Filtering Operators

In this tutorial we are going to learn how to apply blurring filters to images with kornia.filters components.

%%capture
!pip install kornia
!pip install kornia-rs

import io

import requests


def download_image(url: str, filename: str = "") -> str:
    filename = url.split("/")[-1] if len(filename) == 0 else filename
    # Download
    bytesio = io.BytesIO(requests.get(url).content)
    # Save file
    with open(filename, "wb") as outfile:
        outfile.write(bytesio.getbuffer())

    return filename


url = "https://github.com/kornia/data/raw/main/drslump.jpg"
download_image(url)

import kornia as K
import kornia.utils
import numpy as np
import torch
import torchvision
from matplotlib import pyplot as plt
from PIL import Image

We use Kornia to load an image to memory represented directly in a tensor

x_rgb: torch.Tensor = (K.image_to_tensor(np.array(Image.open("doraemon.png").convert("RGB"))).float() / 255.0)[
    None, ...
]  # BxCxHxW

x_gray = K.color.rgb_to_grayscale(x_rgb)

def imshow(input: torch.Tensor):
    if input.shape != x_rgb.shape:
        input = K.geometry.resize(input, size=(x_rgb.shape[-2:]))
    out = torch.cat([x_rgb, input], dim=-1)
    out = torchvision.utils.make_grid(out, nrow=2, padding=5)
    out_np = K.utils.tensor_to_image(out)
    plt.imshow(out_np)
    plt.axis("off")
    plt.show()

imshow(x_rgb)

Box Blur

x_blur: torch.Tensor = K.filters.box_blur(x_rgb, (9, 9))
imshow(x_blur)

Blur Pool

x_blur: torch.Tensor = K.filters.blur_pool2d(x_rgb, kernel_size=9)
imshow(x_blur)

Gaussian Blur

x_blur: torch.Tensor = K.filters.gaussian_blur2d(x_rgb, (11, 11), (11.0, 11.0))
imshow(x_blur)

Max Pool

x_blur: torch.Tensor = K.filters.max_blur_pool2d(x_rgb, kernel_size=11)
imshow(x_blur)

Median Blur

x_blur: torch.Tensor = K.filters.median_blur(x_rgb, (5, 5))
imshow(x_blur)

Motion Blur

x_blur: torch.Tensor = K.filters.motion_blur(x_rgb, 9, 90.0, 1)
imshow(x_blur)