Spaces:
Runtime error
Runtime error
File size: 8,231 Bytes
543b03f d77b7a4 054c8f7 543b03f 97480db 543b03f d77b7a4 543b03f d77b7a4 543b03f d77b7a4 543b03f d77b7a4 543b03f d77b7a4 543b03f d77b7a4 543b03f 77e6c3d 543b03f b757e27 543b03f b757e27 543b03f b757e27 543b03f b757e27 543b03f f43a9e6 b757e27 f43a9e6 b757e27 f43a9e6 b757e27 d77b7a4 b757e27 ce67f34 543b03f d77b7a4 543b03f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 |
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
import chromadb
# 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()
# 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
#Load chromaDB
client = chromadb.PersistentClient(path="./clothesDB")
collection = client.get_collection(name="fashion_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 ['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]))
# 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(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.") |