马氏距离图像分类C++

马氏距离是一种常用的图像分类方法，它可以用于计算两个图像之间的相似度。下面是一个使用C++实现的马氏距离图像分类的例子：

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

using namespace std;
using namespace cv;

// 计算马氏距离
double mahalanobis_distance(Mat& x, Mat& y, Mat& cov_inv) {
    Mat diff = x - y;
    double dist = diff.dot(cov_inv * diff);
    return dist;
}

// 图像分类
void classify_image(Mat& image, Mat& left_ventricle, Mat& right_ventricle, Mat& white_matter, Mat& gray_matter) {
    // 计算各个类别的协方差矩阵和逆矩阵
    Mat cov_left_ventricle, cov_right_ventricle, cov_white_matter, cov_gray_matter;
    Mat cov_inv_left_ventricle, cov_inv_right_ventricle, cov_inv_white_matter, cov_inv_gray_matter;
    calcCovarMatrix(left_ventricle, cov_left_ventricle, mean(left_ventricle), CV_COVAR_NORMAL | CV_COVAR_SCALE);
    calcCovarMatrix(right_ventricle, cov_right_ventricle, mean(right_ventricle), CV_COVAR_NORMAL | CV_COVAR_SCALE);
    calcCovarMatrix(white_matter, cov_white_matter, mean(white_matter), CV_COVAR_NORMAL | CV_COVAR_SCALE);
    calcCovarMatrix(gray_matter, cov_gray_matter, mean(gray_matter), CV_COVAR_NORMAL | CV_COVAR_SCALE);
    invert(cov_left_ventricle, cov_inv_left_ventricle);
    invert(cov_right_ventricle, cov_inv_right_ventricle);
    invert(cov_white_matter, cov_inv_white_matter);
    invert(cov_gray_matter, cov_inv_gray_matter);

    // 计算图像的特征向量
    Mat features(1, 4, CV_64F);
    features.at<double>(0, 0) = mahalanobis_distance(image, left_ventricle, cov_inv_left_ventricle);
    features.at<double>(0, 1) = mahalanobis_distance(image, right_ventricle, cov_inv_right_ventricle);
    features.at<double>(0, 2) = mahalanobis_distance(image, white_matter, cov_inv_white_matter);
    features.at<double>(0, 3) = mahalanobis_distance(image, gray_matter, cov_inv_gray_matter);

    // 根据特征向量进行分类
    int label = 0;
    double min_dist = features.at<double>(0, 0);
    for (int i = 1; i < 4; i++) {
        if (features.at<double>(0, i) < min_dist) {
            label = i;
            min_dist = features.at<double>(0, i);
        }
    }

    // 输出分类结果
    switch (label) {
    case 0:
        cout << "该图像属于左脑室分割图像类别" << endl;
        break;
    case 1:
        cout << "该图像属于右脑室分割图像类别" << endl;
        break;
    case 2:
        cout << "该图像属于白质分割图像类别" << endl;
        break;
    case 3:
        cout << "该图像属于灰质分割图像类别" << endl;
        break;
    }
}

int main() {
    // 读取图像和分割结果
    Mat image = imread("test.jpg", IMREAD_GRAYSCALE);
    Mat left_ventricle = imread("left_ventricle.jpg", IMREAD_GRAYSCALE);
    Mat right_ventricle = imread("right_ventricle.jpg", IMREAD_GRAYSCALE);
    Mat white_matter = imread("white_matter.jpg", IMREAD_GRAYSCALE);
    Mat gray_matter = imread("gray_matter.jpg", IMREAD_GRAYSCALE);

    // 对图像进行分类
    classify_image(image, left_ventricle, right_ventricle, white_matter, gray_matter);

    return 0;
}