Hello world: Planet Kornia

Welcome to Planet Kornia: a set of tutorials to learn about Computer Vision in PyTorch.

This is the first tutorial that show how one can simply start loading images with Torchvision, Kornia and OpenCV.

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!pip install kornia

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

import torch
import torchvision
import kornia as K

/home/docs/checkouts/readthedocs.org/user_builds/kornia-tutorials/envs/latest/lib/python3.7/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Download first an image form internet to start to work.

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!wget https://github.com/kornia/data/raw/main/arturito.jpg

Load an image with OpenCV

We can use OpenCV to load an image. By default, OpenCV loads images in BGR format and casts to a numpy.ndarray with the data layout (H,W,C).

However, because matplotlib saves an image in RGB format, in OpenCV you need to change the BGR to RGB so that an image is displayed properly.

img_bgr: np.array = cv2.imread('arturito.jpg')  # HxWxC / np.uint8
img_rgb: np.array = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)

plt.imshow(img_rgb); plt.axis('off');

Load an image with Torchvision

The images can be also loaded using torchvision which directly returns the images in a torch.Tensor in the shape (C,H,W).

x_rgb: torch.tensor = torchvision.io.read_image('arturito.jpg')  # CxHxW / torch.uint8
x_rgb = x_rgb.unsqueeze(0)  # BxCxHxW
print(x_rgb.shape)

torch.Size([1, 3, 144, 256])

Load an image with Kornia

With Kornia we can do all the preceding.

We have a couple of utilities to cast the image to a torch.Tensor to make it compliant to the other Kornia components and arrange the data in (B,C,H,W).

The utility is kornia.image_to_tensor which casts a numpy.ndarray to a torch.Tensor and permutes the channels to leave the image ready for being used with any other PyTorch or Kornia component.
The image is casted into a 4D torch.Tensor with zero-copy.

x_bgr: torch.tensor = K.image_to_tensor(img_bgr)  # CxHxW / torch.uint8
x_bgr = x_bgr.unsqueeze(0)  # 1xCxHxW
print(f"convert from '{img_bgr.shape}' to '{x_bgr.shape}'")

convert from '(144, 256, 3)' to 'torch.Size([1, 3, 144, 256])'

We can convert from BGR to RGB with a kornia.color component.

x_rgb: torch.tensor = K.color.bgr_to_rgb(x_bgr)  # 1xCxHxW / torch.uint8

Visualize an image with Matplotib

We will use Matplotlib for the visualisation inside the notebook. Matplotlib requires a numpy.ndarray in the (H,W,C) format, and for doing so we will go back with kornia.tensor_to_image which will convert the image to the correct format.

img_bgr: np.array = K.tensor_to_image(x_bgr)
img_rgb: np.array = K.tensor_to_image(x_rgb)

Create a subplot to visualize the original an a modified image

fig, axs = plt.subplots(1, 2, figsize=(32, 16))
axs = axs.ravel()

axs[0].axis('off')
axs[0].imshow(img_rgb)

axs[1].axis('off')
axs[1].imshow(img_bgr)

plt.show()