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import torch |
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import gradio as gr |
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import spaces |
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from inference_gradio import inference_one_image, model_init |
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MODEL_PATH = "./checkpoints/docres.pkl" |
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HEADER = """ |
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<div align="center"> |
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<p> |
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<span style="font-size: 30px; vertical-align: bottom;"> DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks </span> |
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</p> |
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<p style="margin-top: -15px;"> |
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<a href="https://arxiv.org/abs/2405.04408" target="_blank" style="color: grey;">ArXiv Paper</a> |
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<a href="https://github.com/ZZZHANG-jx/DocRes" target="_blank" style="color: grey;">GitHub Repository</a> |
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</p> |
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</div> |
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🖼️ Upload an image of a document (or choose one from examples below). |
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✔️ Choose the tasks you want to perform on the document. |
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🚀 Click "Run" and the model will enhance the document according to the selected tasks! |
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""" |
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possible_tasks = [ |
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"dewarping", |
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"deshadowing", |
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"appearance", |
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"deblurring", |
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"binarization", |
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] |
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@spaces.GPU(duration=60) |
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def run_tasks(image, tasks): |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model = model_init(MODEL_PATH, device) |
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bgr_image = image[..., ::-1].copy() |
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bgr_restored_image = inference_one_image(model, bgr_image, tasks, device) |
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if bgr_restored_image.ndim == 3: |
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rgb_image = bgr_restored_image[..., ::-1] |
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else: |
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rgb_image = bgr_restored_image |
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return rgb_image |
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with gr.Blocks() as demo: |
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gr.Markdown(HEADER) |
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task = gr.CheckboxGroup(choices=possible_tasks, label="Tasks", value=["appearance"]) |
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with gr.Row(): |
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input_image = gr.Image(label="Raw Image", type="numpy") |
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output_image = gr.Image(label="Enhanced Image", type="numpy") |
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button = gr.Button() |
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button.click(run_tasks, inputs=[input_image, task], outputs=[output_image]) |
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gr.Examples( |
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examples=[ |
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["input/218_in.png", ["dewarping", "deshadowing", "appearance"]], |
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["input/151_in.png", ["dewarping", "deshadowing", "appearance"]], |
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["input/for_debluring.png", ["deblurring"]], |
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["input/for_appearance.png", ["appearance"]], |
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["input/for_deshadowing.jpg", ["deshadowing"]], |
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["input/for_dewarping.png", ["dewarping"]], |
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["input/for_binarization.png", ["binarization"]], |
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], |
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inputs=[input_image, task], |
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outputs=[output_image], |
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fn=run_tasks, |
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cache_examples="lazy", |
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) |
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demo.launch() |
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