e52684c37e
The replayed walk and the harness that measures it disagreed with the phone in two ways, so every live number taken from them was measuring a different pipeline than the one that ships. The server cut a window `PAN_LONG_EDGE` tall, which on image6 shows one shelf row: a 720 px band reaches 52 of 66 enrolled facings and gives every other SKU 0% coverage, which reads as a matcher failure when it is only the crop. It now spans the sample's full height and pans horizontally, and `PAN_STEP` is expressed in sent-frame pixels so the pace survives the downscale. `stream_client.py` had the same window bug plus a box-mapping bug: it added the pan offset but never divided by the downscale, so a `--width` below the source size shrank every box and the evaluator matched the wrong facing. It now scales the window itself (so the frame it sends is byte-for-byte the app's 720x1280), records that scale per frame, and divides by it when mapping a label box back to source coordinates. The pan accumulator is clamped at the reversal: a negative `pan_x` satisfied `<= 0` on every later frame and flipped the direction each time, parking the window at the left edge. Measured through the real server at the app's geometry: 57 of 66 enrolled facings covered (86%) at 94% precision, 9 distinct SKUs.
1228 lines
53 KiB
Python
1228 lines
53 KiB
Python
"""
|
|
Dorayaki recognition server (どら焼き).
|
|
|
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The computer-vision model runs here, as a service. Capture clients talk to it
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over a versioned REST API (stills, catalog, inspect) and a WebSocket stream
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(live frames in, tracked products and labels out). Any number of clients can
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connect at once; no client shares state with another.
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|
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Run:
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python code/app.py --port 8080
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python code/app.py --host 0.0.0.0 --port 8080 # LAN, set DORAYAKI_API_TOKEN
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python code/app.py --ssl-certfile cert.pem --ssl-keyfile key.pem
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The web client is served from / and is a pure client of the API; it can also be
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pointed at another server with ?server=https://host:port.
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"""
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from __future__ import annotations
|
|
|
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import argparse
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import asyncio
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|
import base64
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import binascii
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from contextlib import asynccontextmanager
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from dataclasses import dataclass
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import json
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import logging
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import os
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import re
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import sys
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import threading
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import time
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import uuid
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from typing import Any, Dict, List, Optional, Tuple
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|
|
|
# Keep the demo off the network before anything imports ultralytics.
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os.environ.setdefault("YOLO_OFFLINE", "1")
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os.environ.setdefault("YOLO_AUTOINSTALL", "false")
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|
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current_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.dirname(current_dir)
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if os.path.exists(os.path.join(project_root, "stock")):
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os.chdir(project_root)
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if current_dir not in sys.path:
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sys.path.insert(0, current_dir)
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from fastapi import ( # noqa: E402
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Depends,
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FastAPI,
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Header,
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HTTPException,
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Request,
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WebSocket,
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WebSocketDisconnect,
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)
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from fastapi.exceptions import RequestValidationError # noqa: E402
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from fastapi.middleware.cors import CORSMiddleware # noqa: E402
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from fastapi.responses import FileResponse, JSONResponse, Response # noqa: E402
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from fastapi.staticfiles import StaticFiles # noqa: E402
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from pydantic import BaseModel, Field # noqa: E402
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|
|
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from engine import ( # noqa: E402
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CLASSIFY_METHODS,
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COORDINATE_CONVENTION,
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EngineSettings,
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ImageExpired,
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RecognitionEngine,
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STATE_DETECTED,
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UndecodableImage,
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UnknownMethod,
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build_default_engine,
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identity_payload,
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settings_from_env,
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)
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from record import FrameRecorder # noqa: E402
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from live import MAX_CROP_BYTES, LiveSession, LiveSettings # noqa: E402
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from transport import ( # noqa: E402
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BodyLimit,
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ServiceAdvertiser,
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|
advertise_host,
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|
bearer_matches,
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|
declared_length,
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|
is_loopback_host,
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read_bounded,
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|
require_token_for_bind,
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|
token_matches,
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|
)
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|
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LOGGER = logging.getLogger("dorayaki")
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MAX_UPLOAD_BYTES = 20 * 1024 * 1024
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MAX_FRAME_BYTES = 4 * 1024 * 1024
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DEFAULT_MAX_SESSIONS = 4
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WS_IDLE_TIMEOUT_S = 120.0
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CATALOG_VERSION_FALLBACK = "unknown"
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MAX_CROP_PIXELS = 4_000_000
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|
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# The replayed-sample pan: a portrait window at the phone's frame size, JPEG at
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# the quality the device camera sends. `PAN_STEP` is pixels per frame **in the
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# sent frame**, so it is divided by the downscale before slicing: the window is
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# the sample's full height scaled to `PAN_LONG_EDGE`, and a step measured in
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# source pixels would shrink with that scale and silently change the pace.
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PAN_LONG_EDGE = 1280
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PAN_STEP = 5.6
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PAN_JPEG_QUALITY = 70
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|
|
|
|
|
def _jpeg_dimensions(data: bytes) -> Optional[Tuple[int, int]]:
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|
"""Read JPEG SOF dimensions without allocating a decoded image."""
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if not data.startswith(b"\xff\xd8"):
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return None
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offset = 2
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while offset + 4 <= len(data):
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if data[offset] != 0xff:
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return None
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|
while offset < len(data) and data[offset] == 0xff:
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offset += 1
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if offset >= len(data):
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return None
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marker = data[offset]
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offset += 1
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if marker in (0xd8, 0x01) or 0xd0 <= marker <= 0xd7:
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continue
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if marker in (0xd9, 0xda) or offset + 2 > len(data):
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return None
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length = int.from_bytes(data[offset:offset + 2], "big")
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if length < 2 or offset + length > len(data):
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return None
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if marker in (0xc0, 0xc1, 0xc2, 0xc3, 0xc5, 0xc6, 0xc7, 0xc9, 0xca, 0xcb, 0xcd, 0xce, 0xcf):
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if length < 7:
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return None
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return (int.from_bytes(data[offset + 5:offset + 7], "big"),
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int.from_bytes(data[offset + 3:offset + 5], "big"))
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offset += length
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return None
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|
|
|
|
|
# ---------------------------------------------------------------------------
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|
# settings
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|
# ---------------------------------------------------------------------------
|
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|
|
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@dataclass
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class ServerSettings:
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|
host: str = "127.0.0.1"
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port: int = 8080
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token: Optional[str] = None
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# Empty means same-origin only. "*" reflects every caller and is not a default.
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cors_origins: str = ""
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max_sessions: int = DEFAULT_MAX_SESSIONS
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live_method: str = "hybrid"
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live_conf: float = 0.1
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|
live_tags: bool = True
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|
# A live session can be recorded so it becomes data to label (record.py).
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|
record_frames: Optional[str] = None
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record_every: int = 3
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log_level: str = "info"
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ssl_certfile: Optional[str] = None
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ssl_keyfile: Optional[str] = None
|
|
|
|
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def server_settings_from_env() -> ServerSettings:
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base = ServerSettings()
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return ServerSettings(
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host=os.environ.get("DORAYAKI_HOST", base.host),
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port=int(os.environ.get("DORAYAKI_PORT", base.port)),
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token=os.environ.get("DORAYAKI_API_TOKEN") or None,
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cors_origins=os.environ.get("DORAYAKI_CORS_ORIGINS", base.cors_origins),
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max_sessions=int(os.environ.get("DORAYAKI_MAX_SESSIONS", base.max_sessions)),
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live_method=os.environ.get("DORAYAKI_LIVE_METHOD", base.live_method),
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live_conf=float(os.environ.get("DORAYAKI_LIVE_CONF", base.live_conf)),
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live_tags=os.environ.get("DORAYAKI_LIVE_TAGS", "1").lower() not in ("0", "false", "no"),
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log_level=os.environ.get("DORAYAKI_LOG_LEVEL", base.log_level),
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ssl_certfile=os.environ.get("DORAYAKI_SSL_CERTFILE") or None,
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ssl_keyfile=os.environ.get("DORAYAKI_SSL_KEYFILE") or None,
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)
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|
|
|
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def load_local_token(path: str = "config/local.env") -> None:
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|
"""Fill DORAYAKI_API_TOKEN from an ignored env file. Never prints the value.
|
|
|
|
ponytail: one KEY=VALUE file, no quoting. Upgrade path is a real env loader
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once a second secret needs the same file.
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"""
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if os.environ.get("DORAYAKI_API_TOKEN"):
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return
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try:
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|
with open(path, "r", encoding="utf-8") as handle:
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|
lines = handle.readlines()
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|
except OSError:
|
|
return
|
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for line in lines:
|
|
key, sep, value = line.strip().partition("=")
|
|
if key == "DORAYAKI_API_TOKEN" and sep and value:
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os.environ["DORAYAKI_API_TOKEN"] = value
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return
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|
|
|
|
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# ---------------------------------------------------------------------------
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# request models
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|
# ---------------------------------------------------------------------------
|
|
|
|
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class StockUpdateBody(BaseModel):
|
|
delta: int = Field(..., ge=-100, le=100, description="units added (+) or dispensed (-)")
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|
|
|
|
class ClassifyBody(BaseModel):
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|
method: Optional[str] = None
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|
detection_ids: Optional[List[int]] = None
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|
|
|
|
|
class InspectBody(BaseModel):
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|
box: List[int] = Field(..., min_length=4, max_length=4)
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|
method: Optional[str] = None
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# helpers
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|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
def _error(code: str, message: str, status_code: int) -> HTTPException:
|
|
return HTTPException(status_code=status_code, detail={"error": {"code": code, "message": message}})
|
|
|
|
|
|
def _require_method(method: Optional[str]) -> str:
|
|
chosen = (method or "hybrid").lower()
|
|
if chosen not in CLASSIFY_METHODS:
|
|
raise _error("BAD_REQUEST", f"unknown method '{method}'", 400)
|
|
return chosen
|
|
|
|
|
|
def get_available_shelf_images(add_dir: str = "add") -> List[Dict[str, str]]:
|
|
"""Same demo set the old /api/shelf_samples returned, now id-keyed."""
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|
items: List[Dict[str, str]] = []
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|
if os.path.exists("image.jpg"):
|
|
items.append({"id": "main_shelf", "path": "image.jpg", "label": "image.jpg (Main Shelf)"})
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|
|
if os.path.exists(add_dir):
|
|
valid_exts = (".webp", ".jpg", ".jpeg", ".png")
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|
files = sorted(
|
|
[f for f in os.listdir(add_dir) if f.lower().endswith(valid_exts)],
|
|
key=lambda x: (len(x), x),
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|
)
|
|
for name in files:
|
|
items.append(
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{
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|
"id": name.rsplit(".", 1)[0],
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|
"path": os.path.join(add_dir, name).replace("\\", "/"),
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|
"label": f"{add_dir}/{name}",
|
|
}
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|
)
|
|
|
|
mosaic = "runs/detect/train/train_batch0.jpg"
|
|
if os.path.exists(mosaic):
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|
items.append({"id": "train_mosaic", "path": mosaic, "label": "train_batch0.jpg (Mosaic)"})
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return items
|
|
|
|
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# app factory
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
def create_app(
|
|
engine: RecognitionEngine,
|
|
settings: Optional[ServerSettings] = None,
|
|
*,
|
|
web_dir: Optional[str] = None,
|
|
) -> FastAPI:
|
|
settings = settings or server_settings_from_env()
|
|
@asynccontextmanager
|
|
async def lifespan(_app: FastAPI):
|
|
yield
|
|
for session in list(sessions.values()):
|
|
if session is not None:
|
|
session.close()
|
|
engine.shutdown()
|
|
|
|
app = FastAPI(
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|
title="Dorayaki Recognition Server",
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|
version="1.0",
|
|
description="Shelf product detection, tracking and classification for capture clients.",
|
|
lifespan=lifespan,
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|
)
|
|
# Same-origin is the default. A configured list is echoed back; "*" is not.
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|
cors = [o.strip() for o in settings.cors_origins.split(",") if o.strip() and o.strip() != "*"]
|
|
if cors:
|
|
app.add_middleware(
|
|
CORSMiddleware,
|
|
allow_origins=cors,
|
|
allow_credentials=False,
|
|
allow_methods=["GET", "POST"],
|
|
allow_headers=["Authorization", "Content-Type"],
|
|
)
|
|
|
|
# A live session can be recorded so a real scan becomes data to label later.
|
|
recorder = FrameRecorder(
|
|
settings.record_frames,
|
|
every=settings.record_every,
|
|
identity_backend=engine.settings.identity_backend,
|
|
) 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()
|
|
# path -> ((mtime_ns, size), decoded image). The pan re-cuts this per frame.
|
|
sample_images: Dict[str, Any] = {}
|
|
sample_image_lock = threading.Lock()
|
|
sessions: Dict[str, LiveSession] = {}
|
|
sessions_lock = asyncio.Lock()
|
|
app.state.engine = engine
|
|
app.state.sessions = sessions
|
|
|
|
async def authorize(authorization: Optional[str] = Header(default=None)) -> None:
|
|
if not settings.token:
|
|
return
|
|
if not bearer_matches(authorization, settings.token):
|
|
raise _error("UNAUTHORIZED", "missing or invalid bearer token", 401)
|
|
|
|
# ---------------------------------------------------------------- health
|
|
@app.get("/v1/health")
|
|
async def health() -> Dict[str, Any]:
|
|
loop = asyncio.get_running_loop()
|
|
|
|
def _health() -> Dict[str, Any]:
|
|
gallery = (
|
|
engine.identity_gallery()
|
|
if getattr(engine.settings, "identity_backend", "") == "gallery"
|
|
else None
|
|
)
|
|
return {
|
|
"status": "ok",
|
|
"model": {
|
|
"version": engine.model_version(),
|
|
"device": getattr(engine.settings, "device", None) or "auto",
|
|
"imgsz": getattr(engine.settings, "imgsz", 640),
|
|
"warm": True,
|
|
},
|
|
"catalog": {"version": engine.catalog_version(), "sku_count": engine.sku_count()},
|
|
"identity": {
|
|
"backend": getattr(engine.settings, "identity_backend", "matcher"),
|
|
"gallery_views": gallery.view_count() if gallery is not None else None,
|
|
},
|
|
"ocr": {
|
|
# Without this an operator could not tell a broken OCR from a shelf
|
|
# with no readable tags.
|
|
"engine": getattr(engine.ocr_engine, "name", "none"),
|
|
"available": bool(getattr(engine.ocr_engine, "available", lambda: False)()),
|
|
},
|
|
"sessions_active": sum(session is not None for session in sessions.values()),
|
|
"uptime_s": round(engine.uptime_s(), 1),
|
|
}
|
|
|
|
return await loop.run_in_executor(None, _health)
|
|
|
|
# ---------------------------------------------------------------- catalog
|
|
@app.get("/v1/catalog", dependencies=[Depends(authorize)])
|
|
async def catalog() -> Dict[str, Any]:
|
|
loop = asyncio.get_running_loop()
|
|
|
|
def _catalog() -> Dict[str, Any]:
|
|
return {"catalog_version": engine.catalog_version(), "products": engine.catalog()}
|
|
|
|
return await loop.run_in_executor(None, _catalog)
|
|
|
|
@app.get("/v1/catalog/{sku_id}/image", dependencies=[Depends(authorize)])
|
|
async def catalog_image(sku_id: str) -> Response:
|
|
loop = asyncio.get_running_loop()
|
|
path = await loop.run_in_executor(None, engine.catalog_image_path, sku_id)
|
|
if not path:
|
|
raise _error("NOT_FOUND", f"no catalog image for '{sku_id}'", 404)
|
|
return FileResponse(path)
|
|
|
|
@app.post("/v1/catalog/{sku_id}/stock", dependencies=[Depends(authorize)])
|
|
async def update_stock(sku_id: str, body: StockUpdateBody) -> Dict[str, Any]:
|
|
if body.delta == 0:
|
|
raise _error("VALIDATION_ERROR", "delta must not be zero", 422)
|
|
loop = asyncio.get_running_loop()
|
|
product = await loop.run_in_executor(None, engine.update_stock, sku_id, body.delta)
|
|
if product is None:
|
|
raise _error("NOT_FOUND", f"unknown sku '{sku_id}'", 404)
|
|
return {"status": "success", "product": product}
|
|
|
|
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 _panned_jpeg(path: str, index: int) -> Optional[bytes]:
|
|
"""One portrait window of `path`, slid `index` steps along the shelf.
|
|
|
|
The window is the shape the phone sends - 9:16, long edge
|
|
`PAN_LONG_EDGE` - but it spans the sample's **full height** and pans
|
|
horizontally. A window that is only `PAN_LONG_EDGE` tall shows one
|
|
shelf row: on `image6` (1500x2000, rows at y 200/560/870/1150/1440) a
|
|
720 px band centred at 640-1360 reaches 52 of 66 enrolled facings and
|
|
gives every other SKU 0% coverage, which reads as a matcher failure
|
|
when it is only the crop. Full height reaches all of them, and the
|
|
downscale to `PAN_LONG_EDGE` is the same 0.64 the evaluator's live
|
|
resolution uses.
|
|
|
|
It steps `PAN_STEP` pixels per frame and reverses at each edge, so the
|
|
walk is continuous rather than a jump back to the start.
|
|
|
|
The decoded image is cached: re-reading a 1500x2000 JPEG per frame cost
|
|
more than the encode, which made the walk slower than `PAN_STEP` says.
|
|
"""
|
|
import cv2
|
|
|
|
image = _decoded_sample(path)
|
|
if image is None:
|
|
return None
|
|
height, width = image.shape[:2]
|
|
window_h = height
|
|
window_w = min(int(window_h * 9 / 16), width)
|
|
max_x = max(0, width - window_w)
|
|
# `PAN_STEP` is in sent-frame pixels; the slice is in source pixels.
|
|
scale = min(1.0, PAN_LONG_EDGE / float(window_h))
|
|
step = PAN_STEP / scale if scale > 0 else PAN_STEP
|
|
travel = index * step
|
|
if max_x == 0:
|
|
x = 0
|
|
else:
|
|
# Triangle wave: pan out to the edge, then back, forever.
|
|
period = 2 * max_x
|
|
phase = travel % period
|
|
x = int(phase if phase <= max_x else period - phase)
|
|
window = image[:, x:x + window_w]
|
|
if scale < 1.0:
|
|
window = cv2.resize(
|
|
window,
|
|
(int(round(window.shape[1] * scale)), int(round(window.shape[0] * scale))),
|
|
interpolation=cv2.INTER_AREA,
|
|
)
|
|
ok, encoded = cv2.imencode(".jpg", window, [int(cv2.IMWRITE_JPEG_QUALITY), PAN_JPEG_QUALITY])
|
|
return encoded.tobytes() if ok else None
|
|
|
|
def _decoded_sample(path: str) -> Any:
|
|
"""The sample decoded once, re-read only when the file changes."""
|
|
try:
|
|
stat = os.stat(path)
|
|
except OSError:
|
|
with sample_image_lock:
|
|
sample_images.pop(path, None)
|
|
return None
|
|
key = (stat.st_mtime_ns, stat.st_size)
|
|
with sample_image_lock:
|
|
cached = sample_images.get(path)
|
|
if cached is not None and cached[0] == key:
|
|
return cached[1]
|
|
import cv2
|
|
|
|
image = cv2.imread(path)
|
|
if image is None:
|
|
return None
|
|
with sample_image_lock:
|
|
sample_images[path] = (key, image)
|
|
return image
|
|
|
|
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']}"}
|
|
dimensions = _sample_dimensions(item["path"])
|
|
if dimensions is not None:
|
|
entry["width"], entry["height"] = dimensions
|
|
listing.append(entry)
|
|
return listing
|
|
|
|
@app.get("/v1/samples", dependencies=[Depends(authorize)])
|
|
async def list_samples() -> Dict[str, Any]:
|
|
loop = asyncio.get_running_loop()
|
|
return {"samples": await loop.run_in_executor(None, _sample_listing)}
|
|
|
|
@app.get("/v1/samples/{sample_id}", dependencies=[Depends(authorize)])
|
|
async def get_sample(sample_id: str, pan: Optional[int] = None) -> Response:
|
|
item = samples.get(sample_id)
|
|
if not item or not await asyncio.get_running_loop().run_in_executor(None, os.path.exists, item["path"]):
|
|
raise _error("NOT_FOUND", f"no sample '{sample_id}'", 404)
|
|
if pan is None:
|
|
return FileResponse(item["path"])
|
|
# A still replayed as frames is a frozen shelf: every frame carries the
|
|
# same boxes, so the live path never exercises a track that moves. `pan`
|
|
# is the frame index, and the window slides one step per frame and
|
|
# reverses at the edges - the same walk `stream_client.py` drives, so a
|
|
# simulator sees what a real slow pan produces.
|
|
loop = asyncio.get_running_loop()
|
|
jpeg = await loop.run_in_executor(None, _panned_jpeg, item["path"], max(0, pan))
|
|
if jpeg is None:
|
|
raise _error("NOT_FOUND", f"cannot read sample '{sample_id}'", 404)
|
|
return Response(content=jpeg, media_type="image/jpeg")
|
|
|
|
# ---------------------------------------------------------------- recognize
|
|
@app.post("/v1/recognize", dependencies=[Depends(authorize)])
|
|
async def recognize(
|
|
request: Request,
|
|
classify: bool = True,
|
|
method: Optional[str] = None,
|
|
conf: Optional[float] = None,
|
|
empty_slots: bool = True,
|
|
tags: bool = False,
|
|
) -> Dict[str, Any]:
|
|
if method is not None:
|
|
_require_method(method)
|
|
content_type = (request.headers.get("content-type") or "").split(";")[0].strip().lower()
|
|
announced = declared_length(request.headers.get("content-length"))
|
|
if announced is not None and announced > MAX_UPLOAD_BYTES:
|
|
raise _error("PAYLOAD_TOO_LARGE", f"image larger than {MAX_UPLOAD_BYTES} bytes", 413)
|
|
if content_type == "multipart/form-data":
|
|
form = await request.form(max_part_size=MAX_UPLOAD_BYTES)
|
|
upload = form.get("file")
|
|
if upload is None or not hasattr(upload, "read"):
|
|
raise _error("BAD_REQUEST", "multipart body needs a 'file' field", 400)
|
|
payload = await read_bounded(upload, MAX_UPLOAD_BYTES)
|
|
else:
|
|
if content_type and not content_type.startswith("image/") and content_type != "application/octet-stream":
|
|
raise _error("UNSUPPORTED_MEDIA_TYPE", f"unsupported content type '{content_type}'", 415)
|
|
payload = await read_bounded(request.stream(), MAX_UPLOAD_BYTES)
|
|
if not payload:
|
|
raise _error("BAD_REQUEST", "empty request body", 400)
|
|
if len(payload) > MAX_UPLOAD_BYTES:
|
|
raise _error("PAYLOAD_TOO_LARGE", f"image larger than {MAX_UPLOAD_BYTES} bytes", 413)
|
|
loop = asyncio.get_running_loop()
|
|
try:
|
|
image = await loop.run_in_executor(None, engine.decode_image, payload)
|
|
except UndecodableImage as exc:
|
|
raise _error("UNDECODABLE_IMAGE", str(exc), 422)
|
|
try:
|
|
result = await loop.run_in_executor(
|
|
None,
|
|
lambda: engine.recognize(
|
|
image,
|
|
classify=classify,
|
|
method=method,
|
|
conf=conf,
|
|
with_empty_slots=empty_slots,
|
|
),
|
|
)
|
|
except UnknownMethod as exc:
|
|
raise _error("BAD_REQUEST", str(exc), 400)
|
|
|
|
if tags:
|
|
# One submit for the whole still-tag operation, on the same
|
|
# single-worker executor the live sessions read tags on. The default
|
|
# pool would let a photo's OCR run beside a live session's Vision
|
|
# work: pyobjc holds the GIL, so the two would interleave and both
|
|
# would slow down. `_attach_still_tags` calls `run_pass` itself
|
|
# (reconcile.attach_tags_to_boxes), never submits, so nothing here
|
|
# queues behind the worker it is running on.
|
|
result = await loop.run_in_executor(
|
|
engine.tag_executor, _attach_still_tags, result, image
|
|
)
|
|
return result
|
|
|
|
def _is_price_card(crop: Any) -> bool:
|
|
"""Is this detection a shelf price card rather than a product?
|
|
|
|
Measured on a supermarket photo whose shelf edge carries big price cards:
|
|
the detector boxes them as products, because a card is roughly the shape of a
|
|
pack. Their text gives them away - a price card's characters are 30-44 %
|
|
digits and it prints the tax-excluded price (本体), while a product's are 3-19 %
|
|
digits. Reporting a price card as a product would put a box labelled with a
|
|
product name on a price tag, which is worse than not detecting it.
|
|
|
|
ponytail: every surviving detection pays one Vision read. There is no cheap
|
|
pre-filter to add, because a card is only distinguishable by its text - its
|
|
box is pack-shaped, so the aspect/position bounds in tags.py (measured for
|
|
real tag regions, 1.8-3.8:1) would exclude the boxes cards actually arrive
|
|
in. Re-measure boxed-card aspect from real photos before narrowing this; a
|
|
card that slips through is a product name printed on a price tag.
|
|
"""
|
|
ocr_engine = getattr(engine, "ocr_engine", None)
|
|
if ocr_engine is None or not getattr(ocr_engine, "available", lambda: False)():
|
|
return False
|
|
try:
|
|
import cv2
|
|
|
|
big = cv2.resize(crop, None, fx=2.0, fy=2.0, interpolation=cv2.INTER_CUBIC)
|
|
lines = ocr_engine.read(big).lines
|
|
except Exception:
|
|
return False
|
|
text = " ".join(line.text for line in lines)
|
|
compact = re.sub(r"\s", "", text)
|
|
if not compact:
|
|
return False
|
|
digits = sum(1 for char in compact if char.isdigit() or char in "¥￥./") / len(compact)
|
|
return ("本体" in text and digits >= 0.20) or digits >= 0.30
|
|
|
|
def _drop_price_cards(result: Dict[str, Any], image: Any) -> int:
|
|
"""Remove detections that are price cards, and say how many were removed."""
|
|
kept, dropped = [], 0
|
|
for detection in result["detections"]:
|
|
crop = engine.crop(image, detection["box"])
|
|
if crop is not None and crop.size and _is_price_card(crop):
|
|
dropped += 1
|
|
continue
|
|
kept.append(detection)
|
|
if dropped:
|
|
result["detections"] = kept
|
|
result["summary"] = engine.summarize(kept, result.get("empty_slots") or [])
|
|
result["price_cards_dropped"] = dropped
|
|
return dropped
|
|
|
|
def _attach_still_tags(result: Dict[str, Any], image: Any) -> Dict[str, Any]:
|
|
"""Run one synchronous tag pass over a still and merge its evidence."""
|
|
ocr_engine = getattr(engine, "ocr_engine", None)
|
|
if ocr_engine is None or not getattr(ocr_engine, "available", lambda: False)():
|
|
result["price_tags"] = {"readable": 0, "unreadable": 0, "absent": 0}
|
|
result["compliance"] = {"violations": 0, "by_rule": {}, "rows": [], "hints": []}
|
|
result["ocr_engine"] = "none"
|
|
return result
|
|
|
|
from reconcile import attach_tags_to_boxes
|
|
from tags import TagService, TagSettings
|
|
|
|
# Before anything else: a price card is not a product.
|
|
_drop_price_cards(result, image)
|
|
|
|
boxes = [
|
|
{"id": detection["id"], "box": detection["box"]} for detection in result["detections"]
|
|
]
|
|
identities = {
|
|
int(detection["id"]): detection["identity"] for detection in result["detections"]
|
|
}
|
|
catalog_by_sku = {product["sku_id"]: product for product in engine.catalog()}
|
|
service = TagService(ocr_engine, settings=TagSettings())
|
|
started = time.perf_counter()
|
|
bundle = attach_tags_to_boxes(service, image, boxes, catalog_by_sku, identities)
|
|
elapsed_ms = (time.perf_counter() - started) * 1000.0
|
|
|
|
# Tag-first identity: the store's own label, read from the tag printed
|
|
# under the facings it names. It fills identities the packaging pass could
|
|
# not resolve and never overwrites one it did.
|
|
tag_identities = bundle.get("tag_identities") or {}
|
|
for detection in result["detections"]:
|
|
derived = tag_identities.get(int(detection["id"]))
|
|
if not derived:
|
|
continue
|
|
# The tag's claim is always reported, so a client can compare the two
|
|
# identity channels; it only *fills* the identity when the packaging
|
|
# pass could not name the product.
|
|
detection["tag_identity"] = dict(derived)
|
|
if (detection.get("identity") or {}).get("state") == "recognized" \
|
|
and (detection["identity"].get("sku_id") == derived["sku_id"]
|
|
or detection["identity"].get("source") == "tag_name"):
|
|
continue
|
|
product = catalog_by_sku.get(derived["sku_id"]) or {}
|
|
detection["identity"] = {
|
|
"state": "recognized",
|
|
"sku_id": derived["sku_id"],
|
|
"identity_level": "tag_derived",
|
|
"confidence": derived["score"],
|
|
"score": derived["score"],
|
|
"source": "tag_name",
|
|
"candidates": [],
|
|
"attempts": 1,
|
|
"method": "tag_name",
|
|
}
|
|
detection["product"] = {
|
|
"sku_id": product.get("sku_id"),
|
|
"product_name": product.get("product_name"),
|
|
"price": product.get("price"),
|
|
}
|
|
|
|
for detection in result["detections"]:
|
|
box_id = int(detection["id"])
|
|
tag = bundle["by_box"].get(box_id)
|
|
if tag is not None:
|
|
detection["price_tag"] = tag
|
|
reconciliation = bundle["reconciliations"].get(box_id)
|
|
if reconciliation is not None:
|
|
detection["reconciliation"] = reconciliation
|
|
|
|
result["price_tags"] = bundle["counts"]
|
|
result["compliance"] = bundle["compliance"]
|
|
result["ocr_engine"] = ocr_engine.name
|
|
result["tag_timing_ms"] = round(elapsed_ms, 1)
|
|
engine.image_store.set_tag_bundle(result["image_id"], bundle)
|
|
return result
|
|
|
|
@app.post("/v1/images/{image_id}/classify", dependencies=[Depends(authorize)])
|
|
async def classify_image(image_id: str, body: ClassifyBody) -> Dict[str, Any]:
|
|
method = _require_method(body.method)
|
|
try:
|
|
stored = engine.image_store.get(image_id)
|
|
except ImageExpired:
|
|
raise _error("IMAGE_EXPIRED", f"image '{image_id}' is no longer available", 404)
|
|
detections = stored.detections
|
|
if body.detection_ids is not None:
|
|
wanted = set(int(i) for i in body.detection_ids)
|
|
detections = [d for d in detections if int(d["id"]) in wanted]
|
|
started = time.perf_counter()
|
|
loop = asyncio.get_running_loop()
|
|
rows = await loop.run_in_executor(
|
|
None, lambda: engine.classify_boxes(stored.image, detections, method=method)
|
|
)
|
|
rows_by_id = {row["detection_id"]: row for row in rows}
|
|
for detection in stored.detections:
|
|
row = rows_by_id.get(int(detection["id"]))
|
|
if row is not None:
|
|
detection["identity"] = row["identity"]
|
|
engine.image_store.update_detections(image_id, stored.detections)
|
|
|
|
# Tags were read before the identities existed; reconcile them now.
|
|
compliance = None
|
|
if stored.tag_bundle:
|
|
from reconcile import reconcile_bundle
|
|
|
|
catalog_by_sku = {product["sku_id"]: product for product in engine.catalog()}
|
|
refreshed = reconcile_bundle(stored.tag_bundle, stored.detections, catalog_by_sku)
|
|
for detection in stored.detections:
|
|
decision = refreshed["reconciliations"].get(int(detection["id"]))
|
|
if decision is not None:
|
|
detection["reconciliation"] = decision
|
|
compliance = refreshed["compliance"]
|
|
identities = []
|
|
for detection in detections:
|
|
row = {
|
|
"detection_id": int(detection["id"]),
|
|
"box": list(detection["box"]),
|
|
"identity": detection["identity"],
|
|
}
|
|
# Re-reconciled after classification; without it a client would keep
|
|
# the `tag_only` verdict it got when identities did not exist yet.
|
|
if detection.get("reconciliation") is not None:
|
|
row["reconciliation"] = detection["reconciliation"]
|
|
if detection.get("price_tag") is not None:
|
|
row["price_tag"] = detection["price_tag"]
|
|
identities.append(row)
|
|
summary = engine.summarize(stored.detections, [])
|
|
payload = {
|
|
"image_id": image_id,
|
|
"method": method,
|
|
"identities": identities,
|
|
"summary": summary,
|
|
"timing_ms": {"classify": round((time.perf_counter() - started) * 1000.0, 2)},
|
|
}
|
|
if compliance is not None:
|
|
payload["compliance"] = compliance
|
|
return payload
|
|
|
|
@app.post("/v1/images/{image_id}/inspect", dependencies=[Depends(authorize)])
|
|
async def inspect_image(image_id: str, body: InspectBody) -> Dict[str, Any]:
|
|
method = _require_method(body.method)
|
|
try:
|
|
stored = engine.image_store.get(image_id)
|
|
except ImageExpired:
|
|
raise _error("IMAGE_EXPIRED", f"image '{image_id}' is no longer available", 404)
|
|
loop = asyncio.get_running_loop()
|
|
return await loop.run_in_executor(
|
|
None, lambda: engine.inspect(stored.image, body.box, method=method)
|
|
)
|
|
|
|
# ---------------------------------------------------------------- live stream
|
|
@app.websocket("/v1/stream")
|
|
async def stream(websocket: WebSocket) -> None:
|
|
# Query tokens are rejected. A wrong header is rejected before hello.
|
|
# A missing header is the browser case: hello goes out, then the first
|
|
# client text must be {"type":"auth","token":"..."} or the socket closes.
|
|
if websocket.query_params.get("token"):
|
|
await websocket.close(code=1008)
|
|
return
|
|
header = websocket.headers.get("authorization")
|
|
if settings.token and header and not bearer_matches(header, settings.token):
|
|
await websocket.close(code=1008)
|
|
return
|
|
loop = asyncio.get_running_loop()
|
|
async with sessions_lock:
|
|
if len(sessions) >= settings.max_sessions:
|
|
await websocket.close(code=1013)
|
|
return
|
|
session_id = str(uuid.uuid4())
|
|
sessions[session_id] = None # reserve before any disk work
|
|
session = None
|
|
try:
|
|
model_version = await loop.run_in_executor(None, engine.model_version)
|
|
session = LiveSession(
|
|
session_id,
|
|
engine,
|
|
settings=LiveSettings(
|
|
method=settings.live_method,
|
|
conf=settings.live_conf,
|
|
max_inflight_per_session=engine.settings.live_inflight,
|
|
max_attempts=engine.settings.live_attempts,
|
|
tags=settings.live_tags,
|
|
),
|
|
)
|
|
await loop.run_in_executor(None, session.refresh_catalog)
|
|
catalog_version = session._catalog_version
|
|
if settings.live_tags:
|
|
await loop.run_in_executor(None, session.enable_tags)
|
|
async with sessions_lock:
|
|
sessions[session_id] = session
|
|
except BaseException:
|
|
if session is not None:
|
|
session.close()
|
|
async with sessions_lock:
|
|
sessions.pop(session_id, None)
|
|
raise
|
|
try:
|
|
await websocket.accept()
|
|
send_lock = asyncio.Lock()
|
|
|
|
async def send(payload: Dict[str, Any]) -> None:
|
|
async with send_lock:
|
|
await websocket.send_text(json.dumps(payload))
|
|
await send(
|
|
{
|
|
"type": "hello",
|
|
"protocol": 1,
|
|
"session_id": session_id,
|
|
"catalog_version": catalog_version,
|
|
"model": {
|
|
"version": model_version,
|
|
"device": getattr(engine.settings, "device", None) or "auto",
|
|
"imgsz": getattr(engine.settings, "imgsz", 640),
|
|
},
|
|
"config": session.config(),
|
|
"limits": {
|
|
"max_frame_bytes": MAX_FRAME_BYTES,
|
|
"max_crop_bytes": MAX_CROP_BYTES,
|
|
},
|
|
}
|
|
)
|
|
except BaseException:
|
|
session.close()
|
|
async with sessions_lock:
|
|
sessions.pop(session_id, None)
|
|
raise
|
|
|
|
async def processing_loop() -> None:
|
|
while not session.closed:
|
|
pending = await session.take_pending_async()
|
|
if pending is None:
|
|
continue
|
|
frame_id, payload = pending
|
|
try:
|
|
result = await loop.run_in_executor(None, session.process_frame, frame_id, payload)
|
|
except UndecodableImage:
|
|
await send({"type": "error", "code": "UNDECODABLE_IMAGE", "frame_id": frame_id,
|
|
"message": "frame is not a readable image"})
|
|
continue
|
|
except Exception as exc: # keep the stream alive on one bad frame
|
|
LOGGER.exception("live frame failed: %s", exc)
|
|
await send({"type": "error", "code": "INTERNAL", "frame_id": frame_id,
|
|
"message": "frame processing failed"})
|
|
continue
|
|
if recorder is not None:
|
|
# Disk writes must not sit on the event loop between results.
|
|
await loop.run_in_executor(
|
|
None, recorder.write, session_id, frame_id, payload, result
|
|
)
|
|
await send(result)
|
|
|
|
async def handle_inspect(message: Dict[str, Any]) -> None:
|
|
request_id = message.get("request_id")
|
|
try:
|
|
track_id = int(message.get("track_id"))
|
|
except (TypeError, ValueError):
|
|
await send({"type": "error", "code": "BAD_MESSAGE", "request_id": request_id,
|
|
"message": "track_id required"})
|
|
return
|
|
method = message.get("method")
|
|
if method is not None:
|
|
try:
|
|
method = _require_method(method)
|
|
except HTTPException as exc:
|
|
await send({"type": "error", "code": "BAD_MESSAGE", "request_id": request_id,
|
|
"message": str(exc.detail)})
|
|
return
|
|
try:
|
|
result = await loop.run_in_executor(None, session.inspect, track_id, method)
|
|
except KeyError:
|
|
await send({"type": "error", "code": "TRACK_NOT_IN_VIEW", "request_id": request_id,
|
|
"message": "track is not in the latest frame"})
|
|
return
|
|
except Exception as exc:
|
|
LOGGER.exception("inspect failed: %s", exc)
|
|
await send({"type": "error", "code": "INTERNAL", "request_id": request_id,
|
|
"message": "inspect failed"})
|
|
return
|
|
payload = {"type": "inspect_result", "request_id": request_id, "track_id": track_id}
|
|
payload.update(result)
|
|
await send(payload)
|
|
|
|
async def handle_capabilities(message: Dict[str, Any]) -> None:
|
|
"""A client that can answer with a full-resolution crop says so here.
|
|
|
|
Until this arrives the session keeps the protocol-v1 behaviour: no
|
|
result carries a crop request, so a v1 client is never asked. A
|
|
capability is believed whole or not at all: a message that does not
|
|
name the lane with a limit the server can honour is refused and the
|
|
lane stays off, rather than half-applied on the client's say-so.
|
|
"""
|
|
if message.get("crop") is not True or "max_crop_bytes" not in message:
|
|
await send({"type": "error", "code": "BAD_MESSAGE",
|
|
"message": "crop must be true and state max_crop_bytes"})
|
|
return
|
|
try:
|
|
capabilities = session.enable_crops(message["max_crop_bytes"])
|
|
except ValueError:
|
|
await send({"type": "error", "code": "BAD_MESSAGE",
|
|
"message": "max_crop_bytes must be 1..%d" % MAX_CROP_BYTES})
|
|
return
|
|
await send({"type": "capabilities_ack", "capabilities": capabilities})
|
|
|
|
async def handle_crop(message: Dict[str, Any]) -> None:
|
|
"""Score a crop the client returned for a track we asked about.
|
|
|
|
base64 travels in a text control message, so the binary frame path
|
|
(4-byte id + JPEG) is untouched and the frame id space is unchanged.
|
|
"""
|
|
request_id = message.get("request_id")
|
|
encoded = message.get("jpeg")
|
|
if not isinstance(encoded, str) or not encoded:
|
|
await send({"type": "crop_error", "request_id": request_id,
|
|
"code": "CROP_BAD_MESSAGE", "message": "jpeg is required"})
|
|
return
|
|
# Base64 expands ~4/3. Refuse on the wire length before decoding it,
|
|
# the same way the REST frame route does.
|
|
if len(encoded) > (MAX_CROP_BYTES * 4) // 3 + 8:
|
|
await send({"type": "crop_error", "request_id": request_id,
|
|
"code": "CROP_TOO_LARGE", "message": "crop over the limit"})
|
|
return
|
|
try:
|
|
crop_bytes = base64.b64decode(encoded, validate=True)
|
|
except (binascii.Error, ValueError):
|
|
await send({"type": "crop_error", "request_id": request_id,
|
|
"code": "CROP_BAD_MESSAGE", "message": "jpeg is not valid base64"})
|
|
return
|
|
dimensions = _jpeg_dimensions(crop_bytes)
|
|
if dimensions is not None and dimensions[0] * dimensions[1] > MAX_CROP_PIXELS:
|
|
await send({"type": "crop_error", "request_id": request_id,
|
|
"code": "CROP_TOO_LARGE", "message": "crop dimensions exceed the limit"})
|
|
return
|
|
reply = await loop.run_in_executor(
|
|
None, session.submit_crop, request_id, message.get("track_id"),
|
|
message.get("frame_id"), crop_bytes,
|
|
)
|
|
await send(reply)
|
|
|
|
processing = asyncio.create_task(processing_loop())
|
|
|
|
authed = not settings.token or bearer_matches(
|
|
websocket.headers.get("authorization"), settings.token
|
|
)
|
|
|
|
async def handle_control(message: Dict[str, Any]) -> bool:
|
|
"""Apply one control message. False closes the socket (auth failed)."""
|
|
nonlocal authed
|
|
kind = message.get("type")
|
|
if not authed:
|
|
presented = message.get("token") if kind == "auth" else None
|
|
if isinstance(presented, str) and token_matches(presented, settings.token):
|
|
authed = True
|
|
return True
|
|
await websocket.close(code=1008)
|
|
return False
|
|
if kind == "auth":
|
|
return True
|
|
if kind == "config":
|
|
try:
|
|
config = session.configure(
|
|
method=message.get("method"),
|
|
conf=message.get("conf"),
|
|
empty_slots=message.get("empty_slots"),
|
|
tags=message.get("tags"),
|
|
)
|
|
except (TypeError, ValueError):
|
|
await send({"type": "error", "code": "BAD_MESSAGE", "message": "bad config values"})
|
|
return True
|
|
await send({"type": "config_ack", "config": config})
|
|
elif kind == "reset":
|
|
session.reset()
|
|
await send({"type": "reset_ack"})
|
|
elif kind == "ping":
|
|
await send({"type": "pong", "t": message.get("t")})
|
|
elif kind == "inspect":
|
|
await handle_inspect(message)
|
|
elif kind == "capabilities":
|
|
await handle_capabilities(message)
|
|
elif kind == "crop":
|
|
await handle_crop(message)
|
|
else:
|
|
await send({"type": "error", "code": "BAD_MESSAGE", "message": f"unknown type '{kind}'"})
|
|
return True
|
|
|
|
try:
|
|
while True:
|
|
try:
|
|
message = await asyncio.wait_for(websocket.receive(), timeout=WS_IDLE_TIMEOUT_S)
|
|
except asyncio.TimeoutError:
|
|
break
|
|
if message.get("type") == "websocket.disconnect":
|
|
break
|
|
text = message.get("text")
|
|
data = message.get("bytes")
|
|
if data is not None:
|
|
if not authed:
|
|
await websocket.close(code=1008)
|
|
break
|
|
if len(data) < 5:
|
|
await send({"type": "error", "code": "BAD_FRAME", "message": "frame shorter than 5 bytes"})
|
|
continue
|
|
if len(data) > MAX_FRAME_BYTES + 4:
|
|
await send({"type": "error", "code": "PAYLOAD_TOO_LARGE", "message": "frame too large"})
|
|
continue
|
|
frame_id = int.from_bytes(data[:4], "big")
|
|
session.submit_frame(frame_id, data[4:])
|
|
elif text is not None:
|
|
try:
|
|
parsed = json.loads(text)
|
|
if not isinstance(parsed, dict):
|
|
raise ValueError("not an object")
|
|
except (json.JSONDecodeError, ValueError):
|
|
await send({"type": "error", "code": "BAD_MESSAGE", "message": "invalid JSON"})
|
|
continue
|
|
if not await handle_control(parsed):
|
|
break
|
|
except WebSocketDisconnect:
|
|
pass
|
|
finally:
|
|
session.close()
|
|
processing.cancel()
|
|
try:
|
|
await processing
|
|
except (asyncio.CancelledError, Exception):
|
|
pass
|
|
async with sessions_lock:
|
|
sessions.pop(session_id, None)
|
|
|
|
# ---------------------------------------------------------------- errors
|
|
@app.exception_handler(RequestValidationError)
|
|
async def validation_exception_handler(request: Request, exc: RequestValidationError) -> JSONResponse:
|
|
first = (exc.errors() or [{}])[0]
|
|
location = ".".join(str(part) for part in first.get("loc", []) if part != "body")
|
|
message = first.get("msg", "invalid request")
|
|
detail = f"{location}: {message}" if location else message
|
|
return JSONResponse(
|
|
status_code=422, content={"error": {"code": "VALIDATION_ERROR", "message": detail}}
|
|
)
|
|
|
|
@app.exception_handler(HTTPException)
|
|
async def http_exception_handler(request: Request, exc: HTTPException) -> JSONResponse:
|
|
detail = exc.detail
|
|
if isinstance(detail, dict) and "error" in detail:
|
|
body = detail
|
|
else:
|
|
body = {"error": {"code": "HTTP_ERROR", "message": str(detail)}}
|
|
return JSONResponse(status_code=exc.status_code, content=body)
|
|
|
|
@app.exception_handler(Exception)
|
|
async def unhandled_exception_handler(request: Request, exc: Exception) -> JSONResponse:
|
|
LOGGER.exception("unhandled error on %s: %s", request.url.path, exc)
|
|
return JSONResponse(
|
|
status_code=500, content={"error": {"code": "INTERNAL", "message": "internal server error"}}
|
|
)
|
|
|
|
# ---------------------------------------------------------------- static web client
|
|
resolved_web_dir = web_dir or os.path.join(current_dir, "web")
|
|
app.add_middleware(BodyLimit, max_bytes=MAX_UPLOAD_BYTES)
|
|
if os.path.isdir(resolved_web_dir):
|
|
app.mount("/", StaticFiles(directory=resolved_web_dir, html=True), name="web")
|
|
|
|
return app
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# entry point
|
|
# ---------------------------------------------------------------------------
|
|
def main() -> None:
|
|
parser = argparse.ArgumentParser(description="Dorayaki - shelf recognition server")
|
|
parser.add_argument("--host", type=str, default=None)
|
|
parser.add_argument("--port", type=int, default=None)
|
|
parser.add_argument("--device", type=str, default=None, help="cpu | mps | cuda | cuda:0")
|
|
parser.add_argument("--imgsz", type=int, default=None)
|
|
parser.add_argument("--weights", type=str, default=None)
|
|
parser.add_argument("--stock-dir", type=str, default=None)
|
|
parser.add_argument("--classify-workers", type=int, default=None)
|
|
parser.add_argument("--live-method", type=str, default=None, choices=list(CLASSIFY_METHODS))
|
|
parser.add_argument("--live-conf", type=float, default=None)
|
|
parser.add_argument("--identity", type=str, default=None,
|
|
choices=["matcher", "local", "gallery", "van"],
|
|
help="which mechanism names products (default local; "
|
|
"van is the always-argmax baseline)")
|
|
parser.add_argument("--live-inflight", type=int, default=None,
|
|
help="classifications in flight per session")
|
|
parser.add_argument("--live-attempts", type=int, default=None,
|
|
help="classification attempts per track")
|
|
parser.add_argument("--record-frames", type=str, default=None,
|
|
help="write processed frames and their results under this directory")
|
|
parser.add_argument("--record-every", type=int, default=None,
|
|
help="record one frame in every N (default 3)")
|
|
parser.add_argument("--no-tags", action="store_true", help="disable price-tag OCR")
|
|
parser.add_argument("--max-sessions", type=int, default=None)
|
|
parser.add_argument("--ssl-certfile", type=str, default=None)
|
|
parser.add_argument("--ssl-keyfile", type=str, default=None)
|
|
parser.add_argument("--log-level", type=str, default=None)
|
|
args = parser.parse_args()
|
|
|
|
logging.basicConfig(
|
|
level=(args.log_level or server_settings_from_env().log_level or "info").upper(),
|
|
format="%(asctime)s %(levelname)s %(name)s: %(message)s",
|
|
)
|
|
|
|
server = server_settings_from_env()
|
|
if args.host is not None:
|
|
server.host = args.host
|
|
if args.port is not None:
|
|
server.port = args.port
|
|
if args.max_sessions is not None:
|
|
server.max_sessions = args.max_sessions
|
|
if args.live_method is not None:
|
|
server.live_method = args.live_method
|
|
if args.live_conf is not None:
|
|
server.live_conf = args.live_conf
|
|
if args.no_tags:
|
|
server.live_tags = False
|
|
if args.ssl_certfile is not None:
|
|
server.ssl_certfile = args.ssl_certfile
|
|
if args.ssl_keyfile is not None:
|
|
server.ssl_keyfile = args.ssl_keyfile
|
|
if args.log_level is not None:
|
|
server.log_level = args.log_level
|
|
|
|
load_local_token()
|
|
if not server.token:
|
|
server.token = os.environ.get("DORAYAKI_API_TOKEN") or None
|
|
require_token_for_bind(server.host, server.token)
|
|
|
|
engine_settings = settings_from_env()
|
|
if args.record_frames is not None:
|
|
server.record_frames = args.record_frames
|
|
if args.record_every is not None:
|
|
server.record_every = args.record_every
|
|
if args.identity is not None:
|
|
engine_settings.identity_backend = args.identity
|
|
if args.live_inflight is not None:
|
|
engine_settings.live_inflight = args.live_inflight
|
|
if args.live_attempts is not None:
|
|
engine_settings.live_attempts = args.live_attempts
|
|
if args.device is not None:
|
|
engine_settings.device = args.device
|
|
if args.imgsz is not None:
|
|
engine_settings.imgsz = args.imgsz
|
|
if args.weights is not None:
|
|
engine_settings.weights_path = args.weights
|
|
if args.stock_dir is not None:
|
|
engine_settings.stock_dir = args.stock_dir
|
|
if args.classify_workers is not None:
|
|
engine_settings.classify_workers = args.classify_workers
|
|
|
|
LOGGER.info("Loading model and catalog (device=%s, imgsz=%s)...",
|
|
engine_settings.device or "auto", engine_settings.imgsz)
|
|
engine = build_default_engine(engine_settings)
|
|
if (engine_settings.identity_backend or "").lower() == "gallery":
|
|
if engine.identity_gallery() is None:
|
|
engine.shutdown()
|
|
raise SystemExit(
|
|
"gallery identity backend requires local DINOv2 weights; refusing to start"
|
|
)
|
|
warm_ms = engine.warm_up()
|
|
LOGGER.info(
|
|
"Ready: %d SKUs, catalog %s, warmup %.0f ms, classify workers %d",
|
|
engine.sku_count(), engine.catalog_version(), warm_ms, engine.classify_workers,
|
|
)
|
|
|
|
app = create_app(engine, server)
|
|
|
|
scheme = "https" if server.ssl_certfile else "http"
|
|
LOGGER.info("Dorayaki recognition server on %s://%s:%d (token %s)",
|
|
scheme, server.host, server.port, "required" if server.token else "off")
|
|
if is_loopback_host(server.host):
|
|
LOGGER.info("Open %s://127.0.0.1:%d in a browser for the web client", scheme, server.port)
|
|
else:
|
|
LOGGER.info("LAN mode: live camera needs https (secure context); use --ssl-certfile or a tunnel")
|
|
|
|
import uvicorn
|
|
|
|
advertised = advertise_host(server.host)
|
|
advertiser = ServiceAdvertiser(advertised, server.port) if advertised else None
|
|
if advertiser is not None and advertiser.start():
|
|
LOGGER.info("Advertising _dorayaki._tcp on %s:%d", advertised, server.port)
|
|
elif advertised:
|
|
LOGGER.info("No dns-sd on this host; _dorayaki._tcp was not advertised")
|
|
|
|
try:
|
|
uvicorn.run(
|
|
app,
|
|
host=server.host,
|
|
port=server.port,
|
|
log_level=(server.log_level or "info").lower(),
|
|
access_log=False, # request lines can carry Authorization; keep them off
|
|
ws="websockets-sansio",
|
|
ssl_certfile=server.ssl_certfile,
|
|
ssl_keyfile=server.ssl_keyfile,
|
|
)
|
|
finally:
|
|
if advertiser is not None:
|
|
advertiser.close()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|