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import gradio as gr |
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from fastai.vision.all import * |
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from pathlib import Path |
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import PIL |
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import torchvision.transforms as transforms |
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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.cpu() |
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model.eval() |
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def transform_image(image): |
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my_transforms = transforms.Compose([transforms.ToTensor(), |
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transforms.Normalize( |
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[0.485, 0.456, 0.406], |
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[0.229, 0.224, 0.225])]) |
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image_aux = image |
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image = transforms.Resize((480,640))(Image.fromarray(image)) |
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tensor = my_transforms(image_aux).unsqueeze(0).to(device) |
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model.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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gr.Interface(fn=transform_image, inputs=gr.inputs.Image(shape=(640, 480)), outputs=gr.outputs.Image(),examples=['color_188.jpg','color_189.jpg']).launch(share=False) |
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