如何在knn算法中使用SIFT描述符对图像进行分类

Question

我已经分别成功实现了 OpenCV 的 SIFT 类和 KNN，但是现在我的任务是使用 SIFT 特征和 KNN 对图像进行分类。因为 SIFT 特征本身不是图像 - 而只是图像的单个点 我如何使用 OpenCV 的 KNearest() 和提取的 SIFT 特征来对图像进行分类？

这些是正在使用的各个类 筛选：

std::string pathSIFT = "C:path of images";
cv::Ptr<cv::SiftFeatureDetector> detector =
    cv::SiftFeatureDetector::create();
cv::Ptr<cv::SiftDescriptorExtractor> descriptor =
    cv::SiftDescriptorExtractor::create();

std::vector<cv::KeyPoint> keypoints;
cv::Mat output;
cv::Mat descriptors;

for (int j = 0; j < images2SIFT.size(); j++) {
    detector->detect(images2SIFT.at(j), keypoints);
    std::string s = std::to_string(j);
    cv::drawKeypoints(images2SIFT.at(j), keypoints, output);
    detector->detectAndCompute(images2SIFT.at(j), Mat(), keypoints, descriptors);

    // Append the descriptors to the matrix
    if (i == 0){
        // If this is the first image, create the matrix
        descriptors.copyTo(descriptors);
    }
    else{
        // Otherwise, append the descriptors to the matrix
        vconcat(descriptors, descriptors, descriptors);
    }
    if (!std::filesystem::exists(pathSIFT + s + ".jpg")) {
        cv::imwrite(pathSIFT + s + ".jpg", output);
    }
    waitKey(200);
}

KNN算法：

Mat samples(data.size(), data[0].rows* data[0].cols, CV_32F);
for (int i = 0; i < samples.rows; i++)
{
    Mat rowData = samples.row(i);
    data[i].reshape(1, 1).convertTo(rowData, CV_32F);
}

Ptr<KNearest> knn = KNearest::create();
knn->setAlgorithmType(KNearest::Types::BRUTE_FORCE);
knn->train(samples, ROW_SAMPLE, labels);

// Classify new image from directory 2
vector<float> predictions;
for (int i = 0; i < dataToTest.size(); i++) {
    Mat img = dataToTest.at(i);
    Mat sample;
    img.reshape(1, 1).convertTo(sample, CV_32F);
    float response = knn->findNearest(sample, 1, noArray());
    predictions.push_back(response);
}