features.py 文件源码

def track_features(self, image_ref, image_cur):
        """Track Features

        Parameters
        ----------
        image_ref : np.array
            Reference image
        image_cur : np.array
            Current image

        """
        # Re-detect new feature points if too few
        if len(self.tracks_tracking) < self.min_nb_features:
            self.tracks_tracking = []  # reset alive feature tracks
            self.detect(image_ref)

        # LK parameters
        win_size = (21, 21)
        max_level = 2
        criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 30, 0.03)

        # Convert reference keypoints to numpy array
        self.kp_ref = self.last_keypoints()

        # Perform LK tracking
        lk_params = {"winSize": win_size,
                     "maxLevel": max_level,
                     "criteria": criteria}
        self.kp_cur, statuses, err = cv2.calcOpticalFlowPyrLK(image_ref,
                                                              image_cur,
                                                              self.kp_ref,
                                                              None,
                                                              **lk_params)

        # Filter out bad matches (choose only good keypoints)
        status = statuses.reshape(statuses.shape[0])
        still_alive = []
        for i in range(len(status)):
            if status[i] == 1:
                track_id = self.tracks_tracking[i]
                still_alive.append(track_id)
                kp = KeyPoint(self.kp_cur[i], 0)
                self.tracks[track_id].update(self.frame_id, kp)

        self.tracks_tracking = still_alive