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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, scale: str, sigma_y: float) -> str:
out_name = image_path.split("/")[-1].split(".")[0]
subprocess.run(
shlex.split(
f"python main.py --config confs/inet256.yml --resize_y --deg sr_averagepooling --scale {scale} --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 = [
[
"DDNM/hq_demo/data/datasets/gts/inet256/323.png",
"monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
"4",
0,
],
[
"DDNM/hq_demo/data/datasets/gts/inet256/orange.png",
"orange",
"4",
0,
],
[
"DDNM/hq_demo/data/datasets/gts/inet256/monarch.png",
"monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
"4",
0.5,
],
[
"DDNM/hq_demo/data/datasets/gts/inet256/bear.png",
"brown bear, bruin, Ursus arctos",
"4",
0,
],
[
"DDNM/hq_demo/data/datasets/gts/inet256/flamingo.png",
"flamingo",
"2",
0,
],
[
"DDNM/hq_demo/data/datasets/gts/inet256/kimono.png",
"kimono",
"2",
0,
],
[
"DDNM/hq_demo/data/datasets/gts/inet256/zebra.png",
"zebra",
"4",
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)
scale = gr.Dropdown(label="Scale", choices=["2", "4", "8"], value="4")
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,
scale,
sigma_y,
],
)
run_button.click(
fn=run,
inputs=[
image,
class_index,
scale,
sigma_y,
],
outputs=result,
)
return demo
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