mirror of
https://github.com/ruby-opencv/ruby-opencv
synced 2023-03-27 23:22:12 -04:00
159 lines
5.6 KiB
C++
159 lines
5.6 KiB
C++
/************************************************************
|
|
|
|
cvhaarclassifercascade.cpp -
|
|
|
|
$Author: lsxi $
|
|
|
|
Copyright (C) 2005-2007 Masakazu Yonekura
|
|
|
|
************************************************************/
|
|
#include "cvhaarclassifiercascade.h"
|
|
/*
|
|
* Document-class: OpenCV::CvHaarClassifierCascade
|
|
*
|
|
* CvHaarClassifierCascade object is "fast-object-detector".
|
|
* This detector can discover object (e.g. human's face) from image.
|
|
*
|
|
* Find face-area from picture "lena"...
|
|
* link:../images/face_detect_from_lena.jpg
|
|
*/
|
|
__NAMESPACE_BEGIN_OPENCV
|
|
__NAMESPACE_BEGIN_CVHAARCLASSIFERCASCADE
|
|
|
|
VALUE rb_klass;
|
|
|
|
VALUE
|
|
rb_class()
|
|
{
|
|
return rb_klass;
|
|
}
|
|
|
|
void define_ruby_class()
|
|
{
|
|
if (rb_klass)
|
|
return;
|
|
/*
|
|
* opencv = rb_define_module("OpenCV");
|
|
*
|
|
* note: this comment is used by rdoc.
|
|
*/
|
|
VALUE opencv = rb_module_opencv();
|
|
|
|
rb_klass = rb_define_class_under(opencv, "CvHaarClassifierCascade", rb_cObject);
|
|
rb_define_alloc_func(rb_klass, rb_allocate);
|
|
rb_define_singleton_method(rb_klass, "load", RUBY_METHOD_FUNC(rb_load), 1);
|
|
rb_define_method(rb_klass, "detect_objects", RUBY_METHOD_FUNC(rb_detect_objects), -1);
|
|
}
|
|
|
|
VALUE
|
|
rb_allocate(VALUE klass)
|
|
{
|
|
return OPENCV_OBJECT(klass, 0);
|
|
}
|
|
|
|
VALUE
|
|
cvhaarclassifiercascade_free(void* ptr)
|
|
{
|
|
if (ptr) {
|
|
CvHaarClassifierCascade* cascade = (CvHaarClassifierCascade*)ptr;
|
|
cvReleaseHaarClassifierCascade(&cascade);
|
|
}
|
|
}
|
|
|
|
/*
|
|
* call-seq:
|
|
* CvHaarClassiferCascade.load(<i>path</i>) -> object-detector
|
|
*
|
|
* Load trained cascade of haar classifers from file.
|
|
* Object detection classifiers are stored in XML or YAML files.
|
|
* sample of object detection classifier files is included by OpenCV.
|
|
*
|
|
* You can found these at
|
|
* C:\Program Files\OpenCV\data\haarcascades\*.xml (Windows, default install path)
|
|
*
|
|
* e.g. you want to try to detect human's face.
|
|
* detector = CvHaarClassiferCascade.load("haarcascade_frontalface_alt.xml")
|
|
*/
|
|
VALUE
|
|
rb_load(VALUE klass, VALUE path)
|
|
{
|
|
CvHaarClassifierCascade *cascade = (CvHaarClassifierCascade*)cvLoad(StringValueCStr(path), 0, 0, 0);
|
|
if(!CV_IS_HAAR_CLASSIFIER(cascade))
|
|
rb_raise(rb_eArgError, "invalid format haar classifier cascade file.");
|
|
return Data_Wrap_Struct(klass, 0, cvhaarclassifiercascade_free, cascade);
|
|
}
|
|
|
|
/*
|
|
* 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);
|
|
|
|
if (!rb_obj_is_kind_of(image, cCvMat::rb_class()))
|
|
raise_typeerror(image, cCvMat::rb_class());
|
|
|
|
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"));
|
|
}
|
|
|
|
CvSeq *seq = cvHaarDetectObjects(CVMAT(image), CVHAARCLASSIFIERCASCADE(self), CVMEMSTORAGE(storage_val),
|
|
scale_factor, min_neighbors, flags, min_size, max_size);
|
|
VALUE 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));
|
|
}
|
|
return result;
|
|
}
|
|
|
|
__NAMESPACE_END_CVHAARCLASSIFERCASCADE
|
|
__NAMESPACE_END_OPENCV
|