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 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') 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_onnx_model(): session = ort.InferenceSession("./accessary_weights.onnx") return session onnx_session = load_onnx_model() 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 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_onnx(image): input_image = np.array(image.resize((640, 640)), dtype=np.float32) input_image = np.transpose(input_image, [2, 0, 1]) input_image = np.expand_dims(input_image, axis=0) input_image /= 255.0 inputs = {onnx_session.get_inputs()[0].name: input_image} outputs = onnx_session.run(None, inputs) detections = outputs[0] categories = [] for detection in detections: x1, y1, x2, y2, conf, cls = detection category = str(int(cls)) if category in ['Bracelets', 'Broches', '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 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("Advanced Fashion 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 Clothing"): 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_onnx(query_image) if st.session_state.detections: st.session_state.step = 'select_category' else: st.warning("No clothing 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.") # Update the 'select_category' step 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']}") st.write(f"Category: {img['info']['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']}") st.write(f"Category: {img['info']['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.")