An Overview of OpenCV
Introduction to OpenCV
You know we are all fools, right? We’re saying that in relation to software development because we haven’t yet created software that could save numerous lives when there are millions of software developers around the world. Have you ever thought about how the world could become a better place and you could take part in doing that by creating software that could be a lifesaver?
Here’s one tool that you can use to create a life changing product! OpenCV – an open source computer vision and machine learning software library whose main goal is to provide a common infrastructure for computer vision applications, and to speed up the use of machine recognition of images and objects in the commercial market.
Our overview of OpenCV article discusses why this tool is important in the field of computer vision. But first, we must learn about the Python programming language and its important connection to OpenCV.
What is Python?
Python is a fast, reliable, interpreted, imperative, functional, procedural, object-oriented, and high-level programming language with dynamic semantics. Python has existed since the 1980s and was created by Guido Van Rossum. It’s a programming language that you can use for desktop GUI applications, web applications, search engines, machine learning, business applications, facial recognition, and much more! You can do all sorts of stuff with Python.
One great thing about
You can learn Python through their website (python.org) and there are also tons of tutorials and resources on the Internet, but let’s not dig too deep into that. Let’s talk about OpenCV!
Overview of OpenCV
OpenCV is a cross-platform library wherein we can develop real-time computer vision applications. OpenCV has features for Image Processing, Pattern Recognition, and Photogrammetry fields. The Image Processing feature allows you to manipulate images through things such as resizing, cropping, changing the color grade, and other types of image manipulation. Pattern Recognition is a technique wherein OpenCV can detect objects in the image. Photogrammetry is about obtaining the accurate measurements of an image.
OpenCV was originally developed in C++, then sometime after they also developed OpenCV for Python and Java. OpenCV runs in various platforms such as Windows, OSX, and Linux.
What are Computer Vision Applications?
These are applications that enable computers to recognize features like faces, cars, objects, and many more. Computer vision deals with how computers can be made to gain information through digital images or videos. From the engineer’s point of view, they seek to automate tasks when human vision is involved, and also they wish to make a product that can exceed the capabilities of the human eye.
Where is OpenCV being applied?
OpenCV is being used in robotics, medicine, industrial automation, security, and transportation.
For robotics, OpenCV can be used to determine a robot’s location. It can also be used in navigation, Obstacle avoidance,
What are the features of OpenCV and what can it do?
OpenCV can read and write images from scratch. Basically you can draw an image through code, capture and save videos (write a command in which you can have access to your webcam or any sort of camera where it can capture frames of images and turn them into a video), process images (filters, adjust contrast, colorations, sharpness, etc), perform feature detection, detect specific objects and analyze videos, and determine the direction and the motion of an object.
Computer vision can also track motion and store various information regarding the movement of a specific item. Computer vision can compare images and can tell which ones are the same as another picture. It can also measure the distance from one point to another. It can even identify what kind of object is in the video or picture depending on the given datasets. It also can detect corners of an object; therefore, we can add a box indication following the object where it moves.
The OpenCV community
One great thing about OpenCV is they have a large community wherein you can ask questions that are OpenCV related and they will be happy to help you. But they have rules and etiquette to posting a question.
They would not like you to ask inappropriate questions like general debugging/programming questions, questions about software that is not related to
Sending questions about problems to OpenCV
Send all your questions to OpenCV Answers or other corresponding mailing lists, and report all bugs to the bug tracker. You also have to specify your platform (Windows 32/64-bit, Linux x86/x64/PPC/…, MacOSX 32/64bit/PPC), compiler version, OpenCV version/revision, whether IPP, OpenMP, MMX, SSE … have been enabled or not, and any other information that lets them reproduce the environment, identify and localize the problem. A sample code reproducing the problem would also be very helpful. Provide the output of cv::getBuildInformation() function every time you report a bug about the trunk version of OpenCV. The problem can be in the build settings so the log can really help us.
If your application is complex, and the problem happens somewhere in the middle, it is still possible to create a short standalone sample: capture and store the data that you pass to the function using FileStorage, copy the function call, and put the corresponding reading from FileStorage in front of it. Describe specifically what you are doing or are trying to do and what went wrong. If you say: “OpenCV doesn’t work!” – they will not be able to help you. You can also include your backtraces, relevant config files, and screenshots or videos to demonstrate the problem.
Let’s talk about Computer Vision
Aside from an overview of OpenCV, let’s talk about computer vision, which is the technological basis of OpenCV. Computer vision is similar to what our eyes are doing. Our eyes analyse what we see; we can recognize faces, objects, movements and determine if something is good or bad. It’s the same with computer vision, and sometimes even more precise. It can also look at complex images and compare them to other images, and define the differences.
Computer vision aims to make our lives easier because it can be applied in the areas of agriculture, augmented reality, autonomous vehicles, biometrics, character recognition, forensics, industrial quality inspection, face recognition, gesture analysis, geoscience, image restoration, medical image analysis, pollution monitoring, process control, remote sensing, robotics, security and surveillance, transport, and many more to come! Computer vision deals with how computers can be made to gain information through digital images or videos. From the engineer’s point of view, they seek to automate tasks involving visualization.
One example of a computer vision application is Amazon Go. It is a store and a mobile app in which consumers can go inside and get everything that they find useful for them and go out with the item that they wish to purchase. As soon as they reach the exit, there will be a prompt that appears on their smartphones with a confirmation of what they took, perhaps similar to a checkout feature in some online stores. The technology doesn’t use QR codes nor barcodes but instead, they use hundreds of cameras placed on the ceiling of a store. The cameras act as the eyes for the store. That is just one of the hundreds of uses for computer vision.
Importance of Computer Vision
Computer vision has been helpful to a lot of businesses and vital to some. Companies use computer vision for OCR, vision biometrics, object recognition, special effects, 3D printing and image capture, sports, smart cars, and medical imaging. These are just some of the uses of computer vision. Computer vision has been very helpful for the growth of technology in our modern day society. It helped a lot of people in ways that could save lives like in operations, tumor searching, x-ray analyzations and many more. This just shows that computer vision truly has evolved from theory to reality. After learning the overview of OpenCV and computer vision, we’ll discuss about the skills of a computer vision developer.
Skill set and experiences of a Computer Vision Developer
A CV Developer needs to have a cross-functional skill set, creativity, and skills in machine learning, robotics and control systems to solve exciting product development challenges. They must be good in math, linear algebra, stats which are needed very extensively, advanced image processing, and pattern recognition skills. Other needed skills are a good geometry base for CV applications, efficient programming skills if you are doing system level C/C++ or for testing purposes, Matlab imaging toolbox, machine learning fundamentals for complex problems, and fluency in C++ or Python, OpenCV, and PCL.
You must also have experience in 3D computer vision and video analytics algorithms such as image segmentation, 3D reconstruction, object and motion detection, tracking and classification. Experience in machine learning algorithms for vision problems including deep learning and experience in real-time video analytics pipeline is also preferable.
Tasks of a Computer Vision Developer:
- Develop and analyze vision algorithms to control robots and advanced hardware systems that were created for human interaction environments
- Develop and assess state-of-the-art computer vision algorithms for real-time control of robots and other hardware devices
- Port, carry
out,and optimize analytics and machine learning algorithms using special purpose computing architectures
- Recommend and carry out creative and efficient solutions for vision and control problems, and contribute to expanding the company’s intellectual property portfolio
- Execute technical performance benchmarking and analyses to support engineering decisions
- Ascertain project specifications and project schedule by studying product requirements and specifications, calculating time requirements and sequencing project elements
- Aid in the installation and running of product demonstrations
- Debugging with no additional support
- Participate in design reviews
- Documentation for design control
Here are some of the companies that use Computer Vision with their respective applications/products:
ClmTec– Medical Image Analysis, digital pathology
- Ikomed – Medical Image Analysis
- Algolux – Imaging and vision system optimization
- Wrnch – Poste
- Adaptive Vision – Industrial image analysis applications
- Plastream – SAAS AI analytics video platform
WebSight – Machine Vision solutions on demand
- Pleiades techs – Contract company
- DxO Labs – Image enhancement technologies
- GeoSys – Agriculture performance
- Exelis VIS – Visual information solutions
- Vidatis – Video processing
- Moodstocks – Mobile image recognition
- Diotasoft – 3D virtual reality
Pimaia– Picture & Illustration Modeling Automatism
- Total Immersion – Augmented Reality Solutions for retail
- VideoStitch – 360 video made easy
- SentioSports – Sport Analytics
- Gentex Corporation – Smart driving systems
- A9 – Visual search solutions
- COGNEX – Industrial applications
- HP Labs – Machine learning
- Philips Healthcare – Medical imagery
- Interphase – Desktop virtualization solutions
- NOKIA – 3D mapping technology
- KLA-Tencor – Metrology, optics, and inspection platforms
- Immersive Labs – Computer vision based advertising technology
- Intuitive Surgical – Robot-assisted surgery
- IBG – Biometric systems
- Imimtek – User interfaces
- PPT Vision – Automated inspection systems
- HHMI – Medical imagery
- Occipital – Natural user interfaces
- Northrop Grumman – Security and
- Justin.tv – Live video platform
- Incogna – Image search engine
- Video Surf – Video search engine
tyzx– 3D sensors
- Object Video – Video processing algorithms
- Honeywell – Video Analytics
- SkyBox – Satellite Imagery
- Park Assist – Parking
- AiLive – Motion recognition and tracking
- Hover – 3D Mapping
- Aptina – CMOS Technology
- OptraScan – Digital Pathology Solutions
- Honda Research – Scene Understanding and Risk Assessment
- Image Metrics – Facial analysis
- Pelican Imaging – 3D photography
- Graftek Imaging – Vision System & Parts
- NVIDIA – Visual Computing & Computer Graphics
- Microscan – Computer Vision Systems
- DAQRI – 4D Augmented Reality
- Kairos – Facial Recognition as a Service
- LTU Technologies – Image Recognition Platform
- Code Laboratories – Real-time imaging Solutions
- FotoNation – Real-time imaging Solutions
Imerit– Machine learning, Segmentation
- Sighthound – SDK and cloud APIs for businesses
- Second Spectrum – Real Time sports analytics
- Curalate – Visual Commerce Software
- Pixma – Computer Vision based phishing protection
- GumGum – An applied computer vision company
- Aitoe Labs – Image-based Artificial Intelligence
- GE Global Research – Medical image analysis
- Yahoo! – Machine learning, data mining, and information retrieval
- Optra Systems – Algorithm development
- Vizzitec – Visual Inspection Solutions
- Pixuate – Image Processing as a Service
- ARLab – Augmented reality for desktops and smartphones
- EllipseAR – Mobile real-time tracking
VISIZA– Object recognition and people recognition
- Samsamia – Machine learning apps for the fashion market
- LetGo – Marketplace for
- Itseez – Computer vision software and consulting services
- ISS Art – Custom Computer for vision software and solutions
- Carl Zeiss – Medical industry, optics
- Atlas Elektronik – Naval electronics
- Nerian Vision GmbH – 3D camera systems
- Vathos Robotics – Image processing based robot reprogramming
Tordivel – Industrial machine vision, 3D modeling
Abto Software – Computer Vision & AI solutions
- Spacemetric – Geospatial imagery management
- Smarteye – Eye tracking technology
- Axis Communications – IP video solutions
- Cella Vision – Body fluids analysis
- Scalado – Mobile imagery solutions
- Anoto – Digital pen technology
- Tobii – Gaze tracking technology
- Oculus – Image recognition technology
- Flir – Thermal imaging
- Vicator – Augmented reality design
- Image Metrics – 3D facial animation
- Nine Point Medical – Optical systems for in vivo diagnostics
- Sony – Gaming interface
- Skin Analytics – Skin cancer prevention
- Birdi – Satellite imagery API
Christian Michelsen Research – Visualization and data analysis
Kade Vision – Video analytics
- ThirdSight – Emotion and Face recognition
- Philips – Medical imagery
- Virtual Proteins – 3D medical imagery
- Layar – Mobile augmented reality
- Ps-tech – Optical tracking device
- TNO – All kinds of Image Processing
- SightCorp – Face Analysis Technologies
- Euvision Technologies – Automatic Image Moderation and Classification
- Eagle Vision – Industrial Computer Vision
Traficon – Traffic video analytics
- Ayonix – Facial recognition
- Morpho – Computational photography, deep learning
Crisalix – Aesthetic and plastic surgery
- PicScout – Track, license and monetize imagery
- RSIP Vision – Image Processing Services
Odd Concepts – Image recognition platform for commerce
- Visenze – Image Recognition for Advertisers
- Graymatics – Cognitive Media Processing
BitRefine Group – Machine Learning and Computer Vision solutions
- Face++ – Facial and Body recognition
- SenseTime – Facial recognition
- Thundersoft – Smart car solutions
- TuSimple – Autonomous driving
Listen to Episode 108 of the Startup Hustle Podcast – Computer Vision
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