Normalize line endings and whitespace
This commit is contained in:
OpenCV Buildbot
committed by
Andrey Kamaev
Andrey Kamaev
parent
69020da607
commit
04384a71e4
194
samples/python2/lk_track.py
Normal file → Executable file
194
samples/python2/lk_track.py
Normal file → Executable file
@@ -1,97 +1,97 @@
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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: video_src = sys.argv[1]
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except: 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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'''
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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: video_src = sys.argv[1]
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except: 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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