/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
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//
//M*/
#include "_cvaux.h"
#if 0
#define LN2PI 1.837877f
#define BIG_FLT 1.e+10f
#define _CV_ERGODIC 1
#define _CV_CAUSAL 2
#define _CV_LAST_STATE 1
#define _CV_BEST_STATE 2
//*F///////////////////////////////////////////////////////////////////////////////////////
// Name: icvForward1DHMM
// Purpose: The function performs baum-welsh algorithm
// Context:
// Parameters: obs_info - addres of pointer to CvImgObsInfo structure
// num_hor_obs - number of horizontal observation vectors
// num_ver_obs - number of horizontal observation vectors
// obs_size - length of observation vector
//
// Returns: error status
//
// Notes:
//F*/
#if 0
CvStatus icvForward1DHMM( int num_states, int num_obs, CvMatr64d A,
CvMatr64d B,
double* scales)
{
// assume that observation and transition
// probabilities already computed
int m_HMMType = _CV_CAUSAL;
double* m_pi = icvAlloc( num_states* sizeof( double) );
/* alpha is matrix
rows throuhg states
columns through time
*/
double* alpha = icvAlloc( num_states*num_obs * sizeof( double ) );
/* All calculations will be in non-logarithmic domain */
/* Initialization */
/* set initial state probabilities */
m_pi[0] = 1;
for (i = 1; i < num_states; i++)
{
m_pi[i] = 0.0;
}
for (i = 0; i < num_states; i++)
{
alpha[i] = m_pi[i] * m_b[ i];
}
/******************************************************************/
/* Induction */
if ( m_HMMType == _CV_ERGODIC )
{
int t;
for (t = 1 ; t < num_obs; t++)
{
for (j = 0; j < num_states; j++)
{
double sum = 0.0;
int i;
for (i = 0; i < num_states; i++)
{
sum += alpha[(t - 1) * num_states + i] * A[i * num_states + j];
}
alpha[(t - 1) * num_states + j] = sum * B[t * num_states + j];
/* add computed alpha to scale factor */
sum_alpha += alpha[(t - 1) * num_states + j];
}
double scale = 1/sum_alpha;
/* scale alpha */
for (j = 0; j < num_states; j++)
{
alpha[(t - 1) * num_states + j] *= scale;
}
scales[t] = scale;
}
}
#endif
//*F///////////////////////////////////////////////////////////////////////////////////////
// Name: icvCreateObsInfo
// Purpose: The function allocates memory for CvImgObsInfo structure
// and its inner stuff
// Context:
// Parameters: obs_info - addres of pointer to CvImgObsInfo structure
// num_hor_obs - number of horizontal observation vectors
// num_ver_obs - number of horizontal observation vectors
// obs_size - length of observation vector
//
// Returns: error status
//
// Notes:
//F*/
/*CvStatus icvCreateObsInfo( CvImgObsInfo** obs_info,
CvSize num_obs, int obs_size )
{
int total = num_obs.height * num_obs.width;
CvImgObsInfo* obs = (CvImgObsInfo*)icvAlloc( sizeof( CvImgObsInfo) );
obs->obs_x = num_obs.width;
obs->obs_y = num_obs.height;
obs->obs = (float*)icvAlloc( total * obs_size * sizeof(float) );
obs->state = (int*)icvAlloc( 2 * total * sizeof(int) );
obs->mix = (int*)icvAlloc( total * sizeof(int) );
obs->obs_size = obs_size;
obs_info[0] = obs;
return CV_NO_ERR;
}*/
/*CvStatus icvReleaseObsInfo( CvImgObsInfo** p_obs_info )
{
CvImgObsInfo* obs_info = p_obs_info[0];
icvFree( &(obs_info->obs) );
icvFree( &(obs_info->mix) );
icvFree( &(obs_info->state) );
icvFree( &(obs_info) );
p_obs_info[0] = NULL;
return CV_NO_ERR;
} */
//*F///////////////////////////////////////////////////////////////////////////////////////
// Name: icvCreate1DHMM
// Purpose: The function allocates memory for 1-dimensional HMM
// and its inner stuff
// Context:
// Parameters: hmm - addres of pointer to CvEHMM structure
// state_number - number of states in HMM
// num_mix - number of gaussian mixtures in HMM states
// size of array is defined by previous parameter
// obs_size - length of observation vectors
//
// Returns: error status
// Notes:
//F*/
CvStatus icvCreate1DHMM( CvEHMM** this_hmm,
int state_number, int* num_mix, int obs_size )
{
int i;
int real_states = state_number;
CvEHMMState* all_states;
CvEHMM* hmm;
int total_mix = 0;
float* pointers;
/* allocate memory for hmm */
hmm = (CvEHMM*)icvAlloc( sizeof(CvEHMM) );
/* set number of superstates */
hmm->num_states = state_number;
hmm->level = 0;
/* allocate memory for all states */
all_states = (CvEHMMState *)icvAlloc( real_states * sizeof( CvEHMMState ) );
/* assign number of mixtures */
for( i = 0; i < real_states; i++ )
{
all_states[i].num_mix = num_mix[i];
}
/* compute size of inner of all real states */
for( i = 0; i < real_states; i++ )
{
total_mix += num_mix[i];
}
/* allocate memory for states stuff */
pointers = (float*)icvAlloc( total_mix * (2/*for mu invvar */ * obs_size +
2/*for weight and log_var_val*/ ) * sizeof( float) );
/* organize memory */
for( i = 0; i < real_states; i++ )
{
all_states[i].mu = pointers; pointers += num_mix[i] * obs_size;
all_states[i].inv_var = pointers; pointers += num_mix[i] * obs_size;
all_states[i].log_var_val = pointers; pointers += num_mix[i];
all_states[i].weight = pointers; pointers += num_mix[i];
}
hmm->u.state = all_states;
hmm->transP = icvCreateMatrix_32f( hmm->num_states, hmm->num_states );
hmm->obsProb = NULL;
/* if all ok - return pointer */
*this_hmm = hmm;
return CV_NO_ERR;
}
CvStatus icvRelease1DHMM( CvEHMM** phmm )
{
CvEHMM* hmm = phmm[0];
icvDeleteMatrix( hmm->transP );
if (hmm->obsProb != NULL)
{
int* tmp = ((int*)(hmm->obsProb)) - 3;
icvFree( &(tmp) );
}
icvFree( &(hmm->u.state->mu) );
icvFree( &(hmm->u.state) );
phmm[0] = NULL;
return CV_NO_ERR;
}
/*can be used in CHMM & DHMM */
CvStatus icvUniform1DSegm( Cv1DObsInfo* obs_info, CvEHMM* hmm )
{
/* implementation is very bad */
int i;
CvEHMMState* first_state;
/* check arguments */
if ( !obs_info || !hmm ) return CV_NULLPTR_ERR;
first_state = hmm->u.state;
for (i = 0; i < obs_info->obs_x; i++)
{
//bad line (division )
int state = (i * hmm->num_states)/obs_info->obs_x;
obs_info->state[i] = state;
}
return CV_NO_ERR;
}
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: InitMixSegm
// Purpose: The function implements the mixture segmentation of the states of the embedded HMM
// Context: used with the Viterbi training of the embedded HMM
// Function uses K-Means algorithm for clustering
//
// Parameters: obs_info_array - array of pointers to image observations
// num_img - length of above array
// hmm - pointer to HMM structure
//
// Returns: error status
//
// Notes:
//F*/
CvStatus icvInit1DMixSegm(Cv1DObsInfo** obs_info_array, int num_img, CvEHMM* hmm)
{
int k, i, j;
int* num_samples; /* number of observations in every state */
int* counter; /* array of counters for every state */
int** a_class; /* for every state - characteristic array */
CvVect32f** samples; /* for every state - pointer to observation vectors */
int*** samples_mix; /* for every state - array of pointers to vectors mixtures */
CvTermCriteria criteria = cvTermCriteria( CV_TERMCRIT_EPS|CV_TERMCRIT_ITER,
1000, /* iter */
0.01f ); /* eps */
int total = hmm->num_states;
CvEHMMState* first_state = hmm->u.state;
/* for every state integer is allocated - number of vectors in state */
num_samples = (int*)icvAlloc( total * sizeof(int) );
/* integer counter is allocated for every state */
counter = (int*)icvAlloc( total * sizeof(int) );
samples = (CvVect32f**)icvAlloc( total * sizeof(CvVect32f*) );
samples_mix = (int***)icvAlloc( total * sizeof(int**) );
/* clear */
memset( num_samples, 0 , total*sizeof(int) );
memset( counter, 0 , total*sizeof(int) );
/* for every state the number of vectors which belong to it is computed (smth. like histogram) */
for (k = 0; k < num_img; k++)
{
CvImgObsInfo* obs = obs_info_array[k];
for (i = 0; i < obs->obs_x; i++)
{
int state = obs->state[ i ];
num_samples[state] += 1;
}
}
/* for every state int* is allocated */
a_class = (int**)icvAlloc( total*sizeof(int*) );
for (i = 0; i < total; i++)
{
a_class[i] = (int*)icvAlloc( num_samples[i] * sizeof(int) );
samples[i] = (CvVect32f*)icvAlloc( num_samples[i] * sizeof(CvVect32f) );
samples_mix[i] = (int**)icvAlloc( num_samples[i] * sizeof(int*) );
}
/* for every state vectors which belong to state are gathered */
for (k = 0; k < num_img; k++)
{
CvImgObsInfo* obs = obs_info_array[k];
int num_obs = obs->obs_x;
float* vector = obs->obs;
for (i = 0; i < num_obs; i++, vector+=obs->obs_size )
{
int state = obs->state[i];
samples[state][counter[state]] = vector;
samples_mix[state][counter[state]] = &(obs->mix[i]);
counter[state]++;
}
}
/* clear counters */
memset( counter, 0, total*sizeof(int) );
/* do the actual clustering using the K Means algorithm */
for (i = 0; i < total; i++)
{
if ( first_state[i].num_mix == 1)
{
for (k = 0; k < num_samples[i]; k++)
{
/* all vectors belong to one mixture */
a_class[i][k] = 0;
}
}
else if( num_samples[i] )
{
/* clusterize vectors */
icvKMeans( first_state[i].num_mix, samples[i], num_samples[i],
obs_info_array[0]->obs_size, criteria, a_class[i] );
}
}
/* for every vector number of mixture is assigned */
for( i = 0; i < total; i++ )
{
for (j = 0; j < num_samples[i]; j++)
{
samples_mix[i][j][0] = a_class[i][j];
}
}
for (i = 0; i < total; i++)
{
icvFree( &(a_class[i]) );
icvFree( &(samples[i]) );
icvFree( &(samples_mix[i]) );
}
icvFree( &a_class );
icvFree( &samples );
icvFree( &samples_mix );
icvFree( &counter );
icvFree( &num_samples );
return CV_NO_ERR;
}
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: ComputeUniModeGauss
// Purpose: The function computes the Gaussian pdf for a sample vector
// Context:
// Parameters: obsVeq - pointer to the sample vector
// mu - pointer to the mean vector of the Gaussian pdf
// var - pointer to the variance vector of the Gaussian pdf
// VecSize - the size of sample vector
//
// Returns: the pdf of the sample vector given the specified Gaussian
//
// Notes:
//F*/
/*float icvComputeUniModeGauss(CvVect32f vect, CvVect32f mu,
CvVect32f inv_var, float log_var_val, int vect_size)
{
int n;
double tmp;
double prob;
prob = -log_var_val;
for (n = 0; n < vect_size; n++)
{
tmp = (vect[n] - mu[n]) * inv_var[n];
prob = prob - tmp * tmp;
}
//prob *= 0.5f;
return (float)prob;
}*/
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: ComputeGaussMixture
// Purpose: The function computes the mixture Gaussian pdf of a sample vector.
// Context:
// Parameters: obsVeq - pointer to the sample vector
// mu - two-dimensional pointer to the mean vector of the Gaussian pdf;
// the first dimension is indexed over the number of mixtures and
// the second dimension is indexed along the size of the mean vector
// var - two-dimensional pointer to the variance vector of the Gaussian pdf;
// the first dimension is indexed over the number of mixtures and
// the second dimension is indexed along the size of the variance vector
// VecSize - the size of sample vector
// weight - pointer to the wights of the Gaussian mixture
// NumMix - the number of Gaussian mixtures
//
// Returns: the pdf of the sample vector given the specified Gaussian mixture.
//
// Notes:
//F*/
/* Calculate probability of observation at state in logarithmic scale*/
/*float icvComputeGaussMixture( CvVect32f vect, float* mu,
float* inv_var, float* log_var_val,
int vect_size, float* weight, int num_mix )
{
double prob, l_prob;
prob = 0.0f;
if (num_mix == 1)
{
return icvComputeUniModeGauss( vect, mu, inv_var, log_var_val[0], vect_size);
}
else
{
int m;
for (m = 0; m < num_mix; m++)
{
if ( weight[m] > 0.0)
{
l_prob = icvComputeUniModeGauss(vect, mu + m*vect_size,
inv_var + m * vect_size,
log_var_val[m],
vect_size);
prob = prob + weight[m]*exp((double)l_prob);
}
}
prob = log(prob);
}
return (float)prob;
}
*/
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: EstimateObsProb
// Purpose: The function computes the probability of every observation in every state
// Context:
// Parameters: obs_info - observations
// hmm - hmm
// Returns: error status
//
// Notes:
//F*/
CvStatus icvEstimate1DObsProb(CvImgObsInfo* obs_info, CvEHMM* hmm )
{
int j;
int total_states = 0;
/* check if matrix exist and check current size
if not sufficient - realloc */
int status = 0; /* 1 - not allocated, 2 - allocated but small size,
3 - size is enough, but distribution is bad, 0 - all ok */
/*for( j = 0; j < hmm->num_states; j++ )
{
total_states += hmm->u.ehmm[j].num_states;
}*/
total_states = hmm->num_states;
if ( hmm->obsProb == NULL )
{
/* allocare memory */
int need_size = ( obs_info->obs_x /* * obs_info->obs_y*/ * total_states * sizeof(float) /* +
obs_info->obs_y * hmm->num_states * sizeof( CvMatr32f) */);
int* buffer = (int*)icvAlloc( need_size + 3 * sizeof(int) );
buffer[0] = need_size;
buffer[1] = obs_info->obs_y;
buffer[2] = obs_info->obs_x;
hmm->obsProb = (float**) (buffer + 3);
status = 3;
}
else
{
/* check current size */
int* total= (int*)(((int*)(hmm->obsProb)) - 3);
int need_size = ( obs_info->obs_x /* * obs_info->obs_y*/ * total_states * sizeof(float) /* +
obs_info->obs_y * hmm->num_states * sizeof( CvMatr32f(float*) )*/ );
assert( sizeof(float*) == sizeof(int) );
if ( need_size > (*total) )
{
int* buffer = ((int*)(hmm->obsProb)) - 3;
icvFree( &buffer);
buffer = (int*)icvAlloc( need_size + 3);
buffer[0] = need_size;
buffer[1] = obs_info->obs_y;
buffer[2] = obs_info->obs_x;
hmm->obsProb = (float**)(buffer + 3);
status = 3;
}
}
if (!status)
{
int* obsx = ((int*)(hmm->obsProb)) - 1;
//int* obsy = ((int*)(hmm->obsProb)) - 2;
assert( /*(*obsy > 0) &&*/ (*obsx > 0) );
/* is good distribution? */
if ( (obs_info->obs_x > (*obsx) ) /* || (obs_info->obs_y > (*obsy) ) */ )
status = 3;
}
assert( (status == 0) || (status == 3) );
/* if bad status - do reallocation actions */
if ( status )
{
float** tmp = hmm->obsProb;
//float* tmpf;
/* distribute pointers of ehmm->obsProb */
/* for( i = 0; i < hmm->num_states; i++ )
{
hmm->u.ehmm[i].obsProb = tmp;
tmp += obs_info->obs_y;
}
*/
//tmpf = (float*)tmp;
/* distribute pointers of ehmm->obsProb[j] */
/* for( i = 0; i < hmm->num_states; i++ )
{
CvEHMM* ehmm = &( hmm->u.ehmm[i] );
for( j = 0; j < obs_info->obs_y; j++ )
{
ehmm->obsProb[j] = tmpf;
tmpf += ehmm->num_states * obs_info->obs_x;
}
}
*/
hmm->obsProb = tmp;
}/* end of pointer distribution */
#if 1
{
#define MAX_BUF_SIZE 1200
float local_log_mix_prob[MAX_BUF_SIZE];
double local_mix_prob[MAX_BUF_SIZE];
int vect_size = obs_info->obs_size;
CvStatus res = CV_NO_ERR;
float* log_mix_prob = local_log_mix_prob;
double* mix_prob = local_mix_prob;
int max_size = 0;
int obs_x = obs_info->obs_x;
/* calculate temporary buffer size */
//for( i = 0; i < hmm->num_states; i++ )
//{
// CvEHMM* ehmm = &(hmm->u.ehmm[i]);
CvEHMMState* state = hmm->u.state;
int max_mix = 0;
for( j = 0; j < hmm->num_states; j++ )
{
int t = state[j].num_mix;
if( max_mix < t ) max_mix = t;
}
max_mix *= hmm->num_states;
/*if( max_size < max_mix )*/ max_size = max_mix;
//}
max_size *= obs_x * vect_size;
/* allocate buffer */
if( max_size > MAX_BUF_SIZE )
{
log_mix_prob = (float*)icvAlloc( max_size*(sizeof(float) + sizeof(double)));
if( !log_mix_prob ) return CV_OUTOFMEM_ERR;
mix_prob = (double*)(log_mix_prob + max_size);
}
memset( log_mix_prob, 0, max_size*sizeof(float));
/*****************computing probabilities***********************/
/* loop through external states */
//for( i = 0; i < hmm->num_states; i++ )
{
// CvEHMM* ehmm = &(hmm->u.ehmm[i]);
CvEHMMState* state = hmm->u.state;
int max_mix = 0;
int n_states = hmm->num_states;
/* determine maximal number of mixtures (again) */
for( j = 0; j < hmm->num_states; j++ )
{
int t = state[j].num_mix;
if( max_mix < t ) max_mix = t;
}
/* loop through rows of the observation matrix */
//for( j = 0; j < obs_info->obs_y; j++ )
{
int m, n;
float* obs = obs_info->obs;/* + j * obs_x * vect_size; */
float* log_mp = max_mix > 1 ? log_mix_prob : (float*)(hmm->obsProb);
double* mp = mix_prob;
/* several passes are done below */
/* 1. calculate logarithms of probabilities for each mixture */
/* loop through mixtures */
/* !!!! */ for( m = 0; m < max_mix; m++ )
{
/* set pointer to first observation in the line */
float* vect = obs;
/* cycles through obseravtions in the line */
for( n = 0; n < obs_x; n++, vect += vect_size, log_mp += n_states )
{
int k, l;
for( l = 0; l < n_states; l++ )
{
if( state[l].num_mix > m )
{
float* mu = state[l].mu + m*vect_size;
float* inv_var = state[l].inv_var + m*vect_size;
double prob = -state[l].log_var_val[m];
for( k = 0; k < vect_size; k++ )
{
double t = (vect[k] - mu[k])*inv_var[k];
prob -= t*t;
}
log_mp[l] = MAX( (float)prob, -500 );
}
}
}
}
/* skip the rest if there is a single mixture */
if( max_mix != 1 )
{
/* 2. calculate exponent of log_mix_prob
(i.e. probability for each mixture) */
res = icvbExp_32f64f( log_mix_prob, mix_prob,
max_mix * obs_x * n_states );
if( res < 0 ) goto processing_exit;
/* 3. sum all mixtures with weights */
/* 3a. first mixture - simply scale by weight */
for( n = 0; n < obs_x; n++, mp += n_states )
{
int l;
for( l = 0; l < n_states; l++ )
{
mp[l] *= state[l].weight[0];
}
}
/* 3b. add other mixtures */
for( m = 1; m < max_mix; m++ )
{
int ofs = -m*obs_x*n_states;
for( n = 0; n < obs_x; n++, mp += n_states )
{
int l;
for( l = 0; l < n_states; l++ )
{
if( m < state[l].num_mix )
{
mp[l + ofs] += mp[l] * state[l].weight[m];
}
}
}
}
/* 4. Put logarithms of summary probabilities to the destination matrix */
res = icvbLog_64f32f( mix_prob, (float*)(hmm->obsProb),//[j],
obs_x * n_states );
if( res < 0 ) goto processing_exit;
}
}
}
processing_exit:
if( log_mix_prob != local_log_mix_prob ) icvFree( &log_mix_prob );
return res;
#undef MAX_BUF_SIZE
}
#else
/* for( i = 0; i < hmm->num_states; i++ )
{
CvEHMM* ehmm = &(hmm->u.ehmm[i]);
CvEHMMState* state = ehmm->u.state;
for( j = 0; j < obs_info->obs_y; j++ )
{
int k,m;
int obs_index = j * obs_info->obs_x;
float* B = ehmm->obsProb[j];
// cycles through obs and states
for( k = 0; k < obs_info->obs_x; k++ )
{
CvVect32f vect = (obs_info->obs) + (obs_index + k) * vect_size;
float* matr_line = B + k * ehmm->num_states;
for( m = 0; m < ehmm->num_states; m++ )
{
matr_line[m] = icvComputeGaussMixture( vect, state[m].mu, state[m].inv_var,
state[m].log_var_val, vect_size, state[m].weight,
state[m].num_mix );
}
}
}
}
*/
#endif
}
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: EstimateTransProb
// Purpose: The function calculates the state and super state transition probabilities
// of the model given the images,
// the state segmentation and the input parameters
// Context:
// Parameters: obs_info_array - array of pointers to image observations
// num_img - length of above array
// hmm - pointer to HMM structure
// Returns: void
//
// Notes:
//F*/
CvStatus icvEstimate1DTransProb( Cv1DObsInfo** obs_info_array,
int num_seq,
CvEHMM* hmm )
{
int i, j, k;
/* as a counter we will use transP matrix */
/* initialization */
/* clear transP */
icvSetZero_32f( hmm->transP, hmm->num_states, hmm->num_states );
/* compute the counters */
for (i = 0; i < num_seq; i++)
{
int counter = 0;
Cv1DObsInfo* info = obs_info_array[i];
for (k = 0; k < info->obs_x; k++, counter++)
{
/* compute how many transitions from state to state
occured */
int state;
int nextstate;
state = info->state[counter];
if (k < info->obs_x - 1)
{
int transP_size = hmm->num_states;
nextstate = info->state[counter+1];
hmm->transP[ state * transP_size + nextstate] += 1;
}
}
}
/* estimate superstate matrix */
for( i = 0; i < hmm->num_states; i++)
{
float total = 0;
float inv_total;
for( j = 0; j < hmm->num_states; j++)
{
total += hmm->transP[i * hmm->num_states + j];
}
//assert( total );
inv_total = total ? 1.f/total : 0;
for( j = 0; j < hmm->num_states; j++)
{
hmm->transP[i * hmm->num_states + j] =
hmm->transP[i * hmm->num_states + j] ?
(float)log( hmm->transP[i * hmm->num_states + j] * inv_total ) : -BIG_FLT;
}
}
return CV_NO_ERR;
}
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: MixSegmL2
// Purpose: The function implements the mixture segmentation of the states of the embedded HMM
// Context: used with the Viterbi training of the embedded HMM
//
// Parameters:
// obs_info_array
// num_img
// hmm
// Returns: void
//
// Notes:
//F*/
CvStatus icv1DMixSegmL2(CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
{
int k, i, m;
CvEHMMState* state = hmm->u.state;
for (k = 0; k < num_img; k++)
{
//int counter = 0;
CvImgObsInfo* info = obs_info_array[k];
for (i = 0; i < info->obs_x; i++)
{
int e_state = info->state[i];
float min_dist;
min_dist = icvSquareDistance((info->obs) + (i * info->obs_size),
state[e_state].mu, info->obs_size);
info->mix[i] = 0;
for (m = 1; m < state[e_state].num_mix; m++)
{
float dist=icvSquareDistance( (info->obs) + (i * info->obs_size),
state[e_state].mu + m * info->obs_size,
info->obs_size);
if (dist < min_dist)
{
min_dist = dist;
/* assign mixture with smallest distance */
info->mix[i] = m;
}
}
}
}
return CV_NO_ERR;
}
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: icvEViterbi
// Purpose: The function calculates the embedded Viterbi algorithm
// for 1 image
// Context:
// Parameters:
// obs_info - observations
// hmm - HMM
//
// Returns: the Embedded Viterbi probability (float)
// and do state segmentation of observations
//
// Notes:
//F*/
float icvViterbi(Cv1DObsInfo* obs_info, CvEHMM* hmm)
{
int i, counter;
float log_likelihood;
//CvEHMMState* first_state = hmm->u.state;
/* memory allocation for superB */
/*CvMatr32f superB = picvCreateMatrix_32f(hmm->num_states, obs_info->obs_x );*/
/* memory allocation for q */
int* super_q = (int*)icvAlloc( obs_info->obs_x * sizeof(int) );
/* perform Viterbi segmentation (process 1D HMM) */
icvViterbiSegmentation( hmm->num_states, obs_info->obs_x,
hmm->transP, (float*)(hmm->obsProb), 0,
_CV_LAST_STATE, &super_q, obs_info->obs_x,
obs_info->obs_x, &log_likelihood );
log_likelihood /= obs_info->obs_x ;
counter = 0;
/* assign new state to observation vectors */
for (i = 0; i < obs_info->obs_x; i++)
{
int state = super_q[i];
obs_info->state[i] = state;
}
/* memory deallocation for superB */
/*picvDeleteMatrix( superB );*/
icvFree( &super_q );
return log_likelihood;
}
CvStatus icvEstimate1DHMMStateParams(CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm)
{
/* compute gamma, weights, means, vars */
int k, i, j, m;
int counter = 0;
int total = 0;
int vect_len = obs_info_array[0]->obs_size;
float start_log_var_val = LN2PI * vect_len;
CvVect32f tmp_vect = icvCreateVector_32f( vect_len );
CvEHMMState* first_state = hmm->u.state;
assert( sizeof(float) == sizeof(int) );
total+= hmm->num_states;
/***************Gamma***********************/
/* initialize gamma */
for( i = 0; i < total; i++ )
{
for (m = 0; m < first_state[i].num_mix; m++)
{
((int*)(first_state[i].weight))[m] = 0;
}
}
/* maybe gamma must be computed in mixsegm process ?? */
/* compute gamma */
counter = 0;
for (k = 0; k < num_img; k++)
{
CvImgObsInfo* info = obs_info_array[k];
int num_obs = info->obs_y * info->obs_x;
for (i = 0; i < num_obs; i++)
{
int state, mixture;
state = info->state[i];
mixture = info->mix[i];
/* computes gamma - number of observations corresponding
to every mixture of every state */
((int*)(first_state[state].weight))[mixture] += 1;
}
}
/***************Mean and Var***********************/
/* compute means and variances of every item */
/* initially variance placed to inv_var */
/* zero mean and variance */
for (i = 0; i < total; i++)
{
memset( (void*)first_state[i].mu, 0, first_state[i].num_mix * vect_len *
sizeof(float) );
memset( (void*)first_state[i].inv_var, 0, first_state[i].num_mix * vect_len *
sizeof(float) );
}
/* compute sums */
for (i = 0; i < num_img; i++)
{
CvImgObsInfo* info = obs_info_array[i];
int total_obs = info->obs_x;// * info->obs_y;
float* vector = info->obs;
for (j = 0; j < total_obs; j++, vector+=vect_len )
{
int state = info->state[j];
int mixture = info->mix[j];
CvVect32f mean = first_state[state].mu + mixture * vect_len;
CvVect32f mean2 = first_state[state].inv_var + mixture * vect_len;
icvAddVector_32f( mean, vector, mean, vect_len );
icvAddSquare_32f_C1IR( vector, vect_len * sizeof(float),
mean2, vect_len * sizeof(float), cvSize(vect_len, 1) );
}
}
/*compute the means and variances */
/* assume gamma already computed */
counter = 0;
for (i = 0; i < total; i++)
{
CvEHMMState* state = &(first_state[i]);
for (m = 0; m < state->num_mix; m++)
{
int k;
CvVect32f mu = state->mu + m * vect_len;
CvVect32f invar = state->inv_var + m * vect_len;
if ( ((int*)state->weight)[m] > 1)
{
float inv_gamma = 1.f/((int*)(state->weight))[m];
icvScaleVector_32f( mu, mu, vect_len, inv_gamma);
icvScaleVector_32f( invar, invar, vect_len, inv_gamma);
}
icvMulVectors_32f(mu, mu, tmp_vect, vect_len);
icvSubVector_32f( invar, tmp_vect, invar, vect_len);
/* low bound of variance - 0.01 (Ara's experimental result) */
for( k = 0; k < vect_len; k++ )
{
invar[k] = (invar[k] > 0.01f) ? invar[k] : 0.01f;
}
/* compute log_var */
state->log_var_val[m] = start_log_var_val;
for( k = 0; k < vect_len; k++ )
{
state->log_var_val[m] += (float)log( invar[k] );
}
state->log_var_val[m] *= 0.5;
/* compute inv_var = 1/sqrt(2*variance) */
icvScaleVector_32f(invar, invar, vect_len, 2.f );
icvbInvSqrt_32f(invar, invar, vect_len );
}
}
/***************Weights***********************/
/* normilize gammas - i.e. compute mixture weights */
//compute weights
for (i = 0; i < total; i++)
{
int gamma_total = 0;
float norm;
for (m = 0; m < first_state[i].num_mix; m++)
{
gamma_total += ((int*)(first_state[i].weight))[m];
}
norm = gamma_total ? (1.f/(float)gamma_total) : 0.f;
for (m = 0; m < first_state[i].num_mix; m++)
{
first_state[i].weight[m] = ((int*)(first_state[i].weight))[m] * norm;
}
}
icvDeleteVector( tmp_vect);
return CV_NO_ERR;
}
#endif