rearrange tracker for user modifiability

app.py

@@ -18,22 +18,15 @@ import psutil
 
 app = FastAPI()
 
-# Remove global model and class_names
-# model = YOLO("yolov8n.pt")
-# if torch.cuda.is_available():
-#     model.to('cuda')
-# class_names = model.names
-
-# Introduce models dictionary
 models = {}
 
 with open("config.json", "r") as f:
     config = json.load(f)
 
 poll_interval = config.get("poll_interval_ms", 100)
-reconnect_interval = config.get("reconnect_interval_sec", 5)  # New setting
+reconnect_interval = config.get("reconnect_interval_sec", 5) 
-TARGET_FPS = config.get("target_fps", 10)  # Add TARGET_FPS
+TARGET_FPS = config.get("target_fps", 10)
-poll_interval = 1000 / TARGET_FPS  # Adjust poll_interval based on TARGET_FPS
+poll_interval = 1000 / TARGET_FPS
 logging.info(f"Poll interval: {poll_interval}ms")
 max_streams = config.get("max_streams", 5)
 max_retries = config.get("max_retries", 3)
@@ -64,6 +57,31 @@ async def detect(websocket: WebSocket):
 
     streams = {}
 
+    # This function is user-modifiable
+    # Save data you want to persist across frames in the persistent_data dictionary
+    async def handle_detection(camera_id, stream, frame, websocket, model: YOLO, persistent_data):
+        boxes = []
+        for r in model(frame, stream=False):
+            for box in r.boxes:
+                boxes.append({
+                    "class": model.names[int(box.cls[0])],
+                    "confidence": float(box.conf[0]),
+                })
+        # Broadcast to all subscribers of this URL
+        detection_data = {
+            "type": "imageDetection",
+            "cameraIdentifier": camera_id,
+            "timestamp": time.time(),
+            "data": {
+                "detections": boxes,
+                "modelId": stream['modelId'],
+                "modelName": stream['modelName']
+            }
+        }
+        logging.debug(f"Sending detection data for camera {camera_id}: {detection_data}")
+        await websocket.send_json(detection_data)
+        return persistent_data
+
     def frame_reader(camera_id, cap, buffer, stop_event):
         import time
         retries = 0
@@ -110,10 +128,9 @@ async def detect(websocket: WebSocket):
                 break
 
     async def process_streams():
-        # Remove global model and class_names
-        # global model, class_names
         global models
         logging.info("Started processing streams")
+        persistent_data_dict = {} 
         try:
             while True:
                 start_time = time.time()
@@ -122,32 +139,11 @@ async def detect(websocket: WebSocket):
                     buffer = stream['buffer']
                     if not buffer.empty():
                         frame = buffer.get()
-                        # Get the model for this stream's modelId
                         model = models.get(stream['modelId'])
-                        if not model:
+                        key = (camera_id, stream['modelId'])
-                            logging.error(f"Model {stream['modelId']} not loaded for camera {camera_id}")
+                        persistent_data = persistent_data_dict.get(key, {})
-                            continue
+                        updated_persistent_data = await handle_detection(camera_id, stream, frame, websocket, model, persistent_data)
-                        results = model(frame, stream=False)
+                        persistent_data_dict[key] = updated_persistent_data
-                        boxes = []
-                        for r in results:
-                            for box in r.boxes:
-                                boxes.append({
-                                    "class": model.names[int(box.cls[0])],
-                                    "confidence": float(box.conf[0]),
-                                })
-                        # Broadcast to all subscribers of this URL
-                        detection_data = {
-                            "type": "imageDetection",
-                            "cameraIdentifier": camera_id,
-                            "timestamp": time.time(),
-                            "data": {
-                                "detections": boxes,
-                                "modelId": stream['modelId'],
-                                "modelName": stream['modelName']
-                            }
-                        }
-                        logging.debug(f"Sending detection data for camera {camera_id}: {detection_data}")
-                        await websocket.send_json(detection_data)
                 elapsed_time = (time.time() - start_time) * 1000  # in ms
                 sleep_time = max(poll_interval - elapsed_time, 0)
                 logging.debug(f"Elapsed time: {elapsed_time}ms, sleeping for: {sleep_time}ms")
@@ -195,8 +191,6 @@ async def detect(websocket: WebSocket):
                 break
 
     async def on_message():
-        # Change from nonlocal to global
-        # global model, class_names
         global models
         while True:
             msg = await websocket.receive_text()