| 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() | |