Through the Sobel operator I have been able to determine the gradient magnitude of an image. I display this below: Now I wish to determine the gradient orientation. To do so, I am following this post, which makes use of the function cv2.phase. Following this, angles are hard coded a particular colour depending on the returned degrees of the function. My issue is that the values this function returns for me are between the range of 0 and 90 degrees. Consequently, I am getting an image consisting of only red and cyan. My code is as follows: # where gray_blur is a grayscale image of dimension 512 by 512 # 3x3 sobel filters for edge detection sobel_x = np.array([[ -1, 0, 1], [ -2, 0, 2], [ -1, 0, 1]]) sobel_y = np.array([[ -1, -2, -1], [ 0, 0, 0], [ 1, 2, 1]]) # Filter the blurred grayscale images using filter2D filtered_blurred_x = cv2.filter2D(gray_blur, -1, sobel_x) filtered_blurred_y = cv2.filter2D(gray_blur, -1, sobel_y) # Compute the orientation of the image orien = cv2.phase(np.array(filtered_blurred_x, np.float32), np.array(filtered_blurred_y, dtype=np.float32), angleInDegrees=True) image_map = np.zeros((orien.shape[0], orien.shape[1], 3), dtype=np.int16) # Define RGB colours red = np.array([255, 0, 0]) cyan = np.array([0, 255, 255]) green = np.array([0, 255, 0]) yellow = np.array([255, 255, 0]) # Set colours corresponding to angles for i in range(0, image_map.shape[0]): for j in range(0, image_map.shape[1]): if orien[j] < 90.0: image_map[i, j, :] = red elif orien[j] >= 90.0 and orien[j] < 180.0: image_map[i, j, :] = cyan elif orien[j] >= 180.0 and orien[j] < 270.0: image_map[i, j, :] = green elif orien[j] >= 270.0 and orien[j] < 360.0: image_map[i, j, :] = yellow # Display gradient orientation f, ax1 = plt.subplots(1, 1, figsize=(20,10)) ax1.set_title('gradient orientation') ax1.imshow(image_map) Which displays the image: Thanks in advance. Continue reading...
