Added descriptors' test

modules/line_descriptor/test/test_descriptors_regression.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
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+ //                           License Agreement
+ //                For Open Source Computer Vision Library
+ //
+ // Copyright (C) 2014, Biagio Montesano, all rights reserved.
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+ // This software is provided by the copyright holders and contributors "as is" and
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+ // In no event shall the Intel Corporation or contributors be liable for any direct,
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+ // (including, but not limited to, procurement of substitute goods or services;
+ // loss of use, data, or profits; or business interruption) however caused
+ // and on any theory of liability, whether in contract, strict liability,
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+ //M*/
+
+#include "test_precomp.hpp"
+
+using namespace cv;
+
+/****************************************************************************************\
+*            Regression tests for line detector comparing keylines.                 *
+ \****************************************************************************************/
+
+const std::string LINE_DESCRIPTOR_DIR = "line_descriptor";
+const std::string IMAGE_FILENAME = "cameraman.jpg";
+const std::string DESCRIPTORS_DIR = LINE_DESCRIPTOR_DIR + "/descriptors";
+
+template<class Distance>
+class CV_BD_DescriptorsTest : public cvtest::BaseTest
+{
+
+ public:
+  typedef typename Distance::ValueType ValueType;
+  typedef typename Distance::ResultType DistanceType;
+
+  CV_BD_DescriptorsTest( std::string fs, DistanceType _maxDist ): maxDist(_maxDist)
+  {
+    bd = BinaryDescriptor::createBinaryDescriptor();
+    fs_name = fs;
+  }
+
+ protected:
+//  void compareDescriptors( const Mat& validDescriptors, const Mat& calcDescriptors );
+//  void createVecFromMat( Mat& inputMat, std::vector<KeyLine>& output );
+//  virtual bool writeDescriptors( Mat& descs );
+//  virtual Mat readDescriptors();
+//  void emptyDataTest();
+//  void regressionTest();
+//  virtual void run( int );
+
+  Ptr<BinaryDescriptor> bd;
+  std::string fs_name;
+  const DistanceType maxDist;
+  Distance distance;
+
+//};
+
+  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( validDescriptors.type() == CV_8U );
+
+    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;
+    }
+
+    std::stringstream ss;
+    ss << "Max distance between valid and computed descriptors " << curMaxDist;
+
+    if( curMaxDist < maxDist )
+      ss << "." << std::endl;
+
+    else
+    {
+      ss << ">" << maxDist << " - bad accuracy!" << "\n";
+      ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
+    }
+
+    ts->printf( cvtest::TS::LOG, ss.str().c_str() );
+  }
+
+  Mat readDescriptors()
+  {
+    Mat descriptors;
+    FileStorage fs( std::string( ts->get_data_path() ) + LINE_DESCRIPTOR_DIR + "/descriptors/" + fs_name, FileStorage::READ );
+    fs["descriptors"] >> descriptors;
+
+    return descriptors;
+  }
+
+  bool writeDescriptors( Mat& descs )
+  {
+    FileStorage fs( std::string( ts->get_data_path() ) + LINE_DESCRIPTOR_DIR + "/descriptors/" + fs_name, FileStorage::WRITE );
+    fs << "descriptors" << descs;
+
+    return true;
+  }
+
+  void createMatFromVec( const std::vector<KeyLine>& linesVec, Mat& output )
+  {
+    output = Mat( (int) linesVec.size(), 17, CV_32FC1 );
+
+    for ( int i = 0; i < (int) linesVec.size(); i++ )
+    {
+      std::vector<float> klData;
+      KeyLine kl = linesVec[i];
+      klData.push_back( kl.angle );
+      klData.push_back( (float) kl.class_id );
+      klData.push_back( kl.ePointInOctaveX );
+      klData.push_back( kl.ePointInOctaveY );
+      klData.push_back( kl.endPointX );
+      klData.push_back( kl.endPointY );
+      klData.push_back( kl.lineLength );
+      klData.push_back( (float) kl.numOfPixels );
+      klData.push_back( (float) kl.octave );
+      klData.push_back( kl.pt.x );
+      klData.push_back( kl.pt.y );
+      klData.push_back( kl.response );
+      klData.push_back( kl.sPointInOctaveX );
+      klData.push_back( kl.sPointInOctaveY );
+      klData.push_back( kl.size );
+      klData.push_back( kl.startPointX );
+      klData.push_back( kl.startPointY );
+
+      float* pointerToRow = output.ptr<float>( i );
+      for ( int j = 0; j < 17; j++ )
+      {
+        *pointerToRow = klData[j];
+        pointerToRow++;
+      }
+    }
+  }
+
+  void createVecFromMat( Mat& inputMat, std::vector<KeyLine>& output )
+  {
+    for ( int i = 0; i < inputMat.rows; i++ )
+    {
+      std::vector<float> tempFloat;
+      KeyLine kl;
+      float* pointerToRow = inputMat.ptr<float>( i );
+
+      for ( int j = 0; j < 17; j++ )
+      {
+        tempFloat.push_back( *pointerToRow );
+        pointerToRow++;
+      }
+
+      kl.angle = tempFloat[0];
+      kl.class_id = (int) tempFloat[1];
+      kl.ePointInOctaveX = tempFloat[2];
+      kl.ePointInOctaveY = tempFloat[3];
+      kl.endPointX = tempFloat[4];
+      kl.endPointY = tempFloat[5];
+      kl.lineLength = tempFloat[6];
+      kl.numOfPixels = (int) tempFloat[7];
+      kl.octave = (int) tempFloat[8];
+      kl.pt.x = tempFloat[9];
+      kl.pt.y = tempFloat[10];
+      kl.response = tempFloat[11];
+      kl.sPointInOctaveX = tempFloat[12];
+      kl.sPointInOctaveY = tempFloat[13];
+      kl.size = tempFloat[14];
+      kl.startPointX = tempFloat[15];
+      kl.startPointY = tempFloat[16];
+
+      output.push_back( kl );
+    }
+  }
+
+  void emptyDataTest()
+  {
+    assert( bd );
+
+    // One image.
+    Mat image;
+    std::vector<KeyLine> keypoints;
+    Mat descriptors;
+
+    try
+    {
+      bd->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
+    {
+      bd->compute( image, keypoints, descriptors );
+    }
+
+    catch ( ... )
+    {
+      ts->printf( cvtest::TS::LOG, "compute() on nonempty image and empty keylines must not generate exception (1).\n" );
+      ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
+    }
+
+    // Several images.
+    std::vector<Mat> images;
+    std::vector<std::vector<KeyLine> > keylinesCollection;
+    std::vector<Mat> descriptorsCollection;
+    try
+    {
+      bd->compute( images, keylinesCollection, descriptorsCollection );
+    }
+
+    catch ( ... )
+    {
+      ts->printf( cvtest::TS::LOG, "compute() on empty images and empty keylines collection must not generate exception (2).\n" );
+      ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
+    }
+
+  }
+
+  void regressionTest()
+  {
+    assert( bd );
+
+    // Read the test image.
+    std::string imgFilename = std::string( ts->get_data_path() ) + LINE_DESCRIPTOR_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;
+    }
+
+    std::vector<KeyLine> keylines;
+    FileStorage fs( std::string( ts->get_data_path() ) + LINE_DESCRIPTOR_DIR + "/detectors/edl_detector_keylines_cameraman.yaml", FileStorage::READ );
+    if( fs.isOpened() )
+    {
+      //read( fs.getFirstTopLevelNode(), keypoints );
+
+      /* load keylines */
+      Mat loadedKeylines;
+      fs["keylines"] >> loadedKeylines;
+      createVecFromMat( loadedKeylines, keylines );
+
+      /* compute descriptors */
+      Mat calcDescriptors;
+      double t = (double) getTickCount();
+      bd->compute( img, keylines, 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) keylines.size() )
+      {
+        ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keylines count must be equal.\n" );
+        ts->printf( cvtest::TS::LOG, "Count of keylines is            %d.\n", (int) keylines.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 != bd->descriptorSize() / 8 || calcDescriptors.type() != bd->descriptorType() )
+      {
+        ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" );
+        ts->printf( cvtest::TS::LOG, "Expected size is   %d.\n", bd->descriptorSize() );
+        ts->printf( cvtest::TS::LOG, "Calculated size is %d.\n", calcDescriptors.cols );
+        ts->printf( cvtest::TS::LOG, "Expected type is   %d.\n", bd->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;
+        }
+      }
+
+    }
+    else
+    {
+      ts->printf( cvtest::TS::LOG, "Compute and write keylines.\n" );
+      fs.open( std::string( ts->get_data_path() ) + LINE_DESCRIPTOR_DIR + "/detectors/edl_detector_keylines_cameraman.yaml", FileStorage::WRITE );
+      if( fs.isOpened() )
+      {
+        bd->detect( img, keylines );
+        Mat keyLinesToYaml;
+        createMatFromVec( keylines, keyLinesToYaml );
+        fs << "keylines" << keyLinesToYaml;
+      }
+      else
+      {
+        ts->printf( cvtest::TS::LOG, "File for writting keylines can not be opened.\n" );
+        ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
+        return;
+      }
+    }
+  }
+
+  void run( int )
+  {
+    if( !bd )
+    {
+      ts->printf( cvtest::TS::LOG, "Feature detector 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 );
+  }
+
+ private:
+  CV_BD_DescriptorsTest& operator=( const CV_BD_DescriptorsTest& )
+  {
+    return *this;
+  }
+};
+/****************************************************************************************\
+*                                Tests registrations                                     *
+ \****************************************************************************************/
+
+TEST( BinaryDescriptor_Descriptors, regression )
+{
+  CV_BD_DescriptorsTest<Hamming> test( std::string( "lbd_descriptors_cameraman" ), 1 );
+  test.safe_run();
+}

modules/line_descriptor/test/test_detector_regression.cpp

@@ -257,8 +257,6 @@ void CV_BinaryDescriptorDetectorTest::regressionTest()
   std::string imgFilename = std::string( ts->get_data_path() ) + LINE_DESCRIPTOR_DIR + "/" + IMAGE_FILENAME;
   std::string resFilename = std::string( ts->get_data_path() ) + DETECTOR_DIR + "/" + fs_name + ".yaml";
 
-  std::cout << "PATH " << std::string( ts->get_data_path() ) << std::endl;
-
   // Read the test image.
   Mat image = imread( imgFilename );
   if( image.empty() )