edit: api 변경 - 벡터이미지검색 -> 결과물에 parts 추가, 파츠검색 api 추가

.gitignore

@@ -145,8 +145,6 @@ log
 
 *output
 *temp
-datas/eyewear_all/*
+datas/*
-datas/clip*
-datas/openai*
 
 *result

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/const.py

@@ -18,11 +18,28 @@ ViTL14_336 = "clip-vit-large-patch14-336"
 
 #download/save path
 PRETRAINED_MODEL_PATH = "./datas"
-FAISS_VECTOR_PATH = "./datas"
+FAISS_VECTOR_PATH = "./datas/Eyewear_001~500_ComponentsCapture_20251121"
-VECTOR_IMG_LIST_PATH = "./datas"
+VECTOR_IMG_LIST_PATH = "./datas/Eyewear_001~500_ComponentsCapture_20251121"
 
 INDEX_IMAGES_PATH = "./datas/eyewear_all"
 
+class VectorSearchItem:
+    glass = "glass"
+    bridge = "Bridge"
+    hinge = "Hinges"
+    lens = "Lens"
+    nosepad = "Nose pad"
+    other = "other"
+    padarm = "Padarm"
+    rim = "Rim"
+    rivet = "Rivet"
+    soltex = "Soltex"
+    temple = "Temple"
+    templetip = "Temple tip"
+    
+class ImageDepths:
+    parts = "Parts"
+    thumnails = "Thumbnails"
 
 # report image consts
 class ReportInfoConst:

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/faiss_functions.py

@@ -100,21 +100,37 @@ def save_report_image(report_info, report_image_path):
     table_setting(report_info, report_image_path)
 
 
-def get_clip_info(model, query_image_path, top_k=4):
+def get_clip_info(model, query_image_path, item_info, top_k=4):
     """
     이미지 유사도 검색
     """
-    index_file_path = os.path.join(VECTOR_IMG_LIST_PATH,f'{model.name}_{os.path.basename(model.value[1].trained_model)}_{model.value[1].index_type}_{DEFAULT_INDEX_NAME_SUFFIX}')
+    index_file_path = os.path.join(VECTOR_IMG_LIST_PATH,f'{model.name}_{os.path.basename(model.value[1].trained_model)}_{model.value[1].index_type}_{item_info}_{DEFAULT_INDEX_NAME_SUFFIX}')
-    txt_file_path = os.path.join(VECTOR_IMG_LIST_PATH,f'{model.name}_{os.path.basename(model.value[1].trained_model)}_{model.value[1].index_type}.txt')
+    txt_file_path = os.path.join(VECTOR_IMG_LIST_PATH,f'{model.name}_{os.path.basename(model.value[1].trained_model)}_{model.value[1].index_type}_{item_info}.txt')
     vector_model = VectorSimilarity(model_info=model, 
                             index_file_path=index_file_path,
                             txt_file_path=txt_file_path)
 
+    image_lists = []
+    if not os.path.exists(txt_file_path):
+        if item_info == VectorSearchItem.glass:
+            image_lists = find_glass_folder_images(VECTOR_IMG_LIST_PATH)
+        else:
+            image_lists = find_parts_folder_images(VECTOR_IMG_LIST_PATH, item_info)
+            
+        if image_lists:
+            with open(txt_file_path, 'w') as f:
+                for img_path in image_lists:
+                    f.write(f"{img_path}\n")
+    else:
+        with open(txt_file_path, 'r') as f:
+            image_lists = [line.strip() for line in f.readlines()]
+        
     # vector_model.create_feature_extraction_model(FEMUsageInfo.openaiclipvit)
 
-    vector_model.save_index_from_files(images_path=INDEX_IMAGES_PATH,
+    vector_model.save_index_from_files(images_path_lists=image_lists,
                             save_index_dir=FAISS_VECTOR_PATH,
                             save_txt_dir=VECTOR_IMG_LIST_PATH,
+                            item_info=item_info,
                             index_type=model.value[1].index_type)
                                 
     inference_times, result_img_paths, result_percents = vector_model.query_faiss(query_image_path, top_k=top_k)
@@ -131,5 +147,56 @@ def get_clip_info(model, query_image_path, top_k=4):
     )
     
     return report_info
+
+def find_glass_folder_images(root_folder):
+    """
+    glass(thumnails) 폴더 내의 모든 이미지 파일 경로를 재귀적으로 탐색하여 리스트로 반환합니다.
+    
+    Args:
+        root_folder (str): 탐색을 시작할 최상위 폴더
+    
+    Returns:
+        list: 발견된 이미지 파일 전체 경로의 리스트.
+    """
+    image_paths = []
+    
+    for dirpath, dirnames, filenames in os.walk(root_folder):
+        
+        if os.path.basename(dirpath) == ImageDepths.thumnails:
+            
+            for filename in filenames:
+                if filename.lower().endswith(('.png', '.jpg', '.jpeg')):
+                
+                    # 파일의 전체 경로를 생성하여 리스트에 추가
+                    full_path = os.path.join(dirpath, filename)
+                    image_paths.append(full_path)
+    
+    return image_paths
+
+
+def find_parts_folder_images(root_folder, parts):
+    """
+    parts 폴더 내의 해당하는 파츠 이미지 파일 경로를 재귀적으로 탐색하여 리스트로 반환합니다.
+    
+    Args:
+        root_folder (str): 탐색을 시작할 최상위 폴더
+    
+    Returns:
+        list: 발견된 이미지 파일 경로의 리스트.
+    """
+    image_paths = []
+    
+    for dirpath, dirnames, filenames in os.walk(root_folder):
+        
+        if os.path.basename(dirpath) == ImageDepths.parts:
+            
+            for filename in filenames:
+                if filename.lower().endswith(('.png', '.jpg', '.jpeg')) and parts in filename:
+                
+                    # 파일의 전체 경로를 생성하여 리스트에 추가
+                    full_path = os.path.join(dirpath, filename)
+                    image_paths.append(full_path)
+    
+    return image_paths
     
     

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/faiss_similarity_search.py

@@ -234,73 +234,67 @@ class VectorSimilarity:
         feature_vectors = self.fem_model.image_embedding(image_data_np)
         return feature_vectors
 
-    def save_index_from_files(self, images_path=None, save_index_dir=None, save_txt_dir=None, index_type=INDEX_TYPE_L2):
+    def save_index_from_files(self, images_path_lists=None, save_index_dir=None, save_txt_dir=None, item_info=None, index_type=INDEX_TYPE_L2):
         """
         인덱스 정보 저장
         :param images_path: 이미지 파일 경로
         :param save_index_dir: 인덱스 저장 디렉토리
         :param save_txt_dir: 이미지 경로 저장 디렉토리
+        :param item_info: 아이템 정보
         :param index_type: 인덱스 타입 (L2, Cosine)
         :return: 이미지 임베딩 차원 정보
         """
         assert self.fem_model is not None, 'invalid fem_model'
-        assert images_path is not None, 'images_path is required'
+        assert images_path_lists is not None, 'images_path is required'
 
-        if not os.path.exists(images_path):
-            log.error(f'image_path={images_path} does not exist')
-
-        image_folder = images_path
         result = None
 
-        # 모든 이미지 임베딩 모으기
+        image_paths = images_path_lists
-        if os.path.exists(image_folder):
+        image_vectors = np.vstack([self.image_embedding_from_file(image_file) for image_file in image_paths])
-            image_paths = [os.path.join(image_folder, image_file) for image_file in os.listdir(image_folder)]
+        log.debug(f'image_vectors.shape={image_vectors.shape}')
-            image_vectors = np.vstack([self.image_embedding_from_file(image_file) for image_file in image_paths])
+        result = image_vectors.shape
-            log.debug(f'image_vectors.shape={image_vectors.shape}')
-            result = image_vectors.shape
 
-            # 인덱스 생성 (L2)
+        # 인덱스 생성 (L2)
-            dimension = image_vectors.shape[1]
+        dimension = image_vectors.shape[1]
 
-            if index_type == INDEX_TYPE_L2:
+        if index_type == INDEX_TYPE_L2:
-                index = faiss.IndexFlatL2(dimension)  # L2
+            index = faiss.IndexFlatL2(dimension)  # L2
-            elif index_type == INDEX_TYPE_COSINE:
+        elif index_type == INDEX_TYPE_COSINE:
-                index = faiss.IndexFlatIP(dimension) # cosine
+            index = faiss.IndexFlatIP(dimension) # cosine
-                faiss.normalize_L2(image_vectors)
+            faiss.normalize_L2(image_vectors)
-            else:
-                raise Exception(f'Invalid index_type={index_type}')
-
-            index.add(image_vectors)
-
-            # 인덱스 저장
-            if save_index_dir is None:
-                save_index_path = os.path.join(FILE_BASE_PATH,f'{self.fem_model_name}_{os.path.basename(self.fem_model_args.trained_model)}_{index_type}_{DEFAULT_SAVE_INDEX_SUFFIX}')
-                self.index_file_path = save_index_path
-            else:
-                save_index_path = os.path.join(save_index_dir,f'{self.fem_model_name}_{os.path.basename(self.fem_model_args.trained_model)}_{index_type}_{DEFAULT_SAVE_INDEX_SUFFIX}')
-                os.makedirs(save_index_dir, exist_ok=True)
-            
-            # 이미지 경로 파일 저장    
-            if save_txt_dir is None:
-                save_txt_path = os.path.join(FILE_BASE_PATH, f'{self.fem_model_name}_{os.path.basename(self.fem_model_args.trained_model)}_{index_type}.txt')
-                self.txt_file_path = save_txt_path
-            else:
-                save_txt_path = os.path.join(save_txt_dir, f'{self.fem_model_name}_{os.path.basename(self.fem_model_args.trained_model)}_{index_type}.txt')
-                os.makedirs(save_txt_dir, exist_ok=True)
-
-            if not os.path.exists(save_txt_path):
-                faiss.write_index(index, save_index_path)
-                log.debug(f'save_index_path={save_index_path}')
-            
-            if not os.path.exists(save_txt_path):
-                # 이미지 경로 저장
-                with open(save_txt_path, 'w') as f:
-                    for path in image_paths:
-                        f.write(f"{path}\n")
-                log.debug(f'save_txt_path={save_txt_path}')
-            
         else:
-            log.error(f'Image folder {image_folder} does not exist')
+            raise Exception(f'Invalid index_type={index_type}')
+
+        index.add(image_vectors)
+
+        # 인덱스 저장
+        if save_index_dir is None:
+            save_index_path = os.path.join(FILE_BASE_PATH,f'{self.fem_model_name}_{os.path.basename(self.fem_model_args.trained_model)}_{index_type}_{item_info}_{DEFAULT_SAVE_INDEX_SUFFIX}')
+            self.index_file_path = save_index_path
+        else:
+            save_index_path = os.path.join(save_index_dir,f'{self.fem_model_name}_{os.path.basename(self.fem_model_args.trained_model)}_{index_type}_{item_info}_{DEFAULT_SAVE_INDEX_SUFFIX}')
+            os.makedirs(save_index_dir, exist_ok=True)
+        
+        # # 이미지 경로 파일 저장    
+        # if save_txt_dir is None:
+        #     save_txt_path = os.path.join(FILE_BASE_PATH, f'{self.fem_model_name}_{os.path.basename(self.fem_model_args.trained_model)}_{index_type}.txt')
+        #     self.txt_file_path = save_txt_path
+        # else:
+        #     save_txt_path = os.path.join(save_txt_dir, f'{self.fem_model_name}_{os.path.basename(self.fem_model_args.trained_model)}_{index_type}.txt')
+        #     os.makedirs(save_txt_dir, exist_ok=True)
+
+        if not os.path.exists(save_index_path):
+            faiss.write_index(index, save_index_path)
+            log.debug(f'save_index_path={save_index_path}')
+        
+        # if not os.path.exists(save_txt_path):
+        #     # 이미지 경로 저장
+        #     with open(save_txt_path, 'w') as f:
+        #         for path in image_paths:
+        #             f.write(f"{path}\n")
+        #     log.debug(f'save_txt_path={save_txt_path}')
+            
+
         return result
 
     def set_index_path(self, index_path):
@@ -394,7 +388,7 @@ def test():
     
     # cm.create_feature_extraction_model(FEMUsageInfo.openaiclipvit)
 
-    cm.save_index_from_files(images_path=INDEX_IMAGES_PATH,
+    cm.save_index_from_files(images_path_lists=INDEX_IMAGES_PATH,
                             save_index_dir=FAISS_VECTOR_PATH,
                             save_txt_dir=VECTOR_IMG_LIST_PATH,
                             index_type=model.value[1].index_type)

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/utils.py

@@ -0,0 +1,31 @@
+import os
+from pathlib import Path
+
+from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.const import *
+
+def search_glass_parts(image_path_list):
+    result = []
+    
+    for image_path in image_path_list:
+        parts_path = Path(os.path.join(os.path.dirname(os.path.dirname(image_path)),ImageDepths.parts))
+        parts_files_generator = parts_path.rglob('*.png')
+
+        parts_list = []
+        # parts_list = [str(p.resolve()) for p in parts_files_generator] # 풀경로
+        
+        for p in parts_files_generator:
+            # name,ext = os.path.splitext(p.name)
+            parts_list.append(p.name)
+        result.append(parts_list)
+    
+    return result
+
+def file_name_to_parts(file_path):
+    filename, ext = os.path.splitext(os.path.basename(file_path))
+    
+    name_list = filename.split('_')
+    
+    result = name_list[2:][0]
+        
+    return result
+    

main_rest/app/models.py

@@ -30,7 +30,7 @@ from main_rest.app.common.consts import (
     DEFAULT_USER_ACCOUNT_PW
 )
 from main_rest.app.utils.date_utils import D
-
+from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.const import VectorSearchItem
 
 class SWInfo(BaseModel):
     """
@@ -581,6 +581,11 @@ class VitModelType:
     b16 = "b16"
     l14 = "l14"
     l14_336 = "l14_336"
+    
+    
+class PartsType(str, Enum):
+    bridge = VectorSearchItem.bridge
+    hinge = VectorSearchItem.hinge
 
 
 class ImageGenerateReq(BaseModel):
@@ -616,7 +621,7 @@ class VectorImageSearchVitReq(BaseModel):
     searchNum : int = Field(4, description='검색결과 이미지 갯수', example=4)
     
     
-class VectorImageSearchVitInputImgReq(BaseModel):
+class VectorGlassesImageSearchVitInputImgReq(BaseModel):
     """
     ### [Request] vector image search vit - input image
     """
@@ -626,6 +631,14 @@ class VectorImageSearchVitInputImgReq(BaseModel):
     searchNum : int = Field(4, description='검색결과 이미지 갯수', example=4)
 
 
+class VectorPartsImageSearchVitInputImgReq(VectorGlassesImageSearchVitInputImgReq):
+    """
+    ### [Request] vector image search vit data - input image
+    """
+    inputImage : str = Field(description='파츠이미지 이름', example='')
+    
+
+
 class VectorImageSearchVitDataReq(VectorImageSearchVitReq):
     """
     ### [Request] vector image search vit data
@@ -642,11 +655,17 @@ class VectorImageSearchVitReportReq(BaseModel):
     indexType : str = Field(VitIndexType.l2, description='인덱스 타입', example=VitIndexType.l2)
     
     
-class VectorImageResult(BaseModel):
+class VectorGlassesImageResult(BaseModel):
     image : str = Field("", description='이미지 데이터', example='')
     percents: float = Field(0.0, description='percents 값', example='')
     imageInfo : str = Field("", description='원본이미지 이름', example='')
+    parts: list = Field([], description='안경 파츠 정보', example=[])
+    
 
+class VectorPartsImageResult(BaseModel):
+    image : str = Field("", description='이미지 데이터', example='')
+    percents: float = Field(0.0, description='percents 값', example='')
+    imageInfo : str = Field("", description='원본이미지 이름', example='')
 #===============================================================================
 #===============================================================================
 #===============================================================================
@@ -716,12 +735,12 @@ class BingCookieSetRes(ResponseBase):
         return BingCookieSetRes
     
 
-class VectorImageSerachDataRes(ResponseBase):
+class VectorGlassesImageSerachDataRes(ResponseBase):
     """
     ### vector image data response
     """
     queryImage : str = Field("", description='쿼리 이미지', example="")
-    vectorResult : List[VectorImageResult] = Field([], description='벡터 검색 결과', example=[])
+    vectorResult : List[VectorGlassesImageResult] = Field([], description='벡터 검색 결과', example=[])
     
     @staticmethod
     def set_error(error,vector_result=[],query_img=""):
@@ -739,4 +758,8 @@ class VectorImageSerachDataRes(ResponseBase):
         ImageGenerateRes.vectorResult = vector_result
         ImageGenerateRes.queryImage = query_img
         
-        return ImageGenerateRes
+        return ImageGenerateRes
+    
+
+class VectorPartsImageSerachDataRes(VectorGlassesImageSerachDataRes):
+        vectorResult : List[VectorPartsImageResult] = Field([], description='벡터 검색 결과', example=[])

main_rest/app/routes/dev.py

@@ -135,3 +135,75 @@ async def test(request: Request):
     return a
 
 
+# @router.post("/vectorImageSearch/vit/imageGenerate/imagen/report", summary="벡터 이미지 검색(clip-vit) - imagen, report 생성", response_model=M.ResponseBase)
+# async def vactor_vit_report(request: Request, request_body_info: M.VectorImageSearchVitReportReq):
+    # """
+    # ## 벡터 이미지 검색(clip-vit) - imagen, report 생성
+    # > imagen AI를 이용하여 이미지 생성 후 vector 검색 그후 종합결과 이미지 생성
+    
+    # ### Requriements
+    # > - googlecli 설치(https://cloud.google.com/sdk/docs/install?hl=ko#linux)
+    
+    # ### options
+    # > - modelType -> b32,b16,l14,l14_336
+    # > - indexType -> l2,cos
+    
+    # """
+    # response = M.ResponseBase()
+    # try:       
+    #     if request_body_info.modelType not in [M.VitModelType.b32, M.VitModelType.b16, M.VitModelType.l14, M.VitModelType.l14_336]:
+    #         raise Exception(f"modelType is invalid (current value = {request_body_info.modelType})")
+        
+    #     if request_body_info.indexType not in [M.VitIndexType.cos, M.VitIndexType.l2]:
+    #         raise Exception(f"indexType is invalid (current value = {request_body_info.indexType})")
+        
+    #     # query_image_path = imagen_generate_temp_image_path(image_prompt=request_body_info.prompt) #imagen
+    #     query_image_path = gemini_image(request_body_info.prompt) #gemini
+    #     report_image_path = f"{os.path.splitext(query_image_path)[0]}_report.png"
+        
+    #     vactor_request_data = {'query_image_path' : query_image_path,
+    #                            'index_type' : request_body_info.indexType, 
+    #                            'model_type' : request_body_info.modelType,
+    #                            'report_path' : report_image_path}
+
+    #     vactor_response = requests.post('http://localhost:51002/api/services/faiss/vector/search/vit/report', data=json.dumps(vactor_request_data))
+        
+    #     if vactor_response.status_code != 200:
+    #         raise Exception(f"response error: {json.loads(vactor_response.text)['error']}")
+        
+    #     if json.loads(vactor_response.text)["error"] != None:
+    #         raise Exception(f"vector error: {json.loads(vactor_response.text)['error']}")
+        
+    #     if rest_config.config != 'release':
+    #         # remote 폴더에 이미지 저장
+    #         sftp_client.remote_copy_data(local_path=report_image_path, 
+    #                                     remote_path=os.path.join(rest_config.remote_folder, f"imagen_report_vit_{request_body_info.prompt}_{D.date_file_name()}.png"))
+    #     else:
+    #         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"))
+         
+    #     # Clean up temporary files
+    #     if 'query_image_path' in locals():
+    #         if os.path.exists(query_image_path):
+    #             os.remove(query_image_path)
+    #             del query_image_path
+    #     if 'report_image_path' in locals():
+    #         if os.path.exists(report_image_path):
+    #             os.remove(report_image_path)
+    #             del report_image_path
+        
+    #     return response.set_message()
+    
+    # except Exception as e:
+    #     LOG.error(traceback.format_exc())
+        
+    #     # Clean up temporary files
+    #     if 'query_image_path' in locals():
+    #         if os.path.exists(query_image_path):
+    #             os.remove(query_image_path)
+    #             del query_image_path
+    #     if 'report_image_path' in locals():
+    #         if os.path.exists(report_image_path):
+    #             os.remove(report_image_path)
+    #             del report_image_path
+                
+    #     return response.set_error(error=e)

main_rest/app/routes/services.py

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

vector_rest/app/models.py

@@ -583,7 +583,7 @@ class VitModelType(str, Enum):
     l14_336 = "l14_336"
 
 
-class VactorSearchReq(BaseModel):
+class VectorSearchReq(BaseModel):
     """
     ### [Request] vector 검색
     """
@@ -592,7 +592,7 @@ class VactorSearchReq(BaseModel):
     search_num : int = Field(4, description='검색결과 이미지 갯수', example=4)
 
 
-class VactorSearchVitReportReq(BaseModel):
+class VectorSearchVitReportReq(BaseModel):
     """
     ### [Request] vector 검색(vit) 후 리포트 이미지 생성
     """
@@ -602,7 +602,7 @@ class VactorSearchVitReportReq(BaseModel):
     report_path : str = Field(description='리포트 이미지 저장 경로', example='path')
     
     
-class VactorSearchVitReq(BaseModel):
+class VectorSearchVitReq(BaseModel):
     """
     ### [Request] vector 검색(vit) 후 이미지 생성
     """
@@ -611,22 +611,21 @@ class VactorSearchVitReq(BaseModel):
     model_type : VitModelType = Field(VitModelType.l14, description='pretrained 모델 정보', example=VitModelType.l14)
     search_num : int = Field(4, description='검색결과 이미지 갯수', example=4)
     
-    
+class VectorSearchVitRes(ResponseBase):
-class VactorSearchVitRes(ResponseBase):
     img_list : dict = Field({}, description='이미지 결과 리스트', example={})
     
     @staticmethod
     def set_error(error):
-        VactorSearchVitRes.img_list = {}
+        VectorSearchVitRes.img_list = {}
-        VactorSearchVitRes.result = False
+        VectorSearchVitRes.result = False
-        VactorSearchVitRes.error = str(error)
+        VectorSearchVitRes.error = str(error)
 
-        return VactorSearchVitRes
+        return VectorSearchVitRes
     
     @staticmethod
     def set_message(msg):
-        VactorSearchVitRes.img_list = msg
+        VectorSearchVitRes.img_list = msg
-        VactorSearchVitRes.result = True
+        VectorSearchVitRes.result = True
-        VactorSearchVitRes.error = None
+        VectorSearchVitRes.error = None
         
-        return VactorSearchVitRes
+        return VectorSearchVitRes

vector_rest/app/routes/services.py

@@ -23,12 +23,14 @@ from custom_apps.faiss_imagenet.main import search_idxs
 from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.faiss_functions import get_clip_info, save_report_image, get_models
 
 from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.faiss_similarity_search import FEMUsageInfo
+from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.const import VectorSearchItem
+from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.utils import search_glass_parts, file_name_to_parts
 
 router = APIRouter(prefix="/services")
 
 
 @router.post("/faiss/vector/search/imagenet", summary="imagenet search", response_model=M.ResponseBase)
-async def vactor_search(request: Request, request_body_info: M.VactorSearchReq):
+async def vactor_search(request: Request, request_body_info: M.VectorSearchReq):
     """
     ## 벡터검색
     
@@ -54,7 +56,7 @@ async def vactor_search(request: Request, request_body_info: M.VactorSearchReq):
     
 
 @router.post("/faiss/vector/search/vit/report", summary="vit search report", response_model=M.ResponseBase)
-async def vactor_report_vit(request: Request, request_body_info: M.VactorSearchVitReportReq):
+async def vactor_report_vit(request: Request, request_body_info: M.VectorSearchVitReportReq):
     """
     이미지 경로를 입력받아 repport image 저장
     """
@@ -75,23 +77,59 @@ async def vactor_report_vit(request: Request, request_body_info: M.VactorSearchV
         return response.set_error(e)
     
 
-@router.post("/faiss/vector/search/vit", summary="vit search", response_model=M.VactorSearchVitRes)
+@router.post("/faiss/vector/search/vit", summary="vit search", response_model=M.VectorSearchVitRes)
-async def vactor_vit(request: Request, request_body_info: M.VactorSearchVitReq):
+async def vactor_vit(request: Request, request_body_info: M.VectorSearchVitReq):
     """
     이미지 경로 를 입력받아 vit 방식으로 검색
     """
-    response = M.VactorSearchVitRes()
+    response = M.VectorSearchVitRes()
     try:
         if not os.path.exists(request_body_info.query_image_path):
             raise FileNotFoundError(f"File {request_body_info.query_image_path} does not exist.")
         
         model = get_models(index_type=request_body_info.index_type, model_type=request_body_info.model_type)
         
-        report_info = get_clip_info(model,request_body_info.query_image_path,top_k=request_body_info.search_num)
+        report_info = get_clip_info(model=model,
+                                    query_image_path=request_body_info.query_image_path,
+                                    item_info=VectorSearchItem.glass,
+                                    top_k=request_body_info.search_num)
+        
+        parts = search_glass_parts(report_info.result_image_paths)
         
         return response.set_message({
             'result_image_paths': report_info.result_image_paths,
-            'result_percents': report_info.result_percents
+            'result_percents': report_info.result_percents,
+            'result_parts_list': parts
+        })
+       
+    except Exception as e:
+        LOG.error(traceback.format_exc())
+        return response.set_error(e)
+    
+
+@router.post("/faiss/vector/search/vit/parts", summary="vit search parts", response_model=M.VectorSearchVitRes)
+async def vactor_vit_parts(request: Request, request_body_info: M.VectorSearchVitReq):
+    """
+    이미지 경로 를 입력받아 vit 방식으로 검색
+    """
+    response = M.VectorSearchVitRes()
+    try:
+        if not os.path.exists(request_body_info.query_image_path):
+            raise FileNotFoundError(f"File {request_body_info.query_image_path} does not exist.")
+        
+        model = get_models(index_type=request_body_info.index_type, model_type=request_body_info.model_type)
+        
+        #TODO parts 데이터 확인필요
+        parts = file_name_to_parts(request_body_info.query_image_path)
+        
+        report_info = get_clip_info(model=model,
+                                    query_image_path=request_body_info.query_image_path,
+                                    item_info=parts,
+                                    top_k=request_body_info.search_num)
+        
+        return response.set_message({
+            'result_image_paths': report_info.result_image_paths,
+            'result_percents': report_info.result_percents,
         })
        
     except Exception as e: