INTRODUCTION TO COMPUTER VISION AND BASIC FUNCTIONS OF OPENCV
Computer vision is a field of study which enables computers to replicate the human visual system. It’s a subset of artificial intelligence which collects information from digital images or videos and processes them to define the attributes.
The entire process involves image acquiring, screening, analysing, identifying and extracting information. This extensive processing helps computers to understand any visual content and act on it accordingly.
Computer vision projects translate digital visual content into explicit descriptions to gather multi-dimensional data. This data is then turned into computer-readable language to aid the decision-making process. The main objective of this branch of artificial intelligence is to teach machines to collect information from pixels.
Automatic cars aim at reducing the need for human intervention while driving, through various AI systems. Computer vision is part of such a system which focuses on imitating the logics behind human vision to help the machines take data-based decisions.
CV systems will scan live objects and categorise them, based on which the car will keep running or make a stop. If the car comes across an obstacle or a traffic light, it will analyse the image, create a 3D version of it, consider the features and decide on an action- all within a second.
Computer Vision primarily relies on pattern recognition techniques to self-train and understand visual data. The wide availability of data and the willingness of companies to share them has made it possible for deep learning experts to use this data to make the process more accurate and fast.
While machine learning algorithms were previously used for computer vision applications, now deep learning methods have evolved as a better solution for this domain. For instance, machine learning techniques require a humongous amount of data and active human monitoring in the initial phase monitoring to ensure that the results are as accurate as possible.
Deep learning on the other hand, relies on neural networks, and uses examples for problem solving. It self-learns by using labeled data to recognise common patterns in the examples.
Apart from automating a lot of functions, computer vision also ensures moderation and monitoring of online visual content. One of the main tasks involved in online content curation is indexing. Since the content available on the internet is mainly of two types, namely text, visual, and audio categorisation becomes easy.
Computer vision uses algorithms to read and index images. Popular search engines like Google and Youtube use computer vision to scan through images and videos to approve them for featuring. By way of doing so, they not only provide users with relevant content but also protect against online abuse and “toxicity”.
Computer vision is not a new concept; in fact, it dates back to the 1960s. It all started with an MIT project -“Summer Vision Project” which analysed scenes to identify objects. David Marr, the celebrated neuroscientist, laid down the building blocks of computer vision, taking a cue from the functions of the cerebellum, hippocampus, and cortex of human perception. He has been dubbed the father of computer vision since, and the field has evolved to include much more complicated functionalities.
Here are some examples of basic functions while writing an opencv code in Python:
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img = cv2.imread('click.jpeg')--> used to import the image from current directory. -
cv2.imshow('logo',img)--> used to view the image as an output. -
cv2.imwrite('click_2.jpeg',img)--> used to save the image to the output durectory.
Depending on the uses, computer vision has the following uses:
Laying the Foundation: Probability, statistics, linear algebra, calculus and basic statistical knowledge are prerequisites of getting into the domain. Similarly, knowledge of programming languages like Python and MATLAB will help you grasp the concepts better.
Digital Image Processing: Learn how to compress image and videos using JPEG and MPEG files. Knowledge of basic image processing tools like histogram equalisation, median filtering and more are required. Once you know the basics of image processing and restoration, you will be ready to pick up the more critical skills of computer vision.
Machine Learning Basics: Knowledge of Convoluted Neural Networks, fully connected neural networks, support vector machines, recurrent neural networks, generative adversarial network, and autoencoders are necessary to get started with computer vision.
Basic Computer Vision: The next step in the process is to decode the mathematical models involved in the image and video formulations. Once you understand how pattern recognition and signal processing works, you can get into advanced learning.
Computer vision engineers are in high demand in the market today, thanks to the enormous amount of visual content that needs to be worked upon.
- A computer vision engineer creates and uses vision algorithms to work on the pixels of any visual content (images, videos and more)
- They use a data-based approach to develop solutions.
- They usually come with a background in AIML and have experience working on a variety of systems, including segmentation, machine learning, and image processing.
- If you want to become a computer vision engineer, you need to pick up the basic skills of the domain and work on projects that will give you a hands-on experience of industry-relevant problem-solving. Great Learning’s Deep Learning certificate program introduces you to all the basics of the domain and sets you on the path of becoming a computer vision engineer.
We have several programming language choices for computer vision – OpenCV using C++, OpenCV using Python, or MATLAB. However, most engineers have a personal favourite, depending on the task they perform.
Beginners often pick OpenCV with Python for its flexibility. It’s a language most programmers are familiar with, and owing to its versatility is very popular among developers.
Computer vision experts recommend Python for the following reasons:
- Easy to Use: Python is easy to learn, especially for beginners. It is one of the first programming languages learnt by most users. This language is also easily adaptable for all kinds of programming needs.
- Most Used computing language: Python offers a complete learning environment for people who want to use it for various kinds of Computer Vision and Machine Learning experiments. Its numpy, scikit-learn, matplotlib and OpenCV provides an exhaustive resource for any computer vision applications.
- Debugging and Visualisation: Python has an in-built debugger, ‘PDB’ which makes debugging codes in this programming language more accessible. Similarly, Matplotlib is a convenient resource for visualisation.
- Web Backend Development: Frameworks like Django, Flask, and Web2py are excellent web page builders. Python is compatible with these frameworks and can be easily tweaked to fit your requirements.
- MATLAB is the other programming language popular with computer experts: Let’s look into the advantages of using MATLAB:
- Toolboxes: MATLAB has one the most exhaustive toolboxes; whether it is a statistical and machine learning toolbox, or an image processing toolbox, MATLAB has one included for all kinds of needs. The clean interfaces of each of these toolboxes enables you to implement a range of algorithms. MATLAB also has an optimisation toolbox which ensures that all algorithms perform at their best.
- Powerful Matrix Library: Images and other visual content contains multi-dimensional matrices along with linear algebra in different algorithms which becomes easier to work within MATLAB. The linear algebra routines included in MATLAB work fast and effective.
- Debugging and Visualisation: Since there is a single integrated platform for coding in MATLAB, writing, visualising and debugging codes become easy.
- Excellent Documentation: MATLAB enables you to document your work adequately so that it is accessible later. Documentation is essential not just for future reference but also to help coders work faster. MATLAB’s documentation allows users to work twice the speed of OpenCV.
- Medical Imaging: Computer vision helps in MRI reconstruction, automatic pathology, diagnosis, machine aided surgeries and more.
- AR/VR: Object occlusion (dense depth estimation), outside-in tracking, inside-out tracking for virtual and augmented reality.
- Smartphones: All the photo filters (including animation filters on social media), QR code scanners, panorama construction, Computational photography, face detectors, image detectors (Google Lens, Night Sight) that you use are computer vision applications.
- Internet: Image search, geolocalisation, image captioning, ariel imaging for maps, video categorisation and more.