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import os
import random
import textwrap
import cv2
import gradio as gr
import numpy as np
from PIL import Image
from cv2.ximgproc import guidedFilter
from imgutils.data import load_image
from imgutils.restore import restore_with_nafnet, restore_with_scunet
def dynamic_clean_adverse(
input_image: Image.Image,
diameter_min: int = 4,
diameter_max: int = 6,
sigma_color_min: float = 6.0,
sigma_color_max: float = 10.0,
sigma_space_min: float = 6.0,
sigma_space_max: float = 10.0,
radius_min: int = 3,
radius_max: int = 6,
eps_min: float = 16.0,
eps_max: float = 24.0,
b_iters: int = 64,
g_iters: int = 4,
):
img = np.array(input_image).astype(np.float32)
y = img.copy()
for _ in range(b_iters):
diameter = random.randint(diameter_min, diameter_max)
sigma_color = random.random() * (sigma_color_max - sigma_color_min) + sigma_color_min
sigma_space = random.random() * (sigma_space_max - sigma_space_min) + sigma_space_min
y = cv2.bilateralFilter(y, diameter, sigma_color, sigma_space)
for _ in range(g_iters):
radius = random.randint(radius_min, radius_max)
eps = random.random() * (eps_max - eps_min) + eps_min
y = guidedFilter(img, y, radius, eps)
output_image = Image.fromarray(y.clip(0, 255).astype(np.uint8))
return output_image
def clean(
image: Image.Image,
diameter_min: int = 4,
diameter_max: int = 6,
sigma_color_min: float = 6.0,
sigma_color_max: float = 10.0,
sigma_space_min: float = 6.0,
sigma_space_max: float = 10.0,
radius_min: int = 3,
radius_max: int = 6,
eps_min: float = 16.0,
eps_max: float = 24.0,
b_iters: int = 64,
g_iters: int = 4,
use_scunet_clean: bool = False,
use_nafnet_clean: bool = False
) -> Image.Image:
image = load_image(image)
image = dynamic_clean_adverse(
image,
diameter_min, diameter_max,
sigma_color_min, sigma_color_max,
sigma_space_min, sigma_space_max,
radius_min, radius_max,
eps_min, eps_max,
b_iters, g_iters
)
if use_scunet_clean:
image = restore_with_scunet(image)
if use_nafnet_clean:
image = restore_with_nafnet(image)
return image
if __name__ == '__main__':
with gr.Blocks() as demo:
with gr.Row():
gr_markdown = gr.Markdown(textwrap.dedent("""
Cleaner for [MIST](https://github.com/mist-project/mist-v2)(**M**IST **I**s **S**tupid **T**rash) noises.
Inspired by https://github.com/lllyasviel/AdverseCleaner
* **Update 2023.12.18**, allow random dynamic adversarial clean and iterate steps.
""").strip())
with gr.Row():
with gr.Column():
gr_input_image = gr.Image(label='Input Image', type="pil")
gr_submit = gr.Button(value='MIST = MIST is Stupid Trash', variant='primary')
with gr.Accordion("Advanced Config", open=False):
with gr.Row():
gr_diameter_min = gr.Slider(
minimum=1, maximum=30, step=1, value=4,
label="Diameter Min (default = 4)", interactive=True,
)
gr_diameter_max = gr.Slider(
minimum=1, maximum=30, step=1, value=6,
label="Diameter Max (default = 6)", interactive=True,
)
with gr.Row():
gr_sigma_color_min = gr.Slider(
minimum=1, maximum=30, step=1, value=6,
label="SigmaColor Min (default = 6)", interactive=True,
)
gr_sigma_color_max = gr.Slider(
minimum=1, maximum=30, step=1, value=10,
label="SigmaColor Max (default = 10)", interactive=True,
)
with gr.Row():
gr_sigma_space_min = gr.Slider(
minimum=1, maximum=30, step=1, value=6,
label="SigmaSpace Min (default = 6)", interactive=True,
)
gr_sigma_space_max = gr.Slider(
minimum=1, maximum=30, step=1, value=10,
label="SigmaSpace Max (default = 10)", interactive=True,
)
with gr.Row():
gr_radius_min = gr.Slider(
minimum=1, maximum=30, step=1, value=3,
label="Radius Min (default = 3)", interactive=True,
)
gr_radius_max = gr.Slider(
minimum=1, maximum=30, step=1, value=6,
label="Radius Max (default = 6)", interactive=True,
)
with gr.Row():
gr_eps_min = gr.Slider(
minimum=1, maximum=30, step=1, value=16,
label="Accuracy Min (default = 16)", interactive=True,
)
gr_eps_max = gr.Slider(
minimum=1, maximum=30, step=1, value=24,
label="Accuracy Max (default = 24)", interactive=True,
)
with gr.Row():
gr_b_iters = gr.Slider(
minimum=1, maximum=256, step=1, value=64,
label="Bilateral Filter Iters (default = 64)", interactive=True,
)
gr_g_iters = gr.Slider(
minimum=1, maximum=32, step=1, value=4,
label="Guided Filter Iters (default = 4)", interactive=True,
)
with gr.Accordion("Extra Restoration", open=False):
with gr.Row():
gr_scunet = gr.Checkbox(label='Use SCUNET', value=False)
gr_nafnet = gr.Checkbox(label='Use NAFNET', value=False)
with gr.Column():
gr_output_image = gr.Image(label='Output Image', type="pil")
gr_submit.click(
fn=clean,
inputs=[
gr_input_image,
gr_diameter_min,
gr_diameter_max,
gr_sigma_color_min,
gr_sigma_color_max,
gr_sigma_space_min,
gr_sigma_space_max,
gr_radius_min,
gr_radius_max,
gr_eps_min,
gr_eps_max,
gr_b_iters,
gr_g_iters,
gr_scunet,
gr_nafnet,
],
outputs=[gr_output_image],
)
demo.queue(os.cpu_count()).launch()
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