OpenCV haartraining源码注解

/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of Intel Corporation may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #ifdef HAVE_CONFIG_H #include <cvconfig.h> #endif #ifdef HAVE_MALLOC_H #include <malloc.h> #endif #include <stdio.h> #include <memory.h> #include <float.h> #include <math.h> #include <time.h> #include <limits.h> #include <_cvcommon.h> #include <cvclassifier.h> #ifdef _OPENMP #include <omp.h> #endif /* _OPENMP */ #define CV_BOOST_IMPL typedef struct CvValArray { uchar* data; size_t step; } CvValArray; #define CMP_VALUES( idx1, idx2 ) \ ( *( (float*) (aux->data + ((int) (idx1)) * aux->step ) ) < \ *( (float*) (aux->data + ((int) (idx2)) * aux->step ) ) ) CV_IMPLEMENT_QSORT_EX( icvSortIndexedValArray_16s, short, CMP_VALUES, CvValArray* ) CV_IMPLEMENT_QSORT_EX( icvSortIndexedValArray_32s, int, CMP_VALUES, CvValArray* ) CV_IMPLEMENT_QSORT_EX( icvSortIndexedValArray_32f, float, CMP_VALUES, CvValArray* ) CV_BOOST_IMPL void cvGetSortedIndices( CvMat* val, CvMat* idx, int sortcols ) { int idxtype = 0; uchar* data = NULL; size_t istep = 0; size_t jstep = 0; int i = 0; int j = 0; CvValArray va; assert( idx != NULL ); assert( val != NULL ); idxtype = CV_MAT_TYPE( idx->type ); assert( idxtype == CV_16SC1 || idxtype == CV_32SC1 || idxtype == CV_32FC1 ); assert( CV_MAT_TYPE( val->type ) == CV_32FC1 ); if( sortcols ) { assert( idx->rows == val->cols ); assert( idx->cols == val->rows ); istep = CV_ELEM_SIZE( val->type ); jstep = val->step; } else { assert( idx->rows == val->rows ); assert( idx->cols == val->cols ); istep = val->step; jstep = CV_ELEM_SIZE( val->type ); } va.data = val->data.ptr; va.step = jstep; switch( idxtype ) { case CV_16SC1: for( i = 0; i < idx->rows; i++ ) { for( j = 0; j < idx->cols; j++ ) { CV_MAT_ELEM( *idx, short, i, j ) = (short) j; } icvSortIndexedValArray_16s( (short*) (idx->data.ptr + i * idx->step), idx->cols, &va ); va.data += istep; } break; case CV_32SC1: for( i = 0; i < idx->rows; i++ ) { for( j = 0; j < idx->cols; j++ ) { CV_MAT_ELEM( *idx, int, i, j ) = j; } icvSortIndexedValArray_32s( (int*) (idx->data.ptr + i * idx->step), idx->cols, &va ); va.data += istep; } break; case CV_32FC1: for( i = 0; i < idx->rows; i++ ) { for( j = 0; j < idx->cols; j++ ) { CV_MAT_ELEM( *idx, float, i, j ) = (float) j; } icvSortIndexedValArray_32f( (float*) (idx->data.ptr + i * idx->step), idx->cols, &va ); va.data += istep; } break; default: assert( 0 ); break; } } CV_BOOST_IMPL void cvReleaseStumpClassifier( CvClassifier** classifier ) { cvFree( classifier ); *classifier = 0; } CV_BOOST_IMPL float cvEvalStumpClassifier( CvClassifier* classifier, CvMat* sample ) { assert( classifier != NULL ); assert( sample != NULL ); assert( CV_MAT_TYPE( sample->type ) == CV_32FC1 ); if( (CV_MAT_ELEM( (*sample), float, 0, ((CvStumpClassifier*) classifier)->compidx )) < ((CvStumpClassifier*) classifier)->threshold ) { return ((CvStumpClassifier*) classifier)->left; } else { return ((CvStumpClassifier*) classifier)->right; } return 0.0F; } #define ICV_DEF_FIND_STUMP_THRESHOLD( suffix, type, error ) \ CV_BOOST_IMPL int icvFindStumpThreshold_##suffix( \ uchar* data, size_t datastep, \ uchar* wdata, size_t wstep, \ uchar* ydata, size_t ystep, \ uchar* idxdata, size_t idxstep, int num, \ float* lerror, \ float* rerror, \ float* threshold, float* left, float* right, \ float* sumw, float* sumwy, float* sumwyy ) \ { \ int found = 0; \ float wyl = 0.0F; \ float wl = 0.0F; \ float wyyl = 0.0F; \ float wyr = 0.0F; \ float wr = 0.0F; \ \ float curleft = 0.0F; \ float curright = 0.0F; \ float* prevval = NULL;