#!/usr/bin/env python
from __future__ import annotations
import StopRunning # 模型还不能跑，这个是我随便遍的import，如果要运行，注释掉这行代码即可
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 = '''
'''
FOOTER = ''
ARTICLE = r"""
If VToonify is helpful, please help to ⭐ the Github Repo. Thanks!
[](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 S-Lab License 1.0.
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 williamyang@pku.edu.cn.
"""
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
''')
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.
- **Solution to [Error: no face detected!]**: 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.
''')
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('''
''')
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=True,
)
if __name__ == '__main__':
main()