The recent launch of mini-programs and apps related to garbage classification has once again brought image recognition into the spotlight. In China, image recognition technology ranks among the top in the world.
Prior to this, many technologies have been applied to image recognition, such as Alipay’s facial recognition payment, facial recognition entry to hotels, and Taobao’s image recognition for shopping. With the popularization of artificial intelligence, image recognition will become increasingly popular in the future.
Additionally, the salary of image recognition engineers makes many people envious, and those who want to switch careers for high pay often don’t know how to start!
Little Zhu believes that these all belong to the IT industry, and the best way to start is to learn a programming language suitable for image recognition.
With so many programming languages, which one is the most suitable for image recognition?
1. Python
Currently, Python is considered one of the most popular programming languages. Its simplicity and versatility are some of the reasons many programmers enjoy using it. Despite its simplicity, Python is a language capable of executing complex tasks. It can be used to create image processing and recognition functions.
The popularity of Python has made its resources abundant. It contains several libraries needed for various functions, including image recognition. One of the most powerful and effective libraries is the Scikit-Learn machine learning library. In addition to image recognition, these libraries can also be used for smarter functions like facial recognition and motion detection.
2. Matlab
Matlab is a standalone programming language that has its own framework and integrated development environment (IDE) with a more powerful workspace. This programming language provides a wide range of tools that you can use to easily handle more technical programming tasks. Image recognition and facial processing are some of the tasks that Matlab can handle.
Matlab offers a set of built-in tools for matrix calculations. It is important to note that image recognition and matrix calculations go hand in hand. Some tools available in Matlab can perform complex image processing tasks such as cropping, rotation, masking, etc.
There is also a dedicated Matlab code for calling and implementing facial recognition functionality. This code uses the AT&T database. Therefore, you should download the AT&T database before using this code. The most advanced facial recognition applications are written in Matlab.
3. C / C++ / C#
When it comes to the C series of programming languages, you can never go wrong. They are powerful and can do anything, including creating image processing and recognition functions. The C series of programming languages provides two options for creating image processing functions. You can choose to code all from scratch, manually writing the code. The second option is to use existing libraries specifically designed for these programming languages. These libraries include OpenGL, EmguCV, OpenCV, etc. They have intelligent image processing capabilities for image recognition.
4. Java
Like C and C++, never underestimate the power of the Java programming language. This language is capable of executing complex functions. It can be used to create applications for image processing and image recognition.
5. OpenCV Tool
Open Source Computer Vision (OpenCV) is an advanced tool for real-time processing. It is also a cross-platform tool, so it can be integrated into any programming language to perform image processing and recognition functions. It can be integrated with C, C++, Java, Python, and Android programming languages.
OpenCV provides patent-free algorithms that you can use without any legal restrictions. It can be used for commercial and academic purposes. It has a dedicated face recognition class that you can use to experiment with the capabilities of image recognition without any hassle. This class comes with informative documentation that will show you how to implement image recognition functions. Overall, OpenCV is the preferred tool for image recognition, regardless of the programming language used.
You can also use several other programming languages to develop image recognition functions. Before starting with any language, please learn how to handle matrices, as it is the building block of image recognition programming.
