edit : 벡터파일 생성 관련 분리

README.md

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

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/const.py

@@ -18,24 +18,23 @@ ViTL14_336 = "clip-vit-large-patch14-336"
 
 #download/save path
 PRETRAINED_MODEL_PATH = "./datas"
-FAISS_VECTOR_PATH = "./datas/Eyewear_001~500_ComponentsCapture_20251121"
+FAISS_VECTOR_PATH = "./datas"
-VECTOR_IMG_LIST_PATH = "./datas/Eyewear_001~500_ComponentsCapture_20251121"
+IMG_LIST_PATH = "../Eyewear_data"
-
-INDEX_IMAGES_PATH = "./datas/eyewear_all"
 
+# INDEX_IMAGES_PATH = "./datas/eyewear_all"
 class VectorSearchItem:
     glass = "glass"
-    bridge = "Bridge"
+    tips = "Tips"
-    hinge = "Hinges"
-    lens = "Lens"
-    nosepad = "Nose pad"
-    other = "other"
-    padarm = "Padarm"
-    rim = "Rim"
-    rivet = "Rivet"
-    soltex = "Soltex"
     temple = "Temple"
-    templetip = "Temple tip"
+    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"

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/faiss_functions.py

@@ -104,34 +104,28 @@ 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}_{item_info}_{DEFAULT_INDEX_NAME_SUFFIX}')
+    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(VECTOR_IMG_LIST_PATH,f'{model.name}_{os.path.basename(model.value[1].trained_model)}_{model.value[1].index_type}_{item_info}.txt')
+    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 = []
+    # image_lists = []
-    if not os.path.exists(txt_file_path):
+
-        if item_info == VectorSearchItem.glass:
+    # with open(txt_file_path, 'r') as f:
-            image_lists = find_glass_folder_images(VECTOR_IMG_LIST_PATH)
+    #     image_lists = [line.strip() for line in f.readlines()]
-        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_lists=image_lists,
+    # vector_model.save_index_from_files(images_path_lists=image_lists,
-                            save_index_dir=FAISS_VECTOR_PATH,
+    #                         save_index_dir=FAISS_VECTOR_PATH,
-                            save_txt_dir=VECTOR_IMG_LIST_PATH,
+    #                         save_txt_dir=VECTOR_IMG_LIST_PATH,
-                            item_info=item_info,
+    #                         item_info=item_info,
-                            index_type=model.value[1].index_type)
+    #                         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)
 

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/faiss_similarity_search.py

@@ -372,33 +372,33 @@ class VectorSimilarity:
                 
         return inference_times, result_img_paths, result_percents
 
-def test():
+# def test():
-    """
+#     """
-    module test function
+#     module test function
-    """
+#     """
-    log.info('\nModule: faiss_similarity_search.py')
+#     log.info('\nModule: faiss_similarity_search.py')
 
-    # index_file_path = f'{FILE_BASE_PATH}openaiclipvit_clip-vit-base-patch32_index.faiss'
+#     # index_file_path = f'{FILE_BASE_PATH}openaiclipvit_clip-vit-base-patch32_index.faiss'
-    model = FEMUsageInfo.openaiclip_vit_l14_cos
+#     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 = 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, 
+#     cm = VectorSimilarity(model_info=model, 
-                          index_file_path=index_file_path,
+#                           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'))
+#                           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_lists=INDEX_IMAGES_PATH,
+#     cm.save_index_from_files(images_path_lists=INDEX_IMAGES_PATH,
-                            save_index_dir=FAISS_VECTOR_PATH,
+#                             save_index_dir=FAISS_VECTOR_PATH,
-                            save_txt_dir=VECTOR_IMG_LIST_PATH,
+#                             save_txt_dir=VECTOR_IMG_LIST_PATH,
-                            index_type=model.value[1].index_type)
+#                             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

@@ -21,6 +21,12 @@ def search_glass_parts(image_path_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('_')
@@ -28,4 +34,6 @@ def file_name_to_parts(file_path):
     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_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/models.py

@@ -583,11 +583,6 @@ class VitModelType:
     l14_336 = "l14_336"
     
     
-class PartsType(str, Enum):
-    bridge = VectorSearchItem.bridge
-    hinge = VectorSearchItem.hinge
-
-
 class ImageGenerateReq(BaseModel):
     """
     ### [Request] image generate request
@@ -663,7 +658,7 @@ class VectorGlassesImageResult(BaseModel):
     
 
 class VectorPartsImageResult(BaseModel):
-    image : str = Field("", description='이미지 데이터', example='')
+    image : str | None = Field("", description='이미지 데이터', example='')
     percents: float = Field(0.0, description='percents 값', example='')
     imageInfo : str = Field("", description='원본이미지 이름', example='')
 #===============================================================================

main_rest/app/routes/services.py

@@ -446,7 +446,7 @@ async def image_generator(request: Request, request_body_info: M.ImageGenerateRe
         
         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) #TODO(jwkim) imagen3 으로 변경예정
+        temp_image_path = gemini_image(full_prompt)
         
         if os.path.exists(temp_image_path):
             image_data = image_to_base64_string(temp_image_path)
@@ -483,7 +483,7 @@ async def vactor_vit_input_glasses_img_data(request: Request, request_body_info:
     > 입력이미지(inputImage)는 base64로 변환된 데이터 입력
     
     ### Output
-    > - 결과(vectorResult)는 base64로 변환된 데이터(image), 유사도(percents)가 쌍으로 나오며, 요청한 searchNum 갯수에 맞춰서 결과가 나옴 
+    > - 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
@@ -534,7 +534,9 @@ async def vactor_vit_input_glasses_img_data(request: Request, request_body_info:
         vector_image_results = []
         for img, percents, parts in zip(result_image_paths, result_percents, result_parts):
             
-            b64_data = image_to_base64_string(img)
+            b64_data = None
+            if os.path.exists(img):
+                b64_data = image_to_base64_string(img)
             float_percent = float(percents)
             
             info = M.VectorGlassesImageResult(image=b64_data, percents=float_percent, imageInfo=os.path.split(img)[-1], parts=parts)
@@ -568,7 +570,8 @@ async def vactor_vit_input_parts_img_data(request: Request, request_body_info: M
     > 입력된 이미지로 vector 검색 그후 결과 이미지데이터 return
     
     ### Input
-    > 입력이미지(inputImage)는 파츠이미지 이름 (확장자 포함)
+    > 입력이미지(inputImage)는 파츠 이미지파일 이름 (확장자 포함)  
+    > ⚠️ /vectorImageSearch/vit/inputImage/data/glasses API response인 parts 값중 하나를 선택
     
     ### Output
     > - 결과(vectorResult)는 base64로 변환된 데이터(image), 유사도(percents)가 쌍으로 나오며, 요청한 searchNum 갯수에 맞춰서 결과가 나옴 
@@ -592,7 +595,7 @@ async def vactor_vit_input_parts_img_data(request: Request, request_body_info: M
             raise Exception(f"indexType is invalid (current value = {request_body_info.indexType})")
        
         glass_name = f"{request_body_info.inputImage.split('_')[0]}_{request_body_info.inputImage.split('_')[1]}"
-        query_image_path = os.path.join(VECTOR_IMG_LIST_PATH, glass_name, ImageDepths.parts, request_body_info.inputImage)
+        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,
@@ -617,7 +620,9 @@ async def vactor_vit_input_parts_img_data(request: Request, request_body_info: M
         vector_image_results = []
         for img, percents in zip(result_image_paths, result_percents):
             
-            b64_data = image_to_base64_string(img)
+            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])

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/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

@@ -110,7 +110,7 @@ async def vactor_vit(request: Request, request_body_info: M.VectorSearchVitReq):
 @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
+    이미지 경로 를 입력받아 vit 방식으로 parts 검색
     """
     response = M.VectorSearchVitRes()
     try:
@@ -119,7 +119,6 @@ async def vactor_vit_parts(request: Request, request_body_info: M.VectorSearchVi
         
         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,