dis_opt_flow.py

#!/usr/bin/env python

'''
example to show optical flow estimation using DISOpticalFlow

USAGE: dis_opt_flow.py [<video_source>]

Keys:
 1  - toggle HSV flow visualization
 2  - toggle glitch
 3  - toggle spatial propagation of flow vectors
 4  - toggle temporal propagation of flow vectors
ESC - exit
'''

# Python 2/3 compatibility
from __future__ import print_function

import numpy as np
import cv2
import video


def draw_flow(img, flow, step=16):
    h, w = img.shape[:2]
    y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1).astype(int)
    fx, fy = flow[y,x].T
    lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)
    lines = np.int32(lines + 0.5)
    vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
    cv2.polylines(vis, lines, 0, (0, 255, 0))
    for (x1, y1), (x2, y2) in lines:
        cv2.circle(vis, (x1, y1), 1, (0, 255, 0), -1)
    return vis


def draw_hsv(flow):
    h, w = flow.shape[:2]
    fx, fy = flow[:,:,0], flow[:,:,1]
    ang = np.arctan2(fy, fx) + np.pi
    v = np.sqrt(fx*fx+fy*fy)
    hsv = np.zeros((h, w, 3), np.uint8)
    hsv[...,0] = ang*(180/np.pi/2)
    hsv[...,1] = 255
    hsv[...,2] = np.minimum(v*4, 255)
    bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
    return bgr


def warp_flow(img, flow):
    h, w = flow.shape[:2]
    flow = -flow
    flow[:,:,0] += np.arange(w)
    flow[:,:,1] += np.arange(h)[:,np.newaxis]
    res = cv2.remap(img, flow, None, cv2.INTER_LINEAR)
    return res


if __name__ == '__main__':
    import sys
    print(__doc__)
    try:
        fn = sys.argv[1]
    except IndexError:
        fn = 0

    cam = video.create_capture(fn)
    ret, prev = cam.read()
    prevgray = cv2.cvtColor(prev, cv2.COLOR_BGR2GRAY)
    show_hsv = False
    show_glitch = False
    use_spatial_propagation = False
    use_temporal_propagation = True
    cur_glitch = prev.copy()
    inst = cv2.optflow.createOptFlow_DIS(cv2.optflow.DISOPTICAL_FLOW_PRESET_MEDIUM)
    inst.setUseSpatialPropagation(use_spatial_propagation)

    flow = None
    while True:
        ret, img = cam.read()
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        if flow is not None and use_temporal_propagation:
            #warp previous flow to get an initial approximation for the current flow:
            flow = inst.calc(prevgray, gray, warp_flow(flow,flow))
        else:
            flow = inst.calc(prevgray, gray, None)
        prevgray = gray

        cv2.imshow('flow', draw_flow(gray, flow))
        if show_hsv:
            cv2.imshow('flow HSV', draw_hsv(flow))
        if show_glitch:
            cur_glitch = warp_flow(cur_glitch, flow)
            cv2.imshow('glitch', cur_glitch)

        ch = 0xFF & cv2.waitKey(5)
        if ch == 27:
            break
        if ch == ord('1'):
            show_hsv = not show_hsv
            print('HSV flow visualization is', ['off', 'on'][show_hsv])
        if ch == ord('2'):
            show_glitch = not show_glitch
            if show_glitch:
                cur_glitch = img.copy()
            print('glitch is', ['off', 'on'][show_glitch])
        if ch == ord('3'):
            use_spatial_propagation = not use_spatial_propagation
            inst.setUseSpatialPropagation(use_spatial_propagation)
            print('spatial propagation is', ['off', 'on'][use_spatial_propagation])
        if ch == ord('4'):
            use_temporal_propagation = not use_temporal_propagation
            print('temporal propagation is', ['off', 'on'][use_temporal_propagation])
    cv2.destroyAllWindows()