relarse 0.2.0

.gitignore

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

README.md

@@ -1,4 +1,4 @@
-# FM_TEST_REST_SERVER
+# GLASSES_AI_SERVER
 
 RESTful API Server
 

config.py

@@ -26,6 +26,7 @@ class Config:
         self.sftp_port = 22
         self.sftp_id = "fermat"
         self.sftp_pw = "fermat3514"
+        self.db_pw = "1234"
         
         if not os.path.exists(self.remote_folder):
             os.makedirs(self.remote_folder)
@@ -34,6 +35,8 @@ class Config:
         self.config = 'release'
         self.local_folder = "./result" # 결과물 저장 폴더
         
+        self.db_pw = "Fermat3514!"
+        
         if not os.path.exists(self.local_folder):
             os.makedirs(self.local_folder)
 

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/const.py

@@ -19,10 +19,26 @@ ViTL14_336 = "clip-vit-large-patch14-336"
 #download/save path
 PRETRAINED_MODEL_PATH = "./datas"
 FAISS_VECTOR_PATH = "./datas"
-VECTOR_IMG_LIST_PATH = "./datas"
-
-INDEX_IMAGES_PATH = "./datas/eyewear_all"
+IMG_LIST_PATH = "../Eyewear_data"
 
+# INDEX_IMAGES_PATH = "./datas/eyewear_all"
+class VectorSearchItem:
+    glass = "glass"
+    tips = "Tips"
+    temple = "Temple"
+    rim = "Rim"
+    rim2 = "Rim2"
+    noesPad = "Nose pad"
+    noseArm = "Nose arm"
+    lens = "Lens"
+    endPiece = "End piece"
+    hinges = "Hinges"
+    screw = "Screw"
+    bridge = "Bridge"
+    
+class ImageDepths:
+    parts = "Parts"
+    thumnails = "Thumbnails"
 
 # report image consts
 class ReportInfoConst:

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/faiss_functions.py

@@ -100,22 +100,32 @@ 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}')
-    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')
+    index_file_path = os.path.join(FAISS_VECTOR_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(FAISS_VECTOR_PATH,f'{model.name}_{os.path.basename(model.value[1].trained_model)}_{model.value[1].index_type}_{item_info}.txt')
+    
+    if not os.path.exists(index_file_path) or not os.path.exists(txt_file_path):
+        raise FileNotFoundError(f"Index file {index_file_path} or txt file {txt_file_path} does not exist.")
+    
     vector_model = VectorSimilarity(model_info=model, 
                             index_file_path=index_file_path,
                             txt_file_path=txt_file_path)
 
+    # image_lists = []
+
+    # 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,
-                            save_index_dir=FAISS_VECTOR_PATH,
-                            save_txt_dir=VECTOR_IMG_LIST_PATH,
-                            index_type=model.value[1].index_type)
+    # 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 +141,56 @@ 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
-        if os.path.exists(image_folder):
-            image_paths = [os.path.join(image_folder, image_file) for image_file in os.listdir(image_folder)]
-            image_vectors = np.vstack([self.image_embedding_from_file(image_file) for image_file in image_paths])
-            log.debug(f'image_vectors.shape={image_vectors.shape}')
-            result = image_vectors.shape
+        image_paths = images_path_lists
+        image_vectors = np.vstack([self.image_embedding_from_file(image_file) for image_file in image_paths])
+        log.debug(f'image_vectors.shape={image_vectors.shape}')
+        result = image_vectors.shape
 
-            # 인덱스 생성 (L2)
-            dimension = image_vectors.shape[1]
+        # 인덱스 생성 (L2)
+        dimension = image_vectors.shape[1]
 
-            if index_type == INDEX_TYPE_L2:
-                index = faiss.IndexFlatL2(dimension)  # L2
-            elif index_type == INDEX_TYPE_COSINE:
-                index = faiss.IndexFlatIP(dimension) # cosine
-                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}')
-            
+        if index_type == INDEX_TYPE_L2:
+            index = faiss.IndexFlatL2(dimension)  # L2
+        elif index_type == INDEX_TYPE_COSINE:
+            index = faiss.IndexFlatIP(dimension) # cosine
+            faiss.normalize_L2(image_vectors)
         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):
@@ -378,33 +372,33 @@ class VectorSimilarity:
                 
         return inference_times, result_img_paths, result_percents
 
-def test():
-    """
-    module test function
-    """
-    log.info('\nModule: faiss_similarity_search.py')
+# def test():
+#     """
+#     module test function
+#     """
+#     log.info('\nModule: faiss_similarity_search.py')
 
-    # index_file_path = f'{FILE_BASE_PATH}openaiclipvit_clip-vit-base-patch32_index.faiss'
-    model = FEMUsageInfo.openaiclip_vit_l14_cos
-    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 = f'{FILE_BASE_PATH}openaiclipvit_clip-vit-base-patch32_index.faiss'
+#     model = FEMUsageInfo.openaiclip_vit_l14_cos
+#     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}')
 
-    cm = VectorSimilarity(model_info=model, 
-                          index_file_path=index_file_path,
-                          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'))
+#     cm = VectorSimilarity(model_info=model, 
+#                           index_file_path=index_file_path,
+#                           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'))
     
-    # cm.create_feature_extraction_model(FEMUsageInfo.openaiclipvit)
+#     # cm.create_feature_extraction_model(FEMUsageInfo.openaiclipvit)
 
-    cm.save_index_from_files(images_path=INDEX_IMAGES_PATH,
-                            save_index_dir=FAISS_VECTOR_PATH,
-                            save_txt_dir=VECTOR_IMG_LIST_PATH,
-                            index_type=model.value[1].index_type)
+#     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)
                              
 
-    cm.query_faiss(os.path.join(INDEX_IMAGES_PATH,"img1.png"))
+#     cm.query_faiss(os.path.join(INDEX_IMAGES_PATH,"img1.png"))
 
 
 if __name__ == '__main__':
     """
     test module
     """
-    test()
+    # test()

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/utils.py

@@ -0,0 +1,39 @@
+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):
+    """
+    파일경로로 파츠이름 파싱
+
+    :param file_path: 파일경로
+    :return: 파츠이름
+    """
+    filename, ext = os.path.splitext(os.path.basename(file_path))
+    
+    name_list = filename.split('_')
+    
+    result = name_list[2:][0]
+        
+    return result
+
+if __name__ == '__main__':
+    print(file_name_to_parts(os.path.join(FAISS_VECTOR_PATH,"Glass_001",ImageDepths.parts,"Glass_001_Temple_L.png")))

custom_apps/gemini/main.py

@@ -14,11 +14,11 @@ def gemini_image(prompt, folder=None):
     from custom_logger.main_log import main_logger as LOG
     
     image_path = ''
-    client = genai.Client(api_key="AIzaSyB7tu67y9gOkJkpQtvI5OAYSzUzwv9qwnE")
+    client = genai.Client(api_key="AIzaSyCSw4pcPDYdAnjzBB7J9ZKXtRJJvunjWtA") # a2tec key
 
     for i in range(3):
         response = client.models.generate_content(
-            model="gemini-2.0-flash-preview-image-generation",
+            model="gemini-2.5-flash-image",
             contents=prompt,
             config=types.GenerateContentConfig(
             response_modalities=['TEXT', 'IMAGE']

custom_logger/custom_log.py

@@ -2,7 +2,7 @@
 """
 @file : custom_log.py
 @author: hsj100
-@license: DAOOLDNS
+@license: A2TEC
 @brief: 
 @section Modify History
 - 2024-05-08 오후 2:33 hsj100 base

main_rest/app/common/consts.py

@@ -8,15 +8,16 @@
 - 2022-01-14/hsj100@a2tec.co.kr : refactoring
 @brief: consts
 """
+from config import rest_config
 
 # SUPPORT PROJECT
 SUPPORT_PROJECT_BASIC = 'PROJECT_BASIC'
 
-PROJECT_NAME = 'FERMAT-TEST'
+PROJECT_NAME = 'K_EYEWEAR'
 SW_TITLE= f'{PROJECT_NAME} - REST API'
-SW_VERSION = '0.1.0'
+SW_VERSION = '0.2.0'
 SW_DESCRIPTION = f'''
-### FERMAT-TEST REST API
+### K_EYEWEAR REST API
 
 ## API 이용법
   - 개별 API 설명과 Request/Response schema 참조
@@ -87,7 +88,7 @@ NUM_RETRY_UUID_GEN = 3
 DB_ADDRESS = "localhost"
 DB_PORT = 53306
 DB_USER_ID = 'root'
-DB_USER_PW = '1234'
+DB_USER_PW = rest_config.db_pw
 DB_NAME = 'FM_TEST'
 DB_CHARSET = 'utf8mb4'
 

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):
     """
@@ -581,12 +581,13 @@ class VitModelType:
     b16 = "b16"
     l14 = "l14"
     l14_336 = "l14_336"
-
-
+    
+    
 class ImageGenerateReq(BaseModel):
     """
     ### [Request] image generate request
     """
+    prefix : Optional[str] = Field(default="하얀색 배경", description="접두사", example="하얀색 배경")
     prompt : str = Field(description='프롬프트', example='검은색 안경')    
     
 class BingCookieSetReq(BaseModel):
@@ -610,21 +611,29 @@ class VectorImageSearchVitReq(BaseModel):
     ### [Request] vector image search vit
     """
     prompt : str = Field(description='프롬프트', example='검은색 안경')
-    modelType : str = Field(VitModelType.l14, description='pretrained model 타입', example=VitModelType.l14)
+    modelType : str = Field(VitModelType.b32, description='pretrained model 타입', example=VitModelType.b32)
     indexType : str = Field(VitIndexType.l2, description='인덱스 타입', example=VitIndexType.l2)
     searchNum : int = Field(4, description='검색결과 이미지 갯수', example=4)
     
     
-class VectorImageSearchVitInputImgReq(BaseModel):
+class VectorGlassesImageSearchVitInputImgReq(BaseModel):
     """
     ### [Request] vector image search vit - input image
     """
     inputImage : str = Field(description='base64 이미지', example='')
-    modelType : str = Field(VitModelType.l14, description='pretrained model 타입', example=VitModelType.l14)
+    modelType : str = Field(VitModelType.b32, description='pretrained model 타입', example=VitModelType.b32)
     indexType : str = Field(VitIndexType.l2, description='인덱스 타입', example=VitIndexType.l2)
     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
@@ -641,10 +650,17 @@ 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 | None = Field("", description='이미지 데이터', example='')
+    percents: float = Field(0.0, description='percents 값', example='')
+    imageInfo : str = Field("", description='원본이미지 이름', example='')
 #===============================================================================
 #===============================================================================
 #===============================================================================
@@ -714,12 +730,12 @@ 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=""):
@@ -737,4 +753,8 @@ 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)
@@ -123,88 +125,88 @@ router = APIRouter(prefix="/services")
 #         LOG.error(traceback.format_exc())
 #         return response.set_error(e)
     
-@router.post("/imageGenerate/imagen", summary="이미지 생성(AI) - imagen", response_model=M.ResponseBase)
-async def imagen(request: Request, request_body_info: M.ImageGenerateReq):
-    """
-    ## 이미지 생성(AI) - imagen
-    > imagen AI를 이용하여 이미지 생성
-    """
-    # imagen 사용중단 gemini로 변경
+# @router.post("/imageGenerate/imagen", summary="이미지 생성(AI) - imagen", response_model=M.ResponseBase)
+# async def imagen(request: Request, request_body_info: M.ImageGenerateReq):
+#     """
+#     ## 이미지 생성(AI) - imagen
+#     > imagen AI를 이용하여 이미지 생성
+#     """
+#     # imagen 사용중단 gemini로 변경
     
-    response = M.ResponseBase()
-    try:
-        # NOTE(JWKIM) : imagen 사용 중단
-        # img_length = imagen_generate_image(prompt=request_body_info.prompt,
-        #                       download_count=request_body_info.downloadCount
-        #                       )
-        temp_image_path = gemini_image(request_body_info.prompt)
+#     response = M.ResponseBase()
+#     try:
+#         # NOTE(JWKIM) : imagen 사용 중단
+#         # img_length = imagen_generate_image(prompt=request_body_info.prompt,
+#         #                       download_count=request_body_info.downloadCount
+#         #                       )
+        # temp_image_path = gemini_image(request_body_info.prompt)
         
-        if rest_config.config != 'release':
-            _remote_folder = os.path.join(rest_config.remote_folder,"imagen")
-            # remote save
-            sftp_client.remote_copy_data(
-                temp_image_path, 
-                os.path.join(_remote_folder,f"imagen_{request_body_info.prompt}_{1}_{D.date_file_name()}.png"))
-        else:
-            _local_forder = os.path.join(rest_config.local_folder,"image_generate","imagen")
-            os.makedirs(_local_forder,exist_ok=True)
-            shutil.copy(temp_image_path, os.path.join(_local_forder,f"imagen_{request_body_info.prompt}_{1}_{D.date_file_name()}.png"))
+#         if rest_config.config != 'release':
+#             _remote_folder = os.path.join(rest_config.remote_folder,"imagen")
+#             # remote save
+#             sftp_client.remote_copy_data(
+#                 temp_image_path, 
+#                 os.path.join(_remote_folder,f"imagen_{request_body_info.prompt}_{1}_{D.date_file_name()}.png"))
+#         else:
+#             _local_forder = os.path.join(rest_config.local_folder,"image_generate","imagen")
+#             os.makedirs(_local_forder,exist_ok=True)
+#             shutil.copy(temp_image_path, os.path.join(_local_forder,f"imagen_{request_body_info.prompt}_{1}_{D.date_file_name()}.png"))
         
-        # Clean up temporary files
-        if 'temp_image_path' in locals():
-            if os.path.exists(temp_image_path):
-                os.remove(temp_image_path)
-                del temp_image_path
+#         # Clean up temporary files
+#         if 'temp_image_path' in locals():
+#             if os.path.exists(temp_image_path):
+#                 os.remove(temp_image_path)
+#                 del temp_image_path
         
-        return response.set_message()
+#         return response.set_message()
     
-    except Exception as e:
-        LOG.error(traceback.format_exc())
+#     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
+#         # 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
         
-        return response.set_error(error=e)
+#         return response.set_error(error=e)
     
-@router.post("/imageGenerate/imagen/data", summary="이미지 생성(AI) - imagen(data)", response_model=M.ImageGenerateDataRes)
-async def imagen_data(request: Request, request_body_info: M.ImageGenerateReq):
-    """
-    ## 이미지 생성(AI) - imagen
-    > imagen AI를 이용하여 이미지 데이터생성
-    """
-    # imagen 사용중단 gemini로 변경
+# @router.post("/imageGenerate/imagen/data", summary="이미지 생성(AI) - imagen(data)", response_model=M.ImageGenerateDataRes)
+# async def imagen_data(request: Request, request_body_info: M.ImageGenerateReq):
+#     """
+#     ## 이미지 생성(AI) - imagen
+#     > imagen AI를 이용하여 이미지 데이터생성
+#     """
+#     # imagen 사용중단 gemini로 변경
     
-    response = M.ImageGenerateDataRes()
-    try:
-        # NOTE(JWKIM) : imagen 사용 중단
-        # img_length = imagen_generate_image(prompt=request_body_info.prompt,
-        #                       download_count=request_body_info.downloadCount
-        #                       )
-        temp_image_path = gemini_image(request_body_info.prompt)
+#     response = M.ImageGenerateDataRes()
+#     try:
+#         # NOTE(JWKIM) : imagen 사용 중단
+#         # img_length = imagen_generate_image(prompt=request_body_info.prompt,
+#         #                       download_count=request_body_info.downloadCount
+#         #                       )
+#         temp_image_path = gemini_image(request_body_info.prompt)
         
-        b64data = image_to_base64_string(temp_image_path)
+#         b64data = image_to_base64_string(temp_image_path)
         
-        # Clean up temporary files
-        if 'temp_image_path' in locals():
-            if os.path.exists(temp_image_path):
-                os.remove(temp_image_path)
-                del temp_image_path
+#         # Clean up temporary files
+#         if 'temp_image_path' in locals():
+#             if os.path.exists(temp_image_path):
+#                 os.remove(temp_image_path)
+#                 del temp_image_path
         
-        return response.set_message(b64data)
+#         return response.set_message(b64data)
     
-    except Exception as e:
-        LOG.error(traceback.format_exc())
+#     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
+#         # 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
         
-        return response.set_error(error=e)
+#         return response.set_error(error=e)
 
 # @router.post("/vectorImageSearch/imagenet/imageGenerate/imagen", summary="벡터 이미지 검색(imagenet) - imagen", response_model=M.ResponseBase)
 # async def vactor_imagenet(request: Request, request_body_info: M.vectorImageSearchReq):
@@ -256,181 +258,245 @@ async def imagen_data(request: Request, request_body_info: M.ImageGenerateReq):
 #         LOG.error(traceback.format_exc())
 #         return response.set_error(error=e)
 
-@router.post("/vectorImageSearch/vit/imageGenerate/imagen", summary="벡터 이미지 검색(clip-vit) - imagen", response_model=M.ResponseBase)
-async def vactor_vit(request: Request, request_body_info: M.VectorImageSearchVitReq):
-    """
-    ## 벡터 이미지 검색(clip-vit) - imagen
-    > imagen AI를 이용하여 이미지 생성 후 vector 검색 그후 결과 이미지 생성
+# @router.post("/vectorImageSearch/vit/imageGenerate/imagen", summary="벡터 이미지 검색(clip-vit) - imagen", response_model=M.ResponseBase)
+# async def vactor_vit(request: Request, request_body_info: M.VectorImageSearchVitReq):
+#     """
+#     ## 벡터 이미지 검색(clip-vit) - imagen
+#     > imagen AI를 이용하여 이미지 생성 후 vector 검색 그후 결과 이미지 생성
     
-    ### options
-    > - modelType -> b32,b16,l14,l14_336
-    > - indexType -> l2,cos
+#     ### options
+#     > - modelType -> b32,b16,l14,l14_336
+#     > - indexType -> l2,cos
     
-    """
-    response = M.ResponseBase()
-    try:  
-        if not download_range(request_body_info.searchNum, max=10):
-            raise Exception(f"downloadCound is invalid (current value = {request_body_info.searchNum})")
+#     """
+#     response = M.ResponseBase()
+#     try:  
+#         if not download_range(request_body_info.searchNum, max=10):
+#             raise Exception(f"downloadCound is invalid (current value = {request_body_info.searchNum})")
         
-        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.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})")
+#         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
+#         # 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
         
-        vector_request_data = {'query_image_path' : query_image_path,
-                               'index_type' : request_body_info.indexType, 
-                               'model_type' : request_body_info.modelType,
-                               'search_num' : request_body_info.searchNum}
+#         vector_request_data = {'query_image_path' : query_image_path,
+#                                'index_type' : request_body_info.indexType, 
+#                                'model_type' : request_body_info.modelType,
+#                                'search_num' : request_body_info.searchNum}
 
-        vector_response = requests.post('http://localhost:51002/api/services/faiss/vector/search/vit', data=json.dumps(vector_request_data))
+#         vector_response = requests.post('http://localhost:51002/api/services/faiss/vector/search/vit', data=json.dumps(vector_request_data))
         
-        vector_response_dict = json.loads(vector_response.text)
+#         vector_response_dict = json.loads(vector_response.text)
         
-        if vector_response.status_code != 200:
-            raise Exception(f"response error: {vector_response_dict['error']}")
+#         if vector_response.status_code != 200:
+#             raise Exception(f"response error: {vector_response_dict['error']}")
         
-        if vector_response_dict["error"] != None:
-            raise Exception(f"vector error: {vector_response_dict['error']}")
+#         if vector_response_dict["error"] != None:
+#             raise Exception(f"vector error: {vector_response_dict['error']}")
         
-        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_image_paths = vector_response_dict.get('img_list').get('result_image_paths')
+#         result_percents = vector_response_dict.get('img_list').get('result_percents')
         
-        if rest_config.config != 'release':
-            # 원격지 폴더 생성
-            remote_directory = os.path.join(rest_config.remote_folder, f"imagen_query_{request_body_info.modelType}_{request_body_info.indexType}_{request_body_info.prompt}_{D.date_file_name()}")
-            sftp_client.remote_mkdir(remote_directory)
+#         if rest_config.config != 'release':
+#             # 원격지 폴더 생성
+#             remote_directory = os.path.join(rest_config.remote_folder, f"imagen_query_{request_body_info.modelType}_{request_body_info.indexType}_{request_body_info.prompt}_{D.date_file_name()}")
+#             sftp_client.remote_mkdir(remote_directory)
             
-            # 원격지에 이미지 저장
-            sftp_client.remote_copy_data(local_path=query_image_path, remote_path=os.path.join(remote_directory,"query.png"))
+#             # 원격지에 이미지 저장
+#             sftp_client.remote_copy_data(local_path=query_image_path, remote_path=os.path.join(remote_directory,"query.png"))
             
-            for img_path, img_percent in zip(result_image_paths,result_percents):
-                sftp_client.remote_copy_data(local_path=img_path, remote_path=os.path.join(remote_directory,f"search_{img_percent}.png"))
+#             for img_path, img_percent in zip(result_image_paths,result_percents):
+#                 sftp_client.remote_copy_data(local_path=img_path, remote_path=os.path.join(remote_directory,f"search_{img_percent}.png"))
+#         else:
+#             local_directory = os.path.join(rest_config.local_folder, f"imagen_query_{request_body_info.modelType}_{request_body_info.indexType}_{request_body_info.prompt}_{D.date_file_name()}")
+#             os.makedirs(local_directory, exist_ok=True)
+            
+#             shutil.copy(query_image_path, os.path.join(local_directory,"query.png"))
+            
+#             for img_path, img_percent in zip(result_image_paths,result_percents):
+#                 shutil.copy(img_path, os.path.join(local_directory,f"search_{img_percent}.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
+        
+#         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
+                
+#         return response.set_error(error=e)
+    
+# @router.post("/vectorImageSearch/vit/imageGenerate/imagen/data", summary="벡터 이미지 검색(clip-vit) - imagen(data)", response_model=M.VectorImageSerachDataRes)
+# async def vactor_vit_data(request: Request, request_body_info: M.VectorImageSearchVitDataReq):
+#     """
+#     ## 벡터 이미지 검색(clip-vit) - imagen
+#     > imagen AI를 이용하여 이미지 생성 후 vector 검색 그후 결과 이미지데이터 return
+    
+#     ### options
+#     > - modelType -> b32,b16,l14,l14_336
+#     > - indexType -> l2,cos
+    
+#     """
+#     response = M.VectorImageSerachDataRes()
+#     query_image_data = ''
+    
+#     try: 
+#         if not download_range(request_body_info.searchNum, max=10):
+#             raise Exception(f"downloadCound is invalid (current value = {request_body_info.searchNum})")
+        
+#         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
+    
+#         vector_request_data = {'query_image_path' : query_image_path,
+#                                'index_type' : request_body_info.indexType, 
+#                                'model_type' : request_body_info.modelType,
+#                                'search_num' : request_body_info.searchNum}
+
+#         vector_response = requests.post('http://localhost:51002/api/services/faiss/vector/search/vit', data=json.dumps(vector_request_data))
+        
+#         vector_response_dict = json.loads(vector_response.text)
+        
+#         if vector_response.status_code != 200:
+#             raise Exception(f"response error: {vector_response_dict['error']}")
+        
+#         if vector_response_dict["error"] != None:
+#             raise Exception(f"vector error: {vector_response_dict['error']}")
+        
+#         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.VectorImageResult(image=b64_data,percents=float_percent)
+            
+#             vector_image_results.append(info)
+
+#         if request_body_info.querySend:
+#             query_image_data = image_to_base64_string(query_image_path)
+            
+#         # 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
+        
+#         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
+                
+#         return response.set_error(error=e)
+
+@router.post("/imageGenerator", summary="이미지 데이터 생성", response_model=M.ImageGenerateDataRes)
+async def image_generator(request: Request, request_body_info: M.ImageGenerateReq):
+    """
+    ## 이미지 생성
+    > 입력된 prompt로 이미지 생성 그후 결과 이미지데이터 return
+    
+    ### Input
+    > 접두어(prefix)는 null값을 주어도 기본값 사용, 빈("")값 사용시 prefix는 사용안함
+    > 최종 입력시 prefix, prompt 조합하여 내부적으로 최종 문장 완성
+    
+    ### Output
+    > - 결과(imageData)는 base64로 변환된 데이터(image)
+    
+    ### Options
+    > - prefix -> 접두어 (기본값: 하얀색 배경)
+    > - prompt -> 프롬프트
+    
+    ### Notice
+    > - 최종이미지에 배경이 없어야 이미지 검색시 유리하기때문에 꼭 prefix 옵션은 사용
+    
+    """
+    
+    response = M.ImageGenerateDataRes()
+    try:
+        if request_body_info.prefix == None:
+            request_body_info.prefix = M.ImageGenerateReq.model_fields.get("prefix").default
+        
+        full_prompt = f"{request_body_info.prefix}, {request_body_info.prompt}" if request_body_info.prefix else f"{request_body_info.prompt}"
+        
+        temp_image_path = gemini_image(full_prompt)
+        
+        if os.path.exists(temp_image_path):
+            image_data = image_to_base64_string(temp_image_path)
+            
+            # Clean up temporary files
+            if 'temp_image_path' in locals():
+                if os.path.exists(temp_image_path):
+                    os.remove(temp_image_path)
+                    del temp_image_path
+            
+            return response.set_message(image_data)
         else:
-            local_directory = os.path.join(rest_config.local_folder, f"imagen_query_{request_body_info.modelType}_{request_body_info.indexType}_{request_body_info.prompt}_{D.date_file_name()}")
-            os.makedirs(local_directory, exist_ok=True)
-            
-            shutil.copy(query_image_path, os.path.join(local_directory,"query.png"))
-            
-            for img_path, img_percent in zip(result_image_paths,result_percents):
-                shutil.copy(img_path, os.path.join(local_directory,f"search_{img_percent}.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
+            raise Exception(f"image not generated(prefix: {request_body_info.prefix}, prompt:{request_body_info.prompt})")
         
-        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 'temp_image_path' in locals():
+            if os.path.exists(temp_image_path):
+                os.remove(temp_image_path)
+                del temp_image_path
                 
         return response.set_error(error=e)
-    
-@router.post("/vectorImageSearch/vit/imageGenerate/imagen/data", summary="벡터 이미지 검색(clip-vit) - imagen(data)", response_model=M.VectorImageSerachDataRes)
-async def vactor_vit_data(request: Request, request_body_info: M.VectorImageSearchVitDataReq):
+        
+
+@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_glasses_img_data(request: Request, request_body_info: M.VectorGlassesImageSearchVitInputImgReq):
     """
-    ## 벡터 이미지 검색(clip-vit) - imagen
-    > imagen AI를 이용하여 이미지 생성 후 vector 검색 그후 결과 이미지데이터 return
-    
-    ### options
-    > - modelType -> b32,b16,l14,l14_336
-    > - indexType -> l2,cos
-    
-    """
-    response = M.VectorImageSerachDataRes()
-    query_image_data = ''
-    
-    try: 
-        if not download_range(request_body_info.searchNum, max=10):
-            raise Exception(f"downloadCound is invalid (current value = {request_body_info.searchNum})")
-        
-        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
-    
-        vector_request_data = {'query_image_path' : query_image_path,
-                               'index_type' : request_body_info.indexType, 
-                               'model_type' : request_body_info.modelType,
-                               'search_num' : request_body_info.searchNum}
-
-        vector_response = requests.post('http://localhost:51002/api/services/faiss/vector/search/vit', data=json.dumps(vector_request_data))
-        
-        vector_response_dict = json.loads(vector_response.text)
-        
-        if vector_response.status_code != 200:
-            raise Exception(f"response error: {vector_response_dict['error']}")
-        
-        if vector_response_dict["error"] != None:
-            raise Exception(f"vector error: {vector_response_dict['error']}")
-        
-        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.VectorImageResult(image=b64_data,percents=float_percent)
-            
-            vector_image_results.append(info)
-
-        if request_body_info.querySend:
-            query_image_data = image_to_base64_string(query_image_path)
-            
-        # 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
-        
-        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
-                
-        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)
-async def vactor_vit_input_img_data(request: Request, request_body_info: M.VectorImageSearchVitInputImgReq):
-    """
-    ## 벡터 이미지 검색(clip-vit) - inputimage
+    ## 벡터 이미지 검색(clip-vit) - inputimage : glasses
     > 입력된 이미지로 vector 검색 그후 결과 이미지데이터 return
     
-    ### Requriements
-    > - googlecli 설치(https://cloud.google.com/sdk/docs/install?hl=ko#linux)
+    ### Input
+    > 입력이미지(inputImage)는 base64로 변환된 데이터 입력
     
-    ### options
-    > - modelType -> b32,b16,l14,l14_336
-    > - indexType -> l2,cos
+    ### Output
+    > - 결과(vectorResult)는 base64로 변환된 데이터(image), 유사도(percents), 파츠정보(parts)가 쌍으로 나오며, 요청한 searchNum 갯수에 맞춰서 결과가 나옴 
+    > - 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 - 새로운 조합 (ex l14, cos)으로 처음 요청할경우 모델을 빌드하는 과정이 추가됨
     
     """
-    response = M.VectorImageSerachDataRes()
+    response = M.VectorGlassesImageSerachDataRes()
     query_image_data = ''
     
     try: 
@@ -440,7 +506,9 @@ async def vactor_vit_input_img_data(request: Request, request_body_info: M.Vecto
         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_data = request_body_info.inputImage
         query_image_path = os.path.join(TEMP_FOLDER, f'input_{D.date_file_name()}_query.png')
+        os.makedirs(TEMP_FOLDER, exist_ok=True)
         save_base64_as_image_file(request_body_info.inputImage ,query_image_path)
                
         vector_request_data = {'query_image_path' : query_image_path,
@@ -453,21 +521,25 @@ async def vactor_vit_input_img_data(request: Request, request_body_info: M.Vecto
         vector_response_dict = json.loads(vector_response.text)
         
         if vector_response.status_code != 200:
-            raise Exception(f"response error: {vector_response_dict['error']}")
+            raise Exception(f"search server error: {vector_response_dict['error']}")
         
         if vector_response_dict["error"] != None:
-            raise Exception(f"vector error: {vector_response_dict['error']}")
+            raise Exception(f"search result error: {vector_response_dict['error']}")
         
         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):
-            b64_data = image_to_base64_string(img)
+        for img, percents, parts in zip(result_image_paths, result_percents, result_parts):
+            
+            b64_data = None
+            if os.path.exists(img):
+                b64_data = image_to_base64_string(img)
             float_percent = float(percents)
             
-            info = M.VectorImageResult(image=b64_data,percents=float_percent)
+            info = M.VectorGlassesImageResult(image=b64_data, percents=float_percent, imageInfo=os.path.split(img)[-1], parts=parts)
             
             vector_image_results.append(info)
             
@@ -491,75 +563,75 @@ 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)
-async def vactor_vit_report(request: Request, request_body_info: M.VectorImageSearchVitReportReq):
+@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_input_parts_img_data(request: Request, request_body_info: M.VectorPartsImageSearchVitInputImgReq):
     """
-    ## 벡터 이미지 검색(clip-vit) - imagen, report 생성
-    > imagen AI를 이용하여 이미지 생성 후 vector 검색 그후 종합결과 이미지 생성
+    ## 벡터 이미지 검색(clip-vit) - inputImage : parts
+    > 입력된 이미지로 vector 검색 그후 결과 이미지데이터 return
     
-    ### Requriements
-    > - googlecli 설치(https://cloud.google.com/sdk/docs/install?hl=ko#linux)
+    ### Input
+    > 입력이미지(inputImage)는 파츠 이미지파일 이름 (확장자 포함)  
+    > ⚠️ /vectorImageSearch/vit/inputImage/data/glasses API response인 parts 값중 하나를 선택
     
-    ### options
-    > - modelType -> b32,b16,l14,l14_336
-    > - indexType -> l2,cos
+    ### Output
+    > - 결과(vectorResult)는 base64로 변환된 데이터(image), 유사도(percents)가 쌍으로 나오며, 요청한 searchNum 갯수에 맞춰서 결과가 나옴 
+    > - 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()
-    try:       
+    response = M.VectorPartsImageSerachDataRes()
+    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,
+       
+        glass_name = f"{request_body_info.inputImage.split('_')[0]}_{request_body_info.inputImage.split('_')[1]}"
+        query_image_path = os.path.join(IMG_LIST_PATH, glass_name, ImageDepths.parts, request_body_info.inputImage)
+        query_image_data = image_to_base64_string(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:
-            raise Exception(f"response error: {json.loads(vactor_response.text)['error']}")
+        vector_response_dict = json.loads(vector_response.text)
         
-        if json.loads(vactor_response.text)["error"] != None:
-            raise Exception(f"vector error: {json.loads(vactor_response.text)['error']}")
+        if vector_response.status_code != 200:
+            raise Exception(f"search server error: {vector_response_dict['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
+        if vector_response_dict["error"] != None:
+            raise Exception(f"search result error: {vector_response_dict['error']}")
         
-        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 = None
+            if os.path.exists(img):
+                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)

main_rest/app/utils/parsing_utils.py

@@ -65,8 +65,10 @@ def save_base64_as_image_file(base64_data: str, output_path: str):
     """
     Base64 문자열을 디코딩하여 이미지 파일로 저장합니다.
     """
-    # Base64 문자열을 디코딩하여 이진 데이터로 변환합니다.
-    decoded_data = base64.b64decode(base64_data)
-    with open(output_path, "wb") as image_file:
-        image_file.write(decoded_data)
-    
+    try:
+        # Base64 문자열을 디코딩하여 이진 데이터로 변환합니다.
+        decoded_data = base64.b64decode(base64_data)
+        with open(output_path, "wb") as image_file:
+            image_file.write(decoded_data)
+    except Exception as e:
+        raise Exception(f"input image data error : {e}")

make_vector_files.py

@@ -0,0 +1,117 @@
+# from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.faiss_functions import get_clip_info
+import os
+from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.faiss_similarity_search import VectorSimilarity
+from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.const import *
+from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.faiss_functions import get_models, find_glass_folder_images, find_parts_folder_images
+from vector_rest.app import models as VM
+
+def make_image_files(item_info, index_type:VM.VitIndexType, model_type:VM.VitModelType):
+    model = get_models(index_type=index_type, model_type=model_type)
+    
+    txt_file_path = os.path.join(FAISS_VECTOR_PATH,f'{model.name}_{os.path.basename(model.value[1].trained_model)}_{model.value[1].index_type}_{item_info}.txt')
+    
+    image_lists = []
+    if not os.path.exists(txt_file_path):
+        if item_info == VectorSearchItem.glass:
+            image_lists = find_glass_folder_images(IMG_LIST_PATH)
+        else:
+            image_lists = find_parts_folder_images(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:
+        temp_path = os.path.join(FAISS_VECTOR_PATH,f'{model.name}_{os.path.basename(model.value[1].trained_model)}_{model.value[1].index_type}_{item_info}.bak')
+        
+        try:
+            os.rename(txt_file_path, temp_path)
+            
+            if item_info == VectorSearchItem.glass:
+                image_lists = find_glass_folder_images(IMG_LIST_PATH)
+            else:
+                image_lists = find_parts_folder_images(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")
+        
+        except Exception as e:
+            print(f"Error updating image file list: {e}")
+        
+        finally:
+            if os.path.exists(txt_file_path):
+                os.remove(temp_path)
+            else:
+                os.rename(temp_path, txt_file_path)
+            
+    return txt_file_path
+
+def make_vector_files(item_info, index_type:VM.VitIndexType, model_type:VM.VitModelType):
+    model = get_models(index_type=index_type, model_type=model_type)
+    
+    txt_file_path = make_image_files(item_info=item_info, index_type=index_type, model_type=model_type)
+    
+    index_file_path = os.path.join(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}')
+    
+    if not os.path.exists(index_file_path):
+        
+        vector_model = VectorSimilarity(model_info=model, 
+                                index_file_path=index_file_path,
+                                txt_file_path=txt_file_path)
+
+        with open(txt_file_path, 'r') as f:
+            image_lists = [line.strip() for line in f.readlines()]
+            
+        vector_model.save_index_from_files(images_path_lists=image_lists,
+                                save_index_dir=FAISS_VECTOR_PATH,
+                                save_txt_dir=IMG_LIST_PATH,
+                                item_info=item_info,
+                                index_type=model.value[1].index_type)
+        
+    else:
+        temp_path = os.path.join(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}.bak')
+        
+        try:
+            os.rename(index_file_path, temp_path)
+            
+            vector_model = VectorSimilarity(model_info=model, 
+                                    index_file_path=index_file_path,
+                                    txt_file_path=txt_file_path)
+
+            with open(txt_file_path, 'r') as f:
+                image_lists = [line.strip() for line in f.readlines()]
+                
+            vector_model.save_index_from_files(images_path_lists=image_lists,
+                                    save_index_dir=FAISS_VECTOR_PATH,
+                                    save_txt_dir=IMG_LIST_PATH,
+                                    item_info=item_info,
+                                    index_type=model.value[1].index_type)
+        except Exception as e:
+            print(f"Error updating vector file: {e}")
+        finally:
+            if os.path.exists(index_file_path):
+                os.remove(temp_path)
+            else:
+                os.rename(temp_path, index_file_path)
+    
+    print(f"Vector file created: {index_file_path} :::::::::: item_info: {item_info}, index_type: {index_type}, model_type: {model_type}")
+    
+    return index_file_path
+
+if __name__ == '__main__':
+    
+    import time
+    class_attributes = dict(VectorSearchItem.__dict__)
+
+    pure_data_dict = {
+        key: value
+        for key, value in class_attributes.items()
+        if not key.startswith('__')
+    }
+    
+    for item_key, item_value in pure_data_dict.items():
+        make_vector_files(item_info=item_value, index_type=VM.VitIndexType.l2, model_type=VM.VitModelType.b32)
+        
+        time.sleep(5) # huggingface api 요청 제한 회피 위해 대기 TODO(jwkim) huggingface 로그인은 한번만 진행하게 변경

vector_rest/app/common/consts.py

@@ -8,6 +8,7 @@
 - 2022-01-14/hsj100@a2tec.co.kr : refactoring
 @brief: consts
 """
+from config import rest_config
 
 # SUPPORT PROJECT
 SUPPORT_PROJECT_BASIC = 'PROJECT_BASIC'
@@ -87,7 +88,7 @@ NUM_RETRY_UUID_GEN = 3
 DB_ADDRESS = "localhost"
 DB_PORT = 53306
 DB_USER_ID = 'root'
-DB_USER_PW = '1234'
+DB_USER_PW = rest_config.db_pw
 DB_NAME = 'FM_TEST'
 DB_CHARSET = 'utf8mb4'
 

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 VactorSearchVitRes(ResponseBase):
+class VectorSearchVitRes(ResponseBase):
     img_list : dict = Field({}, description='이미지 결과 리스트', example={})
     
     @staticmethod
     def set_error(error):
-        VactorSearchVitRes.img_list = {}
-        VactorSearchVitRes.result = False
-        VactorSearchVitRes.error = str(error)
+        VectorSearchVitRes.img_list = {}
+        VectorSearchVitRes.result = False
+        VectorSearchVitRes.error = str(error)
 
-        return VactorSearchVitRes
+        return VectorSearchVitRes
     
     @staticmethod
     def set_message(msg):
-        VactorSearchVitRes.img_list = msg
-        VactorSearchVitRes.result = True
-        VactorSearchVitRes.error = None
+        VectorSearchVitRes.img_list = msg
+        VectorSearchVitRes.result = True
+        VectorSearchVitRes.error = None
         
-        return VactorSearchVitRes
+        return VectorSearchVitRes

vector_rest/app/routes/dev.py

@@ -39,7 +39,7 @@ def send_mail():
     sender = 'jolimola@gmail.com'
     sender_pw = '!ghkdtmdwns1'
     # recipient = 'hsj100@a2tec.co.kr'
-    recipient = 'jwkim@daooldns.co.kr'
+    recipient = 'jwkim@a2tec.co.kr'
     list_cc = ['cc1@gmail.com', 'cc2@naver.com']
     str_cc = ','.join(list_cc)
 

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 +77,58 @@ 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)
-async def vactor_vit(request: Request, request_body_info: M.VactorSearchVitReq):
+@router.post("/faiss/vector/search/vit", summary="vit search", response_model=M.VectorSearchVitRes)
+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 parts 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)
+        
+        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: