edit: api 변경 - 벡터이미지검색 -> 결과물에 parts 추가, 파츠검색 api 추가
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@@ -135,3 +135,75 @@ async def test(request: Request):
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return a
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# @router.post("/vectorImageSearch/vit/imageGenerate/imagen/report", summary="벡터 이미지 검색(clip-vit) - imagen, report 생성", response_model=M.ResponseBase)
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# async def vactor_vit_report(request: Request, request_body_info: M.VectorImageSearchVitReportReq):
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# """
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# ## 벡터 이미지 검색(clip-vit) - imagen, report 생성
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# > imagen AI를 이용하여 이미지 생성 후 vector 검색 그후 종합결과 이미지 생성
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# ### Requriements
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# > - googlecli 설치(https://cloud.google.com/sdk/docs/install?hl=ko#linux)
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# ### options
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# > - modelType -> b32,b16,l14,l14_336
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# > - indexType -> l2,cos
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# """
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# response = M.ResponseBase()
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# try:
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# if request_body_info.modelType not in [M.VitModelType.b32, M.VitModelType.b16, M.VitModelType.l14, M.VitModelType.l14_336]:
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# raise Exception(f"modelType is invalid (current value = {request_body_info.modelType})")
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# if request_body_info.indexType not in [M.VitIndexType.cos, M.VitIndexType.l2]:
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# raise Exception(f"indexType is invalid (current value = {request_body_info.indexType})")
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# # query_image_path = imagen_generate_temp_image_path(image_prompt=request_body_info.prompt) #imagen
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# query_image_path = gemini_image(request_body_info.prompt) #gemini
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# report_image_path = f"{os.path.splitext(query_image_path)[0]}_report.png"
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# vactor_request_data = {'query_image_path' : query_image_path,
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# 'index_type' : request_body_info.indexType,
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# 'model_type' : request_body_info.modelType,
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# 'report_path' : report_image_path}
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# vactor_response = requests.post('http://localhost:51002/api/services/faiss/vector/search/vit/report', data=json.dumps(vactor_request_data))
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# if vactor_response.status_code != 200:
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# raise Exception(f"response error: {json.loads(vactor_response.text)['error']}")
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# if json.loads(vactor_response.text)["error"] != None:
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# raise Exception(f"vector error: {json.loads(vactor_response.text)['error']}")
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# if rest_config.config != 'release':
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# # remote 폴더에 이미지 저장
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# sftp_client.remote_copy_data(local_path=report_image_path,
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# remote_path=os.path.join(rest_config.remote_folder, f"imagen_report_vit_{request_body_info.prompt}_{D.date_file_name()}.png"))
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# else:
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# shutil.copy(report_image_path, os.path.join(rest_config.local_folder, f"imagen_report_vit_{request_body_info.prompt}_{D.date_file_name()}.png"))
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# # Clean up temporary files
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# if 'query_image_path' in locals():
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# if os.path.exists(query_image_path):
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# os.remove(query_image_path)
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# del query_image_path
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# if 'report_image_path' in locals():
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# if os.path.exists(report_image_path):
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# os.remove(report_image_path)
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# del report_image_path
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# return response.set_message()
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# except Exception as e:
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# LOG.error(traceback.format_exc())
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# # Clean up temporary files
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# if 'query_image_path' in locals():
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# if os.path.exists(query_image_path):
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# os.remove(query_image_path)
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# del query_image_path
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# if 'report_image_path' in locals():
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# if os.path.exists(report_image_path):
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# os.remove(report_image_path)
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# del report_image_path
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# return response.set_error(error=e)
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@@ -30,6 +30,8 @@ from utils.custom_sftp import sftp_client
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from config import rest_config
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from const import TEMP_FOLDER
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from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.const import *
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router = APIRouter(prefix="/services")
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# @router.post("/bing/cookie/set", summary="bing 관련 쿠키 set", response_model=M.BingCookieSetRes)
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@@ -471,10 +473,10 @@ async def image_generator(request: Request, request_body_info: M.ImageGenerateRe
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return response.set_error(error=e)
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@router.post("/vectorImageSearch/vit/inputImage/data", summary="벡터 이미지 검색(clip-vit) - input image(data)", response_model=M.VectorImageSerachDataRes)
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async def vactor_vit_input_img_data(request: Request, request_body_info: M.VectorImageSearchVitInputImgReq):
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@router.post("/vectorImageSearch/vit/inputImage/data/glasses", summary="벡터 이미지 검색(clip-vit) - input image(data): glasses", response_model=M.VectorGlassesImageSerachDataRes)
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async def vactor_vit_input_glasses_img_data(request: Request, request_body_info: M.VectorGlassesImageSearchVitInputImgReq):
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"""
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## 벡터 이미지 검색(clip-vit) - inputimage
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## 벡터 이미지 검색(clip-vit) - inputimage : glasses
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> 입력된 이미지로 vector 검색 그후 결과 이미지데이터 return
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### Input
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@@ -494,7 +496,7 @@ async def vactor_vit_input_img_data(request: Request, request_body_info: M.Vecto
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> - indexType , modelType - 새로운 조합 (ex l14, cos)으로 처음 요청할경우 모델을 빌드하는 과정이 추가됨
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"""
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response = M.VectorImageSerachDataRes()
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response = M.VectorGlassesImageSerachDataRes()
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query_image_data = ''
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try:
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@@ -526,15 +528,16 @@ async def vactor_vit_input_img_data(request: Request, request_body_info: M.Vecto
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result_image_paths = vector_response_dict.get('img_list').get('result_image_paths')
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result_percents = vector_response_dict.get('img_list').get('result_percents')
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result_parts = vector_response_dict.get('img_list').get('result_parts_list')
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# 이미지 데이터 생성
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vector_image_results = []
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for img, percents in zip(result_image_paths, result_percents):
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for img, percents, parts in zip(result_image_paths, result_percents, result_parts):
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b64_data = image_to_base64_string(img)
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float_percent = float(percents)
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info = M.VectorImageResult(image=b64_data, percents=float_percent, imageInfo=os.path.split(img)[-1])
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info = M.VectorGlassesImageResult(image=b64_data, percents=float_percent, imageInfo=os.path.split(img)[-1], parts=parts)
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vector_image_results.append(info)
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@@ -558,75 +561,72 @@ async def vactor_vit_input_img_data(request: Request, request_body_info: M.Vecto
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return response.set_error(error=e)
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# @router.post("/vectorImageSearch/vit/imageGenerate/imagen/report", summary="벡터 이미지 검색(clip-vit) - imagen, report 생성", response_model=M.ResponseBase)
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# async def vactor_vit_report(request: Request, request_body_info: M.VectorImageSearchVitReportReq):
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@router.post("/vectorImageSearch/vit/inputImage/data/parts", summary="벡터 이미지 검색(clip-vit) - input image(data): parts", response_model=M.VectorPartsImageSerachDataRes)
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async def vactor_vit_input_parts_img_data(request: Request, request_body_info: M.VectorPartsImageSearchVitInputImgReq):
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"""
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## 벡터 이미지 검색(clip-vit) - imagen, report 생성
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> imagen AI를 이용하여 이미지 생성 후 vector 검색 그후 종합결과 이미지 생성
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## 벡터 이미지 검색(clip-vit) - inputImage : parts
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> 입력된 이미지로 vector 검색 그후 결과 이미지데이터 return
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### Requriements
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> - googlecli 설치(https://cloud.google.com/sdk/docs/install?hl=ko#linux)
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### Input
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> 입력이미지(inputImage)는 파츠이미지 이름 (확장자 포함)
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### options
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> - modelType -> b32,b16,l14,l14_336
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> - indexType -> l2,cos
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### Output
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> - 결과(vectorResult)는 base64로 변환된 데이터(image), 유사도(percents)가 쌍으로 나오며, 요청한 searchNum 갯수에 맞춰서 결과가 나옴
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> - queryImage는 Input시 입력한 inputImage 이미지 데이터
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### Options
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> - modelType -> b32, b16, l14, l14_336 (기본값: b32)
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> - indexType -> l2, cos (기본값: l2)
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> - searchNum -> 결과이미지 갯수 (기본값: 4)
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### Notice
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> - 일반검색시 indexType , modelType 은 기본값으로 사용
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> - indexType , modelType, inputImage - 새로운 조합 (ex l14, cos, Lens)으로 처음 요청할경우 모델을 빌드하는 과정이 추가됨
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"""
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response = M.ResponseBase()
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try:
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response = M.VectorPartsImageSerachDataRes()
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try:
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if request_body_info.modelType not in [M.VitModelType.b32, M.VitModelType.b16, M.VitModelType.l14, M.VitModelType.l14_336]:
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raise Exception(f"modelType is invalid (current value = {request_body_info.modelType})")
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if request_body_info.indexType not in [M.VitIndexType.cos, M.VitIndexType.l2]:
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raise Exception(f"indexType is invalid (current value = {request_body_info.indexType})")
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# query_image_path = imagen_generate_temp_image_path(image_prompt=request_body_info.prompt) #imagen
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query_image_path = gemini_image(request_body_info.prompt) #gemini
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report_image_path = f"{os.path.splitext(query_image_path)[0]}_report.png"
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vactor_request_data = {'query_image_path' : query_image_path,
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glass_name = f"{request_body_info.inputImage.split('_')[0]}_{request_body_info.inputImage.split('_')[1]}"
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query_image_path = os.path.join(VECTOR_IMG_LIST_PATH, glass_name, ImageDepths.parts, request_body_info.inputImage)
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query_image_data = image_to_base64_string(query_image_path)
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vector_request_data = {'query_image_path' : query_image_path,
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'index_type' : request_body_info.indexType,
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'model_type' : request_body_info.modelType,
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'report_path' : report_image_path}
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'search_num' : request_body_info.searchNum}
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vactor_response = requests.post('http://localhost:51002/api/services/faiss/vector/search/vit/report', data=json.dumps(vactor_request_data))
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vector_response = requests.post('http://localhost:51002/api/services/faiss/vector/search/vit/parts', data=json.dumps(vector_request_data))
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if vactor_response.status_code != 200:
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raise Exception(f"response error: {json.loads(vactor_response.text)['error']}")
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vector_response_dict = json.loads(vector_response.text)
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if json.loads(vactor_response.text)["error"] != None:
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raise Exception(f"vector error: {json.loads(vactor_response.text)['error']}")
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if vector_response.status_code != 200:
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raise Exception(f"search server error: {vector_response_dict['error']}")
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if rest_config.config != 'release':
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# remote 폴더에 이미지 저장
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sftp_client.remote_copy_data(local_path=report_image_path,
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remote_path=os.path.join(rest_config.remote_folder, f"imagen_report_vit_{request_body_info.prompt}_{D.date_file_name()}.png"))
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else:
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shutil.copy(report_image_path, os.path.join(rest_config.local_folder, f"imagen_report_vit_{request_body_info.prompt}_{D.date_file_name()}.png"))
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# Clean up temporary files
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if 'query_image_path' in locals():
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if os.path.exists(query_image_path):
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os.remove(query_image_path)
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del query_image_path
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if 'report_image_path' in locals():
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if os.path.exists(report_image_path):
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os.remove(report_image_path)
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del report_image_path
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if vector_response_dict["error"] != None:
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raise Exception(f"search result error: {vector_response_dict['error']}")
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return response.set_message()
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result_image_paths = vector_response_dict.get('img_list').get('result_image_paths')
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result_percents = vector_response_dict.get('img_list').get('result_percents')
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# 이미지 데이터 생성
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vector_image_results = []
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for img, percents in zip(result_image_paths, result_percents):
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b64_data = image_to_base64_string(img)
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float_percent = float(percents)
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info = M.VectorPartsImageResult(image=b64_data, percents=float_percent, imageInfo=os.path.split(img)[-1])
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vector_image_results.append(info)
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return response.set_message(vector_result=vector_image_results,query_img=query_image_data)
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except Exception as e:
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LOG.error(traceback.format_exc())
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# Clean up temporary files
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if 'query_image_path' in locals():
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if os.path.exists(query_image_path):
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os.remove(query_image_path)
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del query_image_path
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if 'report_image_path' in locals():
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if os.path.exists(report_image_path):
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os.remove(report_image_path)
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del report_image_path
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return response.set_error(error=e)
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return response.set_error(error=e)
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