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| import gradio as gr | |
| from gradio_client import Client | |
| import numpy as np | |
| #import torch | |
| import requests | |
| from PIL import Image | |
| #from torchvision import transforms | |
| from predict_unet import predict_model | |
| title = "<center><strong><font size='8'> Medical Image Segmentation with UNet </font></strong></center>" | |
| examples = [["examples/50494616.jpg"], ["examples/50494676.jpg"], ["examples/56399783.jpg"], | |
| ["examples/56399789.jpg"], ["examples/56399831.jpg"], ["examples/56399959.jpg"], | |
| ["examples/56400014.jpg"], ["examples/56400119.jpg"], | |
| ["examples/56481903.jpg"], ["examples/70749195.jpg"]] | |
| def run_unetv0(input): | |
| output = predict_model(input, "v0") | |
| normalized_output = np.clip(output, 0, 1) | |
| return normalized_output | |
| def run_unetv1(input): | |
| output = predict_model(input, "v1") | |
| normalized_output = np.clip(output, 0, 1) | |
| return normalized_output | |
| def run_unetv2(input): | |
| output = predict_model(input, "v2") | |
| normalized_output = np.clip(output, 0, 1) | |
| return normalized_output | |
| def run_unetv3(input): | |
| output = predict_model(input, "v3") | |
| normalized_output = np.clip(output, 0, 1) | |
| return normalized_output | |
| input_img_v0 = gr.Image(label="Input", type='numpy') | |
| segm_img_v0 = gr.Image(label="Segmented Image") | |
| input_img_v1 = gr.Image(label="Input", type='numpy') | |
| segm_img_v1 = gr.Image(label="Segmented Image") | |
| input_img_v2 = gr.Image(label="Input", type='numpy') | |
| segm_img_v2 = gr.Image(label="Segmented Image") | |
| input_img_v3 = gr.Image(label="Input", type='numpy') | |
| segm_img_v3 = gr.Image(label="Segmented Image") | |
| with gr.Blocks(title='UNet examples') as demo: | |
| # v0: regular UNet | |
| with gr.Tab("Regular UNet (v0)"): | |
| # display input image and segmented image | |
| with gr.Row(variant="panel"): | |
| with gr.Column(scale=1): | |
| input_img_v0.render() | |
| with gr.Column(scale=1): | |
| segm_img_v0.render() | |
| # submit and clear | |
| with gr.Row(): | |
| with gr.Column(): | |
| segment_btn_v0 = gr.Button("Run Segmentation", variant='primary') | |
| clear_btn_v0 = gr.Button("Clear", variant="secondary") | |
| # load examples | |
| gr.Markdown("Try some of the examples below") | |
| gr.Examples(examples=examples, | |
| inputs=[input_img_v0], | |
| outputs=segm_img_v0, | |
| fn=run_unetv0, | |
| cache_examples=False, | |
| examples_per_page=5) | |
| # just a placeholder for second column | |
| with gr.Column(): | |
| gr.Markdown("") | |
| segment_btn_v0.click(run_unetv0, | |
| inputs=[ | |
| input_img_v0, | |
| ], | |
| outputs=segm_img_v0) | |
| # v1: UNet3+ | |
| with gr.Tab("UNet3+ (v1)"): | |
| # display input image and segmented image | |
| with gr.Row(variant="panel"): | |
| with gr.Column(scale=1): | |
| input_img_v1.render() | |
| with gr.Column(scale=1): | |
| segm_img_v1.render() | |
| # submit and clear | |
| with gr.Row(): | |
| with gr.Column(): | |
| segment_btn_v1 = gr.Button("Run Segmentation", variant='primary') | |
| clear_btn_v1 = gr.Button("Clear", variant="secondary") | |
| # load examples | |
| gr.Markdown("Try some of the examples below") | |
| gr.Examples(examples=examples, | |
| inputs=[input_img_v1], | |
| outputs=segm_img_v1, | |
| fn=run_unetv1, | |
| cache_examples=False, | |
| examples_per_page=5) | |
| # just a placeholder for second column | |
| with gr.Column(): | |
| gr.Markdown("") | |
| segment_btn_v1.click(run_unetv1, | |
| inputs=[ | |
| input_img_v1, | |
| ], | |
| outputs=segm_img_v1) | |
| # v2: UNet3+ with deep supervision | |
| with gr.Tab("UNet3+(v2) with deep supervision"): | |
| # display input image and segmented image | |
| with gr.Row(variant="panel"): | |
| with gr.Column(scale=1): | |
| input_img_v2.render() | |
| with gr.Column(scale=1): | |
| segm_img_v2.render() | |
| # submit and clear | |
| with gr.Row(): | |
| with gr.Column(): | |
| segment_btn_v2 = gr.Button("Run Segmentation", variant='primary') | |
| clear_btn_v2 = gr.Button("Clear", variant="secondary") | |
| # load examples | |
| gr.Markdown("Try some of the examples below") | |
| gr.Examples(examples=examples, | |
| inputs=[input_img_v2], | |
| outputs=segm_img_v2, | |
| fn=run_unetv2, | |
| cache_examples=False, | |
| examples_per_page=5) | |
| # just a placeholder for second column | |
| with gr.Column(): | |
| gr.Markdown("") | |
| segment_btn_v2.click(run_unetv2, | |
| inputs=[ | |
| input_img_v2, | |
| ], | |
| outputs=segm_img_v2) | |
| # v3: UNet3+ with deep supervision and cgm | |
| with gr.Tab("UNet3+(v3) with deep supervision and cgm"): | |
| # display input image and segmented image | |
| with gr.Row(variant="panel"): | |
| with gr.Column(scale=1): | |
| input_img_v3.render() | |
| with gr.Column(scale=1): | |
| segm_img_v3.render() | |
| # submit and clear | |
| with gr.Row(): | |
| with gr.Column(): | |
| segment_btn_v3 = gr.Button("Run Segmentation", variant='primary') | |
| clear_btn_v3 = gr.Button("Clear", variant="secondary") | |
| # load examples | |
| gr.Markdown("Try some of the examples below") | |
| gr.Examples(examples=examples, | |
| inputs=[input_img_v3], | |
| outputs=segm_img_v3, | |
| fn=run_unetv3, | |
| cache_examples=False, | |
| examples_per_page=5) | |
| # just a placeholder for second column | |
| with gr.Column(): | |
| gr.Markdown("") | |
| segment_btn_v3.click(run_unetv3, | |
| inputs=[ | |
| input_img_v3, | |
| ], | |
| outputs=segm_img_v3) | |
| def clear(): | |
| return None, None | |
| clear_btn_v0.click(clear, outputs=[input_img_v0, segm_img_v0]) | |
| clear_btn_v1.click(clear, outputs=[input_img_v1, segm_img_v1]) | |
| clear_btn_v2.click(clear, outputs=[input_img_v2, segm_img_v2]) | |
| clear_btn_v3.click(clear, outputs=[input_img_v3, segm_img_v3]) | |
| demo.queue() | |
| demo.launch() | |