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import os
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 clean_adverse(
input_image: Image.Image,
diameter: float = 5,
sigma_color: float = 8,
sigma_space: float = 8,
radius: float = 4,
eps: float = 16,
) -> Image.Image:
img = np.array(input_image).astype(np.float32)
y = img.copy()
for _ in range(64):
y = cv2.bilateralFilter(y, diameter, sigma_color, sigma_space)
for _ in range(4):
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: float = 5,
sigma_color: float = 8,
sigma_space: float = 8,
radius: float = 4,
eps: float = 16,
use_scunet_clean: bool = False,
use_nafnet_clean: bool = False
) -> Image.Image:
image = load_image(image)
image = clean_adverse(image, diameter, sigma_color, sigma_space, radius, eps)
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():
with gr.Column():
gr_input_image = gr.Image(label='Input Image', type="pil")
gr_submit = gr.Button(value='MIST = Mist IS Trash')
with gr.Accordion("Advanced Config", open=False):
diameter_slider = gr.Slider(
minimum=1,
maximum=30,
step=1,
value=5,
label="Diameter (default = 5)",
interactive=True,
)
sigma_color_slider = gr.Slider(
minimum=1,
maximum=30,
step=1,
value=8,
label="SigmaColor (default = 8)",
interactive=True,
)
sigma_space_slider = gr.Slider(
minimum=1,
maximum=30,
step=1,
value=8,
label="SigmaSpace (default = 8)",
interactive=True,
)
radius_slider = gr.Slider(
minimum=1,
maximum=30,
step=1,
value=4,
label="Radius (default = 4)",
interactive=True,
)
eps_slider = gr.Slider(
minimum=1,
maximum=30,
step=1,
value=16,
label="Accuracy (default = 16)",
interactive=True,
)
with gr.Accordion("Extra Restoration", open=False):
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,
diameter_slider,
sigma_color_slider,
sigma_space_slider,
radius_slider,
eps_slider,
gr_scunet,
gr_nafnet,
],
outputs=[gr_output_image],
)
demo.queue(os.cpu_count()).launch()
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