What are the image processing libraries in Python?

Python has become a mainstream language in the fields of data science and artificial intelligence. Because Python is an easy-to-learn language, it is commonly used in various fields, including image processing.

There are many image processing libraries for Python. In this article, we will introduce some of the major image processing libraries and how to use them.

1. OpenCV

OpenCV is an open source computer vision library and one of the most popular Python image processing libraries. It can be used to read, write and process images from a camera, file or network video stream. It supports computer vision tasks such as object recognition, face detection and motion tracking. OpenCV can also be used to develop machine learning-based applications, including image classification and object detection.

Using the OpenCV library in Python requires the following steps:

import cv2

image = cv2.imread("image.jpg")
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("Gray image", gray_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

This code snippet demonstrates how to read an image from an image file named "image.jpg" and convert it is a grayscale image. Then, use the cv2.imshow() function and it will display the image. Finally, the cv2.waitKey() function waits for the user to press any key, and cv2.destroyAllWindows() is used to close all windows.

2. Pillow

Pillow is an upgraded version of the Python image processing library PIL (Python Imaging Library). It provides many functions related to image processing, such as image scaling, image rotation, image filtering and image conversion.

Using the Pillow library in Python requires the following steps:

from PIL import Image

# Open an image file
image = Image.open("image.jpg")

# Resize image
image = image.resize((200, 200))

# Convert image to grayscale
gray_image = image.convert('L')

# Save image
gray_image.save("gray_image.jpg")

This code snippet demonstrates how to open an image file named "image.jpg", resize the image, and convert it as a grayscale image and save it as "gray_image.jpg". Pillow offers many additional features, appropriate descriptions can be found in the official documentation.

3. scikit-image

scikit-image is a Python image processing library for scientific computing. It contains many functions that implement different algorithms and techniques that can be used for specific image processing tasks, such as visual feature detection and morphological operations.

The following steps are required to use the scikit-image library in Python:

from skimage import io, filters

# Load an image file
image = io.imread("image.jpg")

# Apply Canny edge detector
edges = filters.sobel(image)

# Show the resulting image
io.imshow(edges)
io.show()

This code snippet demonstrates how to read an image from an image file named "image.jpg" and Detect edges using Canny edge detector. Finally, display the processed image using the io.imshow() function and display it using the io.show() function.

4. Matplotlib

Matplotlib is one of the main libraries for Python data visualization. It can also be used to create and display images. The imshow() function in Matplotlib can be used to display images.

The following steps are required to use the Matplotlib library in Python:

import matplotlib.pyplot as plt
import imageio

# Read an image file
image = imageio.imread("image.jpg")

# Display the image
plt.imshow(image)
plt.show()

This code snippet demonstrates how to read an image from an image file named "image.jpg" and use Matplotlib Show it.

Summary

There are many image processing libraries in Python. This article introduces some of the most commonly used libraries, including OpenCV, Pillow, scikit-image, and Matplotlib, and provides some example code for using these libraries for image processing. If you are new to learning Python image processing, this article should help you.

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