mirror of
https://github.com/opencv/opencv.git
synced 2024-11-30 06:10:02 +08:00
377 lines
20 KiB
C++
377 lines
20 KiB
C++
|
/*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
|
||
|
|
//
|
||
|
|
// 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*/
|
||
|
|
|
||
|
|
#ifndef __OPENCV_PRECOMP_H__
|
||
|
|
#define __OPENCV_PRECOMP_H__
|
||
|
|
|
||
|
|
#include "opencv2/core.hpp"
|
||
|
|
#include "old_ml.hpp"
|
||
|
|
#include "opencv2/core/core_c.h"
|
||
|
|
#include "opencv2/core/utility.hpp"
|
||
|
|
|
||
|
|
#include "opencv2/core/private.hpp"
|
||
|
|
|
||
|
|
#include <assert.h>
|
||
|
|
#include <float.h>
|
||
|
|
#include <limits.h>
|
||
|
|
#include <math.h>
|
||
|
|
#include <stdlib.h>
|
||
|
|
#include <stdio.h>
|
||
|
|
#include <string.h>
|
||
|
|
#include <time.h>
|
||
|
|
|
||
|
|
#define ML_IMPL CV_IMPL
|
||
|
|
#define __BEGIN__ __CV_BEGIN__
|
||
|
|
#define __END__ __CV_END__
|
||
|
|
#define EXIT __CV_EXIT__
|
||
|
|
|
||
|
|
#define CV_MAT_ELEM_FLAG( mat, type, comp, vect, tflag ) \
|
||
|
|
(( tflag == CV_ROW_SAMPLE ) \
|
||
|
|
? (CV_MAT_ELEM( mat, type, comp, vect )) \
|
||
|
|
: (CV_MAT_ELEM( mat, type, vect, comp )))
|
||
|
|
|
||
|
|
/* Convert matrix to vector */
|
||
|
|
#define ICV_MAT2VEC( mat, vdata, vstep, num ) \
|
||
|
|
if( MIN( (mat).rows, (mat).cols ) != 1 ) \
|
||
|
|
CV_ERROR( CV_StsBadArg, "" ); \
|
||
|
|
(vdata) = ((mat).data.ptr); \
|
||
|
|
if( (mat).rows == 1 ) \
|
||
|
|
{ \
|
||
|
|
(vstep) = CV_ELEM_SIZE( (mat).type ); \
|
||
|
|
(num) = (mat).cols; \
|
||
|
|
} \
|
||
|
|
else \
|
||
|
|
{ \
|
||
|
|
(vstep) = (mat).step; \
|
||
|
|
(num) = (mat).rows; \
|
||
|
|
}
|
||
|
|
|
||
|
|
/* get raw data */
|
||
|
|
#define ICV_RAWDATA( mat, flags, rdata, sstep, cstep, m, n ) \
|
||
|
|
(rdata) = (mat).data.ptr; \
|
||
|
|
if( CV_IS_ROW_SAMPLE( flags ) ) \
|
||
|
|
{ \
|
||
|
|
(sstep) = (mat).step; \
|
||
|
|
(cstep) = CV_ELEM_SIZE( (mat).type ); \
|
||
|
|
(m) = (mat).rows; \
|
||
|
|
(n) = (mat).cols; \
|
||
|
|
} \
|
||
|
|
else \
|
||
|
|
{ \
|
||
|
|
(cstep) = (mat).step; \
|
||
|
|
(sstep) = CV_ELEM_SIZE( (mat).type ); \
|
||
|
|
(n) = (mat).rows; \
|
||
|
|
(m) = (mat).cols; \
|
||
|
|
}
|
||
|
|
|
||
|
|
#define ICV_IS_MAT_OF_TYPE( mat, mat_type) \
|
||
|
|
(CV_IS_MAT( mat ) && CV_MAT_TYPE( mat->type ) == (mat_type) && \
|
||
|
|
(mat)->cols > 0 && (mat)->rows > 0)
|
||
|
|
|
||
|
|
/*
|
||
|
|
uchar* data; int sstep, cstep; - trainData->data
|
||
|
|
uchar* classes; int clstep; int ncl;- trainClasses
|
||
|
|
uchar* tmask; int tmstep; int ntm; - typeMask
|
||
|
|
uchar* missed;int msstep, mcstep; -missedMeasurements...
|
||
|
|
int mm, mn; == m,n == size,dim
|
||
|
|
uchar* sidx;int sistep; - sampleIdx
|
||
|
|
uchar* cidx;int cistep; - compIdx
|
||
|
|
int k, l; == n,m == dim,size (length of cidx, sidx)
|
||
|
|
int m, n; == size,dim
|
||
|
|
*/
|
||
|
|
#define ICV_DECLARE_TRAIN_ARGS() \
|
||
|
|
uchar* data; \
|
||
|
|
int sstep, cstep; \
|
||
|
|
uchar* classes; \
|
||
|
|
int clstep; \
|
||
|
|
int ncl; \
|
||
|
|
uchar* tmask; \
|
||
|
|
int tmstep; \
|
||
|
|
int ntm; \
|
||
|
|
uchar* missed; \
|
||
|
|
int msstep, mcstep; \
|
||
|
|
int mm, mn; \
|
||
|
|
uchar* sidx; \
|
||
|
|
int sistep; \
|
||
|
|
uchar* cidx; \
|
||
|
|
int cistep; \
|
||
|
|
int k, l; \
|
||
|
|
int m, n; \
|
||
|
|
\
|
||
|
|
data = classes = tmask = missed = sidx = cidx = NULL; \
|
||
|
|
sstep = cstep = clstep = ncl = tmstep = ntm = msstep = mcstep = mm = mn = 0; \
|
||
|
|
sistep = cistep = k = l = m = n = 0;
|
||
|
|
|
||
|
|
#define ICV_TRAIN_DATA_REQUIRED( param, flags ) \
|
||
|
|
if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) ) \
|
||
|
|
{ \
|
||
|
|
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
|
||
|
|
} \
|
||
|
|
else \
|
||
|
|
{ \
|
||
|
|
ICV_RAWDATA( *(param), (flags), data, sstep, cstep, m, n ); \
|
||
|
|
k = n; \
|
||
|
|
l = m; \
|
||
|
|
}
|
||
|
|
|
||
|
|
#define ICV_TRAIN_CLASSES_REQUIRED( param ) \
|
||
|
|
if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) ) \
|
||
|
|
{ \
|
||
|
|
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
|
||
|
|
} \
|
||
|
|
else \
|
||
|
|
{ \
|
||
|
|
ICV_MAT2VEC( *(param), classes, clstep, ncl ); \
|
||
|
|
if( m != ncl ) \
|
||
|
|
{ \
|
||
|
|
CV_ERROR( CV_StsBadArg, "Unmatched sizes" ); \
|
||
|
|
} \
|
||
|
|
}
|
||
|
|
|
||
|
|
#define ICV_ARG_NULL( param ) \
|
||
|
|
if( (param) != NULL ) \
|
||
|
|
{ \
|
||
|
|
CV_ERROR( CV_StsBadArg, #param " parameter must be NULL" ); \
|
||
|
|
}
|
||
|
|
|
||
|
|
#define ICV_MISSED_MEASUREMENTS_OPTIONAL( param, flags ) \
|
||
|
|
if( param ) \
|
||
|
|
{ \
|
||
|
|
if( !ICV_IS_MAT_OF_TYPE( param, CV_8UC1 ) ) \
|
||
|
|
{ \
|
||
|
|
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
|
||
|
|
} \
|
||
|
|
else \
|
||
|
|
{ \
|
||
|
|
ICV_RAWDATA( *(param), (flags), missed, msstep, mcstep, mm, mn ); \
|
||
|
|
if( mm != m || mn != n ) \
|
||
|
|
{ \
|
||
|
|
CV_ERROR( CV_StsBadArg, "Unmatched sizes" ); \
|
||
|
|
} \
|
||
|
|
} \
|
||
|
|
}
|
||
|
|
|
||
|
|
#define ICV_COMP_IDX_OPTIONAL( param ) \
|
||
|
|
if( param ) \
|
||
|
|
{ \
|
||
|
|
if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) ) \
|
||
|
|
{ \
|
||
|
|
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
|
||
|
|
} \
|
||
|
|
else \
|
||
|
|
{ \
|
||
|
|
ICV_MAT2VEC( *(param), cidx, cistep, k ); \
|
||
|
|
if( k > n ) \
|
||
|
|
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
|
||
|
|
} \
|
||
|
|
}
|
||
|
|
|
||
|
|
#define ICV_SAMPLE_IDX_OPTIONAL( param ) \
|
||
|
|
if( param ) \
|
||
|
|
{ \
|
||
|
|
if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) ) \
|
||
|
|
{ \
|
||
|
|
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
|
||
|
|
} \
|
||
|
|
else \
|
||
|
|
{ \
|
||
|
|
ICV_MAT2VEC( *sampleIdx, sidx, sistep, l ); \
|
||
|
|
if( l > m ) \
|
||
|
|
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
|
||
|
|
} \
|
||
|
|
}
|
||
|
|
|
||
|
|
/****************************************************************************************/
|
||
|
|
#define ICV_CONVERT_FLOAT_ARRAY_TO_MATRICE( array, matrice ) \
|
||
|
|
{ \
|
||
|
|
CvMat a, b; \
|
||
|
|
int dims = (matrice)->cols; \
|
||
|
|
int nsamples = (matrice)->rows; \
|
||
|
|
int type = CV_MAT_TYPE((matrice)->type); \
|
||
|
|
int i, offset = dims; \
|
||
|
|
\
|
||
|
|
CV_ASSERT( type == CV_32FC1 || type == CV_64FC1 ); \
|
||
|
|
offset *= ((type == CV_32FC1) ? sizeof(float) : sizeof(double));\
|
||
|
|
\
|
||
|
|
b = cvMat( 1, dims, CV_32FC1 ); \
|
||
|
|
cvGetRow( matrice, &a, 0 ); \
|
||
|
|
for( i = 0; i < nsamples; i++, a.data.ptr += offset ) \
|
||
|
|
{ \
|
||
|
|
b.data.fl = (float*)array[i]; \
|
||
|
|
CV_CALL( cvConvert( &b, &a ) ); \
|
||
|
|
} \
|
||
|
|
}
|
||
|
|
|
||
|
|
/****************************************************************************************\
|
||
|
|
* Auxiliary functions declarations *
|
||
|
|
\****************************************************************************************/
|
||
|
|
|
||
|
|
/* Generates a set of classes centers in quantity <num_of_clusters> that are generated as
|
||
|
|
uniform random vectors in parallelepiped, where <data> is concentrated. Vectors in
|
||
|
|
<data> should have horizontal orientation. If <centers> != NULL, the function doesn't
|
||
|
|
allocate any memory and stores generated centers in <centers>, returns <centers>.
|
||
|
|
If <centers> == NULL, the function allocates memory and creates the matrice. Centers
|
||
|
|
are supposed to be oriented horizontally. */
|
||
|
|
CvMat* icvGenerateRandomClusterCenters( int seed,
|
||
|
|
const CvMat* data,
|
||
|
|
int num_of_clusters,
|
||
|
|
CvMat* centers CV_DEFAULT(0));
|
||
|
|
|
||
|
|
/* Fills the <labels> using <probs> by choosing the maximal probability. Outliers are
|
||
|
|
fixed by <oulier_tresh> and have cluster label (-1). Function also controls that there
|
||
|
|
weren't "empty" clusters by filling empty clusters with the maximal probability vector.
|
||
|
|
If probs_sums != NULL, filles it with the sums of probabilities for each sample (it is
|
||
|
|
useful for normalizing probabilities' matrice of FCM) */
|
||
|
|
void icvFindClusterLabels( const CvMat* probs, float outlier_thresh, float r,
|
||
|
|
const CvMat* labels );
|
||
|
|
|
||
|
|
typedef struct CvSparseVecElem32f
|
||
|
|
{
|
||
|
|
int idx;
|
||
|
|
float val;
|
||
|
|
}
|
||
|
|
CvSparseVecElem32f;
|
||
|
|
|
||
|
|
/* Prepare training data and related parameters */
|
||
|
|
#define CV_TRAIN_STATMODEL_DEFRAGMENT_TRAIN_DATA 1
|
||
|
|
#define CV_TRAIN_STATMODEL_SAMPLES_AS_ROWS 2
|
||
|
|
#define CV_TRAIN_STATMODEL_SAMPLES_AS_COLUMNS 4
|
||
|
|
#define CV_TRAIN_STATMODEL_CATEGORICAL_RESPONSE 8
|
||
|
|
#define CV_TRAIN_STATMODEL_ORDERED_RESPONSE 16
|
||
|
|
#define CV_TRAIN_STATMODEL_RESPONSES_ON_OUTPUT 32
|
||
|
|
#define CV_TRAIN_STATMODEL_ALWAYS_COPY_TRAIN_DATA 64
|
||
|
|
#define CV_TRAIN_STATMODEL_SPARSE_AS_SPARSE 128
|
||
|
|
|
||
|
|
int
|
||
|
|
cvPrepareTrainData( const char* /*funcname*/,
|
||
|
|
const CvMat* train_data, int tflag,
|
||
|
|
const CvMat* responses, int response_type,
|
||
|
|
const CvMat* var_idx,
|
||
|
|
const CvMat* sample_idx,
|
||
|
|
bool always_copy_data,
|
||
|
|
const float*** out_train_samples,
|
||
|
|
int* _sample_count,
|
||
|
|
int* _var_count,
|
||
|
|
int* _var_all,
|
||
|
|
CvMat** out_responses,
|
||
|
|
CvMat** out_response_map,
|
||
|
|
CvMat** out_var_idx,
|
||
|
|
CvMat** out_sample_idx=0 );
|
||
|
|
|
||
|
|
void
|
||
|
|
cvSortSamplesByClasses( const float** samples, const CvMat* classes,
|
||
|
|
int* class_ranges, const uchar** mask CV_DEFAULT(0) );
|
||
|
|
|
||
|
|
void
|
||
|
|
cvCombineResponseMaps (CvMat* _responses,
|
||
|
|
const CvMat* old_response_map,
|
||
|
|
CvMat* new_response_map,
|
||
|
|
CvMat** out_response_map);
|
||
|
|
|
||
|
|
void
|
||
|
|
cvPreparePredictData( const CvArr* sample, int dims_all, const CvMat* comp_idx,
|
||
|
|
int class_count, const CvMat* prob, float** row_sample,
|
||
|
|
int as_sparse CV_DEFAULT(0) );
|
||
|
|
|
||
|
|
/* copies clustering [or batch "predict"] results
|
||
|
|
(labels and/or centers and/or probs) back to the output arrays */
|
||
|
|
void
|
||
|
|
cvWritebackLabels( const CvMat* labels, CvMat* dst_labels,
|
||
|
|
const CvMat* centers, CvMat* dst_centers,
|
||
|
|
const CvMat* probs, CvMat* dst_probs,
|
||
|
|
const CvMat* sample_idx, int samples_all,
|
||
|
|
const CvMat* comp_idx, int dims_all );
|
||
|
|
#define cvWritebackResponses cvWritebackLabels
|
||
|
|
|
||
|
|
#define XML_FIELD_NAME "_name"
|
||
|
|
CvFileNode* icvFileNodeGetChild(CvFileNode* father, const char* name);
|
||
|
|
CvFileNode* icvFileNodeGetChildArrayElem(CvFileNode* father, const char* name,int index);
|
||
|
|
CvFileNode* icvFileNodeGetNext(CvFileNode* n, const char* name);
|
||
|
|
|
||
|
|
|
||
|
|
void cvCheckTrainData( const CvMat* train_data, int tflag,
|
||
|
|
const CvMat* missing_mask,
|
||
|
|
int* var_all, int* sample_all );
|
||
|
|
|
||
|
|
CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, bool check_for_duplicates=false );
|
||
|
|
|
||
|
|
CvMat* cvPreprocessVarType( const CvMat* type_mask, const CvMat* var_idx,
|
||
|
|
int var_all, int* response_type );
|
||
|
|
|
||
|
|
CvMat* cvPreprocessOrderedResponses( const CvMat* responses,
|
||
|
|
const CvMat* sample_idx, int sample_all );
|
||
|
|
|
||
|
|
CvMat* cvPreprocessCategoricalResponses( const CvMat* responses,
|
||
|
|
const CvMat* sample_idx, int sample_all,
|
||
|
|
CvMat** out_response_map, CvMat** class_counts=0 );
|
||
|
|
|
||
|
|
const float** cvGetTrainSamples( const CvMat* train_data, int tflag,
|
||
|
|
const CvMat* var_idx, const CvMat* sample_idx,
|
||
|
|
int* _var_count, int* _sample_count,
|
||
|
|
bool always_copy_data=false );
|
||
|
|
|
||
|
|
namespace cv
|
||
|
|
{
|
||
|
|
struct DTreeBestSplitFinder
|
||
|
|
{
|
||
|
|
DTreeBestSplitFinder(){ splitSize = 0, tree = 0; node = 0; }
|
||
|
|
DTreeBestSplitFinder( CvDTree* _tree, CvDTreeNode* _node);
|
||
|
|
DTreeBestSplitFinder( const DTreeBestSplitFinder& finder, Split );
|
||
|
|
virtual ~DTreeBestSplitFinder() {}
|
||
|
|
virtual void operator()(const BlockedRange& range);
|
||
|
|
void join( DTreeBestSplitFinder& rhs );
|
||
|
|
Ptr<CvDTreeSplit> bestSplit;
|
||
|
|
Ptr<CvDTreeSplit> split;
|
||
|
|
int splitSize;
|
||
|
|
CvDTree* tree;
|
||
|
|
CvDTreeNode* node;
|
||
|
|
};
|
||
|
|
|
||
|
|
struct ForestTreeBestSplitFinder : DTreeBestSplitFinder
|
||
|
|
{
|
||
|
|
ForestTreeBestSplitFinder() : DTreeBestSplitFinder() {}
|
||
|
|
ForestTreeBestSplitFinder( CvForestTree* _tree, CvDTreeNode* _node );
|
||
|
|
ForestTreeBestSplitFinder( const ForestTreeBestSplitFinder& finder, Split );
|
||
|
|
virtual void operator()(const BlockedRange& range);
|
||
|
|
};
|
||
|
|
}
|
||
|
|
|
||
|
|
#endif /* __ML_H__ */
|