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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
from ultralytics import YOLO
import cv2
# Load CLIP model and tokenizer
@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()
# Load YOLOv8 model
@st.cache_resource
def load_yolo_model():
return YOLO("./best.pt")
yolo_model = load_yolo_model()
# Load and process data
@st.cache_data
def load_data():
with open('./musinsa-final.json', 'r', encoding='utf-8') as f:
return json.load(f)
data = load_data()
# Helper functions
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
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()
@st.cache_data
def process_database():
database_embeddings = []
database_info = []
for item in data:
image_url = item['์ด๋ฏธ์ง ๋งํฌ'][0]
image = load_image_from_url(image_url)
if image is not None:
embedding = get_image_embedding(image)
database_embeddings.append(embedding)
database_info.append({
'id': item['\ufeff์ํ ID'],
'category': item['์นดํ
๊ณ ๋ฆฌ'],
'brand': item['๋ธ๋๋๋ช
'],
'name': item['์ ํ๋ช
'],
'price': item['์ ๊ฐ'],
'discount': item['ํ ์ธ์จ'],
'image_url': image_url
})
else:
st.warning(f"Skipping item {item['๏ปฟ์ํ ID']} due to image loading failure")
if database_embeddings:
return np.vstack(database_embeddings), database_info
else:
st.error("No valid embeddings were generated.")
return None, None
database_embeddings, database_info = process_database()
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 find_similar_images(query_embedding, top_k=5):
similarities = np.dot(database_embeddings, query_embedding.T).squeeze()
top_indices = np.argsort(similarities)[::-1][:top_k]
results = []
for idx in top_indices:
results.append({
'info': database_info[idx],
'similarity': similarities[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 ['sunglass','hat','jacket','shirt','pants','shorts','skirt','dress','bag','shoe']:
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]))
# ์ธ์
์ํ ์ด๊ธฐํ
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")
# ๋จ๊ณ๋ณ ์ฒ๋ฆฌ
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(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)
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)
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.") |