+++ /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;
-
- CV_FUNCNAME( "cvCreate2DHMM" );
-
- __BEGIN__;
-
- IPPI_CALL( icvCreate2DHMM( &hmm, state_number, num_mix, obs_size ));
-
- __END__;
-
- return hmm;
-}
-
-CV_IMPL void
-cvRelease2DHMM( CvEHMM ** hmm )
-{
- CV_FUNCNAME( "cvRelease2DHMM" );
-
- __BEGIN__;
-
- IPPI_CALL( icvRelease2DHMM( hmm ));
- __END__;
-}
-
-CV_IMPL CvImgObsInfo*
-cvCreateObsInfo( CvSize num_obs, int obs_size )
-{
- CvImgObsInfo *obs_info = 0;
-
- CV_FUNCNAME( "cvCreateObsInfo" );
-
- __BEGIN__;
-
- IPPI_CALL( icvCreateObsInfo( &obs_info, num_obs, obs_size ));
-
- __END__;
-
- return obs_info;
-}
-
-CV_IMPL void
-cvReleaseObsInfo( CvImgObsInfo ** obs_info )
-{
- CV_FUNCNAME( "cvReleaseObsInfo" );
-
- __BEGIN__;
-
- IPPI_CALL( icvReleaseObsInfo( obs_info ));
-
- __END__;
-}
-
-
-CV_IMPL void
-cvUniformImgSegm( CvImgObsInfo * obs_info, CvEHMM * hmm )
-{
- CV_FUNCNAME( "cvUniformImgSegm" );
-
- __BEGIN__;
-
- IPPI_CALL( icvUniformImgSegm( obs_info, hmm ));
- __CLEANUP__;
- __END__;
-}
-
-CV_IMPL void
-cvInitMixSegm( CvImgObsInfo ** obs_info_array, int num_img, CvEHMM * hmm )
-{
- CV_FUNCNAME( "cvInitMixSegm" );
-
- __BEGIN__;
-
- IPPI_CALL( icvInitMixSegm( obs_info_array, num_img, hmm ));
-
- __END__;
-}
-
-CV_IMPL void
-cvEstimateHMMStateParams( CvImgObsInfo ** obs_info_array, int num_img, CvEHMM * hmm )
-{
- CV_FUNCNAME( "cvEstimateHMMStateParams" );
-
- __BEGIN__;
-
- IPPI_CALL( icvEstimateHMMStateParams( obs_info_array, num_img, hmm ));
-
- __END__;
-}
-
-CV_IMPL void
-cvEstimateTransProb( CvImgObsInfo ** obs_info_array, int num_img, CvEHMM * hmm )
-{
- CV_FUNCNAME( "cvEstimateTransProb" );
-
- __BEGIN__;
-
- IPPI_CALL( icvEstimateTransProb( obs_info_array, num_img, hmm ));
-
- __END__;
-
-}
-
-CV_IMPL void
-cvEstimateObsProb( CvImgObsInfo * obs_info, CvEHMM * hmm )
-{
- CV_FUNCNAME( "cvEstimateObsProb" );
-
- __BEGIN__;
-
- IPPI_CALL( icvEstimateObsProb( obs_info, hmm ));
-
- __END__;
-}
-
-CV_IMPL float
-cvEViterbi( CvImgObsInfo * obs_info, CvEHMM * hmm )
-{
- float result = FLT_MAX;
-
- CV_FUNCNAME( "cvEViterbi" );
-
- __BEGIN__;
-
- if( (obs_info == NULL) || (hmm == NULL) )
- CV_ERROR( CV_BadDataPtr, "Null pointer." );
-
- result = icvEViterbi( obs_info, hmm );
-
- __END__;
-
- return result;
-}
-
-CV_IMPL void
-cvMixSegmL2( CvImgObsInfo ** obs_info_array, int num_img, CvEHMM * hmm )
-{
- CV_FUNCNAME( "cvMixSegmL2" );
-
- __BEGIN__;
-
- IPPI_CALL( icvMixSegmL2( obs_info_array, num_img, hmm ));
-
- __END__;
-}
-
-/* End of file */
-
projects / opencv / blobdiff