OpenCv-C++-03-高斯混合模型GMM
1、与KMeans相比，属于软分类；
2、实现方法--------期望最大化（E-M）；
3、停止条件--------收敛或规定次数达到。
OpenCV代码实现：
#include
#include
using namespace cv;
using namespace cv::ml;
using namespace std;
int main(int argc, char** argv)
{
Mat img = Mat::zeros(500, 500, CV_8UC3); //定义一张500*500的8位3通道图片
RNG rng(12345);
Scalar colorTab[] = { //定义颜色数组
Scalar(0,0,255),
Scalar(255,0,255),
Scalar(0,255,0),
Scalar(255,0,0),
Scalar(0,255,255),
};
int numCluster = rng.uniform(2, 5); //4个分类
printf("number of cluster:%d\n", numCluster); //打印4个分类
cout << "----------------------------------------" << endl;
int sampleCount = rng.uniform(1, 1000); //1000个随机点
Mat points(sampleCount, 2, CV_32FC1);
Mat labels;
Mat centers;
//产生多高斯部分的随机采样点
for (int k = 0; k < numCluster; k++)
{
Point center;
center.x = rng.uniform(0, img.cols);
center.y = rng.uniform(0, img.rows);
cout << "x=" << center.x << " " << "y=" << center.y << endl;
Mat pointChunk = points.rowRange(k*sampleCount / numCluster,
k == numCluster - 1 ?
sampleCount : (k + 1)*sampleCount / numCluster); //定义随机散点
//rng.fill函数，会以center点为中心，产生高斯分布的随机点(位置点），并把位置点保存在矩阵pointChunk中。
rng.fill(pointChunk, RNG::NORMAL, Scalar(center.x, center.y), Scalar(img.cols*0.05, img.rows*0.05));
}
randShuffle(points, 1, &rng);//打乱points中值，第二个参数表示随机交换元素的数量的缩放因子，总的交换次数dst.rows*dst.cols*iterFactor，第三个参数是个随机发生器，决定选哪两个元素交换。
Ptrem_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());
//--------------------------
//分类
Mat sample(1, 2, CV_32FC1);
for (int row = 0; row < img.rows; row++)
{
for (int col = 0; col < img.cols; col++)
{
sample.at(0) = (float)col;
sample.at(1) = (float)row;
int response = cvRound(em_model->predict2(sample, noArray())[1]); //cvRound 返回跟参数最接近的整数值
Scalar c = colorTab[response];
circle(img, Point(col, row), 1, c*0.75, -1);
}
}
//绘制分类的点
for (int i = 0; i < sampleCount; i++)
{
Point p(cvRound(points.at(i, 0)), cvRound(points.at(i, 1)));
circle(img, p, 1, colorTab[labels.at(i)], -1);
}
imshow("final result", img);
waitKey(0);
return 0;
}
运行结果：
基于图像分割的GMM：
#include
#include
using namespace cv;
using namespace cv::ml;
using namespace std;
int main(int argc, char* argv)
{
Mat src = imread("F:/test/toux.jpg",1);
if (src.empty())
{
cout << "图像文件未找到!" << endl;
return -1;
}
imshow("input image", src);
const Scalar colors[] = {
Scalar(255, 0, 0),
Scalar(0, 255, 0),
Scalar(0, 0, 255),
Scalar(255, 255, 0)
};
int clustersNumber = 3;
int width = src.cols;
int height = src.rows;
int channel = src.channels();
int all_samples = width * height; //所有的样本点总数
Mat points(all_samples,channel, 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 < src.rows; row++)
{
for (int col = 0; col < src.cols; col++)
{
index = row * width + col;
Vec3b bgr = src.at(row, col);
points.at(index, 0) = bgr[0];
points.at(index, 1) = bgr[1];
points.at(index, 2) = bgr[2];
}
}
//EM 训练
Ptrem_model = EM::create();
em_model->setClustersNumber(clustersNumber);
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(channel,1,CV_64FC1);
int r=0, g=0, b = 0;
for (int row = 0; row < src.rows; row++)
{
for (int col = 0; col < src.cols; 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];
}
}
imshow("GMM Result",result);
waitKey(0);
return 0;
}
运行结果：
