/*
* call-seq:
* last -> obj or nil
*
* Return last sequence-block.
*/
VALUE
rb_last(VALUE self)
{
CvSeq *seq = CVSEQ(self);
if(!(seq->total > 0))
return Qnil;
return REFER_OBJECT(seqblock_class(seq), cvGetSeqElem(seq, -1), self);
}
/*
* call-seq:
* [<i>index</i>] -> obj or nil
*
* Return sequence-block at <i>index</i>.
*/
VALUE
rb_aref(VALUE self, VALUE index)
{
CvSeq *seq = CVSEQ(self);
int idx = NUM2INT(index);
if(!(seq->total > 0))
return Qnil;
if (idx >= seq->total)
rb_raise(rb_eIndexError, "index %d out of sequence", idx);
return REFER_OBJECT(seqblock_class(seq), cvGetSeqElem(seq, idx), self);
}
/*
* call-seq:
* each{|obj| ... } -> self
*
* Calls block once for each sequence-block in <i>self</i>,
* passing that sequence-block as a parameter.
* seq = CvSeq.new(CvIndex)
* seq.push(5, 6, 7)
* seq.each{|x| print x, " -- " }
* produces:
* 5 -- 6 -- 7 --
*/
VALUE
rb_each(VALUE self)
{
CvSeq *seq = CVSEQ(self);
if(seq->total > 0){
VALUE klass = seqblock_class(seq);
for(int i = 0; i < seq->total; i++){
rb_yield(REFER_OBJECT(klass, cvGetSeqElem(seq, i), self));
}
}
return self;
}
/*
* call-seq:
* [<i>index</i>] -> obj or nil
*
* Return sequence-block at <i>index</i>.
*/
VALUE
rb_aref(VALUE self, VALUE index)
{
CvSeq *seq = CVSEQ(self);
int idx = NUM2INT(index);
if (seq->total == 0)
return Qnil;
if (idx >= seq->total)
rb_raise(rb_eIndexError, "index %d out of sequence", idx);
VALUE result = Qnil;
try {
if (seqblock_class(seq) == rb_cFixnum)
result = INT2NUM(*CV_GET_SEQ_ELEM(int, seq, idx));
else
result = REFER_OBJECT(seqblock_class(seq), cvGetSeqElem(seq, idx), self);
}
/*
* call-seq:
* detect_objects(image[, options]) -> cvseq(include CvAvgComp object)
* detect_objects(image[, options]){|cmp| ... } -> cvseq(include CvAvgComp object)
*
* Detects objects in the image. This method finds rectangular regions in the
* given image that are likely to contain objects the cascade has been trained
* for and return those regions as a sequence of rectangles.
*
* * <i>option</i> should be Hash include these keys.
* :scale_factor (should be > 1.0)
* The factor by which the search window is scaled between the subsequent scans,
* 1.1 mean increasing window by 10%.
* :storage
* Memory storage to store the resultant sequence of the object candidate rectangles
* :flags
* Mode of operation. Currently the only flag that may be specified is CV_HAAR_DO_CANNY_PRUNING .
* If it is set, the function uses Canny edge detector to reject some image regions that contain
* too few or too much edges and thus can not contain the searched object. The particular threshold
* values are tuned for face detection and in this case the pruning speeds up the processing
* :min_neighbors
* Minimum number (minus 1) of neighbor rectangles that makes up an object.
* All the groups of a smaller number of rectangles than min_neighbors - 1 are rejected.
* If min_neighbors is 0, the function does not any grouping at all and returns all the detected
* candidate rectangles, whitch many be useful if the user wants to apply a customized grouping procedure.
* :min_size
* Minimum window size. By default, it is set to size of samples the classifier has been
* trained on (~20x20 for face detection).
* :max_size
* aximum window size to use. By default, it is set to the size of the image.
*/
VALUE
rb_detect_objects(int argc, VALUE *argv, VALUE self)
{
VALUE image, options;
rb_scan_args(argc, argv, "11", &image, &options);
double scale_factor;
int flags, min_neighbors;
CvSize min_size, max_size;
VALUE storage_val;
if (NIL_P(options)) {
scale_factor = 1.1;
flags = 0;
min_neighbors = 3;
min_size = max_size = cvSize(0, 0);
storage_val = cCvMemStorage::new_object();
}
else {
scale_factor = IF_DBL(LOOKUP_CVMETHOD(options, "scale_factor"), 1.1);
flags = IF_INT(LOOKUP_CVMETHOD(options, "flags"), 0);
min_neighbors = IF_INT(LOOKUP_CVMETHOD(options, "min_neighbors"), 3);
VALUE min_size_val = LOOKUP_CVMETHOD(options, "min_size");
min_size = NIL_P(min_size_val) ? cvSize(0, 0) : VALUE_TO_CVSIZE(min_size_val);
VALUE max_size_val = LOOKUP_CVMETHOD(options, "max_size");
max_size = NIL_P(max_size_val) ? cvSize(0, 0) : VALUE_TO_CVSIZE(max_size_val);
storage_val = CHECK_CVMEMSTORAGE(LOOKUP_CVMETHOD(options, "storage"));
}
VALUE result = Qnil;
try {
IplImage *ipl = IPLIMAGE_WITH_CHECK(image);
CvSeq *seq = cvHaarDetectObjects(ipl, CVHAARCLASSIFIERCASCADE(self), CVMEMSTORAGE(storage_val),
scale_factor, min_neighbors, flags, min_size, max_size);
result = cCvSeq::new_sequence(cCvSeq::rb_class(), seq, cCvAvgComp::rb_class(), storage_val);
if (rb_block_given_p()) {
for(int i = 0; i < seq->total; ++i)
rb_yield(REFER_OBJECT(cCvAvgComp::rb_class(), cvGetSeqElem(seq, i), storage_val));
}
}
catch (cv::Exception& e) {
raise_cverror(e);
}
return result;
}
/*
* call-seq:
* detect_objects_with_pruning(image[,scale_factor = 1.1, min_neighbor = 3, min_size = CvSize.new(0,0)]) -> cvseq(include CvAvgComp object)
* detect_objects_with_pruning(image[,scale_factor = 1.1, min_neighbor = 3, min_size = CvSize.new(0,0)]){|cmp| ... } -> cvseq(include CvAvgComp object)
*
* Almost same to #detect_objects (Return detected objects).
*
* Before scanning to image, Canny edge detector to reject some image regions
* that contain too few or too much edges, and thus can not contain the searched object.
*
* note: The particular threshold values are tuned for face detection.
* And in this case the pruning speeds up the processing.
*/
VALUE
rb_detect_objects_with_pruning(int argc, VALUE *argv, VALUE self)
{
VALUE image, storage, scale_factor, min_neighbors, min_size, result;
rb_scan_args(argc, argv, "14", &image, &storage, &scale_factor, &min_neighbors, &min_size);
if (!rb_obj_is_kind_of(image, cCvMat::rb_class()))
rb_raise(rb_eTypeError, "argument 1(target-image) should be %s.", rb_class2name(cCvMat::rb_class()));
double scale = IF_DBL(scale_factor, 1.1);
if (!(scale > 1.0))
rb_raise(rb_eArgError, "argument 2 (scale factor) must > 1.0.");
storage = CHECK_CVMEMSTORAGE(storage);
CvSeq *seq = cvHaarDetectObjects(CVMAT(image), CVHAARCLASSIFIERCASCADE(self), CVMEMSTORAGE(storage),
scale, IF_INT(min_neighbors, 3), CV_HAAR_DO_CANNY_PRUNING, NIL_P(min_size) ? cvSize(0,0) : VALUE_TO_CVSIZE(min_size));
result = cCvSeq::new_sequence(cCvSeq::rb_class(), seq, cCvAvgComp::rb_class(), storage);
if (rb_block_given_p()) {
for(int i = 0; i < seq->total; i++)
rb_yield(REFER_OBJECT(cCvAvgComp::rb_class(), cvGetSeqElem(seq, i), storage));
}
return result;
}
/*
* call-seq:
* [<i>index</i>] -> obj or nil
*
* Return sequence-block at <i>index</i>.
*/
VALUE
rb_aref(VALUE self, VALUE index)
{
CvSeq *seq = CVSEQ(self);
int idx = NUM2INT(index);
if (seq->total == 0) {
return Qnil;
}
if (idx >= seq->total) {
rb_raise(rb_eIndexError, "index %d out of sequence", idx);
}
VALUE result = Qnil;
try {
VALUE klass = seqblock_class(seq);
if (RTEST(rb_class_inherited_p(klass, rb_cInteger))) {
result = INT2NUM(*CV_GET_SEQ_ELEM(int, seq, idx));
}
else {
result = REFER_OBJECT(klass, cvGetSeqElem(seq, idx), self);
}
}
/*
* call-seq:
* center -> cvpoint2d32f
* Return center point of box as CvPoint2D32f.
*/
VALUE
rb_center(VALUE self)
{
return REFER_OBJECT(cCvPoint2D32f::rb_class(), &CVBOX2D(self)->center, self);
}
/*
* call-seq:
* size -> cvsize2d32f
* Return size of box as CvSize2D32f.
*/
VALUE
rb_size(VALUE self)
{
return REFER_OBJECT(cCvSize2D32f::rb_class(), &CVBOX2D(self)->size, self);
}
/*
* Return position of the feature as CvPoint2D32f.
*
* @overload pt
* @return [CvPoint2D32f] Position of the feature.
*/
VALUE
rb_get_pt(VALUE self)
{
return REFER_OBJECT(cCvPoint2D32f::rb_class(), &CVSURFPOINT(self)->pt, self);
}
/*
* Return optional component boundary
*/
VALUE
rb_contour(VALUE self)
{
return REFER_OBJECT(cCvContour::rb_class(), &CVCONNECTEDCOMP(self)->contour, self);
}
/*
* Return ROI of the component.
*/
VALUE
rb_rect(VALUE self)
{
return REFER_OBJECT(cCvRect::rb_class(), &CVCONNECTEDCOMP(self)->rect, self);
}
/*
* Return average color of the connected component.
*/
VALUE
rb_value(VALUE self)
{
return REFER_OBJECT(cCvScalar::rb_class(), &CVCONNECTEDCOMP(self)->value, self);
}
VALUE
rb_p2(VALUE self)
{
return REFER_OBJECT(cCvPoint::rb_class(), &CVCONTOURTREE(self)->p2, self);
}
