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import pathlib |
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import gradio as gr |
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import numpy as np |
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import PIL.Image as Image |
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from model import Model, random_color, vis_mask |
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model = Model() |
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def run(image_path, threshold, max_num_mask): |
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image = np.asarray(Image.open(image_path).convert('RGB')) |
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masks = model(image_path, threshold, max_num_mask) |
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for mask in masks: |
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image = vis_mask(image, mask, random_color(rgb=True)) |
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return image |
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TITLE = 'MaskCut' |
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DESCRIPTION = 'This is an unofficial demo for https://github.com/facebookresearch/CutLER.' |
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paths = sorted(pathlib.Path('CutLER/maskcut/imgs').glob('*.jpg')) |
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demo = gr.Interface( |
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fn=run, |
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inputs=[ |
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gr.Image(label='Input image', type='filepath'), |
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gr.Slider(label='Threshold used for producing binary graph', |
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minimum=0, |
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maximum=1, |
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value=0.15, |
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step=0.01), |
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gr.Slider(label='The maximum number of pseudo-masks per image', |
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minimum=1, |
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maximum=20, |
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value=3, |
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step=1), |
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], |
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outputs=gr.Image(label='Result', type='numpy'), |
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examples=[[path.as_posix(), 0.15, 3] for path in paths], |
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title=TITLE, |
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description=DESCRIPTION) |
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demo.queue().launch() |
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