Edge Detection

In this tutorial we are going to learn how to detect edges in 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/doraemon.png"
download_image(url)

'doraemon.png'

import cv2
import kornia as K
import numpy as np
import torch
import torchvision
from matplotlib import pyplot as plt

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

x_rgb: torch.Tensor = K.io.load_image("doraemon.png", K.io.ImageLoadType.RGB32)[None, ...]  # BxCxHxW

x_gray = K.color.rgb_to_grayscale(x_rgb)

def imshow(input: torch.Tensor):
    out = torchvision.utils.make_grid(input, nrow=2, padding=5)
    out_np: np.ndarray = K.utils.tensor_to_image(out)
    plt.imshow(out_np)
    plt.axis("off")
    plt.show()

imshow(x_gray)

1st order derivates

grads: torch.Tensor = K.filters.spatial_gradient(x_gray, order=1)  # BxCx2xHxW
grads_x = grads[:, :, 0]
grads_y = grads[:, :, 1]

# Show first derivatives in x
imshow(1.0 - grads_x.clamp(0.0, 1.0))

# Show first derivatives in y
imshow(1.0 - grads_y.clamp(0.0, 1.0))

2nd order derivatives

grads: torch.Tensor = K.filters.spatial_gradient(x_gray, order=2)  # BxCx2xHxW
grads_x = grads[:, :, 0]
grads_y = grads[:, :, 1]

# Show second derivatives in x
imshow(1.0 - grads_x.clamp(0.0, 1.0))

# Show second derivatives in y
imshow(1.0 - grads_y.clamp(0.0, 1.0))

Sobel Edges

Once with the gradients in the two directions we can computet the Sobel edges. However, in kornia we already have it implemented.

x_sobel: torch.Tensor = K.filters.sobel(x_gray)
imshow(1.0 - x_sobel)

Laplacian edges

x_laplacian: torch.Tensor = K.filters.laplacian(x_gray, kernel_size=5)
imshow(1.0 - x_laplacian.clamp(0.0, 1.0))

Canny edges

x_laplacian: torch.Tensor = K.filters.canny(x_gray)[0]
imshow(1.0 - x_laplacian.clamp(0.0, 1.0))