app.py

#!/usr/bin/env python

from __future__ import annotations

import argparse
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], api_name="load-model")


        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], api_name="scale-image")
        preprocess_video0_button.click(fn=model.detect_and_align_video,
                                inputs=[input_video, top, bottom, left, right],
                                outputs=[aligned_face, instyle, input_info], api_name="scale-first-frame")
        preprocess_video1_button.click(fn=model.detect_and_align_full_video,
                                inputs=[input_video, top, bottom, left, right],
                                outputs=[aligned_video, instyle, input_info], api_name="scale-video")

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

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

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


if __name__ == '__main__':
    main()