How do you use Akaze in Open CV on python

I searched for the same tutorial and found out the tutorial is given in 3 alternate languages C++, Python & Java. There are 3 hyperlinks for them before the start of code area.

Try this [ https://docs.opencv.org/3.4/db/d70/tutorial_akaze_matching.html ]

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.