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Open Source Computer Vision Library
https://opencv.org/
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86 lines
2.3 KiB
86 lines
2.3 KiB
#/usr/bin/env python |
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import numpy as np |
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import cv2 |
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import video |
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help_message = ''' |
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USAGE: opt_flow.py [<video_source>] |
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Keys: |
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1 - toggle HSV flow visualization |
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2 - toggle glitch |
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''' |
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def draw_flow(img, flow, step=16): |
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h, w = img.shape[:2] |
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y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1) |
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fx, fy = flow[y,x].T |
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lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2) |
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lines = np.int32(lines + 0.5) |
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vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) |
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cv2.polylines(vis, lines, 0, (0, 255, 0)) |
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for (x1, y1), (x2, y2) in lines: |
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cv2.circle(vis, (x1, y1), 1, (0, 255, 0), -1) |
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return vis |
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def draw_hsv(flow): |
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h, w = flow.shape[:2] |
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fx, fy = flow[:,:,0], flow[:,:,1] |
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ang = np.arctan2(fy, fx) + np.pi |
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v = np.sqrt(fx*fx+fy*fy) |
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hsv = np.zeros((h, w, 3), np.uint8) |
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hsv[...,0] = ang*(180/np.pi/2) |
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hsv[...,1] = 255 |
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hsv[...,2] = np.minimum(v*4, 255) |
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bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) |
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return bgr |
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def warp_flow(img, flow): |
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h, w = flow.shape[:2] |
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flow = -flow |
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flow[:,:,0] += np.arange(w) |
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flow[:,:,1] += np.arange(h)[:,np.newaxis] |
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res = cv2.remap(img, flow, None, cv2.INTER_LINEAR) |
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return res |
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if __name__ == '__main__': |
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import sys |
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print help_message |
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try: fn = sys.argv[1] |
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except: fn = 0 |
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cam = video.create_capture(fn) |
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ret, prev = cam.read() |
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prevgray = cv2.cvtColor(prev, cv2.COLOR_BGR2GRAY) |
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show_hsv = False |
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show_glitch = False |
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cur_glitch = prev.copy() |
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while True: |
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ret, img = cam.read() |
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
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flow = cv2.calcOpticalFlowFarneback(prevgray, gray, 0.5, 3, 15, 3, 5, 1.2, 0) |
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prevgray = gray |
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cv2.imshow('flow', draw_flow(gray, flow)) |
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if show_hsv: |
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cv2.imshow('flow HSV', draw_hsv(flow)) |
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if show_glitch: |
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cur_glitch = warp_flow(cur_glitch, flow) |
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cv2.imshow('glitch', cur_glitch) |
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ch = 0xFF & cv2.waitKey(5) |
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if ch == 27: |
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break |
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if ch == ord('1'): |
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show_hsv = not show_hsv |
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print 'HSV flow visualization is', ['off', 'on'][show_hsv] |
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if ch == ord('2'): |
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show_glitch = not show_glitch |
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if show_glitch: |
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cur_glitch = img.copy() |
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print 'glitch is', ['off', 'on'][show_glitch] |
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cv2.destroyAllWindows() |
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