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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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46
modules/ml/src/tree.cpp
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@@ -2036,7 +2036,7 @@ void CvDTree::cluster_categories( const int* vectors, int n, int m,
// TODO: consider adding priors (class weights) and sample weights to the clustering algorithm
int iters = 0, max_iters = 100;
int i, j, idx;
double* buf = (double*)cvStackAlloc( (n + k)*sizeof(buf[0]) );
cv::AutoBuffer<double> buf(n + k);
double *v_weights = buf, *c_weights = buf + n;
bool modified = true;
RNG* r = data->rng;
@@ -3558,40 +3558,30 @@ void CvDTree::free_tree()
CvDTreeNode* CvDTree::predict( const CvMat* _sample,
const CvMat* _missing, bool preprocessed_input ) const
{
CvDTreeNode* result = 0;
int* catbuf = 0;
cv::AutoBuffer<int> catbuf;
CV_FUNCNAME( "CvDTree::predict" );
__BEGIN__;
int i, step, mstep = 0;
const float* sample;
int i, mstep = 0;
const uchar* m = 0;
CvDTreeNode* node = root;
const int* vtype;
const int* vidx;
const int* cmap;
const int* cofs;
if( !node )
CV_ERROR( CV_StsError, "The tree has not been trained yet" );
CV_Error( CV_StsError, "The tree has not been trained yet" );
if( !CV_IS_MAT(_sample) || CV_MAT_TYPE(_sample->type) != CV_32FC1 ||
(_sample->cols != 1 && _sample->rows != 1) ||
(_sample->cols + _sample->rows - 1 != data->var_all && !preprocessed_input) ||
(_sample->cols + _sample->rows - 1 != data->var_count && preprocessed_input) )
CV_ERROR( CV_StsBadArg,
CV_Error( CV_StsBadArg,
"the input sample must be 1d floating-point vector with the same "
"number of elements as the total number of variables used for training" );
sample = _sample->data.fl;
step = CV_IS_MAT_CONT(_sample->type) ? 1 : _sample->step/sizeof(sample[0]);
const float* sample = _sample->data.fl;
int step = CV_IS_MAT_CONT(_sample->type) ? 1 : _sample->step/sizeof(sample[0]);
if( data->cat_count && !preprocessed_input ) // cache for categorical variables
{
int n = data->cat_count->cols;
catbuf = (int*)cvStackAlloc(n*sizeof(catbuf[0]));
catbuf.allocate(n);
for( i = 0; i < n; i++ )
catbuf[i] = -1;
}
@@ -3599,17 +3589,17 @@ CvDTreeNode* CvDTree::predict( const CvMat* _sample,
if( _missing )
{
if( !CV_IS_MAT(_missing) || !CV_IS_MASK_ARR(_missing) ||
!CV_ARE_SIZES_EQ(_missing, _sample) )
CV_ERROR( CV_StsBadArg,
!CV_ARE_SIZES_EQ(_missing, _sample) )
CV_Error( CV_StsBadArg,
"the missing data mask must be 8-bit vector of the same size as input sample" );
m = _missing->data.ptr;
mstep = CV_IS_MAT_CONT(_missing->type) ? 1 : _missing->step/sizeof(m[0]);
}
vtype = data->var_type->data.i;
vidx = data->var_idx && !preprocessed_input ? data->var_idx->data.i : 0;
cmap = data->cat_map ? data->cat_map->data.i : 0;
cofs = data->cat_ofs ? data->cat_ofs->data.i : 0;
const int* vtype = data->var_type->data.i;
const int* vidx = data->var_idx && !preprocessed_input ? data->var_idx->data.i : 0;
const int* cmap = data->cat_map ? data->cat_map->data.i : 0;
const int* cofs = data->cat_ofs ? data->cat_ofs->data.i : 0;
while( node->Tn > pruned_tree_idx && node->left )
{
@@ -3640,7 +3630,7 @@ CvDTreeNode* CvDTree::predict( const CvMat* _sample,
int ival = cvRound(val);
if( ival != val )
CV_ERROR( CV_StsBadArg,
CV_Error( CV_StsBadArg,
"one of input categorical variable is not an integer" );
int sh = 0;
@@ -3678,11 +3668,7 @@ CvDTreeNode* CvDTree::predict( const CvMat* _sample,
node = dir < 0 ? node->left : node->right;
}
result = node;
__END__;
return result;
return node;
}