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Top Python Libraries For Computer Vision in 2022 by Moez Ali Medium

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Cairo is a 2D image manipulation that allows the creation of vector graphics. Vector graphics are attractive since they don’t lose clarity when resized or altered. For instance, it does an excellent job of solving the finding Wally problem, using only a small quantity of programming.

  • Written in C++ and C programming, OpenCV delivers the necessary speed for real-time computer vision.
  • Algorithms are used both in web and mobile projects, and it’s worth noting that these areas are intensively used to prepare data for science.
  • This filter replaces the value of a pixel with the minimum value of the nine pixels in the 3×3 array centered around the pixel.
  • Analytics Drift strives to keep you updated with the latest technologies such as Artificial Intelligence, Data Science, Machine Learning, and Deep Learning.

This is because it offers excellent visualizations that make the model understandable and attractive. Pillow is an image processing library for Python derived from the PIL or the Python Imaging Library. Although it is not as powerful and fast as openCV it can be used for simple image manipulation works like cropping, resizing, rotating and greyscaling the image.

Sci-Kit Image

Similar to scikit-image, SciPy works in tandem with NumPy to process images effortlessly. Due to the speed it offers, you can build several moderate-level workflows like feature extraction, face detection, image sharpening, denoising, geometrical transformations and more. But, SciPy cannot provide you with the flexibility to develop complex projects that require intensive workflows. SimpleITK is an open-source library that offers multi-dimensional image analysis. Unlike most image processing and computer vision libraries that consider images as arrays, it treats images as a set of points in space. It supports languages like Python, R, Java, C#, Lua, Ruby, TCL, and C++.

The open-source Python library Keras was designed primarily for the development and evaluation of neural networks inside machine learning and deep learning models. This library can function on top of TensorFlow and Theano, which means developers can start training their neural networks with not much code. Like NumPy, SciPy is free and open source, making it accessible for everyone. SciPy is based on NumPy and can also be used for technical and scientific computing with large data sets.

Because of how simple and intuitive Matplotlib is, many beginners choose to work with it when starting out in data visualization. It’s also the choice of many developers who already have plenty of experience with other data visualization tools. Matplotlib is an open-source Python library often touted as an alternative to the paid solution MATLAB. Matplotlib, a SciPy extension, was made for the purpose of data visualization as it’s used to create graphs and plots. Matplotlib can also work with the complex data models outputted by Pandas as well as data structures created by NumPy.


Numerous image processing techniques are available through the Numpy and Scipy libraries in Python. PyCaret got its name from being a Python library based on Caret, a machine learning library in the programming language R. This open source library was also created for machine learning, and as a result, it offers some features to help simplify and automate ML programs.

PyTorch is most often used in DL and ML applications, including NLP and computer vision. This library is well-known for being able to execute fast, even when handling heavy loads. PyTorch is also flexible, allowing it to work on CPUs, GPUs, and even simplified processors. It has high-level commands often used for data manipulation and visualization. Kv, or the Kivy language, is an intermediate language dedicated to describing user interactions and interface.

Pillow also has the advantage of being widely used by the Python community, and it doesn’t have the same steep learning curve as some of the other image processing libraries. Pillow and its predecessor, PIL, are the original Python libraries for dealing with images. Even though there are other Python libraries for image processing, Pillow remains an important tool for understanding and dealing with images. Scipy is a Python library that is mostly used for mathematical and scientific computation, but it can also be used for multidimensional image processing. To use the image processing tools you just need to import the scipy.ndimage module from the library.

Image Manipulation With NumPy and Pillow

It provides a high-performance implementation of gradient boosting that can handle large datasets and high-dimensional feature spaces. 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. On the other hand, this library doesn’t support all sorts of formats.

Another benefit is that it can be used without NumPy and Matplotlib. Matplotlib is primarily used for 2D visualizations such as scatter plots, bar graphs, histograms, and many more, but we can also use it for image processing. Simply based on the fact that OpenCV is written in C and C++ whereas PIL is written in Python and C, OpenCV appears to be faster. The extraction of data from thousands of photos requires quick processing.

When it comes to solving data science tasks and challenges, Python never ceases to surprise its users. Most data scientists are already leveraging the power of Python programming every day. Python is an easy-to-learn, easy-to-debug, widely used, object-oriented, open-source, high-performance language, and there are many more benefits to Python programming. Python has been built with extraordinary Python libraries for data science that are used by programmers every day in solving problems. SimpleITK is written in C++, but it’s available for a large number of programming languages including Python.

Top Python Image Processing Libraries

There is, however, an alternative, Pillow, which is an actively developed alternative to PIL, which is simpler to install, works on all the major platforms, and also supports Python 3. The library provides basic image processing functions that include points, filters, the convolution kernels built-in, as well as colour space conversions. SciPy provides some basic image processing operations such as Face Detection, Convolution, Image Segmentation, Reading Images, Feature Extraction, and many more. Along with this, you also perform filtering, draw contour lines on images. PIL is a free Python image library that adds support for viewing, manipulating, and saving various image file types to the Python programming language.

Now, let’s load an image from the internet and apply some filters. Colab doesn’t support cv2.imshow() — but, we have an alternative for that. We will be using Colab as there we don’t need to set thing environment — everything is pre-setup here. The documentation contains installation instructions, examples and even some tutorials to help get started in Mahotas. The official documentation is straightforward and has tons of examples and use cases to follow including the one below. Here’s an example that shows the capabilities of OpenCV-Python in image blending using pyramids to create a new fruit called orapple.

  • The data visualization library eases the creation of dashboards, data applications, and interactive plots.
  • If you want to deal with images directly by manipulating their pixels, then you can use NumPy and SciPy.
  • In this section, you’ll learn about image processing features in the Python Pillow library.
  • Also commonly known as ITK–Insight Segmentation and Registration Toolkit–is a widely used image processing library.
  • Bob is a collection of Python libraries that provide a range of tools and algorithms for machine learning, computer vision, and signal processing.

NumPy can also process multidimensional arrays, which is why so many developers and data scientists use it for AI and ML projects. NumPy is one of the best open source Python modules for scientific and numerical computing and data analyses. In fact, it even provides the foundation for a few other Python libraries, such as SciPy and Sci-Kit Learn. NumPy is most often used for mathematical operations with matrices and arrays.


For example, you can compare the amount of code in Python and C++ for a typical image processing library. This makes Pgmagick a universal powerful image tool for many tasks of building backends. Consider that image processing is multi-threaded using OpenMP which means you can scale image processing as much as how many processors you can add to the OpenMP server.

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Pycairo is a set of Python bindings for the Cairo graphics library. Vector graphics are interesting because they don’t lose clarity when resized or transformed. Today’s world is full of data, and images form a significant part of this data. However, before they can be used, these digital images must be processed—analyzed and manipulated in order to improve their quality or extract some information that can be put to use. Other advantages of the library include the ability to retain the functionality of the code even after iteration. To understand the implementation of the Mahotas, read the preprint paper here.

How to Get User Input in Python

Firstly, Pillow is the open-source library that supports many functionalit//ies that some other libraries do not provide like opening, filtering, saving images. The main thing I like about it that you can resize, convert the images to other formats like jpeg, png etc. easily. As a Python library, OpenCV is comprised of various functions, making it a great tool for computer vision programs in real-time. This highly efficient library can process various visual inputs not just from images but also from video data.

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Bob is designed to be modular and extensible, allowing developers to add new algorithms and models easily. Pipenv generates a Pipfile.lock file that records the exact versions of each package installed in your project’s virtual environment. This ensures that your project always uses the same dependencies, even if newer versions of those packages are released. Pipenv creates a virtual environment for your project and installs the necessary packages inside it. This ensures that your project’s dependencies are isolated from other Python installations on your system. ELI5 can generate human-readable explanations for how a model makes predictions, which can help communicate with non-technical stakeholders.

Top Python Libraries For Computer Vision in 2022

The second argument is the lo in the main image where you want to paste the second picture. The tuple includes the coordinates within the main image where you want to place the top-left corner of the image that you’re pasting in. You achieve thresholding by calling .point() to convert each pixel in the grayscale image into either 255 or 0. The conversion depends on whether the value in the grayscale image is greater or smaller than the threshold value.

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Today, a colossal amount of data is generated due to the rapid increase in smartphones and CCTV cameras. The abundance of image data has let to many companies building data-driven products to streamline business processes. Consequently, being proficient with image processing libraries can differentiate you in the market. OpenCV is arguably the best image processing library in the world due to its wide range of use cases in computer vision.

data analysis

The blurred images show that the box blur filter with a radius of 20 produces an image that’s more blurred than the image generated by the box blur filter with radius 5. The next sections will look at the kernels and image filtering capabilities available in the ImageFilter module in Pillow. The diagram and the discussion above only consider three kernel positions.

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