提高视频帧率

python-inference-service/app/main.py

@@ -129,17 +129,13 @@ async def detect_file(
     model_name: str,
     file: UploadFile = File(...)
 ):
-    # 1. 打印 model_name（直接打印字符串即可）
-    print(f"接收到的 model_name: {model_name}")
-
-    # 2. 打印 file 的基本信息（文件名、内容类型等）
-    print(f"文件名: {file.filename}")
-    print(f"文件内容类型: {file.content_type}")  # 例如 image/jpeg、text/plain 等
     """Detect objects in an uploaded image file"""
+    print(f"接收到的 model_name: {model_name}")
+    print(f"文件名: {file.filename}")
+    print(f"文件内容类型: {file.content_type}")
+    
     global model_manager
 
-
-
     if not model_manager:
         raise HTTPException(status_code=500, detail="Model manager not initialized")
     
@@ -151,16 +147,39 @@ async def detect_file(
     # Read uploaded file
     try:
         contents = await file.read()
+        print(f"文件大小: {len(contents)} 字节")
+        
+        if len(contents) == 0:
+            raise HTTPException(status_code=400, detail="Empty file")
+        
         nparr = np.frombuffer(contents, np.uint8)
+        print(f"numpy数组形状: {nparr.shape}, dtype: {nparr.dtype}")
+        
         image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
         
         if image is None:
-            raise HTTPException(status_code=400, detail="Invalid image data")
+            print("错误: cv2.imdecode 返回 None")
+            raise HTTPException(status_code=400, detail="Invalid image data - failed to decode")
+        
+        print(f"解码后图像形状: {image.shape}, dtype: {image.dtype}")
+        
+    except HTTPException:
+        raise
     except Exception as e:
+        print(f"处理图像时出错: {str(e)}")
+        import traceback
+        traceback.print_exc()
         raise HTTPException(status_code=400, detail=f"Failed to process image: {str(e)}")
     
     # Run detection
-    detections, inference_time = detector.detect(image)
+    try:
+        detections, inference_time = detector.detect(image)
+        print(f"检测完成: 找到 {len(detections)} 个目标, 耗时 {inference_time:.2f}ms")
+    except Exception as e:
+        print(f"推理过程中出错: {str(e)}")
+        import traceback
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=f"Detection failed: {str(e)}")
     
     return DetectionResponse(
         model_name=model_name,