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Top 8 Image-Processing Python Libraries Used in Machine Learning

The center of the structuring element identifies the pixel being processed. As the name says, image processing means processing the image and this may include many different techniques until we reach our goal. PyCairo is a set of Python bindings for the graphics library Cairo. Vector graphics are interesting because they don’t lose clarity when you resize or transform them.

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These are some of the most basic operations that can be performed with the OpenCV on an image. Apart from this, OpenCV can perform operations such as Image Segmentation, Face Detection, Object Detection, 3-D reconstruction, feature extraction as well. SimpleITK is often used for image segmentation and image registration, which is the process of overlaying two or more images. Image processing is the process of transforming images into digital forms before performing special operations on them, which yields valuable information. The package is imported as skimage, and most functions are found within the submodules. Simply follow this link to learn how to use Pillow to read, store images and process images.

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The last image processing library in Python on our list is SimpleCV, which is a popular open-source framework for creating computer vision applications with image processing. SimpleCV has a readable interface for cameras, format conversion, image manipulation, feature extraction, and more. Firstly,  Pillow (python image editing library) is the open-source library that supports many functionalit//ies that some other libraries do not provide like opening, filtering, saving images.

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NumPy is one of the core libraries in Python programming and provides support for arrays. 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, you can modify the pixel values of an image. The image can be loaded using skimage and displayed using Matplotlib.

  1. The square structuring element ‘A’ fits in the object we want to select, the ‘B’ intersects the object and ‘C’ is out of the object.
  2. An image can be represented as a 2D function F(x,y) where x and y are spatial coordinates.
  3. In this entire tutorial, you will know the best image processing library in python.
  4. For a better understanding, try zoom in on an image as much as possible.
  5. There are other kernels that perform different functions, including different blurring methods, edge detection, sharpening, and more.

It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that. 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. Matplotlib is specialized in 2D plots of arrays as a multi-platform data visualization library on Numpy arrays. SimpleITK works a little differently than the other image processing libraries on this list.

You can see a list of all the filters available in the ImageFilter documentation. One of the methods that’s used in image processing is image convolution using kernels. The aim of this tutorial is not to give a detailed explanation of image processing theory. If you’re interested in the science of image processing, one of the best resources that you can use is Digital Image Processing by Gonzalez and Woods. The red band alone, stored in the variable red, is a grayscale image with mode L.

Therefore, you’ll still need to use PIL when importing into your code. You can place this image file in the project folder that you’re working in. With those images in hand, you’re now ready to get started with Pillow.

For example, it does a good job with the Finding Wally problem with a minimum amount of code. The documentation has instructions for installation as well as examples covering every module of the library. If you’re not sure which to choose, learn more about installing packages. And check the image being saved in the current working directory.

Other than this, it can also be used for machine learning tasks. It is written in C++ but developers have provided Python and Java bindings. image manipulation Mahotas is a computer vision library in Python that provides a wide range of image processing and computer vision capabilities.

Data scientists can process these images and feed them into machine learning (ML) models to gain deep insights for a business. This article looks at 10 of the most commonly used Python libraries for image manipulation tasks. These libraries provide an easy and intuitive way to transform images and make sense of the underlying data. All these can be performed using various image processing libraries like OpenCV, Mahotas, PIL, scikit-learn. Python is one of the widely used programming languages for this purpose.

Matplotlib’s flexibility and extensive documentation make it a valuable tool for image visualization and analysis tasks in Python-based projects. SciPy is a top library primarily created for mathematical and scientific computations, with around 15.2K dependent packages and 43.4K dependent repositories. It builds on the foundation of NumPy, another popular Python library for numerical operations, and extends its capabilities to encompass numerous scientific domains. This library is another excellent alternative if you are looking for various applications such as image segmentation, analyzing images, face detection, feature extraction, and more. Scikit-Image is an open-source Python library for image processing and computer vision tasks. It is part of the larger scientific Python ecosystem and is built on top of other popular libraries like NumPy and SciPy.

However, to be put to any use, these images need to be processed. Image processing is how we analyze and manipulate a digital image to improve its quality or extract information from it. In the next section, you’ll go a step further and create a GIF animation using NumPy and Pillow. These functions make it easier to experiment with erosion and dilation for an image. You’ll use these functions in the next section as you continue working on placing the cat into the monastery. However, you’d like to have an image in which all the pixels that correspond to the cat are white and all other pixels are black.

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