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import gradio as gr
import monai
import torch
from monai.networks.nets import UNet
from PIL import Image
import albumentations as A
from albumentations.pytorch import ToTensorV2
import numpy as np
model = UNet(
spatial_dims=2,
in_channels=3,
out_channels=1,
channels=[16, 32, 64, 128, 256, 512],
strides=(2, 2, 2, 2, 2),
num_res_units=4,
dropout=0.15,
)
model.load_state_dict(torch.load("best_model.pth", map_location=torch.device('cpu')))
model.eval()
def process_image(image):
image = image / 255.0
image = image.astype(np.float32)
inference_transforms = A.Compose([
A.Resize(height=512, width=512),
ToTensorV2(),
])
image = inference_transforms(image=image)["image"]
image = image.unsqueeze(0)
with torch.no_grad():
mask_pred = torch.sigmoid(model(image))
return mask_pred[0, 0, :, :].numpy()
demo = gr.Interface(
fn=process_image,
title="Histapathology segmentation",
inputs=[
gr.Image(
label="Input image",
image_mode="RGB",
height=400,
type="numpy",
width=400,
)
],
outputs=[
gr.Image(
label="Model Prediction",
image_mode="L",
height=400,
width=400,
)
],
# examples=[
# os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg"),
# os.path.join(os.path.dirname(__file__), "images/lion.jpg"),
# os.path.join(os.path.dirname(__file__), "images/logo.png"),
# os.path.join(os.path.dirname(__file__), "images/tower.jpg"),
# ],
)
demo.launch()
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