1 去除多余模块的
#-*- coding:utf-8 -*- ''' Lucas-Kanade tracker ==================== Lucas-Kanade sparse optical flow demo. Uses goodFeaturesToTrack for track initialization and back-tracking for match verification between frames. Lucas Kanade稀疏光流Demo。使用goodfeaturestotrack 用于轨迹初始化和跟踪跟踪的匹配验证 帧间。 Usage ----- lk_track.py [<video_source>] Keys ---- ESC - exit ''' # Python 2/3 compatibility import numpy as np import cv2 lk_params = dict( winSize = (15, 15), maxLevel = 3, criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)) feature_params = dict( maxCorners = 800, qualityLevel = 0.3, minDistance = 7, blockSize = 7 ) class App: def __init__(self, video_src): self.track_len = 10 #跟踪轨迹长度10 self.detect_interval = 5 self.tracks = [] #储存跟踪点 self.cam = cv2.VideoCapture(video_src) self.frame_idx = 0 def run(self): while True: ret, frame = self.cam.read() frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) vis = frame.copy() if len(self.tracks) > 0: img0, img1 = self.prev_gray, frame_gray p0 = np.float32([tr[-1] for tr in self.tracks]).reshape(-1, 1, 2) p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params) p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params) d = abs(p0-p0r).reshape(-1, 2).max(-1) good = d < 1 new_tracks = [] for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good): if not good_flag: continue tr.append((x, y)) if len(tr) > self.track_len: del tr[0] new_tracks.append(tr) cv2.circle(vis, (x, y), 2, (0, 255, 0), -1) self.tracks = new_tracks cv2.polylines(vis, [np.int32(tr) for tr in self.tracks], False, (0, 255, 0)) # draw_str(vis, (20, 20), 'track count: %d' % len(self.tracks)) if self.frame_idx % self.detect_interval == 0: mask = np.zeros_like(frame_gray) mask[:] = 255 for x, y in [np.int32(tr[-1]) for tr in self.tracks]: cv2.circle(mask, (x, y), 5, 0, -1) p = cv2.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params) if p is not None: for x, y in np.float32(p).reshape(-1, 2): self.tracks.append([(x, y)]) self.frame_idx += 1 self.prev_gray = frame_gray cv2.imshow('lk_track', vis) ch = cv2.waitKey(1) if ch == 27: break def main(): video_src = 'traffic.flv' App(video_src).run() cv2.destroyAllWindows() if __name__ == '__main__': main()
2。还原成普通函数
#-*- coding:utf-8 -*- import numpy as np import cv2 #ShiTomasi 角检测的参数 lk_params = dict( winSize = (15, 15), maxLevel = 3, criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)) # l-k 光流参数 feature_params = dict( maxCorners = 800, qualityLevel = 0.3, minDistance = 7, blockSize = 7 ) track_len = 10 # 跟踪轨迹长度10 detect_interval = 5 tracks = [] # 储存跟踪点 frame_idx = 0 # 1,定义一个对象，存储读取的视频 video_src = 'traffic.flv' cam = cv2.VideoCapture(video_src) while True: ret, frame = cam.read() frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) vis = frame.copy() if len(tracks) > 0: img0, img1 = prev_gray, frame_gray p0 = np.float32([tr[-1] for tr in tracks]).reshape(-1, 1, 2) p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params) p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params) d = abs(p0 - p0r).reshape(-1, 2).max(-1) good = d < 1 new_tracks = [] for tr, (x, y), good_flag in zip(tracks, p1.reshape(-1, 2), good): if not good_flag: continue tr.append((x, y)) if len(tr) > track_len: del tr[0] new_tracks.append(tr) cv2.circle(vis, (x, y), 2, (0, 255, 0), -1) tracks = new_tracks cv2.polylines(vis, [np.int32(tr) for tr in tracks], False, (0, 255, 0)) # draw_str(vis, (20, 20), 'track count: %d' % len(self.tracks)) if frame_idx % detect_interval == 0: mask = np.zeros_like(frame_gray) mask[:] = 255 for x, y in [np.int32(tr[-1]) for tr in tracks]: cv2.circle(mask, (x, y), 5, 0, -1) p = cv2.goodFeaturesToTrack(frame_gray, mask=mask, **feature_params) if p is not None: for x, y in np.float32(p).reshape(-1, 2): tracks.append([(x, y)]) frame_idx += 1 prev_gray = frame_gray cv2.imshow('lk_track', vis) ch = cv2.waitKey(1) if ch == 27: break cv2.destroyAllWindows() cam.release()
3。效果图
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