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Table of contents
1.
Introduction
2.
What is an Image?
3.
What is Image Processing?
4.
Popular Python Libraries for Image Processing
5.
OpenCV
6.
Pillow()
7.
Frequently Asked Questions
8.
Conclusion
Last Updated: Mar 27, 2024
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Image Processing with Python

Author Prakriti
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Basics of Python
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Introduction

What contributes to a great-looking website? Absolutely the perfect combination of colors and fonts can work wonders, but first impressions are of the images we see on the website, and as the saying goes, the first impression is the last.

We are in a dynamic era where Images have a HUGE impact! Our brain can interpret images much faster than textual data, and that is why any idea can be communicated instantaneously using images. Images define the world. They portray our feelings. Images affect everything, be it our food or clothes, give depth to our context, and provide an immersive experience.

“Also See, Python Round Function, Swapcase in Python” 

What is an Image?

We all understand the intuition of an image, but it is essential to know how to describe an image mathematically. Image is represented using its dimensions, i.e., height and width depending on pixels. A pixel is an element that contains the smallest item of information in an image. For example, if an image has dimensions 100 X 200, we will have 20,000 pixels in that image. Each pixel has a color, opacity, or shade, represented using the following:

  • Grayscale
    Each pixel represents an integer value between 0 to 255, where 0 implies complete black color and 255 implies complete white color.
  • RGB
    Each pixel contains three integers between 0 to 255, representing the intensity of Red, Green, and Blue colors, respectively.
  • RGBA
    RGBA is an extension of RGB. It contains an additional alpha field for the opacity of the image.

Also Read About, Python for Data Science

Also See, Image Sampling 

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What is Image Processing?

As the name suggests, Image Processing is a method used to process the images to extract useful information from images or get enhanced images. It has applications in almost every field. For example, we can identify criminals from their images much faster than just using their textual descriptions with face identification. It is helpful in object detection, self-driving cars, robotics, pattern recognition, in the medical field to identify diseases using images, etc.

Also See, Intersection in Python and Convert String to List Python.

Popular Python Libraries for Image Processing

Python is a widely used language for Image Processing. It contains multiple libraries and frameworks for quick implementation of image processing methods.

Some examples of the tools are OpenCV and Pillow().

OpenCV

OpenCV - Open Source Computer Vision is a crucial library for image processing containing 2000+ optimized codes for machine learning and computer vision.

 It can be used for multiple tasks like

  • Reading an image
  • Extract RGB value
  • Resizing, rotating of image
  • Changing the color space of an image
  • Smoothing of images
  • Extracting foreground
  • Image segmentation

Follow this link to know more about OpenCV.

Pillow()

Pillow(), also known as Python Image Library (PIL), is one of the most potent image processing libraries.

In PIL, we have a library called Image, which can carry out various operations like resizing, rotating the image, etc.

Like OpenCV, Pillow() can be used for various functions like

  • Load an image - using the open() function.
  • Display an image - using the show() function.
  • Know the format of an image - using the format() function.
  • Save the image after processing in png format - using the save() function.
  • Resize the image - using the resize() function.
  • Crop the image into desired dimensions - using the crop() function.
  • Know the size of the image - using the size attribute.
  • Know about the pixel - using the mode attribute.
  • Flip the image - using the transform() function.

Follow this link to know more about the Pillow() library.

You can also practice with the help of Online Python Compiler

Frequently Asked Questions

1. What is image processing?
Image Processing is the method in which we perform operations on an image to extract useful information from the image or get an enhanced image.

2. What is the best image processing library in Python?
OpenCV, Pillow(), and Scikit-image are among Python’s best image processing libraries.

3. Why do we need image processing?
We need image processing as it helps us extract useful information from images. For example, image processing can be used to identify thieves quickly compared to identifying thieves using just the textual descriptions of the person.

4. Which is better for image processing, Python or MATLAB?
Python is faster than MATLAB, which is a great advantage. For example, OpenCV libraries are much faster in Python than MATLAB as MATLAB has a lot of wrappers that consume time.

5. What is DN in pixel?
Every pixel consists of a numeric value known as Digital Number, which stores the electromagnetic energy measured for the ground resolution cell.

Conclusion

This article discussed Image Processing with Python, OpenCV, and Pillow.

We hope this blog has helped you enhance your knowledge regarding image Processing with Python and if you would like to learn more, check out our free content on NLP and more unique courses. Do upvote our blog to help other ninjas grow.

Happy Coding!

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