# 视频稳定 if prev_frame is not None: # 计算帧间差分 diff = cv2.absdiff(frame, prev_frame) # 计算运动向量 _, motion = cv2.optflow.calcOpticalFlowFarneback(prev_frame, frame, None, 0.5, 3, 15, 3, 5, 1.2, 0) # 平移每一帧 M = np.float32([[1, 0, motion[:,:,0].mean()], [0, 1, motion[:,:,1].mean()]]) frame = cv2.warpAffine(frame, M, (frame.shape[1], frame.shape[0])) diff = cv2.warpAffine(diff, M, (diff.shape[1], diff.shape[0])) # 显示帧间差分 cv2.imshow('diff', diff) # 更新上一帧 prev_frame = frame.copy()将这段代码改为4.5.3版本opencv能用的代码

在 OpenCV 4.5.3 中，`calcOpticalFlowFarneback` 函数已经被移动到了 `cv2.optflow` 模块的子模块 `optflow_sparse` 中，并且函数名也发生了变化。下面是修改后的代码：

# 初始化上一帧
prev_frame = None

while True:
    # 读取视频帧
    ret, frame = cap.read()
    if not ret:
        break
    
    if prev_frame is not None:
        # 计算帧间差分
        diff = cv2.absdiff(frame, prev_frame)
        
        # 计算运动向量
        prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        flow = cv2.optflow.calcOpticalFlowSparseToDense(prev_gray, gray)
        
        # 平移每一帧
        M = np.float32([[1, 0, flow[:,:,0].mean()], [0, 1, flow[:,:,1].mean()]])
        frame = cv2.warpAffine(frame, M, (frame.shape[1], frame.shape[0]))
        diff = cv2.warpAffine(diff, M, (diff.shape[1], diff.shape[0]))
        
        # 显示帧间差分
        cv2.imshow('diff', diff)
    
    # 更新上一帧
    prev_frame = frame.copy()

    # 显示视频帧
    cv2.imshow('frame', frame)
    
    # 按下 q 键退出循环
    if cv2.waitKey(1) == ord('q'):
        break

# 释放视频读取器和窗口
cap.release()
cv2.destroyAllWindows()

需要注意的是，我们需要先将当前帧和上一帧转换为灰度图像，然后再使用 `calcOpticalFlowSparseToDense` 函数计算运动向量。此外，由于光流法计算的运动向量可能存在噪点，因此在计算平移矩阵时，我们使用了运动向量的平均值。