How to use C++ combined with OpenCV for image processing and classification

In the field of computer vision, OpenCV (Open Source Computer Vision Library) is a powerful open source library that provides rich image processing and computer vision capabilities. C++ is an efficient and widely used programming language. Combining OpenCV and C++, we can perform image processing and classification tasks efficiently.

Advantages of OpenCV and C++

• performance:C++ is a compiled language with high execution efficiency and is suitable for processing large-scale image data.
• Rich features:OpenCV provides from basic image processing to complex machine learning algorithms.
• flexibility: The combination of C++ and OpenCV provides flexible programming methods, and solutions can be customized according to requirements.

Install OpenCV

In Windows systems, the steps to install OpenCV usually include:

• Download the precompiled library or source code for OpenCV.
• Add OpenCV's include directory to the project's include path.
• Add OpenCV's library file to the project's linker settings.
• Configure environment variables and add OpenCV's bin directory to the system's PATH.

In Ubuntu systems, you can use the following command to install the OpenCV library:

sudo apt-get install libopencv-dev

Image processing basics

Image reading and display

#include <opencv2/>
#include <iostream>
int main() {
    // Read the image    cv::Mat image = cv::imread("");
    // Check whether the image is successfully read    if (()) {
        std::cout << "Cannot open image file" << std::endl;
        return -1;
    }
    // Show image    cv::imshow("Display Image", image);
    cv::waitKey(0); // Wait for the button to press    return 0;
}

Image preprocessing

Image preprocessing includes operations such as grayscale, binarization, and filtering.

#include <opencv2/>
#include <iostream>
int main() {
    cv::Mat image = cv::imread("", cv::IMREAD_GRAYSCALE); // Read grayscale images    if (()) {
        std::cout << "Cannot open image file" << std::endl;
        return -1;
    }
    cv::Mat blurredImage;
    cv::GaussianBlur(image, blurredImage, cv::Size(5, 5), 1.5); // Gaussian fuzzy processing    cv::imshow("Original Image", image);
    cv::imshow("Blurred Image", blurredImage);
    cv::waitKey(0);
    return 0;
}

Image classification

Image classification is an important task in computer vision. In C++, we can use OpenCV's machine learning module for image classification.

Image classification using SVM

Support vector machine (SVM) is a commonly used classification algorithm. In OpenCV, you can usecv::ml::SVMClasses are classified in SVM.

#include <opencv2/>
#include <opencv2/>
#include <iostream>
int main() {
    // Read training data and tags    std::vector<cv::Mat> trainData;
    std::vector<int> labels;
    // ... (code that loads training data and tags)    // Create SVM model    cv::Ptr<cv::ml::SVM> svm = cv::ml::SVM::create();
    svm->setType(cv::ml::SVM::C_SVC);
    svm->setKernel(cv::ml::SVM::RBF);
    svm->setTermCriteria(cv::TermCriteria(CV_TERMCRIT_ITER, 100, 1e-6));
    // Training SVM model    svm->train(trainData, cv::ml::ROW_SAMPLE, labels);
    // Classify new images    cv::Mat newImage = cv::imread("new_example.jpg");
    std::vector<float> classLabels;
    svm->predict(newImage, classLabels);
    std::cout << "Predicted class label: " << classLabels[0] << std::endl;
    return 0;
}

Image classification using deep learning

OpenCV's DNN module supports loading and inference of deep learning models. Image classification can be performed using pre-trained deep learning models.

#include <opencv2/>
#include <opencv2/>
#include <iostream>
int main() {
    // Load pretrained model and weights    cv::dnn::Net net = cv::dnn::readNetFromCaffe("", "res10_300x300_ssd_iter_140000_fp16.caffemodel");
    // Read the image    cv::Mat image = cv::imread("");
    cv::Mat blob = cv::dnn::blobFromImage(image, 1.0, cv::Size(300, 300), cv::Scalar(104.0, 177.0, 123.0), true, false);
    // Set network input    (blob);
    // Forward propagation    cv::Mat detection = ();
    // Process the test results    // ... (code that processes the detection result)    return 0;
}

in conclusion

By combining OpenCV and C++, we can perform image processing and classification tasks efficiently. Whether it is traditional machine learning algorithms or modern deep learning models, OpenCV provides powerful support. Through these tools, we can build complex visual systems and solve practical problems.

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