trainer_matches.py 文件源码

def get_matches(self, train, corr):
        train_img = cv2.imread(train, 0)
        query_img = self.query
        # Initiate SIFT detector
        sift = cv2.xfeatures2d.SIFT_create()

        # find the keypoints and descriptors with SIFT
        kp1, des1 = sift.detectAndCompute(train_img, None)
        kp2, des2 = sift.detectAndCompute(query_img, None)

        # create BFMatcher object
        bf = cv2.BFMatcher()
        try:
            matches = bf.knnMatch(des1, des2, k=2)
        except cv2.error:
            return False
        good_matches = []
        cluster = []
        for m, n in matches:
            img2_idx = m.trainIdx
            img1_idx = m.queryIdx
            (x1, y1) = kp1[img1_idx].pt
            (x2, y2) = kp2[img2_idx].pt
            # print("Comare %d to %d and %d to %d" % (x1,x2,y1,y2))
            if m.distance < 0.8 * n.distance and y2 > self.yThreshold and x2 < self.xThreshold:
                good_matches.append([m])
                cluster.append([int(x2), int(y2)])
        if len(cluster) <= corr:
            return False
        self.kmeans = KMeans(n_clusters=1, random_state=0).fit(cluster)
        new_cluster = self.compare_distances(train_img, cluster)
        if len(new_cluster) == 0 or len(new_cluster) / len(cluster) < .5:
            return False
        img3 = cv2.drawMatchesKnn(
            train_img, kp1, query_img, kp2, good_matches, None, flags=2)
        if self._debug:
            self.images.append(img3)
            self.debug_matcher(img3)
        return True