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import torch, torchvision
from monai.networks.nets import UNet
from monai.networks.layers import Norm
from monai.inferers import sliding_window_inference
import PIL
from torchvision.utils import save_image
import numpy as np
model = UNet(
spatial_dims=3,
in_channels=1,
out_channels=2,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
norm=Norm.BATCH,
)
model.load_state_dict(torch.load("weights/model.pt", map_location=torch.device('cpu')))
import gradio as gr
def load_image0():
return load_image(0)
def load_image1():
return load_image(1)
def load_image2():
return load_image(2)
def load_image3():
return load_image(3)
def load_image4():
return load_image(4)
def load_image5():
return load_image(5)
def load_image6():
return load_image(6)
def load_image7():
return load_image(7)
def load_image8():
return load_image(8)
def load_image(index):
return [index, f"thumbnails/val_image{index}.png", f"thumbnails_label/val_label{index}.png"]
def predict(index):
val_data = torch.load(f"samples/val_data{index}.pt")
model.eval()
with torch.no_grad():
roi_size = (160, 160, 160)
sw_batch_size = 4
val_outputs = sliding_window_inference(val_data, roi_size, sw_batch_size, model)
meta_tsr = torch.argmax(val_outputs, dim=1)[0, :, :, 80]
pil_image = torchvision.transforms.functional.to_pil_image(meta_tsr.to(torch.float32))
return pil_image
with gr.Blocks(title="Spleen 3D segmentation with MONAI - ClassCat",
css=".gradio-container {background:azure;}"
) as demo:
sample_index = gr.State([])
gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">Spleen 3D segmentation with MONAI</div>""")
gr.HTML("""<h4 style="color:navy;">1. Select an example, which includes input images and label images, by clicking "Example x" button.</h4>""")
with gr.Row():
input_image = gr.Image(label="a piece of input image data", type="filepath")
label_image = gr.Image(label="label image", type="filepath")
output_image = gr.Image(label="predicted image", type="pil")
with gr.Row():
with gr.Column():
ex_btn0 = gr.Button("Example 1")
ex_btn0.style(full_width=False, css="width:20px;")
ex_image0 = gr.Image(value='thumbnails/val_image0.png', interactive=False, label='ex 1')
ex_image0.style(width=128, height=128)
with gr.Column():
ex_btn1 = gr.Button("Example 2")
ex_btn1.style(full_width=False, css="width:20px;")
ex_image1 = gr.Image(value='thumbnails/val_image1.png', interactive=False, label='ex 2')
ex_image1.style(width=128, height=128)
with gr.Column():
ex_btn2 = gr.Button("Example 3")
ex_btn2.style(full_width=False, css="width:20px;")
ex_image2 = gr.Image(value='thumbnails/val_image2.png', interactive=False, label='ex 3')
ex_image2.style(width=128, height=128)
with gr.Column():
ex_btn3 = gr.Button("Example 4")
ex_btn3.style(full_width=False, css="width:20px;")
ex_image3 = gr.Image(value='thumbnails/val_image3.png', interactive=False, label='ex 4')
ex_image3.style(width=128, height=128)
ex_btn0.click(fn=load_image0, outputs=[sample_index, input_image, label_image])
ex_btn1.click(fn=load_image1, outputs=[sample_index, input_image, label_image])
ex_btn2.click(fn=load_image2, outputs=[sample_index, input_image, label_image])
ex_btn3.click(fn=load_image3, outputs=[sample_index, input_image, label_image])
gr.HTML("""<br/>""")
gr.HTML("""<h4 style="color:navy;">2. Then, click "Infer" button to predict segmentation images. It will take about 30 seconds (on cpu)</h4>""")
send_btn = gr.Button("Infer")
send_btn.click(fn=predict, inputs=[sample_index], outputs=[output_image])
#demo.queue()
demo.launch(debug=True)
### EOF ###
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