update five videos test scriupt

five_sensors.py

@@ -0,0 +1,296 @@
+"""
+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, cal_path, result_queue, stop_event, target_fps=None):
+    """
+    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
+        cal_path: 标定路径
+        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
+        target_fps: 目标帧率；若设置则用定时器限速，使多传感器 FPS 接近一致
+    """
+    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,
+            cal_path=cal_path
+        )
+
+        print(f"传感器 {sensor_id} 已启动" + (f" (target_fps={target_fps})" if target_fps else ""))
+
+        frame_interval = 1.0 / target_fps if target_fps and target_fps > 0 else 0.0
+
+        while not stop_event.is_set():
+            t_start = time.perf_counter()
+            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 Exception:
+                    # 如果队列已满，跳过这一帧
+                    pass
+
+            except Exception as e:
+                print(f"传感器 {sensor_id} 处理错误: {e}")
+                time.sleep(0.1)
+
+            # 步骤7: 精确限速（time.sleep 在 Windows 上精度仅 ~15.6ms，需留足余量后 spin-wait）
+            if frame_interval > 0:
+                deadline = t_start + frame_interval
+                remaining = deadline - time.perf_counter()
+                if remaining > 0.018:
+                    time.sleep(remaining - 0.018)  # coarse sleep; leave 18ms margin for Windows granularity
+                while time.perf_counter() < deadline:
+                    pass  # spin-wait for the last few ms
+        
+        # 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
+    # =========================================================================
+    # 目标帧率：设为 None 不限速（可跑满 100+ FPS）；设为正数则 sleep 限速，使 FPS ≤ target_fps
+    target_fps = 60  # 不限速；设为 30 等可 cap 帧率
+
+    num_sensors = 5
+    sensor_configs = [
+        {
+            'sensor_id': 0,
+            'vid_src': './s1_480p.mp4',
+            'config_name': './config/prototype_of3_480p.json',
+            'cal_path': './config/dd01a_0_480p.npy'
+        },
+        {
+            'sensor_id': 1,
+            'vid_src': './s1_480p.mp4',  # 可以改为不同的视频源或摄像头索引
+            'config_name': './config/prototype_of3_480p.json',
+            'cal_path': './config/dd01a_0_480p.npy'
+        },
+                {
+            'sensor_id': 2,
+            'vid_src': './s1_480p.mp4',  # 可以改为不同的视频源或摄像头索引
+            'config_name': './config/prototype_of3_480p.json',
+            'cal_path': './config/dd01a_0_480p.npy'
+        },
+                {
+            'sensor_id': 3,
+            'vid_src': './s1_480p.mp4',  # 可以改为不同的视频源或摄像头索引
+            'config_name': './config/prototype_of3_480p.json',
+            'cal_path': './config/dd01a_0_480p.npy'
+        },
+                {
+            'sensor_id': 4,
+            'vid_src': './s1_480p.mp4',  # 可以改为不同的视频源或摄像头索引
+            'config_name': './config/prototype_of3_480p.json',
+            'cal_path': './config/dd01a_0_480p.npy'
+        },
+    ]
+
+    # =========================================================================
+    # 2. 创建进程间通信对象
+    # =========================================================================
+    # 增大队列大小以缓冲更多帧，避免因主进程处理延迟导致的丢帧
+    result_queues = [Queue(maxsize=30) for _ in sensor_configs]
+    stop_events = [mp.Event() for _ in sensor_configs]
+
+    # =========================================================================
+    # 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'], config['cal_path'],
+                  result_queues[i], stop_events[i], target_fps)
+        )
+        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)
+    
+    # 用于控制打印频率，减少I/O开销
+    print_counter = 0
+    print_interval = 30  # 每30帧打印一次，进一步减少I/O
+    
+    # 用于控制按键检查频率
+    key_check_counter = 0
+    key_check_interval = 5  # 每5帧检查一次按键
+    
+    try:
+        # =====================================================================
+        # 4. 主循环：从队列获取数据并显示
+        # =====================================================================
+        while True:
+            # 从所有队列中快速获取最新数据（高效清空队列，只保留最新帧）
+            for i, queue in enumerate(result_queues):
+                # 快速清空队列，只保留最后一帧
+                data = None
+                # 一次性获取所有可用数据，只保留最后一个
+                while True:
+                    try:
+                        data = queue.get_nowait()
+                    except:
+                        break
+                
+                if data is not None:
+                    latest_data[i] = data
+            
+            # 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"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))
+            
+            # 降低打印频率以减少I/O开销（在主循环级别控制）
+            print_counter += 1
+            if print_counter >= print_interval:
+                for data in latest_data:
+                    if data is not None and 'error' not in data:
+                        sid = data['sensor_id']
+                        print(f"传感器 {sid}: FPS={data['fps']:5.1f}")
+                print_counter = 0
+            
+            # 降低按键检查频率以减少开销
+            key_check_counter += 1
+            if key_check_counter >= key_check_interval:
+                key = cv2.waitKey(1) & 0xFF
+                if key == ord('q'):
+                    print("\n退出程序...")
+                    break
+                key_check_counter = 0
+    
+    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()