Color space conversions

In this tutorial we are going to learn how to convert image from different image spaces using kornia.color.

from matplotlib import pyplot as plt
import cv2
import numpy as np

import torch
import kornia
import torchvision

We use OpenCV to load an image to memory represented in a numpy.ndarray

img_bgr: np.ndarray = cv2.imread('./data/simba.png', cv2.IMREAD_COLOR)

Convert the numpy array to torch

x_bgr: torch.Tensor = kornia.image_to_tensor(img_bgr, keepdim=False)

Using kornia we easily perform color transformation in batch mode.

def hflip(input: torch.Tensor) -> torch.Tensor:
    return torch.flip(input, [-1])


def vflip(input: torch.Tensor) -> torch.Tensor:
    return torch.flip(input, [-2])


def rot180(input: torch.Tensor) -> torch.Tensor:
    return torch.flip(input, [-2, -1])


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

Create a batch of images

xb_bgr = torch.cat([x_bgr, hflip(x_bgr), vflip(x_bgr), rot180(x_bgr)])
imshow(xb_bgr)

Out:

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

Convert BGR to RGB

xb_rgb = kornia.bgr_to_rgb(xb_bgr)
imshow(xb_rgb)

Out:

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

Convert RGB to grayscale NOTE: image comes in torch.uint8, and kornia assumes floating point type

xb_gray = kornia.rgb_to_grayscale(xb_rgb.float() / 255.)
imshow(xb_gray)

Convert RGB to HSV

xb_hsv = kornia.rgb_to_hsv(xb_rgb.float() / 255.)
imshow(xb_hsv[:, 2:3])

Convert RGB to YUV NOTE: image comes in torch.uint8, and kornia assumes floating point type

yuv = kornia.rgb_to_yuv(xb_rgb.float() / 255.)
y_channel = torchvision.utils.make_grid(yuv, nrow=2)[0, :, :]
plt.imshow(y_channel, cmap='gray', vmin=0, vmax=1)  # Displaying only y channel
plt.axis('off')
plt.show()