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| 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 | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| def read_image(f_name): | |
| image_to_tensor = transforms.ToTensor() | |
| img = image_to_tensor(cv2.imread(f_name, cv2.IMREAD_COLOR)) | |
| resized_image = resize(img,(50, 50)) | |
| return resized_image | |
| def predict(images, eps): | |
| eps = float(eps) | |
| f_names = [img.name for img in images] | |
| images = [read_image(f) for f in f_names] | |
| 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 sjould be atleast 2 images!* | |
| Learn more about [ZCA Whitening and Kornia](https://kornia.readthedocs.io/en/latest/_modules/kornia/enhance/zca.html)''' | |
| iface = gr.Interface(fn=predict, | |
| inputs=['files', gr.Slider(0.01, 1)], | |
| outputs=gr.Image(), | |
| allow_flagging="never", | |
| title=title, | |
| description=description, | |
| examples=[[ | |
| [ | |
| 'irises.jpg', | |
| 'roses.jpg', | |
| 'sunflower.jpg', | |
| 'violets.jpg', | |
| 'chamomile.jpg', | |
| 'tulips.jpg', | |
| 'Alstroemeria.jpg', | |
| 'Carnation.jpg', | |
| 'Orchid.jpg', | |
| 'Peony.jpg' | |
| ], 0.01]] | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch(show_error=True) |