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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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19
modules/ml/src/ann_mlp.cpp
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@@ -265,30 +265,25 @@ void CvANN_MLP::create( const CvMat* _layer_sizes, int _activ_func,
float CvANN_MLP::predict( const CvMat* _inputs, CvMat* _outputs ) const
{
CV_FUNCNAME( "CvANN_MLP::predict" );
__BEGIN__;
double* buf;
int i, j, n, dn = 0, l_count, dn0, buf_sz, min_buf_sz;
if( !layer_sizes )
CV_ERROR( CV_StsError, "The network has not been initialized" );
CV_Error( CV_StsError, "The network has not been initialized" );
if( !CV_IS_MAT(_inputs) || !CV_IS_MAT(_outputs) ||
!CV_ARE_TYPES_EQ(_inputs,_outputs) ||
(CV_MAT_TYPE(_inputs->type) != CV_32FC1 &&
CV_MAT_TYPE(_inputs->type) != CV_64FC1) ||
_inputs->rows != _outputs->rows )
CV_ERROR( CV_StsBadArg, "Both input and output must be floating-point matrices "
CV_Error( CV_StsBadArg, "Both input and output must be floating-point matrices "
"of the same type and have the same number of rows" );
if( _inputs->cols != layer_sizes->data.i[0] )
CV_ERROR( CV_StsBadSize, "input matrix must have the same number of columns as "
CV_Error( CV_StsBadSize, "input matrix must have the same number of columns as "
"the number of neurons in the input layer" );
if( _outputs->cols != layer_sizes->data.i[layer_sizes->cols - 1] )
CV_ERROR( CV_StsBadSize, "output matrix must have the same number of columns as "
CV_Error( CV_StsBadSize, "output matrix must have the same number of columns as "
"the number of neurons in the output layer" );
n = dn0 = _inputs->rows;
min_buf_sz = 2*max_count;
@@ -301,7 +296,7 @@ float CvANN_MLP::predict( const CvMat* _inputs, CvMat* _outputs ) const
buf_sz = dn0*min_buf_sz;
}
buf = (double*)cvStackAlloc( buf_sz*sizeof(buf[0]) );
cv::AutoBuffer<double> buf(buf_sz);
l_count = layer_sizes->cols;
for( i = 0; i < n; i += dn )
@@ -310,7 +305,7 @@ float CvANN_MLP::predict( const CvMat* _inputs, CvMat* _outputs ) const
dn = MIN( dn0, n - i );
cvGetRows( _inputs, layer_in, i, i + dn );
cvInitMatHeader( layer_out, dn, layer_in->cols, CV_64F, buf );
cvInitMatHeader( layer_out, dn, layer_in->cols, CV_64F, &buf[0] );
scale_input( layer_in, layer_out );
CV_SWAP( layer_in, layer_out, temp );
@@ -332,8 +327,6 @@ float CvANN_MLP::predict( const CvMat* _inputs, CvMat* _outputs ) const
scale_output( layer_in, layer_out );
}
__END__;
return 0.f;
}