Add confidence check on model

siwatsystem/pympta.py

@@ -561,6 +561,9 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None):
 
         # ─── Detection stage - Multi-class support ──────────────────
         tk = node["triggerClassIndices"]
+        logger.debug(f"Running detection for node {node['modelId']} with trigger classes: {node.get('triggerClasses', [])} (indices: {tk})")
+        logger.debug(f"Node configuration: minConfidence={node['minConfidence']}, multiClass={node.get('multiClass', False)}")
+        
         res = node["model"].track(
             frame,
             stream=False,
@@ -573,12 +576,17 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None):
         all_boxes = []
         regions_dict = {}
         
-        for box in res.boxes:
+        logger.debug(f"Raw detection results from model: {len(res.boxes) if res.boxes is not None else 0} detections")
+        
+        for i, box in enumerate(res.boxes):
             conf = float(box.cpu().conf[0])
             cid = int(box.cpu().cls[0])
             name = node["model"].names[cid]
             
+            logger.debug(f"Detection {i}: class='{name}' (id={cid}), confidence={conf:.3f}, threshold={node['minConfidence']}")
+            
             if conf < node["minConfidence"]:
+                logger.debug(f"  -> REJECTED: confidence {conf:.3f} < threshold {node['minConfidence']}")
                 continue
                 
             xy = box.cpu().xyxy[0]
@@ -595,6 +603,8 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None):
             all_detections.append(detection)
             all_boxes.append(bbox)
             
+            logger.debug(f"  -> ACCEPTED: {name} with confidence {conf:.3f}, bbox={bbox}")
+            
             # Store highest confidence detection for each class
             if name not in regions_dict or conf > regions_dict[name]["confidence"]:
                 regions_dict[name] = {
@@ -602,8 +612,13 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None):
                     "confidence": conf,
                     "detection": detection
                 }
+                logger.debug(f"  -> Updated regions_dict['{name}'] with confidence {conf:.3f}")
+
+        logger.info(f"Detection summary: {len(all_detections)} accepted detections from {len(res.boxes) if res.boxes is not None else 0} total")
+        logger.info(f"Detected classes: {list(regions_dict.keys())}")
 
         if not all_detections:
+            logger.warning("No detections above confidence threshold - returning null")
             return (None, None) if return_bbox else None
 
         # ─── Multi-class validation ─────────────────────────────────
@@ -611,13 +626,25 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None):
             expected_classes = node["expectedClasses"]
             detected_classes = list(regions_dict.keys())
             
-            # Check if all expected classes are detected
+            logger.info(f"Multi-class validation: expected={expected_classes}, detected={detected_classes}")
+            
+            # Check if at least one expected class is detected (flexible mode)
+            matching_classes = [cls for cls in expected_classes if cls in detected_classes]
             missing_classes = [cls for cls in expected_classes if cls not in detected_classes]
-            if missing_classes:
+            
-                logger.debug(f"Missing expected classes: {missing_classes}. Detected: {detected_classes}")
+            logger.debug(f"Matching classes: {matching_classes}, Missing classes: {missing_classes}")
+            
+            if not matching_classes:
+                # No expected classes found at all
+                logger.warning(f"PIPELINE REJECTED: No expected classes detected. Expected: {expected_classes}, Detected: {detected_classes}")
                 return (None, None) if return_bbox else None
             
-            logger.info(f"Multi-class detection success: {detected_classes}")
+            if missing_classes:
+                logger.info(f"Partial multi-class detection: {matching_classes} found, {missing_classes} missing")
+            else:
+                logger.info(f"Complete multi-class detection success: {detected_classes}")
+        else:
+            logger.debug("No multi-class validation - proceeding with all detections")
 
         # ─── Execute actions with region information ────────────────
         detection_result = {
@@ -628,6 +655,11 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None):
         
         # ─── Create initial database record when Car+Frontal detected ────
         if node.get("db_manager") and node.get("multiClass", False):
+            # Only create database record if we have both Car and Frontal
+            has_car = "Car" in regions_dict
+            has_frontal = "Frontal" in regions_dict
+            
+            if has_car and has_frontal:
                 # Generate UUID session_id since client session is None for now
                 import uuid as uuid_lib
                 from datetime import datetime
@@ -648,6 +680,8 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None):
                     detection_result["session_id"] = inserted_session_id
                     detection_result["timestamp"] = timestamp  # Update with proper timestamp
                     logger.info(f"Created initial database record with session_id: {inserted_session_id}")
+            else:
+                logger.debug(f"Database record not created - missing required classes. Has Car: {has_car}, Has Frontal: {has_frontal}")
         
         execute_actions(node, frame, detection_result, regions_dict)