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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, Biagio Montesano, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (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,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "perf_precomp.hpp"
using namespace cv;
using namespace cv::line_descriptor;
using namespace std;
using namespace perf;
using std::tr1::make_tuple;
using std::tr1::get;
#define QUERY_DES_COUNT 300
#define DIM 32
#define COUNT_FACTOR 4
#define RADIUS 3
void generateData( Mat& query, Mat& train );
uchar invertSingleBits( uchar dividend_char, int numBits );
/* invert numBits bits in input char */
uchar invertSingleBits( uchar dividend_char, int numBits )
{
std::vector<int> bin_vector;
long dividend;
long bin_num;
/* convert input char to a long */
dividend = (long) dividend_char;
/*if a 0 has been obtained, just generate a 8-bit long vector of zeros */
if( dividend == 0 )
bin_vector = std::vector<int>( 8, 0 );
/* else, apply classic decimal to binary conversion */
else
{
while ( dividend >= 1 )
{
bin_num = dividend % 2;
dividend /= 2;
bin_vector.push_back( bin_num );
}
}
/* ensure that binary vector always has length 8 */
if( bin_vector.size() < 8 )
{
std::vector<int> zeros( 8 - bin_vector.size(), 0 );
bin_vector.insert( bin_vector.end(), zeros.begin(), zeros.end() );
}
/* invert numBits bits */
for ( int index = 0; index < numBits; index++ )
{
if( bin_vector[index] == 0 )
bin_vector[index] = 1;
else
bin_vector[index] = 0;
}
/* reconvert to decimal */
uchar result = 0;
for ( int i = (int) bin_vector.size() - 1; i >= 0; i-- )
result += (uchar) ( bin_vector[i] * ( 1 << i ) );
return result;
}
void generateData( Mat& query, Mat& train )
{
RNG& rng = theRNG();
Mat buf( QUERY_DES_COUNT, DIM, CV_8UC1 );
rng.fill( buf, RNG::UNIFORM, Scalar( 0 ), Scalar( 255 ) );
buf.convertTo( query, CV_8UC1 );
for ( int i = 0; i < query.rows; i++ )
{
for ( int j = 0; j < COUNT_FACTOR; j++ )
{
train.push_back( query.row( i ) );
int randCol = rand() % 32;
uchar u = query.at<uchar>( i, randCol );
uchar modified_u = invertSingleBits( u, j + 1 );
train.at<uchar>( i * COUNT_FACTOR + j, randCol ) = modified_u;
}
}
}
PERF_TEST(matching, single_match)
{
Mat query, train;
std::vector<DMatch> dm;
Ptr<BinaryDescriptorMatcher> bd = BinaryDescriptorMatcher::createBinaryDescriptorMatcher();
generateData( query, train );
TEST_CYCLE()
bd->match( query, train, dm );
SANITY_CHECK_NOTHING();
}
PERF_TEST(knn_matching, knn_match_distances_test)
{
Mat query, train, distances;
std::vector<std::vector<DMatch> > dm;
Ptr<BinaryDescriptorMatcher> bd = BinaryDescriptorMatcher::createBinaryDescriptorMatcher();
generateData( query, train );
TEST_CYCLE()
{
bd->knnMatch( query, train, dm, QUERY_DES_COUNT );
for ( int i = 0; i < (int) dm.size(); i++ )
{
for ( int j = 0; j < (int) dm[i].size(); j++ )
distances.push_back( dm[i][j].distance );
}
}
SANITY_CHECK_NOTHING();
}
PERF_TEST(radius_match, radius_match_distances_test)
{
Mat query, train, distances;
std::vector<std::vector<DMatch> > dm;
Ptr<BinaryDescriptorMatcher> bd = BinaryDescriptorMatcher::createBinaryDescriptorMatcher();
generateData( query, train );
TEST_CYCLE()
{
bd->radiusMatch( query, train, dm, RADIUS );
for ( int i = 0; i < (int) dm.size(); i++ )
{
for ( int j = 0; j < (int) dm[i].size(); j++ )
distances.push_back( dm[i][j].distance );
}
}
SANITY_CHECK_NOTHING();
}
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