ocr1.0

rtc/rtc.py

@@ -4,6 +4,7 @@ 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 OCRViolationDetector
 
 
 class VideoTrack(MediaStreamTrack):
@@ -47,7 +48,7 @@ async def rtc_frame_receiver(url, frame_queue):
                     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}")
+                        # 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")
@@ -93,23 +94,35 @@ async def frame_consumer(frame_queue):
 
     Args: frame_queue: 帧队列
     """
+    # 创建OCR检测器实例（请替换为实际的违禁词文件路径）
+    ocr_detector = OCRViolationDetector(
+        forbidden_words_path=r"D:\Git\bin\video\ocr\forbidden_words.txt",  # 替换为实际路径
+        ocr_confidence_threshold=0.5,)
+
     while True:
         # 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()
 
+        ocr_detector.detect(current_frame)
+
+
+
+
+
+
         # 验证这是cv2可以处理的帧
-        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cv2帧、尺寸: {current_frame.shape}、数据类型: {current_frame.dtype}")
 
         # 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")
+        # print("cv2帧处理完成")
 
         # 标记任务完成
         frame_queue.task_done()
-        print("帧处理完成、队列已清空")
+        # print("帧处理完成、队列已清空")
 
 
 async def main():