Upload New File- Convolutes the kernel for blurring out images

extraction/convolution.py

+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)
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