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| import gradio as gr | |
| from detectron2.data import MetadataCatalog | |
| from segment_anything import SamAutomaticMaskGenerator | |
| metadata = MetadataCatalog.get('coco_2017_train_panoptic') | |
| print(metadata) | |
| class ImageMask(gr.components.Image): | |
| """ | |
| Sets: source="canvas", tool="sketch" | |
| """ | |
| is_template = True | |
| def __init__(self, **kwargs): | |
| super().__init__(source="upload", tool="sketch", interactive=True, **kwargs) | |
| def preprocess(self, x): | |
| return super().preprocess(x) | |
| demo = gr.Blocks() | |
| image = ImageMask( | |
| label="Input", | |
| type="pil", | |
| brush_radius=20.0, | |
| brush_color="#FFFFFF") | |
| slider = gr.Slider( | |
| minimum=1, | |
| maximum=3, | |
| value=2, | |
| label="Granularity", | |
| info="Choose in [1, 1.5), [1.5, 2.5), [2.5, 3] for [seem, semantic-sam (multi-level), sam]") | |
| mode = gr.Radio( | |
| choices=['Automatic', 'Interactive', ], | |
| value='Automatic', | |
| label="Segmentation Mode") | |
| image_out = gr.Image(label="Auto generation", type="pil") | |
| slider_alpha = gr.Slider( | |
| minimum=0, | |
| maximum=1, | |
| value=0.1, | |
| label="Mask Alpha", | |
| info="Choose in [0, 1]") | |
| label_mode = gr.Radio( | |
| choices=['Number', 'Alphabet'], | |
| value='Number', | |
| label="Mark Mode") | |
| anno_mode = gr.CheckboxGroup( | |
| choices=["Mask", "Box", "Mark"], | |
| value=['Mask', 'Mark'], | |
| label="Annotation Mode") | |
| runBtn = gr.Button("Run") | |
| title = "Set-of-Mark (SoM) Prompting for Visual Grounding in GPT-4V" | |
| 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>!" | |
| with demo: | |
| gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>") | |
| 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>") | |
| gr.Markdown(f"<h3 style='margin-bottom: 1rem'>{description}</h3>") | |
| with gr.Row(): | |
| with gr.Column(): | |
| image.render() | |
| slider.render() | |
| with gr.Row(): | |
| mode.render() | |
| anno_mode.render() | |
| with gr.Row(): | |
| slider_alpha.render() | |
| label_mode.render() | |
| with gr.Column(): | |
| image_out.render() | |
| runBtn.render() | |
| # with gr.Row(): | |
| # example = gr.Examples( | |
| # examples=[ | |
| # ["examples/ironing_man.jpg"], | |
| # ], | |
| # inputs=image, | |
| # cache_examples=False, | |
| # ) | |
| # example = gr.Examples( | |
| # examples=[ | |
| # ["examples/ironing_man_som.png"], | |
| # ], | |
| # inputs=image, | |
| # cache_examples=False, | |
| # label='Marked Examples', | |
| # ) | |
| # runBtn.click(inference, inputs=[image, slider, mode, slider_alpha, label_mode, anno_mode], | |
| # outputs = image_out) | |
| demo.queue().launch() | |