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  #include "test_precomp.hpp"
  
  #include <string>
  #include <iostream>
  #include <fstream>
  #include <iterator>
  #include <limits>
  #include <numeric>
  
  using namespace cv;
  using namespace std;
  
  class CV_RigidTransform_Test : public cvtest::BaseTest
  {
  public:
      CV_RigidTransform_Test();
      ~CV_RigidTransform_Test();
  protected:
      void run(int);
  
      bool testNPoints(int);
      bool testImage();
  };
  
  CV_RigidTransform_Test::CV_RigidTransform_Test()
  {
  }
  CV_RigidTransform_Test::~CV_RigidTransform_Test() {}
  
  struct WrapAff2D
  {
      const double *F;
      WrapAff2D(const Mat& aff) : F(aff.ptr<double>()) {}
      Point2f operator()(const Point2f& p)
      {
          return Point2d( p.x * F[0] + p.y * F[1] +  F[2],
                          p.x * F[3] + p.y * F[4] +  F[5]);
      }
  };
  
  bool CV_RigidTransform_Test::testNPoints(int from)
  {
      cv::RNG rng = ts->get_rng();
  
      int progress = 0;
      int k, ntests = 10000;
  
      for( k = from; k < ntests; k++ )
      {
          ts->update_context( this, k, true );
          progress = update_progress(progress, k, ntests, 0);
  
          Mat aff(2, 3, CV_64F);
          rng.fill(aff, CV_RAND_UNI, Scalar(-2), Scalar(2));
  
          int n = (unsigned)rng % 100 + 10;
  
          Mat fpts(1, n, CV_32FC2);
          Mat tpts(1, n, CV_32FC2);
  
          rng.fill(fpts, CV_RAND_UNI, Scalar(0,0), Scalar(10,10));
          transform(fpts.ptr<Point2f>(), fpts.ptr<Point2f>() + n, tpts.ptr<Point2f>(), WrapAff2D(aff));
  
          Mat noise(1, n, CV_32FC2);
          rng.fill(noise, CV_RAND_NORMAL, Scalar::all(0), Scalar::all(0.001*(n<=7 ? 0 : n <= 30 ? 1 : 10)));
          tpts += noise;
  
          Mat aff_est = estimateRigidTransform(fpts, tpts, true);
  
          double thres = 0.1*norm(aff);
          double d = norm(aff_est, aff, NORM_L2);
          if (d > thres)
          {
              double dB=0, nB=0;
              if (n <= 4)
              {
                  Mat A = fpts.reshape(1, 3);
                  Mat B = A - repeat(A.row(0), 3, 1), Bt = B.t();
                  B = Bt*B;
                  dB = cv::determinant(B);
                  nB = norm(B);
                  if( fabs(dB) < 0.01*nB )
                      continue;
              }
              ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
              ts->printf( cvtest::TS::LOG, "Threshold = %f, norm of difference = %f", thres, d );
              return false;
          }
      }
      return true;
  }
  
  bool CV_RigidTransform_Test::testImage()
  {
      Mat img;
      Mat testImg = imread( string(ts->get_data_path()) + "shared/graffiti.png", 1);
      if (testImg.empty())
      {
         ts->printf( ts->LOG, "test image can not be read");
         ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
         return false;
      }
      pyrDown(testImg, img);
  
      Mat aff = cv::getRotationMatrix2D(Point(img.cols/2, img.rows/2), 1, 0.99);
      aff.ptr<double>()[2]+=3;
      aff.ptr<double>()[5]+=3;
  
      Mat rotated;
      warpAffine(img, rotated, aff, img.size());
  
      Mat aff_est = estimateRigidTransform(img, rotated, true);
  
      const double thres = 0.03;
      if (norm(aff_est, aff, NORM_INF) > thres)
      {
          ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
          ts->printf( cvtest::TS::LOG, "Threshold = %f, norm of difference = %f", thres,
              norm(aff_est, aff, NORM_INF) );
          return false;
      }
  
      return true;
  }
  
  void CV_RigidTransform_Test::run( int start_from )
  {
      cvtest::DefaultRngAuto dra;
  
      if (!testNPoints(start_from))
          return;
  
      if (!testImage())
          return;
  
      ts->set_failed_test_info(cvtest::TS::OK);
  }
  
  TEST(Video_RigidFlow, accuracy) { CV_RigidTransform_Test test; test.safe_run(); }
  