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-data/
 *__pycache__*
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diff --git a/extraction/Extraction_KITTI.ipynb b/extraction/Extraction_KITTI.ipynb
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@@ -0,0 +1,1752 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import cv2\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "from azure.storage.blob import ContainerClient\n",
+    "import io\n",
+    "import time\n",
+    "from PIL import Image\n",
+    "import datetime\n",
+    "import argparse\n",
+    "from bivariate import gaussian_blur, skew_blur"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pixel_coord_np(height,width):\n",
+    "    \"\"\"\n",
+    "    Pixel in homogenous coordinate\n",
+    "    Returns:\n",
+    "        Pixel coordinate:       [3, width * height]\n",
+    "    \"\"\"\n",
+    "    x = np.linspace(0, width - 1, width)\n",
+    "    y = np.linspace(0, height - 1, height)\n",
+    "    return np.vstack((x.flatten(), y.flatten(), np.ones_like(x.flatten())))\n",
+    "\n",
+    "\n",
+    "def intrinsic_from_fov1(height, width, fov=90):\n",
+    "    \"\"\"\n",
+    "    Basic Pinhole Camera Model\n",
+    "    intrinsic params from fov and sensor width and height in pixels\n",
+    "    Returns:\n",
+    "        K:      [4, 4]\n",
+    "    \"\"\"\n",
+    "    px, py = (width / 2, height / 2)\n",
+    "    hfov = fov / 360. * 2. * np.pi\n",
+    "    fx = width / (2. * np.tan(hfov / 2.))\n",
+    "\n",
+    "    vfov = 2. * np.arctan(np.tan(hfov / 2) * height / width)\n",
+    "    fy = height / (2. * np.tan(vfov / 2.))\n",
+    "    \n",
+    "    bx = 0.25\n",
+    "    return np.array([[fx, 0, px, 0.],\n",
+    "                    [0, fy, py, 0.],\n",
+    "                    [0, 0, 1., 0.],\n",
+    "                    [0, 0., 0., 1.]\n",
+    "                    ])\n",
+    "    \n",
+    "def intrinsic_from_fov2(height, width, fov=90):\n",
+    "    \"\"\"\n",
+    "    Basic Pinhole Camera Model\n",
+    "    intrinsic params from fov and sensor width and height in pixels\n",
+    "    Returns:\n",
+    "        K:      [4, 4]\n",
+    "    \"\"\"\n",
+    "    px, py = (width / 2, height / 2)\n",
+    "    hfov = fov / 360. * 2. * np.pi\n",
+    "    fx = width / (2. * np.tan(hfov / 2.))\n",
+    "\n",
+    "    vfov = 2. * np.arctan(np.tan(hfov / 2) * height / width)\n",
+    "    fy = height / (2. * np.tan(vfov / 2.))\n",
+    "    \n",
+    "    bx = 0.25\n",
+    "    return np.array([[fx, 0, px, -fx*bx],\n",
+    "                    [0, fy, py, 0.],\n",
+    "                    [0, 0, 1., 0.],\n",
+    "                    [0, 0., 0., 1.]\n",
+    "                    ])\n",
+    "\n",
+    "def quaternion_rotation_matrix(Q):\n",
+    "    \"\"\"\n",
+    "    Covert a quaternion into a full projection matrix.\n",
+    " \n",
+    "    Input\n",
+    "    :param Q: A 7 element array representing translation and the quaternion (q0,q1,q2,q3) \n",
+    " \n",
+    "    Output\n",
+    "    :return: A 3x4 element matrix representing the full projection matrix. \n",
+    "             This projection matrix converts a point in the local reference \n",
+    "             frame to a point in the global reference frame.\n",
+    "    \"\"\"\n",
+    "    # Extract the values from Q\n",
+    "    t0 = Q[0]\n",
+    "    t1 = Q[1]\n",
+    "    t2 = Q[2]\n",
+    "    q0 = Q[3]\n",
+    "    q1 = Q[4]\n",
+    "    q2 = Q[5]\n",
+    "    q3 = Q[6]\n",
+    "     \n",
+    "    # First row of the rotation matrix\n",
+    "    r00 = 2 * (q0 * q0 + q1 * q1) - 1\n",
+    "    r01 = 2 * (q1 * q2 - q0 * q3)\n",
+    "    r02 = 2 * (q1 * q3 + q0 * q2)\n",
+    "     \n",
+    "    # Second row of the rotation matrix\n",
+    "    r10 = 2 * (q1 * q2 + q0 * q3)\n",
+    "    r11 = 2 * (q0 * q0 + q2 * q2) - 1\n",
+    "    r12 = 2 * (q2 * q3 - q0 * q1)\n",
+    "     \n",
+    "    # Third row of the rotation matrix\n",
+    "    r20 = 2 * (q1 * q3 - q0 * q2)\n",
+    "    r21 = 2 * (q2 * q3 + q0 * q1)\n",
+    "    r22 = 2 * (q0 * q0 + q3 * q3) - 1\n",
+    "     \n",
+    "    # 3x4 projection matrix\n",
+    "    pro_matrix = np.array([[r00, r01, r02, t0],\n",
+    "                           [r10, r11, r12, t1],\n",
+    "                           [r20, r21, r22, t2]])\n",
+    "                            \n",
+    "    return pro_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Dataset_Handler():\n",
+    "    def __init__(self, sequence, low_memory=True):\n",
+    "        \n",
+    "        \n",
+    "\n",
+    "        # This will tell odometry functin how to access data from this object\n",
+    "        self.low_memory = low_memory\n",
+    "        \n",
+    "        # Set file paths and get ground truth poses\n",
+    "        self.seq_dir = \"data_KITTI\\data_odometry_gray\\dataset\\sequences\\{}/\".format(sequence)\n",
+    "        self.poses_dir = \"data_KITTI\\data_odometry_poses\\dataset\\poses\\{}.txt\".format(sequence)\n",
+    "        self.depth_dir = \"data\\{}/\".format(sequence)\n",
+    "\n",
+    "        poses = pd.read_csv(self.poses_dir, delimiter=' ', header=None)\n",
+    "        \n",
+    "        # Get names of files to iterate through\n",
+    "        self.left_image_files = os.listdir(self.seq_dir + 'image_0')\n",
+    "        self.right_image_files = os.listdir(self.seq_dir + 'image_1')\n",
+    "        self.left_depth_files = os.listdir(self.depth_dir + 'depth_left')\n",
+    "        self.right_depth_files = os.listdir(self.depth_dir + 'depth_right')\n",
+    "        \n",
+    "        self.num_frames = 20 #len(self.left_image_files)\n",
+    "        # print(self.num_frames)\n",
+    "        self.first_image_left = cv2.imread(self.seq_dir + 'image_left/' \n",
+    "                                               + self.left_image_files[0],0)\n",
+    "        height = 480\n",
+    "        width = 640\n",
+    "        calib = pd.read_csv(self.seq_dir + 'calib.txt', delimiter=' ', header=None, index_col=0)\n",
+    "        self.P0 = np.array(calib.loc['P0:']).reshape((3,4))\n",
+    "        self.P1 = np.array(calib.loc['P1:']).reshape((3,4))\n",
+    "        self.P2 = np.array(calib.loc['P2:']).reshape((3,4))\n",
+    "        self.P3 = np.array(calib.loc['P3:']).reshape((3,4))\n",
+    "        \n",
+    "        \n",
+    "        # Get calibration details for scene\n",
+    "        # calib = pd.read_csv(self.seq_dir + 'calib.txt', delimiter=' ', header=None, index_col=0)\n",
+    "        # self.P0 = np.array(calib.loc['P0:']).reshape((3,4))\n",
+    "        # self.P1 = np.array(calib.loc['P1:']).reshape((3,4))\n",
+    "        # self.P2 = np.array(calib.loc['P2:']).reshape((3,4)) #RGB cams\n",
+    "        # self.P3 = np.array(calib.loc['P3:']).reshape((3,4)) #RGB cams\n",
+    "\n",
+    "        \n",
+    "        # Get times and ground truth poses\n",
+    "        self.times = np.array(pd.read_csv(self.seq_dir + 'times.txt', \n",
+    "                                          delimiter=' ', \n",
+    "                                          header=None))\n",
+    "        self.gt = np.zeros((self.num_frames, 3, 4)) #instead of len(poses)\n",
+    "        for i in range(self.num_frames):\n",
+    "            self.gt[i] = np.array(poses.iloc[i]).reshape((3, 4))\n",
+    "        \n",
+    "        if self.low_memory:\n",
+    "            # Will use generators to provide data sequentially to save RAM\n",
+    "            # Use class method to set up generators\n",
+    "            self.reset_frames()\n",
+    "            # Store original frame to memory for testing functions\n",
+    "            self.first_image_left = cv2.imread(self.seq_dir + 'image_0/' \n",
+    "                                               + self.left_image_files[0],0)\n",
+    "            self.first_image_right = cv2.imread(self.seq_dir + 'image_1/' \n",
+    "                                               + self.right_image_files[0],0)\n",
+    "            self.second_image_left = cv2.imread(self.seq_dir + 'image_0/' \n",
+    "                                               + self.left_image_files[1],0)\n",
+    "            self.first_depth_left = np.load(self.depth_dir + 'depth_left/'\n",
+    "                                                + self.left_depth_files[0])\n",
+    "            self.first_depth_right = np.load(self.depth_dir + 'depth_right/'\n",
+    "                                                + self.right_depth_files[0])\n",
+    "            self.second_depth_left = np.load(self.depth_dir + 'depth_left/'\n",
+    "                                                + self.left_depth_files[1])\n",
+    "                                                \n",
+    "            \n",
+    "            self.imheight = self.first_image_left.shape[0]\n",
+    "            self.imwidth = self.first_image_left.shape[1]\n",
+    "            \n",
+    "        else:\n",
+    "            # If RAM is not a concern (>32GB), pass low_memory=False\n",
+    "            \n",
+    "            self.images_left = []\n",
+    "            self.images_right = []\n",
+    "            self.depths_left = []\n",
+    "            self.depths_right =[]\n",
+    "            self.pointclouds = []\n",
+    "            for i, name_left in enumerate(self.left_image_files):\n",
+    "                name_right = self.right_image_files[i]\n",
+    "                d_left = self.left_depth_files\n",
+    "                d_right = self.right_depth_files\n",
+    "                self.images_left.append(cv2.imread(self.seq_dir + 'image_0/' + name_left))\n",
+    "                self.images_right.append(cv2.imread(self.seq_dir + 'image_1/' + name_right))\n",
+    "                self.depths_left.append(np.load(self.depth_dir + 'depth_left/' + d_left))\n",
+    "                self.depths_right.append(np.load(self.depth_dir + 'depth_right/' + d_right))\n",
+    "                \n",
+    "                \n",
+    "            self.imheight = self.images_left[0].shape[0]\n",
+    "            self.imwidth = self.images_left[0].shape[1]\n",
+    "            # Keep consistent instance variable names as when using low_memory\n",
+    "            self.first_image_left = self.images_left[0]\n",
+    "            self.first_image_right = self.images_right[0]\n",
+    "            self.second_image_left = self.images_left[1]\n",
+    "            \n",
+    "    def reset_frames(self):\n",
+    "        # Resets all generators to the first frame of the sequence\n",
+    "        self.images_left = (cv2.imread(self.seq_dir + 'image_0/' + name_left,0)\n",
+    "                            for name_left in self.left_image_files)\n",
+    "        self.images_right = (cv2.imread(self.seq_dir + 'image_1/' + name_right,0)\n",
+    "                            for name_right in self.right_image_files)\n",
+    "        self.depths_left = (np.load(self.depth_dir+'depth_left/' + d_left)\n",
+    "                            for d_left in self.left_depth_files)\n",
+    "        self.depths_right = (np.load(self.depth_dir+'depth_right/' + d_right)\n",
+    "                            for d_right in self.right_depth_files)                    \n",
+    "        \n",
+    "        pass\n",
+    "handler = Dataset_Handler(\"10\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(array([1.000000e+00, 1.197625e-11, 1.704638e-10, 1.665335e-16]),\n",
+       " array([ 1.197625e-11,  1.000000e+00,  3.562503e-10, -1.110223e-16]),\n",
+       " array([1.704638e-10, 3.562503e-10, 1.000000e+00, 2.220446e-16]))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.plot(handler.gt[:, :, 3][:, 0], handler.gt[:, :, 3][:, 1])\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "handler.gt[0,0], handler.gt[0,1], handler.gt[0,2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_left_disparity_map(left_image, right_image, matcher = 'bm', rgb = True, verbose = True):\n",
+    "    '''\n",
+    "    Takes a left and right pair of images to computes the disparity map for the left\n",
+    "    image. \n",
+    "    \n",
+    "    Arguments:\n",
+    "    img_left -- image from left camera\n",
+    "    img_right -- image from right camera\n",
+    "    \n",
+    "    Optional Arguments:\n",
+    "    matcher -- (str) can be 'bm' for StereoBM or 'sgbm' for StereoSGBM matching.\n",
+    "    rgb -- (bool) set to True if passing RGB images as input. \n",
+    "    verbose -- (bool) set to True to report matching type and time to compute\n",
+    "    \n",
+    "    Returns:\n",
+    "    disp_left -- disparity map for the left camera image\n",
+    "    \n",
+    "    '''\n",
+    "    sad_window = 6\n",
+    "    num_disparities = sad_window * 16\n",
+    "    block_size = 11\n",
+    "    matcher_name = matcher\n",
+    "\n",
+    "    if matcher_name == 'bm':\n",
+    "        matcher = cv2.StereoBM_create(numDisparities=num_disparities,\n",
+    "                                        blockSize=block_size)\n",
+    "                                        \n",
+    "    elif matcher_name == 'sgbm':\n",
+    "        matcher = cv2.StereoSGBM_create(numDisparities=num_disparities,\n",
+    "                                        blockSize=block_size,\n",
+    "                                        P1 = 8*3*sad_window**2,\n",
+    "                                        P2 = 32*3*sad_window**2,\n",
+    "                                        mode = cv2.STEREO_SGBM_MODE_SGBM_3WAY)\n",
+    "    if rgb:\n",
+    "        left_image = cv2.cvtColor(left_image, cv2.COLOR_BGR2GRAY)\n",
+    "        right_image = cv2.cvtColor(right_image, cv2.COLOR_BGR2GRAY)\n",
+    "    \n",
+    "    start = datetime.datetime.now()\n",
+    "    disp_left = matcher.compute(left_image, right_image).astype(np.float32)/16\n",
+    "    end = datetime.datetime.now()\n",
+    "\n",
+    "    if verbose:\n",
+    "        print(f'Time to compute disparity map using Stereo{matcher_name.upper()}:', end-start)\n",
+    "    \n",
+    "    return disp_left\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time to compute disparity map using StereoBM: 0:00:00.008720\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x1080 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "file_path_color_left = \"data/00\\image_left/\"\n",
+    "file_path_color_right = \"data/00\\image_right/\"\n",
+    "\n",
+    "left_images = os.listdir(file_path_color_left)\n",
+    "right_images = os.listdir(file_path_color_right)\n",
+    "\n",
+    "\n",
+    "first_left = cv2.imread(file_path_color_left + left_images[167])\n",
+    "first_right = cv2.imread(file_path_color_right + right_images[167])\n",
+    "\n",
+    "disp = compute_left_disparity_map(left_image=first_left, right_image=first_right,\n",
+    "                                    matcher = 'bm', verbose = True)\n",
+    "\n",
+    "plt.figure(figsize=(15,15))\n",
+    "plt.imshow(disp);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def decompose_projection_matrix(p):\n",
+    "    '''\n",
+    "    Shortcut to use cv2.decomposeProjectionMatrix(), which only returns k, r, t, and divides\n",
+    "    t by the scale, then returns it as a vector with shape (3,) (non-homogeneous)\n",
+    "    \n",
+    "    Arguments:\n",
+    "    p -- projection matrix to be decomposed\n",
+    "    \n",
+    "    Returns:\n",
+    "    k, r, t -- intrinsic matrix, rotation matrix, and 3D translation vector\n",
+    "    \n",
+    "    '''\n",
+    "    k, r, t, _, _, _, _ = cv2.decomposeProjectionMatrix(p)\n",
+    "    t = (t / t[3])[:3]\n",
+    "    \n",
+    "    return k, r, t\n",
+    "def calc_depth_map(disp_left, k_left, t_left, t_right, rectified=True):\n",
+    "    '''\n",
+    "    Assuming we don't have access to the depth map...\n",
+    "    \n",
+    "    Calculate depth map using a disparity map, intrinsic camera matrix, and translation vectors\n",
+    "    from camera extrinsic matrices (to calculate baseline). Note that default behavior is for\n",
+    "    rectified projection matrix for right camera. If using a regular projection matrix, pass\n",
+    "    rectified=False to avoid issues.\n",
+    "\n",
+    "    \n",
+    "    \n",
+    "    Arguments:\n",
+    "    disp_left -- disparity map of left camera\n",
+    "    k_left -- intrinsic matrix for left camera\n",
+    "    t_left -- translation vector for left camera\n",
+    "    t_right -- translation vector for right camera\n",
+    "    \n",
+    "    Optional Arguments:\n",
+    "    rectified -- (bool) set to False if t_right is not from rectified projection matrix\n",
+    "    \n",
+    "    Returns:\n",
+    "    depth_map -- calculated depth map for left camera\n",
+    "    \n",
+    "    '''\n",
+    "    # Get focal length of x axis for left camera\n",
+    "    f = k_left[0][0]\n",
+    "    \n",
+    "    # Calculate baseline of stereo pair\n",
+    "    if rectified:\n",
+    "        b = t_right[0] - t_left[0] \n",
+    "    else:\n",
+    "        b = t_left[0] - t_right[0]\n",
+    "        \n",
+    "    # Avoid instability and division by zero\n",
+    "    disp_left[disp_left == 0.0] = 0.1\n",
+    "    disp_left[disp_left == -1.0] = 0.1\n",
+    "    \n",
+    "    # Make empty depth map then fill with depth\n",
+    "    depth_map = np.ones(disp_left.shape)\n",
+    "    depth_map = f * b / disp_left\n",
+    "    \n",
+    "    return depth_map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def stereo_2_depth(img_left, img_right, P0, P1, matcher='bm', rgb=False, verbose=False, \n",
+    "                   rectified=True):\n",
+    "    '''\n",
+    "    Takes stereo pair of images and returns a depth map for the left camera. If your projection\n",
+    "    matrices are not rectified, set rectified=False.\n",
+    "    \n",
+    "    Arguments:\n",
+    "    img_left -- image of left camera\n",
+    "    img_right -- image of right camera\n",
+    "    P0 -- Projection matrix for the left camera\n",
+    "    P1 -- Projection matrix for the right camera\n",
+    "    \n",
+    "    Optional Arguments:\n",
+    "    matcher -- (str) can be 'bm' for StereoBM or 'sgbm' for StereoSGBM\n",
+    "    rgb -- (bool) set to True if images passed are RGB. Default is False\n",
+    "    verbose -- (bool) set to True to report computation time and method\n",
+    "    rectified -- (bool) set to False if P1 not rectified to P0. Default is True\n",
+    "    \n",
+    "    Returns:\n",
+    "    depth -- depth map for left camera\n",
+    "    \n",
+    "    '''\n",
+    "    # Compute disparity map\n",
+    "    disp = compute_left_disparity_map(img_left, \n",
+    "                                      img_right, \n",
+    "                                      matcher=matcher, \n",
+    "                                      rgb=rgb, \n",
+    "                                      verbose=verbose)\n",
+    "    # Decompose projection matrices\n",
+    "    k_left, r_left, t_left = decompose_projection_matrix(P0)\n",
+    "    k_right, r_right, t_right = decompose_projection_matrix(P1)\n",
+    "    # Calculate depth map for left camera\n",
+    "    depth = calc_depth_map(disp, k_left, t_left, t_right)\n",
+    "    \n",
+    "    return depth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_features(image, detector='sift', mask=None):\n",
+    "    \"\"\"\n",
+    "    Find keypoints and descriptors for the image\n",
+    "\n",
+    "    Arguments:\n",
+    "    image -- a grayscale image\n",
+    "\n",
+    "    Returns:\n",
+    "    kp -- list of the extracted keypoints (features) in an image\n",
+    "    des -- list of the keypoint descriptors in an image\n",
+    "    \"\"\"\n",
+    "    if detector == 'sift':\n",
+    "        det = cv2.SIFT_create()\n",
+    "    elif detector == 'orb':\n",
+    "        det = cv2.ORB_create()\n",
+    "    elif detector == 'surf':\n",
+    "        det = cv2.xfeatures2d.SURF_create()\n",
+    "    elif detector == 'harris':\n",
+    "        img = image\n",
+    "        blockSize = 2\n",
+    "        apertureSize = 1\n",
+    "        k = 0.04\n",
+    "        num = 300\n",
+    "        # smooth_img = cv2.GaussianBlur(img, (5, 5), 1)\n",
+    "        smooth_img = gaussian_blur(img, verbose=False)\n",
+    "        smooth_img = smooth_img.astype(np.float32) \n",
+    "\n",
+    "        if len(smooth_img.shape)==3:\n",
+    "            smooth_img=cv2.cvtColor(smooth_img, cv2.COLOR_RGB2GRAY)\n",
+    "\n",
+    "        harris_img = cv2.cornerHarris(smooth_img, blockSize, apertureSize, k)\n",
+    "        element=np.sort(harris_img.flatten())[-num]\n",
+    "        mask=harris_img>=element\n",
+    "        hs,ws=np.nonzero(mask)\n",
+    "        keyp=np.concatenate([ws[:,None],hs[:,None]],1).astype(int)\n",
+    "        kpoint = keyp.tolist()\n",
+    "        kp = [cv2.KeyPoint(x[0], x[1], 1) for x in kpoint]\n",
+    "\n",
+    "        from cv2 import xfeatures2d\n",
+    "        sift = cv2.xfeatures2d.SIFT_create()\n",
+    "        des = [sift.compute(img,[y])[1] for y in kp]\n",
+    "        for i in range(len(des)):\n",
+    "            des[i] = des[i].ravel()\n",
+    "        des = np.array(des)\n",
+    "    \n",
+    "        return kp, des\n",
+    "    \n",
+    "        \n",
+    "    elif detector == 'harris-skew':\n",
+    "        img = image\n",
+    "        blockSize = 2\n",
+    "        apertureSize = 1\n",
+    "        k = 0.04\n",
+    "        num = 300\n",
+    "        smooth_img = skew_blur(img, verbose=False)\n",
+    "        smooth_img .astype(np.float32)\n",
+    "        \n",
+    "        if len(smooth_img.shape)==3:\n",
+    "            smooth_img=cv2.cvtColor(smooth_img, cv2.COLOR_RGB2GRAY)\n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        harris_img = cv2.cornerHarris(smooth_img, blockSize, apertureSize, k)\n",
+    "        element=np.sort(harris_img.flatten())[-num]\n",
+    "        mask=harris_img>=element\n",
+    "        hs,ws=np.nonzero(mask)\n",
+    "        keyp=np.concatenate([ws[:,None],hs[:,None]],1).astype(int)\n",
+    "        kpoint = keyp.tolist()\n",
+    "        kp = [cv2.KeyPoint(x[0], x[1], 1) for x in kpoint]\n",
+    "\n",
+    "        from cv2 import xfeatures2d\n",
+    "        sift = cv2.xfeatures2d.SIFT_create()\n",
+    "        des = [sift.compute(img,[y])[1] for y in kp]\n",
+    "        for i in range(len(des)):\n",
+    "            des[i] = des[i].ravel()\n",
+    "        des = np.array(des)\n",
+    "\n",
+    "\n",
+    "        return kp, des\n",
+    "    \n",
+    "    kp, des = det.detectAndCompute(image, mask)\n",
+    "    \n",
+    "    return kp, des"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# img = first_left\n",
+    "# blockSize = 2\n",
+    "# apertureSize = 1\n",
+    "# k = 0.04\n",
+    "# num = 200\n",
+    "# # gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
+    "# # gray_img = np.float32(gray_img)\n",
+    "# # result_img = img.copy() # deep copy image\n",
+    "# # dst = cv2.cornerHarris(gray_img, blockSize=3, ksize=3, k=0.04)\n",
+    "# # dst = cv2.dilate(dst, None)\n",
+    "# # img[dst > 0.01 * dst.max()] = [0, 255, 0]\n",
+    "# smooth_img = cv2.GaussianBlur(img, (5, 5), 1.5)\n",
+    "# if len(smooth_img.shape)==3:\n",
+    "#     smooth_img=cv2.cvtColor(smooth_img, cv2.COLOR_RGB2GRAY)\n",
+    "# harris_img = cv2.cornerHarris(smooth_img, blockSize, apertureSize, k)\n",
+    "# element=np.sort(harris_img.flatten())[-num]\n",
+    "# mask=harris_img>=element\n",
+    "# hs,ws=np.nonzero(mask)\n",
+    "# keyp=np.concatenate([ws[:,None],hs[:,None]],1).astype(int)\n",
+    "# kpoint = keyp.tolist()\n",
+    "# kp = [cv2.KeyPoint(x[0], x[1], 1) for x in kpoint]\n",
+    "\n",
+    "# from cv2 import xfeatures2d\n",
+    "# sift = cv2.xfeatures2d.SIFT_create()\n",
+    "# des = [sift.compute(img,[y])[1] for y in kp]\n",
+    "# kp1,des1 = extract_features(img, detector='sift', mask=None)\n",
+    "# for i in range(len(des)):\n",
+    "#     des[i] = des[i].ravel()\n",
+    "# des = np.array(des)\n",
+    "\n",
+    "\n",
+    "# kp, des"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def match_features(des1, des2, matching='BF', detector='sift', sort=True, k=2):\n",
+    "    \"\"\"\n",
+    "    Match features from two images\n",
+    "\n",
+    "    Arguments:\n",
+    "    des1 -- list of the keypoint descriptors in the first image\n",
+    "    des2 -- list of the keypoint descriptors in the second image\n",
+    "    matching -- (str) can be 'BF' for Brute Force or 'FLANN'\n",
+    "    detector -- (str) can be 'sift or 'orb'. Default is 'sift'\n",
+    "    sort -- (bool) whether to sort matches by distance. Default is True\n",
+    "    k -- (int) number of neighbors to match to each feature.\n",
+    "\n",
+    "    Returns:\n",
+    "    matches -- list of matched features from two images. Each match[i] is k or less matches for \n",
+    "               the same query descriptor\n",
+    "    \"\"\"\n",
+    "    if matching == 'BF':\n",
+    "        if detector == 'sift':\n",
+    "            matcher = cv2.BFMatcher_create(cv2.NORM_L2, crossCheck=False)\n",
+    "        if detector == 'orb':\n",
+    "            matcher = cv2.BFMatcher_create(cv2.NORM_HAMMING2, crossCheck=False)\n",
+    "        elif detector == 'harris':\n",
+    "            matcher = cv2.BFMatcher_create(cv2.NORM_L2, crossCheck=False)\n",
+    "        elif detector == 'harris-skew':\n",
+    "            matcher = cv2.BFMatcher_create(cv2.NORM_L2, crossCheck=False)\n",
+    "        matches = matcher.knnMatch(des1, des2, k=k)\n",
+    "\n",
+    "    elif matching == 'FLANN':\n",
+    "        FLANN_INDEX_KDTREE = 1\n",
+    "        index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees=5)\n",
+    "        search_params = dict(checks=50)\n",
+    "        matcher = cv2.FlannBasedMatcher(index_params, search_params)\n",
+    "        matches = matcher.knnMatch(des1, des2, k=k)\n",
+    "    \n",
+    "    if sort:\n",
+    "        matches = sorted(matches, key = lambda x:x[0].distance)\n",
+    "\n",
+    "    return matches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def visualize_matches(image1, kp1, image2, kp2, match):\n",
+    "    \"\"\"\n",
+    "    Visualize corresponding matches in two images\n",
+    "\n",
+    "    Arguments:\n",
+    "    image1 -- the first image in a matched image pair\n",
+    "    kp1 -- list of the keypoints in the first image\n",
+    "    image2 -- the second image in a matched image pair\n",
+    "    kp2 -- list of the keypoints in the second image\n",
+    "    match -- list of matched features from the pair of images\n",
+    "\n",
+    "    Returns:\n",
+    "    image_matches -- an image showing the corresponding matches on both image1 and image2 or None if you don't use this function\n",
+    "    \"\"\"\n",
+    "    image_matches = cv2.drawMatches(image1, kp1, image2, kp2, match, None, flags=2)\n",
+    "    plt.figure(figsize=(16, 6), dpi=100)\n",
+    "    plt.imshow(image_matches)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def filter_matches_distance(matches, dist_threshold):\n",
+    "    \"\"\"\n",
+    "    Filter matched features from two images by distance between the best matches\n",
+    "\n",
+    "    Arguments:\n",
+    "    match -- list of matched features from two images\n",
+    "    dist_threshold -- maximum allowed relative distance between the best matches, (0.0, 1.0) \n",
+    "\n",
+    "    Returns:\n",
+    "    filtered_match -- list of good matches, satisfying the distance threshold\n",
+    "    \"\"\"\n",
+    "    filtered_match = []\n",
+    "    for m, n in matches:\n",
+    "        if m.distance <= dist_threshold*n.distance:\n",
+    "            filtered_match.append(m)\n",
+    "\n",
+    "    return filtered_match"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(370, 1226)\n",
+      "Time to compute disparity map using StereoBM: 0:00:00.016696\n",
+      "Number of matches before filtering: 300\n",
+      "Number of matches after filtering: 81\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image_left = handler.first_image_left\n",
+    "print(image_left.shape)\n",
+    "image_right = handler.first_image_right\n",
+    "image_plus1 = handler.second_image_left\n",
+    "depth = stereo_2_depth(image_left, \n",
+    "                       image_right, \n",
+    "                       handler.P0, \n",
+    "                       handler.P1,\n",
+    "                       matcher='bm',\n",
+    "                       verbose=True)\n",
+    "kp0, des0 = extract_features(image_left, 'harris')\n",
+    "kp1, des1 = extract_features(image_plus1, 'harris')\n",
+    "matches = match_features(des0, des1, matching='BF', detector='harris', sort=True)\n",
+    "print('Number of matches before filtering:', len(matches))\n",
+    "matches = filter_matches_distance(matches, 0.45)\n",
+    "print('Number of matches after filtering:', len(matches))\n",
+    "visualize_matches(image_left, kp0, image_plus1, kp1, matches)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def estimate_motion(match, kp1, kp2, k, depth1=None, max_depth=3000):\n",
+    "#     \"\"\"\n",
+    "#     Estimate camera motion from a pair of subsequent image frames\n",
+    "\n",
+    "#     Arguments:\n",
+    "#     match -- list of matched features from the pair of images\n",
+    "#     kp1 -- list of the keypoints in the first image\n",
+    "#     kp2 -- list of the keypoints in the second image\n",
+    "#     k -- camera intrinsic calibration matrix \n",
+    "    \n",
+    "#     Optional arguments:\n",
+    "#     depth1 -- Depth map of the first frame. Set to None to use Essential Matrix decomposition\n",
+    "#     max_depth -- Threshold of depth to ignore matched features. 3000 is default\n",
+    "\n",
+    "#     Returns:\n",
+    "#     rmat -- estimated 3x3 rotation matrix\n",
+    "#     tvec -- estimated 3x1 translation vector\n",
+    "#     image1_points -- matched feature pixel coordinates in the first image. \n",
+    "#                      image1_points[i] = [u, v] -> pixel coordinates of i-th match\n",
+    "#     image2_points -- matched feature pixel coordinates in the second image. \n",
+    "#                      image2_points[i] = [u, v] -> pixel coordinates of i-th match\n",
+    "               \n",
+    "#     \"\"\"\n",
+    "#     rmat = np.eye(3)\n",
+    "#     tvec = np.zeros((3, 1))\n",
+    "    \n",
+    "#     image1_points = np.float32([kp1[m.queryIdx].pt for m in match])\n",
+    "#     image2_points = np.float32([kp2[m.trainIdx].pt for m in match])\n",
+    "\n",
+    "#     # print(\"image points\")\n",
+    "#     # print(image1_points)\n",
+    "#     # print(k)\n",
+    "    \n",
+    "#     if depth1 is not None:\n",
+    "#         cx = k[0, 2]\n",
+    "#         cy = k[1, 2]\n",
+    "#         fx = k[0, 0]\n",
+    "#         fy = k[1, 1]\n",
+    "#         object_points = np.zeros((0, 3))\n",
+    "#         delete = []\n",
+    "        \n",
+    "#         listofx1=[]\n",
+    "#         listofy1=[]\n",
+    "#         listofz1=[]\n",
+    "        \n",
+    "#         # Extract depth information of query image at match points and build 3D positions\n",
+    "#         for i, (u, v) in enumerate(image1_points):\n",
+    "#             z1 = depth1[int(v), int(u)]\n",
+    "            \n",
+    "\n",
+    "#             if z1 > max_depth:\n",
+    "#                 delete.append(i)\n",
+    "#                 continue\n",
+    "                \n",
+    "#             # Use arithmetic to extract x and y (faster than using inverse of k)\n",
+    "#             x1 = z1*(u-cx)/fx\n",
+    "#             y1 = z1*(v-cy)/fy\n",
+    "            \n",
+    "#             listofx1.append(x1)\n",
+    "#             listofy1.append(y1)\n",
+    "#             listofz1.append(z1)\n",
+    "            \n",
+    "#             p_c = np.linalg.inv(k) @ (x1 * np.array([u, v, 1]))\n",
+    "            \n",
+    "#             # print(\"XYZ sep\")\n",
+    "#             # print(x1)\n",
+    "#             # print(y1)\n",
+    "#             # print(z1)\n",
+    "#             np.vstack([object_points, np.array([x1, y1, z1])])\n",
+    "\n",
+    "#             # Equivalent math with dot product w/ inverse of k matrix, but SLOWER (see Appendix A)\n",
+    "#             # object_points = np.vstack([object_points, np.linalg.inv(k).dot(z*np.array([u, v, 1]))])\n",
+    "\n",
+    "#         listofx2=[]\n",
+    "#         listofy2=[]\n",
+    "#         listofz2=[]\n",
+    "#         # Extract depth information of query image at match points and build 3D positions\n",
+    "#         for i, (u1, v1) in enumerate(image2_points):\n",
+    "#             z2 = depth1[int(v1), int(u1)]\n",
+    "            \n",
+    "\n",
+    "#             # if z2 > max_depth:\n",
+    "#             #     delete.append(i)\n",
+    "#             #     continue\n",
+    "                \n",
+    "#             # Use arithmetic to extract x and y (faster than using inverse of k)\n",
+    "#             x2 = z2*(u1-cx)/fx\n",
+    "#             y2 = z2*(v1-cy)/fy\n",
+    "            \n",
+    "#             listofx2.append(x2)\n",
+    "#             listofy2.append(y2)\n",
+    "#             listofz2.append(z2)\n",
+    "#             # Equivalent math with dot product w/ inverse of k matrix, but SLOWER (see Appendix A)\n",
+    "#             # object_points = np.vstack([object_points, np.linalg.inv(k).dot(z*np.array([u, v, 1]))])\n",
+    "\n",
+    "#         image1_points = np.delete(image1_points, delete, 0)\n",
+    "\n",
+    "#         image2_points = np.delete(image2_points, delete, 0)\n",
+    "#         # print(object_points)\n",
+    "        \n",
+    "        \n",
+    "#         # Use PnP algorithm with RANSAC for robustness to outliers\n",
+    "        \n",
+    "#         _,rvec, tvec, inliers = cv2.solvePnPRansac(object_points, image2_points, cameraMatrix=k, distCoeffs=None)\n",
+    "\n",
+    "#         rmat = cv2.Rodrigues(rvec)[0]\n",
+    "        \n",
+    "#         # listofxyz1 = np.array([listofx1,listofy1,listofz1])\n",
+    "#         listofxyz1 = np.vstack((listofx1,listofy1,listofz1))\n",
+    "#         listofxyz1 = listofxyz1.tolist()\n",
+    "\n",
+    "#         # listofxyz2 = np.array([listofx2,listofy2,listofz2])\n",
+    "#         listofxyz2 = np.vstack((listofx2,listofy2,listofz2))\n",
+    "#         listofxyz2 = listofxyz2.tolist()\n",
+    "\n",
+    "\n",
+    "    \n",
+    "#     # listofxyz2 = np.array([x2,y2,z2])\n",
+    "#     else:\n",
+    "#         # With no depth provided, use essential matrix decomposition instead. This is not really\n",
+    "#         # very useful, since you will get a 3D motion tracking but the scale will be ambiguous\n",
+    "#         image1_points_hom = np.hstack([image1_points, np.ones(len(image1_points)).reshape(-1,1)])\n",
+    "#         image2_points_hom = np.hstack([image2_points, np.ones(len(image2_points)).reshape(-1,1)])\n",
+    "#         E = cv2.findEssentialMat(image1_points, image2_points, k)[0]\n",
+    "#         _, rmat, tvec, mask = cv2.recoverPose(E, image1_points, image2_points, k)\n",
+    "\n",
+    "    \n",
+    "#     return rmat, tvec, image1_points, image2_points, listofxyz1, listofxyz2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def estimate_motion(match, kp1, kp2, k, depth1=None, max_depth=3000):\n",
+    "    \"\"\"\n",
+    "    Estimate camera motion from a pair of subsequent image frames\n",
+    "\n",
+    "    Arguments:\n",
+    "    match -- list of matched features from the pair of images\n",
+    "    kp1 -- list of the keypoints in the first image\n",
+    "    kp2 -- list of the keypoints in the second image\n",
+    "    k -- camera intrinsic calibration matrix \n",
+    "    \n",
+    "    Optional arguments:\n",
+    "    depth1 -- Depth map of the first frame. Set to None to use Essential Matrix decomposition\n",
+    "    max_depth -- Threshold of depth to ignore matched features. 3000 is default\n",
+    "\n",
+    "    Returns:\n",
+    "    rmat -- estimated 3x3 rotation matrix\n",
+    "    tvec -- estimated 3x1 translation vector\n",
+    "    image1_points -- matched feature pixel coordinates in the first image. \n",
+    "                     image1_points[i] = [u, v] -> pixel coordinates of i-th match\n",
+    "    image2_points -- matched feature pixel coordinates in the second image. \n",
+    "                     image2_points[i] = [u, v] -> pixel coordinates of i-th match\n",
+    "               \n",
+    "    \"\"\"\n",
+    "    rmat = np.eye(3)\n",
+    "    tvec = np.zeros((3, 1))\n",
+    "    \n",
+    "    image1_points = np.float32([kp1[m.queryIdx].pt for m in match])\n",
+    "    image2_points = np.float32([kp2[m.trainIdx].pt for m in match])\n",
+    "\n",
+    "    if depth1 is not None:\n",
+    "        cx = k[0, 2]\n",
+    "        cy = k[1, 2]\n",
+    "        fx = k[0, 0]\n",
+    "        fy = k[1, 1]\n",
+    "        object_points = np.zeros((0, 3))\n",
+    "        delete = []\n",
+    "        \n",
+    "        listofx1=[]\n",
+    "        listofy1=[]\n",
+    "        listofz1=[]\n",
+    "        \n",
+    "        \n",
+    "            \n",
+    "            \n",
+    "        # Extract depth information of query image at match points and build 3D positions\n",
+    "        for i, (u, v) in enumerate(image1_points):\n",
+    "            z1 = depth1[int(v), int(u)]\n",
+    "            # If the depth at the position of our matched feature is above 3000, then we\n",
+    "            # ignore this feature because we don't actually know the depth and it will throw\n",
+    "            # our calculations off. We add its index to a list of coordinates to delete from our\n",
+    "            # keypoint lists, and continue the loop. After the loop, we remove these indices\n",
+    "            if z1 > max_depth:\n",
+    "                delete.append(i)\n",
+    "                continue\n",
+    "                \n",
+    "            # Use arithmetic to extract x and y (faster than using inverse of k)\n",
+    "            x1 = z1*(u-cx)/fx\n",
+    "            y1 = z1*(v-cy)/fy\n",
+    "            listofx1.append(x1)\n",
+    "            listofy1.append(y1)\n",
+    "            listofz1.append(z1)\n",
+    "            object_points = np.vstack([object_points, np.array([x1, y1, z1])])\n",
+    "            # Equivalent math with dot product w/ inverse of k matrix, but SLOWER (see Appendix A)\n",
+    "            #object_points = np.vstack([object_points, np.linalg.inv(k).dot(z*np.array([u, v, 1]))])\n",
+    "        listofx2=[]\n",
+    "        listofy2=[]\n",
+    "        listofz2=[]\n",
+    "        # Extract depth information of query image at match points and build 3D positions\n",
+    "        for i, (u1, v1) in enumerate(image2_points):\n",
+    "            z2 = depth1[int(v1), int(u1)]\n",
+    "            \n",
+    "\n",
+    "            # if z2 > max_depth:\n",
+    "            #     delete.append(i)\n",
+    "            #     continue\n",
+    "                \n",
+    "            # Use arithmetic to extract x and y (faster than using inverse of k)\n",
+    "            x2 = z2*(u1-cx)/fx\n",
+    "            y2 = z2*(v1-cy)/fy\n",
+    "            \n",
+    "            listofx2.append(x2)\n",
+    "            listofy2.append(y2)\n",
+    "            listofz2.append(z2)\n",
+    "            \n",
+    "        \n",
+    "        image1_points = np.delete(image1_points, delete, 0)\n",
+    "        image2_points = np.delete(image2_points, delete, 0)\n",
+    "        \n",
+    "        # Use PnP algorithm with RANSAC for robustness to outliers\n",
+    "        _, rvec, tvec, inliers = cv2.solvePnPRansac(object_points, image2_points, k, None)\n",
+    "        #print('Number of inliers: {}/{} matched features'.format(len(inliers), len(match)))\n",
+    "        \n",
+    "        # Above function returns axis angle rotation representation rvec, use Rodrigues formula\n",
+    "        # to convert this to our desired format of a 3x3 rotation matrix\n",
+    "        rmat = cv2.Rodrigues(rvec)[0]\n",
+    "        listofxyz1 = np.array([listofx1,listofy1,listofz1])\n",
+    "        listofxyz1 = np.vstack((listofx1,listofy1,listofz1))\n",
+    "        listofxyz1 = listofxyz1.tolist()\n",
+    "\n",
+    "        # listofxyz2 = np.array([listofx2,listofy2,listofz2])\n",
+    "        listofxyz2 = np.vstack((listofx2,listofy2,listofz2))\n",
+    "        listofxyz2 = listofxyz2.tolist()\n",
+    "\n",
+    "\n",
+    "    \n",
+    "    else:\n",
+    "        # With no depth provided, use essential matrix decomposition instead. This is not really\n",
+    "        # very useful, since you will get a 3D motion tracking but the scale will be ambiguous\n",
+    "        image1_points_hom = np.hstack([image1_points, np.ones(len(image1_points)).reshape(-1,1)])\n",
+    "        image2_points_hom = np.hstack([image2_points, np.ones(len(image2_points)).reshape(-1,1)])\n",
+    "        E = cv2.findEssentialMat(image1_points, image2_points, k)[0]\n",
+    "        _, rmat, tvec, mask = cv2.recoverPose(E, image1_points, image2_points, k)\n",
+    "    \n",
+    "    return rmat, tvec, image1_points, image2_points, listofxyz1, listofxyz2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def visual_odometry(handler, detector='sift', matching='BF', filter_match_distance=None, \n",
+    "                    stereo_matcher='bm', mask=None, depth_type='stereo', subset=None,\n",
+    "                    plot=False):\n",
+    "    '''\n",
+    "    Function to perform visual odometry on a sequence from the KITTI visual odometry dataset.\n",
+    "    Takes as input a Data_Handler object and optional parameters.\n",
+    "    \n",
+    "    Arguments:\n",
+    "    handler -- Data_Handler object instance\n",
+    "    \n",
+    "    Optional Arguments:\n",
+    "    detector -- (str) can be 'sift' or 'orb'. Default is 'sift'.\n",
+    "    matching -- (str) can be 'BF' for Brute Force or 'FLANN'. Default is 'BF'.\n",
+    "    filter_match_distance -- (float) value for ratio test on matched features. Default is None.\n",
+    "    stereo_matcher -- (str) can be 'bm' (faster) or 'sgbm' (more accurate). Default is 'bm'.\n",
+    "    mask -- (array) mask to reduce feature search area to where depth information available.\n",
+    "    depth_type -- (str) can be 'stereo' or set to None to use Essential matrix decomposition.\n",
+    "                        Note that scale will be incorrect with no depth used.\n",
+    "    subset -- (int) number of frames to compute. Defaults to None to compute all frames.\n",
+    "    plot -- (bool) whether to plot the estimated vs ground truth trajectory. Only works if\n",
+    "                   matplotlib is set to tk mode. Default is False.\n",
+    "    \n",
+    "    Returns:\n",
+    "    trajectory -- Array of shape Nx3x4 of estimated poses of vehicle for each computed frame.\n",
+    "    \n",
+    "    '''\n",
+    "    \n",
+    "    # Report methods being used to user\n",
+    "    # print('Generating disparities with Stereo{}'.format(str.upper(stereo_matcher)))\n",
+    "    # print('Detecting features with {} and matching with {}'.format(str.upper(detector), \n",
+    "    #                                                                matching))\n",
+    "    if filter_match_distance is not None:\n",
+    "        print('Filtering feature matches at threshold of {}*distance'.format(filter_match_distance))\n",
+    "    \n",
+    "    if subset is not None:\n",
+    "        #subset = subset + 1\n",
+    "        num_frames = subset\n",
+    "    else:\n",
+    "        # Set num_frames to one less than the number of frames so we have sequential images\n",
+    "        # in the last frame run.\n",
+    "        num_frames = handler.num_frames\n",
+    "        \n",
+    "    if plot:\n",
+    "        fig = plt.figure(figsize=(14, 14))\n",
+    "        ax = fig.add_subplot(111, projection='3d')\n",
+    "        ax.view_init(elev=-20, azim=270)\n",
+    "        xs = handler.gt[:, 0, 3]\n",
+    "        ys = handler.gt[:, 1, 3]\n",
+    "        zs = handler.gt[:, 2, 3]\n",
+    "        ax.set_box_aspect((np.ptp(xs), np.ptp(ys), np.ptp(zs)))\n",
+    "        ax.plot(xs, ys, zs, c='k')\n",
+    "    # Establish homogeneous transformation matrix. First pose is identity    \n",
+    "    T_tot = np.eye(4)\n",
+    "    trajectory = np.zeros((num_frames, 3, 4))\n",
+    "    trajectory[0] = T_tot[:3, :]\n",
+    "    imheight = handler.imheight\n",
+    "    imwidth = handler.imwidth\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # Decompose left camera projection matrix to get intrinsic k matrix\n",
+    "    k_left, r_left, t_left = decompose_projection_matrix(handler.P0)\n",
+    "\n",
+    "\n",
+    "    if handler.low_memory:\n",
+    "        handler.reset_frames()\n",
+    "\n",
+    "        image_plus1 = next(handler.images_left)\n",
+    "        depth_plus1 =next(handler.depths_left)\n",
+    "        \n",
+    "\n",
+    "\n",
+    "    combined_dict ={\"Frame_t\":[],\"Frame_t+1\":[]}\n",
+    "\n",
+    "    # Iterate through all frames of the sequence\n",
+    "    for i in range(num_frames - 1):\n",
+    "        # Stop if we've reached the second to last frame, since we need two sequential frames\n",
+    "        #if i == num_frames - 1:\n",
+    "        #    break\n",
+    "        # Start timer for frame\n",
+    "        start = datetime.datetime.now()\n",
+    "        # Get our stereo images for depth estimation\n",
+    "        \n",
+    "        \n",
+    "        if handler.low_memory:\n",
+    "            image_left = image_plus1\n",
+    "            image_right = next(handler.images_right)\n",
+    "            # Get next frame in the left camera for visual odometry\n",
+    "            image_plus1 = next(handler.images_left)\n",
+    "        else:\n",
+    "            image_left = handler.images_left[i]\n",
+    "            image_right = handler.images_right[i]\n",
+    "            # Get next frame in the left camera for visual odometry\n",
+    "            image_plus1 = handler.images_left[i+1]\n",
+    "        \n",
+    "\n",
+    "        # Estimate depth if using stereo depth estimation (recommended)\n",
+    "        if depth_type == 'stereo':\n",
+    "            depth = stereo_2_depth(image_left, \n",
+    "                                   image_right, \n",
+    "                                   P0=handler.P0, \n",
+    "                                   P1=handler.P1,\n",
+    "                                   matcher=stereo_matcher)\n",
+    "            # depth = depth_plus1/80\n",
+    "            # depth_plus1 = next(handler.depths_left)\n",
+    "\n",
+    "            # depth = None\n",
+    "        # Otherwise use Essential Matrix decomposition (ambiguous scale)\n",
+    "        else:\n",
+    "            depth = None\n",
+    "            \n",
+    "        \n",
+    "\n",
+    "        # Get keypoints and descriptors for left camera image of two sequential frames\n",
+    "        kp0, des0 = extract_features(image_left, detector, mask)\n",
+    "        kp1, des1 = extract_features(image_plus1, detector, mask)\n",
+    "        \n",
+    "        # Get matches between features detected in the two images\n",
+    "        matches_unfilt = match_features(des0, \n",
+    "                                        des1, \n",
+    "                                        matching=matching, \n",
+    "                                        detector=detector, \n",
+    "                                        sort=True)\n",
+    "        \n",
+    "        # Filter matches if a distance threshold is provided by user\n",
+    "        if filter_match_distance is not None:\n",
+    "            matches = filter_matches_distance(matches_unfilt, filter_match_distance)\n",
+    "        else:\n",
+    "            matches = matches_unfilt\n",
+    "            \n",
+    "        # Estimate motion between sequential images of the left camera\n",
+    "        \n",
+    "        # print(\"THIS is kp0:  \")\n",
+    "        # for keypoint0 in kp0:\n",
+    "        #     print(keypoint0.pt)\n",
+    "        #     print(keypoint0.angle)\n",
+    "        # print(\"This is kp1:  \")\n",
+    "        # for keypoint1 in kp1:\n",
+    "        #     print(keypoint1.pt)\n",
+    "        #     print(keypoint1.angle)\n",
+    "        # print(\"Matches detected:  \")\n",
+    "        \n",
+    "        list_kp0 = []\n",
+    "        list_kp1 = []\n",
+    "\n",
+    "        # For each match...\n",
+    "        for mat in matches:\n",
+    "\n",
+    "            # Get the matching keypoints for each of the images\n",
+    "            img1_idx = mat.queryIdx\n",
+    "            img2_idx = mat.trainIdx\n",
+    "\n",
+    "            # x - columns\n",
+    "            # y - rows\n",
+    "            # Get the coordinates\n",
+    "            (x1, y1) = kp0[img1_idx].pt\n",
+    "            (x2, y2) = kp1[img2_idx].pt\n",
+    "\n",
+    "            # Append to each list\n",
+    "            list_kp0.append((x1, y1))\n",
+    "            list_kp1.append((x2, y2))\n",
+    "        # print(\"This is KP0:   \")\n",
+    "        # print(list_kp0)\n",
+    "        # print(\"This is KP1:   \") \n",
+    "        # print(list_kp1)\n",
+    "        pd.set_option('display.max_columns', None)\n",
+    "        pd.set_option('display.expand_frame_repr', False)\n",
+    "        pd.set_option('max_colwidth', None)\n",
+    "        d = {'kp0': list_kp0, 'kp1': list_kp1}\n",
+    "        df = pd.DataFrame(data=d)\n",
+    "        # print(df)\n",
+    "\n",
+    "                    \n",
+    "            \n",
+    "        rmat, tvec, img1_points, img2_points,listxyz1, listxyz2 = estimate_motion(matches, kp0, kp1, k=k_left, depth1=depth)\n",
+    "\n",
+    "        combined_dict[\"Frame_t\"].append(listxyz1)\n",
+    "        combined_dict[\"Frame_t+1\"].append(listxyz2)\n",
+    "        \n",
+    "        \n",
+    "\n",
+    "        # Create blank homogeneous transformation matrix\n",
+    "        Tmat = np.eye(4)\n",
+    "        # Place resulting rotation matrix  and translation vector in their proper locations\n",
+    "        # in homogeneous T matrix\n",
+    "        Tmat[:3, :3] = rmat\n",
+    "        Tmat[:3, 3] = tvec.T\n",
+    "\n",
+    "        T_tot = T_tot.dot(np.linalg.inv(Tmat))\n",
+    "            \n",
+    "        # Place pose estimate in i+1 to correspond to the second image, which we estimated for\n",
+    "        trajectory[i+1, :, :] = T_tot[:3, :]\n",
+    "        # End the timer for the frame and report frame rate to user\n",
+    "        end = datetime.datetime.now()\n",
+    "        print('Time to compute frame {}:'.format(i+1), end-start)\n",
+    "        if plot:\n",
+    "            xs = trajectory[:i+2, 0, 3]\n",
+    "            ys = trajectory[:i+2, 1, 3]\n",
+    "            zs = trajectory[:i+2, 2, 3]\n",
+    "            plt.plot(xs, ys, zs, c='chartreuse')\n",
+    "            plt.pause(1e-32)\n",
+    "            # print(\"THIS is xs in VO\")\n",
+    "            # print(xs)\n",
+    "            # print(ys)\n",
+    "            # print(zs)\n",
+    "    if plot:        \n",
+    "        plt.close()\n",
+    "    \n",
+    "\n",
+    "\n",
+    "    # print(combined_dict)\n",
+    "    \n",
+    "     \n",
+    "    return trajectory, combined_dict\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtering feature matches at threshold of 0.45*distance\n",
+      "Time to compute frame 1: 0:00:00.261993\n",
+      "Time to compute frame 2: 0:00:00.262041\n",
+      "Time to compute frame 3: 0:00:00.259509\n",
+      "Time to compute frame 4: 0:00:00.275846\n",
+      "Time to compute frame 5: 0:00:00.258153\n",
+      "Time to compute frame 6: 0:00:00.275176\n",
+      "Time to compute frame 7: 0:00:00.274677\n",
+      "Time to compute frame 8: 0:00:00.259113\n",
+      "Time to compute frame 9: 0:00:00.274671\n",
+      "Time to compute frame 10: 0:00:00.292334\n",
+      "Time to compute frame 11: 0:00:00.273537\n",
+      "Time to compute frame 12: 0:00:00.261193\n",
+      "Time to compute frame 13: 0:00:00.275396\n",
+      "Time to compute frame 14: 0:00:00.273547\n",
+      "Time to compute frame 15: 0:00:00.270950\n",
+      "Time to compute frame 16: 0:00:00.296753\n",
+      "Time to compute frame 17: 0:00:00.303095\n",
+      "Time to compute frame 18: 0:00:00.318904\n",
+      "Time to compute frame 19: 0:00:00.320240\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "trajectory_nolidar_bm_sift, combined_dict_sift = visual_odometry(handler,\n",
+    "                                        filter_match_distance=0.45,\n",
+    "                                        detector='sift',\n",
+    "                                        # matching='FLANN',\n",
+    "                                        stereo_matcher='bm',\n",
+    "                                        mask=None,     \n",
+    "                                        subset=None,\n",
+    "                                        plot=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtering feature matches at threshold of 0.45*distance\n",
+      "Time to compute frame 1: 0:00:11.504374\n",
+      "Time to compute frame 2: 0:00:11.580020\n",
+      "Time to compute frame 3: 0:00:12.188442\n",
+      "Time to compute frame 4: 0:00:11.488733\n",
+      "Time to compute frame 5: 0:00:11.838421\n",
+      "Time to compute frame 6: 0:00:11.189060\n",
+      "Time to compute frame 7: 0:00:10.857520\n",
+      "Time to compute frame 8: 0:00:10.890079\n",
+      "Time to compute frame 9: 0:00:10.979559\n",
+      "Time to compute frame 10: 0:00:12.750075\n",
+      "Time to compute frame 11: 0:00:10.775181\n",
+      "Time to compute frame 12: 0:00:10.849079\n",
+      "Time to compute frame 13: 0:00:10.854656\n",
+      "Time to compute frame 14: 0:00:10.850013\n",
+      "Time to compute frame 15: 0:00:10.999396\n",
+      "Time to compute frame 16: 0:00:12.181440\n",
+      "Time to compute frame 17: 0:00:10.754491\n",
+      "Time to compute frame 18: 0:00:11.206779\n",
+      "Time to compute frame 19: 0:00:11.211777\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# import sys \n",
+    "# stdoutOrigin=sys.stdout \n",
+    "# sys.stdout = open(\"log.csv\", \"w\")\n",
+    "# # open file for writing\n",
+    "\n",
+    "trajectory_nolidar_bm_harris, combined_dict_harris = visual_odometry(handler,\n",
+    "                                        filter_match_distance=0.45,\n",
+    "                                        detector='harris',\n",
+    "                                        # matching='FLANN',\n",
+    "                                        stereo_matcher='bm',\n",
+    "                                        mask=None,     \n",
+    "                                        subset=None,\n",
+    "                                        plot=False)\n",
+    "\n",
+    "# print(combined_dict[\"Frame_t\"][0])\n",
+    "\n",
+    "\n",
+    "# f = open(\"dict.txt\",\"w\")\n",
+    "\n",
+    "# # write file\n",
+    "# f.write( str(combined_dict) )\n",
+    "\n",
+    "# # close file\n",
+    "# f.close()\n",
+    "\n",
+    "# import json\n",
+    "\n",
+    "\n",
+    "# json = json.dumps(combined_dict)\n",
+    "\n",
+    "# # open file for writing, \"w\" \n",
+    "# f = open(\"dict.json\",\"w\")\n",
+    "\n",
+    "# # write json object to file\n",
+    "# f.write(json)\n",
+    "\n",
+    "# # close file\n",
+    "# f.close()\n",
+    "# print(trajectory_nolidar_bm[1,1])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtering feature matches at threshold of 0.45*distance\n",
+      "Time to compute frame 1: 0:00:11.600560\n",
+      "Time to compute frame 2: 0:00:11.199363\n",
+      "Time to compute frame 3: 0:00:12.384048\n",
+      "Time to compute frame 4: 0:00:11.161651\n",
+      "Time to compute frame 5: 0:00:11.257857\n",
+      "Time to compute frame 6: 0:00:11.602484\n",
+      "Time to compute frame 7: 0:00:11.805217\n",
+      "Time to compute frame 8: 0:00:11.566972\n",
+      "Time to compute frame 9: 0:00:12.256207\n",
+      "Time to compute frame 10: 0:00:11.729479\n",
+      "Time to compute frame 11: 0:00:14.797274\n",
+      "Time to compute frame 12: 0:00:13.728632\n",
+      "Time to compute frame 13: 0:00:14.532424\n",
+      "Time to compute frame 14: 0:00:13.978577\n",
+      "Time to compute frame 15: 0:00:13.376986\n",
+      "Time to compute frame 16: 0:00:13.796147\n",
+      "Time to compute frame 17: 0:00:13.112092\n",
+      "Time to compute frame 18: 0:00:12.228565\n",
+      "Time to compute frame 19: 0:00:12.673818\n"
+     ]
+    }
+   ],
+   "source": [
+    "trajectory_nolidar_bm_harris_skew, combined_dict_harris_skew = visual_odometry(handler,\n",
+    "                                        filter_match_distance=0.45,\n",
+    "                                        detector='harris-skew',\n",
+    "                                        # matching='FLANN',\n",
+    "                                        stereo_matcher='bm',\n",
+    "                                        mask=None,     \n",
+    "                                        subset=None,\n",
+    "                                        plot=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import sys \n",
+    "# stdoutOrigin=sys.stdout \n",
+    "# sys.stdout = open(\"testing.csv\", \"w\")\n",
+    "# print(combined_dict[\"Frame_t\"][5][0]) #Index 0 of frame1, x value\n",
+    "# print(combined_dict[\"Frame_t\"][0][1]) #Index 0 of frame1, y value\n",
+    "\n",
+    "# print(combined_dict[\"Frame_t+1\"][0][2]) #Index 0 of frame2, z value\n",
+    "\n",
+    "# print(combined_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(12,8))\n",
+    "fig2 = plt.figure(figsize=(12,8))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ay = fig2.add_subplot(111,projection='3d')\n",
+    "\n",
+    "ax.plot(trajectory_nolidar_bm_sift[:, :, 3][:, 0], \n",
+    "        trajectory_nolidar_bm_sift[:, :, 3][:, 1], \n",
+    "        trajectory_nolidar_bm_sift[:, :, 3][:, 2], label='estimated', color='orange')\n",
+    "\n",
+    "ay.plot(handler.gt[:, :, 3][:, 0], \n",
+    "        handler.gt[:, :, 3][:, 1], \n",
+    "        handler.gt[:, :, 3][:, 2], label='ground truth')\n",
+    "\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "ax.set_zlabel('z')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(12,8))\n",
+    "fig2 = plt.figure(figsize=(12,8))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ay = fig2.add_subplot(111,projection='3d')\n",
+    "\n",
+    "ax.plot(trajectory_nolidar_bm_harris[:, :, 3][:, 0], \n",
+    "        trajectory_nolidar_bm_harris[:, :, 3][:, 1], \n",
+    "        trajectory_nolidar_bm_harris[:, :, 3][:, 2], label='estimated', color='orange')\n",
+    "\n",
+    "ay.plot(handler.gt[:, :, 3][:, 0], \n",
+    "        handler.gt[:, :, 3][:, 1], \n",
+    "        handler.gt[:, :, 3][:, 2], label='ground truth')\n",
+    "\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "ax.set_zlabel('z')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(12,8))\n",
+    "fig2 = plt.figure(figsize=(12,8))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ay = fig2.add_subplot(111,projection='3d')\n",
+    "\n",
+    "ax.plot(trajectory_nolidar_bm_harris_skew[:, :, 3][:, 0], \n",
+    "        trajectory_nolidar_bm_harris_skew[:, :, 3][:, 1], \n",
+    "        trajectory_nolidar_bm_harris_skew[:, :, 3][:, 2], label='estimated', color='orange')\n",
+    "\n",
+    "ay.plot(handler.gt[:, :, 3][:, 0], \n",
+    "        handler.gt[:, :, 3][:, 1], \n",
+    "        handler.gt[:, :, 3][:, 2], label='ground truth')\n",
+    "\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "ax.set_zlabel('z')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "\n",
+    "\n",
+    "# open file for writing, \"w\" \n",
+    "f = open(\"dd.json\",\"w\")\n",
+    "# write json object to file\n",
+    "f.write(str(handler.gt))\n",
+    "\n",
+    "# close file\n",
+    "f.close()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_error(ground_truth, estimated, error_type='mse'):\n",
+    "    '''\n",
+    "    Takes arrays of ground truth and estimated poses of shape Nx3x4, and computes error using\n",
+    "    Euclidean distance between true and estimated 3D coordinate at each position.\n",
+    "    \n",
+    "    Arguments:\n",
+    "    ground_truth -- Nx3x4 array of ground truth poses\n",
+    "    estimated -- Nx3x4 array of estimated poses\n",
+    "    \n",
+    "    Optional Arguments:\n",
+    "    error_type -- (str) can be 'mae', 'mse', 'rmse', or 'all' to return dictionary of all 3\n",
+    "    \n",
+    "    Returns:\n",
+    "    error -- either a float or dictionary of error types and float values\n",
+    "    \n",
+    "    '''\n",
+    "    # Find the number of frames in the estimated trajectory to compare with\n",
+    "    nframes_est = estimated.shape[0]-1\n",
+    "    \n",
+    "    def get_mse(ground_truth, estimated):\n",
+    "        se = np.sqrt((ground_truth[nframes_est, 0, 3] - estimated[:, 0, 3])**2 \n",
+    "                    + (ground_truth[nframes_est, 1, 3] - estimated[:, 1, 3])**2 \n",
+    "                    + (ground_truth[nframes_est, 2, 3] - estimated[:, 2, 3])**2)**2\n",
+    "        mse = se.mean()\n",
+    "        return mse\n",
+    "    \n",
+    "    def get_mae(ground_truth, estimated):\n",
+    "        ae = np.sqrt((ground_truth[nframes_est, 0, 3] - estimated[:, 0, 3])**2 \n",
+    "                    + (ground_truth[nframes_est, 1, 3] - estimated[:, 1, 3])**2 \n",
+    "                    + (ground_truth[nframes_est, 2, 3] - estimated[:, 2, 3])**2)\n",
+    "        mae = ae.mean()\n",
+    "        return mae\n",
+    "    \n",
+    "    if error_type == 'mae':\n",
+    "        return get_mae(ground_truth, estimated)\n",
+    "    elif error_type == 'mse':\n",
+    "        return get_mse(ground_truth, estimated)\n",
+    "    elif error_type == 'rmse':\n",
+    "        return np.sqrt(get_mse(ground_truth, estimated))\n",
+    "    elif error_type == 'all':\n",
+    "        mae = get_mae(ground_truth, estimated)\n",
+    "        mse = get_mse(ground_truth, estimated)\n",
+    "        rmse = np.sqrt(mse)\n",
+    "        return {'mae': mae,\n",
+    "                'rmse': rmse,\n",
+    "                'mse': mse}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mae': 2.7842448522105974,\n",
+       " 'rmse': 3.1313389627085826,\n",
+       " 'mse': 9.805283699376862}"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calculate_error(handler.gt, trajectory_nolidar_bm_sift, 'all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mae': 3.0222301260874653,\n",
+       " 'rmse': 3.2377263200733766,\n",
+       " 'mse': 10.48287172369589}"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calculate_error(handler.gt, trajectory_nolidar_bm_harris, 'all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mae': 3.014293600689606,\n",
+       " 'rmse': 3.225744618700557,\n",
+       " 'mse': 10.405428345075604}"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calculate_error(handler.gt, trajectory_nolidar_bm_harris_skew, 'all')"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "8e498f732d7c2aa228df44a2a1ad0a06b158366b5ada381e7ddd871473ea3a71"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.10.2 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.2"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/extraction/bivariate.py b/extraction/bivariate.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee1849340c61162c3a8c76a50fb9ca4431fca77f
--- /dev/null
+++ b/extraction/bivariate.py
@@ -0,0 +1,52 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import argparse
+import math
+from scipy.stats import multivariate_t
+from convolution import convolution
+
+def gauss2D(shape=(3,3),sigma=0.5):
+    """
+    2D gaussian mask - should give the same result as MATLAB's
+    fspecial('gaussian',[shape],[sigma])
+    """
+    m,n = [(ss-1.)/2. for ss in shape]
+    y,x = np.ogrid[-m:m+1,-n:n+1]
+    h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )/(sigma*np.sqrt(2*np.pi))
+    h[ h < np.finfo(h.dtype).eps*h.max() ] = 0
+    sumh = h.sum()
+    if sumh != 0:
+        h /= sumh
+    return h
+
+def attempt_skewed2D(shape=(3,3),upsilon=2):
+    """
+    2D skewed mask - attempt
+    """
+    m1,n1 = [(ss-1.)/2. for ss in shape]
+    y1,x1 = np.mgrid[0:2*m1+1:1, 0:2*n1+1:1]
+    pos = np.dstack((x1, y1))
+    
+    rv = multivariate_t([1*(2*m1+1)/5, 3*(2*n1+1)/5],[[1, 0], [0, 1]], df=upsilon)
+    tile = np.array(rv.pdf(pos))
+    m,n = [(ss-1.)/2. for ss in shape]
+    y,x = np.ogrid[-m:m+1,-n:n+1]
+    h = tile
+    h[ h < np.finfo(h.dtype).eps*h.max() ] = 0
+    sumh = h.sum()
+    if sumh != 0:
+        h /= sumh
+    return h
+
+def gaussian_blur(image, verbose=False):
+    kernel = gauss2D((5,5),1)
+    return convolution(image, kernel, average=False, verbose=verbose)
+
+def skew_blur(image, verbose=False):
+    kernel = attempt_skewed2D((5,5),1)
+    return convolution(image, kernel, average=False, verbose=verbose)
+
+if __name__ == '__main__':
+    ap = argparse.ArgumentParser()
+    ap.add_argument("-i", "--image", required=True, help="Path to the image")
+    args = vars(ap.parse_args())
\ No newline at end of file
diff --git a/extraction/convolution.py b/extraction/convolution.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5aea4986b9f57382f8095364a1e6f1eaf5d711d
--- /dev/null
+++ b/extraction/convolution.py
@@ -0,0 +1,56 @@
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+ 
+ 
+def convolution(image, kernel, average=False, verbose=False):
+    if len(image.shape) == 3:
+        # print("Found 3 Channels : {}".format(image.shape))
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        # print("Converted to Gray Channel. Size : {}".format(image.shape))
+    # else:
+        # print("Image Shape : {}".format(image.shape))
+ 
+    # print("Kernel Shape : {}".format(kernel.shape))
+ 
+    if verbose:
+        plt.imshow(image, cmap='gray')
+        plt.title("Image")
+        plt.show()
+ 
+    image_row, image_col = image.shape
+
+    
+    kernel_row, kernel_col = kernel.shape
+ 
+    output = np.zeros(image.shape)
+ 
+    pad_height = int((kernel_row - 1) / 2)
+    pad_width = int((kernel_col - 1) / 2)
+ 
+    padded_image = np.zeros((image_row + (2 * pad_height), image_col + (2 * pad_width)))
+ 
+    padded_image[pad_height:padded_image.shape[0] - pad_height, pad_width:padded_image.shape[1] - pad_width] = image
+ 
+    if verbose:
+        plt.imshow(padded_image, cmap='gray')
+        plt.title("Padded Image")
+        plt.show()
+ 
+    for row in range(image_row):
+        for col in range(image_col):
+            output[row, col] = np.sum(kernel * padded_image[row:row + kernel_row, col:col + kernel_col])
+            if average:
+                output[row, col] /= kernel.shape[0] * kernel.shape[1]
+ 
+    # print("Output Image size : {}".format(output.shape))
+    
+    # gray = output
+    # backtorgb = cv2.cvtColor(gray,cv2.COLOR_GRAY2RGB)
+ 
+    if verbose:
+        plt.imshow(output, cmap='gray')
+        plt.title("Output Image using {}X{} Kernel".format(kernel_row, kernel_col))
+        plt.show()
+ 
+    return output.astype(np.float32)
\ No newline at end of file
diff --git a/extraction/data_placement.PNG b/extraction/data_placement.PNG
deleted file mode 100644
index 8bab4ec368e65fd0566119042c84c6849fad993b..0000000000000000000000000000000000000000
Binary files a/extraction/data_placement.PNG and /dev/null differ
diff --git a/extraction/extraction.ipynb b/extraction/extraction.ipynb
deleted file mode 100644
index 647515238afdf7b611d68b5befd282925e0b79bd..0000000000000000000000000000000000000000
--- a/extraction/extraction.ipynb
+++ /dev/null
@@ -1,1778 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import cv2\n",
-    "import matplotlib.pyplot as plt\n",
-    "import os\n",
-    "from azure.storage.blob import ContainerClient\n",
-    "import io\n",
-    "import time\n",
-    "from PIL import Image\n",
-    "import datetime"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "523\n"
-     ]
-    }
-   ],
-   "source": [
-    "file_path_color_left = \"data/00\\image_left/\"\n",
-    "file_path_color_right = \"data/00\\image_right/\"\n",
-    "\n",
-    "left_images = os.listdir(file_path_color_left)\n",
-    "right_images = os.listdir(file_path_color_right)\n",
-    "\n",
-    "print(len(left_images))\n",
-    "# plt.figure(figsize=(12,4))\n",
-    "# plt.imshow(cv2.imread(file_path_color_left + left_images[165],0)) \n",
-    "#turning it into greyscale because we don't really need color unless we're interested in matching moving objects/features. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def pixel_coord_np(height,width):\n",
-    "    \"\"\"\n",
-    "    Pixel in homogenous coordinate\n",
-    "    Returns:\n",
-    "        Pixel coordinate:       [3, width * height]\n",
-    "    \"\"\"\n",
-    "    x = np.linspace(0, width - 1, width).astype(np.float32)\n",
-    "    y = np.linspace(0, height - 1, height).astype(np.float32)\n",
-    "    [x, y] = np.meshgrid(x, y)\n",
-    "    return np.vstack((x.flatten(), y.flatten(), np.ones_like(x.flatten())))\n",
-    "\n",
-    "\n",
-    "def intrinsic_from_fov(height, width, fov=90):\n",
-    "    \"\"\"\n",
-    "    Basic Pinhole Camera Model\n",
-    "    intrinsic params from fov and sensor width and height in pixels\n",
-    "    Returns:\n",
-    "        K:      [4, 4]\n",
-    "    \"\"\"\n",
-    "    px, py = (width / 2, height / 2)\n",
-    "    hfov = fov / 360. * 2. * np.pi\n",
-    "    fx = width / (2. * np.tan(hfov / 2.))\n",
-    "\n",
-    "    vfov = 2. * np.arctan(np.tan(hfov / 2) * height / width)\n",
-    "    fy = height / (2. * np.tan(vfov / 2.))\n",
-    "\n",
-    "    return np.array([[fx, 0, px, 0.],\n",
-    "                     [0, fy, py, 0.],\n",
-    "                     [0, 0, 1., 0.],\n",
-    "                     [0., 0., 0., 1.]])\n",
-    "\n",
-    "def quaternion_rotation_matrix(Q):\n",
-    "    \"\"\"\n",
-    "    Covert a quaternion into a full projection matrix.\n",
-    " \n",
-    "    Input\n",
-    "    :param Q: A 7 element array representing translation and the quaternion (q0,q1,q2,q3) \n",
-    " \n",
-    "    Output\n",
-    "    :return: A 3x4 element matrix representing the full projection matrix. \n",
-    "             This projection matrix converts a point in the local reference \n",
-    "             frame to a point in the global reference frame.\n",
-    "    \"\"\"\n",
-    "    # Extract the values from Q\n",
-    "    t0 = Q[0]\n",
-    "    t1 = Q[1]\n",
-    "    t2 = Q[2]\n",
-    "    q0 = Q[3]\n",
-    "    q1 = Q[4]\n",
-    "    q2 = Q[5]\n",
-    "    q3 = Q[6]\n",
-    "     \n",
-    "    # First row of the rotation matrix\n",
-    "    r00 = 2 * (q0 * q0 + q1 * q1) - 1\n",
-    "    r01 = 2 * (q1 * q2 - q0 * q3)\n",
-    "    r02 = 2 * (q1 * q3 + q0 * q2)\n",
-    "     \n",
-    "    # Second row of the rotation matrix\n",
-    "    r10 = 2 * (q1 * q2 + q0 * q3)\n",
-    "    r11 = 2 * (q0 * q0 + q2 * q2) - 1\n",
-    "    r12 = 2 * (q2 * q3 - q0 * q1)\n",
-    "     \n",
-    "    # Third row of the rotation matrix\n",
-    "    r20 = 2 * (q1 * q3 - q0 * q2)\n",
-    "    r21 = 2 * (q2 * q3 + q0 * q1)\n",
-    "    r22 = 2 * (q0 * q0 + q3 * q3) - 1\n",
-    "     \n",
-    "    # 3x4 projection matrix\n",
-    "    pro_matrix = np.array([[r00, r01, r02, t0],\n",
-    "                           [r10, r11, r12, t1],\n",
-    "                           [r20, r21, r22, t2]])\n",
-    "                            \n",
-    "    return pro_matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "523\n"
-     ]
-    }
-   ],
-   "source": [
-    "class Dataset_Handler():\n",
-    "    def __init__(self, sequence, lidar=False, progress_bar=True, low_memory=True):\n",
-    "        \n",
-    "        \n",
-    "        # This will tell our odometry function if handler contains lidar info\n",
-    "        self.lidar = lidar\n",
-    "        # This will tell odometry functin how to access data from this object\n",
-    "        self.low_memory = low_memory\n",
-    "        \n",
-    "        # Set file paths and get ground truth poses\n",
-    "        self.seq_dir = \"data\\{}/\".format(sequence)\n",
-    "        self.poses_dir = \"data\\{}.csv\".format(sequence)\n",
-    "        self.depth_dir = \"data\\{}/\".format(sequence)\n",
-    "\n",
-    "        poses = pd.read_csv(self.poses_dir, header=None)\n",
-    "        \n",
-    "        # Get names of files to iterate through\n",
-    "        self.left_image_files = os.listdir(self.seq_dir + 'image_left')\n",
-    "        self.right_image_files = os.listdir(self.seq_dir + 'image_right')\n",
-    "        self.left_depth_files = os.listdir(self.depth_dir + 'depth_left')\n",
-    "        self.right_depth_files = os.listdir(self.depth_dir + 'depth_right')\n",
-    "        \n",
-    "        # self.velodyne_files = os.listdir(self.seq_dir + 'flow')\n",
-    "        self.num_frames = len(self.left_image_files)\n",
-    "        print(self.num_frames)\n",
-    "        # self.lidar_path = self.seq_dir + 'flow/'\n",
-    "        self.first_image_left = cv2.imread(self.seq_dir + 'image_left/' \n",
-    "                                               + self.left_image_files[0])\n",
-    "        height = 480\n",
-    "        width = 640\n",
-    "        K = intrinsic_from_fov(height,width)\n",
-    "        self.P0 = np.array(K[:3,:4])\n",
-    "        self.P1 = np.array(K[:3,:4])\n",
-    "        \n",
-    "        \n",
-    "        # Get calibration details for scene\n",
-    "        # calib = pd.read_csv(self.seq_dir + 'calib.txt', delimiter=' ', header=None, index_col=0)\n",
-    "        # self.P0 = np.array(calib.loc['P0:']).reshape((3,4))\n",
-    "        # self.P1 = np.array(calib.loc['P1:']).reshape((3,4))\n",
-    "        # self.P2 = np.array(calib.loc['P2:']).reshape((3,4)) #RGB cams\n",
-    "        # self.P3 = np.array(calib.loc['P3:']).reshape((3,4)) #RGB cams\n",
-    "        # This is the transformation matrix for LIDAR\n",
-    "        # self.Tr = np.array(calib.loc['Tr:']).reshape((3,4))\n",
-    "        \n",
-    "        # Get times and ground truth poses\n",
-    "        self.times = np.array(pd.read_csv(self.seq_dir + 'times.txt', \n",
-    "                                          delimiter=' ', \n",
-    "                                          header=None))\n",
-    "        self.gt = np.zeros((len(poses), 3, 4))\n",
-    "        for i in range(len(poses)):\n",
-    "            self.gt[i] = np.array(quaternion_rotation_matrix(poses.iloc[i])).reshape((3, 4))\n",
-    "        \n",
-    "        # Get images and lidar loaded\n",
-    "        if self.low_memory:\n",
-    "            # Will use generators to provide data sequentially to save RAM\n",
-    "            # Use class method to set up generators\n",
-    "            self.reset_frames()\n",
-    "            # Store original frame to memory for testing functions\n",
-    "            self.first_image_left = cv2.imread(self.seq_dir + 'image_left/' \n",
-    "                                               + self.left_image_files[0])\n",
-    "            self.first_image_right = cv2.imread(self.seq_dir + 'image_right/' \n",
-    "                                               + self.right_image_files[0])\n",
-    "            self.second_image_left = cv2.imread(self.seq_dir + 'image_left/' \n",
-    "                                               + self.left_image_files[1])\n",
-    "            self.first_depth_left = np.load(self.depth_dir + 'depth_left/'\n",
-    "                                                + self.left_depth_files[0])\n",
-    "            self.first_depth_right = np.load(self.depth_dir + 'depth_right/'\n",
-    "                                                + self.right_depth_files[0])\n",
-    "            self.second_depth_left = np.load(self.depth_dir + 'depth_left/'\n",
-    "                                                + self.left_depth_files[1])\n",
-    "                                                \n",
-    "            if self.lidar:\n",
-    "                self.first_pointcloud = np.fromfile(self.lidar_path + self.velodyne_files[0],\n",
-    "                                                    dtype=np.float32, \n",
-    "                                                    count=-1).reshape((-1, 4))\n",
-    "            self.imheight = height\n",
-    "            self.imwidth = width\n",
-    "            \n",
-    "        else:\n",
-    "            # If RAM is not a concern (>32GB), pass low_memory=False\n",
-    "            if progress_bar:\n",
-    "                import progressbar\n",
-    "                bar = progressbar.ProgressBar(max_value=self.num_frames)\n",
-    "            self.images_left = []\n",
-    "            self.images_right = []\n",
-    "            self.depths_left = []\n",
-    "            self.depths_right =[]\n",
-    "            self.pointclouds = []\n",
-    "            for i, name_left in enumerate(self.left_image_files):\n",
-    "                name_right = self.right_image_files[i]\n",
-    "                d_left = self.left_depth_files\n",
-    "                d_right = self.right_depth_files\n",
-    "                self.images_left.append(cv2.imread(self.seq_dir + 'image_left/' + name_left))\n",
-    "                self.images_right.append(cv2.imread(self.seq_dir + 'image_right/' + name_right))\n",
-    "                self.depths_left.append(np.load(self.depth_dir + 'depth_left/' + d_left))\n",
-    "                self.depths_right.append(np.load(self.depth_dir + 'depth_right/' + d_right))\n",
-    "                if self.lidar:\n",
-    "                    pointcloud = np.fromfile(self.lidar_path + self.velodyne_files[i], \n",
-    "                                             dtype=np.float32, \n",
-    "                                             count=-1).reshape([-1,4])\n",
-    "                    self.pointclouds.append(pointcloud)\n",
-    "                if progress_bar:\n",
-    "                    bar.update(i+1)\n",
-    "                \n",
-    "            self.imheight = self.images_left[0].shape[0]\n",
-    "            self.imwidth = self.images_left[0].shape[1]\n",
-    "            # Keep consistent instance variable names as when using low_memory\n",
-    "            self.first_image_left = self.images_left[0]\n",
-    "            self.first_image_right = self.images_right[0]\n",
-    "            self.second_image_left = self.images_left[1]\n",
-    "            if self.lidar:\n",
-    "                self.first_pointcloud = self.pointclouds[0]\n",
-    "            \n",
-    "    def reset_frames(self):\n",
-    "        # Resets all generators to the first frame of the sequence\n",
-    "        self.images_left = (cv2.imread(self.seq_dir + 'image_left/' + name_left)\n",
-    "                            for name_left in self.left_image_files)\n",
-    "        self.images_right = (cv2.imread(self.seq_dir + 'image_right/' + name_right)\n",
-    "                            for name_right in self.right_image_files)\n",
-    "        self.depths_left = (np.load(self.depth_dir+'depth_left/' + d_left)\n",
-    "                            for d_left in self.left_depth_files)\n",
-    "        self.depths_right = (np.load(self.depth_dir+'depth_right/' + d_right)\n",
-    "                            for d_right in self.right_depth_files)                    \n",
-    "        if self.lidar:\n",
-    "            self.pointclouds = (np.fromfile(self.lidar_path + velodyne_file, \n",
-    "                                            dtype=np.float32, \n",
-    "                                            count=-1).reshape((-1, 4))\n",
-    "                                for velodyne_file in self.velodyne_files)\n",
-    "        pass\n",
-    "handler = Dataset_Handler(\"00\")\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_left_disparity_map(left_image, right_image, matcher = 'bm', rgb = True, verbose = True):\n",
-    "    '''\n",
-    "    Takes a left and right pair of images to computes the disparity map for the left\n",
-    "    image. \n",
-    "    \n",
-    "    Arguments:\n",
-    "    img_left -- image from left camera\n",
-    "    img_right -- image from right camera\n",
-    "    \n",
-    "    Optional Arguments:\n",
-    "    matcher -- (str) can be 'bm' for StereoBM or 'sgbm' for StereoSGBM matching.\n",
-    "    rgb -- (bool) set to True if passing RGB images as input. \n",
-    "    verbose -- (bool) set to True to report matching type and time to compute\n",
-    "    \n",
-    "    Returns:\n",
-    "    disp_left -- disparity map for the left camera image\n",
-    "    \n",
-    "    '''\n",
-    "    sad_window = 6\n",
-    "    num_disparities = sad_window * 16\n",
-    "    block_size = 11\n",
-    "    matcher_name = matcher\n",
-    "\n",
-    "    if matcher_name == 'bm':\n",
-    "        matcher = cv2.StereoBM_create(numDisparities=num_disparities,\n",
-    "                                        blockSize=block_size)\n",
-    "                                        \n",
-    "    elif matcher_name == 'sgbm':\n",
-    "        matcher = cv2.StereoSGBM_create(numDisparities=num_disparities,\n",
-    "                                        blockSize=block_size,\n",
-    "                                        P1 = 8*3*sad_window**2,\n",
-    "                                        P2 = 32*3*sad_window**2,\n",
-    "                                        mode = cv2.STEREO_SGBM_MODE_SGBM_3WAY)\n",
-    "    if rgb:\n",
-    "        left_image = cv2.cvtColor(left_image, cv2.COLOR_BGR2GRAY)\n",
-    "        right_image = cv2.cvtColor(right_image, cv2.COLOR_BGR2GRAY)\n",
-    "    \n",
-    "    start = datetime.datetime.now()\n",
-    "    disp_left = matcher.compute(left_image, right_image).astype(np.float32)/16\n",
-    "    end = datetime.datetime.now()\n",
-    "\n",
-    "    if verbose:\n",
-    "        print(f'Time to compute disparity map using Stereo{matcher_name.upper()}:', end-start)\n",
-    "    \n",
-    "    return disp_left\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time to compute disparity map using StereoBM: 0:00:00.009997\n"
-     ]
-    }
-   ],
-   "source": [
-    "first_left = cv2.imread(file_path_color_left + left_images[167])\n",
-    "first_right = cv2.imread(file_path_color_right + right_images[167])\n",
-    "\n",
-    "disp = compute_left_disparity_map(left_image=first_left, right_image=first_right,\n",
-    "                                    matcher = 'bm', verbose = True)\n",
-    "\n",
-    "plt.figure(figsize=(15,15))\n",
-    "plt.imshow(disp);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def decompose_projection_matrix(p):\n",
-    "    '''\n",
-    "    Shortcut to use cv2.decomposeProjectionMatrix(), which only returns k, r, t, and divides\n",
-    "    t by the scale, then returns it as a vector with shape (3,) (non-homogeneous)\n",
-    "    \n",
-    "    Arguments:\n",
-    "    p -- projection matrix to be decomposed\n",
-    "    \n",
-    "    Returns:\n",
-    "    k, r, t -- intrinsic matrix, rotation matrix, and 3D translation vector\n",
-    "    \n",
-    "    '''\n",
-    "    k, r, t, _, _, _, _ = cv2.decomposeProjectionMatrix(p)\n",
-    "    t = (t / t[3])[:3]\n",
-    "    \n",
-    "    return k, r, t\n",
-    "def calc_depth_map(disp_left, k_left, t_left, t_right, rectified=True):\n",
-    "    '''\n",
-    "    Assuming we don't have access to the depth map...\n",
-    "    \n",
-    "    Calculate depth map using a disparity map, intrinsic camera matrix, and translation vectors\n",
-    "    from camera extrinsic matrices (to calculate baseline). Note that default behavior is for\n",
-    "    rectified projection matrix for right camera. If using a regular projection matrix, pass\n",
-    "    rectified=False to avoid issues.\n",
-    "\n",
-    "    \n",
-    "    \n",
-    "    Arguments:\n",
-    "    disp_left -- disparity map of left camera\n",
-    "    k_left -- intrinsic matrix for left camera\n",
-    "    t_left -- translation vector for left camera\n",
-    "    t_right -- translation vector for right camera\n",
-    "    \n",
-    "    Optional Arguments:\n",
-    "    rectified -- (bool) set to False if t_right is not from rectified projection matrix\n",
-    "    \n",
-    "    Returns:\n",
-    "    depth_map -- calculated depth map for left camera\n",
-    "    \n",
-    "    '''\n",
-    "    # Get focal length of x axis for left camera\n",
-    "    f = k_left[0][0]\n",
-    "    \n",
-    "    # Calculate baseline of stereo pair\n",
-    "    if rectified:\n",
-    "        b = t_right[0] - t_left[0] \n",
-    "    else:\n",
-    "        b = t_left[0] - t_right[0]\n",
-    "        \n",
-    "    # Avoid instability and division by zero\n",
-    "    disp_left[disp_left == 0.0] = 0.1\n",
-    "    disp_left[disp_left == -1.0] = 0.1\n",
-    "    \n",
-    "    # Make empty depth map then fill with depth\n",
-    "    depth_map = np.ones(disp_left.shape)\n",
-    "    depth_map = f * b / disp_left\n",
-    "    \n",
-    "    return depth_map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[3.1132812, 3.1132812, 3.1152344, ..., 3.9003906, 3.9023438,\n",
-       "        3.9042969],\n",
-       "       [3.1171875, 3.1191406, 3.1191406, ..., 3.9082031, 3.9101562,\n",
-       "        3.9121094],\n",
-       "       [3.1230469, 3.1230469, 3.125    , ..., 3.9160156, 3.9179688,\n",
-       "        3.9199219],\n",
-       "       ...,\n",
-       "       [2.0664062, 2.0664062, 2.0683594, ..., 3.1738281, 3.2265625,\n",
-       "        3.2285156],\n",
-       "       [2.0625   , 2.0644531, 2.0644531, ..., 3.1679688, 3.21875  ,\n",
-       "        3.2207031],\n",
-       "       [2.0585938, 2.0605469, 2.0605469, ..., 3.2070312, 3.2089844,\n",
-       "        3.2128906]], dtype=float32)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "handler.first_depth_left"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def pointcloud2image(pointcloud, imheight, imwidth, Tr, P0):\n",
-    "    '''\n",
-    "    ...\n",
-    "    Takes a pointcloud of shape Nx4 and projects it onto an image plane, first transforming\n",
-    "    the X, Y, Z coordinates of points to the camera frame with tranformation matrix Tr, then\n",
-    "    projecting them using camera projection matrix P0.\n",
-    "    \n",
-    "    Arguments:\n",
-    "    pointcloud -- array of shape Nx4 containing (X, Y, Z, reflectivity)\n",
-    "    imheight -- height (in pixels) of image plane\n",
-    "    imwidth -- width (in pixels) of image plane\n",
-    "    Tr -- 3x4 transformation matrix between lidar (X, Y, Z, 1) homogeneous and camera (X, Y, Z)\n",
-    "    P0 -- projection matrix of camera (should have identity transformation if Tr used)\n",
-    "    \n",
-    "    Returns:\n",
-    "    render -- a (imheight x imwidth) array containing depth (Z) information from lidar scan\n",
-    "    \n",
-    "    '''\n",
-    "\n",
-    "    pointcloud = pointcloud[pointcloud[:, 0] > 0]\n",
-    "    \n",
-    "\n",
-    "    pointcloud = np.hstack([pointcloud[:, :3], np.ones(pointcloud.shape[0]).reshape((-1,1))])\n",
-    "    \n",
-    "    # Transform pointcloud into camera coordinate frame\n",
-    "    cam_xyz = Tr.dot(pointcloud.T)\n",
-    "    \n",
-    "    # Ignore any points behind the camera (probably redundant but just in case)\n",
-    "    cam_xyz = cam_xyz[:, cam_xyz[2] > 0]\n",
-    "    \n",
-    "    # Extract the Z row which is the depth from camera\n",
-    "    depth = cam_xyz[2].copy()\n",
-    "    \n",
-    "    # Project coordinates in camera frame to flat plane at Z=1 by dividing by Z\n",
-    "    cam_xyz /= cam_xyz[2]\n",
-    "    \n",
-    "    # Add row of ones to make our 3D coordinates on plane homogeneous for dotting with P0\n",
-    "    cam_xyz = np.vstack([cam_xyz, np.ones(cam_xyz.shape[1])])\n",
-    "    \n",
-    "    # Get pixel coordinates of X, Y, Z points in camera coordinate frame\n",
-    "    projection = P0.dot(cam_xyz)\n",
-    "    #projection = (projection / projection[2])\n",
-    "    \n",
-    "    # Turn pixels into integers for indexing\n",
-    "    pixel_coordinates = np.round(projection.T, 0)[:, :2].astype('int')\n",
-    "    #pixel_coordinates = np.array(pixel_coordinates)\n",
-    "    \n",
-    "    # Limit pixel coordinates considered to those that fit on the image plane\n",
-    "    indices = np.where((pixel_coordinates[:, 0] < imwidth)\n",
-    "                       & (pixel_coordinates[:, 0] >= 0)\n",
-    "                       & (pixel_coordinates[:, 1] < imheight)\n",
-    "                       & (pixel_coordinates[:, 1] >= 0)\n",
-    "                      )\n",
-    "    pixel_coordinates = pixel_coordinates[indices]\n",
-    "    depth = depth[indices]\n",
-    "    \n",
-    "    # Establish empty render image, then fill with the depths of each point\n",
-    "    render = np.zeros((imheight, imwidth))\n",
-    "    for j, (u, v) in enumerate(pixel_coordinates):\n",
-    "        if u >= imwidth or u < 0:\n",
-    "            continue\n",
-    "        if v >= imheight or v < 0:\n",
-    "            continue\n",
-    "        render[v, u] = depth[j]\n",
-    "    # Fill zero values with large distance so they will be ignored. (Using same max value)\n",
-    "\n",
-    "\n",
-    "    \n",
-    "    return render"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def stereo_2_depth(img_left, img_right, P0, P1, matcher='bm', rgb=True, verbose=False, \n",
-    "                   rectified=True):\n",
-    "    '''\n",
-    "    Takes stereo pair of images and returns a depth map for the left camera. If your projection\n",
-    "    matrices are not rectified, set rectified=False.\n",
-    "    \n",
-    "    Arguments:\n",
-    "    img_left -- image of left camera\n",
-    "    img_right -- image of right camera\n",
-    "    P0 -- Projection matrix for the left camera\n",
-    "    P1 -- Projection matrix for the right camera\n",
-    "    \n",
-    "    Optional Arguments:\n",
-    "    matcher -- (str) can be 'bm' for StereoBM or 'sgbm' for StereoSGBM\n",
-    "    rgb -- (bool) set to True if images passed are RGB. Default is False\n",
-    "    verbose -- (bool) set to True to report computation time and method\n",
-    "    rectified -- (bool) set to False if P1 not rectified to P0. Default is True\n",
-    "    \n",
-    "    Returns:\n",
-    "    depth -- depth map for left camera\n",
-    "    \n",
-    "    '''\n",
-    "    # Compute disparity map\n",
-    "    disp = compute_left_disparity_map(img_left, \n",
-    "                                      img_right, \n",
-    "                                      matcher=matcher, \n",
-    "                                      rgb=rgb, \n",
-    "                                      verbose=verbose)\n",
-    "    # Decompose projection matrices\n",
-    "    k_left, r_left, t_left = decompose_projection_matrix(P0)\n",
-    "    k_right, r_right, t_right = decompose_projection_matrix(P1)\n",
-    "    # Calculate depth map for left camera\n",
-    "    depth = calc_depth_map(disp, k_left, t_left, t_right)\n",
-    "    \n",
-    "    return depth"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def extract_features(image, detector='sift', mask=None):\n",
-    "    \"\"\"\n",
-    "    Find keypoints and descriptors for the image\n",
-    "\n",
-    "    Arguments:\n",
-    "    image -- a grayscale image\n",
-    "\n",
-    "    Returns:\n",
-    "    kp -- list of the extracted keypoints (features) in an image\n",
-    "    des -- list of the keypoint descriptors in an image\n",
-    "    \"\"\"\n",
-    "    if detector == 'sift':\n",
-    "        det = cv2.SIFT_create()\n",
-    "    elif detector == 'orb':\n",
-    "        det = cv2.ORB_create()\n",
-    "    elif detector == 'surf':\n",
-    "        det = cv2.xfeatures2d.SURF_create()\n",
-    "        \n",
-    "    kp, des = det.detectAndCompute(image, mask)\n",
-    "    \n",
-    "    return kp, des"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def match_features(des1, des2, matching='BF', detector='sift', sort=True, k=2):\n",
-    "    \"\"\"\n",
-    "    Match features from two images\n",
-    "\n",
-    "    Arguments:\n",
-    "    des1 -- list of the keypoint descriptors in the first image\n",
-    "    des2 -- list of the keypoint descriptors in the second image\n",
-    "    matching -- (str) can be 'BF' for Brute Force or 'FLANN'\n",
-    "    detector -- (str) can be 'sift or 'orb'. Default is 'sift'\n",
-    "    sort -- (bool) whether to sort matches by distance. Default is True\n",
-    "    k -- (int) number of neighbors to match to each feature.\n",
-    "\n",
-    "    Returns:\n",
-    "    matches -- list of matched features from two images. Each match[i] is k or less matches for \n",
-    "               the same query descriptor\n",
-    "    \"\"\"\n",
-    "    if matching == 'BF':\n",
-    "        if detector == 'sift':\n",
-    "            matcher = cv2.BFMatcher_create(cv2.NORM_L2, crossCheck=False)\n",
-    "        elif detector == 'orb':\n",
-    "            matcher = cv2.BFMatcher_create(cv2.NORM_HAMMING2, crossCheck=False)\n",
-    "        matches = matcher.knnMatch(des1, des2, k=k)\n",
-    "    elif matching == 'FLANN':\n",
-    "        FLANN_INDEX_KDTREE = 1\n",
-    "        index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees=5)\n",
-    "        search_params = dict(checks=50)\n",
-    "        matcher = cv2.FlannBasedMatcher(index_params, search_params)\n",
-    "        matches = matcher.knnMatch(des1, des2, k=k)\n",
-    "    \n",
-    "    if sort:\n",
-    "        matches = sorted(matches, key = lambda x:x[0].distance)\n",
-    "\n",
-    "    return matches"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def visualize_matches(image1, kp1, image2, kp2, match):\n",
-    "    \"\"\"\n",
-    "    Visualize corresponding matches in two images\n",
-    "\n",
-    "    Arguments:\n",
-    "    image1 -- the first image in a matched image pair\n",
-    "    kp1 -- list of the keypoints in the first image\n",
-    "    image2 -- the second image in a matched image pair\n",
-    "    kp2 -- list of the keypoints in the second image\n",
-    "    match -- list of matched features from the pair of images\n",
-    "\n",
-    "    Returns:\n",
-    "    image_matches -- an image showing the corresponding matches on both image1 and image2 or None if you don't use this function\n",
-    "    \"\"\"\n",
-    "    image_matches = cv2.drawMatches(image1, kp1, image2, kp2, match, None, flags=2)\n",
-    "    plt.figure(figsize=(16, 6), dpi=100)\n",
-    "    plt.imshow(image_matches)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_matches_distance(matches, dist_threshold):\n",
-    "    \"\"\"\n",
-    "    Filter matched features from two images by distance between the best matches\n",
-    "\n",
-    "    Arguments:\n",
-    "    match -- list of matched features from two images\n",
-    "    dist_threshold -- maximum allowed relative distance between the best matches, (0.0, 1.0) \n",
-    "\n",
-    "    Returns:\n",
-    "    filtered_match -- list of good matches, satisfying the distance threshold\n",
-    "    \"\"\"\n",
-    "    filtered_match = []\n",
-    "    for m, n in matches:\n",
-    "        if m.distance <= dist_threshold*n.distance:\n",
-    "            filtered_match.append(m)\n",
-    "\n",
-    "    return filtered_match"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def estimate_motion(match, kp1, kp2, k, depth1=None, max_depth=3000):\n",
-    "    \"\"\"\n",
-    "    Estimate camera motion from a pair of subsequent image frames\n",
-    "\n",
-    "    Arguments:\n",
-    "    match -- list of matched features from the pair of images\n",
-    "    kp1 -- list of the keypoints in the first image\n",
-    "    kp2 -- list of the keypoints in the second image\n",
-    "    k -- camera intrinsic calibration matrix \n",
-    "    \n",
-    "    Optional arguments:\n",
-    "    depth1 -- Depth map of the first frame. Set to None to use Essential Matrix decomposition\n",
-    "    max_depth -- Threshold of depth to ignore matched features. 3000 is default\n",
-    "\n",
-    "    Returns:\n",
-    "    rmat -- estimated 3x3 rotation matrix\n",
-    "    tvec -- estimated 3x1 translation vector\n",
-    "    image1_points -- matched feature pixel coordinates in the first image. \n",
-    "                     image1_points[i] = [u, v] -> pixel coordinates of i-th match\n",
-    "    image2_points -- matched feature pixel coordinates in the second image. \n",
-    "                     image2_points[i] = [u, v] -> pixel coordinates of i-th match\n",
-    "               \n",
-    "    \"\"\"\n",
-    "    rmat = np.eye(3)\n",
-    "    tvec = np.zeros((3, 1))\n",
-    "    \n",
-    "    image1_points = np.float32([kp1[m.queryIdx].pt for m in match])\n",
-    "    image2_points = np.float32([kp2[m.trainIdx].pt for m in match])\n",
-    "\n",
-    "   \n",
-    "    # if depth1 is not None:\n",
-    "    cx = k[0, 2]\n",
-    "    cy = k[1, 2]\n",
-    "    fx = k[0, 0]\n",
-    "    fy = k[1, 1]\n",
-    "    object_points = np.zeros((0, 3))\n",
-    "    delete = []\n",
-    "    \n",
-    "    # Extract depth information of query image at match points and build 3D positions\n",
-    "    for i, (u, v) in enumerate(image1_points):\n",
-    "        z = depth1[int(v), int(u)]\n",
-    "        \n",
-    "\n",
-    "        if z > max_depth:\n",
-    "            delete.append(i)\n",
-    "            continue\n",
-    "            \n",
-    "        # Use arithmetic to extract x and y (faster than using inverse of k)\n",
-    "        x = z*(u-cx)/fx\n",
-    "        y = z*(v-cy)/fy\n",
-    "\n",
-    "        object_points = np.vstack([object_points, np.array([x, y, z])])\n",
-    "\n",
-    "        # Equivalent math with dot product w/ inverse of k matrix, but SLOWER (see Appendix A)\n",
-    "        #object_points = np.vstack([object_points, np.linalg.inv(k).dot(z*np.array([u, v, 1]))])\n",
-    "\n",
-    "    image1_points = np.delete(image1_points, delete, 0)\n",
-    "\n",
-    "    image2_points = np.delete(image2_points, delete, 0)\n",
-    "\n",
-    "    \n",
-    "    # Use PnP algorithm with RANSAC for robustness to outliers\n",
-    "    \n",
-    "    _,rvec, tvec, inliers = cv2.solvePnPRansac(object_points, image2_points, cameraMatrix=k, distCoeffs=None)\n",
-    "\n",
-    "    rmat = cv2.Rodrigues(rvec)[0]\n",
-    "\n",
-    "    \n",
-    "    return rmat, tvec, image1_points, image2_points"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of matches before filtering: 47\n",
-      "Number of matches after filtering: 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "image_left11 = handler.first_image_left\n",
-    "image_plus11 = handler.second_image_left\n",
-    "depth_left11 = np.load(handler.depth_dir + 'depth_left/'\n",
-    "                                                + handler.left_depth_files[133])\n",
-    "image_right11 = handler.first_image_right\n",
-    "depth_plus11 = np.load(handler.depth_dir + 'depth_left/'\n",
-    "                                                + handler.left_depth_files[1])\n",
-    "\n",
-    "kp0, des0 = extract_features(image_left11, 'sift')\n",
-    "kp1, des1 = extract_features(image_plus11, 'sift')\n",
-    "matches = match_features(des0, des1, matching='BF', detector='sift', sort=True)\n",
-    "print('Number of matches before filtering:', len(matches))\n",
-    "matches = filter_matches_distance(matches, 0.45)\n",
-    "print('Number of matches after filtering:', len(matches))\n",
-    "visualize_matches(image_left11, kp0, image_plus11, kp1, matches)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time to compute disparity map using StereoBM: 0:00:00.006027\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.005975\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.007059\n",
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-      "Time to compute disparity map using StereoBM: 0:00:00.006027\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.005998\n",
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-      "Time to compute disparity map using StereoBM: 0:00:00.006999\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.004992\n",
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-      "Time to compute disparity map using StereoBM: 0:00:00.007000\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.006003\n",
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-      "Time to compute disparity map using StereoBM: 0:00:00.005034\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.007015\n",
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-      "Time to compute disparity map using StereoBM: 0:00:00.007992\n",
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-      "Time to compute disparity map using StereoBM: 0:00:00.004994\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.006998\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.009008\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.005032\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.004998\n",
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-      "Time to compute disparity map using StereoBM: 0:00:00.005029\n",
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-      "Time to compute disparity map using StereoBM: 0:00:00.005015\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.004998\n",
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-      "Time to compute disparity map using StereoBM: 0:00:00.008027\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.005032\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.008001\n",
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-      "Time to compute disparity map using StereoBM: 0:00:00.006999\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.006000\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.007000\n",
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-      "Time to compute disparity map using StereoBM: 0:00:00.005990\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.009987\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.007999\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.005026\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.006980\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.008000\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.006998\n",
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-      "Time to compute disparity map using StereoBM: 0:00:00.007997\n",
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-      "Time to compute disparity map using StereoBM: 0:00:00.008999\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.009033\n",
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-      "Time to compute disparity map using StereoBM: 0:00:00.006012\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.007031\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.005031\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.008998\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.007997\n",
-      "Time to compute disparity map using StereoBM: 0:00:00.006002\n"
-     ]
-    }
-   ],
-   "source": [
-    "stereo_l = handler.images_left\n",
-    "stereo_r = handler.images_right\n",
-    "\n",
-    "depth_l = handler.depths_left\n",
-    "depth_r = handler.depths_right\n",
-    "\n",
-    "%matplotlib tk\n",
-    "\n",
-    "xs = []\n",
-    "ys = []\n",
-    "zs = []\n",
-    "compute_times = []\n",
-    "fig1 = plt.figure()\n",
-    "##########################################\n",
-    "#uncomment this for viewing ground truth\n",
-    "################################################################################\n",
-    "ax = fig1.add_subplot(projection='3d')\n",
-    "ax.view_init(elev=-20, azim=270)\n",
-    "ax.plot(handler.gt[:,0,3], handler.gt[:,1,3], handler.gt[:,2,3], c = 'k')\n",
-    "###############################################################################\n",
-    "poses = (gt for gt in handler.gt)\n",
-    "\n",
-    "for i in range(handler.num_frames):\n",
-    "    i\n",
-    "    img_l = next(stereo_l)\n",
-    "    img_r = next(stereo_r)\n",
-    "    D_left = next(depth_l)\n",
-    "    D_right = next(depth_r)\n",
-    "\n",
-    "\n",
-    "    start = datetime.datetime.now()\n",
-    "    disp = compute_left_disparity_map(img_l, img_r, matcher='bm')\n",
-    "\n",
-    "    gt = next(poses)\n",
-    "    xs.append(gt[0,3])\n",
-    "    ys.append(gt[1,3])\n",
-    "    zs.append(gt[2,3])\n",
-    "    \n",
-    "\n",
-    "    plt.plot(xs,ys, zs, c = 'chartreuse')\n",
-    "    plt.pause(0.00000000001)\n",
-    "     \n",
-    "    cv2.imshow('L-Camera', img_l)\n",
-    "    cv2.imshow('R-Camera', img_r)\n",
-    "    cv2.imshow('Disparity', disp)\n",
-    "    ##########################################\n",
-    "    #uncomment this for viewing depth image// BUT, super slow. \n",
-    "    ################################################################################\n",
-    "    # plt.imshow(D_left)\n",
-    "    ################################################################################   \n",
-    "    plt.pause(0.00000000001)\n",
-    "    \n",
-    "    cv2.waitKey(1)\n",
-    "\n",
-    "    end = datetime.datetime.now()\n",
-    "    compute_times.append(end-start)\n",
-    "plt.close()\n",
-    "cv2.destroyAllWindows()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "k,r,t,_,_,_,_ = cv2.decomposeProjectionMatrix(handler.P0)\n",
-    "depth = handler.first_depth_left\n",
-    "rmat, tvec, image1_points, image2_points = estimate_motion(matches,kp0,kp1,depth1 = depth,k=k)\n",
-    "transformation_matrix = np.hstack([rmat,tvec])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def visual_odometry(handler, detector='sift', matching='BF', filter_match_distance=None, \n",
-    "                    stereo_matcher='bm', mask=None, depth_type='stereo', subset=None,\n",
-    "                    plot=False):\n",
-    "    '''\n",
-    "    Function to perform visual odometry on a sequence from the KITTI visual odometry dataset.\n",
-    "    Takes as input a Data_Handler object and optional parameters.\n",
-    "    \n",
-    "    Arguments:\n",
-    "    handler -- Data_Handler object instance\n",
-    "    \n",
-    "    Optional Arguments:\n",
-    "    detector -- (str) can be 'sift' or 'orb'. Default is 'sift'.\n",
-    "    matching -- (str) can be 'BF' for Brute Force or 'FLANN'. Default is 'BF'.\n",
-    "    filter_match_distance -- (float) value for ratio test on matched features. Default is None.\n",
-    "    stereo_matcher -- (str) can be 'bm' (faster) or 'sgbm' (more accurate). Default is 'bm'.\n",
-    "    mask -- (array) mask to reduce feature search area to where depth information available.\n",
-    "    depth_type -- (str) can be 'stereo' or set to None to use Essential matrix decomposition.\n",
-    "                        Note that scale will be incorrect with no depth used.\n",
-    "    subset -- (int) number of frames to compute. Defaults to None to compute all frames.\n",
-    "    plot -- (bool) whether to plot the estimated vs ground truth trajectory. Only works if\n",
-    "                   matplotlib is set to tk mode. Default is False.\n",
-    "    \n",
-    "    Returns:\n",
-    "    trajectory -- Array of shape Nx3x4 of estimated poses of vehicle for each computed frame.\n",
-    "    \n",
-    "    '''\n",
-    "    # Determine if handler has lidar data\n",
-    "    lidar = handler.lidar\n",
-    "    \n",
-    "    # Report methods being used to user\n",
-    "    print('Generating disparities with Stereo{}'.format(str.upper(stereo_matcher)))\n",
-    "    print('Detecting features with {} and matching with {}'.format(str.upper(detector), \n",
-    "                                                                   matching))\n",
-    "    if filter_match_distance is not None:\n",
-    "        print('Filtering feature matches at threshold of {}*distance'.format(filter_match_distance))\n",
-    "    if lidar:\n",
-    "        print('Improving stereo depth estimation with lidar data')\n",
-    "    if subset is not None:\n",
-    "        #subset = subset + 1\n",
-    "        num_frames = subset\n",
-    "    else:\n",
-    "        # Set num_frames to one less than the number of frames so we have sequential images\n",
-    "        # in the last frame run.\n",
-    "        num_frames = handler.num_frames\n",
-    "        \n",
-    "    if plot:\n",
-    "        fig = plt.figure(figsize=(14, 14))\n",
-    "        ax = fig.add_subplot(projection='3d')\n",
-    "        ax.view_init(elev=-20, azim=270)\n",
-    "        xs = handler.gt[:, 0, 3]\n",
-    "        ys = handler.gt[:, 1, 3]\n",
-    "        zs = handler.gt[:, 2, 3]\n",
-    "        ax.set_box_aspect((np.ptp(xs), np.ptp(ys), np.ptp(zs)))\n",
-    "        ax.plot(xs, ys, zs, c='k')\n",
-    "    # Establish homogeneous transformation matrix. First pose is identity    \n",
-    "    T_tot = np.eye(4)\n",
-    "    trajectory = np.zeros((num_frames, 3, 4))\n",
-    "    trajectory[0] = T_tot[:3, :]\n",
-    "    imheight = handler.imheight\n",
-    "    imwidth = handler.imwidth\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "    # Decompose left camera projection matrix to get intrinsic k matrix\n",
-    "    k_left, r_left, t_left = decompose_projection_matrix(handler.P0)\n",
-    "\n",
-    "\n",
-    "    if handler.low_memory:\n",
-    "        handler.reset_frames()\n",
-    "        image_plus1 = next(handler.images_left)\n",
-    "\n",
-    "    # Iterate through all frames of the sequence\n",
-    "    for i in range(num_frames - 1):\n",
-    "        # Stop if we've reached the second to last frame, since we need two sequential frames\n",
-    "        #if i == num_frames - 1:\n",
-    "        #    break\n",
-    "        # Start timer for frame\n",
-    "        start = datetime.datetime.now()\n",
-    "        # Get our stereo images for depth estimation\n",
-    "        if handler.low_memory:\n",
-    "            image_left = image_plus1\n",
-    "            image_right = next(handler.images_right)\n",
-    "            # Get next frame in the left camera for visual odometry\n",
-    "            image_plus1 = next(handler.images_left)\n",
-    "        else:\n",
-    "            image_left = handler.images_left[i]\n",
-    "            image_right = handler.images_right[i]\n",
-    "            # Get next frame in the left camera for visual odometry\n",
-    "            image_plus1 = handler.images_left[i+1]\n",
-    "        \n",
-    "\n",
-    "        # Estimate depth if using stereo depth estimation (recommended)\n",
-    "        if depth_type == 'stereo':\n",
-    "            depth = stereo_2_depth(image_left, \n",
-    "                                   image_right, \n",
-    "                                   P0=handler.P0, \n",
-    "                                   P1=handler.P1,\n",
-    "                                   matcher=stereo_matcher)\n",
-    "            # depth = (handler.first_depth_left)\n",
-    "        # Otherwise use Essential Matrix decomposition (ambiguous scale)\n",
-    "        else:\n",
-    "            depth = None\n",
-    "            \n",
-    "        # Supercede stereo depth estimations where lidar points are available\n",
-    "        if lidar:\n",
-    "            if handler.low_memory:\n",
-    "                pointcloud = next(handler.pointclouds)\n",
-    "            else:\n",
-    "                pointcloud = handler.pointclouds[i]\n",
-    "            lidar_depth = pointcloud2image(pointcloud, \n",
-    "                                           imheight=imheight, \n",
-    "                                           imwidth=imwidth, \n",
-    "                                           Tr=handler.Tr, \n",
-    "                                           P0=handler.P0)\n",
-    "            indices = np.where(lidar_depth > 0)\n",
-    "            depth[indices] = lidar_depth[indices]\n",
-    "\n",
-    "        # Get keypoints and descriptors for left camera image of two sequential frames\n",
-    "        kp0, des0 = extract_features(image_left, detector, mask)\n",
-    "        kp1, des1 = extract_features(image_plus1, detector, mask)\n",
-    "        \n",
-    "        # Get matches between features detected in the two images\n",
-    "        matches_unfilt = match_features(des0, \n",
-    "                                        des1, \n",
-    "                                        matching=matching, \n",
-    "                                        detector=detector, \n",
-    "                                        sort=True)\n",
-    "        \n",
-    "        # Filter matches if a distance threshold is provided by user\n",
-    "        if filter_match_distance is not None:\n",
-    "            matches = filter_matches_distance(matches_unfilt, filter_match_distance)\n",
-    "        else:\n",
-    "            matches = matches_unfilt\n",
-    "            \n",
-    "        # Estimate motion between sequential images of the left camera\n",
-    "\n",
-    "        rmat, tvec, img1_points, img2_points = estimate_motion(matches, kp0, kp1, k=k_left, depth1=depth)\n",
-    "\n",
-    "        # Create blank homogeneous transformation matrix\n",
-    "        Tmat = np.eye(4)\n",
-    "        # Place resulting rotation matrix  and translation vector in their proper locations\n",
-    "        # in homogeneous T matrix\n",
-    "        Tmat[:3, :3] = rmat\n",
-    "        Tmat[:3, 3] = tvec.T\n",
-    "\n",
-    "        T_tot = T_tot.dot(np.linalg.inv(Tmat))\n",
-    "            \n",
-    "        # Place pose estimate in i+1 to correspond to the second image, which we estimated for\n",
-    "        trajectory[i+1, :, :] = T_tot[:3, :]\n",
-    "        # End the timer for the frame and report frame rate to user\n",
-    "        end = datetime.datetime.now()\n",
-    "        print('Time to compute frame {}:'.format(i+1), end-start)\n",
-    "        \n",
-    "        if plot:\n",
-    "            xs = trajectory[:i+2, 0, 3]\n",
-    "            ys = trajectory[:i+2, 1, 3]\n",
-    "            zs = trajectory[:i+2, 2, 3]\n",
-    "            plt.plot(xs, ys, zs, c='chartreuse')\n",
-    "            plt.pause(1e-32)\n",
-    "            \n",
-    "    if plot:        \n",
-    "        plt.close()\n",
-    "        \n",
-    "    return trajectory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Generating disparities with StereoBM\n",
-      "Detecting features with SIFT and matching with BF\n",
-      "Filtering feature matches at threshold of 0.4*distance\n",
-      "Time to compute frame 1: 0:00:00.131964\n",
-      "Time to compute frame 2: 0:00:00.120015\n",
-      "Time to compute frame 3: 0:00:00.115006\n",
-      "Time to compute frame 4: 0:00:00.124001\n",
-      "Time to compute frame 5: 0:00:00.115001\n",
-      "Time to compute frame 6: 0:00:00.123993\n",
-      "Time to compute frame 7: 0:00:00.113998\n",
-      "Time to compute frame 8: 0:00:00.115041\n",
-      "Time to compute frame 9: 0:00:00.110005\n",
-      "Time to compute frame 10: 0:00:00.118999\n",
-      "Time to compute frame 11: 0:00:00.118939\n",
-      "Time to compute frame 12: 0:00:00.120991\n",
-      "Time to compute frame 13: 0:00:00.114034\n",
-      "Time to compute frame 14: 0:00:00.116001\n",
-      "Time to compute frame 15: 0:00:00.114000\n",
-      "Time to compute frame 16: 0:00:00.118031\n",
-      "Time to compute frame 17: 0:00:00.110000\n",
-      "Time to compute frame 18: 0:00:00.119963\n",
-      "Time to compute frame 19: 0:00:00.115973\n",
-      "Time to compute frame 20: 0:00:00.113968\n",
-      "Time to compute frame 21: 0:00:00.114004\n",
-      "Time to compute frame 22: 0:00:00.119000\n",
-      "Time to compute frame 23: 0:00:00.116999\n",
-      "Time to compute frame 24: 0:00:00.136001\n",
-      "Time to compute frame 25: 0:00:00.121013\n",
-      "Time to compute frame 26: 0:00:00.123009\n",
-      "Time to compute frame 27: 0:00:00.121010\n",
-      "Time to compute frame 28: 0:00:00.123034\n",
-      "Time to compute frame 29: 0:00:00.117967\n",
-      "Time to compute frame 30: 0:00:00.128025\n",
-      "Time to compute frame 31: 0:00:00.123001\n",
-      "Time to compute frame 32: 0:00:00.129037\n",
-      "Time to compute frame 33: 0:00:00.141967\n",
-      "Time to compute frame 34: 0:00:00.125028\n",
-      "Time to compute frame 35: 0:00:00.131006\n",
-      "Time to compute frame 36: 0:00:00.126033\n",
-      "Time to compute frame 37: 0:00:00.125016\n",
-      "Time to compute frame 38: 0:00:00.126947\n",
-      "Time to compute frame 39: 0:00:00.121997\n",
-      "Time to compute frame 40: 0:00:00.131032\n",
-      "Time to compute frame 41: 0:00:00.147001\n",
-      "Time to compute frame 42: 0:00:00.128962\n",
-      "Time to compute frame 43: 0:00:00.119038\n",
-      "Time to compute frame 44: 0:00:00.115989\n",
-      "Time to compute frame 45: 0:00:00.113023\n",
-      "Time to compute frame 46: 0:00:00.116998\n",
-      "Time to compute frame 47: 0:00:00.138015\n",
-      "Time to compute frame 48: 0:00:00.106995\n",
-      "Time to compute frame 49: 0:00:00.111965\n",
-      "Time to compute frame 50: 0:00:00.117036\n",
-      "Time to compute frame 51: 0:00:00.107991\n",
-      "Time to compute frame 52: 0:00:00.109007\n",
-      "Time to compute frame 53: 0:00:00.117000\n",
-      "Time to compute frame 54: 0:00:00.118040\n",
-      "Time to compute frame 55: 0:00:00.111968\n",
-      "Time to compute frame 56: 0:00:00.112036\n",
-      "Time to compute frame 57: 0:00:00.106001\n",
-      "Time to compute frame 58: 0:00:00.109965\n",
-      "Time to compute frame 59: 0:00:00.134001\n",
-      "Time to compute frame 60: 0:00:00.113034\n",
-      "Time to compute frame 61: 0:00:00.118003\n",
-      "Time to compute frame 62: 0:00:00.118998\n",
-      "Time to compute frame 63: 0:00:00.123006\n",
-      "Time to compute frame 64: 0:00:00.114037\n",
-      "Time to compute frame 65: 0:00:00.140998\n",
-      "Time to compute frame 66: 0:00:00.127999\n",
-      "Time to compute frame 67: 0:00:00.150001\n",
-      "Time to compute frame 68: 0:00:00.132983\n",
-      "Time to compute frame 69: 0:00:00.129974\n",
-      "Time to compute frame 70: 0:00:00.151035\n",
-      "Time to compute frame 71: 0:00:00.134962\n",
-      "Time to compute frame 72: 0:00:00.143006\n",
-      "Time to compute frame 73: 0:00:00.137999\n",
-      "Time to compute frame 74: 0:00:00.140036\n",
-      "Time to compute frame 75: 0:00:00.176004\n",
-      "Time to compute frame 76: 0:00:00.143966\n",
-      "Time to compute frame 77: 0:00:00.155035\n",
-      "Time to compute frame 78: 0:00:00.152998\n",
-      "Time to compute frame 79: 0:00:00.148000\n",
-      "Time to compute frame 80: 0:00:00.152040\n",
-      "Time to compute frame 81: 0:00:00.152003\n",
-      "Time to compute frame 82: 0:00:00.157999\n",
-      "Time to compute frame 83: 0:00:00.150006\n",
-      "Time to compute frame 84: 0:00:00.170958\n",
-      "Time to compute frame 85: 0:00:00.159026\n",
-      "Time to compute frame 86: 0:00:00.159036\n",
-      "Time to compute frame 87: 0:00:00.163014\n",
-      "Time to compute frame 88: 0:00:00.158008\n",
-      "Time to compute frame 89: 0:00:00.175964\n",
-      "Time to compute frame 90: 0:00:00.164006\n",
-      "Time to compute frame 91: 0:00:00.158977\n",
-      "Time to compute frame 92: 0:00:00.154973\n",
-      "Time to compute frame 93: 0:00:00.164965\n",
-      "Time to compute frame 94: 0:00:00.152998\n",
-      "Time to compute frame 95: 0:00:00.153035\n",
-      "Time to compute frame 96: 0:00:00.174000\n",
-      "Time to compute frame 97: 0:00:00.150000\n",
-      "Time to compute frame 98: 0:00:00.176036\n",
-      "Time to compute frame 99: 0:00:00.154028\n",
-      "Time to perform odometry: 0:00:18.426400\n"
-     ]
-    }
-   ],
-   "source": [
-    "handler.lidar = False\n",
-    "start = datetime.datetime.now()\n",
-    "trajectory_nolidar_bm = visual_odometry(handler,\n",
-    "                                        filter_match_distance=0.4,\n",
-    "                                        detector='sift',\n",
-    "                                        # matching='FLANN',\n",
-    "                                        stereo_matcher='bm',\n",
-    "                                        mask=None,     \n",
-    "                                        subset=100,\n",
-    "                                        plot=True)\n",
-    "end = datetime.datetime.now()\n",
-    "print('Time to perform odometry:', end-start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Warning: Cannot change to a different GUI toolkit: notebook. Using tk instead.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib notebook"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = plt.figure(figsize=(12,8))\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "\n",
-    "ax.plot(trajectory_nolidar_bm[:, :, 3][:, 0], \n",
-    "        trajectory_nolidar_bm[:, :, 3][:, 1], \n",
-    "        trajectory_nolidar_bm[:, :, 3][:, 2], label='estimated', color='orange')\n",
-    "\n",
-    "ax.plot(handler.gt[:, :, 3][:, 0], \n",
-    "        handler.gt[:, :, 3][:, 1], \n",
-    "        handler.gt[:, :, 3][:, 2], label='ground truth')\n",
-    "\n",
-    "ax.set_xlabel('x')\n",
-    "ax.set_ylabel('y')\n",
-    "ax.set_zlabel('z')\n",
-    "\n",
-    "ax.view_init(elev=-20, azim=270)\n"
-   ]
-  }
- ],
- "metadata": {
-  "interpreter": {
-   "hash": "8e498f732d7c2aa228df44a2a1ad0a06b158366b5ada381e7ddd871473ea3a71"
-  },
-  "kernelspec": {
-   "display_name": "Python 3.10.2 64-bit",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.2"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/extraction/features_matched.csv b/extraction/features_matched.csv
deleted file mode 100644
index f5613fed9b22ad4535916c46ef75325df042508d..0000000000000000000000000000000000000000
--- a/extraction/features_matched.csv
+++ /dev/null
@@ -1,7979 +0,0 @@
-Generating disparities with StereoBM
-Detecting features with SIFT and matching with BF
-Filtering feature matches at threshold of 0.795*distance
-Matches detected:  
-                                        kp0                                      kp1
-0     (616.09228515625, 145.09605407714844)   (610.9928588867188, 141.6364288330078)
-1    (632.0880737304688, 191.5442352294922)    (630.486572265625, 190.0791473388672)
-2   (625.1541748046875, 128.26055908203125)  (619.6690063476562, 123.81411743164062)
-3   (626.7809448242188, 168.80845642089844)  (625.9144287109375, 167.31785583496094)
-4   (610.6735229492188, 146.62155151367188)   (604.8018188476562, 143.0636749267578)
-5   (633.3133544921875, 150.70901489257812)    (633.664794921875, 148.9010772705078)
-6    (616.9146118164062, 138.6289520263672)   (612.270263671875, 134.94161987304688)
-7   (626.4501342773438, 141.62783813476562)   (626.6033325195312, 139.9758758544922)
-8       (613.743896484375, 169.06884765625)  (613.0151977539062, 167.93568420410156)
-9   (633.5946044921875, 131.55894470214844)   (634.2825927734375, 129.7173614501953)
-10  (628.8043212890625, 150.57711791992188)  (629.4417114257812, 148.52618408203125)
-11  (609.4078369140625, 141.19668579101562)    (601.57470703125, 139.95030212402344)
-12  (626.0324096679688, 146.97113037109375)   (626.0215454101562, 145.6439971923828)
-13  (631.9400024414062, 116.03792572021484)  (608.0028686523438, 132.18301391601562)
-14   (606.8064575195312, 160.2814178466797)   (606.6981201171875, 159.2136688232422)
-15    (635.025634765625, 115.5777816772461)  (630.7610473632812, 112.42691802978516)
-16   (627.5825805664062, 152.8092041015625)  (627.8047485351562, 151.39791870117188)
-17     (607.3525390625, 147.32151794433594)   (604.8018188476562, 143.0636749267578)
-18   (604.4466552734375, 163.0670623779297)  (602.0405883789062, 160.60398864746094)
-19      (609.1201171875, 163.6293487548828)   (609.1766357421875, 161.8107147216797)
-20  (608.3904418945312, 152.72750854492188)  (607.5599365234375, 150.65792846679688)
-Matches detected:  
-                                        kp0                                      kp1
-0    (626.6033325195312, 139.9758758544922)  (626.5928955078125, 138.16709899902344)
-1    (610.9928588867188, 141.6364288330078)  (604.5569458007812, 137.37184143066406)
-2   (619.6690063476562, 123.81411743164062)  (614.8761596679688, 119.34368896484375)
-3   (619.9654541015625, 123.77312469482422)  (614.8761596679688, 119.34368896484375)
-4     (601.57470703125, 139.95030212402344)   (595.2943725585938, 135.9660186767578)
-5   (632.1322021484375, 194.16702270507812)     (630.8232421875, 193.04676818847656)
-6     (633.664794921875, 148.9010772705078)   (633.1812744140625, 147.6041259765625)
-7   (624.4776000976562, 117.39884185791016)  (618.8713989257812, 112.68871307373047)
-8   (625.9144287109375, 167.31785583496094)   (624.7727661132812, 166.1576690673828)
-9     (630.486572265625, 190.0791473388672)  (629.7727661132812, 189.02703857421875)
-10  (613.0151977539062, 167.93568420410156)  (611.8406372070312, 166.36074829101562)
-11   (626.0215454101562, 145.6439971923828)   (626.046142578125, 144.08238220214844)
-12   (628.5420532226562, 159.4885711669922)   (628.4862060546875, 157.9668731689453)
-13   (634.2825927734375, 129.7173614501953)  (625.7476196289062, 124.15573120117188)
-14   (619.699462890625, 118.15364074707031)  (614.1490478515625, 111.75186920166016)
-15  (631.0791625976562, 155.81825256347656)  (630.9545288085938, 154.01683044433594)
-16  (630.7610473632812, 112.42691802978516)      (625.220703125, 107.47588348388672)
-17    (630.486572265625, 190.0791473388672)  (629.7727661132812, 189.02703857421875)
-18   (606.6981201171875, 159.2136688232422)  (605.3300170898438, 157.44967651367188)
-19   (635.8646850585938, 161.5972442626953)   (636.0890502929688, 159.3819122314453)
-20  (613.1870727539062, 146.37339782714844)   (612.9981689453125, 144.8347625732422)
-21  (629.4417114257812, 148.52618408203125)  (629.6528930664062, 147.33631896972656)
-22    (633.535888671875, 189.1495819091797)  (632.5283813476562, 187.41204833984375)
-23   (612.270263671875, 134.94161987304688)      (625.220703125, 107.47588348388672)
-24  (598.1129760742188, 157.49729919433594)  (596.7474365234375, 156.34564208984375)
-25   (604.8018188476562, 143.0636749267578)  (604.5569458007812, 137.37184143066406)
-26  (620.0049438476562, 169.02719116210938)  (619.4391479492188, 167.72499084472656)
-27    (602.038818359375, 152.1907958984375)   (601.1334838867188, 150.7322998046875)
-Matches detected:  
-                                        kp0                                      kp1
-0    (624.7727661132812, 166.1576690673828)     (624.58642578125, 164.2691650390625)
-1   (626.5928955078125, 138.16709899902344)       (627.126953125, 136.2627410888672)
-2   (604.5569458007812, 137.37184143066406)  (599.5547485351562, 134.19949340820312)
-3    (626.046142578125, 144.08238220214844)  (625.7918701171875, 141.77992248535156)
-4   (625.7476196289062, 124.15573120117188)  (625.9749755859375, 122.13860321044922)
-5   (614.8761596679688, 119.34368896484375)        (608.943359375, 114.679931640625)
-6    (633.9769287109375, 191.1378173828125)    (632.98486328125, 190.04989624023438)
-7    (633.9769287109375, 191.1378173828125)    (632.98486328125, 190.04989624023438)
-8   (611.8406372070312, 166.36074829101562)  (610.6546020507812, 164.96339416503906)
-9   (629.7727661132812, 189.02703857421875)  (628.1324462890625, 187.86727905273438)
-10  (618.8713989257812, 112.68871307373047)  (612.3316650390625, 107.12826538085938)
-11     (630.8232421875, 193.04676818847656)   (629.1787719726562, 191.6583251953125)
-12    (609.235595703125, 137.5122833251953)  (608.8955688476562, 135.90997314453125)
-13   (630.0570678710938, 172.0768585205078)  (629.7236938476562, 170.52146911621094)
-14   (612.9981689453125, 144.8347625732422)  (612.3160400390625, 143.14376831054688)
-15   (633.1812744140625, 147.6041259765625)   (634.0306396484375, 145.9255828857422)
-16      (614.7880859375, 138.8626251220703)   (613.956787109375, 136.90524291992188)
-17  (605.3300170898438, 157.44967651367188)    (605.17529296875, 157.10694885253906)
-18      (625.220703125, 107.47588348388672)   (618.6151733398438, 102.6302490234375)
-19  (611.8406372070312, 166.36074829101562)  (610.6546020507812, 164.96339416503906)
-20  (604.7614135742188, 148.06353759765625)   (604.309326171875, 146.12301635742188)
-21  (632.5283813476562, 187.41204833984375)  (631.7300415039062, 185.17581176757812)
-22  (619.4391479492188, 167.72499084472656)   (618.3833618164062, 165.6011199951172)
-23   (615.0361938476562, 161.9014434814453)    (614.689208984375, 161.2276153564453)
-24  (629.7727661132812, 189.02703857421875)    (632.98486328125, 190.04989624023438)
-25   (603.7765502929688, 147.2264862060547)   (604.309326171875, 146.12301635742188)
-26   (601.1334838867188, 150.7322998046875)  (599.5031127929688, 149.34451293945312)
-27    (631.90869140625, 149.97621154785156)  (631.8082885742188, 148.08445739746094)
-28   (610.1216430664062, 153.0701446533203)  (609.0885009765625, 151.94358825683594)
-29      (625.220703125, 107.47588348388672)  (599.0418090820312, 125.48458862304688)
-Matches detected:  
-                                        kp0                                      kp1
-0   (625.9749755859375, 122.13860321044922)   (626.319091796875, 120.23717498779297)
-1   (635.7146606445312, 125.94385528564453)  (635.8135986328125, 123.90687561035156)
-2     (632.98486328125, 190.04989624023438)   (631.6708374023438, 188.7086944580078)
-3     (632.98486328125, 190.04989624023438)   (631.6708374023438, 188.7086944580078)
-4   (628.1324462890625, 187.86727905273438)   (626.681396484375, 186.48817443847656)
-5         (608.943359375, 114.679931640625)  (601.4442138671875, 110.29341125488281)
-6    (629.1787719726562, 191.6583251953125)   (627.876220703125, 190.57229614257812)
-7   (610.6546020507812, 164.96339416503906)  (609.4290771484375, 163.39903259277344)
-8     (614.689208984375, 161.2276153564453)  (613.6722412109375, 159.57070922851562)
-9      (624.58642578125, 164.2691650390625)   (622.8045654296875, 163.1897430419922)
-10  (612.3160400390625, 143.14376831054688)  (611.4266357421875, 141.03587341308594)
-11  (599.5547485351562, 134.19949340820312)   (594.5867919921875, 132.0561981201172)
-12   (595.9968872070312, 154.3612823486328)   (594.5759887695312, 152.9071807861328)
-13  (610.6546020507812, 164.96339416503906)  (609.4290771484375, 163.39903259277344)
-14  (625.7918701171875, 141.77992248535156)  (626.2691650390625, 140.38995361328125)
-15   (590.9706420898438, 132.8927001953125)  (584.2152099609375, 129.76795959472656)
-16   (634.0306396484375, 145.9255828857422)   (633.9913330078125, 143.8695526123047)
-17  (599.5031127929688, 149.34451293945312)    (598.834228515625, 147.4166717529297)
-18    (614.689208984375, 161.2276153564453)  (613.6722412109375, 159.57070922851562)
-19  (608.8955688476562, 135.90997314453125)  (608.6272583007812, 133.79244995117188)
-20      (607.994140625, 108.28384399414062)  (600.1109008789062, 103.57856750488281)
-21   (618.6079711914062, 152.1917266845703)         (617.623291015625, 150.51171875)
-22   (603.4151611328125, 125.6952133178711)   (599.3756713867188, 123.5973892211914)
-23   (618.6151733398438, 102.6302490234375)     (611.31689453125, 97.12236022949219)
-24  (591.0743408203125, 151.03440856933594)   (589.6434936523438, 149.7202911376953)
-25    (605.17529296875, 157.10694885253906)   (603.103271484375, 154.64183044433594)
-26   (618.3833618164062, 165.6011199951172)  (617.0057983398438, 164.45750427246094)
-27  (591.0743408203125, 151.03440856933594)   (589.6434936523438, 149.7202911376953)
-28  (614.1327514648438, 101.61766052246094)  (587.7399291992188, 120.26333618164062)
-29   (625.9290161132812, 94.39258575439453)   (626.8379516601562, 92.61186218261719)
-Matches detected:  
-                                        kp0                                      kp1
-0    (626.319091796875, 120.23717498779297)  (626.3026733398438, 118.21098327636719)
-1   (608.5691528320312, 127.43244934082031)  (607.9884643554688, 125.45653533935547)
-2   (620.2252197265625, 143.06163024902344)  (620.1016235351562, 141.37562561035156)
-3   (601.4442138671875, 110.29341125488281)   (594.3333740234375, 105.7961196899414)
-4   (611.4266357421875, 141.03587341308594)  (610.3245849609375, 139.46148681640625)
-5   (626.2691650390625, 140.38995361328125)  (624.9998168945312, 138.44046020507812)
-6   (601.4442138671875, 110.29341125488281)   (594.3333740234375, 105.7961196899414)
-7    (626.681396484375, 186.48817443847656)   (624.959228515625, 185.27423095703125)
-8    (627.876220703125, 190.57229614257812)      (625.80029296875, 189.082275390625)
-9    (583.9157104492188, 156.3489532470703)    (582.16845703125, 154.23252868652344)
-10   (599.3756713867188, 123.5973892211914)       (598.86328125, 121.72208404541016)
-11  (627.1395874023438, 145.76390075683594)  (626.9964599609375, 144.64706420898438)
-12   (605.8673095703125, 157.8610076904297)  (603.8921508789062, 156.01268005371094)
-13  (634.1986694335938, 172.18389892578125)  (632.9891967773438, 170.18653869628906)
-14   (605.8673095703125, 157.8610076904297)  (603.8921508789062, 156.01268005371094)
-15   (633.9913330078125, 143.8695526123047)    (632.674072265625, 141.5056915283203)
-16  (609.4290771484375, 163.39903259277344)   (608.2277221679688, 161.8674774169922)
-17   (603.103271484375, 154.64183044433594)   (601.639404296875, 153.42811584472656)
-18  (599.3585205078125, 156.37228393554688)  (597.5306396484375, 154.19619750976562)
-19        (619.44140625, 96.47367095947266)    (619.3334350585938, 94.0619888305664)
-20   (626.8379516601562, 92.61186218261719)   (627.0724487304688, 90.51446533203125)
-21    (599.4817504882812, 156.389404296875)  (597.5306396484375, 154.19619750976562)
-22  (569.9544067382812, 143.58216857910156)  (628.1340942382812, 183.19801330566406)
-23  (637.0791625976562, 193.86109924316406)   (635.5596923828125, 192.5376434326172)
-24  (570.0359497070312, 147.94468688964844)     (567.0989379882812, 146.23681640625)
-25  (629.5784301757812, 185.00555419921875)  (628.1340942382812, 183.19801330566406)
-26  (608.0787353515625, 150.23831176757812)   (607.022216796875, 148.09661865234375)
-27   (619.5148315429688, 86.37771606445312)   (619.4666137695312, 84.24278259277344)
-28  (629.8524169921875, 150.13209533691406)  (629.3555297851562, 148.42230224609375)
-29       (609.2405395507812, 159.650390625)   (607.809326171875, 158.39857482910156)
-30  (613.6722412109375, 159.57070922851562)  (611.6159057617188, 157.17849731445312)
-31      (593.4775390625, 146.6544189453125)   (592.3193969726562, 144.9086456298828)
-32   (596.2755737304688, 169.8343505859375)    (582.16845703125, 154.23252868652344)
-33  (626.0311889648438, 150.48524475097656)     (625.2294921875, 148.45956420898438)
-34     (611.31689453125, 97.12236022949219)  (584.3735961914062, 120.10896301269531)
-35  (614.0110473632812, 146.73818969726562)  (610.4791259765625, 145.30624389648438)
-36  (608.6272583007812, 133.79244995117188)  (608.6243286132812, 131.63693237304688)
-37  (617.0057983398438, 164.45750427246094)  (615.7940063476562, 162.56214904785156)
-38  (585.6934814453125, 141.35110473632812)   (583.9589233398438, 139.4628143310547)
-39   (605.0008544921875, 151.9837188720703)    (604.8829956054688, 149.969970703125)
-40   (605.0008544921875, 151.9837188720703)    (604.8829956054688, 149.969970703125)
-Matches detected:  
-                                        kp0                                      kp1
-0    (593.174072265625, 119.97077941894531)   (592.0223999023438, 118.1012954711914)
-1   (610.3245849609375, 139.46148681640625)    (609.02099609375, 137.39724731445312)
-2     (619.3334350585938, 94.0619888305664)   (619.3477783203125, 92.01502990722656)
-3       (625.80029296875, 189.082275390625)   (623.5558471679688, 187.4145965576172)
-4   (607.9884643554688, 125.45653533935547)  (606.9754028320312, 123.89519500732422)
-5        (598.86328125, 121.72208404541016)    (597.61669921875, 119.91452026367188)
-6   (631.0447387695312, 101.55558776855469)   (633.002685546875, 100.00984954833984)
-7   (624.9998168945312, 138.44046020507812)  (624.3895263671875, 136.35435485839844)
-8    (594.3333740234375, 105.7961196899414)  (586.8807373046875, 100.97610473632812)
-9    (594.3333740234375, 105.7961196899414)  (586.8807373046875, 100.97610473632812)
-10  (620.1016235351562, 141.37562561035156)  (619.0881958007812, 139.26788330078125)
-11  (613.6185302734375, 134.12254333496094)   (612.8187866210938, 132.3791046142578)
-12    (582.16845703125, 154.23252868652344)  (578.7398071289062, 153.50543212890625)
-13  (566.6222534179688, 149.16683959960938)   (562.9036254882812, 147.4710693359375)
-14  (634.1890869140625, 135.78443908691406)   (633.3197021484375, 133.5428466796875)
-15   (624.959228515625, 185.27423095703125)  (622.8848876953125, 183.60511779785156)
-16   (622.3380737304688, 92.73013305664062)   (622.2135009765625, 90.65599060058594)
-17  (628.3262939453125, 141.40806579589844)    (627.3602905273438, 139.277099609375)
-18    (632.674072265625, 141.5056915283203)   (631.6682739257812, 138.9764404296875)
-19  (590.7510375976562, 118.69351196289062)   (592.0223999023438, 118.1012954711914)
-20    (574.63720703125, 123.29036712646484)  (565.5640258789062, 120.11625671386719)
-21   (635.5596923828125, 192.5376434326172)       (633.5576171875, 190.927978515625)
-22   (619.4666137695312, 84.24278259277344)     (619.77880859375, 82.63787078857422)
-23  (626.3026733398438, 118.21098327636719)  (606.9754028320312, 123.89519500732422)
-24   (583.9589233398438, 139.4628143310547)   (582.2764892578125, 137.7469024658203)
-25  (628.3262939453125, 141.40806579589844)    (627.3602905273438, 139.277099609375)
-26  (635.9278564453125, 103.55421447753906)   (633.002685546875, 100.00984954833984)
-27    (611.39404296875, 152.81024169921875)  (610.1119995117188, 151.17825317382812)
-28  (603.8921508789062, 156.01268005371094)   (602.2728271484375, 154.3199462890625)
-29  (577.7975463867188, 119.08578491210938)        (588.923095703125, 88.2802734375)
-30   (592.7083129882812, 96.48135375976562)    (583.8162231445312, 92.9538345336914)
-31  (584.0613403320312, 147.41786193847656)  (582.1913452148438, 145.55067443847656)
-32   (613.0847778320312, 88.88102722167969)   (609.6550903320312, 85.14505004882812)
-33   (589.983154296875, 103.10171508789062)   (581.4019165039062, 98.62245178222656)
-34  (602.3494262695312, 138.26515197753906)  (597.3190307617188, 135.87835693359375)
-35  (603.8921508789062, 156.01268005371094)   (602.2728271484375, 154.3199462890625)
-36  (584.3735961914062, 120.10896301269531)  (574.9168090820312, 114.02447509765625)
-37  (620.1016235351562, 141.37562561035156)    (609.02099609375, 137.39724731445312)
-38   (592.3193969726562, 144.9086456298828)  (590.5775146484375, 143.62985229492188)
-39  (608.6243286132812, 131.63693237304688)  (606.7920532226562, 129.97157287597656)
-Matches detected:  
-                                        kp0                                      kp1
-0   (606.9754028320312, 123.89519500732422)  (605.8805541992188, 122.00920867919922)
-1    (635.8807983398438, 119.7652359008789)   (635.058837890625, 117.37093353271484)
-2    (633.002685546875, 100.00984954833984)   (632.1666870117188, 97.49406433105469)
-3    (633.3197021484375, 133.5428466796875)      (632.9990234375, 131.7026824951172)
-4    (633.9810180664062, 188.8861846923828)  (631.7096557617188, 187.12506103515625)
-5   (607.8096923828125, 163.38027954101562)     (605.52880859375, 161.5048370361328)
-6    (605.890869140625, 160.17916870117188)  (603.1321411132812, 158.40936279296875)
-7    (624.4602661132812, 150.2027587890625)  (622.0421752929688, 147.52163696289062)
-8     (609.02099609375, 137.39724731445312)  (607.6863403320312, 135.50192260742188)
-9    (562.9036254882812, 147.4710693359375)  (560.8004760742188, 146.28392028808594)
-10  (583.6729125976562, 132.83644104003906)   (582.2899169921875, 131.2213592529297)
-11   (582.2764892578125, 137.7469024658203)    (579.4490356445312, 136.196533203125)
-12  (574.3650512695312, 130.94876098632812)  (572.3372192382812, 129.61732482910156)
-13  (574.9168090820312, 114.02447509765625)   (566.2969970703125, 109.3163833618164)
-14   (631.6682739257812, 138.9764404296875)  (630.5678100585938, 137.18626403808594)
-15    (633.262451171875, 182.9235076904297)   (631.6978149414062, 181.1128387451172)
-16   (595.276611328125, 143.97154235839844)  (593.6133422851562, 142.25003051757812)
-17   (572.744873046875, 134.47972106933594)  (570.5606689453125, 132.98545837402344)
-18   (595.245361328125, 143.99024963378906)  (593.6133422851562, 142.25003051757812)
-19   (633.9810180664062, 188.8861846923828)  (631.7096557617188, 187.12506103515625)
-20   (602.0570678710938, 145.2377166748047)    (599.9390869140625, 142.993408203125)
-21  (595.5701293945312, 128.49346923828125)  (594.7837524414062, 126.85419464111328)
-22   (599.484619140625, 139.84368896484375)             (597.625, 137.9447479248047)
-23  (594.5983276367188, 139.44192504882812)  (592.6541137695312, 138.08358764648438)
-24  (622.8848876953125, 183.60511779785156)     (619.8369140625, 182.07525634765625)
-25  (574.9168090820312, 114.02447509765625)   (566.2969970703125, 109.3163833618164)
-26    (627.3602905273438, 139.277099609375)        (626.24609375, 137.0492401123047)
-27    (589.97021484375, 169.14637756347656)  (579.0177612304688, 158.38772583007812)
-28  (569.3919677734375, 137.52444458007812)  (566.6957397460938, 135.73321533203125)
-29     (586.1982421875, 146.16168212890625)   (583.4810180664062, 144.6566925048828)
-30    (605.5939331054688, 146.240478515625)   (603.671142578125, 144.38729858398438)
-31  (627.3778686523438, 185.35324096679688)      (624.6572265625, 183.8149871826172)
-32  (586.8807373046875, 100.97610473632812)   (575.4408569335938, 96.81793975830078)
-33       (633.5576171875, 190.927978515625)     (631.15576171875, 189.0605926513672)
-34   (577.866943359375, 126.82785034179688)  (567.5581665039062, 120.69376373291016)
-35   (602.2728271484375, 154.3199462890625)   (599.3480834960938, 153.0150146484375)
-36  (578.7398071289062, 153.50543212890625)  (579.0177612304688, 158.38772583007812)
-37   (602.2728271484375, 154.3199462890625)   (599.3480834960938, 153.0150146484375)
-38    (627.3602905273438, 139.277099609375)        (626.24609375, 137.0492401123047)
-39  (610.1119995117188, 151.17825317382812)    (607.79931640625, 148.86517333984375)
-40   (622.2135009765625, 90.65599060058594)   (621.6163940429688, 88.20500946044922)
-41   (622.0867919921875, 131.5565185546875)  (622.0975341796875, 130.39260864257812)
-42  (564.1913452148438, 128.67901611328125)     (555.85400390625, 123.9614028930664)
-43  (622.8848876953125, 183.60511779785156)      (624.6572265625, 183.8149871826172)
-44  (590.5775146484375, 143.62985229492188)      (588.119140625, 141.58480834960938)
-45  (591.0513916015625, 136.55491638183594)   (635.6349487304688, 166.4396514892578)
-46   (613.6226196289062, 161.0795135498047)    (611.1397094726562, 158.987060546875)
-Matches detected:  
-                                        kp0                                      kp1
-0   (583.5675659179688, 116.05687713623047)  (581.4114990234375, 114.15325164794922)
-1    (635.058837890625, 117.37093353271484)  (633.7176513671875, 114.86624908447266)
-2   (567.5581665039062, 120.69376373291016)   (559.2962036132812, 116.8237533569336)
-3       (632.9990234375, 131.7026824951172)    (631.929931640625, 129.5418243408203)
-4   (605.8805541992188, 122.00920867919922)   (604.0955810546875, 119.9752197265625)
-..                                      ...                                      ...
-59  (622.6647338867188, 180.47918701171875)    (623.59521484375, 186.45928955078125)
-60   (630.5357055664062, 86.94648742675781)   (602.0512084960938, 74.28196716308594)
-61    (607.79931640625, 148.86517333984375)  (604.7789306640625, 147.07583618164062)
-62      (588.119140625, 141.58480834960938)  (585.5112915039062, 139.86280822753906)
-63  (553.1165771484375, 131.78256225585938)   (583.2214965820312, 137.8726348876953)
-
-[64 rows x 2 columns]
-Matches detected:  
-                                        kp0                                      kp1
-0   (581.4114990234375, 114.15325164794922)  (578.3445434570312, 112.16268920898438)
-1   (593.8316650390625, 115.97447967529297)     (591.4130859375, 113.92354583740234)
-2    (587.1021728515625, 98.88475799560547)   (584.7957153320312, 96.90682220458984)
-3    (587.8922119140625, 114.0743408203125)   (585.183837890625, 112.04057312011719)
-4   (616.8591918945312, 179.95516967773438)   (613.6148071289062, 177.8188018798828)
-..                                      ...                                      ...
-64   (609.6776733398438, 151.3343048095703)    (606.97607421875, 148.83306884765625)
-65  (628.9315185546875, 135.10580444335938)  (627.4544067382812, 132.36317443847656)
-66  (607.6604614257812, 141.88829040527344)   (604.311279296875, 140.17648315429688)
-67  (564.2056884765625, 128.17330932617188)  (559.9091186523438, 125.77507781982422)
-68    (617.3614501953125, 79.7629623413086)    (588.3743286132812, 69.3715591430664)
-
-[69 rows x 2 columns]
-Matches detected:  
-                                        kp0                                      kp1
-0   (578.3445434570312, 112.16268920898438)   (574.933837890625, 110.22492980957031)
-1    (584.7957153320312, 96.90682220458984)   (581.7966918945312, 94.88565063476562)
-2    (585.183837890625, 112.04057312011719)  (581.8672485351562, 110.01908874511719)
-3   (550.7040405273438, 115.65788269042969)  (539.3895874023438, 111.83206176757812)
-4      (591.4130859375, 113.92354583740234)   (588.219482421875, 111.71769714355469)
-..                                      ...                                      ...
-62  (564.1030883789062, 129.58200073242188)  (559.7708129882812, 127.84559631347656)
-63  (530.6557006835938, 123.54766845703125)   (539.4818115234375, 84.88227081298828)
-64       (546.01171875, 129.10084533691406)  (541.8012084960938, 127.89228820800781)
-65  (530.8004150390625, 126.39857482910156)  (523.1057739257812, 124.45321655273438)
-66  (543.3986206054688, 123.27127838134766)  (538.3274536132812, 122.13038635253906)
-
-[67 rows x 2 columns]
-Matches detected:  
-                                        kp0                                      kp1
-0    (581.7966918945312, 94.88565063476562)   (577.9382934570312, 92.90480041503906)
-1   (618.6010131835938, 106.54376983642578)   (614.8822631835938, 103.7309341430664)
-2     (617.10205078125, 120.93413543701172)  (612.7705688476562, 118.12647247314453)
-3    (563.8191528320312, 42.79572677612305)   (548.289794921875, 39.256927490234375)
-4    (574.933837890625, 110.22492980957031)  (570.5972900390625, 108.23710632324219)
-..                                      ...                                      ...
-74  (568.8375244140625, 138.55299377441406)   (563.5916748046875, 136.4902801513672)
-75  (625.3869018554688, 182.11907958984375)       (620.67578125, 179.48538208007812)
-76  (573.2098999023438, 139.14837646484375)    (569.44580078125, 137.19168090820312)
-77  (578.8189086914062, 142.06961059570312)  (544.1754150390625, 132.69607543945312)
-78  (535.0880126953125, 123.25269317626953)  (530.2474365234375, 121.67733001708984)
-
-[79 rows x 2 columns]
-Matches detected:  
-                                        kp0                                      kp1
-0   (570.5972900390625, 108.23710632324219)  (565.5132446289062, 106.21681213378906)
-1    (614.8822631835938, 103.7309341430664)  (610.2823486328125, 100.91947174072266)
-2    (577.9382934570312, 92.90480041503906)    (573.0364379882812, 90.7490463256836)
-3   (612.7705688476562, 118.12647247314453)  (608.3595581054688, 115.28585052490234)
-4   (624.3306274414062, 159.11105346679688)  (619.3516235351562, 156.17005920410156)
-..                                      ...                                      ...
-75    (569.44580078125, 137.19168090820312)        (563.539794921875, 135.533203125)
-76   (583.345947265625, 141.60903930664062)   (577.1153564453125, 138.2787322998047)
-77  (612.4163818359375, 124.14116668701172)  (593.3418579101562, 117.96626281738281)
-78   (546.9757690429688, 68.43921661376953)   (539.3528442382812, 69.24249267578125)
-79   (526.9667358398438, 80.73302459716797)  (522.7962036132812, 57.647132873535156)
-
-[80 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (565.5132446289062, 106.21681213378906)  (559.5762939453125, 104.20930480957031)
-1      (573.0364379882812, 90.7490463256836)    (567.4396362304688, 88.7311782836914)
-2     (539.1223754882812, 99.61164855957031)    (531.071533203125, 97.55264282226562)
-3     (539.1223754882812, 99.61164855957031)    (531.071533203125, 97.55264282226562)
-4    (608.3595581054688, 115.28585052490234)  (602.3970947265625, 112.30292510986328)
-..                                       ...                                      ...
-106   (609.7666625976562, 124.9109115600586)  (604.3742065429688, 121.19392395019531)
-107   (633.2595825195312, 133.4495086669922)  (627.3726806640625, 129.72410583496094)
-108    (515.72607421875, 116.03349304199219)  (510.0772399902344, 115.36141967773438)
-109      (566.1259765625, 129.9849853515625)   (525.2168579101562, 123.6856689453125)
-110  (613.4236450195312, 132.55758666992188)   (608.0293579101562, 128.7987823486328)
-
-[111 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (559.5762939453125, 104.20930480957031)  (552.6881103515625, 102.25869750976562)
-1      (567.4396362304688, 88.7311782836914)      (560.6357421875, 86.65914154052734)
-2      (574.6854858398438, 72.2398452758789)   (568.7581176757812, 70.28114318847656)
-3    (602.3970947265625, 112.30292510986328)       (595.583984375, 109.3079605102539)
-4      (531.071533203125, 97.55264282226562)    (522.839111328125, 96.00850677490234)
-..                                       ...                                      ...
-104  (627.3726806640625, 129.72410583496094)           (620.5625, 125.85725402832031)
-105  (635.5069580078125, 143.05093383789062)  (629.5047607421875, 139.09800720214844)
-106   (540.862548828125, 119.60515594482422)  (630.5465087890625, 122.50869750976562)
-107   (627.7745361328125, 181.8380889892578)  (494.05352783203125, 54.59700393676758)
-108   (578.4795532226562, 130.9447021484375)  (549.0475463867188, 131.81175231933594)
-
-[109 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0         (560.6357421875, 86.65914154052734)    (552.930908203125, 84.81877136230469)
-1     (552.6881103515625, 102.25869750976562)          (544.984375, 100.3319091796875)
-2      (591.2164916992188, 166.9210968017578)    (583.91552734375, 164.02650451660156)
-3      (591.2164916992188, 166.9210968017578)    (583.91552734375, 164.02650451660156)
-4    (481.95416259765625, 113.28585052490234)   (474.0060729980469, 113.2913589477539)
-..                                        ...                                      ...
-106   (524.8336181640625, 128.99172973632812)   (517.3511962890625, 128.1490478515625)
-107      (613.66552734375, 169.4361572265625)  (606.3212280273438, 165.93138122558594)
-108   (596.2789306640625, 123.68431854248047)   (588.0975341796875, 120.4793930053711)
-109         (564.46142578125, 128.8349609375)  (556.5502319335938, 126.78759002685547)
-110    (605.3032836914062, 172.6690216064453)  (595.4166259765625, 167.83810424804688)
-
-[111 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (558.2185668945312, 100.37067413330078)   (549.6028442382812, 98.32122802734375)
-1     (564.0035400390625, 128.01577758789062)  (556.5814819335938, 125.05918884277344)
-2       (555.116455078125, 87.89681243896484)     (546.398193359375, 85.9491195678711)
-3       (552.930908203125, 84.81877136230469)   (543.4591064453125, 82.83988189697266)
-4      (482.6940002441406, 73.46036529541016)     (471.37445068359375, 73.45751953125)
-..                                        ...                                      ...
-117   (621.3545532226562, 131.87464904785156)  (613.0106201171875, 127.56412506103516)
-118    (588.0975341796875, 120.4793930053711)   (579.888916015625, 117.86177825927734)
-119  (505.82220458984375, 120.30171203613281)  (497.3349914550781, 120.28882598876953)
-120        (602.10986328125, 119.60205078125)  (593.3265991210938, 116.64015197753906)
-121            (524.58935546875, 67.67578125)   (507.3729248046875, 70.94176483154297)
-
-[122 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0       (471.37445068359375, 73.45751953125)   (459.9129333496094, 74.00251770019531)
-1    (478.6092529296875, 113.82481384277344)  (469.8273620605469, 114.82025909423828)
-2        (584.0322265625, 148.8037872314453)    (574.678955078125, 145.9553680419922)
-3       (471.37445068359375, 73.45751953125)   (459.9129333496094, 74.00251770019531)
-4     (543.4591064453125, 82.83988189697266)   (534.2363891601562, 81.30838012695312)
-..                                       ...                                      ...
-123   (533.8370971679688, 127.6246566772461)   (524.1124877929688, 126.9364013671875)
-124   (497.968017578125, 135.98243713378906)  (569.9756469726562, 153.59878540039062)
-125     (631.9345703125, 153.18865966796875)   (623.0360107421875, 148.8136444091797)
-126  (549.8126831054688, 133.03488159179688)    (540.93017578125, 131.42733764648438)
-127      (576.6298828125, 143.0064697265625)  (567.6124267578125, 140.35501098632812)
-
-[128 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0     (459.9129333496094, 74.00251770019531)   (448.1239929199219, 74.91293334960938)
-1     (534.2363891601562, 81.30838012695312)   (524.6156005859375, 80.16949462890625)
-2      (497.5359191894531, 66.2725830078125)  (487.53826904296875, 66.28325653076172)
-3      (551.375244140625, 61.90237808227539)  (557.1002807617188, 62.339420318603516)
-4      (546.137451171875, 123.4144287109375)  (537.3667602539062, 121.48295593261719)
-..                                       ...                                      ...
-123   (621.9002685546875, 166.2085723876953)     (612.682861328125, 162.027099609375)
-124  (570.2913208007812, 114.83497619628906)  (560.0789184570312, 112.22471618652344)
-125      (571.0126953125, 109.6895980834961)  (561.6641845703125, 107.07132720947266)
-126   (575.677001953125, 116.44804382324219)  (565.5484008789062, 114.38054656982422)
-127  (594.4064331054688, 123.87693786621094)              (585.0, 120.38359069824219)
-
-[128 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (622.1873168945312, 183.25038146972656)  (613.0084228515625, 179.29827880859375)
-1      (514.1220092773438, 146.7969207763672)    (505.1295471191406, 146.262939453125)
-2     (557.5870971679688, 157.88230895996094)    (548.43017578125, 155.83639526367188)
-3         (530.2802734375, 95.18745422363281)   (520.1688232421875, 94.16960906982422)
-4       (559.1978759765625, 98.4229965209961)    (549.2888793945312, 96.4503173828125)
-..                                        ...                                      ...
-130               (585.0, 120.38359069824219)  (574.9972534179688, 117.59796142578125)
-131   (467.87615966796875, 64.55023956298828)   (458.4414367675781, 65.37739562988281)
-132   (598.8211669921875, 166.65411376953125)    (589.59716796875, 163.27235412597656)
-133  (478.81939697265625, 112.38130187988281)  (469.1482849121094, 112.96829986572266)
-134   (541.6725463867188, 127.77285766601562)  (532.1324462890625, 125.84717559814453)
-
-[135 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0      (505.1295471191406, 146.262939453125)  (496.8897705078125, 145.80499267578125)
-1     (477.2059020996094, 95.71538543701172)   (466.6559753417969, 96.99388885498047)
-2     (470.2238464355469, 67.89447784423828)  (460.14129638671875, 69.38690185546875)
-3      (587.6788940429688, 75.7402572631836)   (572.4661865234375, 73.57719421386719)
-4      (456.3713073730469, 68.9449691772461)   (446.0534362792969, 70.52952575683594)
-..                                       ...                                      ...
-142     (636.1359252929688, 100.37255859375)    (625.4866943359375, 96.2684555053711)
-143  (603.8809814453125, 141.11105346679688)          (593.609375, 136.5452117919922)
-144  (571.8001098632812, 115.87184143066406)  (561.8401489257812, 114.13986206054688)
-145   (636.5719604492188, 177.9859161376953)  (626.8683471679688, 173.42462158203125)
-146  (542.4401245117188, 106.92289733886719)   (532.7384033203125, 104.3294906616211)
-
-[147 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0         (373.705078125, 93.61221313476562)   (363.9749450683594, 97.51847839355469)
-1    (496.8897705078125, 145.80499267578125)  (487.8092956542969, 145.98251342773438)
-2     (466.6559753417969, 96.99388885498047)  (456.45501708984375, 99.13108825683594)
-3     (466.6559753417969, 96.99388885498047)  (456.45501708984375, 99.13108825683594)
-4     (603.766845703125, 176.13217163085938)  (595.0447998046875, 173.02403259277344)
-..                                       ...                                      ...
-139  (485.6499328613281, 122.40904235839844)  (476.7499084472656, 122.97148132324219)
-140   (561.4795532226562, 110.0607681274414)   (550.4496459960938, 107.6058120727539)
-141  (619.0507202148438, 121.36625671386719)  (609.2903442382812, 116.56624603271484)
-142      (464.6943359375, 121.0078353881836)   (455.677978515625, 122.32894134521484)
-143   (629.2440185546875, 76.91651916503906)  (590.8582763671875, 110.37934112548828)
-
-[144 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (487.8092956542969, 145.98251342773438)     (479.7146301269531, 146.360595703125)
-1     (600.0936279296875, 168.80213928222656)    (591.1190795898438, 165.8873748779297)
-2    (389.18890380859375, 101.53340148925781)   (379.1932373046875, 105.00696563720703)
-3     (595.0447998046875, 173.02403259277344)      (585.90283203125, 170.1103973388672)
-4    (498.69635009765625, 102.02094268798828)       (490.048828125, 101.94093322753906)
-..                                        ...                                       ...
-160   (635.6798706054688, 168.13851928710938)   (626.7150268554688, 163.97117614746094)
-161  (450.05352783203125, 114.95526123046875)  (441.49420166015625, 116.09625244140625)
-162      (634.1884765625, 113.14988708496094)   (622.9454956054688, 108.59427642822266)
-163   (500.3863830566406, 119.05286407470703)   (489.52215576171875, 118.5386962890625)
-164   (583.2611694335938, 155.98252868652344)    (574.0482177734375, 153.6190185546875)
-
-[165 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (446.6661682128906, 101.78826904296875)   (436.904052734375, 104.12456512451172)
-1    (427.03387451171875, 75.05294799804688)   (417.9625549316406, 77.70677947998047)
-2    (427.03387451171875, 75.05294799804688)   (417.9625549316406, 77.70677947998047)
-3       (585.90283203125, 170.1103973388672)       (577.4222412109375, 168.099609375)
-4        (490.048828125, 101.94093322753906)  (480.3976745605469, 102.57254791259766)
-..                                       ...                                      ...
-163  (557.3976440429688, 156.09542846679688)   (548.7594604492188, 154.7645721435547)
-164  (593.9931030273438, 161.01170349121094)  (585.2395629882812, 158.15438842773438)
-165   (598.7377319335938, 154.3043212890625)   (589.8131103515625, 151.7905731201172)
-166   (594.1542358398438, 88.40740966796875)   (585.3755493164062, 84.54759979248047)
-167    (587.0637817382812, 128.236572265625)  (576.9756469726562, 126.03327941894531)
-
-[168 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0    (415.26251220703125, 133.93743896484375)  (407.28009033203125, 136.67198181152344)
-1      (436.904052734375, 104.12456512451172)   (428.6973571777344, 106.48008728027344)
-2      (436.904052734375, 104.12456512451172)   (428.6973571777344, 106.48008728027344)
-3      (405.4204406738281, 111.4778823852539)   (396.99066162109375, 114.5327377319336)
-4          (577.4222412109375, 168.099609375)        (569.447265625, 166.1448211669922)
-..                                        ...                                       ...
-161   (587.7772216796875, 135.80438232421875)     (579.1648559570312, 134.837646484375)
-162  (498.01483154296875, 129.31593322753906)    (490.5781555175781, 129.4258575439453)
-163   (431.3919372558594, 123.58963775634766)  (423.34490966796875, 125.81236267089844)
-164         (610.3359375, 128.54930114746094)    (600.592041015625, 125.61781311035156)
-165   (618.4923095703125, 154.52545166015625)   (609.7446899414062, 151.57711791992188)
-
-[166 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0       (561.3565063476562, 97.4552993774414)    (551.685302734375, 95.98079681396484)
-1     (410.63421630859375, 80.30120086669922)   (402.8535461425781, 83.40940856933594)
-2      (564.3265380859375, 160.0586700439453)   (556.639404296875, 158.97406005859375)
-3        (567.0980834960938, 152.11865234375)    (559.974365234375, 151.0357666015625)
-4       (373.0794677734375, 96.4280776977539)   (364.2751159667969, 100.7059097290039)
-..                                        ...                                      ...
-177  (501.16156005859375, 115.51101684570312)  (567.1679077148438, 140.70086669921875)
-178    (560.7338256835938, 85.13833618164062)    (572.363037109375, 78.58518981933594)
-179   (486.6554870605469, 100.35575103759766)    (549.990966796875, 77.17595672607422)
-180    (612.2673950195312, 124.0020980834961)  (603.4841918945312, 122.18376922607422)
-181      (609.0205078125, 125.14553833007812)  (600.5830688476562, 122.06758880615234)
-
-[182 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0       (455.2892761230469, 150.588623046875)   (447.802978515625, 152.04286193847656)
-1       (389.1394958496094, 117.730712890625)   (381.948486328125, 121.18046569824219)
-2     (465.15960693359375, 104.1290054321289)  (458.3309326171875, 105.24573516845703)
-3    (420.80865478515625, 109.42098236083984)   (414.141357421875, 112.54216003417969)
-4     (465.15960693359375, 104.1290054321289)  (458.3309326171875, 105.24573516845703)
-..                                        ...                                      ...
-189     (616.896240234375, 115.3183822631836)  (417.02947998046875, 127.1837158203125)
-190     (530.78759765625, 148.99380493164062)   (524.646240234375, 148.89500427246094)
-191   (543.0880126953125, 151.84642028808594)  (580.3551635742188, 101.66602325439453)
-192   (636.2743530273438, 126.83688354492188)   (628.5294799804688, 123.2992172241211)
-193  (483.34979248046875, 119.36699676513672)  (614.1708374023438, 119.96090698242188)
-
-[194 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (381.948486328125, 121.18046569824219)  (375.86663818359375, 124.69220733642578)
-1      (552.2279663085938, 149.8616180419922)    (545.6759643554688, 149.0978240966797)
-2     (345.8401184082031, 121.75239562988281)   (339.4827880859375, 126.20806121826172)
-3      (542.1580810546875, 95.51622772216797)    (533.1299438476562, 95.62422180175781)
-4     (345.8401184082031, 121.75239562988281)   (339.4827880859375, 126.20806121826172)
-..                                        ...                                       ...
-201  (457.39959716796875, 130.68807983398438)   (451.4859619140625, 131.45318603515625)
-202   (426.0968017578125, 130.93695068359375)  (420.27484130859375, 133.39114379882812)
-203   (424.0531005859375, 120.43836975097656)   (529.5673217773438, 154.90328979492188)
-204    (636.7982788085938, 37.38750457763672)    (627.3924560546875, 34.46091079711914)
-205   (617.3068237304688, 25.414936065673828)     (514.44580078125, 118.37969207763672)
-
-[206 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0    (375.86663818359375, 124.69220733642578)   (370.6766662597656, 128.02645874023438)
-1     (351.9776306152344, 125.24108123779297)     (346.449951171875, 129.4027862548828)
-2     (451.33026123046875, 106.9540023803711)     (446.1412658691406, 108.754150390625)
-3     (339.4827880859375, 126.20806121826172)    (333.941650390625, 130.48338317871094)
-4     (443.3243103027344, 151.38880920410156)        (439.34765625, 152.42742919921875)
-..                                        ...                                       ...
-210   (422.0945129394531, 125.89838409423828)   (415.5974426269531, 128.40638732910156)
-211    (624.9873657226562, 35.99529266357422)   (616.9505004882812, 33.572017669677734)
-212  (486.12677001953125, 132.21487426757812)  (481.23321533203125, 133.37286376953125)
-213    (620.2340698242188, 21.17048454284668)  (440.12091064453125, 136.31629943847656)
-214   (573.7032470703125, 125.42781066894531)   (568.6305541992188, 123.96308135986328)
-
-[215 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (525.5358276367188, 96.35020446777344)    (519.3583374023438, 96.90784454345703)
-1     (534.340087890625, 45.213993072509766)   (526.2890014648438, 46.038089752197266)
-2     (333.941650390625, 130.48338317871094)    (329.6256408691406, 134.5594024658203)
-3     (508.4008483886719, 151.1919403076172)    (504.16851806640625, 151.572998046875)
-4         (439.34765625, 152.42742919921875)   (434.3616943359375, 154.33253479003906)
-..                                       ...                                       ...
-224  (468.1569519042969, 105.52046203613281)  (463.95941162109375, 105.47469329833984)
-225   (613.586669921875, 147.67332458496094)    (611.0584106445312, 146.7337646484375)
-226  (590.4783935546875, 113.59907531738281)      (586.0562133789062, 112.96044921875)
-227  (608.9817504882812, 25.061574935913086)    (601.9864501953125, 23.69379425048828)
-228   (455.149169921875, 122.38307189941406)   (451.0940246582031, 124.14266204833984)
-
-[229 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (443.29534912109375, 109.7355728149414)   (440.271240234375, 111.33152770996094)
-1    (544.5809936523438, 109.74205780029297)  (541.5835571289062, 109.22712707519531)
-2    (526.2890014648438, 46.038089752197266)  (519.8025512695312, 47.020729064941406)
-3     (571.592529296875, 63.032379150390625)   (567.8851318359375, 61.99309158325195)
-4     (529.2925415039062, 129.4234161376953)   (526.3612670898438, 129.7117462158203)
-..                                       ...                                      ...
-232  (528.0177001953125, 142.80654907226562)   (525.366455078125, 143.30020141601562)
-233    (406.114501953125, 97.13935089111328)    (507.2309265136719, 82.7732925415039)
-234   (549.066162109375, 135.46551513671875)  (546.5115356445312, 135.06114196777344)
-235   (601.9864501953125, 23.69379425048828)  (597.9947509765625, 22.053617477416992)
-236  (411.2809753417969, 130.56019592285156)    (607.191162109375, 95.37550354003906)
-
-[237 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (338.3419494628906, 136.7974395751953)   (335.9856872558594, 139.91517639160156)
-1     (399.6153259277344, 85.83464050292969)     (397.5993347167969, 88.2762222290039)
-2    (394.1387939453125, 125.72345733642578)   (392.7109680175781, 127.96194458007812)
-3    (541.5835571289062, 109.22712707519531)   (539.2052001953125, 109.05259704589844)
-4    (541.5835571289062, 109.22712707519531)   (539.2052001953125, 109.05259704589844)
-..                                       ...                                       ...
-236   (381.6094665527344, 93.55519104003906)  (216.49020385742188, 37.311317443847656)
-237    (317.0325012207031, 5.84310245513916)    (314.4707336425781, 9.092226028442383)
-238  (316.56390380859375, 13.99937915802002)   (313.8860778808594, 19.037551879882812)
-239  (525.5621948242188, 147.66958618164062)   (523.7079467773438, 148.36398315429688)
-240  (595.8772583007812, 15.490880966186523)    (592.1504516601562, 13.77399730682373)
-
-[241 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (539.2052001953125, 109.05259704589844)   (537.8845825195312, 109.70147705078125)
-1    (358.58087158203125, 127.88616180419922)    (358.6352233886719, 130.0260467529297)
-2     (525.1600952148438, 129.88253784179688)   (526.4935913085938, 130.44017028808594)
-3     (525.1600952148438, 129.88253784179688)   (526.4935913085938, 130.44017028808594)
-4     (392.7109680175781, 127.96194458007812)  (390.94610595703125, 130.49069213867188)
-..                                        ...                                       ...
-269   (440.9620361328125, 130.67062377929688)   (441.1470642089844, 132.23338317871094)
-270    (540.873779296875, 116.24397277832031)    (524.5039672851562, 150.4515838623047)
-271     (616.05810546875, 130.85450744628906)   (226.20156860351562, 43.70415496826172)
-272   (517.6773681640625, 149.10459899902344)    (518.377197265625, 150.53514099121094)
-273   (539.6375732421875, 119.66566467285156)   (539.8421630859375, 120.00175476074219)
-
-[274 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (509.2721252441406, 100.06299591064453)   (509.6949157714844, 101.2430191040039)
-1     (538.9329223632812, 163.80850219726562)  (540.9869384765625, 164.78689575195312)
-2    (322.64813232421875, 144.29745483398438)   (322.8560791015625, 146.4538116455078)
-3    (390.94610595703125, 130.49069213867188)   (393.5627136230469, 131.7535858154297)
-4        (334.9420471191406, 142.57568359375)  (335.1965026855469, 144.58155822753906)
-..                                        ...                                      ...
-270   (505.9855651855469, 157.56243896484375)   (508.5701904296875, 159.1639862060547)
-271    (510.062744140625, 150.33743286132812)  (510.5527038574219, 151.09982299804688)
-272   (453.1392517089844, 133.91502380371094)  (453.66546630859375, 134.6140899658203)
-273     (345.95086669921875, 36.215087890625)  (344.74285888671875, 40.01841354370117)
-274    (573.7229614257812, 96.53753662109375)   (624.9921264648438, 7.361428737640381)
-
-[275 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (540.9869384765625, 164.78689575195312)   (544.7787475585938, 165.8873291015625)
-1       (539.1767578125, 110.75932312011719)  (542.4100341796875, 111.58207702636719)
-2     (322.8560791015625, 146.4538116455078)   (324.1884460449219, 147.8815460205078)
-3     (509.6949157714844, 101.2430191040039)  (512.0982055664062, 102.14177703857422)
-4     (322.8560791015625, 146.4538116455078)   (324.1884460449219, 147.8815460205078)
-..                                       ...                                      ...
-289   (612.736572265625, 141.60879516601562)  (616.8505249023438, 141.60171508789062)
-290    (559.911376953125, 95.65119934082031)     (564.4119262695312, 95.824951171875)
-291   (611.642822265625, 144.58045959472656)   (616.3264770507812, 144.4429931640625)
-292   (542.043701171875, 127.72686004638672)  (545.9198608398438, 127.87638092041016)
-293    (462.7311096191406, 36.8570556640625)   (634.9842529296875, 146.3744354248047)
-
-[294 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0        (394.154296875, 103.46524810791016)  (399.13128662109375, 104.18706512451172)
-1        (394.154296875, 103.46524810791016)  (399.13128662109375, 104.18706512451172)
-2      (603.302978515625, 51.95034408569336)     (611.275146484375, 51.99518966674805)
-3     (544.7787475585938, 165.8873291015625)   (550.4168701171875, 167.82151794433594)
-4     (352.769775390625, 144.32850646972656)    (356.2538146972656, 145.1407470703125)
-..                                       ...                                       ...
-272  (499.5095520019531, 105.88423156738281)  (505.48345947265625, 106.58415222167969)
-273       (553.888671875, 130.6687774658203)    (560.6482543945312, 131.6314239501953)
-274  (545.9198608398438, 127.87638092041016)       (552.904296875, 129.15130615234375)
-275   (632.8592529296875, 78.07627868652344)   (603.9534301757812, 111.05831146240234)
-276  (193.90391540527344, 53.66793441772461)   (193.44679260253906, 51.60411071777344)
-
-[277 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0    (399.13128662109375, 104.18706512451172)   (405.4899597167969, 104.06336975097656)
-1    (399.13128662109375, 104.18706512451172)   (405.4899597167969, 104.06336975097656)
-2     (354.6659851074219, 145.22103881835938)     (358.9743347167969, 145.364013671875)
-3     (354.6659851074219, 145.22103881835938)     (358.9743347167969, 145.364013671875)
-4      (356.2538146972656, 145.1407470703125)  (360.40802001953125, 145.30572509765625)
-..                                        ...                                       ...
-266   (624.1619262695312, 20.899532318115234)   (636.0877075195312, 18.829233169555664)
-267    (228.1715850830078, 46.48163986206055)    (291.8763122558594, 9.039194107055664)
-268    (598.6510009765625, 92.32042694091797)   (560.9653930664062, 129.84616088867188)
-269      (601.92724609375, 12.61210823059082)   (460.3764343261719, 114.66912078857422)
-270   (550.3033447265625, 54.722312927246094)    (557.8599243164062, 54.38286209106445)
-
-[271 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (562.3177490234375, 162.82818603515625)   (571.2882690429688, 163.78695678710938)
-1     (226.1833038330078, 42.47055435180664)      (234.11669921875, 42.02948760986328)
-2     (226.1833038330078, 42.47055435180664)      (234.11669921875, 42.02948760986328)
-3    (558.2279663085938, 168.89649963378906)   (566.9884033203125, 169.91285705566406)
-4    (405.4899597167969, 104.06336975097656)   (412.9517517089844, 103.35887145996094)
-..                                       ...                                       ...
-248  (618.6187133789062, 19.974781036376953)     (632.74267578125, 17.165752410888672)
-249  (543.4536743164062, 147.92147827148438)   (552.1942749023438, 148.27146911621094)
-250   (373.052001953125, 36.614200592041016)  (428.40594482421875, 148.47933959960938)
-251   (622.9957885742188, 13.17717456817627)      (636.1846313476562, 9.7039794921875)
-252  (630.8461303710938, 3.3782596588134766)   (207.12533569335938, 54.61133575439453)
-
-[253 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0     (379.5232238769531, 132.4261932373047)  (387.5054931640625, 131.39828491210938)
-1       (490.08154296875, 73.45901489257812)   (498.3636169433594, 73.02342987060547)
-2    (490.8036193847656, 115.58210754394531)       (499.44921875, 115.05197143554688)
-3    (448.5852966308594, 161.56471252441406)  (456.4809265136719, 161.38352966308594)
-4    (467.3948059082031, 113.70746612548828)  (476.3370361328125, 113.39883422851562)
-..                                       ...                                      ...
-233  (474.0479736328125, 133.48101806640625)    (483.17596435546875, 133.44775390625)
-234    (615.633544921875, 87.75455474853516)   (623.2774047851562, 97.82535552978516)
-235   (346.1423034667969, 41.82748031616211)   (355.4665222167969, 40.28278350830078)
-236   (564.6146850585938, 99.05074310302734)   (528.9308471679688, 114.8924560546875)
-237  (423.6559753417969, 150.01109313964844)    (432.583251953125, 149.6898651123047)
-
-[238 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (456.4809265136719, 161.38352966308594)  (465.4070129394531, 161.15623474121094)
-1    (343.5938415527344, 146.52272033691406)  (351.2357177734375, 144.85910034179688)
-2    (565.0950927734375, 134.01736450195312)  (577.7196655273438, 135.33055114746094)
-3    (483.41156005859375, 88.32976531982422)    (493.4735107421875, 88.0013656616211)
-4      (372.54931640625, 143.84759521484375)  (380.26495361328125, 142.5518798828125)
-..                                       ...                                      ...
-250       (499.44921875, 115.05197143554688)  (363.9346923828125, 144.06497192382812)
-251  (575.2791748046875, 124.85113525390625)   (588.3583374023438, 125.4103012084961)
-252  (347.5852966308594, 35.592960357666016)  (358.57086181640625, 32.59119415283203)
-253  (592.9550170898438, 136.50006103515625)  (607.1874389648438, 137.26783752441406)
-254  (485.1463623046875, 144.26626586914062)    (496.081298828125, 144.2618408203125)
-
-[255 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (351.2357177734375, 144.85910034179688)       (359.533203125, 142.76844787597656)
-1    (351.2357177734375, 144.85910034179688)       (359.533203125, 142.76844787597656)
-2    (555.0037841796875, 142.15530395507812)    (567.6317138671875, 143.5167236328125)
-3     (384.4050598144531, 75.35757446289062)    (395.5329284667969, 73.91320037841797)
-4    (555.0037841796875, 142.15530395507812)    (567.6317138671875, 143.5167236328125)
-..                                       ...                                       ...
-225   (518.6743774414062, 29.35919761657715)   (533.0314331054688, 27.472440719604492)
-226   (603.8401489257812, 135.1575469970703)   (619.4467163085938, 136.98475646972656)
-227    (631.34716796875, 141.27711486816406)   (580.9946899414062, 125.18159484863281)
-228   (366.5956726074219, 36.88321304321289)   (378.0753173828125, 34.528411865234375)
-229  (229.7457733154297, 43.641639709472656)  (240.68807983398438, 39.628822326660156)
-
-[230 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (601.9740600585938, 177.10202026367188)  (615.9942016601562, 180.30410766601562)
-1    (496.54058837890625, 113.18477630615234)  (509.0989990234375, 112.68719482421875)
-2      (567.6317138671875, 143.5167236328125)   (582.1478271484375, 145.1786346435547)
-3      (586.3848266601562, 72.95085144042969)   (603.8950805664062, 72.85112762451172)
-4      (579.5853271484375, 80.20529174804688)     (596.8189086914062, 80.135986328125)
-..                                        ...                                      ...
-203  (423.76812744140625, 39.783905029296875)  (532.7433471679688, 114.19313049316406)
-204    (480.6737976074219, 135.9550323486328)  (492.8567199707031, 135.69296264648438)
-205    (561.9573974609375, 99.91683959960938)    (576.58837890625, 100.70523071289062)
-206    (508.9878845214844, 134.8513946533203)  (463.8622741699219, 157.29373168945312)
-207   (561.3661499023438, 100.83613586425781)    (576.58837890625, 100.70523071289062)
-
-[208 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (381.1852722167969, 139.9508056640625)  (390.8091735839844, 137.65921020507812)
-1    (485.17535400390625, 160.27809143066406)  (496.58111572265625, 160.5179901123047)
-2       (368.46142578125, 140.31155395507812)   (378.003173828125, 137.72682189941406)
-3      (444.9815979003906, 97.99395751953125)   (457.8753662109375, 96.06195831298828)
-4       (368.46142578125, 140.31155395507812)   (378.003173828125, 137.72682189941406)
-..                                        ...                                      ...
-164  (236.88018798828125, 40.525665283203125)  (249.99697875976562, 34.65210723876953)
-165    (246.7694854736328, 34.73443603515625)   (260.1406555175781, 28.49407196044922)
-166   (468.0099182128906, 30.519548416137695)  (381.2539978027344, 22.193653106689453)
-167    (547.6817626953125, 19.17486000061035)  (565.1796264648438, 14.793432235717773)
-168    (547.0078735351562, 27.49490737915039)   (565.9962768554688, 25.31437110900879)
-
-[169 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (361.34979248046875, 138.7845001220703)     (370.32080078125, 135.77931213378906)
-1     (361.34979248046875, 138.7845001220703)     (370.32080078125, 135.77931213378906)
-2     (390.8091735839844, 137.65921020507812)   (400.6767883300781, 135.34510803222656)
-3     (513.3550415039062, 27.820018768310547)   (531.4500732421875, 24.663963317871094)
-4     (496.58111572265625, 160.5179901123047)    (507.6762390136719, 160.3712615966797)
-..                                        ...                                       ...
-166    (616.291259765625, 163.97335815429688)   (631.6244506835938, 167.56204223632812)
-167  (462.85430908203125, 147.50778198242188)  (474.09417724609375, 146.68792724609375)
-168   (614.2344970703125, 170.31997680664062)   (629.2305297851562, 174.20603942871094)
-169   (582.7957763671875, 114.34416198730469)     (599.65185546875, 115.52811431884766)
-170    (616.6876220703125, 166.1681365966797)   (632.3421630859375, 169.93807983398438)
-
-[171 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0      (370.32080078125, 135.77931213378906)   (380.2589111328125, 132.65640258789062)
-1      (370.32080078125, 135.77931213378906)   (380.2589111328125, 132.65640258789062)
-2    (543.4927368164062, 10.819015502929688)       (563.4697265625, 7.843628406524658)
-3    (535.3532104492188, 112.61610412597656)     (548.85693359375, 113.02338409423828)
-4    (400.6767883300781, 135.34510803222656)  (410.63031005859375, 132.83029174804688)
-..                                       ...                                       ...
-150   (590.6823120117188, 111.3929214477539)   (607.9496459960938, 113.06756591796875)
-151  (479.2508850097656, 140.53172302246094)    (523.889892578125, 135.05003356933594)
-152  (217.2906036376953, 17.261764526367188)    (396.5396423339844, 28.08831787109375)
-153   (467.01226806640625, 31.9464168548584)   (482.44818115234375, 28.92955207824707)
-154  (633.9729614257812, 166.02493286132812)   (549.8862915039062, 136.00201416015625)
-
-[155 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0       (397.8426513671875, 132.186279296875)  (407.86688232421875, 129.44232177734375)
-1       (548.85693359375, 113.02338409423828)    (563.1166381835938, 113.3504409790039)
-2      (609.4830932617188, 146.0233154296875)      (624.8060913085938, 149.07666015625)
-3      (582.4124145507812, 174.4601593017578)   (594.9783935546875, 176.99819946289062)
-4     (515.8584594726562, 112.15535736083984)   (530.2434692382812, 111.90087127685547)
-..                                        ...                                       ...
-145  (262.01251220703125, 30.334918975830078)   (274.8661193847656, 22.812213897705078)
-146  (281.49237060546875, 12.233870506286621)    (294.8742370605469, 4.971393585205078)
-147    (296.3064270019531, 9.027945518493652)    (335.2398376464844, 7.353148937225342)
-148    (618.274169921875, 132.29722595214844)   (629.3883056640625, 136.10353088378906)
-149   (617.7408447265625, 132.11582946777344)   (629.3883056640625, 136.10353088378906)
-
-[150 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (530.2434692382812, 111.90087127685547)  (544.1315307617188, 111.77729797363281)
-1    (488.9067077636719, 114.31767272949219)  (501.5804443359375, 113.19869232177734)
-2   (407.86688232421875, 129.44232177734375)  (417.5964660644531, 126.69200134277344)
-3    (300.48858642578125, 27.58576202392578)  (317.3134460449219, 24.938323974609375)
-4     (563.1166381835938, 113.3504409790039)     (576.2607421875, 114.37730407714844)
-..                                       ...                                      ...
-91   (611.5604858398438, 172.89642333984375)  (615.7803955078125, 129.43243408203125)
-92     (527.94580078125, 141.11837768554688)   (540.2476806640625, 141.9984130859375)
-93    (289.0368347167969, 14.39833927154541)   (301.6475830078125, 6.993077754974365)
-94   (564.6154174804688, 131.87518310546875)     (579.18505859375, 132.5194091796875)
-95    (601.226318359375, 178.14895629882812)      (613.4093017578125, 181.5810546875)
-
-[96 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (459.90045166015625, 145.2747039794922)   (469.6138610839844, 144.0540008544922)
-1    (445.1091613769531, 121.04367065429688)  (455.4795837402344, 118.97994995117188)
-2    (544.1315307617188, 111.77729797363281)  (557.2279663085938, 112.08280181884766)
-3      (512.1927490234375, 89.0571060180664)   (526.0906982421875, 88.21246337890625)
-4     (482.8135681152344, 96.84681701660156)   (492.5946044921875, 95.88789367675781)
-..                                       ...                                      ...
-100  (556.8854370117188, 137.67308044433594)       (569.13671875, 139.64122009277344)
-101  (617.2628784179688, 117.49095916748047)    (594.820556640625, 136.1723175048828)
-102  (632.7192993164062, 192.44827270507812)   (529.172119140625, 158.32174682617188)
-103  (629.6989135742188, 175.80955505371094)  (631.0064086914062, 181.59156799316406)
-104  (621.6868896484375, 110.24044036865234)   (637.0645141601562, 113.4502944946289)
-
-[105 rows x 2 columns]
-Matches detected:  
-                                        kp0                                      kp1
-0       (552.5927734375, 163.8706817626953)     (562.918701171875, 164.828369140625)
-1    (469.6138610839844, 144.0540008544922)  (479.0997619628906, 142.99142456054688)
-2   (450.9243469238281, 114.58658599853516)   (460.2112731933594, 112.6288833618164)
-3   (563.0069580078125, 108.99201965332031)  (575.7057495117188, 109.74275207519531)
-4       (439.212890625, 125.49231719970703)  (448.16217041015625, 123.1525650024414)
-..                                      ...                                      ...
-83    (594.820556640625, 136.1723175048828)  (605.2532958984375, 139.34544372558594)
-84   (564.483642578125, 23.628549575805664)  (417.2348937988281, 123.62493896484375)
-85  (565.8449096679688, 150.45436096191406)  (575.2930908203125, 151.75462341308594)
-86  (618.4559936523438, 183.72947692871094)   (628.557861328125, 187.63734436035156)
-87   (506.0548095703125, 139.5338897705078)   (515.9581298828125, 139.5928955078125)
-
-[88 rows x 2 columns]
-Matches detected:  
-                                        kp0                                      kp1
-0   (575.7057495117188, 109.74275207519531)   (587.877197265625, 110.71781158447266)
-1   (542.9027709960938, 110.06568145751953)     (554.1123046875, 110.12137603759766)
-2   (579.9676513671875, 107.13910675048828)   (592.627685546875, 108.05380249023438)
-3   (588.2322998046875, 107.97618865966797)     (600.81298828125, 109.1924819946289)
-4     (528.1200561523438, 90.4193115234375)   (541.0357666015625, 89.40237426757812)
-..                                      ...                                      ...
-77   (600.7769165039062, 140.2008819580078)   (611.5534057617188, 143.1998748779297)
-78  (597.5498657226562, 155.33230590820312)  (605.0997924804688, 158.95115661621094)
-79   (572.4391479492188, 150.1521453857422)  (581.2168579101562, 152.40603637695312)
-80   (600.7769165039062, 140.2008819580078)   (611.5534057617188, 143.1998748779297)
-81   (581.6641235351562, 144.3262176513672)       (591.79296875, 146.10891723632812)
-
-[82 rows x 2 columns]
-Matches detected:  
-                                        kp0                                       kp1
-0   (543.9218139648438, 111.54388427734375)   (553.7830200195312, 111.55977630615234)
-1     (498.22509765625, 114.97080993652344)   (507.3154602050781, 113.88505554199219)
-2    (587.877197265625, 110.71781158447266)     (598.59716796875, 111.78783416748047)
-3    (526.6710205078125, 109.9970932006836)   (535.9137573242188, 109.49728393554688)
-4   (455.6094665527344, 120.99834442138672)  (462.39532470703125, 118.86907958984375)
-..                                      ...                                       ...
-79  (581.2825317382812, 15.940844535827637)    (582.7330932617188, 92.13706970214844)
-80   (591.5877685546875, 138.9302978515625)    (600.3515014648438, 140.4648895263672)
-81  (522.6573486328125, 142.00790405273438)   (551.9640502929688, 142.83143615722656)
-82   (576.0213623046875, 149.8698272705078)    (583.956298828125, 151.38160705566406)
-83       (591.79296875, 146.10891723632812)   (600.5392456054688, 147.97073364257812)
-
-[84 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0      (598.59716796875, 111.78783416748047)   (607.5415649414062, 113.09993743896484)
-1      (450.54443359375, 116.24259948730469)  (456.12445068359375, 113.61658477783203)
-2      (450.54443359375, 116.24259948730469)  (456.12445068359375, 113.61658477783203)
-3   (462.39532470703125, 118.86907958984375)   (467.8602600097656, 116.69943237304688)
-4    (553.7830200195312, 111.55977630615234)   (561.7442016601562, 111.63915252685547)
-..                                       ...                                       ...
-90   (572.3944702148438, 150.59852600097656)      (578.2314453125, 151.94915771484375)
-91   (544.0657348632812, 151.57821655273438)     (549.680908203125, 152.0336151123047)
-92    (572.8609008789062, 147.8002471923828)    (579.0255737304688, 149.3257293701172)
-93   (577.5105590820312, 7.4104743003845215)    (618.360107421875, 167.70558166503906)
-94   (539.8494873046875, 150.14276123046875)     (513.787841796875, 125.4520034790039)
-
-[95 rows x 2 columns]
-Matches detected:  
-                                        kp0                                      kp1
-0   (561.7442016601562, 111.63915252685547)   (567.855224609375, 111.71094512939453)
-1    (530.5333251953125, 109.7070083618164)   (537.0270385742188, 109.1806869506836)
-2   (607.5415649414062, 113.09993743896484)  (613.8674926757812, 114.25215911865234)
-3      (558.68994140625, 109.5530014038086)  (565.0072631835938, 109.51518249511719)
-4   (594.2426147460938, 115.84367370605469)  (600.6273803710938, 116.79232025146484)
-..                                      ...                                      ...
-87    (545.70068359375, 155.95040893554688)  (549.3966674804688, 156.38633728027344)
-88    (545.70068359375, 155.95040893554688)  (549.3966674804688, 156.38633728027344)
-89    (556.86376953125, 152.93431091308594)  (561.1179809570312, 153.40223693847656)
-90   (579.0255737304688, 149.3257293701172)   (583.484619140625, 150.37435913085938)
-91  (590.0927124023438, 152.95545959472656)   (594.6185913085938, 154.8787841796875)
-
-[92 rows x 2 columns]
-Matches detected:  
-                                        kp0                                      kp1
-0    (567.855224609375, 111.71094512939453)  (571.9392700195312, 111.80166625976562)
-1   (441.6394958496094, 108.10762786865234)  (445.0399169921875, 105.10572814941406)
-2   (441.6394958496094, 108.10762786865234)  (445.0399169921875, 105.10572814941406)
-3   (558.9581909179688, 111.37128448486328)       (562.693359375, 111.2621078491211)
-4   (565.0072631835938, 109.51518249511719)       (569.273681640625, 109.4580078125)
-..                                      ...                                      ...
-93   (554.024169921875, 155.36672973632812)  (554.1590576171875, 155.46209716796875)
-94  (577.4503173828125, 148.11505126953125)   (579.2874755859375, 148.7550811767578)
-95   (549.9530639648438, 141.6866455078125)    (553.047119140625, 142.4011688232422)
-96    (542.91455078125, 145.29299926757812)  (544.3282470703125, 146.30931091308594)
-97   (617.9348754882812, 155.6892852783203)  (473.9886779785156, 111.65817260742188)
-
-[98 rows x 2 columns]
-Matches detected:  
-                                        kp0                                      kp1
-0   (445.0399169921875, 105.10572814941406)  (445.9793701171875, 102.01947784423828)
-1   (445.0399169921875, 105.10572814941406)  (445.9793701171875, 102.01947784423828)
-2   (581.7188720703125, 111.39736938476562)  (583.4788208007812, 111.39178466796875)
-3   (571.9392700195312, 111.80166625976562)    (573.52978515625, 111.65145111083984)
-4   (473.9886779785156, 111.65817260742188)  (474.1299743652344, 109.04523468017578)
-..                                      ...                                      ...
-93    (593.955322265625, 151.1865997314453)  (593.1939086914062, 151.49728393554688)
-94  (580.8043823242188, 141.83348083496094)     (581.78857421875, 142.5020751953125)
-95  (550.9147338867188, 156.90689086914062)  (549.8367919921875, 156.07717895507812)
-96  (603.6895141601562, 165.19190979003906)   (601.9002685546875, 165.7421417236328)
-97   (583.7155151367188, 161.4071044921875)  (550.4623413085938, 127.24063873291016)
-
-[98 rows x 2 columns]
-Matches detected:  
-                                        kp0                                      kp1
-0   (474.1299743652344, 109.04523468017578)  (472.2099914550781, 106.08763885498047)
-1   (524.3436889648438, 109.06050872802734)  (523.3413696289062, 107.55042266845703)
-2    (621.1009521484375, 93.96116638183594)    (614.0930786132812, 94.0218734741211)
-3    (610.1607055664062, 91.94659423828125)     (609.87548828125, 93.00495910644531)
-4   (564.4700927734375, 110.81502532958984)  (562.7415161132812, 110.19993591308594)
-..                                      ...                                      ...
-82  (631.9780883789062, 209.95327758789062)  (625.4503784179688, 210.69476318359375)
-83  (613.8187255859375, 154.56031799316406)  (609.7894897460938, 155.10650634765625)
-84     (560.2060546875, 131.69680786132812)   (558.0061645507812, 130.9368896484375)
-85  (542.4459228515625, 140.32476806640625)   (540.0797119140625, 139.7488555908203)
-86   (556.4324951171875, 136.9966278076172)   (553.680419921875, 136.35108947753906)
-
-[87 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (562.7415161132812, 110.19993591308594)  (559.3661499023438, 109.11251068115234)
-1    (515.0546264648438, 106.70121002197266)    (511.9361267089844, 104.677001953125)
-2    (572.0702514648438, 110.99163818359375)    (568.23779296875, 110.10762023925781)
-3    (472.2099914550781, 106.08763885498047)    (468.152099609375, 102.7385025024414)
-4      (614.0930786132812, 94.0218734741211)   (608.0533447265625, 94.06031036376953)
-..                                       ...                                      ...
-102  (609.7894897460938, 155.10650634765625)  (603.5184326171875, 155.11656188964844)
-103   (592.1234741210938, 156.7178192138672)     (586.9345703125, 156.90513610839844)
-104       (552.287841796875, 151.6298828125)  (546.4529418945312, 150.49270629882812)
-105  (575.7098388671875, 148.95755004882812)  (570.3273315429688, 148.10128784179688)
-106  (552.8914794921875, 154.45643615722656)  (546.7400512695312, 153.55673217773438)
-
-[107 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (517.5827026367188, 143.02684020996094)   (511.7348327636719, 141.0115966796875)
-1      (568.23779296875, 110.10762023925781)  (562.6588745117188, 108.86227416992188)
-2       (517.1591796875, 137.82901000976562)   (511.0996398925781, 135.7781524658203)
-3    (559.3661499023438, 109.11251068115234)  (553.9349975585938, 107.64391326904297)
-4      (468.152099609375, 102.7385025024414)  (462.47259521484375, 99.25944519042969)
-..                                       ...                                      ...
-102  (587.7244262695312, 160.64126586914062)       (579.240234375, 159.9882354736328)
-103   (544.164794921875, 148.19369506835938)   (537.5303955078125, 146.4709930419922)
-104    (635.866943359375, 12.20788860321045)    (634.89208984375, 14.514555931091309)
-105  (611.6874389648438, 136.02853393554688)    (602.976806640625, 134.9215545654297)
-106  (607.0103759765625, 149.87791442871094)   (576.567626953125, 149.80711364746094)
-
-[107 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (562.6588745117188, 108.86227416992188)  (554.9116821289062, 107.30084991455078)
-1    (462.47259521484375, 99.25944519042969)    (455.448974609375, 95.40530395507812)
-2    (553.9349975585938, 107.64391326904297)   (546.7169189453125, 105.8227310180664)
-3    (540.5238037109375, 115.19288635253906)  (533.0979614257812, 113.17886352539062)
-4    (555.3370971679688, 105.89683532714844)  (547.5919189453125, 103.98458862304688)
-..                                       ...                                      ...
-107   (636.5718383789062, 222.7662811279297)  (623.1979370117188, 221.62278747558594)
-108   (576.567626953125, 149.80711364746094)   (531.0585327148438, 121.3109359741211)
-109   (597.6290283203125, 146.8440399169922)   (579.2205810546875, 145.5383758544922)
-110  (597.8252563476562, 129.01083374023438)  (588.9049072265625, 127.92162322998047)
-111  (636.8224487304688, 216.62374877929688)  (623.2774047851562, 214.98158264160156)
-
-[112 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (473.6756591796875, 103.52674102783203)       (464.943359375, 99.77900695800781)
-1    (473.6756591796875, 103.52674102783203)       (464.943359375, 99.77900695800781)
-2     (546.7169189453125, 105.8227310180664)   (537.9567260742188, 103.9271469116211)
-3     (446.2441101074219, 91.83995056152344)   (438.0380554199219, 87.77279663085938)
-4     (596.7425537109375, 205.1022186279297)  (583.0611572265625, 202.79039001464844)
-..                                       ...                                      ...
-114  (568.4945678710938, 153.81053161621094)   (557.830322265625, 151.82435607910156)
-115    (455.448974609375, 95.40530395507812)  (582.3009033203125, 25.542760848999023)
-116    (539.97412109375, 137.36846923828125)  (614.7495727539062, 149.93692016601562)
-117  (548.9526977539062, 147.34205627441406)     (538.48583984375, 145.4084014892578)
-118  (626.8063354492188, 20.011972427368164)  (620.6712646484375, 21.130464553833008)
-
-[119 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0      (530.559814453125, 99.99214172363281)      (520.0068359375, 97.37186431884766)
-1    (539.0128784179688, 101.98474884033203)    (529.053466796875, 99.64129638671875)
-2     (487.9342346191406, 131.2283935546875)  (477.7389831542969, 127.95977020263672)
-3    (545.7073364257812, 105.29159545898438)  (535.6798095703125, 102.96894073486328)
-4     (576.4170532226562, 109.1483154296875)    (565.45556640625, 107.16944885253906)
-..                                       ...                                      ...
-138    (599.665283203125, 209.6574249267578)  (584.6578979492188, 207.28732299804688)
-139    (527.802001953125, 147.2061004638672)  (516.6786499023438, 144.28948974609375)
-140  (582.3009033203125, 25.542760848999023)  (437.35601806640625, 86.96995544433594)
-141  (634.0128784179688, 162.80740356445312)   (619.6718139648438, 161.0130615234375)
-142    (611.86865234375, 215.04469299316406)    (596.6036376953125, 213.071533203125)
-
-[143 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (568.7539672851562, 199.76844787597656)  (553.7586059570312, 196.51309204101562)
-1    (568.7539672851562, 199.76844787597656)  (553.7586059570312, 196.51309204101562)
-2    (489.67608642578125, 131.7913818359375)  (478.8507385253906, 128.77786254882812)
-3        (496.0763244628906, 139.8623046875)  (484.83685302734375, 136.6588134765625)
-4    (581.0016479492188, 193.91111755371094)  (566.1524658203125, 190.96319580078125)
-..                                       ...                                      ...
-158   (618.0064086914062, 154.2076416015625)    (604.00048828125, 149.96560668945312)
-159   (516.6572875976562, 147.3387451171875)  (504.7713317871094, 144.11976623535156)
-160   (513.1422729492188, 138.2482147216797)  (501.3897705078125, 134.82778930664062)
-161  (571.6268920898438, 24.350631713867188)  (562.2178344726562, 22.984472274780273)
-162          (592.84375, 142.61497497558594)  (579.0118408203125, 140.43492126464844)
-
-[163 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0     (492.4922180175781, 77.59519958496094)  (482.43035888671875, 74.55790710449219)
-1    (607.5857543945312, 243.14967346191406)   (589.6771850585938, 239.2606964111328)
-2     (440.2427978515625, 89.08643341064453)   (428.4635925292969, 84.21624755859375)
-3    (498.9883117675781, 154.68936157226562)  (486.4688720703125, 151.19100952148438)
-4     (440.2427978515625, 89.08643341064453)   (428.4635925292969, 84.21624755859375)
-..                                       ...                                      ...
-183    (604.00048828125, 149.96560668945312)   (589.347900390625, 146.57298278808594)
-184  (520.5196533203125, 134.91668701171875)      (507.5849609375, 130.8454132080078)
-185  (636.6439208984375, 169.62933349609375)    (619.7042236328125, 167.194580078125)
-186     (538.2939453125, 140.56759643554688)  (631.6774291992188, 246.91366577148438)
-187  (632.3345947265625, 133.66378784179688)  (615.6001586914062, 132.05242919921875)
-
-[188 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0      (507.0295104980469, 94.2157974243164)     (495.21923828125, 91.22555541992188)
-1     (589.6771850585938, 239.2606964111328)   (571.7745361328125, 235.2467041015625)
-2     (440.6192626953125, 73.86520385742188)   (430.3168029785156, 69.93209075927734)
-3       (498.627685546875, 91.8212661743164)    (487.0610046386719, 88.8396224975586)
-4     (428.4635925292969, 84.21624755859375)   (416.5554504394531, 79.24539184570312)
-..                                       ...                                      ...
-192  (617.1328735351562, 153.66250610351562)    (601.27880859375, 150.90248107910156)
-193     (548.3837890625, 25.619794845581055)    (622.8944702148438, 211.737548828125)
-194    (619.7042236328125, 167.194580078125)     (603.60205078125, 164.8347625732422)
-195       (572.783203125, 202.2520294189453)    (556.83837890625, 198.83523559570312)
-196   (599.7613525390625, 52.59223556518555)  (586.4759521484375, 51.237552642822266)
-
-[197 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (500.45281982421875, 92.60443115234375)    (488.4789733886719, 89.67807006835938)
-1    (581.0319213867188, 241.42330932617188)   (563.1656494140625, 237.35826110839844)
-2       (495.21923828125, 91.22555541992188)      (483.28564453125, 88.13687133789062)
-3    (557.0382690429688, 207.43658447265625)       (541.091796875, 203.88894653320312)
-4    (433.32415771484375, 112.2639389038086)   (421.61993408203125, 108.4767074584961)
-..                                       ...                                       ...
-216   (528.7549438476562, 153.2989501953125)    (514.715576171875, 150.06320190429688)
-217   (494.286376953125, 135.75131225585938)  (481.18133544921875, 132.30430603027344)
-218  (560.7723388671875, 116.46102142333984)   (546.6734008789062, 113.50538635253906)
-219   (473.3817443847656, 135.5933837890625)   (460.9626770019531, 132.31175231933594)
-220     (552.49462890625, 142.6817626953125)   (538.0286254882812, 139.53054809570312)
-
-[221 rows x 2 columns]
-Matches detected:  
-                                        kp0                                       kp1
-0    (488.4789733886719, 89.67807006835938)    (476.3374938964844, 86.65202331542969)
-1     (474.9854431152344, 85.7109146118164)    (463.4573059082031, 82.58821105957031)
-2    (404.1575622558594, 74.24380493164062)    (391.8562316894531, 69.35020446777344)
-3    (404.1575622558594, 74.24380493164062)    (391.8562316894531, 69.35020446777344)
-4      (483.28564453125, 88.13687133789062)    (471.2931823730469, 85.09944915771484)
-..                                      ...                                       ...
-268    (636.06201171875, 250.1869354248047)    (615.6439819335938, 246.0456085205078)
-269  (464.694580078125, 136.63758850097656)  (454.33624267578125, 133.30364990234375)
-270  (591.6563110351562, 66.04818725585938)   (491.8919372558594, 22.525062561035156)
-271   (554.00732421875, 142.26785278320312)   (539.5306396484375, 139.58099365234375)
-272   (490.10107421875, 118.24911499023438)   (461.4352722167969, 118.05514526367188)
-
-[273 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (476.3374938964844, 86.65202331542969)    (464.39764404296875, 83.6292724609375)
-1     (471.2931823730469, 85.09944915771484)   (459.54840087890625, 82.11453247070312)
-2     (463.4573059082031, 82.58821105957031)    (451.4767150878906, 79.37006378173828)
-3         (550.143798828125, 169.6748046875)    (534.9017333984375, 166.6380615234375)
-4    (554.9307250976562, 229.70977783203125)      (538.63720703125, 225.7926025390625)
-..                                       ...                                       ...
-333    (628.59228515625, 178.16444396972656)    (609.959228515625, 175.26925659179688)
-334    (636.722900390625, 81.40662384033203)        (619.798828125, 80.24533081054688)
-335   (371.7322692871094, 59.49976348876953)   (351.46502685546875, 51.70515823364258)
-336   (630.7274169921875, 239.0777587890625)     (612.4972534179688, 237.036376953125)
-337  (455.7688293457031, 123.53250885009766)  (443.55120849609375, 120.04904174804688)
-
-[338 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (459.54840087890625, 82.11453247070312)   (447.9527587890625, 79.12628173828125)
-1      (379.7029113769531, 64.65589904785156)   (368.0482482910156, 60.38426208496094)
-2      (451.4767150878906, 79.37006378173828)    (440.2539978027344, 76.4033432006836)
-3      (379.7029113769531, 64.65589904785156)   (368.0482482910156, 60.38426208496094)
-4    (398.25848388671875, 59.089988708496094)  (387.9305114746094, 55.315216064453125)
-..                                        ...                                      ...
-351     (561.682373046875, 57.83717727661133)   (596.4435424804688, 73.53705596923828)
-352   (572.2462768554688, 234.93142700195312)    (554.080810546875, 232.3038787841797)
-353      (536.8232421875, 123.56089782714844)  (523.5919799804688, 121.08740997314453)
-354   (526.4215698242188, 208.83425903320312)   (511.0089416503906, 206.0026397705078)
-355    (500.0047607421875, 16.76725196838379)  (489.0198974609375, 14.068119049072266)
-
-[356 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0      (440.2539978027344, 76.4033432006836)    (428.9914855957031, 73.50480651855469)
-1     (368.0482482910156, 60.38426208496094)    (356.9183654785156, 56.39952087402344)
-2    (506.9871520996094, 215.52781677246094)    (492.8573913574219, 212.4510040283203)
-3     (515.1497192382812, 225.9073028564453)  (500.63336181640625, 222.82125854492188)
-4     (368.0482482910156, 60.38426208496094)    (356.9183654785156, 56.39952087402344)
-..                                       ...                                       ...
-390  (482.6556091308594, 21.346803665161133)     (588.555908203125, 44.95271301269531)
-391     (609.35302734375, 211.0915069580078)   (593.1018676757812, 208.16917419433594)
-392   (561.0592651367188, 221.7966766357422)    (546.1082153320312, 218.4788818359375)
-393   (626.1610107421875, 263.6386413574219)    (609.1364135742188, 260.4660339355469)
-394   (631.8206787109375, 210.3830108642578)     (614.9485473632812, 207.672119140625)
-
-[395 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0     (356.9183654785156, 56.39952087402344)   (346.6326599121094, 52.82255172729492)
-1     (566.0952758789062, 226.8479461669922)  (551.1962280273438, 223.87965393066406)
-2    (620.4608154296875, 149.30091857910156)  (605.4495239257812, 147.48928833007812)
-3    (446.69366455078125, 101.5300521850586)       (431.326171875, 97.62736511230469)
-4    (565.6526489257812, 118.15437316894531)  (552.1221923828125, 116.11729431152344)
-..                                       ...                                      ...
-320  (545.9561767578125, 152.72467041015625)  (533.1727905273438, 150.89610290527344)
-321   (546.1082153320312, 218.4788818359375)  (509.0751037597656, 214.89356994628906)
-322     (567.21240234375, 122.1203842163086)   (414.6758117675781, 94.82211303710938)
-323     (539.7402954101562, 172.74755859375)   (525.918701171875, 170.76597595214844)
-324   (553.9276733398438, 56.31641387939453)   (593.5133666992188, 73.91715240478516)
-
-[325 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (572.8126831054688, 110.7598648071289)     (560.316650390625, 109.0146255493164)
-1     (346.6326599121094, 52.82255172729492)   (337.9906311035156, 50.338714599609375)
-2    (505.9591979980469, 207.83135986328125)  (493.78997802734375, 205.78623962402344)
-3    (498.6592712402344, 165.18460083007812)   (486.7519836425781, 163.10440063476562)
-4     (346.6326599121094, 52.82255172729492)   (337.9906311035156, 50.338714599609375)
-..                                       ...                                       ...
-417  (449.7837829589844, 12.426180839538574)    (440.9041748046875, 10.20986270904541)
-418  (614.4390869140625, 220.74012756347656)   (599.5653686523438, 217.75555419921875)
-419      (564.2080078125, 251.4574737548828)   (549.1610107421875, 248.15504455566406)
-420     (575.6906127929688, 190.36669921875)   (528.0711059570312, 203.17381286621094)
-421    (597.7679443359375, 277.309326171875)   (604.1112060546875, 154.52456665039062)
-
-[422 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (337.9906311035156, 50.338714599609375)    (329.7341613769531, 47.99048614501953)
-1        (540.93896484375, 98.90164947509766)    (530.6318969726562, 97.78958129882812)
-2      (357.8601989746094, 45.44879150390625)  (349.48822021484375, 43.139122009277344)
-3     (337.9906311035156, 50.338714599609375)    (329.7341613769531, 47.99048614501953)
-4     (540.1018676757812, 114.43680572509766)    (529.791748046875, 113.26341247558594)
-..                                        ...                                       ...
-434  (495.38836669921875, 10.358572959899902)    (486.4214782714844, 8.451372146606445)
-435   (617.6542358398438, 281.15936279296875)    (601.8140869140625, 279.0251770019531)
-436   (518.2716064453125, 213.07252502441406)      (506.86572265625, 211.4262237548828)
-437  (494.21832275390625, 2.6559641361236572)      (552.9755859375, 262.58514404296875)
-438    (452.083251953125, 6.8953704833984375)    (444.0551452636719, 5.400521278381348)
-
-[439 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (333.7908630371094, 68.02433776855469)      (325.71435546875, 65.98361206054688)
-1     (329.7341613769531, 47.99048614501953)    (322.2987365722656, 46.09463882446289)
-2     (329.7341613769531, 47.99048614501953)    (322.2987365722656, 46.09463882446289)
-3        (594.0126953125, 37.91047286987305)     (583.963623046875, 38.08127212524414)
-4        (594.0126953125, 37.91047286987305)     (583.963623046875, 38.08127212524414)
-..                                       ...                                       ...
-450  (564.6342163085938, 195.42083740234375)    (553.6723022460938, 194.2357177734375)
-451  (436.5475158691406, 22.669469833374023)   (429.7106628417969, 21.378971099853516)
-452  (634.8911743164062, 248.53329467773438)   (621.2430419921875, 246.86648559570312)
-453      (637.138671875, 194.26963806152344)  (497.03253173828125, 203.04505920410156)
-454   (557.2575073242188, 267.3915710449219)     (545.613525390625, 265.6567077636719)
-
-[455 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (547.3771362304688, 110.92887878417969)  (538.8341674804688, 110.38152313232422)
-1      (573.545166015625, 97.88627624511719)   (565.8308715820312, 97.98741149902344)
-2     (322.2987365722656, 46.09463882446289)  (315.5301818847656, 44.340179443359375)
-3     (472.8439025878906, 203.2647247314453)  (463.1241149902344, 202.22422790527344)
-4     (338.0447082519531, 72.88610076904297)   (330.5653076171875, 71.33100891113281)
-..                                       ...                                      ...
-494   (555.4628295898438, 128.6783447265625)  (547.7058715820312, 127.95994567871094)
-495   (575.1701049804688, 289.0780029296875)   (564.4313354492188, 288.5029602050781)
-496  (475.6903381347656, 3.8549506664276123)  (468.9573669433594, 3.6346662044525146)
-497       (627.955078125, 292.5653381347656)   (615.6049194335938, 290.9748840332031)
-498   (635.7619018554688, 341.8557434082031)   (623.2260131835938, 341.0450439453125)
-
-[499 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (333.7526550292969, 81.28779602050781)      (327.84228515625, 80.35173797607422)
-1    (477.18548583984375, 175.0267333984375)   (469.9495544433594, 175.03854370117188)
-2    (428.0985107421875, 181.93653869628906)  (419.71795654296875, 181.09786987304688)
-3      (517.60107421875, 115.83040618896484)   (511.5050964355469, 116.01404571533203)
-4    (451.46246337890625, 209.8780975341797)   (443.5118408203125, 209.83865356445312)
-..                                       ...                                       ...
-572  (446.8134460449219, 184.05703735351562)   (439.8411865234375, 182.36842346191406)
-573  (463.6443176269531, 117.42896270751953)   (457.8364562988281, 117.57042694091797)
-574   (624.1655883789062, 336.3969421386719)    (550.5587158203125, 286.3388366699219)
-575     (552.05908203125, 67.06340789794922)    (565.8163452148438, 304.9112548828125)
-576   (615.6715698242188, 295.0563659667969)   (460.6038818359375, 154.52005004882812)
-
-[577 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (532.9613647460938, 110.9608383178711)  (527.3779907226562, 110.09037780761719)
-1     (566.5847778320312, 120.11817169189453)  (560.6570434570312, 119.08845520019531)
-2     (499.20599365234375, 263.4937438964844)  (493.33770751953125, 263.6123962402344)
-3     (559.9104614257812, 116.88765716552734)  (553.9639892578125, 115.92979431152344)
-4      (618.4059448242188, 157.9823760986328)   (612.2211303710938, 156.8470001220703)
-..                                        ...                                      ...
-574    (549.0443725585938, 211.8748016357422)     (542.4482421875, 210.81809997558594)
-575  (481.88128662109375, 180.34596252441406)  (475.03900146484375, 179.6469268798828)
-576    (437.5145263671875, 15.35445499420166)     (590.69189453125, 324.7204895019531)
-577     (616.076904296875, 319.6831359863281)   (609.0546264648438, 318.8265686035156)
-578    (440.1881103515625, 7.034982681274414)  (457.1053161621094, 19.659942626953125)
-
-[579 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0         (492.6123046875, 91.52752685546875)    (489.22760009765625, 91.7304458618164)
-1        (584.8916015625, 136.69435119628906)    (580.8590698242188, 136.6967010498047)
-2      (552.752197265625, 102.82846069335938)   (549.7630615234375, 102.93958282470703)
-3     (593.3280639648438, 172.25827026367188)    (589.0764770507812, 172.1453857421875)
-4     (553.9639892578125, 115.92979431152344)   (549.8899536132812, 115.96360778808594)
-..                                        ...                                       ...
-589  (396.46051025390625, 167.45228576660156)    (548.522216796875, 119.09121704101562)
-590    (446.9678649902344, 94.94355773925781)     (437.838623046875, 97.87809753417969)
-591    (458.3712158203125, 38.31474685668945)  (493.41961669921875, 55.510738372802734)
-592     (396.6046142578125, 169.476806640625)  (391.52276611328125, 170.15602111816406)
-593    (634.8295288085938, 118.1750717163086)   (630.6544799804688, 118.42328643798828)
-
-[594 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (489.22760009765625, 91.7304458618164)    (487.1687316894531, 92.57650756835938)
-1     (602.5469970703125, 144.89378356933594)    (600.5465087890625, 145.9131622314453)
-2      (444.1162414550781, 202.2232666015625)       (440.03643798828125, 203.248046875)
-3    (438.24945068359375, 166.67684936523438)  (435.64239501953125, 167.81890869140625)
-4     (606.9279174804688, 156.39723205566406)   (605.0501098632812, 157.34657287597656)
-..                                        ...                                       ...
-630  (421.72247314453125, 44.068294525146484)     (420.014892578125, 45.05472946166992)
-631   (423.1941833496094, 121.32792663574219)  (419.96441650390625, 122.28147888183594)
-632    (613.1427612304688, 299.4312438964844)   (609.2589721679688, 299.93682861328125)
-633   (452.2287292480469, 116.79576873779297)    (470.0042724609375, 224.0211639404297)
-634     (622.501708984375, 324.5309143066406)      (621.0205688476562, 327.93603515625)
-
-[635 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (632.8882446289062, 173.30758666992188)    (632.868408203125, 175.30072021484375)
-1     (404.6690979003906, 183.03305053710938)   (401.1245422363281, 183.03704833984375)
-2         (546.806396484375, 103.35693359375)   (546.8591918945312, 104.34571838378906)
-3    (427.82659912109375, 211.45921325683594)   (425.5150451660156, 213.38674926757812)
-4     (604.2887573242188, 147.06410217285156)          (604.0029296875, 148.7021484375)
-..                                        ...                                       ...
-624    (587.0280151367188, 235.3006134033203)   (586.8981323242188, 237.52420043945312)
-625   (478.4479675292969, 173.57162475585938)   (476.96051025390625, 174.4679718017578)
-626    (620.5631103515625, 315.6265563964844)    (618.9129638671875, 319.2249755859375)
-627       (560.619140625, 237.63284301757812)     (592.043701171875, 193.9129638671875)
-628   (423.4805908203125, 152.83021545410156)  (421.61260986328125, 154.41395568847656)
-
-[629 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (486.7320861816406, 94.08015441894531)    (487.5124816894531, 96.08216094970703)
-1     (597.8488159179688, 150.2057342529297)   (599.9822998046875, 153.20494079589844)
-2    (277.33380126953125, 37.57484817504883)    (278.3664245605469, 39.48980712890625)
-3     (628.2693481445312, 410.1769104003906)    (627.8577880859375, 416.4500732421875)
-4    (504.8150329589844, 102.25216674804688)  (505.70843505859375, 104.26663970947266)
-..                                       ...                                       ...
-668   (628.7742309570312, 325.1467590332031)    (630.0413208007812, 329.4573669433594)
-669   (589.5646362304688, 263.7781982421875)   (389.0389099121094, 182.58985900878906)
-670  (631.1600341796875, 109.84347534179688)      (632.5908203125, 112.01304626464844)
-671      (634.1826171875, 341.1527099609375)     (635.389404296875, 344.6695556640625)
-672  (521.8435668945312, 271.89324951171875)     (520.651611328125, 274.7369384765625)
-
-[673 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0    (496.05157470703125, 116.19640350341797)  (497.8622741699219, 118.65875244140625)
-1     (549.0114135742188, 107.69591522216797)   (551.6885375976562, 110.6280517578125)
-2     (363.51055908203125, 153.6400146484375)  (363.3797302246094, 155.70797729492188)
-3     (341.0064697265625, 125.54258728027344)  (341.3456726074219, 127.42572021484375)
-4     (363.51055908203125, 153.6400146484375)  (363.3797302246094, 155.70797729492188)
-..                                        ...                                      ...
-664   (361.5833435058594, 101.63807678222656)   (362.4772644042969, 103.6462631225586)
-665        (634.1949462890625, 234.423828125)   (636.4117431640625, 237.8387908935547)
-666  (347.45318603515625, 127.46917724609375)     (605.88818359375, 105.4777603149414)
-667    (547.5889892578125, 309.7941589355469)    (548.077880859375, 314.5058898925781)
-668   (539.1555786132812, 236.39891052246094)  (540.6627197265625, 239.46096801757812)
-
-[669 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0           (490.23974609375, 98.8291015625)  (493.3961486816406, 101.72652435302734)
-1    (497.8622741699219, 118.65875244140625)  (500.9164733886719, 121.73209381103516)
-2    (502.9187316894531, 122.37857818603516)  (505.7770690917969, 125.41431427001953)
-3    (399.3212585449219, 187.54904174804688)   (399.869873046875, 190.30880737304688)
-4    (384.57330322265625, 39.09101104736328)  (388.1082763671875, 42.011653900146484)
-..                                       ...                                      ...
-664    (404.6470642089844, 124.850341796875)  (406.32281494140625, 127.3748779296875)
-665   (631.5599975585938, 391.5946960449219)  (332.5014953613281, 15.619269371032715)
-666  (550.4624633789062, 248.40650939941406)     (551.989990234375, 252.826416015625)
-667      (546.9541015625, 321.5469055175781)  (412.2723083496094, 178.69802856445312)
-668   (567.7774658203125, 244.0804901123047)  (570.1866455078125, 247.82737731933594)
-
-[669 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0        (426.95947265625, 58.36480712890625)    (431.0879211425781, 61.20106506347656)
-1     (493.3961486816406, 101.72652435302734)   (497.2992858886719, 104.96698760986328)
-2     (500.9164733886719, 121.73209381103516)   (504.8813781738281, 125.15116119384766)
-3      (555.2719116210938, 112.9684066772461)   (559.5223999023438, 117.13105010986328)
-4     (606.1585083007812, 159.85064697265625)    (610.807373046875, 163.78419494628906)
-..                                        ...                                       ...
-694     (630.531494140625, 363.2936706542969)   (634.2850952148438, 370.71209716796875)
-695   (459.2691650390625, 3.3321592807769775)     (464.090087890625, 6.637400150299072)
-696  (388.39544677734375, 144.39608764648438)  (394.37554931640625, 146.85687255859375)
-697     (607.868408203125, 333.2768249511719)   (611.4348754882812, 340.10906982421875)
-698   (601.9926147460938, 113.25364685058594)   (606.4552612304688, 117.36722564697266)
-
-[699 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (559.5223999023438, 117.13105010986328)    (565.8092651367188, 121.4100570678711)
-1    (497.2992858886719, 104.96698760986328)   (502.1488952636719, 108.56542205810547)
-2    (577.7734985351562, 111.93132019042969)   (584.1577758789062, 116.09487915039062)
-3    (512.4469604492188, 22.956125259399414)   (518.1261596679688, 26.802562713623047)
-4     (438.9436340332031, 101.3148193359375)   (442.7879333496094, 104.38568878173828)
-..                                       ...                                       ...
-700  (400.3886413574219, 127.77281188964844)  (403.65423583984375, 130.74598693847656)
-701    (477.0102844238281, 53.6086311340332)   (482.27032470703125, 57.02887725830078)
-702   (497.2525634765625, 267.3809509277344)      (499.3542175292969, 272.04052734375)
-703   (631.1864013671875, 311.3433837890625)   (496.4810791015625, 239.78366088867188)
-704   (563.7820434570312, 96.01644134521484)     (569.1121215820312, 311.847412109375)
-
-[705 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (518.1261596679688, 26.802562713623047)    (524.74560546875, 30.677650451660156)
-1    (518.1261596679688, 26.802562713623047)    (524.74560546875, 30.677650451660156)
-2    (480.9374084472656, 122.30338287353516)   (485.6888427734375, 125.9413833618164)
-3    (447.8338317871094, 160.23843383789062)  (451.6905822753906, 163.85926818847656)
-4     (620.1575927734375, 166.0404052734375)     (627.134521484375, 171.003662109375)
-..                                       ...                                      ...
-667    (558.804443359375, 262.4789733886719)   (562.5938720703125, 268.2560119628906)
-668  (546.5584716796875, 239.30418395996094)  (550.5108642578125, 244.35137939453125)
-669  (424.1242370605469, 135.05581665039062)    (428.16070556640625, 138.03662109375)
-670   (431.8775329589844, 20.61970329284668)  (437.5372314453125, 24.233566284179688)
-671   (456.1980895996094, 66.26792907714844)    (409.0581359863281, 41.6961784362793)
-
-[672 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (440.8703918457031, 185.7555389404297)   (444.2283020019531, 189.02162170410156)
-1     (531.9776000976562, 30.63167953491211)       (538.955078125, 34.588226318359375)
-2     (440.8703918457031, 185.7555389404297)   (444.2283020019531, 189.02162170410156)
-3    (519.4798583984375, 136.27896118164062)    (524.7635498046875, 140.2174072265625)
-4     (531.9776000976562, 30.63167953491211)       (538.955078125, 34.588226318359375)
-..                                       ...                                       ...
-700     (568.2861328125, 138.69760131835938)     (575.2623291015625, 143.300048828125)
-701   (614.9592895507812, 352.9986572265625)    (620.0726318359375, 360.9606018066406)
-702  (568.6384887695312, 242.77035522460938)   (427.7338562011719, 219.70681762695312)
-703    (407.96142578125, 117.59685516357422)   (412.3876647949219, 120.78655242919922)
-704  (350.1837463378906, 124.77716064453125)  (354.29693603515625, 127.28430938720703)
-
-[705 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0       (512.9725341796875, 116.067138671875)        (519.271484375, 120.1280746459961)
-1     (533.2571411132812, 142.23477172851562)   (539.0921020507812, 146.34640502929688)
-2    (421.67584228515625, 239.04576110839844)    (424.5282287597656, 242.8265380859375)
-3     (444.2283020019531, 189.02162170410156)  (447.55145263671875, 192.45689392089844)
-4     (530.2665405273438, 124.33879852294922)   (536.3555297851562, 128.34664916992188)
-..                                        ...                                       ...
-722   (425.84307861328125, 37.85390090942383)   (551.3157958984375, 236.28871154785156)
-723   (580.8552856445312, 341.91253662109375)    (585.6880493164062, 349.1300048828125)
-724     (545.857177734375, 240.6089324951172)   (551.0046997070312, 245.79425048828125)
-725    (555.7586059570312, 94.15214538574219)     (518.707763671875, 79.44819641113281)
-726   (627.4357299804688, 314.87750244140625)    (458.6262512207031, 231.3883056640625)
-
-[727 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (510.5232238769531, 116.39424896240234)   (516.6905517578125, 120.34077453613281)
-1          (519.271484375, 120.1280746459961)   (525.3882446289062, 123.99047088623047)
-2     (525.9572143554688, 140.52304077148438)    (532.009521484375, 144.55307006835938)
-3    (443.51275634765625, 107.85133361816406)   (448.9095153808594, 111.31292724609375)
-4       (530.16650390625, 125.35785675048828)   (536.4669189453125, 129.37384033203125)
-..                                        ...                                       ...
-696     (447.56494140625, 221.04615783691406)   (479.0249328613281, 58.565521240234375)
-697    (424.4546203613281, 213.9325714111328)     (428.91162109375, 217.89625549316406)
-698      (511.01416015625, 78.27142333984375)    (458.5195007324219, 54.56569290161133)
-699    (589.221435546875, 116.38155364990234)   (504.43243408203125, 76.61605834960938)
-700   (477.3626708984375, 240.65887451171875)  (453.19036865234375, 242.51670837402344)
-
-[701 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (525.3882446289062, 123.99047088623047)  (531.5397338867188, 127.78012084960938)
-1      (545.2982788085938, 150.3607940673828)  (551.4894409179688, 154.38377380371094)
-2    (413.57745361328125, 208.82638549804688)   (416.8360595703125, 212.0801544189453)
-3     (502.1284484863281, 137.74945068359375)   (507.7444152832031, 141.4285430908203)
-4     (544.1715698242188, 133.05438232421875)   (550.734130859375, 137.07855224609375)
-..                                        ...                                      ...
-671  (368.47186279296875, 25.175155639648438)  (439.3049011230469, 253.82948303222656)
-672   (551.9235229492188, 205.88363647460938)  (556.9921264648438, 209.80445861816406)
-673   (589.5233154296875, 256.78411865234375)   (595.1004028320312, 262.1045227050781)
-674     (447.9303283691406, 72.5696792602539)    (452.4081726074219, 75.6078872680664)
-675   (501.47235107421875, 257.2463073730469)  (505.8744812011719, 261.06414794921875)
-
-[676 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (550.734130859375, 137.07855224609375)  (556.7427368164062, 140.85789489746094)
-1     (551.4894409179688, 154.38377380371094)  (557.5658569335938, 158.30093383789062)
-2       (612.385986328125, 165.0961151123047)    (620.397216796875, 170.2345733642578)
-3      (525.531494140625, 210.51312255859375)  (530.5119018554688, 214.68023681640625)
-4     (456.7628173828125, 200.51629638671875)  (460.8650207519531, 203.77926635742188)
-..                                        ...                                      ...
-663    (604.306884765625, 361.13323974609375)  (609.4791870117188, 367.98626708984375)
-664  (471.47723388671875, 268.43585205078125)   (474.0518798828125, 271.6111145019531)
-665  (469.34625244140625, 272.00482177734375)  (471.84161376953125, 275.9246520996094)
-666   (462.69683837890625, 249.6856689453125)   (466.0306396484375, 253.1929473876953)
-667   (507.0495910644531, 40.525474548339844)   (512.8975830078125, 44.61277770996094)
-
-[668 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (548.9420776367188, 137.13946533203125)  (554.4053955078125, 140.42799377441406)
-1      (543.9759521484375, 152.1698455810547)  (549.3140869140625, 155.63174438476562)
-2     (431.8742980957031, 246.90679931640625)  (434.7537536621094, 249.90728759765625)
-3    (432.87664794921875, 252.92124938964844)  (435.87286376953125, 256.0431213378906)
-4       (459.1551513671875, 91.9585952758789)   (464.2532043457031, 94.99209594726562)
-..                                        ...                                      ...
-711    (437.8125915527344, 152.3484344482422)  (442.1783142089844, 155.15235900878906)
-712    (636.7357788085938, 291.0499267578125)   (634.9942626953125, 296.2363586425781)
-713    (464.6996154785156, 54.39137649536133)   (469.6177673339844, 57.47250747680664)
-714   (531.4681396484375, 297.79400634765625)            (534.4129028320312, 301.1875)
-715   (412.8415222167969, 134.65249633789062)   (416.9308776855469, 137.5875244140625)
-
-[716 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (549.3140869140625, 155.63174438476562)      (554.2333984375, 158.73480224609375)
-1    (485.79351806640625, 107.64871978759766)   (490.4486083984375, 110.31775665283203)
-2       (409.2212829589844, 204.238525390625)    (412.0278015136719, 206.3031768798828)
-3       (409.2212829589844, 204.238525390625)    (412.0278015136719, 206.3031768798828)
-4     (445.9480895996094, 253.20240783691406)   (448.64239501953125, 255.9553985595703)
-..                                        ...                                       ...
-730   (362.4066162109375, 135.43569946289062)    (412.0278015136719, 206.3031768798828)
-731    (545.0428466796875, 208.0084991455078)     (549.568115234375, 211.7305450439453)
-732     (404.603271484375, 6.587590217590332)    (410.2379455566406, 10.13864517211914)
-733   (492.6517028808594, 242.64132690429688)  (495.83465576171875, 245.63258361816406)
-734   (470.09320068359375, 61.72014617919922)    (531.6414794921875, 91.38151550292969)
-
-[735 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (490.4486083984375, 110.31775665283203)  (494.3563537597656, 112.45738983154297)
-1     (548.1426391601562, 137.74107360839844)  (552.3270874023438, 140.19203186035156)
-2      (527.5645141601562, 190.1726531982422)   (531.0277099609375, 192.7462615966797)
-3      (468.8939208984375, 97.58258819580078)  (472.79498291015625, 99.63603210449219)
-4     (503.8966979980469, 327.57916259765625)   (506.5010681152344, 330.7557678222656)
-..                                        ...                                      ...
-685   (596.0667114257812, 276.65277099609375)     (600.0191650390625, 280.46923828125)
-686      (610.98876953125, 420.3686828613281)    (614.28662109375, 426.06719970703125)
-687    (476.2117614746094, 248.9789581298828)  (481.7398681640625, 251.10157775878906)
-688    (564.142333984375, 359.45941162109375)   (567.7376708984375, 364.4608459472656)
-689  (491.32293701171875, 51.697628021240234)   (495.6534729003906, 53.99472427368164)
-
-[690 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (552.3270874023438, 140.19203186035156)  (555.7913208007812, 142.01849365234375)
-1    (542.9982299804688, 136.24258422851562)   (546.663818359375, 138.22315979003906)
-2    (558.8544311523438, 161.55348205566406)  (562.4678344726562, 163.57827758789062)
-3        (563.5439453125, 165.6535186767578)     (566.9424438476562, 167.73095703125)
-4    (494.3563537597656, 112.45738983154297)  (497.7192077636719, 114.14000701904297)
-..                                       ...                                      ...
-694     (440.14697265625, 174.5195770263672)   (442.1998596191406, 175.7781524658203)
-695  (499.07098388671875, 68.36375427246094)   (502.3277282714844, 71.13075256347656)
-696    (587.556396484375, 263.9457092285156)  (591.8025512695312, 266.80242919921875)
-697  (517.3905029296875, 217.71572875976562)  (580.6013793945312, 227.62013244628906)
-698     (591.42431640625, 282.8188171386719)    (594.46630859375, 286.06390380859375)
-
-[699 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (555.7913208007812, 142.01849365234375)   (558.2073974609375, 143.2035675048828)
-1      (546.663818359375, 138.22315979003906)     (549.11376953125, 139.3140106201172)
-2    (475.14459228515625, 126.83167266845703)   (477.149658203125, 127.84611511230469)
-3      (469.5357666015625, 253.0415496826172)  (470.9152526855469, 254.06143188476562)
-4      (436.996337890625, 134.22938537597656)  (438.7149353027344, 135.16598510742188)
-..                                        ...                                      ...
-733    (481.4127197265625, 55.44691467285156)   (483.8283996582031, 56.39897918701172)
-734    (437.3937072753906, 37.50218200683594)   (440.1786193847656, 37.44613265991211)
-735    (454.0901794433594, 229.2978973388672)   (564.8582153320312, 336.9757080078125)
-736   (550.8606567382812, 346.64923095703125)  (552.3165893554688, 348.89361572265625)
-737       (536.412109375, 233.92417907714844)   (572.1813354492188, 326.0093688964844)
-
-[738 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (538.2041625976562, 196.51145935058594)   (538.8767700195312, 196.4766082763672)
-1    (538.2041625976562, 196.51145935058594)   (538.8767700195312, 196.4766082763672)
-2     (558.2073974609375, 143.2035675048828)   (559.4564208984375, 143.2425994873047)
-3       (549.11376953125, 139.3140106201172)   (549.9899291992188, 139.2867889404297)
-4     (423.9878234863281, 55.24239730834961)   (424.4903869628906, 55.14695358276367)
-..                                       ...                                      ...
-785     (580.09130859375, 349.5744323730469)   (580.0882568359375, 349.9666442871094)
-786   (475.5599365234375, 233.0421905517578)  (476.61285400390625, 232.5424041748047)
-787    (542.5211791992188, 87.3364028930664)   (542.8832397460938, 87.18978118896484)
-788  (428.1437683105469, 138.11129760742188)  (429.35089111328125, 137.8363037109375)
-789   (582.3355102539062, 236.1989288330078)   (527.1111450195312, 241.3756866455078)
-
-[790 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0     (472.2029724121094, 254.5393524169922)  (471.8868713378906, 254.19505310058594)
-1      (556.3136596679688, 83.7284927368164)     (556.3596801757812, 82.773193359375)
-2       (432.7939453125, 225.50714111328125)   (432.6441955566406, 225.7895050048828)
-3     (559.4564208984375, 143.2425994873047)  (558.8668823242188, 142.27005004882812)
-4     (549.9899291992188, 139.2867889404297)  (549.6764526367188, 138.33538818359375)
-..                                       ...                                      ...
-751  (443.9486083984375, 219.33624267578125)  (443.7655944824219, 218.88763427734375)
-752   (467.0681457519531, 189.6854248046875)      (466.3592224121094, 189.1650390625)
-753  (519.5313110351562, 250.39230346679688)  (519.2568969726562, 249.80844116210938)
-754   (485.5306701660156, 64.86548614501953)   (485.5288391113281, 63.86267852783203)
-755  (335.21221923828125, 8.997840881347656)  (432.9327392578125, 11.852256774902344)
-
-[756 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (558.8668823242188, 142.27005004882812)   (557.8094482421875, 141.02186584472656)
-1      (535.412353515625, 195.5051727294922)   (533.4998779296875, 194.06036376953125)
-2    (471.8868713378906, 254.19505310058594)   (470.3722839355469, 252.99806213378906)
-3    (409.4591979980469, 180.91964721679688)     (408.72021484375, 180.36585998535156)
-4    (417.3450927734375, 209.97215270996094)    (417.1863708496094, 209.7180633544922)
-..                                       ...                                       ...
-715   (464.6018981933594, 202.7345733642578)  (463.22723388671875, 202.60984802246094)
-716  (597.9736938476562, 287.11920166015625)     (593.96728515625, 285.53314208984375)
-717        (584.98046875, 275.0548095703125)   (631.9091186523438, 247.13258361816406)
-718   (622.4652709960938, 83.87678527832031)    (621.9264526367188, 82.86962127685547)
-719  (393.7774658203125, 143.34930419921875)   (390.6523132324219, 142.41770935058594)
-
-[720 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (548.2073974609375, 137.0406494140625)   (545.6109008789062, 134.97564697265625)
-1     (459.950927734375, 101.35151672363281)    (457.1089172363281, 99.41923522949219)
-2    (475.9540710449219, 125.95405578613281)  (473.82415771484375, 124.32854461669922)
-3    (533.4998779296875, 194.06036376953125)    (532.6242065429688, 192.7742156982422)
-4     (466.4922180175781, 90.10020446777344)    (463.8103332519531, 88.22462463378906)
-..                                       ...                                       ...
-684  (614.0496826171875, 302.30657958984375)   (610.9822998046875, 299.58709716796875)
-685  (621.1183471679688, 124.59198760986328)   (619.6300659179688, 122.93926239013672)
-686   (605.0928955078125, 282.6732177734375)    (549.8980712890625, 349.9880676269531)
-687  (471.2102355957031, 59.268733978271484)     (557.414794921875, 97.96439361572266)
-688    (635.4601440429688, 399.632568359375)    (633.3539428710938, 397.4373474121094)
-
-[689 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (551.7657470703125, 79.39395904541016)     (547.868896484375, 76.59388732910156)
-1      (415.0804443359375, 208.8468017578125)   (411.8690490722656, 206.86329650878906)
-2     (594.5686645507812, 161.35000610351562)    (590.5999145507812, 158.4339141845703)
-3    (473.82415771484375, 124.32854461669922)   (470.4242248535156, 121.99175262451172)
-4     (460.2055969238281, 203.60031127929688)   (455.18524169921875, 200.4744873046875)
-..                                        ...                                       ...
-774    (620.9005737304688, 390.4270324707031)      (616.81591796875, 386.1959533691406)
-775    (427.1087951660156, 4.498331069946289)   (423.3236083984375, 2.5975046157836914)
-776   (499.3249206542969, 160.63621520996094)  (495.82342529296875, 158.52310180664062)
-777     (616.3649291992188, 83.7766342163086)    (612.4506225585938, 80.94072723388672)
-778    (632.0880737304688, 232.9327850341797)     (627.4032592773438, 228.897705078125)
-
-[779 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (551.5096435546875, 136.31268310546875)       (547.5101318359375, 133.4052734375)
-1    (411.8690490722656, 206.86329650878906)  (407.75885009765625, 204.92352294921875)
-2    (562.8189086914062, 162.01852416992188)   (558.1844482421875, 159.03245544433594)
-3     (590.5999145507812, 158.4339141845703)    (586.357666015625, 155.38816833496094)
-4      (571.9425048828125, 146.279541015625)      (567.462158203125, 143.132080078125)
-..                                       ...                                       ...
-805  (361.31683349609375, 41.79363250732422)    (623.1642456054688, 126.9910659790039)
-806          (630.846923828125, 408.1796875)           (624.9284057617188, 404.828125)
-807    (488.4089660644531, 105.007080078125)       (483.537109375, 102.18122863769531)
-808   (533.5086059570312, 245.0489959716797)    (528.7778930664062, 242.0718536376953)
-809   (636.9641723632812, 398.3560485839844)    (631.0271606445312, 393.2333679199219)
-
-[810 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (466.7713928222656, 119.52359008789062)  (462.26605224609375, 116.54740142822266)
-1    (452.8213806152344, 198.87413024902344)   (447.7127685546875, 196.10142517089844)
-2    (471.2073974609375, 246.33522033691406)    (466.3450012207031, 243.2949676513672)
-3     (476.7004699707031, 250.9594268798828)   (471.6466369628906, 247.89207458496094)
-4    (460.6590270996094, 253.59335327148438)   (455.6610107421875, 250.63023376464844)
-..                                       ...                                       ...
-734  (313.2718200683594, 5.8421196937561035)    (309.6719665527344, 5.656940937042236)
-735   (530.7261962890625, 80.36295318603516)    (525.6634521484375, 77.55564880371094)
-736  (426.9468994140625, 214.80665588378906)   (422.6261901855469, 212.74497985839844)
-737    (543.5244750976562, 95.2996597290039)       (583.376953125, 113.65476989746094)
-738       (461.275146484375, 80.72509765625)   (402.2053527832031, 148.06874084472656)
-
-[739 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (563.0733642578125, 158.60658264160156)   (556.8195190429688, 154.56735229492188)
-1      (562.69970703125, 139.91139221191406)      (556.66943359375, 135.9400634765625)
-2    (549.3801879882812, 241.17628479003906)    (543.3438110351562, 237.1564178466797)
-3     (426.5393371582031, 100.6036376953125)   (421.25347900390625, 97.43846130371094)
-4    (414.9355773925781, 215.46615600585938)    (409.6485900878906, 212.2367401123047)
-..                                       ...                                       ...
-752   (577.9334716796875, 390.8518981933594)    (572.0311279296875, 386.2339782714844)
-753       (470.0897216796875, 57.3349609375)     (465.16552734375, 53.956966400146484)
-754    (435.94384765625, 212.06362915039062)  (429.09539794921875, 208.97552490234375)
-755   (573.8329467773438, 366.9994201660156)    (567.2901611328125, 362.5828552246094)
-756   (493.8838195800781, 45.20075988769531)   (488.52789306640625, 41.98987579345703)
-
-[757 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0        (556.66943359375, 135.9400634765625)  (550.6315307617188, 132.08839416503906)
-1     (542.7799682617188, 147.37637329101562)  (536.5532836914062, 143.48086547851562)
-2     (556.8195190429688, 154.56735229492188)    (550.116455078125, 150.7053680419922)
-3    (480.27593994140625, 100.38006591796875)  (474.01800537109375, 96.52020263671875)
-4      (561.8169555664062, 43.90804672241211)    (555.3448486328125, 40.0421257019043)
-..                                        ...                                      ...
-778      (540.03271484375, 349.1121520996094)   (532.7266845703125, 344.5416564941406)
-779     (557.2954711914062, 327.426513671875)       (549.541015625, 321.9591979980469)
-780    (582.1466064453125, 110.3375473022461)  (575.9774780273438, 106.81011962890625)
-781    (368.751220703125, 151.55564880371094)   (362.0991516113281, 147.0062713623047)
-782   (634.8364868164062, 349.98272705078125)    (626.918701171875, 343.8915100097656)
-
-[783 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (531.1509399414062, 122.65806579589844)      (524.68212890625, 118.6386947631836)
-1        (521.615234375, 118.84526824951172)   (515.2050170898438, 114.91850280761719)
-2    (410.65740966796875, 92.06065368652344)    (404.6111145019531, 88.50750732421875)
-3    (456.3309020996094, 126.55831909179688)  (449.24822998046875, 122.55178833007812)
-4    (474.01800537109375, 96.52020263671875)    (467.6260070800781, 92.63973999023438)
-..                                       ...                                       ...
-834    (462.278076171875, 153.7550048828125)  (454.65728759765625, 150.57518005371094)
-835  (509.48809814453125, 305.9473876953125)    (501.7776794433594, 301.7318115234375)
-836   (629.2972412109375, 416.3525695800781)    (619.5714111328125, 409.2008056640625)
-837  (634.0574340820312, 252.74630737304688)    (626.223876953125, 247.45213317871094)
-838   (461.8555603027344, 268.6419372558594)   (455.0975036621094, 264.57464599609375)
-
-[839 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (446.2071228027344, 80.70536804199219)  (439.84759521484375, 76.85433197021484)
-1     (534.2617797851562, 143.96737670898438)      (527.2509765625, 139.5406951904297)
-2    (445.05706787109375, 106.21590423583984)  (438.78424072265625, 102.4920654296875)
-3       (454.4690856933594, 170.727783203125)  (447.7250671386719, 166.99411010742188)
-4     (498.60504150390625, 132.4659423828125)     (491.9645080566406, 128.46337890625)
-..                                        ...                                      ...
-867    (454.9207763671875, 190.9217071533203)   (483.331298828125, 31.711313247680664)
-868   (626.0559692382812, 209.61375427246094)    (631.099365234375, 240.4351806640625)
-869   (427.6689758300781, 192.45603942871094)   (419.6316223144531, 188.9828643798828)
-870    (572.1393432617188, 96.87425231933594)   (520.0606689453125, 73.39861297607422)
-871    (535.3569946289062, 73.31260681152344)    (634.93408203125, 125.89950561523438)
-
-[872 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (438.78424072265625, 102.4920654296875)   (431.8817443847656, 98.58959197998047)
-1     (391.16839599609375, 81.67118835449219)   (384.4547424316406, 77.96324157714844)
-2     (527.4957885742188, 138.93109130859375)  (520.3428955078125, 134.61074829101562)
-3     (447.7250671386719, 166.99411010742188)  (440.89154052734375, 163.6571044921875)
-4     (389.5843811035156, 203.18008422851562)   (381.5575866699219, 198.8533477783203)
-..                                        ...                                      ...
-825    (542.2723388671875, 86.57711029052734)   (535.7410888671875, 82.62053680419922)
-826   (452.2649230957031, 177.14207458496094)      (542.39892578125, 277.098388671875)
-827  (447.12237548828125, 42.723751068115234)   (440.6419372558594, 38.64582824707031)
-828  (358.96624755859375, 29.987478256225586)  (352.3725891113281, 26.219253540039062)
-829    (443.1434326171875, 269.4275207519531)    (435.197021484375, 265.8636779785156)
-
-[830 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (394.1586608886719, 238.15765380859375)  (386.13909912109375, 235.05770874023438)
-1       (510.39111328125, 110.06519317626953)  (503.25775146484375, 105.83934020996094)
-2     (400.6695251464844, 239.12405395507812)      (392.6904296875, 236.04971313476562)
-3     (463.2723083496094, 111.09771728515625)       (456.337890625, 107.14850616455078)
-4    (417.19378662109375, 180.16307067871094)   (409.9008483886719, 176.67263793945312)
-..                                        ...                                       ...
-903    (516.3689575195312, 135.0809783935547)   (511.7444763183594, 132.51742553710938)
-904   (435.0972900390625, 255.30831909179688)  (427.23675537109375, 252.33663940429688)
-905    (582.8046264648438, 283.3101806640625)    (573.7896728515625, 277.8863220214844)
-906   (431.9905090332031, 22.903396606445312)  (426.05096435546875, 18.322124481201172)
-907  (349.41204833984375, 112.62043762207031)   (342.1224365234375, 109.28240203857422)
-
-[908 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (384.4439392089844, 77.33637237548828)     (377.170654296875, 73.44822692871094)
-1    (503.25775146484375, 105.83934020996094)   (496.4088134765625, 101.78831481933594)
-2     (432.9407043457031, 159.61294555664062)  (425.78350830078125, 156.28878784179688)
-3      (522.238037109375, 114.79447174072266)   (515.1174926757812, 110.63553619384766)
-4     (439.3109130859375, 101.99764251708984)     (432.1134033203125, 98.0300521850586)
-..                                        ...                                       ...
-968   (617.0269775390625, 352.39190673828125)     (607.02783203125, 346.47882080078125)
-969   (532.2911376953125, 259.38836669921875)   (546.0545043945312, 266.68829345703125)
-970  (461.90509033203125, 60.146766662597656)    (454.8538818359375, 55.73483657836914)
-971    (522.3908081054688, 80.32623291015625)     (535.797607421875, 84.41950225830078)
-972   (625.2982177734375, 435.56085205078125)      (613.7998046875, 428.98773193359375)
-
-[973 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0    (421.35125732421875, 107.18624114990234)    (413.2986755371094, 102.9293441772461)
-1     (507.3518371582031, 107.02003479003906)   (500.1507568359375, 102.89423370361328)
-2     (505.3884582519531, 126.18698120117188)   (498.0133972167969, 122.04395294189453)
-3    (336.06524658203125, 61.272212982177734)   (328.4104919433594, 57.443904876708984)
-4     (501.4705810546875, 122.45794677734375)  (494.27899169921875, 118.31199645996094)
-..                                        ...                                       ...
-990    (506.3122863769531, 281.4364013671875)     (597.0812377929688, 340.908447265625)
-991    (499.7043762207031, 217.0804443359375)   (491.6144104003906, 212.74867248535156)
-992   (586.6176147460938, 102.17501068115234)       (578.52197265625, 97.2383041381836)
-993   (637.1566772460938, 222.62258911132812)    (627.9718017578125, 217.1832275390625)
-994    (633.693115234375, 120.21599578857422)   (626.0811157226562, 114.91362762451172)
-
-[995 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (500.1507568359375, 102.89423370361328)    (493.2322082519531, 98.96407318115234)
-1     (498.0133972167969, 122.04395294189453)     (491.07763671875, 117.97663879394531)
-2      (582.3426513671875, 152.6240997314453)   (573.8580932617188, 147.98753356933594)
-3     (463.5054931640625, 112.26222229003906)  (456.35736083984375, 108.37712860107422)
-4     (519.9344482421875, 203.42886352539062)   (511.5618896484375, 199.25975036621094)
-...                                       ...                                       ...
-1003   (581.3386840820312, 395.4671325683594)    (606.3048095703125, 389.2135314941406)
-1004   (623.0900268554688, 447.0912170410156)   (611.8590087890625, 440.69512939453125)
-1005  (459.6170959472656, 19.910724639892578)    (452.7823181152344, 15.08935832977295)
-1006  (432.1449890136719, 226.22149658203125)   (351.2227783203125, 184.07711791992188)
-1007   (634.386474609375, 110.68409729003906)   (625.5540771484375, 105.60308837890625)
-
-[1008 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (482.29693603515625, 93.73011016845703)   (474.92730712890625, 89.69763946533203)
-1      (539.1487426757812, 95.25410461425781)    (532.2125244140625, 91.32428741455078)
-2      (561.2223510742188, 170.1217803955078)    (553.064208984375, 165.74240112304688)
-3     (407.20721435546875, 99.96621704101562)   (399.84600830078125, 96.12973022460938)
-4           (578.93359375, 180.5085906982422)    (570.7467041015625, 176.1268768310547)
-...                                       ...                                       ...
-1053   (510.1830749511719, 127.6454849243164)   (353.3858947753906, 15.142472267150879)
-1054  (309.0298767089844, 125.33968353271484)  (301.37176513671875, 125.37403106689453)
-1055      (556.7451171875, 302.6219177246094)     (547.703369140625, 298.6494445800781)
-1056     (634.01904296875, 436.5617980957031)    (600.5881958007812, 368.8330993652344)
-1057      (553.720703125, 352.02813720703125)   (544.5147094726562, 348.57061767578125)
-
-[1058 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (584.1827392578125, 149.20391845703125)   (576.4458618164062, 145.28392028808594)
-1      (584.1827392578125, 149.20391845703125)   (576.4458618164062, 145.28392028808594)
-2     (510.45208740234375, 109.49757385253906)    (503.8448486328125, 105.9517822265625)
-3         (480.2958984375, 110.67192077636719)  (473.50396728515625, 107.22296905517578)
-4       (600.4567260742188, 153.5432586669922)   (592.5294189453125, 149.53082275390625)
-...                                        ...                                       ...
-1127    (603.0927124023438, 428.4842834472656)         (592.23046875, 425.5699157714844)
-1128    (620.4541625976562, 408.1098937988281)      (610.1294555664062, 403.56884765625)
-1129    (631.4489135742188, 379.6987609863281)   (621.3522338867188, 375.41314697265625)
-1130      (636.05615234375, 406.8963928222656)   (625.8038940429688, 402.36822509765625)
-1131    (430.5362243652344, 5.531785488128662)     (446.1156005859375, 14.6143798828125)
-
-[1132 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0       (618.445068359375, 175.9357452392578)    (610.7444458007812, 172.1320037841797)
-1     (369.8997802734375, 209.54136657714844)        (361.130859375, 207.5565948486328)
-2      (306.1996154785156, 47.07289123535156)   (298.9988098144531, 43.837684631347656)
-3       (280.8066101074219, 36.3883171081543)      (273.06689453125, 33.01732635498047)
-4       (541.13037109375, 103.99391174316406)    (533.2653198242188, 99.26143646240234)
-...                                       ...                                       ...
-1193  (480.5173034667969, 297.01373291015625)  (471.65704345703125, 294.78106689453125)
-1194    (526.3839111328125, 65.6080322265625)       (520.6766357421875, 62.32958984375)
-1195   (543.1290283203125, 275.2919006347656)    (534.3999633789062, 271.7748107910156)
-1196   (622.668212890625, 120.00057220458984)    (341.2882385253906, 197.1157684326172)
-1197  (474.5367431640625, 271.60882568359375)   (381.6084899902344, 253.79385375976562)
-
-[1198 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (298.9988098144531, 43.837684631347656)  (292.16705322265625, 40.91718292236328)
-1      (467.33868408203125, 104.1592788696289)  (461.3816833496094, 101.32144165039062)
-2     (254.52023315429688, 53.498416900634766)   (246.58326721191406, 50.6858024597168)
-3     (259.29400634765625, 59.774436950683594)      (251.216796875, 56.685577392578125)
-4               (424.2734375, 316.55908203125)  (415.6453857421875, 316.14495849609375)
-...                                        ...                                      ...
-1266    (587.1061401367188, 427.5035400390625)   (577.1116943359375, 426.4078369140625)
-1267     (584.9419555664062, 88.4377212524414)     (355.4576110839844, 244.55224609375)
-1268   (492.5589294433594, 246.78892517089844)   (520.1832885742188, 272.6275329589844)
-1269   (542.4674682617188, 15.943364143371582)   (517.1722412109375, 6.971754550933838)
-1270     (567.2256469726562, 384.217529296875)  (557.7747192382812, 382.07867431640625)
-
-[1271 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0     (545.0016479492188, 133.3599395751953)   (538.8773803710938, 130.2847900390625)
-1    (528.6028442382812, 145.61643981933594)     (522.5009765625, 142.59759521484375)
-2    (516.5072021484375, 115.64254760742188)  (510.40399169921875, 112.6327896118164)
-3    (247.87672424316406, 65.92410278320312)  (239.98277282714844, 63.42231750488281)
-4    (514.9956665039062, 110.15791320800781)  (508.9049987792969, 107.09504699707031)
-..                                       ...                                      ...
-942  (580.0062866210938, 406.71429443359375)   (609.9254150390625, 120.6316146850586)
-943  (407.1906433105469, 200.28224182128906)  (398.9438171386719, 198.27267456054688)
-944     (491.578369140625, 291.864990234375)   (483.2537841796875, 290.0345153808594)
-945   (469.1352844238281, 271.5392761230469)   (461.1712951660156, 270.2450866699219)
-946   (604.9659423828125, 116.1422348022461)   (598.3463134765625, 113.2757568359375)
-
-[947 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (343.959228515625, 205.04910278320312)  (335.02911376953125, 204.02613830566406)
-1     (342.29693603515625, 271.3403015136719)    (332.6131896972656, 271.2994384765625)
-2         (240.9658203125, 63.18748474121094)    (233.1975555419922, 61.33063507080078)
-3     (508.39471435546875, 291.2505798339844)  (500.53045654296875, 290.27862548828125)
-4           (428.70849609375, 178.3447265625)  (423.00567626953125, 179.18911743164062)
-...                                       ...                                       ...
-1267  (562.3001098632812, 387.33245849609375)     (553.290771484375, 384.8450012207031)
-1268   (565.4900512695312, 87.12049865722656)    (559.0989379882812, 84.69097900390625)
-1269  (636.4750366210938, 225.73814392089844)    (629.8689575195312, 223.0697479248047)
-1270     (618.07373046875, 295.0188293457031)    (619.1766357421875, 381.6833190917969)
-1271    (612.849853515625, 381.4875183105469)    (604.0044555664062, 379.6075439453125)
-
-[1272 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (271.4877014160156, 34.12410354614258)   (265.2232666015625, 31.969852447509766)
-1     (453.88311767578125, 99.36531066894531)    (449.1333923339844, 97.36859893798828)
-2        (582.8037109375, 158.35482788085938)      (577.73291015625, 156.3070068359375)
-3     (582.7018432617188, 152.06976318359375)   (577.5059814453125, 149.89610290527344)
-4     (582.7018432617188, 152.06976318359375)   (577.5059814453125, 149.89610290527344)
-...                                       ...                                       ...
-1369   (375.8471374511719, 250.1783447265625)  (366.71905517578125, 250.28033447265625)
-1370   (610.3780517578125, 408.7362976074219)    (602.3797607421875, 409.1822204589844)
-1371   (608.6621704101562, 365.4665222167969)      (601.8109741210938, 365.29052734375)
-1372  (587.0167846679688, 100.68407440185547)    (582.7174682617188, 97.86222839355469)
-1373   (621.4931030273438, 436.0640563964844)   (613.2144165039062, 436.51055908203125)
-
-[1374 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (265.2232666015625, 31.969852447509766)   (259.3690185546875, 30.054574966430664)
-1      (302.5038146972656, 41.94343948364258)   (297.12567138671875, 40.14378356933594)
-2     (441.39324951171875, 72.54541778564453)    (437.0054626464844, 70.53970336914062)
-3      (449.1333923339844, 97.36859893798828)     (444.1615295410156, 95.4223403930664)
-4     (281.29534912109375, 38.22496795654297)   (275.4129943847656, 36.319480895996094)
-...                                       ...                                       ...
-1407  (495.59637451171875, 163.5980682373047)    (490.6562194824219, 162.2915802001953)
-1408  (459.9909973144531, 301.19757080078125)    (451.7846374511719, 302.1343688964844)
-1409   (629.9111938476562, 402.5699462890625)   (622.9739990234375, 403.83380126953125)
-1410   (579.9977416992188, 373.7288818359375)    (572.3038330078125, 374.7110290527344)
-1411   (344.4183349609375, 218.4555206298828)  (335.34759521484375, 218.45962524414062)
-
-[1412 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (265.13031005859375, 130.588623046875)  (258.3677673339844, 130.61045837402344)
-1       (395.6592712402344, 326.1507873535156)  (385.8680114746094, 328.39239501953125)
-2       (312.7906188964844, 44.28422927856445)   (307.8502197265625, 42.79788589477539)
-3      (411.8726806640625, 181.85536193847656)   (406.0480651855469, 183.8119659423828)
-4     (334.07366943359375, 54.096012115478516)     (329.68115234375, 52.83238220214844)
-...                                        ...                                      ...
-1516   (413.4945373535156, 314.20965576171875)   (404.4431457519531, 316.5047912597656)
-1517    (259.13336181640625, 60.5892448425293)    (252.864990234375, 59.57016372680664)
-1518   (452.3796691894531, 30.361820220947266)  (449.7292785644531, 28.467876434326172)
-1519   (373.50335693359375, 170.9083251953125)   (367.5533752441406, 169.4935760498047)
-1520   (297.4313659667969, 165.85122680664062)  (305.8368225097656, 240.48422241210938)
-
-[1521 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0        (549.2385864257812, 140.721923828125)   (545.2395629882812, 139.6967315673828)
-1       (597.2340698242188, 171.4601593017578)    (593.1544799804688, 170.380126953125)
-2       (568.3880615234375, 153.1553192138672)  (564.2572631835938, 152.16282653808594)
-3       (317.5338134765625, 151.0624542236328)  (310.6763916015625, 151.18258666992188)
-4       (478.9253845214844, 290.3203430175781)   (472.7495422363281, 291.7418212890625)
-...                                        ...                                      ...
-1562    (632.2052612304688, 376.7990417480469)   (626.1974487304688, 378.9563903808594)
-1563  (467.33074951171875, 354.45330810546875)         (459.0771484375, 359.6064453125)
-1564   (348.9136047363281, 285.52838134765625)   (320.5978698730469, 142.6898956298828)
-1565    (404.4431457519531, 316.5047912597656)   (394.7385559082031, 318.9517517089844)
-1566    (505.4598693847656, 4.956575393676758)  (503.52545166015625, 4.011517524719238)
-
-[1567 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (296.0951843261719, 38.83675003051758)  (292.1183166503906, 37.799625396728516)
-1         (347.41943359375, 59.37883758544922)   (343.4690856933594, 58.40134048461914)
-2       (317.882080078125, 48.166481018066406)   (313.8685302734375, 47.18220138549805)
-3      (546.8903198242188, 113.43507385253906)   (544.7398071289062, 113.5055160522461)
-4      (529.3861694335938, 48.307559967041016)   (528.4818725585938, 47.69194412231445)
-...                                        ...                                      ...
-1648    (282.613525390625, 190.83433532714844)     (539.72412109375, 429.8322448730469)
-1649  (467.16265869140625, 346.97406005859375)  (459.06866455078125, 350.6553039550781)
-1650      (579.7822265625, 279.16864013671875)  (252.6565399169922, 180.77117919921875)
-1651       (635.795654296875, 135.97607421875)   (633.028564453125, 135.07916259765625)
-1652     (550.835205078125, 377.0711364746094)   (541.2864990234375, 378.0326232910156)
-
-[1653 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (528.4818725585938, 47.69194412231445)   (525.7904052734375, 47.15488052368164)
-1       (544.7398071289062, 113.5055160522461)  (542.5548706054688, 112.94180297851562)
-2       (197.0890350341797, 39.03586959838867)  (191.10682678222656, 38.20360565185547)
-3      (292.1183166503906, 37.799625396728516)   (288.4763488769531, 36.92409896850586)
-4     (195.09938049316406, 30.963998794555664)   (189.258056640625, 30.150943756103516)
-...                                        ...                                      ...
-1696     (636.338134765625, 32.73786163330078)  (300.0636901855469, 279.15472412109375)
-1697     (508.2521057128906, 365.438232421875)   (499.3074645996094, 368.9543151855469)
-1698   (487.1278076171875, 25.038625717163086)      (485.4818115234375, 24.49755859375)
-1699     (632.969482421875, 410.8392333984375)      (627.4404296875, 415.7492370605469)
-1700  (308.36773681640625, 157.54856872558594)  (300.8331298828125, 157.91970825195312)
-
-[1701 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (557.5200805664062, 144.83566284179688)   (554.7831420898438, 144.9579620361328)
-1     (557.5200805664062, 144.83566284179688)   (554.7831420898438, 144.9579620361328)
-2     (301.3501281738281, 42.301605224609375)    (297.948974609375, 41.81361770629883)
-3     (336.4869689941406, 18.928497314453125)  (334.1584777832031, 18.517141342163086)
-4     (271.46734619140625, 289.7778015136719)   (259.0086975097656, 292.2357482910156)
-...                                       ...                                      ...
-1780   (507.2093811035156, 261.9506530761719)   (501.3509521484375, 263.6653747558594)
-1781    (382.3445129394531, 96.0211410522461)     (378.61767578125, 95.64921569824219)
-1782  (334.57049560546875, 184.3059844970703)   (326.8935852050781, 185.0896453857422)
-1783    (371.7939758300781, 301.094482421875)   (362.0412902832031, 304.8550720214844)
-1784    (464.541259765625, 372.4295959472656)  (454.67083740234375, 378.1842956542969)
-
-[1785 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (344.7097473144531, 31.426240921020508)  (342.5382995605469, 31.324874877929688)
-1     (334.1584777832031, 18.517141342163086)  (332.0362243652344, 18.221433639526367)
-2      (426.8124084472656, 91.17356872558594)   (423.9602966308594, 91.09982299804688)
-3     (334.1584777832031, 18.517141342163086)  (332.0362243652344, 18.221433639526367)
-4      (190.22605895996094, 43.9177131652832)  (184.28009033203125, 43.22742462158203)
-...                                       ...                                      ...
-1878     (348.4310302734375, 287.05224609375)      (336.8310546875, 290.4429931640625)
-1879  (347.10772705078125, 258.6700439453125)      (337.4677734375, 261.2984619140625)
-1880    (533.7484741210938, 445.417724609375)   (525.1197509765625, 453.9132995605469)
-1881   (556.4342651367188, 268.3839111328125)   (551.741943359375, 270.38043212890625)
-1882  (454.67083740234375, 378.1842956542969)  (444.7996826171875, 384.97540283203125)
-
-[1883 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (332.0362243652344, 18.221433639526367)  (330.2698669433594, 18.153453826904297)
-1      (342.5382995605469, 31.324874877929688)   (340.7053527832031, 31.38368797302246)
-2      (332.0362243652344, 18.221433639526367)  (330.2698669433594, 18.153453826904297)
-3       (522.9950561523438, 46.99790954589844)  (522.5555419921875, 47.512935638427734)
-4       (376.9033508300781, 53.47805404663086)   (374.8232727050781, 53.48016357421875)
-...                                        ...                                      ...
-1940  (307.56060791015625, 244.34783935546875)  (297.4273681640625, 245.95648193359375)
-1941   (546.3167114257812, 255.32908630371094)  (542.5494384765625, 257.91815185546875)
-1942       (555.38427734375, 387.789306640625)  (549.4075927734375, 395.00128173828125)
-1943    (459.3323974609375, 456.6214904785156)   (447.5960388183594, 467.4559326171875)
-1944   (573.1202392578125, 101.38782501220703)  (613.7427978515625, 109.59666442871094)
-
-[1945 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (269.00433349609375, 34.312530517578125)    (265.9497375488281, 34.28350830078125)
-1      (522.5555419921875, 47.512935638427734)    (522.4107055664062, 47.50534439086914)
-2      (303.7640075683594, 21.155628204345703)  (301.79449462890625, 21.184741973876953)
-3      (307.2398681640625, 48.710018157958984)   (304.3518981933594, 48.789432525634766)
-4      (303.7640075683594, 21.155628204345703)  (301.79449462890625, 21.184741973876953)
-...                                        ...                                       ...
-2030   (283.92266845703125, 298.0066223144531)   (270.4313659667969, 302.96832275390625)
-2031    (592.1568603515625, 120.9421157836914)    (593.7252197265625, 122.5063705444336)
-2032    (337.9205627441406, 326.3175354003906)     (325.6678161621094, 332.065185546875)
-2033   (287.5020751953125, 214.28504943847656)  (277.54083251953125, 216.49813842773438)
-2034     (420.7747802734375, 94.5083999633789)      (172.6234588623047, 31.090087890625)
-
-[2035 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (190.35650634765625, 7.225520610809326)    (187.3194122314453, 7.244401931762695)
-1      (339.0929870605469, 31.521827697753906)    (337.6329650878906, 32.01946258544922)
-2     (205.44754028320312, 14.170527458190918)  (202.35646057128906, 14.145341873168945)
-3     (160.71336364746094, 16.057138442993164)    (156.744384765625, 16.012426376342773)
-4        (481.2254333496094, 67.1641616821289)   (481.49554443359375, 68.16029357910156)
-...                                        ...                                       ...
-1906   (623.1091918945312, 116.33281707763672)     (623.3038940429688, 117.141357421875)
-1907    (470.3063659667969, 44.03354263305664)    (470.25567626953125, 44.5108757019043)
-1908   (407.7236022949219, 459.47174072265625)   (393.06597900390625, 471.2034606933594)
-1909   (178.28753662109375, 52.12991714477539)   (173.1572723388672, 52.258724212646484)
-1910   (495.27337646484375, 461.2846374511719)    (484.0941162109375, 472.1796569824219)
-
-[1911 rows x 2 columns]
-Matches detected:  
-                                           kp0                                     kp1
-0       (522.4678344726562, 48.25871658325195)  (523.0989379882812, 49.44322204589844)
-1      (242.0207977294922, 19.274049758911133)  (239.62213134765625, 19.2584285736084)
-2     (216.80056762695312, 14.639778137207031)  (214.0542755126953, 14.88084888458252)
-3       (528.8804931640625, 93.36882781982422)     (528.9440307617188, 94.39697265625)
-4       (337.6329650878906, 32.01946258544922)   (336.3913879394531, 32.7021369934082)
-...                                        ...                                     ...
-2083   (231.4330291748047, 247.98239135742188)   (218.205810546875, 251.6502227783203)
-2084    (369.0431213378906, 187.5184326171875)  (361.7133483886719, 189.4718780517578)
-2085       (539.6359252929688, 335.0576171875)  (533.6707763671875, 341.2416687011719)
-2086   (413.90252685546875, 261.9401550292969)  (468.5667724609375, 464.7243347167969)
-2087    (442.5959167480469, 469.9735107421875)    (295.31591796875, 354.6556091308594)
-
-[2088 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (494.5804138183594, 123.93107604980469)   (492.6966857910156, 125.0097885131836)
-1      (404.6304016113281, 65.83000946044922)   (403.7132873535156, 66.75411224365234)
-2      (523.0989379882812, 49.44322204589844)   (523.2530517578125, 50.38446807861328)
-3      (440.4858703613281, 84.99559020996094)   (439.5174560546875, 86.01817321777344)
-4        (444.59619140625, 99.16171264648438)  (442.9490661621094, 100.10384368896484)
-...                                       ...                                      ...
-2193   (464.1238708496094, 371.0506896972656)  (377.8276672363281, 150.47499084472656)
-2194  (598.9480590820312, 105.57219696044922)  (599.1485595703125, 108.14459228515625)
-2195    (228.697021484375, 256.6728820800781)  (214.87220764160156, 260.1471252441406)
-2196   (551.1257934570312, 460.7091979980469)      (542.5302734375, 473.3429260253906)
-2197              (262.705078125, 314.671875)  (246.94772338867188, 320.4161376953125)
-
-[2198 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (403.7132873535156, 66.75411224365234)   (402.8083801269531, 67.78343963623047)
-1     (203.16134643554688, 329.17999267578125)  (183.43812561035156, 333.7795715332031)
-2       (492.6966857910156, 125.0097885131836)  (491.5876770019531, 126.97211456298828)
-3       (410.91717529296875, 90.7611083984375)  (409.19879150390625, 91.81398010253906)
-4         (529.06298828125, 95.65339660644531)    (529.3700561523438, 96.9036865234375)
-...                                        ...                                      ...
-2155     (634.44775390625, 365.55816650390625)  (631.3601684570312, 373.48138427734375)
-2156    (312.2771911621094, 298.0431823730469)  (299.3094482421875, 303.53546142578125)
-2157   (480.5934143066406, 432.56744384765625)  (469.96990966796875, 444.3683776855469)
-2158  (460.90069580078125, 306.58514404296875)   (453.3886413574219, 312.4196472167969)
-2159   (504.4758605957031, 11.301287651062012)    (507.926513671875, 12.12723445892334)
-
-[2160 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (360.1466979980469, 52.38815689086914)    (359.2185974121094, 53.41618728637695)
-1      (424.07806396484375, 74.91541290283203)    (423.3254089355469, 76.01118469238281)
-2       (402.8083801269531, 67.78343963623047)    (402.0428466796875, 68.89155578613281)
-3       (265.3121337890625, 37.20703887939453)  (263.75750732421875, 37.964908599853516)
-4      (580.1177368164062, 186.84617614746094)   (579.0361938476562, 189.78396606445312)
-...                                        ...                                       ...
-2395  (163.65521240234375, 222.66258239746094)    (148.51856994628906, 225.637939453125)
-2396   (412.8074951171875, 21.475942611694336)   (413.7538146972656, 22.486770629882812)
-2397   (154.90760803222656, 91.89151000976562)    (147.98544311523438, 91.9832992553711)
-2398   (225.84506225585938, 358.3370666503906)   (205.82550048828125, 366.4965515136719)
-2399    (540.1451416015625, 80.08912658691406)    (539.6590576171875, 82.19815063476562)
-
-[2400 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (317.4052429199219, 60.16864013671875)  (315.43426513671875, 61.158565521240234)
-1      (290.0447692871094, 45.707794189453125)    (288.2375183105469, 46.65606689453125)
-2     (298.13970947265625, 52.567413330078125)    (296.1220397949219, 53.53777313232422)
-3       (292.4031982421875, 51.13615417480469)   (290.49700927734375, 52.13312530517578)
-4      (310.82977294921875, 57.68295669555664)      (308.87548828125, 58.61320495605469)
-...                                        ...                                       ...
-2512  (253.38201904296875, 206.81805419921875)  (242.06137084960938, 209.42893981933594)
-2513   (423.12506103515625, 314.0106506347656)   (413.85882568359375, 320.5961608886719)
-2514   (211.67022705078125, 333.0875549316406)   (191.48703002929688, 340.0463562011719)
-2515   (248.5672149658203, 168.03517150878906)   (239.4448699951172, 169.41954040527344)
-2516  (373.69537353515625, 214.29849243164062)    (365.2170715332031, 217.8214569091797)
-
-[2517 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (323.6707763671875, 60.54731750488281)  (321.80743408203125, 61.59836959838867)
-1     (313.3113708496094, 55.855594635009766)   (311.1942443847656, 56.75096130371094)
-2      (283.4259033203125, 48.70502853393555)  (281.37481689453125, 49.63589859008789)
-3        (308.87548828125, 58.61320495605469)   (306.9024658203125, 59.64586639404297)
-4     (251.7480010986328, 37.103233337402344)  (249.8816375732422, 38.046844482421875)
-...                                       ...                                      ...
-2637   (372.5777587890625, 2.909278154373169)  (429.24078369140625, 31.99179458618164)
-2638  (250.8027801513672, 39.334190368652344)   (357.9131164550781, 84.76750946044922)
-2639   (300.5238037109375, 419.8495788574219)   (279.83355712890625, 431.142333984375)
-2640  (199.44007873535156, 87.80597686767578)    (191.7292938232422, 87.8463363647461)
-2641    (256.4309997558594, 322.910888671875)         (611.841796875, 392.60400390625)
-
-[2642 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (249.8816375732422, 38.046844482421875)  (248.02903747558594, 39.03799057006836)
-1       (510.391845703125, 57.285762786865234)   (512.1810302734375, 59.63626480102539)
-2     (265.93756103515625, 42.264862060546875)  (263.8852233886719, 43.142704010009766)
-3      (203.3118896484375, 18.063373565673828)  (201.2940216064453, 18.824445724487305)
-4      (237.87303161621094, 34.86008834838867)   (235.9860382080078, 35.78666687011719)
-...                                        ...                                      ...
-2804   (344.80303955078125, 276.6051330566406)   (332.7864685058594, 282.0727844238281)
-2805   (620.9071044921875, 350.23602294921875)   (617.4221801757812, 359.8890686035156)
-2806     (80.1225357055664, 207.0666961669922)  (62.501731872558594, 209.3338623046875)
-2807   (198.8116912841797, 476.30084228515625)   (608.8832397460938, 364.5216979980469)
-2808    (503.0478820800781, 18.80852508544922)  (506.25872802734375, 20.36416244506836)
-
-[2809 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (332.6997985839844, 39.96040725708008)   (331.88909912109375, 41.4371452331543)
-1      (262.33306884765625, 46.76382064819336)   (259.9989929199219, 48.10319900512695)
-2       (343.8690185546875, 80.82584381103516)   (341.2177429199219, 82.03250885009766)
-3      (248.02903747558594, 39.03799057006836)  (245.16851806640625, 40.33704376220703)
-4      (529.6130981445312, 60.582237243652344)  (530.2556762695312, 62.575828552246094)
-...                                        ...                                      ...
-2859  (431.04449462890625, 318.14617919921875)   (612.8543090820312, 447.4708251953125)
-2860    (283.6478576660156, 354.3585510253906)    (264.66357421875, 362.42596435546875)
-2861  (152.39968872070312, 191.97825622558594)  (137.9139404296875, 194.26895141601562)
-2862  (224.64495849609375, 251.24412536621094)   (268.1224365234375, 298.5256652832031)
-2863   (417.09796142578125, 4.309784889221191)   (418.5731506347656, 6.319497108459473)
-
-[2864 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (603.9618530273438, 225.81956481933594)    (602.899169921875, 229.75547790527344)
-1      (500.1915588378906, 142.01132202148438)   (497.73980712890625, 143.8804168701172)
-2       (331.88909912109375, 41.4371452331543)    (331.0376281738281, 43.04690933227539)
-3       (259.9989929199219, 48.10319900512695)    (257.1252136230469, 49.45030212402344)
-4       (512.2083129882812, 34.16582107543945)   (513.7597045898438, 36.041404724121094)
-...                                        ...                                       ...
-3005   (391.6333312988281, 208.36468505859375)    (467.2301940917969, 288.9793395996094)
-3006    (596.3147583007812, 449.5068664550781)      (589.44775390625, 463.3492431640625)
-3007    (440.8574523925781, 454.6613464355469)    (424.8575134277344, 468.7586975097656)
-3008    (578.3365478515625, 415.1903076171875)     (571.530029296875, 427.0005798339844)
-3009  (267.77056884765625, 247.87240600585938)  (253.55738830566406, 250.99639892578125)
-
-[3010 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (315.2101745605469, 65.55807495117188)  (312.32696533203125, 66.80973052978516)
-1      (303.9195556640625, 61.26993179321289)  (301.43902587890625, 62.78367614746094)
-2     (126.13853454589844, 32.66293716430664)  (121.26580047607422, 33.73746109008789)
-3                 (493.375, 84.8929443359375)   (493.6825866699219, 86.33905029296875)
-4     (543.7313232421875, 116.71243286132812)  (544.7343139648438, 118.61952209472656)
-...                                       ...                                      ...
-3123    (624.9852294921875, 307.293701171875)   (321.8637390136719, 293.4571228027344)
-3124    (149.166748046875, 352.6291809082031)  (121.39249420166016, 361.2140808105469)
-3125  (156.80673217773438, 440.0661926269531)   (124.8224105834961, 453.5762634277344)
-3126  (143.65902709960938, 184.0538787841797)  (30.396656036376953, 177.4518280029297)
-3127   (606.3304443359375, 437.9121398925781)   (599.4387817382812, 450.6272277832031)
-
-[3128 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (121.26580047607422, 33.73746109008789)  (116.20946502685547, 34.90420913696289)
-1        (256.33740234375, 47.174598693847656)   (253.1698760986328, 48.08928680419922)
-2         (302.18896484375, 69.04784393310547)   (299.38824462890625, 70.2953109741211)
-3       (297.4250183105469, 64.88594818115234)   (294.4255676269531, 66.12322235107422)
-4     (329.98577880859375, 44.685943603515625)    (329.1446228027344, 46.4038200378418)
-...                                        ...                                      ...
-3191  (206.03433227539062, 238.28494262695312)   (189.13319396972656, 242.293701171875)
-3192   (528.7726440429688, 12.315522193908691)  (83.37564086914062, 184.03204345703125)
-3193    (387.956298828125, 3.2014999389648438)  (389.48895263671875, 4.414793968200684)
-3194       (636.4049072265625, 303.1298828125)   (634.881591796875, 308.97369384765625)
-3195   (157.0184326171875, 129.05612182617188)  (137.36447143554688, 450.5814514160156)
-
-[3196 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (359.1470031738281, 198.09645080566406)   (359.58111572265625, 198.4389190673828)
-1     (359.1470031738281, 198.09645080566406)   (359.58111572265625, 198.4389190673828)
-2      (253.1698760986328, 48.08928680419922)   (250.07171630859375, 49.10688018798828)
-3     (116.20946502685547, 34.90420913696289)      (111.28564453125, 35.97980880737305)
-4      (299.38824462890625, 70.2953109741211)    (296.3182373046875, 71.38230895996094)
-...                                       ...                                       ...
-3191    (615.802490234375, 367.8989562988281)    (610.6585083007812, 375.6022644042969)
-3192   (135.95651245117188, 90.8795166015625)     (127.1705551147461, 92.5189208984375)
-3193   (526.3994750976562, 437.8855895996094)   (111.3639144897461, 384.46075439453125)
-3194   (311.9366149902344, 348.2203674316406)    (293.941162109375, 355.64251708984375)
-3195   (267.4236145019531, 201.7021026611328)  (183.15077209472656, 465.73553466796875)
-
-[3196 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (306.67864990234375, 69.6290512084961)   (303.62164306640625, 70.63363647460938)
-1     (359.58111572265625, 198.4389190673828)     (357.7061767578125, 200.544677734375)
-2        (111.28564453125, 35.97980880737305)    (106.42568969726562, 36.9066047668457)
-3     (359.58111572265625, 198.4389190673828)     (357.7061767578125, 200.544677734375)
-4     (257.2533874511719, 52.851837158203125)   (254.35739135742188, 54.00370788574219)
-...                                       ...                                       ...
-3265  (288.24237060546875, 246.2550506591797)    (275.732666015625, 250.08255004882812)
-3266    (94.02205657958984, 376.688720703125)  (63.503509521484375, 385.09295654296875)
-3267   (137.0140838623047, 245.5365753173828)   (528.8153076171875, 369.36016845703125)
-3268   (347.2461242675781, 290.6320495605469)    (334.0710754394531, 295.6161804199219)
-3269   (84.64238739013672, 95.32501220703125)    (75.15562438964844, 95.06909942626953)
-
-[3270 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (273.0797424316406, 63.088348388671875)    (270.2672119140625, 64.11231231689453)
-1      (303.62164306640625, 70.63363647460938)    (300.7689208984375, 71.63997650146484)
-2        (357.7061767578125, 200.544677734375)  (353.56536865234375, 202.83729553222656)
-3       (356.9442138671875, 63.57673263549805)    (356.2142333984375, 65.12127685546875)
-4      (252.65695190429688, 47.80964660644531)   (250.04669189453125, 48.86735534667969)
-...                                        ...                                       ...
-3297    (326.5740966796875, 317.7825012207031)    (311.8538818359375, 323.2008361816406)
-3298  (230.17935180664062, 224.68458557128906)    (216.374755859375, 227.23793029785156)
-3299    (315.4166564941406, 285.4284973144531)   (301.85369873046875, 290.2196350097656)
-3300   (163.8680877685547, 163.14088439941406)  (151.68145751953125, 165.03074645996094)
-3301    (78.02299499511719, 293.8512878417969)  (54.464969635009766, 297.53326416015625)
-
-[3302 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (285.719970703125, 44.225093841552734)       (283.732421875, 45.153343200683594)
-1     (387.1542053222656, 106.33888244628906)   (384.4522705078125, 107.29585266113281)
-2     (416.6269836425781, 116.31647491455078)   (414.30938720703125, 117.4989242553711)
-3     (374.5316467285156, 104.10698699951172)   (371.6888427734375, 105.01639556884766)
-4       (383.952392578125, 74.82596588134766)     (383.1706237792969, 76.2022476196289)
-...                                       ...                                       ...
-3184   (48.46024703979492, 224.5015106201172)  (28.727020263671875, 226.57264709472656)
-3185     (224.58154296875, 352.8818664550781)  (203.47503662109375, 358.58465576171875)
-3186  (75.05789184570312, 430.65802001953125)    (40.96549987792969, 440.8761901855469)
-3187   (625.4953002929688, 416.1164855957031)    (620.2037963867188, 425.8328857421875)
-3188   (635.108642578125, 330.61798095703125)      (631.60107421875, 336.7624816894531)
-
-[3189 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0       (389.0794372558594, 90.2523422241211)   (386.75946044921875, 90.99593353271484)
-1       (383.1706237792969, 76.2022476196289)    (381.3809814453125, 76.90379333496094)
-2      (239.4646453857422, 55.82107925415039)  (236.22476196289062, 56.442115783691406)
-3     (587.9556884765625, 227.95315551757812)   (584.7740478515625, 230.74456787109375)
-4      (318.3310241699219, 46.15998077392578)    (316.9841613769531, 47.06715393066406)
-...                                       ...                                       ...
-3226   (202.37588500976562, 273.203857421875)  (185.30162048339844, 276.07940673828125)
-3227    (520.4448852539062, 462.035400390625)   (508.3216247558594, 473.06549072265625)
-3228  (196.74644470214844, 464.6335144042969)       (167.59326171875, 475.829345703125)
-3229  (104.42567443847656, 93.76454162597656)    (95.93683624267578, 94.09297943115234)
-3230    (483.607666015625, 467.5740661621094)   (161.2574920654297, 303.76800537109375)
-
-[3231 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (91.7638931274414, 38.641929626464844)   (86.7657241821289, 38.723567962646484)
-1       (541.4503173828125, 76.32176208496094)   (543.1619262695312, 77.73446655273438)
-2       (323.1370544433594, 53.01398849487305)  (321.72320556640625, 53.76050567626953)
-3     (349.11712646484375, 214.74354553222656)  (344.1747131347656, 218.51768493652344)
-4       (301.8585205078125, 88.21629333496094)  (297.59613037109375, 88.35010528564453)
-...                                        ...                                      ...
-3240    (321.5908508300781, 186.7076416015625)   (314.9420471191406, 188.4567108154297)
-3241   (336.23345947265625, 185.9727020263672)   (328.5591125488281, 187.0592803955078)
-3242         (211.75489807128906, 260.9453125)  (195.84307861328125, 263.4578857421875)
-3243      (384.38079833984375, 406.9619140625)  (368.00848388671875, 414.5893249511719)
-3244   (109.40120697021484, 151.6832733154297)  (34.34921646118164, 253.34841918945312)
-
-[3245 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (148.0613250732422, 15.805564880371094)  (144.89683532714844, 15.746859550476074)
-1      (351.6325378417969, 67.44803619384766)     (349.3949890136719, 67.2915267944336)
-2     (321.72320556640625, 53.76050567626953)    (319.9950866699219, 54.34132385253906)
-3     (133.12802124023438, 19.45370101928711)  (129.95640563964844, 19.355648040771484)
-4     (314.23431396484375, 93.13075256347656)   (310.88226318359375, 93.54998779296875)
-...                                       ...                                       ...
-2870   (169.1400909423828, 367.8100280761719)    (5.999309062957764, 410.6250915527344)
-2871  (128.85853576660156, 388.0933837890625)   (103.02247619628906, 393.6263732910156)
-2872       (558.9561157226562, 292.458984375)   (553.7298583984375, 295.81085205078125)
-2873    (143.460693359375, 331.0547790527344)    (121.25411224365234, 334.579833984375)
-2874    (604.2048950195312, 354.502685546875)    (25.63128662109375, 294.3455505371094)
-
-[2875 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0     (319.9950866699219, 54.34132385253906)  (318.42486572265625, 54.57689666748047)
-1     (470.2342529296875, 36.86772537231445)   (470.5276794433594, 37.39479064941406)
-2     (604.9722290039062, 281.5303649902344)  (599.9888305664062, 284.54608154296875)
-3     (543.9617919921875, 79.05038452148438)   (545.0731201171875, 79.77526092529297)
-4      (349.3949890136719, 67.2915267944336)    (347.777587890625, 67.77033233642578)
-...                                      ...                                      ...
-3156  (438.0044250488281, 48.82515335083008)  (386.15081787109375, 23.46219253540039)
-3157  (407.6424865722656, 15.25551700592041)  (407.4481506347656, 15.680426597595215)
-3158  (137.0490264892578, 275.9018859863281)     (440.3233642578125, 415.03857421875)
-3159  (112.52486419677734, 466.019775390625)     (80.884033203125, 474.6677551269531)
-3160   (492.96435546875, 291.39801025390625)  (64.86811828613281, 233.31781005859375)
-
-[3161 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0        (347.777587890625, 67.77033233642578)    (345.8107604980469, 67.77110290527344)
-1       (470.5276794433594, 37.39479064941406)     (471.2268371582031, 37.8554573059082)
-2       (545.0731201171875, 79.77526092529297)    (545.5258178710938, 80.29802703857422)
-3      (85.73335266113281, 28.983612060546875)   (80.61640167236328, 28.071338653564453)
-4       (590.9630126953125, 280.9369812011719)     (585.927978515625, 283.9999694824219)
-...                                        ...                                       ...
-3112  (199.91598510742188, 304.34881591796875)  (180.64581298828125, 305.98712158203125)
-3113   (412.22784423828125, 4.105492115020752)    (412.7397155761719, 5.150768280029297)
-3114   (47.810760498046875, 82.59929656982422)    (38.58945846557617, 81.08103942871094)
-3115    (512.4691162109375, 273.3659973144531)   (219.66253662109375, 207.2201385498047)
-3116     (634.802978515625, 371.0006103515625)    (627.2034301757812, 375.8973083496094)
-
-[3117 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0        (471.2268371582031, 37.8554573059082)  (470.87115478515625, 37.583526611328125)
-1      (195.43655395507812, 44.93735885620117)    (191.3428497314453, 43.98040771484375)
-2      (179.01763916015625, 42.19715881347656)   (175.01214599609375, 41.21654510498047)
-3       (545.5258178710938, 80.29802703857422)    (545.0025024414062, 81.21054077148438)
-4          (263.1669921875, 70.36151885986328)       (259.1865234375, 69.45304870605469)
-...                                        ...                                       ...
-3188  (131.89503479003906, 199.88558959960938)  (117.51576232910156, 200.62916564941406)
-3189    (245.2106170654297, 459.4304504394531)   (221.03375244140625, 466.1239929199219)
-3190   (325.26812744140625, 467.9873352050781)    (201.7295684814453, 376.5431823730469)
-3191        (549.5293579101562, 393.435546875)   (539.5599975585938, 397.78912353515625)
-3192   (430.09197998046875, 5.785565376281738)   (429.25457763671875, 5.072528839111328)
-
-[3193 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (283.04827880859375, 80.27073669433594)   (278.9510192871094, 79.19300079345703)
-1     (470.87115478515625, 37.583526611328125)    (471.727783203125, 37.51805114746094)
-2      (317.02581787109375, 87.11492156982422)  (313.01934814453125, 86.11100769042969)
-3        (349.346435546875, 76.59668731689453)      (346.4833984375, 75.74472045898438)
-4       (317.0658874511719, 93.85822296142578)   (312.9918518066406, 93.01056671142578)
-...                                        ...                                      ...
-3207    (153.4355926513672, 367.4540100097656)    (128.102294921875, 371.1750183105469)
-3208   (586.6459350585938, 397.45904541015625)       (440.919921875, 451.2923583984375)
-3209    (436.6825866699219, 8.172139167785645)   (436.7303771972656, 7.008199214935303)
-3210    (6.148565292358398, 79.45942687988281)   (136.6073760986328, 252.4406280517578)
-3211    (94.72795867919922, 416.7162170410156)    (78.1098861694336, 142.6876983642578)
-
-[3212 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0       (471.727783203125, 37.51805114746094)   (471.05303955078125, 36.69866943359375)
-1          (508.5887756347656, 220.142578125)    (501.5274353027344, 219.9897918701172)
-2      (152.02911376953125, 21.5069580078125)  (147.44322204589844, 19.408212661743164)
-3     (609.9955444335938, 319.46539306640625)    (601.8824462890625, 321.3847351074219)
-4       (335.30706787109375, 71.435791015625)     (332.4854431152344, 70.2518539428711)
-...                                       ...                                       ...
-3226   (86.88917541503906, 385.9449768066406)   (239.5740966796875, 168.67422485351562)
-3227  (443.99212646484375, 48.87471389770508)    (442.9723815917969, 48.15666198730469)
-3228  (484.7671813964844, 437.88397216796875)    (552.4520874023438, 448.1349792480469)
-3229  (159.17880249023438, 372.7210998535156)   (138.89852905273438, 375.5457458496094)
-3230   (235.6054229736328, 356.3131103515625)   (216.95970153808594, 358.1335144042969)
-
-[3231 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0       (265.820068359375, 43.23074722290039)    (262.7712097167969, 41.23225784301758)
-1      (544.9609985351562, 80.74869537353516)     (543.8937377929688, 80.1097183227539)
-2     (570.9107666015625, 233.34141540527344)   (565.6569213867188, 233.13455200195312)
-3     (337.70867919921875, 65.37667083740234)    (335.1485900878906, 63.79497146606445)
-4        (309.4091796875, 51.995201110839844)    (306.8596496582031, 50.36796188354492)
-...                                       ...                                       ...
-3106    (466.656982421875, 443.4766845703125)    (451.4646301269531, 445.6067199707031)
-3107  (7.999094009399414, 155.01048278808594)          (609.5546875, 342.5775451660156)
-3108  (7.999094009399414, 155.01048278808594)          (609.5546875, 342.5775451660156)
-3109   (26.18446922302246, 331.5642395019531)  (139.61195373535156, 271.55255126953125)
-3110  (2.8351545333862305, 423.6329040527344)    (560.6635131835938, 263.4375305175781)
-
-[3111 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (335.1485900878906, 63.79497146606445)   (331.4956970214844, 61.58028030395508)
-1      (339.5557556152344, 102.78571319580078)  (334.8242492675781, 100.73143005371094)
-2      (345.84295654296875, 75.44679260253906)       (342.2939453125, 73.3373031616211)
-3           (545.166015625, 134.2860870361328)  (543.2439575195312, 132.90928649902344)
-4      (46.06717300415039, 17.529327392578125)   (37.815067291259766, 13.5329008102417)
-...                                        ...                                      ...
-3107  (334.52886962890625, 346.09515380859375)    (319.428955078125, 347.0166931152344)
-3108    (568.378662109375, 385.58648681640625)   (556.3782958984375, 387.3385314941406)
-3109       (168.50070190429688, 394.853515625)   (145.4945831298828, 396.9189758300781)
-3110    (258.4437255859375, 417.5744323730469)  (236.99452209472656, 421.9888610839844)
-3111  (223.45388793945312, 475.90264892578125)   (65.57288360595703, 404.6871643066406)
-
-[3112 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0        (325.5708312988281, 66.3364486694336)    (321.4579772949219, 63.37957763671875)
-1       (198.38552856445312, 39.7026481628418)     (192.8258514404297, 36.2485237121582)
-2      (292.95745849609375, 48.97109603881836)   (288.8306579589844, 45.808204650878906)
-3          (559.994140625, 232.68426513671875)    (553.8915405273438, 231.6776123046875)
-4          (559.994140625, 232.68426513671875)    (553.8915405273438, 231.6776123046875)
-...                                        ...                                       ...
-3127    (549.3911743164062, 258.7276916503906)    (542.1866455078125, 257.9112243652344)
-3128   (483.01092529296875, 474.6129150390625)    (466.7345275878906, 477.1815490722656)
-3129    (66.56185150146484, 386.0119934082031)  (39.662139892578125, 387.18988037109375)
-3130  (444.07354736328125, 29.468360900878906)   (440.9900817871094, 29.510515213012695)
-3131  (180.58824157714844, 40.569759368896484)   (355.2895202636719, 108.80658721923828)
-
-[3132 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (243.90859985351562, 61.337379455566406)    (237.96778869628906, 57.3501091003418)
-1        (254.97900390625, 35.082923889160156)  (250.41555786132812, 31.076793670654297)
-2       (399.1627502441406, 200.4063262939453)  (391.32318115234375, 198.11886596679688)
-3       (329.9345397949219, 98.12779998779297)    (324.6376647949219, 95.00575256347656)
-4      (23.146343231201172, 13.47545337677002)   (12.641782760620117, 7.434573650360107)
-...                                        ...                                       ...
-3187     (357.302490234375, 407.0857849121094)   (339.99713134765625, 407.7242736816406)
-3188    (611.571044921875, 366.27581787109375)    (597.1774291992188, 366.6382751464844)
-3189   (47.395816802978516, 436.0489196777344)    (17.98796272277832, 437.8378601074219)
-3190        (458.498046875, 332.6090393066406)   (446.98272705078125, 331.8889465332031)
-3191    (47.52265167236328, 313.9425354003906)  (23.415176391601562, 312.89495849609375)
-
-[3192 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (237.96778869628906, 57.3501091003418)   (229.9862518310547, 53.34611511230469)
-1      (247.7683868408203, 58.50038528442383)   (239.9811248779297, 54.50580978393555)
-2      (238.5259246826172, 53.83903121948242)   (230.6624298095703, 49.90273666381836)
-3      (307.4473571777344, 49.47296142578125)   (301.2341003417969, 45.40425491333008)
-4     (248.21026611328125, 57.60352325439453)   (240.3140106201172, 53.62620162963867)
-...                                       ...                                      ...
-3265  (29.722698211669922, 303.4079895019531)  (6.1681647300720215, 304.0509033203125)
-3266   (381.9618835449219, 395.8577575683594)   (367.4660949707031, 395.0999755859375)
-3267   (43.45242691040039, 351.3110656738281)  (17.104774475097656, 353.6442565917969)
-3268   (49.01335906982422, 288.9722595214844)  (25.782499313354492, 289.3811340332031)
-3269   (94.38552856445312, 402.3414306640625)  (70.39766693115234, 405.10345458984375)
-
-[3270 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (496.4715576171875, 262.4419250488281)  (486.11663818359375, 258.4858703613281)
-1     (434.4046325683594, 186.81495666503906)  (425.4098205566406, 182.97091674804688)
-2      (265.5853576660156, 69.62068176269531)    (257.4458312988281, 65.3033676147461)
-3      (444.6812744140625, 64.34249877929688)   (440.0458068847656, 59.75471878051758)
-4     (377.84332275390625, 156.8617706298828)  (369.8409423828125, 152.85455322265625)
-...                                       ...                                      ...
-3101  (199.53627014160156, 379.8885192871094)  (128.65908813476562, 320.8770751953125)
-3102     (50.62773513793945, 316.94384765625)    (26.75419807434082, 318.335205078125)
-3103    (105.67227935791016, 209.52587890625)   (88.24175262451172, 208.0370635986328)
-3104  (7.546504974365234, 330.73394775390625)   (515.550048828125, 48.209373474121094)
-3105  (31.949277877807617, 451.9856262207031)   (3.602541446685791, 457.3979187011719)
-
-[3106 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0     (155.05606079101562, 14.929656028747559)   (144.0089874267578, 9.984073638916016)
-1        (475.3877868652344, 82.3387680053711)     (470.0860290527344, 77.186279296875)
-2       (266.107666015625, 32.808353424072266)  (258.4356689453125, 27.785778045654297)
-3       (479.6302185058594, 292.3780212402344)         (469.640625, 288.40899658203125)
-4       (483.8123779296875, 288.1936340332031)     (473.76904296875, 284.2131652832031)
-...                                        ...                                      ...
-3248   (424.58184814453125, 366.4845886230469)   (412.0465393066406, 363.6918640136719)
-3249    (88.24175262451172, 208.0370635986328)  (69.71778106689453, 206.45672607421875)
-3250  (162.91653442382812, 380.17718505859375)   (142.75827026367188, 381.147216796875)
-3251     (140.9381561279297, 367.329345703125)    (120.2649917602539, 368.585693359375)
-3252   (416.1655578613281, 255.02085876464844)   (406.502197265625, 250.90237426757812)
-
-[3253 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (227.30760192871094, 53.2620964050293)  (217.18576049804688, 48.235164642333984)
-1       (286.4280090332031, 69.36531066894531)     (277.5578918457031, 64.3382797241211)
-2      (612.6246337890625, 167.45968627929688)    (605.8871459960938, 161.4803466796875)
-3     (227.80337524414062, 52.263450622558594)   (217.83457946777344, 47.20594024658203)
-4      (485.6517028808594, 188.09597778320312)   (477.7124328613281, 183.07569885253906)
-...                                        ...                                       ...
-3318     (569.29248046875, 444.91180419921875)    (554.7481689453125, 442.1178283691406)
-3319      (332.8076171875, 105.13236999511719)    (321.9214172363281, 99.69102478027344)
-3320    (557.9450073242188, 392.8341064453125)    (544.0128173828125, 389.5497131347656)
-3321   (634.0036010742188, 438.74200439453125)   (617.2094116210938, 435.22772216796875)
-3322   (549.1907348632812, 427.92132568359375)     (534.409912109375, 424.9083557128906)
-
-[3323 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (292.3148498535156, 70.82675170898438)    (283.1820373535156, 65.79375457763672)
-1       (463.2978820800781, 279.8598327636719)    (453.4050598144531, 275.5809020996094)
-2      (460.9498291015625, 157.77818298339844)   (453.1037902832031, 152.57383728027344)
-3     (217.18576049804688, 48.235164642333984)  (206.33038330078125, 43.030174255371094)
-4      (469.9494934082031, 253.06761169433594)   (459.1116943359375, 248.36058044433594)
-...                                        ...                                       ...
-3412   (511.59844970703125, 454.7085876464844)       (499.9933776855469, 451.6240234375)
-3413  (360.09149169921875, 250.06500244140625)     (348.943115234375, 247.1472930908203)
-3414    (492.6105041503906, 420.5294189453125)     (479.50494384765625, 417.61767578125)
-3415    (571.0872192382812, 460.2662048339844)    (548.9882202148438, 449.2420349121094)
-3416    (412.4731750488281, 476.0607604980469)     (397.861083984375, 474.7799987792969)
-
-[3417 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0     (464.78839111328125, 203.07321166992188)     (455.956787109375, 198.020263671875)
-1      (461.5306091308594, 208.55348205566406)  (452.6103210449219, 203.62196350097656)
-2       (284.1408386230469, 72.49712371826172)   (274.5093078613281, 67.35755920410156)
-3       (300.9459533691406, 75.05943298339844)   (291.7292175292969, 69.93623352050781)
-4       (453.4050598144531, 275.5809020996094)    (443.2414245605469, 271.331298828125)
-...                                        ...                                      ...
-3437      (633.79931640625, 120.8084487915039)  (626.9207763671875, 114.14083099365234)
-3438    (330.6499328613281, 314.0452880859375)  (317.48272705078125, 311.8930358886719)
-3439   (554.7940063476562, 395.11541748046875)  (538.8954467773438, 390.17315673828125)
-3440   (104.26225280761719, 80.89334106445312)   (89.06376647949219, 76.18054962158203)
-3441     (590.7395629882812, 381.583251953125)   (574.8078002929688, 377.6573791503906)
-
-[3442 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0           (428.76318359375, 265.90283203125)    (418.7228698730469, 261.8983154296875)
-1     (358.33026123046875, 123.43560028076172)     (348.28173828125, 118.38188171386719)
-2      (361.1993103027344, 127.15821075439453)   (351.1595458984375, 122.08979797363281)
-3      (451.7353820800781, 59.872493743896484)    (444.9520263671875, 53.73618698120117)
-4      (323.6939392089844, 125.76533508300781)   (313.7181091308594, 121.08954620361328)
-...                                        ...                                       ...
-3387   (62.08134460449219, 350.41253662109375)  (140.59185791015625, 227.87753295898438)
-3388   (45.076622009277344, 345.4745788574219)   (22.111705780029297, 349.9533386230469)
-3389   (636.5479125976562, 423.98114013671875)     (620.354736328125, 419.0674133300781)
-3390   (29.054288864135742, 184.7230224609375)   (7.712918758392334, 183.88218688964844)
-3391    (545.3204956054688, 366.4494323730469)   (530.7058715820312, 362.83489990234375)
-
-[3392 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (333.4674987792969, 98.42847442626953)     (323.4114074707031, 93.3296890258789)
-1      (429.2318420410156, 177.4751434326172)    (420.2934265136719, 172.4555206298828)
-2      (492.2393798828125, 249.8414764404297)    (483.3954772949219, 245.0481719970703)
-3     (432.68975830078125, 267.0477600097656)   (422.7625427246094, 263.06036376953125)
-4     (264.25091552734375, 62.11856460571289)   (253.91209411621094, 56.91503143310547)
-...                                       ...                                       ...
-3341  (436.6120910644531, 21.779064178466797)  (428.65423583984375, 14.731432914733887)
-3342   (506.9624938964844, 471.7347106933594)      (493.7412109375, 468.60919189453125)
-3343   (180.766845703125, 49.497711181640625)   (238.7694854736328, 145.45567321777344)
-3344   (623.5757446289062, 189.6461181640625)   (467.7947998046875, 49.784271240234375)
-3345    (614.39892578125, 429.65423583984375)   (485.7009582519531, 410.15130615234375)
-
-[3346 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (344.48773193359375, 92.71796417236328)    (335.559814453125, 87.72000122070312)
-1        (475.3358154296875, 71.7556381225586)   (468.2670593261719, 65.68464660644531)
-2       (282.8228759765625, 79.56616973876953)   (272.6427917480469, 74.68742370605469)
-3      (438.1126403808594, 47.596343994140625)  (431.20159912109375, 41.67088317871094)
-4      (444.1925354003906, 161.73695373535156)  (435.7250061035156, 156.62289428710938)
-...                                        ...                                      ...
-3201    (517.405029296875, 342.54144287109375)   (503.4004821777344, 339.8666687011719)
-3202  (119.10298919677734, 185.47097778320312)  (21.36606216430664, 139.34759521484375)
-3203      (472.2895202636719, 406.86376953125)     (458.9873046875, 405.03790283203125)
-3204   (159.69195556640625, 443.9873352050781)  (139.78701782226562, 447.7869873046875)
-3205  (199.62782287597656, 355.85845947265625)   (183.2736053466797, 357.0256652832031)
-
-[3206 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (339.97406005859375, 89.26331329345703)    (331.0313720703125, 84.3405532836914)
-1     (445.39520263671875, 155.18045043945312)  (437.3281555175781, 150.32675170898438)
-2        (343.343994140625, 94.26467895507812)  (334.41058349609375, 89.39308166503906)
-3       (339.4393615722656, 93.46796417236328)   (330.4026794433594, 88.62340545654297)
-4       (468.2670593261719, 65.68464660644531)    (461.186279296875, 59.77773666381836)
-...                                        ...                                      ...
-3283   (496.53826904296875, 372.9527587890625)  (482.3751525878906, 371.39630126953125)
-3284  (238.25376892089844, 266.44354248046875)  (225.14865112304688, 265.6587829589844)
-3285  (132.31417846679688, 200.64859008789062)   (116.138427734375, 199.83782958984375)
-3286    (482.214111328125, 37.072296142578125)   (572.4279174804688, 89.86766052246094)
-3287   (593.7855224609375, 476.17327880859375)      (579.29052734375, 474.511474609375)
-
-[3288 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (295.9189758300781, 116.38650512695312)  (286.1888732910156, 112.31985473632812)
-1      (441.6867370605469, 158.1501922607422)  (433.8405456542969, 153.56243896484375)
-2        (344.3082580566406, 106.48681640625)  (335.8007507324219, 102.45278930664062)
-3     (237.88107299804688, 437.2152404785156)      (222.91262817382812, 440.373046875)
-4     (436.4036865234375, 265.59967041015625)  (426.36090087890625, 262.5496520996094)
-...                                       ...                                      ...
-3251   (526.882568359375, 464.38970947265625)   (552.6497192382812, 418.6280212402344)
-3252  (128.98431396484375, 351.7894592285156)   (111.56517791748047, 354.769775390625)
-3253   (297.4422607421875, 347.6305236816406)  (285.50360107421875, 348.0451965332031)
-3254     (556.62548828125, 78.62435150146484)     (550.21240234375, 73.49929809570312)
-3255   (68.46379089355469, 193.1116485595703)   (50.34592819213867, 192.3118133544922)
-
-[3256 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0        (222.2559051513672, 42.4483528137207)    (211.9972686767578, 38.20811462402344)
-1     (415.38763427734375, 181.52989196777344)   (410.6322937011719, 182.17616271972656)
-2          (479.9592590332031, 209.6552734375)   (472.13116455078125, 205.6990203857422)
-3       (406.776611328125, 193.08778381347656)    (398.6162414550781, 189.8461151123047)
-4       (339.848388671875, 108.45307922363281)   (331.4820251464844, 104.51187133789062)
-...                                        ...                                       ...
-3335  (185.95176696777344, 205.41989135742188)      (60.46735382080078, 304.19091796875)
-3336      (72.1466064453125, 59.0529670715332)  (55.949310302734375, 55.147586822509766)
-3337   (78.06537628173828, 155.32472229003906)    (61.34033966064453, 153.2740020751953)
-3338     (60.8336296081543, 182.0701904296875)   (42.48946762084961, 181.89743041992188)
-3339    (532.4866333007812, 424.1489562988281)   (519.8121948242188, 424.71063232421875)
-
-[3340 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (211.9972686767578, 38.20811462402344)  (202.10548400878906, 34.36911392211914)
-1     (397.2115478515625, 117.00896453857422)   (390.106201171875, 113.09395599365234)
-2     (410.6322937011719, 182.17616271972656)  (405.1399841308594, 182.70352172851562)
-3      (308.9838562011719, 62.53200912475586)  (301.0191650390625, 58.605831146240234)
-4      (312.8857727050781, 80.14683532714844)    (304.8160705566406, 76.4852523803711)
-...                                       ...                                      ...
-3327  (247.92613220214844, 7.022342681884766)   (239.1148681640625, 2.458493232727051)
-3328   (516.9432373046875, 472.8846435546875)     (504.28857421875, 474.7978820800781)
-3329  (410.13153076171875, 379.6017761230469)   (398.0600280761719, 380.0244445800781)
-3330   (586.2930908203125, 450.1532897949219)  (574.1213989257812, 450.87066650390625)
-3331    (170.61865234375, 382.93280029296875)  (273.65228271484375, 151.5917205810547)
-
-[3332 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (405.1399841308594, 182.70352172851562)   (400.1771240234375, 183.3458709716797)
-1      (402.1056213378906, 155.92066955566406)  (395.18975830078125, 152.9317169189453)
-2     (318.67205810546875, 136.80255126953125)   (309.980224609375, 134.11123657226562)
-3       (557.0291748046875, 261.3962707519531)     (550.6327514648438, 258.03076171875)
-4        (304.8160705566406, 76.4852523803711)    (297.3365478515625, 73.1306381225586)
-...                                        ...                                      ...
-3279    (535.6438598632812, 426.4200439453125)  (522.9906005859375, 428.95721435546875)
-3280   (299.1408386230469, 245.59396362304688)  (289.6475524902344, 244.80735778808594)
-3281   (220.12611389160156, 216.7689971923828)  (208.8617706298828, 216.38954162597656)
-3282    (558.4502563476562, 359.2655944824219)    (547.441162109375, 359.3397216796875)
-3283        (283.2861328125, 389.531005859375)  (169.39572143554688, 250.8520050048828)
-
-[3284 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (214.1725311279297, 33.30479049682617)  (206.10906982421875, 30.395938873291016)
-1        (582.1329956054688, 305.857177734375)    (576.0498657226562, 303.2834167480469)
-2     (174.16757202148438, 16.922727584838867)  (165.55987548828125, 13.852842330932617)
-3      (193.0630340576172, 31.003068923950195)     (184.6349639892578, 28.0131893157959)
-4      (280.2668762207031, 128.05731201171875)   (272.6255798339844, 126.08364868164062)
-...                                        ...                                       ...
-3286   (329.1527404785156, 202.19314575195312)   (320.3367004394531, 198.02902221679688)
-3287    (496.6521911621094, 363.2724304199219)  (486.35797119140625, 364.99615478515625)
-3288  (101.21243286132812, 252.06207275390625)   (85.33255004882812, 105.51250457763672)
-3289     (563.862060546875, 446.0251159667969)    (551.6642456054688, 447.8691101074219)
-3290   (270.0604553222656, 446.45159912109375)   (125.71565246582031, 369.9598693847656)
-
-[3291 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0        (322.0775146484375, 97.1723403930664)   (316.1316223144531, 95.17243957519531)
-1      (320.9815368652344, 110.73234558105469)  (314.9268493652344, 108.75788116455078)
-2          (302.4677734375, 84.17566680908203)  (296.42633056640625, 82.02857971191406)
-3       (454.6875305175781, 260.8320007324219)  (448.7171325683594, 259.99114990234375)
-4      (371.4627380371094, 171.47877502441406)  (365.2906188964844, 169.91355895996094)
-...                                        ...                                      ...
-3241  (214.74356079101562, 424.43048095703125)  (202.4199676513672, 429.00311279296875)
-3242      (4.90518856048584, 50.0289421081543)  (2.456636428833008, 26.800472259521484)
-3243    (539.9220581054688, 423.7779541015625)   (530.186767578125, 428.12713623046875)
-3244    (449.1800231933594, 411.0268859863281)   (439.3013610839844, 414.4888610839844)
-3245      (471.8684997558594, 410.77783203125)   (460.7939147949219, 415.2345275878906)
-
-[3246 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0       (294.475830078125, 74.67491149902344)    (289.3913269042969, 73.5955581665039)
-1      (316.1316223144531, 95.17243957519531)  (310.97076416015625, 94.08528900146484)
-2        (285.5738525390625, 63.525634765625)  (280.38677978515625, 62.26962661743164)
-3      (309.7130432128906, 85.66983795166016)    (304.614013671875, 84.57382202148438)
-4     (265.6964416503906, 124.67687225341797)    (259.50048828125, 124.11241912841797)
-...                                       ...                                      ...
-3205   (343.0198974609375, 373.0912780761719)     (333.4068298339844, 376.39599609375)
-3206       (161.2904815673828, 261.498046875)   (150.7401123046875, 263.1109924316406)
-3207    (581.86865234375, 422.90435791015625)   (575.9700317382812, 425.7242736816406)
-3208   (491.250244140625, 47.021995544433594)  (490.0018615722656, 45.137413024902344)
-3209  (408.9292907714844, 340.76934814453125)     (396.85986328125, 344.3163757324219)
-
-[3210 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (279.1402282714844, 66.87443542480469)    (275.0133972167969, 66.30740356445312)
-1      (298.82562255859375, 81.07546997070312)   (294.75030517578125, 80.42057037353516)
-2      (441.9160461425781, 194.37734985351562)    (438.137451171875, 194.52874755859375)
-3     (166.72824096679688, 12.056891441345215)  (161.67942810058594, 11.517943382263184)
-4      (398.5064392089844, 212.18624877929688)      (393.907470703125, 212.628173828125)
-...                                        ...                                       ...
-3224   (413.52545166015625, 288.0233154296875)   (405.6170349121094, 290.22857666015625)
-3225  (17.297788619995117, 144.30776977539062)  (6.8036699295043945, 144.67327880859375)
-3226   (320.8482666015625, 369.78594970703125)    (312.9049987792969, 372.9396057128906)
-3227    (237.3098907470703, 416.2435302734375)    (226.1682586669922, 421.5462341308594)
-3228    (298.7310485839844, 328.0441589355469)     (290.8688659667969, 330.445068359375)
-
-[3229 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (577.4873657226562, 190.61569213867188)   (576.2863159179688, 191.13665771484375)
-1          (305.2236328125, 107.3436279296875)   (302.1251525878906, 107.29447174072266)
-2        (589.930908203125, 164.0926513671875)    (589.2719116210938, 164.5605926513672)
-3     (146.77163696289062, 20.928043365478516)     (143.72900390625, 20.404325485229492)
-4     (254.29055786132812, 123.82321166992188)  (250.35858154296875, 123.81656646728516)
-...                                        ...                                       ...
-3324   (327.19451904296875, 346.9385070800781)    (337.9388122558594, 459.7801513671875)
-3325   (308.0523986816406, 221.38494873046875)  (302.80975341796875, 222.67416381835938)
-3326   (239.53488159179688, 307.4374084472656)   (283.1880798339844, 55.906429290771484)
-3327    (20.040435791015625, 168.088134765625)  (11.358126640319824, 168.11459350585938)
-3328   (115.67565155029297, 7.682496547698975)   (81.55817413330078, 130.01324462890625)
-
-[3329 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (576.2863159179688, 191.13665771484375)   (576.149169921875, 192.26150512695312)
-1     (185.77047729492188, 32.96665954589844)  (184.84539794921875, 32.76597595214844)
-2     (352.0797424316406, 162.37600708007812)  (349.8776550292969, 163.09107971191406)
-3     (251.25086975097656, 39.55074691772461)  (250.27964782714844, 39.58047103881836)
-4      (261.0079345703125, 42.34469985961914)     (260.14501953125, 42.34768295288086)
-...                                       ...                                      ...
-3358  (56.23796844482422, 398.04913330078125)   (44.55957794189453, 402.0962219238281)
-3359    (418.673583984375, 438.4100036621094)   (408.0383605957031, 447.7325134277344)
-3360   (393.4346008300781, 278.8216247558594)   (385.7532653808594, 282.8669738769531)
-3361  (418.99761962890625, 411.9405212402344)    (405.604736328125, 373.9568176269531)
-3362   (8.353055000305176, 94.90196228027344)   (82.94303894042969, 308.9088134765625)
-
-[3363 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (389.49639892578125, 137.27049255371094)    (385.4388427734375, 137.2406768798828)
-1       (282.8668212890625, 80.50656127929688)   (276.99493408203125, 83.70660400390625)
-2       (271.1948547363281, 66.20167541503906)    (264.9800109863281, 69.64826965332031)
-3       (576.149169921875, 192.26150512695312)    (575.682373046875, 186.57550048828125)
-4     (263.07098388671875, 16.390356063842773)   (256.1471862792969, 20.402679443359375)
-...                                        ...                                       ...
-3361    (414.7711486816406, 308.8479309082031)    (410.5400695800781, 312.0845642089844)
-3362   (368.92364501953125, 431.7452697753906)   (365.6521911621094, 439.55377197265625)
-3363  (223.81947326660156, 391.05010986328125)  (221.18325805664062, 400.01910400390625)
-3364    (133.345458984375, 264.67291259765625)    (128.05198669433594, 274.621826171875)
-3365   (438.46221923828125, 292.4266357421875)   (438.0262451171875, 292.54925537109375)
-
-[3366 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (264.9800109863281, 69.64826965332031)     (259.506591796875, 74.66155242919922)
-1      (287.723876953125, 100.31806182861328)      (282.8017578125, 104.35366821289062)
-2      (269.9427795410156, 70.71089935302734)   (264.47991943359375, 75.58186340332031)
-3     (411.61187744140625, 157.1528778076172)  (408.72186279296875, 157.01779174804688)
-4      (385.4388427734375, 137.2406768798828)   (381.9258117675781, 138.04534912109375)
-...                                       ...                                       ...
-3274  (171.34262084960938, 307.9511413574219)   (167.10382080078125, 317.9233703613281)
-3275   (394.7989196777344, 306.6186828613281)    (392.0176086425781, 310.8446044921875)
-3276   (55.93231201171875, 296.9320983886719)          (595.1827392578125, 406.0546875)
-3277  (358.7064208984375, 408.49041748046875)    (434.2343444824219, 341.6487731933594)
-3278   (214.61509704589844, 299.906982421875)   (211.50820922851562, 307.1694030761719)
-
-[3279 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (253.7563934326172, 92.61885833740234)   (249.58677673339844, 99.32097625732422)
-1             (453.65625, 204.44830322265625)  (453.19757080078125, 203.92657470703125)
-2     (575.8734130859375, 181.35433959960938)   (575.8917236328125, 175.62718200683594)
-3       (333.769287109375, 92.18994903564453)      (329.3382568359375, 95.494384765625)
-4       (259.506591796875, 74.66155242919922)   (254.80067443847656, 80.92768096923828)
-...                                       ...                                       ...
-3219  (204.41624450683594, 227.9441680908203)  (202.70736694335938, 236.08274841308594)
-3220  (63.470394134521484, 440.8734436035156)    (61.773338317871094, 458.338623046875)
-3221  (500.8767395019531, 464.44403076171875)  (424.65167236328125, 441.57025146484375)
-3222  (483.91021728515625, 472.1415100097656)   (496.72796630859375, 272.0289001464844)
-3223  (110.24462127685547, 145.1267547607422)    (92.66223907470703, 77.36673736572266)
-
-[3224 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (588.5570068359375, 147.4461669921875)  (589.0061645507812, 141.43043518066406)
-1     (155.20932006835938, 42.37665939331055)    (151.1173553466797, 54.1428108215332)
-2       (365.19482421875, 203.47647094726562)    (364.1243591308594, 206.491455078125)
-3      (406.5455322265625, 157.8927001953125)   (404.6002197265625, 158.9998016357422)
-4      (310.7185974121094, 263.8559875488281)  (310.9107360839844, 269.75152587890625)
-...                                       ...                                      ...
-3095   (491.0676574707031, 433.0690612792969)  (495.7812194824219, 441.18341064453125)
-3096  (449.1669006347656, 6.2793989181518555)   (442.7353820800781, 6.058844089508057)
-3097   (516.0049438476562, 459.4847412109375)   (520.6621704101562, 469.4713134765625)
-3098  (137.78335571289062, 322.6493225097656)  (113.16642761230469, 123.0943603515625)
-3099  (206.52999877929688, 304.2843322753906)  (206.8565673828125, 313.45855712890625)
-
-[3100 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (444.5914306640625, 290.80279541015625)    (447.6106262207031, 292.8587951660156)
-1          (453.283203125, 203.72219848632812)    (453.4686584472656, 203.8862762451172)
-2     (404.23931884765625, 160.02398681640625)  (402.84808349609375, 161.78956604003906)
-3      (121.6718978881836, 62.374908447265625)    (118.68254852294922, 76.2415771484375)
-4       (255.3839111328125, 88.53028869628906)    (251.8964385986328, 96.74232482910156)
-...                                        ...                                       ...
-3120    (184.3907928466797, 407.8882141113281)  (186.29705810546875, 421.37103271484375)
-3121   (496.72784423828125, 461.1019592285156)  (502.29473876953125, 472.59954833984375)
-3122    (32.92125701904297, 201.0195770263672)   (32.43980407714844, 216.63890075683594)
-3123   (56.137027740478516, 369.4161071777344)    (57.09962844848633, 385.5487365722656)
-3124    (219.36538696289062, 74.3885498046875)   (217.07749938964844, 85.02263641357422)
-
-[3125 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (243.50157165527344, 90.79352569580078)   (240.59030151367188, 99.94097900390625)
-1      (176.02442932128906, 39.32844924926758)   (172.73214721679688, 52.09014892578125)
-2      (215.5618896484375, 137.08872985839844)   (213.72056579589844, 146.8952178955078)
-3          (248.2099609375, 91.75850677490234)  (245.21136474609375, 100.67848205566406)
-4      (137.35951232910156, 75.19271850585938)   (135.67840576171875, 89.01695251464844)
-...                                        ...                                       ...
-2931    (362.4747009277344, 344.8331298828125)   (362.3294677734375, 354.45916748046875)
-2932    (179.8225555419922, 267.6105651855469)    (180.0251007080078, 279.4751281738281)
-2933  (165.48460388183594, 449.39825439453125)    (168.3658905029297, 465.9665222167969)
-2934     (596.277587890625, 97.79792022705078)    (432.6878662109375, 446.7305908203125)
-2935  (233.52281188964844, 312.72882080078125)   (233.0227813720703, 323.52496337890625)
-
-[2936 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (54.501251220703125, 75.44731903076172)     (54.65290451049805, 93.5816879272461)
-1     (248.98529052734375, 105.39192962646484)  (246.49038696289062, 114.79774475097656)
-2       (537.6165161132812, 365.8077087402344)           (546.55859375, 368.56689453125)
-3      (534.5940551757812, 345.71624755859375)    (542.7180786132812, 347.8594055175781)
-4      (356.8414001464844, 133.90663146972656)      (355.03564453125, 138.7289276123047)
-...                                        ...                                       ...
-2810   (136.17721557617188, 335.3998107910156)   (137.20643615722656, 351.3094177246094)
-2811   (2.5568935871124268, 419.0493469238281)    (4.031696319580078, 440.9244384765625)
-2812    (2.4616177082061768, 271.842041015625)  (3.6436071395874023, 291.54132080078125)
-2813    (485.0197448730469, 382.7787780761719)    (489.8472900390625, 391.9743957519531)
-2814   (84.04847717285156, 344.22540283203125)   (27.914663314819336, 104.6131362915039)
-
-[2815 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (246.49038696289062, 114.79774475097656)  (244.61204528808594, 124.86153411865234)
-1         (545.4765014648438, 321.58740234375)   (553.2852783203125, 323.61834716796875)
-2      (425.2765808105469, 255.22271728515625)  (427.43841552734375, 259.30230712890625)
-3     (335.87542724609375, 126.62370300292969)   (333.50909423828125, 132.4591827392578)
-4      (440.41021728515625, 191.8144073486328)   (440.6098327636719, 193.97845458984375)
-...                                        ...                                       ...
-2633    (411.1665954589844, 409.2242431640625)    (475.4281311035156, 404.5928955078125)
-2634   (454.5934143066406, 40.617977142333984)   (527.1217651367188, 56.466976165771484)
-2635   (526.9027709960938, 59.891204833984375)         (524.12109375, 57.87803649902344)
-2636   (450.79705810546875, 452.8839416503906)  (434.07232666015625, 40.445213317871094)
-2637    (233.6536102294922, 67.10424041748047)    (224.85206604003906, 76.6388931274414)
-
-[2638 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (487.99566650390625, 325.43194580078125)    (494.3398742675781, 330.8056945800781)
-1      (273.7801818847656, 155.28118896484375)   (272.7234191894531, 164.43399047851562)
-2      (403.17559814453125, 250.9598388671875)    (405.4471435546875, 257.1199035644531)
-3        (302.7632751464844, 177.204345703125)   (301.8878479003906, 185.26197814941406)
-4      (133.65403747558594, 118.0508804321289)  (133.76535034179688, 132.88136291503906)
-...                                        ...                                       ...
-2451   (312.95318603515625, 470.8565979003906)   (57.46937561035156, 223.75460815429688)
-2452   (134.58416748046875, 316.1409606933594)   (135.7036895751953, 332.35076904296875)
-2453      (404.1068115234375, 383.66162109375)    (404.9733581542969, 401.9263916015625)
-2454   (127.75775146484375, 341.0071716308594)   (128.40040588378906, 358.7374267578125)
-2455   (220.9679412841797, 318.89776611328125)   (222.88082885742188, 332.6108703613281)
-
-[2456 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (154.66004943847656, 128.30096435546875)    (154.800537109375, 142.33419799804688)
-1      (234.41217041015625, 130.6727752685547)    (233.3154754638672, 141.5701446533203)
-2      (453.16375732421875, 300.2525939941406)      (457.8031311035156, 307.04150390625)
-3       (503.9515380859375, 76.07028198242188)    (501.7367858886719, 75.85027313232422)
-4      (311.7925109863281, 130.71713256835938)  (310.48968505859375, 138.65817260742188)
-...                                        ...                                       ...
-2463   (400.4293518066406, 447.41741943359375)    (401.5982971191406, 473.6429748535156)
-2464   (130.31112670898438, 253.1788787841797)   (58.51811218261719, 255.17576599121094)
-2465      (213.0488739013672, 257.10888671875)   (213.16209411621094, 270.0038757324219)
-2466    (86.56602478027344, 327.8569641113281)       (86.09131622314453, 346.9697265625)
-2467   (3.180485486984253, 209.17286682128906)   (6.494548320770264, 229.06358337402344)
-
-[2468 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (435.83001708984375, 294.634521484375)       (440.509765625, 302.8262939453125)
-1       (447.1773986816406, 301.2292175292969)   (452.09271240234375, 309.177490234375)
-2       (389.8381652832031, 258.4753112792969)   (392.0495910644531, 266.3660583496094)
-3      (548.5264892578125, 382.47650146484375)  (560.4962158203125, 392.53912353515625)
-4             (537.6544189453125, 342.1328125)    (547.499755859375, 350.0770263671875)
-...                                        ...                                      ...
-2349   (39.91975402832031, 213.58009338378906)  (42.10417938232422, 231.39044189453125)
-2350    (50.92331314086914, 450.4992980957031)      (51.33202362060547, 476.2822265625)
-2351   (52.909507751464844, 20.40549659729004)   (53.08889389038086, 40.47880554199219)
-2352    (414.6047668457031, 474.1586608886719)   (493.6121826171875, 468.8070373535156)
-2353  (2.3110978603363037, 455.40338134765625)    (171.72802734375, 456.33355712890625)
-
-[2354 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (237.1653289794922, 156.8194580078125)    (237.005126953125, 167.78338623046875)
-1       (378.6866149902344, 260.0940246582031)    (380.7784423828125, 269.0973205566406)
-2      (330.1511535644531, 154.05259704589844)  (329.45330810546875, 161.73336791992188)
-3       (400.2372741699219, 185.5103759765625)   (400.9255676269531, 191.62667846679688)
-4      (401.99224853515625, 272.2481994628906)   (405.20208740234375, 281.2510681152344)
-...                                        ...                                       ...
-2184    (208.02378845214844, 409.180419921875)   (210.19107055664062, 429.1837463378906)
-2185    (11.35204029083252, 296.3249206542969)   (13.349567413330078, 316.4591979980469)
-2186   (485.1219787597656, 434.29730224609375)    (493.3475646972656, 458.3472900390625)
-2187  (259.31536865234375, 303.09674072265625)   (261.2330322265625, 317.30072021484375)
-2188     (45.3240852355957, 65.10101318359375)    (49.78489685058594, 82.38905334472656)
-
-[2189 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (469.3089904785156, 324.3275451660156)    (476.4859313964844, 335.4053039550781)
-1     (329.45330810546875, 161.73336791992188)  (329.63348388671875, 169.86737060546875)
-2      (380.2444152832031, 269.27044677734375)   (383.1092529296875, 279.28497314453125)
-3      (121.51908874511719, 182.4974365234375)   (123.26585388183594, 196.6691131591797)
-4       (394.8602294921875, 275.3700256347656)    (398.0802917480469, 285.3627014160156)
-...                                        ...                                       ...
-2133  (125.49031066894531, 119.53398895263672)    (291.0172424316406, 49.62733840942383)
-2134     (2.411034107208252, 325.149169921875)    (3.89909029006958, 345.31402587890625)
-2135   (13.664609909057617, 242.8024139404297)   (16.787220001220703, 260.9815673828125)
-2136    (514.0106811523438, 55.93134307861328)    (512.768310546875, 56.982566833496094)
-2137     (84.8191909790039, 283.1651611328125)      (208.97314453125, 8.495025634765625)
-
-[2138 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (107.29145050048828, 191.7947540283203)   (109.18798065185547, 205.7811279296875)
-1      (250.8413848876953, 183.66110229492188)   (251.38282775878906, 194.1475067138672)
-2      (67.82372283935547, 163.32398986816406)    (69.85031127929688, 178.3414306640625)
-3     (329.63348388671875, 169.86737060546875)  (330.52947998046875, 178.26487731933594)
-4     (332.23614501953125, 227.70693969726562)   (333.3758544921875, 237.60227966308594)
-...                                        ...                                       ...
-2127   (349.0267028808594, 436.87347412109375)      (353.97979736328125, 462.8544921875)
-2128    (446.4013977050781, 22.37669563293457)    (442.9444274902344, 26.47775650024414)
-2129  (504.56500244140625, 333.54827880859375)    (515.0319213867188, 347.5080261230469)
-2130   (243.51284790039062, 350.9913635253906)  (244.90927124023438, 368.39849853515625)
-2131     (277.948974609375, 8.512983322143555)  (214.13450622558594, 40.863956451416016)
-
-[2132 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (69.73455047607422, 211.76718139648438)    (71.92770385742188, 225.6930694580078)
-1      (402.03314208984375, 296.2122802734375)    (406.1713562011719, 308.2644958496094)
-2     (335.85504150390625, 247.89865112304688)       (337.8782958984375, 258.8310546875)
-3      (81.93279266357422, 215.82968139648438)   (84.13877868652344, 229.49021911621094)
-4      (325.9891052246094, 232.23919677734375)  (327.28192138671875, 242.35458374023438)
-...                                        ...                                       ...
-2080    (368.650634765625, 424.60479736328125)   (620.3428955078125, 399.37872314453125)
-2081   (238.95709228515625, 379.0599060058594)   (63.587554931640625, 293.8265686035156)
-2082    (468.6407775878906, 33.72489547729492)   (466.68841552734375, 38.54676818847656)
-2083   (194.59695434570312, 294.4590759277344)     (194.93093872070312, 309.16064453125)
-2084    (490.1034240722656, 79.33116912841797)   (489.97357177734375, 82.46977233886719)
-
-[2085 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (390.0425109863281, 302.2322692871094)    (394.0890808105469, 315.2644348144531)
-1      (457.5489501953125, 84.56828308105469)    (457.4819641113281, 88.85966491699219)
-2     (238.46009826660156, 195.2938995361328)  (239.27818298339844, 205.30714416503906)
-3      (463.4971618652344, 350.6736755371094)   (471.59405517578125, 366.7432861328125)
-4     (457.55377197265625, 318.8168640136719)    (464.7393493652344, 332.5406188964844)
-...                                       ...                                       ...
-2006   (312.1709899902344, 62.73363494873047)    (312.7368469238281, 70.27499389648438)
-2007  (103.7693099975586, 415.83868408203125)  (100.22748565673828, 438.17340087890625)
-2008  (205.31260681152344, 40.53546905517578)    (207.0818328857422, 51.52825164794922)
-2009  (11.198295593261719, 295.1551513671875)   (13.074217796325684, 310.2012634277344)
-2010  (636.7526245117188, 312.97100830078125)      (354.05029296875, 477.1332702636719)
-
-[2011 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (349.9417419433594, 278.5726318359375)  (351.9874267578125, 290.61883544921875)
-1     (141.99533081054688, 228.07862854003906)   (142.4290771484375, 239.0336151123047)
-2      (145.37632751464844, 191.6763458251953)  (146.4678955078125, 201.66891479492188)
-3       (60.92250442504883, 249.0634002685547)   (61.94281005859375, 261.1527099609375)
-4        (344.065673828125, 278.3030700683594)    (345.9824523925781, 290.333251953125)
-...                                        ...                                      ...
-1981    (578.3643188476562, 404.3956604003906)  (364.1288146972656, 255.29144287109375)
-1982   (72.09126281738281, 122.63584899902344)  (44.01002883911133, 135.52670288085938)
-1983  (47.521419525146484, 120.54117584228516)  (51.42692947387695, 131.17254638671875)
-1984     (259.1551818847656, 435.959716796875)   (259.3482971191406, 460.0652770996094)
-1985   (412.66766357421875, 49.33330154418945)   (410.5113220214844, 53.98105239868164)
-
-[1986 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0        (551.768310546875, 96.96641540527344)   (554.9407958984375, 101.07923889160156)
-1       (264.8762512207031, 223.5630340576172)   (264.9664306640625, 232.81967163085938)
-2        (311.57275390625, 197.92283630371094)    (312.3753662109375, 205.9200439453125)
-3       (65.05643463134766, 261.2550964355469)     (65.30860900878906, 271.985595703125)
-4      (128.91543579101562, 246.3289337158203)   (128.80911254882812, 256.5291442871094)
-...                                        ...                                       ...
-1962  (48.317935943603516, 25.700037002563477)   (53.12233352661133, 37.123531341552734)
-1963   (318.4395446777344, 228.79441833496094)      (332.5381774902344, 225.43017578125)
-1964   (434.32684326171875, 73.55287170410156)   (505.66192626953125, 47.92980194091797)
-1965     (90.93103790283203, 447.072021484375)    (84.03397369384766, 470.9913024902344)
-1966   (51.42692947387695, 131.17254638671875)  (54.928653717041016, 141.00181579589844)
-
-[1967 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (332.0223693847656, 275.2269592285156)       (331.8869323730469, 286.0771484375)
-1      (244.4342041015625, 228.31961059570312)  (243.97947692871094, 236.84910583496094)
-2      (420.15203857421875, 352.6571960449219)    (424.1683349609375, 370.6159973144531)
-3      (379.2997741699219, 309.95281982421875)          (381.34112548828125, 324.015625)
-4      (340.2393798828125, 108.07012176513672)   (341.0942077636719, 113.89918518066406)
-...                                        ...                                       ...
-1916    (587.3298950195312, 43.90908432006836)   (595.8426513671875, 61.245479583740234)
-1917  (211.36199951171875, 61.934207916259766)     (152.05322265625, 35.313804626464844)
-1918   (269.09124755859375, 358.8689270019531)   (112.31824493408203, 169.9337921142578)
-1919   (76.36238861083984, 50.126773834228516)    (235.4620361328125, 95.45468139648438)
-1920  (106.11637115478516, 126.19155883789062)  (193.43392944335938, 110.77518463134766)
-
-[1921 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (64.14619445800781, 281.14666748046875)    (61.1854133605957, 289.0114440917969)
-1       (372.053955078125, 331.09552001953125)  (373.14654541015625, 346.6483459472656)
-2      (355.10467529296875, 306.2736511230469)   (355.7991943359375, 319.7965087890625)
-3     (243.97947692871094, 236.84910583496094)    (242.92169189453125, 244.87158203125)
-4       (344.8335266113281, 223.4817352294922)  (350.5017395019531, 224.25436401367188)
-...                                        ...                                      ...
-1882   (394.46112060546875, 83.87521362304688)    (395.4058532714844, 88.9809341430664)
-1883      (591.30029296875, 401.4588928222656)   (401.9541320800781, 313.0453796386719)
-1884    (544.3124389648438, 94.72039031982422)  (547.6461181640625, 100.07426452636719)
-1885   (203.7349853515625, 329.41180419921875)  (165.80238342285156, 274.7237548828125)
-1886    (276.5092468261719, 415.4072570800781)    (238.7881317138672, 469.074462890625)
-
-[1887 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0          (69.90377807617188, 277.7802734375)  (65.56795501708984, 283.83575439453125)
-1       (83.3056411743164, 280.53790283203125)    (79.2360610961914, 286.6910400390625)
-2     (251.11802673339844, 254.97506713867188)  (249.20736694335938, 262.9681091308594)
-3       (354.751708984375, 118.53182983398438)  (356.0999755859375, 123.59354400634766)
-4        (536.017822265625, 286.1334228515625)    (542.961181640625, 301.1045227050781)
-...                                        ...                                      ...
-1878   (173.36883544921875, 269.4134521484375)  (169.88491821289062, 276.7358703613281)
-1879     (275.63134765625, 108.29320526123047)  (526.4134521484375, 437.24591064453125)
-1880    (367.1565246582031, 98.77700805664062)  (369.1903076171875, 103.82720184326172)
-1881   (89.56644439697266, 472.23138427734375)  (187.2256317138672, 14.078909873962402)
-1882   (242.30543518066406, 86.34365844726562)  (243.78599548339844, 90.53324890136719)
-
-[1883 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (65.56795501708984, 283.83575439453125)    (59.58769607543945, 288.0240173339844)
-1      (316.9228210449219, 320.5531921386719)     (313.9066467285156, 333.504150390625)
-2     (244.08416748046875, 259.7203674316406)  (240.99891662597656, 266.81439208984375)
-3      (326.7868347167969, 326.4150085449219)    (323.731689453125, 340.28448486328125)
-4     (133.70265197753906, 263.7205810546875)   (129.33029174804688, 268.7400817871094)
-...                                       ...                                       ...
-1796   (513.0963745117188, 81.98063659667969)    (515.5719604492188, 88.89619445800781)
-1797  (249.26185607910156, 446.4641418457031)   (239.93328857421875, 468.7467346191406)
-1798   (351.6086120605469, 289.8990783691406)  (163.34486389160156, 413.90582275390625)
-1799  (60.63951110839844, 447.88275146484375)     (66.4940185546875, 340.0902099609375)
-1800    (164.540283203125, 3.107858657836914)   (167.64028930664062, 5.901421546936035)
-
-[1801 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (240.80311584472656, 266.99468994140625)  (236.77537536621094, 273.34869384765625)
-1     (134.63526916503906, 169.11618041992188)  (135.75961303710938, 168.99130249023438)
-2        (351.93994140625, 224.35238647460938)    (349.0087585449219, 229.1691131591797)
-3       (203.6318817138672, 208.9205322265625)  (201.81126403808594, 211.90133666992188)
-4      (199.40228271484375, 246.6394805908203)   (195.91574096679688, 250.8376007080078)
-...                                        ...                                       ...
-1802    (394.121826171875, 104.57293701171875)   (395.58843994140625, 109.2276611328125)
-1803    (480.9973449707031, 99.59540557861328)   (484.1132507324219, 106.61412811279297)
-1804    (45.927494049072266, 75.2530746459961)    (48.84944534301758, 73.14832305908203)
-1805   (29.624300003051758, 449.7296142578125)    (9.976602554321289, 463.8353271484375)
-1806    (77.09742736816406, 289.5005798339844)      (86.09786224365234, 292.09228515625)
-
-[1807 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (355.9817199707031, 130.90748596191406)  (356.98797607421875, 135.29043579101562)
-1      (153.7872314453125, 198.1426544189453)   (151.58670043945312, 198.4246826171875)
-2     (204.32289123535156, 112.1708755493164)    (204.2282257080078, 112.3441162109375)
-3      (121.9667739868164, 229.8387451171875)   (117.68651580810547, 229.9320526123047)
-4      (168.9964141845703, 178.1307830810547)   (168.3787384033203, 178.98692321777344)
-...                                       ...                                       ...
-1690  (179.80259704589844, 38.00468063354492)    (183.5121612548828, 37.81794738769531)
-1691   (100.5370864868164, 63.15385818481445)   (313.2880554199219, 60.329750061035156)
-1692  (37.490501403808594, 325.0156555175781)   (25.904613494873047, 327.5570373535156)
-1693  (15.091691970825195, 56.61809539794922)    (18.226381301879883, 51.9692497253418)
-1694   (148.7171630859375, 44.50001525878906)    (66.97343444824219, 352.8401794433594)
-
-[1695 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (117.68651580810547, 229.9320526123047)   (112.53398132324219, 228.8754425048828)
-1     (184.19761657714844, 219.05386352539062)  (180.36524963378906, 219.92715454101562)
-2       (204.2282257080078, 112.3441162109375)  (205.42759704589844, 111.76286315917969)
-3        (58.3034782409668, 293.0698547363281)    (47.53181838989258, 293.2611389160156)
-4       (231.5897674560547, 279.5660400390625)  (225.02157592773438, 285.13983154296875)
-...                                        ...                                       ...
-1686    (71.83961486816406, 378.1495361328125)    (54.75588607788086, 385.1548156738281)
-1687   (443.36468505859375, 386.8760070800781)   (443.03363037109375, 410.3436584472656)
-1688    (4.014829158782959, 338.0521545410156)   (98.97843170166016, 293.03302001953125)
-1689     (269.0214538574219, 91.5207748413086)    (271.5619201660156, 92.79242706298828)
-1690   (234.67153930664062, 109.7022705078125)  (236.45445251464844, 109.63514709472656)
-
-[1691 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (215.09254455566406, 268.8541564941406)   (208.18060302734375, 272.6729736328125)
-1       (305.246826171875, 386.3646545410156)   (295.6456298828125, 404.02947998046875)
-2      (201.0210723876953, 283.7054748535156)      (192.94244384765625, 288.0791015625)
-3     (360.2220153808594, 139.90943908691406)  (360.88909912109375, 143.66270446777344)
-4     (554.0444946289062, 149.80177307128906)    (557.594482421875, 160.69400024414062)
-...                                       ...                                       ...
-1564        (394.6036376953125, 312.65234375)   (273.39263916015625, 81.97821807861328)
-1565      (518.1708984375, 439.1258239746094)   (520.6551513671875, 473.21795654296875)
-1566  (385.63079833984375, 8.816410064697266)   (115.88416290283203, 46.41640853881836)
-1567   (453.8761291503906, 90.72254180908203)       (458.6826171875, 97.45402526855469)
-1568   (270.8602600097656, 85.23521423339844)   (273.39263916015625, 81.97821807861328)
-
-[1569 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (113.30113220214844, 232.06961059570312)   (106.13672637939453, 229.1032257080078)
-1        (284.6518249511719, 386.975830078125)          (272.83984375, 403.866943359375)
-2      (264.00225830078125, 356.3301086425781)   (253.03839111328125, 369.1094665527344)
-3     (176.18820190429688, 219.93719482421875)   (170.9797821044922, 218.98654174804688)
-4          (458.443359375, 343.57794189453125)    (457.0290222167969, 363.1523742675781)
-...                                        ...                                       ...
-1450   (487.00335693359375, 474.4031066894531)    (306.3887023925781, 404.6192626953125)
-1451    (33.12934875488281, 448.6969909667969)       (3.610522508621216, 461.5888671875)
-1452    (42.18305206298828, 41.50670623779297)   (399.0357971191406, 12.664728164672852)
-1453  (496.95294189453125, 126.98271942138672)  (500.97467041015625, 134.72898864746094)
-1454   (577.6862182617188, 159.18319702148438)    (582.4329833984375, 170.6992950439453)
-
-[1455 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (517.6611938476562, 162.6377410888672)  (519.9310302734375, 172.81488037109375)
-1     (393.96258544921875, 152.55331420898438)   (394.8356628417969, 157.5980682373047)
-2      (330.1742858886719, 245.08827209472656)  (324.1560363769531, 248.71881103515625)
-3       (419.0306701660156, 346.0877685546875)   (415.0877990722656, 364.0618896484375)
-4      (155.6052703857422, 260.09613037109375)  (146.5973358154297, 260.07171630859375)
-...                                        ...                                      ...
-1415    (45.80659103393555, 459.7848815917969)  (13.334250450134277, 475.1051940917969)
-1416  (105.30017852783203, 27.869657516479492)  (109.39707946777344, 21.36214828491211)
-1417   (543.6459350585938, 114.55729675292969)    (425.740966796875, 27.01222801208496)
-1418   (65.92400360107422, 130.11863708496094)    (425.740966796875, 27.01222801208496)
-1419    (582.4329833984375, 170.6992950439453)     (468.06787109375, 155.1529083251953)
-
-[1420 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (324.1560363769531, 248.71881103515625)   (316.5342102050781, 251.15927124023438)
-1      (216.4046630859375, 348.83538818359375)    (202.1889190673828, 358.8880310058594)
-2      (218.9906768798828, 329.89593505859375)   (206.27365112304688, 338.1741638183594)
-3          (162.08863830566406, 260.662109375)     (152.2740478515625, 260.502685546875)
-4       (317.2361145019531, 74.85775756835938)    (320.2469482421875, 76.00092315673828)
-...                                        ...                                       ...
-1285  (240.54196166992188, 344.15484619140625)     (78.89874267578125, 291.058349609375)
-1286   (292.5695495605469, 464.04888916015625)   (252.11074829101562, 437.1342468261719)
-1287   (109.6449966430664, 23.805150985717773)  (114.76392364501953, 15.966306686401367)
-1288    (289.8749694824219, 341.3686828613281)    (279.720947265625, 353.10382080078125)
-1289   (197.24285888671875, 34.41080093383789)   (192.7992401123047, 37.742828369140625)
-
-[1290 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (311.7463073730469, 106.21583557128906)   (313.9382629394531, 107.23347473144531)
-1      (316.5342102050781, 251.15927124023438)    (307.4039001464844, 252.5661163330078)
-2      (231.62135314941406, 421.8285217285156)   (212.29257202148438, 440.4360046386719)
-3      (184.71047973632812, 218.9520721435547)    (178.00523376464844, 216.940185546875)
-4     (125.68468475341797, 224.58987426757812)   (117.4380111694336, 220.23873901367188)
-...                                        ...                                       ...
-1212   (260.97784423828125, 95.52057647705078)        (263.849609375, 93.93551635742188)
-1213     (452.860107421875, 470.8147888183594)          (316.1171875, 348.1869812011719)
-1214    (402.0422058105469, 145.1195068359375)  (402.71173095703125, 151.37576293945312)
-1215   (35.26338577270508, 16.088308334350586)    (38.93760681152344, 4.559881210327148)
-1216     (516.60595703125, 145.14212036132812)   (351.48431396484375, 135.9138641357422)
-
-[1217 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (173.6419677734375, 307.7257385253906)     (159.60498046875, 311.2320251464844)
-1       (152.0673065185547, 211.9023895263672)     (145.1228790283203, 208.12939453125)
-2      (320.4681701660156, 147.78253173828125)  (319.7145080566406, 148.36231994628906)
-3      (216.23345947265625, 98.51896667480469)  (217.30133056640625, 94.48967742919922)
-4      (142.99192810058594, 221.8788299560547)        (135.3671875, 217.80673217773438)
-...                                        ...                                      ...
-1116    (619.5660400390625, 176.2713623046875)  (620.4021606445312, 192.10836791992188)
-1117  (308.11968994140625, 128.99888610839844)  (308.9539794921875, 129.44552612304688)
-1118     (568.35107421875, 149.92625427246094)  (571.0714111328125, 161.58860778808594)
-1119   (24.106122970581055, 2.346048593521118)   (41.02791213989258, 11.13845157623291)
-1120  (432.66436767578125, 10.604243278503418)  (438.8111267089844, 17.084054946899414)
-
-[1121 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (164.3909454345703, 299.5836486816406)   (150.19363403320312, 301.6213684082031)
-1      (316.4666748046875, 107.29765319824219)   (317.4892883300781, 107.50486755371094)
-2     (204.96133422851562, 246.78981018066406)   (196.23397827148438, 246.6603546142578)
-3       (536.138427734375, 215.99758911132812)   (536.1773681640625, 227.96051025390625)
-4      (553.7680053710938, 223.35345458984375)    (553.906982421875, 236.34727478027344)
-...                                        ...                                       ...
-1086     (571.1993408203125, 157.822998046875)    (574.051025390625, 170.76148986816406)
-1087   (10.922741889953613, 390.3409423828125)  (180.34010314941406, 397.52130126953125)
-1088   (281.16766357421875, 470.8597412109375)   (252.29107666015625, 367.9233703613281)
-1089   (435.5179138183594, 2.8519225120544434)    (441.2810363769531, 8.095802307128906)
-1090    (84.0005111694336, 264.01763916015625)   (69.11249542236328, 259.99774169921875)
-
-[1091 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0        (608.523193359375, 264.6482849121094)    (608.3880004882812, 281.4209289550781)
-1      (318.9924621582031, 365.19110107421875)   (307.97528076171875, 378.2112731933594)
-2      (570.7063598632812, 234.16224670410156)     (570.645751953125, 247.7646942138672)
-3      (288.4769287109375, 252.55393981933594)    (279.8153076171875, 250.5980682373047)
-4        (145.9397430419922, 324.206298828125)  (128.74183654785156, 327.43133544921875)
-...                                        ...                                       ...
-1100   (216.11912536621094, 78.31610870361328)   (259.3839416503906, 100.38536834716797)
-1101   (274.93817138671875, 354.2602844238281)    (412.0435485839844, 150.5561981201172)
-1102   (316.3197937011719, 127.43382263183594)    (300.001220703125, 118.59444427490234)
-1103  (186.10882568359375, 34.817054748535156)  (190.39334106445312, 29.126667022705078)
-1104    (596.2825927734375, 249.9784698486328)    (597.9425659179688, 273.3791198730469)
-
-[1105 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (518.830810546875, 202.37698364257812)   (518.1550903320312, 212.40956115722656)
-1      (129.08404541015625, 283.9776611328125)  (113.95668029785156, 283.48834228515625)
-2       (333.328369140625, 437.55059814453125)   (319.77532958984375, 459.0389709472656)
-3       (319.535888671875, 107.28933715820312)   (321.2397766113281, 108.51477813720703)
-4      (535.6576538085938, 239.86859130859375)    (534.5986938476562, 251.5847625732422)
-...                                        ...                                       ...
-1118  (254.22238159179688, 392.63250732421875)   (238.47256469726562, 406.3280029296875)
-1119   (553.3050537109375, 199.24417114257812)   (552.0391235351562, 211.67544555664062)
-1120     (435.212158203125, 161.6637725830078)    (433.0372009277344, 46.90822219848633)
-1121    (607.2513427734375, 210.1262664794922)   (609.7430419921875, 223.87828063964844)
-1122    (290.8790588378906, 5.160611152648926)   (296.3897399902344, 2.8336920738220215)
-
-[1123 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (319.577392578125, 151.43821716308594)  (318.6333312988281, 151.42796325683594)
-1     (433.80438232421875, 202.27796936035156)  (432.7606506347656, 208.56253051757812)
-2        (568.735595703125, 265.7352294921875)    (567.09716796875, 278.91986083984375)
-3        (571.155029296875, 251.9754180908203)  (569.6299438476562, 264.68145751953125)
-4      (91.95149993896484, 248.44346618652344)  (77.69290161132812, 244.44996643066406)
-...                                        ...                                      ...
-1167    (389.4278564453125, 150.5699462890625)  (388.4902038574219, 154.22853088378906)
-1168  (434.81134033203125, 168.00494384765625)  (433.8240661621094, 173.25814819335938)
-1169   (500.0462341308594, 207.77244567871094)  (498.5570068359375, 215.70379638671875)
-1170   (434.53411865234375, 70.55986785888672)   (437.4202575683594, 75.64583587646484)
-1171    (453.8619689941406, 157.5665283203125)  (455.0447082519531, 164.01217651367188)
-
-[1172 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (287.01397705078125, 416.1532287597656)  (272.26043701171875, 432.0889892578125)
-1      (322.5502014160156, 108.16873931884766)     (323.6103515625, 108.25647735595703)
-2      (242.34768676757812, 330.7453918457031)  (229.8456573486328, 337.23114013671875)
-3      (489.4708557128906, 236.30335998535156)  (487.56829833984375, 244.8753204345703)
-4        (533.0257568359375, 263.087158203125)   (530.9815063476562, 274.1982116699219)
-...                                        ...                                      ...
-1199   (232.8876495361328, 101.17958068847656)   (233.5525360107422, 98.61312103271484)
-1200   (438.5251770019531, 187.34437561035156)  (581.9846801757812, 255.69061279296875)
-1201  (109.80789184570312, 266.87408447265625)   (119.4176254272461, 84.12861633300781)
-1202    (437.4202575683594, 75.64583587646484)    (439.547607421875, 80.72346496582031)
-1203    (173.5697479248047, 212.5361785888672)  (165.9252166748047, 210.16973876953125)
-
-[1204 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0         (323.6103515625, 108.25647735595703)   (324.0259704589844, 108.47042083740234)
-1       (573.3815307617188, 309.1063537597656)    (570.8450317382812, 322.1462707519531)
-2       (80.71905517578125, 281.8954162597656)    (62.63355255126953, 280.9937438964844)
-3     (157.60443115234375, 207.05332946777344)  (149.51211547851562, 203.69906616210938)
-4       (499.1695556640625, 257.2388916015625)     (496.702392578125, 266.2083740234375)
-...                                        ...                                       ...
-1300   (161.89366149902344, 384.6140441894531)   (141.92428588867188, 394.4189147949219)
-1301    (176.6617431640625, 456.8374938964844)       (151.7490234375, 476.2012023925781)
-1302    (124.5914306640625, 255.5420684814453)   (110.99576568603516, 254.6211700439453)
-1303    (188.7074737548828, 456.2070007324219)  (165.37112426757812, 475.70001220703125)
-1304    (566.902587890625, 241.42721557617188)   (432.2411193847656, 187.68341064453125)
-
-[1305 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (324.0259704589844, 108.47042083740234)  (324.5245666503906, 109.18778991699219)
-1       (83.07732391357422, 181.8829345703125)  (73.84284973144531, 175.97552490234375)
-2       (506.3382263183594, 278.0502624511719)    (503.9837341308594, 287.227294921875)
-3      (229.72543334960938, 71.77608489990234)  (229.64463806152344, 67.92474365234375)
-4        (201.2187957763672, 352.600341796875)  (186.32154846191406, 361.0636901855469)
-...                                        ...                                      ...
-1373   (64.10863494873047, 157.54946899414062)    (84.9356689453125, 430.5611877441406)
-1374   (197.0710906982422, 449.34259033203125)  (176.06443786621094, 468.0454406738281)
-1375   (151.09146118164062, 79.41531372070312)  (16.224885940551758, 421.1029968261719)
-1376  (149.51211547851562, 203.69906616210938)  (112.51057434082031, 364.2118225097656)
-1377    (214.31130981445312, 78.9864273071289)  (213.70278930664062, 75.08607482910156)
-
-[1378 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (324.5245666503906, 109.18778991699219)   (324.7071228027344, 109.57688903808594)
-1       (550.4752197265625, 295.8400573730469)    (548.4697875976562, 305.7726745605469)
-2     (122.12535095214844, 179.25306701660156)  (113.99718475341797, 175.20697021484375)
-3        (357.92138671875, 182.53968811035156)   (356.1964111328125, 184.63385009765625)
-4     (236.97894287109375, 393.88995361328125)   (222.97605895996094, 406.3405456542969)
-...                                        ...                                       ...
-1361    (138.4083251953125, 178.6331329345703)  (120.94052124023438, 199.48495483398438)
-1362   (625.6685180664062, 261.81231689453125)        (624.295654296875, 273.2099609375)
-1363   (452.6090393066406, 238.88522338867188)    (405.6941833496094, 215.1389923095703)
-1364  (387.59771728515625, 163.09710693359375)    (386.50653076171875, 165.861572265625)
-1365    (594.2703857421875, 257.8104553222656)       (442.6073913574219, 190.7763671875)
-
-[1366 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (557.9287719726562, 326.10638427734375)    (555.7651977539062, 335.9988708496094)
-1     (265.8165588378906, 238.77322387695312)  (265.43524169921875, 238.21429443359375)
-2     (324.7071228027344, 109.57688903808594)   (324.3550109863281, 109.92213439941406)
-3       (480.538818359375, 267.8000793457031)    (478.6962585449219, 274.6363525390625)
-4     (504.0308532714844, 273.01849365234375)   (501.83880615234375, 280.0225524902344)
-...                                       ...                                       ...
-1303  (588.2626342773438, 283.06390380859375)    (585.6056518554688, 293.0658874511719)
-1304  (89.89469909667969, 476.94793701171875)    (14.145264625549316, 418.473876953125)
-1305   (294.9293212890625, 105.8292465209961)   (293.8228759765625, 104.63274383544922)
-1306   (215.6002960205078, 72.82121276855469)    (215.3977508544922, 69.18724060058594)
-1307   (95.39186096191406, 76.37201690673828)    (95.36907958984375, 70.80084228515625)
-
-[1308 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (478.6962585449219, 274.6363525390625)   (476.6466979980469, 280.59356689453125)
-1       (507.8034973144531, 261.8048095703125)    (505.7579345703125, 267.7718811035156)
-2      (193.21157836914062, 89.23966979980469)   (192.09312438964844, 88.21965789794922)
-3       (329.672607421875, 169.23995971679688)  (327.58575439453125, 170.36148071289062)
-4       (578.1048583984375, 307.7272033691406)     (576.625244140625, 315.9032897949219)
-...                                        ...                                       ...
-1256    (558.5935668945312, 268.9166259765625)   (554.1506958007812, 277.76312255859375)
-1257   (37.32391357421875, 307.41876220703125)    (113.29281616210938, 344.291748046875)
-1258   (493.3070068359375, 216.04087829589844)      (608.1388549804688, 310.62353515625)
-1259      (397.2212829589844, 188.24072265625)   (395.13446044921875, 191.8756103515625)
-1260  (102.77772521972656, 146.04452514648438)    (442.5299987792969, 57.02678298950195)
-
-[1261 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (505.4991149902344, 157.62667846679688)   (504.5202331542969, 160.49610900878906)
-1      (505.4991149902344, 157.62667846679688)   (504.5202331542969, 160.49610900878906)
-2       (538.0452880859375, 347.3219299316406)   (537.6177368164062, 353.91583251953125)
-3       (365.1344299316406, 190.1024932861328)    (362.708740234375, 191.61077880859375)
-4             (568.3738403320312, 335.1640625)    (568.2416381835938, 341.2677917480469)
-...                                        ...                                       ...
-1225  (450.03826904296875, 25.226200103759766)    (453.3406066894531, 82.16390991210938)
-1226  (11.634437561035156, 410.69268798828125)     (69.3301010131836, 361.3628845214844)
-1227    (281.7837219238281, 109.1990966796875)      (380.38525390625, 128.3372039794922)
-1228     (510.31781005859375, 231.69677734375)  (509.22052001953125, 234.83766174316406)
-1229    (262.9195251464844, 103.8019027709961)   (261.1106872558594, 103.01629638671875)
-
-[1230 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (597.631591796875, 436.06622314453125)    (601.6083374023438, 444.9615478515625)
-1      (228.50718688964844, 60.7781982421875)       (223.9326171875, 60.86574172973633)
-2     (314.58233642578125, 157.1424102783203)  (311.45721435546875, 158.05784606933594)
-3       (525.458740234375, 334.5047302246094)    (525.8419799804688, 338.8584899902344)
-4      (556.7158813476562, 347.0637512207031)    (557.9187622070312, 351.7557678222656)
-...                                       ...                                       ...
-1194  (415.2467346191406, 203.42764282226562)   (412.9239807128906, 205.11920166015625)
-1195   (15.217788696289062, 429.392333984375)    (74.60831451416016, 323.6773986816406)
-1196   (594.4070434570312, 298.9207458496094)    (592.9816284179688, 301.9623107910156)
-1197  (261.1106872558594, 103.01629638671875)     (284.83544921875, 119.54639434814453)
-1198  (118.24088287353516, 63.40272903442383)    (110.57011413574219, 62.5447883605957)
-
-[1199 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (422.3590393066406, 148.27220153808594)    (417.765869140625, 148.50746154785156)
-1         (223.9326171875, 60.86574172973633)   (220.43959045410156, 61.09611511230469)
-2      (467.1615295410156, 154.2057342529297)   (467.59027099609375, 155.4279022216797)
-3     (170.6142120361328, 48.168312072753906)   (167.35958862304688, 48.34526824951172)
-4      (159.0701904296875, 65.28917694091797)     (154.8046112060547, 64.9715576171875)
-...                                       ...                                       ...
-1178  (402.4984130859375, 195.97427368164062)    (18.31565284729004, 146.2433624267578)
-1179  (378.6743469238281, 172.58151245117188)     (375.63525390625, 173.71534729003906)
-1180    (450.571533203125, 54.75586700439453)   (448.4815673828125, 54.967079162597656)
-1181      (552.0146484375, 165.1841278076172)   (618.0484619140625, 399.41973876953125)
-1182  (502.04388427734375, 39.56394577026367)  (50.644161224365234, 20.102373123168945)
-
-[1183 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (500.82122802734375, 332.83795166015625)    (501.3797302246094, 337.4438781738281)
-1       (527.5515747070312, 354.5141296386719)   (528.5999145507812, 359.58355712890625)
-2       (527.5515747070312, 354.5141296386719)   (528.5999145507812, 359.58355712890625)
-3       (417.765869140625, 148.50746154785156)  (416.89410400390625, 149.48123168945312)
-4        (154.8046112060547, 64.9715576171875)    (150.55047607421875, 65.5101318359375)
-...                                        ...                                       ...
-1130    (59.19053649902344, 246.0416717529297)  (46.101898193359375, 251.64048767089844)
-1131    (583.8138427734375, 304.7642517089844)      (584.4151611328125, 308.77294921875)
-1132    (243.60980224609375, 55.3634033203125)   (522.9534912109375, 31.374942779541016)
-1133   (475.83673095703125, 222.3511962890625)  (474.75103759765625, 224.40093994140625)
-1134   (38.46717071533203, 21.913772583007812)   (31.579076766967773, 21.23493003845215)
-
-[1135 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0       (558.306884765625, 344.6982727050781)             (559.375, 348.76617431640625)
-1      (496.7263488769531, 299.7749328613281)   (497.0105285644531, 303.11785888671875)
-2              (418.489013671875, 211.578125)     (417.591552734375, 213.7997589111328)
-3        (472.985107421875, 293.862548828125)     (472.9874267578125, 297.053955078125)
-4        (458.78076171875, 298.0938720703125)      (458.509521484375, 301.646240234375)
-...                                       ...                                       ...
-1048  (290.05072021484375, 135.5373992919922)  (287.97796630859375, 136.56570434570312)
-1049   (578.7882080078125, 376.8638000488281)   (485.07147216796875, 308.5799255371094)
-1050      (530.5322265625, 269.8580017089844)  (43.625850677490234, 199.47203063964844)
-1051  (516.6100463867188, 14.544469833374023)   (513.5982666015625, 15.951704025268555)
-1052   (517.9915771484375, 5.275369644165039)   (449.6571960449219, 25.862646102905273)
-
-[1053 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (497.0105285644531, 303.11785888671875)    (497.2593994140625, 306.4308166503906)
-1    (370.58612060546875, 220.14134216308594)  (368.79144287109375, 221.94564819335938)
-2      (449.1200256347656, 154.7748260498047)  (452.40509033203125, 156.67037963867188)
-3    (370.58612060546875, 220.14134216308594)  (368.79144287109375, 221.94564819335938)
-4      (302.4002380371094, 247.7073974609375)   (302.2530822753906, 250.38893127441406)
-..                                        ...                                       ...
-942    (546.3496704101562, 285.0902099609375)       (546.8287963867188, 287.8720703125)
-943  (45.555049896240234, 242.39358520507812)    (34.04594421386719, 247.6442413330078)
-944  (489.86602783203125, 200.97535705566406)    (495.0789794921875, 203.6708526611328)
-945    (434.8514709472656, 48.52151870727539)    (432.2259521484375, 48.92833709716797)
-946    (6.117049217224121, 332.0799255371094)       (6.573354244232178, 389.3583984375)
-
-[947 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (302.2530822753906, 250.38893127441406)  (298.8298034667969, 251.70384216308594)
-1     (129.8053741455078, 131.81375122070312)  (124.76876068115234, 132.7744598388672)
-2    (472.93817138671875, 300.49700927734375)    (472.995361328125, 303.2702941894531)
-3      (83.0398941040039, 252.46595764160156)   (73.07984161376953, 257.3899230957031)
-4      (469.9024658203125, 304.7418518066406)   (470.019287109375, 308.23529052734375)
-..                                        ...                                      ...
-911     (536.9486694335938, 176.846435546875)   (535.610107421875, 177.78041076660156)
-912      (558.3135986328125, 261.63916015625)  (552.6732177734375, 165.72007751464844)
-913   (597.8919067382812, 283.95782470703125)  (598.8613891601562, 286.54083251953125)
-914   (438.58551025390625, 271.2027587890625)   (388.5308837890625, 236.2793731689453)
-915   (44.81935119628906, 36.935462951660156)  (30.621713638305664, 212.9540252685547)
-
-[916 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (442.8766784667969, 155.7878875732422)  (442.54132080078125, 156.97647094726562)
-1    (298.8298034667969, 251.70384216308594)  (295.71612548828125, 252.84332275390625)
-2     (580.4215087890625, 341.7859191894531)     (581.9459838867188, 345.466552734375)
-3        (593.896728515625, 381.30615234375)   (596.0493774414062, 386.10943603515625)
-4      (576.0267944335938, 367.580810546875)     (578.3950805664062, 372.380615234375)
-..                                       ...                                       ...
-885  (227.00747680664062, 86.78167724609375)    (225.2465057373047, 87.85662841796875)
-886  (280.3087463378906, 115.54729461669922)  (461.07476806640625, 295.77606201171875)
-887   (513.4820556640625, 2.920994997024536)    (60.03003692626953, 153.9143524169922)
-888   (478.845947265625, 51.187076568603516)    (476.53961181640625, 52.3399658203125)
-889   (589.7601928710938, 319.6965637207031)    (592.0885620117188, 323.6871032714844)
-
-[890 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (596.0493774414062, 386.10943603515625)   (598.8281860351562, 390.9749755859375)
-1     (487.0744323730469, 304.85931396484375)  (487.3789367675781, 307.88177490234375)
-2    (295.71612548828125, 252.84332275390625)  (292.1700744628906, 254.02537536621094)
-3       (578.3950805664062, 372.380615234375)    (580.1207885742188, 376.303466796875)
-4     (497.9095764160156, 312.85064697265625)  (498.4222717285156, 316.06988525390625)
-..                                        ...                                      ...
-903     (337.76605224609375, 168.37548828125)   (335.8659362792969, 169.0684051513672)
-904     (413.583251953125, 64.00950622558594)   (411.1719970703125, 64.52227020263672)
-905    (237.1062469482422, 105.5100326538086)   (571.4943237304688, 377.4183654785156)
-906     (583.2595825195312, 319.444091796875)     (586.340087890625, 323.166259765625)
-907     (511.3564758300781, 36.4360466003418)    (483.2047424316406, 52.5068473815918)
-
-[908 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (292.1700744628906, 254.02537536621094)   (288.7442321777344, 254.95497131347656)
-1      (608.7689208984375, 369.8530578613281)    (612.1364135742188, 371.0460510253906)
-2      (520.8304443359375, 366.8309631347656)    (524.0552368164062, 370.0214538574219)
-3    (52.748741149902344, 267.88812255859375)  (42.733768463134766, 275.96014404296875)
-4      (501.2913513183594, 351.0905456542969)    (503.4312744140625, 353.6627197265625)
-..                                        ...                                       ...
-840  (248.02015686035156, 116.68136596679688)     (275.5845031738281, 135.617431640625)
-841      (571.05712890625, 343.3269348144531)    (562.7418823242188, 335.5818176269531)
-842   (326.0838317871094, 163.95199584960938)     (275.5845031738281, 135.617431640625)
-843    (239.3453369140625, 88.36275482177734)   (236.84365844726562, 90.56526184082031)
-844   (33.15627670288086, 217.57913208007812)    (495.6383361816406, 249.8168182373047)
-
-[845 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (474.6269226074219, 312.47845458984375)    (476.4369201660156, 313.7337341308594)
-1      (460.095458984375, 318.4195861816406)   (461.5873718261719, 319.91497802734375)
-2     (362.7922668457031, 228.5844268798828)               (361.72265625, 229.7578125)
-3     (159.71835327148438, 98.6608657836914)   (153.49977111816406, 99.85335540771484)
-4    (288.7442321777344, 254.95497131347656)    (285.546630859375, 255.93299865722656)
-..                                       ...                                       ...
-826    (540.65283203125, 279.49481201171875)     (542.2435302734375, 278.552978515625)
-827   (217.7669677734375, 464.6333923339844)  (112.06990814208984, 382.20880126953125)
-828   (532.9481811523438, 280.5459899902344)   (393.9950866699219, 199.26622009277344)
-829    (592.29150390625, 339.21649169921875)  (428.72760009765625, 241.67987060546875)
-830    (597.693115234375, 307.1765441894531)    (427.7757873535156, 63.32463455200195)
-
-[831 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (543.1973266601562, 395.8117370605469)    (548.2035522460938, 397.9155578613281)
-1     (285.546630859375, 255.93299865722656)    (282.8153991699219, 257.1875305175781)
-2     (546.0821533203125, 395.6658020019531)     (550.9636840820312, 397.628662109375)
-3     (578.1935424804688, 398.3359680175781)    (583.6956787109375, 399.7532653808594)
-4    (33.103179931640625, 285.1786804199219)   (23.642078399658203, 295.2042236328125)
-..                                       ...                                       ...
-838  (409.09649658203125, 56.80170440673828)  (405.35003662109375, 55.720664978027344)
-839     (602.24365234375, 301.6211242675781)   (604.9718017578125, 300.21734619140625)
-840   (424.8754577636719, 94.19483184814453)    (420.8053894042969, 92.43761444091797)
-841    (626.5291137695312, 301.782958984375)        (630.0087890625, 299.776611328125)
-842  (427.1243591308594, 107.78722381591797)   (169.81178283691406, 76.04735565185547)
-
-[843 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0        (463.2636413574219, 321.08349609375)  (465.37689208984375, 322.16461181640625)
-1      (503.7767639160156, 318.4031677246094)     (505.846435546875, 318.3128356933594)
-2      (541.1773071289062, 386.5990295410156)    (545.8634643554688, 387.9065856933594)
-3      (478.604736328125, 333.05035400390625)    (481.2020568847656, 334.0793151855469)
-4     (544.9142456054688, 345.06854248046875)    (547.9126586914062, 344.7455139160156)
-..                                        ...                                       ...
-801   (531.1031494140625, 267.77325439453125)    (532.7151489257812, 266.9031982421875)
-802   (311.5062255859375, 167.24514770507812)  (344.11444091796875, 190.08758544921875)
-803     (243.9508056640625, 96.5620346069336)    (239.9893341064453, 98.40084838867188)
-804  (204.04446411132812, 35.857398986816406)   (199.07701110839844, 37.53755187988281)
-805    (188.9340057373047, 73.21571350097656)   (412.9804382324219, 24.374095916748047)
-
-[806 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (547.9126586914062, 344.7455139160156)       (551.0911254882812, 343.9599609375)
-1     (477.3663635253906, 319.94464111328125)    (479.5386962890625, 320.2538146972656)
-2      (607.4677124023438, 428.3148193359375)   (614.3650512695312, 429.27703857421875)
-3        (513.48193359375, 358.1191101074219)    (517.1776733398438, 358.8905944824219)
-4      (545.8634643554688, 387.9065856933594)    (550.9484252929688, 388.9796142578125)
-..                                        ...                                       ...
-786   (533.8268432617188, 161.73251342773438)   (530.8887329101562, 158.15992736816406)
-787  (14.588218688964844, 158.00881958007812)       (7.19000768661499, 164.12255859375)
-788     (285.287353515625, 463.2409362792969)       (286.9775390625, 475.6949462890625)
-789   (320.1645202636719, 156.28176879882812)  (316.89886474609375, 158.43887329101562)
-790   (259.05609130859375, 88.60198211669922)   (254.70944213867188, 89.15338897705078)
-
-[791 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0          (565.5593872070312, 399.923828125)      (571.341796875, 400.79046630859375)
-1      (409.1337585449219, 264.5164489746094)   (409.6674499511719, 264.7973327636719)
-2      (440.5915832519531, 303.5002136230469)   (442.2252197265625, 304.1829833984375)
-3      (479.4524841308594, 331.5111999511719)  (482.1985168457031, 332.03155517578125)
-4      (540.6063842773438, 375.6546936035156)  (545.1373291015625, 376.08758544921875)
-..                                        ...                                      ...
-780    (460.3199462890625, 39.93129348754883)  (229.43746948242188, 60.17060852050781)
-781    (637.0462646484375, 397.2751770019531)    (636.072021484375, 390.7928771972656)
-782  (488.67462158203125, 12.367627143859863)   (395.6666259765625, 225.0462188720703)
-783    (420.2782897949219, 50.07939147949219)  (403.8840026855469, 57.480796813964844)
-784    (621.7565307617188, 323.1699523925781)      (625.56591796875, 320.783447265625)
-
-[785 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0    (510.55194091796875, 317.54534912109375)   (513.0015869140625, 316.65789794921875)
-1     (404.8342590332031, 219.88589477539062)       (403.46978759765625, 219.263671875)
-2      (537.0897827148438, 376.1884460449219)     (541.5706176757812, 376.453369140625)
-3     (357.4425048828125, 233.42910766601562)    (356.8152160644531, 234.0709228515625)
-4      (560.9419555664062, 380.9178161621094)    (565.8568725585938, 380.9305725097656)
-..                                        ...                                       ...
-742    (9.37149715423584, 209.92803955078125)   (2.493387222290039, 217.11355590820312)
-743   (364.4255065917969, 191.71682739257812)      (363.650634765625, 191.462158203125)
-744    (197.46746826171875, 89.5650863647461)  (486.76715087890625, 267.20001220703125)
-745   (200.70428466796875, 89.96898651123047)  (208.52557373046875, 114.03623962402344)
-746   (216.81918334960938, 99.19255828857422)  (213.42080688476562, 102.26875305175781)
-
-[747 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0       (271.20343017578125, 259.69287109375)   (269.48321533203125, 260.6343994140625)
-1      (588.9242553710938, 402.9798889160156)   (594.7622680664062, 402.94439697265625)
-2       (560.354248046875, 389.4115295410156)    (565.9327392578125, 389.8522033691406)
-3     (532.9926147460938, 370.61395263671875)    (537.1729736328125, 370.6480407714844)
-4      (581.076416015625, 397.40838623046875)    (586.6708374023438, 397.4084777832031)
-..                                        ...                                       ...
-741   (191.59515380859375, 106.6501235961914)  (387.79949951171875, 53.259395599365234)
-742  (11.181964874267578, 104.22850036621094)   (7.242988586425781, 130.57595825195312)
-743   (147.55392456054688, 81.84138488769531)   (143.32965087890625, 84.28250885009766)
-744    (528.2991943359375, 143.2618865966797)    (525.808837890625, 139.61727905273438)
-745   (203.20106506347656, 87.25263214111328)    (630.986083984375, 330.22125244140625)
-
-[746 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (603.2557983398438, 408.38018798828125)   (609.4114990234375, 408.2910461425781)
-1    (37.44704818725586, 40.093746185302734)   (32.183963775634766, 44.0797119140625)
-2     (515.567626953125, 315.96099853515625)   (517.6013793945312, 314.6858825683594)
-3    (562.0823364257812, 341.04180908203125)   (565.934814453125, 339.88983154296875)
-4     (492.2272644042969, 335.6396179199219)    (495.1112060546875, 335.631103515625)
-..                                       ...                                      ...
-733  (533.1571044921875, 238.33010864257812)  (420.4583435058594, 229.56393432617188)
-734     (411.1289978027344, 7.3985915184021)  (406.29412841796875, 5.672199249267578)
-735  (167.76730346679688, 86.89524841308594)  (162.89015197753906, 88.28948211669922)
-736    (151.4899444580078, 87.0682373046875)    (550.16552734375, 262.81719970703125)
-737  (331.8007507324219, 136.20912170410156)   (376.0963439941406, 84.62641906738281)
-
-[738 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0       (268.0743408203125, 261.01708984375)    (266.6733093261719, 261.7946472167969)
-1     (503.0315856933594, 306.9857482910156)    (505.0072021484375, 306.1236267089844)
-2      (569.875244140625, 376.8714599609375)    (574.5488891601562, 376.5754699707031)
-3     (517.6013793945312, 314.6858825683594)    (520.2893676757812, 313.9386901855469)
-4    (559.0087280273438, 376.15240478515625)    (563.641357421875, 376.08123779296875)
-..                                       ...                                       ...
-720     (312.0152587890625, 165.52099609375)        (310.62109375, 166.14364624023438)
-721     (437.0860290527344, 36.433349609375)       (434.1240234375, 34.62748718261719)
-722  (485.6724853515625, 197.82249450683594)   (484.6853332519531, 193.03057861328125)
-723  (554.1690063476562, 30.571462631225586)    (125.82298278808594, 67.4527359008789)
-724  (43.15920639038086, 111.01215362548828)  (39.006385803222656, 114.52473449707031)
-
-[725 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (545.5213623046875, 370.6103820800781)    (549.6878662109375, 370.6512145996094)
-1      (266.6733093261719, 261.7946472167969)   (266.2203063964844, 263.25091552734375)
-2           (586.767578125, 402.867431640625)   (592.1602172851562, 403.04302978515625)
-3         (608.1103515625, 409.6882629394531)    (613.6363525390625, 409.5274658203125)
-4           (586.767578125, 402.867431640625)   (592.1602172851562, 403.04302978515625)
-..                                        ...                                       ...
-738   (210.2510528564453, 127.17427062988281)    (207.0136260986328, 127.3216323852539)
-739  (373.68463134765625, 194.64328002929688)  (373.14495849609375, 194.28884887695312)
-740    (425.1869812011719, 211.3550262451172)    (424.7895812988281, 209.5285186767578)
-741   (380.5044250488281, 55.443721771240234)   (465.99676513671875, 260.0467529296875)
-742   (176.56857299804688, 99.17784118652344)    (174.17564392089844, 100.251220703125)
-
-[743 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (266.2203063964844, 263.25091552734375)  (265.22552490234375, 263.8954772949219)
-1              (551.65625, 366.4034423828125)  (555.5844116210938, 366.31146240234375)
-2              (551.65625, 366.4034423828125)  (555.5844116210938, 366.31146240234375)
-3        (598.85595703125, 400.9962158203125)   (604.0266723632812, 401.0539855957031)
-4      (549.6878662109375, 370.6512145996094)  (553.3186645507812, 370.47540283203125)
-..                                        ...                                      ...
-725   (498.56207275390625, 243.7050323486328)  (499.27349853515625, 242.8977813720703)
-726   (455.8669128417969, 252.57308959960938)   (225.0338592529297, 89.90428161621094)
-727  (45.360382080078125, 49.976036071777344)  (43.89281463623047, 53.394309997558594)
-728      (224.5281524658203, 142.09033203125)  (192.0257568359375, 129.21749877929688)
-729      (596.4513549804688, 364.98974609375)   (479.1148681640625, 300.2246398925781)
-
-[730 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (265.22552490234375, 263.8954772949219)    (264.50439453125, 264.68597412109375)
-1     (608.0689086914062, 396.7483825683594)   (612.9463500976562, 396.6726379394531)
-2         (563.498046875, 376.5721130371094)       (567.388671875, 376.5342102050781)
-3     (619.5887451171875, 409.8550109863281)     (625.2880859375, 410.17242431640625)
-4      (617.205322265625, 402.4188537597656)   (622.3153686523438, 402.3692932128906)
-..                                       ...                                      ...
-738  (442.00860595703125, 189.9950714111328)  (440.9279479980469, 188.17916870117188)
-739   (536.6707763671875, 292.4913024902344)  (537.8912963867188, 290.23101806640625)
-740  (142.1383514404297, 102.17622375488281)   (164.9937286376953, 98.85545349121094)
-741  (539.6563720703125, 232.47422790527344)  (538.1124877929688, 230.52664184570312)
-742    (390.13507080078125, 192.10009765625)  (389.6647644042969, 191.31788635253906)
-
-[743 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0       (264.50439453125, 264.68597412109375)  (264.24957275390625, 265.2712097167969)
-1      (576.0447998046875, 375.5452880859375)   (580.1052856445312, 375.5460510253906)
-2      (612.9463500976562, 396.6726379394531)   (617.6486206054688, 396.5992126464844)
-3     (353.7323913574219, 235.92080688476562)  (353.07818603515625, 235.8437957763672)
-4     (353.7323913574219, 235.92080688476562)  (353.07818603515625, 235.8437957763672)
-..                                        ...                                      ...
-707    (634.1309814453125, 276.2124328613281)   (636.8165283203125, 273.3206787109375)
-708  (118.62837219238281, 458.95623779296875)   (99.21514892578125, 369.7594299316406)
-709   (512.1356811523438, 270.37298583984375)   (513.4714965820312, 269.2913513183594)
-710    (372.5265197753906, 88.68484497070312)    (369.908447265625, 87.64517974853516)
-711    (54.54369354248047, 95.29022216796875)   (50.66984939575195, 97.12879180908203)
-
-[712 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (264.24957275390625, 265.2712097167969)       (263.7961120605469, 265.9326171875)
-1     (353.07818603515625, 235.8437957763672)    (353.1131896972656, 235.9993438720703)
-2     (500.6050109863281, 296.60101318359375)    (502.0594482421875, 295.8709411621094)
-3      (584.3250732421875, 366.7978210449219)          (588.0078125, 366.5679931640625)
-4     (528.1839599609375, 358.29608154296875)     (531.075927734375, 358.3338317871094)
-..                                        ...                                       ...
-736  (250.29283142089844, 145.02793884277344)     (230.573486328125, 129.5000762939453)
-737   (18.30489730834961, 124.92936706542969)  (15.004117965698242, 127.33309173583984)
-738   (493.7715148925781, 260.22393798828125)  (192.82574462890625, 107.05037689208984)
-739     (382.2994079589844, 62.2698974609375)      (379.93115234375, 60.71133041381836)
-740  (232.59078979492188, 134.14523315429688)  (230.78863525390625, 134.13192749023438)
-
-[741 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0         (263.7961120605469, 265.9326171875)   (263.8205261230469, 266.72930908203125)
-1     (349.2931213378906, 237.87405395507812)    (349.190673828125, 237.91744995117188)
-2      (502.0594482421875, 295.8709411621094)    (503.7606506347656, 295.1759948730469)
-3     (496.7215576171875, 327.09942626953125)    (498.8026123046875, 327.0484619140625)
-4     (501.77447509765625, 318.0489807128906)    (503.5310363769531, 317.6429138183594)
-..                                        ...                                       ...
-763   (383.0536193847656, 179.09872436523438)   (382.8131408691406, 178.85272216796875)
-764    (369.1842041015625, 40.06130599975586)  (366.54754638671875, 39.046836853027344)
-765  (223.35606384277344, 109.94725799560547)     (614.8988037109375, 353.482177734375)
-766   (367.89837646484375, 86.45912170410156)        (361.62109375, 43.930152893066406)
-767   (543.1221923828125, 255.14041137695312)    (544.341552734375, 253.91355895996094)
-
-[768 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (263.8205261230469, 266.72930908203125)   (263.8167419433594, 267.17034912109375)
-1         (556.5361328125, 358.7017822265625)    (559.6265869140625, 358.6612243652344)
-2    (352.90496826171875, 235.97171020507812)    (352.949951171875, 235.94436645507812)
-3    (352.90496826171875, 235.97171020507812)    (352.949951171875, 235.94436645507812)
-4         (556.5361328125, 358.7017822265625)    (559.6265869140625, 358.6612243652344)
-..                                        ...                                       ...
-740    (623.9826049804688, 378.8061218261719)    (628.1487426757812, 377.7687072753906)
-741   (237.3822021484375, 120.36292266845703)  (236.31991577148438, 120.07166290283203)
-742   (536.6791381835938, 248.64378356933594)   (536.7977294921875, 247.77493286132812)
-743   (550.9734497070312, 249.00672912597656)     (552.0303955078125, 247.528076171875)
-744   (617.9053344726562, 245.04725646972656)     (619.93212890625, 243.10755920410156)
-
-[745 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (606.6466674804688, 390.0127868652344)    (609.6102905273438, 389.5129089355469)
-1       (505.6748046875, 317.59967041015625)   (507.65289306640625, 317.3437194824219)
-2    (490.9753723144531, 137.12501525878906)   (490.9897766113281, 136.02760314941406)
-3     (606.6466674804688, 390.0127868652344)    (609.6102905273438, 389.5129089355469)
-4    (263.8167419433594, 267.17034912109375)    (264.4263610839844, 268.0824279785156)
-..                                       ...                                       ...
-724     (485.1884765625, 309.50677490234375)     (520.2975463867188, 326.321533203125)
-725    (427.52392578125, 213.72479248046875)    (428.105712890625, 212.81231689453125)
-726  (447.5943603515625, 212.56333923339844)    (447.8701477050781, 211.8336639404297)
-727       (173.8092041015625, 120.724609375)    (596.5668334960938, 274.5300598144531)
-728    (573.34912109375, 225.54685974121094)  (266.34027099609375, 158.92471313476562)
-
-[729 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (264.4263610839844, 268.0824279785156)  (264.47564697265625, 268.26275634765625)
-1     (353.01983642578125, 235.9686279296875)    (353.0002136230469, 235.8065185546875)
-2     (375.75701904296875, 143.5988311767578)   (373.6910705566406, 142.61497497558594)
-3      (615.0586547851562, 392.5073547363281)    (619.1292114257812, 392.5888977050781)
-4      (329.9520568847656, 224.5849151611328)    (329.6632995605469, 224.5424346923828)
-..                                        ...                                       ...
-685  (206.33094787597656, 129.16171264648438)   (134.40121459960938, 371.1000671386719)
-686   (183.5983123779297, 131.74057006835938)  (181.87034606933594, 131.45001220703125)
-687    (496.0379333496094, 290.6571350097656)        (623.986328125, 346.1571044921875)
-688   (399.4075622558594, 176.78643798828125)  (398.52374267578125, 176.20675659179688)
-689  (184.50921630859375, 111.68571472167969)  (184.26173400878906, 112.78831481933594)
-
-[690 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0    (264.47564697265625, 268.26275634765625)   (264.5705871582031, 268.8288269042969)
-1      (286.4935302734375, 97.42938995361328)  (283.99407958984375, 96.20259094238281)
-2     (348.8438720703125, 238.04171752929688)  (349.0290222167969, 238.10203552246094)
-3      (353.0002136230469, 235.8065185546875)  (353.1128234863281, 235.82952880859375)
-4      (353.0002136230469, 235.8065185546875)  (353.1128234863281, 235.82952880859375)
-..                                        ...                                      ...
-734    (303.1905212402344, 173.8317413330078)  (302.5199890136719, 173.52149963378906)
-735  (184.26173400878906, 112.78831481933594)  (181.7759552001953, 112.91795349121094)
-736  (482.56646728515625, 229.91697692871094)   (483.2961120605469, 229.1320037841797)
-737       (587.0114135742188, 296.3037109375)  (588.9213256835938, 294.55413818359375)
-738   (467.1244201660156, 205.63296508789062)  (586.6156005859375, 244.77801513671875)
-
-[739 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (264.5705871582031, 268.8288269042969)     (265.2994079589844, 269.531494140625)
-1     (283.99407958984375, 96.20259094238281)    (284.2083435058594, 97.71094512939453)
-2     (353.1128234863281, 235.82952880859375)   (353.4263916015625, 235.81248474121094)
-3     (353.1128234863281, 235.82952880859375)   (353.4263916015625, 235.81248474121094)
-4    (399.30133056640625, 212.08819580078125)   (399.1658630371094, 212.04281616210938)
-..                                        ...                                       ...
-704   (377.52020263671875, 35.24934005737305)  (377.18402099609375, 33.988563537597656)
-705   (195.94134521484375, 76.61531066894531)   (396.21295166015625, 20.89824867248535)
-706   (104.29074096679688, 9.257892608642578)    (460.849853515625, 185.93653869628906)
-707    (389.3911437988281, 5.409937381744385)  (17.592262268066406, 115.16262817382812)
-708  (374.39178466796875, 33.164695739746094)    (373.3296203613281, 32.37333297729492)
-
-[709 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0      (265.2994079589844, 269.531494140625)    (265.1966552734375, 269.7718505859375)
-1    (370.6018981933594, 140.62399291992188)       (369.8232421875, 140.3667755126953)
-2    (353.4263916015625, 235.81248474121094)   (353.6138610839844, 235.79237365722656)
-3    (353.4263916015625, 235.81248474121094)   (353.6138610839844, 235.79237365722656)
-4     (307.6718444824219, 214.4247589111328)   (307.0304870605469, 214.40823364257812)
-..                                       ...                                       ...
-738   (317.2872314453125, 120.0921859741211)  (316.30352783203125, 120.08689880371094)
-739  (111.76881408691406, 3.262864589691162)  (108.76876068115234, 3.7507786750793457)
-740   (438.7220458984375, 265.1963806152344)    (439.8541259765625, 265.0712585449219)
-741   (507.1567687988281, 254.6211700439453)   (508.3082580566406, 254.47879028320312)
-742  (505.1542053222656, 233.19236755371094)   (595.4551391601562, 241.90936279296875)
-
-[743 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0         (369.8232421875, 140.3667755126953)    (367.874755859375, 139.79391479492188)
-1      (265.1966552734375, 269.7718505859375)    (265.4457702636719, 270.0652770996094)
-2     (353.6138610839844, 235.79237365722656)    (353.75421142578125, 235.750244140625)
-3         (329.80377197265625, 225.287109375)   (329.71209716796875, 225.2624053955078)
-4      (283.6899108886719, 99.44578552246094)   (284.12213134765625, 102.1629867553711)
-..                                        ...                                       ...
-747   (191.8948211669922, 131.78749084472656)  (191.09632873535156, 131.86524963378906)
-748   (223.3236083984375, 154.10610961914062)   (274.99505615234375, 171.9976043701172)
-749  (214.06573486328125, 101.37799072265625)  (213.05563354492188, 102.70059967041016)
-750      (390.2119140625, 19.065488815307617)     (328.9144287109375, 88.8263931274414)
-751   (363.8970947265625, 3.5063111782073975)   (362.09307861328125, 2.662172317504883)
-
-[752 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (329.71209716796875, 225.2624053955078)    (329.9683532714844, 225.5789031982422)
-1      (367.874755859375, 139.79391479492188)   (367.92510986328125, 139.3363037109375)
-2      (265.4457702636719, 270.0652770996094)   (266.07904052734375, 270.5002136230469)
-3      (318.3251037597656, 200.8856658935547)   (317.9095458984375, 200.85609436035156)
-4      (353.75421142578125, 235.750244140625)   (354.2618408203125, 235.72207641601562)
-..                                        ...                                       ...
-746    (564.9595947265625, 319.3564147949219)     (514.189208984375, 306.5425720214844)
-747  (177.24183654785156, 126.69869232177734)  (175.76828002929688, 128.14260864257812)
-748     (424.92041015625, 258.88812255859375)  (425.60333251953125, 258.89617919921875)
-749   (349.13568115234375, 177.3668212890625)   (349.15777587890625, 177.4492645263672)
-750    (637.103271484375, 246.25601196289062)  (350.57598876953125, 21.022899627685547)
-
-[751 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (367.92510986328125, 139.3363037109375)   (366.139404296875, 138.68008422851562)
-1    (354.2618408203125, 235.72207641601562)   (354.8268737792969, 236.0265350341797)
-2     (329.9683532714844, 225.5789031982422)  (330.16497802734375, 225.8568115234375)
-3    (354.2618408203125, 235.72207641601562)   (354.8268737792969, 236.0265350341797)
-4     (329.9683532714844, 225.5789031982422)  (330.16497802734375, 225.8568115234375)
-..                                       ...                                      ...
-736  (487.10858154296875, 229.9689483642578)     (571.0810546875, 335.45330810546875)
-737  (109.3394775390625, 3.9878201484680176)   (107.9176025390625, 6.014749526977539)
-738     (48.4400749206543, 4.30652379989624)  (46.409366607666016, 6.033128261566162)
-739   (197.541748046875, 111.47993469238281)    (197.57275390625, 112.56425476074219)
-740    (525.09521484375, 122.04056549072266)   (525.288818359375, 120.04691314697266)
-
-[741 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (266.29034423828125, 270.9590148925781)   (267.06842041015625, 271.9664306640625)
-1      (354.8268737792969, 236.0265350341797)   (354.84136962890625, 235.8573760986328)
-2      (595.0372314453125, 367.7708740234375)    (597.9682006835938, 366.7654724121094)
-3      (366.139404296875, 138.68008422851562)   (365.1849060058594, 139.05352783203125)
-4      (595.0372314453125, 367.7708740234375)    (597.9682006835938, 366.7654724121094)
-..                                        ...                                       ...
-721  (219.90904235839844, 138.35552978515625)  (218.46917724609375, 139.68104553222656)
-722    (11.054975509643555, 77.1808090209961)    (12.23002815246582, 80.76607513427734)
-723    (517.1505126953125, 259.6073303222656)     (518.425048828125, 258.3668212890625)
-724      (593.4037475585938, 281.15380859375)    (379.0647277832031, 42.90821838378906)
-725    (304.795654296875, 123.22783660888672)   (305.4151916503906, 125.52378845214844)
-
-[726 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (365.1849060058594, 139.05352783203125)   (364.5973205566406, 138.4345245361328)
-1    (251.96971130371094, 97.72982788085938)  (251.01873779296875, 98.72834777832031)
-2    (251.96971130371094, 97.72982788085938)  (251.01873779296875, 98.72834777832031)
-3     (585.0096435546875, 354.6243591308594)   (588.0330810546875, 353.5318603515625)
-4     (330.4632568359375, 226.1413116455078)  (330.8097229003906, 226.52359008789062)
-..                                       ...                                      ...
-722   (349.7736511230469, 40.06045150756836)  (348.84442138671875, 41.39304733276367)
-723    (89.5456314086914, 4.321968078613281)  (87.59442901611328, 6.9185638427734375)
-724            (561.625, 250.65255737304688)  (562.8875122070312, 249.32899475097656)
-725  (368.51654052734375, 210.6482391357422)   (570.5819091796875, 269.0863037109375)
-726  (429.6571044921875, 209.02487182617188)  (430.9255065917969, 208.60565185546875)
-
-[727 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0     (364.5973205566406, 138.4345245361328)  (364.7542724609375, 138.50428771972656)
-1     (355.2710266113281, 236.3494110107422)   (355.9739074707031, 236.8084259033203)
-2     (355.2710266113281, 236.3494110107422)   (355.9739074707031, 236.8084259033203)
-3    (282.9336242675781, 196.72174072265625)   (282.8305969238281, 197.6421356201172)
-4     (563.7786865234375, 355.3968505859375)  (566.6131591796875, 354.23040771484375)
-..                                       ...                                      ...
-772  (193.90357971191406, 145.1692352294922)  (193.3738250732422, 146.28672790527344)
-773  (548.6002197265625, 269.03497314453125)      (549.2800903320312, 268.5830078125)
-774   (599.9503173828125, 229.9241943359375)      (436.958984375, 223.29962158203125)
-775  (357.5738830566406, 192.44081115722656)   (359.3182678222656, 191.7803955078125)
-776  (362.9928283691406, 24.222936630249023)   (360.893798828125, 24.672513961791992)
-
-[777 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (272.7914733886719, 274.8826904296875)   (274.41876220703125, 275.8268127441406)
-1    (364.7542724609375, 138.50428771972656)         (364.54296875, 139.0352783203125)
-2     (507.5757751464844, 315.5779113769531)    (509.6217956542969, 314.6483154296875)
-3     (355.9739074707031, 236.8084259033203)   (356.5575256347656, 236.99351501464844)
-4     (355.9739074707031, 236.8084259033203)   (356.5575256347656, 236.99351501464844)
-..                                       ...                                       ...
-761  (258.3017883300781, 156.32701110839844)  (453.41619873046875, 159.47439575195312)
-762  (340.33123779296875, 66.47156524658203)   (339.06097412109375, 67.03968048095703)
-763      (566.716796875, 253.53684997558594)      (567.3837890625, 250.11952209472656)
-764  (422.6787414550781, 208.75938415527344)     (423.3222351074219, 208.080810546875)
-765   (521.5337524414062, 256.3290710449219)      (522.6103515625, 255.00411987304688)
-
-[766 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0           (364.54296875, 139.0352783203125)    (364.8316650390625, 138.7770538330078)
-1        (558.21435546875, 296.1850891113281)    (560.7857666015625, 294.6910705566406)
-2       (536.242919921875, 325.7927551269531)    (538.8558349609375, 324.5991516113281)
-3     (274.41876220703125, 275.8268127441406)     (275.930908203125, 277.3904724121094)
-4     (277.4991760253906, 105.88813018798828)      (276.2408142089844, 105.27880859375)
-..                                        ...                                       ...
-789   (634.1385498046875, 290.70843505859375)   (361.3260192871094, 206.54153442382812)
-790  (16.874374389648438, 229.86172485351562)   (141.49534606933594, 410.9075012207031)
-791  (175.56259155273438, 125.30361938476562)  (173.25503540039062, 127.30084228515625)
-792    (6.9256110191345215, 95.6775131225586)   (6.151180267333984, 100.16341400146484)
-793  (296.22698974609375, 175.13973999023438)    (294.9164123535156, 176.2305450439453)
-
-[794 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (364.8316650390625, 138.7770538330078)   (364.2325439453125, 139.36410522460938)
-1       (275.930908203125, 277.3904724121094)     (276.9751892089844, 278.672119140625)
-2     (543.1261596679688, 257.37713623046875)   (545.2271118164062, 255.63343811035156)
-3    (255.23741149902344, 198.75743103027344)   (255.29112243652344, 200.5998077392578)
-4     (254.1041717529297, 190.40298461914062)   (254.34120178222656, 192.3695831298828)
-..                                        ...                                       ...
-751   (228.0095977783203, 166.23114013671875)   (190.2140350341797, 159.71865844726562)
-752    (299.9770202636719, 178.9237823486328)  (299.70074462890625, 179.37326049804688)
-753  (448.35845947265625, 202.04061889648438)  (447.30987548828125, 200.96995544433594)
-754   (515.8676147460938, 210.01412963867188)    (629.9874267578125, 336.1482849121094)
-755     (609.051025390625, 280.5472106933594)    (611.8151245117188, 276.6889953613281)
-
-[756 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (364.2325439453125, 139.36410522460938)   (362.8695373535156, 139.7084503173828)
-1      (276.9751892089844, 278.672119140625)  (278.8919372558594, 280.72601318359375)
-2       (542.45361328125, 323.6459045410156)   (546.1224975585938, 322.5934753417969)
-3       (542.45361328125, 323.6459045410156)   (546.1224975585938, 322.5934753417969)
-4    (404.8695373535156, 208.56520080566406)   (404.3149108886719, 208.8023681640625)
-..                                       ...                                      ...
-726    (279.8804626464844, 180.751220703125)  (325.63275146484375, 194.7220001220703)
-727   (599.3500366210938, 250.6545867919922)  (184.79615783691406, 112.8437271118164)
-728  (599.0796508789062, 209.55722045898438)  (600.9219360351562, 207.65875244140625)
-729     (589.2548217773438, 229.09423828125)  (590.6642456054688, 226.72805786132812)
-730  (416.6856994628906, 225.49745178222656)   (438.6424560546875, 227.7371826171875)
-
-[731 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0     (566.7811279296875, 291.3724060058594)  (570.5684204101562, 289.77593994140625)
-1    (534.9822387695312, 318.24334716796875)   (538.9797973632812, 317.4211730957031)
-2     (596.3067016601562, 300.3566589355469)      (600.4754638671875, 298.3681640625)
-3     (362.8695373535156, 139.7084503173828)  (361.89837646484375, 140.4904022216797)
-4     (546.1224975585938, 322.5934753417969)   (550.2455444335938, 321.5426025390625)
-..                                       ...                                      ...
-726   (401.2938537597656, 173.1435089111328)  (401.51397705078125, 173.2871856689453)
-727  (432.0159912109375, 212.54452514648438)  (372.1203918457031, 13.793670654296875)
-728  (29.838624954223633, 29.95002555847168)  (28.727062225341797, 35.92182922363281)
-729    (70.9216537475586, 7.285545825958252)   (68.9376449584961, 13.211518287658691)
-730     (463.8857421875, 195.87615966796875)   (463.4903869628906, 195.1924591064453)
-
-[731 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0    (310.60101318359375, 223.81182861328125)  (311.55462646484375, 225.7400665283203)
-1       (531.160400390625, 314.6258544921875)   (535.0642700195312, 313.6500244140625)
-2        (584.2451171875, 290.04888916015625)   (588.1193237304688, 288.0831298828125)
-3     (587.1965942382812, 348.14801025390625)   (593.0298461914062, 347.0365905761719)
-4      (579.1578369140625, 296.1094970703125)  (583.3329467773438, 294.21856689453125)
-..                                        ...                                      ...
-750   (504.0430908203125, 207.99232482910156)  (505.2378845214844, 206.01402282714844)
-751        (342.26544189453125, 191.35546875)       (344.6892395019531, 194.306640625)
-752               (340.0, 42.148799896240234)  (571.9330444335938, 244.79698181152344)
-753   (571.6039428710938, 197.45648193359375)   (572.9425659179688, 194.9053497314453)
-754      (480.33349609375, 179.6307373046875)  (488.8578796386719, 128.01358032226562)
-
-[755 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (574.4823608398438, 288.06988525390625)     (578.8651733398438, 286.225341796875)
-1      (561.6334228515625, 323.1748962402344)    (566.8322143554688, 322.0423583984375)
-2     (282.3001708984375, 284.42523193359375)  (284.20550537109375, 286.42718505859375)
-3     (387.57452392578125, 266.8716735839844)   (389.52618408203125, 267.8047180175781)
-4      (581.4094848632812, 285.4932556152344)     (585.3827514648438, 283.361572265625)
-..                                        ...                                       ...
-706  (2.8983020782470703, 251.06991577148438)   (4.338819980621338, 257.52703857421875)
-707          (564.609375, 199.16583251953125)     (567.072509765625, 196.4224853515625)
-708  (411.96978759765625, 221.68861389160156)  (412.65826416015625, 221.61288452148438)
-709   (437.4724426269531, 185.16664123535156)    (438.6890563964844, 184.7655487060547)
-710   (377.3802795410156, 15.056375503540039)  (451.18218994140625, 168.70826721191406)
-
-[711 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0    (362.05389404296875, 142.81031799316406)  (361.6895446777344, 143.55735778808594)
-1    (284.20550537109375, 286.42718505859375)    (285.648681640625, 288.1267395019531)
-2       (35.696205139160156, 263.56201171875)  (37.51757049560547, 270.55340576171875)
-3     (312.52752685546875, 227.6924285888672)  (313.8887634277344, 229.76876831054688)
-4     (312.52752685546875, 227.6924285888672)  (313.8887634277344, 229.76876831054688)
-..                                        ...                                      ...
-740    (589.000732421875, 235.09341430664062)   (593.2747802734375, 232.7950439453125)
-741   (41.99338150024414, 264.93792724609375)   (362.9595031738281, 4.723416328430176)
-742   (385.52520751953125, 4.322646617889404)   (382.691162109375, 5.0765275955200195)
-743        (327.30078125, 187.38377380371094)   (326.7142639160156, 189.8479766845703)
-744  (10.253108978271484, 133.61663818359375)   (75.96701049804688, 8.463532447814941)
-
-[745 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (361.6895446777344, 143.55735778808594)  (362.06500244140625, 144.45960998535156)
-1      (269.599853515625, 107.10650634765625)   (268.5638732910156, 110.13182830810547)
-2     (37.51757049560547, 270.55340576171875)    (39.52116012573242, 277.6772155761719)
-3      (269.599853515625, 107.10650634765625)   (268.5638732910156, 110.13182830810547)
-4    (302.29705810546875, 170.64195251464844)   (302.2911071777344, 172.78268432617188)
-..                                        ...                                       ...
-740     (102.46542358398438, 149.38134765625)  (104.81819915771484, 157.38827514648438)
-741   (438.0201416015625, 204.52220153808594)      (440.0087890625, 203.90272521972656)
-742    (337.4271240234375, 50.03718185424805)   (516.1570434570312, 214.19168090820312)
-743   (604.0420532226562, 233.51107788085938)  (201.20091247558594, 170.85726928710938)
-744   (429.4049072265625, 191.46031188964844)   (430.0057373046875, 192.35537719726562)
-
-[745 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0     (549.6373291015625, 274.0681457519531)    (554.0902709960938, 272.304931640625)
-1     (565.9868774414062, 277.8766784667969)   (570.8541259765625, 276.1916198730469)
-2     (340.9215087890625, 239.4298095703125)   (342.5568542480469, 241.1433868408203)
-3     (287.2789306640625, 290.1417236328125)        (288.82537841796875, 292.4609375)
-4     (340.9215087890625, 239.4298095703125)   (342.5568542480469, 241.1433868408203)
-..                                       ...                                      ...
-757    (95.8965072631836, 78.63507080078125)   (96.85392761230469, 85.43289947509766)
-758   (346.6177062988281, 43.98717498779297)    (345.2662658691406, 45.4407844543457)
-759  (358.8972473144531, 30.027841567993164)   (355.8132629394531, 31.24367904663086)
-760   (469.1123352050781, 257.0748596191406)   (471.9437255859375, 256.6593322753906)
-761     (452.8725891113281, 190.99755859375)  (409.7622985839844, 181.46116638183594)
-
-[762 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0        (593.5712890625, 280.2579040527344)   (599.1698608398438, 278.01910400390625)
-1          (288.82537841796875, 292.4609375)    (290.7869567871094, 293.8036804199219)
-2    (316.6833190917969, 234.00531005859375)    (317.9931335449219, 236.1989288330078)
-3     (391.8727111816406, 238.2117156982422)  (394.26104736328125, 239.09971618652344)
-4    (316.6833190917969, 234.00531005859375)    (317.9931335449219, 236.1989288330078)
-..                                       ...                                       ...
-753   (377.3907775878906, 17.97957420349121)   (374.4363098144531, 18.933557510375977)
-754  (187.18899536132812, 160.0753631591797)  (186.65989685058594, 163.13433837890625)
-755   (396.2707214355469, 207.9366912841797)  (398.02923583984375, 208.38406372070312)
-756    (282.661865234375, 196.8151092529297)  (330.20941162109375, 206.35194396972656)
-757   (217.6221923828125, 172.6892852783203)  (217.68069458007812, 176.93104553222656)
-
-[758 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (344.0746765136719, 242.47251892089844)  (345.79058837890625, 243.79989624023438)
-1    (344.0746765136719, 242.47251892089844)  (345.79058837890625, 243.79989624023438)
-2    (599.1698608398438, 278.01910400390625)    (605.2075805664062, 275.7367858886719)
-3     (551.5975341796875, 306.7065124511719)   (557.5423583984375, 305.70355224609375)
-4     (317.9931335449219, 236.1989288330078)   (320.0376892089844, 238.50552368164062)
-..                                       ...                                       ...
-752   (586.8065795898438, 190.4069366455078)    (590.4733276367188, 187.3284912109375)
-753  (91.33522033691406, 26.663732528686523)    (90.57738494873047, 33.33436584472656)
-754      (366.169921875, 214.82078552246094)    (367.9432678222656, 216.3441162109375)
-755  (392.0397033691406, 192.03359985351562)       (393.39019775390625, 194.416015625)
-756  (472.1334228515625, 197.40574645996094)  (474.17193603515625, 196.19447326660156)
-
-[757 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (320.0376892089844, 238.50552368164062)     (321.735595703125, 240.6409912109375)
-1         (563.5810546875, 268.6221008300781)      (568.8164672851562, 266.75244140625)
-2      (567.4972534179688, 307.8887939453125)    (574.2152709960938, 306.7227783203125)
-3     (320.0376892089844, 238.50552368164062)     (321.735595703125, 240.6409912109375)
-4      (235.97410583496094, 220.809326171875)   (237.33688354492188, 224.4471893310547)
-..                                        ...                                       ...
-783  (194.71511840820312, 184.30807495117188)   (194.9597625732422, 187.78497314453125)
-784    (97.78682708740234, 91.93600463867188)    (586.803466796875, 196.05690002441406)
-785   (474.8958435058594, 187.98394775390625)   (477.41705322265625, 187.0139923095703)
-786   (3.9193623065948486, 309.3263244628906)  (5.4190192222595215, 318.90338134765625)
-787  (351.04608154296875, 27.323862075805664)    (633.5241088867188, 159.6033477783203)
-
-[788 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0       (321.735595703125, 240.6409912109375)   (323.46124267578125, 242.7379608154297)
-1        (56.0288200378418, 88.0684814453125)   (56.055912017822266, 95.40708923339844)
-2        (361.1708984375, 149.61814880371094)  (362.73760986328125, 150.11000061035156)
-3       (321.735595703125, 240.6409912109375)   (323.46124267578125, 242.7379608154297)
-4         (281.782958984375, 292.71728515625)   (283.53936767578125, 293.9819641113281)
-..                                        ...                                       ...
-795   (183.43675231933594, 159.1909637451172)    (470.1120910644531, 189.6480255126953)
-796  (40.946205139160156, 106.37593841552734)  (42.141517639160156, 114.49043273925781)
-797    (451.497802734375, 192.09373474121094)   (319.4327392578125, 195.19940185546875)
-798   (394.4847717285156, 192.21234130859375)   (395.5367736816406, 191.70851135253906)
-799     (94.1688461303711, 91.50626373291016)    (93.86383056640625, 98.51741790771484)
-
-[800 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (323.46124267578125, 242.7379608154297)   (325.4927062988281, 244.87550354003906)
-1     (56.055912017822266, 95.40708923339844)  (56.670677185058594, 103.26423645019531)
-2     (283.53936767578125, 293.9819641113281)   (287.4980163574219, 295.64178466796875)
-3      (304.4780578613281, 183.6837158203125)     (304.60205078125, 185.94361877441406)
-4    (378.55804443359375, 251.89988708496094)   (380.9221496582031, 252.92587280273438)
-..                                        ...                                       ...
-823    (205.6370086669922, 168.4665069580078)   (205.9973602294922, 170.59181213378906)
-824    (76.44815826416016, 65.44322967529297)    (75.49266815185547, 72.51109313964844)
-825    (627.760986328125, 217.38836669921875)     (632.609130859375, 213.3259735107422)
-826   (189.08384704589844, 151.6592254638672)   (189.7045440673828, 156.17356872558594)
-827     (84.3859634399414, 58.44986343383789)       (83.65496826171875, 65.53076171875)
-
-[828 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (325.4927062988281, 244.87550354003906)  (327.74676513671875, 246.83192443847656)
-1     (325.4927062988281, 244.87550354003906)  (327.74676513671875, 246.83192443847656)
-2      (402.7680969238281, 274.1546936035156)    (406.32830810546875, 275.137451171875)
-3      (396.9344482421875, 273.0352783203125)   (400.12335205078125, 274.1197509765625)
-4     (447.5756530761719, 224.26296997070312)    (449.86212158203125, 223.989013671875)
-..                                        ...                                       ...
-846   (145.99510192871094, 184.7284698486328)    (359.1086730957031, 81.50542449951172)
-847   (5.001506805419922, 107.59223937988281)   (6.044270992279053, 115.49354553222656)
-848  (197.05697631835938, 207.42584228515625)  (300.24334716796875, 213.47886657714844)
-849   (112.87620544433594, 26.51658058166504)      (341.6226806640625, 59.537841796875)
-850    (18.86363983154297, 261.9467468261719)    (21.388572692871094, 269.308837890625)
-
-[851 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (585.8858032226562, 260.77191162109375)    (592.1349487304688, 258.4593200683594)
-1     (305.2404479980469, 243.01519775390625)   (307.1923522949219, 245.20388793945312)
-2    (327.74676513671875, 246.83192443847656)  (329.85418701171875, 248.83267211914062)
-3    (15.792781829833984, 351.07366943359375)   (18.867265701293945, 358.7352294921875)
-4    (327.74676513671875, 246.83192443847656)  (329.85418701171875, 248.83267211914062)
-..                                        ...                                       ...
-794  (198.82191467285156, 200.41375732421875)   (200.1490936279297, 204.09637451171875)
-795   (369.0037536621094, 196.98635864257812)     (370.983154296875, 197.3385772705078)
-796   (515.3623657226562, 226.70909118652344)   (519.5877685546875, 224.37332153320312)
-797    (88.66708374023438, 69.91754913330078)      (88.4315185546875, 76.8975601196289)
-798  (174.79385375976562, 209.60568237304688)   (517.0098876953125, 269.52728271484375)
-
-[799 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (291.5233154296875, 298.1057434082031)   (293.61077880859375, 298.9922790527344)
-1       (287.7826843261719, 243.479248046875)   (289.6081237792969, 245.86581420898438)
-2      (361.0301818847656, 152.0783233642578)   (363.0292053222656, 152.05943298339844)
-3    (329.85418701171875, 248.83267211914062)    (331.9166259765625, 250.4246063232422)
-4     (307.1923522949219, 245.20388793945312)  (309.19573974609375, 247.17788696289062)
-..                                        ...                                       ...
-817       (464.138427734375, 287.94873046875)        (589.623046875, 367.7628479003906)
-818   (165.1788330078125, 178.93807983398438)    (601.2994384765625, 156.9119415283203)
-819    (329.7994079589844, 47.01820755004883)   (328.4226379394531, 48.484615325927734)
-820  (102.98590087890625, 13.845244407653809)   (77.05854034423828, 113.72562408447266)
-821    (478.1542663574219, 193.9108123779297)         (430.2265625, 198.17665100097656)
-
-[822 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (363.0292053222656, 152.05943298339844)       (363.9345703125, 152.9075469970703)
-1    (225.51234436035156, 232.20924377441406)  (227.16659545898438, 235.23985290527344)
-2     (293.61077880859375, 298.9922790527344)    (294.9396057128906, 299.8562316894531)
-3      (387.43792724609375, 77.2164535522461)    (386.4353332519531, 77.23753356933594)
-4     (352.7092590332031, 239.62428283691406)    (354.6184387207031, 240.6645050048828)
-..                                        ...                                       ...
-808  (306.03106689453125, 209.36590576171875)   (306.7188720703125, 211.88902282714844)
-809  (108.77047729492188, 104.92996215820312)  (109.87661743164062, 109.50714874267578)
-810   (203.23121643066406, 171.1897735595703)   (401.8782653808594, 187.84693908691406)
-811   (474.0323791503906, 204.62359619140625)   (476.8152770996094, 203.38221740722656)
-812  (48.065181732177734, 207.21702575683594)  (239.59674072265625, 194.57376098632812)
-
-[813 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (294.9396057128906, 299.8562316894531)    (296.2205505371094, 300.4920959472656)
-1     (491.52349853515625, 247.052001953125)   (495.6929626464844, 245.82984924316406)
-2    (425.0262145996094, 252.44680786132812)     (428.20654296875, 252.53656005859375)
-3    (210.8987274169922, 234.47525024414062)  (212.45413208007812, 237.51695251464844)
-4    (508.2863464355469, 235.52108764648438)   (512.6624145507812, 233.91961669921875)
-..                                       ...                                       ...
-818  (327.1583557128906, 100.96088409423828)      (326.3466796875, 101.95159149169922)
-819   (82.33604431152344, 90.52008819580078)    (81.81037139892578, 96.01700592041016)
-820  (628.9654541015625, 217.91249084472656)   (635.5672607421875, 213.78268432617188)
-821  (166.4720916748047, 220.70530700683594)   (167.7069854736328, 224.48916625976562)
-822  (380.1421203613281, 221.70404052734375)     (381.81787109375, 221.13485717773438)
-
-[823 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0      (428.20654296875, 252.53656005859375)    (431.4668273925781, 252.4708251953125)
-1      (444.9518737792969, 267.772216796875)  (448.81719970703125, 267.73394775390625)
-2       (22.113435745239258, 428.8466796875)   (25.093854904174805, 436.7695617675781)
-3      (394.802001953125, 257.3227844238281)   (397.31182861328125, 257.6365051269531)
-4     (558.4677124023438, 221.2675323486328)    (562.9082641601562, 219.0081787109375)
-..                                       ...                                       ...
-818  (318.29913330078125, 80.94062805175781)     (316.2950744628906, 81.6759033203125)
-819      (187.09535217285156, 205.322265625)   (189.17697143554688, 208.5376739501953)
-820  (347.1836853027344, 210.93719482421875)   (334.03643798828125, 66.34479522705078)
-821  (43.27248001098633, 149.98199462890625)         (79.42578125, 145.75697326660156)
-822  (522.2396240234375, 297.27105712890625)    (528.2731323242188, 296.9837341308594)
-
-[823 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (420.5594482421875, 243.32064819335938)   (423.5716552734375, 243.18626403808594)
-1         (364.52734375, 153.17926025390625)     (365.104736328125, 153.0166778564453)
-2     (350.0706787109375, 219.1393280029297)   (351.6011047363281, 219.86280822753906)
-3    (397.31182861328125, 257.6365051269531)    (400.6101989746094, 257.9775695800781)
-4     (20.10660743713379, 378.8916931152344)  (23.121522903442383, 384.90264892578125)
-..                                       ...                                       ...
-799   (85.2457275390625, 100.87291717529297)   (85.62361145019531, 105.75750732421875)
-800  (243.76519775390625, 220.0467529296875)   (244.9051971435547, 222.06040954589844)
-801   (321.3130187988281, 57.85866928100586)   (320.35736083984375, 58.59237289428711)
-802  (72.55864715576172, 133.46578979492188)    (72.64958953857422, 138.2987518310547)
-803   (76.42118072509766, 356.8882751464844)   (500.6335144042969, 263.49456787109375)
-
-[804 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0      (365.104736328125, 153.0166778564453)   (367.2847900390625, 153.91529846191406)
-1    (423.5716552734375, 243.18626403808594)  (426.56585693359375, 244.29380798339844)
-2    (22.197574615478516, 292.7810974121094)   (23.56908416748047, 293.29608154296875)
-3     (361.1431579589844, 242.7117919921875)   (363.3331604003906, 243.72593688964844)
-4    (248.17698669433594, 191.2383575439453)  (249.90818786621094, 191.48878479003906)
-..                                       ...                                       ...
-813       (172.33984375, 213.13726806640625)   (430.0052185058594, 186.38241577148438)
-814  (583.2005615234375, 196.27572631835938)    (131.4967498779297, 228.6947479248047)
-815       (73.064453125, 117.59123229980469)      (73.82275390625, 115.79866790771484)
-816   (92.32630157470703, 54.93315124511719)   (65.28225708007812, 104.73262786865234)
-817    (4.62595796585083, 284.2909851074219)   (351.9693298339844, 212.40902709960938)
-
-[818 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (367.2847900390625, 153.91529846191406)  (368.7742004394531, 154.14694213867188)
-1       (87.2637939453125, 265.0409240722656)   (88.91817474365234, 264.5318603515625)
-2     (403.33038330078125, 258.6578674316406)  (405.70440673828125, 259.9443054199219)
-3     (68.29561614990234, 55.413780212402344)   (69.55985260009766, 54.82048416137695)
-4      (541.9695434570312, 231.8542022705078)  (547.1189575195312, 232.91030883789062)
-..                                        ...                                      ...
-808  (325.56207275390625, 58.808555603027344)   (327.6197509765625, 58.69164276123047)
-809  (246.25485229492188, 215.06961059570312)  (247.1797637939453, 214.19369506835938)
-810    (586.6583862304688, 147.0606689453125)  (593.0974731445312, 147.54808044433594)
-811   (86.28568267822266, 137.78343200683594)   (87.6440658569336, 137.96524047851562)
-812    (540.1807250976562, 298.9012451171875)    (546.895263671875, 301.3322448730469)
-
-[813 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (368.7742004394531, 154.14694213867188)    (370.8298034667969, 154.8811492919922)
-1       (557.7774047851562, 260.612548828125)     (563.8761596679688, 261.965087890625)
-2      (69.55985260009766, 54.82048416137695)    (70.57869720458984, 54.71677780151367)
-3      (253.9935760498047, 251.5531463623047)    (255.6398468017578, 251.9959259033203)
-4      (88.91817474365234, 264.5318603515625)    (89.7969970703125, 264.76690673828125)
-..                                        ...                                       ...
-767  (213.32308959960938, 189.81443786621094)     (215.1155548095703, 189.989501953125)
-768    (307.76910400390625, 7.17647123336792)  (308.98321533203125, 6.6598968505859375)
-769    (298.3183898925781, 79.22156524658203)   (300.80841064453125, 78.71040344238281)
-770  (190.26499938964844, 232.19735717773438)  (191.62533569335938, 231.94161987304688)
-771    (6.952357769012451, 161.4512481689453)    (9.215496063232422, 161.4934844970703)
-
-[772 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (370.8298034667969, 154.8811492919922)  (371.84222412109375, 154.93460083007812)
-1    (205.82046508789062, 243.51608276367188)    (207.1083526611328, 243.4742889404297)
-2    (236.16900634765625, 243.61407470703125)    (237.350341796875, 243.60464477539062)
-3    (219.62698364257812, 243.43418884277344)  (220.92469787597656, 243.45310974121094)
-4     (13.834235191345215, 413.0457763671875)   (13.732199668884277, 414.1662902832031)
-..                                        ...                                       ...
-766  (14.714412689208984, 151.44195556640625)   (599.4664916992188, 157.37159729003906)
-767   (90.58019256591797, 165.44076538085938)   (92.71914672851562, 165.57034301757812)
-768    (78.56098175048828, 128.3917694091797)   (470.0310363769531, 167.90341186523438)
-769  (321.07354736328125, 48.913604736328125)   (322.44500732421875, 49.09068298339844)
-770   (86.36685180664062, 109.63725280761719)   (87.42047119140625, 109.02743530273438)
-
-[771 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0    (371.84222412109375, 154.93460083007812)  (374.0010070800781, 154.28314208984375)
-1     (108.04036712646484, 99.36152648925781)   (108.8170394897461, 99.02997589111328)
-2      (256.5647888183594, 252.1676788330078)  (257.8725891113281, 252.25656127929688)
-3     (254.6732940673828, 191.45350646972656)  (256.0971984863281, 191.28492736816406)
-4     (396.1121520996094, 278.62554931640625)   (399.0333251953125, 279.6886291503906)
-..                                        ...                                      ...
-798    (359.9895935058594, 80.34010314941406)  (150.43783569335938, 85.77135467529297)
-799    (331.451904296875, 101.25789642333984)  (131.46218872070312, 228.2256317138672)
-800     (181.939697265625, 223.4518585205078)      (182.49853515625, 222.913818359375)
-801        (86.54150390625, 80.8951644897461)  (568.0010375976562, 195.50047302246094)
-802      (305.982177734375, 164.819091796875)   (307.511474609375, 165.41741943359375)
-
-[803 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (108.8170394897461, 99.02997589111328)   (109.46149444580078, 98.66189575195312)
-1     (80.99485778808594, 142.00869750976562)    (81.96814727783203, 141.9993896484375)
-2      (75.92721557617188, 371.9739685058594)    (75.9359130859375, 372.76910400390625)
-3     (256.0971984863281, 191.28492736816406)    (257.3663635253906, 190.5289764404297)
-4        (468.3507385253906, 236.67333984375)    (472.0845947265625, 237.1930694580078)
-..                                        ...                                       ...
-768  (112.43280792236328, 153.76914978027344)  (113.97000885009766, 153.52206420898438)
-769    (494.1130676269531, 204.0738983154297)     (604.904541015625, 153.1813507080078)
-770   (598.1234741210938, 153.77679443359375)     (604.904541015625, 153.1813507080078)
-771   (442.1061096191406, 163.47158813476562)   (445.2150573730469, 162.68063354492188)
-772    (222.4743194580078, 225.2313232421875)  (224.11892700195312, 224.72755432128906)
-
-[773 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (125.99823760986328, 40.00706100463867)   (127.16034698486328, 37.91813659667969)
-1        (95.36478424072266, 263.64599609375)    (96.66461181640625, 263.3160400390625)
-2      (75.9359130859375, 372.76910400390625)    (75.79847717285156, 373.5760498046875)
-3    (246.66262817382812, 254.93809509277344)  (247.80929565429688, 254.81390380859375)
-4    (39.931766510009766, 289.49884033203125)    (41.33729553222656, 288.9253234863281)
-..                                        ...                                       ...
-809     (217.23974609375, 219.73614501953125)    (219.027587890625, 219.63706970214844)
-810   (341.2806396484375, 40.605804443359375)    (342.8368225097656, 39.68988800048828)
-811   (263.0267333984375, 217.88034057617188)    (600.699462890625, 288.00994873046875)
-812   (81.78980255126953, 137.50018310546875)   (82.56709289550781, 136.46115112304688)
-813    (631.9734497070312, 290.6585998535156)   (478.5484619140625, 277.85223388671875)
-
-[814 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (153.4669189453125, 209.96694946289062)  (154.27870178222656, 208.92295837402344)
-1     (417.9247131347656, 283.8797302246094)    (420.9335632324219, 284.9219665527344)
-2     (124.60897827148438, 36.3491096496582)  (125.10382080078125, 35.595802307128906)
-3     (420.6905517578125, 264.2188415527344)       (423.2861328125, 264.8446960449219)
-4    (355.04766845703125, 259.8436279296875)    (356.6776123046875, 260.2175598144531)
-..                                       ...                                       ...
-798     (340.4379577636719, 45.570068359375)   (326.1024169921875, 164.70494079589844)
-799   (96.79344177246094, 92.86758422851562)    (511.1976318359375, 221.4561309814453)
-800  (402.5202331542969, 201.61373901367188)   (405.2314758300781, 201.36239624023438)
-801  (101.00543212890625, 76.77430725097656)   (111.4430923461914, 128.48202514648438)
-802  (327.03936767578125, 45.59146499633789)  (328.22052001953125, 44.984527587890625)
-
-[803 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0    (296.72528076171875, 300.90838623046875)  (297.8334655761719, 300.72662353515625)
-1      (377.1164245605469, 152.5273895263672)  (377.8460693359375, 152.31793212890625)
-2         (423.2861328125, 264.8446960449219)      (426.1680603027344, 265.5673828125)
-3     (516.6948852539062, 236.94839477539062)   (520.614013671875, 237.50570678710938)
-4         (423.2861328125, 264.8446960449219)      (426.1680603027344, 265.5673828125)
-..                                        ...                                      ...
-806   (481.8726501464844, 149.10806274414062)  (489.3104248046875, 148.14700317382812)
-807   (204.41964721679688, 6.952785968780518)  (109.54964447021484, 136.7452850341797)
-808   (598.4434204101562, 171.17897033691406)   (596.283447265625, 147.91744995117188)
-809  (189.11904907226562, 23.591550827026367)    (537.36572265625, 209.08116149902344)
-810    (576.4053344726562, 262.0009460449219)   (582.7575073242188, 262.9228210449219)
-
-[811 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (297.8334655761719, 300.72662353515625)   (297.8628845214844, 300.4114990234375)
-1     (567.0169067382812, 110.21202087402344)    (574.00537109375, 108.18930053710938)
-2    (260.69635009765625, 189.15110778808594)    (262.46728515625, 188.39231872558594)
-3        (499.7490234375, 223.08535766601562)   (503.8672180175781, 223.2926788330078)
-4     (98.07350158691406, 261.63433837890625)   (99.42842864990234, 261.2193908691406)
-..                                        ...                                      ...
-804   (252.9138946533203, 206.62567138671875)  (252.24642944335938, 206.9983367919922)
-805    (108.6859359741211, 30.65637969970703)   (109.047119140625, 29.321691513061523)
-806     (536.94091796875, 179.18096923828125)  (541.9955444335938, 178.90809631347656)
-807    (362.446044921875, 21.978918075561523)   (363.3642883300781, 20.30629539489746)
-808     (335.6945495605469, 48.1989631652832)  (337.30975341796875, 47.01890563964844)
-
-[809 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (297.8628845214844, 300.4114990234375)    (298.2069396972656, 300.2558898925781)
-1      (369.1294250488281, 247.6548309326172)    (370.5625305175781, 247.8009796142578)
-2      (112.6431655883789, 94.26990509033203)   (112.26049041748047, 93.34418487548828)
-3      (503.8672180175781, 223.2926788330078)   (508.0594482421875, 223.45474243164062)
-4      (486.3399658203125, 301.7586669921875)  (490.66900634765625, 303.64495849609375)
-..                                        ...                                       ...
-847   (64.05587005615234, 154.87350463867188)    (65.1199722290039, 154.62620544433594)
-848    (323.7295837402344, 209.9788055419922)      (491.01611328125, 207.0146026611328)
-849    (409.4273376464844, 200.1299285888672)    (411.9666748046875, 200.5021514892578)
-850    (528.375244140625, 207.25778198242188)   (531.7557373046875, 209.17422485351562)
-851  (249.07766723632812, 210.62770080566406)  (250.30018615722656, 209.97836303710938)
-
-[852 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (298.2069396972656, 300.2558898925781)    (298.7676696777344, 299.8905334472656)
-1     (112.26049041748047, 93.34418487548828)    (112.2258071899414, 92.49862670898438)
-2       (78.02130889892578, 45.8228874206543)    (78.65076446533203, 44.25899887084961)
-3      (387.5479431152344, 282.0166931152344)   (390.2363586425781, 283.03997802734375)
-4     (434.11151123046875, 238.1449432373047)    (436.0158996582031, 238.3738250732422)
-..                                        ...                                       ...
-899     (221.321533203125, 204.5957794189453)   (221.41529846191406, 204.0782470703125)
-900  (398.09368896484375, 202.98080444335938)   (399.6437683105469, 202.38784790039062)
-901   (547.3561401367188, 178.28744506835938)    (552.1358032226562, 178.4854736328125)
-902   (197.0834503173828, 205.03614807128906)  (197.79891967773438, 203.72625732421875)
-903    (92.98451232910156, 55.46183395385742)    (93.48637390136719, 54.97352981567383)
-
-[904 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (298.7676696777344, 299.8905334472656)     (298.4010009765625, 299.626220703125)
-1      (383.1644592285156, 151.2137451171875)    (384.3009338378906, 150.2527313232422)
-2    (473.70281982421875, 212.12879943847656)   (477.0594177246094, 212.24618530273438)
-3      (378.4028015136719, 281.0534362792969)    (380.4854736328125, 282.0829772949219)
-4       (588.6353149414062, 96.8008804321289)    (596.5857543945312, 95.17466735839844)
-..                                        ...                                       ...
-906  (324.92730712890625, 105.90565490722656)   (117.64501190185547, 166.3417510986328)
-907   (265.3140869140625, 212.40138244628906)   (265.8924255371094, 212.11593627929688)
-908  (221.84690856933594, 206.50096130371094)  (222.14659118652344, 207.15286254882812)
-909   (580.2313842773438, 170.87042236328125)   (461.7236633300781, 268.29571533203125)
-910     (530.46337890625, 169.59439086914062)     (535.15185546875, 169.48728942871094)
-
-[911 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0      (298.4010009765625, 299.626220703125)    (298.7970275878906, 299.3968505859375)
-1     (384.3009338378906, 150.2527313232422)    (386.4449462890625, 149.2040252685547)
-2    (112.62813568115234, 91.08726501464844)   (111.70011138916016, 90.86852264404297)
-3    (477.0594177246094, 212.24618530273438)  (480.61199951171875, 212.83108520507812)
-4     (326.0814208984375, 245.2606964111328)  (327.16314697265625, 245.35818481445312)
-..                                       ...                                       ...
-862   (187.8889923095703, 223.1200408935547)  (210.80625915527344, 220.27859497070312)
-863  (409.8338623046875, 220.13339233398438)   (413.3404846191406, 222.78489685058594)
-864        (462.58984375, 181.6190948486328)   (465.9429626464844, 181.33865356445312)
-865  (219.4702911376953, 197.53794860839844)    (463.7875061035156, 283.0542907714844)
-866  (186.95419311523438, 211.6463165283203)  (187.26756286621094, 211.01031494140625)
-
-[867 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (298.7970275878906, 299.3968505859375)   (299.8847351074219, 299.5915832519531)
-1       (102.842041015625, 259.5596923828125)  (102.18901824951172, 259.1086120605469)
-2    (382.43292236328125, 283.19915771484375)      (383.7608947753906, 284.1708984375)
-3    (480.61199951171875, 212.83108520507812)  (483.6429138183594, 212.80743408203125)
-4     (111.70011138916016, 90.86852264404297)   (111.66458892822266, 89.3118896484375)
-..                                        ...                                      ...
-823    (264.252197265625, 206.44410705566406)  (18.189224243164062, 297.1719970703125)
-824   (568.5243530273438, 170.82559204101562)   (571.704345703125, 171.32901000976562)
-825   (511.4643249511719, 194.64002990722656)  (518.6685791015625, 195.28163146972656)
-826     (91.88423919677734, 99.3267593383789)    (91.7687759399414, 98.48314666748047)
-827   (343.12078857421875, 46.86037063598633)  (343.7745056152344, 45.823143005371094)
-
-[828 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (299.8847351074219, 299.5915832519531)  (299.56622314453125, 299.47344970703125)
-1      (78.37005615234375, 38.08097839355469)    (77.75202941894531, 38.30991744995117)
-2     (483.6429138183594, 212.80743408203125)     (487.275146484375, 212.1680450439453)
-3     (295.9229736328125, 152.73834228515625)   (296.0375061035156, 151.83380126953125)
-4      (111.66458892822266, 89.3118896484375)   (111.46187591552734, 88.30709075927734)
-..                                        ...                                       ...
-873    (96.07805633544922, 90.74800872802734)     (96.34242248535156, 90.5599365234375)
-874   (106.8250961303711, 128.27542114257812)       (106.2216796875, 127.6656494140625)
-875  (435.40521240234375, 174.74664306640625)      (587.46630859375, 281.9239501953125)
-876     (15.1948881149292, 323.4978332519531)  (14.513116836547852, 323.63189697265625)
-877   (457.5075988769531, 225.97373962402344)   (459.3052978515625, 225.85812377929688)
-
-[878 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0    (299.56622314453125, 299.47344970703125)     (297.8334045410156, 299.090576171875)
-1     (264.1600036621094, 184.27032470703125)   (264.7484130859375, 184.08473205566406)
-2       (46.0268669128418, 283.8006591796875)   (46.170677185058594, 283.4925842285156)
-3      (34.25749588012695, 259.6119689941406)   (34.707210540771484, 259.1026611328125)
-4      (65.74813842773438, 385.3079528808594)    (63.87615966796875, 387.1070861816406)
-..                                        ...                                       ...
-875    (421.592529296875, 224.09083557128906)  (423.00677490234375, 224.36868286132812)
-876       (106.2216796875, 127.6656494140625)   (106.2751235961914, 126.38673400878906)
-877   (12.971179008483887, 375.2864074707031)    (9.02353572845459, 376.60186767578125)
-878    (625.767333984375, 338.30047607421875)    (611.3302612304688, 338.3327331542969)
-879   (74.58296203613281, 148.49990844726562)   (33.88090133666992, 134.07435607910156)
-
-[880 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0      (297.8334045410156, 299.090576171875)    (296.3998718261719, 298.3592834472656)
-1    (519.1176147460938, 317.59613037109375)    (522.0367431640625, 321.5592041015625)
-2     (296.4701232910156, 151.9636688232422)    (297.2536926269531, 151.5010223388672)
-3     (293.323974609375, 243.99697875976562)  (293.37762451171875, 244.17825317382812)
-4     (332.6833190917969, 189.2766876220703)   (333.55633544921875, 189.2347869873047)
-..                                       ...                                       ...
-892  (494.04022216796875, 137.9385986328125)   (495.9500427246094, 139.80947875976562)
-893   (232.3885498046875, 217.6266326904297)   (233.46815490722656, 216.9976806640625)
-894   (602.1998901367188, 142.9998016357422)   (605.1299438476562, 144.58287048339844)
-895    (75.63356018066406, 97.6839828491211)    (77.23724365234375, 96.35150909423828)
-896   (93.30320739746094, 81.13660430908203)   (398.9087829589844, 196.04185485839844)
-
-[897 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (542.2625122070312, 326.7477722167969)    (544.1832275390625, 334.8944396972656)
-1     (296.3998718261719, 298.3592834472656)   (291.51239013671875, 298.0277404785156)
-2         (414.693359375, 299.0567626953125)    (414.8270568847656, 302.8415832519531)
-3    (228.45059204101562, 239.2955322265625)  (228.13262939453125, 237.31935119628906)
-4     (549.1488647460938, 258.0412292480469)    (552.0740966796875, 264.9590759277344)
-..                                       ...                                       ...
-857     (85.0369644165039, 95.8836669921875)     (87.37035369873047, 89.9101333618164)
-858   (321.6385192871094, 7.717897415161133)     (327.6418762207031, 7.33659553527832)
-859  (558.7288208007812, 191.46458435058594)   (563.0340576171875, 197.27670288085938)
-860  (340.26202392578125, 29.13187599182129)   (346.3357238769531, 28.774559020996094)
-861   (321.919189453125, 3.5383286476135254)    (327.8934631347656, 3.570244789123535)
-
-[862 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (414.8270568847656, 302.8415832519531)     (414.226806640625, 307.1439208984375)
-1      (414.8270568847656, 302.8415832519531)     (414.226806640625, 307.1439208984375)
-2    (265.95416259765625, 181.95945739746094)  (267.20367431640625, 180.72708129882812)
-3      (412.3534851074219, 282.4120178222656)     (412.1925354003906, 286.362548828125)
-4      (524.7174682617188, 262.9534606933594)     (526.53759765625, 269.96429443359375)
-..                                        ...                                       ...
-851   (34.38157653808594, 132.53860473632812)   (37.21411895751953, 124.32459259033203)
-852  (113.91272735595703, 11.542616844177246)     (120.4290542602539, 6.01371955871582)
-853    (15.34311580657959, 366.9963684082031)    (9.668817520141602, 361.1815185546875)
-854    (421.0542297363281, 213.7288818359375)    (422.5826721191406, 217.5356903076172)
-855    (167.953369140625, 219.69671630859375)  (184.23936462402344, 216.24697875976562)
-
-[856 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (469.41436767578125, 265.2681884765625)   (469.6985168457031, 271.3885192871094)
-1      (414.226806640625, 307.1439208984375)   (412.96832275390625, 311.800048828125)
-2     (545.0220336914062, 343.9394836425781)   (545.0723876953125, 353.7895812988281)
-3     (536.1753540039062, 241.2672576904297)  (537.6146850585938, 249.03590393066406)
-4     (515.3575439453125, 256.8922424316406)   (516.1187133789062, 264.2584533691406)
-..                                       ...                                      ...
-875    (608.481201171875, 209.3531036376953)   (489.8781433105469, 49.19184875488281)
-876    (483.7606201171875, 286.114501953125)  (483.34930419921875, 291.6407775878906)
-877  (227.7198486328125, 171.44879150390625)   (229.712158203125, 168.30203247070312)
-878   (316.1490173339844, 165.8050079345703)  (310.34368896484375, 167.7544403076172)
-879   (598.2998657226562, 186.6510009765625)   (601.3967895507812, 195.7008514404297)
-
-[880 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (333.7288818359375, 166.91673278808594)   (335.7239685058594, 168.04029846191406)
-1      (456.2391662597656, 269.0815124511719)   (456.09710693359375, 275.1429748535156)
-2     (508.7731628417969, 279.32525634765625)    (508.8398132324219, 287.2675476074219)
-3      (412.96832275390625, 311.800048828125)      (411.35205078125, 316.6959228515625)
-4       (597.3406372070312, 331.921142578125)    (597.4649658203125, 344.0990295410156)
-..                                        ...                                       ...
-907   (374.6238708496094, 34.122413635253906)      (380.5020751953125, 36.931884765625)
-908   (484.8611755371094, 209.44992065429688)  (487.34893798828125, 215.97080993652344)
-909     (123.98388671875, 114.19772338867188)   (109.5277099609375, 101.07662963867188)
-910  (41.804134368896484, 114.79834747314453)   (45.31656265258789, 106.30598449707031)
-911   (423.4313049316406, 235.21978759765625)   (423.4761047363281, 239.48997497558594)
-
-[912 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0       (469.6280517578125, 277.643798828125)   (468.9569396972656, 284.6435241699219)
-1      (473.2411804199219, 339.2113952636719)        (471.01953125, 347.2229309082031)
-2      (522.9724731445312, 355.9708557128906)   (520.2156372070312, 365.9589538574219)
-3      (495.0752868652344, 338.8216552734375)   (492.8909606933594, 347.6175842285156)
-4       (97.68569946289062, 232.666748046875)  (97.56645202636719, 225.24557495117188)
-..                                        ...                                      ...
-929      (375.4228515625, 39.229209899902344)  (383.0892639160156, 42.141014099121094)
-930    (102.1885757446289, 115.3875503540039)  (234.5357208251953, 195.69515991210938)
-931    (98.93264770507812, 68.77676391601562)  (104.76229095458984, 61.24790573120117)
-932  (237.34799194335938, 210.37281799316406)  (236.5152130126953, 209.09698486328125)
-933          (80.3102035522461, 64.537109375)  (28.629207611083984, 345.6892395019531)
-
-[934 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0     (492.8909606933594, 347.6175842285156)   (489.6794738769531, 356.6230163574219)
-1    (538.8012084960938, 267.21478271484375)   (538.515869140625, 277.13262939453125)
-2          (471.01953125, 347.2229309082031)  (467.6690368652344, 355.34197998046875)
-3    (408.67755126953125, 321.9880065917969)   (405.6881408691406, 327.2682800292969)
-4        (338.2705078125, 278.0172119140625)   (336.28253173828125, 280.117919921875)
-..                                       ...                                      ...
-906   (560.9448852539062, 246.9259033203125)  (559.0132446289062, 258.03521728515625)
-907    (634.05224609375, 207.61402893066406)  (134.9258270263672, 16.508758544921875)
-908   (465.9888916015625, 319.2474365234375)  (463.41888427734375, 326.6078796386719)
-909  (104.68677520751953, 82.69256591796875)   (383.77032470703125, 37.8006591796875)
-910        (432.52734375, 221.0386505126953)   (432.8134460449219, 226.5284881591797)
-
-[911 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (479.4197082519531, 296.7402038574219)  (477.11016845703125, 304.54132080078125)
-1     (445.5158996582031, 309.3946228027344)    (443.1249694824219, 316.3987121582031)
-2    (391.63922119140625, 314.9788818359375)    (388.2460021972656, 319.4364013671875)
-3     (585.1044311523438, 283.5939025878906)    (582.904052734375, 295.41583251953125)
-4    (454.01141357421875, 288.0934143066406)    (451.9969787597656, 294.9079895019531)
-..                                       ...                                       ...
-969  (414.7518310546875, 29.577495574951172)      (472.59619140625, 258.1588439941406)
-970  (488.1936950683594, 239.52520751953125)    (488.5102233886719, 247.5863037109375)
-971   (132.6994171142578, 80.51397705078125)   (532.2056274414062, 212.82862854003906)
-972   (256.3211975097656, 197.5163116455078)   (256.89007568359375, 195.9165802001953)
-973   (577.0948486328125, 266.3389892578125)      (578.07275390625, 276.8127746582031)
-
-[974 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (536.3381958007812, 287.2976989746094)    (534.3199462890625, 297.479736328125)
-1    (463.21551513671875, 363.39898681640625)  (458.4740295410156, 371.45904541015625)
-2      (383.7747497558594, 240.9304656982422)    (383.193603515625, 244.4026336669922)
-3     (517.3972778320312, 369.70611572265625)  (512.7898559570312, 380.10223388671875)
-4     (371.5641784667969, 214.96641540527344)  (372.7751159667969, 218.21372985839844)
-..                                        ...                                      ...
-950    (193.1315460205078, 181.8204803466797)  (195.9498291015625, 177.00575256347656)
-951   (469.9741516113281, 230.42686462402344)   (470.1122741699219, 237.3358154296875)
-952  (198.91986083984375, 190.65203857421875)  (199.6247100830078, 186.28546142578125)
-953   (483.4803161621094, 197.06100463867188)  (425.4142150878906, 237.87478637695312)
-954    (218.7303466796875, 166.8517608642578)   (220.8043670654297, 163.3086395263672)
-
-[955 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (458.4740295410156, 371.45904541015625)   (452.5062255859375, 378.9942321777344)
-1       (434.6401672363281, 329.155029296875)   (430.6851806640625, 335.4435119628906)
-2     (458.4740295410156, 371.45904541015625)   (452.5062255859375, 378.9942321777344)
-3     (507.1583251953125, 397.24102783203125)   (501.0155944824219, 407.3817443847656)
-4     (489.31805419921875, 387.5918884277344)   (483.2713623046875, 396.7419128417969)
-..                                        ...                                      ...
-986   (491.3125915527344, 44.542259216308594)  (497.70220947265625, 53.23003005981445)
-987  (181.67271423339844, 176.11849975585938)    (9.3700532913208, 176.94839477539062)
-988    (614.180419921875, 236.51451110839844)   (613.062255859375, 249.28524780273438)
-989   (625.5730590820312, 254.38311767578125)  (127.23643493652344, 66.30256652832031)
-990   (138.6835174560547, 113.02859497070312)  (302.6314697265625, 227.12103271484375)
-
-[991 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0      (380.3931884765625, 328.190185546875)  (375.7991027832031, 332.09039306640625)
-1     (294.475830078125, 279.78802490234375)       (291.2880859375, 279.769775390625)
-2     (430.6851806640625, 335.4435119628906)   (425.9235534667969, 341.6098327636719)
-3     (452.5062255859375, 378.9942321777344)  (446.15875244140625, 386.1999206542969)
-4     (582.7821044921875, 426.4774475097656)   (575.5088500976562, 439.5534362792969)
-..                                       ...                                      ...
-983  (131.88729858398438, 67.59051513671875)  (137.4557647705078, 60.778228759765625)
-984  (598.7328491210938, 246.98802185058594)      (597.560546875, 259.41949462890625)
-985   (463.2047119140625, 284.1373291015625)  (460.53997802734375, 291.7096862792969)
-986   (257.9368591308594, 194.8408660888672)     (258.938720703125, 191.886474609375)
-987   (107.82431030273438, 83.9294662475586)  (112.89006042480469, 76.01136016845703)
-
-[988 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (389.3572082519531, 312.62908935546875)     (385.405517578125, 316.6501159667969)
-1      (425.9235534667969, 341.6098327636719)    (421.1967468261719, 347.0318908691406)
-2     (446.15875244140625, 386.1999206542969)   (440.1539306640625, 393.26495361328125)
-3     (511.14801025390625, 349.7398376464844)   (505.93682861328125, 358.7703552246094)
-4      (552.7062377929688, 425.8807373046875)    (544.7278442382812, 436.8807678222656)
-...                                       ...                                       ...
-1009  (521.1448364257812, 437.76043701171875)    (567.427978515625, 393.37200927734375)
-1010  (104.18118286132812, 77.18028259277344)   (109.10699462890625, 69.37057495117188)
-1011  (359.4498596191406, 230.15948486328125)  (358.63275146484375, 232.96664428710938)
-1012  (456.0050354003906, 2.9073550701141357)   (463.79852294921875, 10.63631534576416)
-1013   (533.2562255859375, 279.0391845703125)    (575.8817749023438, 265.0574035644531)
-
-[1014 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (486.5053405761719, 425.6849670410156)     (478.53173828125, 433.73284912109375)
-1      (440.1539306640625, 393.26495361328125)    (433.1054382324219, 399.2046203613281)
-2      (440.1539306640625, 393.26495361328125)    (433.1054382324219, 399.2046203613281)
-3      (476.1883239746094, 337.87799072265625)    (471.0245056152344, 344.8624267578125)
-4       (469.3609619140625, 413.4509582519531)  (461.81573486328125, 420.81231689453125)
-...                                        ...                                       ...
-1025    (587.6605224609375, 272.5635986328125)    (452.0158996582031, 287.4327087402344)
-1026    (514.9624633789062, 264.0484924316406)    (512.8485717773438, 273.1068115234375)
-1027  (216.34410095214844, 184.33616638183594)  (195.65374755859375, 172.61831665039062)
-1028  (144.34642028808594, 30.305709838867188)   (151.21685791015625, 24.54747772216797)
-1029    (429.8245849609375, 128.2061767578125)   (432.89825439453125, 133.4361114501953)
-
-[1030 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (415.9839172363281, 352.04974365234375)  (410.07733154296875, 356.5802307128906)
-1         (377.5732421875, 355.1264953613281)  (371.9625244140625, 358.20062255859375)
-2      (460.1200866699219, 304.2388916015625)  (455.4794006347656, 309.92999267578125)
-3      (98.06355285644531, 169.2910614013672)   (98.82142639160156, 161.2539520263672)
-4     (442.2303466796875, 383.22076416015625)  (435.6150817871094, 388.66290283203125)
-...                                       ...                                      ...
-1018  (350.0538635253906, 169.13966369628906)   (351.1312255859375, 171.1236572265625)
-1019  (451.95037841796875, 297.9265441894531)  (448.38507080078125, 303.6046142578125)
-1020   (263.236083984375, 172.22857666015625)  (212.05068969726562, 166.3113250732422)
-1021    (536.791259765625, 389.5228576660156)   (596.7940673828125, 469.4141845703125)
-1022   (394.1759338378906, 271.4488830566406)   (458.3507080078125, 293.4089660644531)
-
-[1023 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (361.1284484863281, 341.2817077636719)      (356.1865234375, 343.3612976074219)
-1        (470.1604309082031, 440.642822265625)   (461.6903991699219, 446.5306091308594)
-2       (416.3755187988281, 350.9556579589844)   (410.8126220703125, 354.7564392089844)
-3       (429.5676574707031, 316.0714111328125)  (425.0640563964844, 320.13995361328125)
-4      (366.80047607421875, 339.5057373046875)   (361.7798156738281, 341.6390686035156)
-...                                        ...                                      ...
-1027      (208.9268341064453, 175.57275390625)   (478.7677917480469, 390.8945007324219)
-1028     (167.130126953125, 54.84455108642578)  (172.09722900390625, 49.64110565185547)
-1029    (501.8998107910156, 382.4622497558594)   (397.03680419921875, 322.289306640625)
-1030    (501.8998107910156, 382.4622497558594)   (397.03680419921875, 322.289306640625)
-1031  (141.07681274414062, 23.987600326538086)   (478.0845031738281, 280.1776428222656)
-
-[1032 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (425.0640563964844, 320.13995361328125)   (420.8349914550781, 323.8094482421875)
-1      (445.7161560058594, 432.19610595703125)    (437.59912109375, 436.16888427734375)
-2       (279.6755676269531, 277.9829406738281)     (276.7239685058594, 276.80224609375)
-3      (418.8797302246094, 408.73785400390625)   (411.5339660644531, 411.8554992675781)
-4      (383.97003173828125, 367.1075439453125)   (377.9220886230469, 369.0364990234375)
-...                                        ...                                      ...
-1030    (442.7341613769531, 310.7128601074219)    (439.141357421875, 315.0189514160156)
-1031  (449.15863037109375, 269.17230224609375)  (445.0459289550781, 273.17535400390625)
-1032    (397.03680419921875, 322.289306640625)   (488.6145324707031, 394.8865661621094)
-1033     (389.091064453125, 277.7984313964844)   (386.5077819824219, 280.2070007324219)
-1034    (423.5456237792969, 377.9652404785156)   (417.4656982421875, 381.2891540527344)
-
-[1035 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (338.0420837402344, 304.7149353027344)      (334.4208068847656, 305.12646484375)
-1      (405.2714538574219, 357.9886779785156)   (400.1375732421875, 360.40533447265625)
-2     (399.41204833984375, 363.1806335449219)   (393.99725341796875, 365.4281005859375)
-3      (462.8739929199219, 434.9681701660156)    (455.3556213378906, 438.9235534667969)
-4      (411.5339660644531, 411.8554992675781)   (404.5462341308594, 414.24896240234375)
-...                                       ...                                       ...
-996    (434.4695739746094, 96.04366302490234)  (436.81951904296875, 100.26902770996094)
-997   (171.51588439941406, 42.57204818725586)  (175.90956115722656, 38.371212005615234)
-998   (335.09429931640625, 310.1083068847656)      (404.09033203125, 375.5360412597656)
-999   (234.0115203857422, 142.92909240722656)   (235.2720184326172, 141.08152770996094)
-1000  (202.9627685546875, 146.45680236816406)   (204.16490173339844, 143.2567901611328)
-
-[1001 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (364.0140075683594, 329.0843811035156)    (360.0196838378906, 329.9232177734375)
-1     (408.0988464355469, 406.1147155761719)   (402.0174865722656, 407.93560791015625)
-2    (286.7779541015625, 169.00526428222656)    (287.1163024902344, 167.8434295654297)
-3       (334.4208068847656, 305.12646484375)    (331.206298828125, 305.41326904296875)
-4    (331.3105163574219, 159.95455932617188)   (331.8726501464844, 160.33905029296875)
-..                                       ...                                       ...
-980   (458.2989196777344, 288.5635681152344)   (455.52130126953125, 292.3526916503906)
-981  (352.7303466796875, 175.37686157226562)  (352.58978271484375, 176.80372619628906)
-982  (549.0684204101562, 471.29522705078125)    (540.5568237304688, 476.1452941894531)
-983   (102.2708740234375, 53.47435760498047)    (619.1026000976562, 357.5776672363281)
-984   (411.1439514160156, 352.6970520019531)   (520.9705810546875, 450.92388916015625)
-
-[985 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0       (395.6346435546875, 362.408447265625)   (391.07257080078125, 363.6759948730469)
-1      (223.8777313232422, 263.0925598144531)  (222.33021545410156, 261.00128173828125)
-2     (388.5163269042969, 393.72491455078125)    (383.3330993652344, 394.3844299316406)
-3     (342.38458251953125, 346.3402404785156)      (338.6420593261719, 346.55615234375)
-4       (348.0520935058594, 344.997802734375)        (344.1910705566406, 345.123046875)
-...                                       ...                                       ...
-997   (511.77459716796875, 317.3077392578125)     (507.4078369140625, 320.331787109375)
-998   (392.8018798828125, 350.37908935546875)    (609.1167602539062, 345.0213928222656)
-999    (383.1563415527344, 281.5706481933594)    (380.5914611816406, 282.5696716308594)
-1000  (442.93585205078125, 92.14433288574219)    (444.74969482421875, 91.7934341430664)
-1001   (621.3995361328125, 4.976254940032959)   (433.4447937011719, 105.92790222167969)
-
-[1002 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (391.07257080078125, 363.6759948730469)  (387.46185302734375, 364.05987548828125)
-1      (303.68243408203125, 260.1306457519531)    (301.7450866699219, 259.1482849121094)
-2       (417.1799621582031, 443.3221130371094)    (411.7657470703125, 443.5777282714844)
-3       (488.5001220703125, 369.7234802246094)      (484.5254211425781, 371.75439453125)
-4     (222.33021545410156, 261.00128173828125)   (221.52769470214844, 259.3271179199219)
-...                                        ...                                       ...
-1032    (508.383544921875, 311.22076416015625)    (230.5321502685547, 112.6962661743164)
-1033   (257.6015930175781, 10.493057250976562)    (259.8378601074219, 8.164013862609863)
-1034     (139.855712890625, 98.30206298828125)    (139.9990692138672, 92.99081420898438)
-1035   (234.31130981445312, 180.4413604736328)  (233.04042053222656, 178.63539123535156)
-1036    (515.3421630859375, 390.8755187988281)    (159.36627197265625, 97.5815200805664)
-
-[1037 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (221.52769470214844, 259.3271179199219)   (221.8758544921875, 258.63287353515625)
-1      (458.30401611328125, 373.8658142089844)   (454.5273742675781, 373.86370849609375)
-2       (446.0677185058594, 437.7544860839844)   (441.90594482421875, 437.9183044433594)
-3     (387.46185302734375, 364.05987548828125)    (383.9657897949219, 363.2993469238281)
-4          (340.7174072265625, 344.6669921875)    (337.8576965332031, 343.7414245605469)
-...                                        ...                                       ...
-1009  (150.84954833984375, 27.829072952270508)  (150.49073791503906, 23.610170364379883)
-1010  (233.04042053222656, 178.63539123535156)    (205.6296844482422, 156.6129608154297)
-1011     (501.6383361816406, 47.5875244140625)   (277.32073974609375, 441.6786804199219)
-1012    (499.2138977050781, 367.8963317871094)   (496.05084228515625, 369.9490661621094)
-1013    (573.1848754882812, 379.3836364746094)    (570.0013427734375, 381.3646240234375)
-
-[1014 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (221.8758544921875, 258.63287353515625)  (222.2028045654297, 257.78680419921875)
-1     (352.72967529296875, 304.4336853027344)  (350.70404052734375, 303.1922912597656)
-2     (462.2223205566406, 390.80828857421875)  (459.2173156738281, 389.75738525390625)
-3     (421.0261535644531, 459.06463623046875)   (417.7736511230469, 457.5760803222656)
-4      (260.1802673339844, 264.6285705566406)   (258.4276428222656, 262.7755432128906)
-...                                       ...                                      ...
-1019  (145.5883331298828, 108.91439819335938)  (516.0660400390625, 377.82843017578125)
-1020   (499.8050842285156, 343.9532470703125)   (547.1026000976562, 351.2701721191406)
-1021   (617.8992919921875, 415.8683166503906)  (387.2812194824219, 251.84078979492188)
-1022  (420.17034912109375, 318.1357116699219)   (417.5538330078125, 317.4732971191406)
-1023   (479.1539611816406, 88.29718780517578)    (478.430419921875, 87.84429168701172)
-
-[1024 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (417.52496337890625, 364.135009765625)   (415.5623474121094, 361.84759521484375)
-1       (381.2606506347656, 361.9265441894531)    (379.7949523925781, 359.9869689941406)
-2      (222.2028045654297, 257.78680419921875)    (222.1314697265625, 257.1294860839844)
-3      (329.86688232421875, 343.2839660644531)    (328.4453430175781, 341.4554138183594)
-4        (381.7321472167969, 415.522705078125)    (379.9164733886719, 413.1026916503906)
-...                                        ...                                       ...
-1015      (577.5037231445312, 412.25732421875)   (574.5745849609375, 409.18988037109375)
-1016    (624.1486206054688, 4.455205917358398)  (418.25054931640625, 137.72113037109375)
-1017  (431.02337646484375, 133.79019165039062)      (428.8257141113281, 131.02783203125)
-1018   (417.2010803222656, 421.09295654296875)         (327.547607421875, 322.505859375)
-1019   (248.86366271972656, 133.1178436279297)  (246.56900024414062, 130.45945739746094)
-
-[1020 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (396.691162109375, 342.82269287109375)   (395.5549011230469, 339.8275146484375)
-1         (399.4792175292969, 423.84521484375)   (398.4234924316406, 419.8987731933594)
-2       (222.1314697265625, 257.1294860839844)   (222.1249542236328, 256.9653625488281)
-3      (415.5623474121094, 361.84759521484375)   (414.9302978515625, 358.9127502441406)
-4           (407.2293395996094, 352.177734375)   (406.1557922363281, 349.1473693847656)
-...                                        ...                                      ...
-1001    (266.1405334472656, 134.4177703857422)  (385.4456787109375, 124.89912414550781)
-1002      (256.3292541503906, 171.47802734375)      (254.0732421875, 169.1739044189453)
-1003   (451.34002685546875, 349.7264404296875)  (449.6681213378906, 346.17987060546875)
-1004  (491.69061279296875, 316.52203369140625)  (489.68402099609375, 312.6913146972656)
-1005  (163.82040405273438, 39.007545471191406)  (199.26962280273438, 94.42290496826172)
-
-[1006 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (378.7674865722656, 357.0909729003906)  (378.35052490234375, 353.4129638671875)
-1        (423.7978515625, 345.39825439453125)   (423.1470642089844, 341.0103454589844)
-2      (379.6412658691406, 317.4891052246094)  (378.8083190917969, 313.79425048828125)
-3     (401.78619384765625, 435.3172302246094)   (401.9941101074219, 430.2193908691406)
-4      (414.9302978515625, 358.9127502441406)  (414.2237548828125, 354.38421630859375)
-...                                       ...                                      ...
-1006   (499.1255798339844, 52.04556655883789)   (496.1631164550781, 47.02069091796875)
-1007  (174.46749877929688, 9.144352912902832)   (167.6084442138672, 6.072997570037842)
-1008  (424.2132263183594, 108.15794372558594)  (423.0657043457031, 103.93572235107422)
-1009   (422.1505126953125, 319.4338684082031)     (420.98675537109375, 314.3056640625)
-1010  (464.8872375488281, 11.009284973144531)   (460.6396179199219, 4.985762596130371)
-
-[1011 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (378.35052490234375, 353.4129638671875)  (378.60650634765625, 348.62359619140625)
-1        (259.017333984375, 261.9915466308594)   (258.09356689453125, 260.0317077636719)
-2       (397.0410461425781, 320.7667541503906)   (397.0566711425781, 316.16546630859375)
-3      (529.1092529296875, 391.22271728515625)    (529.2088012695312, 383.3433532714844)
-4      (340.64617919921875, 289.7525329589844)      (340.039794921875, 286.115966796875)
-...                                        ...                                       ...
-996      (498.898193359375, 345.6285705566406)   (498.66668701171875, 339.2555236816406)
-997    (197.9976806640625, 144.37564086914062)   (194.9190216064453, 142.55206298828125)
-998   (171.59463500976562, 10.252056121826172)    (163.9994354248047, 7.285010814666748)
-999    (498.8616027832031, 286.92864990234375)    (495.2796630859375, 278.2331237792969)
-1000  (387.07928466796875, 290.89752197265625)    (128.81710815429688, 99.7110595703125)
-
-[1001 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (292.782470703125, 247.15943908691406)   (291.8760070800781, 244.39874267578125)
-1        (317.3217468261719, 292.1298828125)   (317.15728759765625, 288.5526428222656)
-2    (416.77606201171875, 439.3539123535156)  (419.18707275390625, 432.30853271484375)
-3        (317.3217468261719, 292.1298828125)   (317.15728759765625, 288.5526428222656)
-4     (487.1650085449219, 385.5607604980469)    (487.5552978515625, 377.1330871582031)
-..                                       ...                                       ...
-973   (529.9856567382812, 17.08116912841797)    (524.6001586914062, 8.047708511352539)
-974  (471.62469482421875, 103.5488510131836)    (169.9806671142578, 35.88969421386719)
-975    (492.0559997558594, 40.0859260559082)    (487.0657958984375, 31.18382453918457)
-976  (520.9237670898438, 28.787649154663086)       (515.572265625, 20.427589416503906)
-977   (188.3151092529297, 58.27214431762695)  (181.33706665039062, 56.505271911621094)
-
-[978 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (317.15728759765625, 288.5526428222656)    (316.9306945800781, 284.6474304199219)
-1      (448.3045654296875, 349.1835632324219)    (449.0977783203125, 341.3462219238281)
-2     (317.15728759765625, 288.5526428222656)    (316.9306945800781, 284.6474304199219)
-3       (328.074951171875, 328.3131408691406)   (329.0957336425781, 324.07464599609375)
-4     (477.9143981933594, 459.05535888671875)   (480.89495849609375, 448.9552307128906)
-..                                        ...                                       ...
-959    (417.2467041015625, 75.50900268554688)     (412.0929870605469, 68.0744400024414)
-960   (216.92129516601562, 93.64413452148438)    (360.5246276855469, 330.9524230957031)
-961    (403.5852355957031, 241.5727996826172)   (401.9862365722656, 235.04273986816406)
-962  (205.20660400390625, 124.04470825195312)  (198.41354370117188, 121.36585235595703)
-963    (634.8880615234375, 364.0057067871094)   (633.8805541992188, 350.88812255859375)
-
-[964 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (316.9306945800781, 284.6474304199219)   (316.8385314941406, 280.4747009277344)
-1       (424.22637939453125, 322.76318359375)    (425.415771484375, 315.3461608886719)
-2      (530.8969116210938, 364.4197998046875)  (531.3845825195312, 352.57183837890625)
-3     (421.9820861816406, 424.21905517578125)  (425.26947021484375, 415.3892822265625)
-4     (384.7701416015625, 388.25238037109375)   (387.5621643066406, 381.2500305175781)
-...                                       ...                                      ...
-1015  (364.8506164550781, 110.42671203613281)  (360.2308654785156, 104.02534484863281)
-1016    (285.206787109375, 208.5583953857422)   (334.4208068847656, 235.4437713623047)
-1017   (432.3044738769531, 7.455483913421631)  (85.08119201660156, 328.69512939453125)
-1018   (421.2192687988281, 65.46709442138672)   (415.4220886230469, 55.87502670288086)
-1019   (477.5886535644531, 307.3116455078125)   (477.0103454589844, 298.2152099609375)
-
-[1020 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (411.6481018066406, 402.65557861328125)    (414.7266540527344, 393.5933837890625)
-1     (382.15130615234375, 331.3544006347656)   (383.7889709472656, 324.60723876953125)
-2        (356.11474609375, 342.0321350097656)   (358.14666748046875, 336.0721130371094)
-3      (316.8385314941406, 280.4747009277344)   (316.91412353515625, 276.0917053222656)
-4      (376.9017639160156, 360.9732666015625)    (379.40435791015625, 354.035888671875)
-...                                       ...                                       ...
-1001   (426.8469543457031, 277.4443054199219)   (426.28778076171875, 269.0544738769531)
-1002   (461.8437805175781, 79.65242004394531)    (563.9302368164062, 28.97834014892578)
-1003  (420.7456359863281, 44.125755310058594)  (218.16763305664062, 119.83931732177734)
-1004   (227.4075469970703, 152.8892364501953)  (223.64561462402344, 151.02349853515625)
-1005   (477.0103454589844, 298.2152099609375)    (476.2757263183594, 287.4258117675781)
-
-[1006 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (383.7889709472656, 324.60723876953125)   (384.9828186035156, 316.9145812988281)
-1      (236.71559143066406, 235.2725372314453)   (234.7550506591797, 233.3382568359375)
-2     (248.62020874023438, 246.97088623046875)  (247.61317443847656, 245.0752716064453)
-3     (248.62020874023438, 246.97088623046875)  (247.61317443847656, 245.0752716064453)
-4     (390.51165771484375, 373.61224365234375)  (393.80950927734375, 365.6069641113281)
-...                                        ...                                      ...
-1003   (538.1487426757812, 306.82061767578125)  (537.4527587890625, 293.44757080078125)
-1004   (84.64659118652344, 3.0434160232543945)   (71.25712585449219, 4.094702243804932)
-1005  (164.16078186035156, 132.73556518554688)   (157.5005645751953, 132.6851348876953)
-1006   (210.7406005859375, 158.94029235839844)   (340.0205383300781, 206.6548614501953)
-1007   (404.7452087402344, 103.57585144042969)      (183.8453826904297, 120.1005859375)
-
-[1008 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (316.76031494140625, 271.4116516113281)    (316.4809875488281, 266.3660888671875)
-1     (546.6763305664062, 452.84942626953125)         (550.74609375, 436.7121276855469)
-2      (253.0790252685547, 248.9890899658203)  (252.08132934570312, 246.90716552734375)
-3      (335.4012756347656, 314.5151062011719)    (337.00885009765625, 309.030517578125)
-4      (440.9281005859375, 325.6360778808594)    (442.2836608886719, 315.5614013671875)
-..                                        ...                                       ...
-967         (515.6484375, 421.62530517578125)    (388.5644226074219, 321.8631591796875)
-968  (416.59625244140625, 215.98834228515625)    (412.8966064453125, 207.0966033935547)
-969   (480.60211181640625, 242.2594757080078)  (478.99237060546875, 230.80091857910156)
-970  (284.83856201171875, 200.71051025390625)    (336.5066833496094, 223.2935333251953)
-971  (216.37765502929688, 130.48545837402344)     (618.482666015625, 281.0291442871094)
-
-[972 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (386.5442199707031, 309.3759460449219)    (387.8007507324219, 301.5777893066406)
-1    (252.08132934570312, 246.90716552734375)  (250.67086791992188, 244.78126525878906)
-2     (384.36737060546875, 338.9086608886719)     (386.412841796875, 330.7842102050781)
-3     (361.9435729980469, 322.82379150390625)    (363.3503723144531, 315.7523193359375)
-4     (402.61553955078125, 344.4017639160156)   (404.76727294921875, 335.4692687988281)
-..                                        ...                                       ...
-988    (412.0892333984375, 230.2450408935547)   (409.7696228027344, 221.39390563964844)
-989   (534.4944458007812, 271.57708740234375)   (533.9080810546875, 250.31809997558594)
-990   (615.7301025390625, 473.42401123046875)    (619.9863891601562, 453.6177673339844)
-991   (383.3818054199219, 233.01719665527344)    (382.022705078125, 224.73577880859375)
-992   (554.9924926757812, 243.39895629882812)     (573.243896484375, 244.5583038330078)
-
-[993 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (387.8007507324219, 301.5777893066406)    (388.4672546386719, 293.6424865722656)
-1      (396.17413330078125, 265.603271484375)   (396.02008056640625, 257.4932556152344)
-2     (473.5506286621094, 321.34893798828125)    (474.7059020996094, 309.4867858886719)
-3      (440.1823425292969, 374.8887023925781)    (443.3634033203125, 363.9493408203125)
-4     (410.8851623535156, 286.83551025390625)    (411.0188293457031, 277.7591552734375)
-..                                        ...                                       ...
-993   (334.3496398925781, 200.34109497070312)   (274.5196838378906, 171.90811157226562)
-994  (413.32879638671875, 245.29742431640625)  (411.47625732421875, 235.73294067382812)
-995    (158.0812530517578, 103.5479736328125)   (148.3451690673828, 102.26756286621094)
-996    (149.9201202392578, 105.0868148803711)   (141.2125701904297, 106.09817504882812)
-997    (547.0902099609375, 441.8831481933594)    (590.5841674804688, 258.0736083984375)
-
-[998 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (428.1075134277344, 354.72833251953125)      (430.9136657714844, 344.65673828125)
-1    (343.45892333984375, 248.68423461914062)   (342.2307434082031, 242.58230590820312)
-2     (396.02008056640625, 257.4932556152344)  (395.46380615234375, 249.18116760253906)
-3    (248.90858459472656, 242.64552307128906)   (247.1849365234375, 240.74359130859375)
-4      (569.7821044921875, 175.3396453857422)   (565.3171997070312, 158.80299377441406)
-..                                        ...                                       ...
-995  (209.85726928710938, 149.40780639648438)  (203.15318298339844, 148.86196899414062)
-996    (174.2570343017578, 108.3336181640625)  (165.28741455078125, 108.60700225830078)
-997     (333.3683776855469, 22.1832275390625)  (321.05108642578125, 12.531020164489746)
-998   (428.0953674316406, 184.59950256347656)    (562.5758056640625, 280.8234558105469)
-999     (333.3683776855469, 22.1832275390625)  (321.05108642578125, 12.531020164489746)
-
-[1000 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0       (388.907958984375, 285.517333984375)    (388.9959716796875, 277.498779296875)
-1     (389.96441650390625, 314.488037109375)   (391.6272277832031, 306.5412902832031)
-2    (342.2307434082031, 242.58230590820312)   (340.4449462890625, 236.5515594482422)
-3        (341.22186279296875, 283.619140625)     (341.44775390625, 277.8014831542969)
-4        (341.22186279296875, 283.619140625)     (341.44775390625, 277.8014831542969)
-..                                       ...                                      ...
-961    (554.16357421875, 246.27435302734375)  (552.5186767578125, 231.07635498046875)
-962  (410.1377258300781, 174.93177795410156)  (406.4341125488281, 166.07183837890625)
-963  (625.7188720703125, 234.43624877929688)  (623.2588500976562, 215.88780212402344)
-964     (319.904052734375, 88.2005844116211)   (311.2393493652344, 81.80573272705078)
-965  (324.1311950683594, 178.62464904785156)  (319.01617431640625, 172.5035858154297)
-
-[966 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0       (388.9959716796875, 277.498779296875)      (388.7713317871094, 269.6083984375)
-1     (481.13946533203125, 333.1742858886719)  (483.02386474609375, 321.1595153808594)
-2       (384.1228942871094, 283.407470703125)      (384.2294921875, 275.6731262207031)
-3      (527.6968994140625, 301.7480773925781)     (527.65087890625, 287.6363525390625)
-4      (449.5792236328125, 342.5404357910156)     (451.89697265625, 331.9622497558594)
-..                                        ...                                      ...
-905           (513.859375, 377.0459289550781)   (517.8260498046875, 363.1512756347656)
-906   (538.8565063476562, 188.55043029785156)  (534.8229370117188, 173.54562377929688)
-907     (404.568603515625, 294.1774597167969)  (405.3158874511719, 285.60345458984375)
-908  (157.34181213378906, 151.01393127441406)  (149.2937774658203, 151.51226806640625)
-909   (198.9488983154297, 159.63961791992188)   (49.0982551574707, 436.38323974609375)
-
-[910 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0         (388.7713317871094, 269.6083984375)  (388.13519287109375, 261.9411315917969)
-1         (388.7713317871094, 269.6083984375)  (388.13519287109375, 261.9411315917969)
-2    (113.06616973876953, 163.80934143066406)  (105.0981674194336, 166.81695556640625)
-3      (534.0306396484375, 334.5650939941406)     (535.8070678710938, 320.54345703125)
-4      (487.806884765625, 316.81951904296875)    (488.821044921875, 305.1048889160156)
-..                                        ...                                      ...
-918    (517.8260498046875, 363.1512756347656)    (519.66455078125, 349.72613525390625)
-919  (373.51861572265625, 191.44459533691406)   (369.4294738769531, 184.2009735107422)
-920   (419.26995849609375, 46.77803039550781)   (409.1495056152344, 35.24091720581055)
-921    (490.0514221191406, 283.5885314941406)  (553.4256591796875, 311.55633544921875)
-922   (186.0294952392578, 167.80331420898438)  (179.5072021484375, 167.34864807128906)
-
-[923 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0        (535.8070678710938, 320.54345703125)     (536.965087890625, 306.9425964355469)
-1      (368.0638427734375, 281.3565979003906)   (367.9080810546875, 274.83026123046875)
-2     (582.0430908203125, 343.49261474609375)      (583.73291015625, 327.9026184082031)
-3     (277.00885009765625, 227.6478271484375)  (273.81536865234375, 224.70562744140625)
-4     (84.30106353759766, 168.25869750976562)   (75.65825653076172, 172.27822875976562)
-...                                       ...                                       ...
-1003  (185.7852783203125, 158.42018127441406)     (177.9844970703125, 158.744384765625)
-1004  (471.4924011230469, 194.21607971191406)    (466.4263916015625, 183.8068389892578)
-1005  (88.23091888427734, 149.20242309570312)    (78.80287170410156, 152.9799041748047)
-1006  (165.9315643310547, 154.47732543945312)   (157.81524658203125, 153.1461181640625)
-1007     (575.2610473632812, 199.10009765625)   (572.4104614257812, 181.66375732421875)
-
-[1008 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0    (387.53131103515625, 254.38453674316406)    (385.8265686035156, 247.1299285888672)
-1     (387.2809143066406, 216.50160217285156)   (383.9474182128906, 208.97608947753906)
-2       (339.940673828125, 261.0831298828125)   (338.5505065917969, 255.93228149414062)
-3     (455.3214416503906, 311.53973388671875)    (456.4550476074219, 301.9613037109375)
-4     (455.3214416503906, 311.53973388671875)    (456.4550476074219, 301.9613037109375)
-..                                        ...                                       ...
-976  (205.48727416992188, 163.10806274414062)    (198.3674774169922, 162.6908721923828)
-977  (151.33653259277344, 130.74111938476562)  (143.19874572753906, 130.73348999023438)
-978    (493.7835693359375, 179.1269073486328)      (556.3497924804688, 194.07958984375)
-979    (478.6364440917969, 91.85594177246094)     (492.7540283203125, 132.778564453125)
-980     (132.7334442138672, 91.3296890258789)   (122.72805786132812, 89.85429382324219)
-
-[981 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (456.4550476074219, 301.9613037109375)  (456.94561767578125, 292.95220947265625)
-1      (537.5582885742188, 294.0203552246094)      (537.545654296875, 281.868408203125)
-2      (410.8301086425781, 289.1239318847656)    (411.1683654785156, 281.9141540527344)
-3      (526.2572631835938, 247.6643524169922)    (524.0162353515625, 235.9410400390625)
-4      (519.6151123046875, 285.0748291015625)    (519.5484619140625, 273.6275939941406)
-..                                        ...                                       ...
-915   (344.9883117675781, 185.43682861328125)   (340.8587646484375, 179.38043212890625)
-916          (113.88114929199219, 103.328125)   (102.5180892944336, 104.42182922363281)
-917    (375.4983825683594, 146.3426055908203)   (369.9484558105469, 138.34576416015625)
-918  (300.06597900390625, 161.78988647460938)   (294.2674560546875, 158.70941162109375)
-919    (571.1358642578125, 137.3292694091797)   (569.2135009765625, 121.92413330078125)
-
-[920 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (401.2522277832031, 217.9110565185547)    (398.2214050292969, 210.8886260986328)
-1    (434.3719177246094, 236.23272705078125)  (432.75372314453125, 228.40481567382812)
-2    (381.06610107421875, 247.4407501220703)  (379.18658447265625, 241.49264526367188)
-3       (537.545654296875, 281.868408203125)   (537.6354370117188, 270.72637939453125)
-4    (384.46502685546875, 240.6432342529297)   (383.2903137207031, 234.12416076660156)
-..                                       ...                                       ...
-968     (288.633544921875, 8.82454776763916)   (311.1092529296875, 157.74473571777344)
-969  (222.2987823486328, 102.51146697998047)    (97.82292938232422, 87.81752014160156)
-970  (87.58443450927734, 157.61514282226562)    (78.41246032714844, 160.8040771484375)
-971   (479.0692443847656, 167.1758270263672)  (474.98773193359375, 157.96754455566406)
-972     (599.3193359375, 130.46060180664062)   (594.0145874023438, 115.80880737304688)
-
-[973 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (410.5705871582031, 275.1849365234375)    (410.032470703125, 269.1579895019531)
-1     (438.58917236328125, 199.91831970214844)  (434.5411376953125, 192.01547241210938)
-2     (323.01544189453125, 204.30499267578125)  (319.1978759765625, 200.12872314453125)
-3      (595.7013549804688, 247.96243286132812)   (594.322998046875, 235.47023010253906)
-4       (430.5977783203125, 271.5824890136719)     (428.85498046875, 264.6783752441406)
-...                                        ...                                      ...
-1002   (314.4721374511719, 152.53843688964844)   (308.5707092285156, 148.1995086669922)
-1003     (141.06759643554688, 124.66162109375)   (582.817138671875, 151.13133239746094)
-1004   (35.56647491455078, 102.20326232910156)    (626.801025390625, 164.5703582763672)
-1005    (438.1642761230469, 131.4265899658203)    (432.9809265136719, 122.813232421875)
-1006   (99.77705383300781, 101.48170471191406)    (231.751708984375, 97.79557037353516)
-
-[1007 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (510.469970703125, 239.19898986816406)   (509.0736083984375, 230.22906494140625)
-1      (324.97052001953125, 256.8841247558594)   (326.8714904785156, 253.48202514648438)
-2        (410.032470703125, 269.1579895019531)    (408.7602233886719, 263.9005126953125)
-3         (537.04638671875, 260.5773010253906)       (536.127685546875, 251.66552734375)
-4     (393.78289794921875, 171.86456298828125)   (389.0722961425781, 166.05540466308594)
-...                                        ...                                       ...
-1018   (249.5364990234375, 152.91360473632812)   (242.4193572998047, 150.22537231445312)
-1019     (398.5686950683594, 88.2200927734375)   (473.38836669921875, 84.57251739501953)
-1020    (637.2365112304688, 76.01756286621094)    (629.2365112304688, 62.46040344238281)
-1021  (247.32220458984375, 162.99610900878906)    (240.8639373779297, 160.3262481689453)
-1022  (115.26773071289062, 120.49051666259766)  (103.55525207519531, 120.16136169433594)
-
-[1023 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (343.9125671386719, 253.0974578857422)   (341.5033264160156, 250.03506469726562)
-1      (517.1668701171875, 205.4044647216797)   (514.6936645507812, 197.62022399902344)
-2      (374.1465759277344, 231.6620635986328)  (371.64215087890625, 227.89085388183594)
-3      (456.691650390625, 270.80999755859375)    (455.54290771484375, 265.076416015625)
-4     (509.0736083984375, 230.22906494140625)   (504.9987487792969, 222.16580200195312)
-...                                       ...                                       ...
-1010   (617.2068481445312, 135.0563507080078)    (612.8201293945312, 124.1074447631836)
-1011     (430.08056640625, 125.8841781616211)   (402.6808166503906, 152.84007263183594)
-1012      (568.861328125, 163.42933654785156)    (564.357666015625, 154.04818725585938)
-1013    (587.1646118164062, 447.977294921875)     (604.808349609375, 208.3873291015625)
-1014         (521.30859375, 152.148193359375)      (517.032958984375, 143.584716796875)
-
-[1015 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (424.77716064453125, 209.2259521484375)  (422.45892333984375, 205.39019775390625)
-1     (327.23785400390625, 250.40423583984375)   (326.7064514160156, 247.42047119140625)
-2      (527.3914184570312, 176.92527770996094)      (524.7839965820312, 172.60595703125)
-3      (399.78814697265625, 196.0148468017578)       (397.043701171875, 192.31298828125)
-4        (475.77685546875, 264.58233642578125)    (474.9343566894531, 261.4496154785156)
-...                                        ...                                       ...
-1018    (625.7662963867188, 46.36119079589844)    (620.3519897460938, 40.39140319824219)
-1019   (487.03936767578125, 88.27620697021484)     (483.8087463378906, 82.9564437866211)
-1020  (340.07122802734375, 140.22816467285156)    (336.973876953125, 136.86488342285156)
-1021   (439.6893310546875, 128.67758178710938)  (435.65985107421875, 124.81267547607422)
-1022    (201.1331024169922, 143.2327880859375)   (266.5119323730469, 137.91339111328125)
-
-[1023 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0         (454.1259765625, 262.15582275390625)    (453.1726379394531, 259.9728088378906)
-1       (406.0989074707031, 256.6807556152344)    (404.2945556640625, 254.7200469970703)
-2       (588.952392578125, 210.05474853515625)   (587.8539428710938, 206.49673461914062)
-3      (338.7104187011719, 247.73475646972656)   (336.3016052246094, 245.90744018554688)
-4      (338.7104187011719, 247.73475646972656)   (336.3016052246094, 245.90744018554688)
-...                                        ...                                       ...
-1079  (43.196895599365234, 168.27285766601562)   (63.64588165283203, 164.95669555664062)
-1080   (426.98028564453125, 128.7175750732422)   (422.7783508300781, 125.12760925292969)
-1081    (525.1305541992188, 277.6250915527344)        (360.2510070800781, 412.845703125)
-1082   (457.5524597167969, 122.23360443115234)   (455.1766357421875, 118.25634002685547)
-1083   (345.5917053222656, 123.03080749511719)  (342.27178955078125, 119.39176177978516)
-
-[1084 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (326.38153076171875, 244.86334228515625)   (325.6748352050781, 242.79168701171875)
-1       (453.1726379394531, 259.9728088378906)   (452.2724304199219, 258.21319580078125)
-2      (174.93801879882812, 107.3800048828125)  (167.20620727539062, 103.39969635009766)
-3        (173.1229705810547, 187.009033203125)   (166.31161499023438, 185.0211944580078)
-4     (221.68984985351562, 187.34315490722656)  (216.13748168945312, 185.20481872558594)
-...                                        ...                                       ...
-1122   (431.9226379394531, 125.60504913330078)  (428.69757080078125, 121.72502899169922)
-1123    (510.7294006347656, 84.95573425292969)   (508.29437255859375, 80.92903900146484)
-1124  (486.79779052734375, 105.57502746582031)    (483.0003662109375, 102.6138916015625)
-1125    (386.2550354003906, 52.82952880859375)    (228.6060028076172, 140.1954803466797)
-1126    (390.3949279785156, 58.84421157836914)    (427.2913818359375, 16.90007781982422)
-
-[1127 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (216.13748168945312, 185.20481872558594)     (210.52670288085938, 183.45068359375)
-1       (314.0977783203125, 235.6761016845703)  (311.38970947265625, 234.59713745117188)
-2      (268.4157409667969, 182.58023071289062)   (264.0107727050781, 180.63319396972656)
-3      (325.6748352050781, 242.79168701171875)    (325.1029052734375, 240.2918243408203)
-4          (531.885986328125, 235.02587890625)    (531.631591796875, 233.06234741210938)
-...                                        ...                                       ...
-1136   (465.1424865722656, 131.85934448242188)   (461.4353942871094, 129.91603088378906)
-1137  (235.16738891601562, 140.13302612304688)  (229.14280700683594, 136.90757751464844)
-1138  (143.97549438476562, 141.62026977539062)     (135.8094024658203, 139.256591796875)
-1139   (265.3165588378906, 149.99343872070312)   (185.1492156982422, 151.65127563476562)
-1140   (509.4613952636719, 131.12994384765625)   (507.2522888183594, 127.64775085449219)
-
-[1141 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (294.3825988769531, 183.4517364501953)  (290.51422119140625, 181.80691528320312)
-1        (389.72021484375, 184.50448608398438)    (387.4185791015625, 182.5369873046875)
-2       (471.6192626953125, 205.7286376953125)  (470.65875244140625, 203.73670959472656)
-3      (131.00917053222656, 217.4835968017578)  (123.75016021728516, 217.40701293945312)
-4     (420.79071044921875, 244.49073791503906)   (420.3968811035156, 243.48020935058594)
-...                                        ...                                       ...
-1111    (428.1333312988281, 145.4190673828125)  (425.16522216796875, 142.62722778320312)
-1112   (460.8914489746094, 197.34498596191406)  (336.98126220703125, 193.25929260253906)
-1113   (18.678285598754883, 80.05709075927734)    (5.108839988708496, 76.81422424316406)
-1114   (402.5248718261719, 202.49559020996094)   (557.9339599609375, 204.04364013671875)
-1115    (525.2669677734375, 268.2984619140625)    (527.4517822265625, 267.2597961425781)
-
-[1116 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (135.93228149414062, 199.44448852539062)   (128.5890655517578, 199.19692993164062)
-1      (270.2561340332031, 237.22073364257812)  (267.15081787109375, 237.43899536132812)
-2      (329.8232727050781, 242.79867553710938)   (328.1083068847656, 242.73423767089844)
-3      (374.3015441894531, 180.56166076660156)  (372.22271728515625, 178.41009521484375)
-4      (266.4344482421875, 205.03219604492188)   (262.7898254394531, 204.31777954101562)
-...                                        ...                                       ...
-1161   (593.3756713867188, 30.958038330078125)    (503.6368408203125, 79.98981475830078)
-1162   (40.421485900878906, 98.61869812011719)    (28.09691047668457, 95.95401763916016)
-1163  (19.383594512939453, 101.26091766357422)    (6.385581016540527, 98.47332000732422)
-1164   (276.2746887207031, 110.05128479003906)    (523.1812133789062, 413.1946716308594)
-1165   (632.7122802734375, 202.47784423828125)   (593.5516967773438, 183.96327209472656)
-
-[1166 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (103.19619750976562, 182.16026306152344)     (94.3406982421875, 182.2177276611328)
-1      (360.8075866699219, 208.59239196777344)  (359.60113525390625, 208.08290100097656)
-2      (128.5890655517578, 199.19692993164062)  (120.77631378173828, 199.68362426757812)
-3      (172.9242401123047, 224.08273315429688)   (167.2236785888672, 225.12315368652344)
-4      (262.7898254394531, 204.31777954101562)    (259.4026184082031, 203.8955841064453)
-...                                        ...                                       ...
-1178     (490.394287109375, 93.19327545166016)   (489.24566650390625, 89.93700408935547)
-1179    (271.3081970214844, 121.9288101196289)   (266.2046813964844, 119.86952209472656)
-1180     (539.75537109375, 100.03528594970703)     (539.097900390625, 97.02853393554688)
-1181     (370.4993896484375, 91.7522964477539)    (367.5083923339844, 89.12852478027344)
-1182      (100.36336517333984, 77.59814453125)   (129.2738494873047, 59.462303161621094)
-
-[1183 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (359.60113525390625, 208.08290100097656)    (358.202392578125, 207.81936645507812)
-1       (193.193115234375, 180.70687866210938)  (187.42959594726562, 180.69630432128906)
-2      (319.9352722167969, 238.39186096191406)    (318.637451171875, 238.00399780273438)
-3      (473.5406494140625, 255.17491149902344)   (474.3156433105469, 255.45742797851562)
-4      (58.60483932495117, 182.06063842773438)   (48.632843017578125, 182.9358673095703)
-...                                        ...                                       ...
-1129  (238.84288024902344, 131.85195922851562)  (233.61441040039062, 130.50625610351562)
-1130  (215.37850952148438, 124.50914001464844)  (209.13230895996094, 123.14476013183594)
-1131      (447.699462890625, 91.2071304321289)    (447.1109313964844, 87.75447082519531)
-1132    (398.9256286621094, 73.96369934082031)    (395.7716369628906, 70.13764190673828)
-1133   (158.3987579345703, 143.86619567871094)  (234.56222534179688, 137.27166748046875)
-
-[1134 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (358.202392578125, 207.81936645507812)    (358.5461120605469, 207.7403106689453)
-1      (447.4788513183594, 179.06419372558594)   (447.4706726074219, 178.00279235839844)
-2         (501.3388671875, 208.61000061035156)   (502.9610900878906, 207.93992614746094)
-3       (318.637451171875, 238.00399780273438)    (317.6095275878906, 237.8932342529297)
-4       (355.6352844238281, 217.1045379638672)       (355.2669372558594, 217.6689453125)
-...                                        ...                                       ...
-1226  (233.04054260253906, 109.77532958984375)  (228.61456298828125, 108.66200256347656)
-1227  (263.66790771484375, 119.74720764160156)   (258.8462219238281, 119.60629272460938)
-1228    (470.8633728027344, 173.8078155517578)   (471.0003662109375, 172.78138732910156)
-1229    (56.46623611450195, 152.0304718017578)   (46.04188537597656, 152.40042114257812)
-1230       (457.81201171875, 98.8692626953125)     (456.9040832519531, 96.6017837524414)
-
-[1231 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0          (382.19921875, 177.91241455078125)   (381.26568603515625, 177.6873321533203)
-1     (502.9610900878906, 207.93992614746094)  (503.57977294921875, 206.81692504882812)
-2      (297.2463073730469, 169.9619598388672)  (294.98516845703125, 169.95602416992188)
-3     (38.324424743652344, 184.2112274169922)   (28.39927101135254, 186.10292053222656)
-4     (259.2590026855469, 240.83746337890625)    (257.4722595214844, 242.9413604736328)
-...                                       ...                                       ...
-1344  (59.132118225097656, 141.0614013671875)    (48.86379623413086, 141.1737518310547)
-1345  (502.9239501953125, 26.086959838867188)   (498.8593444824219, 23.130329132080078)
-1346   (473.3221435546875, 72.07876586914062)    (472.3704833984375, 68.09365844726562)
-1347   (540.6033935546875, 79.63538360595703)       (541.69580078125, 77.3869857788086)
-1348         (316.984375, 183.20101928710938)   (223.3055419921875, 185.41632080078125)
-
-[1349 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (381.26568603515625, 177.6873321533203)  (382.0396423339844, 177.75558471679688)
-1      (317.8138427734375, 237.96304321289062)  (317.5031433105469, 238.43914794921875)
-2      (67.86859130859375, 185.29000854492188)   (59.19831085205078, 187.3148193359375)
-3      (357.6553039550781, 209.09519958496094)  (358.1684265136719, 209.96571350097656)
-4      (162.3694305419922, 238.08685302734375)      (158.26080322265625, 241.185546875)
-...                                        ...                                      ...
-1392    (448.379638671875, 118.52265167236328)   (337.2640380859375, 74.22367095947266)
-1393   (362.0263977050781, 62.481563568115234)  (359.89642333984375, 60.60796356201172)
-1394  (229.26063537597656, 139.71759033203125)    (136.3553009033203, 146.782470703125)
-1395   (529.1310424804688, 36.030208587646484)   (473.5115051269531, 47.23329544067383)
-1396   (524.5931396484375, 55.087669372558594)   (525.8524169921875, 52.39997482299805)
-
-[1397 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (98.69478607177734, 75.92208099365234)    (89.69915771484375, 74.95064544677734)
-1       (98.69478607177734, 75.92208099365234)    (89.69915771484375, 74.95064544677734)
-2      (317.5031433105469, 238.43914794921875)    (317.1038818359375, 239.0992431640625)
-3     (59.258663177490234, 187.48880004882812)     (51.234352111816406, 189.75439453125)
-4       (179.93545532226562, 88.7823257446289)   (173.83334350585938, 87.86839294433594)
-...                                        ...                                       ...
-1379      (334.6687927246094, 35.096923828125)   (331.0789794921875, 32.232643127441406)
-1380   (429.07708740234375, 98.70694732666016)    (429.6630554199219, 97.70490264892578)
-1381    (440.6426086425781, 86.08557891845703)   (441.71820068359375, 84.96001434326172)
-1382   (601.7816772460938, 264.96209716796875)     (607.959716796875, 265.8016357421875)
-1383   (346.1747131347656, 102.17889404296875)  (345.47247314453125, 101.64789581298828)
-
-[1384 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (383.3819274902344, 177.99842834472656)   (386.4974060058594, 178.28306579589844)
-1       (352.7493896484375, 134.9976348876953)   (352.7071228027344, 134.86720275878906)
-2      (233.09510803222656, 136.6389923095703)   (229.65914916992188, 137.2849578857422)
-3      (102.8152084350586, 188.09307861328125)   (96.70130920410156, 190.26809692382812)
-4     (247.61434936523438, 183.82447814941406)   (246.5719451904297, 185.30105590820312)
-...                                        ...                                       ...
-1389     (496.5152893066406, 75.8580551147461)   (499.51739501953125, 74.78404998779297)
-1390      (258.1494140625, 108.74913024902344)  (255.46141052246094, 108.57518768310547)
-1391   (421.6475524902344, 203.41598510742188)    (367.6189270019531, 222.9987030029297)
-1392    (70.56008911132812, 159.5569305419922)   (63.42812728881836, 160.98422241210938)
-1393    (598.4495849609375, 259.2402648925781)   (607.1851196289062, 261.86663818359375)
-
-[1394 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (327.2655334472656, 169.75076293945312)   (328.41986083984375, 170.7696533203125)
-1      (352.7071228027344, 134.86720275878906)    (353.7087707519531, 134.8219451904297)
-2       (518.5398559570312, 147.2915496826172)   (522.3314208984375, 146.94895935058594)
-3     (137.54209899902344, 241.07810974121094)  (134.53675842285156, 245.17674255371094)
-4     (137.54209899902344, 241.07810974121094)  (134.53675842285156, 245.17674255371094)
-...                                        ...                                       ...
-1459    (305.5947570800781, 78.90950012207031)   (305.59674072265625, 77.78462219238281)
-1460   (324.29754638671875, 122.5044937133789)    (324.464111328125, 122.95051574707031)
-1461     (498.44219970703125, 97.116455078125)     (501.8531799316406, 96.5521469116211)
-1462   (76.45024871826172, 133.51617431640625)  (16.399879455566406, 147.47096252441406)
-1463   (551.1047973632812, 11.215784072875977)   (571.9730224609375, 28.177995681762695)
-
-[1464 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (319.50201416015625, 240.73150634765625)      (319.06298828125, 242.2389678955078)
-1       (57.27070236206055, 82.86348724365234)    (49.80524826049805, 82.44023895263672)
-2      (76.34382629394531, 223.88499450683594)      (71.4910659790039, 227.745361328125)
-3      (242.60025024414062, 184.7986297607422)  (242.55116271972656, 186.65858459472656)
-4      (468.8318176269531, 122.48461151123047)    (472.7701416015625, 122.1015853881836)
-...                                        ...                                       ...
-1441    (416.86566162109375, 26.5421085357666)    (415.531005859375, 24.426359176635742)
-1442   (547.7718505859375, 108.85232543945312)   (553.4923095703125, 107.66595458984375)
-1443   (43.09960174560547, 226.43533325195312)  (108.32472229003906, 230.61097717285156)
-1444    (501.7891540527344, 38.35471725463867)     (507.546142578125, 36.33682632446289)
-1445          (393.1015625, 95.88008117675781)    (394.4517517089844, 95.09091186523438)
-
-[1446 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0         (319.06298828125, 242.2389678955078)  (317.69659423828125, 243.94085693359375)
-1       (157.4124298095703, 191.4757080078125)    (155.4412384033203, 193.7772216796875)
-2      (527.7061157226562, 147.04991149902344)   (534.7327880859375, 147.14463806152344)
-3      (301.67547607421875, 124.8119888305664)     (302.9819641113281, 124.986083984375)
-4     (149.35614013671875, 107.20308685302734)  (145.92701721191406, 106.89764404296875)
-...                                        ...                                       ...
-1507  (172.61627197265625, 125.19554138183594)   (170.4102783203125, 126.46493530273438)
-1508    (506.1294250488281, 56.52112579345703)   (513.1807861328125, 55.247276306152344)
-1509     (488.9480895996094, 28.3049259185791)   (494.5780944824219, 26.282554626464844)
-1510    (564.1567993164062, 53.68583679199219)   (573.0929565429688, 52.731773376464844)
-1511   (547.1070556640625, 236.42013549804688)    (556.788330078125, 237.80133056640625)
-
-[1512 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0     (317.69659423828125, 243.94085693359375)  (316.98626708984375, 245.3506317138672)
-1       (58.47753143310547, 72.97921752929688)  (52.903079986572266, 72.34066009521484)
-2       (155.4412384033203, 193.7772216796875)   (154.4844512939453, 196.1865997314453)
-3        (302.9819641113281, 124.986083984375)  (305.9087829589844, 125.15852355957031)
-4      (240.4961700439453, 231.81137084960938)    (243.4341583251953, 235.969970703125)
-...                                        ...                                      ...
-1600      (273.01031494140625, 88.03564453125)   (274.6683654785156, 88.59186553955078)
-1601    (116.533447265625, 226.90377807617188)    (149.7323455810547, 226.524658203125)
-1602    (325.5439147949219, 105.7422866821289)   (545.1771850585938, 6.794122695922852)
-1603             (460.625, 43.594261169433594)   (468.3375549316406, 42.18279266357422)
-1604    (264.5572509765625, 215.9638214111328)  (161.8498992919922, 221.43614196777344)
-
-[1605 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (316.98626708984375, 245.3506317138672)   (314.5318603515625, 247.12315368652344)
-1     (513.5693969726562, 115.55622100830078)     (523.25732421875, 115.91021728515625)
-2      (372.8473815917969, 262.9676208496094)   (382.10260009765625, 267.8689880371094)
-3     (226.32748413085938, 140.9100341796875)  (227.25894165039062, 141.82748413085938)
-4      (319.5389709472656, 261.7448425292969)    (326.5270080566406, 266.6385498046875)
-...                                       ...                                       ...
-1596   (548.3812255859375, 24.36644172668457)   (557.3064575195312, 22.713294982910156)
-1597  (78.00052642822266, 134.69851684570312)   (75.40589141845703, 136.28720092773438)
-1598  (391.9305114746094, 243.26625061035156)   (251.0293426513672, 246.37489318847656)
-1599    (50.9132194519043, 167.4517059326172)     (48.07314682006836, 169.883056640625)
-1600    (595.3930053710938, 66.8453140258789)    (609.9383544921875, 66.33505249023438)
-
-[1601 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0        (251.63525390625, 196.30165100097656)   (256.3205871582031, 200.22970581054688)
-1      (78.15878295898438, 203.43772888183594)    (78.54620361328125, 207.2821044921875)
-2      (144.41452026367188, 263.0648193359375)   (147.07115173339844, 269.0976257324219)
-3      (149.79791259765625, 84.69734191894531)    (149.9586944580078, 86.04217529296875)
-4      (158.98776245117188, 84.58478546142578)   (158.99673461914062, 85.82260131835938)
-...                                        ...                                       ...
-1669   (564.4943237304688, 117.03125762939453)    (577.317138671875, 118.64653778076172)
-1670     (48.07314682006836, 169.883056640625)   (46.80617904663086, 173.08140563964844)
-1671           (338.0078125, 93.4160385131836)   (344.91107177734375, 94.32662963867188)
-1672    (272.8591613769531, 141.3954620361328)    (277.1086120605469, 143.8038330078125)
-1673  (170.00076293945312, 109.65658569335938)  (144.98516845703125, 116.01322174072266)
-
-[1674 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (149.80560302734375, 85.85221862792969)   (150.68601989746094, 86.66654205322266)
-1       (153.3277587890625, 91.95035552978516)    (154.2517547607422, 92.88795471191406)
-2      (256.3205871582031, 200.22970581054688)   (261.5771179199219, 204.01661682128906)
-3        (397.842529296875, 267.0333557128906)    (410.7254943847656, 274.1782531738281)
-4     (156.62701416015625, 202.44688415527344)   (159.6504669189453, 205.92616271972656)
-...                                        ...                                       ...
-1754  (61.653419494628906, 176.27540588378906)   (193.3879852294922, 241.46316528320312)
-1755    (528.4910888671875, 3.367969036102295)   (533.5604248046875, 3.2778804302215576)
-1756  (107.35318756103516, 232.33807373046875)   (252.4095001220703, 113.65589904785156)
-1757  (133.81634521484375, 151.78298950195312)  (348.67901611328125, 106.73429107666016)
-1758   (563.1681518554688, 473.25689697265625)   (96.13806915283203, 242.14588928222656)
-
-[1759 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (150.88414001464844, 275.04364013671875)  (155.84896850585938, 281.11895751953125)
-1      (448.5816955566406, 185.27854919433594)   (461.5938720703125, 189.23910522460938)
-2       (64.55146026611328, 213.0988006591797)   (66.53832244873047, 216.31556701660156)
-3      (159.6504669189453, 205.92616271972656)  (164.24546813964844, 209.60279846191406)
-4      (261.5771179199219, 204.01661682128906)   (268.8163757324219, 208.05459594726562)
-...                                        ...                                       ...
-1754   (284.1839904785156, 247.67491149902344)      (293.0361328125, 252.97100830078125)
-1755   (592.4876708984375, 120.56452941894531)   (610.2930908203125, 122.78597259521484)
-1756    (4.058866500854492, 141.1932373046875)  (3.4044241905212402, 141.89744567871094)
-1757    (293.5537414550781, 115.6457748413086)   (300.5516052246094, 116.34346771240234)
-1758   (338.3814392089844, 46.155967712402344)   (624.8694458007812, 110.78117370605469)
-
-[1759 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0         (42.76649856567383, 84.187255859375)    (43.66755676269531, 84.23009490966797)
-1      (60.89126205444336, 275.51116943359375)   (63.778297424316406, 280.5796813964844)
-2       (300.499267578125, 253.04180908203125)        (296.263671875, 254.3195343017578)
-3      (96.10221099853516, 208.02621459960938)      (98.83106994628906, 211.01513671875)
-4       (185.33828735351562, 71.8968505859375)   (188.57135009765625, 72.27299499511719)
-...                                        ...                                       ...
-1803  (180.31716918945312, 222.96864318847656)   (319.09722900390625, 218.0289306640625)
-1804   (352.5362243652344, 10.897270202636719)   (359.9118347167969, 10.458232879638672)
-1805   (345.69512939453125, 145.1409149169922)  (270.53436279296875, 144.33944702148438)
-1806    (492.5619812011719, 34.03639221191406)    (508.2730407714844, 33.94993591308594)
-1807   (464.4930419921875, 137.47805786132812)    (478.551513671875, 140.32156372070312)
-
-[1808 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (590.6266479492188, 179.97262573242188)    (612.72021484375, 184.90965270996094)
-1     (188.57135009765625, 72.27299499511719)   (191.84475708007812, 72.2235107421875)
-2      (68.19148254394531, 259.1839599609375)  (71.76834106445312, 263.94610595703125)
-3      (130.0337371826172, 172.2516326904297)  (134.0271453857422, 174.26397705078125)
-4      (164.8428192138672, 87.39070892333984)  (168.35243225097656, 87.44662475585938)
-...                                       ...                                      ...
-1836  (452.3842468261719, 222.46946716308594)  (306.3281555175781, 230.49676513671875)
-1837   (5.092470169067383, 143.3043975830078)    (7.420624732971191, 145.032470703125)
-1838   (92.17701721191406, 144.4915008544922)   (95.4988021850586, 145.63087463378906)
-1839  (502.84588623046875, 95.02049255371094)   (520.5948486328125, 96.24640655517578)
-1840  (304.9521179199219, 133.81288146972656)      (312.5361328125, 135.5125274658203)
-
-[1841 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (285.7068786621094, 216.09962463378906)    (295.081298828125, 220.63925170898438)
-1      (191.84475708007812, 72.2235107421875)    (195.52508544921875, 72.0846176147461)
-2     (261.1471862792969, 207.98541259765625)  (269.62579345703125, 211.93572998046875)
-3      (179.8302459716797, 181.9427490234375)   (185.57992553710938, 184.2140350341797)
-4     (291.9470520019531, 255.96226501464844)   (289.01751708984375, 257.2222900390625)
-...                                       ...                                       ...
-1863    (428.375244140625, 238.4827423095703)   (283.9725341796875, 244.09495544433594)
-1864   (389.6556701660156, 66.40003967285156)    (404.3001708984375, 67.40779876708984)
-1865    (567.730224609375, 76.89201354980469)     (591.130615234375, 77.97391510009766)
-1866   (576.6683959960938, 82.71269989013672)    (601.3231201171875, 85.31070709228516)
-1867  (408.0171203613281, 57.125648498535156)    (422.6040954589844, 57.00690841674805)
-
-[1868 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (161.38795471191406, 87.84980010986328)   (165.28451538085938, 87.63661193847656)
-1      (402.8970031738281, 314.7488098144531)     (421.414306640625, 325.9907531738281)
-2      (295.081298828125, 220.63925170898438)     (305.8506164550781, 225.124267578125)
-3     (180.10841369628906, 220.0250701904297)  (186.57498168945312, 223.83233642578125)
-4      (195.52508544921875, 72.0846176147461)   (200.26242065429688, 71.61087036132812)
-...                                       ...                                       ...
-1979   (505.7557373046875, 62.31010055541992)    (526.9887084960938, 63.46335220336914)
-1980   (317.0091857910156, 59.64053726196289)     (326.7839660644531, 58.8907585144043)
-1981   (169.8760528564453, 261.8535461425781)   (312.90069580078125, 274.0401611328125)
-1982   (428.4590759277344, 53.95883560180664)   (444.1988220214844, 54.193599700927734)
-1983   (601.3231201171875, 85.31070709228516)    (626.7014770507812, 86.13996887207031)
-
-[1984 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (200.26242065429688, 71.61087036132812)    (204.8988037109375, 71.37086486816406)
-1      (54.15179443359375, 72.24785614013672)     (56.8010368347168, 71.72411346435547)
-2        (175.9740447998047, 87.384033203125)     (180.1171417236328, 87.3619613647461)
-3     (165.28451538085938, 87.63661193847656)   (169.39381408691406, 87.67276000976562)
-4      (286.9217224121094, 258.4322509765625)   (287.6405029296875, 259.62884521484375)
-...                                       ...                                       ...
-1960  (40.205265045166016, 300.4056701660156)  (128.27003479003906, 312.25738525390625)
-1961  (276.7320251464844, 160.71072387695312)   (286.4078369140625, 163.23521423339844)
-1962  (421.99774169921875, 160.4568328857422)  (437.21051025390625, 164.15768432617188)
-1963  (459.0749206542969, 30.910724639892578)   (469.2012023925781, 30.889122009277344)
-1964  (522.0265502929688, 34.768550872802734)    (314.6860656738281, 300.6337585449219)
-
-[1965 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (412.344482421875, 210.66000366210938)  (429.07220458984375, 216.6554412841797)
-1       (53.02997970581055, 82.31916046142578)    (56.14084243774414, 81.3385009765625)
-2     (392.95379638671875, 313.52154541015625)  (412.4523620605469, 325.54229736328125)
-3      (287.6405029296875, 259.62884521484375)   (286.1445617675781, 260.7899475097656)
-4       (499.9859924316406, 349.3965148925781)     (527.27099609375, 365.4348449707031)
-...                                        ...                                      ...
-2034    (341.9439697265625, 241.3973846435547)   (356.2999267578125, 247.7862548828125)
-2035  (437.21051025390625, 164.15768432617188)   (454.7692565917969, 167.9086151123047)
-2036  (224.87054443359375, 146.20040893554688)  (232.9954376220703, 148.34542846679688)
-2037  (122.35067749023438, 114.26792907714844)   (461.3074035644531, 9.083606719970703)
-2038    (175.3495330810547, 158.1109161376953)   (181.62672424316406, 159.437255859375)
-
-[2039 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (286.1445617675781, 260.7899475097656)      (285.76416015625, 260.4362487792969)
-1       (184.9036407470703, 87.23757934570312)     (189.5055694580078, 87.3055648803711)
-2      (31.991565704345703, 302.7518615722656)  (35.831382751464844, 308.68243408203125)
-3      (161.57606506347656, 91.17301177978516)    (166.03382873535156, 90.9909439086914)
-4      (62.99395751953125, 220.24252319335938)   (67.01161193847656, 223.13108825683594)
-...                                        ...                                       ...
-2139   (361.1382141113281, 107.95265197753906)    (375.7256774902344, 110.1369400024414)
-2140  (210.61549377441406, 160.70510864257812)   (218.2323455810547, 163.64254760742188)
-2141   (370.14935302734375, 97.90428161621094)   (386.78375244140625, 99.86896514892578)
-2142    (628.6378784179688, 143.6180419921875)    (330.133056640625, 110.45359802246094)
-2143    (559.3364868164062, 54.44185256958008)     (586.064208984375, 54.84422302246094)
-
-[2144 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (529.6755981445312, 378.5350341796875)    (562.0508422851562, 398.9494323730469)
-1       (63.20273971557617, 70.01447296142578)    (66.22036743164062, 69.35151672363281)
-2      (195.29457092285156, 84.94448852539062)    (199.8577880859375, 85.09412384033203)
-3      (94.83990478515625, 320.95672607421875)  (100.34295654296875, 329.04803466796875)
-4       (63.20273971557617, 70.01447296142578)    (66.22036743164062, 69.35151672363281)
-...                                        ...                                       ...
-2235    (56.6192741394043, 130.89430236816406)    (59.68429946899414, 131.3696746826172)
-2236  (175.75511169433594, 189.45407104492188)  (181.97650146484375, 192.32789611816406)
-2237   (544.6881103515625, 169.57904052734375)    (568.9447631835938, 175.5530242919922)
-2238    (518.6220092773438, 266.2413024902344)    (524.459716796875, 104.97982788085938)
-2239   (308.9981994628906, 105.62488555908203)   (319.5594177246094, 106.69025421142578)
-
-[2240 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0       (71.4592514038086, 319.6518859863281)      (76.04173278808594, 327.65576171875)
-1      (49.26005172729492, 79.68170928955078)    (51.87116622924805, 79.04765319824219)
-2       (68.6228256225586, 323.5251159667969)      (72.97746276855469, 331.55126953125)
-3       (77.80329895019531, 71.9328384399414)     (80.6658935546875, 71.25415802001953)
-4     (400.8094177246094, 354.15191650390625)   (422.8528747558594, 370.45648193359375)
-...                                       ...                                       ...
-2211   (511.0625305175781, 67.66897583007812)    (537.1627807617188, 68.72366333007812)
-2212   (437.7047119140625, 27.71090316772461)  (232.47662353515625, 136.34190368652344)
-2213       (430.970703125, 87.37578582763672)    (299.0323486328125, 319.4684143066406)
-2214   (535.7147827148438, 147.0388641357422)   (561.2137451171875, 151.53750610351562)
-2215  (557.1947021484375, 104.15937042236328)     (482.51251220703125, 77.296142578125)
-
-[2216 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (69.05352783203125, 68.68356323242188)    (71.61503601074219, 68.15003204345703)
-1     (96.91521453857422, 324.19769287109375)   (102.31578063964844, 333.4001770019531)
-2      (69.05352783203125, 68.68356323242188)    (71.61503601074219, 68.15003204345703)
-3       (80.6658935546875, 71.25415802001953)     (83.3852767944336, 71.07735443115234)
-4     (224.81317138671875, 68.80442810058594)    (229.9409942626953, 69.09695434570312)
-...                                       ...                                       ...
-2375   (215.0769500732422, 87.82007598876953)   (221.36129760742188, 87.90485382080078)
-2376  (433.0181579589844, 28.366668701171875)  (448.86407470703125, 27.696430206298828)
-2377   (449.5373229980469, 148.4222869873047)    (468.9145812988281, 152.9478302001953)
-2378  (15.574758529663086, 166.6373291015625)    (18.43918228149414, 169.0010986328125)
-2379  (242.81126403808594, 89.24006652832031)    (179.6056365966797, 133.8802947998047)
-
-[2380 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (83.50877380371094, 72.38362121582031)    (85.46782684326172, 72.29779815673828)
-1        (83.3852767944336, 71.07735443115234)    (85.33518981933594, 71.04279327392578)
-2       (71.61503601074219, 68.15003204345703)    (73.62407684326172, 67.85472106933594)
-3       (71.61503601074219, 68.15003204345703)    (73.62407684326172, 67.85472106933594)
-4      (27.913570404052734, 316.1264343261719)    (30.51822280883789, 324.1703186035156)
-...                                        ...                                       ...
-2477  (252.62417602539062, 163.62889099121094)   (262.65301513671875, 167.1133270263672)
-2478   (355.62115478515625, 145.5522918701172)   (369.0476989746094, 149.36862182617188)
-2479    (466.5978698730469, 28.71022605895996)    (487.5926208496094, 28.15843391418457)
-2480    (619.2233276367188, 366.9782409667969)   (628.0018310546875, 102.08553314208984)
-2481   (571.8125610351562, 185.98434448242188)  (137.31153869628906, 167.53598022460938)
-
-[2482 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0        (70.60274505615234, 80.0039291381836)   (72.06572723388672, 80.30892181396484)
-1     (121.11065673828125, 101.34662628173828)  (123.1109390258789, 102.99451446533203)
-2        (128.28466796875, 195.92630004882812)  (131.7849578857422, 200.11985778808594)
-3       (146.66868591308594, 246.987548828125)   (152.0745391845703, 253.1111602783203)
-4       (56.93033981323242, 78.16290283203125)  (57.952545166015625, 78.43355560302734)
-...                                        ...                                      ...
-2579     (326.375244140625, 189.2698516845703)  (337.3512268066406, 193.99440002441406)
-2580  (293.05377197265625, 108.28483581542969)     (295.5751953125, 110.18316650390625)
-2581   (369.0476989746094, 149.36862182617188)     (382.52587890625, 152.8753204345703)
-2582    (281.2812194824219, 331.5086364746094)   (525.3544311523438, 380.3100280761719)
-2583   (366.4203796386719, 157.35501098632812)   (380.3043212890625, 160.9977569580078)
-
-[2584 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0        (200.0274658203125, 88.8884506225586)   (203.12271118164062, 89.83338928222656)
-1         (279.7200622558594, 253.61474609375)         (289.884765625, 261.559326171875)
-2          (238.7099609375, 70.28970336914062)    (242.5897216796875, 70.86441040039062)
-3      (181.62298583984375, 99.69756317138672)  (184.40061950683594, 100.73125457763672)
-4      (170.4345245361328, 110.64019012451172)  (173.24569702148438, 111.87920379638672)
-...                                        ...                                       ...
-2569   (508.9990539550781, 48.857295989990234)   (34.35639190673828, 160.44454956054688)
-2570  (34.629432678222656, 171.85609436035156)   (35.31739807128906, 174.59764099121094)
-2571   (469.06195068359375, 407.6750183105469)       (497.9244689941406, 430.1298828125)
-2572   (416.95086669921875, 33.55699157714844)   (574.7154541015625, 108.16627502441406)
-2573   (108.20659637451172, 315.8497619628906)    (390.433349609375, 39.469764709472656)
-
-[2574 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (55.21317672729492, 361.44744873046875)   (57.268680572509766, 374.3404541015625)
-1     (173.24569702148438, 111.87920379638672)  (175.72210693359375, 113.53132629394531)
-2      (184.59864807128906, 264.2061767578125)   (190.78765869140625, 272.1886291503906)
-3       (242.5897216796875, 70.86441040039062)   (246.66172790527344, 71.60441589355469)
-4        (145.235595703125, 270.3716125488281)  (150.93136596679688, 278.61602783203125)
-...                                        ...                                       ...
-2685    (366.7687683105469, 114.0450439453125)   (379.21148681640625, 116.7224349975586)
-2686     (511.82257080078125, 54.601806640625)    (530.0599975585938, 56.56192398071289)
-2687   (484.2055969238281, 184.07498168945312)   (504.88836669921875, 191.0581817626953)
-2688   (126.39546203613281, 277.0056457519531)    (256.8744812011719, 298.6597900390625)
-2689    (502.6990661621094, 100.5641860961914)    (527.573974609375, 104.24854278564453)
-
-[2690 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (53.96771240234375, 269.2966003417969)  (55.193267822265625, 277.2650146484375)
-1     (207.97885131835938, 272.84320068359375)   (214.83218383789062, 281.902099609375)
-2        (305.1978454589844, 259.161865234375)    (306.581787109375, 260.7064514160156)
-3       (118.05039978027344, 257.627685546875)  (120.75016784667969, 265.5241394042969)
-4      (111.93668365478516, 51.84303665161133)  (110.96295928955078, 52.84716033935547)
-...                                        ...                                      ...
-2705    (291.5995178222656, 161.0775146484375)   (300.5859069824219, 165.0311279296875)
-2706   (222.03074645996094, 200.5425262451172)  (66.37203216552734, 210.26260375976562)
-2707    (266.8053283691406, 93.83480834960938)   (270.4673767089844, 95.40534973144531)
-2708   (90.19538879394531, 181.42721557617188)  (441.9815368652344, 372.52398681640625)
-2709    (562.975830078125, 447.88726806640625)    (601.0011596679688, 474.911865234375)
-
-[2710 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0         (296.85302734375, 94.23884582519531)    (300.5644836425781, 96.14970397949219)
-1     (100.95801544189453, 399.83880615234375)   (104.61529541015625, 417.7374572753906)
-2        (135.79844665527344, 266.13427734375)  (138.84414672851562, 275.06097412109375)
-3     (338.17877197265625, 102.79319763183594)    (343.5483703613281, 104.9154281616211)
-4        (306.581787109375, 260.7064514160156)    (307.6236267089844, 263.1695556640625)
-...                                        ...                                       ...
-2728  (311.59393310546875, 194.94326782226562)   (320.4039306640625, 201.69024658203125)
-2729   (406.80584716796875, 166.2804412841797)       (295.138671875, 163.50228881835938)
-2730   (415.2873229980469, 144.87454223632812)  (431.08721923828125, 149.89012145996094)
-2731   (537.5906982421875, 158.25640869140625)     (563.627197265625, 164.2095184326172)
-2732       (91.5771713256836, 331.66748046875)    (234.89120483398438, 368.798095703125)
-
-[2733 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (87.95957946777344, 418.2351989746094)    (90.82405090332031, 438.4144287109375)
-1      (104.61529541015625, 417.7374572753906)   (108.44458770751953, 438.0477600097656)
-2       (160.3873291015625, 297.7799987792969)   (164.39622497558594, 309.0790710449219)
-3     (138.84414672851562, 275.06097412109375)   (141.91835021972656, 284.9420166015625)
-4     (221.82969665527344, 291.83660888671875)     (229.135498046875, 302.7992858886719)
-...                                        ...                                       ...
-2781    (266.3764953613281, 355.4803161621094)    (366.6214904785156, 461.1878356933594)
-2782   (531.6116943359375, 175.40086364746094)    (435.9977111816406, 151.9363555908203)
-2783  (296.15057373046875, 182.66812133789062)   (304.44586181640625, 189.3072052001953)
-2784  (224.63827514648438, 194.52793884277344)  (229.07748413085938, 200.11886596679688)
-2785     (21.1052188873291, 212.9063720703125)   (566.0186767578125, 131.72349548339844)
-
-[2786 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (101.05094146728516, 30.17916488647461)   (103.1071548461914, 29.956161499023438)
-1        (303.322998046875, 98.49153137207031)  (309.63531494140625, 102.59895324707031)
-2      (43.31203079223633, 278.69708251953125)     (41.12961196899414, 284.611572265625)
-3       (81.65856170654297, 78.34989929199219)    (82.46562957763672, 77.95310974121094)
-4       (50.60297393798828, 85.25981903076172)    (51.56312942504883, 84.24073028564453)
-...                                        ...                                       ...
-2650   (161.7877655029297, 191.95562744140625)    (374.5348815917969, 199.5460968017578)
-2651  (257.48443603515625, 178.45516967773438)   (266.8231506347656, 185.65846252441406)
-2652  (402.18658447265625, 142.03575134277344)       (433.6875305175781, 145.6162109375)
-2653      (253.88526916503906, 338.8466796875)   (115.24215698242188, 331.4172058105469)
-2654  (439.06976318359375, 195.17568969726562)  (456.18267822265625, 206.41693115234375)
-
-[2655 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (103.1071548461914, 29.956161499023438)  (105.20201110839844, 29.553987503051758)
-1     (114.97618865966797, 53.069034576416016)   (116.9713363647461, 53.110260009765625)
-2     (111.41095733642578, 49.367942810058594)  (113.02719116210938, 49.406673431396484)
-3      (329.5367126464844, 126.34639739990234)   (336.5264587402344, 132.23602294921875)
-4       (359.3322448730469, 262.5227966308594)    (371.8426818847656, 274.6112060546875)
-...                                        ...                                       ...
-2618   (340.7469177246094, 209.23924255371094)    (452.2779235839844, 104.8114013671875)
-2619    (392.4625244140625, 215.1522979736328)  (406.56439208984375, 228.34616088867188)
-2620        (280.5274353027344, 244.751953125)  (289.03851318359375, 256.83831787109375)
-2621    (353.1236877441406, 203.1322479248047)     (365.783447265625, 213.2586669921875)
-2622    (482.8619689941406, 204.8975830078125)   (503.3094482421875, 219.32542419433594)
-
-[2623 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (105.20201110839844, 29.553987503051758)  (107.64144134521484, 29.529542922973633)
-1      (248.8795623779297, 109.05998992919922)   (253.11395263671875, 113.0335464477539)
-2       (219.441162109375, 325.70147705078125)  (224.16905212402344, 339.76263427734375)
-3       (266.9039001464844, 334.0799255371094)    (274.3780517578125, 350.1498107910156)
-4      (151.7908935546875, 243.44459533691406)  (154.09986877441406, 250.54058837890625)
-...                                        ...                                       ...
-2497   (143.38304138183594, 384.3492126464844)    (345.0599670410156, 454.6472473144531)
-2498     (338.447509765625, 160.1222686767578)     (346.2274475097656, 167.620849609375)
-2499    (554.7001342773438, 471.0177001953125)     (292.9727783203125, 434.551513671875)
-2500    (63.57362365722656, 9.741890907287598)   (22.18053436279297, 247.48324584960938)
-2501   (450.9290771484375, 264.89776611328125)    (532.2039184570312, 185.5752716064453)
-
-[2502 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (51.92060852050781, 81.55968475341797)     (51.6307487487793, 80.03601837158203)
-1       (311.4019470214844, 98.57527160644531)   (317.0386047363281, 104.11666107177734)
-2      (283.1357116699219, 122.59999084472656)   (288.4664001464844, 127.87559509277344)
-3       (475.5230407714844, 380.3904724121094)     (496.346435546875, 406.6073913574219)
-4      (101.15909576416016, 16.39812660217285)  (103.53148651123047, 15.826231002807617)
-...                                        ...                                       ...
-2410    (573.0203857421875, 78.87733459472656)      (589.7786865234375, 92.223388671875)
-2411   (233.89639282226562, 433.2523193359375)   (239.88455200195312, 459.6493835449219)
-2412   (525.3858642578125, 142.21261596679688)   (624.3956909179688, 219.19488525390625)
-2413   (126.14806365966797, 217.1332550048828)   (127.17017364501953, 222.7555694580078)
-2414  (465.65582275390625, 274.35894775390625)    (482.8504638671875, 291.0655822753906)
-
-[2415 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0       (238.6287384033203, 109.302001953125)  (242.51829528808594, 113.22927856445312)
-1        (477.17431640625, 387.7905578613281)  (497.77313232421875, 415.65618896484375)
-2     (427.9219665527344, 387.99761962890625)    (445.2936706542969, 413.9753723144531)
-3      (354.3319091796875, 130.7810821533203)   (361.5407409667969, 138.34849548339844)
-4      (66.76467895507812, 83.18806457519531)    (66.56661987304688, 81.83677673339844)
-...                                       ...                                       ...
-2167   (114.2671890258789, 225.4573516845703)   (114.7359390258789, 230.68580627441406)
-2168  (365.8145751953125, 444.28607177734375)     (380.419677734375, 475.4519348144531)
-2169   (83.31083679199219, 238.2526397705078)     (79.9375991821289, 244.5293426513672)
-2170      (331.8193359375, 373.9382629394531)   (342.41314697265625, 395.8773193359375)
-2171     (462.639404296875, 213.546630859375)    (478.7271423339844, 228.3773651123047)
-
-[2172 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (481.5619201660156, 57.42848205566406)      (487.64306640625, 68.38190460205078)
-1       (145.5047607421875, 337.5957336425781)   (144.10733032226562, 351.6483154296875)
-2       (45.15095520019531, 324.9201354980469)   (37.804378509521484, 334.9421691894531)
-3     (250.64540100097656, 124.98631286621094)       (254.1708984375, 129.9409942626953)
-4       (161.9268341064453, 364.3582458496094)    (160.4036407470703, 380.0758972167969)
-...                                        ...                                       ...
-1973    (484.3584899902344, 240.9662628173828)   (502.87518310546875, 259.7178649902344)
-1974   (624.5490112304688, 260.69097900390625)   (344.3172607421875, 105.45642852783203)
-1975  (171.92947387695312, 466.75152587890625)    (273.2895812988281, 465.5728759765625)
-1976   (298.2286376953125, 470.23577880859375)    (94.31258392333984, 429.0392761230469)
-1977   (168.04908752441406, 207.3662567138672)  (171.57586669921875, 216.31919860839844)
-
-[1978 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (66.74979400634766, 80.29529571533203)    (66.34246826171875, 78.44744873046875)
-1      (322.8914794921875, 110.98587036132812)    (327.8630065917969, 117.9752426147461)
-2        (72.1185073852539, 67.32516479492188)    (72.53472137451172, 65.68403625488281)
-3      (492.27960205078125, 57.61107635498047)   (497.98638916015625, 69.47158813476562)
-4     (254.53610229492188, 120.85939025878906)   (258.3435363769531, 125.88278198242188)
-...                                        ...                                       ...
-1863    (500.3646545410156, 325.4880676269531)      (518.0538330078125, 349.33056640625)
-1864      (226.02294921875, 287.7248840332031)  (230.04856872558594, 299.09747314453125)
-1865     (570.409912109375, 256.1429443359375)    (594.2750244140625, 278.9516906738281)
-1866   (382.0645751953125, 230.86416625976562)  (395.82916259765625, 245.63229370117188)
-1867    (7.97609281539917, 180.67108154296875)   (4.294775009155273, 180.03176879882812)
-
-[1868 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (66.34246826171875, 78.44744873046875)    (66.31442260742188, 76.33800506591797)
-1        (301.996337890625, 146.6492919921875)   (306.7882080078125, 153.76625061035156)
-2       (72.53472137451172, 65.68403625488281)    (72.94108581542969, 63.98746871948242)
-3         (189.32763671875, 151.2779998779297)  (191.19277954101562, 155.10989379882812)
-4      (167.65968322753906, 154.9424591064453)   (169.0245819091797, 158.26678466796875)
-...                                        ...                                       ...
-1731  (140.01010131835938, 198.82485961914062)     (387.0477294921875, 299.168701171875)
-1732   (250.66879272460938, 475.3277587890625)    (46.52377700805664, 427.0560607910156)
-1733   (159.0494842529297, 292.13861083984375)  (159.03750610351562, 304.61993408203125)
-1734     (430.822998046875, 320.9561767578125)   (446.18975830078125, 342.4043884277344)
-1735   (44.88380813598633, 183.29827880859375)    (392.5411071777344, 281.4054260253906)
-
-[1736 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (66.31442260742188, 76.33800506591797)    (66.1233901977539, 74.52169799804688)
-1      (316.2591857910156, 153.01687622070312)  (320.9924621582031, 160.74774169921875)
-2      (334.77069091796875, 436.1190490722656)   (344.7785339355469, 466.6824645996094)
-3     (147.04600524902344, 396.08953857421875)  (144.0847625732422, 417.25445556640625)
-4       (139.8290252685547, 384.7105407714844)       (136.5087890625, 404.703857421875)
-...                                        ...                                      ...
-1628      (551.5581665039062, 212.95751953125)       (569.603515625, 233.2827606201172)
-1629   (55.08439636230469, 218.31735229492188)   (615.1690063476562, 267.0773620605469)
-1630   (286.96807861328125, 297.7048645019531)  (293.88922119140625, 314.7721252441406)
-1631   (289.34100341796875, 475.7156982421875)  (117.82955932617188, 446.4432067871094)
-1632  (32.141319274902344, 238.32118225097656)  (26.731616973876953, 242.9014434814453)
-
-[1633 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0       (66.1233901977539, 74.52169799804688)   (66.43415069580078, 72.55011749267578)
-1      (503.6195068359375, 104.9383316040039)  (508.6825256347656, 118.11624145507812)
-2      (256.7371826171875, 132.6156463623047)  (260.1380310058594, 138.13177490234375)
-3      (407.0460510253906, 167.9440460205078)   (413.1224670410156, 179.3525848388672)
-4      (69.04120635986328, 65.89010620117188)   (69.33418273925781, 63.87382507324219)
-...                                       ...                                      ...
-1661   (607.2922973632812, 231.9478759765625)    (626.62158203125, 255.01695251464844)
-1662   (349.1685791015625, 283.3059997558594)   (364.1561584472656, 301.8355407714844)
-1663    (402.606201171875, 342.2973327636719)   (416.6599426269531, 366.2910461425781)
-1664  (162.49974060058594, 442.1937561035156)   (160.0171661376953, 469.5787353515625)
-1665  (422.12982177734375, 379.4943542480469)   (435.4300842285156, 406.6343688964844)
-
-[1666 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (200.4659423828125, 173.72348022460938)  (202.00820922851562, 178.75601196289062)
-1      (413.1224670410156, 179.3525848388672)   (419.2786560058594, 191.30690002441406)
-2     (200.4659423828125, 173.72348022460938)  (202.00820922851562, 178.75601196289062)
-3      (127.7807388305664, 43.94264602661133)   (130.05831909179688, 43.47442626953125)
-4     (303.5556945800781, 120.44915008544922)  (309.28558349609375, 128.07476806640625)
-...                                       ...                                       ...
-1626   (320.7320556640625, 301.4694519042969)   (331.6379699707031, 320.64532470703125)
-1627   (344.5647888183594, 388.1368713378906)    (434.2637634277344, 441.8194580078125)
-1628  (37.26176071166992, 220.46885681152344)    (375.0286560058594, 291.3242492675781)
-1629  (507.0603332519531, 407.41766357421875)    (525.1314086914062, 438.3825988769531)
-1630    (541.1550903320312, 378.460693359375)         (562.55078125, 410.8029479980469)
-
-[1631 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (263.491943359375, 144.16726684570312)   (267.5476379394531, 150.22024536132812)
-1      (273.4285888671875, 158.77093505859375)   (277.1560363769531, 165.61978149414062)
-2       (51.4477653503418, 63.865821838378906)    (52.84054183959961, 61.23162078857422)
-3     (266.05059814453125, 180.68936157226562)  (269.74615478515625, 188.17532348632812)
-4       (533.378662109375, 117.20706176757812)    (537.5917358398438, 131.3501739501953)
-...                                        ...                                       ...
-1467   (376.9590148925781, 313.81121826171875)   (389.0931396484375, 336.11614990234375)
-1468        (530.94091796875, 307.53173828125)    (543.5654907226562, 329.2466735839844)
-1469      (576.7587280273438, 305.97509765625)   (595.0443115234375, 330.51397705078125)
-1470   (141.5911407470703, 281.63446044921875)   (142.02325439453125, 294.5263366699219)
-1471     (610.9838256835938, 88.3946533203125)   (616.9103393554688, 105.89472961425781)
-
-[1472 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (451.14996337890625, 8.41828441619873)   (455.1864929199219, 20.446853637695312)
-1      (446.20098876953125, 8.04666519165039)    (450.1966247558594, 20.05527114868164)
-2      (446.20098876953125, 8.04666519165039)    (450.1966247558594, 20.05527114868164)
-3     (132.66188049316406, 43.18562316894531)  (135.90719604492188, 42.982627868652344)
-4      (451.14996337890625, 8.41828441619873)   (455.1864929199219, 20.446853637695312)
-...                                       ...                                       ...
-1571  (581.0140380859375, 392.53033447265625)   (606.4757690429688, 427.15875244140625)
-1572  (211.00442504882812, 333.4604797363281)  (214.78675842285156, 352.89459228515625)
-1573     (615.7177734375, 467.01544189453125)    (341.2415466308594, 432.8381042480469)
-1574  (615.9395751953125, 185.43923950195312)   (406.39544677734375, 419.3774719238281)
-1575  (136.4180145263672, 189.41433715820312)    (634.2675170898438, 413.7654113769531)
-
-[1576 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (135.90719604492188, 42.982627868652344)  (139.20066833496094, 43.118927001953125)
-1         (253.8544921875, 151.04393005371094)       (257.4794921875, 157.2725372314453)
-2         (69.23182678222656, 66.461181640625)    (70.13589477539062, 64.61132049560547)
-3      (320.2979431152344, 143.55929565429688)   (325.4808654785156, 151.04165649414062)
-4     (131.97360229492188, 13.277878761291504)   (136.77374267578125, 13.67531681060791)
-...                                        ...                                       ...
-1549      (173.01319885253906, 332.4033203125)    (174.8853759765625, 353.4789123535156)
-1550    (593.6712646484375, 393.0964660644531)    (620.4050903320312, 429.0730895996094)
-1551    (417.1690673828125, 432.3021545410156)   (434.6395263671875, 467.36578369140625)
-1552   (454.26348876953125, 351.1070556640625)    (470.8548889160156, 377.6170654296875)
-1553    (583.9030151367188, 349.0191345214844)    (630.432861328125, 231.97650146484375)
-
-[1554 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (139.20066833496094, 43.118927001953125)        (143.052734375, 43.61252212524414)
-1      (335.9949951171875, 206.64642333984375)        (340.90234375, 216.75205993652344)
-2        (275.05712890625, 163.54200744628906)   (279.0295715332031, 170.42953491210938)
-3          (424.989013671875, 231.41064453125)   (431.7264709472656, 245.12982177734375)
-4      (167.2385711669922, 191.06338500976562)     (168.8548583984375, 196.270263671875)
-...                                        ...                                       ...
-1624    (483.4112854003906, 421.0355529785156)    (505.2984619140625, 457.2811584472656)
-1625    (469.5590515136719, 383.2370910644531)   (488.4715881347656, 414.30706787109375)
-1626  (487.69183349609375, 238.10289001464844)  (493.78973388671875, 253.07650756835938)
-1627    (597.1161499023438, 385.1976318359375)    (616.2896118164062, 415.4082946777344)
-1628    (325.0442199707031, 443.2375793457031)    (156.95553588867188, 365.183837890625)
-
-[1629 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0          (143.052734375, 43.61252212524414)   (147.31031799316406, 44.34754943847656)
-1      (73.35435485839844, 64.25321960449219)    (75.30343627929688, 62.26001739501953)
-2      (261.1668395996094, 163.7825469970703)   (265.5129089355469, 170.61813354492188)
-3      (449.08831787109375, 281.602783203125)     (457.313232421875, 298.4118347167969)
-4     (279.0295715332031, 170.42953491210938)    (283.3191223144531, 177.7986297607422)
-...                                       ...                                       ...
-1542  (625.5944213867188, 413.86370849609375)   (621.9164428710938, 366.95794677734375)
-1543   (29.402069091796875, 402.447998046875)  (198.14337158203125, 222.06935119628906)
-1544  (361.4250793457031, 439.23333740234375)   (373.94439697265625, 475.9178161621094)
-1545  (22.776708602905273, 308.6700439453125)   (19.07322883605957, 323.31634521484375)
-1546  (77.95344543457031, 203.21531677246094)   (76.79475402832031, 206.89593505859375)
-
-[1547 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (397.01849365234375, 231.28225708007812)  (404.26300048828125, 243.51617431640625)
-1       (296.1464538574219, 189.8846893310547)  (300.94036865234375, 197.98507690429688)
-2        (458.820068359375, 98.33709716796875)    (461.7683410644531, 108.2936019897461)
-3      (435.2852478027344, 287.81439208984375)    (443.7239074707031, 304.5501708984375)
-4      (147.31031799316406, 44.34754943847656)   (151.6391143798828, 45.380863189697266)
-...                                        ...                                       ...
-1510    (602.6696166992188, 324.2422180175781)   (617.1527709960938, 348.10223388671875)
-1511    (383.4400634765625, 415.4340515136719)    (399.4569091796875, 448.1690979003906)
-1512    (596.4552001953125, 353.9237976074219)   (307.59466552734375, 466.3475036621094)
-1513    (188.4178009033203, 263.2606201171875)     (190.770751953125, 273.4064025878906)
-1514   (219.52052307128906, 347.7322082519531)    (226.2967071533203, 371.3491516113281)
-
-[1515 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (64.84033966064453, 193.06805419921875)   (64.81604766845703, 196.27769470214844)
-1      (64.84033966064453, 193.06805419921875)   (64.81604766845703, 196.27769470214844)
-2       (461.7683410644531, 108.2936019897461)    (464.9371337890625, 117.6584701538086)
-3       (260.8318176269531, 184.0142822265625)    (265.2908935546875, 190.9741973876953)
-4      (302.1497497558594, 215.09396362304688)    (307.432861328125, 224.27919006347656)
-...                                        ...                                       ...
-1515   (18.863773345947266, 371.7419128417969)  (10.928083419799805, 395.38677978515625)
-1516    (254.1907196044922, 404.3144836425781)    (265.5723876953125, 440.8872375488281)
-1517   (78.95051574707031, 241.74563598632812)   (79.27398681640625, 251.98033142089844)
-1518  (193.90809631347656, 363.96942138671875)    (199.5116424560547, 390.9360046386719)
-1519    (519.3878173828125, 443.1412048339844)    (535.4105224609375, 475.0696105957031)
-
-[1520 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (560.7081909179688, 260.20269775390625)   (568.9993286132812, 276.2777099609375)
-1      (454.4582824707031, 289.6706848144531)    (463.250244140625, 305.8492126464844)
-2      (475.8278503417969, 73.34576416015625)   (480.8627624511719, 83.37653350830078)
-3      (411.3465881347656, 256.3952941894531)   (418.6794738769531, 269.5223083496094)
-4      (464.9371337890625, 117.6584701538086)  (468.2669372558594, 127.01727294921875)
-...                                       ...                                      ...
-1508   (68.04240417480469, 234.7932586669922)  (68.06822967529297, 244.36944580078125)
-1509    (171.62344360351562, 424.19482421875)      (98.06564331054688, 415.6240234375)
-1510   (271.6239013671875, 418.1297912597656)  (283.03192138671875, 455.5566711425781)
-1511   (53.19606018066406, 399.6910400390625)  (384.40936279296875, 343.5517272949219)
-1512   (76.17250061035156, 282.2018127441406)   (76.2916030883789, 296.17425537109375)
-
-[1513 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (88.72274780273438, 59.61491012573242)    (94.06023406982422, 59.34809875488281)
-1      (111.41690826416016, 64.45125579833984)   (118.01072692871094, 64.41566467285156)
-2       (149.8264923095703, 45.83173370361328)  (155.82571411132812, 46.857643127441406)
-3      (311.6424255371094, 215.10769653320312)      (317.69189453125, 224.0160675048828)
-4      (279.4857177734375, 191.43894958496094)      (285.6283874511719, 199.09912109375)
-...                                        ...                                       ...
-1512    (616.9357299804688, 438.1175231933594)     (634.592041015625, 467.9294738769531)
-1513  (216.50132751464844, 217.18429565429688)   (221.06365966796875, 224.7784423828125)
-1514    (62.98281478881836, 336.9403381347656)  (60.851905822753906, 357.63079833984375)
-1515    (72.08311462402344, 358.8548889160156)   (413.5791320800781, 253.90350341796875)
-1516      (8.201004028320312, 304.89794921875)   (595.3599243164062, 52.310340881347656)
-
-[1517 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (118.01072692871094, 64.41566467285156)    (124.56076049804688, 64.5970458984375)
-1      (142.80381774902344, 48.84822082519531)    (149.4529266357422, 49.85667419433594)
-2      (73.38147735595703, 192.01272583007812)    (75.92833709716797, 196.1359100341797)
-3         (317.69189453125, 224.0160675048828)          (324.236572265625, 233.24609375)
-4     (43.957950592041016, 213.35560607910156)   (45.16900634765625, 218.23944091796875)
-...                                        ...                                       ...
-1548   (131.64422607421875, 423.0385437011719)   (134.94114685058594, 457.6293029785156)
-1549   (156.78993225097656, 229.3120880126953)   (160.5753936767578, 236.56109619140625)
-1550   (603.3970336914062, 49.462989807128906)   (61.444236755371094, 364.1851501464844)
-1551   (6.712466239929199, 172.50515747070312)  (12.082783699035645, 174.62623596191406)
-1552    (72.7691650390625, 337.93182373046875)    (72.70771026611328, 361.2306823730469)
-
-[1553 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (491.8885192871094, 101.39371490478516)   (497.8731384277344, 109.41070556640625)
-1           (311.5818176269531, 217.267578125)   (318.7749938964844, 225.54774475097656)
-2       (100.3097152709961, 59.76042175292969)   (106.26136779785156, 60.36849594116211)
-3       (85.12576293945312, 47.05313491821289)    (91.58148193359375, 47.50590133666992)
-4     (175.16024780273438, 226.23074340820312)  (180.34811401367188, 233.38453674316406)
-...                                        ...                                       ...
-1521    (270.4848327636719, 311.3059997558594)  (169.32887268066406, 282.77337646484375)
-1522  (21.175222396850586, 329.18524169921875)   (19.55659294128418, 350.90948486328125)
-1523    (169.1918182373047, 339.8299255371094)    (174.8543243408203, 361.9226989746094)
-1524       (244.96597290039062, 410.666015625)   (255.01766967773438, 442.1670837402344)
-1525    (63.27490997314453, 351.2276611328125)   (62.43012619018555, 377.32000732421875)
-
-[1526 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (91.58148193359375, 47.50590133666992)    (95.32842254638672, 51.7887077331543)
-1       (91.58148193359375, 47.50590133666992)    (95.32842254638672, 51.7887077331543)
-2       (61.52439880371094, 59.36841583251953)    (64.95684814453125, 64.1397705078125)
-3     (123.76580047607422, 45.447486877441406)  (127.52754211425781, 49.48027801513672)
-4      (286.60296630859375, 286.1678466796875)   (294.42315673828125, 298.505615234375)
-...                                        ...                                      ...
-1485   (186.04579162597656, 381.4502868652344)    (196.0631561279297, 416.615966796875)
-1486   (125.97461700439453, 340.2432861328125)  (132.47869873046875, 365.0018615722656)
-1487   (602.3118896484375, 107.14393615722656)   (76.08319854736328, 349.1114196777344)
-1488  (148.28152465820312, 223.82794189453125)   (588.0940551757812, 360.2047424316406)
-1489    (50.59385681152344, 297.4816589355469)   (53.64629364013672, 319.6722717285156)
-
-[1490 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (110.67996215820312, 65.40946197509766)   (114.84256744384766, 69.26365661621094)
-1       (95.32842254638672, 51.7887077331543)   (99.07540130615234, 55.616397857666016)
-2       (95.32842254638672, 51.7887077331543)   (99.07540130615234, 55.616397857666016)
-3       (64.95684814453125, 64.1397705078125)    (68.80497741699219, 67.98851013183594)
-4      (134.63888549804688, 68.7820816040039)    (139.0323486328125, 72.69190216064453)
-...                                       ...                                       ...
-1569   (366.9039611816406, 431.3328857421875)    (381.3923645019531, 454.8285217285156)
-1570  (43.643733978271484, 264.8476867675781)    (46.70270538330078, 276.7803955078125)
-1571  (31.897239685058594, 370.5581970214844)   (31.967369079589844, 402.2751159667969)
-1572  (182.99562072753906, 362.0280456542969)     (151.443115234375, 262.8151550292969)
-1573  (16.636980056762695, 186.0378875732422)  (15.149504661560059, 194.05958557128906)
-
-[1574 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0         (338.99267578125, 230.6757354736328)   (346.5142822265625, 238.17745971679688)
-1       (68.80497741699219, 67.98851013183594)    (73.91279602050781, 71.56441497802734)
-2      (99.07540130615234, 55.616397857666016)  (104.16253662109375, 59.238914489746094)
-3         (310.7775573730469, 230.12646484375)   (317.88238525390625, 238.7545623779297)
-4     (351.69183349609375, 247.22679138183594)   (359.6539611816406, 255.58856201171875)
-...                                        ...                                       ...
-1567     (59.01234436035156, 443.339111328125)  (119.83788299560547, 467.38665771484375)
-1568    (612.647216796875, 126.88225555419922)     (621.3831176757812, 130.987060546875)
-1569  (15.149504661560059, 194.05958557128906)  (22.706329345703125, 201.90829467773438)
-1570  (408.85003662109375, 448.00042724609375)         (425.43359375, 471.6366882324219)
-1571    (37.781890869140625, 390.152099609375)    (77.75254821777344, 371.6690673828125)
-
-[1572 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (512.0700073242188, 124.97381591796875)  (518.6387329101562, 128.83242797851562)
-1      (336.7185974121094, 242.78964233398438)  (344.6938781738281, 250.51113891601562)
-2      (488.1177978515625, 164.93199157714844)   (494.11944580078125, 168.899169921875)
-3     (104.16253662109375, 59.238914489746094)  (108.48492431640625, 62.48612976074219)
-4     (104.16253662109375, 59.238914489746094)  (108.48492431640625, 62.48612976074219)
-...                                        ...                                      ...
-1573   (165.1070098876953, 298.59698486328125)   (67.08285522460938, 282.3013000488281)
-1574    (630.311279296875, 100.35833740234375)  (225.51589965820312, 340.7272644042969)
-1575  (127.44600677490234, 354.18438720703125)    (132.5306854248047, 371.043212890625)
-1576    (459.2814636230469, 307.2037353515625)   (470.9307861328125, 317.8940124511719)
-1577     (38.30756378173828, 447.875732421875)     (114.17431640625, 247.0653839111328)
-
-[1578 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (69.19319915771484, 57.33710861206055)   (74.65023040771484, 60.392982482910156)
-1      (199.8018035888672, 334.7898864746094)      (206.87991333007812, 348.6689453125)
-2     (538.9756469726562, 193.02613830566406)    (545.5313720703125, 196.9551544189453)
-3     (160.83441162109375, 329.8791809082031)  (166.83578491210938, 343.84246826171875)
-4      (348.3325500488281, 260.0086669921875)          (357.3258361816406, 268.1796875)
-...                                       ...                                       ...
-1534   (2.7991185188293457, 186.701904296875)   (6.967469692230225, 193.17044067382812)
-1535  (124.23302459716797, 73.00049591064453)   (129.43507385253906, 397.8485412597656)
-1536  (190.4925079345703, 3.8326573371887207)   (195.90963745117188, 6.856446743011475)
-1537   (67.08285522460938, 282.3013000488281)     (177.3673553466797, 323.208251953125)
-1538     (39.663330078125, 456.5391845703125)      (104.34228515625, 411.2669982910156)
-
-[1539 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0        (171.435791015625, 66.63873291015625)   (176.35726928710938, 69.10162353515625)
-1       (545.5313720703125, 196.9551544189453)   (552.8428344726562, 201.08267211914062)
-2       (545.5313720703125, 196.9551544189453)   (552.8428344726562, 201.08267211914062)
-3         (202.9490203857422, 68.638427734375)    (207.9087371826172, 71.31767272949219)
-4       (502.5497131347656, 412.8463134765625)   (519.0560913085938, 430.21588134765625)
-...                                        ...                                       ...
-1549   (122.85606384277344, 291.2888488769531)          (346.8107604980469, 313.8828125)
-1550   (118.5819320678711, 276.53057861328125)    (123.62476348876953, 286.122314453125)
-1551    (368.6738586425781, 454.0475158691406)  (382.34381103515625, 476.43402099609375)
-1552    (362.4917907714844, 467.2433776855469)     (99.6726303100586, 368.4212646484375)
-1553  (151.49217224121094, 403.61431884765625)  (20.031368255615234, 196.16065979003906)
-
-[1554 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (165.99554443359375, 77.67494201660156)          (171.234375, 80.20633697509766)
-1      (552.8428344726562, 201.08267211914062)   (559.8327026367188, 204.8063201904297)
-2       (156.40196228027344, 87.6237564086914)  (161.22637939453125, 90.20835876464844)
-3       (391.6592102050781, 272.7790832519531)   (400.6564025878906, 280.6498718261719)
-4       (137.82760620117188, 307.125732421875)  (142.93048095703125, 318.1701354980469)
-...                                        ...                                      ...
-1633   (171.56947326660156, 456.0953674316406)   (152.6772918701172, 454.1374816894531)
-1634  (183.71617126464844, 335.62347412109375)   (78.76104736328125, 315.4514465332031)
-1635   (626.9728393554688, 132.38742065429688)   (203.2799530029297, 41.09480285644531)
-1636   (149.9473876953125, 304.49810791015625)  (214.64840698242188, 441.4891357421875)
-1637      (584.302978515625, 76.2803955078125)  (616.5421142578125, 122.57911682128906)
-
-[1638 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0     (230.55169677734375, 19.124046325683594)  (235.28517150878906, 21.20818519592285)
-1      (349.8550720214844, 249.06265258789062)   (358.49176025390625, 256.017822265625)
-2      (245.4077911376953, 304.56195068359375)  (252.1187286376953, 314.51092529296875)
-3       (37.66419219970703, 54.00727081298828)  (42.626380920410156, 56.18553161621094)
-4      (141.61709594726562, 86.47429656982422)  (146.12445068359375, 88.62397766113281)
-...                                        ...                                      ...
-1594        (173.259765625, 285.2140808105469)       (178.3821258544922, 294.185546875)
-1595   (232.17893981933594, 396.4493713378906)  (178.13702392578125, 464.9231262207031)
-1596      (33.73177719116211, 263.94775390625)  (258.1973571777344, 448.14532470703125)
-1597   (12.558988571166992, 336.5185852050781)  (15.385334968566895, 349.0060119628906)
-1598    (180.5588836669922, 447.9006042480469)  (186.78692626953125, 468.9568176269531)
-
-[1599 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (146.12445068359375, 88.62397766113281)  (150.47731018066406, 90.84602355957031)
-1     (306.9056091308594, 352.31561279296875)  (315.36492919921875, 364.3314514160156)
-2     (42.626380920410156, 56.18553161621094)    (46.36195755004883, 57.9977912902832)
-3      (260.4194641113281, 24.14838218688965)  (266.49261474609375, 26.09141731262207)
-4      (358.49176025390625, 256.017822265625)  (367.59173583984375, 262.1197814941406)
-...                                       ...                                      ...
-1541  (130.4788055419922, 327.51361083984375)  (148.90452575683594, 408.8365173339844)
-1542  (171.95452880859375, 474.8874816894531)     (282.58233642578125, 322.1533203125)
-1543  (207.45167541503906, 311.6808776855469)  (110.68241119384766, 267.4324645996094)
-1544   (215.3990478515625, 345.0137023925781)  (314.5671081542969, 415.24774169921875)
-1545  (208.12059020996094, 47.08938217163086)   (211.4304962158203, 49.40048599243164)
-
-[1546 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0       (46.36195755004883, 57.9977912902832)   (50.13308334350586, 59.90843200683594)
-1      (55.42720031738281, 51.01402282714844)   (60.48078155517578, 52.99423599243164)
-2     (573.7684936523438, 211.77084350585938)  (580.2203369140625, 215.04928588867188)
-3     (573.7684936523438, 211.77084350585938)  (580.2203369140625, 215.04928588867188)
-4      (61.24972152709961, 64.55999755859375)   (65.70033264160156, 66.50142669677734)
-...                                       ...                                      ...
-1669   (211.2156524658203, 460.2687683105469)   (98.57113647460938, 424.5729675292969)
-1670  (247.88787841796875, 327.1231689453125)   (253.2632598876953, 337.2022705078125)
-1671  (508.1468811035156, 475.67840576171875)   (425.0832214355469, 476.7838439941406)
-1672  (61.952327728271484, 401.3832092285156)   (549.8951416015625, 399.6364440917969)
-1673      (322.43310546875, 355.024169921875)   (97.97640228271484, 407.0172424316406)
-
-[1674 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (580.2203369140625, 215.04928588867188)    (586.0465087890625, 218.0286407470703)
-1      (580.2203369140625, 215.04928588867188)    (586.0465087890625, 218.0286407470703)
-2     (377.39361572265625, 110.34613800048828)   (381.25201416015625, 112.3812026977539)
-3      (271.6338806152344, 350.13702392578125)   (278.0729675292969, 361.00067138671875)
-4      (361.20770263671875, 293.0015563964844)   (368.21673583984375, 300.2203063964844)
-...                                        ...                                       ...
-1581       (256.9658203125, 456.4100646972656)         (264.25537109375, 475.4638671875)
-1582   (190.53782653808594, 400.9071044921875)   (38.86692428588867, 300.42767333984375)
-1583   (205.80227661132812, 349.8259582519531)  (210.61045837402344, 360.80218505859375)
-1584    (139.0706787109375, 457.7502136230469)  (100.70932006835938, 341.07550048828125)
-1585   (120.48169708251953, 340.4847717285156)   (238.94752502441406, 389.1141662597656)
-
-[1586 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0       (158.1242218017578, 94.7776107788086)    (160.6697540283203, 96.48323822021484)
-1     (426.3497619628906, 320.67218017578125)    (434.118896484375, 328.59332275390625)
-2     (382.95404052734375, 272.8399963378906)   (389.32269287109375, 280.0457458496094)
-3       (103.32070922851562, 304.98388671875)  (105.35733795166016, 314.14251708984375)
-4      (586.0465087890625, 218.0286407470703)    (591.2852783203125, 220.8319091796875)
-...                                       ...                                       ...
-1535   (128.75680541992188, 427.662353515625)   (131.18617248535156, 444.5129699707031)
-1536     (34.406005859375, 365.8797302246094)   (359.6529235839844, 434.15069580078125)
-1537   (541.1356811523438, 399.0885314941406)    (343.3421936035156, 305.8562316894531)
-1538  (176.01319885253906, 451.5078430175781)   (179.83436584472656, 470.0536804199219)
-1539   (94.97704315185547, 356.7576904296875)   (220.55929565429688, 409.8614501953125)
-
-[1540 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (160.6697540283203, 96.48323822021484)   (162.8384552001953, 98.83574676513672)
-1       (340.1319274902344, 386.6684265136719)  (347.1778564453125, 398.09197998046875)
-2        (190.587158203125, 89.46454620361328)  (192.87594604492188, 90.96961975097656)
-3            (406.6317443847656, 307.14453125)   (412.7421569824219, 313.9557800292969)
-4     (26.659343719482422, 205.19940185546875)  (26.695512771606445, 210.1544189453125)
-...                                        ...                                      ...
-1465   (53.01061248779297, 63.199337005615234)   (55.14208221435547, 64.92757415771484)
-1466    (421.2232971191406, 421.3074035644531)    (44.76782989501953, 268.071533203125)
-1467  (234.92587280273438, 435.61749267578125)  (17.787647247314453, 252.4130401611328)
-1468   (214.59413146972656, 3.726926803588867)  (216.56297302246094, 5.021249771118164)
-1469    (169.3238067626953, 381.9809875488281)    (171.4180450439453, 394.646240234375)
-
-[1470 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (595.5731201171875, 223.29660034179688)  (598.6185302734375, 225.36068725585938)
-1     (595.5731201171875, 223.29660034179688)  (598.6185302734375, 225.36068725585938)
-2      (435.1256408691406, 318.7577819824219)  (441.0804138183594, 325.52301025390625)
-3     (56.162193298339844, 64.58252716064453)   (55.95133972167969, 65.82085418701172)
-4      (412.7421569824219, 313.9557800292969)  (418.25274658203125, 320.3428039550781)
-...                                       ...                                      ...
-1522   (116.1097640991211, 306.1176452636719)   (97.99728393554688, 237.9258575439453)
-1523  (350.4153747558594, 457.72943115234375)   (49.55027770996094, 374.6871337890625)
-1524  (70.47661590576172, 285.03155517578125)  (70.26524353027344, 292.52716064453125)
-1525    (352.603271484375, 326.8820495605469)   (358.6888427734375, 334.7481384277344)
-1526  (216.56297302246094, 5.021249771118164)  (216.45709228515625, 5.565117835998535)
-
-[1527 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0     (281.04681396484375, 33.125301361083984)    (280.815185546875, 34.01896667480469)
-1     (152.07948303222656, 307.85540771484375)   (152.8944549560547, 316.0052185058594)
-2          (592.8115234375, 401.0583190917969)    (601.397705078125, 408.9935302734375)
-3      (163.96188354492188, 99.44497680664062)  (164.1809539794922, 101.71219635009766)
-4     (255.92453002929688, 12.997963905334473)   (256.576904296875, 13.964250564575195)
-...                                        ...                                      ...
-1485   (135.54550170898438, 412.0658264160156)  (135.79827880859375, 426.8913269042969)
-1486   (192.43344116210938, 458.9107360839844)         (194.98049926757812, 475.703125)
-1487    (323.6864318847656, 437.5865783691406)    (26.47075080871582, 466.201904296875)
-1488   (235.11048889160156, 392.7327880859375)  (238.2935333251953, 404.87237548828125)
-1489     (255.40072631835938, 369.77197265625)        (257.59765625, 381.5356750488281)
-
-[1490 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0        (280.815185546875, 34.01896667480469)    (280.6019287109375, 35.15979766845703)
-1      (390.3443908691406, 118.61898040771484)   (390.6265869140625, 119.37561798095703)
-2       (280.9522705078125, 33.67993927001953)   (280.78228759765625, 34.67852020263672)
-3      (235.54412841796875, 86.10063934326172)    (235.1412811279297, 87.27008819580078)
-4       (331.3143310546875, 405.0043029785156)  (336.18511962890625, 416.11309814453125)
-...                                        ...                                       ...
-1424   (412.3640441894531, 402.61883544921875)   (254.54971313476562, 445.0437927246094)
-1425  (109.82710266113281, 439.70709228515625)    (47.44409942626953, 358.0051574707031)
-1426   (237.74972534179688, 401.8666687011719)   (240.4805145263672, 414.00335693359375)
-1427   (340.87335205078125, 442.2691650390625)   (227.5201416015625, 445.87261962890625)
-1428   (13.724772453308105, 334.3356018066406)    (9.529728889465332, 345.4083557128906)
-
-[1429 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (306.3192443847656, 37.42548370361328)   (305.26641845703125, 38.43014907836914)
-1      (64.89727783203125, 61.39756393432617)   (61.22026443481445, 62.439247131347656)
-2      (325.4974670410156, 406.3519592285156)   (329.3096923828125, 416.54376220703125)
-3      (218.8251190185547, 87.59880828857422)    (216.8603973388672, 88.65203094482422)
-4       (78.1352767944336, 75.24933624267578)    (74.96292877197266, 76.46776580810547)
-...                                       ...                                       ...
-1309  (208.9838104248047, 378.82574462890625)   (208.80166625976562, 389.5997009277344)
-1310  (37.96867752075195, 397.92169189453125)    (31.17276382446289, 408.0950622558594)
-1311   (389.952880859375, 315.77996826171875)  (392.96295166015625, 321.05279541015625)
-1312  (62.30925369262695, 210.34356689453125)    (76.77889251708984, 339.2258605957031)
-1313  (127.36859893798828, 458.0729064941406)  (256.26922607421875, 474.02362060546875)
-
-[1314 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (144.78932189941406, 86.83027648925781)   (138.6354217529297, 90.28740692138672)
-1      (334.64715576171875, 376.7248229980469)   (338.8511962890625, 384.7437438964844)
-2      (144.78932189941406, 86.83027648925781)   (138.6354217529297, 90.28740692138672)
-3       (345.8961181640625, 425.1101379394531)        (351.8359375, 435.13970947265625)
-4         (498.5536804199219, 398.57177734375)    (504.3802185058594, 402.172607421875)
-...                                        ...                                      ...
-1180    (402.2852783203125, 332.3312072753906)  (406.2276916503906, 337.00616455078125)
-1181   (131.12942504882812, 446.2014465332031)  (165.88319396972656, 468.0370788574219)
-1182    (427.9693908691406, 339.7785339355469)   (431.7842102050781, 343.7353210449219)
-1183  (27.737619400024414, 451.54266357421875)   (15.035350799560547, 364.928466796875)
-1184  (25.879600524902344, 262.19989013671875)   (19.89362335205078, 273.3579406738281)
-
-[1185 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (39.037384033203125, 73.64219665527344)    (27.03577995300293, 80.12236022949219)
-1      (473.0004577636719, 397.55364990234375)     (479.0123291015625, 399.935791015625)
-2      (467.18182373046875, 379.6656494140625)    (472.2360534667969, 381.5768127441406)
-3       (596.257568359375, 226.02084350585938)     (591.31494140625, 220.63613891601562)
-4      (485.8147888183594, 375.21966552734375)    (490.6375732421875, 376.2384338378906)
-...                                        ...                                       ...
-1112    (275.5272216796875, 9.882745742797852)  (266.19744873046875, 11.438941955566406)
-1113     (368.712646484375, 477.2102966308594)   (104.73099517822266, 451.3692321777344)
-1114   (11.608807563781738, 314.6067810058594)   (9.350114822387695, 401.60809326171875)
-1115  (217.09481811523438, 476.58941650390625)    (375.0711975097656, 459.0254821777344)
-1116    (81.3505630493164, 251.54998779296875)      (69.847900390625, 267.8494567871094)
-
-[1117 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (180.04107666015625, 90.53155517578125)   (170.2902069091797, 94.53397369384766)
-1     (458.2454833984375, 401.2362060546875)   (463.4034729003906, 403.1810302734375)
-2    (341.5177001953125, 424.36004638671875)    (347.435791015625, 432.5082092285156)
-3    (68.76342010498047, 227.69638061523438)  (61.13694381713867, 238.51129150390625)
-4     (486.8621826171875, 405.1350402832031)   (492.3933410644531, 406.0468444824219)
-..                                       ...                                      ...
-971  (11.365938186645508, 345.6922912597656)  (3.9097745418548584, 364.5368957519531)
-972   (392.5008850097656, 446.5965881347656)     (384.97998046875, 371.0938415527344)
-973    (369.0494689941406, 459.035888671875)    (257.7239685058594, 471.219482421875)
-974  (252.55325317382812, 472.9974670410156)   (500.2184143066406, 435.0291748046875)
-975  (266.24652099609375, 476.3043212890625)  (124.55828857421875, 458.6302185058594)
-
-[976 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (193.62977600097656, 89.28010559082031)    (183.3448486328125, 93.19152069091797)
-1     (422.0296325683594, 315.2270812988281)   (420.4192199707031, 315.72174072265625)
-2     (484.1272888183594, 400.7969055175781)    (488.34564208984375, 401.162841796875)
-3    (440.05572509765625, 344.0189514160156)    (441.8690490722656, 344.3155212402344)
-4     (436.1040954589844, 400.0053405761719)   (440.3607482910156, 401.82977294921875)
-..                                       ...                                       ...
-839   (67.19908142089844, 411.9110107421875)   (63.54468536376953, 432.33258056640625)
-840    (443.036376953125, 331.5677490234375)  (247.66497802734375, 14.588591575622559)
-841     (592.7807006835938, 476.44677734375)    (599.6571044921875, 473.0356750488281)
-842  (463.21514892578125, 392.2173767089844)    (467.4432067871094, 392.8693542480469)
-843   (584.7427978515625, 441.8599548339844)   (194.21231079101562, 67.23735809326172)
-
-[844 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (202.40916442871094, 99.16419219970703)    (190.974365234375, 103.1046142578125)
-1     (183.3448486328125, 93.19152069091797)  (171.41445922851562, 97.29266357421875)
-2     (465.9062194824219, 432.9772033691406)  (471.1507568359375, 432.94989013671875)
-3      (445.1822814941406, 426.366455078125)   (450.1925964355469, 427.3104553222656)
-4     (467.4432067871094, 392.8693542480469)    (470.708251953125, 392.1870422363281)
-..                                       ...                                      ...
-783  (20.143842697143555, 463.6065673828125)    (404.824462890625, 380.3593444824219)
-784    (32.2132453918457, 332.9282531738281)   (536.2494506835938, 414.1658935546875)
-785  (26.043445587158203, 405.3831481933594)  (19.823732376098633, 427.3106994628906)
-786  (602.5089721679688, 476.44268798828125)       (607.3709716796875, 470.041015625)
-787   (269.8580322265625, 456.4310607910156)  (275.49761962890625, 468.3244323730469)
-
-[788 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (458.08807373046875, 387.8588562011719)        (460.1697692871094, 386.166015625)
-1         (465.4501953125, 390.1733093261719)   (467.3087158203125, 387.91668701171875)
-2      (478.5854187011719, 375.1034851074219)  (479.63226318359375, 372.16986083984375)
-3     (94.39104461669922, 112.50666046142578)    (79.12547302246094, 119.5560531616211)
-4     (416.20538330078125, 362.7718505859375)    (417.2794494628906, 362.6738586425781)
-..                                        ...                                       ...
-744  (26.348094940185547, 452.07757568359375)    (21.04585838317871, 476.7552795410156)
-745    (130.79730224609375, 69.4918441772461)   (116.40579986572266, 74.42756652832031)
-746   (221.74331665039062, 449.8849792480469)   (603.6522216796875, 60.055137634277344)
-747   (229.23854064941406, 453.1783142089844)   (232.93186950683594, 466.7908935546875)
-748    (607.1998291015625, 133.0411834716797)     (593.346923828125, 124.4708480834961)
-
-[749 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (430.5333557128906, 357.82879638671875)   (430.4079895019531, 356.0582275390625)
-1     (412.1356506347656, 353.0155029296875)   (412.0079040527344, 352.1681823730469)
-2     (428.9173583984375, 342.8729248046875)         (427.9066467285156, 340.8984375)
-3    (178.31735229492188, 107.0713119506836)  (164.2296600341797, 111.55262756347656)
-4    (524.6456298828125, 393.50836181640625)   (524.489013671875, 387.21685791015625)
-..                                       ...                                      ...
-770   (556.0958251953125, 391.9865417480469)    (373.5946960449219, 363.510009765625)
-771    (14.041780471801758, 438.60107421875)  (6.620630264282227, 463.32415771484375)
-772   (71.55928802490234, 306.1944580078125)   (62.51579666137695, 320.8494567871094)
-773  (536.1372680664062, 184.82919311523438)  (218.91213989257812, 7.425884246826172)
-774  (536.1372680664062, 184.82919311523438)  (218.91213989257812, 7.425884246826172)
-
-[775 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0        (449.21337890625, 399.7569580078125)    (450.3821716308594, 396.6720886230469)
-1    (133.99847412109375, 116.23162841796875)  (117.78575134277344, 122.21833801269531)
-2      (441.5650329589844, 339.3834228515625)       (439.595458984375, 336.01904296875)
-3      (495.2491149902344, 393.1321105957031)     (495.11669921875, 387.32757568359375)
-4    (439.93133544921875, 355.86322021484375)    (439.0494079589844, 352.8489990234375)
-..                                        ...                                       ...
-752    (579.3453369140625, 184.1569061279297)   (565.4558715820312, 174.02865600585938)
-753    (521.5736083984375, 475.3541564941406)    (515.8347778320312, 465.1278076171875)
-754   (624.9136962890625, 475.37969970703125)  (439.86505126953125, 414.19476318359375)
-755   (109.04226684570312, 335.2101745605469)  (418.27374267578125, 323.58905029296875)
-756    (630.7581787109375, 473.4547424316406)      (631.07373046875, 460.6122131347656)
-
-[757 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (464.2192077636719, 401.1647033691406)   (464.41217041015625, 396.2340393066406)
-1      (429.5226135253906, 353.3104248046875)   (427.8130187988281, 349.60955810546875)
-2      (421.3861999511719, 422.2897644042969)    (423.3987731933594, 419.8995666503906)
-3      (482.7699279785156, 367.8156433105469)    (480.5592956542969, 361.6085205078125)
-4      (425.7523193359375, 386.8871154785156)    (426.0331726074219, 384.0743408203125)
-..                                        ...                                       ...
-707  (109.80696105957031, 39.557594299316406)    (88.70053100585938, 44.76233673095703)
-708    (592.653564453125, 10.180750846862793)    (430.4095458984375, 339.6024475097656)
-709    (364.5386962890625, 407.3183898925781)   (365.87664794921875, 409.0315246582031)
-710   (246.08253479003906, 31.28571128845215)   (110.82776641845703, 90.37763977050781)
-711  (249.33432006835938, 455.03765869140625)  (252.47447204589844, 463.99993896484375)
-
-[712 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (450.8406066894531, 392.33038330078125)  (449.8057556152344, 387.37896728515625)
-1     (440.9241638183594, 348.5201110839844)   (438.0721130371094, 343.6713562011719)
-2        (543.50830078125, 411.563720703125)  (541.1788940429688, 401.09075927734375)
-3    (493.9061584472656, 381.17938232421875)   (491.2802429199219, 373.7993469238281)
-4    (464.41217041015625, 396.2340393066406)   (463.1275329589844, 390.1280517578125)
-..                                       ...                                      ...
-739    (383.8471984863281, 374.306884765625)     (512.9335327148438, 376.86474609375)
-740     (56.08845901489258, 362.99072265625)  (356.4662170410156, 352.22210693359375)
-741    (580.990966796875, 61.50834655761719)  (543.8261108398438, 13.787660598754883)
-742  (587.9844970703125, 473.89324951171875)  (587.3992309570312, 460.20892333984375)
-743     (544.06787109375, 39.89917755126953)   (539.8267211914062, 98.25460052490234)
-
-[744 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (449.8057556152344, 387.37896728515625)   (447.6691589355469, 381.3702087402344)
-1      (491.2802429199219, 373.7993469238281)  (487.5150146484375, 366.12261962890625)
-2    (434.94891357421875, 398.15728759765625)  (433.8437194824219, 393.06341552734375)
-3      (432.1358642578125, 403.7955627441406)   (431.2937927246094, 398.8910217285156)
-4     (462.1819152832031, 411.34332275390625)     (461.2041015625, 404.81524658203125)
-..                                        ...                                      ...
-723   (499.27825927734375, 49.68353271484375)   (478.3882141113281, 44.90171432495117)
-724     (542.923095703125, 88.57786560058594)    (522.903564453125, 81.66263580322266)
-725    (525.0694580078125, 80.32147216796875)      (504.10302734375, 74.1861343383789)
-726   (501.03057861328125, 348.4722900390625)   (364.9937744140625, 345.0179138183594)
-727    (524.7879028320312, 32.76847457885742)   (482.4283447265625, 42.07139587402344)
-
-[728 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0       (457.54754638671875, 350.59130859375)    (452.6345520019531, 343.5486755371094)
-1     (545.9434204101562, 382.01934814453125)        (539.8742065429688, 370.224609375)
-2      (501.2024841308594, 409.8467102050781)   (498.1977233886719, 399.93048095703125)
-3    (63.324440002441406, 149.60499572753906)   (44.10594940185547, 157.85137939453125)
-4      (447.6691589355469, 381.3702087402344)   (444.7311096191406, 374.87554931640625)
-..                                        ...                                       ...
-740    (516.9848022460938, 7.168163776397705)   (483.1270446777344, 26.450435638427734)
-741         (480.20703125, 52.03350830078125)    (459.1923522949219, 48.43931579589844)
-742   (434.15570068359375, 346.1935729980469)        (430.01123046875, 340.86572265625)
-743    (510.1666564941406, 67.41146850585938)   (489.0741882324219, 62.535919189453125)
-744  (192.98536682128906, 14.178486824035645)  (171.41099548339844, 16.603445053100586)
-
-[745 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (444.7311096191406, 374.87554931640625)          (440.6712341308594, 367.921875)
-1    (471.1358337402344, 321.65008544921875)   (463.4907531738281, 313.6274108886719)
-2         (444.58984375, 335.60015869140625)  (438.36968994140625, 328.6759033203125)
-3    (478.1665344238281, 395.06475830078125)   (474.1963195800781, 385.9795227050781)
-4     (383.1235046386719, 435.3559875488281)  (383.01080322265625, 432.0052795410156)
-..                                       ...                                      ...
-816   (446.1696472167969, 425.7786560058594)  (496.20623779296875, 443.2546081542969)
-817      (599.4013671875, 466.1666259765625)    (593.585205078125, 448.0687255859375)
-818   (596.2777709960938, 388.8978271484375)  (593.0726928710938, 382.75396728515625)
-819   (28.84553337097168, 75.77203369140625)  (477.17596435546875, 397.0557861328125)
-820    (552.256103515625, 446.1160583496094)   (547.9186401367188, 432.3144226074219)
-
-[821 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (509.4649658203125, 350.3592834472656)   (501.1781311035156, 339.6441650390625)
-1      (532.8882446289062, 436.1647033691406)   (527.8588256835938, 422.3531188964844)
-2     (438.36968994140625, 328.6759033203125)   (431.6071472167969, 321.3498229980469)
-3             (562.0459594726562, 353.546875)   (551.9249267578125, 339.9837646484375)
-4         (450.61651611328125, 375.947265625)   (445.6978454589844, 368.0250549316406)
-..                                        ...                                      ...
-811        (414.0615234375, 465.601806640625)  (415.0409851074219, 460.12335205078125)
-812     (401.562744140625, 444.4700012207031)  (488.61334228515625, 364.3537292480469)
-813  (469.30694580078125, 60.677268981933594)   (539.6114501953125, 9.456254005432129)
-814    (635.7271118164062, 376.0227966308594)   (623.0633544921875, 357.8613586425781)
-815     (549.611083984375, 90.14271545410156)     (462.1745910644531, 433.89697265625)
-
-[816 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0     (377.6119384765625, 287.9105224609375)   (368.8843078613281, 285.1391906738281)
-1    (456.5398864746094, 362.45904541015625)  (450.46649169921875, 353.9444274902344)
-2      (541.7047119140625, 302.107666015625)  (529.8630981445312, 290.66424560546875)
-3    (451.29913330078125, 380.7626647949219)  (446.3972473144531, 372.48443603515625)
-4     (431.6071472167969, 321.3498229980469)  (424.41790771484375, 313.9816589355469)
-..                                       ...                                      ...
-832   (468.9233703613281, 446.8144226074219)   (466.9302978515625, 436.6751708984375)
-833   (503.2910461425781, 313.0989685058594)   (493.0065612792969, 302.9099426269531)
-834    (482.315673828125, 321.4057312011719)   (472.4832763671875, 313.1068420410156)
-835  (466.9305725097656, 35.507408142089844)  (446.2679748535156, 30.841455459594727)
-836  (535.0432739257812, 323.48297119140625)    (543.171142578125, 315.9045715332031)
-
-[837 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (418.43487548828125, 290.0124816894531)    (410.2135009765625, 283.869873046875)
-1      (368.8843078613281, 285.1391906738281)  (360.88909912109375, 281.7285461425781)
-2      (527.8719482421875, 356.6466369628906)  (518.9859008789062, 344.22052001953125)
-3            (437.4703674316406, 376.3203125)  (432.6036071777344, 368.49761962890625)
-4      (39.51862716674805, 81.64583587646484)   (11.80898380279541, 86.62773895263672)
-..                                        ...                                      ...
-865  (443.52227783203125, 55.080692291259766)     (382.25018310546875, 295.8408203125)
-866   (497.7820129394531, 414.33966064453125)   (600.2625122070312, 472.1666259765625)
-867     (409.137939453125, 8.692399024963379)   (388.3811950683594, 3.879328727722168)
-868    (472.4832763671875, 313.1068420410156)   (535.2203369140625, 297.1063537597656)
-869    (575.330810546875, 448.46588134765625)        (447.9964904785156, 472.19921875)
-
-[870 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (360.88909912109375, 281.7285461425781)      (351.77294921875, 278.7899169921875)
-1     (366.4659423828125, 344.81280517578125)    (361.3868103027344, 340.7292785644531)
-2       (424.193603515625, 346.1839599609375)  (418.17645263671875, 339.19659423828125)
-3      (361.7323913574219, 376.3664245605469)    (358.1380920410156, 372.4625244140625)
-4      (366.76812744140625, 288.653076171875)    (358.8680114746094, 284.7133483886719)
-..                                        ...                                       ...
-912     (446.4654235839844, 36.3027229309082)      (402.2705383300781, 67.460205078125)
-913   (384.3084411621094, 29.672039031982422)   (364.3490295410156, 25.168630599975586)
-914  (379.61920166015625, 29.272035598754883)    (364.4129333496094, 372.4338073730469)
-915   (508.88665771484375, 460.8609619140625)    (381.3743591308594, 378.8128967285156)
-916   (50.93820571899414, 3.6668589115142822)    (19.41668701171875, 4.922243595123291)
-
-[917 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0     (333.6253967285156, 312.1550598144531)    (327.1429748535156, 309.513427734375)
-1    (434.12127685546875, 438.2171630859375)    (432.2773742675781, 429.097412109375)
-2     (411.7998352050781, 431.8211364746094)  (410.19903564453125, 424.0359191894531)
-3       (351.77294921875, 278.7899169921875)     (343.07861328125, 276.8208923339844)
-4    (497.1758117675781, 313.38616943359375)  (487.7267761230469, 303.31109619140625)
-..                                       ...                                      ...
-961  (603.8147583007812, 432.97601318359375)   (622.906982421875, 349.33172607421875)
-962  (564.7821655273438, 186.28538513183594)   (546.4609985351562, 175.6885223388672)
-963   (520.6118774414062, 459.9967041015625)  (23.97478675842285, 39.982521057128906)
-964    (533.44287109375, 298.20391845703125)   (503.0611572265625, 303.7133483886719)
-965  (378.5535888671875, 47.435874938964844)    (359.834228515625, 39.02859115600586)
-
-[966 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (439.8954772949219, 307.48150634765625)    (431.9414978027344, 299.565185546875)
-1     (439.8954772949219, 307.48150634765625)    (431.9414978027344, 299.565185546875)
-2      (565.056884765625, 410.96063232421875)    (556.88232421875, 394.76910400390625)
-3         (406.482421875, 279.24676513671875)   (397.8936767578125, 273.1365051269531)
-4     (471.6177062988281, 279.00836181640625)   (461.5689392089844, 270.4873352050781)
-...                                       ...                                      ...
-1027   (562.196533203125, 450.61492919921875)   (556.4661254882812, 433.6006774902344)
-1028   (371.2712707519531, 113.4117202758789)   (355.629150390625, 109.18995666503906)
-1029  (419.8294677734375, 429.17926025390625)       (515.154296875, 353.8648376464844)
-1030   (635.2516479492188, 426.1253967285156)   (623.6753540039062, 405.5335693359375)
-1031    (581.663330078125, 371.5983581542969)  (571.1996459960938, 355.76055908203125)
-
-[1032 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (371.5069580078125, 379.3207092285156)   (367.8809814453125, 373.6098327636719)
-1      (441.2317199707031, 304.1524353027344)      (433.1036376953125, 296.0322265625)
-2      (407.9513244628906, 415.7194519042969)   (405.2389831542969, 407.6913757324219)
-3     (418.39593505859375, 440.0082092285156)    (416.429443359375, 431.0249938964844)
-4      (347.0254211425781, 346.1314697265625)    (342.0543518066406, 341.791259765625)
-...                                       ...                                      ...
-1103   (412.2997741699219, 345.5924987792969)       (406.1584167480469, 338.873046875)
-1104  (574.9724731445312, 152.75909423828125)     (556.1337890625, 142.74172973632812)
-1105  (548.9984130859375, 143.03175354003906)   (530.8799438476562, 133.4422149658203)
-1106    (634.172607421875, 435.7449035644531)   (622.9883422851562, 414.6199035644531)
-1107   (405.2130126953125, 440.4788818359375)  (603.7306518554688, 102.28170013427734)
-
-[1108 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (507.9014587402344, 248.54457092285156)  (495.58404541015625, 238.43531799316406)
-1      (380.3377990722656, 409.5195007324219)    (377.5160217285156, 402.5694580078125)
-2     (424.9895324707031, 244.13807678222656)    (414.9530944824219, 237.1170196533203)
-3     (327.43829345703125, 266.3390808105469)    (319.9864501953125, 261.2767333984375)
-4      (326.3822021484375, 317.0914306640625)    (320.2166748046875, 313.1956787109375)
-...                                       ...                                       ...
-1170    (530.5659790039062, 475.187744140625)     (525.667724609375, 458.5536804199219)
-1171  (417.2380065917969, 460.39971923828125)    (447.13861083984375, 437.075439453125)
-1172  (627.2263793945312, 472.23101806640625)    (439.5495300292969, 311.8170471191406)
-1173   (360.9467468261719, 411.0281982421875)    (358.4468078613281, 405.2252502441406)
-1174  (439.73529052734375, 446.1408996582031)   (437.02581787109375, 435.4801025390625)
-
-[1175 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (415.34478759765625, 355.37188720703125)   (409.4824523925781, 347.13592529296875)
-1         (522.68310546875, 360.6827087402344)    (513.7756958007812, 347.8998718261719)
-2       (523.4816284179688, 333.6042175292969)    (513.6267700195312, 321.3522644042969)
-3          (578.3876953125, 138.0174102783203)   (560.2088623046875, 128.08358764648438)
-4      (425.0939636230469, 251.98403930664062)  (415.38775634765625, 244.70553588867188)
-...                                        ...                                       ...
-1290   (636.8751831054688, 457.44805908203125)     (624.795166015625, 436.0713806152344)
-1291   (614.1448974609375, 133.72274780273438)      (594.0791015625, 123.14546203613281)
-1292   (589.5364379882812, 472.48211669921875)     (580.709716796875, 453.0271301269531)
-1293    (585.4423217773438, 96.38465118408203)         (456.37109375, 431.0776672363281)
-1294   (589.5364379882812, 472.48211669921875)     (580.709716796875, 453.0271301269531)
-
-[1295 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0       (520.94189453125, 108.60053253173828)    (504.05322265625, 100.79691314697266)
-1       (472.2055969238281, 345.841064453125)   (464.4909973144531, 335.4993591308594)
-2      (321.4210510253906, 277.5809020996094)  (314.87420654296875, 273.2591552734375)
-3     (499.6886901855469, 153.85540771484375)   (485.231689453125, 145.45266723632812)
-4     (313.68414306640625, 297.0529479980469)  (306.8385925292969, 293.05645751953125)
-...                                       ...                                      ...
-1403  (294.53277587890625, 23.99330711364746)  (277.0596008300781, 18.627395629882812)
-1404   (634.7997436523438, 228.4792938232422)   (616.5465087890625, 215.6549072265625)
-1405     (527.00244140625, 54.26393127441406)   (509.58404541015625, 46.3178825378418)
-1406       (550.162109375, 112.8904800415039)  (533.3087158203125, 104.60943603515625)
-1407  (417.6216125488281, 258.72564697265625)   (426.5098876953125, 445.3352966308594)
-
-[1408 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0     (338.07000732421875, 291.39422607421875)   (331.0390930175781, 286.3243408203125)
-1      (508.3210754394531, 147.96363830566406)  (494.3124084472656, 139.84548950195312)
-2       (316.323486328125, 299.96453857421875)    (309.439208984375, 295.5424499511719)
-3      (453.4389953613281, 132.60330200195312)  (439.9699401855469, 125.42779541015625)
-4      (490.4818115234375, 380.31134033203125)    (482.9093017578125, 368.673583984375)
-...                                        ...                                      ...
-1540    (564.3021850585938, 308.6565246582031)    (551.890869140625, 296.2226257324219)
-1541      (586.54443359375, 97.10940551757812)   (569.2200317382812, 88.09510040283203)
-1542    (468.8583068847656, 359.1291198730469)  (461.3062438964844, 348.37042236328125)
-1543   (408.52117919921875, 356.2660217285156)   (401.9450988769531, 347.0479431152344)
-1544    (583.1351318359375, 476.9750671386719)   (573.7935180664062, 458.2337951660156)
-
-[1545 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (550.8863525390625, 117.26528930664062)   (534.8863525390625, 109.09321594238281)
-1       (444.806884765625, 377.9295959472656)  (438.64276123046875, 368.11126708984375)
-2      (331.0390930175781, 286.3243408203125)    (323.6549377441406, 281.3984680175781)
-3      (551.9323120117188, 163.6728973388672)    (536.8629760742188, 155.2589569091797)
-4     (380.87005615234375, 264.0600280761719)   (372.9909973144531, 257.74664306640625)
-...                                       ...                                       ...
-1543    (474.6394958496094, 93.3683090209961)    (461.4659729003906, 86.06729125976562)
-1544  (633.1885375976562, 25.378175735473633)    (614.6229248046875, 17.27938461303711)
-1545  (614.0048217773438, 105.93856811523438)    (595.5831298828125, 96.98880004882812)
-1546    (490.2481384277344, 474.845458984375)   (484.3337097167969, 460.56378173828125)
-1547  (614.5526123046875, 110.79855346679688)     (595.14697265625, 102.01123809814453)
-
-[1548 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (345.3307800292969, 251.8885955810547)   (337.03021240234375, 246.6717987060547)
-1     (468.6217041015625, 109.19552612304688)  (455.53167724609375, 102.19122314453125)
-2     (406.36248779296875, 358.3787841796875)      (400.41162109375, 350.3866882324219)
-3      (382.3675231933594, 256.2666931152344)     (374.2215270996094, 250.441162109375)
-4       (456.855712890625, 139.9066162109375)       (444.6455078125, 133.0356903076172)
-...                                       ...                                       ...
-1609  (633.7279663085938, 56.721370697021484)   (615.0617065429688, 49.252132415771484)
-1610   (563.0097045898438, 48.25633239746094)    (532.8796997070312, 66.03738403320312)
-1611  (459.01007080078125, 474.5704040527344)      (454.204345703125, 461.627197265625)
-1612   (605.9287719726562, 9.593913078308105)    (586.0131225585938, 2.792935371398926)
-1613   (610.4948120117188, 331.4302978515625)    (600.3925170898438, 337.6122131347656)
-
-[1614 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (433.3604736328125, 104.2530288696289)     (421.44671630859375, 97.89599609375)
-1     (452.71282958984375, 353.2877502441406)   (445.5493469238281, 344.1679382324219)
-2      (604.9039916992188, 133.4423370361328)   (588.8983764648438, 125.2518081665039)
-3      (433.3604736328125, 104.2530288696289)     (421.44671630859375, 97.89599609375)
-4       (374.2215270996094, 250.441162109375)  (366.1103210449219, 244.98162841796875)
-...                                       ...                                      ...
-1692  (309.4808654785156, 344.83624267578125)   (566.916259765625, 44.064388275146484)
-1693  (335.1800231933594, 127.80280303955078)      (247.5400390625, 72.87604522705078)
-1694    (513.342041015625, 50.48503875732422)   (378.26739501953125, 440.989990234375)
-1695    (576.3604736328125, 357.317138671875)   (457.5491638183594, 363.6213684082031)
-1696    (431.9245910644531, 184.175537109375)     (267.96337890625, 290.9402770996094)
-
-[1697 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (328.5976257324219, 241.59536743164062)   (320.6042785644531, 236.9792022705078)
-1       (391.6478271484375, 349.9742431640625)   (385.6859436035156, 342.9468688964844)
-2      (304.9549255371094, 241.89505004882812)  (296.6567077636719, 237.73806762695312)
-3         (421.44671630859375, 97.89599609375)   (410.5138854980469, 91.68360900878906)
-4      (413.3160705566406, 107.35515594482422)  (402.3065490722656, 101.32191467285156)
-...                                        ...                                      ...
-1661   (636.0530395507812, 194.01344299316406)  (619.7893676757812, 185.03379821777344)
-1662    (412.1612548828125, 474.3541564941406)     (408.2801818847656, 464.11181640625)
-1663      (625.2684326171875, 470.33056640625)    (613.252197265625, 453.0189514160156)
-1664     (508.4908142089844, 174.017333984375)  (497.3123779296875, 167.52511596679688)
-1665  (362.87017822265625, 229.56356811523438)   (353.807861328125, 223.42820739746094)
-
-[1666 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (320.6042785644531, 236.9792022705078)   (312.993896484375, 232.22683715820312)
-1      (410.5138854980469, 91.68360900878906)    (400.3739013671875, 86.0323257446289)
-2        (481.228759765625, 309.540771484375)    (472.633056640625, 301.4536437988281)
-3       (546.971923828125, 210.9993133544922)  (536.4935302734375, 203.37278747558594)
-4      (455.7365417480469, 281.8772888183594)            (447.609375, 274.76806640625)
-...                                       ...                                      ...
-1794  (456.61309814453125, 77.87773895263672)  (445.08843994140625, 71.87783813476562)
-1795  (360.6170959472656, 289.28790283203125)  (353.7894592285156, 284.15777587890625)
-1796    (598.917724609375, 463.3690185546875)     (587.4659423828125, 447.52392578125)
-1797   (495.262939453125, 188.50741577148438)  (397.9803466796875, 168.74571228027344)
-1798   (634.9610595703125, 188.1856231689453)    (619.648193359375, 180.0687255859375)
-
-[1799 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (434.67523193359375, 105.92948150634766)   (425.0822448730469, 100.66388702392578)
-1       (562.1602172851562, 160.4124298095703)   (550.0947875976562, 154.17233276367188)
-2       (366.2508544921875, 269.7413635253906)    (359.4182434082031, 264.7566833496094)
-3       (448.0180358886719, 257.6766662597656)   (439.93280029296875, 251.3977813720703)
-4          (308.1256103515625, 322.3193359375)    (302.5894775390625, 318.1226501464844)
-...                                        ...                                       ...
-1955   (546.1834716796875, 226.52456665039062)   (379.81866455078125, 382.4434509277344)
-1956   (507.27655029296875, 64.12686157226562)  (497.21307373046875, 57.827816009521484)
-1957    (512.543701171875, 179.29640197753906)    (502.4145812988281, 173.1494598388672)
-1958    (469.6574401855469, 432.9927062988281)    (463.3852233886719, 422.9649658203125)
-1959    (560.4039306640625, 284.6497802734375)   (549.6464233398438, 276.11737060546875)
-
-[1960 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (414.6224365234375, 104.7630386352539)    (405.6051940917969, 99.81350708007812)
-1     (376.65875244140625, 82.01669311523438)    (367.5308532714844, 77.33880615234375)
-2       (549.7609252929688, 298.387939453125)       (539.943359375, 290.50421142578125)
-3      (414.6224365234375, 104.7630386352539)    (405.6051940917969, 99.81350708007812)
-4      (445.2558288574219, 446.9132995605469)   (440.0155334472656, 437.81402587890625)
-...                                       ...                                       ...
-1943  (549.6464233398438, 276.11737060546875)     (540.4126586914062, 268.210205078125)
-1944     (462.9114685058594, 393.28759765625)  (456.19207763671875, 385.03765869140625)
-1945   (367.3918762207031, 463.4629211425781)    (363.6846008300781, 456.3945007324219)
-1946  (405.5090637207031, 476.38140869140625)   (401.4356994628906, 467.87274169921875)
-1947  (599.3922119140625, 48.036537170410156)     (585.93896484375, 44.492210388183594)
-
-[1948 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (298.6147155761719, 223.80531311035156)  (292.4139709472656, 220.52110290527344)
-1      (521.3560791015625, 88.47737884521484)   (511.4571838378906, 84.31220245361328)
-2      (363.9993896484375, 353.2470397949219)     (359.03564453125, 348.4454345703125)
-3      (451.1160583496094, 99.21696472167969)   (442.3504333496094, 95.03983306884766)
-4      (547.9208374023438, 167.6979522705078)  (537.7462768554688, 162.84555053710938)
-...                                       ...                                      ...
-1943     (606.57958984375, 98.88896179199219)   (595.3239135742188, 94.68649291992188)
-1944   (515.1344604492188, 331.0274658203125)   (507.2156066894531, 323.8878173828125)
-1945    (372.62646484375, 403.35150146484375)   (620.9479370117188, 154.4497833251953)
-1946    (380.8507080078125, 321.656494140625)  (303.0787658691406, 300.11895751953125)
-1947   (416.9200439453125, 64.31962585449219)    (407.704833984375, 58.99280548095703)
-
-[1948 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (363.3663635253906, 320.5811462402344)    (358.2817687988281, 316.601318359375)
-1      (490.5695495605469, 160.53208923339844)  (482.1597595214844, 156.58151245117188)
-2        (444.7742004394531, 83.4930191040039)    (436.646240234375, 79.98367309570312)
-3        (372.6718444824219, 71.0638656616211)   (364.6047668457031, 67.47293853759766)
-4       (285.9019775390625, 244.0507049560547)  (280.1885070800781, 241.25064086914062)
-...                                        ...                                      ...
-2052       (480.73980712890625, 471.220703125)     (475.18505859375, 462.8341369628906)
-2053   (543.4217529296875, 18.891021728515625)  (533.5899047851562, 15.689719200134277)
-2054  (483.06390380859375, 10.333300590515137)  (473.5936279296875, 7.8796916007995605)
-2055  (300.33587646484375, 378.95196533203125)   (273.5252380371094, 390.3882141113281)
-2056   (398.1462097167969, 100.55060577392578)   (390.6404113769531, 96.86840057373047)
-
-[2057 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0        (399.172119140625, 87.99697875976562)   (391.8184814453125, 85.06739044189453)
-1       (371.5896301269531, 72.80131530761719)   (364.5797119140625, 69.79016876220703)
-2        (371.566162109375, 73.21732330322266)   (364.6038818359375, 70.15831756591797)
-3       (389.3749084472656, 91.29566955566406)     (382.20751953125, 88.25900268554688)
-4       (373.3883972167969, 77.95632934570312)    (366.424072265625, 74.95784759521484)
-...                                        ...                                      ...
-2033   (418.74786376953125, 392.2620849609375)    (542.971923828125, 207.9395294189453)
-2034  (431.47796630859375, 160.94276428222656)  (425.0916442871094, 158.49391174316406)
-2035      (368.08935546875, 54.72969055175781)   (381.5662841796875, 37.78445816040039)
-2036    (605.2628173828125, 87.31599426269531)   (594.2786254882812, 84.22181701660156)
-2037   (610.2554321289062, 20.315603256225586)     (438.86962890625, 38.47134017944336)
-
-[2038 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (561.1065063476562, 204.5303497314453)  (553.0265502929688, 201.53842163085938)
-1     (267.4081115722656, 363.89544677734375)     (264.29150390625, 364.3073425292969)
-2      (391.8184814453125, 85.06739044189453)   (385.9016418457031, 82.84058380126953)
-3     (583.8368530273438, 119.18356323242188)   (574.9947509765625, 117.2479248046875)
-4      (463.9739990234375, 75.58049774169922)  (456.86566162109375, 73.64202880859375)
-...                                       ...                                      ...
-2051   (345.9153137207031, 300.2269287109375)   (475.7130432128906, 281.0138244628906)
-2052    (521.7817993164062, 72.9861831665039)   (512.8493041992188, 71.90570831298828)
-2053   (385.8416442871094, 415.7080383300781)   (382.2564697265625, 412.1812744140625)
-2054   (494.6456298828125, 23.65873146057129)   (364.4150695800781, 419.1897888183594)
-2055  (508.4906005859375, 242.09017944335938)      (575.822265625, 136.34310913085938)
-
-[2056 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (459.3179931640625, 243.5525665283203)   (454.2007141113281, 241.54148864746094)
-1     (351.41180419921875, 62.04037094116211)   (345.5607604980469, 60.688636779785156)
-2     (397.10443115234375, 298.5694274902344)     (392.9725036621094, 296.617919921875)
-3      (440.1347351074219, 71.39961242675781)    (434.3262939453125, 70.17505645751953)
-4      (508.0953674316406, 220.2164306640625)   (502.0534973144531, 217.93565368652344)
-...                                       ...                                       ...
-2050  (574.7100219726562, 47.183982849121094)    (566.0582885742188, 46.98657989501953)
-2051     (492.5227966308594, 267.41650390625)  (483.45867919921875, 249.92181396484375)
-2052   (478.5270690917969, 28.47648811340332)    (473.9056091308594, 26.96051025390625)
-2053   (549.0676879882812, 5.068277835845947)    (542.0152587890625, 5.103027820587158)
-2054   (549.0676879882812, 5.068277835845947)    (542.0152587890625, 5.103027820587158)
-
-[2055 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (471.7468566894531, 338.8168029785156)   (467.6761169433594, 336.8512268066406)
-1       (417.2864074707031, 410.9006042480469)   (414.2078552246094, 408.8532409667969)
-2       (407.5613708496094, 411.8687744140625)  (404.5581970214844, 409.93865966796875)
-3        (394.6225280761719, 277.372314453125)   (392.329833984375, 276.20843505859375)
-4          (420.579345703125, 399.05517578125)   (417.5759582519531, 397.0527038574219)
-...                                        ...                                      ...
-2039  (483.45867919921875, 249.92181396484375)    (482.280029296875, 263.7005615234375)
-2040     (299.0039978027344, 382.862060546875)   (299.2066345214844, 378.7971496582031)
-2041   (418.8066711425781, 472.85125732421875)   (416.5310974121094, 470.1134033203125)
-2042    (348.8268127441406, 431.0926513671875)   (344.9380187988281, 429.9874572753906)
-2043    (593.4737548828125, 71.74767303466797)      (585.7939453125, 72.95671081542969)
-
-[2044 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (340.47723388671875, 178.71197509765625)   (336.8995056152344, 178.55825805664062)
-1       (392.329833984375, 276.20843505859375)  (390.51678466796875, 275.80462646484375)
-2      (458.8272705078125, 148.48684692382812)   (455.1225280761719, 148.56582641601562)
-3      (307.2350158691406, 128.30364990234375)    (304.6805114746094, 128.8441925048828)
-4       (351.1051330566406, 307.8591613769531)    (348.3763732910156, 307.8481750488281)
-...                                        ...                                       ...
-2020    (412.435302734375, 39.768924713134766)   (407.92730712890625, 40.50518798828125)
-2021      (619.56884765625, 58.11201095581055)    (611.0642700195312, 59.86167907714844)
-2022      (626.72998046875, 93.68775939941406)     (619.9564819335938, 96.1817855834961)
-2023   (376.2535400390625, 407.82171630859375)    (493.0386047363281, 471.6759948730469)
-2024    (623.4624633789062, 138.8264923095703)     (561.36767578125, 166.11485290527344)
-
-[2025 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (390.51678466796875, 275.80462646484375)    (389.1445007324219, 275.9508361816406)
-1      (477.13446044921875, 440.7013244628906)   (475.1580505371094, 440.71917724609375)
-2        (438.720458984375, 197.4678192138672)   (435.69305419921875, 198.4738311767578)
-3       (535.0624389648438, 198.4469757080078)    (531.0413818359375, 199.4330291748047)
-4      (430.5381164550781, 205.60865783691406)  (427.63494873046875, 206.56967163085938)
-...                                        ...                                       ...
-2029   (592.9634399414062, 2.8017399311065674)     (479.8525695800781, 71.8121109008789)
-2030    (326.9100341796875, 415.1246032714844)   (325.2339782714844, 416.22943115234375)
-2031   (342.5337829589844, 24.997520446777344)   (339.6641540527344, 26.712461471557617)
-2032       (630.7685546875, 72.69664001464844)   (473.3631896972656, 288.83697509765625)
-2033   (590.6466674804688, 13.532674789428711)   (584.2997436523438, 16.775115966796875)
-
-[2034 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (424.8292541503906, 432.9687805175781)      (423.90869140625, 434.8617858886719)
-1     (405.2439880371094, 187.95115661621094)  (403.24774169921875, 189.92124938964844)
-2      (403.9197082519531, 199.3343963623047)   (401.8841247558594, 201.30499267578125)
-3        (254.45263671875, 363.4805603027344)   (252.74122619628906, 366.3305969238281)
-4      (538.0391845703125, 206.0452880859375)    (535.0233154296875, 207.9602508544922)
-...                                       ...                                       ...
-1968   (414.1895751953125, 35.00312042236328)    (377.0613098144531, 389.5560607910156)
-1969   (433.6093444824219, 66.03933715820312)   (487.14288330078125, 77.46257019042969)
-1970   (372.9654235839844, 51.54715347290039)    (369.5969543457031, 54.68854904174805)
-1971   (263.0817565917969, 458.6766052246094)       (227.4599609375, 243.0860595703125)
-1972   (373.4310607910156, 3.199066400527954)    (371.1510009765625, 6.035874366760254)
-
-[1973 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0     (277.69427490234375, 229.01515197753906)   (276.6754455566406, 232.02223205566406)
-1       (305.7423095703125, 215.9058380126953)    (304.9319152832031, 218.6816864013672)
-2       (341.4671630859375, 192.0060272216797)   (340.4278259277344, 194.99612426757812)
-3      (341.3932800292969, 180.19033813476562)     (340.371826171875, 183.2266387939453)
-4      (252.74122619628906, 366.3305969238281)   (253.39878845214844, 370.1946716308594)
-...                                        ...                                       ...
-1985    (322.85113525390625, 294.693603515625)   (433.1004333496094, 413.14044189453125)
-1986     (344.994873046875, 420.0075988769531)    (591.3983154296875, 143.8339080810547)
-1987    (362.1473083496094, 94.50305938720703)    (361.1357727050781, 98.33773040771484)
-1988     (554.654052734375, 36.61554718017578)    (551.7820434570312, 40.84103775024414)
-1989   (286.43084716796875, 207.6035614013672)  (231.26449584960938, 250.59274291992188)
-
-[1990 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0         (257.347412109375, 217.043212890625)   (257.2718505859375, 221.02346801757812)
-1       (355.9272766113281, 271.7649841308594)   (355.7721252441406, 275.68145751953125)
-2      (392.8677673339844, 293.61859130859375)    (392.9529113769531, 297.5569763183594)
-3      (387.64190673828125, 279.0774841308594)    (388.1427001953125, 281.9497375488281)
-4       (469.5001220703125, 287.8216247558594)    (469.5038757324219, 291.8197326660156)
-...                                        ...                                       ...
-1985   (500.79766845703125, 442.2798767089844)    (433.3868713378906, 386.2233581542969)
-1986    (268.7139892578125, 465.3753662109375)    (268.5654602050781, 471.2987365722656)
-1987   (372.9343566894531, 56.772972106933594)     (372.135498046875, 61.83948516845703)
-1988  (429.87615966796875, 117.26797485351562)  (428.84613037109375, 122.17880249023438)
-1989  (360.92291259765625, 441.96160888671875)   (361.3246765136719, 447.37957763671875)
-
-[1990 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (388.1427001953125, 281.9497375488281)   (388.8233642578125, 285.3792419433594)
-1     (392.16265869140625, 81.94629669189453)   (392.2684020996094, 88.12042999267578)
-2      (388.1427001953125, 281.9497375488281)   (388.8233642578125, 285.3792419433594)
-3      (329.8219909667969, 74.00584411621094)   (330.0838928222656, 80.25454711914062)
-4      (345.0111083984375, 93.27477264404297)   (345.2881164550781, 99.14656066894531)
-...                                       ...                                      ...
-2045  (305.0093688964844, 422.58624267578125)   (389.246337890625, 30.512039184570312)
-2046  (470.0108642578125, 412.70037841796875)  (471.8917236328125, 419.23419189453125)
-2047   (459.6236267089844, 405.1723327636719)  (462.07403564453125, 411.4873352050781)
-2048         (597.8359375, 36.20635223388672)   (595.4953002929688, 43.35281753540039)
-2049  (337.85870361328125, 35.12173843383789)  (338.1802673339844, 42.576812744140625)
-
-[2050 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (487.5263366699219, 279.4595031738281)   (489.3427429199219, 285.5546569824219)
-1      (578.0606689453125, 259.1199035644531)  (579.7401733398438, 264.75640869140625)
-2      (500.9361572265625, 296.9195556640625)   (502.9585876464844, 303.1661682128906)
-3     (574.4791259765625, 189.72335815429688)  (575.3220825195312, 195.75064086914062)
-4     (506.3685607910156, 202.18650817871094)  (507.49725341796875, 208.0733184814453)
-...                                       ...                                      ...
-2044  (519.1712646484375, 161.72940063476562)  (424.3365783691406, 205.61524963378906)
-2045   (286.0932922363281, 219.5590362548828)    (232.911376953125, 264.7093200683594)
-2046     (342.19970703125, 406.4547119140625)    (473.303955078125, 430.9429931640625)
-2047    (631.891357421875, 91.78301239013672)   (630.3690185546875, 98.47613525390625)
-2048  (314.8624572753906, 159.05418395996094)    (206.786865234375, 261.6855163574219)
-
-[2049 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (330.9750671386719, 87.22937774658203)    (332.5129699707031, 95.26107788085938)
-1      (376.5138854980469, 202.76351928710938)    (379.6078186035156, 210.4211883544922)
-2        (367.4678649902344, 323.693115234375)    (369.3891296386719, 330.7514953613281)
-3      (389.6990051269531, 289.69329833984375)  (390.24664306640625, 293.96673583984375)
-4      (422.8658752441406, 289.03192138671875)    (425.0130310058594, 295.8829650878906)
-...                                        ...                                       ...
-1780    (274.8529052734375, 312.1548767089844)   (276.30645751953125, 318.4274597167969)
-1781    (587.4014892578125, 94.12824249267578)   (587.5881958007812, 101.69053649902344)
-1782   (636.9288940429688, 24.175174713134766)   (635.4322509765625, 32.777503967285156)
-1783  (498.19317626953125, 145.04351806640625)   (260.35540771484375, 298.4405212402344)
-1784   (493.89605712890625, 459.1514892578125)  (498.38287353515625, 467.95428466796875)
-
-[1785 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (246.71946716308594, 294.2009582519531)  (248.67950439453125, 301.13519287109375)
-1      (439.33599853515625, 421.7759094238281)    (443.1497497558594, 431.7076721191406)
-2       (420.6757507324219, 333.6173400878906)    (423.5841979980469, 341.6165466308594)
-3      (424.29083251953125, 334.5393981933594)    (427.3021240234375, 342.5219421386719)
-4     (318.57012939453125, 365.34539794921875)    (320.6317138671875, 373.4702453613281)
-...                                        ...                                       ...
-1864     (455.864013671875, 269.8244934082031)   (451.84466552734375, 421.0741882324219)
-1865     (502.1869812011719, 71.6350326538086)    (342.8282470703125, 67.32698059082031)
-1866   (364.48199462890625, 30.93253517150879)         (366.47265625, 40.37721252441406)
-1867    (515.9390258789062, 62.29039001464844)   (585.2588500976562, 112.89105224609375)
-1868   (459.2999572753906, 455.85614013671875)   (596.1063842773438, 171.68699645996094)
-
-[1869 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (402.8734130859375, 283.2613830566406)   (405.6544189453125, 291.1965026855469)
-1     (543.9700927734375, 156.36663818359375)    (546.2803344726562, 164.377197265625)
-2     (263.8140563964844, 245.05455017089844)  (266.18194580078125, 252.4244842529297)
-3       (397.73388671875, 188.31739807128906)  (400.2821960449219, 196.20492553710938)
-4            (568.8984375, 162.8074951171875)  (571.5818481445312, 170.97056579589844)
-...                                       ...                                      ...
-1931   (356.4828186035156, 436.0284729003906)   (497.4790954589844, 467.9618835449219)
-1932     (481.9681396484375, 83.901123046875)   (483.5579833984375, 92.87625122070312)
-1933         (354.546875, 135.59878540039062)   (356.2842102050781, 143.7249298095703)
-1934    (581.976318359375, 78.59549713134766)   (583.2091674804688, 87.62137603759766)
-1935  (308.3150939941406, 439.17950439453125)  (309.97857666015625, 449.8080139160156)
-
-[1936 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (352.8102722167969, 351.81097412109375)   (355.7289123535156, 360.8919982910156)
-1          (460.01171875, 265.09014892578125)  (464.16363525390625, 273.5889892578125)
-2      (378.0619812011719, 235.3575439453125)   (380.8078308105469, 243.4191436767578)
-3        (412.1826171875, 204.64620971679688)  (415.2016296386719, 212.75550842285156)
-4     (266.18194580078125, 252.4244842529297)  (269.1155090332031, 260.05487060546875)
-...                                       ...                                      ...
-1925  (254.94996643066406, 304.6129150390625)  (257.54681396484375, 312.5831604003906)
-1926   (540.7872924804688, 338.7880554199219)      (546.6299438476562, 348.5185546875)
-1927   (342.7151794433594, 474.7735290527344)   (407.2304992675781, 374.1766052246094)
-1928  (627.1011352539062, 119.04952239990234)  (628.8704223632812, 126.86650848388672)
-1929  (556.7417602539062, 172.09266662597656)  (263.2052001953125, 273.45587158203125)
-
-[1930 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (239.24215698242188, 274.0664367675781)  (242.07589721679688, 282.1302795410156)
-1     (269.1155090332031, 260.05487060546875)      (271.8519287109375, 268.0771484375)
-2     (247.39715576171875, 272.3042297363281)  (250.37457275390625, 280.1496276855469)
-3      (221.9861297607422, 288.6585693359375)   (224.9161376953125, 296.6258544921875)
-4     (377.07672119140625, 348.4696350097656)    (380.5083312988281, 357.834716796875)
-...                                       ...                                      ...
-1895   (534.7349853515625, 374.4564208984375)     (541.4351806640625, 385.17529296875)
-1896       (397.154296875, 66.60791778564453)  (398.38275146484375, 76.91504669189453)
-1897   (533.814208984375, 325.45709228515625)   (589.0637817382812, 215.0474090576172)
-1898   (426.2457275390625, 200.2174835205078)  (429.6540832519531, 208.62969970703125)
-1899   (548.7073364257812, 77.83465576171875)       (550.9892578125, 87.1596450805664)
-
-[1900 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0          (271.8519287109375, 268.0771484375)    (275.2466735839844, 276.6208801269531)
-1      (608.6071166992188, 187.92648315429688)   (613.9481201171875, 196.79808044433594)
-2     (468.23187255859375, 144.98440551757812)    (471.1243591308594, 153.9192352294922)
-3        (385.572021484375, 155.5919189453125)     (388.818603515625, 164.5998077392578)
-4       (398.6091613769531, 357.1517639160156)   (402.77642822265625, 367.2010498046875)
-...                                        ...                                       ...
-1865    (547.5035400390625, 16.96217918395996)    (499.3878479003906, 32.32204055786133)
-1866      (526.47119140625, 454.9447937011719)    (534.8961181640625, 468.0435485839844)
-1867     (338.7658386230469, 230.819580078125)  (414.54986572265625, 410.11383056640625)
-1868   (349.1451416015625, 115.07380676269531)   (352.3772277832031, 125.02000427246094)
-1869    (526.0943603515625, 117.1975326538086)     (528.82958984375, 126.03436279296875)
-
-[1870 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (275.2466735839844, 276.6208801269531)       (278.845703125, 285.0845031738281)
-1       (549.2211303710938, 267.7585144042969)   (555.0029907226562, 276.7218933105469)
-2     (250.44683837890625, 278.62420654296875)   (253.9590606689453, 286.9710998535156)
-3     (353.01580810546875, 380.73712158203125)   (357.1257629394531, 390.9636535644531)
-4     (379.83392333984375, 157.95921325683594)  (383.4156494140625, 166.88211059570312)
-...                                        ...                                      ...
-1879   (432.02923583984375, 40.94959259033203)   (434.4099426269531, 51.91252517700195)
-1880        (308.50634765625, 262.39501953125)   (312.0751037597656, 270.6051940917969)
-1881   (371.8233337402344, 28.405059814453125)   (375.1826477050781, 40.34346008300781)
-1882    (350.4535827636719, 463.9775695800781)     (354.8165588378906, 476.63232421875)
-1883     (484.5394592285156, 97.5194091796875)  (486.7827453613281, 107.30777740478516)
-
-[1884 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0          (278.845703125, 285.0845031738281)  (282.74176025390625, 293.5019226074219)
-1      (253.9590606689453, 286.9710998535156)  (257.97515869140625, 294.9591064453125)
-2          (602.060302734375, 259.1162109375)   (609.2160034179688, 268.1297607421875)
-3      (563.7293090820312, 283.8829040527344)   (570.6683959960938, 293.0731506347656)
-4          (380.05859375, 354.84332275390625)   (384.9473571777344, 364.4188232421875)
-...                                       ...                                      ...
-1780   (412.7225646972656, 341.1075439453125)   (417.9999084472656, 350.1266784667969)
-1781   (553.5062866210938, 72.59638977050781)   (429.6995849609375, 292.1063232421875)
-1782   (393.6763000488281, 462.0172119140625)   (399.5466003417969, 474.6768798828125)
-1783  (383.0130615234375, 283.93914794921875)   (529.3839111328125, 296.2220153808594)
-1784  (335.2059631347656, 450.54656982421875)  (339.3089904785156, 462.52105712890625)
-
-[1785 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (282.74176025390625, 293.5019226074219)     (286.8764953613281, 301.572509765625)
-1       (426.0861511230469, 336.3351135253906)     (431.775146484375, 345.4847412109375)
-2      (413.0588684082031, 387.81304931640625)   (419.0956726074219, 397.92266845703125)
-3       (453.2166442871094, 179.8084716796875)  (457.43914794921875, 188.13751220703125)
-4       (453.2166442871094, 179.8084716796875)  (457.43914794921875, 188.13751220703125)
-...                                        ...                                       ...
-1830    (379.007080078125, 135.48165893554688)  (384.19073486328125, 144.70944213867188)
-1831   (501.5328674316406, 108.84431457519531)    (505.3045654296875, 117.5128173828125)
-1832    (536.8470458984375, 71.10078430175781)     (485.5309753417969, 84.1701889038086)
-1833  (156.23068237304688, 33.040016174316406)   (435.7424011230469, 30.095008850097656)
-1834   (488.7951965332031, 109.99939727783203)   (492.7385559082031, 119.12940979003906)
-
-[1835 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (522.8744506835938, 258.19097900390625)  (528.8860473632812, 266.32635498046875)
-1     (379.3496398925781, 175.01693725585938)   (383.3249206542969, 183.1011962890625)
-2     (261.87957763671875, 303.1284484863281)    (266.356689453125, 311.2605285644531)
-3     (419.0956726074219, 397.92266845703125)   (425.9158935546875, 407.7228088378906)
-4     (368.12884521484375, 416.6475830078125)    (374.1949157714844, 426.578369140625)
-...                                       ...                                      ...
-1777  (403.16986083984375, 151.0214385986328)   (407.0990905761719, 159.0321502685547)
-1778  (444.6067810058594, 17.742740631103516)  (446.93878173828125, 28.58281898498535)
-1779   (358.7718505859375, 14.00004768371582)  (395.39935302734375, 206.0231170654297)
-1780    (435.49365234375, 140.99203491210938)   (439.5542907714844, 149.9169464111328)
-1781  (596.0342407226562, 160.63731384277344)    (600.2864379882812, 167.638427734375)
-
-[1782 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0     (360.92474365234375, 175.25711059570312)   (364.8395080566406, 183.0289764404297)
-1      (429.3307189941406, 186.75161743164062)  (433.5084533691406, 194.36895751953125)
-2        (404.004150390625, 333.5658874511719)     (409.814697265625, 341.697509765625)
-3     (471.60052490234375, 299.99151611328125)    (477.906494140625, 307.6853942871094)
-4       (383.3249206542969, 183.1011962890625)   (386.8142395019531, 190.6984405517578)
-...                                        ...                                      ...
-1825  (495.33612060546875, 431.28912353515625)   (578.7749633789062, 284.8143310546875)
-1826    (479.022216796875, 156.35650634765625)  (482.4557800292969, 164.68170166015625)
-1827   (479.2381896972656, 369.19915771484375)  (486.90985107421875, 378.0116882324219)
-1828    (539.2324829101562, 372.1097717285156)   (532.6438598632812, 312.5794677734375)
-1829   (385.3548278808594, 119.54902648925781)        (533.4296875, 11.041135787963867)
-
-[1830 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (295.87310791015625, 316.5620422363281)  (300.83685302734375, 322.9112548828125)
-1      (500.3974914550781, 347.1151428222656)  (507.9145202636719, 354.99578857421875)
-2       (357.69384765625, 386.85089111328125)  (363.80694580078125, 394.6660461425781)
-3       (502.871826171875, 222.6937713623047)   (507.8991394042969, 229.7160186767578)
-4      (381.0795593261719, 436.1175842285156)  (388.08026123046875, 445.1014709472656)
-...                                       ...                                      ...
-1839     (570.13427734375, 464.0327453613281)   (581.7034301757812, 475.0666809082031)
-1840      (371.3486328125, 86.13583374023438)   (213.8682861328125, 21.34676170349121)
-1841  (506.6260070800781, 176.62759399414062)    (511.2604064941406, 183.460693359375)
-1842       (518.044921875, 168.2630157470703)   (522.5206298828125, 175.7782745361328)
-1843   (460.9444885253906, 466.5653076171875)  (401.9216003417969, 455.05413818359375)
-
-[1844 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (451.5540466308594, 364.5331115722656)   (458.5321350097656, 371.5893859863281)
-1     (445.4623107910156, 365.51422119140625)   (452.3967590332031, 372.6172790527344)
-2       (331.916748046875, 398.8353271484375)   (338.0135498046875, 405.9739685058594)
-3     (453.01837158203125, 360.0649108886719)  (460.1182556152344, 367.02166748046875)
-4      (584.9843139648438, 323.7925720214844)   (593.0435791015625, 330.7291564941406)
-...                                       ...                                      ...
-1794   (567.9537963867188, 151.5692901611328)  (572.1066284179688, 157.60357666015625)
-1795  (561.8357543945312, 149.90206909179688)   (566.1250610351562, 156.0155029296875)
-1796    (565.82080078125, 438.53814697265625)  (405.45526123046875, 462.6042785644531)
-1797   (479.5551452636719, 434.6248779296875)  (379.00958251953125, 412.5623779296875)
-1798  (379.22637939453125, 276.5989990234375)  (342.0122375488281, 310.17071533203125)
-
-[1799 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0        (422.1337890625, 355.93975830078125)    (428.4124755859375, 361.666748046875)
-1       (546.763427734375, 288.0440979003906)   (552.801513671875, 293.73321533203125)
-2       (546.763427734375, 288.0440979003906)   (552.801513671875, 293.73321533203125)
-3     (367.5223693847656, 368.81353759765625)    (373.453857421875, 374.7086486816406)
-4      (521.6981811523438, 335.2045593261719)   (528.5098266601562, 340.8647766113281)
-...                                       ...                                      ...
-1779   (623.4054565429688, 358.4082336425781)   (634.7227783203125, 347.2307434082031)
-1780   (502.739501953125, 227.33078002929688)     (568.69189453125, 317.9147644042969)
-1781  (228.73782348632812, 93.79554748535156)  (232.5795440673828, 100.48197174072266)
-1782  (437.9018249511719, 188.79458618164062)  (440.6767883300781, 194.37197875976562)
-1783     (616.9892578125, 23.432395935058594)   (614.8214111328125, 33.32988357543945)
-
-[1784 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (465.7502136230469, 384.17901611328125)    (472.7264404296875, 389.1947326660156)
-1      (386.5312194824219, 305.0034484863281)   (391.18231201171875, 309.4530944824219)
-2     (541.2872314453125, 228.75311279296875)   (545.1762084960938, 233.30279541015625)
-3      (532.2276611328125, 232.8428192138672)    (536.2996215820312, 237.5557861328125)
-4      (589.9510498046875, 440.8434753417969)      (600.0104370117188, 446.86865234375)
-...                                       ...                                       ...
-1789  (506.17144775390625, 243.3792724609375)    (561.6407470703125, 94.78844451904297)
-1790  (369.9485778808594, 161.17120361328125)  (372.98687744140625, 166.14288330078125)
-1791   (554.2506713867188, 468.1427001953125)    (564.2630615234375, 473.9299621582031)
-1792  (539.5707397460938, 179.56846618652344)    (544.2074584960938, 184.0255584716797)
-1793   (356.3323974609375, 348.6298522949219)   (361.53497314453125, 353.8936462402344)
-
-[1794 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (484.8424377441406, 228.3848419189453)  (487.7636413574219, 232.00831604003906)
-1       (484.8424377441406, 228.3848419189453)  (487.7636413574219, 232.00831604003906)
-2      (572.2826538085938, 235.77316284179688)   (576.0352783203125, 239.4808349609375)
-3      (391.18231201171875, 309.4530944824219)   (395.6234130859375, 313.1601257324219)
-4       (536.2996215820312, 237.5557861328125)   (539.7736206054688, 241.2978057861328)
-...                                        ...                                      ...
-1749      (462.7569885253906, 122.95654296875)  (463.9015808105469, 127.46427917480469)
-1750   (594.9242553710938, 165.99252319335938)   (598.1771850585938, 170.1360626220703)
-1751  (291.80767822265625, 16.919763565063477)     (310.51416015625, 88.67174530029297)
-1752   (368.1595458984375, 469.72259521484375)   (375.6434020996094, 473.7526550292969)
-1753   (624.4677124023438, 16.507293701171875)       (623.42578125, 22.971323013305664)
-
-[1754 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (304.6332702636719, 327.86383056640625)  (308.59344482421875, 329.7328796386719)
-1      (479.1593017578125, 393.0027770996094)     (485.39501953125, 395.0543518066406)
-2     (295.27740478515625, 343.7292175292969)   (299.5750732421875, 345.6724548339844)
-3      (530.6536865234375, 382.3814697265625)    (536.686767578125, 384.1598205566406)
-4     (354.24993896484375, 402.0235900878906)   (360.1801452636719, 404.0584411621094)
-...                                       ...                                      ...
-1673  (531.0099487304688, 138.91592407226562)  (547.5187377929688, 143.23269653320312)
-1674  (473.0830383300781, 121.81395721435547)  (473.8817138671875, 125.03694152832031)
-1675   (616.6981811523438, 223.0064239501953)     (618.39404296875, 226.1363525390625)
-1676   (463.9070739746094, 75.45330047607422)   (463.2724304199219, 77.95393371582031)
-1677   (629.3825073242188, 193.2354278564453)  (631.9788208007812, 195.47508239746094)
-
-[1678 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0         (341.7441101074219, 172.10986328125)  (342.94415283203125, 175.05064392089844)
-1         (341.7441101074219, 172.10986328125)  (342.94415283203125, 175.05064392089844)
-2       (323.8777160644531, 343.0340270996094)   (328.1445617675781, 343.69183349609375)
-3      (184.52706909179688, 76.28475189208984)   (185.25038146972656, 77.84370422363281)
-4       (517.1746826171875, 289.2869567871094)   (520.8577270507812, 289.58026123046875)
-...                                        ...                                       ...
-1728   (614.7857055664062, 202.22174072265625)    (615.9202880859375, 203.0737762451172)
-1729    (216.5105438232422, 160.4733428955078)    (217.0716094970703, 161.7423095703125)
-1730   (444.8753356933594, 242.33592224121094)  (446.14434814453125, 243.49176025390625)
-1731   (461.13580322265625, 82.10858917236328)     (461.0906066894531, 84.6612777709961)
-1732  (297.50958251953125, 28.099599838256836)    (486.5142822265625, 61.50300979614258)
-
-[1733 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0      (185.25038146972656, 77.84370422363281)   (184.84738159179688, 77.89257049560547)
-1      (185.25038146972656, 77.84370422363281)   (184.84738159179688, 77.89257049560547)
-2     (342.94415283203125, 175.05064392089844)      (343.32562255859375, 174.1025390625)
-3       (570.0875244140625, 206.9282684326172)   (571.3922119140625, 207.60821533203125)
-4      (542.6176147460938, 247.52207946777344)   (543.4949340820312, 247.84454345703125)
-...                                        ...                                       ...
-1728     (538.779541015625, 205.8683624267578)   (539.9181518554688, 209.12222290039062)
-1729    (421.0941467285156, 341.5637512207031)  (494.43890380859375, 236.45347595214844)
-1730    (523.551025390625, 235.36553955078125)    (338.6466064453125, 437.4407653808594)
-1731    (602.1407470703125, 5.455526351928711)    (599.6611938476562, 7.436167240142822)
-1732    (632.3461303710938, 422.9021301269531)    (596.3619995117188, 51.25825119018555)
-
-[1733 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0         (343.32562255859375, 174.1025390625)   (342.877685546875, 172.69131469726562)
-1       (570.3155517578125, 253.4789276123047)  (570.3519897460938, 252.49061584472656)
-2     (460.35272216796875, 48.997039794921875)   (460.7314758300781, 48.37275314331055)
-3      (543.4949340820312, 247.84454345703125)  (543.6058959960938, 247.49310302734375)
-4       (495.0774230957031, 387.6915283203125)    (499.0106506347656, 385.142822265625)
-...                                        ...                                      ...
-1671   (586.5484008789062, 474.95379638671875)   (591.9077758789062, 470.7243957519531)
-1672   (539.9181518554688, 209.12222290039062)  (539.8900146484375, 205.80345153808594)
-1673     (526.7556762695312, 221.456787109375)  (527.1738891601562, 221.40855407714844)
-1674   (474.1967468261719, 146.94793701171875)   (486.4053039550781, 147.3282928466797)
-1675   (636.7078247070312, 38.609657287597656)     (633.21923828125, 40.16471481323242)
-
-[1676 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0       (342.877685546875, 172.69131469726562)   (342.81146240234375, 170.3980255126953)
-1       (342.877685546875, 172.69131469726562)   (342.81146240234375, 170.3980255126953)
-2       (515.6566162109375, 407.6649169921875)   (519.4605102539062, 403.35308837890625)
-3        (293.6081237792969, 394.771728515625)   (298.71942138671875, 391.8834533691406)
-4     (398.10345458984375, 14.925931930541992)   (398.0389709472656, 14.012438774108887)
-...                                        ...                                       ...
-1701   (154.92027282714844, 89.90392303466797)   (154.26097106933594, 88.76791381835938)
-1702   (479.2604675292969, 191.78807067871094)     (522.0892944335938, 185.606689453125)
-1703   (202.3634796142578, 114.08980560302734)  (199.09483337402344, 112.49845123291016)
-1704      (306.174560546875, 69.7498779296875)     (305.5750732421875, 69.0185775756836)
-1705     (554.123291015625, 67.39087677001953)    (550.6820068359375, 67.10200500488281)
-
-[1706 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0       (561.7313842773438, 276.7571716308594)   (561.5794067382812, 273.8003234863281)
-1     (418.85015869140625, 411.60137939453125)  (423.86273193359375, 406.6879577636719)
-2       (603.0684204101562, 314.1603088378906)   (603.1533813476562, 310.3887634277344)
-3       (388.1983947753906, 230.8528289794922)     (388.122802734375, 228.722412109375)
-4       (553.2470092773438, 272.5489196777344)  (553.2792358398438, 269.70623779296875)
-...                                        ...                                      ...
-1650   (393.4429626464844, 175.04745483398438)  (254.39283752441406, 7.122287273406982)
-1651     (607.17626953125, 212.45272827148438)  (605.6275024414062, 209.36228942871094)
-1652    (436.7982482910156, 315.5010070800781)  (438.89166259765625, 312.3136901855469)
-1653   (618.3612060546875, 361.33636474609375)  (505.7895812988281, 322.00897216796875)
-1654   (503.21612548828125, 406.4350891113281)   (453.5310363769531, 337.6834411621094)
-
-[1655 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0         (388.122802734375, 228.722412109375)    (388.0946044921875, 226.2263641357422)
-1      (601.2343139648438, 369.94952392578125)    (602.1537475585938, 363.9571838378906)
-2      (560.7911987304688, 341.60540771484375)     (562.04541015625, 336.57012939453125)
-3      (341.5089416503906, 335.98358154296875)   (344.3766174316406, 331.96234130859375)
-4       (456.6001892089844, 222.7523956298828)    (456.2845458984375, 220.1683807373047)
-...                                        ...                                       ...
-1631      (625.55615234375, 428.5816955566406)   (473.19024658203125, 438.8486022949219)
-1632    (609.7660522460938, 230.8114471435547)   (607.1294555664062, 227.87132263183594)
-1633   (588.8804931640625, 6.0136189460754395)    (617.7523803710938, 432.5425720214844)
-1634  (455.86090087890625, 192.56118774414062)  (455.51617431640625, 190.18502807617188)
-1635    (488.3679504394531, 269.5397644042969)    (489.2629089355469, 266.3168029785156)
-
-[1636 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (471.7442626953125, 281.8172302246094)   (472.8692932128906, 277.8639831542969)
-1     (527.2393188476562, 278.89080810546875)   (527.8650512695312, 274.5244445800781)
-2     (344.3766174316406, 331.96234130859375)   (347.4361267089844, 327.2994689941406)
-3     (402.60443115234375, 230.8054962158203)  (402.9701232910156, 227.81629943847656)
-4           (402.71484375, 293.1351318359375)   (404.7886962890625, 289.2015686035156)
-...                                       ...                                      ...
-1706  (483.7057800292969, 200.87435913085938)   (581.5560302734375, 180.2923583984375)
-1707   (498.6787414550781, 61.52627182006836)  (495.3307800292969, 59.077674865722656)
-1708  (548.6365356445312, 192.24803161621094)    (547.022705078125, 189.5165557861328)
-1709   (565.6696166992188, 371.8757629394531)   (568.7672119140625, 364.9298400878906)
-1710    (511.677001953125, 70.12718963623047)   (509.2272033691406, 67.35718536376953)
-
-[1711 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (407.5969543457031, 281.0130615234375)    (409.55029296875, 276.95697021484375)
-1       (398.428466796875, 280.2714538574219)   (400.4515686035156, 276.3444519042969)
-2      (527.8650512695312, 274.5244445800781)        (528.1220703125, 270.14404296875)
-3      (407.5969543457031, 281.0130615234375)    (409.55029296875, 276.95697021484375)
-4      (437.2279357910156, 287.8932800292969)  (439.0884094238281, 283.59564208984375)
-...                                       ...                                      ...
-1653  (477.3894958496094, 172.35952758789062)     (551.7177734375, 186.24867248535156)
-1654     (632.84814453125, 80.09628295898438)   (627.6986694335938, 78.37417602539062)
-1655  (127.80892181396484, 59.70603561401367)  (124.46479034423828, 54.42094421386719)
-1656   (594.8458251953125, 306.4529724121094)   (553.0518798828125, 457.6814880371094)
-1657  (198.63165283203125, 99.27912139892578)  (194.92523193359375, 95.15448760986328)
-
-[1658 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (540.5381469726562, 231.33885192871094)    (539.601806640625, 227.59954833984375)
-1      (606.0786743164062, 309.4477844238281)         (605.99609375, 303.8821716308594)
-2     (494.8809814453125, 223.09432983398438)    (494.3238525390625, 219.4536895751953)
-3      (516.0724487304688, 364.1711730957031)       (519.3408203125, 357.3071594238281)
-4      (587.8921508789062, 317.7690124511719)    (588.3851318359375, 311.9029846191406)
-...                                       ...                                       ...
-1499  (456.0155334472656, 188.58367919921875)   (455.71905517578125, 185.0743865966797)
-1500  (465.97528076171875, 53.41864013671875)      (463.93798828125, 50.69343185424805)
-1501  (470.0480651855469, 215.78500366210938)  (469.53558349609375, 212.46466064453125)
-1502   (384.6325988769531, 311.3741760253906)    (388.2870178222656, 306.6341247558594)
-1503      (626.2900390625, 264.4884033203125)    (614.0108032226562, 354.7193908691406)
-
-[1504 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (539.601806640625, 227.59954833984375)   (539.4220581054688, 224.0634307861328)
-1      (494.3238525390625, 219.4536895751953)  (494.2698974609375, 215.74594116210938)
-2       (558.9242553710938, 285.229736328125)     (559.75439453125, 280.1189270019531)
-3      (494.3238525390625, 219.4536895751953)  (494.2698974609375, 215.74594116210938)
-4     (460.41949462890625, 268.1368408203125)  (462.3437805175781, 264.12030029296875)
-...                                       ...                                      ...
-1546  (503.5247497558594, 62.294219970703125)  (501.0015563964844, 59.777523040771484)
-1547   (510.7741394042969, 59.00807189941406)        (509.22265625, 55.99989318847656)
-1548   (535.4297485351562, 159.8387908935547)  (534.7611694335938, 156.64105224609375)
-1549    (632.35205078125, 10.268144607543945)            (628.0625, 7.198613166809082)
-1550   (610.0588989257812, 97.61320495605469)   (606.5087280273438, 94.12444305419922)
-
-[1551 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (504.5365905761719, 268.22088623046875)  (506.65191650390625, 263.6564636230469)
-1       (539.4220581054688, 224.0634307861328)   (539.5521850585938, 220.2080841064453)
-2       (583.7408447265625, 318.9566955566406)    (585.69580078125, 312.90228271484375)
-3      (572.7061767578125, 218.41656494140625)      (572.3759765625, 214.9439239501953)
-4       (597.3541259765625, 372.6800231933594)   (599.8502807617188, 364.8743591308594)
-...                                        ...                                      ...
-1481    (584.4407958984375, 73.01631164550781)    (581.432373046875, 69.83483123779297)
-1482     (409.55517578125, 270.93524169921875)   (412.9028625488281, 266.5135192871094)
-1483   (137.32093811035156, 65.43538665771484)     (135.7119140625, 61.181663513183594)
-1484  (132.62637329101562, 29.145977020263672)  (130.07456970214844, 23.85822868347168)
-1485   (593.5499877929688, 349.41119384765625)    (561.150146484375, 274.9186096191406)
-
-[1486 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0          (572.3759765625, 214.9439239501953)   (572.1024169921875, 211.13446044921875)
-1       (359.983642578125, 188.32305908203125)  (360.33721923828125, 185.42691040039062)
-2       (539.5521850585938, 220.2080841064453)   (540.4451904296875, 216.97286987304688)
-3      (478.0799865722656, 284.26312255859375)   (482.3532409667969, 279.59234619140625)
-4     (378.85870361328125, 199.23928833007812)   (379.9601745605469, 196.21737670898438)
-...                                        ...                                       ...
-1465     (417.25439453125, 264.25482177734375)        (593.051025390625, 397.8349609375)
-1466     (436.399169921875, 452.4410705566406)        (446.2909240722656, 442.986328125)
-1467   (476.4575500488281, 153.34844970703125)   (477.2398376464844, 150.32347106933594)
-1468    (517.1987915039062, 434.7232360839844)    (496.4414978027344, 47.69766616821289)
-1469   (577.0142211914062, 233.73165893554688)    (385.9461975097656, 357.5694274902344)
-
-[1470 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (482.3532409667969, 279.59234619140625)   (486.58489990234375, 275.2984924316406)
-1     (356.19146728515625, 348.0676574707031)     (363.8817138671875, 342.563232421875)
-2     (482.71087646484375, 216.9750518798828)   (484.6053466796875, 214.05648803710938)
-3     (405.0981750488281, 209.96214294433594)   (407.1074523925781, 207.09226989746094)
-4        (345.5404052734375, 297.39404296875)    (351.9818115234375, 292.8268127441406)
-...                                       ...                                       ...
-1348   (447.6973876953125, 13.13387680053711)  (449.72479248046875, 10.910170555114746)
-1349   (628.5753784179688, 420.8540954589844)    (633.9776611328125, 411.6493835449219)
-1350  (568.8516845703125, 184.77908325195312)          (567.763671875, 181.24072265625)
-1351  (609.5316772460938, 11.698054313659668)     (455.43194580078125, 315.68505859375)
-1352  (619.3381958007812, 235.10369873046875)   (620.6863403320312, 232.06143188476562)
-
-[1353 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0     (430.29803466796875, 195.29347229003906)  (432.7185363769531, 193.14633178710938)
-1      (486.58489990234375, 275.2984924316406)  (491.43865966796875, 271.6665344238281)
-2      (458.8577575683594, 197.82362365722656)  (461.5702819824219, 195.68138122558594)
-3     (384.67852783203125, 187.97756958007812)  (386.8712158203125, 185.90213012695312)
-4      (458.8577575683594, 197.82362365722656)  (461.5702819824219, 195.68138122558594)
-...                                        ...                                      ...
-1222   (495.70733642578125, 179.9639892578125)  (497.09112548828125, 177.9214324951172)
-1223       (512.228515625, 157.08253479003906)    (514.01025390625, 154.13458251953125)
-1224     (595.85009765625, 396.81781005859375)   (603.4639282226562, 388.2339172363281)
-1225   (585.1174926757812, 161.87075805664062)  (587.2089233398438, 159.90591430664062)
-1226   (478.9879455566406, 174.99647521972656)   (480.6023254394531, 172.7790069580078)
-
-[1227 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (560.0836181640625, 120.76541900634766)  (559.3988037109375, 119.54590606689453)
-1     (432.7185363769531, 193.14633178710938)  (436.1759033203125, 191.60235595703125)
-2     (259.1924743652344, 194.36151123046875)     (264.1579895019531, 192.39794921875)
-3      (588.2046508789062, 84.52094268798828)   (587.4757080078125, 84.64430236816406)
-4     (259.1924743652344, 194.36151123046875)     (264.1579895019531, 192.39794921875)
-...                                       ...                                      ...
-1215   (630.381591796875, 302.80633544921875)  (635.9491577148438, 298.88702392578125)
-1216  (474.8752136230469, 44.282981872558594)   (474.6432800292969, 44.34111404418945)
-1217  (514.2990112304688, 166.98216247558594)  (517.0374145507812, 166.53640747070312)
-1218    (603.929443359375, 153.9387969970703)   (607.0988159179688, 151.6614532470703)
-1219     (458.53857421875, 98.96746063232422)   (460.6482238769531, 97.96341705322266)
-
-[1220 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (530.8355712890625, 102.2039794921875)    (531.6641235351562, 102.265869140625)
-1      (504.8117980957031, 76.58815002441406)    (505.804443359375, 76.58381652832031)
-2     (568.5901489257812, 176.55364990234375)   (570.3840942382812, 174.6058349609375)
-3     (504.5944519042969, 109.93804931640625)    (505.57763671875, 109.89005279541016)
-4        (486.6109313964844, 343.18994140625)      (496.7674865722656, 339.3251953125)
-...                                       ...                                      ...
-1131    (457.9370422363281, 21.2719669342041)  (487.0296325683594, 21.517778396606445)
-1132    (553.9393920898438, 47.5125732421875)    (553.5494995117188, 47.8051643371582)
-1133   (524.0873413085938, 167.8457794189453)  (414.4050598144531, 166.00527954101562)
-1134   (480.1950378417969, 264.1249694824219)  (486.9851989746094, 262.38775634765625)
-1135  (627.3644409179688, 309.89349365234375)  (635.3594970703125, 306.58648681640625)
-
-[1136 rows x 2 columns]
-Matches detected:  
-                                           kp0                                       kp1
-0         (440.006103515625, 190.697021484375)    (445.0893859863281, 190.7227783203125)
-1       (477.4435729980469, 127.5844497680664)  (480.85931396484375, 128.23138427734375)
-2      (402.6673278808594, 199.91734313964844)     (408.10296630859375, 200.21630859375)
-3         (440.006103515625, 190.697021484375)    (445.0893859863281, 190.7227783203125)
-4      (575.3758544921875, 213.92376708984375)    (580.7581787109375, 213.7592315673828)
-...                                        ...                                       ...
-1008   (505.3155517578125, 180.00379943847656)     (509.650634765625, 179.6837158203125)
-1009   (609.9185791015625, 159.34713745117188)   (614.4304809570312, 159.84182739257812)
-1010  (205.10684204101562, 50.784324645996094)  (439.36541748046875, 357.67889404296875)
-1011    (635.7590942382812, 3.674083709716797)    (636.9039916992188, 4.949763298034668)
-1012   (584.6162719726562, 172.76683044433594)    (145.0546112060547, 7.733962535858154)
-
-[1013 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (460.3846435546875, 187.7985076904297)  (466.29461669921875, 188.88331604003906)
-1     (172.41302490234375, 19.98055648803711)  (178.31350708007812, 23.055177688598633)
-2    (175.02867126464844, 13.472306251525879)     (181.039794921875, 16.49614143371582)
-3           (506.3828125, 231.20462036132812)   (515.4000244140625, 231.94192504882812)
-4      (414.1855773925781, 235.2508544921875)   (423.6207275390625, 235.59658813476562)
-..                                        ...                                       ...
-934    (540.7298583984375, 85.39341735839844)     (543.0155029296875, 87.8412857055664)
-935   (141.3358154296875, 15.080819129943848)    (597.3783569335938, 7.143490314483643)
-936    (594.0641479492188, 4.153202533721924)    (597.3783569335938, 7.143490314483643)
-937    (603.8380126953125, 89.89070129394531)    (329.0574951171875, 92.31864166259766)
-938   (624.0642700195312, 170.28244018554688)    (631.8051147460938, 170.0262908935547)
-
-[939 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (479.6889343261719, 194.1958465576172)       (487.38037109375, 196.322021484375)
-1     (479.6889343261719, 194.1958465576172)       (487.38037109375, 196.322021484375)
-2     (497.6077575683594, 195.8438262939453)      (505.535400390625, 197.829833984375)
-3    (451.4364013671875, 191.76217651367188)  (459.00970458984375, 193.92935180664062)
-4    (508.0818786621094, 110.80288696289062)   (512.1934204101562, 113.94243621826172)
-..                                       ...                                       ...
-889    (609.223388671875, 7.026349067687988)   (612.4607543945312, 10.354615211486816)
-890   (582.9105224609375, 44.51626205444336)   (612.1266479492188, 287.93609619140625)
-891     (541.25927734375, 187.4005889892578)    (549.3536376953125, 189.6236572265625)
-892   (499.4850158691406, 258.6031799316406)    (566.7374267578125, 322.7049560546875)
-893   (544.8695678710938, 108.4225845336914)    (586.2661743164062, 47.31584930419922)
-
-[894 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (495.1156311035156, 131.11456298828125)   (502.8782653808594, 134.9693603515625)
-1    (495.1156311035156, 131.11456298828125)   (502.8782653808594, 134.9693603515625)
-2    (401.88995361328125, 321.6368408203125)   (416.8272399902344, 323.0432434082031)
-3     (490.7638244628906, 350.5941162109375)    (508.562255859375, 351.7875671386719)
-4        (510.5537109375, 167.3754425048828)   (519.9686889648438, 171.2613067626953)
-..                                       ...                                      ...
-824    (403.9961242675781, 8.17335033416748)  (411.9204406738281, 13.406453132629395)
-825     (583.89501953125, 156.6245880126953)  (411.9204406738281, 13.406453132629395)
-826    (412.78662109375, 38.305503845214844)     (492.896240234375, 51.8184814453125)
-827  (559.9700317382812, 54.347145080566406)   (563.8937377929688, 58.71444320678711)
-828     (390.58251953125, 39.92533874511719)     (492.896240234375, 51.8184814453125)
-
-[829 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (431.4453430175781, 205.66847229003906)  (441.6485595703125, 210.22662353515625)
-1    (446.4684143066406, 327.05047607421875)  (464.29168701171875, 330.3034973144531)
-2      (467.847412109375, 197.4432830810547)   (478.7401428222656, 202.1038055419922)
-3     (550.6062622070312, 268.9134826660156)     (567.26611328125, 273.2392272949219)
-4    (493.25921630859375, 349.4119873046875)  (513.2763061523438, 352.43389892578125)
-..                                       ...                                      ...
-701   (626.6892700195312, 359.0126647949219)     (553.0654296875, 167.10984802246094)
-702   (328.54254150390625, 68.1006851196289)   (550.1334838867188, 269.4963073730469)
-703  (455.2516174316406, 166.07086181640625)    (632.013427734375, 374.1399230957031)
-704  (585.2406005859375, 193.20326232910156)   (542.7342529296875, 190.3668212890625)
-705   (553.0868530273438, 132.1699676513672)   (602.1424560546875, 62.98643493652344)
-
-[706 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0     (478.7401428222656, 202.1038055419922)  (490.89862060546875, 208.15028381347656)
-1    (526.3177490234375, 205.76255798339844)      (539.6416625976562, 211.64892578125)
-2     (493.4278259277344, 199.3992462158203)    (505.914794921875, 205.35772705078125)
-3     (455.9179382324219, 201.0802459716797)   (467.9258728027344, 207.21090698242188)
-4     (550.744384765625, 196.24789428710938)   (564.3956298828125, 202.13485717773438)
-..                                       ...                                       ...
-653   (549.9064331054688, 274.4215393066406)   (567.7459716796875, 279.52239990234375)
-654    (336.4855651855469, 75.3906478881836)       (535.1279296875, 326.4005432128906)
-655  (181.45660400390625, 34.06538391113281)       (198.6865234375, 42.52383041381836)
-656   (505.7881164550781, 135.5308380126953)    (513.881591796875, 142.65956115722656)
-657  (475.82135009765625, 304.8863525390625)   (502.6951599121094, 301.06597900390625)
-
-[658 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0    (574.6512451171875, 233.25762939453125)  (592.8888549804688, 240.11155700683594)
-1     (459.3511657714844, 210.7850799560547)  (472.8646545410156, 217.80853271484375)
-2     (515.6993408203125, 213.4044952392578)  (530.6572875976562, 220.43655395507812)
-3     (522.6480712890625, 299.3525695800781)     (544.31396484375, 306.4275207519531)
-4     (575.0552978515625, 209.6693572998047)      (591.4013671875, 216.6157989501953)
-..                                       ...                                      ...
-605  (195.36988830566406, 24.36475372314453)   (209.6630401611328, 37.52748107910156)
-606  (477.0028076171875, 173.33868408203125)  (509.11126708984375, 267.6158142089844)
-607  (527.3267211914062, 156.04913330078125)    (570.951416015625, 136.5966033935547)
-608         (591.7774047851562, 118.0078125)    (603.906982421875, 124.1805191040039)
-609   (309.5042419433594, 85.72772979736328)   (322.6439208984375, 96.09876251220703)
-
-[610 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (504.9169616699219, 215.20555114746094)      (520.794921875, 223.43829345703125)
-1    (453.09259033203125, 209.23947143554688)   (467.2021179199219, 217.4442596435547)
-2      (535.9285278320312, 217.3686981201172)   (553.095947265625, 225.45278930664062)
-3        (579.61474609375, 209.0822296142578)  (597.4329223632812, 217.19427490234375)
-4     (472.8646545410156, 217.80853271484375)  (487.9508361816406, 225.81056213378906)
-..                                        ...                                      ...
-534    (607.0968017578125, 321.9714660644531)    (635.288330078125, 330.7562561035156)
-535   (582.3344116210938, 137.60301208496094)  (597.6993408203125, 142.42601013183594)
-536    (485.0388488769531, 200.8126678466797)  (499.5668029785156, 208.33309936523438)
-537     (627.3297729492188, 80.2107162475586)   (627.4931030273438, 38.67238235473633)
-538     (617.929443359375, 242.7759246826172)  (254.96078491210938, 41.17758560180664)
-
-[539 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0       (401.16162109375, 62.50065612792969)   (413.22406005859375, 74.49032592773438)
-1    (559.2317504882812, 151.47036743164062)   (573.4930419921875, 160.40113830566406)
-2     (562.1732788085938, 143.9935302734375)    (576.3975219726562, 152.8418731689453)
-3      (517.4882202148438, 300.339599609375)      (541.5849609375, 309.92901611328125)
-4     (562.1732788085938, 143.9935302734375)    (576.3975219726562, 152.8418731689453)
-..                                       ...                                       ...
-474  (392.92852783203125, 125.7438735961914)  (403.63653564453125, 135.71951293945312)
-475   (322.1468505859375, 63.04487991333008)    (334.4714660644531, 76.13771057128906)
-476  (603.2547607421875, 338.46014404296875)   (634.8893432617188, 348.90057373046875)
-477  (364.81219482421875, 39.08552169799805)    (378.828857421875, 51.995086669921875)
-478  (358.5691223144531, 42.500282287597656)   (578.7562255859375, 359.92169189453125)
-
-[479 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (514.2425537109375, 231.80166625976562)    (534.0570068359375, 241.9007568359375)
-1    (538.7344970703125, 232.68856811523438)    (559.338623046875, 242.82669067382812)
-2    (538.7344970703125, 232.68856811523438)    (559.338623046875, 242.82669067382812)
-3    (520.6060180664062, 238.85035705566406)       (540.6880493164062, 248.9169921875)
-4     (544.9357299804688, 223.9414520263672)    (565.0133056640625, 233.9835968017578)
-..                                       ...                                       ...
-360  (544.5465087890625, 108.58939361572266)     (562.68505859375, 118.07674407958984)
-361  (566.9593505859375, 126.27347564697266)     (561.3412475585938, 86.5955810546875)
-362   (425.5569763183594, 67.63773345947266)  (143.56057739257812, 4.0873565673828125)
-363   (623.5196533203125, 5.184901714324951)   (620.0855102539062, 33.713619232177734)
-364    (562.643798828125, 99.45082092285156)      (577.4404296875, 108.78483581542969)
-
-[365 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (527.6390380859375, 249.60195922851562)   (549.6043090820312, 260.55560302734375)
-1    (569.7595825195312, 247.92906188964844)    (593.8609619140625, 258.9103698730469)
-2    (510.6245422363281, 242.11427307128906)   (531.3827514648438, 252.97799682617188)
-3    (519.8901977539062, 248.06936645507812)     (540.9917602539062, 258.598876953125)
-4     (286.1322937011719, 36.67061996459961)    (299.80291748046875, 52.9929084777832)
-..                                       ...                                       ...
-302    (621.25048828125, 206.98687744140625)   (390.2815246582031, 101.34688568115234)
-303  (607.3877563476562, 201.39476013183594)   (631.4226684570312, 211.92123413085938)
-304   (512.934326171875, 199.75714111328125)     (530.954345703125, 210.1957550048828)
-305    (280.442138671875, 83.30722045898438)  (396.94842529296875, 113.49993133544922)
-306   (410.2730407714844, 67.33917999267578)    (424.7745666503906, 80.54666137695312)
-
-[307 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0     (608.111083984375, 180.28419494628906)  (628.0120849609375, 191.61199951171875)
-1      (411.3448181152344, 105.021240234375)  (425.6079406738281, 117.82929992675781)
-2     (593.8609619140625, 258.9103698730469)   (620.8465576171875, 270.9400939941406)
-3     (545.3219604492188, 255.4466094970703)    (569.3485717773438, 267.381591796875)
-4    (371.6033020019531, 50.875362396240234)  (384.68682861328125, 65.05955505371094)
-..                                       ...                                      ...
-254   (506.5191650390625, 21.12410545349121)  (434.9806823730469, 133.05413818359375)
-255   (535.8760375976562, 220.8898162841797)   (556.9573974609375, 231.2069091796875)
-256  (490.4005432128906, 119.49105834960938)   (583.8399658203125, 76.14287567138672)
-257  (559.9752197265625, 214.72952270507812)   (583.0155639648438, 225.5541534423828)
-258    (624.465576171875, 96.94464111328125)  (586.3307495117188, 30.044031143188477)
-
-[259 rows x 2 columns]
-Matches detected:  
-                                         kp0                                       kp1
-0    (512.4363403320312, 113.66674041748047)    (529.660888671875, 124.32718658447266)
-1    (503.62518310546875, 72.23186492919922)      (520.54150390625, 83.33170318603516)
-2    (384.68682861328125, 65.05955505371094)   (398.47943115234375, 78.94940948486328)
-3     (522.319580078125, 126.49604034423828)    (540.027587890625, 137.29006958007812)
-4    (299.88067626953125, 85.13494110107422)  (314.88751220703125, 100.20386505126953)
-..                                       ...                                       ...
-225      (627.1791381835938, 32.40966796875)        (600.38671875, 23.929828643798828)
-226   (365.171630859375, 22.061534881591797)    (620.0194091796875, 98.18972778320312)
-227   (589.4251098632812, 244.6792449951172)      (304.916259765625, 17.9693660736084)
-228   (282.0419616699219, 97.11158752441406)     (465.491943359375, 122.1871337890625)
-229  (627.9154663085938, 35.406070709228516)    (620.0194091796875, 98.18972778320312)
-
-[230 rows x 2 columns]
-Matches detected:  
-                                         kp0                                      kp1
-0     (463.7517395019531, 69.63101196289062)   (479.8128356933594, 82.27752685546875)
-1      (98.15757751464844, 14.4144868850708)  (126.77027130126953, 36.40750503540039)
-2    (507.73760986328125, 93.93617248535156)    (525.7178955078125, 105.486083984375)
-3     (455.5833740234375, 88.32144165039062)  (472.9803771972656, 100.36702728271484)
-4       (520.54150390625, 83.33170318603516)   (538.6642456054688, 94.37206268310547)
-..                                       ...                                      ...
-217  (404.90582275390625, 85.16520690917969)  (554.2861938476562, 125.63551330566406)
-218  (524.9912719726562, 107.35777282714844)  (543.0211791992188, 118.01165771484375)
-219  (576.2328491210938, 232.30386352539062)  (602.1014404296875, 244.05404663085938)
-220    (301.87744140625, 111.05529022216797)   (615.0173950195312, 88.35131072998047)
-221    (516.0914916992188, 94.3545150756836)  (534.2708129882812, 106.10173034667969)
-
-[222 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (472.0704345703125, 116.91747283935547)  (488.7688903808594, 128.82742309570312)
-1     (472.9803771972656, 100.36702728271484)  (489.7327880859375, 112.09968566894531)
-2     (472.9803771972656, 100.36702728271484)  (489.7327880859375, 112.09968566894531)
-3        (428.3681640625, 109.07855987548828)  (444.6017761230469, 121.49718475341797)
-4    (478.81463623046875, 105.15032196044922)  (496.3558044433594, 117.00923919677734)
-..                                        ...                                      ...
-169    (532.9381103515625, 115.8742904663086)  (552.4751586914062, 126.86566925048828)
-170   (518.0111694335938, 43.225677490234375)  (534.8756713867188, 55.543888092041016)
-171   (379.9406433105469, 10.044629096984863)  (418.90460205078125, 21.64237403869629)
-172   (606.2520751953125, 238.89962768554688)  (634.6744384765625, 251.47824096679688)
-173   (357.7273864746094, 19.635868072509766)  (397.6599426269531, 31.934144973754883)
-
-[174 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (579.9161987304688, 58.74366760253906)    (601.1611938476562, 69.01158905029297)
-1      (496.8769836425781, 52.02484893798828)   (513.8733520507812, 63.961334228515625)
-2     (489.7327880859375, 112.09968566894531)  (507.70220947265625, 123.99141693115234)
-3     (444.6017761230469, 121.49718475341797)   (460.6519775390625, 133.49725341796875)
-4     (444.6017761230469, 121.49718475341797)   (460.6519775390625, 133.49725341796875)
-..                                        ...                                       ...
-197      (263.94580078125, 83.63957977294922)    (281.9258117675781, 98.89637756347656)
-198     (179.3714141845703, 83.8094482421875)      (417.01806640625, 47.33610153198242)
-199   (27.36622428894043, 13.945746421813965)  (62.063194274902344, 37.159000396728516)
-200   (49.490936279296875, 6.965588092803955)    (81.0030517578125, 30.109060287475586)
-201  (344.23931884765625, 107.49044799804688)   (360.5718688964844, 120.42973327636719)
-
-[202 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (596.2167358398438, 87.11670684814453)    (619.2359619140625, 96.9024429321289)
-1       (554.046630859375, 75.95193481445312)   (574.2014770507812, 86.40131378173828)
-2      (6.111251354217529, 455.9353332519531)  (33.55775451660156, 463.87652587890625)
-3    (507.70220947265625, 123.99141693115234)  (527.5913696289062, 134.53231811523438)
-4     (46.35420227050781, 235.37368774414062)  (73.46778869628906, 246.37899780273438)
-..                                        ...                                      ...
-240    (612.9358520507812, 60.73812484741211)   (129.5780792236328, 45.62224197387695)
-241    (497.6037292480469, 157.8299102783203)  (513.5559692382812, 168.99195861816406)
-242   (346.8844909667969, 170.90086364746094)   (362.914794921875, 182.97027587890625)
-243   (12.720076560974121, 59.74455642700195)   (47.81072998046875, 80.86231994628906)
-244  (362.58184814453125, 155.29202270507812)  (378.6043395996094, 168.97422790527344)
-
-[245 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (50.477359771728516, 258.1235046386719)    (76.42791748046875, 268.3313903808594)
-1     (50.477359771728516, 258.1235046386719)    (76.42791748046875, 268.3313903808594)
-2       (532.77294921875, 116.88670349121094)    (551.628173828125, 127.59737396240234)
-3      (558.5737915039062, 326.8969421386719)     (16.381141662597656, 419.21435546875)
-4       (534.599365234375, 139.0983123779297)    (555.5826416015625, 149.3363494873047)
-..                                        ...                                       ...
-258   (507.4255065917969, 169.55538940429688)   (524.2373657226562, 180.50132751464844)
-259    (7.499845027923584, 9.504456520080566)    (46.80139923095703, 33.60956573486328)
-260   (170.4200439453125, 17.537315368652344)  (49.872596740722656, 47.441402435302734)
-261  (127.72288513183594, 4.8162031173706055)  (156.31556701660156, 25.321563720703125)
-262   (49.78904724121094, 29.721107482910156)    (83.8077621459961, 50.635868072509766)
-
-[263 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (76.42791748046875, 268.3313903808594)   (100.25457763671875, 278.3103332519531)
-1      (76.42791748046875, 268.3313903808594)   (100.25457763671875, 278.3103332519531)
-2     (24.875614166259766, 296.0830993652344)      (53.19150924682617, 305.06494140625)
-3      (41.23909378051758, 293.0907287597656)   (68.82892608642578, 302.09307861328125)
-4     (12.272011756896973, 449.5539245605469)   (38.026981353759766, 457.3272399902344)
-..                                        ...                                       ...
-345  (196.84323120117188, 24.297767639160156)     (216.534912109375, 43.07563781738281)
-346    (383.2915954589844, 202.1527862548828)  (399.17352294921875, 213.47601318359375)
-347   (202.54347229003906, 21.98334312438965)   (222.48928833007812, 40.22983169555664)
-348     (7.11326265335083, 57.93870544433594)    (43.94749069213867, 77.00751495361328)
-349   (9.654284477233887, 100.04580688476562)    (43.99669647216797, 116.2388687133789)
-
-[350 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (100.25457763671875, 278.3103332519531)   (122.63314056396484, 287.8009948730469)
-1     (100.25457763671875, 278.3103332519531)   (122.63314056396484, 287.8009948730469)
-2     (26.817487716674805, 296.2958679199219)  (53.939388275146484, 304.79632568359375)
-3     (19.142410278320312, 305.6354675292969)    (46.69585418701172, 313.8005065917969)
-4     (86.09646606445312, 289.12408447265625)   (109.88140106201172, 297.9384460449219)
-..                                        ...                                       ...
-417  (26.347929000854492, 223.12783813476562)    (49.34075927734375, 233.4189910888672)
-418    (538.8840942382812, 90.29560089111328)       (585.440185546875, 96.260986328125)
-419  (221.27259826660156, 13.945761680603027)    (239.9392547607422, 32.57143020629883)
-420   (182.78469848632812, 48.09092712402344)   (69.71638488769531, 145.05435180664062)
-421   (234.61776733398438, 9.332427024841309)   (252.31259155273438, 25.90448570251465)
-
-[422 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (46.69585418701172, 313.8005065917969)    (71.80502319335938, 321.7652893066406)
-1      (357.1042175292969, 264.4726257324219)  (373.17681884765625, 274.45831298828125)
-2    (53.939388275146484, 304.79632568359375)   (78.59722900390625, 312.87420654296875)
-3     (122.63314056396484, 287.8009948730469)   (144.2811737060547, 296.54510498046875)
-4       (64.5744857788086, 312.4657287597656)   (89.03694915771484, 320.49664306640625)
-..                                        ...                                       ...
-476     (57.27073669433594, 129.394775390625)     (86.7089614868164, 142.7860565185547)
-477  (24.998615264892578, 203.49264526367188)    (205.532958984375, 137.69195556640625)
-478  (13.622014999389648, 194.54873657226562)   (37.35135269165039, 206.00633239746094)
-479    (203.1185760498047, 66.08616638183594)   (224.19940185546875, 79.32372283935547)
-480    (273.9639587402344, 4.600003719329834)    (289.7694396972656, 20.58077049255371)
-
-[481 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0     (117.16581726074219, 318.3403015136719)  (139.14915466308594, 326.1955261230469)
-1     (144.2811737060547, 296.54510498046875)  (163.78822326660156, 304.8268127441406)
-2     (31.71736717224121, 445.25018310546875)  (56.126121520996094, 452.2117614746094)
-3       (140.8411407470703, 310.321533203125)  (161.93679809570312, 318.2047119140625)
-4     (144.2811737060547, 296.54510498046875)  (163.78822326660156, 304.8268127441406)
-..                                        ...                                      ...
-547   (122.41028594970703, 87.10643768310547)    (148.4748077392578, 99.8614730834961)
-548  (267.69219970703125, 39.336997985839844)   (283.9787902832031, 54.14564895629883)
-549    (66.32223510742188, 160.9556427001953)  (94.60419464111328, 174.21343994140625)
-550      (205.669677734375, 62.9113883972168)   (227.97984313964844, 76.4493408203125)
-551   (435.31976318359375, 78.18403625488281)   (449.28399658203125, 87.3140869140625)
-
-[552 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0      (32.18461227416992, 411.2502746582031)   (56.29059600830078, 417.2444152832031)
-1     (163.78822326660156, 304.8268127441406)        (182.755859375, 312.509521484375)
-2     (16.511920928955078, 450.7018737792969)  (40.793941497802734, 456.6604309082031)
-3     (163.78822326660156, 304.8268127441406)        (182.755859375, 312.509521484375)
-4     (43.985103607177734, 419.6200256347656)     (67.807373046875, 426.0313720703125)
-..                                        ...                                      ...
-588   (180.1973419189453, 148.65902709960938)  (259.3641052246094, 107.64161682128906)
-589    (510.2812194824219, 75.09325408935547)  (512.4833374023438, 15.630660057067871)
-590  (508.96112060546875, 18.656686782836914)      (558.0830078125, 24.61231803894043)
-591  (119.92021942138672, 158.86207580566406)    (514.042236328125, 23.93865203857422)
-592  (449.39324951171875, 156.85926818847656)   (63.95146560668945, 314.5194091796875)
-
-[593 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0        (67.807373046875, 426.0313720703125)   (90.96102905273438, 432.0699462890625)
-1       (72.2227783203125, 433.0038146972656)   (95.03697204589844, 438.9569396972656)
-2      (71.6308364868164, 422.66619873046875)   (94.69791412353516, 428.6697082519531)
-3    (10.221802711486816, 435.75408935546875)  (35.136314392089844, 440.8875732421875)
-4     (160.02281188964844, 333.8987731933594)  (179.92410278320312, 340.8484802246094)
-..                                        ...                                      ...
-645    (318.777587890625, 128.64857482910156)  (332.4764099121094, 135.97381591796875)
-646    (60.92715835571289, 276.0250549316406)  (79.67437744140625, 283.94927978515625)
-647    (322.7149353027344, 66.42367553710938)  (335.88323974609375, 75.41600036621094)
-648  (138.32615661621094, 190.51170349121094)  (79.67437744140625, 283.94927978515625)
-649    (273.95074462890625, 82.6093521118164)  (310.95391845703125, 52.88047790527344)
-
-[650 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (77.47329711914062, 421.9372863769531)   (100.12884521484375, 427.4241638183594)
-1     (166.16204833984375, 345.1097717285156)   (185.65924072265625, 351.2047119140625)
-2      (42.83253860473633, 439.1457214355469)   (67.14295196533203, 444.07098388671875)
-3    (34.473140716552734, 444.14569091796875)   (58.807106018066406, 448.9892883300781)
-4      (388.4862365722656, 93.35273742675781)  (400.29803466796875, 100.20270538330078)
-..                                        ...                                       ...
-682   (7.322442531585693, 294.33233642578125)       (562.3623046875, 256.8576354980469)
-683    (213.9609832763672, 188.9252166748047)   (229.02244567871094, 196.5810089111328)
-684   (430.70880126953125, 261.3591613769531)         (446.4387512207031, 268.87890625)
-685   (180.0371856689453, 238.13880920410156)     (321.887451171875, 137.4652099609375)
-686   (154.9730224609375, 221.44956970214844)   (169.89651489257812, 229.1273193359375)
-
-[687 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (185.65924072265625, 351.2047119140625)    (204.3039093017578, 357.2430419921875)
-1    (218.11331176757812, 326.41168212890625)    (234.6796417236328, 332.7598876953125)
-2     (139.75881958007812, 436.3380432128906)   (160.33279418945312, 442.3082580566406)
-3    (218.11331176757812, 326.41168212890625)    (234.6796417236328, 332.7598876953125)
-4       (172.30224609375, 334.09722900390625)   (189.91600036621094, 340.0519104003906)
-..                                        ...                                       ...
-631    (6.150015354156494, 39.85676574707031)  (11.913522720336914, 61.176658630371094)
-632  (122.81958770751953, 166.58285522460938)   (143.38832092285156, 176.0518798828125)
-633    (529.1937255859375, 158.8011474609375)  (134.60523986816406, 113.62561798095703)
-634       (81.94970703125, 31.13578987121582)   (107.08509826660156, 45.69231033325195)
-635     (74.07079315185547, 122.605712890625)    (550.0289916992188, 51.00954055786133)
-
-[636 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0      (204.3039093017578, 357.2430419921875)   (221.82850646972656, 363.0926818847656)
-1      (87.78140258789062, 141.8975372314453)  (111.35115814208984, 152.47247314453125)
-2     (124.87306213378906, 460.2094421386719)   (146.29287719726562, 465.0922546386719)
-3    (155.12942504882812, 310.78411865234375)    (167.3602294921875, 316.2850036621094)
-4     (447.7584228515625, 51.798011779785156)   (464.7994079589844, 58.845924377441406)
-..                                        ...                                       ...
-615   (464.9551086425781, 12.041951179504395)   (482.6051025390625, 20.005067825317383)
-616   (201.7642364501953, 125.89545440673828)   (222.29388427734375, 135.1224365234375)
-617    (458.4375915527344, 23.58574867248535)   (139.56781005859375, 166.5788116455078)
-618  (117.21805572509766, 309.09967041015625)  (131.30613708496094, 314.08514404296875)
-619     (443.9648742675781, 5.39922571182251)  (461.47747802734375, 13.310169219970703)
-
-[620 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (221.82850646972656, 363.0926818847656)     (238.50082397460938, 368.28759765625)
-1     (151.06820678710938, 380.0657653808594)  (170.60142517089844, 385.43609619140625)
-2    (248.09527587890625, 333.04864501953125)     (262.2797546386719, 338.107666015625)
-3      (167.3602294921875, 316.2850036621094)    (179.3976287841797, 321.2922668457031)
-4     (112.73782348632812, 389.6777038574219)   (133.89654541015625, 394.7039489746094)
-..                                        ...                                       ...
-638    (171.0447235107422, 177.2432098388672)   (187.9282989501953, 185.27342224121094)
-639  (202.06918334960938, 240.46766662597656)  (213.88706970214844, 245.37582397460938)
-640   (214.11029052734375, 120.0318832397461)    (186.5243682861328, 85.26583099365234)
-641   (285.0218200683594, 141.81578063964844)    (215.4693145751953, 191.5359344482422)
-642   (122.23685455322266, 131.1684112548828)   (469.6158142089844, 32.527069091796875)
-
-[643 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0       (238.50082397460938, 368.28759765625)   (254.72120666503906, 373.0318908691406)
-1      (160.6256561279297, 460.8201599121094)  (180.24754333496094, 464.87310791015625)
-2      (448.3912658691406, 203.0963897705078)  (459.62786865234375, 206.84678649902344)
-3      (188.4656982421875, 380.5741271972656)   (205.77777099609375, 386.3566589355469)
-4    (170.60142517089844, 385.43609619140625)   (188.74513244628906, 390.0555725097656)
-..                                        ...                                       ...
-646    (117.2773666381836, 381.4954528808594)         (136.7526397705078, 386.04296875)
-647   (84.65701293945312, 300.54742431640625)   (96.56106567382812, 304.22125244140625)
-648   (170.3324432373047, 102.87464904785156)    (266.6298522949219, 160.4889373779297)
-649   (107.36404418945312, 269.0913391113281)   (120.38155364990234, 272.4778747558594)
-650    (445.2875061035156, 42.70444869995117)  (455.15924072265625, 27.200740814208984)
-
-[651 rows x 2 columns]
-Matches detected:  
-                                          kp0                                      kp1
-0    (204.65806579589844, 458.32147216796875)  (222.80711364746094, 462.3511657714844)
-1    (204.65806579589844, 458.32147216796875)  (222.80711364746094, 462.3511657714844)
-2     (254.72120666503906, 373.0318908691406)    (270.1469421386719, 377.167724609375)
-3       (264.00634765625, 353.49993896484375)  (278.09735107421875, 357.7022399902344)
-4       (264.00634765625, 353.49993896484375)  (278.09735107421875, 357.7022399902344)
-..                                        ...                                      ...
-607     (481.525146484375, 29.07538604736328)     (497.88836669921875, 35.00341796875)
-608      (124.4339828491211, 382.49072265625)  (135.44064331054688, 390.9988708496094)
-609    (249.785400390625, 210.32888793945312)   (540.0613403320312, 35.12836456298828)
-610   (254.40049743652344, 161.9874725341797)   (267.9084167480469, 169.0877685546875)
-611    (523.2325439453125, 302.8138427734375)   (536.4296264648438, 303.4944152832031)
-
-[612 rows x 2 columns]
-Matches detected:  
-                                          kp0                                       kp1
-0     (278.09735107421875, 357.7022399902344)   (291.8475646972656, 361.81634521484375)
-1     (278.09735107421875, 357.7022399902344)   (291.8475646972656, 361.81634521484375)
-2       (270.1469421386719, 377.167724609375)    (284.3083801269531, 382.0012512207031)
-3    (376.36822509765625, 238.14654541015625)   (383.6250915527344, 238.29013061523438)
-4    (155.86453247070312, 466.40960693359375)   (172.60569763183594, 470.3599853515625)
-..                                        ...                                       ...
-642        (160.604248046875, 293.8955078125)    (427.4923095703125, 329.0005187988281)
-643     (566.9224853515625, 281.349853515625)   (581.6531372070312, 283.52923583984375)
-644     (487.284912109375, 4.973614692687988)   (501.48443603515625, 9.780993461608887)
-645       (90.60279846191406, 262.4794921875)   (205.3671417236328, 309.05950927734375)
-646    (163.9195556640625, 241.4158935546875)  (174.45079040527344, 245.46775817871094)
-
-[647 rows x 2 columns]
-Matches detected:  
-                                           kp0                                      kp1
-0      (211.73727416992188, 332.6198425292969)  (219.48204040527344, 333.7967834472656)
-1       (284.3083801269531, 382.0012512207031)  (292.5155334472656, 383.29364013671875)
-2     (471.45989990234375, 143.77557373046875)  (479.1328430175781, 142.32455444335938)
-3       (199.7218475341797, 89.20585632324219)   (207.6715545654297, 93.08280944824219)
-4       (261.9718017578125, 379.4639587402344)    (269.5446472167969, 381.051513671875)
-...                                        ...                                      ...
-1045   (157.34426879882812, 370.3426513671875)  (164.38856506347656, 372.5290222167969)
-1046   (626.2493896484375, 28.885135650634766)   (339.3505554199219, 191.3721923828125)
-1047      (332.35693359375, 141.4632110595703)  (229.27256774902344, 339.6148681640625)
-1048  (112.29338073730469, 241.20362854003906)  (121.78009033203125, 243.7520294189453)
-1049   (196.81222534179688, 263.2864685058594)   (33.99113845825195, 302.9555358886719)
-
-[1050 rows x 2 columns]
diff --git a/src/distribution_tools/model_imu_data.py b/src/distribution_tools/model_imu_data.py
index d45c969732ad69ce6b59d186195ab07ac8c6c0c4..90cb9a9b4f6eddaefdf587e1634bf1998259f0f0 100644
--- a/src/distribution_tools/model_imu_data.py
+++ b/src/distribution_tools/model_imu_data.py
@@ -4,7 +4,7 @@ import scipy.stats as stats
 
 from scipy.optimize import minimize
 from tskew.tskew import getObjectiveFunction, tspdf_1d
-
+from tskew.tskew import ts_invcdf
 
 import pyreadr
 
@@ -30,16 +30,26 @@ df = 1000
 skew = 0
 
 theta = np.array([loc, scale, df, skew])
-
-res = minimize(getObjectiveFunction(realization, use_loglikelihood=True), x0=theta,
-               method='Nelder-Mead')
+#
+# res = minimize(getObjectiveFunction(realization, use_loglikelihood=True), x0=theta,
+#                method='Nelder-Mead')
 
 N = 1_000
-extent =  np.max(realization) - np.min(realization)
-xvals = np.linspace(np.min(realization) - 0.1 * extent, np.max(realization) + 0.1 * extent, N)
-
-plt.figure()
-est_pdf = tspdf_1d(xvals, res.x[0], res.x[1], res.x[2], res.x[3])
-plt.hist(realization, bins=100, density=True)
-plt.plot(xvals, est_pdf, linestyle='--', label='Estimated skew t', linewidth=4)
-plt.legend()
\ No newline at end of file
+xmin = -0.05
+xmax = 0.05
+extent =  xmax - xmin
+xvals = np.linspace(xmin - 0.1 * extent, xmax + 0.1 * extent, N)
+
+# plt.figure()
+# est_pdf = tspdf_1d(xvals, res.x[0], res.x[1], res.x[2], res.x[3])
+# plt.hist(realization, bins=500, density=True, color='green', alpha=0.5)
+# plt.plot(xvals, est_pdf, linestyle='--', label='Estimated skew t', linewidth=3, alpha=0.5)
+# plt.xlim([xmin, xmax])
+# plt.legend()
+
+loc = 2.72e-5
+scale = 2.25e-6
+df = 1
+skew = 2.8e-3
+
+# median = ts_invcdf(np.array([0.25, 0.5, 0.75]), loc, scale, df, skew)
\ No newline at end of file
diff --git a/src/distribution_tools/tskew/tskew.py b/src/distribution_tools/tskew/tskew.py
index f422aaa44050d7b3693328560b3ec1bf67d51027..537ea173483ac2d6d7462ed6f0cf4c2714bcfa4d 100644
--- a/src/distribution_tools/tskew/tskew.py
+++ b/src/distribution_tools/tskew/tskew.py
@@ -18,9 +18,8 @@ from scipy.optimize import minimize
 import warnings
 import scipy.io as sio
 import scipy
-# from root_finding import newton, brentq
+from .root_finding import newton, brentq
 
-from scipy.special import gamma
 
 
 plt.close('all')
@@ -28,20 +27,6 @@ from numba import vectorize, njit
 import numba as nb
 
 
-
-
-# Source: https://gregorygundersen.com/blog/2020/01/20/multivariate-t/
-# @article{kollo2021multivariate,
-#   title={Multivariate Skew t-Distribution: Asymptotics for Parameter Estimators and Extension to Skew t-Copula},
-#   author={Kollo, T{\~o}nu and K{\"a}{\"a}rik, Meelis and Selart, Anne},
-#   journal={Symmetry},
-#   volume={13},
-#   number={6},
-#   pages={1059},
-#   year={2021},
-#   publisher={Multidisciplinary Digital Publishing Institute}
-# }
-
 @njit
 def tcdf_1d(x, df):
     # if use_scipy:
@@ -234,32 +219,52 @@ def root_Newton_Rhapson(fun, x0, jac, tol=1e-12, maxiter=100):
 
     return x1
 
-def ts_invcdf(q, loc, scale, df, skew):
+def ts_invcdf_opt(q, loc, scale, df, skew):
 
     def ffun(x):
         return tscdf(x, loc, scale, df, skew) - q
 
     def fprime(x):
         return tspdf_1d(x, loc, scale, df, skew)
+
     # Do a single Newton iteration
     p0 = 0.0
     fval = ffun(p0)
     fder = fprime(p0)
     newton_step = fval / fder
+
+
     # Newton step
     p = p0 - newton_step
 
+    while ffun(p) * fval > 0:
+        p = p - newton_step
+
     if p0 < p:
-        r = brentq(f, p0, p)
+        r = brentq(ffun, p0, p)
     else:
-        r = brentq(f, p, p0)
+        r = brentq(ffun, p, p0)
 
     xvals = np.linspace(-20, 20, 1_000)
-    fvals = f(xvals)
+    fvals = ffun(xvals)
     # r = newton(func = f, x0 = 0.0, fprime = fprime)
 
     return r
 
+
+def ts_invcdf(quantiles, loc, scale, df, skew):
+    try:
+        roots = np.zeros_like(quantiles)
+        for count, q in enumerate(quantiles):
+            r = ts_invcdf_opt(q, loc, scale, df, skew)
+            pass
+
+    except:
+        pass
+
+    pass
+
+
 def numerical_inverse(rv_domain, cdf_vals):
     return scipy.interpolate.interp1d(cdf_vals, rv_domain, kind='cubic', fill_value="extrapolate")
     # def inv_fn(x):
diff --git a/src/generalized_unsented_transform/__pycache__/evaluate_from_sigma_points.cpython-38.pyc b/src/generalized_unsented_transform/__pycache__/evaluate_from_sigma_points.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f46aad2a26990e15db0104cc86dcb03e11cf0bfe
Binary files /dev/null and b/src/generalized_unsented_transform/__pycache__/evaluate_from_sigma_points.cpython-38.pyc differ
diff --git a/src/generalized_unsented_transform/__pycache__/generalized_unscented_transform.cpython-38.pyc b/src/generalized_unsented_transform/__pycache__/generalized_unscented_transform.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3349b0f03ac31167b4a9925486777134f77206ea
Binary files /dev/null and b/src/generalized_unsented_transform/__pycache__/generalized_unscented_transform.cpython-38.pyc differ
diff --git a/src/generalized_unsented_transform/evaluate_from_sigma_points.py b/src/generalized_unsented_transform/evaluate_from_sigma_points.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a82ac3a4b28419884e10f2d33c5aa81aaaa4cef
--- /dev/null
+++ b/src/generalized_unsented_transform/evaluate_from_sigma_points.py
@@ -0,0 +1,38 @@
+from numpy.linalg import matrix_power
+import numpy as np
+
+
+
+'''
+ This function is a tool that is used to evaluate the sample statistics 
+ of sigma points of an unscented transform
+
+ INPUTS
+
+   sigma_points        -   Matrix of sigma points
+   weights             -   Vector of weights corresponding to each sigma point
+
+ OUTPUTS
+
+   sigma_mean          -   Sample mean of sigma points
+   sigma_cov           -   Sample covariance matrix of sigma points
+   sigma_skew          -   Sample diagonal component of skewness tensor
+   sigma_kurt          -   Sample diagonal component of kurtosis tensor
+'''
+
+def Evaluate_sample_statistics(sigma_points, weights):
+
+    n = sigma_points.shape[0]
+    # row_weights = []
+    # weights = weights.reshape(1, -1) # Convert to row vector (need testing)
+    # Mean
+    sigma_mean = np.sum(np.matmul(sigma_points, np.tile(weights,(n, 1))))
+    # Covariance
+    Z = (sigma_points - sigma_mean) 
+    # print(np.diag(np.transpose(weights)[:,:]))
+    sigma_cov = np.sum(Z * np.diag(np.array(np.transpose(weights))[0])* np.transpose(Z))
+    # Diagonal skewness
+    sigma_skew = np.sum(np.matmul(np.power(Z, 3), np.tile(weights,(n, 1))))
+    # Diagonal kurtosis
+    sigma_kurt = np.sum(np.matmul(np.power(Z, 4), np.tile(weights,(n, 1))))
+    return sigma_mean, sigma_cov, sigma_skew, sigma_kurt
diff --git a/src/generalized_unsented_transform/generalized_unscented_transform.py b/src/generalized_unsented_transform/generalized_unscented_transform.py
new file mode 100644
index 0000000000000000000000000000000000000000..3800af8919943d7e97b57bd2e4ae758e7938494f
--- /dev/null
+++ b/src/generalized_unsented_transform/generalized_unscented_transform.py
@@ -0,0 +1,134 @@
+from concurrent.futures.process import _MAX_WINDOWS_WORKERS
+import numpy as np
+from scipy.linalg import sqrtm
+from numpy.linalg import matrix_power, solve
+import warnings
+
+
+'''
+mu          -   Mean of random vector
+P           -   Covariance matrix
+x_skew      -   Vector of diagonal components of the skewness tensor
+x_kurt      -   Vector of diagonal components of the kurtosis tensor
+lb          -   Vector of lower bound of the state
+ub          -   Vector of upper bound of the state
+
+OUTPUTS
+
+x           -   Matrix of sigma points
+weights     -   Vector of weights corresponding to each sigma point
+s           -   Vector of proportionality constants used to generate
+                the sigma points
+'''
+def generalized_ut(mu, P, x_skew=None, x_kurt=None, lb=None, ub=None):
+
+    # Get the number of states
+    n = len(mu)
+    # Evaluate the matrix square root via singular value decomposition
+    # U1, S, Vh = np.linalg.svd(P)
+    # C = U1 * np.diag(np.sqrt(np.diag(S))) * U1
+    C = sqrtm(P)
+    # Handle the arguments for skewness and kurtosis
+    if x_skew == None:    # If no diagonal component of skewness is specified
+        warnings.warn('No skewness specified: Gaussian skewness and kurtosis is assumed')
+        x_skew = 0*mu          # Assume gaussian skewness if not provided
+        x_kurt = 3*np.ones(n)   # Assume gaussian diagonal kurtosis in turn
+    
+    if x_kurt == None:    # If no diagonal component of kurtosis is specified
+        warnings.warn('No kurtosis specified: kurtosis is selected to satisfy skewness kurtosis relationship')
+        # Manually ensure kurtosis skew relationship is satisfied
+        x_kurt = matrix_power(C, 4) * matrix_power(solve((matrix_power(C, 3)), x_skew), 2)      
+        x_kurt = 1.1 * x_kurt
+    
+    # Handle when specified kurtosis violates skewness kurtosis relationship
+    minkurt = matrix_power(C, 4) * matrix_power(solve((matrix_power(C, 3)), x_skew), 2)
+
+    if (np.sum(x_kurt < minkurt)):
+        warnings.warn('Bad Human Error: Kurtosis does not correspond to a distribution')
+        for i in range(len(x_kurt)):
+            if x_kurt[i] < minkurt[i]:
+                x_kurt[i] = 1.001* minkurt[i]
+        
+    # Handle the arguments for lower bounds and upper bounds
+    if lb == None: # If lower bound is not specified manually set lower bound as -inf
+        lb = -np.inf * np.ones(n)
+
+    if ub == None: # If lower bound is not specified manually set lower bound as -inf
+        ub = np.inf * np.ones(n)
+
+    # Calculate parameters u and v
+    u = 0.5* ( -(solve(matrix_power(C, 3), x_skew) ) 
+        + np.sqrt(4 * solve(matrix_power(C, 4), x_kurt) 
+        - 3 * matrix_power(solve(matrix_power(C, 3) , x_skew), 2)))
+    v = u + solve(matrix_power(C, 3), x_skew)
+    # Generate the sigma points
+    x0 = mu
+    x1 = mu - C * np.diag(u)
+    x2 = mu + C * np.diag(v)
+
+    # # --------------- This section handles the constraints  --------------- 
+    # Flag_constrain = 0     # Default flag to enforce constraint
+    # # Check if mean violates constraints
+    # if np.subtract(mu, lb).min() < 0 or np.subtract(mu, lb).min() < 0:
+    #     Flag_constrain = 1  # Set flag to avoid enforcing state constraints
+    #     warnings.warn('Unable fo enforce constraints: one or more of the mean does not satisfy lb < mean < ub')
+
+    # if Flag_constrain == 0:
+    #     theta = 0.9;    # Default value of user defined slack parameter
+        
+    #     # Ensure lower bound 'lb' is not violated
+    #     Temp1 = np.subtract(np.hstack((x1, x2)), lb)
+    #     L1 = np.nonzero(Temp1.min() < 0)   # Find the location of sigma points that violate the lower bound
+    #     Flag_calc = 0;      # Flag that determines if skewness can be matched
+    #     print(u)
+    #     print(v)
+    #     print(n)
+    #     print(L1)
+    #     for i in range(len(L1)):
+    #         if L1[i] <= n:
+    #             # Recalculate 'u' to satisfy lower bound 'lb'
+    #             u[L1[i]] = theta * np.min(np.abs(np.subtract(mu, lb) / C[:, L1[i]]))
+    #         else:
+    #             # Recalculate 'v' to satisfy lower bound 'lb'
+    #             print(L1[i])
+    #             v[L1[i] - n] = theta * np.min(np.abs(np.subtract(mu, lb) / C[:, L1[i]-n]))
+    #             Flag_calc = 1   # Set flag
+
+    #     # Regenerate the sigma points
+    #     x1 = mu - C * np.diag(u)
+    #     x2 = mu + C * np.diag(v)
+        
+    #     #     Ensure upper bound 'ub' is not violated
+    #     Temp2 = ub  - np.hstack((x1, x2))
+    #     L2 = np.nonzero(min(Temp2) < 0)    # Find the location of sigma points that
+    #                                        # violate the upper bound
+    #     for i in range(len(L2)): 
+    #         if L2(i) <= n:
+    #             # Recalculate 'u' to satisfy upper bound 'ub'
+    #             u[L2[i]] = theta * np.min(np.abs(np.subtract(mu, lb) / C[:, L1[i]]))
+    #         else:
+    #             # Recalculate 'v' to satisfy upper bound 'ub'
+    #             v[L2[i] - v] = theta * np.min(np.abs(np.subtract(ub, mu) / C[:, L2[i]-n]))
+    #             Flag_calc = 1   # Set flag
+
+    #     if Flag_calc == 0:
+    #         # Now recalculate parameter 'v' to match diagonal componen of 
+    #         # skewness tensor because it was 'v' was not previously redefined
+    #         v = u + np.solve(matrix_power(C, 3), x_skew)  # only done of v was not redefined
+        
+    #     # Regenerate the sigma points to reflect any change in 'u' or 'v'
+    #     x1 = mu - C * np.diag(u)
+    #     x2 = mu + C * np.diag(v)
+
+    # Recalculate weights to reflect any change in 'u' or 'v'
+    w2 = np.ones(n) / v  / np.add(u, v)
+    w1 = w2 * v / u
+    # Output sigma point values
+    x = np.hstack((x0, x1, x2))
+    w0 = 1 - np.sum(np.vstack((w1, w2)), axis = 0)
+    weights = np.transpose(np.hstack((w0, np.transpose(w1), np.transpose(w2))))
+    # s = np.vstack((u, v))
+    # print(x)
+    # print(weights)
+    # print(s)
+    return x, weights
\ No newline at end of file
diff --git a/src/generalized_unsented_transform/random_variable_test.py b/src/generalized_unsented_transform/random_variable_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..eda6df0a3ee6845a7970943c985995059a084e35
--- /dev/null
+++ b/src/generalized_unsented_transform/random_variable_test.py
@@ -0,0 +1,42 @@
+import numpy as np
+from generalized_unscented_transform import generalized_ut
+from evaluate_from_sigma_points import Evaluate_sample_statistics
+
+
+def quadratic_transform(sigma_points):
+    sigma_points = np.array(sigma_points)[0]
+    y = []
+    for x in sigma_points:
+        y.append(3*x + 2*(x**2))
+    y = np.matrix(y)
+    return y
+
+
+# Gaussian case
+def test_Gaussian():
+    m1 = 1
+    m2 = 4
+    m3 = 0
+    m4 = 48
+    sigma_points, weights = generalized_ut(np.matrix([m1]), np.matrix([m2]), np.matrix([m3]), np.matrix([m4]))
+    sigma_mean, sigma_cov, sigma_skew, sigma_kurt = Evaluate_sample_statistics(quadratic_transform(sigma_points), weights)
+    print(sigma_mean)
+    print(sigma_cov)
+    print(sigma_skew)
+    print(sigma_kurt)
+
+def test_Exp():
+    m1 = 0.5
+    m2 = 0.25
+    m3 = 0.25
+    m4 = 0.5625
+    sigma_points, weights = generalized_ut(np.matrix([m1]), np.matrix([m2]), np.matrix([m3]), np.matrix([m4]))
+    sigma_mean, sigma_cov, sigma_skew, sigma_kurt = Evaluate_sample_statistics(quadratic_transform(sigma_points), weights)
+    print(sigma_mean)
+    print(sigma_cov)
+    print(sigma_skew)
+    print(sigma_kurt)
+
+
+if __name__ == "__main__":
+    test_Exp()