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# created with great guidance from https://github.com/NimaBoscarino
import os
import gradio as gr
import kornia as K
from kornia.core import Tensor
import torch
def filters(file, box_blur, blur_pool2d, gaussian_blur2d, max_blur_pool2d, median_blur):
"""
Apply a series of image filters to the input image.
"""
# Handle filepath string and file object
if isinstance(file, str):
filepath = file
else:
filepath = file.name
# Check if file exists
if not os.path.exists(filepath):
raise ValueError(f"File not found: {filepath}")
# load the image using the rust backend
img: Tensor = K.io.load_image(filepath, K.io.ImageLoadType.RGB32)
img = img[None] # 1xCxHxW / fp32 / [0, 1]
# apply tensor image enhancement
x_out: Tensor = K.filters.box_blur(img, (int(box_blur), int(box_blur)))
x_out = K.filters.blur_pool2d(x_out, int(blur_pool2d))
x_out = K.filters.gaussian_blur2d(x_out,
(int(gaussian_blur2d), int(gaussian_blur2d)),
(float(gaussian_blur2d), float(gaussian_blur2d)))
x_out = K.filters.max_blur_pool2d(x_out, int(max_blur_pool2d))
x_out = K.filters.median_blur(x_out, (int(median_blur), int(median_blur)))
# Ensure output is in the range [0, 1]
x_out = torch.clamp(x_out, 0, 1)
# Convert to numpy array and scale to [0, 255]
output_image = (x_out.squeeze().permute(1, 2, 0).cpu().numpy() * 255).astype('uint8')
return output_image
examples = [
["examples/ninja_turtles.jpg", 1, 1, 1, 1, 1],
["examples/kitty.jpg", 1, 1, 1, 1, 1],
]
demo = gr.Interface(
fn=filters,
inputs=[
gr.Image(type="filepath", label="Input Image"),
gr.Slider(minimum=1, maximum=10, step=1, value=1, label="Box Blur"),
gr.Slider(minimum=1, maximum=10, step=1, value=1, label="Blur Pool"),
gr.Slider(minimum=1, maximum=21, step=2, value=1, label="Gaussian Blur"),
gr.Slider(minimum=1, maximum=20, step=1, value=1, label="Max Pool"),
gr.Slider(minimum=1, maximum=5, step=2, value=1, label="Median Blur"),
],
outputs=gr.Image(type="numpy", label="Output Image"),
examples=examples,
live=True
)
demo.launch()
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