Add: Detailed debug logs for search-similar-objects API

api.py

@@ -759,30 +759,42 @@ def add_detected_objects():
 @app.route('/api/search-similar-objects', methods=['POST'])
 def search_similar_objects():
     """유사한 객체 검색 API"""
+    print("[DEBUG] Received request in search-similar-objects")
+    
     if clip_model is None or object_collection is None:
+        print("[DEBUG] Error: Image embedding model or vector DB not available")
         return jsonify({"error": "Image embedding model or vector DB not available"})
 
     try:
         # 요청 데이터 추출
         data = request.json
+        print(f"[DEBUG] Request data keys: {list(data.keys()) if data else 'None'}")
         
         if not data:
+            print("[DEBUG] Error: Missing request data")
             return jsonify({"error": "Missing request data"})
         
         # 검색 유형 결정
         search_type = data.get('searchType', 'image')
-        n_results = int(data.get('nResults', 5))  # 결과 개수
+        n_results = int(data.get('n_results', 5))  # 결과 개수
+        print(f"[DEBUG] Search type: {search_type}, n_results: {n_results}")
         
         query_embedding = None
         
         if search_type == 'image' and 'image' in data:
             # 이미지로 검색하는 경우
+            print("[DEBUG] Searching by image")
             image_data = data['image']
             if image_data.startswith('data:image'):
                 image_data = image_data.split(',')[1]
             
-            image = Image.open(BytesIO(base64.b64decode(image_data))).convert('RGB')
-            query_embedding = generate_image_embedding(image)
+            try:
+                image = Image.open(BytesIO(base64.b64decode(image_data))).convert('RGB')
+                query_embedding = generate_image_embedding(image)
+                print(f"[DEBUG] Generated image embedding: {type(query_embedding)}, shape: {len(query_embedding) if query_embedding is not None else 'None'}")
+            except Exception as e:
+                print(f"[DEBUG] Error generating image embedding: {e}")
+                return jsonify({"error": f"Error processing image: {str(e)}"}), 500
         
         elif search_type == 'object' and 'objectId' in data:
             # 객체 ID로 검색하는 경우
@@ -792,43 +804,65 @@ def search_similar_objects():
             if result and "embeddings" in result and len(result["embeddings"]) > 0:
                 query_embedding = result["embeddings"][0]
         
-        elif search_type == 'class' and 'className' in data:
+        elif search_type == 'class' and 'class_name' in data:
             # 클래스 이름으로 검색하는 경우
-            class_name = data['className']
+            print("[DEBUG] Searching by class name")
+            class_name = data['class_name']
+            print(f"[DEBUG] Class name: {class_name}")
             filter_query = {"class": {"$eq": class_name}}
             
-            # 클래스로 필터링하여 검색
+            try:
+                # 클래스로 필터링하여 검색
+                print(f"[DEBUG] Querying with filter: {filter_query}")
+                results = object_collection.query(
+                    query_embeddings=None,
+                    where=filter_query,
+                    n_results=n_results,
+                    include=["metadatas", "distances"]
+                )
+                
+                print(f"[DEBUG] Query results: {results['ids'][0] if 'ids' in results and len(results['ids']) > 0 else 'No results'}")
+                formatted_results = format_object_results(results)
+                print(f"[DEBUG] Formatted results count: {len(formatted_results)}")
+                
+                return jsonify({
+                    "success": True,
+                    "searchType": "class",
+                    "results": formatted_results
+                })
+            except Exception as e:
+                print(f"[DEBUG] Error in class search: {e}")
+                return jsonify({"error": f"Error in class search: {str(e)}"}), 500
+        
+        else:
+            print(f"[DEBUG] Invalid search parameters: {data}")
+            return jsonify({"error": "Invalid search parameters"})
+        
+        if query_embedding is None:
+            print("[DEBUG] Error: Failed to generate query embedding")
+            return jsonify({"error": "Failed to generate query embedding"})
+        
+        try:
+            # 유사도 검색 실행
+            print(f"[DEBUG] Running similarity search with embedding of length {len(query_embedding)}")
             results = object_collection.query(
-                query_embeddings=None,
-                where=filter_query,
+                query_embeddings=[query_embedding],
                 n_results=n_results,
                 include=["metadatas", "distances"]
             )
             
+            print(f"[DEBUG] Query results: {results['ids'][0] if 'ids' in results and len(results['ids']) > 0 else 'No results'}")
+            formatted_results = format_object_results(results)
+            print(f"[DEBUG] Formatted results count: {len(formatted_results)}")
+            
             return jsonify({
                 "success": True,
-                "searchType": "class",
-                "results": format_object_results(results)
+                "searchType": search_type,
+                "results": formatted_results
             })
-        
-        else:
-            return jsonify({"error": "Invalid search parameters"})
-        
-        if query_embedding is None:
-            return jsonify({"error": "Failed to generate query embedding"})
-        
-        # 유사도 검색 실행
-        results = object_collection.query(
-            query_embeddings=[query_embedding],
-            n_results=n_results,
-            include=["metadatas", "distances"]
-        )
-        
-        return jsonify({
-            "success": True,
-            "searchType": search_type,
-            "results": format_object_results(results)
-        })
+        except Exception as e:
+            print(f"[DEBUG] Error in similarity search: {e}")
+            return jsonify({"error": f"Error in similarity search: {str(e)}"}), 500
     
     except Exception as e:
         print(f"Error in search-similar-objects API: {e}")
@@ -838,17 +872,49 @@ def format_object_results(results):
     """검색 결과 포맷팅"""
     formatted_results = []
     
-    if len(results['ids']) > 0 and len(results['ids'][0]) > 0:
+    print(f"[DEBUG] Formatting results: {results.keys() if results else 'None'}")
+    
+    if not results or 'ids' not in results or len(results['ids']) == 0 or len(results['ids'][0]) == 0:
+        print("[DEBUG] No results to format")
+        return formatted_results
+    
+    try:
         for i, obj_id in enumerate(results['ids'][0]):
-            result_item = {
-                "id": obj_id,
-                "metadata": results['metadatas'][0][i] if 'metadatas' in results else {}
-            }
-            
-            if 'distances' in results:
-                result_item["distance"] = float(results['distances'][0][i])
-            
-            formatted_results.append(result_item)
+            try:
+                # 객체 정보 추출
+                metadata = results['metadatas'][0][i] if 'metadatas' in results and len(results['metadatas']) > 0 else {}
+                
+                # bbox가 JSON 문자열로 저장되어 있는 경우 역직렬화
+                if 'bbox' in metadata and isinstance(metadata['bbox'], str):
+                    try:
+                        metadata['bbox'] = json.loads(metadata['bbox'])
+                    except:
+                        pass  # 역직렬화 실패 시 그대로 유지
+                
+                result_item = {
+                    "id": obj_id,
+                    "metadata": metadata
+                }
+                
+                if 'distances' in results and len(results['distances']) > 0:
+                    result_item["distance"] = float(results['distances'][0][i])
+                
+                # 이미지 가져오기 시도
+                try:
+                    # 이미지 ID를 통해 원본 이미지 가져오기
+                    image_id = metadata.get('image_id')
+                    if image_id:
+                        print(f"[DEBUG] Getting image for object {obj_id} with image_id {image_id}")
+                        # 이미지 가져오기 로직 추가 필요
+                
+                except Exception as e:
+                    print(f"[DEBUG] Error getting image for result {i}: {e}")
+                
+                formatted_results.append(result_item)
+            except Exception as e:
+                print(f"[DEBUG] Error formatting result {i}: {e}")
+    except Exception as e:
+        print(f"[DEBUG] Error in format_object_results: {e}")
     
     return formatted_results