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import os |
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import torch |
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import gradio as gr |
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import numpy as np |
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import supervision as sv |
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from typing import List |
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from segment_anything import sam_model_registry, SamAutomaticMaskGenerator |
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HOME = os.getenv("HOME") |
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DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') |
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SAM_CHECKPOINT = os.path.join(HOME, "app/weights/sam_vit_h_4b8939.pth") |
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SAM_MODEL_TYPE = "vit_h" |
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MARKDOWN = """ |
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<h1 style='text-align: center'> |
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<img |
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src='https://som-gpt4v.github.io/website/img/som_logo.png' |
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style='height:50px; display:inline-block' |
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/> |
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Set-of-Mark (SoM) Prompting Unleashes Extraordinary Visual Grounding in GPT-4V |
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</h1> |
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""" |
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sam = sam_model_registry[SAM_MODEL_TYPE](checkpoint=SAM_CHECKPOINT).to(device=DEVICE) |
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mask_generator = SamAutomaticMaskGenerator(sam) |
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def inference(image: np.ndarray, annotation_mode: List[str]) -> np.ndarray: |
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return image |
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image_input = gr.Image( |
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label="Input", |
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type="numpy") |
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checkbox_annotation_mode = gr.CheckboxGroup( |
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choices=["Mark", "Mask", "Box"], |
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value=['Mark'], |
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label="Annotation Mode") |
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image_output = gr.Image( |
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label="SoM Visual Prompt", |
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type="numpy", |
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height=512) |
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run_button = gr.Button("Run") |
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with gr.Blocks() as demo: |
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gr.Markdown(MARKDOWN) |
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with gr.Row(): |
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with gr.Column(): |
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image_input.render() |
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with gr.Accordion(label="Detailed prompt settings (e.g., mark type)", open=False): |
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checkbox_annotation_mode.render() |
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with gr.Column(): |
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image_output.render() |
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run_button.render() |
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run_button.click( |
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fn=inference, |
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inputs=[image_input, checkbox_annotation_mode], |
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outputs=image_output) |
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demo.queue().launch(debug=False, show_error=True) |
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