chore: the film-making shortcut leaves the server, and failures stop hiding
From the deep review's hygiene list: - The demo replay (a still recognition replayed as if it were live tracking) moves out of app.py into demo_replay.py, and every payload it produces is marked `synthetic: true`. It had been committed inside an unrelated feature commit, which made a film-making device look like part of the server. - `/v1/health` reports the OCR engine and whether it is available. Without it an operator could not tell a broken OCR from a shelf with no readable tags. - A failed OCR pass is counted and logged instead of swallowed: `TagService.stats()` now carries `passes_failed` and `last_pass_error`. - The live classification budget goes back to the measured production defaults (16 in flight, 2 attempts); a demo walk over a nearly static shelf asks for more through `--live-inflight` / `--live-attempts` rather than silently changing the default for everyone. 162 tests pass. Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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162
recognization/code/demo_replay.py
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recognization/code/demo_replay.py
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"""
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Demo-only: replay a shelf's recognition as if it were live tracking.
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`DemoRecognition` recognises the whole shelf once, with the real model and the real
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matcher, and then hands those results to the walk's frames as if the live session
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had produced them. That is useful for filming and for exercising a client without a
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camera, and it is *not* a perception result: the boxes do not come from the frame
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being processed, and no tracking or classification happened on it. Every payload it
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touches is marked `synthetic: true` so a client can tell.
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It lives here rather than in `app.py` because it was committed inside an unrelated
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feature once, which made a film-making device look like part of the server.
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"""
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from __future__ import annotations
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import threading
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from typing import Any, Dict, List, Optional
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from demo import DemoSourceError
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class DemoRecognition:
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"""The demo shortcut.
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A real walk recognises each facing as it appears, which on a shelf the
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catalog only partly covers leaves most boxes unlabelled. For a demo the
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shelf is recognised ONCE, in full, with the same model and matcher, and
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those results are then handed to the walk's frames as if the live session
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had just produced them - every box already green, with name and price.
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Demo-only: enabled by --demo-walk.
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"""
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_STILL_TO_LIVE = {
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# The still payload already speaks the live vocabulary for its states;
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# the decision-level names are kept for older shapes.
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"recognized": "recognized",
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"needs_confirmation": "needs_confirmation",
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"unrecognized": "unrecognized",
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"accepted": "recognized",
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"ambiguous": "needs_confirmation",
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"unknown": "unrecognized",
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"rejected": "unrecognized",
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}
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def __init__(self, engine: Any, camera: Any, samples: Dict[str, Any], tag_attacher: Any) -> None:
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self._engine = engine
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self._camera = camera
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self._samples = samples
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self._tag_attacher = tag_attacher
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self._catalog_by_sku = {product["sku_id"]: product for product in engine.catalog()}
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self._cache: Dict[str, Any] = {}
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self._lock = threading.Lock()
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def steps(self, sample_id: str) -> int:
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return self._camera.plan([sample_id], (1280, 720)).get("steps", 0)
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def recognition(self, sample_id: str) -> Any:
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cached = self._cache.get(sample_id)
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if cached is not None:
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return cached
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import cv2
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path = self._samples[sample_id]["path"]
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image = cv2.imread(path)
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if image is None:
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raise DemoSourceError(f"cannot read sample '{sample_id}'")
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result = self._engine.recognize(image, classify=True, method="hybrid", with_empty_slots=True)
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if self._tag_attacher is not None:
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result = self._tag_attacher(result, image)
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with self._lock:
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self._cache[sample_id] = (result, int(image.shape[1]), int(image.shape[0]))
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return self._cache[sample_id]
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def warm(self, sample_id: str) -> None:
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self.recognition(sample_id)
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def apply(self, result: Dict[str, Any], sample_id: str, step: int) -> Dict[str, Any]:
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"""Swap the live tracks for the shelf's recognised ones, in frame space."""
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still, _, _ = self.recognition(sample_id)
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position = self._camera.window_at([sample_id], step)
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ox, oy = position["x"], position["y"]
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win_w, win_h = position["width"], position["height"]
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tracks = []
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for index, detection in enumerate(still.get("detections") or []):
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box = [int(v) for v in detection.get("box") or []]
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if len(box) != 4:
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continue
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x1, y1, x2, y2 = box
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if x2 - ox <= 0 or y2 - oy <= 0 or x1 - ox >= win_w or y1 - oy >= win_h:
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continue
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frame_box = [x1 - ox, y1 - oy, x2 - ox, y2 - oy]
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identity = detection.get("identity") or {}
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state = self._STILL_TO_LIVE.get(identity.get("state") or "", "unrecognized")
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sku_id = identity.get("sku_id")
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product = self._catalog_by_sku.get(sku_id or "") if sku_id else None
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if state == "recognized" and product is None:
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state = "unrecognized"
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payload: Dict[str, Any] = {
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"track_id": index + 1,
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"box": frame_box,
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"raw_box": frame_box,
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"confidence": round(float(detection.get("confidence") or 0.0), 4),
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"first_seen_seq": step,
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"relabels": 0,
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"restarts": 0,
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"inherited": False,
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"identity": {
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"state": state,
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"sku_id": sku_id if state == "recognized" else None,
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"identity_level": identity.get("identity_level"),
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"confidence": identity.get("confidence"),
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"score": identity.get("score"),
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"candidates": list(identity.get("candidates") or []),
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"attempts": 1,
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"method": "hybrid",
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},
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}
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if product is not None and state == "recognized":
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payload["product"] = {
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"sku_id": product.get("sku_id"),
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"product_name": product.get("product_name"),
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"price": product.get("price"),
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}
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tracks.append(payload)
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slots = []
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for slot in still.get("empty_slots") or []:
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box = [int(v) for v in slot.get("box") or []]
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if len(box) != 4:
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continue
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x1, y1, x2, y2 = box
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if x2 - ox <= 0 or y2 - oy <= 0 or x1 - ox >= win_w or y1 - oy >= win_h:
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continue
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entry = dict(slot)
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entry["box"] = [x1 - ox, y1 - oy, x2 - ox, y2 - oy]
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slots.append(entry)
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result["tracks"] = tracks
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result["empty_slots"] = slots
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counts: Dict[str, int] = {}
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for track in tracks:
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counts[track["identity"]["state"]] = counts.get(track["identity"]["state"], 0) + 1
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result["summary"] = {
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"in_view": len(tracks),
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"recognized": counts.get("recognized", 0),
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"needs_confirmation": counts.get("needs_confirmation", 0),
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"unrecognized": counts.get("unrecognized", 0),
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"detected": 0,
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"unconfirmed": counts.get("unconfirmed", 0),
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"identifying": 0,
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"empty_slots": len(slots),
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"price_tags": (still.get("price_tags") or {"readable": 0, "unreadable": 0}),
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"reconciliation": (still.get("reconciliation") or {}),
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"compliance": (still.get("compliance") or {"violations": 0, "rows": [], "hints": []}),
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}
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# A client must be able to tell these boxes did not come from the frame it
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# just sent.
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result["synthetic"] = True
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result["demo"] = {"sample": sample_id, "step": step, "bay": position["bay_index"] + 1}
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return result
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