C++opencv 中lucas-kanade光流计算自实现可以处理视频的代码

以下是使用OpenCV实现Lucas-Kanade光流算法处理视频的代码示例：

import cv2
import numpy as np

# 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))

# Capture video from camera
cap = cv2.VideoCapture(0)

# Take first frame and convert to grayscale
ret, old_frame = cap.read()
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)

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

while True:
    # Read new frame
    ret, frame = cap.read()

    # Convert to grayscale
    frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Calculate optical flow using Lucas-Kanade algorithm
    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), (0, 255, 0), 2)
        frame = cv2.circle(frame, (a, b), 5, (0, 0, 255), -1)

    img = cv2.add(frame, mask)

    # Display the resulting frame
    cv2.imshow('frame', img)

    # Exit if ESC key is pressed
    k = cv2.waitKey(30) & 0xff
    if k == 27:
        break

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

# Release video capture and close all windows
cap.release()
cv2.destroyAllWindows()

在此示例中，我们首先为Lucas-Kanade算法设置了一些参数，如窗口大小、最大金字塔级别和停止准则。然后，我们从摄像头捕获视频流，并将第一帧转换为灰度图像。我们还创建了一个掩码图像，用于绘制光流轨迹。在循环中，我们读取新帧并将其转换为灰度图像。然后，我们使用Lucas-Kanade算法计算光流，并选择好的点。最后，我们在帧上绘制出光流轨迹，并将其与掩码图像合并以显示结果。如果按下ESC键，循环将终止并释放视频捕获。