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import spaces |
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import gradio as gr |
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from transformers import pipeline |
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pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog") |
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@spaces.GPU |
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def predict(input_img): |
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predictions = pipeline(input_img) |
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return input_img, {p["label"]: p["score"] for p in predictions} |
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_HEADER_ = ''' |
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<h2>Toon3D: Seeing Cartoons from a New Perspective</h2> |
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Toon3D lifts cartoons into 3D via aligning and warping backprojected monocular depth predictions.<br> |
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Project page @ <a href='https://toon3d.studio/' target='_blank'>https://toon3d.studio/</a> |
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**Important Notes:** |
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- TODO 1 |
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- TODO 2 |
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''' |
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def check_input_images(input_images): |
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if input_images is None: |
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raise gr.Error("No images uploaded!") |
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def process_images(input_images): |
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for image in input_images: |
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print(image) |
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return input_images |
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gradio_app = gr.Interface( |
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predict, |
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inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"), |
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outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)], |
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title="Toon3D", |
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) |
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with gr.Blocks() as demo: |
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gr.Markdown(_HEADER_) |
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with gr.Row(variant="panel"): |
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with gr.Column(): |
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with gr.Row(): |
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input_images = gr.File(label="Upload Images", file_count="multiple", file_types=[".jpg", "jpeg", "png"]) |
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with gr.Row(): |
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process_data_button = gr.Button("Process Data", elem_id="process_data", variant="primary") |
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with gr.Row(): |
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processed_data_zip = gr.File(label="Processed Data", file_count="single", file_types=[".zip"]) |
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with gr.Column(): |
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with gr.Row(): |
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labeled_data = gr.File(label="Labeled Points", file_count="single", file_types=[".json"]) |
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process_data_button.click(fn=check_input_images, inputs=[input_images]).success( |
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fn=process_images, |
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inputs=[input_images], |
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outputs=[processed_data_zip], |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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