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https://github.com/ruby-opencv/ruby-opencv
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08a9655dc1
Added IplImage#smoothness that returns :smooth, :messy, or :blank. Rake compile will now compile the C extension code.
200 lines
4 KiB
C++
200 lines
4 KiB
C++
/************************************************************
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cvhistogram.cpp -
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$Author: lsxi $
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Copyright (C) 2005-2008 Masakazu Yonekura
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************************************************************/
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#include "cvhistogram.h"
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/*
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* Document-class: OpenCV::CvHistogram
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*
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* Muti-dimensional histogram.
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*/
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__NAMESPACE_BEGIN_OPENCV
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__NAMESPACE_BEGIN_CVHISTOGRAM
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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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define_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, "CvHistogram", rb_cObject);
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rb_define_method(rb_klass, "is_uniform?", RUBY_METHOD_FUNC(rb_is_uniform), 0);
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rb_define_method(rb_klass, "is_sparse?", RUBY_METHOD_FUNC(rb_is_sparse), 0);
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rb_define_method(rb_klass, "has_range?", RUBY_METHOD_FUNC(rb_has_range), 0);
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rb_define_method(rb_klass, "dims", RUBY_METHOD_FUNC(rb_dims), 0);
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rb_define_method(rb_klass, "normalize", RUBY_METHOD_FUNC(rb_normalize), 1);
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rb_define_method(rb_klass, "normalize!", RUBY_METHOD_FUNC(rb_normalize_bang), 1);
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rb_define_method(rb_klass, "thresh", RUBY_METHOD_FUNC(rb_thresh), 1);
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rb_define_alias(rb_klass, "threshold", "thresh");
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rb_define_method(rb_klass, "thresh!", RUBY_METHOD_FUNC(rb_thresh_bang), 1);
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rb_define_alias(rb_klass, "threshold!", "thresh!");
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}
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VALUE
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rb_allocate(VALUE klass)
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{
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// not yet
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return Qnil;
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}
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/*
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* call-seq:
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* is_uniform? -> true or false
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*
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*/
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VALUE
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rb_is_uniform(VALUE self)
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{
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return CV_IS_UNIFORM_HIST(CVHISTOGRAM(self)) ? Qtrue : Qfalse;
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}
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/*
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* call-seq:
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* is_sparse? -> true or false
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*
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*/
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VALUE
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rb_is_sparse(VALUE self)
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{
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return CV_IS_SPARSE_HIST(CVHISTOGRAM(self)) ? Qtrue : Qfalse;
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}
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/*
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* call-seq:
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* has_range? -> true or false
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*/
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VALUE
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rb_has_range(VALUE self)
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{
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return CV_HIST_HAS_RANGES(CVHISTOGRAM(self)) ? Qtrue : Qfalse;
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}
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/*
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* call-seq:
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* dims -> [int[,int...]]
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*/
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VALUE
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rb_dims(VALUE self)
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{
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int size[CV_MAX_DIM];
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int dims = cvGetDims(CVHISTOGRAM(self)->bins, size);
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VALUE result = rb_ary_new2(dims);
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for(int i = 0; i < dims; i++){
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rb_ary_store(result, i, INT2FIX(size[i]));
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}
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return result;
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}
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/*
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* call-seq:
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* bins -> cvmatnd or cvsparsemat
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*/
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VALUE
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rb_bins(VALUE self)
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{
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CvHistogram *hist = CVHISTOGRAM(self);
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return REFER_OBJECT(CV_IS_SPARSE_HIST(hist) ? cCvSparseMat::rb_class() : cCvMatND::rb_class(), hist->bins, self);
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}
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/*
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* call-seq:
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* copy -> cvhist
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*
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* Clone histogram.
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*/
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VALUE
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rb_copy(VALUE self)
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{
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VALUE dest = 0;
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CvHistogram *hist = CVHISTOGRAM(dest);
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cvCopyHist(CVHISTOGRAM(self), &hist);
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return dest;
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}
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/*
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* call-seq:
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* clear!
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*
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* Sets all histogram bins to 0 in case of dense histogram and removes all histogram bins in case of sparse array.
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*/
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VALUE
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rb_clear_bang(VALUE self)
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{
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cvClearHist(CVHISTOGRAM(self));
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return self;
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}
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/*
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* call-seq:
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* normalize(<i>factor</i>) -> cvhist
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*
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* Return normalized the histogram bins by scaling them, such that the sum of the bins becomes equal to <i>factor</i>.
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*/
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VALUE
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rb_normalize(VALUE self, VALUE factor)
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{
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return rb_normalize_bang(rb_copy(self), factor);
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}
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/*
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* call-seq:
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* normalize!(<i>factor</i>) -> self
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*
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* normalizes the histogram bins by scaling them, such that the sum of the bins becomes equal to <i>factor</i>.
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*/
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VALUE
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rb_normalize_bang(VALUE self, VALUE factor)
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{
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cvNormalizeHist(CVHISTOGRAM(self), NUM2DBL(factor));
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return self;
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}
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/*
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* call-seq:
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* thresh(<i>factor</i>) -> cvhist
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*
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* Return cleared histogram bins that are below the specified threshold.
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*/
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VALUE
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rb_thresh(VALUE self, VALUE factor)
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{
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return rb_thresh_bang(rb_copy(self), factor);
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}
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/*
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* call-seq:
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* thresh!(<i>factor</i>) -> self
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*
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* Cleares histogram bins that are below the specified threshold.
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*/
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VALUE
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rb_thresh_bang(VALUE self, VALUE factor)
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{
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cvThreshHist(CVHISTOGRAM(self), NUM2DBL(factor));
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return self;
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}
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__NAMESPACE_END_CVHISTOGRAM
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__NAMESPACE_END_OPENCV
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