10 Best Python Libraries for Image Processing

Here we have compiled a list of the 10 best Python libraries for image processing. You can use any of these Python libraries to work with images and handle most file formats to develop custom functionality for your app or software. Do check out these best Python image processing libraries to pick the right one for your next project.

In today’s modern world, digital information is expected to skyrocket, especially in the business world. But, among the different types of digital information consumed online and offline, many of it is made up of images and videos. Because of this, proper image processing is necessary to translate and obtain information for the business.

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10 Best Python Libraries for Image Processing

Typically, most data experts and scientists process the images before using them in specific machine learning models for better results.

In most cases, machine learning is essential as it gives businesses and companies new trends in business operations, customer behavior, and more importantly, helps develop new products.

As such, you need the assistance of several Python libraries to make image processing much easier and faster.

Below are the ten best Python libraries for image processing that businesses can use:

1. Mahotas

One of the great Python libraries for image processing is the Mahotas. It comes with advanced features and functionalities that allow developers to perform image processing by obtaining information from the images.

For instance, they can take advantage of computer vision capabilities that let you conduct processes like convolution, morphological processing, and many more.

Lastly, with the use of Mahotas, the developers can quickly generate 2D and 3D images through its innovative features, including local binary patterns, harlick, and many others.

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2. Scikit-Image

Another fantastic and straightforward Python library that developers and business owners should consider is the Scikit-Image.

That’s because of its variety of uses in any computer-vision task. Whether they need feature detection, segmentation, geometric segmentation, color space manipulation, and many more, using this image processing library can be an excellent open-source.

Thus, for most businesses, including reliable moving companies like roadwaymoving.com, using the Scikit-Image Python library can help them extract information from images that you can use to analyze customer behavior and improve their services.

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3. SimplelTK

For business owners and developers who want to treat images as a set of points on a region in space, using SimplelTK as an image-processing library can be a good idea.

This procedure allows it to process images more effectively, regardless of whether they’re in 2D, 3D, or 4D dimensions.

4. SciPy

While SciPy has been known for scientific and mathematical computations, developers can also utilize it as a practical Python library for image processing.

Using its advanced features and other capabilities, they can process images by implementing algorithms for image manipulation.

For example, SciPy allows them to perform object measurements, adjust interpolation, linear segmentation, and non-linear filtering on specific images.

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5. Numpy

Another Python library that you can use in image processing is Numpy. It contains data structures in the form of multi-dimensional and matrix arrays.

But, for purposes of image processing, Numpy can help manipulate pixels, image cropping, and masking pixel values.

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6. Matplotlib

When it comes to leveraging image processing, using Matplotlib as a Python library can be a good idea.

Although it’s mainly utilized for 2D visualizations such as histograms, scatter plots, bar graphs, and many more, this library for image processing is excellent for altering images to obtain information from it. On the other hand, this library doesn’t support all sorts of formats.

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7. OpenCV

One of the most popular libraries for image processing is the OpenCV. It’s supported by GitHub to enhance it for effective image processing.

Usually written in C++, it contains 2500 algorithms used to build machine learning applications and computer vision.

Generally, the algorithms in OpenCV are designed to carry out various tasks, including face recognition and detection and identification of objects.

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8. Python Image Library (PIL/Pillow)

For most image processing tasks, using PIL or Python Image Library can be good. Unlike other libraries, it offers unique functionalities such as manipulating and saving images, filtering, and opening.

Also, what makes this library more efficient is that it supports all sorts of file formats, and it offers specialized capabilities, including Image Display, Image Archives, and many more.

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9. Pgmagick

This is a widely-used Python library for image processing because of its variety of functionalities.

When using Pgmagick, developers and data scientists can perform many tasks on images, such as resizing, drawing texts, sharpening, rotation, blurring, scaling, and many more.

10. Seaborn

When creating various visualizations from images, Seaborn can be one of the most-used Python libraries nowadays.

One of the excellent features of this library is its ability to display correlations that aren’t obvious in a visual context, thereby letting developers and data scientists understand models more efficiently.

Due to its intuitive interfaces, it can provide well-designed data visualizations to make every model more attractive.

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The Bottom Line

With this, we sum up our list of the 10 best Python libraries for image processing. Please let me know which one you like the most and use it in your next project by writing to me at [email protected]. Also, don’t forget to subscribe to our newsletter.

Due to its many uses in the business industry, image processing has been adopted around the globe. It also plays an essential part in artificial intelligence and data science for extracting information out of images. Thus, if you believe your business will benefit from the advantages it offers, keep these widely-used Python libraries, and you’re good to go.

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