sp_mat make_fourier_basis(const Points & points,
uint K,double max_freq){
uint N = points.n_rows;
mat basis = mat(N,K);
basis.col(0) = ones<vec>(N);
basis.col(1) = sin(datum::pi*sum(points,1));
basis.col(2) = sin(datum::pi*(points.col(0) - points.col(1)));
for(uint i = 3; i < K; i++){
vec freq = 2.0*datum::pi * randi<vec>(2, distr_param(1,(uint)max_freq));
vec flip = randn<vec>(1);
if(flip(0) > 0.5)
basis.col(i) = sin(points * freq);
else
basis.col(i) = cos(points * freq);
}
basis = orth(basis); // Explicitly orthonormalize
return sp_mat(basis);
}
inline
cube
randg(const uword n_rows, const uword n_cols, const uword n_slices, const distr_param& param = distr_param())
{
arma_extra_debug_sigprint();
return randg<cube>(n_rows, n_cols, n_slices, param);
}
inline
cube_type
randg(const uword n_rows, const uword n_cols, const uword n_slices, const distr_param& param = distr_param(), const typename arma_Cube_only<cube_type>::result* junk = 0)
{
arma_extra_debug_sigprint();
arma_ignore(junk);
#if defined(ARMA_USE_CXX11)
{
cube_type out(n_rows, n_cols, n_slices);
double a;
double b;
if(param.state == 0)
{
a = double(1);
b = double(1);
}
else
if(param.state == 1)
{
a = double(param.a_int);
b = double(param.b_int);
}
else
{
a = param.a_double;
b = param.b_double;
}
arma_debug_check( ((a <= double(0)) || (b <= double(0))), "randg(): a and b must be greater than zero" );
#if defined(ARMA_USE_EXTERN_CXX11_RNG)
{
arma_rng_cxx11_instance.randg_fill(out.memptr(), out.n_elem, a, b);
}
#else
{
arma_rng_cxx11 local_arma_rng_cxx11_instance;
typedef typename arma_rng_cxx11::seed_type seed_type;
local_arma_rng_cxx11_instance.set_seed( seed_type(arma_rng::randi<seed_type>()) );
local_arma_rng_cxx11_instance.randg_fill(out.memptr(), out.n_elem, a, b);
}
#endif
return out;
}
#else
{
arma_ignore(n_rows);
arma_ignore(n_cols);
arma_ignore(n_slices);
arma_ignore(param);
arma_stop("randg(): C++11 compiler required");
return cube_type();
}
#endif
}
inline
void
BoW::create_vocabulary(int N_cent, const string path_run_folders)
{
cout << "Calculating Vocabulary " << endl;
cout << "# clusters: " << N_cent << endl;
mat uni_features;
for (uword pe=0; pe<peo_train.n_rows; ++pe) {
mat mat_features_tmp;
mat mat_features;
for (uword act = 0 ; act < actions.n_rows; ++act) {
for (uword sc = 1 ; sc <= 4; ++sc) {
mat mat_features_video_i;
std::stringstream ssName_feat_video;
//ssName_feat_video << "./run"<< run <<"/features/train/feat_vec" << peo_train(pe) << "_" << actions(act) << "_d" << sc;
ssName_feat_video << path_run_folders <<"/features_all_nor/feat_vec_" << peo_train(pe) << "_" << actions(act) << "_d" << sc;
mat_features_video_i.load( ssName_feat_video.str() );
if ( mat_features_video_i.n_cols>0 )
{
mat_features_tmp = join_rows( mat_features_tmp, mat_features_video_i );
}
else
{
cout << "# vectors = 0 in " << ssName_feat_video.str() << endl;
}
}
}
cout << "mat_features_tmp.n_cols "<< mat_features_tmp.n_cols << endl;
const uword N_max = 100000; // maximum number of vectors per action to create universal GMM
//const uword N_max = 100000; //???
if (mat_features_tmp.n_cols > N_max)
{
ivec tmp1 = randi( N_max, distr_param(0,mat_features_tmp.n_cols-1) );
ivec tmp2 = unique(tmp1);
uvec my_indices = conv_to<uvec>::from(tmp2);
mat_features = mat_features_tmp.cols(my_indices); // extract a subset of the columns
}
else
{
mat_features = mat_features_tmp;
}
cout << "mat_features.n_cols "<< mat_features.n_cols << endl;
if ( mat_features.n_cols>0 )
{
uni_features = join_rows( uni_features, mat_features );
}
else
{
cout << "# vectors = 0 in uni_features" << endl;
}
//uni_features = join_rows( uni_features, mat_features );
mat_features_tmp.reset();
mat_features.reset();
}
cout << "r&c "<< uni_features.n_rows << " & " << uni_features.n_cols << endl;
bool is_finite = uni_features.is_finite();
if (!is_finite )
{
cout << "is_finite?? " << is_finite << endl;
cout << uni_features.n_rows << " " << uni_features.n_cols << endl;
getchar();
}
fmat f_uni_features = conv_to< fmat >::from(uni_features);
//uni_features.reset();
cv::Mat featuresUnclustered(f_uni_features.n_cols, dim, CV_32FC1, f_uni_features.memptr() );
//cv::Mat featuresUnclustered( featuresUnclusteredTMP.t() );
int rows = featuresUnclustered.rows;
int cols = featuresUnclustered.cols;
cout << "OpenCV rows & cols " << rows << " & " << cols << endl;
//cout << "Press a Key" << endl;
//getchar();
//cout << f_uni_features.col(1000) << endl;
//cout << uni_features.col(1000) << endl;
//cout << featuresUnclustered.row(1000) << endl;
//cout << "Press a Key" << endl;
//getchar();
//Construct BOWKMeansTrainer
//the number of bags
int dictionarySize = N_cent;
//define Term Criteria
cv::TermCriteria tc(CV_TERMCRIT_ITER,100,0.001);
//retries number
int retries=1;
//necessary flags
int flags=cv::KMEANS_PP_CENTERS;
//Create the BoW (or BoF) trainer
cv::BOWKMeansTrainer bowTrainer(dictionarySize,tc,retries,flags);
//cluster the feature vectors
cv::Mat dictionary = bowTrainer.cluster(featuresUnclustered);
//Displaying # of Rows&Cols
int rows_dic = dictionary.rows;
int cols_dic = dictionary.cols;
cout << "OpenCV Dict rows & cols " << rows_dic << " & " << cols_dic << endl;
//store the vocabulary
std::stringstream name_vocabulary;
name_vocabulary << "./run"<< run <<"/visual_vocabulary/means_Ng" << N_cent << "_dim" <<dim << "_all_sc" << ".yml";
cv::FileStorage fs(name_vocabulary.str(), cv::FileStorage::WRITE);
fs << "vocabulary" << dictionary;
fs.release();
cout << "DONE"<< endl;
}
arma_warn_unused
inline
obj_type
randg(const uword n_rows, const uword n_cols, const distr_param& param = distr_param(), const typename arma_Mat_Col_Row_only<obj_type>::result* junk = 0)
{
arma_extra_debug_sigprint();
arma_ignore(junk);
#if defined(ARMA_USE_CXX11)
{
if(is_Col<obj_type>::value == true)
{
arma_debug_check( (n_cols != 1), "randg(): incompatible size" );
}
else
if(is_Row<obj_type>::value == true)
{
arma_debug_check( (n_rows != 1), "randg(): incompatible size" );
}
obj_type out(n_rows, n_cols);
double a;
double b;
if(param.state == 0)
{
a = double(1);
b = double(1);
}
else
if(param.state == 1)
{
a = double(param.a_int);
b = double(param.b_int);
}
else
{
a = param.a_double;
b = param.b_double;
}
arma_debug_check( ((a <= double(0)) || (b <= double(0))), "randg(): a and b must be greater than zero" );
#if defined(ARMA_USE_EXTERN_CXX11_RNG)
{
arma_rng_cxx11_instance.randg_fill(out.memptr(), out.n_elem, a, b);
}
#else
{
arma_rng_cxx11 local_arma_rng_cxx11_instance;
typedef typename arma_rng_cxx11::seed_type seed_type;
local_arma_rng_cxx11_instance.set_seed( seed_type(arma_rng::randi<seed_type>()) );
local_arma_rng_cxx11_instance.randg_fill(out.memptr(), out.n_elem, a, b);
}
#endif
return out;
}
#else
{
arma_ignore(n_rows);
arma_ignore(n_cols);
arma_ignore(param);
arma_stop_logic_error("randg(): C++11 compiler required");
return obj_type();
}
#endif
}
// Random integer in [0,n-1]
size_t randint(const size_t & n){
return randi<vec>(1,distr_param(0,static_cast<int>(n-1)))(0);
}
//! Random integer in [a,b]
int randint(int a, int b){
return randi<vec>(1,distr_param(a,b))(0);
}
inline
void
BoW::create_universal_gmm(int N_cent, const string path_run_folders)
{
cout << "Calculating Universal GMM " << endl;
cout << "# clusters: " << N_cent << endl;
mat uni_features;
for (uword pe=0; pe<peo_train.n_rows; ++pe) {
mat mat_features_tmp;
mat mat_features;
for (uword act = 0 ; act < actions.n_rows; ++act) {
for (uword sc = 1 ; sc <= 4; ++sc) {
mat mat_features_video_i;
std::stringstream ssName_feat_video;
//ssName_feat_video << "./run"<< run <<"/features/train/feat_vec" << peo_train(pe) << "_" << actions(act) << "_d" << sc;
ssName_feat_video << path_run_folders <<"/features_all_nor/feat_vec_" << peo_train(pe) << "_" << actions(act) << "_d" << sc;
mat_features_video_i.load( ssName_feat_video.str() );
if ( mat_features_video_i.n_cols>0 )
{
mat_features_tmp = join_rows( mat_features_tmp, mat_features_video_i );
}
else
{
cout << "# vectors = 0 in " << ssName_feat_video.str() << endl;
}
}
}
cout << "mat_features_tmp.n_cols "<< mat_features_tmp.n_cols << endl;
const uword N_max = 100000*4; // 4 sc. maximum number of vectors per action to create universal GMM
//const uword N_max = 100000; //???
if (mat_features_tmp.n_cols > N_max)
{
ivec tmp1 = randi( N_max, distr_param(0,mat_features_tmp.n_cols-1) );
ivec tmp2 = unique(tmp1);
uvec my_indices = conv_to<uvec>::from(tmp2);
mat_features = mat_features_tmp.cols(my_indices); // extract a subset of the columns
}
else
{
mat_features = mat_features_tmp;
}
cout << "mat_features.n_cols "<< mat_features.n_cols << endl;
if ( mat_features.n_cols>0 )
{
uni_features = join_rows( uni_features, mat_features );
}
else
{
cout << "# vectors = 0 in uni_features" << endl;
}
//uni_features = join_rows( uni_features, mat_features );
mat_features_tmp.reset();
mat_features.reset();
}
cout << "r&c "<< uni_features.n_rows << " & " << uni_features.n_cols << endl;
bool is_finite = uni_features.is_finite();
if (!is_finite )
{
cout << "is_finite?? " << is_finite << endl;
cout << uni_features.n_rows << " " << uni_features.n_cols << endl;
getchar();
}
cout << "universal GMM" << endl;
gmm_diag gmm_model;
gmm_diag bg_model;
bool status_em = false;
int rep_em=0;
int km_iter = 10;
double var_floor = 1e-10;
bool print_mode = true;
bool status_kmeans = false;
int rep_km = 0;
while (!status_kmeans)
{
arma_rng::set_seed_random();
status_kmeans = gmm_model.learn(uni_features, N_cent, eucl_dist, random_subset, km_iter, 0, var_floor, print_mode); //Only Kmeans
bg_model = gmm_model;
rep_km++;
}
cout <<"K-means was repeated " << rep_km << endl;
means = gmm_model.means;
//Saving statistics
std::stringstream ss_means;
ss_means << "./run"<< run <<"/visual_vocabulary/means_Ng" << N_cent << "_dim" <<dim << "_all_sc" << ".dat";
means.save( ss_means.str(), raw_ascii );
}
arma_warn_unused
inline
icube
randi(const uword n_rows, const uword n_cols, const uword n_slices, const distr_param& param = distr_param())
{
arma_extra_debug_sigprint();
return randi<icube>(n_rows, n_cols, n_slices, param);
}
arma_warn_unused
inline
cube_type
randi(const uword n_rows, const uword n_cols, const uword n_slices, const distr_param& param = distr_param(), const typename arma_Cube_only<cube_type>::result* junk = 0)
{
arma_extra_debug_sigprint();
arma_ignore(junk);
typedef typename cube_type::elem_type eT;
cube_type out(n_rows, n_cols, n_slices);
int a;
int b;
if(param.state == 0)
{
a = 0;
b = arma_rng::randi<eT>::max_val();
}
else
if(param.state == 1)
{
a = param.a_int;
b = param.b_int;
}
else
{
a = int(param.a_double);
b = int(param.b_double);
}
arma_debug_check( (a > b), "randi(): incorrect distribution parameters: a must be less than b" );
arma_rng::randi<eT>::fill(out.memptr(), out.n_elem, a, b);
return out;
}
