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Merge pull request #9804 from woodychow:optimize_cveigen
This commit is contained in:
Alexander Alekhin
commit
ce20efb8e7
@ -394,7 +394,13 @@ public class CoreTest extends OpenCVTestCase {
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}
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public void testEigen() {
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Mat src = new Mat(3, 3, CvType.CV_32FC1, new Scalar(2.0));
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Mat src = new Mat(3, 3, CvType.CV_32FC1) {
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{
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put(0, 0, 2, 0, 0);
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put(1, 0, 0, 6, 0);
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put(2, 0, 0, 0, 4);
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}
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};
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Mat eigenVals = new Mat();
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Mat eigenVecs = new Mat();
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@ -402,18 +408,22 @@ public class CoreTest extends OpenCVTestCase {
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Mat expectedEigenVals = new Mat(3, 1, CvType.CV_32FC1) {
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{
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put(0, 0, 6, 0, 0);
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}
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};
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Mat expectedEigenVecs = new Mat(3, 3, CvType.CV_32FC1) {
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{
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put(0, 0, 0.57735026, 0.57735026, 0.57735032);
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put(1, 0, 0.70710677, -0.70710677, 0);
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put(2, 0, -0.40824831, -0.40824831, 0.81649661);
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put(0, 0, 6, 4, 2);
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}
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};
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assertMatEqual(eigenVals, expectedEigenVals, EPS);
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assertMatEqual(eigenVecs, expectedEigenVecs, EPS);
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// check by definition
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double eps = 1e-3;
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for(int i = 0; i < 3; i++)
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{
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Mat vec = eigenVecs.row(i).t();
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Mat lhs = new Mat(3, 1, CvType.CV_32FC1);
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Core.gemm(src, vec, 1.0, new Mat(), 1.0, lhs);
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Mat rhs = new Mat(3, 1, CvType.CV_32FC1);
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Core.gemm(vec, eigenVals.row(i), 1.0, new Mat(), 1.0, rhs);
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assertMatEqual(lhs, rhs, eps);
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}
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}
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public void testExp() {
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@ -1326,7 +1336,8 @@ public class CoreTest extends OpenCVTestCase {
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Mat vectors = new Mat();
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Core.PCACompute(data, mean, vectors);
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//System.out.println(mean.dump());
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//System.out.println(vectors.dump());
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Mat mean_truth = new Mat(1, 4, CvType.CV_32F) {
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{
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put(0, 0, 2, 4, 4, 8);
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@ -1338,7 +1349,21 @@ public class CoreTest extends OpenCVTestCase {
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}
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};
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assertMatEqual(mean_truth, mean, EPS);
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assertMatEqual(vectors_truth, vectors, EPS);
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// eigenvectors are normalized (length = 1),
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// but direction is unknown (v and -v are both eigen vectors)
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// so this direct check doesn't work:
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// assertMatEqual(vectors_truth, vectors, EPS);
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for(int i = 0; i < 3; i++)
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{
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Mat vec0 = vectors_truth.row(i);
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Mat vec1 = vectors.row(i);
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Mat vec1_ = new Mat();
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Core.subtract(new Mat(1, 4, CvType.CV_32F, new Scalar(0)), vec1, vec1_);
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double scale1 = Core.norm(vec0, vec1);
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double scale2 = Core.norm(vec0, vec1_);
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assertTrue(Math.min(scale1, scale2) < EPS);
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}
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}
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public void testPCAComputeMatMatMatInt() {
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@ -1365,7 +1390,20 @@ public class CoreTest extends OpenCVTestCase {
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}
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};
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assertMatEqual(mean_truth, mean, EPS);
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assertMatEqual(vectors_truth, vectors, EPS);
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// eigenvectors are normalized (length = 1),
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// but direction is unknown (v and -v are both eigen vectors)
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// so this direct check doesn't work:
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// assertMatEqual(vectors_truth, vectors, EPS);
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for(int i = 0; i < 1; i++)
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{
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Mat vec0 = vectors_truth.row(i);
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Mat vec1 = vectors.row(i);
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Mat vec1_ = new Mat();
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Core.subtract(new Mat(1, 4, CvType.CV_32F, new Scalar(0)), vec1, vec1_);
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double scale1 = Core.norm(vec0, vec1);
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double scale2 = Core.norm(vec0, vec1_);
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assertTrue(Math.min(scale1, scale2) < EPS);
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}
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}
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public void testPCAProject() {
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@ -43,6 +43,12 @@
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#include "precomp.hpp"
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#include <limits>
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#ifdef HAVE_EIGEN
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#include <Eigen/Core>
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#include <Eigen/Eigenvalues>
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#include "opencv2/core/eigen.hpp"
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#endif
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#if defined _M_IX86 && defined _MSC_VER && _MSC_VER < 1700
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#pragma float_control(precise, on)
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#endif
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@ -1396,6 +1402,47 @@ bool cv::eigen( InputArray _src, OutputArray _evals, OutputArray _evects )
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v = _evects.getMat();
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}
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#ifdef HAVE_EIGEN
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const bool evecNeeded = _evects.needed();
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const int esOptions = evecNeeded ? Eigen::ComputeEigenvectors : Eigen::EigenvaluesOnly;
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_evals.create(n, 1, type);
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cv::Mat evals = _evals.getMat();
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if ( type == CV_64F )
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{
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Eigen::MatrixXd src_eig, zeros_eig;
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cv::cv2eigen(src, src_eig);
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Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> es;
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es.compute(src_eig, esOptions);
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if ( es.info() == Eigen::Success )
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{
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cv::eigen2cv(es.eigenvalues().reverse().eval(), evals);
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if ( evecNeeded )
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{
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cv::Mat evects = _evects.getMat();
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cv::eigen2cv(es.eigenvectors().rowwise().reverse().transpose().eval(), v);
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}
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return true;
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}
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} else { // CV_32F
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Eigen::MatrixXf src_eig, zeros_eig;
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cv::cv2eigen(src, src_eig);
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Eigen::SelfAdjointEigenSolver<Eigen::MatrixXf> es;
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es.compute(src_eig, esOptions);
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if ( es.info() == Eigen::Success )
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{
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cv::eigen2cv(es.eigenvalues().reverse().eval(), evals);
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if ( evecNeeded )
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{
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cv::eigen2cv(es.eigenvectors().rowwise().reverse().transpose().eval(), v);
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}
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return true;
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}
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}
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return false;
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#else
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size_t elemSize = src.elemSize(), astep = alignSize(n*elemSize, 16);
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AutoBuffer<uchar> buf(n*astep + n*5*elemSize + 32);
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uchar* ptr = alignPtr((uchar*)buf, 16);
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@ -1408,6 +1455,7 @@ bool cv::eigen( InputArray _src, OutputArray _evals, OutputArray _evects )
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w.copyTo(_evals);
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return ok;
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#endif
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}
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namespace cv
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@ -59,7 +59,7 @@ using namespace std;
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#define MESSAGE_ERROR_DIFF_1 "Accuracy of eigen values computing less than required."
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#define MESSAGE_ERROR_DIFF_2 "Accuracy of eigen vectors computing less than required."
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#define MESSAGE_ERROR_ORTHO "Matrix of eigen vectors is not orthogonal."
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#define MESSAGE_ERROR_ORDER "Eigen values are not sorted in ascending order."
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#define MESSAGE_ERROR_ORDER "Eigen values are not sorted in descending order."
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const int COUNT_NORM_TYPES = 3;
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const int NORM_TYPE[COUNT_NORM_TYPES] = {cv::NORM_L1, cv::NORM_L2, cv::NORM_INF};
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@ -164,8 +164,8 @@ void Core_EigenTest_32::run(int) { check_full(CV_32FC1); }
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void Core_EigenTest_64::run(int) { check_full(CV_64FC1); }
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Core_EigenTest::Core_EigenTest()
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: eps_val_32(1e-3f), eps_vec_32(12e-3f),
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eps_val_64(1e-4f), eps_vec_64(1e-3f), ntests(100) {}
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: eps_val_32(1e-3f), eps_vec_32(1e-3f),
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eps_val_64(1e-4f), eps_vec_64(1e-4f), ntests(100) {}
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Core_EigenTest::~Core_EigenTest() {}
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bool Core_EigenTest::check_pair_count(const cv::Mat& src, const cv::Mat& evalues, int low_index, int high_index)
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@ -234,7 +234,7 @@ bool Core_EigenTest::check_orthogonality(const cv::Mat& U)
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for (int i = 0; i < COUNT_NORM_TYPES; ++i)
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{
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double diff = cvtest::norm(UUt, E, NORM_TYPE[i]);
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double diff = cvtest::norm(UUt, E, NORM_TYPE[i] | cv::NORM_RELATIVE);
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if (diff > eps_vec)
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{
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std::cout << endl; std::cout << "Checking orthogonality of matrix " << U << ": ";
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@ -257,7 +257,7 @@ bool Core_EigenTest::check_pairs_order(const cv::Mat& eigen_values)
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if (!(eigen_values.at<float>(i, 0) > eigen_values.at<float>(i+1, 0)))
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{
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std::cout << endl; std::cout << "Checking order of eigen values vector " << eigen_values << "..." << endl;
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std::cout << "Pair of indexes with non ascending of eigen values: (" << i << ", " << i+1 << ")." << endl;
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std::cout << "Pair of indexes with non descending of eigen values: (" << i << ", " << i+1 << ")." << endl;
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std::cout << endl;
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CV_Error(CORE_EIGEN_ERROR_ORDER, MESSAGE_ERROR_ORDER);
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return false;
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@ -272,9 +272,9 @@ bool Core_EigenTest::check_pairs_order(const cv::Mat& eigen_values)
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if (!(eigen_values.at<double>(i, 0) > eigen_values.at<double>(i+1, 0)))
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{
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std::cout << endl; std::cout << "Checking order of eigen values vector " << eigen_values << "..." << endl;
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std::cout << "Pair of indexes with non ascending of eigen values: (" << i << ", " << i+1 << ")." << endl;
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std::cout << "Pair of indexes with non descending of eigen values: (" << i << ", " << i+1 << ")." << endl;
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std::cout << endl;
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CV_Error(CORE_EIGEN_ERROR_ORDER, "Eigen values are not sorted in ascending order.");
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CV_Error(CORE_EIGEN_ERROR_ORDER, "Eigen values are not sorted in descending order.");
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return false;
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}
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@ -307,43 +307,28 @@ bool Core_EigenTest::test_pairs(const cv::Mat& src)
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cv::Mat eigen_vectors_t; cv::transpose(eigen_vectors, eigen_vectors_t);
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cv::Mat src_evec(src.rows, src.cols, type);
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src_evec = src*eigen_vectors_t;
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// Check:
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// src * eigenvector = eigenval * eigenvector
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cv::Mat lhs(src.rows, src.cols, type);
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cv::Mat rhs(src.rows, src.cols, type);
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cv::Mat eval_evec(src.rows, src.cols, type);
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lhs = src*eigen_vectors_t;
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switch (type)
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for (int i = 0; i < src.cols; ++i)
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{
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case CV_32FC1:
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double eigenval = 0;
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switch (type)
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{
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for (int i = 0; i < src.cols; ++i)
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{
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cv::Mat tmp = eigen_values.at<float>(i, 0) * eigen_vectors_t.col(i);
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for (int j = 0; j < src.rows; ++j) eval_evec.at<float>(j, i) = tmp.at<float>(j, 0);
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}
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break;
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case CV_32FC1: eigenval = eigen_values.at<float>(i, 0); break;
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case CV_64FC1: eigenval = eigen_values.at<double>(i, 0); break;
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}
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case CV_64FC1:
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{
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for (int i = 0; i < src.cols; ++i)
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{
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cv::Mat tmp = eigen_values.at<double>(i, 0) * eigen_vectors_t.col(i);
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for (int j = 0; j < src.rows; ++j) eval_evec.at<double>(j, i) = tmp.at<double>(j, 0);
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}
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break;
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}
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default:;
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cv::Mat rhs_v = eigenval * eigen_vectors_t.col(i);
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rhs_v.copyTo(rhs.col(i));
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}
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cv::Mat disparity = src_evec - eval_evec;
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for (int i = 0; i < COUNT_NORM_TYPES; ++i)
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{
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double diff = cvtest::norm(disparity, NORM_TYPE[i]);
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double diff = cvtest::norm(lhs, rhs, NORM_TYPE[i] | cv::NORM_RELATIVE);
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if (diff > eps_vec)
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{
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std::cout << endl; std::cout << "Checking accuracy of eigen vectors computing for matrix " << src << ": ";
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@ -372,7 +357,7 @@ bool Core_EigenTest::test_values(const cv::Mat& src)
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for (int i = 0; i < COUNT_NORM_TYPES; ++i)
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{
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double diff = cvtest::norm(eigen_values_1, eigen_values_2, NORM_TYPE[i]);
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double diff = cvtest::norm(eigen_values_1, eigen_values_2, NORM_TYPE[i] | cv::NORM_RELATIVE);
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if (diff > eps_val)
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{
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std::cout << endl; std::cout << "Checking accuracy of eigen values computing for matrix " << src << ": ";
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@ -419,7 +404,7 @@ static void testEigen(const Mat_<T>& src, const Mat_<T>& expected_eigenvalues, b
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SCOPED_TRACE(runSymmetric ? "cv::eigen" : "cv::eigenNonSymmetric");
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int type = traits::Type<T>::value;
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const T eps = 1e-6f;
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const T eps = src.type() == CV_32F ? 1e-4f : 1e-6f;
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Mat eigenvalues, eigenvectors, eigenvalues0;
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@ -286,258 +286,188 @@ void Core_ReduceTest::run( int )
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#define CHECK_C
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class Core_PCATest : public cvtest::BaseTest
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TEST(Core_PCA, accuracy)
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{
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public:
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Core_PCATest() {}
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protected:
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void run(int)
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const Size sz(200, 500);
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double diffPrjEps, diffBackPrjEps,
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prjEps, backPrjEps,
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evalEps, evecEps;
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int maxComponents = 100;
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double retainedVariance = 0.95;
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Mat rPoints(sz, CV_32FC1), rTestPoints(sz, CV_32FC1);
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RNG rng(12345);
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rng.fill( rPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) );
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rng.fill( rTestPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) );
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PCA rPCA( rPoints, Mat(), CV_PCA_DATA_AS_ROW, maxComponents ), cPCA;
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// 1. check C++ PCA & ROW
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Mat rPrjTestPoints = rPCA.project( rTestPoints );
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Mat rBackPrjTestPoints = rPCA.backProject( rPrjTestPoints );
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Mat avg(1, sz.width, CV_32FC1 );
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cv::reduce( rPoints, avg, 0, CV_REDUCE_AVG );
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Mat Q = rPoints - repeat( avg, rPoints.rows, 1 ), Qt = Q.t(), eval, evec;
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Q = Qt * Q;
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Q = Q /(float)rPoints.rows;
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eigen( Q, eval, evec );
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/*SVD svd(Q);
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evec = svd.vt;
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eval = svd.w;*/
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Mat subEval( maxComponents, 1, eval.type(), eval.ptr() ),
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subEvec( maxComponents, evec.cols, evec.type(), evec.ptr() );
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#ifdef CHECK_C
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Mat prjTestPoints, backPrjTestPoints, cPoints = rPoints.t(), cTestPoints = rTestPoints.t();
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CvMat _points, _testPoints, _avg, _eval, _evec, _prjTestPoints, _backPrjTestPoints;
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#endif
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// check eigen()
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double eigenEps = 1e-4;
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double err;
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for(int i = 0; i < Q.rows; i++ )
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{
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const Size sz(200, 500);
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Mat v = evec.row(i).t();
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Mat Qv = Q * v;
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double diffPrjEps, diffBackPrjEps,
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prjEps, backPrjEps,
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evalEps, evecEps;
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int maxComponents = 100;
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double retainedVariance = 0.95;
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Mat rPoints(sz, CV_32FC1), rTestPoints(sz, CV_32FC1);
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RNG& rng = ts->get_rng();
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rng.fill( rPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) );
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rng.fill( rTestPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) );
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PCA rPCA( rPoints, Mat(), CV_PCA_DATA_AS_ROW, maxComponents ), cPCA;
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// 1. check C++ PCA & ROW
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Mat rPrjTestPoints = rPCA.project( rTestPoints );
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Mat rBackPrjTestPoints = rPCA.backProject( rPrjTestPoints );
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Mat avg(1, sz.width, CV_32FC1 );
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cv::reduce( rPoints, avg, 0, CV_REDUCE_AVG );
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Mat Q = rPoints - repeat( avg, rPoints.rows, 1 ), Qt = Q.t(), eval, evec;
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Q = Qt * Q;
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Q = Q /(float)rPoints.rows;
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eigen( Q, eval, evec );
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/*SVD svd(Q);
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evec = svd.vt;
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eval = svd.w;*/
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Mat subEval( maxComponents, 1, eval.type(), eval.ptr() ),
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subEvec( maxComponents, evec.cols, evec.type(), evec.ptr() );
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#ifdef CHECK_C
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Mat prjTestPoints, backPrjTestPoints, cPoints = rPoints.t(), cTestPoints = rTestPoints.t();
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CvMat _points, _testPoints, _avg, _eval, _evec, _prjTestPoints, _backPrjTestPoints;
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#endif
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// check eigen()
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double eigenEps = 1e-6;
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double err;
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for(int i = 0; i < Q.rows; i++ )
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Mat lv = eval.at<float>(i,0) * v;
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err = cvtest::norm(Qv, lv, NORM_L2 | NORM_RELATIVE);
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EXPECT_LE(err, eigenEps) << "bad accuracy of eigen(); i = " << i;
|
||||
}
|
||||
// check pca eigenvalues
|
||||
evalEps = 1e-5, evecEps = 5e-3;
|
||||
err = cvtest::norm(rPCA.eigenvalues, subEval, NORM_L2 | NORM_RELATIVE);
|
||||
EXPECT_LE(err , evalEps) << "pca.eigenvalues is incorrect (CV_PCA_DATA_AS_ROW)";
|
||||
// check pca eigenvectors
|
||||
for(int i = 0; i < subEvec.rows; i++)
|
||||
{
|
||||
Mat r0 = rPCA.eigenvectors.row(i);
|
||||
Mat r1 = subEvec.row(i);
|
||||
// eigenvectors have normalized length, but both directions v and -v are valid
|
||||
double err1 = cvtest::norm(r0, r1, NORM_L2 | NORM_RELATIVE);
|
||||
double err2 = cvtest::norm(r0, -r1, NORM_L2 | NORM_RELATIVE);
|
||||
err = std::min(err1, err2);
|
||||
if (err > evecEps)
|
||||
{
|
||||
Mat v = evec.row(i).t();
|
||||
Mat Qv = Q * v;
|
||||
Mat tmp;
|
||||
absdiff(rPCA.eigenvectors, subEvec, tmp);
|
||||
double mval = 0; Point mloc;
|
||||
minMaxLoc(tmp, 0, &mval, 0, &mloc);
|
||||
|
||||
Mat lv = eval.at<float>(i,0) * v;
|
||||
err = cvtest::norm( Qv, lv, NORM_L2 );
|
||||
if( err > eigenEps )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "bad accuracy of eigen(); err = %f\n", err );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
}
|
||||
// check pca eigenvalues
|
||||
evalEps = 1e-6, evecEps = 1e-3;
|
||||
err = cvtest::norm( rPCA.eigenvalues, subEval, NORM_L2 );
|
||||
if( err > evalEps )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "pca.eigenvalues is incorrect (CV_PCA_DATA_AS_ROW); err = %f\n", err );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
// check pca eigenvectors
|
||||
for(int i = 0; i < subEvec.rows; i++)
|
||||
{
|
||||
Mat r0 = rPCA.eigenvectors.row(i);
|
||||
Mat r1 = subEvec.row(i);
|
||||
err = cvtest::norm( r0, r1, CV_L2 );
|
||||
if( err > evecEps )
|
||||
{
|
||||
r1 *= -1;
|
||||
double err2 = cvtest::norm(r0, r1, CV_L2);
|
||||
if( err2 > evecEps )
|
||||
{
|
||||
Mat tmp;
|
||||
absdiff(rPCA.eigenvectors, subEvec, tmp);
|
||||
double mval = 0; Point mloc;
|
||||
minMaxLoc(tmp, 0, &mval, 0, &mloc);
|
||||
|
||||
ts->printf( cvtest::TS::LOG, "pca.eigenvectors is incorrect (CV_PCA_DATA_AS_ROW); err = %f\n", err );
|
||||
ts->printf( cvtest::TS::LOG, "max diff is %g at (i=%d, j=%d) (%g vs %g)\n",
|
||||
mval, mloc.y, mloc.x, rPCA.eigenvectors.at<float>(mloc.y, mloc.x),
|
||||
subEvec.at<float>(mloc.y, mloc.x));
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
prjEps = 1.265, backPrjEps = 1.265;
|
||||
for( int i = 0; i < rTestPoints.rows; i++ )
|
||||
{
|
||||
// check pca project
|
||||
Mat subEvec_t = subEvec.t();
|
||||
Mat prj = rTestPoints.row(i) - avg; prj *= subEvec_t;
|
||||
err = cvtest::norm(rPrjTestPoints.row(i), prj, CV_RELATIVE_L2);
|
||||
if( err > prjEps )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "bad accuracy of project() (CV_PCA_DATA_AS_ROW); err = %f\n", err );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
// check pca backProject
|
||||
Mat backPrj = rPrjTestPoints.row(i) * subEvec + avg;
|
||||
err = cvtest::norm( rBackPrjTestPoints.row(i), backPrj, CV_RELATIVE_L2 );
|
||||
if( err > backPrjEps )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "bad accuracy of backProject() (CV_PCA_DATA_AS_ROW); err = %f\n", err );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
// 2. check C++ PCA & COL
|
||||
cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, maxComponents );
|
||||
diffPrjEps = 1, diffBackPrjEps = 1;
|
||||
Mat ocvPrjTestPoints = cPCA.project(rTestPoints.t());
|
||||
err = cvtest::norm(cv::abs(ocvPrjTestPoints), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 );
|
||||
if( err > diffPrjEps )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "bad accuracy of project() (CV_PCA_DATA_AS_COL); err = %f\n", err );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
err = cvtest::norm(cPCA.backProject(ocvPrjTestPoints), rBackPrjTestPoints.t(), CV_RELATIVE_L2 );
|
||||
if( err > diffBackPrjEps )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "bad accuracy of backProject() (CV_PCA_DATA_AS_COL); err = %f\n", err );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
|
||||
// 3. check C++ PCA w/retainedVariance
|
||||
cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, retainedVariance );
|
||||
diffPrjEps = 1, diffBackPrjEps = 1;
|
||||
Mat rvPrjTestPoints = cPCA.project(rTestPoints.t());
|
||||
|
||||
if( cPCA.eigenvectors.rows > maxComponents)
|
||||
err = cvtest::norm(cv::abs(rvPrjTestPoints.rowRange(0,maxComponents)), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 );
|
||||
else
|
||||
err = cvtest::norm(cv::abs(rvPrjTestPoints), cv::abs(rPrjTestPoints.colRange(0,cPCA.eigenvectors.rows).t()), CV_RELATIVE_L2 );
|
||||
|
||||
if( err > diffPrjEps )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "bad accuracy of project() (CV_PCA_DATA_AS_COL); retainedVariance=0.95; err = %f\n", err );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
err = cvtest::norm(cPCA.backProject(rvPrjTestPoints), rBackPrjTestPoints.t(), CV_RELATIVE_L2 );
|
||||
if( err > diffBackPrjEps )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "bad accuracy of backProject() (CV_PCA_DATA_AS_COL); retainedVariance=0.95; err = %f\n", err );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
|
||||
#ifdef CHECK_C
|
||||
// 4. check C PCA & ROW
|
||||
_points = rPoints;
|
||||
_testPoints = rTestPoints;
|
||||
_avg = avg;
|
||||
_eval = eval;
|
||||
_evec = evec;
|
||||
prjTestPoints.create(rTestPoints.rows, maxComponents, rTestPoints.type() );
|
||||
backPrjTestPoints.create(rPoints.size(), rPoints.type() );
|
||||
_prjTestPoints = prjTestPoints;
|
||||
_backPrjTestPoints = backPrjTestPoints;
|
||||
|
||||
cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_ROW );
|
||||
cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints );
|
||||
cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints );
|
||||
|
||||
err = cvtest::norm(prjTestPoints, rPrjTestPoints, CV_RELATIVE_L2);
|
||||
if( err > diffPrjEps )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_ROW); err = %f\n", err );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
err = cvtest::norm(backPrjTestPoints, rBackPrjTestPoints, CV_RELATIVE_L2);
|
||||
if( err > diffBackPrjEps )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_ROW); err = %f\n", err );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
|
||||
// 5. check C PCA & COL
|
||||
_points = cPoints;
|
||||
_testPoints = cTestPoints;
|
||||
avg = avg.t(); _avg = avg;
|
||||
eval = eval.t(); _eval = eval;
|
||||
evec = evec.t(); _evec = evec;
|
||||
prjTestPoints = prjTestPoints.t(); _prjTestPoints = prjTestPoints;
|
||||
backPrjTestPoints = backPrjTestPoints.t(); _backPrjTestPoints = backPrjTestPoints;
|
||||
|
||||
cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_COL );
|
||||
cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints );
|
||||
cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints );
|
||||
|
||||
err = cvtest::norm(cv::abs(prjTestPoints), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 );
|
||||
if( err > diffPrjEps )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_COL); err = %f\n", err );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
err = cvtest::norm(backPrjTestPoints, rBackPrjTestPoints.t(), CV_RELATIVE_L2);
|
||||
if( err > diffBackPrjEps )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_COL); err = %f\n", err );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
#endif
|
||||
// Test read and write
|
||||
FileStorage fs( "PCA_store.yml", FileStorage::WRITE );
|
||||
rPCA.write( fs );
|
||||
fs.release();
|
||||
|
||||
PCA lPCA;
|
||||
fs.open( "PCA_store.yml", FileStorage::READ );
|
||||
lPCA.read( fs.root() );
|
||||
err = cvtest::norm( rPCA.eigenvectors, lPCA.eigenvectors, CV_RELATIVE_L2 );
|
||||
if( err > 0 )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "bad accuracy of write/load functions (YML); err = %f\n", err );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
}
|
||||
err = cvtest::norm( rPCA.eigenvalues, lPCA.eigenvalues, CV_RELATIVE_L2 );
|
||||
if( err > 0 )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "bad accuracy of write/load functions (YML); err = %f\n", err );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
}
|
||||
err = cvtest::norm( rPCA.mean, lPCA.mean, CV_RELATIVE_L2 );
|
||||
if( err > 0 )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "bad accuracy of write/load functions (YML); err = %f\n", err );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
EXPECT_LE(err, evecEps) << "pca.eigenvectors is incorrect (CV_PCA_DATA_AS_ROW) at " << i << " "
|
||||
<< cv::format("max diff is %g at (i=%d, j=%d) (%g vs %g)\n",
|
||||
mval, mloc.y, mloc.x, rPCA.eigenvectors.at<float>(mloc.y, mloc.x),
|
||||
subEvec.at<float>(mloc.y, mloc.x))
|
||||
<< "r0=" << r0 << std::endl
|
||||
<< "r1=" << r1 << std::endl
|
||||
<< "err1=" << err1 << " err2=" << err2
|
||||
;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
prjEps = 1.265, backPrjEps = 1.265;
|
||||
for( int i = 0; i < rTestPoints.rows; i++ )
|
||||
{
|
||||
// check pca project
|
||||
Mat subEvec_t = subEvec.t();
|
||||
Mat prj = rTestPoints.row(i) - avg; prj *= subEvec_t;
|
||||
err = cvtest::norm(rPrjTestPoints.row(i), prj, NORM_L2 | NORM_RELATIVE);
|
||||
if (err < prjEps)
|
||||
{
|
||||
EXPECT_LE(err, prjEps) << "bad accuracy of project() (CV_PCA_DATA_AS_ROW)";
|
||||
continue;
|
||||
}
|
||||
// check pca backProject
|
||||
Mat backPrj = rPrjTestPoints.row(i) * subEvec + avg;
|
||||
err = cvtest::norm(rBackPrjTestPoints.row(i), backPrj, NORM_L2 | NORM_RELATIVE);
|
||||
if (err > backPrjEps)
|
||||
{
|
||||
EXPECT_LE(err, backPrjEps) << "bad accuracy of backProject() (CV_PCA_DATA_AS_ROW)";
|
||||
continue;
|
||||
}
|
||||
}
|
||||
|
||||
// 2. check C++ PCA & COL
|
||||
cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, maxComponents );
|
||||
diffPrjEps = 1, diffBackPrjEps = 1;
|
||||
Mat ocvPrjTestPoints = cPCA.project(rTestPoints.t());
|
||||
err = cvtest::norm(cv::abs(ocvPrjTestPoints), cv::abs(rPrjTestPoints.t()), NORM_L2 | NORM_RELATIVE);
|
||||
ASSERT_LE(err, diffPrjEps) << "bad accuracy of project() (CV_PCA_DATA_AS_COL)";
|
||||
err = cvtest::norm(cPCA.backProject(ocvPrjTestPoints), rBackPrjTestPoints.t(), NORM_L2 | NORM_RELATIVE);
|
||||
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of backProject() (CV_PCA_DATA_AS_COL)";
|
||||
|
||||
// 3. check C++ PCA w/retainedVariance
|
||||
cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, retainedVariance );
|
||||
diffPrjEps = 1, diffBackPrjEps = 1;
|
||||
Mat rvPrjTestPoints = cPCA.project(rTestPoints.t());
|
||||
|
||||
if( cPCA.eigenvectors.rows > maxComponents)
|
||||
err = cvtest::norm(cv::abs(rvPrjTestPoints.rowRange(0,maxComponents)), cv::abs(rPrjTestPoints.t()), NORM_L2 | NORM_RELATIVE);
|
||||
else
|
||||
err = cvtest::norm(cv::abs(rvPrjTestPoints), cv::abs(rPrjTestPoints.colRange(0,cPCA.eigenvectors.rows).t()), NORM_L2 | NORM_RELATIVE);
|
||||
|
||||
ASSERT_LE(err, diffPrjEps) << "bad accuracy of project() (CV_PCA_DATA_AS_COL); retainedVariance=" << retainedVariance;
|
||||
err = cvtest::norm(cPCA.backProject(rvPrjTestPoints), rBackPrjTestPoints.t(), NORM_L2 | NORM_RELATIVE);
|
||||
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of backProject() (CV_PCA_DATA_AS_COL); retainedVariance=" << retainedVariance;
|
||||
|
||||
#ifdef CHECK_C
|
||||
// 4. check C PCA & ROW
|
||||
_points = rPoints;
|
||||
_testPoints = rTestPoints;
|
||||
_avg = avg;
|
||||
_eval = eval;
|
||||
_evec = evec;
|
||||
prjTestPoints.create(rTestPoints.rows, maxComponents, rTestPoints.type() );
|
||||
backPrjTestPoints.create(rPoints.size(), rPoints.type() );
|
||||
_prjTestPoints = prjTestPoints;
|
||||
_backPrjTestPoints = backPrjTestPoints;
|
||||
|
||||
cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_ROW );
|
||||
cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints );
|
||||
cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints );
|
||||
|
||||
err = cvtest::norm(prjTestPoints, rPrjTestPoints, NORM_L2 | NORM_RELATIVE);
|
||||
ASSERT_LE(err, diffPrjEps) << "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_ROW)";
|
||||
err = cvtest::norm(backPrjTestPoints, rBackPrjTestPoints, NORM_L2 | NORM_RELATIVE);
|
||||
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_ROW)";
|
||||
|
||||
// 5. check C PCA & COL
|
||||
_points = cPoints;
|
||||
_testPoints = cTestPoints;
|
||||
avg = avg.t(); _avg = avg;
|
||||
eval = eval.t(); _eval = eval;
|
||||
evec = evec.t(); _evec = evec;
|
||||
prjTestPoints = prjTestPoints.t(); _prjTestPoints = prjTestPoints;
|
||||
backPrjTestPoints = backPrjTestPoints.t(); _backPrjTestPoints = backPrjTestPoints;
|
||||
|
||||
cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_COL );
|
||||
cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints );
|
||||
cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints );
|
||||
|
||||
err = cvtest::norm(cv::abs(prjTestPoints), cv::abs(rPrjTestPoints.t()), NORM_L2 | NORM_RELATIVE);
|
||||
ASSERT_LE(err, diffPrjEps) << "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_COL)";
|
||||
err = cvtest::norm(backPrjTestPoints, rBackPrjTestPoints.t(), NORM_L2 | NORM_RELATIVE);
|
||||
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_COL)";
|
||||
#endif
|
||||
// Test read and write
|
||||
FileStorage fs( "PCA_store.yml", FileStorage::WRITE );
|
||||
rPCA.write( fs );
|
||||
fs.release();
|
||||
|
||||
PCA lPCA;
|
||||
fs.open( "PCA_store.yml", FileStorage::READ );
|
||||
lPCA.read( fs.root() );
|
||||
err = cvtest::norm(rPCA.eigenvectors, lPCA.eigenvectors, NORM_L2 | NORM_RELATIVE);
|
||||
EXPECT_LE(err, 0) << "bad accuracy of write/load functions (YML)";
|
||||
err = cvtest::norm(rPCA.eigenvalues, lPCA.eigenvalues, NORM_L2 | NORM_RELATIVE);
|
||||
EXPECT_LE(err, 0) << "bad accuracy of write/load functions (YML)";
|
||||
err = cvtest::norm(rPCA.mean, lPCA.mean, NORM_L2 | NORM_RELATIVE);
|
||||
EXPECT_LE(err, 0) << "bad accuracy of write/load functions (YML)";
|
||||
}
|
||||
|
||||
class Core_ArrayOpTest : public cvtest::BaseTest
|
||||
{
|
||||
@ -1227,7 +1157,6 @@ protected:
|
||||
}
|
||||
};
|
||||
|
||||
TEST(Core_PCA, accuracy) { Core_PCATest test; test.safe_run(); }
|
||||
TEST(Core_Reduce, accuracy) { Core_ReduceTest test; test.safe_run(); }
|
||||
TEST(Core_Array, basic_operations) { Core_ArrayOpTest test; test.safe_run(); }
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user