Merge branch 'ai/feat/53-detection-autolabel' into 'ai/develop'

Feat: Detection 오토 레이블링 API 구현 - S11P21S002-53

See merge request s11-s-project/S11P21S002!37

ai/.gitignore

@@ -9,6 +9,7 @@ __pycache__/
 *.env
 
 # 패키지 디렉토리
+.venv/
 venv/
 env/
 
@@ -29,3 +30,6 @@ dist/
 
 # MacOS 관련 파일
 .DS_Store
+
+# 테스트 파일
+test-data/

ai/app/api/yolo/detection.py

@@ -0,0 +1,64 @@
+from fastapi import APIRouter, HTTPException
+from schemas.predict_request import PredictRequest
+from schemas.predict_response import PredictResponse, LabelData
+from services.ai_service import load_detection_model
+from typing import List
+
+router = APIRouter()
+
+@router.post("/predict", response_model=List[PredictResponse])
+def predict(request: PredictRequest):
+    version = "0.1.0"
+
+    # 모델 로드
+    try:
+        model = load_detection_model()
+    except Exception as e:
+        raise HTTPException(status_code=500, detail="load model exception: "+str(e))
+    
+    # 추론
+    results = []
+    try:
+        for image in request.image_list:
+            predict_results = model.predict(
+                source=image.image_url, 
+                iou=request.iou_threshold, 
+                conf=request.conf_threshold,
+                classes=request.classes)
+            results.append(predict_results[0])
+    except Exception as e:
+        raise HTTPException(status_code=500, detail="model predict exception: "+str(e))
+    
+    # 추론 결과 -> 레이블 객체 파싱
+    response = []
+    try:
+        for (image, result) in zip(request.image_list, results):
+            label_data:LabelData = {
+                "version": version,
+                "task_type": "det",
+                "shapes": [
+                    {
+                        "label": summary['name'],
+                        "color": "#ff0000",
+                        "points": [
+                            [summary['box']['x1'], summary['box']['y1']],
+                            [summary['box']['x2'], summary['box']['y2']]
+                        ],
+                        "group_id": summary['class'],
+                        "shape_type": "rectangle",
+                        "flags": {}
+                    }    
+                    for summary in result.summary()
+                ],
+                "split": "none",
+                "imageHeight": result.orig_shape[0],
+                "imageWidth": result.orig_shape[1],
+                "imageDepth": 1
+            }
+            response.append({
+                "image_id":image.image_id,
+                "data":label_data
+                })
+    except Exception as e:
+        raise HTTPException(status_code=500, detail="label parsing exception: "+str(e))
+    return response

ai/app/main.py

@@ -1,6 +1,12 @@
 from fastapi import FastAPI
-from app.api.endpoints import router
+from api.yolo.detection import router as yolo_detection_router
 
 app = FastAPI()
 
-app.include_router(router)
+# 각 기능별 라우터를 애플리케이션에 등록
+app.include_router(yolo_detection_router, prefix="/api/yolo/detection")
+
+# 애플리케이션 실행
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("main:app", reload=True)

ai/app/schemas/predict_request.py

@@ -0,0 +1,14 @@
+from pydantic import BaseModel
+from typing import List, Optional
+
+class ImageInfo(BaseModel):
+    image_id: int
+    image_url: str    
+
+class PredictRequest(BaseModel):
+    project_id: int
+    image_list: List[ImageInfo]
+    version: Optional[str] = "latest"
+    conf_threshold: Optional[float] = 0.25
+    iou_threshold: Optional[float] = 0.45
+    classes: Optional[List[int]] = None

ai/app/schemas/predict_response.py

@@ -0,0 +1,23 @@
+from pydantic import BaseModel
+from typing import List, Optional, Tuple, Dict
+
+class Shape(BaseModel):
+    label: str
+    color: str
+    points: List[Tuple[float, float]]
+    group_id: Optional[int] = None
+    shape_type: str
+    flags: Dict[str, Optional[bool]] = {}
+
+class LabelData(BaseModel):
+    version: str
+    task_type: str
+    shapes: List[Shape]
+    split: str
+    imageHeight: int
+    imageWidth: int
+    imageDepth: int
+
+class PredictResponse(BaseModel):
+    image_id: int
+    data: LabelData

ai/app/services/ai_service.py

@@ -0,0 +1,31 @@
+# ai_service.py
+
+from ultralytics import YOLO # Ultralytics YOLO 모델을 가져오기
+from typing import List
+import os
+
+def load_detection_model(model_path: str = "test-data/model/yolov8n.pt", device:str ="cpu"): 
+    """
+    지정된 경로에서 YOLO 모델을 로드합니다.
+
+    Args:
+        model_path (str): 모델 파일 경로.
+        device (str): 모델을 로드할 장치. 기본값은 'cpu'.
+                      'cpu' 또는 'cuda'와 같은 장치를 지정할 수 있습니다.
+
+    Returns:
+        YOLO: 로드된 YOLO 모델 인스턴스
+    """
+    
+    if not os.path.exists(model_path) and model_path != "test-data/model/yolov8n.pt":
+        raise FileNotFoundError(f"Model file not found at path: {model_path}")
+
+    try:
+        model = YOLO(model_path)
+        model.to(device)
+        # Detection 모델인지 검증
+        # 코드 추가 
+        return model
+    except Exception as e:
+        raise RuntimeError(f"Failed to load the model from {model_path}. Error: {str(e)}")
+    

ai/requirements.txt

@@ -1,16 +1,7 @@
-# FastAPI 웹 프레임워크
-fastapi
-
-# ASGI 서버를 위한 Uvicorn
-uvicorn
-
-# YOLOv8 모델을 위한 ultralytics
-ultralytics
-
-# 테스트 도구
-# pytest
-# pytest-asyncio  # 비동기 테스트 지원
-
-# 환경 변수 로드
-python-dotenv
-
+fastapi==0.104.1
+uvicorn==0.30.6
+torch==2.4.0 -f https://download.pytorch.org/whl/cpu
+torchaudio==2.4.0 -f https://download.pytorch.org/whl/cpu
+torchvision==0.19.0 -f https://download.pytorch.org/whl/cpu
+ultralytics==8.2.82
+ultralytics-thop==2.0.5