光流法计算速度python

光流法是一种用于计算图像序列中物体运动的方法，它基于像素间的亮度差异来估计物体的运动速度。在Python中，可以使用OpenCV库来实现光流法计算速度。

下面是一个使用光流法计算速度的示例代码：

import cv2
import numpy as np

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

# 使用Lucas-Kanade方法进行光流估计
lk_params = dict(winSize=(15, 15), maxLevel=2, criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

ret, old_frame = cap.read()
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
p0 = cv2.goodFeaturesToTrack(old_gray, mask=None, maxCorners=100, qualityLevel=0.3, minDistance=7, blockSize=7)

mask = np.zeros_like(old_frame)

while True:
    ret, frame = cap.read()
    if not ret:
        break
    
    frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
    # 计算光流
    p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
    
    # 根据状态选择良好的特征点
    good_new = p1[st == 1]
    good_old = p0[st == 1]
    
    # 绘制光流轨迹
    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), (0, 255, 0), 2)
        frame = cv2.circle(frame, (a, b), 5, (0, 255, 0), -1)
    
    img = cv2.add(frame, mask)
    cv2.imshow('frame', img)
    
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
    
    # 更新旧帧和特征点
    old_gray = frame_gray.copy()
    p0 = good_new.reshape(-1, 1, 2)

cap.release()
cv2.destroyAllWindows()