提高视频帧率

python-inference-service/app/detector.py

@@ -12,7 +12,7 @@ from app.models import Detection
 class PythonModelDetector:
     """Object detector using native Python models"""
     
-    def __init__(self, model_name: str, model_path: str, input_width: int, input_height: int, color: int = 0x00FF00):
+    def __init__(self, model_name: str, model_path: str, input_width: int, input_height: int, color: int = 0x00FF00, model_config: dict = None):
         """
         Initialize detector with Python model
         
@@ -22,11 +22,13 @@ class PythonModelDetector:
             input_width: Input width for the model
             input_height: Input height for the model
             color: RGB color for detection boxes (default: green)
+            model_config: Additional configuration to pass to the model
         """
         self.model_name = model_name
         self.input_width = input_width
         self.input_height = input_height
         self.color = color
+        self.model_config = model_config or {}
         
         # Convert color from RGB to BGR (OpenCV uses BGR)
         self.color_bgr = ((color & 0xFF) << 16) | (color & 0xFF00) | ((color >> 16) & 0xFF)
@@ -72,8 +74,18 @@ class PythonModelDetector:
         if not hasattr(model_module, "Model"):
             raise AttributeError(f"Model module must define a 'Model' class: {model_path}")
         
-        # Create model instance
-        self.model = model_module.Model()
+        # Create model instance with config
+        # Try to pass config to model constructor if it accepts parameters
+        import inspect
+        model_class = model_module.Model
+        sig = inspect.signature(model_class.__init__)
+        
+        if len(sig.parameters) > 1:  # Has parameters beyond 'self'
+            # Pass all config as keyword arguments
+            self.model = model_class(**self.model_config)
+        else:
+            # No parameters, create without arguments
+            self.model = model_class()
         
         # Check if model has the required methods
         if not hasattr(self.model, "predict"):
@@ -113,18 +125,16 @@ class PythonModelDetector:
         # Original image dimensions
         img_height, img_width = img.shape[:2]
         
-        # Preprocess image
-        processed_img = self.preprocess(img)
-        
         # Measure inference time
         start_time = time.time()
         
         try:
             # Run inference using model's predict method
+            # Note: Pass original image to model, let it handle preprocessing
             # Expected return format from model's predict:
             # List of dicts with keys: 'bbox', 'class_id', 'confidence'
             # bbox: (x, y, w, h) normalized [0-1]
-            model_results = self.model.predict(processed_img)
+            model_results = self.model.predict(img)
             
             # Calculate inference time in milliseconds
             inference_time = (time.time() - start_time) * 1000
@@ -279,13 +289,22 @@ class ModelManager:
                 # Use color from palette
                 color = palette[i % len(palette)]
                 
+                # Extract model-specific config (model_file, model_name, etc.)
+                # These will be passed to the Model class __init__
+                model_init_config = {}
+                if "model_file" in model_config:
+                    model_init_config["model_file"] = model_config["model_file"]
+                if "display_name" in model_config:
+                    model_init_config["model_name"] = model_config["display_name"]
+                
                 # Create detector for Python model
                 detector = PythonModelDetector(
                     model_name=name,
                     model_path=path,
                     input_width=size[0],
                     input_height=size[1],
-                    color=color
+                    color=color,
+                    model_config=model_init_config
                 )
                 
                 self.models[name] = detector

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,