OpenCV Features that you can’t miss out in 2023

In this tutorial, we will talk about the features of the OpenCV library.

We will take an interesting look at how do we represent an image in a computer.

Further on, we will see why computer vision is a complex topic and what are the things one should keep in mind before starting projects.

There are several features for the OpenCV library, some of the general features are as follows:

• Open-source
• Fast speed
• Easy to integrate
• Ease of coding
• Fast prototyping

Let us go through each of them in detail.

OpenCV Features

Here are the essential features of the OpenCV library:

1. Open-source

The library is open-source which means that the source code is publicly available.

We can customize the code to meet the specific business requirements.

We can even write more code to add extra functionality.

It is available for free to use in commercial products.

2. Fast Speed

Since the OpenCV library is originally written in C/C++, it is fast and efficient.

Although Python is slower as compared to C++, Python is easily extended with C/C++ which allows us to write computationally intensive code in C++.

It is easy to create Python wrappers that we can use as Python modules.

So the execution of the program will be as fast as C++ since the original code in the background is in C++.

3. Easy to Integrate

The OpenCV Python library makes use of numpy, which is a highly optimized library to perform numerical operations on multidimensional arrays.

So all the OpenCV array-like structures are converted into numpy arrays and this makes it easier to integrate with other libraries that use numpy like SciPy and Matplotlib.

OpenCV contains implementations of more than 2500 algorithms. It also has interfaces for multiple languages like Python, java, etc that makes it easy to integrate.

4. Ease of Coding

Python has a rich set of libraries that provides a powerful environment for scientific calculations.

The libraries like SciPy, numpy, scikit-learn, matplotlib made complex tasks easier to do in Python.

5. Fast Prototyping

Building prototypes application is fast in Python.

We can integrate our application with web frameworks like Django which is a high-level web framework that encourages rapid development.

Challenges Faced in Computer Vision

Computer vision is a field of study where we get computers to understand digital images or a group of images.

It’s very easy for humans to recognize an object by looking at them but in computers, it is not an easy task.

Let us first understand how the computer interprets an image.

To represent an image in a computer, we use a grid structure in which each pixel in the grid contains the color of that particular pixel.

Each color is represented by 3 values (c1,c2,c3) which are basically red, green and blue and their values range from 0-255 depending on the intensity of each color.

For example – the red color can be represented as (255,0,0), green color is represented as (0,255,0). Using different values for different colors, we can create any color we want.

So for an image of size 200×200 px we will have 40,000 pixels and each pixel has 3 values. 40,000 * 3 = 1,20,000 numbers to represent an image of size 200×200 px.

Now imagine, we have a group of photos of different people and we have to classify which photo is of which person.

The computer vision algorithms do this task by operating on the data on each image and look for patterns that help in classifying problems.

The visual system of humans has evolved over thousands of years.

The images that are projected on our retina are converted to neuron signals.

Computer vision understands how human vision works and then we apply this knowledge on computers.

It becomes challenging to build robust applications because the minute changes in any of the parameters of the image can change the resulting outcomes.

The way our machine perceives an image will differ according to lighting conditions, rotation of the image or even the perspective of the image.

This is why it is a complex task and needs careful attention to the small details while implementing computer vision solutions.

With many uses, computer vision is a quickly developing science, but it also has its share of difficulties. The following are some of the main difficulties in computer vision:

1. Complexity of Visual Data: Images and videos include a great deal of visual information, making it difficult to grasp and comprehend. The analysis can be made more difficult by varying illumination, angles, occlusions, and background clutter.

2. The identification and localization of objects within an image or video stream provide a substantial difficulty. Computer vision systems may encounter objects in a variety of shapes, positions, and sizes, making it challenging to reliably identify and detect them.

3. Semantic Segmentation: It is difficult to precisely divide a picture into meaningful parts. Accurate object and boundary differentiation needs sophisticated algorithms and large amounts of processing power.

4. Handling huge Datasets: Training deep learning algorithms frequently calls for huge annotated datasets. Such datasets can be time- and money-consuming to gather and annotate.

5. Computational Resources: To efficiently analyse enormous volumes of data, many computer vision techniques, particularly deep learning models, require strong hardware, such as GPUs.

Summary

So far, we understood the features of the OpenCV library.

In addition, we saw how does the computer represents an image and why computer vision is a challenging task.

So we need to be aware of certain things while implementing computer vision in our projects.