请在这个代码中加入opencv稀释光流算法

好的，以下是加入opencv稀释光流算法的代码示例：

import cv2

# 初始化光流法参数
lk_params = dict(winSize=(15, 15), maxLevel=2, criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

# 读取视频帧
cap = cv2.VideoCapture('video.mp4')
ret, old_frame = cap.read()

# 转换为灰度图像
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)

# 初始化图像点
p0 = cv2.goodFeaturesToTrack(old_gray, mask=None, maxCorners=100, qualityLevel=0.3, minDistance=7, blockSize=7)

# 创建随机颜色
color = (0, 255, 0)

while True:
    # 读取视频帧
    ret, frame = cap.read()

    # 转换为灰度图像
    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)

    # 选择好的点
    good_new = p1[st == 1]
    good_old = p0[st == 1]

    # 绘制轨迹
    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), color, 2)
        frame = cv2.circle(frame, (a, b), 5, color, -1)

    # 显示图像
    img = cv2.add(frame, mask)
    cv2.imshow('frame', img)

    # 更新图像点
    old_gray = frame_gray.copy()
    p0 = good_new.reshape(-1, 1, 2)

    # 等待按键
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放资源并关闭窗口
cap.release()
cv2.destroyAllWindows()

这段代码使用了cv2.calcOpticalFlowPyrLK函数来计算光流，其中lk_params参数为光流法的参数，p0为图像点，good_new为优化后的点，good_old为优化前的点。同时，还绘制了轨迹和点。