#Python resize image code

It was forked from the original Pillow and brought to the next processing efficiency level by Alex Karpinsky, an Uploadcare developer. Uploadcare uses Pillow-SIMD at its image-processing core and it’s one of the most efficient Python libraries. The original library has been forked multiple times and some of these branches widened the functionality or improved image processing performance. You can resize, apply Python image compression for JPEGs and other lossy formats, and perform other types of image manipulations with it. It features all types of downscaling and filters, including Bilinear, Bicubic and Lanczos (Antialias). It’s a successor to PIL and it works with about 30 types of image files, including all popular image formats used on the web, such as JPEG, PNG, GIF and WebP. Three picture downsampling types and their respective window function shapes PillowĪmong all the alternatives, the Pillow library is one of the most functional and popular image processing libraries in Python.

Lanczos, aka Antialias, has the widest functionality among these three and is overkill for most cases as it works in the 8x8 pixel neighborhood.įor some reason, PIL uses Lanczos by default, and therefore it’s very slow in resizing image tasks. Bicubic is a more advanced function that preserves more sharp edges when resizing and works with 4x4 pixel cubes.

Bilinear, a simple one, may produce a few resizing artifacts but is good enough for most images. To make a long story short, there are three interpolation resizing methods (which feature window functions). It’s a well-known library, but it doesn’t use the most optimal way of resizing images. It offers methods to open, manipulate (including resizing and compression) and save image files. It works with Python version 2, which was discontinued in 2020. In the beginning, there was PIL, an open-source Python Imaging Library.

#Python resize image code

Therefore, it’s easier to read the source code and understand what exactly Python functions are doing. Python scripts are usually shorter than the same scripts in functionality made with the C language family, Java and other popular programming languages. In turn, it directly affects the average shopping cart value for both existing users and first-time visitors. It’s commercially viable for online stores (with a picture traffic of up to 80%), because the shorter the loading time, the more pages users will open. Image optimization is a must-have for any website that has pictures that take up to 60% of the traffic. While the algorithms for JPEG resizing & compression are quite complicated and include functional math and operations like convolution and window functions, we won’t go that deep today we’ll just quickly go over the interfaces for developers and do some efficiency comparisons. Popular Python libraries for image processing A bit more on that later in this article. All these libraries are open source, so you can just take them and use their methods and functions to deal with images, or you can fork the libraries on GitHub and customize them for your needs or optimize them, as Alex Karpinsky did with Pillow. In this article, we’ll run through the most popular Python libraries that you can use on your website to optimize and resize images on the fly.

Python is also used for image compression and image resize operations. It’s very powerful at doing mathematical calculations and it works with dynamic data types. Whenever you code in this or another programming language, you can include Python libraries into your project and utilize their classes and methods, thanks to its object-oriented structure. Python is a great and accessible programming language for automation scripts and components of web services.