10 Best Python Libraries for Image Processing

In the earlier section discussing kernels, the box blur filter that you used was a 3×3 filter. This means that it had a radius of 1, because the filter extends by one pixel from the center. The overall size of the display is calculated from the size of the images and the number of images used.

However, when you read an image programmatically with Python or any other language, the computer sees an array of numbers. In this tutorial, you’ll learn how to manipulate images and perform basic image processing using the Python Pillow library. Matplotlib is another great option for an image processing library. It is especially useful as an image module for working with images in Python, and it includes two specific methods for reading and displaying images.

If you like, you can replace the absolute path to the image here and try reading it from your local computer or even from the internet! If the image is present in your current working directory, you only need to specify the image name with its extension type. Another computer vision libraries fundamental feature of Python that draws many of its users is its vast collection of open-source libraries. These libraries can be used by programmers of all experience levels for tasks involving ML and AI, data science, image and data manipulation, and much more.

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An image is essentially a standard NumPy array containing pixels of data points. Therefore, by using basic NumPy operations, such as slicing, masking and fancy indexing, we can modify the pixel values of an image. You can then load the image using skimage and display it using Matplotlib. Thresholding can be used to segment images when the object to segment is distinct from the background.

To use its developers’ own description, Pillow is the friendly PIL fork that kept the library alive and includes support for Python 3. A colored image consists of 3 color channels where a gray image only consists of 1 Color channel which carries intensity information for each pixel showing the image as black-and-white. SimpleITK is often used for image segmentation and image registration, which is the process of overlaying two or more images. The second argument of Image.new() is the size of the image as a tuple of width and height in pixels. PIL was originally developed by Fredrik Lundh and Contributors in 1995.

Let’s see an example to understand what gaussian filters do to an image. Structuring element is a matrix consisting of only 0’s and 1’s that can have any arbitrary shape and size. It is positioned at all possible locations in the image and it is compared with the corresponding neighbourhood of pixels. An image can be represented as a 2D function F(x,y) where x and y are spatial coordinates. The amplitude of F at a particular value of x,y is known as the intensity of an image at that point. If x,y, and the amplitude value is finite then we call it a digital image.

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Within the for loop, you create NumPy arrays for the red, green, and blue channels, as you did in the previous section. The array containing the green layer is always the same and represents a square in the center of the image. Thanks for reading this article on python libraries for image processing and for your patience. Share this article, it will give me the motivation to write more blogs for the data science community.

It is an open-source numerical library that can be used to perform various mathematical operations on different matrices. NumPy is considered one of the most used scientific libraries, which is why many data scientists rely on it to analyze data. 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.

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It acts as a data analysis library that analyzes and manipulates data, and it enables developers to easily work with structured multidimensional data and time series concepts. The programming language includes all of NumPy’s functions, but it turns them into user-friendly, scientific tools. It is often used for image manipulation and provides basic processing features for high-level, non-scientific mathematical functions. NumPy is widely regarded as the best Python library for machine learning and AI.

Above code translates an image from one coordinate to a different coordinate.

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The Gaussian kernel puts more weight on the pixels at the center of the kernel than those at the edges, and this leads to smoother blurring than what’s obtained with the box blur. For this reason, Gaussian blurring can give better results in many cases. The .BoxBlur() filter is similar to the one described in the previous section introducing convolution kernels.

Let’s install the widely used package (OpenCV) to get started with and we are going to run the codes in each cell in a Jupyter Notebook. Plotly is one of the best data visualization tools on the market, and it is built on top of the D3.js, HTML, and CSS visualization toolkit. Written in Python, it uses the Django framework and can help create interactive graphs. It works on different data analytics and visualization tools and enables you to easily import data into a chart. Matplotlib is a unity of NumPy and SciPy, and it was designed to replace the need to use proprietary MATLAB statistical language.

It provides image processing features that are similar to ones found in image processing software such as Photoshop. Pillow is often the preferred option for high-level image processing tasks that don’t require more advanced image processing expertise. It’s also often used for exploratory work when dealing with images. To manipulate and process images, Pillow provides tools that are similar to ones found in image processing software such as Photoshop. Some of the more modern Python image processing libraries are built on top of Pillow and often provide more advanced functionality. It’s important to note that Matplotlib doesn’t support all file formats.

This Python AI library is a stellar choice for developers focused on common computer vision applications as opposed to highly customized solutions. They use systems of nodes (modeled after the neurons in human brains) with each node representing a particular variable or computation. The pathways that lead to a more successful/closer mapping to the output are strengthened and kept while failing/inaccurate pathways are weakened and discarded.

• While it’s not a Python AI library itself, AirSim allows developers to test and experiment with autonomous vehicle algorithms without actually needing to possess the physical hardware for it.
• In the earlier section discussing kernels, the box blur filter that you used was a 3×3 filter.
• From cameras to model sight and radar to model physical touch, AI algorithms must interpret the data from these sensors to allow a machine to function safely and effectively.
• Mahotas has many popular functions such as Watershed, Convex points calculations, morphological processing, and template matching.
• Theano is a highly specific library, and it is mostly used by machine learning and deep learning developers and programmers.

So today, we are going to go through the creation of a simple image recognition system so that you can get familiar with the various AI libraries and tools Python has to offer. ImageAI provides abstracted and convenient implementations of state-of-the-art Computer Vision technologies. All of ImageAI implementations and code can work on any computer system with moderate CPU capacity. However, the speed of processing for operations like image prediction, object detection and others on CPU is slow and not suitable for real-time applications. To perform real-time Computer Vision operations with high performance, you need to use GPU enabled technologies. Erosion is the process of removing white pixels from the boundaries in an image.

We can choose to interpret an image however we want, but there is usually one interpretation that is optimal. For example, we could interpret a black and white image with RGB pixel values, but it is more efficient to view it as a grayscale image (3x fewer integers used). On the other hand, it would https://forexhero.info/ be unwise to interpret a colored image using grayscale pixels, since the pixels won’t be able to capture any of the actual colors. Python has grown in popularity over the years to become one of the most popular programming languages for machine learning (ML) and artificial intelligence (AI) tasks.

• Remember that it is good to play around with the analysis and see how adjusting it changes the results, as this will help you begin to make estimates on your needs for future projects.
• It’s incredibly easy to use, allowing for the creation, training and running of an artificial neural network in just three function calls.
• PIL stands for Python Image Library and Pillow is the friendly PIL fork by Alex Clark and Contributors.
• With robotics, AI meets the physical world through a plethora of different sensors modeling the human senses.

OpenCV is a pre-built, open-source CPU-only library (package) that is widely used for computer vision, machine learning, and image processing applications. It supports a good variety of programming languages including Python. The Dataset class makes it easier and more efficient to perform tasks with all the image files. After we create a dataset with the image files, we will need to decode each file’s contents into usable pixel data. Since the decode_image function works for single image files, we will need to use the dataset object’s map function to apply decode_image to each image file in our dataset. Theano is a highly specific library, and it is mostly used by machine learning and deep learning developers and programmers.

One more top image processing library in Python is Mahotas, which was originally designed for bioimage informatics. Mahotas enables developers to take advantage of advanced features like local binary patterns and haralick. It can compute 2D and 3D images through its mahotas.features.haralick module, and it extracts information from pictures to perform advanced image processing.