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Open Source Computer Vision Library
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101 lines
3.0 KiB
101 lines
3.0 KiB
#!/usr/bin/env python |
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''' |
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Lucas-Kanade tracker |
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==================== |
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Lucas-Kanade sparse optical flow demo. Uses goodFeaturesToTrack |
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for track initialization and back-tracking for match verification |
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between frames. |
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Usage |
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----- |
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lk_track.py [<video_source>] |
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Keys |
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---- |
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ESC - exit |
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''' |
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import numpy as np |
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import cv2 |
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import video |
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from common import anorm2, draw_str |
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from time import clock |
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lk_params = dict( winSize = (15, 15), |
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maxLevel = 2, |
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criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)) |
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feature_params = dict( maxCorners = 500, |
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qualityLevel = 0.3, |
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minDistance = 7, |
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blockSize = 7 ) |
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class App: |
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def __init__(self, video_src): |
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self.track_len = 10 |
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self.detect_interval = 5 |
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self.tracks = [] |
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self.cam = video.create_capture(video_src) |
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self.frame_idx = 0 |
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def run(self): |
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while True: |
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ret, frame = self.cam.read() |
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frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) |
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vis = frame.copy() |
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if len(self.tracks) > 0: |
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img0, img1 = self.prev_gray, frame_gray |
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p0 = np.float32([tr[-1] for tr in self.tracks]).reshape(-1, 1, 2) |
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p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params) |
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p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params) |
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d = abs(p0-p0r).reshape(-1, 2).max(-1) |
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good = d < 1 |
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new_tracks = [] |
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for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good): |
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if not good_flag: |
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continue |
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tr.append((x, y)) |
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if len(tr) > self.track_len: |
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del tr[0] |
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new_tracks.append(tr) |
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cv2.circle(vis, (x, y), 2, (0, 255, 0), -1) |
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self.tracks = new_tracks |
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cv2.polylines(vis, [np.int32(tr) for tr in self.tracks], False, (0, 255, 0)) |
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draw_str(vis, (20, 20), 'track count: %d' % len(self.tracks)) |
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if self.frame_idx % self.detect_interval == 0: |
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mask = np.zeros_like(frame_gray) |
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mask[:] = 255 |
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for x, y in [np.int32(tr[-1]) for tr in self.tracks]: |
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cv2.circle(mask, (x, y), 5, 0, -1) |
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p = cv2.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params) |
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if p is not None: |
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for x, y in np.float32(p).reshape(-1, 2): |
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self.tracks.append([(x, y)]) |
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self.frame_idx += 1 |
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self.prev_gray = frame_gray |
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cv2.imshow('lk_track', vis) |
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ch = 0xFF & cv2.waitKey(1) |
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if ch == 27: |
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break |
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def main(): |
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import sys |
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try: |
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video_src = sys.argv[1] |
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except: |
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video_src = 0 |
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print __doc__ |
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App(video_src).run() |
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cv2.destroyAllWindows() |
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if __name__ == '__main__': |
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main()
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