--- /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"
+
+#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
+
projects / opencv / blobdiff