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Computer vision is a branch of artificial intelligence aimed at enabling computers to observe and interpret the world in a human-like manner. This includes understanding visual data captured by sensors (such as cameras) and high-level concepts that allow humans to comprehend this data.
Computer vision is a key area of research and development in artificial intelligence. It focuses on automatically extracting, analyzing, and understanding useful information from digital images and videos. The market size for computer vision is difficult to estimate. However, it is rapidly growing, with projections indicating that the global market will reach $30.7 billion by 2025.
There are many computer vision libraries and frameworks available in Python. Each library has its own advantages and disadvantages, and which library to use to meet specific user needs is up to the user. In this article, we will review some of the most popular computer vision libraries/frameworks in Python for 2022.
OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. The establishment of OpenCV was aimed at providing a common infrastructure for computer vision applications and accelerating the use of machine perception in commercial products. It includes algorithms for object detection, video analysis, and image recognition.
It integrates over 2500 optimized algorithms, including a wide range of traditional and cutting-edge computer vision and machine learning techniques. These algorithms can be used to find similar images from an image database, remove red-eye from photos taken with a flash, track eye movements, recognize landscapes, and establish covered markers. They can also be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, generate 3D point clouds from stereo cameras, stitch images together to produce high-resolution images of entire scenes, extract 3D models of objects from stereo cameras, and extract 3D models of objects.
Scikit-image is an open-source Python library that provides a range of algorithms for image processing, including denoising, color conversion, and feature detection. It is built on top of the SciPy library and utilizes NumPy arrays for efficient storage and computation.
It includes algorithms for segmentation, geometric transformations, color space operations, analysis, filtering, morphology, feature detection, and more. Scikit-image is primarily written in Python, with some core algorithms written in Cython for performance improvements.
Example of image processing using scikit-image
SimpleCV is an open-source Python library that allows users to access and manipulate digital images. This library provides various functions for image processing, including filters, morphological operations, color conversion, and edge detection.
It offers a clear, readable interface for camera, image editing, feature extraction, and format conversion. This library is designed to provide a complex programming interface for professional users and a thorough interface for basic machine vision tasks for general users.
SimpleCV also provides an interface to access webcam images and can be used with the Raspberry Pi camera module.
Example usage of SimpleCV library
Pytesseract is an optical character recognition (OCR) tool for Python. In other words, it recognizes and “reads” the text embedded in images. Pytesseract cannot be installed via pip, so if you want to install it, you need to build it from source. Python-tesseract is a wrapper for Google’s Tesseract-OCR engine.
Imutils is a Python library that makes it easy to work with images and videos. It provides a set of functions for loading, saving, and processing images and videos. It also includes a set of functions for performing common image processing tasks such as resizing, cropping, and converting between different image formats.
Image conversion tasks using Imutils
OpenVINO is a Python library that allows developers to easily create and deploy computer vision applications. The library includes a variety of tools and utilities that make working with computer vision easier, including a model optimizer, computer vision engine, and a set of pre-trained models. OpenVINO also includes many examples and tutorials demonstrating how to use the library to build various types of applications.
In recent years, the Python programming language has become increasingly popular. This popularity is partly due to Python being relatively easy to learn, with a syntax that is simpler compared to other programming languages. Python is also widely used in the scientific community, with libraries that support robust numerical computation and data visualization.
What computer vision libraries have you all used?
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Reply "Python Vision Practical Project" in the "Beginner Learning Vision" public account backend to download 31 practical vision projects including image segmentation, mask detection, lane line detection, vehicle counting, eyeliner addition, license plate recognition, character recognition, emotion detection, text content extraction, face recognition, etc., to help quickly learn computer vision.
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