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#!/usr/bin/env python
from __future__ import annotations
import json
import shlex
import subprocess
import gradio as gr
def run(image_path: str, class_index: int, sigma_y: float) -> str:
out_name = image_path.split('/')[-1].split('.')[0]
subprocess.run(shlex.split(
f'python main.py --config confs/inet256.yml --deg colorization --scale 1 --class {class_index} --path_y {image_path} --save_path {out_name} --sigma_y {sigma_y}'
),
cwd='DDNM/hq_demo')
return f'DDNM/hq_demo/results/{out_name}/final/00000.png'
def create_demo():
examples = [
[
'sample_images/monarch_gray.png',
'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',
0,
],
[
'sample_images/tiger_gray.png',
'tiger, Panthera tigris',
0,
],
]
with open('imagenet_classes.json') as f:
imagenet_class_names = json.load(f)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
image = gr.Image(label='Input image', type='filepath')
class_index = gr.Dropdown(label='Class name',
choices=imagenet_class_names,
type='index',
value=950)
sigma_y = gr.Number(label='sigma_y', value=0, precision=2)
run_button = gr.Button('Run')
with gr.Column():
result = gr.Image(label='Result', type='filepath')
gr.Examples(
examples=examples,
inputs=[
image,
class_index,
sigma_y,
],
)
run_button.click(
fn=run,
inputs=[
image,
class_index,
sigma_y,
],
outputs=result,
)
return demo
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