CvSeq*
create_seq(int seq_flags, size_t header_size, VALUE storage_value)
{
VALUE klass = Qnil;
int eltype = seq_flags & CV_SEQ_ELTYPE_MASK;
storage_value = CHECK_CVMEMSTORAGE(storage_value);
if (!eltype2class(eltype, &klass)) {
seq_flags = CV_SEQ_ELTYPE_POINT | CV_SEQ_KIND_GENERIC;
}
int mat_type = CV_MAT_TYPE(seq_flags);
size_t elem_size = (size_t)(CV_ELEM_SIZE(mat_type));
CvSeq* seq = NULL;
try {
seq = cvCreateSeq(seq_flags, header_size, elem_size, CVMEMSTORAGE(storage_value));
}
catch (cv::Exception& e) {
raise_cverror(e);
}
register_elem_class(seq, klass);
register_root_object(seq, storage_value);
return seq;
}
/*
* call-seq:
* contour(<i>[criteria = 0]</i>) -> cvcontour
*
* Restores the contour from its binary tree representation.
* The parameter criteria determines the accuracy and/or the number of tree levels
* used for reconstruction, so it is possible to build approximated contour.
*/
VALUE
rb_contour(int argc, VALUE *argv, VALUE self)
{
VALUE criteria, storage;
rb_scan_args(argc, argv, "01", &criteria);
CvSeq *contour = cvContourFromContourTree(CVCONTOURTREE(self), CVMEMSTORAGE(storage), VALUE_TO_CVTERMCRITERIA(criteria));
return cCvSeq::new_sequence(cCvContour::rb_class(), contour, cCvPoint::rb_class(), storage);
}
VALUE
new_object()
{
VALUE storage = cCvMemStorage::new_object();
CvSeq *seq = NULL;
try {
seq = cvCreateSeq(CV_SEQ_CHAIN_CONTOUR, sizeof(CvChain), sizeof(char), CVMEMSTORAGE(storage));
}
catch (cv::Exception& e) {
raise_cverror(e);
}
return cCvSeq::new_sequence(cCvChain::rb_class(), seq, T_FIXNUM, storage);
}
/*
* call-seq:
* CvSeq.new(type[,storage])
*
* Return a new CvSeq. <i>type</i> should be following classes.
*
* * CvIndex
* * CvPoint
*/
VALUE
rb_initialize(int argc, VALUE *argv, VALUE self)
{
VALUE klass, storage_value;
rb_scan_args(argc, argv, "11", &klass, &storage_value);
if (!rb_obj_is_kind_of(klass, rb_cClass))
raise_typeerror(klass, rb_cClass);
int type = 0, size = 0;
if (klass == rb_cFixnum) {
type = CV_SEQ_ELTYPE_INDEX;
size = sizeof(int);
}
else if (klass == cCvPoint::rb_class()) {
type = CV_SEQ_ELTYPE_POINT;
size = sizeof(CvPoint);
}
else if (klass == cCvPoint2D32f::rb_class()) {
type = CV_SEQ_ELTYPE_POINT;
size = sizeof(CvPoint2D32f);
}
else if (klass == cCvPoint3D32f::rb_class()) {
type = CV_SEQ_ELTYPE_POINT3D;
size = sizeof(CvPoint3D32f);
}
else
rb_raise(rb_eArgError, "unsupport %s class for sequence-block.", rb_class2name(klass));
CvSeq* seq = NULL;
if (NIL_P(storage_value)) {
storage_value = cCvMemStorage::new_object(0);
}
else {
storage_value = CHECK_CVMEMSTORAGE(storage_value);
}
try {
seq = cvCreateSeq(type, sizeof(CvSeq), size, CVMEMSTORAGE(storage_value));
}
catch (cv::Exception& e) {
raise_cverror(e);
}
DATA_PTR(self) = seq;
register_elem_class(seq, klass);
register_root_object(seq, storage_value);
return self;
}
/*
* Creates hierarchical representation of contour
* @overload create_tree(threshold = 0.0)
* @param threshold [Number] If <= 0, the method creates full binary tree representation.
* If > 0, the method creates representation with the precision threshold.
* @return [CvContourTree] Hierarchical representation of the contour
* @opencv_func cvCreateContourTree
*/
VALUE
rb_create_tree(int argc, VALUE *argv, VALUE self)
{
VALUE threshold, storage;
rb_scan_args(argc, argv, "01", &threshold);
storage = cCvMemStorage::new_object();
CvContourTree *tree = NULL;
try {
tree = cvCreateContourTree(CVSEQ(self), CVMEMSTORAGE(storage), IF_DBL(threshold, 0.0));
}
catch (cv::Exception& e) {
raise_cverror(e);
}
return cCvSeq::new_sequence(cCvContourTree::rb_class(), (CvSeq*)tree, cCvPoint::rb_class(), storage);
}
/*
* Approximates polygonal curves with desired precision
* @overload approx_poly(options)
* @param options [Hash] Parameters
* @option options [Symbol] :method Approximation method (default :dp)
* * :dp - Douglas-Peucker algorithm.
* @option options [Number] :accuracy Parameter specifying the approximation accuracy.
* This is the maximum distance between the original curve and its approximation.
* @option options [Boolean] :recursive Recursion flag. If true, the function approximates
* all the contours accessible from curve by h_next and v_next links.
* @return [CvContour] Result of the approximation
* @return [nil] Approximation faied
* @opencv_func cvApproxPoly
*/
VALUE
rb_approx_poly(int argc, VALUE *argv, VALUE self)
{
VALUE approx_poly_option;
rb_scan_args(argc, argv, "01", &approx_poly_option);
approx_poly_option = APPROX_POLY_OPTION(approx_poly_option);
VALUE storage = cCvMemStorage::new_object();
CvSeq *contour = cvApproxPoly(CVCONTOUR(self), sizeof(CvContour), CVMEMSTORAGE(storage),
APPROX_POLY_METHOD(approx_poly_option),
APPROX_POLY_ACCURACY(approx_poly_option),
APPROX_POLY_RECURSIVE(approx_poly_option));
if (contour && contour->total > 0) {
return cCvSeq::new_sequence(cCvContour::rb_class(), contour, cCvPoint::rb_class(), storage);
}
return Qnil;
}
CvSeq*
create_seq(int seq_flags, size_t header_size, VALUE storage_value)
{
VALUE klass = Qnil;
int eltype = seq_flags & CV_SEQ_ELTYPE_MASK;
storage_value = CHECK_CVMEMSTORAGE(storage_value);
switch (eltype) {
case CV_SEQ_ELTYPE_POINT:
klass = cCvPoint::rb_class();
break;
case CV_32FC2:
klass = cCvPoint2D32f::rb_class();
break;
case CV_SEQ_ELTYPE_POINT3D:
klass = cCvPoint3D32f::rb_class();
break;
case CV_SEQ_ELTYPE_CODE:
case CV_SEQ_ELTYPE_INDEX:
klass = rb_cFixnum;
break;
case CV_SEQ_ELTYPE_PPOINT: // or CV_SEQ_ELTYPE_PTR:
// Not supported
rb_raise(rb_eArgError, "seq_flags %d is not supported.", eltype);
break;
default:
seq_flags = CV_SEQ_ELTYPE_POINT | CV_SEQ_KIND_GENERIC;
klass = cCvPoint::rb_class();
break;
}
int mat_type = CV_MAT_TYPE(seq_flags);
size_t elem_size = (size_t)(CV_ELEM_SIZE(mat_type));
CvSeq* seq = NULL;
try {
seq = cvCreateSeq(seq_flags, header_size, elem_size, CVMEMSTORAGE(storage_value));
}
catch (cv::Exception& e) {
raise_cverror(e);
}
register_elem_class(seq, klass);
register_root_object(seq, storage_value);
return seq;
}
/*
* 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:
* approx_chains(<i>[approx_chain_option]</i>) -> cvcontour
*
* Approximates Freeman chain(s) with polygonal curve.
* <i>approx_chain_option</i> should be Hash include these keys.
* :method - Approximation method.
* :approx_none - translate all the points from the chain code into points;
* :approx_simple(default) - compress horizontal, vertical, and diagonal segments, that is,
* the function leaves only their ending points.
* :approx_tc89_l1
* :approx_tc89_kcos - apply one of the flavors of Teh-Chin chain approximation algorithm.
* If set the difference between the current pixel and seed pixel is considered,
* otherwise difference between neighbor pixels is considered (the range is floating).
* :parameter - Method parameter (not used now).
* :minimal_perimeter (default 0)
* Approximates only those contours whose perimeters are not less than minimal_perimeter. Other chains are removed from the resulting structure.
* :recursive (default false)
* If not nil or false, the function approximates all chains that access can be obtained to
* from self by h_next or v_next links. If 0, the single chain is approximated.
*
*/
VALUE
rb_approx_chains(int argc, VALUE *argv, VALUE self)
{
VALUE approx_chain_option;
rb_scan_args(argc, argv, "01", &approx_chain_option);
approx_chain_option = APPROX_CHAIN_OPTION(approx_chain_option);
VALUE storage = cCvMemStorage::new_object();
CvSeq *seq = cvApproxChains(CVSEQ(self), CVMEMSTORAGE(storage),
APPROX_CHAIN_METHOD(approx_chain_option),
APPROX_CHAIN_PARAMETER(approx_chain_option),
APPROX_CHAIN_MINIMAL_PERIMETER(approx_chain_option),
APPROX_CHAIN_RECURSIVE(approx_chain_option));
if (seq && seq->total > 0) {
return cCvSeq::new_sequence(cCvChain::rb_class(), seq, cCvPoint::rb_class(), storage);
}
return Qnil;
}
/*
* call-seq:
* CvSeq.new(<i>type[,storage]</i>)
*
* Return a new CvSeq. <i>type</i> should be following classes.
*
* * CvIndex
* * CvPoint
*/
VALUE
rb_initialize(int argc, VALUE *argv, VALUE self)
{
VALUE klass, storage_value;
CvMemStorage *storage;
if (rb_scan_args(argc, argv, "11", &klass, &storage_value) > 1) {
storage_value = CHECK_CVMEMSTORAGE(storage_value);
storage = CVMEMSTORAGE(storage_value);
}
else
storage = cvCreateMemStorage(0);
if(!rb_obj_is_kind_of(klass, rb_cClass))
rb_raise(rb_eTypeError, "argument 1 (sequence-block class) should be %s.", rb_class2name(rb_cClass));
int type = 0, size = 0;
if (klass == cCvIndex::rb_class()) {
type = CV_SEQ_ELTYPE_INDEX;
size = sizeof(CvIndex);
}
else if (klass == cCvPoint::rb_class()) {
type = CV_SEQ_ELTYPE_POINT;
size = sizeof(CvPoint);
}
else if (klass == cCvPoint2D32f::rb_class()) {
type = CV_SEQ_ELTYPE_POINT;
size = sizeof(CvPoint2D32f);
}
else if (klass == cCvPoint3D32f::rb_class()) {
type = CV_SEQ_ELTYPE_POINT3D;
size = sizeof(CvPoint3D32f);
}
// todo: more various class will be support.
if (!size)
rb_raise(rb_eTypeError, "unsupport %s class for sequence-block.", rb_class2name(klass));
CvSeq* seq = cvCreateSeq(type, sizeof(CvSeq), size, storage);
DATA_PTR(self) = seq;
resist_class_information_of_sequence(seq, klass);
return self;
}
VALUE
rb_initialize(int argc, VALUE *argv, VALUE self)
{
CvMemStorage *storage;
VALUE storage_value;
if (rb_scan_args(argc, argv, "01", &storage_value) > 0) {
storage_value = CHECK_CVMEMSTORAGE(storage_value);
storage = CVMEMSTORAGE(storage_value);
}
else
storage = rb_cvCreateMemStorage(0);
try {
DATA_PTR(self) = (CvChain*)cvCreateSeq(CV_SEQ_ELTYPE_CODE, sizeof(CvChain),
sizeof(char), storage);
}
catch (cv::Exception& e) {
raise_cverror(e);
}
return self;
}
/*
* 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;
}
/*
* Constructor
* @overload new(storage = nil)
* @param [CvMemStorage] storage Sequence location
* @return [CvContour] self
* @opencv_func cvCreateSeq
*/
VALUE
rb_initialize(int argc, VALUE *argv, VALUE self)
{
VALUE storage;
rb_scan_args(argc, argv, "01", &storage);
if (NIL_P(storage))
storage = cCvMemStorage::new_object(0);
else
storage = CHECK_CVMEMSTORAGE(storage);
try {
DATA_PTR(self) = (CvContour*)cvCreateSeq(CV_SEQ_ELTYPE_POINT, sizeof(CvContour),
sizeof(CvPoint), CVMEMSTORAGE(storage));
}
catch (cv::Exception& e) {
raise_cverror(e);
}
cCvSeq::register_elem_class(self, cCvPoint::rb_class());
register_root_object(CVSEQ(self), storage);
return self;
}
