Let us view the original image and the one with key-points marked to see what key-points were extracted.
Note:
cv2.drawKeypoint(input_image, input_image_key_points, output_image, color) method of OpenCV returns the output_image drawn with its key-points in given color, given the input_image and its key-points input_image_key_points, color as input arguments.Get the img_right_kp, the resultant of plotting img_right with its key-points kp1 drawn using cv2.drawKeypoints.
img_right_kp = cv2.<< your code comes here >>(img_right, kp1, np.array([]), color=(0,0, 255))
Observe, we pass np.array([]) in place of output image. The np.array([]) will be the output image with key-points drawn. If you want to draw the key-points to be drawn on the same input image, you could replace it with that image. In order to maintain modularity, let's pass a new empty NumPy array.
Get the img_left_kp, the resultant of plotting img_left with its key-points kp2 drawn in
cv2.drawKeypoints.
img_left_kp = cv2.<< your code comes here >>(img_left, kp2, np.array([]), color=(0,0, 255))
Visualize the img_left_kp and img_right_kp side-by-side.
plt.figure(figsize=(30,20))
plt.subplot(1,2,1)
plt.imshow(fixColor(img_left_kp ))
plt.subplot(1,2,2)
plt.imshow(fixColor(img_right_kp ))
plt.tight_layout()
Note - Having trouble with the assessment engine? Follow the steps listed here
Loading comments...