# 主函数：播放视频并实时分析
def analyze_and_display(video_source=0):
    cap = cv2.VideoCapture(video_source)
    global paused
    while cap.isOpened():
        if not paused:
            ret, frame = cap.read()
            if not ret:
                break

            image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            result = pose.process(image_rgb)

            if result.pose_landmarks:
                # 绘制骨架
                mp_draw.draw_landmarks(frame, result.pose_landmarks, mp_pose.POSE_CONNECTIONS)

        # 在循环内部处理每一帧时加入以下逻辑
        original_height, original_width = frame.shape[:2]
        # 设置缩放比例和最小高度
        scale_percent = 50
        min_height = 600
        # 计算缩放后的尺寸
        new_width = int(original_width * scale_percent / 100)
        new_height = int(original_height * scale_percent / 100)
        # 如果缩放后高度太小，则限制为最小高度并保持宽高比
        if new_height < min_height:
            new_height = min_height
            new_width = int(original_width * new_height / original_height)
        # 缩放图像
        resized_frame = cv2.resize(frame, (new_width, new_height))
        # 显示画面
        cv2.imshow('Test', resized_frame)
        cv2.setMouseCallback('Test', toggle_pause)

        # 按 'q' 键退出
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    cap.release()
    cv2.destroyAllWindows()
    
# 下载远程视频到本地临时文件
def download_video(url):
    response = requests.get(url, stream=True)
    fd, path = tempfile.mkstemp(suffix='.mp4')
    with os.fdopen(fd, 'wb') as tmp:
        for chunk in response.iter_content(chunk_size=1024 * 1024):
            if chunk:
                tmp.write(chunk)
    return path

# 主函数入口
if __name__ == "__main__":
    import sys

    if len(sys.argv) != 2:
        print("Usage: python squat_analyzer.py <video_file_or_url>")
        sys.exit(1)

    video_input = sys.argv[1]

    if video_input.startswith(("http://", "https://")):
        print("正在下载视频...")
        video_path = download_video(video_input)
    else:
        video_path = video_input

    result = analyze_and_display(video_path)
    print(result)

# 初始化MediaPipe Pose模块
mp_pose = mp.solutions.pose
pose = mp_pose.Pose(static_image_mode=False, model_complexity=1,
                    enable_segmentation=False, min_detection_confidence=0.5)


# 然后看analyze_and_display函数
image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
result = pose.process(image_rgb)
    if result.pose_landmarks:
        landmarks = result.pose_landmarks.landmark
        ....

print(landmarks[12])