demo.py

#!/usr/bin/env python

from __future__ import annotations

import argparse
import pathlib
import torch
import gradio as gr

from vtoonify_model import Model


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument("--device", type=str, default="cpu")
    parser.add_argument("--theme", type=str)
    parser.add_argument("--share", action="store_true")
    parser.add_argument("--port", type=int)
    parser.add_argument("--disable-queue", dest="enable_queue", action="store_false")
    return parser.parse_args()


DESCRIPTION = """
<div align=center>
<h1 style="font-weight: 900; margin-bottom: 7px;">
   Portrait Style Transfer with <a href="https://github.com/williamyang1991/VToonify">VToonify</a>
</h1>
<p>For faster inference without waiting in queue, you may duplicate the space and use the GPU setting.
<br/>
<a href="https://huggingface.co/spaces/PKUWilliamYang/VToonify?duplicate=true">
<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
<p/>
<video id="video" width=50% controls="" preload="none" poster="https://repository-images.githubusercontent.com/534480768/53715b0f-a2df-4daa-969c-0e74c102d339">
<source id="mp4" src="https://user-images.githubusercontent.com/18130694/189483939-0fc4a358-fb34-43cc-811a-b22adb820d57.mp4
" type="video/mp4">
</videos>
</div>
"""
FOOTER = '<div align=center><img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.laobi.icu/badge?page_id=williamyang1991/VToonify" /></div>'

ARTICLE = r"""
If VToonify is helpful, please help to ⭐ the <a href='https://github.com/williamyang1991/VToonify' target='_blank'>Github Repo</a>. Thanks! 
[![GitHub Stars](https://img.shields.io/github/stars/williamyang1991/VToonify?style=social)](https://github.com/williamyang1991/VToonify)
---
📝 **Citation**
If our work is useful for your research, please consider citing:
```bibtex
@article{yang2022Vtoonify,
  title={VToonify: Controllable High-Resolution Portrait Video Style Transfer},
  author={Yang, Shuai and Jiang, Liming and Liu, Ziwei and Loy, Chen Change},
  journal={ACM Transactions on Graphics (TOG)},
  volume={41},
  number={6},
  articleno={203},
  pages={1--15},
  year={2022},
  publisher={ACM New York, NY, USA},
  doi={10.1145/3550454.3555437},
}
```

📋 **License**
This project is licensed under <a rel="license" href="https://github.com/williamyang1991/VToonify/blob/main/LICENSE.md">S-Lab License 1.0</a>. 
Redistribution and use for non-commercial purposes should follow this license.

📧 **Contact**
If you have any questions, please feel free to reach me out at <b>williamyang@pku.edu.cn</b>.
"""


def update_slider(choice: str) -> dict:
    if type(choice) == str and choice.endswith("-d"):
        return gr.Slider.update(maximum=1, minimum=0, value=0.5)
    else:
        return gr.Slider.update(maximum=0.5, minimum=0.5, value=0.5)


def set_example_image(example: list) -> dict:
    return gr.Image.update(value=example[0])


def set_example_video(example: list) -> dict:
    return (gr.Video.update(value=example[0]),)


sample_video = [
    "./vtoonify/data/529_2.mp4",
    "./vtoonify/data/7154235.mp4",
    "./vtoonify/data/651.mp4",
    "./vtoonify/data/908.mp4",
]
sample_vid = gr.Video(label="Video file")  # for displaying the example
example_videos = gr.components.Dataset(
    components=[sample_vid],
    samples=[[path] for path in sample_video],
    type="values",
    label="Video Examples",
)


def main():
    args = parse_args()
    args.device = "cuda" if torch.cuda.is_available() else "cpu"
    print("*** Now using %s." % (args.device))
    model = Model(device=args.device)

    with gr.Blocks(theme=args.theme, css="style.css") as demo:
        gr.Markdown(DESCRIPTION)

        with gr.Box():
            gr.Markdown(
                """## Step 1(Select Style)
    - Select **Style Type**.
        - Type with `-d` means it supports style degree adjustment.
        - Type without `-d` usually has better toonification quality.

    """
            )
            with gr.Row():
                with gr.Column():
                    gr.Markdown("""Select Style Type""")
                    with gr.Row():
                        style_type = gr.Radio(
                            label="Style Type",
                            choices=[
                                "cartoon1",
                                "cartoon1-d",
                                "cartoon2-d",
                                "cartoon3-d",
                                "cartoon4",
                                "cartoon4-d",
                                "cartoon5-d",
                                "comic1-d",
                                "comic2-d",
                                "arcane1",
                                "arcane1-d",
                                "arcane2",
                                "arcane2-d",
                                "caricature1",
                                "caricature2",
                                "pixar",
                                "pixar-d",
                                "illustration1-d",
                                "illustration2-d",
                                "illustration3-d",
                                "illustration4-d",
                                "illustration5-d",
                            ],
                        )
                        exstyle = gr.Variable()
                    with gr.Row():
                        loadmodel_button = gr.Button("Load Model")
                    with gr.Row():
                        load_info = gr.Textbox(
                            label="Process Information",
                            interactive=False,
                            value="No model loaded.",
                        )
                with gr.Column():
                    gr.Markdown(
                        """Reference Styles
    ![example](https://raw.githubusercontent.com/williamyang1991/tmpfile/master/vtoonify/style.jpg)"""
                    )

        with gr.Box():
            gr.Markdown(
                """## Step 2 (Preprocess Input Image / Video)
    - Drop an image/video containing a near-frontal face to the **Input Image**/**Input Video**.
    - Hit the **Rescale Image**/**Rescale First Frame** button.
        - Rescale the input to make it best fit the model.
        - The final image result will be based on this **Rescaled Face**. Use padding parameters to adjust the background space.
        - **<font color=red>Solution to [Error: no face detected!]</font>**: VToonify uses dlib.get_frontal_face_detector but sometimes it fails to detect a face. You can try several times or use other images until a face is detected, then switch back to the original image.
    - For video input, further hit the **Rescale Video** button.
        - The final video result will be based on this **Rescaled Video**. To avoid overload, video is cut to at most **100/300** frames for CPU/GPU, respectively.

    """
            )
            with gr.Row():
                with gr.Box():
                    with gr.Column():
                        gr.Markdown(
                            """Choose the padding parameters.
        ![example](https://raw.githubusercontent.com/williamyang1991/tmpfile/master/vtoonify/rescale.jpg)"""
                        )
                        with gr.Row():
                            top = gr.Slider(128, 256, value=200, step=8, label="top")
                        with gr.Row():
                            bottom = gr.Slider(
                                128, 256, value=200, step=8, label="bottom"
                            )
                        with gr.Row():
                            left = gr.Slider(128, 256, value=200, step=8, label="left")
                        with gr.Row():
                            right = gr.Slider(
                                128, 256, value=200, step=8, label="right"
                            )
                with gr.Box():
                    with gr.Column():
                        gr.Markdown("""Input""")
                        with gr.Row():
                            input_image = gr.Image(label="Input Image", type="filepath")
                        with gr.Row():
                            preprocess_image_button = gr.Button("Rescale Image")
                        with gr.Row():
                            input_video = gr.Video(
                                label="Input Video",
                                mirror_webcam=False,
                                type="filepath",
                            )
                        with gr.Row():
                            preprocess_video0_button = gr.Button("Rescale First Frame")
                            preprocess_video1_button = gr.Button("Rescale Video")

                with gr.Box():
                    with gr.Column():
                        gr.Markdown("""View""")
                        with gr.Row():
                            input_info = gr.Textbox(
                                label="Process Information",
                                interactive=False,
                                value="n.a.",
                            )
                        with gr.Row():
                            aligned_face = gr.Image(
                                label="Rescaled Face", type="numpy", interactive=False
                            )
                            instyle = gr.Variable()
                        with gr.Row():
                            aligned_video = gr.Video(
                                label="Rescaled Video", type="mp4", interactive=False
                            )
            with gr.Row():
                with gr.Column():
                    paths = [
                        "./vtoonify/data/pexels-andrea-piacquadio-733872.jpg",
                        "./vtoonify/data/i5R8hbZFDdc.jpg",
                        "./vtoonify/data/yRpe13BHdKw.jpg",
                        "./vtoonify/data/ILip77SbmOE.jpg",
                        "./vtoonify/data/077436.jpg",
                        "./vtoonify/data/081680.jpg",
                    ]
                    example_images = gr.Dataset(
                        components=[input_image],
                        samples=[[path] for path in paths],
                        label="Image Examples",
                    )
                with gr.Column():
                    # example_videos = gr.Dataset(components=[input_video], samples=[['./vtoonify/data/529.mp4']], type='values')
                    # to render video example on mouse hover/click
                    example_videos.render()

                    # to load sample video into input_video upon clicking on it
                    def load_examples(video):
                        # print("****** inside load_example() ******")
                        # print("in_video is : ", video[0])
                        return video[0]

                    example_videos.click(load_examples, example_videos, input_video)

        with gr.Box():
            gr.Markdown("""## Step 3 (Generate Style Transferred Image/Video)""")
            with gr.Row():
                with gr.Column():
                    gr.Markdown(
                        """

                        - Adjust **Style Degree**.
                        - Hit **Toonify!** to toonify one frame. Hit **VToonify!** to toonify full video.
                            - Estimated time on 1600x1440 video of 300 frames: 1 hour (CPU); 2 mins (GPU)
                        """
                    )
                    style_degree = gr.Slider(
                        0, 1, value=0.5, step=0.05, label="Style Degree"
                    )
                with gr.Column():
                    gr.Markdown(
                        """![example](https://raw.githubusercontent.com/williamyang1991/tmpfile/master/vtoonify/degree.jpg)
                        """
                    )
            with gr.Row():
                output_info = gr.Textbox(
                    label="Process Information", interactive=False, value="n.a."
                )
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        result_face = gr.Image(
                            label="Result Image", type="numpy", interactive=False
                        )
                    with gr.Row():
                        toonify_button = gr.Button("Toonify!")
                with gr.Column():
                    with gr.Row():
                        result_video = gr.Video(
                            label="Result Video", type="mp4", interactive=False
                        )
                    with gr.Row():
                        vtoonify_button = gr.Button("VToonify!")

        gr.Markdown(ARTICLE)
        gr.Markdown(FOOTER)

        loadmodel_button.click(
            fn=model.load_model, inputs=[style_type], outputs=[exstyle, load_info]
        )

        style_type.change(fn=update_slider, inputs=style_type, outputs=style_degree)

        preprocess_image_button.click(
            fn=model.detect_and_align_image,
            inputs=[input_image, top, bottom, left, right],
            outputs=[aligned_face, instyle, input_info],
        )
        preprocess_video0_button.click(
            fn=model.detect_and_align_video,
            inputs=[input_video, top, bottom, left, right],
            outputs=[aligned_face, instyle, input_info],
        )
        preprocess_video1_button.click(
            fn=model.detect_and_align_full_video,
            inputs=[input_video, top, bottom, left, right],
            outputs=[aligned_video, instyle, input_info],
        )

        toonify_button.click(
            fn=model.image_toonify,
            inputs=[aligned_face, instyle, exstyle, style_degree, style_type],
            outputs=[result_face, output_info],
        )
        vtoonify_button.click(
            fn=model.video_tooniy,
            inputs=[aligned_video, instyle, exstyle, style_degree, style_type],
            outputs=[result_video, output_info],
        )

        example_images.click(
            fn=set_example_image,
            inputs=example_images,
            outputs=example_images.components,
        )

    # demo.launch(
    #     enable_queue=args.enable_queue,
    #     server_port=args.port,
    #     share=args.share,
    # )

    demo.queue(concurrency_count=1, max_size=4)
    demo.launch(server_port=8266)


if __name__ == "__main__":
    main()