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import os |
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from PIL import Image |
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import torch |
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from torchvision import transforms |
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import gradio as gr |
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model = torch.hub.load('hustvl/yolop', 'yolop', pretrained=True) |
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normalize = transforms.Normalize( |
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] |
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) |
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transform=transforms.Compose([ |
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transforms.ToTensor(), |
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]) |
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def inference(img): |
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img = img.resize((640, 640)) |
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img = torch.unsqueeze(transform(img), dim=0) |
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det_out, da_seg_out,ll_seg_out = model(img) |
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ll_out = ll_seg_out[0][0, :, :].detach().numpy() |
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da_out = da_seg_out[0][0, :, :].detach().numpy() |
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return da_out,ll_out |
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gr.Interface(inference,gr.inputs.Image(type="pil"),["image","image"]).launch(debug=True) |
