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fixed traincascade (#554)

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Maria Dimashova 2010-12-02 13:44:08 +00:00
parent 07e68eb0bb
commit 62cb71092c
1 changed files with 25 additions and 11 deletions
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36
modules/traincascade/boost.cpp
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@ -139,7 +139,7 @@ bool CvCascadeBoostParams::scanAttr( const String prmName, const String val)
{
weight_trim_rate = (float) atof( val.c_str() );
}
else if( !prmName.compare( "-maxDepth" ) )
else if( !prmName.compare( "-maxTreeDepth" ) )
{
max_depth = atoi( val.c_str() );
}
@ -240,9 +240,11 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
if (sample_count < 65536)
is_buf_16u = true;
numPrecalcVal = min( (_precalcValBufSize*1048576) / int(sizeof(float)*sample_count), var_count );
numPrecalcIdx = min( (_precalcIdxBufSize*1048576) /
int((is_buf_16u ? sizeof(unsigned short) : sizeof (int))*sample_count), var_count );
numPrecalcVal = min( cvRound((double)_precalcValBufSize*1048576. / (sizeof(float)*sample_count)), var_count );
numPrecalcIdx = min( cvRound((double)_precalcIdxBufSize*1048576. /
((is_buf_16u ? sizeof(unsigned short) : sizeof (int))*sample_count)), var_count );
assert( numPrecalcIdx >= 0 && numPrecalcVal >= 0 );
valCache.create( numPrecalcVal, sample_count, CV_32FC1 );
var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 );
@ -394,7 +396,7 @@ void CvCascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* o
}
else
{
for( int i = 0; i < nodeSampleCount; i++ )
for( int i = 0; i < nodeSampleCount; i++ )
{
int idx = (*sortedIndices)[i];
idx = sampleIndices[idx];
@ -404,13 +406,26 @@ void CvCascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* o
}
else // vi >= numPrecalcIdx
{
// use sample_indices as temporary buffer for values
vector<float> sampleValuesBuf;
float* sampleValues = 0;
if( sizeof(float) == sizeof(int) )
{
// use sampleIndices as temporary buffer for values
sampleValues = (float*)sampleIndices;
}
else
{
sampleValuesBuf.resize(nodeSampleCount);
sampleValues = &sampleValuesBuf[0];
}
if ( vi < numPrecalcVal )
{
for( int i = 0; i < nodeSampleCount; i++ )
{
sortedIndicesBuf[i] = i;
((float*)sampleIndices)[i] = valCache.at<float>( vi, sampleIndices[i] );
sampleValues[i] = valCache.at<float>( vi, sampleIndices[i] );
}
}
else
@ -418,12 +433,12 @@ void CvCascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* o
for( int i = 0; i < nodeSampleCount; i++ )
{
sortedIndicesBuf[i] = i;
((float*)sampleIndices)[i] = (*featureEvaluator)( vi, sampleIndices[i]);
sampleValues[i] = (*featureEvaluator)( vi, sampleIndices[i]);
}
}
icvSortIntAux( sortedIndicesBuf, sample_count, (float *)sampleIndices );
icvSortIntAux( sortedIndicesBuf, nodeSampleCount, &sampleValues[0] );
for( int i = 0; i < nodeSampleCount; i++ )
ordValuesBuf[i] = ((float*)sampleIndices)[sortedIndicesBuf[i]];
ordValuesBuf[i] = (&sampleValues[0])[sortedIndicesBuf[i]];
*sortedIndices = sortedIndicesBuf;
}
@ -553,7 +568,6 @@ struct FeatureValOnlyPrecalc
void CvCascadeBoostTrainData::precalculate()
{
int minNum = MIN( numPrecalcVal, numPrecalcIdx);
CV_DbgAssert( !valCache.empty() );
double proctime = -TIME( 0 );
parallel_for( BlockedRange(numPrecalcVal, numPrecalcIdx),