Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
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#include "test_precomp.hpp"
using namespace std;
using namespace cv;
void defaultDistribs( vector<Mat>& means, vector<Mat>& covs )
{
float mp0[] = {0.0f, 0.0f}, cp0[] = {0.67f, 0.0f, 0.0f, 0.67f};
float mp1[] = {5.0f, 0.0f}, cp1[] = {1.0f, 0.0f, 0.0f, 1.0f};
float mp2[] = {1.0f, 5.0f}, cp2[] = {1.0f, 0.0f, 0.0f, 1.0f};
Mat m0( 1, 2, CV_32FC1, mp0 ), c0( 2, 2, CV_32FC1, cp0 );
Mat m1( 1, 2, CV_32FC1, mp1 ), c1( 2, 2, CV_32FC1, cp1 );
Mat m2( 1, 2, CV_32FC1, mp2 ), c2( 2, 2, CV_32FC1, cp2 );
means.resize(3), covs.resize(3);
m0.copyTo(means[0]), c0.copyTo(covs[0]);
m1.copyTo(means[1]), c1.copyTo(covs[1]);
m2.copyTo(means[2]), c2.copyTo(covs[2]);
}
// generate points sets by normal distributions
void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const vector<Mat>& means, const vector<Mat>& covs, int labelType )
{
vector<int>::const_iterator sit = sizes.begin();
int total = 0;
for( ; sit != sizes.end(); ++sit )
total += *sit;
assert( means.size() == sizes.size() && covs.size() == sizes.size() );
assert( !data.empty() && data.rows == total );
assert( data.type() == CV_32FC1 );
labels.create( data.rows, 1, labelType );
randn( data, Scalar::all(0.0), Scalar::all(1.0) );
vector<Mat>::const_iterator mit = means.begin(), cit = covs.begin();
int bi, ei = 0;
sit = sizes.begin();
for( int p = 0, l = 0; sit != sizes.end(); ++sit, ++mit, ++cit, l++ )
{
bi = ei;
ei = bi + *sit;
assert( mit->rows == 1 && mit->cols == data.cols );
assert( cit->rows == data.cols && cit->cols == data.cols );
for( int i = bi; i < ei; i++, p++ )
{
Mat r(1, data.cols, CV_32FC1, data.ptr<float>(i));
r = r * (*cit) + *mit;
if( labelType == CV_32FC1 )
labels.at<float>(p, 0) = (float)l;
else
labels.at<int>(p, 0) = l;
}
}
}
int maxIdx( const vector<int>& count )
{
int idx = -1;
int maxVal = -1;
vector<int>::const_iterator it = count.begin();
for( int i = 0; it != count.end(); ++it, i++ )
{
if( *it > maxVal)
{
maxVal = *it;
idx = i;
}
}
assert( idx >= 0);
return idx;
}
bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap )
{
int total = 0, setCount = (int)sizes.size();
vector<int>::const_iterator sit = sizes.begin();
for( ; sit != sizes.end(); ++sit )
total += *sit;
assert( !labels.empty() );
assert( labels.rows == total && labels.cols == 1 );
assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
bool isFlt = labels.type() == CV_32FC1;
labelsMap.resize(setCount);
vector<int>::iterator lmit = labelsMap.begin();
vector<bool> buzy(setCount, false);
int bi, ei = 0;
for( sit = sizes.begin(); sit != sizes.end(); ++sit, ++lmit )
{
vector<int> count( setCount, 0 );
bi = ei;
ei = bi + *sit;
if( isFlt )
{
for( int i = bi; i < ei; i++ )
count[(int)labels.at<float>(i, 0)]++;
}
else
{
for( int i = bi; i < ei; i++ )
count[labels.at<int>(i, 0)]++;
}
*lmit = maxIdx( count );
if( buzy[*lmit] )
return false;
buzy[*lmit] = true;
}
return true;
}
float calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, bool labelsEquivalent = true )
{
int err = 0;
assert( !labels.empty() && !origLabels.empty() );
assert( labels.cols == 1 && origLabels.cols == 1 );
assert( labels.rows == origLabels.rows );
assert( labels.type() == origLabels.type() );
assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
vector<int> labelsMap;
bool isFlt = labels.type() == CV_32FC1;
if( !labelsEquivalent )
{
getLabelsMap( labels, sizes, labelsMap );
for( int i = 0; i < labels.rows; i++ )
if( isFlt )
err += labels.at<float>(i, 0) != labelsMap[(int)origLabels.at<float>(i, 0)];
else
err += labels.at<int>(i, 0) != labelsMap[origLabels.at<int>(i, 0)];
}
else
{
for( int i = 0; i < labels.rows; i++ )
if( isFlt )
err += labels.at<float>(i, 0) != origLabels.at<float>(i, 0);
else
err += labels.at<int>(i, 0) != origLabels.at<int>(i, 0);
}
return (float)err / (float)labels.rows;
}
//--------------------------------------------------------------------------------------------
class CV_KMeansTest : public cvtest::BaseTest {
public:
CV_KMeansTest() {}
protected:
virtual void run( int start_from );
};
void CV_KMeansTest::run( int /*start_from*/ )
{
const int iters = 100;
int sizesArr[] = { 5000, 7000, 8000 };
int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
Mat data( pointsCount, 2, CV_32FC1 ), labels;
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
vector<Mat> means, covs;
defaultDistribs( means, covs );
generateData( data, labels, sizes, means, covs, CV_32SC1 );
int code = cvtest::TS::OK;
Mat bestLabels;
// 1. flag==KMEANS_PP_CENTERS
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_PP_CENTERS, None() );
if( calcErr( bestLabels, labels, sizes, false ) > 0.01f )
{
ts->printf( cvtest::TS::LOG, "bad accuracy if flag==KMEANS_PP_CENTERS" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
// 2. flag==KMEANS_RANDOM_CENTERS
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_RANDOM_CENTERS, None() );
if( calcErr( bestLabels, labels, sizes, false ) > 0.01f )
{
ts->printf( cvtest::TS::LOG, "bad accuracy if flag==KMEANS_PP_CENTERS" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
// 3. flag==KMEANS_USE_INITIAL_LABELS
labels.copyTo( bestLabels );
RNG rng;
for( int i = 0; i < 0.5f * pointsCount; i++ )
bestLabels.at<int>( rng.next() % pointsCount, 0 ) = rng.next() % 3;
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_USE_INITIAL_LABELS, None() );
if( calcErr( bestLabels, labels, sizes, false ) > 0.01f )
{
ts->printf( cvtest::TS::LOG, "bad accuracy if flag==KMEANS_PP_CENTERS" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
ts->set_failed_test_info( code );
}
//--------------------------------------------------------------------------------------------
class CV_KNearestTest : public cvtest::BaseTest {
public:
CV_KNearestTest() {}
protected:
virtual void run( int start_from );
};
void CV_KNearestTest::run( int /*start_from*/ )
{
int sizesArr[] = { 500, 700, 800 };
int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
// train data
Mat trainData( pointsCount, 2, CV_32FC1 ), trainLabels;
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
vector<Mat> means, covs;
defaultDistribs( means, covs );
generateData( trainData, trainLabels, sizes, means, covs, CV_32FC1 );
// test data
Mat testData( pointsCount, 2, CV_32FC1 ), testLabels, bestLabels;
generateData( testData, testLabels, sizes, means, covs, CV_32FC1 );
int code = cvtest::TS::OK;
KNearest knearest;
knearest.train( trainData, trainLabels );
knearest.find_nearest( testData, 4, &bestLabels );
if( calcErr( bestLabels, testLabels, sizes, true ) > 0.01f )
{
ts->printf( cvtest::TS::LOG, "bad accuracy on test data" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
ts->set_failed_test_info( code );
}
//--------------------------------------------------------------------------------------------
class CV_EMTest : public cvtest::BaseTest {
public:
CV_EMTest() {}
protected:
virtual void run( int start_from );
};
void CV_EMTest::run( int /*start_from*/ )
{
int sizesArr[] = { 5000, 7000, 8000 };
int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
// train data
Mat trainData( pointsCount, 2, CV_32FC1 ), trainLabels;
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
vector<Mat> means, covs;
defaultDistribs( means, covs );
generateData( trainData, trainLabels, sizes, means, covs, CV_32SC1 );
// test data
Mat testData( pointsCount, 2, CV_32FC1 ), testLabels, bestLabels;
generateData( testData, testLabels, sizes, means, covs, CV_32SC1 );
int code = cvtest::TS::OK;
ExpectationMaximization em;
CvEMParams params;
params.nclusters = 3;
em.train( trainData, Mat(), params, &bestLabels );
// check train error
if( calcErr( bestLabels, trainLabels, sizes, true ) > 0.002f )
{
ts->printf( cvtest::TS::LOG, "bad accuracy on train data" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
// check test error
bestLabels.create( testData.rows, 1, CV_32SC1 );
for( int i = 0; i < testData.rows; i++ )
{
Mat sample( 1, testData.cols, CV_32FC1, testData.ptr<float>(i));
bestLabels.at<int>(i,0) = (int)em.predict( sample, 0 );
}
if( calcErr( bestLabels, testLabels, sizes, true ) > 0.005f )
{
ts->printf( cvtest::TS::LOG, "bad accuracy on test data" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
ts->set_failed_test_info( code );
}
TEST(ML_KMeans, accuracy) { CV_KMeansTest test; test.safe_run(); }
TEST(ML_KNearest, accuracy) { CV_KNearestTest test; test.safe_run(); }
TEST(ML_EMTest, accuracy) { CV_EMTest test; test.safe_run(); }