perf: reuse sample dimensions and catalog digest

recognization/code/app.py

@@ -21,6 +21,7 @@ import argparse
 import asyncio
 import base64
 from contextlib import asynccontextmanager
+from dataclasses import dataclass
 import json
 import logging
 import os
@@ -29,8 +30,7 @@ import sys
 import threading
 import time
 import uuid
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Tuple
 
 # Keep the demo off the network before anything imports ultralytics.
 os.environ.setdefault("YOLO_OFFLINE", "1")
@@ -296,6 +296,9 @@ def create_app(
     recorder = FrameRecorder(settings.record_frames, every=settings.record_every) \
         if settings.record_frames else None
     samples = {item["id"]: item for item in get_available_shelf_images("add")}
+    # path -> (mtime_ns, size, width, height). Dropped when the file changes.
+    sample_dimensions: Dict[str, Tuple[int, int, int, int]] = {}
+    sample_dimensions_lock = threading.Lock()
     sessions: Dict[str, LiveSession] = {}
     sessions_lock = asyncio.Lock()
     app.state.engine = engine
@@ -371,15 +374,37 @@ def create_app(
             raise _error("NOT_FOUND", f"unknown sku '{sku_id}'", 404)
         return {"status": "success", "product": product}
 
-    def _sample_listing() -> List[Dict[str, Any]]:
-        listing = []
+    def _sample_dimensions(path: str) -> Optional[Tuple[int, int]]:
+        try:
+            stat = os.stat(path)
+        except OSError:
+            with sample_dimensions_lock:
+                sample_dimensions.pop(path, None)
+            return None
+        key = (stat.st_mtime_ns, stat.st_size)
+        with sample_dimensions_lock:
+            cached = sample_dimensions.get(path)
+            if cached is not None and cached[:2] == key:
+                return cached[2], cached[3]
         import cv2
 
+        image = cv2.imread(path)
+        if image is None:
+            with sample_dimensions_lock:
+                sample_dimensions.pop(path, None)
+            return None
+        width, height = int(image.shape[1]), int(image.shape[0])
+        with sample_dimensions_lock:
+            sample_dimensions[path] = (key[0], key[1], width, height)
+        return width, height
+
+    def _sample_listing() -> List[Dict[str, Any]]:
+        listing = []
         for item in samples.values():
             entry = {"id": item["id"], "label": item["label"], "url": f"/v1/samples/{item['id']}"}
-            image = cv2.imread(item["path"])
-            if image is not None:
-                entry["width"], entry["height"] = int(image.shape[1]), int(image.shape[0])
+            dimensions = _sample_dimensions(item["path"])
+            if dimensions is not None:
+                entry["width"], entry["height"] = dimensions
             listing.append(entry)
         return listing
 
@@ -693,7 +718,6 @@ def create_app(
             async def send(payload: Dict[str, Any]) -> None:
                 async with send_lock:
                     await websocket.send_text(json.dumps(payload))
-
             await send(
                 {
                     "type": "hello",
@@ -717,7 +741,7 @@ def create_app(
 
         async def processing_loop() -> None:
             while not session.closed:
-                pending = await loop.run_in_executor(None, session.take_pending, 0.25)
+                pending = await session.take_pending_async()
                 if pending is None:
                     continue
                 frame_id, payload = pending