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  #include "test_precomp.hpp"
  
  using namespace std;
  using namespace cv;
  
  const string FEATURES2D_DIR = "features2d";
  const string IMAGE_FILENAME = "tsukuba.png";
  const string DESCRIPTOR_DIR = FEATURES2D_DIR + "/descriptor_extractors";
  
  /****************************************************************************************\
  *                     Regression tests for descriptor extractors.                        *
  \****************************************************************************************/
  static void writeMatInBin( const Mat& mat, const string& filename )
  {
      FILE* f = fopen( filename.c_str(), "wb");
      if( f )
      {
          int type = mat.type();
          fwrite( (void*)&mat.rows, sizeof(int), 1, f );
          fwrite( (void*)&mat.cols, sizeof(int), 1, f );
          fwrite( (void*)&type, sizeof(int), 1, f );
          int dataSize = (int)(mat.step * mat.rows * mat.channels());
          fwrite( (void*)&dataSize, sizeof(int), 1, f );
          fwrite( (void*)mat.ptr(), 1, dataSize, f );
          fclose(f);
      }
  }
  
  static Mat readMatFromBin( const string& filename )
  {
      FILE* f = fopen( filename.c_str(), "rb" );
      if( f )
      {
          int rows, cols, type, dataSize;
          size_t elements_read1 = fread( (void*)&rows, sizeof(int), 1, f );
          size_t elements_read2 = fread( (void*)&cols, sizeof(int), 1, f );
          size_t elements_read3 = fread( (void*)&type, sizeof(int), 1, f );
          size_t elements_read4 = fread( (void*)&dataSize, sizeof(int), 1, f );
          CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1);
  
          int step = dataSize / rows / CV_ELEM_SIZE(type);
          CV_Assert(step >= cols);
  
          Mat m = Mat(rows, step, type).colRange(0, cols);
  
          size_t elements_read = fread( m.ptr(), 1, dataSize, f );
          CV_Assert(elements_read == (size_t)(dataSize));
          fclose(f);
  
          return m;
      }
      return Mat();
  }
  
  template<class Distance>
  class CV_DescriptorExtractorTest : public cvtest::BaseTest
  {
  public:
      typedef typename Distance::ValueType ValueType;
      typedef typename Distance::ResultType DistanceType;
  
      CV_DescriptorExtractorTest( const string _name, DistanceType _maxDist, const Ptr<DescriptorExtractor>& _dextractor,
                                  Distance d = Distance(), Ptr<FeatureDetector> _detector = Ptr<FeatureDetector>()):
          name(_name), maxDist(_maxDist), dextractor(_dextractor), distance(d) , detector(_detector) {}
  
      ~CV_DescriptorExtractorTest()
      {
          if(!detector.empty())
              detector.release();
      }
  protected:
      virtual void createDescriptorExtractor() {}
  
      void compareDescriptors( const Mat& validDescriptors, const Mat& calcDescriptors )
      {
          if( validDescriptors.size != calcDescriptors.size || validDescriptors.type() != calcDescriptors.type() )
          {
              ts->printf(cvtest::TS::LOG, "Valid and computed descriptors matrices must have the same size and type.\n");
              ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
              return;
          }
  
          CV_Assert( DataType<ValueType>::type == validDescriptors.type() );
  
          int dimension = validDescriptors.cols;
          DistanceType curMaxDist = std::numeric_limits<DistanceType>::min();
          for( int y = 0; y < validDescriptors.rows; y++ )
          {
              DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
              if( dist > curMaxDist )
                  curMaxDist = dist;
          }
  
          stringstream ss;
          ss << "Max distance between valid and computed descriptors " << curMaxDist;
          if( curMaxDist < maxDist )
              ss << "." << endl;
          else
          {
              ss << ">" << maxDist  << " - bad accuracy!"<< endl;
              ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
          }
          ts->printf(cvtest::TS::LOG,  ss.str().c_str() );
      }
  
      void emptyDataTest()
      {
          assert( dextractor );
  
          // One image.
          Mat image;
          vector<KeyPoint> keypoints;
          Mat descriptors;
  
          try
          {
              dextractor->compute( image, keypoints, descriptors );
          }
          catch(...)
          {
              ts->printf( cvtest::TS::LOG, "compute() on empty image and empty keypoints must not generate exception (1).\n");
              ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
          }
  
          image.create( 50, 50, CV_8UC3 );
          try
          {
              dextractor->compute( image, keypoints, descriptors );
          }
          catch(...)
          {
              ts->printf( cvtest::TS::LOG, "compute() on nonempty image and empty keypoints must not generate exception (1).\n");
              ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
          }
  
          // Several images.
          vector<Mat> images;
          vector<vector<KeyPoint> > keypointsCollection;
          vector<Mat> descriptorsCollection;
          try
          {
              dextractor->compute( images, keypointsCollection, descriptorsCollection );
          }
          catch(...)
          {
              ts->printf( cvtest::TS::LOG, "compute() on empty images and empty keypoints collection must not generate exception (2).\n");
              ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
          }
      }
  
      void regressionTest()
      {
          assert( dextractor );
  
          // Read the test image.
          string imgFilename =  string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
          Mat img = imread( imgFilename );
          if( img.empty() )
          {
              ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() );
              ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
              return;
          }
          vector<KeyPoint> keypoints;
          FileStorage fs( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::READ );
          if(!detector.empty()) {
              detector->detect(img, keypoints);
          } else {
              read( fs.getFirstTopLevelNode(), keypoints );
          }
          if(!keypoints.empty())
          {
              Mat calcDescriptors;
              double t = (double)getTickCount();
              dextractor->compute( img, keypoints, calcDescriptors );
              t = getTickCount() - t;
              ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms.\n", t/((double)getTickFrequency()*1000.)/calcDescriptors.rows);
  
              if( calcDescriptors.rows != (int)keypoints.size() )
              {
                  ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" );
                  ts->printf( cvtest::TS::LOG, "Count of keypoints is            %d.\n", (int)keypoints.size() );
                  ts->printf( cvtest::TS::LOG, "Count of computed descriptors is %d.\n", calcDescriptors.rows );
                  ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                  return;
              }
  
              if( calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType() )
              {
                  ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" );
                  ts->printf( cvtest::TS::LOG, "Expected size is   %d.\n", dextractor->descriptorSize() );
                  ts->printf( cvtest::TS::LOG, "Calculated size is %d.\n", calcDescriptors.cols );
                  ts->printf( cvtest::TS::LOG, "Expected type is   %d.\n", dextractor->descriptorType() );
                  ts->printf( cvtest::TS::LOG, "Calculated type is %d.\n", calcDescriptors.type() );
                  ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                  return;
              }
  
              // TODO read and write descriptor extractor parameters and check them
              Mat validDescriptors = readDescriptors();
              if( !validDescriptors.empty() )
                  compareDescriptors( validDescriptors, calcDescriptors );
              else
              {
                  if( !writeDescriptors( calcDescriptors ) )
                  {
                      ts->printf( cvtest::TS::LOG, "Descriptors can not be written.\n" );
                      ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
                      return;
                  }
              }
          }
          if(!fs.isOpened())
          {
              ts->printf( cvtest::TS::LOG, "Compute and write keypoints.\n" );
              fs.open( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::WRITE );
              if( fs.isOpened() )
              {
                  ORB fd;
                  fd.detect(img, keypoints);
                  write( fs, "keypoints", keypoints );
              }
              else
              {
                  ts->printf(cvtest::TS::LOG, "File for writting keypoints can not be opened.\n");
                  ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
                  return;
              }
          }
      }
  
      void run(int)
      {
          createDescriptorExtractor();
          if( !dextractor )
          {
              ts->printf(cvtest::TS::LOG, "Descriptor extractor is empty.\n");
              ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
              return;
          }
  
          emptyDataTest();
          regressionTest();
  
          ts->set_failed_test_info( cvtest::TS::OK );
      }
  
      virtual Mat readDescriptors()
      {
          Mat res = readMatFromBin( string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
          return res;
      }
  
      virtual bool writeDescriptors( Mat& descs )
      {
          writeMatInBin( descs,  string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
          return true;
      }
  
      string name;
      const DistanceType maxDist;
      Ptr<DescriptorExtractor> dextractor;
      Distance distance;
      Ptr<FeatureDetector> detector;
  
  private:
      CV_DescriptorExtractorTest& operator=(const CV_DescriptorExtractorTest&) { return *this; }
  };
  
  /****************************************************************************************\
  *                                Tests registrations                                     *
  \****************************************************************************************/
  
  TEST( Features2d_DescriptorExtractor_BRISK, regression )
  {
      CV_DescriptorExtractorTest<Hamming> test( "descriptor-brisk",  (CV_DescriptorExtractorTest<Hamming>::DistanceType)2.f,
                                                   DescriptorExtractor::create("BRISK") );
      test.safe_run();
  }
  
  TEST( Features2d_DescriptorExtractor_ORB, regression )
  {
      // TODO adjust the parameters below
      CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb",  (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
                                                   DescriptorExtractor::create("ORB") );
      test.safe_run();
  }
  
  TEST( Features2d_DescriptorExtractor_KAZE, regression )
  {
      CV_DescriptorExtractorTest< L2<float> > test( "descriptor-kaze",  0.03f,
                                                   DescriptorExtractor::create("KAZE"),
                                                   L2<float>(), FeatureDetector::create("KAZE"));
      test.safe_run();
  }
  
  TEST( Features2d_DescriptorExtractor_AKAZE, regression )
  {
      CV_DescriptorExtractorTest<Hamming> test( "descriptor-akaze",  (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
                                                DescriptorExtractor::create("AKAZE"),
                                                Hamming(), FeatureDetector::create("AKAZE"));
      test.safe_run();
  }
  