836 lines
22 KiB
C++
836 lines
22 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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/*
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* cvhaarclassifier.cpp
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*
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* haar classifiers (stump, CART, stage, cascade)
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*/
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#include "_cvhaartraining.h"
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CvIntHaarClassifier* icvCreateCARTHaarClassifier( int count )
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{
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CvCARTHaarClassifier* cart;
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size_t datasize;
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datasize = sizeof( *cart ) +
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( sizeof( int ) +
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sizeof( CvTHaarFeature ) + sizeof( CvFastHaarFeature ) +
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sizeof( float ) + sizeof( int ) + sizeof( int ) ) * count +
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sizeof( float ) * (count + 1);
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cart = (CvCARTHaarClassifier*) cvAlloc( datasize );
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memset( cart, 0, datasize );
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cart->feature = (CvTHaarFeature*) (cart + 1);
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cart->fastfeature = (CvFastHaarFeature*) (cart->feature + count);
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cart->threshold = (float*) (cart->fastfeature + count);
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cart->left = (int*) (cart->threshold + count);
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cart->right = (int*) (cart->left + count);
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cart->val = (float*) (cart->right + count);
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cart->compidx = (int*) (cart->val + count + 1 );
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cart->count = count;
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cart->eval = icvEvalCARTHaarClassifier;
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cart->save = icvSaveCARTHaarClassifier;
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cart->release = icvReleaseHaarClassifier;
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return (CvIntHaarClassifier*) cart;
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}
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void icvReleaseHaarClassifier( CvIntHaarClassifier** classifier )
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{
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cvFree( classifier );
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*classifier = NULL;
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}
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void icvInitCARTHaarClassifier( CvCARTHaarClassifier* carthaar, CvCARTClassifier* cart,
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CvIntHaarFeatures* intHaarFeatures )
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{
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int i;
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for( i = 0; i < cart->count; i++ )
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{
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carthaar->feature[i] = intHaarFeatures->feature[cart->compidx[i]];
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carthaar->fastfeature[i] = intHaarFeatures->fastfeature[cart->compidx[i]];
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carthaar->threshold[i] = cart->threshold[i];
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carthaar->left[i] = cart->left[i];
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carthaar->right[i] = cart->right[i];
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carthaar->val[i] = cart->val[i];
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carthaar->compidx[i] = cart->compidx[i];
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}
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carthaar->count = cart->count;
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carthaar->val[cart->count] = cart->val[cart->count];
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}
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float icvEvalCARTHaarClassifier( CvIntHaarClassifier* classifier,
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sum_type* sum, sum_type* tilted, float normfactor )
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{
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int idx = 0;
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do
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{
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if( cvEvalFastHaarFeature(
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((CvCARTHaarClassifier*) classifier)->fastfeature + idx, sum, tilted )
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< (((CvCARTHaarClassifier*) classifier)->threshold[idx] * normfactor) )
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{
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idx = ((CvCARTHaarClassifier*) classifier)->left[idx];
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}
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else
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{
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idx = ((CvCARTHaarClassifier*) classifier)->right[idx];
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}
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} while( idx > 0 );
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return ((CvCARTHaarClassifier*) classifier)->val[-idx];
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}
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CvIntHaarClassifier* icvCreateStageHaarClassifier( int count, float threshold )
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{
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CvStageHaarClassifier* stage;
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size_t datasize;
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datasize = sizeof( *stage ) + sizeof( CvIntHaarClassifier* ) * count;
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stage = (CvStageHaarClassifier*) cvAlloc( datasize );
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memset( stage, 0, datasize );
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stage->count = count;
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stage->threshold = threshold;
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stage->classifier = (CvIntHaarClassifier**) (stage + 1);
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stage->eval = icvEvalStageHaarClassifier;
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stage->save = icvSaveStageHaarClassifier;
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stage->release = icvReleaseStageHaarClassifier;
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return (CvIntHaarClassifier*) stage;
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}
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void icvReleaseStageHaarClassifier( CvIntHaarClassifier** classifier )
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{
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int i;
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for( i = 0; i < ((CvStageHaarClassifier*) *classifier)->count; i++ )
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{
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if( ((CvStageHaarClassifier*) *classifier)->classifier[i] != NULL )
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{
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((CvStageHaarClassifier*) *classifier)->classifier[i]->release(
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&(((CvStageHaarClassifier*) *classifier)->classifier[i]) );
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}
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}
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cvFree( classifier );
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*classifier = NULL;
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}
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float icvEvalStageHaarClassifier( CvIntHaarClassifier* classifier,
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sum_type* sum, sum_type* tilted, float normfactor )
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{
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int i;
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float stage_sum;
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stage_sum = 0.0F;
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for( i = 0; i < ((CvStageHaarClassifier*) classifier)->count; i++ )
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{
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stage_sum +=
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((CvStageHaarClassifier*) classifier)->classifier[i]->eval(
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((CvStageHaarClassifier*) classifier)->classifier[i],
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sum, tilted, normfactor );
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}
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return stage_sum;
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}
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CvIntHaarClassifier* icvCreateCascadeHaarClassifier( int count )
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{
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CvCascadeHaarClassifier* ptr;
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size_t datasize;
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datasize = sizeof( *ptr ) + sizeof( CvIntHaarClassifier* ) * count;
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ptr = (CvCascadeHaarClassifier*) cvAlloc( datasize );
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memset( ptr, 0, datasize );
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ptr->count = count;
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ptr->classifier = (CvIntHaarClassifier**) (ptr + 1);
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ptr->eval = icvEvalCascadeHaarClassifier;
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ptr->save = NULL;
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ptr->release = icvReleaseCascadeHaarClassifier;
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return (CvIntHaarClassifier*) ptr;
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}
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void icvReleaseCascadeHaarClassifier( CvIntHaarClassifier** classifier )
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{
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int i;
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for( i = 0; i < ((CvCascadeHaarClassifier*) *classifier)->count; i++ )
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{
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if( ((CvCascadeHaarClassifier*) *classifier)->classifier[i] != NULL )
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{
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((CvCascadeHaarClassifier*) *classifier)->classifier[i]->release(
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&(((CvCascadeHaarClassifier*) *classifier)->classifier[i]) );
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}
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}
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cvFree( classifier );
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*classifier = NULL;
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}
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float icvEvalCascadeHaarClassifier( CvIntHaarClassifier* classifier,
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sum_type* sum, sum_type* tilted, float normfactor )
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{
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int i;
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for( i = 0; i < ((CvCascadeHaarClassifier*) classifier)->count; i++ )
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{
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if( ((CvCascadeHaarClassifier*) classifier)->classifier[i]->eval(
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((CvCascadeHaarClassifier*) classifier)->classifier[i],
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sum, tilted, normfactor )
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< ( ((CvStageHaarClassifier*)
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((CvCascadeHaarClassifier*) classifier)->classifier[i])->threshold
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- CV_THRESHOLD_EPS) )
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{
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return 0.0;
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}
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}
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return 1.0;
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}
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void icvSaveHaarFeature( CvTHaarFeature* feature, FILE* file )
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{
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fprintf( file, "%d\n", ( ( feature->rect[2].weight == 0.0F ) ? 2 : 3) );
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fprintf( file, "%d %d %d %d %d %d\n",
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feature->rect[0].r.x,
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feature->rect[0].r.y,
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feature->rect[0].r.width,
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feature->rect[0].r.height,
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0,
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(int) (feature->rect[0].weight) );
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fprintf( file, "%d %d %d %d %d %d\n",
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feature->rect[1].r.x,
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feature->rect[1].r.y,
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feature->rect[1].r.width,
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feature->rect[1].r.height,
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0,
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(int) (feature->rect[1].weight) );
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if( feature->rect[2].weight != 0.0F )
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{
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fprintf( file, "%d %d %d %d %d %d\n",
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feature->rect[2].r.x,
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feature->rect[2].r.y,
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feature->rect[2].r.width,
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feature->rect[2].r.height,
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0,
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(int) (feature->rect[2].weight) );
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}
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fprintf( file, "%s\n", &(feature->desc[0]) );
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}
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void icvLoadHaarFeature( CvTHaarFeature* feature, FILE* file )
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{
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int nrect;
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int j;
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int tmp;
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int weight;
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nrect = 0;
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int values_read = fscanf( file, "%d", &nrect );
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CV_Assert(values_read == 1);
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assert( nrect <= CV_HAAR_FEATURE_MAX );
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for( j = 0; j < nrect; j++ )
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{
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values_read = fscanf( file, "%d %d %d %d %d %d",
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&(feature->rect[j].r.x),
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&(feature->rect[j].r.y),
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&(feature->rect[j].r.width),
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&(feature->rect[j].r.height),
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&tmp, &weight );
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CV_Assert(values_read == 6);
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feature->rect[j].weight = (float) weight;
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}
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for( j = nrect; j < CV_HAAR_FEATURE_MAX; j++ )
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{
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feature->rect[j].r.x = 0;
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feature->rect[j].r.y = 0;
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feature->rect[j].r.width = 0;
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feature->rect[j].r.height = 0;
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feature->rect[j].weight = 0.0f;
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}
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values_read = fscanf( file, "%s", &(feature->desc[0]) );
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CV_Assert(values_read == 1);
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feature->tilted = ( feature->desc[0] == 't' );
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}
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void icvSaveCARTHaarClassifier( CvIntHaarClassifier* classifier, FILE* file )
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{
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int i;
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int count;
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count = ((CvCARTHaarClassifier*) classifier)->count;
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fprintf( file, "%d\n", count );
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for( i = 0; i < count; i++ )
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{
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icvSaveHaarFeature( &(((CvCARTHaarClassifier*) classifier)->feature[i]), file );
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fprintf( file, "%e %d %d\n",
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((CvCARTHaarClassifier*) classifier)->threshold[i],
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((CvCARTHaarClassifier*) classifier)->left[i],
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((CvCARTHaarClassifier*) classifier)->right[i] );
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}
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for( i = 0; i <= count; i++ )
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{
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fprintf( file, "%e ", ((CvCARTHaarClassifier*) classifier)->val[i] );
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}
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fprintf( file, "\n" );
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}
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CvIntHaarClassifier* icvLoadCARTHaarClassifier( FILE* file, int step )
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{
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CvCARTHaarClassifier* ptr;
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int i;
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int count;
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ptr = NULL;
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int values_read = fscanf( file, "%d", &count );
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CV_Assert(values_read == 1);
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if( count > 0 )
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{
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ptr = (CvCARTHaarClassifier*) icvCreateCARTHaarClassifier( count );
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for( i = 0; i < count; i++ )
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{
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icvLoadHaarFeature( &(ptr->feature[i]), file );
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values_read = fscanf( file, "%f %d %d", &(ptr->threshold[i]), &(ptr->left[i]),
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&(ptr->right[i]) );
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CV_Assert(values_read == 3);
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}
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for( i = 0; i <= count; i++ )
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{
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values_read = fscanf( file, "%f", &(ptr->val[i]) );
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CV_Assert(values_read == 1);
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}
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icvConvertToFastHaarFeature( ptr->feature, ptr->fastfeature, ptr->count, step );
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}
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return (CvIntHaarClassifier*) ptr;
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}
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void icvSaveStageHaarClassifier( CvIntHaarClassifier* classifier, FILE* file )
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{
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int count;
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int i;
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float threshold;
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count = ((CvStageHaarClassifier*) classifier)->count;
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fprintf( file, "%d\n", count );
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for( i = 0; i < count; i++ )
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{
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((CvStageHaarClassifier*) classifier)->classifier[i]->save(
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((CvStageHaarClassifier*) classifier)->classifier[i], file );
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}
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threshold = ((CvStageHaarClassifier*) classifier)->threshold;
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/* to be compatible with the previous implementation */
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/* threshold = 2.0F * ((CvStageHaarClassifier*) classifier)->threshold - count; */
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fprintf( file, "%e\n", threshold );
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}
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static CvIntHaarClassifier* icvLoadCARTStageHaarClassifierF( FILE* file, int step )
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{
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CvStageHaarClassifier* ptr = NULL;
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//CV_FUNCNAME( "icvLoadCARTStageHaarClassifierF" );
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__BEGIN__;
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if( file != NULL )
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{
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int count;
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int i;
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float threshold;
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count = 0;
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int values_read = fscanf( file, "%d", &count );
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CV_Assert(values_read == 1);
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if( count > 0 )
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{
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ptr = (CvStageHaarClassifier*) icvCreateStageHaarClassifier( count, 0.0F );
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for( i = 0; i < count; i++ )
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{
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ptr->classifier[i] = icvLoadCARTHaarClassifier( file, step );
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}
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values_read = fscanf( file, "%f", &threshold );
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CV_Assert(values_read == 1);
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ptr->threshold = threshold;
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/* to be compatible with the previous implementation */
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/* ptr->threshold = 0.5F * (threshold + count); */
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}
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if( feof( file ) )
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{
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ptr->release( (CvIntHaarClassifier**) &ptr );
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ptr = NULL;
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}
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}
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__END__;
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return (CvIntHaarClassifier*) ptr;
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}
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CvIntHaarClassifier* icvLoadCARTStageHaarClassifier( const char* filename, int step )
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{
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CvIntHaarClassifier* ptr = NULL;
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CV_FUNCNAME( "icvLoadCARTStageHaarClassifier" );
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__BEGIN__;
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FILE* file;
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file = fopen( filename, "r" );
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if( file )
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{
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CV_CALL( ptr = icvLoadCARTStageHaarClassifierF( file, step ) );
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fclose( file );
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}
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__END__;
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return ptr;
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}
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/* tree cascade classifier */
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/* evaluates a tree cascade classifier */
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float icvEvalTreeCascadeClassifier( CvIntHaarClassifier* classifier,
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sum_type* sum, sum_type* tilted, float normfactor )
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{
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CvTreeCascadeNode* ptr;
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ptr = ((CvTreeCascadeClassifier*) classifier)->root;
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while( ptr )
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{
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if( ptr->stage->eval( (CvIntHaarClassifier*) ptr->stage,
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sum, tilted, normfactor )
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>= ptr->stage->threshold - CV_THRESHOLD_EPS )
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{
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ptr = ptr->child;
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}
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else
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{
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while( ptr && ptr->next == NULL ) ptr = ptr->parent;
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if( ptr == NULL ) return 0.0F;
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ptr = ptr->next;
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}
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}
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return 1.0F;
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}
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/* sets path int the tree form the root to the leaf node */
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void icvSetLeafNode( CvTreeCascadeClassifier* tcc, CvTreeCascadeNode* leaf )
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{
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CV_FUNCNAME( "icvSetLeafNode" );
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__BEGIN__;
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CvTreeCascadeNode* ptr;
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ptr = NULL;
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while( leaf )
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{
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leaf->child_eval = ptr;
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ptr = leaf;
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leaf = leaf->parent;
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}
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leaf = tcc->root;
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while( leaf && leaf != ptr ) leaf = leaf->next;
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if( leaf != ptr )
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CV_ERROR( CV_StsError, "Invalid tcc or leaf node." );
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tcc->root_eval = ptr;
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__END__;
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}
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|
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/* evaluates a tree cascade classifier. used in filtering */
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float icvEvalTreeCascadeClassifierFilter( CvIntHaarClassifier* classifier, sum_type* sum,
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sum_type* tilted, float normfactor )
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{
|
|
CvTreeCascadeNode* ptr;
|
|
//CvTreeCascadeClassifier* tree;
|
|
|
|
//tree = (CvTreeCascadeClassifier*) classifier;
|
|
|
|
|
|
|
|
ptr = ((CvTreeCascadeClassifier*) classifier)->root_eval;
|
|
while( ptr )
|
|
{
|
|
if( ptr->stage->eval( (CvIntHaarClassifier*) ptr->stage,
|
|
sum, tilted, normfactor )
|
|
< ptr->stage->threshold - CV_THRESHOLD_EPS )
|
|
{
|
|
return 0.0F;
|
|
}
|
|
ptr = ptr->child_eval;
|
|
}
|
|
|
|
return 1.0F;
|
|
}
|
|
|
|
/* creates tree cascade node */
|
|
|
|
CvTreeCascadeNode* icvCreateTreeCascadeNode()
|
|
{
|
|
CvTreeCascadeNode* ptr = NULL;
|
|
|
|
CV_FUNCNAME( "icvCreateTreeCascadeNode" );
|
|
|
|
__BEGIN__;
|
|
size_t data_size;
|
|
|
|
data_size = sizeof( *ptr );
|
|
CV_CALL( ptr = (CvTreeCascadeNode*) cvAlloc( data_size ) );
|
|
memset( ptr, 0, data_size );
|
|
|
|
__END__;
|
|
|
|
return ptr;
|
|
}
|
|
|
|
/* releases all tree cascade nodes accessible via links */
|
|
|
|
void icvReleaseTreeCascadeNodes( CvTreeCascadeNode** node )
|
|
{
|
|
//CV_FUNCNAME( "icvReleaseTreeCascadeNodes" );
|
|
|
|
__BEGIN__;
|
|
|
|
if( node && *node )
|
|
{
|
|
CvTreeCascadeNode* ptr;
|
|
CvTreeCascadeNode* ptr_;
|
|
|
|
ptr = *node;
|
|
|
|
while( ptr )
|
|
{
|
|
while( ptr->child ) ptr = ptr->child;
|
|
|
|
if( ptr->stage ) ptr->stage->release( (CvIntHaarClassifier**) &ptr->stage );
|
|
ptr_ = ptr;
|
|
|
|
while( ptr && ptr->next == NULL ) ptr = ptr->parent;
|
|
if( ptr ) ptr = ptr->next;
|
|
|
|
cvFree( &ptr_ );
|
|
}
|
|
}
|
|
|
|
__END__;
|
|
}
|
|
|
|
|
|
/* releases tree cascade classifier */
|
|
|
|
void icvReleaseTreeCascadeClassifier( CvIntHaarClassifier** classifier )
|
|
{
|
|
if( classifier && *classifier )
|
|
{
|
|
icvReleaseTreeCascadeNodes( &((CvTreeCascadeClassifier*) *classifier)->root );
|
|
cvFree( classifier );
|
|
*classifier = NULL;
|
|
}
|
|
}
|
|
|
|
|
|
void icvPrintTreeCascade( CvTreeCascadeNode* root )
|
|
{
|
|
//CV_FUNCNAME( "icvPrintTreeCascade" );
|
|
|
|
__BEGIN__;
|
|
|
|
CvTreeCascadeNode* node;
|
|
CvTreeCascadeNode* n;
|
|
char buf0[256];
|
|
char buf[256];
|
|
int level;
|
|
int i;
|
|
int max_level;
|
|
|
|
node = root;
|
|
level = max_level = 0;
|
|
while( node )
|
|
{
|
|
while( node->child ) { node = node->child; level++; }
|
|
if( level > max_level ) { max_level = level; }
|
|
while( node && !node->next ) { node = node->parent; level--; }
|
|
if( node ) node = node->next;
|
|
}
|
|
|
|
printf( "\nTree Classifier\n" );
|
|
printf( "Stage\n" );
|
|
for( i = 0; i <= max_level; i++ ) printf( "+---" );
|
|
printf( "+\n" );
|
|
for( i = 0; i <= max_level; i++ ) printf( "|%3d", i );
|
|
printf( "|\n" );
|
|
for( i = 0; i <= max_level; i++ ) printf( "+---" );
|
|
printf( "+\n\n" );
|
|
|
|
node = root;
|
|
|
|
buf[0] = 0;
|
|
while( node )
|
|
{
|
|
sprintf( buf + strlen( buf ), "%3d", node->idx );
|
|
while( node->child )
|
|
{
|
|
node = node->child;
|
|
sprintf( buf + strlen( buf ),
|
|
((node->idx < 10) ? "---%d" : ((node->idx < 100) ? "--%d" : "-%d")),
|
|
node->idx );
|
|
}
|
|
printf( " %s\n", buf );
|
|
|
|
while( node && !node->next ) { node = node->parent; }
|
|
if( node )
|
|
{
|
|
node = node->next;
|
|
|
|
n = node->parent;
|
|
buf[0] = 0;
|
|
while( n )
|
|
{
|
|
if( n->next )
|
|
sprintf( buf0, " | %s", buf );
|
|
else
|
|
sprintf( buf0, " %s", buf );
|
|
strcpy( buf, buf0 );
|
|
n = n->parent;
|
|
}
|
|
printf( " %s |\n", buf );
|
|
}
|
|
}
|
|
printf( "\n" );
|
|
fflush( stdout );
|
|
|
|
__END__;
|
|
}
|
|
|
|
|
|
|
|
CvIntHaarClassifier* icvLoadTreeCascadeClassifier( const char* filename, int step,
|
|
int* splits )
|
|
{
|
|
CvTreeCascadeClassifier* ptr = NULL;
|
|
CvTreeCascadeNode** nodes = NULL;
|
|
|
|
CV_FUNCNAME( "icvLoadTreeCascadeClassifier" );
|
|
|
|
__BEGIN__;
|
|
|
|
size_t data_size;
|
|
CvStageHaarClassifier* stage;
|
|
char stage_name[PATH_MAX];
|
|
char* suffix;
|
|
int i, num;
|
|
FILE* f;
|
|
int result, parent=0, next=0;
|
|
int stub;
|
|
|
|
if( !splits ) splits = &stub;
|
|
|
|
*splits = 0;
|
|
|
|
data_size = sizeof( *ptr );
|
|
|
|
CV_CALL( ptr = (CvTreeCascadeClassifier*) cvAlloc( data_size ) );
|
|
memset( ptr, 0, data_size );
|
|
|
|
ptr->eval = icvEvalTreeCascadeClassifier;
|
|
ptr->release = icvReleaseTreeCascadeClassifier;
|
|
|
|
sprintf( stage_name, "%s/", filename );
|
|
suffix = stage_name + strlen( stage_name );
|
|
|
|
for( i = 0; ; i++ )
|
|
{
|
|
sprintf( suffix, "%d/%s", i, CV_STAGE_CART_FILE_NAME );
|
|
f = fopen( stage_name, "r" );
|
|
if( !f ) break;
|
|
fclose( f );
|
|
}
|
|
num = i;
|
|
|
|
if( num < 1 ) EXIT;
|
|
|
|
data_size = sizeof( *nodes ) * num;
|
|
CV_CALL( nodes = (CvTreeCascadeNode**) cvAlloc( data_size ) );
|
|
|
|
for( i = 0; i < num; i++ )
|
|
{
|
|
sprintf( suffix, "%d/%s", i, CV_STAGE_CART_FILE_NAME );
|
|
f = fopen( stage_name, "r" );
|
|
CV_CALL( stage = (CvStageHaarClassifier*)
|
|
icvLoadCARTStageHaarClassifierF( f, step ) );
|
|
|
|
result = ( f && stage ) ? fscanf( f, "%d%d", &parent, &next ) : 0;
|
|
if( f ) fclose( f );
|
|
|
|
if( result != 2 )
|
|
{
|
|
num = i;
|
|
break;
|
|
}
|
|
|
|
printf( "Stage %d loaded\n", i );
|
|
|
|
if( parent >= i || (next != -1 && next != i + 1) )
|
|
CV_ERROR( CV_StsError, "Invalid tree links" );
|
|
|
|
CV_CALL( nodes[i] = icvCreateTreeCascadeNode() );
|
|
nodes[i]->stage = stage;
|
|
nodes[i]->idx = i;
|
|
nodes[i]->parent = (parent != -1 ) ? nodes[parent] : NULL;
|
|
nodes[i]->next = ( next != -1 ) ? nodes[i] : NULL;
|
|
nodes[i]->child = NULL;
|
|
}
|
|
for( i = 0; i < num; i++ )
|
|
{
|
|
if( nodes[i]->next )
|
|
{
|
|
(*splits)++;
|
|
nodes[i]->next = nodes[i+1];
|
|
}
|
|
if( nodes[i]->parent && nodes[i]->parent->child == NULL )
|
|
{
|
|
nodes[i]->parent->child = nodes[i];
|
|
}
|
|
}
|
|
ptr->root = nodes[0];
|
|
ptr->next_idx = num;
|
|
|
|
__END__;
|
|
|
|
cvFree( &nodes );
|
|
|
|
return (CvIntHaarClassifier*) ptr;
|
|
}
|
|
|
|
|
|
CvTreeCascadeNode* icvFindDeepestLeaves( CvTreeCascadeClassifier* tcc )
|
|
{
|
|
CvTreeCascadeNode* leaves;
|
|
|
|
//CV_FUNCNAME( "icvFindDeepestLeaves" );
|
|
|
|
__BEGIN__;
|
|
|
|
int level, cur_level;
|
|
CvTreeCascadeNode* ptr;
|
|
CvTreeCascadeNode* last;
|
|
|
|
leaves = last = NULL;
|
|
|
|
ptr = tcc->root;
|
|
level = -1;
|
|
cur_level = 0;
|
|
|
|
/* find leaves with maximal level */
|
|
while( ptr )
|
|
{
|
|
if( ptr->child ) { ptr = ptr->child; cur_level++; }
|
|
else
|
|
{
|
|
if( cur_level == level )
|
|
{
|
|
last->next_same_level = ptr;
|
|
ptr->next_same_level = NULL;
|
|
last = ptr;
|
|
}
|
|
if( cur_level > level )
|
|
{
|
|
level = cur_level;
|
|
leaves = last = ptr;
|
|
ptr->next_same_level = NULL;
|
|
}
|
|
while( ptr && ptr->next == NULL ) { ptr = ptr->parent; cur_level--; }
|
|
if( ptr ) ptr = ptr->next;
|
|
}
|
|
}
|
|
|
|
__END__;
|
|
|
|
return leaves;
|
|
}
|
|
|
|
/* End of file. */
|