ykcheungab
277 lines
11 KiB
Python
277 lines
11 KiB
Python
"""
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示例 4: 多传感器并行处理（多进程模式 - 不使用 MultiSensor）
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本示例演示如何使用 multiprocessing 同时运行两个传感器：
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- 每个传感器运行在独立的进程中
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- 每个进程拥有独立的 CUDA 上下文
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- 使用 multiprocessing.Process 和 Queue 进行进程间通信
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架构说明：
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进程 1 (传感器 0): 采集 → 光流 → HHD → 接触检测 → 队列
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进程 2 (传感器 1): 采集 → 光流 → HHD → 接触检测 → 队列
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↓
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主进程: 显示 ← ───────────────┘
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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
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注意事项：
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- Windows 下必须在 if __name__ == '__main__' 中调用 mp.freeze_support()
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- 每个传感器进程会占用独立的 GPU 显存
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"""
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import os
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os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
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import cv2
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import orisys
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import numpy as np
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import multiprocessing as mp
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from multiprocessing import Queue, Process
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import time
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def sensor_worker(sensor_id, vid_src, config_name, cal_path, result_queue, stop_event, target_fps=None):
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"""
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传感器工作进程函数
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Args:
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sensor_id: 传感器编号
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vid_src: 视频源（文件路径或摄像头索引）
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config_name: 配置名称
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cal_path: 标定路径
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result_queue: 结果队列，用于向主进程发送数据
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stop_event: 停止事件，用于通知工作进程退出
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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
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"""
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try:
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# 在每个进程中创建独立的传感器实例
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sensor = orisys.Sensor(
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vid_src,
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isstitch=True,
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cuda=True,
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verbose=False, # 减少子进程输出
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config_name=config_name,
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cal_path=cal_path
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)
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print(f"传感器 {sensor_id} 已启动" + (f" (target_fps={target_fps})" if target_fps else ""))
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frame_interval = 1.0 / target_fps if target_fps and target_fps > 0 else 0.0
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while not stop_event.is_set():
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t_start = time.perf_counter()
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try:
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# 步骤1: 获取图像
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sensor.get_img()
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# 步骤2: 计算形变场
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sensor.compute_deformation()
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# 步骤3: 计算触碰点
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sensor.compute_contact()
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# 步骤4: 读取信息
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fps, flow, vnormal, img, contour, centroid, depth_map = sensor.read_info(
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sensor.info.FPS,
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sensor.info.VRAW,
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sensor.info.VNORMAL,
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sensor.info.IMG,
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sensor.info.CONTOUR,
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sensor.info.CENTROID,
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sensor.info.DEPTH
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)
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# 步骤5: 准备数据字典（确保所有数据都是可序列化的）
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data = {
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'sensor_id': sensor_id,
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'fps': fps,
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'flow': flow.copy() if flow is not None else None,
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'vnormal': vnormal.copy() if vnormal is not None else None,
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'img': img.copy() if img is not None else None,
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'contour': contour,
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'centroid': centroid,
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'depth_map': depth_map.copy() if depth_map is not None else None,
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'timestamp': time.time()
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}
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# 步骤6: 发送数据到主进程（非阻塞）
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try:
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result_queue.put_nowait(data)
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except Exception:
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# 如果队列已满，跳过这一帧
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pass
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except Exception as e:
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print(f"传感器 {sensor_id} 处理错误: {e}")
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time.sleep(0.1)
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# 步骤7: 按 target_fps 限速，使多传感器 FPS 接近
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if frame_interval > 0:
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elapsed = time.perf_counter() - t_start
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sleep_time = frame_interval - elapsed
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if sleep_time > 0.001: # 至少 1ms 才 sleep，避免无意义调度
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time.sleep(sleep_time)
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# 清理资源
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sensor.disconnect()
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print(f"传感器 {sensor_id} 已停止")
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except Exception as e:
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print(f"传感器 {sensor_id} 初始化失败: {e}")
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result_queue.put({'sensor_id': sensor_id, 'error': str(e)})
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def main():
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# =========================================================================
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# 1. 配置多个传感器
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# =========================================================================
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# 目标帧率：设为正数时，两路传感器会按此帧率限速，使 FPS 接近一致；设为 None 则不限速
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target_fps = 30
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num_sensors = 2
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sensor_configs = [
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{
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'sensor_id': 0,
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'vid_src': 0,
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'config_name': './config/ddjx01.json',
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'cal_path': './config/ddjx01.npy'
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},
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{
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'sensor_id': 1,
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'vid_src': 1, # 可以改为不同的视频源或摄像头索引
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'config_name': './config/ddjx01.json',
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'cal_path': './config/ddjx01.npy'
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},
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]
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# =========================================================================
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# 2. 创建进程间通信对象
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# =========================================================================
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# 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
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result_queues = [Queue(maxsize=30) for _ in sensor_configs]
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stop_events = [mp.Event() for _ in sensor_configs]
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# =========================================================================
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# 3. 启动传感器工作进程
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# =========================================================================
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processes = []
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for i, config in enumerate(sensor_configs):
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p = Process(
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target=sensor_worker,
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args=(i, config['vid_src'], config['config_name'], config['cal_path'],
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result_queues[i], stop_events[i], target_fps)
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)
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p.start()
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processes.append(p)
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print(f"\n已启动 {len(processes)} 个传感器进程")
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print("按 'q' 退出程序")
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# 用于存储每个传感器的最新数据
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latest_data = [None] * len(sensor_configs)
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# 用于控制打印频率，减少I/O开销
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print_counter = 0
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print_interval = 30 # 每30帧打印一次，进一步减少I/O
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# 用于控制按键检查频率
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key_check_counter = 0
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key_check_interval = 5 # 每5帧检查一次按键
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try:
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# =====================================================================
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# 4. 主循环：从队列获取数据并显示
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# =====================================================================
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while True:
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# 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）
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for i, queue in enumerate(result_queues):
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# 快速清空队列，只保留最后一帧
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data = None
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# 一次性获取所有可用数据，只保留最后一个
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while True:
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try:
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data = queue.get_nowait()
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except:
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break
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if data is not None:
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latest_data[i] = data
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# 显示所有传感器的数据
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for data in latest_data:
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if data is None or 'error' in data:
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continue
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sid = data['sensor_id']
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# 显示形变场
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if data['img'] is not None and data['flow'] is not None:
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arrows = orisys.util.draw_arrows(
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data['img'],
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data['flow'],
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threshold=5,
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grid_spacing=10,
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arrow_scale=1.0
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)
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cv2.imshow(f"Deformation {sid}", arrows)
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# # 显示触碰点和区域
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# if data['img'] is not None:
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# image_with_foe = orisys.util.draw_contact(
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# data['img'],
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# data['contour'],
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# data['centroid']
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# )
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# cv2.imshow(f"Contact Centroid {sid}", image_with_foe)
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# # 显示深度图
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# if data['depth_map'] is not None and data['depth_map'].size > 0:
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# div_abs = data['depth_map']
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# if div_abs.max() > div_abs.min():
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# div_normalized = ((div_abs - div_abs.min()) /
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# (div_abs.max() - div_abs.min()) * 255).astype(np.uint8)
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# else:
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# div_normalized = np.zeros_like(div_abs, dtype=np.uint8)
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# cv2.imshow(f"Depth Map {sid}",
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# cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))
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# 降低打印频率以减少I/O开销（在主循环级别控制）
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print_counter += 1
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if print_counter >= print_interval:
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for data in latest_data:
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if data is not None and 'error' not in data:
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sid = data['sensor_id']
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print(f"传感器 {sid}: FPS={data['fps']:5.1f}")
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print_counter = 0
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# 降低按键检查频率以减少开销
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key_check_counter += 1
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if key_check_counter >= key_check_interval:
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key = cv2.waitKey(1) & 0xFF
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if key == ord('q'):
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print("\n退出程序...")
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break
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key_check_counter = 0
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finally:
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# =====================================================================
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# 5. 清理资源
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# =====================================================================
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# 通知所有工作进程停止
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for event in stop_events:
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event.set()
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# 等待所有进程结束
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for p in processes:
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p.join(timeout=2)
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if p.is_alive():
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p.terminate()
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p.join()
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cv2.destroyAllWindows()
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print("所有传感器进程已停止")
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if __name__ == '__main__':
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# Windows 下多进程必需的初始化
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mp.freeze_support()
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main()
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