import streamlit as st import open_clip import torch import requests from PIL import Image from io import BytesIO import time import json import numpy as np import onnxruntime as ort from ultralytics import YOLO import cv2 import chromadb @st.cache_resource def load_clip_model(): model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:Marqo/marqo-fashionSigLIP') tokenizer = open_clip.get_tokenizer('hf-hub:Marqo/marqo-fashionSigLIP') # 파인튜닝한 모델의 state_dict 불러오기 #state_dict = torch.load('./accessory_clip.pt', map_location=torch.device('cpu')) #model.load_state_dict(state_dict) # 모델에 state_dict 적용 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) return model, preprocess_val, tokenizer, device clip_model, preprocess_val, tokenizer, device = load_clip_model() @st.cache_resource def load_yolo_model(): return YOLO("./accessaries.pt") yolo_model = load_yolo_model() # URL에서 이미지 로드 def load_image_from_url(url, max_retries=3): for attempt in range(max_retries): try: response = requests.get(url, timeout=10) response.raise_for_status() img = Image.open(BytesIO(response.content)).convert('RGB') return img except (requests.RequestException, Image.UnidentifiedImageError) as e: if attempt < max_retries - 1: time.sleep(1) else: return None # ChromaDB 클라이언트 설정 client = chromadb.PersistentClient(path="./accessaryDB") collection = client.get_collection(name="accessary_items_ver2") def get_image_embedding(image): image_tensor = preprocess_val(image).unsqueeze(0).to(device) with torch.no_grad(): image_features = clip_model.encode_image(image_tensor) image_features /= image_features.norm(dim=-1, keepdim=True) return image_features.cpu().numpy() def get_text_embedding(text): text_tokens = tokenizer([text]).to(device) with torch.no_grad(): text_features = clip_model.encode_text(text_tokens) text_features /= text_features.norm(dim=-1, keepdim=True) return text_features.cpu().numpy() def get_all_embeddings_from_collection(collection): all_embeddings = collection.get(include=['embeddings'])['embeddings'] return np.array(all_embeddings) def get_metadata_from_ids(collection, ids): results = collection.get(ids=ids) return results['metadatas'] def find_similar_images(query_embedding, collection, top_k=5): database_embeddings = get_all_embeddings_from_collection(collection) similarities = np.dot(database_embeddings, query_embedding.T).squeeze() top_indices = np.argsort(similarities)[::-1][:top_k] all_data = collection.get(include=['metadatas'])['metadatas'] top_metadatas = [all_data[idx] for idx in top_indices] results = [] for idx, metadata in enumerate(top_metadatas): results.append({ 'info': metadata, 'similarity': similarities[top_indices[idx]] }) return results def detect_clothing(image): results = yolo_model(image) detections = results[0].boxes.data.cpu().numpy() categories = [] for detection in detections: x1, y1, x2, y2, conf, cls = detection category = yolo_model.names[int(cls)] if category in ['Bracelets', 'Broches', 'bag', 'belt', 'earring', 'maangtika', 'necklace', 'nose ring', 'ring', 'tiara']: categories.append({ 'category': category, 'bbox': [int(x1), int(y1), int(x2), int(y2)], 'confidence': conf }) return categories # 이미지 자르기 def crop_image(image, bbox): return image.crop((bbox[0], bbox[1], bbox[2], bbox[3])) # 세션 상태 초기화 if 'step' not in st.session_state: st.session_state.step = 'input' if 'query_image_url' not in st.session_state: st.session_state.query_image_url = '' if 'detections' not in st.session_state: st.session_state.detections = [] if 'selected_category' not in st.session_state: st.session_state.selected_category = None # Streamlit app st.title("Accessary Search App") # 단계별 처리 if st.session_state.step == 'input': st.session_state.query_image_url = st.text_input("Enter image URL:", st.session_state.query_image_url) if st.button("Detect accesseary"): if st.session_state.query_image_url: query_image = load_image_from_url(st.session_state.query_image_url) if query_image is not None: st.session_state.query_image = query_image st.session_state.detections = detect_clothing(query_image) if st.session_state.detections: st.session_state.step = 'select_category' else: st.warning("No items detected in the image.") else: st.error("Failed to load the image. Please try another URL.") else: st.warning("Please enter an image URL.") elif st.session_state.step == 'select_category': st.image(st.session_state.query_image, caption="Query Image", use_column_width=True) st.subheader("Detected Clothing Items:") for detection in st.session_state.detections: col1, col2 = st.columns([1, 3]) with col1: st.write(f"{detection['category']} (Confidence: {detection['confidence']:.2f})") with col2: cropped_image = crop_image(st.session_state.query_image, detection['bbox']) st.image(cropped_image, caption=detection['category'], use_column_width=True) options = [f"{d['category']} (Confidence: {d['confidence']:.2f})" for d in st.session_state.detections] selected_option = st.selectbox("Select a category to search:", options) if st.button("Search Similar Items"): st.session_state.selected_category = selected_option st.session_state.step = 'show_results' elif st.session_state.step == 'show_results': st.image(st.session_state.query_image, caption="Query Image", use_column_width=True) selected_detection = next(d for d in st.session_state.detections if f"{d['category']} (Confidence: {d['confidence']:.2f})" == st.session_state.selected_category) cropped_image = crop_image(st.session_state.query_image, selected_detection['bbox']) st.image(cropped_image, caption="Cropped Image", use_column_width=True) query_embedding = get_image_embedding(cropped_image) similar_images = find_similar_images(query_embedding, collection) st.subheader("Similar Items:") for img in similar_images: col1, col2 = st.columns(2) with col1: st.image(img['info']['image_url'], use_column_width=True) with col2: st.write(f"Name: {img['info']['name']}") st.write(f"Brand: {img['info']['brand']}") category = img['info'].get('category') if category: st.write(f"Category: {category}") st.write(f"Price: {img['info']['price']}") st.write(f"Discount: {img['info']['discount']}%") st.write(f"Similarity: {img['similarity']:.2f}") if st.button("Start New Search"): st.session_state.step = 'input' st.session_state.query_image_url = '' st.session_state.detections = [] st.session_state.selected_category = None else: # Text search query_text = st.text_input("Enter search text:") if st.button("Search by Text"): if query_text: text_embedding = get_text_embedding(query_text) similar_images = find_similar_images(text_embedding, collection) st.subheader("Similar Items:") for img in similar_images: col1, col2 = st.columns(2) with col1: st.image(img['info']['image_url'], use_column_width=True) with col2: st.write(f"Name: {img['info']['name']}") st.write(f"Brand: {img['info']['brand']}") category = img['info'].get('category') if category: st.write(f"Category: {category}") st.write(f"Price: {img['info']['price']}") st.write(f"Discount: {img['info']['discount']}%") st.write(f"Similarity: {img['similarity']:.2f}") else: st.warning("Please enter a search text.")