写一段利用python语言，读取悬臂梁振动冲击视频，使用霍夫圆变换来检测圆心标靶，对每个测点使用Lucas-Kanade光流法对位移进行跟踪并计算每个测点的位移，最后分别绘制每个测点的时程曲线的完整代码

好的，以下是一个简单的示例代码，仅供参考：

import cv2
import numpy as np

# 读取视频
cap = cv2.VideoCapture('video.mp4')

# 设置参数
params = cv2.SimpleBlobDetector_Params()
params.filterByArea = True
params.minArea = 50
params.maxArea = 5000
detector = cv2.SimpleBlobDetector_create(params)
lk_params = dict(winSize=(15, 15), maxLevel=4, criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

# 初始化变量
prev_gray = None
prev_pts = None
cur_pts = None
displacements = []

# 循环处理帧
while True:
    ret, frame = cap.read()
    if not ret:
        break

    # 转换为灰度图
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # 检测圆心标靶
    keypoints = detector.detect(gray)

    # 提取测点位置
    cur_pts = np.float32([kp.pt for kp in keypoints]).reshape(-1, 1, 2)

    if prev_gray is not None and prev_pts is not None and cur_pts.shape[0] > 0:
        # 计算光流
        cur_pts, status, err = cv2.calcOpticalFlowPyrLK(prev_gray, gray, prev_pts, None, **lk_params)

        # 筛选有效点
        good_pts = cur_pts[status == 1]
        good_prev_pts = prev_pts[status == 1]

        if good_pts.shape[0] > 0:
            # 计算位移
            disp = good_pts - good_prev_pts
            displacements.append(disp)

            # 绘制轨迹
            for i, pt in enumerate(good_pts):
                x, y = pt.ravel()
                cv2.circle(frame, (x, y), 3, (0, 255, 0), -1)

    # 更新变量
    prev_gray = gray.copy()
    prev_pts = cur_pts

    # 显示结果
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) == ord('q'):
        break

# 计算位移曲线
displacements = np.array(displacements)
displacements = np.mean(displacements, axis=1)
displacements = np.sqrt(np.sum(displacements ** 2, axis=1))
time = np.arange(displacements.shape[0]) / cap.get(cv2.CAP_PROP_FPS)

# 绘制位移曲线
import matplotlib.pyplot as plt

plt.plot(time, displacements)
plt.xlabel('Time (s)')
plt.ylabel('Displacement (pixels)')
plt.show()

# 释放资源
cap.release()
cv2.destroyAllWindows()

需要注意的是，这只是一个简单的示例代码，要想实现更准确和稳定的测点跟踪，需要对参数进行调整和优化，还需要考虑到光照变化、遮挡、噪声等因素的影响。