(五)OpenCV图像分割_02_K-means分割图像_机器学习
#include
#include
using namespace std;
using namespace cv;
int main(int argc, char** argv)
{
Mat src,dst;
src = imread("../path.jpg");
if (src.empty())
{
cout << "could not load image1..." << endl;
return -1;
}
namedWindow("src", WINDOW_AUTOSIZE);
imshow("src", src);
//为最大的5种聚类分配5种不同的颜色，用以区分不同类的数据
Scalar colorTab[] = {
Scalar(0, 0, 255),
Scalar(0, 255, 0),
Scalar(255, 0, 0),
Scalar(0, 255, 255),
Scalar(255, 0, 255)
};
int width = src.cols;
int height = src.rows;
int dims = src.channels();
int sampleCount = width * height;//采样数
int clusterCount = 5;//最大聚类数目
Mat points(sampleCount, dims, CV_32F, Scalar(10));//存放样本点，是sampleCount行dims通道的行向量
Mat centers(clusterCount, 1, points.type());////用来存储聚类后的中心点
Mat labels;
// RGB 数据转换到样本数据
int index = 0;
for (int row = 0; row < height; row++)
{
for (int col = 0; col < width; col++)
{
index = row * width + col;
Vec3b bgr = src.at(row, col);
points.at(index, 0) = static_cast(bgr[0]);
points.at(index, 1) = static_cast(bgr[1]);
points.at(index, 2) = static_cast(bgr[2]);
}
}
//K-Means
TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1);
kmeans(points, clusterCount, labels, criteria, 3, KMEANS_PP_CENTERS, centers);
// 显示图像分割结果
dst = Mat::zeros(src.size(), src.type());
for (int row = 0; row < height; row++)
{
for (int col = 0; col < width; col++)
{
index = row * width + col;
int label = labels.at(index, 0);
dst.at(row, col)[0] = colorTab[label][0];
dst.at(row, col)[1] = colorTab[label][1];
dst.at(row, col)[2] = colorTab[label][2];
}
}
for (int i = 0; i < centers.rows; i++)
{
int x = centers.at(i, 0);
int y = centers.at(i, 1);
cout << "聚类中心为center: " << "c.x" << x << ",c.y: " << y << endl;
}
imshow("K-means_dst", dst);
waitKey(0);
return 0;
}
输出结果:
作者：丶小早
