Configure Linux systems to support real-time image processing and computer vision development

Configuring Linux systems to support real-time image processing and computer vision development

Introduction:
Computer vision, as one of the important branches of artificial intelligence, has achieved tremendous development in various fields in recent years. Real-time image processing and computer vision development require a powerful platform to support it, and Linux system, as a free, open and powerful operating system, has become the first choice for developers. This article will introduce how to configure a Linux system to support real-time image processing and computer vision development, and provide code examples for readers' reference.

1. Install the Linux system:
First, we need to choose a suitable Linux distribution and install it. Common Linux distributions include Ubuntu, CentOS, Fedora, etc. We can choose one of them according to our needs and preferences.

2. Install necessary dependent libraries and tools:
Before starting real-time image processing and computer vision development, we need to install some necessary dependent libraries and tools. The following are some commonly used dependent libraries and tools that readers can install according to their own needs.

1. OpenCV: OpenCV is an open source computer vision library that provides a wealth of image processing and computer vision algorithms. We can install OpenCV through the following command:

sudo apt-get install libopencv-dev

2. NumPy: NumPy is a library for scientific computing in the Python language, providing high-performance multi-dimensional array and matrix operations. We can install NumPy through the following command:

sudo apt-get install python-numpy

3. cmake: cmake is a cross-platform automated build tool that we can use to compile and install some libraries that require manual compilation. We can install cmake through the following command:

sudo apt-get install cmake

3. Configure the development environment:
Before configuring the development environment, we need to determine the development language we are using. Common computer vision development languages ​​include C and Python. We can choose one of them according to our preference and familiarity.

1. C development environment configuration:
   If we choose to use C for development, we need to install a C compiler and an integrated development environment (IDE). Commonly used C compilers include GCC and Clang, and commonly used IDEs include Code::Blocks, Eclipse, etc. We can install the GCC compiler through the following command:

sudo apt-get install g++

2. Python development environment configuration:
   If we choose to use Python for development, we need to install a Python interpreter and a Python development environment . Commonly used Python interpreters include Python 2 and Python 3. We can choose one of them according to our needs. Commonly used Python development environments include PyCharm, Jupyter Notebook, etc. We can install the Python interpreter and pip package management tool through the following command:

sudo apt-get install python python-pip

Next, we can use pip to install some commonly used Python libraries, such as:

pip install numpy opencv-python

four , Code example:
After completing the above configuration, we can use the following code example for real-time image processing and computer vision development.

C Sample code:

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

int main() {
    cv::VideoCapture cap(0);
    if (!cap.isOpened()) {
        std::cout << "Failed to open camera" << std::endl;
        return -1;
    }
    cv::Mat frame;
    while (cap.read(frame)) {
        cv::imshow("Camera", frame);
        if (cv::waitKey(30) == 'q') {
            break;
        }
    }
    cap.release();
    cv::destroyAllWindows();
    return 0;
}

Python sample code:

import cv2

cap = cv2.VideoCapture(0)
if not cap.isOpened():
    print("Failed to open camera")
    exit(1)

while True:
    ret, frame = cap.read()
    if not ret:
        print("Failed to read frame")
        break

    cv2.imshow("Camera", frame)

    if cv2.waitKey(30) == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

The above code example uses the OpenCV library to open the camera in real time and display the image captured by the camera, if pressed on the keyboard Press the "q" key to exit the program.

Conclusion:
Through the above configuration and code examples, we can successfully implement real-time image processing and computer vision development on Linux systems. Readers can further learn and explore more computer vision algorithms and technologies according to their own needs and interests.

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