mirror of
https://github.com/azaion/detections.git
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[AZ-173] [AZ-174] Stream-based detection API and DB-driven AI config
Made-with: Cursor
This commit is contained in:
Oleksandr Bezdieniezhnykh
+158
-23
@@ -4,10 +4,10 @@ import json
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import os
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import time
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from concurrent.futures import ThreadPoolExecutor
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from typing import Optional
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from typing import Annotated, Optional
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import requests as http_requests
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from fastapi import FastAPI, UploadFile, File, Form, HTTPException, Request
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from fastapi import Body, FastAPI, UploadFile, File, Form, HTTPException, Request
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel
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@@ -20,6 +20,7 @@ LOADER_URL = os.environ.get("LOADER_URL", "http://loader:8080")
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ANNOTATIONS_URL = os.environ.get("ANNOTATIONS_URL", "http://annotations:8080")
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loader_client = LoaderHttpClient(LOADER_URL)
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annotations_client = LoaderHttpClient(ANNOTATIONS_URL)
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inference = None
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_event_queues: list[asyncio.Queue] = []
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_active_detections: dict[str, asyncio.Task] = {}
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@@ -60,6 +61,29 @@ class TokenManager:
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except Exception:
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return None
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@staticmethod
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def decode_user_id(token: str) -> Optional[str]:
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try:
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payload = token.split(".")[1]
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padding = 4 - len(payload) % 4
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if padding != 4:
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payload += "=" * padding
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data = json.loads(base64.urlsafe_b64decode(payload))
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uid = (
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data.get("sub")
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or data.get("userId")
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or data.get("user_id")
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or data.get("nameid")
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or data.get(
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"http://schemas.xmlsoap.org/ws/2005/05/identity/claims/nameidentifier"
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)
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)
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if uid is None:
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return None
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return str(uid)
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except Exception:
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return None
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def get_inference():
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global inference
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@@ -105,7 +129,115 @@ class AIConfigDto(BaseModel):
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altitude: float = 400
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focal_length: float = 24
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sensor_width: float = 23.5
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paths: list[str] = []
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_AI_SETTINGS_FIELD_KEYS = (
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(
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"frame_period_recognition",
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("frame_period_recognition", "framePeriodRecognition", "FramePeriodRecognition"),
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),
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(
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"frame_recognition_seconds",
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("frame_recognition_seconds", "frameRecognitionSeconds", "FrameRecognitionSeconds"),
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),
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(
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"probability_threshold",
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("probability_threshold", "probabilityThreshold", "ProbabilityThreshold"),
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),
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(
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"tracking_distance_confidence",
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(
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"tracking_distance_confidence",
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"trackingDistanceConfidence",
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"TrackingDistanceConfidence",
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),
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),
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(
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"tracking_probability_increase",
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(
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"tracking_probability_increase",
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"trackingProbabilityIncrease",
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"TrackingProbabilityIncrease",
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),
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),
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(
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"tracking_intersection_threshold",
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(
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"tracking_intersection_threshold",
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"trackingIntersectionThreshold",
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"TrackingIntersectionThreshold",
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),
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),
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(
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"model_batch_size",
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("model_batch_size", "modelBatchSize", "ModelBatchSize"),
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),
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(
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"big_image_tile_overlap_percent",
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(
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"big_image_tile_overlap_percent",
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"bigImageTileOverlapPercent",
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"BigImageTileOverlapPercent",
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),
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),
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(
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"altitude",
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("altitude", "Altitude"),
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),
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(
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"focal_length",
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("focal_length", "focalLength", "FocalLength"),
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),
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(
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"sensor_width",
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("sensor_width", "sensorWidth", "SensorWidth"),
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),
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)
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def _merged_annotation_settings_payload(raw: object) -> dict:
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if not raw or not isinstance(raw, dict):
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return {}
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merged = dict(raw)
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inner = raw.get("aiRecognitionSettings")
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if isinstance(inner, dict):
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merged.update(inner)
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cam = raw.get("cameraSettings")
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if isinstance(cam, dict):
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merged.update(cam)
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out = {}
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for snake, aliases in _AI_SETTINGS_FIELD_KEYS:
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for key in aliases:
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if key in merged and merged[key] is not None:
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out[snake] = merged[key]
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break
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return out
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def _build_media_detect_config_dict(
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media_id: str,
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token_mgr: Optional[TokenManager],
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override: Optional[AIConfigDto],
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) -> dict:
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cfg: dict = {}
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bearer = ""
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if token_mgr:
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bearer = token_mgr.get_valid_token()
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uid = TokenManager.decode_user_id(token_mgr.access_token)
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if uid:
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raw = annotations_client.fetch_user_ai_settings(uid, bearer)
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cfg.update(_merged_annotation_settings_payload(raw))
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if override is not None:
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for k, v in override.model_dump(exclude_defaults=True).items():
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cfg[k] = v
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media_path = annotations_client.fetch_media_path(media_id, bearer)
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if not media_path:
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raise HTTPException(
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status_code=503,
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detail="Could not resolve media path from annotations service",
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)
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cfg["paths"] = [media_path]
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return cfg
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def detection_to_dto(det) -> DetectionDto:
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@@ -150,9 +282,11 @@ async def detect_image(
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file: UploadFile = File(...),
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config: Optional[str] = Form(None),
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):
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import tempfile
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import cv2
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import numpy as np
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from pathlib import Path
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from inference import ai_config_from_dict
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image_bytes = await file.read()
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if not image_bytes:
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@@ -166,21 +300,21 @@ async def detect_image(
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if config:
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config_dict = json.loads(config)
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suffix = os.path.splitext(file.filename or "upload.jpg")[1] or ".jpg"
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tmp = tempfile.NamedTemporaryFile(delete=False, suffix=suffix)
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media_name = Path(file.filename or "upload.jpg").stem.replace(" ", "")
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loop = asyncio.get_event_loop()
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inf = get_inference()
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results = []
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def on_annotation(annotation, percent):
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results.extend(annotation.detections)
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ai_cfg = ai_config_from_dict(config_dict)
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def run_img():
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inf.run_detect_image(image_bytes, ai_cfg, media_name, on_annotation)
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try:
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tmp.write(image_bytes)
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tmp.close()
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config_dict["paths"] = [tmp.name]
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loop = asyncio.get_event_loop()
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inf = get_inference()
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results = []
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def on_annotation(annotation, percent):
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results.extend(annotation.detections)
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await loop.run_in_executor(executor, inf.run_detect, config_dict, on_annotation)
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await loop.run_in_executor(executor, run_img)
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return [detection_to_dto(d) for d in results]
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except RuntimeError as e:
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if "not available" in str(e):
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@@ -188,8 +322,6 @@ async def detect_image(
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raise HTTPException(status_code=422, detail=str(e))
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e))
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finally:
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os.unlink(tmp.name)
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def _post_annotation_to_service(token_mgr: TokenManager, media_id: str,
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@@ -216,7 +348,11 @@ def _post_annotation_to_service(token_mgr: TokenManager, media_id: str,
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@app.post("/detect/{media_id}")
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async def detect_media(media_id: str, request: Request, config: Optional[AIConfigDto] = None):
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async def detect_media(
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media_id: str,
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request: Request,
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config: Annotated[Optional[AIConfigDto], Body()] = None,
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):
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existing = _active_detections.get(media_id)
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if existing is not None and not existing.done():
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raise HTTPException(status_code=409, detail="Detection already in progress for this media")
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@@ -226,8 +362,7 @@ async def detect_media(media_id: str, request: Request, config: Optional[AIConfi
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refresh_token = request.headers.get("x-refresh-token", "")
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token_mgr = TokenManager(access_token, refresh_token) if access_token else None
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cfg = config or AIConfigDto()
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config_dict = cfg.model_dump()
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config_dict = _build_media_detect_config_dict(media_id, token_mgr, config)
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async def run_detection():
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loop = asyncio.get_event_loop()
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