sift_matcher.py 文件源码

def calculate_sift(self, last_frame, new_frame, last_kp=None):
        # find corresponding points in the input image and the template image
        bf = cv2.BFMatcher()
        matches = bf.knnMatch(self.descs[k], scene_desc, k=2)

        # Apply Lowe Ratio Test to the keypoints
        # this should weed out unsure matches
        good_keypoints = []
        for m, n in matches:
            if m.distance < self.good_thresh * n.distance:
                good_keypoints.append(m)

        # put keypoints from template image in template_pts
        # transform the keypoint data into arrays for homography check
        # grab precomputed points
        template_pts = np.float32(
            [self.kps[k][m.queryIdx].pt for m in good_keypoints]
        ).reshape(-1, 1, 2)

        # put corresponding keypoints from input image in scene_img_pts
        scene_img_pts = np.float32(
            [scene_kps[m.trainIdx].pt for m in good_keypoints]
        ).reshape(-1, 1, 2)

        # if we can't find any matching keypoints, bail
        # (probably the scene image was nonexistant/real bad)
        if scene_img_pts.shape[0] == 0:
            return None

        # use OpenCV to calculate optical flow
        new_frame_matched_features, status, error = cv2.calcOpticalFlowPyrLK(
            self.last_frame_gray,
            frame_gray,
            self.last_frame_features,
            None,
            **self.lk_params
        )

        self.publish_interframe_motion(
            self.last_frame_features,
            new_frame_matched_features,
            status,
            error
        )

        # save data for next frame
        self.store_as_last_frame(frame_gray)