Open Source Computer Vision Library or OpenCV is an extraordinary library of multimedia resources. What are its uses, and how can they be exploited?
Computer Vision: Definition
First of all, it is essential to understand what computer vision is. The computer vision allows us to capture images and videos. Included in this process are storage, retrieval and processing procedures of all kinds. Artificial intelligence (AI) has its roots in computer vision. These operations are fundamental to robotics, automatic driving and photographic image processing.
OpenCV: What is it?
OpenCV is a prerequisite for computer vision, machine learning and image manipulation. In fact, it is a major open source library of images and videos. In English, OpenCV stands for Open Source Computer Vision Library.
It makes real-time operations accessible. By exploiting images and videos, we are able to determine the identity of recognized facesof objects to manuscripts.
Combining it with other libraries extends the range of possible functions. Integrated with NumPy from Python, which exploits the structure of the OpenCV table to facilitate analysis. The tools used to identify images, their patterns and characteristics are vector space and mathematical calculations.
Intel Research took the initiative in 1999 to launch OpenCV. At the time, the company wanted to develop CPU-intensive applications. At the conceptual head of this application were many optimization experts from Intel Russia. In addition, the members responsible for the performance library were also part of the design team.
In 2012, OpenCV changed hands and became OpenCV.org when a non-profit foundation decided to buy it. This foundation was already working on a development site.
In 2016, Intel signed a contract to acquire Itseez, one of its core developers.
In 2020, things have moved on, with OpenCV launching Kickstarter for OpenCV AI. This will be a range of hardware added to the library to exploit spatial artificial intelligence.
OpenCV 1.0: what are the features of this version?
There was a first version called OpenCV 1.0published under BSD license. This version was free of charge and was used mainly for commercial and educational purposes. It ran on Windows, Linux, MAC PS, iOS and Android systems. C++, C, Python and Java were available as interfaces. As computer languages used, C and C++ were favored to take advantage of multicore.
Applications that benefit from OpenCV
The OpenCV applications applications have paved the way for many other applications. These include: facial recognition, automatic surveillance systems, census of people in a given place, vehicles and their speed on freeways, artistic platforms.
Thanks to OpenCV, we can easily detect certain defects in the manufacturing cycle of certain products. OpenCV streamlines image processing Street ViewIt can also be used to search and retrieve multimedia files, and to analyze medical images. In 3D movies, it can define structure by studying movement.
What are OpenCV’s features?
OpenCV’s features can be summarized as image processing, object and feature detection, machine learning and CUDA acceleration.
OpenCV is open source by name. It is renowned for the speed of its processing and prototyping. Its ease of integration and coding are among its appreciated features.
Image processing refers to all possible operations on a given image. The aim is to improve quality or extract hidden information. To take this a step further, an image can be represented in an orthonormal dimension. It could respond to the function f(x, y). “x” and “y” at that moment would be its coordinates. The gray level corresponds to the fat amplitude of the coordinate pairs (x, y). This function determines the image pixel value, which in turn determines its brightness and color.
This processing encompasses three precise steps: import, analysis and/or manipulation, and finally output.. The output can be either an output image or a report resulting from image analysis.
OpenCV also provides a number of graphical interface functions. The application can also be used to insert text into an image, and to use slide cursors and mouse controls.
All in all, OpenCV handles almost all the classic image operations. These include reading, displaying, thresholding, smoothing and filtering, segmentation and mathematical morphology of an image.
How can a machine recognize or read a certain image?
The computer does not respond consistently to a spontaneous request such as “Is that me in the picture?”. However, it performs an internal mathematical calculation, reading the given image as a range of values between 0 and 255. And these analyzed values enable the recognition of a certain face. In the case of colors, the analysis is carried out via three primary channels: red, blue and green.
What alternatives are there to OpenCV?
If you can’t make it your own, there are dozens of alternatives. For example, Microsoft Computer Vision API, Azure Visage API, Cloud Vision API, Amazon Recognition, G2 offerings, scikit-image, SimpleCV, Clarifi’s, DeePy, IBM Watson.