update comment

example7_five_sensors.py

@@ -1,12 +1,29 @@
 """
-Example 7: Five Sensors with Async Pipeline
+示例 7: 五传感器网格显示
 
-Demonstrates scaling to 5 sensors running in parallel with async mode.
-Each sensor runs in its own process with 3-stage async pipeline.
+本示例演示如何扩展到 5 个传感器：
+- 使用异步多进程模式运行所有传感器
+- 将 5 个传感器画面拼接成 2x3 网格显示
+- 实时显示每个传感器的 FPS 和力数据
+
+网格布局：
+    ┌─────────┬─────────┬─────────┐
+    │ 传感器0 │ 传感器1 │ 传感器2 │
+    ├─────────┼─────────┼─────────┤
+    │ 传感器3 │ 传感器4 │  (空)   │
+    └─────────┴─────────┴─────────┘
+
+硬件要求估算：
+- CPU: 建议 8 核以上（每个传感器约 2-3 线程）
+- GPU: 建议 8GB 显存以上（每个传感器约 500MB-1GB）
+- 内存: 建议 16GB 以上
+
+注意：本示例使用同一个视频文件模拟 5 个传感器，
+     实际使用时请替换为不同的视频源或摄像头索引。
 """
 
 import os
-os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
+os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"  # Windows 兼容性设置
 
 import cv2
 import orisys
@@ -15,7 +32,13 @@ import numpy as np
 
 
 def main():
-    # Configure 5 sensors (using same video for demo - replace with actual sources)
+    # =========================================================================
+    # 1. 配置 5 个传感器
+    # =========================================================================
+    # 注意：这里使用同一个视频文件作为演示
+    # 实际应用中应替换为不同的视频源：
+    #   - 摄像头: 0, 1, 2, 3, 4
+    #   - 视频文件: './sensor0.mp4', './sensor1.mp4', ...
     configs = [
         {'vid_src': './s1.mp4', 'isstitch': True, 'cuda': True, 'cal_path': './config/cal.npy'},
         {'vid_src': './s1.mp4', 'isstitch': True, 'cuda': True, 'cal_path': './config/cal.npy'},
@@ -24,43 +47,81 @@ def main():
         {'vid_src': './s1.mp4', 'isstitch': True, 'cuda': True, 'cal_path': './config/cal.npy'},
     ]
     
+    # =========================================================================
+    # 2. 启动多传感器异步模式
+    # =========================================================================
     with orisys.MultiSensor.async_context(configs) as multi:
-        print(f"\n{len(configs)} sensors started. Press 'q' to quit")
+        print(f"\n{len(configs)} 个传感器已启动，按 'q' 退出")
         
+        # =====================================================================
+        # 3. 主循环
+        # =====================================================================
         for all_data in multi:
+            # 过滤有效数据（有图像的传感器）
             valid_data = [d for d in all_data if d and d['img'] is not None]
             
-            # Print stats for all sensors
+            # -----------------------------------------------------------------
+            # 3.1 打印所有传感器状态（单行显示）
+            # -----------------------------------------------------------------
             for data in valid_data:
-                print(f"S{data['sensor_id']}: FPS={data['fps']:4.1f} Fn={data['fnormal']:7.1f}", end=" | ")
+                print(f"S{data['sensor_id']}: "
+                      f"FPS={data['fps']:4.1f} "
+                      f"Fn={data['fnormal']:7.1f}", end=" | ")
             if valid_data:
-                print()
+                print()  # 换行
             
-            # Create grid display (2 rows: 3 + 2)
+            # -----------------------------------------------------------------
+            # 3.2 准备网格显示的图像
+            # -----------------------------------------------------------------
             imgs = []
             for data in valid_data:
+                # 绘制光流箭头
                 if data['flow'] is not None:
-                    img = orisys.util.draw_arrows(data['img'].copy(), data['flow'], threshold=5)
+                    img = orisys.util.draw_arrows(
+                        data['img'].copy(),
+                        data['flow'],
+                        threshold=5
+                    )
                 else:
                     img = data['img']
-                # Add sensor ID label
-                cv2.putText(img, f"S{data['sensor_id']}", (10, 30), 
-                           cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+                
+                # 在图像上标注传感器编号
+                cv2.putText(
+                    img,
+                    f"S{data['sensor_id']}",
+                    (10, 30),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.8,
+                    (0, 255, 0),
+                    2
+                )
                 imgs.append(img)
             
+            # -----------------------------------------------------------------
+            # 3.3 创建 2x3 网格布局
+            # -----------------------------------------------------------------
             if len(imgs) >= 5:
-                # Create 2x3 grid (pad with black if needed)
                 h, w = imgs[0].shape[:2]
-                black = np.zeros((h, w, 3), dtype=np.uint8) if len(imgs[0].shape) == 3 else np.zeros((h, w), dtype=np.uint8)
                 
+                # 创建黑色占位图像（用于第6格）
+                if len(imgs[0].shape) == 3:
+                    black = np.zeros((h, w, 3), dtype=np.uint8)
+                else:
+                    black = np.zeros((h, w), dtype=np.uint8)
+                
+                # 拼接网格
+                # 第一行: 传感器 0, 1, 2
                 row1 = np.hstack([imgs[0], imgs[1], imgs[2]])
+                # 第二行: 传感器 3, 4, 空
                 row2 = np.hstack([imgs[3], imgs[4], black])
+                # 合并两行
                 grid = np.vstack([row1, row2])
                 
-                # Resize for display
+                # 缩放到适合显示的尺寸
                 grid = cv2.resize(grid, (1200, 800))
-                cv2.imshow("5 Sensors Grid", grid)
+                cv2.imshow("5 传感器网格视图", grid)
             
+            # 检查退出键
             if cv2.waitKey(1) & 0xFF == ord('q'):
                 break
     
@@ -68,6 +129,6 @@ def main():
 
 
 if __name__ == '__main__':
+    # Windows 下多进程必需的初始化
     mp.freeze_support()
     main()
-