RTC提交

rtc/rtc.py

@@ -1,8 +1,9 @@
 import asyncio
 import aiohttp
+import cv2  # 导入OpenCV库
+import numpy as np
 from aiortc import RTCPeerConnection, RTCSessionDescription, RTCConfiguration
 from aiortc.mediastreams import MediaStreamTrack
-from ocr.ocr_violation_detector import detect
 
 
 class VideoTrack(MediaStreamTrack):
@@ -18,7 +19,7 @@ class VideoTrack(MediaStreamTrack):
 
 async def rtc_frame_receiver(url, frame_queue):
     """
-    对每帧进行检查、只要接收到 RTC 帧且队列为空、就往队列放入数据
+    对每帧进行检查、只要接收到 RTC 帧且队列为空、就往队列放入cv2格式的帧数据
     """
     pc = RTCPeerConnection(RTCConfiguration(iceServers=[]))
     video_track = VideoTrack()
@@ -35,17 +36,23 @@ async def rtc_frame_receiver(url, frame_queue):
             while True:
                 # 接收当前帧并累计计数
                 frame = await track.recv()
-                frame_bgr24 = frame.to_ndarray(format='bgr24')
+                # 转换为cv2兼容的BGR格式numpy数组
-                total_frames += 1
+                frame_cv2 = frame.to_ndarray(format='bgr24')
 
-                # 对每帧都检查队列状态、队列为空则放入
+                # 验证是否为cv2兼容格式
-                if frame_queue.empty():
+                if isinstance(frame_cv2, np.ndarray) and frame_cv2.ndim == 3 and frame_cv2.shape[2] == 3:
-                    # 队列为空、放入当前帧
+                    total_frames += 1
-                    await frame_queue.put(frame_bgr24)
+
-                    print(f"第{total_frames}帧：队列为空、已放入新帧")
+                    # 对每帧都检查队列状态、队列为空则放入
+                    if frame_queue.empty():
+                        # 队列为空、放入当前cv2帧
+                        await frame_queue.put(frame_cv2)
+                        print(f"第{total_frames}帧：队列为空、已放入新的cv2帧，尺寸: {frame_cv2.shape}")
+                    else:
+                        # 队列非空、说明上一帧还未处理、跳过当前帧
+                        print(f"第{total_frames}帧：队列非空、跳过该帧")
                 else:
-                    # 队列非空、说明上一帧还未处理、跳过当前帧
+                    print("帧格式转换失败，不是有效的cv2格式")
-                    print(f"第{total_frames}帧：队列非空、跳过该帧")
 
     # 创建并设置本地offer
     offer = await pc.createOffer()
@@ -82,18 +89,23 @@ async def rtc_frame_receiver(url, frame_queue):
 
 async def frame_consumer(frame_queue):
     """
-    从队列中读取帧并处理（队列空时会阻塞等待）
+    从队列中读取cv2帧并处理（队列空时会阻塞等待）
 
     Args: frame_queue: 帧队列
     """
     while True:
-        # 从队列中获取帧（队列为空时会阻塞等待新帧）
+        # 从队列中获取cv2帧（队列为空时会阻塞等待新帧）
         current_frame = await frame_queue.get()
 
-        # 检测
+        # 验证这是cv2可以处理的帧
-        detect(current_frame)
+        print(f"从队列获取到cv2帧、尺寸: {current_frame.shape}、数据类型: {current_frame.dtype}")
 
-        print(f"从队列获取到帧、尺寸: {current_frame.shape}、进行处理")
+        # 这里可以添加cv2的处理代码，例如显示帧
+        # cv2.imshow('Received Frame', current_frame)
+        # if cv2.waitKey(1) & 0xFF == ord('q'):
+        #     break
+
+        print("cv2帧处理完成")
 
         # 标记任务完成
         frame_queue.task_done()
@@ -116,4 +128,8 @@ async def main():
 
 
 if __name__ == "__main__":
-    asyncio.run(main())
+    try:
+        asyncio.run(main())
+    finally:
+        # 确保关闭所有cv2窗口
+        cv2.destroyAllWindows()