feat: custom bot-sort based tracker

core/models/inference.py

@@ -60,6 +60,8 @@ class YOLOWrapper:
 
         self.model = None
         self._class_names = []
+
+
         self._load_model()
 
         logger.info(f"Initialized YOLO wrapper for {model_id} on {self.device}")
@@ -115,6 +117,7 @@ class YOLOWrapper:
             logger.error(f"Failed to extract class names: {str(e)}")
             self._class_names = {}
 
+
     def infer(
         self,
         image: np.ndarray,
@@ -222,55 +225,30 @@ class YOLOWrapper:
 
         return detections
 
+
     def track(
         self,
         image: np.ndarray,
         confidence_threshold: float = 0.5,
         trigger_classes: Optional[List[str]] = None,
-        persist: bool = True
+        persist: bool = True,
+        camera_id: Optional[str] = None
     ) -> InferenceResult:
         """
-        Run tracking on an image
+        Run detection (tracking will be handled by external tracker)
 
         Args:
             image: Input image as numpy array (BGR format)
             confidence_threshold: Minimum confidence for detections
             trigger_classes: List of class names to filter
-            persist: Whether to persist tracks across frames
+            persist: Ignored - tracking handled externally
+            camera_id: Ignored - tracking handled externally
 
         Returns:
-            InferenceResult containing detections with track IDs
+            InferenceResult containing detections (no track IDs from YOLO)
         """
-        if self.model is None:
-            raise RuntimeError(f"Model {self.model_id} not loaded")
-
-        try:
-            import time
-            start_time = time.time()
-
-            # Run tracking
-            results = self.model.track(
-                image,
-                conf=confidence_threshold,
-                persist=persist,
-                verbose=False
-            )
-
-            inference_time = time.time() - start_time
-
-            # Parse results
-            detections = self._parse_results(results[0], trigger_classes)
-
-            return InferenceResult(
-                detections=detections,
-                image_shape=(image.shape[0], image.shape[1]),
-                inference_time=inference_time,
-                model_id=self.model_id
-            )
-
-        except Exception as e:
-            logger.error(f"Tracking failed for model {self.model_id}: {str(e)}", exc_info=True)
-            raise
+        # Just do detection - no YOLO tracking
+        return self.infer(image, confidence_threshold, trigger_classes)
 
     def predict_classification(
         self,
@@ -350,6 +328,7 @@ class YOLOWrapper:
         """Get the number of classes the model can detect"""
         return len(self._class_names)
 
+
     def clear_cache(self) -> None:
         """Clear the model cache"""
         with self._cache_lock: