OpenCV is an open-source computer vision and machine learning software library developed by Intel. It provides a common infrastructure for applications related to computer vision and its associated fields. It is used to speed up the use of real-time machine recognition of images, objects, and video processing applications.
To have an overview of how OpenCV works, let’s discuss these important topics: Computer Vision, OpenCV’s associated programming languages, and OpenCV applications — to have an in-depth understanding of this powerful library.
What is Computer Vision?
Our eyes send signals to our brain and it analyzes what we see; we can recognize faces, objects, and movements, and determine if something is good or bad in a given scenario. As such, computer vision (CV) wants to achieve what our eyes are doing.
It deals with computers and other electronic equipment to gain information through digital images or videos. It can also analyze complex images, execute comparisons, and establish the differences.
Computer vision is very helpful for the growth of technology in our modern-day society as it evolved from theory to reality. The importance of 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, medical imaging, and many others.
From an IT professional’s point of view, they seek to automate tasks involving visualization. Thus, it fueled huge developments resulting in a massive interest from both entrepreneurs and software development providers. These are people from all avenues in life with different skill levels who collaborate to smoothen the friction between productivity and commercialism. Also, they aim to find the balance of the complexity of computer vision and pitch it perfectly to the world.
Computer vision aims to make our lives easier because it can be applied in almost all areas. These applications create heroes in the world of computing — heroes such as clients and software developers.
The clients who want to solve low-level CV problems using forward-thinking and brave approaches. Plus, the fantastically talented developers cohesively develop CV systems over computers and other equipment.
Overview of OpenCV
There are major domains — image processing, video capture and analysis, face detection, and object detection — associated with computer vision, but it needs a cross-platform library to develop real-time applications. This is where OpenCV came in, which was originally developed in C++ and later followed by Java and Python. It runs on various platforms such as Windows, macOS, Android, iOS, and Linux.
OpenCV is a perfect tool for computer vision, but system development without thinking of its broadest audience is still a huge problem among entrepreneurs. Also, there are times that both parties — clients and developers — have a dilemma on what kind of success they want to achieve. Thus, both parties must have an overview of OpenCV in dealing with computer vision.
The next section will highlight the OpenCV library and its associated modules.
Features of OpenCV Library
The use of the OpenCV library will allow you to:
- Read and write images
- Capture and save videos
- Image processing such as filtering and transformation
- Feature detection
- Video or image object detection such as human body parts, cars, signage, etc.
- Video analysis
OpenCV Library Modules
OpenCV can read and write images from scratch, draw an image through code, capture and save videos, process images, perform feature detection, detect specific objects and analyze videos, and determine the direction and the motion of an object.
Here are the main library modules under the OpenCV library:
- Core Functionality
- The core functions of the OpenCV library cover the basic data structures such as Scalar, Point, Range, etc. To store images, it has the multidimensional array Mat.
- Image Processing
- This module covers various image processing operations such as image filtering, geometric image transformations, color space conversion, histograms, etc.
- This module covers the video analysis concepts such as motion estimation, background subtraction, and object tracking.
- Video I/O
- This module explains the video capturing and video codecs using the OpenCV library.
- This module includes algorithms regarding basic multiple-view geometry algorithms, single and stereo camera calibration, object pose estimation, stereo correspondence, and elements of 3D reconstruction.
- This module includes the concepts of feature detection and description.
- This module includes the detection of objects and instances of the predefined classes such as faces, eyes, traffic lights, people, cars, etc.
- This is an easy-to-use interface with simple UI capabilities.
OpenCV is 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, and Human-Robot Interaction.
For medicine, OpenCV can help patients through the classification and detection of cells or tumors, 2D/3D segmentation, 3D organ reconstruction, and vision-guided robotic surgeries.
As for industrial automation, it can be helpful in terms of determining defects of stock, barcode and packages, object sorting, document analysis, and many more.
For Security, this can be used in surveillance and biometrics, and lastly, for transportation, it can help us detect driver vigilance and develop autonomous vehicles.
Finding the passion and vision for image processing and computer vision applications allow entrepreneurs to empower their clients. With the demand for image-based search engines, both entrepreneurs and software developers will emerge victorious using tools such as OpenCV.
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Computer vision is not just an interesting field but a revenue-generating business. However, the realistic woes that most entrepreneurs faced are expenses and scarcity of resources. Despite the massive interest, there are still areas under computer vision that has limitations. All these issues will be addressed intelligently by our pool of project managers, engineers, and software developers.
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