Mastering MATLAB on Ubuntu - Exploring its Integration with Python and Computer Vision

In this in-depth article, we’ll provide a thorough guide to installing MATLAB on the Ubuntu operating system. This platform is widely used by professionals from various domains including Python Engine …

In this in-depth article, we’ll provide a thorough guide to installing MATLAB on the Ubuntu operating system. This platform is widely used by professionals from various domains including Python Engineers, Data Scientists, and Computer Vision experts who work with large amounts of data and visualization tools. We will also delve into how these powerful tools can be integrated with Python programming language for seamless collaboration in complex projects.

Introduction

MATLAB is a widely-used software application for technical computing developed by MathWorks. It is a high-performance, multithreaded, and extensively documented environment which is capable of handling numerical computation, visualization, data processing, and algorithm development. On the other hand, Ubuntu is an open-source Linux operating system with a strong focus on security, stability, and performance. Integrating MATLAB with Ubuntu opens up numerous possibilities for engineers and computer vision experts to collaborate more effectively in their day-to-day tasks.

In this comprehensive guide, we will explain the step-by-step process of installing MATLAB on your Ubuntu machine, including its use cases and integration with Python programming language. We’ll also demonstrate how to make use of code samples for demonstrating concepts related to both platforms.

Installing MATLAB on Ubuntu

Installing MATLAB on a Linux system like Ubuntu is quite straightforward, but it’s essential to ensure you have the latest updates before proceeding. Here are the steps to follow:

1. Update your Ubuntu system: Open Terminal and type ‘sudo apt-get update’. Hit Enter to execute the command. This will fetch new package information from repositories for updating the system.

2. Install required dependencies: To ensure that MATLAB can run smoothly on your Ubuntu system, install some essential dependencies by running this command: sudo apt-get install build-essential libxcb-render0-dev libgl1-mesa-dev libxv1-dev libxtst6-dev -y

3. Download the MATLAB installation file: Browse to the official MathWorks website (https://www.mathworks.com/products/matlab/buy.html) and locate the “Download Product” section for your required version of MATLAB. Click on “Get a 30-Day Free Trial.”

4. Choose Ubuntu as the target system: In the subsequent window, scroll down to locate the “Choose Your Platform” option. Under Linux, select “Ubuntu 18.04 LTS (64-bit), Ubuntu 20.04 LTS (64-bit)” or the latest available version.

5. Download the MATLAB installation file: Click on the “Download” button next to the chosen platform. This will initiate a download of the appropriate installR1917a_glnxa64.run file (where ‘1917a’ represents the version number). Once the download is complete, move this file to a more convenient location for easy access later.

6. Make the installation file executable: Change its permissions by running this command in Terminal: chmod +x installR1917a_glnxa64.run

7. Execute the MATLAB installation script: To run the installation process, use the following syntax: ./installR1917a_glnxa64.run This will launch a wizard-style installation interface that will guide you through the setup process. Make sure to accept all default settings unless there’s a specific reason for changing them.

8. Complete the installation and reboot: Upon successful completion of the installation, follow the on-screen prompts to finish setting up your MATLAB environment. You may need to restart Ubuntu afterward to fully integrate the system changes made during the installation process.

Using MATLAB and Python in Collaboration

Now that you have MATLAB installed on your Ubuntu machine, it’s worth exploring how this software can interact with Python for more advanced data analysis and visualization tasks. One of the key advantages of this integration is the seamless exchange of data between both platforms. Here are some basic code samples to get started:

1. Launching MATLAB from Python: To launch the MATLAB environment from within your Python program, you can use a system command like ‘os.system(“matlab -r”)’. This will run MATLAB with its prompt ready for executing MATLAB commands and scripts.

2. Import MATLAB variables into Python: You may also want to use the data stored in MATLAB arrays within your Python program. To do this, you can leverage NumPy’s frommat() function which accepts a filename as an argument: arr = np.frommat(“your_file_path.mat”, ‘-') This will read the specified .mat file and store its contents in the arr array that follows the NumPy format.

3. Transferring data between Python and MATLAB: If you want to move data from Python variables to MATLAB, you can use Python’s pickle library to serialize your objects into byte streams, which can then be saved as a file (e.g., ‘my_object.pkl’). These serialized objects can subsequently be unpacked within the MATLAB environment and used as required.

4. Creating custom Python-MATLAB interfaces: For more complex interactions between Python and MATLAB, you may want to develop your own classes or functions that handle communication between these two platforms. This would involve leveraging tools like ctypes (for C/C++ libraries) or the mplexec module from the Python Matlab package for executing MATLAB commands.

Conclusion

In this article, we have provided a detailed guide on how to install MATLAB on an Ubuntu operating system. Additionally, we touched upon various aspects of integrating MATLAB with Python programming language, particularly in the context of data science and computer vision applications. With these tools at your disposal, you can now tackle a range of data-driven tasks while leveraging the power of both platforms to achieve optimal performance. Remember that as you gain experience using MATLAB on Ubuntu, you may want to explore additional use cases and customized workflows tailored to your specific needs.