Optical flow

import numpy as np
import cv2

cap = cv2.VideoCapture('tram_20190930_135346.mp4')

# params for ShiTomasi corner detection
feature_params = dict( maxCorners = 100,
                       qualityLevel = 0.3,
                       minDistance = 7,
                       blockSize = 7 )

# Parameters for lucas kanade optical flow
lk_params = dict( winSize  = (15,15),
                  maxLevel = 2,
                  criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

# Create some random colors
color = np.random.randint(0,255,(100,3))

# Take first frame and find corners in it
ret, old_frame = cap.read()
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)

# Create a mask image for drawing purposes
mask = np.zeros_like(old_frame)

while(1):
    ret,frame = cap.read()
    frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # calculate optical flow
    p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)

    # Select good points
    good_new = p1[st==1]
    good_old = p0[st==1]

    # draw the tracks
    for i,(new,old) in enumerate(zip(good_new,good_old)):
        a,b = new.ravel()
        c,d = old.ravel()
        mask = cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)
        frame = cv2.circle(frame,(a,b),5,color[i].tolist(),-1)
    img = cv2.add(frame,mask)

    cv2.imshow('frame',img)
    k = cv2.waitKey(30) & 0xff
    if k == 27:
        break

    # Now update the previous frame and previous points
    old_gray = frame_gray.copy()
    p0 = good_new.reshape(-1,1,2)

cv2.destroyAllWindows()
cap.release()

#!/usr/bin/env python

'''
example to show optical flow
USAGE: opt_flow.py [<video_source>]
Keys:
 1 - toggle HSV flow visualization
 2 - toggle glitch
Keys:
    ESC    - exit
'''

# Python 2/3 compatibility
from __future__ import print_function

import numpy as np
import cv2 as cv
import time
import farneback3d



def draw_flow(img: np.ndarray, flow: np.ndarray, 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
    mean_flow = np.mean(flow)
    print('Mean: {}'.format(mean_flow))
    lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)
    lines = np.int32(lines + 0.5)
    vis = cv.cvtColor(img, cv.COLOR_GRAY2BGR)
    cv.polylines(vis, lines, 0, (0, 255, 0))
    for (x1, y1), (_x2, _y2) in lines:
        cv.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 = cv.cvtColor(hsv, cv.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 = cv.remap(img, flow, None, cv.INTER_LINEAR)
    return res

def main():
    import sys
    try:
        fn = sys.argv[1]
    except IndexError:
        fn = 0

    crop_x1 = 220
    crop_y1 = 410
    crop_x2 = 905
    crop_y2 = 715

    cam = cv.VideoCapture('/home/wtram/Videos/demo/save-20191127_110845.avi')
    _ret, prev = cam.read()
    prev = prev[crop_y1:crop_y2, crop_x1:crop_x2, :]
    prevgray = cv.cvtColor(prev, cv.COLOR_BGR2GRAY)
    show_hsv = False
    show_glitch = False
    cur_glitch = prev.copy()

    # optflow = farneback3d.Farneback(
    #     pyr_scale=0.8,  # Scaling between multi-scale pyramid levels
    #     levels=6,  # Number of multi-scale levels
    #     num_iterations=5,  # Iterations on each multi-scale level
    #     winsize=9,  # Window size for Gaussian filtering of polynomial coefficients
    #     poly_n=5,  # Size of window for weighted least-square estimation of polynomial coefficients
    #     poly_sigma=1.2,  # Sigma for Gaussian weighting of least-square estimation of polynomial coefficients
    # )

    while True:
        _ret, img = cam.read()
        img = img[crop_y1:crop_y2, crop_x1:crop_x2, :]
        gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

        start_time = time.time()

        flow = cv.calcOpticalFlowFarneback(prevgray, gray, None, 0.5, 3, 15, 3, 5, 1.2, 0)
        # vol0 = prev.astype(np.float32) / 255
        # vol1 = img.astype(np.float32) / 255
        # flow = optflow.calc_flow(vol0, vol1)

        end_time = time.time()
        elapsed_time = end_time - start_time
        print('calcOpticalFlowFarneback time: {:.2f}s'.format(elapsed_time))

        prev = img
        prevgray = gray

        # flow = 255 * flow  # Now scale by 255
        # flow = flow.astype(np.uint8)

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

        ch = cv.waitKey(1)
        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])

    print('Done')


if __name__ == '__main__':
    print(__doc__)
    main()
    cv.destroyAllWindows()