Utilizing OpenCV for Computer Vision

November 16, 2022 | Comments(1) |

TABLE OF CONTENT

1. Introduction
2. OpenCV Python VS OpenCV C++
3. OpenCV Vs MATLAB for Computer Vision
4. Capabilities of OpenCV vision
5. Conclusion
6. About CloudThat
7. FAQs

 

Introduction

In the fields of artificial intelligence and machine learning, computer vision has become extremely popular. Computers can interpret more data than the unaided eye, often quicker and more precisely, or just when people are not actively looking, thanks to the ability to recognize images.

Computer Vision

Computers are trained in this area of artificial intelligence and computer science to comprehend the visual environment. It is a crucial element of machine learning. It focuses on depictions of the natural world (high dimensional data). It performs a variety of activities, including picture understanding and conversion into numerical or symbolic information, as well as acquisition, analysis, and processing.

Object detection, medical diagnosis, and other applications of computer vision are some examples. Users can read, display, and store images in local storage using OpenCV. OpenCV also allows users to read, display, and save images into local storage.

OpenCV

A Python library called OpenCV was created to help with computer vision issues. Numerous programming languages, including C++, Python, Java, etc., are supported by OpenCV. support for several operating systems, such as Windows, macOS, and Linux.

Python represents images as NumPy arrays, which are essentially arrays of pixels. Every time the user scans an image using OpenCV, it will always be a color image with numerous values per pixel, namely for the red, green, and blue channels.

OpenCV Python VS OpenCV C++

Python

  • It cuts down on development time and makes debugging simpler.
  • The management of memory is controlled by Python.
  • As photos are also an n-dimensional array, Python NumPy & SciPy modules can be utilized for array manipulation with great optimization.
  • Cython is a Python extension for C. Depending on your application, it can increase speed by up to 100x or even more when compared to native Python code.

C++

  • Includes a longer duration.
  • Some of OpenCV’s more advanced capabilities might only be accessible in C++ and haven’t yet been converted to Python.

OpenCV Vs MATLAB for Computer Vision

The most truly open computer vision library, OpenCV, has a large user base. OpenCV is substantially faster in execution.

However, MATLAB is more practical for developing and presenting data. MATLAB because its code is simple to debug and handy for quick prototyping. Additionally, it provides excellent documentation and support. The drawbacks of MATLAB include its closed-source nature, expensive licensing, and lack of portability of its applications.

Why does computer vision use OpenCV?

It is open source, simple to use, and uses little RAM. OpenCV is written in C/C++ which is very fast.

Capabilities of OpenCV vision 

The most adaptable computer vision tool is certainly OpenCV, which is used for a variety of computer vision applications, including image identification, 2D or 3D analysis, motion detection, facial recognition, and more. We shall highlight the most important capabilities below:

  • Face Recognition
  • Real-time object detection
  • Image segmentation

Conclusion 

Computers are taught to process images and extract key details from photos or videos in the subfield of artificial intelligence known as computer vision. A C++ written Python library called Open Computer Vision (OpenCV) offers a variety of functions for computer vision applications.

About CloudThat

CloudThat is also the official AWS (Amazon Web Services) Advanced Consulting Partner and Training partner and Microsoft gold partner, helping people develop knowledge of the cloud and help their businesses aim for higher goals using best-in-industry cloud computing practices and expertise. We are on a mission to build a robust cloud computing ecosystem by disseminating knowledge on technological intricacies within the cloud space. Our blogs, webinars, case studies, and white papers enable all the stakeholders in the cloud computing sphere.

Drop a query if you have any questions regarding OpenCV and I will get back to you quickly.

To get started, go through our Consultancy page and Managed Services Package that is CloudThat’s offerings.

FAQs

  1. What are the AWS pre-trained services for Computer vision?

A. Amazon Rekognition is a Computer Vision service that is fully managed by AWS and uses highly scalable deep learning technology to analyze images and video. With Amazon Rekognition, various business challenges can be solved without requiring you to have an expert in machine learning.

To know more on Rekognition here.

  1. What do OpenCV’s erosion and dilation mean?

A. The two most popular morphological image processing procedures in OpenCV, which is a method for altering the geometric structure in the image, are erosion and dilation.

  1. What is Haar Cascade?

A. Despite their size and location in the image, objects can be found in photographs using the Haar cascade technique. This algorithm can function in real-time. A haar-cascade detector can be trained to recognize many different items, including faces, bikes, structures, fruits, etc.

  1. How to access the OpenCV webcam on Google Colab?

A. Google Colab is a cloud service you cannot access your local hardware like a webcam directly into it. But it is possible by using some helper functions written in Javascript given by Google Colab to access the webcam.

Pic1

Source – https://colab.research.google.com/

  1. What is a “digital image”?

A. A digital picture is a representation of a real item that may be captured as data and kept for later use.  Because they are composed of pixels, which are little dots that form an image when placed in a grid, digital images are frequently referred to as “pictures.” Digital images are also referred to as “computer graphics” for this reason.  Digital imaging is used to store and show images on the computer, such as pictures or images. Medical imaging, satellite imagery, and filmmaking are just a few of the various uses for digital imaging.


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