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这是我正在运行并获取对象轨迹的代码

cap = cv2.VideoCapture('F:/Behavoirs/Nice camera angle/1 (5)_Trim.mp4')
backgroundobject = cv2.createBackgroundSubtractorMOG2(history = 500, varThreshold = 16.5 ,detectShadows=True)
kernel = np.ones((3,3),np.uint8)

lower_val = (10, 0, 0)
upper_val = (135,255,255)

low_green = np.array([25, 52, 72])
high_green = np.array([102, 255, 255])

color = np.random.randint(0, 255, (100, 3))

variance_list = [300,150]

lk_params = dict(winSize  = (15, 15),
                maxLevel = 2,
                criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

feature_params = dict(maxCorners = 100,
                    qualityLevel = 0.3,
                    minDistance = 7,
                    blockSize = 7)


trajectory_len = 1000
detect_interval = 10
trajectories = []
frame_idx = 0

while True:
    suc, frame = cap.read()
    resized_frame = cv2.resize(frame, None, fx=0.2, fy=0.2, interpolation = cv2.INTER_LINEAR)# resize the frame 
    org_frame = resized_frame.copy()    
    resized_frame = cv2.GaussianBlur(resized_frame, (7,7), 0)#blur
    
    imgHSV = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2HSV)#tohsv
    maskr = cv2.inRange(imgHSV, lower_val, upper_val)#to remove green
    
    sub = backgroundobject.apply(maskr) #background subtraction
    contours , hi = cv2.findContours(sub, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)# finding contours
    
    for contour in contours: 
        if cv2.contourArea(contour) > 25: #need to get the max contour!!!!
            x, y, width, height = cv2.boundingRect(contour)
            cv2.rectangle(org_frame, (x , y), (x + width, y + height),(0, 0, 255), 2)

    # Calculate optical flow for a sparse feature set using the iterative Lucas-Kanade Method
    if len(trajectories) > 0:
        img0, img1 = prev_gray, sub
        p0 = np.float32([trajectory[-1] for trajectory in trajectories]).reshape(-1, 1, 2)
        p1, _st, _err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
        p0r, _st, _err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
        d = abs(p0-p0r).reshape(-1, 2).max(-1)
        good = d < 1

        new_trajectories = []

        # Get all the trajectories
        for trajectory, (x, y), good_flag in zip(trajectories, p1.reshape(-1, 2), good):
            if not good_flag:
                continue
            trajectory.append((x, y))
            if len(trajectory) > trajectory_len:
                del trajectory[0]
            new_trajectories.append(trajectory)
            # Newest detected point
            cv2.circle(org_frame, (int(x), int(y)), 2, (0, 0, 255), -1)

        trajectories = new_trajectories

        # Draw all the trajectories
        cv2.polylines(org_frame, [np.int32(trajectory) for trajectory in trajectories], False, (0, 255, 0))
        cv2.putText(org_frame, 'track count points: %d' % len(trajectories), (20, 50), cv2.FONT_HERSHEY_PLAIN, 1, (0,255,0), 2)


    # Update interval - When to update and detect new features
    if frame_idx % detect_interval == 0:
        mask = np.zeros_like(sub)
        mask[:] = 255

        # Lastest point in latest trajectory
        for x, y in [np.int32(trajectory[-1]) for trajectory in trajectories]:
            cv2.circle(mask, (x, y), 5, 0, -1)

        # Detect the good features to track
        p = cv2.goodFeaturesToTrack(sub, mask = mask, **feature_params)
        if p is not None:
            # If good features can be tracked - add that to the trajectories
            for x, y in np.float32(p).reshape(-1, 2):
                trajectories.append([(x, y)])


    frame_idx += 1
    prev_gray = sub

    # Show Results
    cv2.imshow('Optical Flow', org_frame)
    
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

可能的问题可能是noobish，但如何为我在框架中检测到的每个对象分配一个id，我使用什么方法？我的代码进行背景减法并去除绿色和高斯模糊然后使用查找轮廓来获取图像中的轮廓然后使用光流来获取对象的轨迹然后绘制它那么如何识别检测到的每个对象？

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