Feat: 백엔드 API 연동
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김용수
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ai/.gitignore
vendored
4
ai/.gitignore
vendored
@ -37,4 +37,6 @@ test-data/
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# 리소스
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resources/
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datasets/
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*.pt
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*.pt
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*.jpg
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@ -23,139 +23,89 @@ async def detection_predict(request: PredictRequest):
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ws_client = WebSocketClient(spring_server_ws_url)
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# 모델 로드
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try:
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model_path = request.m_key and get_model_path(request.project_id, request.m_key)
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model = load_detection_model(model_path=model_path)
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except Exception as e:
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raise HTTPException(status_code=500, detail="load model exception: " + str(e))
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model = load_model(request)
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# 모델 레이블 카테고리 연결
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classes = None
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if request.label_map:
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classes = list(request.label_map)
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classes = list(request.label_map) if request.label_map else None
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# 결과를 저장할 리스트
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response = []
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# 웹소켓 연결
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await connect_to_websocket(ws_client)
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# 추론
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try:
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await ws_client.connect()
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if not ws_client.is_connected():
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raise WebSocketConnectionException()
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# 추론
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total_images = len(request.image_list)
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for idx, image in enumerate(request.image_list):
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try:
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# URL에서 이미지를 메모리로 로드 TODO: 추후 메모리에 할지 어떻게 해야할지 or 병렬 처리 고민
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predict_results = model.predict(
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source=image.image_url,
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iou=request.iou_threshold,
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conf=request.conf_threshold,
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classes=classes
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)
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# 예측 결과 처리
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result = predict_results[0]
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label_data = LabelData(
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version="0.0.0",
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task_type="det",
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shapes=[
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{
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"label": summary['name'],
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"color": "#ff0000",
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"points": [
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[summary['box']['x1'], summary['box']['y1']],
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[summary['box']['x2'], summary['box']['y2']]
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],
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"group_id": request.label_map[summary['class']] if request.label_map else summary['class'],
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"shape_type": "rectangle",
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"flags": {}
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}
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for summary in result.summary()
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],
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split="none",
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imageHeight=result.orig_img.shape[0],
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imageWidth=result.orig_img.shape[1],
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imageDepth=result.orig_img.shape[2]
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)
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result = run_predictions(model, image, request, classes)
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response_item = process_prediction_result(result, request, image)
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response.append(response_item)
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response_item = PredictResponse(
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image_id=image.image_id,
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image_url=image.image_url,
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data=label_data
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)
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# 진행률 계산
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progress = (idx + 1) / total_images * 100
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# 웹소켓으로 예측 결과와 진행률 전송
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message = {
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"project_id": request.project_id,
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"progress": progress,
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"result": response_item.model_dump()
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}
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await ws_client.send_message("/app/ai/predict/progress", json.dumps(message))
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except Exception as e:
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raise HTTPException(status_code=500, detail="model predict exception: " + str(e))
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return Response(status_code=204)
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# 웹소켓 연결 안된 경우
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except WebSocketConnectionException as e:
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# 추론
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response = []
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for image in request.image_list:
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try:
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predict_results = model.predict(
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source=image.image_url,
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iou=request.iou_threshold,
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conf=request.conf_threshold,
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classes=classes
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)
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# 예측 결과 처리
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result = predict_results[0]
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label_data = LabelData(
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version="0.0.0",
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task_type="det",
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shapes=[
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{
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"label": summary['name'],
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"color": "#ff0000",
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"points": [
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[summary['box']['x1'], summary['box']['y1']],
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[summary['box']['x2'], summary['box']['y2']]
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],
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"group_id": request.label_map[summary['class']] if request.label_map else summary['class'],
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"shape_type": "rectangle",
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"flags": {}
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}
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for summary in result.summary()
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],
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split="none",
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imageHeight=result.orig_img.shape[0],
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imageWidth=result.orig_img.shape[1],
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imageDepth=result.orig_img.shape[2]
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)
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response_item = PredictResponse(
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image_id=image.image_id,
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image_url=image.image_url,
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data=label_data
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)
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response.append(response_item)
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except Exception as e:
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raise HTTPException(status_code=500, detail="model predict exception: " + str(e))
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return response
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except Exception as e:
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print(f"Prediction process failed: {str(e)}")
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raise HTTPException(status_code=500, detail="Prediction process failed")
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finally:
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if ws_client.is_connected():
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await ws_client.close()
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# 모델 로드
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def load_model(request: PredictRequest):
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try:
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model_path = request.m_key and get_model_path(request.project_id, request.m_key)
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return load_detection_model(model_path=model_path)
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except Exception as e:
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raise HTTPException(status_code=500, detail="load model exception: " + str(e))
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# 웹소켓 연결
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async def connect_to_websocket(ws_client):
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try:
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await ws_client.connect()
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if not ws_client.is_connected():
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raise WebSocketConnectionException("웹 소켓 연결 실패")
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except Exception as e:
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raise HTTPException(status_code=500, detail="websocket connect failed: " + str(e))
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# 추론 실행 함수
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def run_predictions(model, image, request, classes):
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try:
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predict_results = model.predict(
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source=image.image_url,
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iou=request.iou_threshold,
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conf=request.conf_threshold,
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classes=classes
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)
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return predict_results[0]
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except Exception as e:
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raise HTTPException(status_code=500, detail="model predict exception: " + str(e))
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# 추론 결과 처리 함수
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def process_prediction_result(result, request, image):
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label_data = LabelData(
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version="0.0.0",
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task_type="det",
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shapes=[
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{
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"label": summary['name'],
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"color": "#ff0000",
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"points": [
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[summary['box']['x1'], summary['box']['y1']],
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[summary['box']['x2'], summary['box']['y2']]
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],
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"group_id": request.label_map[summary['class']] if request.label_map else summary['class'],
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"shape_type": "rectangle",
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"flags": {}
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}
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for summary in result.summary()
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],
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split="none",
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imageHeight=result.orig_img.shape[0],
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imageWidth=result.orig_img.shape[1],
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imageDepth=result.orig_img.shape[2]
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)
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return PredictResponse(
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image_id=image.image_id,
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data=json.dumps(label_data.dict())
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)
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@router.post("/train")
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async def detection_train(request: TrainRequest):
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@ -20,5 +20,4 @@ class LabelData(BaseModel):
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class PredictResponse(BaseModel):
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image_id: int
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image_url: str
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data: LabelData
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data: str
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