283 lines
6.6 KiB
C++
283 lines
6.6 KiB
C++
/************************************************************
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cvcondensation.cpp -
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$Author: lsxi $
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Copyright (C) 2005-2006 Masakazu Yonekura
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************************************************************/
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#include "cvcondensation.h"
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/*
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* Document-class: OpenCV::CvConDensation
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*
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*/
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__NAMESPACE_BEGIN_OPENCV
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__NAMESPACE_BEGIN_CVCONDENSATION
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VALUE rb_klass;
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VALUE
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rb_class()
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{
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return rb_klass;
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}
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void
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init_ruby_class()
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{
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if (rb_klass)
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return;
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/*
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* opencv = rb_define_module("OpenCV");
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*
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* note: this comment is used by rdoc.
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*/
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VALUE opencv = rb_module_opencv();
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rb_klass = rb_define_class_under(opencv, "CvConDensation", rb_cObject);
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rb_define_method(rb_klass, "dp", RUBY_METHOD_FUNC(rb_dp), 0);
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rb_define_method(rb_klass, "mp", RUBY_METHOD_FUNC(rb_mp), 0);
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rb_define_method(rb_klass, "dynamic_matrix", RUBY_METHOD_FUNC(rb_dynamic_matrix), 0);
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rb_define_method(rb_klass, "confidence", RUBY_METHOD_FUNC(rb_confidence), 0);
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rb_define_method(rb_klass, "cumulative", RUBY_METHOD_FUNC(rb_cumulative), 0);
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rb_define_method(rb_klass, "state", RUBY_METHOD_FUNC(rb_state), 0);
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rb_define_method(rb_klass, "samples_num", RUBY_METHOD_FUNC(rb_samples_num), 0);
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rb_define_method(rb_klass, "init_sample_set", RUBY_METHOD_FUNC(rb_init_sample_set), 2);
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rb_define_method(rb_klass, "update_by_time", RUBY_METHOD_FUNC(rb_update_by_time), 0);
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rb_define_alias(rb_klass, "update", "update_by_time");
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rb_define_method(rb_klass, "each_sample", RUBY_METHOD_FUNC(rb_each_sample), 0);
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rb_define_method(rb_klass, "calculate_confidence", RUBY_METHOD_FUNC(rb_calculate_confidence), 0);
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}
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/*
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* call-seq:
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* dp -> int
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*
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* Return dimension of state vector
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*/
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VALUE
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rb_dp(VALUE self)
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{
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return INT2NUM(CVCONDENSATION(self)->DP);
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}
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/*
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* call-seq:
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* mp -> int
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*
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* Return demension of measurement vector.
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*/
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VALUE
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rb_mp(VALUE self)
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{
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return INT2NUM(CVCONDENSATION(self)->MP);
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}
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/*
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* call-seq:
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* dynamic_matrix -> mat
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*
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* Return matrix of the linear Dynamics system.
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*/
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VALUE
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rb_dynamic_matrix(VALUE self)
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{
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CvConDensation *cd = CVCONDENSATION(self);
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CvMat* mat = NULL;
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try {
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mat = cvInitMatHeader(ALLOC(CvMat), cd->DP, cd->DP, CV_MAKETYPE(CV_32F, 1), cd->DynamMatr);
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}
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catch (cv::Exception& e) {
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raise_cverror(e);
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}
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return DEPEND_OBJECT(cCvMat::rb_class(), mat, self);
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}
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/*
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* call-seq:
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* confidence -> mat
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*
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* Return confidence for each sample.
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*/
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VALUE
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rb_confidence(VALUE self)
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{
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CvConDensation *cd = CVCONDENSATION(self);
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CvMat* mat = NULL;
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try {
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mat = cvInitMatHeader(ALLOC(CvMat), cd->SamplesNum, 1, CV_MAKETYPE(CV_32F, 1), cd->flConfidence);
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}
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catch (cv::Exception& e) {
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raise_cverror(e);
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}
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return DEPEND_OBJECT(cCvMat::rb_class(), mat, self);
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}
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/*
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* call-seq:
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* cumulative -> mat
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*
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* Return cumulative confidence.
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*/
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VALUE
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rb_cumulative(VALUE self)
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{
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CvConDensation *cd = CVCONDENSATION(self);
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CvMat* mat = NULL;
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try {
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mat = cvInitMatHeader(ALLOC(CvMat), cd->SamplesNum, 1, CV_MAKETYPE(CV_32F, 1), cd->flCumulative);
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}
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catch (cv::Exception& e) {
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raise_cverror(e);
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}
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return DEPEND_OBJECT(cCvMat::rb_class(), mat, self);
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}
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/*
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* call-seq:
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* state -> mat
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*
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* Return vector of state
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*/
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VALUE
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rb_state(VALUE self)
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{
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CvConDensation *cd = CVCONDENSATION(self);
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CvMat* mat = NULL;
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try {
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mat = cvInitMatHeader(ALLOC(CvMat), cd->DP, 1, CV_MAKETYPE(CV_32F, 1), cd->State);
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}
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catch (cv::Exception& e) {
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raise_cverror(e);
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}
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return DEPEND_OBJECT(cCvMat::rb_class(), mat, self);
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}
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/*
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* call-seq:
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* samples_num -> int
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*
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* Return number of the samples
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*/
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VALUE
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rb_samples_num(VALUE self)
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{
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return INT2NUM(CVCONDENSATION(self)->SamplesNum);
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}
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/*
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* call-seq:
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* init_sample_set(upper, lower)
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*
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* Initializes sample set for ConDensation algorithm.
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* Fills the samples with values within specified(lower to upper) ranges.
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*/
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VALUE
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rb_init_sample_set(VALUE self, VALUE lower, VALUE upper)
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{
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CvConDensation *cd = CVCONDENSATION(self);
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CvMat *lower_bound = CVMAT_WITH_CHECK(lower), *upper_bound = CVMAT_WITH_CHECK(upper), lb_stub, ub_stub;
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int lower_type = lower_bound->type, upper_type = lower_bound->type;
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try {
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if (lower_type != CV_32FC1 || lower_bound->cols != 1) {
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if (CV_MAT_DEPTH(lower_type) == CV_32F) {
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lower_bound = cvReshape(lower_bound, &lb_stub, 1, lower_bound->rows * lower_bound->cols);
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}
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else {
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lower = cCvMat::new_object(cvSize(lower_bound->rows * lower_bound->cols, 1), CV_MAKETYPE(CV_32S, 1));
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cvConvertScale(lower_bound, CVARR(lower));
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lower_bound = CVMAT(lower);
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}
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}
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if (upper_type != CV_32FC1 || upper_bound->cols != 1) {
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if (CV_MAT_DEPTH(upper_type) == CV_32F) {
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upper_bound = cvReshape(upper_bound, &ub_stub, 1, upper_bound->rows * upper_bound->cols);
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}
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else {
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upper = cCvMat::new_object(cvSize(upper_bound->rows * upper_bound->cols, 1), CV_MAKETYPE(CV_32F, 1));
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cvConvertScale(upper_bound, CVARR(upper));
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upper_bound = CVMAT(upper);
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}
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}
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if (lower_bound->rows != cd->DP || upper_bound->rows != cd->DP) {
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rb_raise(rb_eTypeError, "sample matrix step unmatch.");
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}
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cvConDensInitSampleSet(cd, lower_bound, upper_bound);
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}
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catch (cv::Exception& e) {
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raise_cverror(e);
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}
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return self;
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}
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/*
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* call-seq:
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* update_by_time
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*
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* Estimates subsequent model state.
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*/
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VALUE
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rb_update_by_time(VALUE self)
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{
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try {
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cvConDensUpdateByTime(CVCONDENSATION(self));
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}
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catch (cv::Exception& e) {
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raise_cverror(e);
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}
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return self;
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}
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/*
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* call-seq:
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* each_sample {|mat| ... }
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*
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* Evaluate each sample by given block.
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*/
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VALUE
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rb_each_sample(VALUE self)
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{
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CvConDensation *cd = CVCONDENSATION(self);
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if (rb_block_given_p()) {
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try {
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for (int i = 0; i < cd->SamplesNum; ++i) {
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CvMat* mat = cvInitMatHeader(ALLOC(CvMat), cd->DP, 1, CV_MAKETYPE(CV_32F, 1), cd->flSamples[i]);
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rb_yield(DEPEND_OBJECT(cCvMat::rb_class(), mat, self));
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}
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}
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catch (cv::Exception& e) {
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raise_cverror(e);
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}
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}
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return self;
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}
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/*
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* call-seq:
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* calculate_confidence {|value| ... }
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*
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* Evalute each sample by given block, then return value set to confidence.
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*/
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VALUE
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rb_calculate_confidence(VALUE self)
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{
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VALUE value;
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CvConDensation *cd = CVCONDENSATION(self);
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if (rb_block_given_p()) {
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try {
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for (int i = 0; i < cd->SamplesNum; ++i) {
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CvMat* mat = cvInitMatHeader(ALLOC(CvMat), cd->DP, 1, CV_MAKETYPE(CV_32F, 1), cd->flSamples[i]);
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value = rb_yield(DEPEND_OBJECT(cCvMat::rb_class(), mat, self));
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cd->flConfidence[i] = NUM2DBL(value);
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}
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}
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catch (cv::Exception& e) {
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raise_cverror(e);
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}
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}
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return self;
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}
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__NAMESPACE_END_CVCONDENSATION
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__NAMESPACE_END_OPENCV
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