Configure Linux systems to support intelligent transportation and traffic signal optimization development

Configuring Linux systems to support the development of intelligent transportation and traffic signal optimization

With the increasing congestion of urban traffic and frequent traffic accidents, intelligent transportation systems and traffic signal optimization have become the key to solving traffic problems. In this information age, Linux system, as an operating system with strong stability and high flexibility, is widely used in the development of intelligent transportation and traffic signal optimization. This article will describe how to configure a Linux system to support intelligent transportation and traffic signal optimization development, and provide some code examples.

1. Install the Linux system

To start configuring the Linux system, you first need to select the appropriate distribution and install it. Common Linux distributions include Ubuntu, CentOS, Debian, etc., all of which provide better stability and ease of use. When choosing a distro, you can choose based on your needs and familiarity. The installation process is relatively simple and will not be described in detail here.

2. Install the development environment

After completing the installation of the Linux system, you need to install a development environment suitable for intelligent transportation and traffic signal optimization development. Commonly used development environments include GCC compiler, Python interpreter, Java development environment, etc. The following uses the Ubuntu system as an example to introduce how to install these development environments.

1. Install the GCC compiler

Run the following command in the terminal to install the GCC compiler:

sudo apt update
sudo apt install gcc

2. Install the Python interpreter

Run the following command in the terminal to install the Python interpreter:

sudo apt update
sudo apt install python3

3. Install the Java development environment

Run the following command in the terminal to install Java development environment:

sudo apt update
sudo apt install default-jdk

3. Install necessary development tools and libraries

After completing the installation of the development environment, you also need to install some necessary development tools and libraries in order to develop intelligent transportation and Traffic signal optimization application. The following uses C language as an example to introduce how to install the necessary development tools and libraries.

1. Install OpenCV

OpenCV is an open source computer vision library that provides a wealth of image processing and machine vision algorithms. Run the following command in the terminal to install OpenCV:

sudo apt update
sudo apt install libopencv-dev

2. Install Boost library

Boost is a C library with extensive functionality that contains a large number of tools and algorithms. Run the following command in the terminal to install the Boost library:

sudo apt update
sudo apt install libboost-all-dev

4. Write code examples

After completing the installation of development tools and libraries, you can write code examples for intelligent transportation and traffic signal optimization . The following is a sample code that uses OpenCV to implement image license plate recognition:

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

int main()
{
    cv::Mat image = cv::imread("car.jpg", cv::IMREAD_COLOR);
    cv::Mat gray;
    cv::cvtColor(image, gray, cv::COLOR_BGR2GRAY);

    cv::CascadeClassifier classifier;
    classifier.load("haarcascade_russian_plate_number.xml");

    std::vector<cv::Rect> plates;
    classifier.detectMultiScale(gray, plates, 1.1, 3);

    for (const auto& plate : plates)
    {
        cv::rectangle(image, plate, cv::Scalar(0, 255, 0), 2);
    }

    cv::imshow("Image", image);
    cv::waitKey(0);

    return 0;
}

The above code uses OpenCV's CascadeClassifier class for license plate recognition. First read a vehicle image and convert it into a grayscale image. Then load the trained license plate classifier and perform multi-scale target detection on the grayscale image to find the possible license plate area. Finally, the found license plate area is plotted on the original image and the results are displayed.

5. Summary

By configuring the Linux system to support the development of intelligent transportation and traffic signal optimization, we can more easily develop related applications. This article briefly introduces the installation of Linux system, installation of development environment, installation of necessary development tools and libraries, and a code example of using OpenCV to implement image license plate recognition. I hope this content will be of some help to you in the development of intelligent transportation and traffic signal optimization.

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