How To Install OpenCV on Manjaro

OpenCV (Open Source Computer Vision Library) is a powerful tool for computer vision and image processing. It provides a comprehensive set of functionalities for real-time computer vision applications. This guide will walk you through the process of installing OpenCV on Manjaro, a popular user-friendly Linux distribution that is based on Arch Linux. Whether you are a beginner or an experienced developer, this article aims to provide clear, step-by-step instructions to help you get OpenCV up and running on your system.

Prerequisites

Before diving into the installation process, it’s essential to ensure that your system meets the necessary requirements.

System Requirements

• Minimum Hardware Specifications: A modern processor (Intel or AMD), at least 4 GB of RAM, and sufficient disk space (10 GB recommended).
• Recommended Software Versions: Manjaro version 21.0 or later, with a kernel version of 5.4 or higher.

Necessary Skills

• Basic command line usage: Familiarity with terminal commands is crucial.
• Understanding package management in Linux: Knowing how to install and manage packages is essential.

Installation of Essential Tools

Ensure that you have the following tools installed:

• Sudo: Make sure your user has sudo privileges to install software.
• C++ Compiler: Install GCC/G++ if it’s not already present. You can do this by running:

sudo pacman -S gcc

Installation via Package Manager

The simplest way to install OpenCV on Manjaro is by using the package manager, Pacman. This method ensures that you get a stable version of OpenCV along with all necessary dependencies.

Step 1: Update Your System

Before installing any new software, it’s good practice to update your system. Run the following command in your terminal:

sudo pacman -Syu

This command synchronizes your package database and updates all installed packages to their latest versions.

Step 2: Install OpenCV and Dependencies

You can install OpenCV directly from the official repositories. Execute the following command:

sudo pacman -S opencv opencv-samples

This command installs OpenCV along with sample programs that can help you test your installation and learn how to use the library effectively.

Step 3: Verify Installation

To ensure that OpenCV has been installed correctly, check the version by running:

opencv_version

If installed successfully, this command will display the version number of OpenCV you have installed.

Building OpenCV from Source

If you need specific features or optimizations not available in the precompiled packages, building OpenCV from source is an excellent option. This method allows for greater customization and access to the latest updates directly from the repository.

Why Build from Source?

Building from source gives you control over which modules to include, enabling optimizations tailored to your hardware.

Step 1: Install Required Dependencies

You will need several development tools and libraries to build OpenCV from source. Use the following command to install them:

sudo pacman -S base-devel cmake git pkg-config numpy python-opencv

Step 2: Download OpenCV Source Code

The next step is to download the OpenCV source code from its GitHub repository. Run these commands:

git clone https://github.com/opencv/opencv.git
cd opencv
git checkout tags/4.5.1

This will clone the repository and switch to version 4.5.1, which is stable and widely used.

Step 3: Create Build Directory and Configure Build

Create a separate build directory for compiling OpenCV:

mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..

This command configures the build environment, specifying release mode and installation path.

Step 4: Compile and Install

The final step in building OpenCV from source is compiling and installing it. Execute the following commands:

make -j$(nproc)
sudo make install

The `make` command compiles the code using all available processor cores, speeding up the process significantly.

Common Pitfalls and Troubleshooting

If you encounter issues during installation or while running OpenCV, consider these common pitfalls:

Incorrect Paths

If you receive errors related to file paths when running programs, ensure that all paths specified in your code are correct. Double-check installation paths if you built from source.

Missing Dependencies

If certain functionalities are not working (e.g., video processing), it may be due to missing dependencies. Make sure all required libraries are installed by referring back to the dependency list provided earlier.

Version Mismatch Issues

If you experience compatibility issues between your code samples and installed OpenCV version, check if any functions have been deprecated or changed in newer versions by consulting the official documentation.

Testing Your Installation

Running Sample Programs

The `opencv-samples` package includes various sample programs that demonstrate how to use different features of OpenCV. Navigate to the samples directory and run some examples:

cd /usr/share/opencv/samples/cpp
./example_cmake .. 

Creating a Simple Test Program

You can also create a simple program to test your installation. Here’s an example code snippet:

#include <opencv4/opencv2/opencv.hpp>
#include <iostream>

int main() {
    std::cout << "OpenCV version: " << CV_VERSION << std::endl;
    return 0;
}

Compiling the Test Program

You can compile this program using g++. Save it as `test.cpp` and run:

g++ test.cpp `pkg-config --cflags --libs opencv4` -o test
./test

If everything is set up correctly, this should print out the version of OpenCV installed on your system.

Congratulations! You have successfully installed OpenCV. Thanks for using this tutorial for installing the OpenCV (Open Source Computer Vision Library) on Manjaro. For additional help or useful information, we recommend you check the official OpenCV website.