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import gradio as gr |
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import torch |
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from PIL import Image |
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torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2016/06/15/01/11/soccer-1457988_1280.jpg', 'soccer.jpg') |
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torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2016/11/21/14/31/vw-bus-1845719_1280.jpg', 'bus.jpg') |
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model = torch.hub.load('ultralytics/yolov3', 'yolov3') |
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def yolo(im, size=640): |
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g = (size / max(im.size)) |
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im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS) |
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results = model(im) |
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results.render() |
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return Image.fromarray(results.imgs[0]) |
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inputs = gr.inputs.Image(type='pil', label="Original Image") |
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outputs = gr.outputs.Image(type="pil", label="Output Image") |
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title = "YOLOv3" |
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description = "YOLOv3 Gradio demo for object detection. Upload an image or click an example image to use." |
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article = "<p style='text-align: center'>YOLOv3 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov3' target='_blank'>Source code</a> |<a href='https://apps.apple.com/app/id1452689527' target='_blank'>iOS App</a></p>" |
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examples = [['soccer.jpg'], ['bus.jpg']] |
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gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch( |
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debug=True) |