Recommended learning resources for popular libraries and frameworks in the C++ ecosystem

Popular libraries and frameworks in the C++ ecosystem include: Boost C++ libraries and frameworks: Provides a wide range of versatile C++ libraries. STL Standard Template Library: C++ Standard Library Reference. Qt framework: cross-platform GUI development tool. TensorFlow: Deep learning and machine learning library. OpenCV: Computer vision and image processing library. By studying these resources, you can gain an in-depth understanding of these tools and improve your C++ development capabilities.

Recommended learning resources for popular libraries and frameworks in the C++ ecosystem

In the C++ ecosystem, there are many powerful libraries and frameworks that help developers build advanced applications. Understanding these tools is critical to improving your C++ development skills.

Boost C++ Library

• [Boost C++ Bibliotheca](https://www.boost.org/doc/libs/1_76_0/): Provided Extensive general-purpose C++ library
• [Boost.org Documentation](https://www.boost.org/doc/): Contains detailed reference to the library

STL (Standard Template Library)

• [C++ Reference - STL](https://en.cppreference.com/w/cpp/header/algorithm)：C++ Standard Library Reference
• [Herb Sutter's book "The C++ Standard Library Tutorial and Reference"](https://www.amazon.com/C-Standard-Library-Tutorial-Reference/dp/0321531979): Learn more about STL

Qt Framework

• [Qt Framework](https://www.qt.io/): Provides tools for cross-platform GUI development
• [Qt Official Documentation](https://doc.qt.io/): Comprehensive documentation and tutorials

TensorFlow

• [TensorFlow official website](https://www.tensorflow.org/): Deep learning and machine learning library
• [TensorFlow tutorial](https://www.tensorflow.org/tutorials/ ): Step-by-step guide

OpenCV

• [OpenCV official website](https://opencv.org/): Computer Vision and Image Processing Library
• [OpenCV Tutorial](https://docs.opencv.org/4.x/d9/df8/tutorial_root.html): Practical tutorials and examples

Practical case: Build an image processing application using Qt and OpenCV

The following code demonstrates how to use Qt and OpenCV to build a simple image processing application:

#include <QtGui/QApplication>
#include <QtCore/QPixmap>
#include <QImage>
#include <opencv2/opencv.hpp>

int main(int argc, char *argv[]) {
    QApplication app(argc, argv);

    // 加载图像
    QPixmap pixmap = QPixmap::fromImage(QImage("image.png"));
    cv::Mat img = cv::imread("image.png");

    // 使用 OpenCV 处理图像
    cv::GaussianBlur(img, img, cv::Size(5, 5), 0);

    // 更新 QPixmap
    QImage newImage(img.data, img.cols, img.rows, img.step, QImage::Format_RGB888);
    pixmap = QPixmap::fromImage(newImage);

    // 显示图像
    QLabel label;
    label.setPixmap(pixmap);
    label.show();

    return app.exec();
}

By integrating these With learning resources and practical cases, you can gain an in-depth understanding of popular libraries and frameworks in the C++ ecosystem and improve your C++ development capabilities.

The above is the detailed content of Recommended learning resources for popular libraries and frameworks in the C++ ecosystem. For more information, please follow other related articles on the PHP Chinese website!