--- /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
+// For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+// * Redistribution's of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+//
+// * Redistribution's in binary form must reproduce the above copyright notice,
+// this list of conditions and the following disclaimer in the documentation
+// and/or other materials provided with the distribution.
+//
+// * The name of Intel Corporation may not be used to endorse or promote products
+// derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "_cvaux.h"
+
+#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: _cvCreateObsInfo
+// 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*/
+static CvStatus CV_STDCALL icvCreateObsInfo( CvImgObsInfo** obs_info,
+ CvSize num_obs, int obs_size )
+{
+ int total = num_obs.height * num_obs.width;
+
+ CvImgObsInfo* obs = (CvImgObsInfo*)cvAlloc( sizeof( CvImgObsInfo) );
+
+ obs->obs_x = num_obs.width;
+ obs->obs_y = num_obs.height;
+
+ obs->obs = (float*)cvAlloc( total * obs_size * sizeof(float) );
+
+ obs->state = (int*)cvAlloc( 2 * total * sizeof(int) );
+ obs->mix = (int*)cvAlloc( total * sizeof(int) );
+
+ obs->obs_size = obs_size;
+
+ obs_info[0] = obs;
+
+ return CV_NO_ERR;
+}
+
+static CvStatus CV_STDCALL icvReleaseObsInfo( CvImgObsInfo** p_obs_info )
+{
+ CvImgObsInfo* obs_info = p_obs_info[0];
+
+ cvFree( &(obs_info->obs) );
+ cvFree( &(obs_info->mix) );
+ cvFree( &(obs_info->state) );
+ cvFree( &(obs_info) );
+
+ p_obs_info[0] = NULL;
+
+ return CV_NO_ERR;
+}
+
+
+//*F///////////////////////////////////////////////////////////////////////////////////////
+// Name: icvCreate2DHMM
+// Purpose: The function allocates memory for 2-dimensional embedded HMM model
+// and its inner stuff
+// Context:
+// Parameters: hmm - addres of pointer to CvEHMM structure
+// state_number - array of hmm sizes (size of array == state_number[0]+1 )
+// num_mix - number of gaussian mixtures in low-level HMM states
+// size of array is defined by previous array values
+// obs_size - length of observation vectors
+//
+// Returns: error status
+//
+// Notes: state_number[0] - number of states in external HMM.
+// state_number[i] - number of states in embedded HMM
+//
+// example for face recognition: state_number = { 5 3 6 6 6 3 },
+// length of num_mix array = 3+6+6+6+3 = 24//
+//
+//F*/
+static CvStatus CV_STDCALL icvCreate2DHMM( CvEHMM** this_hmm,
+ int* state_number, int* num_mix, int obs_size )
+{
+ int i;
+ int real_states = 0;
+
+ CvEHMMState* all_states;
+ CvEHMM* hmm;
+ int total_mix = 0;
+ float* pointers;
+
+ //compute total number of states of all level in 2d EHMM
+ for( i = 1; i <= state_number[0]; i++ )
+ {
+ real_states += state_number[i];
+ }
+
+ /* allocate memory for all hmms (from all levels) */
+ hmm = (CvEHMM*)cvAlloc( (state_number[0] + 1) * sizeof(CvEHMM) );
+
+ /* set number of superstates */
+ hmm[0].num_states = state_number[0];
+ hmm[0].level = 1;
+
+ /* allocate memory for all states */
+ all_states = (CvEHMMState *)cvAlloc( 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*)cvAlloc( 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];
+ }
+
+ /* set pointer to embedded hmm array */
+ hmm->u.ehmm = hmm + 1;
+
+ for( i = 0; i < hmm[0].num_states; i++ )
+ {
+ hmm[i+1].u.state = all_states;
+ all_states += state_number[i+1];
+ hmm[i+1].num_states = state_number[i+1];
+ }
+
+ for( i = 0; i <= state_number[0]; i++ )
+ {
+ hmm[i].transP = icvCreateMatrix_32f( hmm[i].num_states, hmm[i].num_states );
+ hmm[i].obsProb = NULL;
+ hmm[i].level = i ? 0 : 1;
+ }
+
+ /* if all ok - return pointer */
+ *this_hmm = hmm;
+ return CV_NO_ERR;
+}
+
+static CvStatus CV_STDCALL icvRelease2DHMM( CvEHMM** phmm )
+{
+ CvEHMM* hmm = phmm[0];
+ int i;
+ for( i = 0; i < hmm[0].num_states + 1; i++ )
+ {
+ icvDeleteMatrix( hmm[i].transP );
+ }
+
+ if (hmm->obsProb != NULL)
+ {
+ int* tmp = ((int*)(hmm->obsProb)) - 3;
+ cvFree( &(tmp) );
+ }
+
+ cvFree( &(hmm->u.ehmm->u.state->mu) );
+ cvFree( &(hmm->u.ehmm->u.state) );
+
+
+ /* free hmm structures */
+ cvFree( phmm );
+
+ phmm[0] = NULL;
+
+ return CV_NO_ERR;
+}
+
+/* distance between 2 vectors */
+static float icvSquareDistance( CvVect32f v1, CvVect32f v2, int len )
+{
+ int i;
+ double dist0 = 0;
+ double dist1 = 0;
+
+ for( i = 0; i <= len - 4; i += 4 )
+ {
+ double t0 = v1[i] - v2[i];
+ double t1 = v1[i+1] - v2[i+1];
+ dist0 += t0*t0;
+ dist1 += t1*t1;
+
+ t0 = v1[i+2] - v2[i+2];
+ t1 = v1[i+3] - v2[i+3];
+ dist0 += t0*t0;
+ dist1 += t1*t1;
+ }
+
+ for( ; i < len; i++ )
+ {
+ double t0 = v1[i] - v2[i];
+ dist0 += t0*t0;
+ }
+
+ return (float)(dist0 + dist1);
+}
+
+/*can be used in CHMM & DHMM */
+static CvStatus CV_STDCALL
+icvUniformImgSegm( CvImgObsInfo* obs_info, CvEHMM* hmm )
+{
+#if 1
+ /* implementation is very bad */
+ int i, j, counter = 0;
+ CvEHMMState* first_state;
+ float inv_x = 1.f/obs_info->obs_x;
+ float inv_y = 1.f/obs_info->obs_y;
+
+ /* check arguments */
+ if ( !obs_info || !hmm ) return CV_NULLPTR_ERR;
+
+ first_state = hmm->u.ehmm->u.state;
+
+ for (i = 0; i < obs_info->obs_y; i++)
+ {
+ //bad line (division )
+ int superstate = (int)((i * hmm->num_states)*inv_y);/* /obs_info->obs_y; */
+
+ int index = (int)(hmm->u.ehmm[superstate].u.state - first_state);
+
+ for (j = 0; j < obs_info->obs_x; j++, counter++)
+ {
+ int state = (int)((j * hmm->u.ehmm[superstate].num_states)* inv_x); /* / obs_info->obs_x; */
+
+ obs_info->state[2 * counter] = superstate;
+ obs_info->state[2 * counter + 1] = state + index;
+ }
+ }
+#else
+ //this is not ready yet
+
+ int i,j,k,m;
+ CvEHMMState* first_state = hmm->u.ehmm->u.state;
+
+ /* check bad arguments */
+ if ( hmm->num_states > obs_info->obs_y ) return CV_BADSIZE_ERR;
+
+ //compute vertical subdivision
+ float row_per_state = (float)obs_info->obs_y / hmm->num_states;
+ float col_per_state[1024]; /* maximum 1024 superstates */
+
+ //for every horizontal band compute subdivision
+ for( i = 0; i < hmm->num_states; i++ )
+ {
+ CvEHMM* ehmm = &(hmm->u.ehmm[i]);
+ col_per_state[i] = (float)obs_info->obs_x / ehmm->num_states;
+ }
+
+ //compute state bounds
+ int ss_bound[1024];
+ for( i = 0; i < hmm->num_states - 1; i++ )
+ {
+ ss_bound[i] = floor( row_per_state * ( i+1 ) );
+ }
+ ss_bound[hmm->num_states - 1] = obs_info->obs_y;
+
+ //work inside every superstate
+
+ int row = 0;
+
+ for( i = 0; i < hmm->num_states; i++ )
+ {
+ CvEHMM* ehmm = &(hmm->u.ehmm[i]);
+ int index = ehmm->u.state - first_state;
+
+ //calc distribution in superstate
+ int es_bound[1024];
+ for( j = 0; j < ehmm->num_states - 1; j++ )
+ {
+ es_bound[j] = floor( col_per_state[i] * ( j+1 ) );
+ }
+ es_bound[ehmm->num_states - 1] = obs_info->obs_x;
+
+ //assign states to first row of superstate
+ int col = 0;
+ for( j = 0; j < ehmm->num_states; j++ )
+ {
+ for( k = col; k < es_bound[j]; k++, col++ )
+ {
+ obs_info->state[row * obs_info->obs_x + 2 * k] = i;
+ obs_info->state[row * obs_info->obs_x + 2 * k + 1] = j + index;
+ }
+ col = es_bound[j];
+ }
+
+ //copy the same to other rows of superstate
+ for( m = row; m < ss_bound[i]; m++ )
+ {
+ memcpy( &(obs_info->state[m * obs_info->obs_x * 2]),
+ &(obs_info->state[row * obs_info->obs_x * 2]), obs_info->obs_x * 2 * sizeof(int) );
+ }
+
+ row = ss_bound[i];
+ }
+
+#endif
+
+ 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*/
+static CvStatus CV_STDCALL
+icvInitMixSegm( CvImgObsInfo** 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 = 0;
+
+ CvEHMMState* first_state = hmm->u.ehmm->u.state;
+
+ for( i = 0 ; i < hmm->num_states; i++ )
+ {
+ total += hmm->u.ehmm[i].num_states;
+ }
+
+ /* for every state integer is allocated - number of vectors in state */
+ num_samples = (int*)cvAlloc( total * sizeof(int) );
+
+ /* integer counter is allocated for every state */
+ counter = (int*)cvAlloc( total * sizeof(int) );
+
+ samples = (CvVect32f**)cvAlloc( total * sizeof(CvVect32f*) );
+ samples_mix = (int***)cvAlloc( 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];
+ int count = 0;
+
+ for (i = 0; i < obs->obs_y; i++)
+ {
+ for (j = 0; j < obs->obs_x; j++, count++)
+ {
+ int state = obs->state[ 2 * count + 1];
+ num_samples[state] += 1;
+ }
+ }
+ }
+
+ /* for every state int* is allocated */
+ a_class = (int**)cvAlloc( total*sizeof(int*) );
+
+ for (i = 0; i < total; i++)
+ {
+ a_class[i] = (int*)cvAlloc( num_samples[i] * sizeof(int) );
+ samples[i] = (CvVect32f*)cvAlloc( num_samples[i] * sizeof(CvVect32f) );
+ samples_mix[i] = (int**)cvAlloc( 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 ) * ( obs->obs_y );
+ float* vector = obs->obs;
+
+ for (i = 0; i < num_obs; i++, vector+=obs->obs_size )
+ {
+ int state = obs->state[2*i+1];
+
+ 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 */
+ cvKMeans( 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++)
+ {
+ cvFree( &(a_class[i]) );
+ cvFree( &(samples[i]) );
+ cvFree( &(samples_mix[i]) );
+ }
+
+ cvFree( &a_class );
+ cvFree( &samples );
+ cvFree( &samples_mix );
+ cvFree( &counter );
+ cvFree( &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*/
+/*static 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*/
+/*static 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*/
+static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* hmm )
+{
+ int i, 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;
+ }
+
+ 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*)cvAlloc( 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;
+ cvFree( &buffer);
+ buffer = (int*)cvAlloc( 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;
+ }
+ }
+ if (!status)
+ {
+ int* obsx = ((int*)(hmm->obsProb)) - 1;
+ int* obsy = ((int*)(hmm->obsProb)) - 2;
+
+ assert( (*obsx > 0) && (*obsy > 0) );
+
+ /* is good distribution? */
+ if ( (obs_info->obs_x > (*obsx) ) || (obs_info->obs_y > (*obsy) ) )
+ status = 3;
+ }
+
+ /* if bad status - do reallocation actions */
+ assert( (status == 0) || (status == 3) );
+
+ 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;
+ }
+ }
+ }/* 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 = ehmm->u.state;
+
+ int max_mix = 0;
+ for( j = 0; j < ehmm->num_states; j++ )
+ {
+ int t = state[j].num_mix;
+ if( max_mix < t ) max_mix = t;
+ }
+ max_mix *= ehmm->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*)cvAlloc( 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 = ehmm->u.state;
+
+ int max_mix = 0;
+ int n_states = ehmm->num_states;
+
+ /* determine maximal number of mixtures (again) */
+ for( j = 0; j < ehmm->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 : ehmm->obsProb[j];
+ 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 ) continue;
+
+ /* 2. calculate exponent of log_mix_prob
+ (i.e. probability for each mixture) */
+ cvbFastExp( log_mix_prob, mix_prob, max_mix * obs_x * n_states );
+
+ /* 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 */
+ cvbFastLog( mix_prob, ehmm->obsProb[j], obs_x * n_states );
+ }
+ }
+
+ if( log_mix_prob != local_log_mix_prob ) cvFree( &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*/
+static CvStatus CV_STDCALL
+icvEstimateTransProb( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
+{
+ int i, j, k;
+
+ CvEHMMState* first_state = hmm->u.ehmm->u.state;
+ /* as a counter we will use transP matrix */
+
+ /* initialization */
+
+ /* clear transP */
+ icvSetZero_32f( hmm->transP, hmm->num_states, hmm->num_states );
+ for (i = 0; i < hmm->num_states; i++ )
+ {
+ icvSetZero_32f( hmm->u.ehmm[i].transP , hmm->u.ehmm[i].num_states, hmm->u.ehmm[i].num_states );
+ }
+
+ /* compute the counters */
+ for (i = 0; i < num_img; i++)
+ {
+ int counter = 0;
+ CvImgObsInfo* info = obs_info_array[i];
+
+ for (j = 0; j < info->obs_y; j++)
+ {
+ for (k = 0; k < info->obs_x; k++, counter++)
+ {
+ /* compute how many transitions from state to state
+ occured both in horizontal and vertical direction */
+ int superstate, state;
+ int nextsuperstate, nextstate;
+ int begin_ind;
+
+ superstate = info->state[2 * counter];
+ begin_ind = (int)(hmm->u.ehmm[superstate].u.state - first_state);
+ state = info->state[ 2 * counter + 1] - begin_ind;
+
+ if (j < info->obs_y - 1)
+ {
+ int transP_size = hmm->num_states;
+
+ nextsuperstate = info->state[ 2*(counter + info->obs_x) ];
+
+ hmm->transP[superstate * transP_size + nextsuperstate] += 1;
+ }
+
+ if (k < info->obs_x - 1)
+ {
+ int transP_size = hmm->u.ehmm[superstate].num_states;
+
+ nextstate = info->state[2*(counter+1) + 1] - begin_ind;
+ hmm->u.ehmm[superstate].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;
+ }
+ }
+
+ /* estimate other matrices */
+ for( k = 0; k < hmm->num_states; k++ )
+ {
+ CvEHMM* ehmm = &(hmm->u.ehmm[k]);
+
+ for( i = 0; i < ehmm->num_states; i++)
+ {
+ float total = 0;
+ float inv_total;
+ for( j = 0; j < ehmm->num_states; j++)
+ {
+ total += ehmm->transP[i*ehmm->num_states + j];
+ }
+ //assert( total );
+ inv_total = total ? 1.f/total : 0;
+
+ for( j = 0; j < ehmm->num_states; j++)
+ {
+ ehmm->transP[i * ehmm->num_states + j] =
+ (ehmm->transP[i * ehmm->num_states + j]) ?
+ (float)log( ehmm->transP[i * ehmm->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*/
+static CvStatus CV_STDCALL
+icvMixSegmL2( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
+{
+ int k, i, j, m;
+
+ CvEHMMState* state = hmm->u.ehmm[0].u.state;
+
+
+ for (k = 0; k < num_img; k++)
+ {
+ int counter = 0;
+ CvImgObsInfo* info = obs_info_array[k];
+
+ for (i = 0; i < info->obs_y; i++)
+ {
+ for (j = 0; j < info->obs_x; j++, counter++)
+ {
+ int e_state = info->state[2 * counter + 1];
+ float min_dist;
+
+ min_dist = icvSquareDistance((info->obs) + (counter * info->obs_size),
+ state[e_state].mu, info->obs_size);
+ info->mix[counter] = 0;
+
+ for (m = 1; m < state[e_state].num_mix; m++)
+ {
+ float dist=icvSquareDistance( (info->obs) + (counter * 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[counter] = m;
+ }
+ }
+ }
+ }
+ }
+ return CV_NO_ERR;
+}
+
+/*
+CvStatus icvMixSegmProb(CvImgObsInfo* obs_info, int num_img, CvEHMM* hmm )
+{
+ int k, i, j, m;
+
+ CvEHMMState* state = hmm->ehmm[0].state_info;
+
+
+ for (k = 0; k < num_img; k++)
+ {
+ int counter = 0;
+ CvImgObsInfo* info = obs_info + k;
+
+ for (i = 0; i < info->obs_y; i++)
+ {
+ for (j = 0; j < info->obs_x; j++, counter++)
+ {
+ int e_state = info->in_state[counter];
+ float max_prob;
+
+ max_prob = icvComputeUniModeGauss( info->obs[counter], state[e_state].mu[0],
+ state[e_state].inv_var[0],
+ state[e_state].log_var[0],
+ info->obs_size );
+ info->mix[counter] = 0;
+
+ for (m = 1; m < state[e_state].num_mix; m++)
+ {
+ float prob=icvComputeUniModeGauss(info->obs[counter], state[e_state].mu[m],
+ state[e_state].inv_var[m],
+ state[e_state].log_var[m],
+ info->obs_size);
+ if (prob > max_prob)
+ {
+ max_prob = prob;
+ // assign mixture with greatest probability.
+ info->mix[counter] = m;
+ }
+ }
+ }
+ }
+ }
+
+ return CV_NO_ERR;
+}
+*/
+static CvStatus CV_STDCALL
+icvViterbiSegmentation( int num_states, int /*num_obs*/, CvMatr32f transP,
+ CvMatr32f B, int start_obs, int prob_type,
+ int** q, int min_num_obs, int max_num_obs,
+ float* prob )
+{
+ // memory allocation
+ int i, j, last_obs;
+ int m_HMMType = _CV_ERGODIC; /* _CV_CAUSAL or _CV_ERGODIC */
+
+ int m_ProbType = prob_type; /* _CV_LAST_STATE or _CV_BEST_STATE */
+
+ int m_minNumObs = min_num_obs; /*??*/
+ int m_maxNumObs = max_num_obs; /*??*/
+
+ int m_numStates = num_states;
+
+ float* m_pi = (float*)cvAlloc( num_states* sizeof(float) );
+ CvMatr32f m_a = transP;
+
+ // offset brobability matrix to starting observation
+ CvMatr32f m_b = B + start_obs * num_states;
+ //so m_xl will not be used more
+
+ //m_xl = start_obs;
+
+ /* if (muDur != NULL){
+ m_d = new int[m_numStates];
+ m_l = new double[m_numStates];
+ for (i = 0; i < m_numStates; i++){
+ m_l[i] = muDur[i];
+ }
+ }
+ else{
+ m_d = NULL;
+ m_l = NULL;
+ }
+ */
+
+ CvMatr32f m_Gamma = icvCreateMatrix_32f( num_states, m_maxNumObs );
+ int* m_csi = (int*)cvAlloc( num_states * m_maxNumObs * sizeof(int) );
+
+ //stores maximal result for every ending observation */
+ CvVect32f m_MaxGamma = prob;
+
+
+// assert( m_xl + max_num_obs <= num_obs );
+
+ /*??m_q = new int*[m_maxNumObs - m_minNumObs];
+ ??for (i = 0; i < m_maxNumObs - m_minNumObs; i++)
+ ?? m_q[i] = new int[m_minNumObs + i + 1];
+ */
+
+ /******************************************************************/
+ /* Viterbi initialization */
+ /* set initial state probabilities, in logarithmic scale */
+ for (i = 0; i < m_numStates; i++)
+ {
+ m_pi[i] = -BIG_FLT;
+ }
+ m_pi[0] = 0.0f;
+
+ for (i = 0; i < num_states; i++)
+ {
+ m_Gamma[0 * num_states + i] = m_pi[i] + m_b[0 * num_states + i];
+ m_csi[0 * num_states + i] = 0;
+ }
+
+ /******************************************************************/
+ /* Viterbi recursion */
+
+ if ( m_HMMType == _CV_CAUSAL ) //causal model
+ {
+ int t,j;
+
+ for (t = 1 ; t < m_maxNumObs; t++)
+ {
+ // evaluate self-to-self transition for state 0
+ m_Gamma[t * num_states + 0] = m_Gamma[(t-1) * num_states + 0] + m_a[0];
+ m_csi[t * num_states + 0] = 0;
+
+ for (j = 1; j < num_states; j++)
+ {
+ float self = m_Gamma[ (t-1) * num_states + j] + m_a[ j * num_states + j];
+ float prev = m_Gamma[ (t-1) * num_states +(j-1)] + m_a[ (j-1) * num_states + j];
+
+ if ( prev > self )
+ {
+ m_csi[t * num_states + j] = j-1;
+ m_Gamma[t * num_states + j] = prev;
+ }
+ else
+ {
+ m_csi[t * num_states + j] = j;
+ m_Gamma[t * num_states + j] = self;
+ }
+
+ m_Gamma[t * num_states + j] = m_Gamma[t * num_states + j] + m_b[t * num_states + j];
+ }
+ }
+ }
+ else if ( m_HMMType == _CV_ERGODIC ) //ergodic model
+ {
+ int t;
+ for (t = 1 ; t < m_maxNumObs; t++)
+ {
+ for (j = 0; j < num_states; j++)
+ {
+ int i;
+ m_Gamma[ t*num_states + j] = m_Gamma[(t-1) * num_states + 0] + m_a[0*num_states+j];
+ m_csi[t *num_states + j] = 0;
+
+ for (i = 1; i < num_states; i++)
+ {
+ float currGamma = m_Gamma[(t-1) *num_states + i] + m_a[i *num_states + j];
+ if (currGamma > m_Gamma[t *num_states + j])
+ {
+ m_Gamma[t * num_states + j] = currGamma;
+ m_csi[t * num_states + j] = i;
+ }
+ }
+ m_Gamma[t *num_states + j] = m_Gamma[t *num_states + j] + m_b[t * num_states + j];
+ }
+ }
+ }
+
+ for( last_obs = m_minNumObs-1, i = 0; last_obs < m_maxNumObs; last_obs++, i++ )
+ {
+ int t;
+
+ /******************************************************************/
+ /* Viterbi termination */
+
+ if ( m_ProbType == _CV_LAST_STATE )
+ {
+ m_MaxGamma[i] = m_Gamma[last_obs * num_states + num_states - 1];
+ q[i][last_obs] = num_states - 1;
+ }
+ else if( m_ProbType == _CV_BEST_STATE )
+ {
+ int k;
+ q[i][last_obs] = 0;
+ m_MaxGamma[i] = m_Gamma[last_obs * num_states + 0];
+
+ for(k = 1; k < num_states; k++)
+ {
+ if ( m_Gamma[last_obs * num_states + k] > m_MaxGamma[i] )
+ {
+ m_MaxGamma[i] = m_Gamma[last_obs * num_states + k];
+ q[i][last_obs] = k;
+ }
+ }
+ }
+
+ /******************************************************************/
+ /* Viterbi backtracking */
+ for (t = last_obs-1; t >= 0; t--)
+ {
+ q[i][t] = m_csi[(t+1) * num_states + q[i][t+1] ];
+ }
+ }
+
+ /* memory free */
+ cvFree( &m_pi );
+ cvFree( &m_csi );
+ icvDeleteMatrix( m_Gamma );
+
+ 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*/
+static float CV_STDCALL icvEViterbi( CvImgObsInfo* obs_info, CvEHMM* hmm )
+{
+ int i, j, counter;
+ float log_likelihood;
+
+ float inv_obs_x = 1.f / obs_info->obs_x;
+
+ CvEHMMState* first_state = hmm->u.ehmm->u.state;
+
+ /* memory allocation for superB */
+ CvMatr32f superB = icvCreateMatrix_32f(hmm->num_states, obs_info->obs_y );
+
+ /* memory allocation for q */
+ int*** q = (int***)cvAlloc( hmm->num_states * sizeof(int**) );
+ int* super_q = (int*)cvAlloc( obs_info->obs_y * sizeof(int) );
+
+ for (i = 0; i < hmm->num_states; i++)
+ {
+ q[i] = (int**)cvAlloc( obs_info->obs_y * sizeof(int*) );
+
+ for (j = 0; j < obs_info->obs_y ; j++)
+ {
+ q[i][j] = (int*)cvAlloc( obs_info->obs_x * sizeof(int) );
+ }
+ }
+
+ /* start Viterbi segmentation */
+ for (i = 0; i < hmm->num_states; i++)
+ {
+ CvEHMM* ehmm = &(hmm->u.ehmm[i]);
+
+ for (j = 0; j < obs_info->obs_y; j++)
+ {
+ float max_gamma;
+
+ /* 1D HMM Viterbi segmentation */
+ icvViterbiSegmentation( ehmm->num_states, obs_info->obs_x,
+ ehmm->transP, ehmm->obsProb[j], 0,
+ _CV_LAST_STATE, &q[i][j], obs_info->obs_x,
+ obs_info->obs_x, &max_gamma);
+
+ superB[j * hmm->num_states + i] = max_gamma * inv_obs_x;
+ }
+ }
+
+ /* perform global Viterbi segmentation (i.e. process higher-level HMM) */
+
+ icvViterbiSegmentation( hmm->num_states, obs_info->obs_y,
+ hmm->transP, superB, 0,
+ _CV_LAST_STATE, &super_q, obs_info->obs_y,
+ obs_info->obs_y, &log_likelihood );
+
+ log_likelihood /= obs_info->obs_y ;
+
+
+ counter = 0;
+ /* assign new state to observation vectors */
+ for (i = 0; i < obs_info->obs_y; i++)
+ {
+ for (j = 0; j < obs_info->obs_x; j++, counter++)
+ {
+ int superstate = super_q[i];
+ int state = (int)(hmm->u.ehmm[superstate].u.state - first_state);
+
+ obs_info->state[2 * counter] = superstate;
+ obs_info->state[2 * counter + 1] = state + q[superstate][i][j];
+ }
+ }
+
+ /* memory deallocation for superB */
+ icvDeleteMatrix( superB );
+
+ /*memory deallocation for q */
+ for (i = 0; i < hmm->num_states; i++)
+ {
+ for (j = 0; j < obs_info->obs_y ; j++)
+ {
+ cvFree( &q[i][j] );
+ }
+ cvFree( &q[i] );
+ }
+
+ cvFree( &q );
+ cvFree( &super_q );
+
+ return log_likelihood;
+}
+
+static CvStatus CV_STDCALL
+icvEstimateHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
+{
+ /* compute gamma, weights, means, vars */
+ int k, i, j, m;
+ 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.ehmm[0].u.state;
+
+ assert( sizeof(float) == sizeof(int) );
+
+ for(i = 0; i < hmm->num_states; i++ )
+ {
+ total+= hmm->u.ehmm[i].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 */
+ 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[2*i + 1];
+ 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[2 * j + 1];
+ 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 );
+ for( k = 0; k < vect_len; k++ )
+ mean2[k] += vector[k]*vector[k];
+ }
+ }
+
+ /*compute the means and variances */
+ /* assume gamma already computed */
+ 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 - 100 (Ara's experimental result) */
+ for( k = 0; k < vect_len; k++ )
+ {
+ invar[k] = (invar[k] > 100.f) ? invar[k] : 100.f;
+ }
+
+ /* 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] );
+ }
+
+ /* SMOLI 27.10.2000 */
+ state->log_var_val[m] *= 0.5;
+
+
+ /* compute inv_var = 1/sqrt(2*variance) */
+ icvScaleVector_32f(invar, invar, vect_len, 2.f );
+ cvbInvSqrt( 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;
+}
+
+/*
+CvStatus icvLightingCorrection8uC1R( uchar* img, CvSize roi, int src_step )
+{
+ int i, j;
+ int width = roi.width;
+ int height = roi.height;
+
+ float x1, x2, y1, y2;
+ int f[3] = {0, 0, 0};
+ float a[3] = {0, 0, 0};
+
+ float h1;
+ float h2;
+
+ float c1,c2;
+
+ float min = FLT_MAX;
+ float max = -FLT_MAX;
+ float correction;
+
+ float* float_img = icvAlloc( width * height * sizeof(float) );
+
+ x1 = width * (width + 1) / 2.0f; // Sum (1, ... , width)
+ x2 = width * (width + 1 ) * (2 * width + 1) / 6.0f; // Sum (1^2, ... , width^2)
+ y1 = height * (height + 1)/2.0f; // Sum (1, ... , width)
+ y2 = height * (height + 1 ) * (2 * height + 1) / 6.0f; // Sum (1^2, ... , width^2)
+
+
+ // extract grayvalues
+ for (i = 0; i < height; i++)
+ {
+ for (j = 0; j < width; j++)
+ {
+ f[2] = f[2] + j * img[i*src_step + j];
+ f[1] = f[1] + i * img[i*src_step + j];
+ f[0] = f[0] + img[i*src_step + j];
+ }
+ }
+
+ h1 = (float)f[0] * (float)x1 / (float)width;
+ h2 = (float)f[0] * (float)y1 / (float)height;
+
+ a[2] = ((float)f[2] - h1) / (float)(x2*height - x1*x1*height/(float)width);
+ a[1] = ((float)f[1] - h2) / (float)(y2*width - y1*y1*width/(float)height);
+ a[0] = (float)f[0]/(float)(width*height) - (float)y1*a[1]/(float)height -
+ (float)x1*a[2]/(float)width;
+
+ for (i = 0; i < height; i++)
+ {
+ for (j = 0; j < width; j++)
+ {
+
+ correction = a[0] + a[1]*(float)i + a[2]*(float)j;
+
+ float_img[i*width + j] = img[i*src_step + j] - correction;
+
+ if (float_img[i*width + j] < min) min = float_img[i*width+j];
+ if (float_img[i*width + j] > max) max = float_img[i*width+j];
+ }
+ }
+
+ //rescaling to the range 0:255
+ c2 = 0;
+ if (max == min)
+ c2 = 255.0f;
+ else
+ c2 = 255.0f/(float)(max - min);
+
+ c1 = (-(float)min)*c2;
+
+ for (i = 0; i < height; i++)
+ {
+ for (j = 0; j < width; j++)
+ {
+ int value = (int)floor(c2*float_img[i*width + j] + c1);
+ if (value < 0) value = 0;
+ if (value > 255) value = 255;
+ img[i*src_step + j] = (uchar)value;
+ }
+ }
+
+ cvFree( &float_img );
+ return CV_NO_ERR;
+}
+
+
+CvStatus icvLightingCorrection( icvImage* img )
+{
+ CvSize roi;
+ if ( img->type != IPL_DEPTH_8U || img->channels != 1 )
+ return CV_BADFACTOR_ERR;
+
+ roi = _cvSize( img->roi.width, img->roi.height );
+
+ return _cvLightingCorrection8uC1R( img->data + img->roi.y * img->step + img->roi.x,
+ roi, img->step );
+
+}
+
+*/
+
+CV_IMPL CvEHMM*
+cvCreate2DHMM( int *state_number, int *num_mix, int obs_size )
+{
+ CvEHMM* hmm = 0;
+
+ IPPI_CALL( icvCreate2DHMM( &hmm, state_number, num_mix, obs_size ));
+
+ return hmm;
+}
+
+CV_IMPL void
+cvRelease2DHMM( CvEHMM ** hmm )
+{
+ IPPI_CALL( icvRelease2DHMM( hmm ));
+}
+
+CV_IMPL CvImgObsInfo*
+cvCreateObsInfo( CvSize num_obs, int obs_size )
+{
+ CvImgObsInfo *obs_info = 0;
+
+ IPPI_CALL( icvCreateObsInfo( &obs_info, num_obs, obs_size ));
+
+ return obs_info;
+}
+
+CV_IMPL void
+cvReleaseObsInfo( CvImgObsInfo ** obs_info )
+{
+ IPPI_CALL( icvReleaseObsInfo( obs_info ));
+}
+
+
+CV_IMPL void
+cvUniformImgSegm( CvImgObsInfo * obs_info, CvEHMM * hmm )
+{
+ IPPI_CALL( icvUniformImgSegm( obs_info, hmm ));
+}
+
+CV_IMPL void
+cvInitMixSegm( CvImgObsInfo ** obs_info_array, int num_img, CvEHMM * hmm )
+{
+ IPPI_CALL( icvInitMixSegm( obs_info_array, num_img, hmm ));
+}
+
+CV_IMPL void
+cvEstimateHMMStateParams( CvImgObsInfo ** obs_info_array, int num_img, CvEHMM * hmm )
+{
+ IPPI_CALL( icvEstimateHMMStateParams( obs_info_array, num_img, hmm ));
+}
+
+CV_IMPL void
+cvEstimateTransProb( CvImgObsInfo ** obs_info_array, int num_img, CvEHMM * hmm )
+{
+ IPPI_CALL( icvEstimateTransProb( obs_info_array, num_img, hmm ));
+}
+
+CV_IMPL void
+cvEstimateObsProb( CvImgObsInfo * obs_info, CvEHMM * hmm )
+{
+ IPPI_CALL( icvEstimateObsProb( obs_info, hmm ));
+}
+
+CV_IMPL float
+cvEViterbi( CvImgObsInfo * obs_info, CvEHMM * hmm )
+{
+ if( (obs_info == NULL) || (hmm == NULL) )
+ CV_Error( CV_BadDataPtr, "Null pointer." );
+
+ return icvEViterbi( obs_info, hmm );
+}
+
+CV_IMPL void
+cvMixSegmL2( CvImgObsInfo ** obs_info_array, int num_img, CvEHMM * hmm )
+{
+ IPPI_CALL( icvMixSegmL2( obs_info_array, num_img, hmm ));
+}
+
+/* End of file */
+
projects / opencv / blobdiff