kornia

import torch import kornia as K import numpy as np from PIL import Image import matplotlib.pyplot as plt # Simulate loading an image or create a dummy tensor # In a real scenario, replace this with actual image loading (e.g., with Pillow/OpenCV) # and conversion using K.image_to_tensor. # Example with a dummy tensor: image_tensor = torch.rand(1, 3, 256, 256, dtype=torch.float32) # Convert image to grayscale grayscale_tensor = K.color.rgb_to_grayscale(image_tensor) # Apply a Gaussian blur blurred_tensor = K.filters.gaussian_blur2d(grayscale_tensor, kernel_size=(7, 7), sigma=(1.5, 1.5)) print(f"Original image shape: {image_tensor.shape}") print(f"Grayscale image shape: {grayscale_tensor.shape}") print(f"Blurred image shape: {blurred_tensor.shape}") # --- Optional: Visualization (requires matplotlib and numpy) --- # Convert tensors to NumPy arrays for display # You might need to detach from GPU and move to CPU if applicable: # grayscale_np = K.tensor_to_image(grayscale_tensor.detach().cpu()) # blurred_np = K.tensor_to_image(blurred_tensor.detach().cpu()) # fig, axs = plt.subplots(1, 2, figsize=(10, 5)) # axs[0].imshow(grayscale_np) # axs[0].set_title('Grayscale Image') # axs[0].axis('off') # axs[1].imshow(blurred_np) # axs[1].set_title('Blurred Image') # axs[1].axis('off') # plt.show()