restart/ crash/ tracker/ loading with minor fixes..

backend/engine.py

@@ -90,8 +90,9 @@ def run(model, video_path, line, config, on_frame, save_annotated=False, annotat
         annotated_dir = os.path.join(tempfile.gettempdir(), "funky_reports")
         os.makedirs(annotated_dir, exist_ok=True)
         annotated_path = os.path.join(annotated_dir, f"annotated_{os.path.basename(video_path)}.mp4")
+        writer_fps = max(1.0, fps / stride)
         fourcc = cv2.VideoWriter_fourcc(*"mp4v")
-        writer = cv2.VideoWriter(annotated_path, fourcc, fps / stride, (out_w, out_h))
+        writer = cv2.VideoWriter(annotated_path, fourcc, writer_fps, (out_w, out_h))
 
     prev_side = {}
     counted_ids = set()
@@ -144,6 +145,11 @@ def run(model, video_path, line, config, on_frame, save_annotated=False, annotat
 
                 current = _side((cx, cy), a, b)
 
+                # Skip if centroid is exactly on the line (cross-product == 0)
+                # — avoids misfired crossings due to floating-point boundary hits
+                if current == 0:
+                    continue
+
                 if obj_id in prev_side and obj_id not in counted_ids:
                     if prev_side[obj_id] != current:
                         dist = _point_to_segment_dist(cx, cy, a[0], a[1], b[0], b[1])
@@ -160,10 +166,11 @@ def run(model, video_path, line, config, on_frame, save_annotated=False, annotat
 
                 prev_side[obj_id] = current
 
-        # Write annotated frame (only for frames we already process)
+        # Write annotated frame
+        cur_clses = cls if r.boxes.id is not None else None
         if writer is not None:
             frame = r.orig_img.copy()
-            _draw_annotations(frame, cur_boxes, cur_ids, cls if r.boxes.id is not None else None, [a, b], annotated_options)
+            _draw_annotations(frame, cur_boxes, cur_ids, cur_clses, [a, b], annotated_options)
             writer.write(frame)
 
         congestion.append(active)

backend/model.py

@@ -5,27 +5,51 @@ from ultralytics import YOLO
 
 load_dotenv()
 
+# Silence Ultralytics config dir warning on read-only containers
+os.environ.setdefault("YOLO_CONFIG_DIR", "/tmp/Ultralytics")
+
 MODEL_DIR = Path(__file__).parent / "weights"
-PT_PATH = MODEL_DIR / "best.pt"
 OV_DIR = MODEL_DIR / "best_int8_openvino_model"
 
+_HF_BASE = (
+    "https://huggingface.co/Perception365/VehicleNet-Y26s"
+    "/resolve/main/weights/best_int8_openvino_model"
+)
+
+# The three files that make up the OpenVINO INT8 model
+_OV_FILES = ["best.bin", "best.xml", "metadata.yaml"]
+
 
-def ensure_openvino():
-    MODEL_DIR.mkdir(exist_ok=True)
+def _auth_header():
+    token = os.getenv("HF_TOKEN", "")
+    return f'-H "Authorization: Bearer {token}"' if token else ""
 
-    if not PT_PATH.exists():
-        token = os.getenv("HF_TOKEN")
-        os.system(
-            f'curl -L -H "Authorization: Bearer {token}" '
-            f'-o {PT_PATH} '
-            f'https://huggingface.co/Perception365/VehicleNet-Y26s/resolve/main/weights/best.pt'
-        )
 
-    # https://docs.ultralytics.com/modes/export/#arguments
-    if not OV_DIR.exists():
-        YOLO(str(PT_PATH)).export(format="openvino", imgsz=736, dynamic=False, int8=True, device='cpu', batch=1)
+def _download_ov_model():
+    """Download the pre-built OV INT8 model files directly from HF."""
+    OV_DIR.mkdir(parents=True, exist_ok=True)
+    auth = _auth_header()
+    for filename in _OV_FILES:
+        dest = OV_DIR / filename
+        if dest.exists():
+            print(f"[model] {filename} already present, skipping.")
+            continue
+        url = f"{_HF_BASE}/{filename}"
+        print(f"[model] Downloading {filename} ...")
+        ret = os.system(f'curl -L --fail {auth} -o "{dest}" "{url}"')
+        if ret != 0 or not dest.exists() or dest.stat().st_size < 100:
+            raise RuntimeError(
+                f"[model] Failed to download {filename} from HF. "
+                "Check HF_TOKEN and repo visibility."
+            )
+    print("[model] OV model files ready ✅")
 
 
 def load_model():
-    ensure_openvino()
+    if not OV_DIR.exists() or not all(
+        (OV_DIR / f).exists() for f in _OV_FILES
+    ):
+        _download_ov_model()
+    else:
+        print("[model] OV model already present — skipping download.")
     return YOLO(str(OV_DIR), task="detect")

backend/server.py

@@ -17,7 +17,8 @@ from engine import run
 from constants import MODEL_CLASSES, BUSINESS_MAP
 from visualize import generate_all
 
-app = FastAPI()
+from contextlib import asynccontextmanager
+import numpy as np
 
 BASE = Path(__file__).parent.parent
 FRONTEND = BASE / "frontend"
@@ -31,10 +32,23 @@ run_results = {}
 model = None
 
 
-@app.on_event("startup")
-def startup():
+@asynccontextmanager
+async def lifespan(app: FastAPI):
     global model
     model = load_model()
+    # Warm up: run a dummy inference so OpenVINO compiles its graph now,
+    # not on the first real user request
+    try:
+        dummy = np.zeros((1, 3, 640, 640), dtype=np.uint8)
+        model(dummy, verbose=False)
+        print("[BACKEND] Model warm-up complete.")
+    except Exception as e:
+        print(f"[BACKEND] Warm-up skipped: {e}")
+    yield
+    # Shutdown: nothing to clean up
+
+
+app = FastAPI(lifespan=lifespan)
 
 
 @app.get("/")
@@ -135,7 +149,7 @@ async def ws_run(ws: WebSocket):
 
     path = videos.get(video_id)
 
-    loop = asyncio.get_event_loop()
+    loop = asyncio.get_running_loop()
 
     queue = asyncio.Queue()
 

requirements.txt

@@ -9,6 +9,7 @@ python-dotenv==1.0.0
 websockets==12.0
 onnxruntime
 openvino>=2024.0.0
+nncf>=2.14.0,<3.0.0
 onnx>=1.12.0
 onnxslim>=0.1.71
 lap>=0.5.12