itda-segmentation_1020ver / app_accessary.py
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Rename app.py to app_accessary.py
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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
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')
# ํŒŒ์ธํŠœ๋‹ํ•œ ๋ชจ๋ธ์˜ state_dict ๋ถˆ๋Ÿฌ์˜ค๊ธฐ
#state_dict = torch.load('./accessory_clip.pt', map_location=torch.device('cpu'))
#model.load_state_dict(state_dict) # ๋ชจ๋ธ์— state_dict ์ ์šฉ
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()
# URL์—์„œ ์ด๋ฏธ์ง€ ๋กœ๋“œ
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 ํด๋ผ์ด์–ธํŠธ ์„ค์ •
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
# ์ด๋ฏธ์ง€ ์ž๋ฅด๊ธฐ
def crop_image(image, bbox):
return image.crop((bbox[0], bbox[1], bbox[2], bbox[3]))
# ์„ธ์…˜ ์ƒํƒœ ์ดˆ๊ธฐํ™”
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")
# ๋‹จ๊ณ„๋ณ„ ์ฒ˜๋ฆฌ
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.")