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