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import gradio as gr |
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from detectron2.data import MetadataCatalog |
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from segment_anything import SamAutomaticMaskGenerator |
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metadata = MetadataCatalog.get('coco_2017_train_panoptic') |
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print(metadata) |
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class ImageMask(gr.components.Image): |
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""" |
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Sets: source="canvas", tool="sketch" |
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""" |
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is_template = True |
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def __init__(self, **kwargs): |
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super().__init__(source="upload", tool="sketch", interactive=True, **kwargs) |
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def preprocess(self, x): |
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return super().preprocess(x) |
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demo = gr.Blocks() |
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image = ImageMask( |
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label="Input", |
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type="pil", |
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brush_radius=20.0, |
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brush_color="#FFFFFF") |
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slider = gr.Slider( |
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minimum=1, |
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maximum=3, |
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value=2, |
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label="Granularity", |
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info="Choose in [1, 1.5), [1.5, 2.5), [2.5, 3] for [seem, semantic-sam (multi-level), sam]") |
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mode = gr.Radio( |
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choices=['Automatic', 'Interactive', ], |
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value='Automatic', |
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label="Segmentation Mode") |
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image_out = gr.Image(label="Auto generation", type="pil") |
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slider_alpha = gr.Slider( |
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minimum=0, |
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maximum=1, |
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value=0.1, |
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label="Mask Alpha", |
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info="Choose in [0, 1]") |
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label_mode = gr.Radio( |
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choices=['Number', 'Alphabet'], |
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value='Number', |
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label="Mark Mode") |
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anno_mode = gr.CheckboxGroup( |
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choices=["Mask", "Box", "Mark"], |
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value=['Mask', 'Mark'], |
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label="Annotation Mode") |
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runBtn = gr.Button("Run") |
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title = "Set-of-Mark (SoM) Prompting for Visual Grounding in GPT-4V" |
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description = "This is a demo for SoM Prompting to unleash extraordinary visual grounding in GPT-4V. Please upload an image and them click the 'Run' button to get the image with marks. Then try it on <a href='https://chat.openai.com/'>GPT-4V<a>!" |
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with demo: |
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gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>") |
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gr.Markdown("<h3 style='text-align: center; margin-bottom: 1rem'>project: <a href='https://som-gpt4v.github.io/'>link</a>, arXiv: <a href='https://arxiv.org/abs/2310.11441'>link</a>, code: <a href='https://github.com/microsoft/SoM'>link</a></h3>") |
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gr.Markdown(f"<h3 style='margin-bottom: 1rem'>{description}</h3>") |
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with gr.Row(): |
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with gr.Column(): |
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image.render() |
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slider.render() |
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with gr.Row(): |
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mode.render() |
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anno_mode.render() |
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with gr.Row(): |
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slider_alpha.render() |
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label_mode.render() |
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with gr.Column(): |
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image_out.render() |
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runBtn.render() |
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demo.queue().launch() |
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