fix: model calling method

core/detection/branches.py

@@ -438,11 +438,22 @@ class BranchProcessor:
                        f"({input_frame.shape[1]}x{input_frame.shape[0]}) with confidence={min_confidence}")
 
 
-            # Use .predict() method for both detection and classification models
+            # Determine model type and use appropriate calling method (like ML engineer's approach)
             inference_start = time.time()
-            detection_results = model.model.predict(input_frame, conf=min_confidence, verbose=False)
+
+            # Check if this is a classification model based on filename or model structure
+            is_classification = 'cls' in branch_id.lower() or 'classify' in branch_id.lower()
+
+            if is_classification:
+                # Use .predict() method for classification models (like ML engineer's classification_test.py)
+                detection_results = model.model.predict(source=input_frame, verbose=False)
+                logger.info(f"[INFERENCE DONE] {branch_id}: Classification completed in {time.time() - inference_start:.3f}s using .predict()")
+            else:
+                # Use direct model call for detection models (like ML engineer's detection_test.py)
+                detection_results = model.model(input_frame, conf=min_confidence, verbose=False)
+                logger.info(f"[INFERENCE DONE] {branch_id}: Detection completed in {time.time() - inference_start:.3f}s using direct call")
+
             inference_time = time.time() - inference_start
-            logger.info(f"[INFERENCE DONE] {branch_id}: Predict completed in {inference_time:.3f}s using .predict() method")
 
             # Initialize branch_detections outside the conditional
             branch_detections = []
@@ -648,17 +659,11 @@ class BranchProcessor:
             # Format key with context
             key = action.params['key'].format(**context)
 
-            # Convert image to bytes
+            # Get image format parameters
             import cv2
             image_format = action.params.get('format', 'jpeg')
             quality = action.params.get('quality', 90)
 
-            if image_format.lower() == 'jpeg':
-                encode_param = [cv2.IMWRITE_JPEG_QUALITY, quality]
-                _, image_bytes = cv2.imencode('.jpg', image_to_save, encode_param)
-            else:
-                _, image_bytes = cv2.imencode('.png', image_to_save)
-
             # Save to Redis synchronously using a sync Redis client
             try:
                 import redis

core/detection/pipeline.py

@@ -133,32 +133,43 @@ class DetectionPipeline:
 
     async def _initialize_detection_model(self) -> bool:
         """
-        Load and initialize the main detection model.
+        Load and initialize the main detection model from pipeline.json configuration.
 
         Returns:
             True if successful, False otherwise
         """
         try:
             if not self.pipeline_config:
-                logger.warning("No pipeline configuration found")
+                logger.error("No pipeline configuration found - cannot initialize detection model")
                 return False
 
             model_file = getattr(self.pipeline_config, 'model_file', None)
             model_id = getattr(self.pipeline_config, 'model_id', None)
+            min_confidence = getattr(self.pipeline_config, 'min_confidence', 0.6)
+            trigger_classes = getattr(self.pipeline_config, 'trigger_classes', [])
+            crop = getattr(self.pipeline_config, 'crop', False)
 
             if not model_file:
-                logger.warning("No detection model file specified")
+                logger.error("No detection model file specified in pipeline configuration")
                 return False
 
-            # Load detection model
+            # Log complete pipeline configuration for main detection model
-            logger.info(f"Loading detection model: {model_id} ({model_file})")
+            logger.info(f"[MAIN MODEL CONFIG] Initializing from pipeline.json:")
+            logger.info(f"[MAIN MODEL CONFIG]   modelId: {model_id}")
+            logger.info(f"[MAIN MODEL CONFIG]   modelFile: {model_file}")
+            logger.info(f"[MAIN MODEL CONFIG]   minConfidence: {min_confidence}")
+            logger.info(f"[MAIN MODEL CONFIG]   triggerClasses: {trigger_classes}")
+            logger.info(f"[MAIN MODEL CONFIG]   crop: {crop}")
+
+            # Load detection model using model manager
+            logger.info(f"[MAIN MODEL LOADING] Loading {model_file} from model directory {self.model_id}")
             self.detection_model = self.model_manager.get_yolo_model(self.model_id, model_file)
             if not self.detection_model:
-                logger.error(f"Failed to load detection model {model_file} from model {self.model_id}")
+                logger.error(f"[MAIN MODEL ERROR] Failed to load detection model {model_file} from model {self.model_id}")
                 return False
 
             self.detection_model_id = model_id
-            logger.info(f"Detection model {model_id} loaded successfully")
+            logger.info(f"[MAIN MODEL SUCCESS] Detection model {model_id} ({model_file}) loaded successfully")
             return True
 
         except Exception as e:
@@ -462,10 +473,13 @@ class DetectionPipeline:
                 'timestamp_ms': int(time.time() * 1000)
             }
 
-            # Run inference on single snapshot using .predict() method
+            # Run inference using direct model call (like ML engineer's approach)
-            detection_results = self.detection_model.model.predict(
+            # Use minConfidence from pipeline.json configuration
+            model_confidence = getattr(self.pipeline_config, 'min_confidence', 0.6)
+            logger.info(f"[DETECTION PHASE] Running {self.pipeline_config.model_id} with conf={model_confidence} (from pipeline.json)")
+            detection_results = self.detection_model.model(
                 frame,
-                conf=getattr(self.pipeline_config, 'min_confidence', 0.6),
+                conf=model_confidence,
                 verbose=False
             )
 
@@ -477,7 +491,7 @@ class DetectionPipeline:
                 result_obj = detection_results[0]
                 trigger_classes = getattr(self.pipeline_config, 'trigger_classes', [])
 
-                # Handle .predict() results which have .boxes for detection models
+                # Handle direct model call results which have .boxes for detection models
                 if hasattr(result_obj, 'boxes') and result_obj.boxes is not None:
                     logger.info(f"[DETECTION PHASE] Found {len(result_obj.boxes)} raw detections from {getattr(self.pipeline_config, 'model_id', 'unknown')}")
 
@@ -586,10 +600,13 @@ class DetectionPipeline:
 
             # If no detected_regions provided, re-run detection to get them
             if not detected_regions:
-                # Use .predict() method for detection
+                # Use direct model call for detection (like ML engineer's approach)
-                detection_results = self.detection_model.model.predict(
+                # Use minConfidence from pipeline.json configuration
+                model_confidence = getattr(self.pipeline_config, 'min_confidence', 0.6)
+                logger.info(f"[PROCESSING PHASE] Re-running {self.pipeline_config.model_id} with conf={model_confidence} (from pipeline.json)")
+                detection_results = self.detection_model.model(
                     frame,
-                    conf=getattr(self.pipeline_config, 'min_confidence', 0.6),
+                    conf=model_confidence,
                     verbose=False
                 )
 
@@ -742,10 +759,13 @@ class DetectionPipeline:
             }
 
 
-            # Run inference on single snapshot using .predict() method
+            # Run inference using direct model call (like ML engineer's approach)
-            detection_results = self.detection_model.model.predict(
+            # Use minConfidence from pipeline.json configuration
+            model_confidence = getattr(self.pipeline_config, 'min_confidence', 0.6)
+            logger.info(f"[PIPELINE EXECUTE] Running {self.pipeline_config.model_id} with conf={model_confidence} (from pipeline.json)")
+            detection_results = self.detection_model.model(
                 frame,
-                conf=getattr(self.pipeline_config, 'min_confidence', 0.6),
+                conf=model_confidence,
                 verbose=False
             )
 
@@ -757,7 +777,7 @@ class DetectionPipeline:
                 result_obj = detection_results[0]
                 trigger_classes = getattr(self.pipeline_config, 'trigger_classes', [])
 
-                # Handle .predict() results which have .boxes for detection models
+                # Handle direct model call results which have .boxes for detection models
                 if hasattr(result_obj, 'boxes') and result_obj.boxes is not None:
                     logger.info(f"[PIPELINE RAW] Found {len(result_obj.boxes)} raw detections from {getattr(self.pipeline_config, 'model_id', 'unknown')}")
 

core/models/inference.py

@@ -81,8 +81,28 @@ class YOLOWrapper:
                 from ultralytics import YOLO
 
                 logger.info(f"Loading YOLO model from {self.model_path}")
+
+                # Load model normally first
                 self.model = YOLO(str(self.model_path))
 
+                # Determine if this is a classification model based on filename or model structure
+                # Classification models typically have 'cls' in filename
+                is_classification = 'cls' in str(self.model_path).lower()
+
+                # For classification models, create a separate instance with task parameter
+                if is_classification:
+                    try:
+                        # Reload with classification task (like ML engineer's approach)
+                        self.model = YOLO(str(self.model_path), task="classify")
+                        logger.info(f"Loaded classification model {self.model_id} with task='classify'")
+                    except Exception as e:
+                        logger.warning(f"Failed to load with task='classify', using default: {e}")
+                        # Fall back to regular loading
+                        self.model = YOLO(str(self.model_path))
+                        logger.info(f"Loaded model {self.model_id} with default task")
+                else:
+                    logger.info(f"Loaded detection model {self.model_id}")
+
                 # Move model to device
                 if self.device == 'cuda' and torch.cuda.is_available():
                     self.model.to('cuda')
@@ -141,7 +161,7 @@ class YOLOWrapper:
             import time
             start_time = time.time()
 
-            # Run inference
+            # Run inference using direct model call (like ML engineer's approach)
             results = self.model(
                 image,
                 conf=confidence_threshold,
@@ -291,11 +311,11 @@ class YOLOWrapper:
             raise RuntimeError(f"Model {self.model_id} not loaded")
 
         try:
-            # Run inference
+            # Run inference using predict method for classification (like ML engineer's approach)
-            results = self.model(image, verbose=False)
+            results = self.model.predict(source=image, verbose=False)
 
             # For classification models, extract probabilities
-            if hasattr(results[0], 'probs'):
+            if results and len(results) > 0 and hasattr(results[0], 'probs') and results[0].probs is not None:
                 probs = results[0].probs
                 top_indices = probs.top5[:top_k]
                 top_conf = probs.top5conf[:top_k].cpu().numpy()
@@ -307,7 +327,7 @@ class YOLOWrapper:
 
                 return predictions
             else:
-                logger.warning(f"Model {self.model_id} does not support classification")
+                logger.warning(f"Model {self.model_id} does not support classification or no probs found")
                 return {}
 
         except Exception as e:
@@ -350,6 +370,10 @@ class YOLOWrapper:
         """Get the number of classes the model can detect"""
         return len(self._class_names)
 
+    def is_classification_model(self) -> bool:
+        """Check if this is a classification model"""
+        return 'cls' in str(self.model_path).lower() or 'classify' in str(self.model_path).lower()
+
     def clear_cache(self) -> None:
         """Clear the model cache"""
         with self._cache_lock: