opencv_085_flow.py

import numpy as np
import cv2 as cv
import math
cap = cv.VideoCapture('car_flow.mp4')
 
#角点检测参数
feature_params = dict(maxCorners=100, qualityLevel=0.1, minDistance=7, blockSize=7)
 
#KLT光流参数
lk_params = dict(winSize=(15, 15), maxLevel=2, criteria=(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.02))


# 随机颜色
color = np.random.randint(0,255,(100,3))
 
# 读取第一帧
ret, old_frame = cap.read()
old_gray = cv.cvtColor(old_frame, cv.COLOR_BGR2GRAY)
p0 = cv.goodFeaturesToTrack(old_gray, mask=None, **feature_params,useHarrisDetector=False,k=0.04)
good_ini=p0.copy()
 
def caldist(a,b,c,d):
    return abs(a-c)+abs(b-d)
 
mask = np.zeros_like(old_frame)
# 光流跟踪
while True:
    ret, frame = cap.read()
    if ret is False:
        break
    frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
    # 计算光流
    p1, st, err = cv.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
    # 根据状态选择
     
    good_new = p1[st == 1]
    good_old = p0[st == 1]
    #删除静止点
    k=0
    for i, (new0, old0) in enumerate(zip(good_new,good_old)):
        a0,b0 = new0.ravel()
        c0,d0 = old0.ravel()
        dist=caldist(a0,b0,c0,d0)
        if dist>2:
            good_new[k]=good_new[i]
            good_old[k]=good_old[i]
            good_ini[k]=good_ini[i]
            k=k+1

    # 提取动态点
    good_ini=good_ini[:k]
    good_new=good_new[:k]
    good_old=good_old[:k]  
 
    # 绘制跟踪线
    for i, (new, old) in enumerate(zip(good_new,good_old)):
        a,b = new.ravel()
        c,d = old.ravel()
        mask = cv.line(mask, (a,b),(c,d), color[i].tolist(), 2)
        frame = cv.circle(frame,(a,b),5,color[i].tolist(),-1)
    cv.imshow('frame',cv.add(frame,mask))
    k = cv.waitKey(30) & 0xff
    if k == 27:
        cv.imwrite("flow.jpg", cv.add(frame,mask))
        break
 
    # 更新
    old_gray = frame_gray.copy()
    p0 = good_new.reshape(-1, 1, 2)
 
    if good_ini.shape[0]<40:
        p0 = cv.goodFeaturesToTrack(old_gray, mask=None, **feature_params)
        good_ini=p0.copy()
 
cv.destroyAllWindows()
cap.release()