OpenKNN.py

import numpy as np
import cv2

def CompareImage(img1,img2):
 sift = cv2.xfeatures2d.SURF_create()
 kp1, des1 = sift.detectAndCompute(img1, None)
 kp2, des2 = sift.detectAndCompute(img2, None)
 FLANN_INDEX_KDTREE = 0
 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
 search_params = dict(checks=50)

 flann = cv2.FlannBasedMatcher(index_params, search_params)
 matches = flann.knnMatch(des1, des2, k=2)
 FLANN_INDEX_KDTREE = 0
 MIN_MATCH_COUNT = 10
 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
 search_params = dict(checks=50)
 flann = cv2.FlannBasedMatcher(index_params, search_params)
 matches = flann.knnMatch(des1, des2, k=2)
 good = []
 for m, n in matches:
    if m.distance < 0.6 * n.distance:
        good.append(m)

 if len(good) > MIN_MATCH_COUNT:
    src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
    dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)

    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
    matchesMask = mask.ravel().tolist()
    return  [True,"%d/%d" % (len(good), MIN_MATCH_COUNT),matchesMask]
 else:
    matchesMask = None
    return  [False,"%d/%d" % (len(good), MIN_MATCH_COUNT),matchesMask]