What is OpenCV? And why is OpenCV so popular in the Computer Vision Industry? Today, OpenCV is the de facto standard tool for image processing. Hence, the world’s leading companies across industries use OpenCV to develop their computer vision systems. In this article, we will provide an easy-to-read introduction to OpenCV.
In this article, we will cover the following topics:
- Introduction to OpenCV
- What is Open CV?
- What is Computer Vision?
- Developing software with OpenCV
- Use OpenCV without coding
- Vision capabilities of OpenCV
- Commercial use cases of OpenCV
- OpenCV Community and Resources
About us: At viso.ai, we’ve integrated OpenCV into Viso Suite, the end-to-end Computer Vision Platform. Leading organizations use it to build, deploy and scale real-world computer vision applications. We are also an AI software partner of Intel, the creator of OpenCV. Get a demo here.
What is OpenCV?
Initially developed by Intel, OpenCV (Open Source Computer Vision) is a free cross-platform computer vision library for real-time image processing. The OpenCV software has become a de-facto standard tool for all things related to Computer Vision. Today, OpenCV is still highly popular, with over 29’000 downloads every week.
OpenCV is written in C and C++. It runs under the most popular operating systems, such as GNU/Linux, OS X, Windows, Android, iOS, etc. It is available for free under the Apache 2 license. There is active development on interfaces for Python, Ruby, Matlab, and other languages making it easily accessible through commands like “pip install opencv” for Python users and “git opencv” for version control.
The OpenCV library contains over 2500 algorithms, extensive documentation, source code, and sample code for real-time computer vision. Developers using the Python package, along with other Python libraries, can seamlessly integrate OpenCV into their projects using commands like “python opencv.” This integration is made even more convenient through package managers, which provide a simple process for installation and version control.
Since its first release in 2000 under the BSD license and later under the Apache 2 license, the open-source library has been used in numerous cutting-edge applications, products, and research projects. These applications include stitching camera images together in satellite or web maps, image scan alignment, medical image noise reduction, object analysis, security, surveillance and intrusion detection systems, automatic monitoring, and safety systems, manufacturing AI inspection, camera calibration, defense and military applications, and unmanned aerial, ground, and underwater vehicles.
Interesting to know: OpenCV has even been used in sound and music recognition, where vision recognition techniques have been applied to sound spectrogram images.
What is Open CV used for?
OpenCV was built for maximum efficiency and performance of computing-intensive vision tasks. Therefore, it has a strong focus on real-time applications of AI vision. The open-source software is written in optimized C and can take advantage of multicore processors (multi-threading).
The goal of OpenCV is to provide an easy-to-use computer vision infrastructure that helps people build sophisticated vision applications quickly by providing over 500 functions that span many areas in vision. OpenCV is often used in factory product inspection, medical imaging, security analysis, human-machine interface, camera calibration, stereo vision (3D vision), and robotic vision.
The comprehensive image processing capabilities support video stream processing, image stitching (combining multiple cameras), camera calibration, and diverse image pre-processing tasks. Because machine learning is essential in computer vision, OpenCV contains a complete, general-purpose ML Library focused on statistical pattern recognition and clustering.
To enhance performance, OpenCV has supported NVIDIA CUDA and GPU acceleration since 2011, providing functionality for handling installed packages, site packages, and OpenCV binaries, along with explicit control over data movement between CPU and GPU memory through the OpenCV GPU module.
Who uses OpenCV?
OpenCV is used by big enterprises and government institutions, for example, Google, Toyota, IBM, Microsoft, SONY, Siemens, and Facebook. Also, well-known computer vision startups use OpenCV to build powerful computer vision products and AI solutions – including viso.ai. Many research centers use OpenCV, such as Stanford, MIT, INRIA, Cambridge, and CMU.
The use cases of computer vision are vast. While most are aware of rather popular use cases in security and video surveillance or self-driving cars, fewer people get to see the use cases in specific industries such as industrial manufacturing, restaurants, or retail analytics.
The rapid advances in computer vision over the past years have made it possible for companies across industries to develop purpose-built computer vision applications that solve highly specific problems (detect product issues, count objects, analyze behavior, etc.).
Can I use OpenCV for commercial projects?
Yes. The open-source license for OpenCV was chosen so that developers can build commercial products using all or part of OpenCV. Therefore, companies are under no obligation to open-source their product or to return improvements to the public domain (copyleft), though many do and contribute to the large community.
OpenCV versions
Find the latest version of OpenCV in the official GitHub repository. As of the end of 2023, the most recent OpenCV release was its version 4.8.0, which provided significant improvements over previous versions. Key advances include Vulkan backend refactor for better performance (4x faster performance), enhanced OpenVINO support, multimedia enhancements (AVIF support, HEVC/H265 support), and Python/JavaScript bindings improvements.
Earlier versions of OpenCV included the 4.5.4 OpenCV release that provided improved support for more AI models and DNN module patches, version 4.5.3 introduced optimized CUDA and MatMul support, and 4.5.0 was the first of the OpenCV versions licensed under Apache 2.
Who developed OpenCV – and Why?
OpenCV was initially created as part of an Intel Research initiative to advance CPU-intensive applications. One of the authors working for Intel noticed that some top university groups, such as the MIT Media Lab, used well-developed and internally open computer vision infrastructures to accelerate the development work. The code was shared between students and provided a significant head start in developing custom vision applications without reinventing the basic functions from scratch.
Accelerating commercial computer vision development by providing a common infrastructure to build on has always been the primary goal of the OpenCV developer teams at Intel. Advancing vision-based commercial applications by making portable, performance-optimized code available for free would increase the need for fast processors – the core business of Intel.
Driving upgrades to faster processors would generate more income for Intel than selling some extra software. This is probably why the free and open code originated from a hardware vendor rather than a software company.
The open-source library receives many user contributions, and central development has largely moved outside Intel. It was later supported by Willow Garage and the computer vision startup Itseez which Intel acquired in 2016.
Downloading and Installing OpenCV
Since 2012, support for OpenCV was taken over by a non-profit foundation named OpenCV.org, which maintains a developer site and a user website. You can get the official releases, including the Python version, contrib modules, and built binaries, from SourceForge or take the latest sources from the opencv GitHub.
How to install OpenCV on Windows: To install OpenCV, download the executable installation and run it. It will install OpenCV and register DirectShow filters. Once you finally have OpenCV installed, you’re ready to get started.
OpenCV Demo for Testing
Check out this link to a quick demo OpenCV app where you can test selected image processing and image conversion functions and test OpenCV with your images without installing the libraries.
Find the free app with an exemplary OpenCV software demo here.
No-code and low-code OpenCV
Recently, no-code and low-code development has become a new way for enterprises and organizations to deliver and maintain solutions much faster and more efficiently. Computer vision development is usually very complex and requires numerous iteration cycles. Hence, the delivery of computer vision greatly benefits from visual development and an automated deployment infrastructure of no-code technology.
The computer vision platform Viso Suite provides the capabilities of OpenCV as modular building blocks that can be used to rapidly build computer vision applications without writing code from scratch. This allows teams to use OpenCV faster and facilitates integration with different hardware such as cameras, edge computers, and machine learning models.
No-code helps to bridge the gap between seasoned computer vision engineers and business teams and makes it possible to adjust solutions to changing business requirements and advancing technology. The ability to use OpenCV without coding leverages the full economic potential of computer vision and lowers the risk and costs of computer vision.
Hence, we have built Viso Suite to provide an end-to-end solution for teams to build, deploy, and operate all their computer vision applications in one place – using the best software for Computer Vision, including OpenCV.
Implementing Computer Vision
What is Computer Vision?
Computer vision is a primary field of Artificial Intelligence technology that allows computers to extract information from digital images and videos to take specific actions. While there are different definitions of Artificial Intelligence (AI), all concepts generally focus on replicating human intelligence with machines.
How does computer vision work?
The technologies of machine vision aim to imitate human vision; it involves the interpretation of digital images or videos for understanding and recognizing objects and scenes in them.
This is achieved through a combination of software and hardware that mimics the workings of the human visual system. Some common methods used in machine vision are pattern recognition, feature extraction, and image processing.
AI vision, or Computer Vision, involves a set of image-processing techniques to make computers see and understand visual information. Computers act as “the brains” that perform advanced computing tasks and apply sophisticated algorithms to analyze images or videos provided by optical sensors or cameras (the “eyes”). Virtually any image of any camera can be used to apply AI vision algorithms.
Computer Vision In Production
The ability to make computers see with AI and perceive the physical world using visual sensors is becoming an integral technology to digitize and automate operations effectively. In recent years, machine learning technologies – especially deep learning, have shown great success in computer vision applications across industries.
Most applications leverage Artificial Intelligence with IoT (AIoT), Cloud Computing, and Edge AI to deliver and deploy computer vision anywhere and at scale. Leveraging AI vision tech, computers can perform face recognition, read handwriting, recognize objects, classify human movement, perform automated inspection, or detect critical situations automatically with image recognition.
Develop Computer Vision with OpenCV
Computer Vision is non-trivial, and achieving good performance at a reasonable cost is the basis of scalable computer vision. When working with cameras, the image data is frequently corrupted by noise and distortions stemming from variations in the physical world (lighting, reflections, movement, imperfections of the lens (distortion, field of view), sensor, or mechanical setup (angle, position, height).
To overcome these daunting challenges, developers need to build sophisticated computer vision pipelines that model the path of the data flows. This application logic combines different tasks, from acquiring the frames to preprocessing them (denoising, filtering, dewarping, etc.) and fleeting them into one or multiple vision algorithms. OpenCV provides a standard toolset for developers to solve computer vision problems.
In some cases, high-level functionalities in the library will be sufficient to solve the more complex problems in AI vision. However, writing conventional code can quickly become complex, and hard to understand and maintain or update as business requirements or regulations change.
To accelerate the development, the Viso Suite platform uses no-code technology to leverage the capabilities of OpenCV. This allows developers to build their computer vision pipelines visually with modular building blocks. The no-code editor and automated AI model management facilitate collaboration and make building and maintaining computer vision pipelines much faster. Using OpenCV with no code benefits both vision experts and newly trained developers with basic knowledge.
Vision Capabilities of OpenCV
OpenCV is probably the most versatile computer vision tool used in a broad field of computer vision tasks ranging from image recognition and 2D or 3D analysis to motion tracking, facial recognition, and more.
In the following, we will highlight the most prominent capabilities:
1. Real-time object detection
Object detection technology is used to apply image recognition and locate specific objects in video data or images, such as cars, humans, animals, and specific parts or equipment in industrial manufacturing.
2. Image segmentation
Image segmentation applies image processing algorithms to divide an image into different segments. Segmentation is usually applied to simplify, change, or enhance the image, often combined with subsequent computer vision tasks. An example is autonomous driving, where image segmentation is used to determine the road.
3. Movement and gesture recognition
Human pose and gesture recognition are used to interpret and understand the gestures of human beings through video analysis. Body, hand, or facial movements can be recognized and categorized to assign a pre-defined category. Movement analysis is often part of pose estimation to analyze the body movements with reference key points (joints, limbs). Calculating the object pose provides a method to understand how the object is situated in a 3D space, for example, how it is rotated.
4. Face Recognition
Automatic face recognition is used to identify humans by detecting a human face and matching it with a database based on detected facial features. The FaceRecognizer of OpenCV provides a set of popular face recognition algorithms to use in real applications.
5. Augmented Reality
Augmented reality (AR) allows real-time interaction between the real world and the virtual world. Therefore, augmented reality aims to augment the physical world around us with computer-generated perceptual information.
Advanced Computer Vision OpenCV Techniques
OpenCV also offers more sophisticated techniques extending beyond the basic functionalities.
Key Feature Detection and Description
- SIFT (Scale-Invariant Feature Transform): SIFT is an OpenCV algorithm for detecting and describing key features in images. Known for being effective in object recognition and image stitching, SIFT’s scale-invariant nature makes it resilient to variations in object size and orientation.
- SURF (Speeded-Up Robust Features): SURF is another algorithm for keypoint detection and image feature description. Building on SIFT, SURF offers increased computational speed, useful for real-time applications.
Object Detection Techniques
OpenCV provides pre-trained models for object detection. These pre-built models simplify model implementation, which allows users to get started without the need for extensive manual configuration.
- Haar Cascades: Haar Cascades, implemented through
cv2.CascadeClassifier(), are useful in detecting specific patterns like faces and eyes. - Deep Learning-Based Methods: OpenCV provides integrations with popular deep learning models, such as Single Shot Multibox Detector (SSD) and You Only Look Once (YOLO).
Image Segmentation and Motion Analysis
- Segmentation Techniques: You can dive deeper into image segmentation using techniques like GrabCut and MeanShift. These split images based on color, texture, and intensity.
- Optical Flow: Lucas-Kanade and Farneback algorithms can track motion between consecutive frames in a video by calculating the displacement of pixels over time.
Image Morphology and Enhancement
- Morphological Operations: Morphological operations like erosion, dilation, opening, and closing can be used for shape analysis and noise removal. Erosion shrinks objects and removes small details, while dilation expands objects and fills in gaps. Opening, a combination of erosion followed by dilation, helps in noise reduction and breaking narrow connections. Closing, done through dilation followed by erosion, helps in closing small holes and gaps.
- Image Filtering: Gaussian, Sobel, Laplacian, and others can be applied for image enhancement and feature extraction. Gaussian filters smooth images by reducing noise and blurring. Sobel filters highlight edges by computing the gradient. Laplacian filters detect regions of rapid intensity change, emphasizing fine details.
Camera Calibration and Stereo Vision
- Camera Calibration: Camera calibration estimates intrinsic and extrinsic camera parameters to fix distortions and improve the accuracy of image measurements. The
cv2.calibrateCamera()function enables the calibration by capturing images of a calibration pattern from different viewpoints. The parameters, such as focal length and distortion coefficients, allow for precise 3D reconstruction and accurate perspective correction. This is useful in applications like augmented reality. - Stereo Vision Techniques: Stereo vision techniques, such as StereoBM and StereoSGBM, focus on depth estimation from images captured by a stereo camera setup. StereoBM, created with
cv2.StereoBM_create(), uses block matching to determine disparities and then calculate depth information. StereoSGBM, implemented withcv2.StereoSGBM_create(), uses semi-global matching for more robust results.
Background Subtraction and Image Stitching
- Background Subtraction: Background subtraction algorithms, like MOG2 and KNN, isolate moving objects in video streams. These methods use a contrast-based approach, differentiating foreground and background elements. Evaluation metrics often include precision, recall, and F1 score to assess the accuracy of object detection and delineation.
- Image Stitching: Image stitching involves combining multiple images to create a panoramic view. OpenCV provides functions for feature matching, homography estimation, and perspective transformation to align and blend images. Evaluation can include visual inspection for seam quality and geometric accuracy assessment, ensuring that the results are cohesive and distortion-free.
Applications built with OpenCV
Most people are aware of computer vision being widely popular in security and surveillance. Some people are aware of niche applications in safety monitoring, unmanned flying vehicles (drones), or biomedical analysis. But few are aware of how pervasive machine vision has become in industrial manufacturing – virtually everything that is mass-produced has been automatically inspected at some point using computer vision.
Use cases built with OpenCV
Since OpenCV is a development kit, there are countless use cases you can build with OpenCV, including:
- Recognition of objects for counting and object tracking
- Analyzing medical images to support human diagnosis (Medical Imaging)
- Recognition of advertisements in TV footage or logo recognition with AI vision
- Player tracking in sports and fitness, scene recognition, and execution quality analysis
- Counting the number of people in public places such as airports (Crowd analysis)
- Robotic automation for intelligent, vision-based interfaces (Intelligent Screens)
- Automatic inspection and video analysis with always-on computer vision (for example, at airports in aviation).
- Image search on digital platforms, in web-based applications
- Detecting defects or faults during manufacturing processes (Smart Factory)
- Counting the number of vehicles on a highway (Traffic Analysis)
- CCTV camera applications to detect physical violence, attacks, and traffic violations (Smart City)
Explore more in our complete list of computer vision use cases.
Computer Vision by Industry
In many industries, computer vision provides a competitive advantage, allowing the transformation of laborious and expensive processes with highly tailored, integrated, and high-performing computer vision applications.
If you are interested in exploring high-value computer vision applications, consider checking out the industry reports of viso.ai. We constantly scout the latest computer vision applications across industries, including Logistics, Healthcare, Industrial Manufacturing, Automotive, Retail, Utilities and Energy, Oil and Gas, Aviation, Sports, and Insurance.
If you need more information, feel free to reach out.
OpenCV Community and Resources
If you’re looking to learn more from the open-source community or take inspiration from others, OpenCV offers several valuable resources across various platforms. Satya Mallick, the CEO of OpenCV, has curated an extensive portfolio of resources available for those looking to learn more about not only OpenCV, but the world of computer vision at large.
- The official OpenCV forum
- Webinar series by OpenCV with influential leaders in computer vision
- OpenCV free official bootcamp
- OpenCV official podcast series
Where to Start With OpenCV?
Computer Vision is a rapidly growing field, partly due to both cheaper and more portable cameras, decreasing processing costs, and rapidly advancing vision algorithms based on deep learning. With its focus on real-time vision, OpenCV helps professionals and researchers efficiently implement projects from concept to production.
The no-code computer vision platform Viso Suite helps organizations to use OpenCV faster by automating manual coding, ready-made hardware integration, and fully managed infrastructure. Leverage the power of OpenCV, MediaPipe, image annotation, model training, and deployment in one place.
To learn more about Viso Suite, and if you are looking to evaluate the platform for your organization, get in touch with our team.
