scikit-image 0.16.2 - Python

Scikit-image is a collection of algorithms for image processing and computer vision tasks. It is built on top of the SciPy library and offers a simple and efficient way to manipulate image data and perform advanced processing tasks.

Features

• Easy-to-use interface for common image processing operations
• Various types of image filters, including Gaussian, Median, and Sobel filters
• Morphological operations, such as erosion and dilation
• Segmentation algorithms, including thresholding and watershed segmentation
• Feature detection algorithms, such as corner detection and edge detection
• Image restoration and enhancement techniques, such as deblurring and denoising
• Geometric image transformations, such as scaling, rotation, and cropping

Installation

Scikit-image can be installed using pip or conda:

pip install scikit-image

conda install scikit-image

Usage

Reading and Displaying Images

This example shows how to read and display an image using scikit-image:

from skimage import io

# read the image
image = io.imread('image.jpg')

# display the image
io.imshow(image)
io.show()

Image Filtering

This example demonstrates how to apply a Gaussian filter to an image:

from skimage import io, filters

# read the image
image = io.imread('image.jpg')

# apply a Gaussian filter
filtered = filters.gaussian(image, sigma=1)

# display the filtered image
io.imshow(filtered)
io.show()

Image Thresholding

This example demonstrates how to apply thresholding to an image:

from skimage import io, filters

# read the image
image = io.imread('image.jpg', as_gray=True)

# apply thresholding
threshold = filters.threshold_otsu(image)
binary = image > threshold

# display the binary image
io.imshow(binary)
io.show()

Conclusion

Scikit-image is a powerful and easy-to-use library for image processing and computer vision tasks. Its intuitive interface and rich set of features make it a great choice for both novice and advanced users.