基于高斯函数的算法,通过混合单个或多个高斯函数,计算对应像素中概率,哪个分类的概率最高的,则属于哪个类别
高斯分布与概率密度分布 - PDF :
GMM方法跟K - Means相比较,属于软分类
实现方法 - 期望最大化(E - M)
停止条件 - 收敛,或规定的循环次数
代码:
#include
#include
#include
#include
#include
#include
#include
using namespace std;
using namespace cv;
using namespace cv::ml;
int main(int argc, char** argv) {
Mat src = imread("../img/88.jpg");
if (src.empty()) {
printf("could not load iamge...\n");
return -1;
}
imshow("原图", src);
// 初始化
int numCluster = 3;
const Scalar colors[] = {
Scalar(255, 0, 0),
Scalar(0, 255, 0),
Scalar(0, 0, 255),
Scalar(255, 255, 0)
};
int width = src.cols;
int height = src.rows;
int dims = src.channels();
int nsamples = width * height;
Mat points(nsamples, dims, CV_64FC1);
Mat labels;
Mat result = Mat::zeros(src.size(), CV_8UC3);
// 图像RGB像素数据转换为样本数据
int index = 0;
for (int row = 0; row < height; row++) {
for (int col = 0; col < width; col++) {
index = row * width + col;
Vec3b rgb = src.at(row, col);
points.at(index, 0) = static_cast(rgb[0]);
points.at(index, 1) = static_cast(rgb[1]);
points.at(index, 2) = static_cast(rgb[2]);
}
}
// EM Cluster Train
Ptr em_model = EM::create();
em_model->setClustersNumber(numCluster);
em_model->setCovarianceMatrixType(EM::COV_MAT_SPHERICAL);
em_model->setTermCriteria(TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 100, 0.1));
em_model->trainEM(points, noArray(), labels, noArray());
// 对每个像素标记颜色与显示
Mat sample(dims, 1, CV_64FC1);
double time = getTickCount();
int r = 0, g = 0, b = 0;
for (int row = 0; row < height; row++) {
for (int col = 0; col < width; col++) {
index = row * width + col;
b = src.at(row, col)[0];
g = src.at(row, col)[1];
r = src.at(row, col)[2];
sample.at(0) = b;
sample.at(1) = g;
sample.at(2) = r;
int response = cvRound(em_model->predict2(sample, noArray())[1]);
Scalar c = colors[response];
result.at(row, col)[0] = c[0];
result.at(row, col)[1] = c[1];
result.at(row, col)[2] = c[2];
}
}
printf("execution time(ms) : %.2f\n", (getTickCount() - time) / getTickFrequency() * 1000);
imshow("EM-Segmentation", result);
waitKey(0);
return 0;
}
转载:https://blog.csdn.net/CJ_035/article/details/81835833