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Open Source Computer Vision Library
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376 lines
20 KiB
376 lines
20 KiB
/*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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// |
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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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#ifndef OPENCV_PRECOMP_H |
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#define OPENCV_PRECOMP_H |
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#include "opencv2/core.hpp" |
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#include "old_ml.hpp" |
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#include "opencv2/core/core_c.h" |
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#include "opencv2/core/utility.hpp" |
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#include "opencv2/core/private.hpp" |
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#include <assert.h> |
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#include <float.h> |
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#include <limits.h> |
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#include <math.h> |
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#include <stdlib.h> |
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#include <stdio.h> |
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#include <string.h> |
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#include <time.h> |
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#define ML_IMPL CV_IMPL |
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#define __BEGIN__ __CV_BEGIN__ |
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#define __END__ __CV_END__ |
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#define EXIT __CV_EXIT__ |
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#define CV_MAT_ELEM_FLAG( mat, type, comp, vect, tflag ) \ |
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(( tflag == CV_ROW_SAMPLE ) \ |
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? (CV_MAT_ELEM( mat, type, comp, vect )) \ |
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: (CV_MAT_ELEM( mat, type, vect, comp ))) |
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/* Convert matrix to vector */ |
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#define ICV_MAT2VEC( mat, vdata, vstep, num ) \ |
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if( MIN( (mat).rows, (mat).cols ) != 1 ) \ |
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CV_ERROR( cv::Error::StsBadArg, "" ); \ |
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(vdata) = ((mat).data.ptr); \ |
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if( (mat).rows == 1 ) \ |
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{ \ |
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(vstep) = CV_ELEM_SIZE( (mat).type ); \ |
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(num) = (mat).cols; \ |
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} \ |
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else \ |
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{ \ |
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(vstep) = (mat).step; \ |
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(num) = (mat).rows; \ |
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} |
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/* get raw data */ |
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#define ICV_RAWDATA( mat, flags, rdata, sstep, cstep, m, n ) \ |
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(rdata) = (mat).data.ptr; \ |
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if( CV_IS_ROW_SAMPLE( flags ) ) \ |
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{ \ |
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(sstep) = (mat).step; \ |
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(cstep) = CV_ELEM_SIZE( (mat).type ); \ |
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(m) = (mat).rows; \ |
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(n) = (mat).cols; \ |
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} \ |
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else \ |
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{ \ |
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(cstep) = (mat).step; \ |
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(sstep) = CV_ELEM_SIZE( (mat).type ); \ |
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(n) = (mat).rows; \ |
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(m) = (mat).cols; \ |
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} |
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#define ICV_IS_MAT_OF_TYPE( mat, mat_type) \ |
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(CV_IS_MAT( mat ) && CV_MAT_TYPE( mat->type ) == (mat_type) && \ |
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(mat)->cols > 0 && (mat)->rows > 0) |
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/* |
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uchar* data; int sstep, cstep; - trainData->data |
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uchar* classes; int clstep; int ncl;- trainClasses |
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uchar* tmask; int tmstep; int ntm; - typeMask |
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uchar* missed;int msstep, mcstep; -missedMeasurements... |
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int mm, mn; == m,n == size,dim |
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uchar* sidx;int sistep; - sampleIdx |
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uchar* cidx;int cistep; - compIdx |
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int k, l; == n,m == dim,size (length of cidx, sidx) |
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int m, n; == size,dim |
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*/ |
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#define ICV_DECLARE_TRAIN_ARGS() \ |
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uchar* data; \ |
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int sstep, cstep; \ |
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uchar* classes; \ |
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int clstep; \ |
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int ncl; \ |
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uchar* tmask; \ |
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int tmstep; \ |
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int ntm; \ |
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uchar* missed; \ |
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int msstep, mcstep; \ |
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int mm, mn; \ |
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uchar* sidx; \ |
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int sistep; \ |
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uchar* cidx; \ |
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int cistep; \ |
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int k, l; \ |
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int m, n; \ |
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\ |
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data = classes = tmask = missed = sidx = cidx = NULL; \ |
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sstep = cstep = clstep = ncl = tmstep = ntm = msstep = mcstep = mm = mn = 0; \ |
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sistep = cistep = k = l = m = n = 0; |
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#define ICV_TRAIN_DATA_REQUIRED( param, flags ) \ |
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if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) ) \ |
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{ \ |
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CV_ERROR( cv::Error::StsBadArg, "Invalid " #param " parameter" ); \ |
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} \ |
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else \ |
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{ \ |
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ICV_RAWDATA( *(param), (flags), data, sstep, cstep, m, n ); \ |
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k = n; \ |
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l = m; \ |
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} |
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#define ICV_TRAIN_CLASSES_REQUIRED( param ) \ |
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if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) ) \ |
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{ \ |
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CV_ERROR( cv::Error::StsBadArg, "Invalid " #param " parameter" ); \ |
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} \ |
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else \ |
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{ \ |
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ICV_MAT2VEC( *(param), classes, clstep, ncl ); \ |
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if( m != ncl ) \ |
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{ \ |
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CV_ERROR( cv::Error::StsBadArg, "Unmatched sizes" ); \ |
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} \ |
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} |
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#define ICV_ARG_NULL( param ) \ |
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if( (param) != NULL ) \ |
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{ \ |
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CV_ERROR( cv::Error::StsBadArg, #param " parameter must be NULL" ); \ |
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} |
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#define ICV_MISSED_MEASUREMENTS_OPTIONAL( param, flags ) \ |
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if( param ) \ |
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{ \ |
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if( !ICV_IS_MAT_OF_TYPE( param, CV_8UC1 ) ) \ |
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{ \ |
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CV_ERROR( cv::Error::StsBadArg, "Invalid " #param " parameter" ); \ |
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} \ |
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else \ |
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{ \ |
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ICV_RAWDATA( *(param), (flags), missed, msstep, mcstep, mm, mn ); \ |
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if( mm != m || mn != n ) \ |
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{ \ |
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CV_ERROR( cv::Error::StsBadArg, "Unmatched sizes" ); \ |
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} \ |
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} \ |
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} |
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#define ICV_COMP_IDX_OPTIONAL( param ) \ |
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if( param ) \ |
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{ \ |
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if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) ) \ |
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{ \ |
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CV_ERROR( cv::Error::StsBadArg, "Invalid " #param " parameter" ); \ |
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} \ |
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else \ |
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{ \ |
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ICV_MAT2VEC( *(param), cidx, cistep, k ); \ |
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if( k > n ) \ |
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CV_ERROR( cv::Error::StsBadArg, "Invalid " #param " parameter" ); \ |
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} \ |
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} |
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#define ICV_SAMPLE_IDX_OPTIONAL( param ) \ |
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if( param ) \ |
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{ \ |
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if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) ) \ |
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{ \ |
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CV_ERROR( cv::Error::StsBadArg, "Invalid " #param " parameter" ); \ |
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} \ |
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else \ |
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{ \ |
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ICV_MAT2VEC( *sampleIdx, sidx, sistep, l ); \ |
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if( l > m ) \ |
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CV_ERROR( cv::Error::StsBadArg, "Invalid " #param " parameter" ); \ |
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} \ |
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} |
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/****************************************************************************************/ |
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#define ICV_CONVERT_FLOAT_ARRAY_TO_MATRICE( array, matrice ) \ |
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{ \ |
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CvMat a, b; \ |
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int dims = (matrice)->cols; \ |
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int nsamples = (matrice)->rows; \ |
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int type = CV_MAT_TYPE((matrice)->type); \ |
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int i, offset = dims; \ |
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\ |
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CV_ASSERT( type == CV_32FC1 || type == CV_64FC1 ); \ |
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offset *= ((type == CV_32FC1) ? sizeof(float) : sizeof(double));\ |
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\ |
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b = cvMat( 1, dims, CV_32FC1 ); \ |
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cvGetRow( matrice, &a, 0 ); \ |
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for( i = 0; i < nsamples; i++, a.data.ptr += offset ) \ |
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{ \ |
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b.data.fl = (float*)array[i]; \ |
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CV_CALL( cvConvert( &b, &a ) ); \ |
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} \ |
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} |
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/****************************************************************************************\ |
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* Auxiliary functions declarations * |
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\****************************************************************************************/ |
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/* Generates a set of classes centers in quantity <num_of_clusters> that are generated as |
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uniform random vectors in parallelepiped, where <data> is concentrated. Vectors in |
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<data> should have horizontal orientation. If <centers> != NULL, the function doesn't |
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allocate any memory and stores generated centers in <centers>, returns <centers>. |
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If <centers> == NULL, the function allocates memory and creates the matrice. Centers |
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are supposed to be oriented horizontally. */ |
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CvMat* icvGenerateRandomClusterCenters( int seed, |
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const CvMat* data, |
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int num_of_clusters, |
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CvMat* centers CV_DEFAULT(0)); |
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/* Fills the <labels> using <probs> by choosing the maximal probability. Outliers are |
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fixed by <oulier_tresh> and have cluster label (-1). Function also controls that there |
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weren't "empty" clusters by filling empty clusters with the maximal probability vector. |
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If probs_sums != NULL, fills it with the sums of probabilities for each sample (it is |
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useful for normalizing probabilities' matrice of FCM) */ |
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void icvFindClusterLabels( const CvMat* probs, float outlier_thresh, float r, |
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const CvMat* labels ); |
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typedef struct CvSparseVecElem32f |
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{ |
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int idx; |
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float val; |
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} |
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CvSparseVecElem32f; |
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/* Prepare training data and related parameters */ |
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#define CV_TRAIN_STATMODEL_DEFRAGMENT_TRAIN_DATA 1 |
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#define CV_TRAIN_STATMODEL_SAMPLES_AS_ROWS 2 |
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#define CV_TRAIN_STATMODEL_SAMPLES_AS_COLUMNS 4 |
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#define CV_TRAIN_STATMODEL_CATEGORICAL_RESPONSE 8 |
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#define CV_TRAIN_STATMODEL_ORDERED_RESPONSE 16 |
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#define CV_TRAIN_STATMODEL_RESPONSES_ON_OUTPUT 32 |
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#define CV_TRAIN_STATMODEL_ALWAYS_COPY_TRAIN_DATA 64 |
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#define CV_TRAIN_STATMODEL_SPARSE_AS_SPARSE 128 |
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int |
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cvPrepareTrainData( const char* /*funcname*/, |
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const CvMat* train_data, int tflag, |
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const CvMat* responses, int response_type, |
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const CvMat* var_idx, |
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const CvMat* sample_idx, |
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bool always_copy_data, |
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const float*** out_train_samples, |
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int* _sample_count, |
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int* _var_count, |
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int* _var_all, |
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CvMat** out_responses, |
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CvMat** out_response_map, |
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CvMat** out_var_idx, |
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CvMat** out_sample_idx=0 ); |
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void |
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cvSortSamplesByClasses( const float** samples, const CvMat* classes, |
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int* class_ranges, const uchar** mask CV_DEFAULT(0) ); |
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void |
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cvCombineResponseMaps (CvMat* _responses, |
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const CvMat* old_response_map, |
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CvMat* new_response_map, |
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CvMat** out_response_map); |
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void |
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cvPreparePredictData( const CvArr* sample, int dims_all, const CvMat* comp_idx, |
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int class_count, const CvMat* prob, float** row_sample, |
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int as_sparse CV_DEFAULT(0) ); |
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/* copies clustering [or batch "predict"] results |
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(labels and/or centers and/or probs) back to the output arrays */ |
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void |
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cvWritebackLabels( const CvMat* labels, CvMat* dst_labels, |
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const CvMat* centers, CvMat* dst_centers, |
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const CvMat* probs, CvMat* dst_probs, |
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const CvMat* sample_idx, int samples_all, |
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const CvMat* comp_idx, int dims_all ); |
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#define cvWritebackResponses cvWritebackLabels |
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#define XML_FIELD_NAME "_name" |
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cv::FileNode icvFileNodeGetChild( cv::FileNode& father, const char* name ); |
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cv::FileNode icvFileNodeGetChildArrayElem( cv::FileNode& father, const char* name,int index ); |
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cv::FileNode icvFileNodeGetNext( cv::FileNode& n, const char* name ); |
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void cvCheckTrainData( const CvMat* train_data, int tflag, |
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const CvMat* missing_mask, |
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int* var_all, int* sample_all ); |
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CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, bool check_for_duplicates=false ); |
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CvMat* cvPreprocessVarType( const CvMat* type_mask, const CvMat* var_idx, |
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int var_all, int* response_type ); |
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CvMat* cvPreprocessOrderedResponses( const CvMat* responses, |
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const CvMat* sample_idx, int sample_all ); |
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CvMat* cvPreprocessCategoricalResponses( const CvMat* responses, |
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const CvMat* sample_idx, int sample_all, |
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CvMat** out_response_map, CvMat** class_counts=0 ); |
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const float** cvGetTrainSamples( const CvMat* train_data, int tflag, |
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const CvMat* var_idx, const CvMat* sample_idx, |
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int* _var_count, int* _sample_count, |
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bool always_copy_data=false ); |
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namespace cv |
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{ |
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struct DTreeBestSplitFinder |
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{ |
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DTreeBestSplitFinder(){ splitSize = 0, tree = 0; node = 0; } |
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DTreeBestSplitFinder( CvDTree* _tree, CvDTreeNode* _node); |
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DTreeBestSplitFinder( const DTreeBestSplitFinder& finder, Split ); |
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virtual ~DTreeBestSplitFinder() {} |
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virtual void operator()(const BlockedRange& range); |
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void join( DTreeBestSplitFinder& rhs ); |
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Ptr<CvDTreeSplit> bestSplit; |
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Ptr<CvDTreeSplit> split; |
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int splitSize; |
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CvDTree* tree; |
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CvDTreeNode* node; |
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}; |
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struct ForestTreeBestSplitFinder : DTreeBestSplitFinder |
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{ |
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ForestTreeBestSplitFinder() : DTreeBestSplitFinder() {} |
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ForestTreeBestSplitFinder( CvForestTree* _tree, CvDTreeNode* _node ); |
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ForestTreeBestSplitFinder( const ForestTreeBestSplitFinder& finder, Split ); |
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virtual void operator()(const BlockedRange& range); |
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}; |
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} |
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#endif /* __ML_H__ */
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