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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],
]
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,
live=True
)
iface.launch()
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