opencv PCA 求轮廓的形心

PCA的详细功能不是很了解。但是，发现用它来求形心非常好。输入为findcontours之后的轮廓点，输出为形心的坐标。

话不多说，上代码。

//开发环境，opencv3.1.0+vs2013

#include 
#include 

using namespace std;
using namespace cv;

cv::Point chao_getCentroid(std::vector list);//得到形心坐标，

int main()
{
	Mat src = imread("1.png");
	if (!src.data || src.empty())
		{
			cout << "Problem loading image!!!" << endl;
			return -1;
		}

	imshow("src", src);
	Mat gray;
	cvtColor(src, gray, COLOR_BGR2GRAY);
	Mat bw;
	threshold(gray, bw, 50, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
	vector hierarchy;
	vector > contours;
	Mat bw_back = 255 - bw;
	findContours(bw_back, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
	for (size_t i = 0; i < contours.size(); ++i)
	{
		
		drawContours(src, contours, static_cast(i), Scalar(0, 0, 255), 2, 8, hierarchy, 0);
		
		Point center = chao_getCentroid(contours[i]);
		circle(src,center,5,Scalar(0,0,255),-1,8);
	}
	imshow("output", src);
    waitKey(0);
	return 0;
}

cv::Point chao_getCentroid(std::vector list)
{
	Point result_point(0,0);
	//Construct a buffer used by the pca analysis
	int sz = static_cast(list.size());
	Mat data_pts = Mat(sz, 2, CV_64FC1);
	for (int i = 0; i < data_pts.rows; ++i)
	{
		data_pts.at(i, 0) = list[i].x;
		data_pts.at(i, 1) = list[i].y;
	}

	//Perform PCA analysis
	PCA pca_analysis(data_pts, Mat(), CV_PCA_DATA_AS_ROW);

	//Store the center of the object
	Point cntr = Point(static_cast(pca_analysis.mean.at(0, 0)),
		static_cast(pca_analysis.mean.at(0, 1)));
	return cntr;
}

效果如下图所示

关于PCA详细使用，可参考官方例程，opencv3.1.0\sources\samples\cpp\tutorial_code\ml\introduction_to_pca文件夹下的introduction_to_pca.cpp文件