Top 7 Python libraries for Computer Vision

Python Programming language is considered is one of the best programming language for the purpose of computer vision. Computer vision have some powerful advancement due to the obvious reason of Python programming Language.

Python Programming Language

Python is a general purpose programming language which can be used in any sector of technology. The power of python programming language is due to the enrich Python libraries for each purpose. You think of something and there will be a ready made library available for your project.

Computer Vision

Computer vision is a fast growing field in the computer technology. Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.

Face Recognition Photo
Image from Face Recongnition Github

I will introduce you to Top 7 Python Libraries that you can use in your computer vision project. Each of the libraries have big support of the python community and is widely used. Feel free to pick any of the following.;

7 Python Libraries for Computer vision:-

Below is the list of Top 7 Best Computer Vision Libraries

  1. OpenCV :-

    OpenCV is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez. The library is cross-platform and free for use under the open-source Apache 2 License. OpenCV (Open Source Computer Vision Library) is an open-source library that includes several hundreds of computer vision algorithms. OpenCV is the huge open-source library for the computer vision, machine learning, and image processing and now it plays a major role in real-time operation which is very important in today's systems.
  2. SimpleCV :-

    SimpleCV is a framework for Open Source Machine Vision, using OpenCV and the Python programming language. It provides a concise, readable interface for cameras, image manipulation, feature extraction, and format conversion. Our mission is to give casual users a comprehensive interface for basic machine vision functions and an elegant programming interface for advanced users.
  3. Kornia :-

    Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions.
  4. pytorchcv :-

    pytorchcv is a collection of image classification, segmentation, detection, and pose estimation models. Many of them are pretrained on ImageNet-1K, CIFAR-10/100, SVHN, CUB-200-2011, Pascal VOC2012, ADE20K, Cityscapes, and COCO datasets and loaded automatically during use. All pretrained models require the same ordinary normalization. Scripts for training/evaluating/converting models are in the imgclsmob repo.
  5. Face Recognition :-

    Face Recognition is the world's simplest facial recognition api for Python and the command line With Face Recongintion Python Computer vision Library you can Recognize and manipulate faces from Python or from the command line with the world's simplest face recognition library.
  6. EasyOCR :-

    EasyOCR is Especially used for optical character recognition. which can help you extract text from an image. EasyOCR is a Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
  7. tesserocr :-

    tesserocr is A simple, Pillow-friendly, wrapper around the tesseract-ocr API for Optical Character Recognition (OCR). tesserocr integrates directly with Tesseract's C++ API using Cython which allows for a simple Pythonic and easy-to-read source code. It enables real concurrent execution when used with Python's threading module by releasing the GIL while processing an image in tesseract.

Summary and Conclusion:-

This was all about computer vision libraries. Hope you like it. Please support me by subscribing to you Youtube channel. and by sharing this article to your friends. If you are interested in other python tutorials please visit my youtube channel Code with Ali.


I’m Ali, Founder of I am a Software developer, and I love to write articles to help students, developers, and learners. I started because of my love for Python, Java, and WebDev.

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