How do you use Akaze in Open CV on python

Question

I found example in c++: http://docs.opencv.org/3.0-beta/doc/tutorials/features2d/akaze_matching/akaze_matching.html

But there isn't any example in python showing how to use this feature detector (also couldn't find anything more in documentation about AKAZE there is ORB SIFT, SURF, etc but not what I'm looking for) http://docs.opencv.org/3.1.0/db/d27/tutorial_py_table_of_contents_feature2d.html#gsc.tab=0

Can someone could share or show me where I can find information how to match images in python with akaze?

Answer 1

I am not sure on where to find it, the way I made it work was through this function which used the Brute Force matcher:

def kaze_match(im1_path, im2_path):
    # load the image and convert it to grayscale
    im1 = cv2.imread(im1_path)
    im2 = cv2.imread(im2_path)
    gray1 = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
    gray2 = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)    

    # initialize the AKAZE descriptor, then detect keypoints and extract
    # local invariant descriptors from the image
    detector = cv2.AKAZE_create()
    (kps1, descs1) = detector.detectAndCompute(gray1, None)
    (kps2, descs2) = detector.detectAndCompute(gray2, None)

    print("keypoints: {}, descriptors: {}".format(len(kps1), descs1.shape))
    print("keypoints: {}, descriptors: {}".format(len(kps2), descs2.shape))    

    # Match the features
    bf = cv2.BFMatcher(cv2.NORM_HAMMING)
    matches = bf.knnMatch(descs1,descs2, k=2)    # typo fixed

    # Apply ratio test
    good = []
    for m,n in matches:
        if m.distance < 0.9*n.distance:
            good.append([m])

    # cv2.drawMatchesKnn expects list of lists as matches.
    im3 = cv2.drawMatchesKnn(im1, kps1, im2, kps2, good[1:20], None, flags=2)
    cv2.imshow("AKAZE matching", im3)
    cv2.waitKey(0) 

Remember that the feature vectors are binary vectors. Therefore, the similarity is based on the Hamming distance, rather than the commonly used L2 norm or Euclidean distance if you will.