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import gradio as gr |
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from fastai.vision.all import * |
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import torchvision.transforms as transforms |
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import torch |
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from PIL import Image |
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import numpy as np |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model = torch.jit.load("unet.pth") |
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model = model.to(device) |
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model.eval() |
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def transform_image(image): |
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my_transforms = transforms.Compose([ |
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transforms.ToTensor(), |
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) |
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]) |
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image = transforms.Resize((480,640))(Image.fromarray(image)) |
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tensor = my_transforms(image).unsqueeze(0).to(device) |
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with torch.no_grad(): |
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outputs = model(tensor) |
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outputs = torch.argmax(outputs, 1) |
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mask = np.array(outputs.cpu()) |
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mask[mask==0]=255 |
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mask[mask==1]=150 |
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mask[mask==2]=76 |
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mask[mask==3]=25 |
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mask[mask==4]=0 |
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mask = np.reshape(mask, (480, 640)) |
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return Image.fromarray(mask.astype('uint8')) |
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interface = gr.Interface(fn=transform_image, |
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inputs=gr.components.Image(width=640, height=480), |
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outputs=gr.components.Image(), |
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examples=['color_154.jpg', 'color_189.jpg']) |
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interface.launch(share=False) |
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