Feat: Segmentation 오토 레이블링 API 구현 - S11P21S002-118

ai/app/api/yolo/segmentation.py

@@ -0,0 +1,63 @@
+from fastapi import APIRouter, HTTPException
+from schemas.predict_request import PredictRequest
+from schemas.predict_response import PredictResponse, LabelData
+from services.ai_service import load_segmentation_model
+from typing import List
+
+router = APIRouter()
+
+@router.post("/segmentation", response_model=List[PredictResponse])
+def predict(request: PredictRequest):
+    version = "0.1.0"
+
+    # 모델 로드
+    try:
+        model = load_segmentation_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": "seg",
+                "shapes": [
+                    {
+                        "label": summary['name'],
+                        "color": "#ff0000",
+                        "points": list(zip(summary['segments']['x'], summary['segments']['y'])),
+                        "group_id": summary['class'],
+                        "shape_type": "polygon",
+                        "flags": {}
+                    }    
+                    for summary in result.summary()
+                ],
+                "split": "none",
+                "imageHeight": result.orig_img.shape[0],
+                "imageWidth": result.orig_img.shape[1],
+                "imageDepth": result.orig_img.shape[2]
+            }
+            response.append({
+                "image_id":image.image_id,
+                "image_url":image.image_url,
+                "data":label_data
+                })
+    except Exception as e:
+        raise HTTPException(status_code=500, detail="label parsing exception: "+str(e))
+    return response

ai/app/main.py

@@ -1,10 +1,12 @@
 from fastapi import FastAPI
 from api.yolo.detection import router as yolo_detection_router
+from api.yolo.segmentation import router as yolo_segmentation_router
 
 app = FastAPI()
 
 # 각 기능별 라우터를 애플리케이션에 등록
 app.include_router(yolo_detection_router, prefix="/api")
+app.include_router(yolo_segmentation_router, prefix="/api")
 
 # 애플리케이션 실행
 if __name__ == "__main__":

ai/app/services/ai_service.py

@@ -2,7 +2,7 @@
 
 from ultralytics import YOLO # Ultralytics YOLO 모델을 가져오기
 from ultralytics.models.yolo.model import YOLO as YOLO_Model
-from ultralytics.nn.tasks import DetectionModel 
+from ultralytics.nn.tasks import DetectionModel, SegmentationModel
 import os
 import torch
 
@@ -22,21 +22,36 @@ def load_detection_model(model_path: str = "test-data/model/yolov8n.pt", device:
     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 = YOLO(model_path)
+    # Detection 모델인지 검증
-        # Detection 모델인지 검증
+    if not (isinstance(model, YOLO_Model) and isinstance(model.model, DetectionModel)):
-        if not (isinstance(model, YOLO_Model) and isinstance(model.model, DetectionModel)):
+        raise TypeError(f"Invalid model type: {type(model)} (contained model type: {type(model.model)}). Expected a DetectionModel.")
-            raise TypeError(f"Invalid model type: {type(model)} (contained model type: {type(model.model)}). Expected a DetectionModel.")
     
-        # gpu 이용
+    # gpu 이용
-        if (device == "auto" and torch.cuda.is_available()):
+    if (device == "auto" and torch.cuda.is_available()):
-            model.to("cuda")
+        model.to("cuda")
-            print('gpu 가속 활성화')
+        print('gpu 가속 활성화')
-        elif (device == "auto"):
+    elif (device == "auto"):
-            model.to("cpu")
+        model.to("cpu")
-        else:
+    else:
-            model.to(device)
+        model.to(device)
-        return model
+    return model
-    except Exception as e:
-        raise RuntimeError(f"Failed to load the model from {model_path}. Error: {str(e)}")
     
+def load_segmentation_model(model_path: str = "test-data/model/yolov8n-seg.pt", device:str ="auto"): 
+    if not os.path.exists(model_path) and model_path != "test-data/model/yolov8n-seg.pt":
+        raise FileNotFoundError(f"Model file not found at path: {model_path}")
+
+    model = YOLO(model_path)
+    # Segmentation 모델인지 검증
+    if not (isinstance(model, YOLO_Model) and isinstance(model.model, SegmentationModel)):
+        raise TypeError(f"Invalid model type: {type(model)} (contained model type: {type(model.model)}). Expected a SegmentationModel.")
+    
+    # gpu 이용
+    if (device == "auto" and torch.cuda.is_available()):
+        model.to("cuda")
+        print('gpu 가속 활성화')
+    elif (device == "auto"):
+        model.to("cpu")
+    else:
+        model.to(device)
+    return model