"""
示例 4: 多传感器并行处理（多进程模式 - 不使用 MultiSensor）

本示例演示如何使用 multiprocessing 同时运行两个传感器：
- 每个传感器运行在独立的进程中
- 每个进程拥有独立的 CUDA 上下文
- 使用 multiprocessing.Process 和 Queue 进行进程间通信

架构说明：
    进程 1 (传感器 0):  采集 → 光流 → HHD → 接触检测 → 队列
    进程 2 (传感器 1):  采集 → 光流 → HHD → 接触检测 → 队列
                                              ↓
    主进程:            显示 ← ───────────────┘

适用场景：多指触觉传感器、多视角采集、需要 GPU 加速的多路处理

注意事项：
- Windows 下必须在 if __name__ == '__main__' 中调用 mp.freeze_support()
- 每个传感器进程会占用独立的 GPU 显存
"""
import os
os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0" 
import cv2
import orisys
import numpy as np
import multiprocessing as mp
from multiprocessing import Queue, Process
import time


def sensor_worker(sensor_id, vid_src, config_name, result_queue, stop_event):
    """
    传感器工作进程函数
    
    Args:
        sensor_id: 传感器编号
        vid_src: 视频源（文件路径或摄像头索引）
        config_name: 配置名称
        result_queue: 结果队列，用于向主进程发送数据
        stop_event: 停止事件，用于通知工作进程退出
    """
    try:
        # 在每个进程中创建独立的传感器实例
        sensor = orisys.Sensor(
            vid_src, 
            isstitch=True, 
            cuda=True, 
            verbose=False,  # 减少子进程输出
            config_name=config_name
        )
        
        print(f"传感器 {sensor_id} 已启动")
        
        while not stop_event.is_set():
            try:
                # 步骤1: 获取图像
                sensor.get_img()
                
                # 步骤2: 计算形变场
                sensor.compute_deformation()
                
                # 步骤3: 计算触碰点
                sensor.compute_contact()
                
                # 步骤4: 读取信息
                fps, flow, vnormal, img, contour, centroid, depth_map = sensor.read_info(
                    sensor.info.FPS,
                    sensor.info.VRAW,
                    sensor.info.VNORMAL,
                    sensor.info.IMG,
                    sensor.info.CONTOUR,
                    sensor.info.CENTROID,
                    sensor.info.DEPTH
                )
                
                # 步骤5: 准备数据字典（确保所有数据都是可序列化的）
                data = {
                    'sensor_id': sensor_id,
                    'fps': fps,
                    'flow': flow.copy() if flow is not None else None,
                    'vnormal': vnormal.copy() if vnormal is not None else None,
                    'img': img.copy() if img is not None else None,
                    'contour': contour,
                    'centroid': centroid,
                    'depth_map': depth_map.copy() if depth_map is not None else None,
                    'timestamp': time.time()
                }
                
                # 步骤6: 发送数据到主进程（非阻塞）
                try:
                    result_queue.put_nowait(data)
                except:
                    # 如果队列已满，跳过这一帧
                    pass
                
                # 短暂休眠，避免过度占用CPU
                time.sleep(0.001)
                
            except Exception as e:
                print(f"传感器 {sensor_id} 处理错误: {e}")
                time.sleep(0.1)
        
        # 清理资源
        sensor.disconnect()
        print(f"传感器 {sensor_id} 已停止")
        
    except Exception as e:
        print(f"传感器 {sensor_id} 初始化失败: {e}")
        result_queue.put({'sensor_id': sensor_id, 'error': str(e)})


def main():
    # =========================================================================
    # 1. 配置多个传感器
    # =========================================================================
    num_sensors = 2
    sensor_configs = [
        {
            'vid_src': './s1.mp4',
            'config_name': 'prototype'
        },
        {
            'vid_src': './s1.mp4',  # 可以改为不同的视频源或摄像头索引
            'config_name': 'prototype'
        },
    ]
    
    # =========================================================================
    # 2. 创建进程间通信对象
    # =========================================================================
    result_queues = [Queue(maxsize=num_sensors) for _ in sensor_configs]  # 每个传感器一个队列
    stop_events = [mp.Event() for _ in sensor_configs]  # 停止事件
    
    # =========================================================================
    # 3. 启动传感器工作进程
    # =========================================================================
    processes = []
    for i, config in enumerate(sensor_configs):
        p = Process(
            target=sensor_worker,
            args=(i, config['vid_src'], config['config_name'], result_queues[i], stop_events[i])
        )
        p.start()
        processes.append(p)
    
    print(f"\n已启动 {len(processes)} 个传感器进程")
    print("按 'q' 退出程序")
    
    # 用于存储每个传感器的最新数据
    latest_data = [None] * len(sensor_configs)
    
    try:
        # =====================================================================
        # 4. 主循环：从队列获取数据并显示
        # =====================================================================
        while True:
            # 从所有队列中获取最新数据
            for i, queue in enumerate(result_queues):
                try:
                    # 非阻塞获取，获取队列中最新的数据
                    while True:
                        data = queue.get_nowait()
                        latest_data[i] = data
                except:
                    # 队列为空，使用之前的数据
                    pass
            
            # 显示所有传感器的数据
            for data in latest_data:
                if data is None or 'error' in data:
                    continue
                
                sid = data['sensor_id']
                
                # 显示形变场
                if data['img'] is not None and data['flow'] is not None:
                    arrows = orisys.util.draw_arrows(
                        data['img'],
                        data['flow'],
                        threshold=5,
                        grid_spacing=10,
                        arrow_scale=1.0
                    )
                    cv2.imshow(f"形变场 Deformation {sid}", arrows)
                
                # 显示触碰点和区域
                if data['img'] is not None:
                    image_with_foe = orisys.util.draw_contact(
                        data['img'], 
                        data['contour'], 
                        data['centroid']
                    )
                    cv2.imshow(f"Contact Centroid {sid}", image_with_foe)
                
                # 显示深度图
                if data['depth_map'] is not None and data['depth_map'].size > 0:
                    div_abs = data['depth_map']
                    if div_abs.max() > div_abs.min():
                        div_normalized = ((div_abs - div_abs.min()) / 
                                        (div_abs.max() - div_abs.min()) * 255).astype(np.uint8)
                    else:
                        div_normalized = np.zeros_like(div_abs, dtype=np.uint8)
                    cv2.imshow(f"Depth Map {sid}", 
                             cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))
                
                # 打印FPS
                print(f"传感器 {sid}: FPS={data['fps']:5.1f}")
            
            # 检查退出键
            key = cv2.waitKey(1) & 0xFF
            if key == ord('q'):
                print("\n退出程序...")
                break
    
    finally:
        # =====================================================================
        # 5. 清理资源
        # =====================================================================
        # 通知所有工作进程停止
        for event in stop_events:
            event.set()
        
        # 等待所有进程结束
        for p in processes:
            p.join(timeout=2)
            if p.is_alive():
                p.terminate()
                p.join()
        
        cv2.destroyAllWindows()
        print("所有传感器进程已停止")


if __name__ == '__main__':
    # Windows 下多进程必需的初始化
    mp.freeze_support()
    main()