tracker_utils.py 文件源码

def track(self, im0, im1, p0): 
        if p0 is None or not len(p0): 
            return np.array([])

        fflow = cv2.calcOpticalFlowFarneback(im0, im1, **self.farneback_params_)
        fflow = cv2.medianBlur(fflow, 5)

        # Initialize forward flow and propagated points
        p1 = np.ones(shape=p0.shape) * np.nan
        flow_p0 = np.ones(shape=p0.shape) * np.nan
        flow_good = np.ones(shape=p0.shape, dtype=bool)

        # Check finite value for pts, and within image bounds
        valid0 = finite_and_within_bounds(p0, im0.shape)

        # Determine finite flow at points
        xys0 = p0[valid0].astype(int)
        flow_p0[valid0] = fflow[xys0[:,1], xys0[:,0]]

        # Propagate
        p1 = p0 + flow_p0

        # FWD-BWD check
        if self.fb_check_: 
            # Initialize reverse flow and propagated points
            p0r = np.ones(shape=p0.shape) * np.nan
            flow_p1 = np.ones(shape=p0.shape) * np.nan

            rflow = cv2.calcOpticalFlowFarneback(im1, im0, **self.farneback_params_)
            rflow = cv2.medianBlur(rflow, 5)

            # Check finite value for pts, and within image bounds
            valid1 = finite_and_within_bounds(p1, im0.shape)

            # Determine finite flow at points
            xys1 = p1[valid1].astype(int)
            flow_p1[valid1] = rflow[xys1[:,1], xys1[:,0]]

            # Check diff
            p0r = p1 + flow_p1
            fb_good = (np.fabs(p0r-p0) < 3).all(axis=1)

            # Set only good flow 
            flow_p0[~fb_good] = np.nan
            p1 = p0 + flow_p0

        return p1