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| import numpy as np | |
| import streamlit as st | |
| import tensorflow as tf | |
| from PIL import Image | |
| from scipy.spatial.distance import euclidean | |
| st.title("Surrey 2023 - Image Retrieval") | |
| # # Load your TensorFlow model | |
| model = tf.keras.models.load_model("model/adcv_model") | |
| def preprocess_image(image, target_size=(128, 128)): | |
| image = image.resize(target_size) | |
| image_array = np.array(image) # / 255.0 | |
| return np.expand_dims(image_array, axis=0) | |
| def distance_quadruplet(result): | |
| distance_list = [] | |
| for i in range(1, len(result)): | |
| distance = euclidean(result[0], result[i]) | |
| distance_list.append(1 / (1 + distance)) | |
| return distance_list | |
| col1, col2 = st.columns(2) | |
| image_list = st.file_uploader("Choose List Image You Want To Search - No More Than 10 Images", type=["jpg", "jpeg", "png"], accept_multiple_files=True) | |
| image_query = st.file_uploader("Choose Images For Querying", type=["jpg", "jpeg", "png"], accept_multiple_files=False) | |
| image_process_list = [] | |
| image_query_process = [] | |
| if image_list is not None: | |
| if len(image_list) >= 10: | |
| col1.write("Your list image have problem - Try to refresh and upload again") | |
| else: | |
| count = 0 | |
| columns = st.columns(10) | |
| for i, uploaded_file_1 in enumerate(image_list): | |
| img = Image.open(uploaded_file_1).convert('RGB') | |
| img = img.resize((128, 128)) | |
| image_process_list.append(img) | |
| if count <= 10: | |
| count += 1 | |
| columns[i].image(img, caption=f"Uploaded image: {uploaded_file_1.name}", width=64) | |
| else: | |
| col1.write("Upload Image") | |
| if image_query is not None: | |
| img_qr = Image.open(image_query).convert('RGB') | |
| img_qr = img_qr.resize((128, 128)) | |
| image_query_process.append(img_qr) | |
| col1.image(img_qr, caption="Query Image", use_column_width=True, width=98) | |
| else: | |
| col1.write("Upload Image") | |
| image_pr = [] | |
| if st.button("Classify"): | |
| image_pr = image_query_process + image_process_list | |
| image_pr = np.stack(image_pr) | |
| result = model.predict(image_pr) | |
| distance_list = [] | |
| for i in range(1, len(result)): | |
| distance = euclidean(result[0], result[i]) | |
| distance_list.append(1 / (1 + distance)) | |
| max_index = np.argmax(distance_list) | |
| image_matches = image_list[max_index] | |
| col2.write(f"The image that have the most similarity: {image_matches}") | |