Spaces:
Running
Running
import gradio as gr | |
import torch | |
import kornia as K | |
from kornia.geometry.transform import resize | |
import cv2 | |
import numpy as np | |
from torchvision import transforms | |
from torchvision.utils import make_grid | |
from PIL import Image | |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
def read_image(img): | |
image_to_tensor = transforms.ToTensor() | |
if isinstance(img, np.ndarray): | |
img = Image.fromarray(img) | |
img_tensor = image_to_tensor(img) | |
resized_image = resize(img_tensor.unsqueeze(0), (50, 50)).squeeze(0) | |
return resized_image | |
def predict(images, eps): | |
eps = float(eps) | |
images = [read_image(img) for img in images] | |
images = torch.stack(images, dim=0).to(device) | |
zca = K.enhance.ZCAWhitening(eps=eps, compute_inv=True) | |
zca.fit(images) | |
zca_images = zca(images) | |
grid_zca = make_grid(zca_images, nrow=3, normalize=True).cpu().numpy() | |
return np.transpose(grid_zca, [1, 2, 0]) | |
title = 'ZCA Whitening with Kornia!' | |
description = '''[ZCA Whitening](https://paperswithcode.com/method/zca-whitening) is an image preprocessing method that leads to a transformation of data such that the covariance matrix is the identity matrix, leading to decorrelated features: | |
*Note that you can upload only image files, e.g. jpg, png etc and there should be at least 2 images!* | |
Learn more about [ZCA Whitening and Kornia](https://kornia.readthedocs.io/en/v0.6.4/_modules/kornia/enhance/zca.html)''' | |
with gr.Blocks(title=title) as demo: | |
gr.Markdown(f"# {title}") | |
gr.Markdown(description) | |
with gr.Row(): | |
input_images = gr.File(file_count="multiple", label="Input Images") | |
eps_slider = gr.Slider(minimum=0.01, maximum=1, value=0.01, label="Epsilon") | |
output_image = gr.Image(label="ZCA Whitened Images") | |
submit_button = gr.Button("Apply ZCA Whitening") | |
submit_button.click(fn=predict, inputs=[input_images, eps_slider], outputs=output_image) | |
gr.Examples( | |
examples=[ | |
[ | |
['irises.jpg', 'roses.jpg', 'sunflower.jpg', 'violets.jpg', 'chamomile.jpg', | |
'tulips.jpg', 'Alstroemeria.jpg', 'Carnation.jpg', 'Orchid.jpg', 'Peony.jpg'], | |
0.01 | |
] | |
], | |
inputs=[input_images, eps_slider], | |
) | |
if __name__ == "__main__": | |
demo.launch(show_error=True) |