Add app.py

app.py

@@ -0,0 +1,44 @@
+import gradio as gr
+import torch
+import kornia as K
+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 resize_images(f_names):
+    for i, f_name in enumerate(f_names):
+      img = cv2.imread(f_name, cv2.IMREAD_COLOR)
+      resized_image = cv2.resize(img,(70, 70))
+      cv2.imwrite(f_name,resized_image)
+
+def predict(images, eps):
+  eps = float(eps)
+  f_names = [img.name for img in images]
+  resize_images(f_names)
+  convert_tensor = transforms.ToTensor()
+  images = [convert_tensor(cv2.imread(f, cv2.IMREAD_COLOR)) 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=2, 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
+             )
+
+if __name__ == "__main__":
+  iface.launch(show_error=True)