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replaced alloca() (a.k.a. cvStackAlloc) with AutoBuffer or vector() everywhere. cvStackAlloc() is still defined, but we do not need alloca() anymore to compile and run OpenCV (fixes #889 and may be some others)

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This commit is contained in:
Vadim Pisarevsky
2011-02-18 10:29:57 +00:00
parent 7b2ec0a1e6
commit 65a7f13af3
21 changed files with 286 additions and 263 deletions
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modules/ml/src/nbayes.cpp
+13 -35
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@@ -281,28 +281,20 @@ bool CvNormalBayesClassifier::train( const CvMat* _train_data, const CvMat* _res
float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results ) const
{
float value = 0;
void* buffer = 0;
int allocated_buffer = 0;
CV_FUNCNAME( "CvNormalBayesClassifier::predict" );
__BEGIN__;
int i, j, k, cls = -1, _var_count, nclasses;
int i, j, cls = -1;
double opt = FLT_MAX;
CvMat diff;
int rtype = 0, rstep = 0, size;
const int* vidx = 0;
nclasses = cls_labels->cols;
_var_count = avg[0]->cols;
int rtype = 0, rstep = 0;
int nclasses = cls_labels->cols;
int _var_count = avg[0]->cols;
if( !CV_IS_MAT(samples) || CV_MAT_TYPE(samples->type) != CV_32FC1 || samples->cols != var_all )
CV_ERROR( CV_StsBadArg,
CV_Error( CV_StsBadArg,
"The input samples must be 32f matrix with the number of columns = var_all" );
if( samples->rows > 1 && !results )
CV_ERROR( CV_StsNullPtr,
CV_Error( CV_StsNullPtr,
"When the number of input samples is >1, the output vector of results must be passed" );
if( results )
@@ -311,29 +303,20 @@ float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results ) c
CV_MAT_TYPE(results->type) != CV_32SC1) ||
(results->cols != 1 && results->rows != 1) ||
results->cols + results->rows - 1 != samples->rows )
CV_ERROR( CV_StsBadArg, "The output array must be integer or floating-point vector "
CV_Error( CV_StsBadArg, "The output array must be integer or floating-point vector "
"with the number of elements = number of rows in the input matrix" );
rtype = CV_MAT_TYPE(results->type);
rstep = CV_IS_MAT_CONT(results->type) ? 1 : results->step/CV_ELEM_SIZE(rtype);
}
if( var_idx )
vidx = var_idx->data.i;
const int* vidx = var_idx ? var_idx->data.i : 0;
// allocate memory and initializing headers for calculating
size = sizeof(double) * (nclasses + var_count);
if( size <= CV_MAX_LOCAL_SIZE )
buffer = cvStackAlloc( size );
else
{
CV_CALL( buffer = cvAlloc( size ));
allocated_buffer = 1;
}
cv::AutoBuffer<double> buffer(nclasses + var_count);
CvMat diff = cvMat( 1, var_count, CV_64FC1, &buffer[0] );
diff = cvMat( 1, var_count, CV_64FC1, buffer );
for( k = 0; k < samples->rows; k++ )
for( int k = 0; k < samples->rows; k++ )
{
int ival;
@@ -349,7 +332,7 @@ float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results ) c
for( j = 0; j < _var_count; j++ )
diff.data.db[j] = avg_data[j] - x[vidx ? vidx[j] : j];
CV_CALL(cvGEMM( &diff, u, 1, 0, 0, &diff, CV_GEMM_B_T ));
cvGEMM( &diff, u, 1, 0, 0, &diff, CV_GEMM_B_T );
for( j = 0; j < _var_count; j++ )
{
double d = diff.data.db[j];
@@ -385,11 +368,6 @@ float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results ) c
}*/
}
__END__;
if( allocated_buffer )
cvFree( &buffer );
return value;
}