edit : 안경이미지 검색시 temp 이미지 사용 안함

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/faiss_functions.py

@@ -127,7 +127,12 @@ def get_clip_info(model, query_image_path, item_info, top_k=4):
     #                         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)
+    if os.path.exists(query_image_path):
+        inference_times, result_img_paths, result_percents = vector_model.query_faiss(query_image_path, top_k=top_k)
+    elif query_image_path is None or query_image_path == "":
+        raise ValueError("query_image is None or empty.")
+    else:
+        inference_times, result_img_paths, result_percents = vector_model.query_faiss_image_data(query_image_path, top_k=top_k)
     
     for i in range(len(result_percents)):
         if float(result_percents[i]) < 0.0:

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/faiss_similarity_search.py

@@ -53,7 +53,7 @@ from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS import feature_extraction_model
 # 사용할 이미지 임베딩 모델 클래스 추가
 from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.fem_openaiclipvit import FEOpenAIClipViT
 from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.const import *
-
+from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.utils import get_base64_bytes
 
 """
 Definition
@@ -219,6 +219,24 @@ class VectorSimilarity:
         feature_vectors = self.fem_model.image_embedding(image_data_np)
         return feature_vectors
     
+    def image_embedding_from_b64data(self, b64_data=None):
+        """
+        이미지 데이터(base64)에서 특징 벡터 추출
+        :param image_data_np: 이미지 데이터(numpy)
+        :return: 특징 벡터 or None
+        """
+        import io
+        feature_vectors = None
+
+        if b64_data is None:
+            log.error(f'invalid data[{b64_data}]')
+            return feature_vectors
+        
+        image = Image.open(io.BytesIO(b64_data)).convert("RGB")
+
+        feature_vectors = self.fem_model.image_embedding(image)
+        return feature_vectors
+
     def image_embedding_from_data(self, image_data_np=None):
         """
         이미지 데이터(numpy)에서 특징 벡터 추출
@@ -372,6 +390,53 @@ class VectorSimilarity:
                 
         return inference_times, result_img_paths, result_percents
     
+    def query_faiss_image_data(self, query_image_data=None, top_k=4):
+        
+        if os.path.exists(self.txt_file_path):
+            with open(self.txt_file_path, 'r') as f:
+                image_paths = [line.strip() for line in f.readlines()]
+        else:
+            logging.error("Image path list TXT file not found.")
+            image_paths = []
+            
+        b64_data = get_base64_bytes(query_image_data)
+
+        start_vector_time = datetime.now()
+        index = self.load_index(self.index_file_path)
+        query_vector = self.image_embedding_from_b64data(b64_data)
+        end_vector_time = datetime.now()
+        
+        diff_vector_time = self.time_diff(start_vector_time,end_vector_time)
+        
+        if self.index_type == INDEX_TYPE_COSINE:
+            faiss.normalize_L2(query_vector)
+        
+        start_search_time = datetime.now()
+        distances, indices = index.search(query_vector, top_k)
+        end_search_time = datetime.now()
+        
+        diff_search_time = self.time_diff(start_search_time,end_search_time)
+        diff_total_time = self.time_diff(start_vector_time,end_search_time)
+        inference_times = f"Total time - {diff_total_time}, vector_time - {diff_vector_time}, search_time - {diff_search_time}"
+        
+        result_img_paths = []
+        result_percents = []
+        
+        # 결과
+        # for i in range(top_k):
+        #     print(f"{i + 1}: {image_paths[indices[0][i]]}, Distance: {distances[0][i]}")
+        for idx, dist in zip(indices[0], distances[0]):
+            log.debug(f"{idx} (거리: {dist:.4f})")
+            
+            result_img_paths.append(image_paths[idx])
+
+            if self.index_type == INDEX_TYPE_COSINE:
+                result_percents.append(f"{dist*100:.2f}")
+            else:
+                result_percents.append(f"{((1 - dist)*100):.2f}")
+                
+        return inference_times, result_img_paths, result_percents
+
 # def test():
 #     """
 #     module test function

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/utils.py

@@ -1,4 +1,5 @@
 import os
+import base64
 from pathlib import Path
 
 from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.const import *
@@ -43,5 +44,29 @@ def file_name_to_parts(file_path):
         
     return result
 
+
+def get_base64_bytes(data: str) -> bytes:
+    """
+    문자열을 검사하여 유효한 Base64라면 디코딩된 bytes 데이터를 반환하고,
+    그렇지 않으면 ValueError 예외를 발생시킵니다.
+    """
+
+    try:
+        # 1. 입력 문자열 전처리 (공백 제거 등)
+        stripped_data = data.strip()
+        
+        # 2. bytes로 인코딩 (b64decode는 bytes-like object를 필요로 함)
+        encoded_input = stripped_data.encode('ascii')
+        
+        # 3. Base64 디코딩 수행 (validate=True로 엄격한 검사)
+        # 성공 시 b'...' 형태의 바이트 데이터가 생성됨
+        decoded_bytes = base64.b64decode(encoded_input, validate=True)
+        
+        return decoded_bytes
+        
+    except Exception as e:
+        # Base64 형식이 아니거나 패딩 오류 등 발생 시
+        raise ValueError(f"유효한 Base64 데이터가 아닙니다. 변환 불가: {e}")
+
 if __name__ == '__main__':
     print(file_name_to_parts(os.path.join(FAISS_VECTOR_PATH,"Glass_001",ImageDepths.parts,"Glass_001_Temple_L.png")))

main_rest/app/routes/services.py

@@ -507,9 +507,12 @@ async def vactor_vit_input_glasses_img_data(request: Request, request_body_info:
             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)
+        # query_image_path = os.path.join(TEMP_FOLDER, f'input_{D.date_file_name()}_query.png')
-        save_base64_as_image_file(request_body_info.inputImage ,query_image_path)
+        # os.makedirs(TEMP_FOLDER, exist_ok=True)
+        # save_base64_as_image_file(request_body_info.inputImage ,query_image_path)
+        
+        query_image_path = query_image_data
                
         vector_request_data = {'query_image_path' : query_image_path,
                                'index_type' : request_body_info.indexType, 

make_vector_files.py

@@ -118,5 +118,3 @@ if __name__ == '__main__':
     
     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/services.py

@@ -84,8 +84,8 @@ async def vactor_vit(request: Request, request_body_info: M.VectorSearchVitReq):
     """
     response = M.VectorSearchVitRes()
     try:
-        if not os.path.exists(request_body_info.query_image_path):
+        # if not os.path.exists(request_body_info.query_image_path):
-            raise FileNotFoundError(f"File {request_body_info.query_image_path} does not exist.")
+        #     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)