Optical Flow，即光流是一种经典的传统视觉算法。在视频层次的其他任务上，如视频目标检测、跟踪和分割等等，有着很大用武之地。

实现效果：

1.先准备一个video，如果没有，可以用官方video：

wget https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4

2.复制下列代码，新建为optical.py，然后跑代码：

python optical.py slow_traffic_small.mp4

代码如下：（代码大部分借用自官方代码）

import numpy as np
import cv2
import argparse
parser = argparse.ArgumentParser(description='This sample demonstrates Lucas-Kanade Optical Flow calculation. \
                                              The example file can be downloaded from: \
                                              https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4')
parser.add_argument('image', type=str, help='path to image file')
args = parser.parse_args()
cap = cv2.VideoCapture(args.image)
w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
writer = cv2.VideoWriter('demo.mp4', cv2.VideoWriter_fourcc(*'mp4v'),
        30, (w, h))
# 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 ret:
    ret, frame = cap.read()
    if not ret:
        break
    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
    if p1 is not None:
        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, (int(a),int(b)),(int(c),int(d)), color[i].tolist(), 2)
        frame = cv2.circle(frame,(int(a),int(b)),5,color[i].tolist(),-1)
    img = cv2.add(frame, mask)
    # cv.imshow('frame',img)
    writer.write(img)
    # Now update the previous frame and previous points
    old_gray = frame_gray.copy()
    p0 = good_new.reshape(-1,1,2)