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Open Source Computer Vision Library
https://opencv.org/
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122 lines
3.5 KiB
122 lines
3.5 KiB
#!/usr/bin/env python |
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''' |
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example to show optical flow estimation using DISOpticalFlow |
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USAGE: dis_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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3 - toggle spatial propagation of flow vectors |
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4 - toggle temporal propagation of flow vectors |
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ESC - exit |
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''' |
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# Python 2/3 compatibility |
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from __future__ import print_function |
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import numpy as np |
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import cv2 as cv |
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import video |
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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).astype(int) |
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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 = cv.cvtColor(img, cv.COLOR_GRAY2BGR) |
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cv.polylines(vis, lines, 0, (0, 255, 0)) |
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for (x1, y1), (x2, y2) in lines: |
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cv.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 = cv.cvtColor(hsv, cv.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 = cv.remap(img, flow, None, cv.INTER_LINEAR) |
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return res |
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def main(): |
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import sys |
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print(__doc__) |
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try: |
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fn = sys.argv[1] |
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except IndexError: |
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fn = 0 |
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cam = video.create_capture(fn) |
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ret, prev = cam.read() |
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prevgray = cv.cvtColor(prev, cv.COLOR_BGR2GRAY) |
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show_hsv = False |
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show_glitch = False |
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use_spatial_propagation = False |
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use_temporal_propagation = True |
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cur_glitch = prev.copy() |
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inst = cv.DISOpticalFlow.create(cv.DISOPTICAL_FLOW_PRESET_MEDIUM) |
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inst.setUseSpatialPropagation(use_spatial_propagation) |
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flow = None |
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while True: |
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ret, img = cam.read() |
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gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) |
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if flow is not None and use_temporal_propagation: |
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#warp previous flow to get an initial approximation for the current flow: |
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flow = inst.calc(prevgray, gray, warp_flow(flow,flow)) |
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else: |
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flow = inst.calc(prevgray, gray, None) |
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prevgray = gray |
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cv.imshow('flow', draw_flow(gray, flow)) |
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if show_hsv: |
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cv.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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cv.imshow('glitch', cur_glitch) |
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ch = 0xFF & cv.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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if ch == ord('3'): |
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use_spatial_propagation = not use_spatial_propagation |
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inst.setUseSpatialPropagation(use_spatial_propagation) |
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print('spatial propagation is', ['off', 'on'][use_spatial_propagation]) |
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if ch == ord('4'): |
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use_temporal_propagation = not use_temporal_propagation |
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print('temporal propagation is', ['off', 'on'][use_temporal_propagation]) |
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print('Done') |
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if __name__ == '__main__': |
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print(__doc__) |
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main() |
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cv.destroyAllWindows()
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