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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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title="YOLOP" |
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description="Gradio demo for YOLOP: YOLOP pretrained on the BDD100K dataset. To use it, simply upload your image or click on one of the examples to load them. Read more at the links below" |
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article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2108.11250' target='_blank'>YOLOP: You Only Look Once for Panoptic Driving Perception</a> | <a href='https://github.com/hustvl/YOLOP' target='_blank'>Github Repo</a></p>" |
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examples=[['frame_00_delay-0.13s.jpg']] |
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gr.Interface(inference,gr.inputs.Image(type="pil"),[gr.outputs.Image(label='Drivable Area Segmentation'),gr.outputs.Image(label='Lane Detection')],article=article,description=description,title=title,examples=examples).launch() |
