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Hid symbols in static builds, added LTO flags, removed exports from ts

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This commit is contained in:
Pavel Rojtberg
2017-01-25 12:20:57 +01:00
committed by Maksim Shabunin
parent ef04ca9e0f
commit 6fb9d42c3f
25 changed files with 174 additions and 375 deletions
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apps/traincascade/old_ml.hpp
-26
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@@ -1895,32 +1895,6 @@ protected:
cv::RNG* rng;
};
/****************************************************************************************\
* Auxilary functions declarations *
\****************************************************************************************/
/* Generates <sample> from multivariate normal distribution, where <mean> - is an
average row vector, <cov> - symmetric covariation matrix */
CVAPI(void) cvRandMVNormal( CvMat* mean, CvMat* cov, CvMat* sample,
CvRNG* rng CV_DEFAULT(0) );
/* Generates sample from gaussian mixture distribution */
CVAPI(void) cvRandGaussMixture( CvMat* means[],
CvMat* covs[],
float weights[],
int clsnum,
CvMat* sample,
CvMat* sampClasses CV_DEFAULT(0) );
#define CV_TS_CONCENTRIC_SPHERES 0
/* creates test set */
CVAPI(void) cvCreateTestSet( int type, CvMat** samples,
int num_samples,
int num_features,
CvMat** responses,
int num_classes, ... );
/****************************************************************************************\
* Data *
\****************************************************************************************/
apps/traincascade/old_ml_inner_functions.cpp
-191
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@@ -114,153 +114,11 @@ void CvStatModel::write( CvFileStorage*, const char* ) const
OPENCV_ERROR( CV_StsNotImplemented, "CvStatModel::write", "" );
}
void CvStatModel::read( CvFileStorage*, CvFileNode* )
{
OPENCV_ERROR( CV_StsNotImplemented, "CvStatModel::read", "" );
}
/* Calculates upper triangular matrix S, where A is a symmetrical matrix A=S'*S */
static void cvChol( CvMat* A, CvMat* S )
{
int dim = A->rows;
int i, j, k;
float sum;
for( i = 0; i < dim; i++ )
{
for( j = 0; j < i; j++ )
CV_MAT_ELEM(*S, float, i, j) = 0;
sum = 0;
for( k = 0; k < i; k++ )
sum += CV_MAT_ELEM(*S, float, k, i) * CV_MAT_ELEM(*S, float, k, i);
CV_MAT_ELEM(*S, float, i, i) = (float)sqrt(CV_MAT_ELEM(*A, float, i, i) - sum);
for( j = i + 1; j < dim; j++ )
{
sum = 0;
for( k = 0; k < i; k++ )
sum += CV_MAT_ELEM(*S, float, k, i) * CV_MAT_ELEM(*S, float, k, j);
CV_MAT_ELEM(*S, float, i, j) =
(CV_MAT_ELEM(*A, float, i, j) - sum) / CV_MAT_ELEM(*S, float, i, i);
}
}
}
/* Generates <sample> from multivariate normal distribution, where <mean> - is an
average row vector, <cov> - symmetric covariation matrix */
CV_IMPL void cvRandMVNormal( CvMat* mean, CvMat* cov, CvMat* sample, CvRNG* rng )
{
int dim = sample->cols;
int amount = sample->rows;
CvRNG state = rng ? *rng : cvRNG( cvGetTickCount() );
cvRandArr(&state, sample, CV_RAND_NORMAL, cvScalarAll(0), cvScalarAll(1) );
CvMat* utmat = cvCreateMat(dim, dim, sample->type);
CvMat* vect = cvCreateMatHeader(1, dim, sample->type);
cvChol(cov, utmat);
int i;
for( i = 0; i < amount; i++ )
{
cvGetRow(sample, vect, i);
cvMatMulAdd(vect, utmat, mean, vect);
}
cvReleaseMat(&vect);
cvReleaseMat(&utmat);
}
/* Generates <sample> of <amount> points from a discrete variate xi,
where Pr{xi = k} == probs[k], 0 < k < len - 1. */
static void cvRandSeries( float probs[], int len, int sample[], int amount )
{
CvMat* univals = cvCreateMat(1, amount, CV_32FC1);
float* knots = (float*)cvAlloc( len * sizeof(float) );
int i, j;
CvRNG state = cvRNG(-1);
cvRandArr(&state, univals, CV_RAND_UNI, cvScalarAll(0), cvScalarAll(1) );
knots[0] = probs[0];
for( i = 1; i < len; i++ )
knots[i] = knots[i - 1] + probs[i];
for( i = 0; i < amount; i++ )
for( j = 0; j < len; j++ )
{
if ( CV_MAT_ELEM(*univals, float, 0, i) <= knots[j] )
{
sample[i] = j;
break;
}
}
cvFree(&knots);
}
/* Generates <sample> from gaussian mixture distribution */
CV_IMPL void cvRandGaussMixture( CvMat* means[],
CvMat* covs[],
float weights[],
int clsnum,
CvMat* sample,
CvMat* sampClasses )
{
int dim = sample->cols;
int amount = sample->rows;
int i, clss;
int* sample_clsnum = (int*)cvAlloc( amount * sizeof(int) );
CvMat** utmats = (CvMat**)cvAlloc( clsnum * sizeof(CvMat*) );
CvMat* vect = cvCreateMatHeader(1, dim, CV_32FC1);
CvMat* classes;
if( sampClasses )
classes = sampClasses;
else
classes = cvCreateMat(1, amount, CV_32FC1);
CvRNG state = cvRNG(-1);
cvRandArr(&state, sample, CV_RAND_NORMAL, cvScalarAll(0), cvScalarAll(1));
cvRandSeries(weights, clsnum, sample_clsnum, amount);
for( i = 0; i < clsnum; i++ )
{
utmats[i] = cvCreateMat(dim, dim, CV_32FC1);
cvChol(covs[i], utmats[i]);
}
for( i = 0; i < amount; i++ )
{
CV_MAT_ELEM(*classes, float, 0, i) = (float)sample_clsnum[i];
cvGetRow(sample, vect, i);
clss = sample_clsnum[i];
cvMatMulAdd(vect, utmats[clss], means[clss], vect);
}
if( !sampClasses )
cvReleaseMat(&classes);
for( i = 0; i < clsnum; i++ )
cvReleaseMat(&utmats[i]);
cvFree(&utmats);
cvFree(&sample_clsnum);
cvReleaseMat(&vect);
}
CvMat* icvGenerateRandomClusterCenters ( int seed, const CvMat* data,
int num_of_clusters, CvMat* _centers )
{
@@ -317,55 +175,6 @@ CvMat* icvGenerateRandomClusterCenters ( int seed, const CvMat* data,
return _centers ? _centers : centers;
} // end of icvGenerateRandomClusterCenters
// By S. Dilman - begin -
#define ICV_RAND_MAX 4294967296 // == 2^32
// static void cvRandRoundUni (CvMat* center,
// float radius_small,
// float radius_large,
// CvMat* desired_matrix,
// CvRNG* rng_state_ptr)
// {
// float rad, norm, coefficient;
// int dim, size, i, j;
// CvMat *cov, sample;
// CvRNG rng_local;
// CV_FUNCNAME("cvRandRoundUni");
// __BEGIN__
// rng_local = *rng_state_ptr;
// CV_ASSERT ((radius_small >= 0) &&
// (radius_large > 0) &&
// (radius_small <= radius_large));
// CV_ASSERT (center && desired_matrix && rng_state_ptr);
// CV_ASSERT (center->rows == 1);
// CV_ASSERT (center->cols == desired_matrix->cols);
// dim = desired_matrix->cols;
// size = desired_matrix->rows;
// cov = cvCreateMat (dim, dim, CV_32FC1);
// cvSetIdentity (cov);
// cvRandMVNormal (center, cov, desired_matrix, &rng_local);
// for (i = 0; i < size; i++)
// {
// rad = (float)(cvRandReal(&rng_local)*(radius_large - radius_small) + radius_small);
// cvGetRow (desired_matrix, &sample, i);
// norm = (float) cvNorm (&sample, 0, CV_L2);
// coefficient = rad / norm;
// for (j = 0; j < dim; j++)
// CV_MAT_ELEM (sample, float, 0, j) *= coefficient;
// }
// __END__
// }
// By S. Dilman - end -
static int CV_CDECL
icvCmpIntegers( const void* a, const void* b )
{
apps/traincascade/old_ml_tree.cpp
+1 -1
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@@ -1880,7 +1880,7 @@ double CvDTree::calc_node_dir( CvDTreeNode* node )
namespace cv
{
template<> CV_EXPORTS void DefaultDeleter<CvDTreeSplit>::operator ()(CvDTreeSplit* obj) const
template<> void DefaultDeleter<CvDTreeSplit>::operator ()(CvDTreeSplit* obj) const
{
fastFree(obj);
}