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| import streamlit as st | |
| from transformers import pipeline | |
| from tensorflow.keras.applications.resnet50 import ResNet50 | |
| from tensorflow.keras.preprocessing import image | |
| from tensorflow.keras.applications.resnet50 import preprocess_input | |
| from sklearn.neighbors import NearestNeighbors | |
| from PIL import Image | |
| import numpy as np | |
| import glob | |
| import os | |
| resnet_model = ResNet50(weights='imagenet') | |
| st.title("CS634 - Assignment 3") | |
| user_image_input = st.file_uploader("Upload Images", type=["jpg"]) | |
| path='photos/*' | |
| photos=[] | |
| for fold in glob.glob(path, recursive=True): | |
| for subdir, dirs, files in os.walk(fold): | |
| for file in files: | |
| #st.write(file) | |
| photos.append(os.path.join(subdir, file)) | |
| photos.insert(0,"") | |
| celebrity_photo = st.selectbox("Select Photo",photos) | |
| def extract_features(photos, resnet_model): | |
| features = {} | |
| for photo in photos: | |
| if(photo!=""): | |
| img = image.load_img(photo, target_size=(224, 224)) | |
| x = image.img_to_array(img) | |
| x = np.expand_dims(x, axis=0) | |
| x = preprocess_input(x) | |
| features_vector = resnet_model.predict(x) | |
| features_vector = features_vector.flatten() | |
| features[photo] = features_vector | |
| return features | |
| if(len(celebrity_photo) != 0): | |
| #st.image(user_image_input, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto") | |
| user_input_image = None | |
| st.write(celebrity_photo) | |
| #st.write(user_image_input.read()) | |
| size=len(photos) | |
| #st.write(size) | |
| st.write("Query Image: ") | |
| st.image(celebrity_photo) | |
| features = extract_features(photos, resnet_model) | |
| features_array = np.array(list(features.values())) | |
| nn_model = NearestNeighbors(n_neighbors=11, metric='euclidean') | |
| nn_model.fit(features_array) | |
| query_image_path = photos[size-1] | |
| query_image_feature = features[query_image_path].reshape(1, -1) | |
| distances, indices = nn_model.kneighbors(query_image_feature) | |
| st.write("Similar Images:") | |
| for i in range(1,11): | |
| similar_image_path = photos[indices[0][i]] | |
| similar_image_distance = distances[0][i] | |
| st.write("Similar Image #{}: Distance: {}".format(i, similar_image_distance)) | |
| st.image(similar_image_path) | |
| if(user_image_input != None): | |
| celebrity_photo = [] | |
| #st.image(user_image_input, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto") | |
| im = Image.open(user_image_input) | |
| im=im.resize((224,224)) | |
| im.save("input_image.jpg", "JPEG") | |
| photos.append("input_image.jpg") | |
| #st.write(user_image_input.read()) | |
| size=len(photos) | |
| #st.write(size) | |
| st.write("Query Image: ") | |
| st.image(photos[size-1]) | |
| features = extract_features(photos, resnet_model) | |
| features_array = np.array(list(features.values())) | |
| nn_model = NearestNeighbors(n_neighbors=11, metric='euclidean') | |
| nn_model.fit(features_array) | |
| query_image_path = photos[size-1] | |
| query_image_feature = features[query_image_path].reshape(1, -1) | |
| distances, indices = nn_model.kneighbors(query_image_feature) | |
| st.write("Similar Images:") | |
| for i in range(1,11): | |
| similar_image_path = photos[indices[0][i]] | |
| similar_image_distance = distances[0][i] | |
| st.write("Similar Image #{}: Distance: {}".format(i, similar_image_distance)) | |
| st.write(similar_image_path) | |
| st.image(similar_image_path) | |
| #else: | |
| # size=len(photos) | |
| # st.write(size) | |
| # st.image(photos[size-1]) | |
| # features = extract_features(photos, resnet_model) | |