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Open Source Computer Vision Library
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113 lines
3.7 KiB
113 lines
3.7 KiB
13 years ago
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'''
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Lucas-Kanade homography 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. Finds homography between reference and current views.
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Usage
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-----
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lk_homography.py [<video_source>]
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Keys
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----
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ESC - exit
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SPACE - start tracking
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r - toggle RANSAC
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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 draw_str
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lk_params = dict( winSize = (19, 19),
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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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derivLambda = 0.0 )
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feature_params = dict( maxCorners = 1000,
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qualityLevel = 0.01,
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minDistance = 8,
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blockSize = 19 )
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def checkedTrace(img0, img1, p0, back_threshold = 1.0):
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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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status = d < back_threshold
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return p1, status
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green = (0, 255, 0)
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red = (0, 0, 255)
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class App:
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def __init__(self, video_src):
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self.cam = video.create_capture(video_src)
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self.p0 = None
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self.use_ransac = True
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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 self.p0 is not None:
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p2, trace_status = checkedTrace(self.gray1, frame_gray, self.p1)
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self.p1 = p2[trace_status].copy()
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self.p0 = self.p0[trace_status].copy()
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self.gray1 = frame_gray
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if len(self.p0) < 4:
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self.p0 = None
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continue
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H, status = cv2.findHomography(self.p0, self.p1, (0, cv2.RANSAC)[self.use_ransac], 10.0)
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h, w = frame.shape[:2]
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overlay = cv2.warpPerspective(self.frame0, H, (w, h))
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vis = cv2.addWeighted(vis, 0.5, overlay, 0.5, 0.0)
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for (x0, y0), (x1, y1), good in zip(self.p0[:,0], self.p1[:,0], status[:,0]):
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if good:
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cv2.line(vis, (x0, y0), (x1, y1), (0, 128, 0))
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cv2.circle(vis, (x1, y1), 2, (red, green)[good], -1)
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draw_str(vis, (20, 20), 'track count: %d' % len(self.p1))
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if self.use_ransac:
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draw_str(vis, (20, 40), 'RANSAC')
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else:
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p = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
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if p is not None:
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for x, y in p[:,0]:
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cv2.circle(vis, (x, y), 2, green, -1)
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draw_str(vis, (20, 20), 'feature count: %d' % len(p))
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cv2.imshow('lk_homography', vis)
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ch = cv2.waitKey(1)
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if ch == 27:
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break
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if ch == ord(' '):
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self.frame0 = frame.copy()
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self.p0 = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
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if self.p0 is not None:
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self.p1 = self.p0
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self.gray0 = frame_gray
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self.gray1 = frame_gray
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if ch == ord('r'):
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self.use_ransac = not self.use_ransac
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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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if __name__ == '__main__':
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main()
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