scikit-image GGB Tutorial

Introduction

scikit-image or skimage as same as OpenCV uses numpy array as its core for image processing. Thus, this tutorial is similar to the OpenCV ones with a slightly different configuration. This following tutorial will use scikit-image as the main image processing library.

Load Image

First of all, load the useful components to load an image:

from skimage import io
from ggb import GGB, ColorSpace, CVLib

image = io.imread('/path/to/image')

Warning

Users should define /path/to/image to a valid path by themselves for example img/leukocytes.png.

GGB Convert

For the main process, let’s utilize GGB, ColorSpace and CVLib:

ggb_image = GGB(image=image, input_color=ColorSpace.RGB, backend=CVLib.OPENCV).process()

Note

Default scikit-image image in RGB color space. To see the valid color space please see the reference.

Warning

scikit-image backend parameter should be set as CVLib.OPENCV since it is based on numpy array.

Show Result

There are various ways to see the result such as show it directly or write it into a variable or file:

#show it directly
ggb_image.show()

#write it into a variable
final_image = ggb_image.write()

#write it into a file
ggb_write.write('/path/to/image')

Warning

Users should define /path/to/image to a valid path by themselves for example img/leukocytes_ggb.png.