generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

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)

Browse Source
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
Download Patch File Download Diff File Expand all files Collapse all files

41
modules/ml/src/knearest.cpp
View File

@@ -306,43 +306,37 @@ float CvKNearest::find_nearest( const CvMat* _samples, int k, CvMat* _results,
const float** _neighbors, CvMat* _neighbor_responses, CvMat* _dist ) const
{
float result = 0.f;
bool local_alloc = false;
float* buf = 0;
const int max_blk_count = 128, max_buf_sz = 1 << 12;
CV_FUNCNAME( "CvKNearest::find_nearest" );
__BEGIN__;
int i, count, count_scale, blk_count0, blk_count = 0, buf_sz, k1;
if( !samples )
CV_ERROR( CV_StsError, "The search tree must be constructed first using train method" );
CV_Error( CV_StsError, "The search tree must be constructed first using train method" );
if( !CV_IS_MAT(_samples) ||
CV_MAT_TYPE(_samples->type) != CV_32FC1 ||
_samples->cols != var_count )
CV_ERROR( CV_StsBadArg, "Input samples must be floating-point matrix (<num_samples>x<var_count>)" );
CV_Error( CV_StsBadArg, "Input samples must be floating-point matrix (<num_samples>x<var_count>)" );
if( _results && (!CV_IS_MAT(_results) ||
(_results->cols != 1 && _results->rows != 1) ||
_results->cols + _results->rows - 1 != _samples->rows) )
CV_ERROR( CV_StsBadArg,
CV_Error( CV_StsBadArg,
"The results must be 1d vector containing as much elements as the number of samples" );
if( _results && CV_MAT_TYPE(_results->type) != CV_32FC1 &&
(CV_MAT_TYPE(_results->type) != CV_32SC1 || regression))
CV_ERROR( CV_StsUnsupportedFormat,
CV_Error( CV_StsUnsupportedFormat,
"The results must be floating-point or integer (in case of classification) vector" );
if( k < 1 || k > max_k )
CV_ERROR( CV_StsOutOfRange, "k must be within 1..max_k range" );
CV_Error( CV_StsOutOfRange, "k must be within 1..max_k range" );
if( _neighbor_responses )
{
if( !CV_IS_MAT(_neighbor_responses) || CV_MAT_TYPE(_neighbor_responses->type) != CV_32FC1 ||
_neighbor_responses->rows != _samples->rows || _neighbor_responses->cols != k )
CV_ERROR( CV_StsBadArg,
CV_Error( CV_StsBadArg,
"The neighbor responses (if present) must be floating-point matrix of <num_samples> x <k> size" );
}
@@ -350,34 +344,28 @@ float CvKNearest::find_nearest( const CvMat* _samples, int k, CvMat* _results,
{
if( !CV_IS_MAT(_dist) || CV_MAT_TYPE(_dist->type) != CV_32FC1 ||
_dist->rows != _samples->rows || _dist->cols != k )
CV_ERROR( CV_StsBadArg,
CV_Error( CV_StsBadArg,
"The distances from the neighbors (if present) must be floating-point matrix of <num_samples> x <k> size" );
}
count = _samples->rows;
count_scale = k*2*sizeof(float);
count_scale = k*2;
blk_count0 = MIN( count, max_blk_count );
buf_sz = MIN( blk_count0 * count_scale, max_buf_sz );
blk_count0 = MAX( buf_sz/count_scale, 1 );
blk_count0 += blk_count0 % 2;
blk_count0 = MIN( blk_count0, count );
buf_sz = blk_count0 * count_scale + k*sizeof(float);
buf_sz = blk_count0 * count_scale + k;
k1 = get_sample_count();
k1 = MIN( k1, k );
if( buf_sz <= CV_MAX_LOCAL_SIZE )
{
buf = (float*)cvStackAlloc( buf_sz );
local_alloc = true;
}
else
CV_CALL( buf = (float*)cvAlloc( buf_sz ));
cv::AutoBuffer<float> buf(buf_sz);
for( i = 0; i < count; i += blk_count )
{
blk_count = MIN( count - i, blk_count0 );
float* neighbor_responses = buf;
float* dist = buf + blk_count*k;
float* neighbor_responses = &buf[0];
float* dist = neighbor_responses + blk_count*k;
Cv32suf* sort_buf = (Cv32suf*)(dist + blk_count*k);
find_neighbors_direct( _samples, k, i, i + blk_count,
@@ -389,11 +377,6 @@ float CvKNearest::find_nearest( const CvMat* _samples, int k, CvMat* _results,
result = r;
}
__END__;
if( !local_alloc )
cvFree( &buf );
return result;
}