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ruby-opencv/ext/opencv/cvhaarclassifiercascade.cpp

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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