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I can see there are implementations of feature descriptors like SIFT, SURF in OpenCV but they extract sparse key points on the images. I want to do a dense feature matching in two images.

My approach: I can fetch the keypoints on the images this way and now I need to compute the descriptors of these keypoints. step_size = 2 keypoint = [cv2.KeyPoint(x, y, step_size) for y in range(0, img1.shape[0], step_size) for x in range(0, img1.shape[1], step_size)]

I can see there are implementations of feature descriptors like SIFT, SURF in OpenCV but they extract sparse key points on the images. I want to do a dense feature matching in two images.

My approach: I can fetch the keypoints on the images this way and now I need to compute the descriptors of these keypoints. step_size = 2 keypoint = [cv2.KeyPoint(x, y, step_size) for y in range(0, img1.shape[0], step_size) for x in range(0, img1.shape[1], step_size)]

I can see there are implementations of feature descriptors like SIFT, SURF in OpenCV but they extract sparse key points on the images. I want to do a dense feature matching in two images.

My approach: I can fetch the keypoints on the images this way and now I need to compute the descriptors of these keypoints. keypoints.

step_size = 2
keypoint = [cv2.KeyPoint(x, y, step_size) for y in range(0, img1.shape[0], step_size) 
                                    for x in range(0, img1.shape[1], step_size)]
step_size)]

I can see there are implementations of feature descriptors like SIFT, SURF in OpenCV but they extract sparse key points on the images. I want to do a dense feature matching in two images.

My approach: I can fetch the keypoints on the images this way and now I need to compute the descriptors of these keypoints.

step_size = 2
keypoint = [cv2.KeyPoint(x, y, step_size) for y in range(0, img1.shape[0], step_size) 
 for x in range(0, img1.shape[1], step_size)]