#!/usr/bin/env python
from __future__ import annotations
import argparse
import pathlib
import gradio as gr
from dualstylegan import Model
DESCRIPTION = '''# Portrait Style Transfer with DualStyleGAN
'''
FOOTER = '
'
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()
def get_style_image_url(style_name: str) -> str:
base_url = 'https://raw.githubusercontent.com/williamyang1991/DualStyleGAN/main/doc_images'
filenames = {
'cartoon': 'cartoon_overview.jpg',
'caricature': 'caricature_overview.jpg',
'anime': 'anime_overview.jpg',
'arcane': 'Reconstruction_arcane_overview.jpg',
'comic': 'Reconstruction_comic_overview.jpg',
'pixar': 'Reconstruction_pixar_overview.jpg',
'slamdunk': 'Reconstruction_slamdunk_overview.jpg',
}
return f'{base_url}/{filenames[style_name]}'
def get_style_image_markdown_text(style_name: str) -> str:
url = get_style_image_url(style_name)
return f'
'
def update_slider(choice: str) -> dict:
max_vals = {
'cartoon': 316,
'caricature': 198,
'anime': 173,
'arcane': 99,
'comic': 100,
'pixar': 121,
'slamdunk': 119,
}
return gr.Slider.update(maximum=max_vals[choice])
def update_style_image(style_name: str) -> dict:
text = get_style_image_markdown_text(style_name)
return gr.Markdown.update(value=text)
def set_example_image(example: list) -> dict:
return gr.Image.update(value=example[0])
def set_example_styles(example: list) -> list[dict]:
return [
gr.Radio.update(value=example[0]),
gr.Slider.update(value=example[1]),
]
def set_example_weights(example: list) -> list[dict]:
return [
gr.Slider.update(value=example[0]),
gr.Slider.update(value=example[1]),
]
def main():
args = parse_args()
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 (Preprocess Input Image)
- Drop an image containing a near-frontal face to the **Input Image**.
- If there are multiple faces in the image, hit the Edit button in the upper right corner and crop the input image beforehand.
- Hit the **Detect & Align Face** button.
- Hit the **Reconstruct Face** button.
- The final result will be based on this **Reconstructed Face**. So, if the reconstructed image is not satisfactory, you may want to change the input image.
''')
with gr.Row():
with gr.Column():
with gr.Row():
input_image = gr.Image(label='Input Image',
type='file')
with gr.Row():
detect_button = gr.Button('Detect & Align Face')
with gr.Column():
with gr.Row():
aligned_face = gr.Image(label='Aligned Face',
type='numpy',
interactive=False)
with gr.Row():
reconstruct_button = gr.Button('Reconstruct Face')
with gr.Column():
reconstructed_face = gr.Image(label='Reconstructed Face',
type='numpy')
instyle = gr.Variable()
with gr.Row():
paths = sorted(pathlib.Path('images').glob('*.jpg'))
example_images = gr.Dataset(components=[input_image],
samples=[[path.as_posix()]
for path in paths])
with gr.Box():
gr.Markdown('''## Step 2 (Select Style Image)
- Select **Style Type**.
- Select **Style Image Index** from the image table below.
''')
with gr.Row():
with gr.Column():
style_type = gr.Radio(model.style_types,
label='Style Type')
text = get_style_image_markdown_text('cartoon')
style_image = gr.Markdown(value=text)
style_index = gr.Slider(0,
316,
value=26,
step=1,
label='Style Image Index')
with gr.Row():
example_styles = gr.Dataset(
components=[style_type, style_index],
samples=[
['cartoon', 26],
['caricature', 65],
['arcane', 63],
['pixar', 80],
])
with gr.Box():
gr.Markdown('''## Step 3 (Generate Style Transferred Image)
- Adjust **Structure Weight** and **Color Weight**.
- These are weights for the style image, so the larger the value, the closer the resulting image will be to the style image.
- Hit the **Generate** button.
''')
with gr.Row():
with gr.Column():
with gr.Row():
structure_weight = gr.Slider(0,
1,
value=0.6,
step=0.1,
label='Structure Weight')
with gr.Row():
color_weight = gr.Slider(0,
1,
value=1,
step=0.1,
label='Color Weight')
with gr.Row():
structure_only = gr.Checkbox(label='Structure Only')
with gr.Row():
generate_button = gr.Button('Generate')
with gr.Column():
result = gr.Image(label='Result')
with gr.Row():
example_weights = gr.Dataset(
components=[structure_weight, color_weight],
samples=[
[0.6, 1.0],
[0.3, 1.0],
[0.0, 1.0],
[1.0, 0.0],
])
gr.Markdown(FOOTER)
detect_button.click(fn=model.detect_and_align_face,
inputs=input_image,
outputs=aligned_face)
reconstruct_button.click(fn=model.reconstruct_face,
inputs=aligned_face,
outputs=[reconstructed_face, instyle])
style_type.change(fn=update_slider,
inputs=style_type,
outputs=style_index)
style_type.change(fn=update_style_image,
inputs=style_type,
outputs=style_image)
generate_button.click(fn=model.generate,
inputs=[
style_type,
style_index,
structure_weight,
color_weight,
structure_only,
instyle,
],
outputs=result)
example_images.click(fn=set_example_image,
inputs=example_images,
outputs=example_images.components)
example_styles.click(fn=set_example_styles,
inputs=example_styles,
outputs=example_styles.components)
example_weights.click(fn=set_example_weights,
inputs=example_weights,
outputs=example_weights.components)
demo.launch(
enable_queue=args.enable_queue,
server_port=args.port,
share=args.share,
)
if __name__ == '__main__':
main()