feat: ShootTracker SQLite+JWT+YOLOv8

ai-service/main.py

@@ -0,0 +1,169 @@
+"""
+ShootTracker — Microservice IA
+FastAPI + YOLOv8 + OpenCV
+
+Endpoints :
+  POST /analyze  — Analyse d'une photo de cible
+  GET  /health   — Santé du service
+  GET  /         — Infos
+"""
+
+import os
+import logging
+from contextlib import asynccontextmanager
+from typing import Optional
+
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, field_validator
+
+from analyzer import TargetAnalyzer, AnalysisResult
+
+# ─── Logging ──────────────────────────────────────────────────────────────────
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s [%(levelname)s] %(name)s: %(message)s',
+    datefmt='%H:%M:%S'
+)
+logger = logging.getLogger(__name__)
+
+# ─── Modèle global ────────────────────────────────────────────────────────────
+analyzer: Optional[TargetAnalyzer] = None
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Charge le modèle au démarrage."""
+    global analyzer
+    model_path = os.getenv('YOLO_MODEL_PATH', 'yolov8n.pt')
+    logger.info(f"Chargement du modèle YOLOv8 : {model_path}")
+    analyzer = TargetAnalyzer(model_path=model_path)
+    logger.info("✓ Service IA prêt")
+    yield
+    logger.info("Service IA arrêté")
+
+# ─── Application FastAPI ──────────────────────────────────────────────────────
+app = FastAPI(
+    title="ShootTracker AI Service",
+    description="Analyse d'impacts de tir avec YOLOv8 + OpenCV",
+    version="1.0.0",
+    lifespan=lifespan,
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ─── Schémas Pydantic ─────────────────────────────────────────────────────────
+
+class AnalyzeRequest(BaseModel):
+    image_base64: str
+    target_type: str
+    previous_image_base64: Optional[str] = None
+
+    @field_validator('target_type')
+    @classmethod
+    def validate_target_type(cls, v: str) -> str:
+        allowed = ['issf', 'silhouette', 'libre']
+        if v not in allowed:
+            raise ValueError(f"target_type doit être parmi : {allowed}")
+        return v
+
+    @field_validator('image_base64')
+    @classmethod
+    def validate_image(cls, v: str) -> str:
+        if not v or len(v) < 100:
+            raise ValueError("image_base64 invalide ou trop courte")
+        return v
+
+class ImpactOut(BaseModel):
+    x: float
+    y: float
+    zone: Optional[int]
+    points: int
+    confidence: float
+
+class AnalyzeResponse(BaseModel):
+    impacts: list[ImpactOut]
+    total_detected: int
+    score_zone: int
+    score_groupement: int
+    score_total: int
+    dispersion_radius: float
+    center_x: float
+    center_y: float
+    annotated_image_base64: Optional[str]
+    error: Optional[str]
+    warning: Optional[str]
+
+# ─── Endpoints ────────────────────────────────────────────────────────────────
+
+@app.get("/", tags=["Info"])
+async def root():
+    return {
+        "service": "ShootTracker AI",
+        "version": "1.0.0",
+        "status": "running",
+        "model": os.getenv('YOLO_MODEL_PATH', 'yolov8n.pt'),
+    }
+
+@app.get("/health", tags=["Info"])
+async def health():
+    return {
+        "status": "ok",
+        "model_loaded": analyzer is not None and analyzer.model is not None,
+        "service": "shoottracker-ai",
+    }
+
+@app.post("/analyze", response_model=AnalyzeResponse, tags=["Analyse"])
+async def analyze_target(req: AnalyzeRequest):
+    """
+    Analyse une photo de cible de tir.
+
+    - Détecte les impacts avec YOLOv8
+    - Analyse les zones selon le type de cible
+    - Si image_précédente fournie : ne compte que les NOUVEAUX impacts
+    - Calcule le score et le groupement
+    - Retourne la photo annotée en base64
+    """
+    if analyzer is None:
+        raise HTTPException(status_code=503, detail="Service IA non initialisé")
+
+    logger.info(f"Analyse demandée : target_type={req.target_type}, prev={'oui' if req.previous_image_base64 else 'non'}")
+
+    try:
+        result: AnalysisResult = analyzer.analyze(
+            image_b64=req.image_base64,
+            target_type=req.target_type,
+            previous_image_b64=req.previous_image_base64,
+        )
+    except Exception as e:
+        logger.exception(f"Erreur inattendue: {e}")
+        raise HTTPException(status_code=500, detail=f"Erreur interne : {str(e)}")
+
+    if result.error and not result.annotated_image_base64:
+        # Erreur critique (image illisible)
+        raise HTTPException(status_code=422, detail=result.error)
+
+    return AnalyzeResponse(
+        impacts=[ImpactOut(**imp) for imp in result.impacts],
+        total_detected=result.total_detected,
+        score_zone=result.score_zone,
+        score_groupement=result.score_groupement,
+        score_total=result.score_total,
+        dispersion_radius=result.dispersion_radius,
+        center_x=result.center_x,
+        center_y=result.center_y,
+        annotated_image_base64=result.annotated_image_base64,
+        error=result.error,
+        warning=result.warning,
+    )
+
+
+if __name__ == "__main__":
+    import uvicorn
+    port = int(os.getenv("PORT", 8000))
+    uvicorn.run("main:app", host="0.0.0.0", port=port, reload=False, workers=1)