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import gradio as gr
import cv2
import torch
import kornia as K
def load_torch_image(fname):
img = K.image_to_tensor(fname, False).float() / 255.
img = K.color.bgr_to_rgb(img)
return img
def enhance(file, brightness, contrast, saturation, gamma, hue):
fname = file.name
im = cv2.imread(fname)
img = load_torch_image(im)
x_out: torch.Tensor = K.enhance.adjust_brightness(img, float(brightness))
x_out = K.enhance.adjust_contrast(x_out, float(contrast))
x_out = K.enhance.adjust_saturation(x_out, float(saturation))
x_out = K.enhance.adjust_gamma(x_out, float(gamma))
x_out = K.enhance.adjust_hue(x_out, float(hue))
return K.utils.tensor_to_image(x_out)
examples = [
["examples/ninja_turtles.jpg", 0, 1, 1, 1, 0],
["examples/kitty.jpg", 0, 1, 1, 1, 0],
]
title = "Kornia Image Enhancements"
description = "<p style='text-align: center'>This is a Gradio demo for Kornia's Image Enhancements.</p><p style='text-align: center'>To use it, simply upload your image, or click one of the examples to load them, and use the sliders to enhance! Read more at the links at the bottom.</p>"
article = "<p style='text-align: center'><a href='https://kornia.readthedocs.io/en/latest/' target='_blank'>Kornia Docs</a> | <a href='https://github.com/kornia/kornia' target='_blank'>Kornia Github Repo</a> | <a href='https://kornia-tutorials.readthedocs.io/en/latest/image_enhancement.html' target='_blank'>Kornia Enhancements Tutorial</a></p>"
iface = gr.Interface(
enhance,
[
gr.inputs.Image(type="file"),
gr.inputs.Slider(minimum=0, maximum=1, step=0.1, default=0, label="Brightness"),
gr.inputs.Slider(minimum=0, maximum=4, step=0.1, default=1, label="Contrast"),
gr.inputs.Slider(minimum=0, maximum=4, step=0.1, default=1, label="Saturation"),
gr.inputs.Slider(minimum=0, maximum=1, step=0.1, default=1, label="Gamma"),
gr.inputs.Slider(minimum=0, maximum=4, step=0.1, default=0, label="Hue"),
],
"image",
examples=examples,
title=title,
description=description,
article=article,
live=True
)
iface.launch()