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from huggingface_hub import from_pretrained_fastai |
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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 PIL |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model = torch.jit.load("Pr1.pth") |
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model = model.cpu() |
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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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return my_transforms(image_aux).unsqueeze(0).to(device) |
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def predict(img): |
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img_pil = PIL.Image.fromarray(img, 'RGB') |
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image = transforms.Resize((480,640))(img_pil) |
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tensor = transform_image(image=image) |
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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]=0 |
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mask[mask==1]=150 |
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mask[mask==2]=25 |
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mask[mask==3]=74 |
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mask[mask==4]=255 |
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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=predict, inputs=gr.inputs.Image(shape=(480, 640)), outputs=gr.outputs.Image(type="pil"),examples=['grapes1.jpg','grapes2.jpg']).launch(share=False) |
