+++ /dev/null
-/*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*/
-
-#include "_ml.h"
-
-
-CvStatModel::CvStatModel()
-{
- default_model_name = "my_stat_model";
-}
-
-
-CvStatModel::~CvStatModel()
-{
- clear();
-}
-
-
-void CvStatModel::clear()
-{
-}
-
-
-void CvStatModel::save( const char* filename, const char* name )
-{
- CvFileStorage* fs = 0;
-
- CV_FUNCNAME( "CvStatModel::save" );
-
- __BEGIN__;
-
- CV_CALL( fs = cvOpenFileStorage( filename, 0, CV_STORAGE_WRITE ));
- if( !fs )
- CV_ERROR( CV_StsError, "Could not open the file storage. Check the path and permissions" );
-
- write( fs, name ? name : default_model_name );
-
- __END__;
-
- cvReleaseFileStorage( &fs );
-}
-
-
-void CvStatModel::load( const char* filename, const char* name )
-{
- CvFileStorage* fs = 0;
-
- CV_FUNCNAME( "CvStatModel::load" );
-
- __BEGIN__;
-
- CvFileNode* model_node = 0;
-
- CV_CALL( fs = cvOpenFileStorage( filename, 0, CV_STORAGE_READ ));
- if( !fs )
- CV_ERROR( CV_StsError, "Could not open the file storage. Check the path and permissions" );
-
- if( name )
- model_node = cvGetFileNodeByName( fs, 0, name );
- else
- {
- CvFileNode* root = cvGetRootFileNode( fs );
- if( root->data.seq->total > 0 )
- model_node = (CvFileNode*)cvGetSeqElem( root->data.seq, 0 );
- }
-
- read( fs, model_node );
-
- __END__;
-
- cvReleaseFileStorage( &fs );
-}
-
-
-void CvStatModel::write( CvFileStorage*, const char* )
-{
- OPENCV_ERROR( CV_StsNotImplemented, "CvStatModel::write", "" );
-}
-
-
-void CvStatModel::read( CvFileStorage*, CvFileNode* )
-{
- OPENCV_ERROR( CV_StsNotImplemented, "CvStatModel::read", "" );
-}
-
-
-/* Calculates upper triangular matrix S, where A is a symmetrical matrix A=S'*S */
-CV_IMPL void cvChol( CvMat* A, CvMat* S )
-{
- int dim = A->rows;
-
- int i, j, k;
- float sum;
-
- for( i = 0; i < dim; i++ )
- {
- for( j = 0; j < i; j++ )
- CV_MAT_ELEM(*S, float, i, j) = 0;
-
- sum = 0;
- for( k = 0; k < i; k++ )
- sum += CV_MAT_ELEM(*S, float, k, i) * CV_MAT_ELEM(*S, float, k, i);
-
- CV_MAT_ELEM(*S, float, i, i) = (float)sqrt(CV_MAT_ELEM(*A, float, i, i) - sum);
-
- for( j = i + 1; j < dim; j++ )
- {
- sum = 0;
- for( k = 0; k < i; k++ )
- sum += CV_MAT_ELEM(*S, float, k, i) * CV_MAT_ELEM(*S, float, k, j);
-
- CV_MAT_ELEM(*S, float, i, j) =
- (CV_MAT_ELEM(*A, float, i, j) - sum) / CV_MAT_ELEM(*S, float, i, i);
-
- }
- }
-}
-
-/* Generates <sample> from multivariate normal distribution, where <mean> - is an
- average row vector, <cov> - symmetric covariation matrix */
-CV_IMPL void cvRandMVNormal( CvMat* mean, CvMat* cov, CvMat* sample, CvRNG* rng )
-{
- int dim = sample->cols;
- int amount = sample->rows;
-
- CvRNG state = rng ? *rng : cvRNG(time(0));
- cvRandArr(&state, sample, CV_RAND_NORMAL, cvScalarAll(0), cvScalarAll(1) );
-
- CvMat* utmat = cvCreateMat(dim, dim, sample->type);
- CvMat* vect = cvCreateMatHeader(1, dim, sample->type);
-
- cvChol(cov, utmat);
-
- int i;
- for( i = 0; i < amount; i++ )
- {
- cvGetRow(sample, vect, i);
- cvMatMulAdd(vect, utmat, mean, vect);
- }
-
- cvReleaseMat(&vect);
- cvReleaseMat(&utmat);
-}
-
-
-/* Generates <sample> of <amount> points from a discrete variate xi,
- where Pr{xi = k} == probs[k], 0 < k < len - 1. */
-CV_IMPL void cvRandSeries( float probs[], int len, int sample[], int amount )
-{
- CvMat* univals = cvCreateMat(1, amount, CV_32FC1);
- float* knots = (float*)cvAlloc( len * sizeof(float) );
-
- int i, j;
-
- CvRNG state = cvRNG(-1);
- cvRandArr(&state, univals, CV_RAND_UNI, cvScalarAll(0), cvScalarAll(1) );
-
- knots[0] = probs[0];
- for( i = 1; i < len; i++ )
- knots[i] = knots[i - 1] + probs[i];
-
- for( i = 0; i < amount; i++ )
- for( j = 0; j < len; j++ )
- {
- if ( CV_MAT_ELEM(*univals, float, 0, i) <= knots[j] )
- {
- sample[i] = j;
- break;
- }
- }
-
- cvFree(&knots);
-}
-
-/* Generates <sample> from gaussian mixture distribution */
-CV_IMPL void cvRandGaussMixture( CvMat* means[],
- CvMat* covs[],
- float weights[],
- int clsnum,
- CvMat* sample,
- CvMat* sampClasses )
-{
- int dim = sample->cols;
- int amount = sample->rows;
-
- int i, clss;
-
- int* sample_clsnum = (int*)cvAlloc( amount * sizeof(int) );
- CvMat** utmats = (CvMat**)cvAlloc( clsnum * sizeof(CvMat*) );
- CvMat* vect = cvCreateMatHeader(1, dim, CV_32FC1);
-
- CvMat* classes;
- if( sampClasses )
- classes = sampClasses;
- else
- classes = cvCreateMat(1, amount, CV_32FC1);
-
- CvRNG state = cvRNG(-1);
- cvRandArr(&state, sample, CV_RAND_NORMAL, cvScalarAll(0), cvScalarAll(1));
-
- cvRandSeries(weights, clsnum, sample_clsnum, amount);
-
- for( i = 0; i < clsnum; i++ )
- {
- utmats[i] = cvCreateMat(dim, dim, CV_32FC1);
- cvChol(covs[i], utmats[i]);
- }
-
- for( i = 0; i < amount; i++ )
- {
- CV_MAT_ELEM(*classes, float, 0, i) = (float)sample_clsnum[i];
- cvGetRow(sample, vect, i);
- clss = sample_clsnum[i];
- cvMatMulAdd(vect, utmats[clss], means[clss], vect);
- }
-
- if( !sampClasses )
- cvReleaseMat(&classes);
- for( i = 0; i < clsnum; i++ )
- cvReleaseMat(&utmats[i]);
- cvFree(&utmats);
- cvFree(&sample_clsnum);
- cvReleaseMat(&vect);
-}
-
-
-CvMat* icvGenerateRandomClusterCenters ( int seed, const CvMat* data,
- int num_of_clusters, CvMat* _centers )
-{
- CvMat* centers = _centers;
-
- CV_FUNCNAME("icvGenerateRandomClusterCenters");
- __BEGIN__;
-
- CvRNG rng;
- CvMat data_comp, centers_comp;
- CvPoint minLoc, maxLoc; // Not used, just for function "cvMinMaxLoc"
- double minVal, maxVal;
- int i;
- int dim = data ? data->cols : 0;
-
- if( ICV_IS_MAT_OF_TYPE(data, CV_32FC1) )
- {
- if( _centers && !ICV_IS_MAT_OF_TYPE (_centers, CV_32FC1) )
- {
- CV_ERROR(CV_StsBadArg,"");
- }
- else if( !_centers )
- CV_CALL(centers = cvCreateMat (num_of_clusters, dim, CV_32FC1));
- }
- else if( ICV_IS_MAT_OF_TYPE(data, CV_64FC1) )
- {
- if( _centers && !ICV_IS_MAT_OF_TYPE (_centers, CV_64FC1) )
- {
- CV_ERROR(CV_StsBadArg,"");
- }
- else if( !_centers )
- CV_CALL(centers = cvCreateMat (num_of_clusters, dim, CV_64FC1));
- }
- else
- CV_ERROR (CV_StsBadArg,"");
-
- if( num_of_clusters < 1 )
- CV_ERROR (CV_StsBadArg,"");
-
- rng = cvRNG(seed);
- for (i = 0; i < dim; i++)
- {
- CV_CALL(cvGetCol (data, &data_comp, i));
- CV_CALL(cvMinMaxLoc (&data_comp, &minVal, &maxVal, &minLoc, &maxLoc));
- CV_CALL(cvGetCol (centers, ¢ers_comp, i));
- CV_CALL(cvRandArr (&rng, ¢ers_comp, CV_RAND_UNI, cvScalarAll(minVal), cvScalarAll(maxVal)));
- }
-
- __END__;
-
- if( (cvGetErrStatus () < 0) || (centers != _centers) )
- cvReleaseMat (¢ers);
-
- return _centers ? _centers : centers;
-} // end of icvGenerateRandomClusterCenters
-
-// By S. Dilman - begin -
-
-#define ICV_RAND_MAX 4294967296 // == 2^32
-
-CV_IMPL void cvRandRoundUni (CvMat* center,
- float radius_small,
- float radius_large,
- CvMat* desired_matrix,
- CvRNG* rng_state_ptr)
-{
- float rad, norm, coefficient;
- int dim, size, i, j;
- CvMat *cov, sample;
- CvRNG rng_local;
-
- CV_FUNCNAME("cvRandRoundUni");
- __BEGIN__
-
- rng_local = *rng_state_ptr;
-
- CV_ASSERT ((radius_small >= 0) &&
- (radius_large > 0) &&
- (radius_small <= radius_large));
- CV_ASSERT (center && desired_matrix && rng_state_ptr);
- CV_ASSERT (center->rows == 1);
- CV_ASSERT (center->cols == desired_matrix->cols);
-
- dim = desired_matrix->cols;
- size = desired_matrix->rows;
- cov = cvCreateMat (dim, dim, CV_32FC1);
- cvSetIdentity (cov);
- cvRandMVNormal (center, cov, desired_matrix, &rng_local);
-
- for (i = 0; i < size; i++)
- {
- rad = (float)(cvRandReal(&rng_local)*(radius_large - radius_small) + radius_small);
- cvGetRow (desired_matrix, &sample, i);
- norm = (float) cvNorm (&sample, 0, CV_L2);
- coefficient = rad / norm;
- for (j = 0; j < dim; j++)
- CV_MAT_ELEM (sample, float, 0, j) *= coefficient;
- }
-
- __END__
-
-}
-
-// By S. Dilman - end -
-
-/* Aij <- Aji for i > j if lower_to_upper != 0
- for i < j if lower_to_upper = 0 */
-void cvCompleteSymm( CvMat* matrix, int lower_to_upper )
-{
- CV_FUNCNAME("cvCompleteSymm");
-
- __BEGIN__;
-
- int rows, cols;
- int i, j;
- int step;
-
- if( !CV_IS_MAT(matrix))
- CV_ERROR(CV_StsBadArg, "Invalid matrix argument");
-
- rows = matrix->rows;
- cols = matrix->cols;
- step = matrix->step / CV_ELEM_SIZE(matrix->type);
-
- switch(CV_MAT_TYPE(matrix->type))
- {
- case CV_32FC1:
- {
- float* dst = matrix->data.fl;
- if( !lower_to_upper )
- for( i = 1; i < rows; i++ )
- {
- const float* src = (const float*)(matrix->data.fl + i);
- dst += step;
- for( j = 0; j < i; j++, src += step )
- dst[j] = src[0];
- }
- else
- for( i = 0; i < rows-1; i++, dst += step )
- {
- const float* src = (const float*)(matrix->data.fl + (i+1)*step + i);
- for( j = i+1; j < cols; j++, src += step )
- dst[j] = src[0];
- }
- }
- break;
- case CV_64FC1:
- {
- double* dst = matrix->data.db;
- if( !lower_to_upper )
- for( i = 1; i < rows; i++ )
- {
- const double* src = (const double*)(matrix->data.db + i);
- dst += step;
- for( j = 0; j < i; j++, src += step )
- dst[j] = src[0];
- }
- else
- for( i = 0; i < rows-1; i++, dst += step )
- {
- const double* src = (const double*)(matrix->data.db + (i+1)*step + i);
- for( j = i+1; j < cols; j++, src += step )
- dst[j] = src[0];
- }
- }
- break;
- }
-
- __END__;
-}
-
-
-static int CV_CDECL
-icvCmpIntegers( const void* a, const void* b )
-{
- return *(const int*)a - *(const int*)b;
-}
-
-
-static int CV_CDECL
-icvCmpIntegersPtr( const void* _a, const void* _b )
-{
- int a = **(const int**)_a;
- int b = **(const int**)_b;
- return (a < b ? -1 : 0)|(a > b);
-}
-
-
-static int icvCmpSparseVecElems( const void* a, const void* b )
-{
- return ((CvSparseVecElem32f*)a)->idx - ((CvSparseVecElem32f*)b)->idx;
-}
-
-
-CvMat*
-cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, bool check_for_duplicates )
-{
- CvMat* idx = 0;
-
- CV_FUNCNAME( "cvPreprocessIndexArray" );
-
- __BEGIN__;
-
- int i, idx_total, idx_selected = 0, step, type, prev = INT_MIN, is_sorted = 1;
- uchar* srcb = 0;
- int* srci = 0;
- int* dsti;
-
- if( !CV_IS_MAT(idx_arr) )
- CV_ERROR( CV_StsBadArg, "Invalid index array" );
-
- if( idx_arr->rows != 1 && idx_arr->cols != 1 )
- CV_ERROR( CV_StsBadSize, "the index array must be 1-dimensional" );
-
- idx_total = idx_arr->rows + idx_arr->cols - 1;
- srcb = idx_arr->data.ptr;
- srci = idx_arr->data.i;
-
- type = CV_MAT_TYPE(idx_arr->type);
- step = CV_IS_MAT_CONT(idx_arr->type) ? 1 : idx_arr->step/CV_ELEM_SIZE(type);
-
- switch( type )
- {
- case CV_8UC1:
- case CV_8SC1:
- // idx_arr is array of 1's and 0's -
- // i.e. it is a mask of the selected components
- if( idx_total != data_arr_size )
- CV_ERROR( CV_StsUnmatchedSizes,
- "Component mask should contain as many elements as the total number of input variables" );
-
- for( i = 0; i < idx_total; i++ )
- idx_selected += srcb[i*step] != 0;
-
- if( idx_selected == 0 )
- CV_ERROR( CV_StsOutOfRange, "No components/input_variables is selected!" );
-
- if( idx_selected == idx_total )
- EXIT;
- break;
- case CV_32SC1:
- // idx_arr is array of integer indices of selected components
- if( idx_total > data_arr_size )
- CV_ERROR( CV_StsOutOfRange,
- "index array may not contain more elements than the total number of input variables" );
- idx_selected = idx_total;
- // check if sorted already
- for( i = 0; i < idx_total; i++ )
- {
- int val = srci[i*step];
- if( val >= prev )
- {
- is_sorted = 0;
- break;
- }
- prev = val;
- }
- break;
- default:
- CV_ERROR( CV_StsUnsupportedFormat, "Unsupported index array data type "
- "(it should be 8uC1, 8sC1 or 32sC1)" );
- }
-
- CV_CALL( idx = cvCreateMat( 1, idx_selected, CV_32SC1 ));
- dsti = idx->data.i;
-
- if( type < CV_32SC1 )
- {
- for( i = 0; i < idx_total; i++ )
- if( srcb[i*step] )
- *dsti++ = i;
- }
- else
- {
- for( i = 0; i < idx_total; i++ )
- dsti[i] = srci[i*step];
-
- if( !is_sorted )
- qsort( dsti, idx_total, sizeof(dsti[0]), icvCmpIntegers );
-
- if( dsti[0] < 0 || dsti[idx_total-1] >= data_arr_size )
- CV_ERROR( CV_StsOutOfRange, "the index array elements are out of range" );
-
- if( check_for_duplicates )
- {
- for( i = 1; i < idx_total; i++ )
- if( dsti[i] <= dsti[i-1] )
- CV_ERROR( CV_StsBadArg, "There are duplicated index array elements" );
- }
- }
-
- __END__;
-
- if( cvGetErrStatus() < 0 )
- cvReleaseMat( &idx );
-
- return idx;
-}
-
-
-CvMat*
-cvPreprocessVarType( const CvMat* var_type, const CvMat* var_idx,
- int var_all, int* response_type )
-{
- CvMat* out_var_type = 0;
- CV_FUNCNAME( "cvPreprocessVarType" );
-
- if( response_type )
- *response_type = -1;
-
- __BEGIN__;
-
- int i, tm_size, tm_step;
- int* map = 0;
- const uchar* src;
- uchar* dst;
- int var_count = var_all;
-
- if( !CV_IS_MAT(var_type) )
- CV_ERROR( var_type ? CV_StsBadArg : CV_StsNullPtr, "Invalid or absent var_type array" );
-
- if( var_type->rows != 1 && var_type->cols != 1 )
- CV_ERROR( CV_StsBadSize, "var_type array must be 1-dimensional" );
-
- if( !CV_IS_MASK_ARR(var_type))
- CV_ERROR( CV_StsUnsupportedFormat, "type mask must be 8uC1 or 8sC1 array" );
-
- tm_size = var_type->rows + var_type->cols - 1;
- tm_step = var_type->step ? var_type->step/CV_ELEM_SIZE(var_type->type) : 1;
-
- if( /*tm_size != var_count &&*/ tm_size != var_count + 1 )
- CV_ERROR( CV_StsBadArg,
- "type mask must be of <input var count> + 1 size" );
-
- if( response_type && tm_size > var_count )
- *response_type = var_type->data.ptr[var_count*tm_step] != 0;
-
- if( var_idx )
- {
- if( !CV_IS_MAT(var_idx) || CV_MAT_TYPE(var_idx->type) != CV_32SC1 ||
- var_idx->rows != 1 && var_idx->cols != 1 || !CV_IS_MAT_CONT(var_idx->type) )
- CV_ERROR( CV_StsBadArg, "var index array should be continuous 1-dimensional integer vector" );
- if( var_idx->rows + var_idx->cols - 1 > var_count )
- CV_ERROR( CV_StsBadSize, "var index array is too large" );
- map = var_idx->data.i;
- var_count = var_idx->rows + var_idx->cols - 1;
- }
-
- CV_CALL( out_var_type = cvCreateMat( 1, var_count, CV_8UC1 ));
- src = var_type->data.ptr;
- dst = out_var_type->data.ptr;
-
- for( i = 0; i < var_count; i++ )
- {
- int idx = map ? map[i] : i;
- assert( (unsigned)idx < (unsigned)tm_size );
- dst[i] = (uchar)(src[idx*tm_step] != 0);
- }
-
- __END__;
-
- return out_var_type;
-}
-
-
-CvMat*
-cvPreprocessOrderedResponses( const CvMat* responses, const CvMat* sample_idx, int sample_all )
-{
- CvMat* out_responses = 0;
-
- CV_FUNCNAME( "cvPreprocessOrderedResponses" );
-
- __BEGIN__;
-
- int i, r_type, r_step;
- const int* map = 0;
- float* dst;
- int sample_count = sample_all;
-
- if( !CV_IS_MAT(responses) )
- CV_ERROR( CV_StsBadArg, "Invalid response array" );
-
- if( responses->rows != 1 && responses->cols != 1 )
- CV_ERROR( CV_StsBadSize, "Response array must be 1-dimensional" );
-
- if( responses->rows + responses->cols - 1 != sample_count )
- CV_ERROR( CV_StsUnmatchedSizes,
- "Response array must contain as many elements as the total number of samples" );
-
- r_type = CV_MAT_TYPE(responses->type);
- if( r_type != CV_32FC1 && r_type != CV_32SC1 )
- CV_ERROR( CV_StsUnsupportedFormat, "Unsupported response type" );
-
- r_step = responses->step ? responses->step / CV_ELEM_SIZE(responses->type) : 1;
-
- if( r_type == CV_32FC1 && CV_IS_MAT_CONT(responses->type) && !sample_idx )
- {
- out_responses = (CvMat*)responses;
- EXIT;
- }
-
- if( sample_idx )
- {
- if( !CV_IS_MAT(sample_idx) || CV_MAT_TYPE(sample_idx->type) != CV_32SC1 ||
- sample_idx->rows != 1 && sample_idx->cols != 1 || !CV_IS_MAT_CONT(sample_idx->type) )
- CV_ERROR( CV_StsBadArg, "sample index array should be continuous 1-dimensional integer vector" );
- if( sample_idx->rows + sample_idx->cols - 1 > sample_count )
- CV_ERROR( CV_StsBadSize, "sample index array is too large" );
- map = sample_idx->data.i;
- sample_count = sample_idx->rows + sample_idx->cols - 1;
- }
-
- CV_CALL( out_responses = cvCreateMat( 1, sample_count, CV_32FC1 ));
-
- dst = out_responses->data.fl;
- if( r_type == CV_32FC1 )
- {
- const float* src = responses->data.fl;
- for( i = 0; i < sample_count; i++ )
- {
- int idx = map ? map[i] : i;
- assert( (unsigned)idx < (unsigned)sample_all );
- dst[i] = src[idx*r_step];
- }
- }
- else
- {
- const int* src = responses->data.i;
- for( i = 0; i < sample_count; i++ )
- {
- int idx = map ? map[i] : i;
- assert( (unsigned)idx < (unsigned)sample_all );
- dst[i] = (float)src[idx*r_step];
- }
- }
-
- __END__;
-
- return out_responses;
-}
-
-CvMat*
-cvPreprocessCategoricalResponses( const CvMat* responses,
- const CvMat* sample_idx, int sample_all,
- CvMat** out_response_map, CvMat** class_counts )
-{
- CvMat* out_responses = 0;
- int** response_ptr = 0;
-
- CV_FUNCNAME( "cvPreprocessCategoricalResponses" );
-
- if( out_response_map )
- *out_response_map = 0;
-
- if( class_counts )
- *class_counts = 0;
-
- __BEGIN__;
-
- int i, r_type, r_step;
- int cls_count = 1, prev_cls, prev_i;
- const int* map = 0;
- const int* srci;
- const float* srcfl;
- int* dst;
- int* cls_map;
- int* cls_counts = 0;
- int sample_count = sample_all;
-
- if( !CV_IS_MAT(responses) )
- CV_ERROR( CV_StsBadArg, "Invalid response array" );
-
- if( responses->rows != 1 && responses->cols != 1 )
- CV_ERROR( CV_StsBadSize, "Response array must be 1-dimensional" );
-
- if( responses->rows + responses->cols - 1 != sample_count )
- CV_ERROR( CV_StsUnmatchedSizes,
- "Response array must contain as many elements as the total number of samples" );
-
- r_type = CV_MAT_TYPE(responses->type);
- if( r_type != CV_32FC1 && r_type != CV_32SC1 )
- CV_ERROR( CV_StsUnsupportedFormat, "Unsupported response type" );
-
- r_step = responses->step ? responses->step / CV_ELEM_SIZE(responses->type) : 1;
-
- if( sample_idx )
- {
- if( !CV_IS_MAT(sample_idx) || CV_MAT_TYPE(sample_idx->type) != CV_32SC1 ||
- sample_idx->rows != 1 && sample_idx->cols != 1 || !CV_IS_MAT_CONT(sample_idx->type) )
- CV_ERROR( CV_StsBadArg, "sample index array should be continuous 1-dimensional integer vector" );
- if( sample_idx->rows + sample_idx->cols - 1 > sample_count )
- CV_ERROR( CV_StsBadSize, "sample index array is too large" );
- map = sample_idx->data.i;
- sample_count = sample_idx->rows + sample_idx->cols - 1;
- }
-
- CV_CALL( out_responses = cvCreateMat( 1, sample_count, CV_32SC1 ));
-
- if( !out_response_map )
- CV_ERROR( CV_StsNullPtr, "out_response_map pointer is NULL" );
-
- CV_CALL( response_ptr = (int**)cvAlloc( sample_count*sizeof(response_ptr[0])));
-
- srci = responses->data.i;
- srcfl = responses->data.fl;
- dst = out_responses->data.i;
-
- for( i = 0; i < sample_count; i++ )
- {
- int idx = map ? map[i] : i;
- assert( (unsigned)idx < (unsigned)sample_all );
- if( r_type == CV_32SC1 )
- dst[i] = srci[idx*r_step];
- else
- {
- float rf = srcfl[idx*r_step];
- int ri = cvRound(rf);
- if( ri != rf )
- {
- char buf[100];
- sprintf( buf, "response #%d is not integral", idx );
- CV_ERROR( CV_StsBadArg, buf );
- }
- dst[i] = ri;
- }
- response_ptr[i] = dst + i;
- }
-
- qsort( response_ptr, sample_count, sizeof(int*), icvCmpIntegersPtr );
-
- // count the classes
- for( i = 1; i < sample_count; i++ )
- cls_count += *response_ptr[i] != *response_ptr[i-1];
-
- if( cls_count < 2 )
- CV_ERROR( CV_StsBadArg, "There is only a single class" );
-
- CV_CALL( *out_response_map = cvCreateMat( 1, cls_count, CV_32SC1 ));
-
- if( class_counts )
- {
- CV_CALL( *class_counts = cvCreateMat( 1, cls_count, CV_32SC1 ));
- cls_counts = (*class_counts)->data.i;
- }
-
- // compact the class indices and build the map
- prev_cls = ~*response_ptr[0];
- cls_count = -1;
- cls_map = (*out_response_map)->data.i;
-
- for( i = 0, prev_i = -1; i < sample_count; i++ )
- {
- int cur_cls = *response_ptr[i];
- if( cur_cls != prev_cls )
- {
- if( cls_counts && cls_count >= 0 )
- cls_counts[cls_count] = i - prev_i;
- cls_map[++cls_count] = prev_cls = cur_cls;
- prev_i = i;
- }
- *response_ptr[i] = cls_count;
- }
-
- if( cls_counts )
- cls_counts[cls_count] = i - prev_i;
-
- __END__;
-
- cvFree( &response_ptr );
-
- return out_responses;
-}
-
-
-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 )
-{
- float** samples = 0;
-
- CV_FUNCNAME( "cvGetTrainSamples" );
-
- __BEGIN__;
-
- int i, j, var_count, sample_count, s_step, v_step;
- bool copy_data;
- const float* data;
- const int *s_idx, *v_idx;
-
- if( !CV_IS_MAT(train_data) )
- CV_ERROR( CV_StsBadArg, "Invalid or NULL training data matrix" );
-
- var_count = var_idx ? var_idx->cols + var_idx->rows - 1 :
- tflag == CV_ROW_SAMPLE ? train_data->cols : train_data->rows;
- sample_count = sample_idx ? sample_idx->cols + sample_idx->rows - 1 :
- tflag == CV_ROW_SAMPLE ? train_data->rows : train_data->cols;
-
- if( _var_count )
- *_var_count = var_count;
-
- if( _sample_count )
- *_sample_count = sample_count;
-
- copy_data = tflag != CV_ROW_SAMPLE || var_idx || always_copy_data;
-
- CV_CALL( samples = (float**)cvAlloc(sample_count*sizeof(samples[0]) +
- (copy_data ? 1 : 0)*var_count*sample_count*sizeof(samples[0][0])) );
- data = train_data->data.fl;
- s_step = train_data->step / sizeof(samples[0][0]);
- v_step = 1;
- s_idx = sample_idx ? sample_idx->data.i : 0;
- v_idx = var_idx ? var_idx->data.i : 0;
-
- if( !copy_data )
- {
- for( i = 0; i < sample_count; i++ )
- samples[i] = (float*)(data + (s_idx ? s_idx[i] : i)*s_step);
- }
- else
- {
- samples[0] = (float*)(samples + sample_count);
- if( tflag != CV_ROW_SAMPLE )
- CV_SWAP( s_step, v_step, i );
-
- for( i = 0; i < sample_count; i++ )
- {
- float* dst = samples[i] = samples[0] + i*var_count;
- const float* src = data + (s_idx ? s_idx[i] : i)*s_step;
-
- if( !v_idx )
- for( j = 0; j < var_count; j++ )
- dst[j] = src[j*v_step];
- else
- for( j = 0; j < var_count; j++ )
- dst[j] = src[v_idx[j]*v_step];
- }
- }
-
- __END__;
-
- return (const float**)samples;
-}
-
-
-void
-cvCheckTrainData( const CvMat* train_data, int tflag,
- const CvMat* missing_mask,
- int* var_all, int* sample_all )
-{
- CV_FUNCNAME( "cvCheckTrainData" );
-
- if( var_all )
- *var_all = 0;
-
- if( sample_all )
- *sample_all = 0;
-
- __BEGIN__;
-
- // check parameter types and sizes
- if( !CV_IS_MAT(train_data) || CV_MAT_TYPE(train_data->type) != CV_32FC1 )
- CV_ERROR( CV_StsBadArg, "train data must be floating-point matrix" );
-
- if( missing_mask )
- {
- if( !CV_IS_MAT(missing_mask) || !CV_IS_MASK_ARR(missing_mask) ||
- !CV_ARE_SIZES_EQ(train_data, missing_mask) )
- CV_ERROR( CV_StsBadArg,
- "missing value mask must be 8-bit matrix of the same size as training data" );
- }
-
- if( tflag != CV_ROW_SAMPLE && tflag != CV_COL_SAMPLE )
- CV_ERROR( CV_StsBadArg,
- "Unknown training data layout (must be CV_ROW_SAMPLE or CV_COL_SAMPLE)" );
-
- if( var_all )
- *var_all = tflag == CV_ROW_SAMPLE ? train_data->cols : train_data->rows;
-
- if( sample_all )
- *sample_all = tflag == CV_ROW_SAMPLE ? train_data->rows : train_data->cols;
-
- __END__;
-}
-
-
-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 )
-{
- int ok = 0;
- CvMat* _var_idx = 0;
- CvMat* _sample_idx = 0;
- CvMat* _responses = 0;
- int sample_all = 0, sample_count = 0, var_all = 0, var_count = 0;
-
- CV_FUNCNAME( "cvPrepareTrainData" );
-
- // step 0. clear all the output pointers to ensure we do not try
- // to call free() with uninitialized pointers
- if( out_responses )
- *out_responses = 0;
-
- if( out_response_map )
- *out_response_map = 0;
-
- if( out_var_idx )
- *out_var_idx = 0;
-
- if( out_sample_idx )
- *out_sample_idx = 0;
-
- if( out_train_samples )
- *out_train_samples = 0;
-
- if( _sample_count )
- *_sample_count = 0;
-
- if( _var_count )
- *_var_count = 0;
-
- if( _var_all )
- *_var_all = 0;
-
- __BEGIN__;
-
- if( !out_train_samples )
- CV_ERROR( CV_StsBadArg, "output pointer to train samples is NULL" );
-
- CV_CALL( cvCheckTrainData( train_data, tflag, 0, &var_all, &sample_all ));
-
- if( sample_idx )
- CV_CALL( _sample_idx = cvPreprocessIndexArray( sample_idx, sample_all ));
- if( var_idx )
- CV_CALL( _var_idx = cvPreprocessIndexArray( var_idx, var_all ));
-
- if( responses )
- {
- if( !out_responses )
- CV_ERROR( CV_StsNullPtr, "output response pointer is NULL" );
-
- if( response_type == CV_VAR_NUMERICAL )
- {
- CV_CALL( _responses = cvPreprocessOrderedResponses( responses,
- _sample_idx, sample_all ));
- }
- else
- {
- CV_CALL( _responses = cvPreprocessCategoricalResponses( responses,
- _sample_idx, sample_all, out_response_map, 0 ));
- }
- }
-
- CV_CALL( *out_train_samples =
- cvGetTrainSamples( train_data, tflag, _var_idx, _sample_idx,
- &var_count, &sample_count, always_copy_data ));
-
- ok = 1;
-
- __END__;
-
- if( ok )
- {
- if( out_responses )
- *out_responses = _responses, _responses = 0;
-
- if( out_var_idx )
- *out_var_idx = _var_idx, _var_idx = 0;
-
- if( out_sample_idx )
- *out_sample_idx = _sample_idx, _sample_idx = 0;
-
- if( _sample_count )
- *_sample_count = sample_count;
-
- if( _var_count )
- *_var_count = var_count;
-
- if( _var_all )
- *_var_all = var_all;
- }
- else
- {
- if( out_response_map )
- cvReleaseMat( out_response_map );
- cvFree( out_train_samples );
- }
-
- if( _responses != responses )
- cvReleaseMat( &_responses );
- cvReleaseMat( &_var_idx );
- cvReleaseMat( &_sample_idx );
-
- return ok;
-}
-
-
-typedef struct CvSampleResponsePair
-{
- const float* sample;
- const uchar* mask;
- int response;
- int index;
-}
-CvSampleResponsePair;
-
-
-static int
-CV_CDECL icvCmpSampleResponsePairs( const void* a, const void* b )
-{
- int ra = ((const CvSampleResponsePair*)a)->response;
- int rb = ((const CvSampleResponsePair*)b)->response;
- int ia = ((const CvSampleResponsePair*)a)->index;
- int ib = ((const CvSampleResponsePair*)b)->index;
-
- return ra < rb ? -1 : ra > rb ? 1 : ia - ib;
- //return (ra > rb ? -1 : 0)|(ra < rb);
-}
-
-
-void
-cvSortSamplesByClasses( const float** samples, const CvMat* classes,
- int* class_ranges, const uchar** mask )
-{
- CvSampleResponsePair* pairs = 0;
- CV_FUNCNAME( "cvSortSamplesByClasses" );
-
- __BEGIN__;
-
- int i, k = 0, sample_count;
-
- if( !samples || !classes || !class_ranges )
- CV_ERROR( CV_StsNullPtr, "INTERNAL ERROR: some of the args are NULL pointers" );
-
- if( classes->rows != 1 || CV_MAT_TYPE(classes->type) != CV_32SC1 )
- CV_ERROR( CV_StsBadArg, "classes array must be a single row of integers" );
-
- sample_count = classes->cols;
- CV_CALL( pairs = (CvSampleResponsePair*)cvAlloc( (sample_count+1)*sizeof(pairs[0])));
-
- for( i = 0; i < sample_count; i++ )
- {
- pairs[i].sample = samples[i];
- pairs[i].mask = (mask) ? (mask[i]) : 0;
- pairs[i].response = classes->data.i[i];
- pairs[i].index = i;
- assert( classes->data.i[i] >= 0 );
- }
-
- qsort( pairs, sample_count, sizeof(pairs[0]), icvCmpSampleResponsePairs );
- pairs[sample_count].response = -1;
- class_ranges[0] = 0;
-
- for( i = 0; i < sample_count; i++ )
- {
- samples[i] = pairs[i].sample;
- if (mask)
- mask[i] = pairs[i].mask;
- classes->data.i[i] = pairs[i].response;
-
- if( pairs[i].response != pairs[i+1].response )
- class_ranges[++k] = i+1;
- }
-
- __END__;
-
- cvFree( &pairs );
-}
-
-
-void
-cvPreparePredictData( const CvArr* _sample, int dims_all,
- const CvMat* comp_idx, int class_count,
- const CvMat* prob, float** _row_sample,
- int as_sparse )
-{
- float* row_sample = 0;
- int* inverse_comp_idx = 0;
-
- CV_FUNCNAME( "cvPreparePredictData" );
-
- __BEGIN__;
-
- const CvMat* sample = (const CvMat*)_sample;
- float* sample_data;
- int sample_step;
- int is_sparse = CV_IS_SPARSE_MAT(sample);
- int d, sizes[CV_MAX_DIM];
- int i, dims_selected;
- int vec_size;
-
- if( !is_sparse && !CV_IS_MAT(sample) )
- CV_ERROR( !sample ? CV_StsNullPtr : CV_StsBadArg, "The sample is not a valid vector" );
-
- if( cvGetElemType( sample ) != CV_32FC1 )
- CV_ERROR( CV_StsUnsupportedFormat, "Input sample must have 32fC1 type" );
-
- CV_CALL( d = cvGetDims( sample, sizes ));
-
- if( !(is_sparse && d == 1 || !is_sparse && d == 2 && (sample->rows == 1 || sample->cols == 1)) )
- CV_ERROR( CV_StsBadSize, "Input sample must be 1-dimensional vector" );
-
- if( d == 1 )
- sizes[1] = 1;
-
- if( sizes[0] + sizes[1] - 1 != dims_all )
- CV_ERROR( CV_StsUnmatchedSizes,
- "The sample size is different from what has been used for training" );
-
- if( !_row_sample )
- CV_ERROR( CV_StsNullPtr, "INTERNAL ERROR: The row_sample pointer is NULL" );
-
- if( comp_idx && (!CV_IS_MAT(comp_idx) || comp_idx->rows != 1 ||
- CV_MAT_TYPE(comp_idx->type) != CV_32SC1) )
- CV_ERROR( CV_StsBadArg, "INTERNAL ERROR: invalid comp_idx" );
-
- dims_selected = comp_idx ? comp_idx->cols : dims_all;
-
- if( prob )
- {
- if( !CV_IS_MAT(prob) )
- CV_ERROR( CV_StsBadArg, "The output matrix of probabilities is invalid" );
-
- if( (prob->rows != 1 && prob->cols != 1) ||
- CV_MAT_TYPE(prob->type) != CV_32FC1 &&
- CV_MAT_TYPE(prob->type) != CV_64FC1 )
- CV_ERROR( CV_StsBadSize,
- "The matrix of probabilities must be 1-dimensional vector of 32fC1 type" );
-
- if( prob->rows + prob->cols - 1 != class_count )
- CV_ERROR( CV_StsUnmatchedSizes,
- "The vector of probabilities must contain as many elements as "
- "the number of classes in the training set" );
- }
-
- vec_size = !as_sparse ? dims_selected*sizeof(row_sample[0]) :
- (dims_selected + 1)*sizeof(CvSparseVecElem32f);
-
- if( CV_IS_MAT(sample) )
- {
- sample_data = sample->data.fl;
- sample_step = sample->step / sizeof(row_sample[0]);
-
- if( !comp_idx && sample_step <= 1 && !as_sparse )
- *_row_sample = sample_data;
- else
- {
- CV_CALL( row_sample = (float*)cvAlloc( vec_size ));
-
- if( !comp_idx )
- for( i = 0; i < dims_selected; i++ )
- row_sample[i] = sample_data[sample_step*i];
- else
- {
- int* comp = comp_idx->data.i;
- if( !sample_step )
- for( i = 0; i < dims_selected; i++ )
- row_sample[i] = sample_data[comp[i]];
- else
- for( i = 0; i < dims_selected; i++ )
- row_sample[i] = sample_data[sample_step*comp[i]];
- }
-
- *_row_sample = row_sample;
- }
-
- if( as_sparse )
- {
- const float* src = (const float*)row_sample;
- CvSparseVecElem32f* dst = (CvSparseVecElem32f*)row_sample;
-
- dst[dims_selected].idx = -1;
- for( i = dims_selected - 1; i >= 0; i-- )
- {
- dst[i].idx = i;
- dst[i].val = src[i];
- }
- }
- }
- else
- {
- CvSparseNode* node;
- CvSparseMatIterator mat_iterator;
- const CvSparseMat* sparse = (const CvSparseMat*)sample;
- assert( is_sparse );
-
- node = cvInitSparseMatIterator( sparse, &mat_iterator );
- CV_CALL( row_sample = (float*)cvAlloc( vec_size ));
-
- if( comp_idx )
- {
- CV_CALL( inverse_comp_idx = (int*)cvAlloc( dims_all*sizeof(int) ));
- memset( inverse_comp_idx, -1, dims_all*sizeof(int) );
- for( i = 0; i < dims_selected; i++ )
- inverse_comp_idx[comp_idx->data.i[i]] = i;
- }
-
- if( !as_sparse )
- {
- memset( row_sample, 0, vec_size );
-
- for( ; node != 0; node = cvGetNextSparseNode(&mat_iterator) )
- {
- int idx = *CV_NODE_IDX( sparse, node );
- if( inverse_comp_idx )
- {
- idx = inverse_comp_idx[idx];
- if( idx < 0 )
- continue;
- }
- row_sample[idx] = *(float*)CV_NODE_VAL( sparse, node );
- }
- }
- else
- {
- CvSparseVecElem32f* ptr = (CvSparseVecElem32f*)row_sample;
-
- for( ; node != 0; node = cvGetNextSparseNode(&mat_iterator) )
- {
- int idx = *CV_NODE_IDX( sparse, node );
- if( inverse_comp_idx )
- {
- idx = inverse_comp_idx[idx];
- if( idx < 0 )
- continue;
- }
- ptr->idx = idx;
- ptr->val = *(float*)CV_NODE_VAL( sparse, node );
- ptr++;
- }
-
- qsort( row_sample, ptr - (CvSparseVecElem32f*)row_sample,
- sizeof(ptr[0]), icvCmpSparseVecElems );
- ptr->idx = -1;
- }
-
- *_row_sample = row_sample;
- }
-
- __END__;
-
- if( inverse_comp_idx )
- cvFree( &inverse_comp_idx );
-
- if( cvGetErrStatus() < 0 && _row_sample )
- {
- cvFree( &row_sample );
- *_row_sample = 0;
- }
-}
-
-
-static void
-icvConvertDataToSparse( const uchar* src, int src_step, int src_type,
- uchar* dst, int dst_step, int dst_type,
- CvSize size, int* idx )
-{
- CV_FUNCNAME( "icvConvertDataToSparse" );
-
- __BEGIN__;
-
- int i, j;
- src_type = CV_MAT_TYPE(src_type);
- dst_type = CV_MAT_TYPE(dst_type);
-
- if( CV_MAT_CN(src_type) != 1 || CV_MAT_CN(dst_type) != 1 )
- CV_ERROR( CV_StsUnsupportedFormat, "The function supports only single-channel arrays" );
-
- if( src_step == 0 )
- src_step = CV_ELEM_SIZE(src_type);
-
- if( dst_step == 0 )
- dst_step = CV_ELEM_SIZE(dst_type);
-
- // if there is no "idx" and if both arrays are continuous,
- // do the whole processing (copying or conversion) in a single loop
- if( !idx && CV_ELEM_SIZE(src_type)*size.width == src_step &&
- CV_ELEM_SIZE(dst_type)*size.width == dst_step )
- {
- size.width *= size.height;
- size.height = 1;
- }
-
- if( src_type == dst_type )
- {
- int full_width = CV_ELEM_SIZE(dst_type)*size.width;
-
- if( full_width == sizeof(int) ) // another common case: copy int's or float's
- for( i = 0; i < size.height; i++, src += src_step )
- *(int*)(dst + dst_step*(idx ? idx[i] : i)) = *(int*)src;
- else
- for( i = 0; i < size.height; i++, src += src_step )
- memcpy( dst + dst_step*(idx ? idx[i] : i), src, full_width );
- }
- else if( src_type == CV_32SC1 && (dst_type == CV_32FC1 || dst_type == CV_64FC1) )
- for( i = 0; i < size.height; i++, src += src_step )
- {
- uchar* _dst = dst + dst_step*(idx ? idx[i] : i);
- if( dst_type == CV_32FC1 )
- for( j = 0; j < size.width; j++ )
- ((float*)_dst)[j] = (float)((int*)src)[j];
- else
- for( j = 0; j < size.width; j++ )
- ((double*)_dst)[j] = ((int*)src)[j];
- }
- else if( (src_type == CV_32FC1 || src_type == CV_64FC1) && dst_type == CV_32SC1 )
- for( i = 0; i < size.height; i++, src += src_step )
- {
- uchar* _dst = dst + dst_step*(idx ? idx[i] : i);
- if( src_type == CV_32FC1 )
- for( j = 0; j < size.width; j++ )
- ((int*)_dst)[j] = cvRound(((float*)src)[j]);
- else
- for( j = 0; j < size.width; j++ )
- ((int*)_dst)[j] = cvRound(((double*)src)[j]);
- }
- else if( src_type == CV_32FC1 && dst_type == CV_64FC1 ||
- src_type == CV_64FC1 && dst_type == CV_32FC1 )
- for( i = 0; i < size.height; i++, src += src_step )
- {
- uchar* _dst = dst + dst_step*(idx ? idx[i] : i);
- if( src_type == CV_32FC1 )
- for( j = 0; j < size.width; j++ )
- ((double*)_dst)[j] = ((float*)src)[j];
- else
- for( j = 0; j < size.width; j++ )
- ((float*)_dst)[j] = (float)((double*)src)[j];
- }
- else
- CV_ERROR( CV_StsUnsupportedFormat, "Unsupported combination of input and output vectors" );
-
- __END__;
-}
-
-
-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 )
-{
- CV_FUNCNAME( "cvWritebackLabels" );
-
- __BEGIN__;
-
- int samples_selected = samples_all, dims_selected = dims_all;
-
- if( dst_labels && !CV_IS_MAT(dst_labels) )
- CV_ERROR( CV_StsBadArg, "Array of output labels is not a valid matrix" );
-
- if( dst_centers )
- if( !ICV_IS_MAT_OF_TYPE(dst_centers, CV_32FC1) &&
- !ICV_IS_MAT_OF_TYPE(dst_centers, CV_64FC1) )
- CV_ERROR( CV_StsBadArg, "Array of cluster centers is not a valid matrix" );
-
- if( dst_probs && !CV_IS_MAT(dst_probs) )
- CV_ERROR( CV_StsBadArg, "Probability matrix is not valid" );
-
- if( sample_idx )
- {
- CV_ASSERT( sample_idx->rows == 1 && CV_MAT_TYPE(sample_idx->type) == CV_32SC1 );
- samples_selected = sample_idx->cols;
- }
-
- if( comp_idx )
- {
- CV_ASSERT( comp_idx->rows == 1 && CV_MAT_TYPE(comp_idx->type) == CV_32SC1 );
- dims_selected = comp_idx->cols;
- }
-
- if( dst_labels && (!labels || labels->data.ptr != dst_labels->data.ptr) )
- {
- if( !labels )
- CV_ERROR( CV_StsNullPtr, "NULL labels" );
-
- CV_ASSERT( labels->rows == 1 );
-
- if( dst_labels->rows != 1 && dst_labels->cols != 1 )
- CV_ERROR( CV_StsBadSize, "Array of output labels should be 1d vector" );
-
- if( dst_labels->rows + dst_labels->cols - 1 != samples_all )
- CV_ERROR( CV_StsUnmatchedSizes,
- "Size of vector of output labels is not equal to the total number of input samples" );
-
- CV_ASSERT( labels->cols == samples_selected );
-
- CV_CALL( icvConvertDataToSparse( labels->data.ptr, labels->step, labels->type,
- dst_labels->data.ptr, dst_labels->step, dst_labels->type,
- cvSize( 1, samples_selected ), sample_idx ? sample_idx->data.i : 0 ));
- }
-
- if( dst_centers && (!centers || centers->data.ptr != dst_centers->data.ptr) )
- {
- int i;
-
- if( !centers )
- CV_ERROR( CV_StsNullPtr, "NULL centers" );
-
- if( centers->rows != dst_centers->rows )
- CV_ERROR( CV_StsUnmatchedSizes, "Invalid number of rows in matrix of output centers" );
-
- if( dst_centers->cols != dims_all )
- CV_ERROR( CV_StsUnmatchedSizes,
- "Number of columns in matrix of output centers is "
- "not equal to the total number of components in the input samples" );
-
- CV_ASSERT( centers->cols == dims_selected );
-
- for( i = 0; i < centers->rows; i++ )
- CV_CALL( icvConvertDataToSparse( centers->data.ptr + i*centers->step, 0, centers->type,
- dst_centers->data.ptr + i*dst_centers->step, 0, dst_centers->type,
- cvSize( 1, dims_selected ), comp_idx ? comp_idx->data.i : 0 ));
- }
-
- if( dst_probs && (!probs || probs->data.ptr != dst_probs->data.ptr) )
- {
- if( !probs )
- CV_ERROR( CV_StsNullPtr, "NULL probs" );
-
- if( probs->cols != dst_probs->cols )
- CV_ERROR( CV_StsUnmatchedSizes, "Invalid number of columns in output probability matrix" );
-
- if( dst_probs->rows != samples_all )
- CV_ERROR( CV_StsUnmatchedSizes,
- "Number of rows in output probability matrix is "
- "not equal to the total number of input samples" );
-
- CV_ASSERT( probs->rows == samples_selected );
-
- CV_CALL( icvConvertDataToSparse( probs->data.ptr, probs->step, probs->type,
- dst_probs->data.ptr, dst_probs->step, dst_probs->type,
- cvSize( probs->cols, samples_selected ),
- sample_idx ? sample_idx->data.i : 0 ));
- }
-
- __END__;
-}
-
-#if 0
-CV_IMPL void
-cvStatModelMultiPredict( const CvStatModel* stat_model,
- const CvArr* predict_input,
- int flags, CvMat* predict_output,
- CvMat* probs, const CvMat* sample_idx )
-{
- CvMemStorage* storage = 0;
- CvMat* sample_idx_buffer = 0;
- CvSparseMat** sparse_rows = 0;
- int samples_selected = 0;
-
- CV_FUNCNAME( "cvStatModelMultiPredict" );
-
- __BEGIN__;
-
- int i;
- int predict_output_step = 1, sample_idx_step = 1;
- int type;
- int d, sizes[CV_MAX_DIM];
- int tflag = flags == CV_COL_SAMPLE;
- int samples_all, dims_all;
- int is_sparse = CV_IS_SPARSE_MAT(predict_input);
- CvMat predict_input_part;
- CvArr* sample = &predict_input_part;
- CvMat probs_part;
- CvMat* probs1 = probs ? &probs_part : 0;
-
- if( !CV_IS_STAT_MODEL(stat_model) )
- CV_ERROR( !stat_model ? CV_StsNullPtr : CV_StsBadArg, "Invalid statistical model" );
-
- if( !stat_model->predict )
- CV_ERROR( CV_StsNotImplemented, "There is no \"predict\" method" );
-
- if( !predict_input || !predict_output )
- CV_ERROR( CV_StsNullPtr, "NULL input or output matrices" );
-
- if( !is_sparse && !CV_IS_MAT(predict_input) )
- CV_ERROR( CV_StsBadArg, "predict_input should be a matrix or a sparse matrix" );
-
- if( !CV_IS_MAT(predict_output) )
- CV_ERROR( CV_StsBadArg, "predict_output should be a matrix" );
-
- type = cvGetElemType( predict_input );
- if( type != CV_32FC1 ||
- (CV_MAT_TYPE(predict_output->type) != CV_32FC1 &&
- CV_MAT_TYPE(predict_output->type) != CV_32SC1 ))
- CV_ERROR( CV_StsUnsupportedFormat, "The input or output matrix has unsupported format" );
-
- CV_CALL( d = cvGetDims( predict_input, sizes ));
- if( d > 2 )
- CV_ERROR( CV_StsBadSize, "The input matrix should be 1- or 2-dimensional" );
-
- if( !tflag )
- {
- samples_all = samples_selected = sizes[0];
- dims_all = sizes[1];
- }
- else
- {
- samples_all = samples_selected = sizes[1];
- dims_all = sizes[0];
- }
-
- if( sample_idx )
- {
- if( !CV_IS_MAT(sample_idx) )
- CV_ERROR( CV_StsBadArg, "Invalid sample_idx matrix" );
-
- if( sample_idx->cols != 1 && sample_idx->rows != 1 )
- CV_ERROR( CV_StsBadSize, "sample_idx must be 1-dimensional matrix" );
-
- samples_selected = sample_idx->rows + sample_idx->cols - 1;
-
- if( CV_MAT_TYPE(sample_idx->type) == CV_32SC1 )
- {
- if( samples_selected > samples_all )
- CV_ERROR( CV_StsBadSize, "sample_idx is too large vector" );
- }
- else if( samples_selected != samples_all )
- CV_ERROR( CV_StsUnmatchedSizes, "sample_idx has incorrect size" );
-
- sample_idx_step = sample_idx->step ?
- sample_idx->step / CV_ELEM_SIZE(sample_idx->type) : 1;
- }
-
- if( predict_output->rows != 1 && predict_output->cols != 1 )
- CV_ERROR( CV_StsBadSize, "predict_output should be a 1-dimensional matrix" );
-
- if( predict_output->rows + predict_output->cols - 1 != samples_all )
- CV_ERROR( CV_StsUnmatchedSizes, "predict_output and predict_input have uncoordinated sizes" );
-
- predict_output_step = predict_output->step ?
- predict_output->step / CV_ELEM_SIZE(predict_output->type) : 1;
-
- if( probs )
- {
- if( !CV_IS_MAT(probs) )
- CV_ERROR( CV_StsBadArg, "Invalid matrix of probabilities" );
-
- if( probs->rows != samples_all )
- CV_ERROR( CV_StsUnmatchedSizes,
- "matrix of probabilities must have as many rows as the total number of samples" );
-
- if( CV_MAT_TYPE(probs->type) != CV_32FC1 )
- CV_ERROR( CV_StsUnsupportedFormat, "matrix of probabilities must have 32fC1 type" );
- }
-
- if( is_sparse )
- {
- CvSparseNode* node;
- CvSparseMatIterator mat_iterator;
- CvSparseMat* sparse = (CvSparseMat*)predict_input;
-
- if( sample_idx && CV_MAT_TYPE(sample_idx->type) == CV_32SC1 )
- {
- CV_CALL( sample_idx_buffer = cvCreateMat( 1, samples_all, CV_8UC1 ));
- cvZero( sample_idx_buffer );
- for( i = 0; i < samples_selected; i++ )
- sample_idx_buffer->data.ptr[sample_idx->data.i[i*sample_idx_step]] = 1;
- samples_selected = samples_all;
- sample_idx = sample_idx_buffer;
- sample_idx_step = 1;
- }
-
- CV_CALL( sparse_rows = (CvSparseMat**)cvAlloc( samples_selected*sizeof(sparse_rows[0])));
- for( i = 0; i < samples_selected; i++ )
- {
- if( sample_idx && sample_idx->data.ptr[i*sample_idx_step] == 0 )
- continue;
- CV_CALL( sparse_rows[i] = cvCreateSparseMat( 1, &dims_all, type ));
- if( !storage )
- storage = sparse_rows[i]->heap->storage;
- else
- {
- // hack: to decrease memory footprint, make all the sparse matrices
- // reside in the same storage
- int elem_size = sparse_rows[i]->heap->elem_size;
- cvReleaseMemStorage( &sparse_rows[i]->heap->storage );
- sparse_rows[i]->heap = cvCreateSet( 0, sizeof(CvSet), elem_size, storage );
- }
- }
-
- // put each row (or column) of predict_input into separate sparse matrix.
- node = cvInitSparseMatIterator( sparse, &mat_iterator );
- for( ; node != 0; node = cvGetNextSparseNode( &mat_iterator ))
- {
- int* idx = CV_NODE_IDX( sparse, node );
- int idx0 = idx[tflag ^ 1];
- int idx1 = idx[tflag];
-
- if( sample_idx && sample_idx->data.ptr[idx0*sample_idx_step] == 0 )
- continue;
-
- assert( sparse_rows[idx0] != 0 );
- *(float*)cvPtrND( sparse, &idx1, 0, 1, 0 ) = *(float*)CV_NODE_VAL( sparse, node );
- }
- }
-
- for( i = 0; i < samples_selected; i++ )
- {
- int idx = i;
- float response;
-
- if( sample_idx )
- {
- if( CV_MAT_TYPE(sample_idx->type) == CV_32SC1 )
- {
- idx = sample_idx->data.i[i*sample_idx_step];
- if( (unsigned)idx >= (unsigned)samples_all )
- CV_ERROR( CV_StsOutOfRange, "Some of sample_idx elements are out of range" );
- }
- else if( CV_MAT_TYPE(sample_idx->type) == CV_8UC1 &&
- sample_idx->data.ptr[i*sample_idx_step] == 0 )
- continue;
- }
-
- if( !is_sparse )
- {
- if( !tflag )
- cvGetRow( predict_input, &predict_input_part, idx );
- else
- {
- cvGetCol( predict_input, &predict_input_part, idx );
- }
- }
- else
- sample = sparse_rows[idx];
-
- if( probs )
- cvGetRow( probs, probs1, idx );
-
- CV_CALL( response = stat_model->predict( stat_model, (CvMat*)sample, probs1 ));
-
- if( CV_MAT_TYPE(predict_output->type) == CV_32FC1 )
- predict_output->data.fl[idx*predict_output_step] = response;
- else
- {
- CV_ASSERT( cvRound(response) == response );
- predict_output->data.i[idx*predict_output_step] = cvRound(response);
- }
- }
-
- __END__;
-
- if( sparse_rows )
- {
- int i;
- for( i = 0; i < samples_selected; i++ )
- if( sparse_rows[i] )
- {
- sparse_rows[i]->heap->storage = 0;
- cvReleaseSparseMat( &sparse_rows[i] );
- }
- cvFree( &sparse_rows );
- }
-
- cvReleaseMat( &sample_idx_buffer );
- cvReleaseMemStorage( &storage );
-}
-#endif
-
-// By P. Yarykin - begin -
-
-void cvCombineResponseMaps (CvMat* _responses,
- const CvMat* old_response_map,
- CvMat* new_response_map,
- CvMat** out_response_map)
-{
- int** old_data = NULL;
- int** new_data = NULL;
-
- CV_FUNCNAME ("cvCombineResponseMaps");
- __BEGIN__
-
- int i,j;
- int old_n, new_n, out_n;
- int samples, free_response;
- int* first;
- int* responses;
- int* out_data;
-
- if( out_response_map )
- *out_response_map = 0;
-
-// Check input data.
- if ((!ICV_IS_MAT_OF_TYPE (_responses, CV_32SC1)) ||
- (!ICV_IS_MAT_OF_TYPE (old_response_map, CV_32SC1)) ||
- (!ICV_IS_MAT_OF_TYPE (new_response_map, CV_32SC1)))
- {
- CV_ERROR (CV_StsBadArg, "Some of input arguments is not the CvMat")
- }
-
-// Prepare sorted responses.
- first = new_response_map->data.i;
- new_n = new_response_map->cols;
- CV_CALL (new_data = (int**)cvAlloc (new_n * sizeof (new_data[0])));
- for (i = 0; i < new_n; i++)
- new_data[i] = first + i;
- qsort (new_data, new_n, sizeof(int*), icvCmpIntegersPtr);
-
- first = old_response_map->data.i;
- old_n = old_response_map->cols;
- CV_CALL (old_data = (int**)cvAlloc (old_n * sizeof (old_data[0])));
- for (i = 0; i < old_n; i++)
- old_data[i] = first + i;
- qsort (old_data, old_n, sizeof(int*), icvCmpIntegersPtr);
-
-// Count the number of different responses.
- for (i = 0, j = 0, out_n = 0; i < old_n && j < new_n; out_n++)
- {
- if (*old_data[i] == *new_data[j])
- {
- i++;
- j++;
- }
- else if (*old_data[i] < *new_data[j])
- i++;
- else
- j++;
- }
- out_n += old_n - i + new_n - j;
-
-// Create and fill the result response maps.
- CV_CALL (*out_response_map = cvCreateMat (1, out_n, CV_32SC1));
- out_data = (*out_response_map)->data.i;
- memcpy (out_data, first, old_n * sizeof (int));
-
- free_response = old_n;
- for (i = 0, j = 0; i < old_n && j < new_n; )
- {
- if (*old_data[i] == *new_data[j])
- {
- *new_data[j] = (int)(old_data[i] - first);
- i++;
- j++;
- }
- else if (*old_data[i] < *new_data[j])
- i++;
- else
- {
- out_data[free_response] = *new_data[j];
- *new_data[j] = free_response++;
- j++;
- }
- }
- for (; j < new_n; j++)
- {
- out_data[free_response] = *new_data[j];
- *new_data[j] = free_response++;
- }
- CV_ASSERT (free_response == out_n);
-
-// Change <responses> according to out response map.
- samples = _responses->cols + _responses->rows - 1;
- responses = _responses->data.i;
- first = new_response_map->data.i;
- for (i = 0; i < samples; i++)
- {
- responses[i] = first[responses[i]];
- }
-
- __END__
-
- cvFree(&old_data);
- cvFree(&new_data);
-
-}
-
-
-int icvGetNumberOfCluster( double* prob_vector, int num_of_clusters, float r,
- float outlier_thresh, int normalize_probs )
-{
- int max_prob_loc = 0;
-
- CV_FUNCNAME("icvGetNumberOfCluster");
- __BEGIN__;
-
- double prob, maxprob, sum;
- int i;
-
- CV_ASSERT(prob_vector);
- CV_ASSERT(num_of_clusters >= 0);
-
- maxprob = prob_vector[0];
- max_prob_loc = 0;
- sum = maxprob;
- for( i = 1; i < num_of_clusters; i++ )
- {
- prob = prob_vector[i];
- sum += prob;
- if( prob > maxprob )
- {
- max_prob_loc = i;
- maxprob = prob;
- }
- }
- if( normalize_probs && fabs(sum - 1.) > FLT_EPSILON )
- {
- for( i = 0; i < num_of_clusters; i++ )
- prob_vector[i] /= sum;
- }
- if( fabs(r - 1.) > FLT_EPSILON && fabs(sum - 1.) < outlier_thresh )
- max_prob_loc = -1;
-
- __END__;
-
- return max_prob_loc;
-
-} // End of icvGetNumberOfCluster
-
-
-void icvFindClusterLabels( const CvMat* probs, float outlier_thresh, float r,
- const CvMat* labels )
-{
- CvMat* counts = 0;
-
- CV_FUNCNAME("icvFindClusterLabels");
- __BEGIN__;
-
- int nclusters, nsamples;
- int i, j;
- double* probs_data;
-
- CV_ASSERT( ICV_IS_MAT_OF_TYPE(probs, CV_64FC1) );
- CV_ASSERT( ICV_IS_MAT_OF_TYPE(labels, CV_32SC1) );
-
- nclusters = probs->cols;
- nsamples = probs->rows;
- CV_ASSERT( nsamples == labels->cols );
-
- CV_CALL( counts = cvCreateMat( 1, nclusters + 1, CV_32SC1 ) );
- CV_CALL( cvSetZero( counts ));
- for( i = 0; i < nsamples; i++ )
- {
- labels->data.i[i] = icvGetNumberOfCluster( probs->data.db + i*probs->cols,
- nclusters, r, outlier_thresh, 1 );
- counts->data.i[labels->data.i[i] + 1]++;
- }
- CV_ASSERT((int)cvSum(counts).val[0] == nsamples);
- // Filling empty clusters with the vector, that has the maximal probability
- for( j = 0; j < nclusters; j++ ) // outliers are ignored
- {
- int maxprob_loc = -1;
- double maxprob = 0;
-
- if( counts->data.i[j+1] ) // j-th class is not empty
- continue;
- // look for the presentative, which is not lonely in it's cluster
- // and that has a maximal probability among all these vectors
- probs_data = probs->data.db;
- for( i = 0; i < nsamples; i++, probs_data++ )
- {
- int label = labels->data.i[i];
- double prob;
- if( counts->data.i[label+1] == 0 ||
- (counts->data.i[label+1] <= 1 && label != -1) )
- continue;
- prob = *probs_data;
- if( prob >= maxprob )
- {
- maxprob = prob;
- maxprob_loc = i;
- }
- }
- // maxprob_loc == 0 <=> number of vectors less then number of clusters
- CV_ASSERT( maxprob_loc >= 0 );
- counts->data.i[labels->data.i[maxprob_loc] + 1]--;
- labels->data.i[maxprob_loc] = j;
- counts->data.i[j + 1]++;
- }
-
- __END__;
-
- cvReleaseMat( &counts );
-} // End of icvFindClusterLabels
-
-/* End of file */
projects / opencv / blobdiff