samples/python/snippets/dis_opt_flow.py#

An example using the dense optical flow and DIS optical flow algorithms in python

  1#!/usr/bin/env python
  2
  3'''
  4example to show optical flow estimation using DISOpticalFlow
  5
  6USAGE: dis_opt_flow.py [<video_source>]
  7
  8Keys:
  9 1  - toggle HSV flow visualization
 10 2  - toggle glitch
 11 3  - toggle spatial propagation of flow vectors
 12 4  - toggle temporal propagation of flow vectors
 13ESC - exit
 14'''
 15
 16# Python 2/3 compatibility
 17from __future__ import print_function
 18
 19import numpy as np
 20import cv2 as cv
 21
 22import video
 23
 24
 25def draw_flow(img, flow, step=16):
 26    h, w = img.shape[:2]
 27    y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1).astype(int)
 28    fx, fy = flow[y,x].T
 29    lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)
 30    lines = np.int32(lines + 0.5)
 31    vis = cv.cvtColor(img, cv.COLOR_GRAY2BGR)
 32    cv.polylines(vis, lines, 0, (0, 255, 0))
 33    for (x1, y1), (_x2, _y2) in lines:
 34        cv.circle(vis, (x1, y1), 1, (0, 255, 0), -1)
 35    return vis
 36
 37
 38def draw_hsv(flow):
 39    h, w = flow.shape[:2]
 40    fx, fy = flow[:,:,0], flow[:,:,1]
 41    ang = np.arctan2(fy, fx) + np.pi
 42    v = np.sqrt(fx*fx+fy*fy)
 43    hsv = np.zeros((h, w, 3), np.uint8)
 44    hsv[...,0] = ang*(180/np.pi/2)
 45    hsv[...,1] = 255
 46    hsv[...,2] = np.minimum(v*4, 255)
 47    bgr = cv.cvtColor(hsv, cv.COLOR_HSV2BGR)
 48    return bgr
 49
 50
 51def warp_flow(img, flow):
 52    h, w = flow.shape[:2]
 53    flow = -flow
 54    flow[:,:,0] += np.arange(w)
 55    flow[:,:,1] += np.arange(h)[:,np.newaxis]
 56    res = cv.remap(img, flow, None, cv.INTER_LINEAR)
 57    return res
 58
 59
 60def main():
 61    import sys
 62    print(__doc__)
 63    try:
 64        fn = sys.argv[1]
 65    except IndexError:
 66        fn = 0
 67
 68    cam = video.create_capture(fn)
 69    _ret, prev = cam.read()
 70    prevgray = cv.cvtColor(prev, cv.COLOR_BGR2GRAY)
 71    show_hsv = False
 72    show_glitch = False
 73    use_spatial_propagation = False
 74    use_temporal_propagation = True
 75    cur_glitch = prev.copy()
 76    inst = cv.DISOpticalFlow.create(cv.DISOPTICAL_FLOW_PRESET_MEDIUM)
 77    inst.setUseSpatialPropagation(use_spatial_propagation)
 78
 79    flow = None
 80    while True:
 81        _ret, img = cam.read()
 82        gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
 83        if flow is not None and use_temporal_propagation:
 84            #warp previous flow to get an initial approximation for the current flow:
 85            flow = inst.calc(prevgray, gray, warp_flow(flow,flow))
 86        else:
 87            flow = inst.calc(prevgray, gray, None)
 88        prevgray = gray
 89
 90        cv.imshow('flow', draw_flow(gray, flow))
 91        if show_hsv:
 92            cv.imshow('flow HSV', draw_hsv(flow))
 93        if show_glitch:
 94            cur_glitch = warp_flow(cur_glitch, flow)
 95            cv.imshow('glitch', cur_glitch)
 96
 97        ch = 0xFF & cv.waitKey(5)
 98        if ch == 27:
 99            break
100        if ch == ord('1'):
101            show_hsv = not show_hsv
102            print('HSV flow visualization is', ['off', 'on'][show_hsv])
103        if ch == ord('2'):
104            show_glitch = not show_glitch
105            if show_glitch:
106                cur_glitch = img.copy()
107            print('glitch is', ['off', 'on'][show_glitch])
108        if ch == ord('3'):
109            use_spatial_propagation = not use_spatial_propagation
110            inst.setUseSpatialPropagation(use_spatial_propagation)
111            print('spatial propagation is', ['off', 'on'][use_spatial_propagation])
112        if ch == ord('4'):
113            use_temporal_propagation = not use_temporal_propagation
114            print('temporal propagation is', ['off', 'on'][use_temporal_propagation])
115
116    print('Done')
117
118
119if __name__ == '__main__':
120    print(__doc__)
121    main()
122    cv.destroyAllWindows()