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import gradio as gr |
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from predict import predict_masks |
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import glob |
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example_list = glob.glob("examples/*") |
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example_list = list(map(lambda el:[el], example_list)) |
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demo = gr.Blocks() |
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with demo: |
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gr.Markdown("# **<p align='center'>Mask2Former: Masked Attention Mask Transformer for Universal Segmentation</p>**") |
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gr.Markdown("This space demonstrates the use of Mask2Former. It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). \ |
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Before Mask2Former, you'd have to resort to using a specialized architecture designed for solving a particular kind of image segmentation task (i.e. semantic, instance or panoptic segmentation). On the other hand, in the form of Mask2Former, for the first time, we have a single architecture that is capable of solving any segmentation task and performs on par or better than specialized architectures.") |
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with gr.Box(): |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown("**Inputs**") |
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segmentation_task = gr.Dropdown(["semantic", "instance", "panoptic"], value="panoptic", label="Segmentation Task", show_label=True) |
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input_image = gr.Image(type='filepath',label="Input Image", show_label=True) |
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with gr.Column(): |
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gr.Markdown("**Outputs**") |
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output_heading = gr.Textbox(label="Output Type", show_label=True) |
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output_mask = gr.Image(label="Predicted Masks", show_label=True) |
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gr.Markdown("**Predict**") |
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with gr.Box(): |
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with gr.Row(): |
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submit_button = gr.Button("Submit") |
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gr.Markdown("**Examples:**") |
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with gr.Column(): |
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gr.Examples(example_list, [input_image, segmentation_task], [output_mask,output_heading], predict_masks) |
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submit_button.click(predict_masks, inputs=[input_image, segmentation_task], outputs=[output_mask,output_heading]) |
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gr.Markdown('\n Demo created by: <a href=\"https://www.linkedin.com/in/shivalika-singh/\">Shivalika Singh</a>') |
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demo.launch(debug=True) |
