OF PCA

float* ofApp::getOrientation(const vector<cv::Point> &pts) //, Mat &img)
        {
            //http://docs.opencv.org/3.1.0/d1/dee/tutorial_introduction_to_pca.html#gsc.tab=0
            //Construct a buffer used by the pca analysis
            int sz = static_cast<int>(pts.size());
            Mat data_pts = Mat(sz, 2, CV_64FC1);
            for (int i = 0; i < data_pts.rows; ++i)
            {
                data_pts.at<double>(i, 0) = pts[i].x;
                data_pts.at<double>(i, 1) = pts[i].y;
            }
            //Perform PCA analysis
            PCA pca_analysis(data_pts, Mat(), CV_PCA_DATA_AS_ROW);
            //Store the center of the object
            cv::Point cntr = cv::Point(static_cast<int>(pca_analysis.mean.at<double>(0, 0)),
                               static_cast<int>(pca_analysis.mean.at<double>(0, 1)));
            //Store the eigenvalues and eigenvectors
            vector<Point2d> eigen_vecs(2);
            vector<double> eigen_val(2);
            for (int i = 0; i < 2; ++i)
            {
                eigen_vecs[i] = Point2d(pca_analysis.eigenvectors.at<double>(i, 0),
                                        pca_analysis.eigenvectors.at<double>(i, 1));
                eigen_val[i] = pca_analysis.eigenvalues.at<double>(0, i);
            }

            cv::Point p1 = cntr + 0.02 * cv::Point(static_cast<int>(eigen_vecs[0].x * eigen_val[0]), static_cast<int>(eigen_vecs[0].y * eigen_val[0]));
            cv::Point p2 = cntr - 0.02 * cv::Point(static_cast<int>(eigen_vecs[1].x * eigen_val[1]), static_cast<int>(eigen_vecs[1].y * eigen_val[1]));

            orientation_angle = atan2(eigen_vecs[0].y, eigen_vecs[0].x); // orientation in radians
            orientation_cntr = toOf(cntr);
            eigenPoint_a = toOf(p1);
            eigenPoint_b = toOf(p2);
        //    return angle;
            
            static float temp_array[8];
            temp_array[0] = eigen_vecs[0].x;
            temp_array[1] = eigen_vecs[0].y;
            temp_array[2] = eigen_vecs[1].x;
            temp_array[3] = eigen_vecs[1].y;
            temp_array[4] = eigen_val[0];
            temp_array[5] = eigen_val[1];
            temp_array[6] = static_cast<int>(pca_analysis.mean.at<double>(0, 0)); //center x
            temp_array[7] = static_cast<int>(pca_analysis.mean.at<double>(0, 1)); //center y
            
            return temp_array;
        }