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Brain-tumor-3D-segmentation
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import torch
import matplotlib.pyplot as plt

from monai.networks.nets import SegResNet
from monai.inferers import sliding_window_inference

from monai.transforms import (
    Activations,
    AsDiscrete,
    Compose,
)

model = SegResNet(
    blocks_down=[1, 2, 2, 4],
    blocks_up=[1, 1, 1],
    init_filters=16,
    in_channels=4,
    out_channels=3,
    dropout_prob=0.2,
)

model.load_state_dict(
    torch.load("model.pt", map_location=torch.device('cpu'))
)

# define inference method
VAL_AMP = True

def inference(input):

    def _compute(input):
        return sliding_window_inference(
            inputs=input,
            roi_size=(240, 240, 160),
            sw_batch_size=1,
            predictor=model,
            overlap=0.5,
        )

    if VAL_AMP:
        with torch.cuda.amp.autocast():
            return _compute(input)
    else:
        return _compute(input)


post_trans = Compose(
    [Activations(sigmoid=True), AsDiscrete(threshold=0.5)]
)

import gradio as gr

def load_sample1():
    return load_sample(1)

def load_sample2():
    return load_sample(2)

def load_sample3():
    return load_sample(3)

def load_sample4():
    return load_sample(4)

import torchvision

def load_sample(index):
    #sample_index = index

    sample = torch.load(f"val{index-1}.pt")
    imgs = []
    for i in range(4):
        imgs.append(sample["image"][i, :, :, 70])

    pil_images = []
    for i in range(4):
        pil_images.append(torchvision.transforms.functional.to_pil_image(imgs[i]))

    imgs_label = []
    for i in range(3):
        imgs_label.append(sample["label"][i, :, :, 70])

    pil_images_label = []
    for i in range(3):
        pil_images_label.append(torchvision.transforms.functional.to_pil_image(imgs_label[i]))

    return [index, pil_images[0], pil_images[1], pil_images[2], pil_images[3],
            pil_images_label[0], pil_images_label[1], pil_images_label[2]]


def predict(sample_index):
    print(sample_index)
    sample = torch.load(f"val{sample_index-1}.pt")
    model.eval()
    with torch.no_grad():
        # select one image to evaluate and visualize the model output
        val_input = sample["image"].unsqueeze(0)
        roi_size = (128, 128, 64)
        sw_batch_size = 4
        val_output = inference(val_input)
        val_output = post_trans(val_output[0])

    imgs_output = []
    for i in range(3):
        imgs_output.append(val_output[i, :, :, 70])

    pil_images_output = []
    for i in range(3):
        pil_images_output.append(torchvision.transforms.functional.to_pil_image(imgs_output[i]))

    return [pil_images_output[0], pil_images_output[1], pil_images_output[2]]

with gr.Blocks(css=".gradio-container {background:lightyellow;color:red;}", title="テスト"
               ) as demo:
    sample_index = gr.State([])

    gr.HTML('<div style="font-size:12pt; text-align:center; color:yellow;">MNIST 分類器</div>')

    with gr.Row():
        input_image0 = gr.Image(label="image channel 0", type="pil", shape=(240, 240))
        input_image1 = gr.Image(label="image channel 1", type="pil", shape=(240, 240))
        input_image2 = gr.Image(label="image channel 2", type="pil", shape=(240, 240))
        input_image3 = gr.Image(label="image channel 3", type="pil", shape=(240, 240))

    #input_image = gr.Image(label="画像入力", type="pil", image_mode="RGB", shape=(240, 240))

    with gr.Row():
        label_image0 = gr.Image(label="label channel 0", type="pil")
        label_image1 = gr.Image(label="label channel 1", type="pil")
        label_image2 = gr.Image(label="label channel 2", type="pil")

    with gr.Row():
        example1_btn = gr.Button("Example 1")
        example2_btn = gr.Button("Example 2")
        example3_btn = gr.Button("Example 3")
        example4_btn = gr.Button("Example 4")

        example1_btn.click(fn=load_sample1, inputs=None, 
                           outputs=[sample_index, input_image0, input_image1, input_image2, input_image3,
                                    label_image0, label_image1, label_image2])
        example2_btn.click(fn=load_sample2, inputs=None, 
                           outputs=[sample_index, input_image0, input_image1, input_image2, input_image3,
                                    label_image0, label_image1, label_image2])
        example3_btn.click(fn=load_sample3, inputs=None, 
                           outputs=[sample_index, input_image0, input_image1, input_image2, input_image3,
                                    label_image0, label_image1, label_image2])
        example4_btn.click(fn=load_sample4, inputs=None, 
                           outputs=[sample_index, input_image0, input_image1, input_image2, input_image3,
                                    label_image0, label_image1, label_image2])

    with gr.Row():
        output_image0 = gr.Image(label="output channel 0", type="pil")
        output_image1 = gr.Image(label="output channel 1", type="pil")
        output_image2 = gr.Image(label="output channel 2", type="pil")

    #output_label=gr.Label(label="予測確率", num_top_classes=3)
    
    send_btn = gr.Button("予測する")

    #gr.Examples(['2.png', '4.png'], inputs=input_image2)

    send_btn.click(fn=predict, inputs=[sample_index], outputs=[output_image0, output_image1, output_image2])

#demo.queue()
demo.launch(debug=True)



### EOF  ###