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import os
import json
from PIL import Image
import numpy as np
import torch
from torch.utils.data import Dataset
class CARLA_Data(Dataset):
def __init__(self, root, config):
self.config = config
self.seq_len = config.seq_len
self.pred_len = config.pred_len
self.front = []
self.left = []
self.right = []
self.rear = []
self.x = []
self.y = []
self.x_command = []
self.y_command = []
self.theta = []
self.steer = []
self.throttle = []
self.brake = []
self.command = []
self.velocity = []
for sub_root in root:
preload_file = os.path.join(sub_root, 'rg_cilrs_pl_'+str(self.seq_len)+'_'+str(self.pred_len)+'.npy')
# dump to npy if no preload
if not os.path.exists(preload_file):
preload_front = []
preload_left = []
preload_right = []
preload_rear = []
preload_x = []
preload_y = []
preload_x_command = []
preload_y_command = []
preload_theta = []
preload_steer = []
preload_throttle = []
preload_brake = []
preload_command = []
preload_velocity = []
# list sub-directories in root
root_files = os.listdir(sub_root)
routes = [folder for folder in root_files if not os.path.isfile(os.path.join(sub_root,folder))]
for route in routes:
route_dir = os.path.join(sub_root, route)
print(route_dir)
# subtract final frames (pred_len) since there are no future waypoints
# first frame of sequence not used
num_seq = (len(os.listdir(route_dir+"/rgb_front/"))-self.pred_len-2)//self.seq_len
for seq in range(num_seq):
fronts = []
lefts = []
rights = []
rears = []
xs = []
ys = []
thetas = []
# read files sequentially (past and current frames)
for i in range(self.seq_len):
# images
filename = f"{str(seq*self.seq_len+1+i).zfill(4)}.png"
fronts.append(route_dir+"/rgb_front/"+filename)
lefts.append(route_dir+"/rgb_left/"+filename)
rights.append(route_dir+"/rgb_right/"+filename)
rears.append(route_dir+"/rgb_rear/"+filename)
# position
with open(route_dir + f"/measurements/{str(seq*self.seq_len+1+i).zfill(4)}.json", "r") as read_file:
data = json.load(read_file)
xs.append(data['x'])
ys.append(data['y'])
thetas.append(data['theta'])
# get control value of final frame in sequence
preload_x_command.append(data['x_command'])
preload_y_command.append(data['y_command'])
preload_steer.append(data['steer'])
preload_throttle.append(data['throttle'])
preload_brake.append(data['brake'])
preload_command.append(data['command'])
preload_velocity.append(data['speed'])
# read files sequentially (future frames)
for i in range(self.seq_len, self.seq_len + self.pred_len):
# position
with open(route_dir + f"/measurements/{str(seq*self.seq_len+1+i).zfill(4)}.json", "r") as read_file:
data = json.load(read_file)
xs.append(data['x'])
ys.append(data['y'])
# fix for theta=nan in some measurements
if np.isnan(data['theta']):
thetas.append(0)
else:
thetas.append(data['theta'])
preload_front.append(fronts)
preload_left.append(lefts)
preload_right.append(rights)
preload_rear.append(rears)
preload_x.append(xs)
preload_y.append(ys)
preload_theta.append(thetas)
# dump to npy
preload_dict = {}
preload_dict['front'] = preload_front
preload_dict['left'] = preload_left
preload_dict['right'] = preload_right
preload_dict['rear'] = preload_rear
preload_dict['x'] = preload_x
preload_dict['y'] = preload_y
preload_dict['x_command'] = preload_x_command
preload_dict['y_command'] = preload_y_command
preload_dict['theta'] = preload_theta
preload_dict['steer'] = preload_steer
preload_dict['throttle'] = preload_throttle
preload_dict['brake'] = preload_brake
preload_dict['command'] = preload_command
preload_dict['velocity'] = preload_velocity
np.save(preload_file, preload_dict)
# load from npy if available
preload_dict = np.load(preload_file, allow_pickle=True)
self.front += preload_dict.item()['front']
self.left += preload_dict.item()['left']
self.right += preload_dict.item()['right']
self.rear += preload_dict.item()['rear']
self.x += preload_dict.item()['x']
self.y += preload_dict.item()['y']
self.x_command += preload_dict.item()['x_command']
self.y_command += preload_dict.item()['y_command']
self.theta += preload_dict.item()['theta']
self.steer += preload_dict.item()['steer']
self.throttle += preload_dict.item()['throttle']
self.brake += preload_dict.item()['brake']
self.command += preload_dict.item()['command']
self.velocity += preload_dict.item()['velocity']
print("Preloading " + str(len(preload_dict.item()['front'])) + " sequences from " + preload_file)
def __len__(self):
"""Returns the length of the dataset. """
return len(self.front)
def __getitem__(self, index):
"""Returns the item at index idx. """
data = dict()
data['fronts'] = []
data['lefts'] = []
data['rights'] = []
data['rears'] = []
seq_fronts = self.front[index]
seq_lefts = self.left[index]
seq_rights = self.right[index]
seq_rears = self.rear[index]
seq_x = self.x[index]
seq_y = self.y[index]
seq_theta = self.theta[index]
for i in range(self.seq_len):
data['fronts'].append(torch.from_numpy(np.array(
scale_and_crop_image(Image.open(seq_fronts[i]), scale=self.config.scale, crop=self.config.input_resolution))))
if not self.config.ignore_sides:
data['lefts'].append(torch.from_numpy(np.array(
scale_and_crop_image(Image.open(seq_lefts[i]), scale=self.config.scale, crop=self.config.input_resolution))))
data['rights'].append(torch.from_numpy(np.array(
scale_and_crop_image(Image.open(seq_rights[i]), scale=self.config.scale, crop=self.config.input_resolution))))
if not self.config.ignore_rear:
data['rears'].append(torch.from_numpy(np.array(
scale_and_crop_image(Image.open(seq_rears[i]), scale=self.config.scale, crop=self.config.input_resolution))))
# fix for theta=nan in some measurements
if np.isnan(seq_theta[i]):
seq_theta[i] = 0.
ego_x = seq_x[i]
ego_y = seq_y[i]
ego_theta = seq_theta[i]
# lidar and waypoint processing to local coordinates
waypoints = []
for i in range(self.seq_len + self.pred_len):
# waypoint is the transformed version of the origin in local coordinates
# we use 90-theta instead of theta
# LBC code uses 90+theta, but x is to the right and y is downwards here
local_waypoint = transform_2d_points(np.zeros((1,3)),
np.pi/2-seq_theta[i], -seq_x[i], -seq_y[i], np.pi/2-ego_theta, -ego_x, -ego_y)
waypoints.append(tuple(local_waypoint[0,:2]))
data['waypoints'] = waypoints
# convert x_command, y_command to local coordinates
# taken from LBC code (uses 90+theta instead of theta)
R = np.array([
[np.cos(np.pi/2+ego_theta), -np.sin(np.pi/2+ego_theta)],
[np.sin(np.pi/2+ego_theta), np.cos(np.pi/2+ego_theta)]
])
local_command_point = np.array([self.x_command[index]-ego_x, self.y_command[index]-ego_y])
local_command_point = R.T.dot(local_command_point)
data['target_point'] = tuple(local_command_point)
data['steer'] = self.steer[index]
data['throttle'] = self.throttle[index]
data['brake'] = self.brake[index]
data['command'] = self.command[index]
data['velocity'] = self.velocity[index]
return data
def scale_and_crop_image(image, scale=1, crop=256):
"""
Scale and crop a PIL image, returning a channels-first numpy array.
"""
(width, height) = (int(image.width // scale), int(image.height // scale))
im_resized = image.resize((width, height))
image = np.asarray(im_resized)
start_x = height//2 - crop//2
start_y = width//2 - crop//2
cropped_image = image[start_x:start_x+crop, start_y:start_y+crop]
cropped_image = np.transpose(cropped_image, (2,0,1))
return cropped_image
def transform_2d_points(xyz, r1, t1_x, t1_y, r2, t2_x, t2_y):
"""
Build a rotation matrix and take the dot product.
"""
# z value to 1 for rotation
xy1 = xyz.copy()
xy1[:,2] = 1
c, s = np.cos(r1), np.sin(r1)
r1_to_world = np.matrix([[c, s, t1_x], [-s, c, t1_y], [0, 0, 1]])
# np.dot converts to a matrix, so we explicitly change it back to an array
world = np.asarray(r1_to_world @ xy1.T)
c, s = np.cos(r2), np.sin(r2)
r2_to_world = np.matrix([[c, s, t2_x], [-s, c, t2_y], [0, 0, 1]])
world_to_r2 = np.linalg.inv(r2_to_world)
out = np.asarray(world_to_r2 @ world).T
# reset z-coordinate
out[:,2] = xyz[:,2]
return out