enhance detection logic to prioritize highest confidence results; improve logging for detection data and heartbeat state reports

app.py

@@ -114,22 +114,67 @@ async def detect(websocket: WebSocket):
             process_time = (time.time() - start_time) * 1000
             logger.debug(f"Detection for camera {camera_id} completed in {process_time:.2f}ms")
             
+            # Log the raw detection result for debugging
+            logger.debug(f"Raw detection result for camera {camera_id}:\n{json.dumps(detection_result, indent=2, default=str)}")
+            
+            # Direct class result (no detections/classifications structure)
+            if detection_result and isinstance(detection_result, dict) and "class" in detection_result and "confidence" in detection_result:
+                highest_confidence_detection = {
+                    "class": detection_result.get("class", "none"),
+                    "confidence": detection_result.get("confidence", 1.0),
+                    "box": [0, 0, 0, 0]  # Empty bounding box for classifications
+                }
+            # Handle case when no detections found or result is empty
+            elif not detection_result or not detection_result.get("detections"):
+                # Check if we have classification results
+                if detection_result and detection_result.get("classifications"):
+                    # Get the highest confidence classification
+                    classifications = detection_result.get("classifications", [])
+                    highest_confidence_class = max(classifications, key=lambda x: x.get("confidence", 0)) if classifications else None
+                    
+                    if highest_confidence_class:
+                        highest_confidence_detection = {
+                            "class": highest_confidence_class.get("class", "none"),
+                            "confidence": highest_confidence_class.get("confidence", 1.0),
+                            "box": [0, 0, 0, 0]  # Empty bounding box for classifications
+                        }
+                    else:
+                        highest_confidence_detection = {
+                            "class": "none",
+                            "confidence": 1.0,
+                            "box": [0, 0, 0, 0]
+                        }
+                else:
+                    highest_confidence_detection = {
+                        "class": "none",
+                        "confidence": 1.0,
+                        "box": [0, 0, 0, 0]
+                    }
+            else:
+                # Find detection with highest confidence
+                detections = detection_result.get("detections", [])
+                highest_confidence_detection = max(detections, key=lambda x: x.get("confidence", 0)) if detections else {
+                    "class": "none",
+                    "confidence": 1.0,
+                    "box": [0, 0, 0, 0]
+                }
+            
             detection_data = {
                 "type": "imageDetection",
                 "cameraIdentifier": camera_id,
                 "timestamp": time.time(),
                 "data": {
-                    "detection": detection_result if detection_result else None,
+                    "detection": highest_confidence_detection,  # Send only the highest confidence detection
                     "modelId": stream["modelId"],
                     "modelName": stream["modelName"]
                 }
             }
             
-            if detection_result:
+            if highest_confidence_detection["class"] != "none":
-                detection_count = len(detection_result.get("detections", []))
+                logger.info(f"Camera {camera_id}: Detected {highest_confidence_detection['class']} with confidence {highest_confidence_detection['confidence']:.2f} using model {stream['modelName']}")
-                logger.info(f"Camera {camera_id}: Detected {detection_count} objects with model {stream['modelName']}")
             
             await websocket.send_json(detection_data)
+            logger.debug(f"Sent detection data to client for camera {camera_id}:\n{json.dumps(detection_data, indent=2)}")
             return persistent_data
         except Exception as e:
             logger.error(f"Error in handle_detection for camera {camera_id}: {str(e)}", exc_info=True)
@@ -307,7 +352,7 @@ async def detect(websocket: WebSocket):
                     "cameraConnections": camera_connections
                 }
                 await websocket.send_text(json.dumps(state_report))
-                logger.debug("Sent stateReport as heartbeat")
+                logger.debug(f"Sent stateReport as heartbeat: CPU {cpu_usage:.1f}%, Memory {memory_usage:.1f}%, {len(camera_connections)} active cameras")
                 await asyncio.sleep(HEARTBEAT_INTERVAL)
             except Exception as e:
                 logger.error(f"Error sending stateReport heartbeat: {e}")