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| # Copyright (C) 2023, Xu Sun. | |
| # This program is licensed under the Apache License version 2. | |
| # See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0.txt> for full license details. | |
| import torch | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import streamlit as st | |
| from PIL import Image | |
| from glaucoma import GlaucomaModel | |
| run_device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| def main(): | |
| # Wide mode | |
| st.set_page_config(layout="wide") | |
| # Designing the interface | |
| st.title("Glaucoma Screening from Retinal Fundus Images") | |
| # For newline | |
| st.write('\n') | |
| # Author info | |
| st.write('Developed by X. Sun. Find more info about me: https://pamixsun.github.io') | |
| # For newline | |
| st.write('\n') | |
| # Instructions | |
| st.markdown("*Hint: click on the top-right corner of an image to enlarge it!*") | |
| # Set the columns | |
| cols = st.beta_columns((1, 1, 1)) | |
| cols[0].subheader("Input image") | |
| cols[1].subheader("Optic disc and optic cup") | |
| cols[2].subheader("Class activation map") | |
| # set the visualization figure | |
| fig, ax = plt.subplots() | |
| # Sidebar | |
| # File selection | |
| st.sidebar.title("Image selection") | |
| # Disabling warning | |
| st.set_option('deprecation.showfileUploaderEncoding', False) | |
| # Choose your own image | |
| uploaded_file = st.sidebar.file_uploader("Upload image", type=['png', 'jpeg', 'jpg']) | |
| if uploaded_file is not None: | |
| # read the upload image | |
| image = Image.open(uploaded_file).convert('RGB') | |
| image = np.array(image).astype(np.uint8) | |
| # page_idx = 0 | |
| ax.imshow(image) | |
| ax.axis('off') | |
| cols[0].pyplot(fig) | |
| # For newline | |
| st.sidebar.write('\n') | |
| # actions | |
| if st.sidebar.button("Analyze image"): | |
| if uploaded_file is None: | |
| st.sidebar.write("Please upload an image") | |
| else: | |
| with st.spinner('Loading model...'): | |
| # load model | |
| model = GlaucomaModel(device=run_device) | |
| with st.spinner('Analyzing...'): | |
| # Forward the image to the model and get results | |
| disease_idx, disc_cup_image, cam, vcdr = model.process(image) | |
| # plot the optic disc and optic cup image | |
| ax.imshow(disc_cup_image) | |
| ax.axis('off') | |
| cols[1].pyplot(fig) | |
| # plot the stitched image | |
| ax.imshow(cam) | |
| ax.axis('off') | |
| cols[2].pyplot(fig) | |
| # Display JSON | |
| st.subheader(" Screening results:") | |
| st.write('\n') | |
| final_results_as_table = f""" | |
| |Parameters|Outcomes| | |
| |---|---| | |
| |Vertical cup-to-disc ratio|{vcdr:.04f}| | |
| |Category|{model.cls_id2label[disease_idx]}| | |
| """ | |
| st.markdown(final_results_as_table) | |
| if __name__ == '__main__': | |
| main() |