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modified arguments of CvHaarClassifierCascade#detect_objects to set options more easily

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ser1zw 2011-05-21 18:22:27 +09:00
parent ca24f705ac
commit 5266eb051a
6 changed files with 77 additions and 58 deletions
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111
ext/opencv/cvhaarclassifiercascade.cpp
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@ -43,7 +43,6 @@ void define_ruby_class()
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);
rb_define_method(rb_klass, "detect_objects_with_pruning", RUBY_METHOD_FUNC(rb_detect_objects_with_pruning), -1);
}
VALUE
@ -86,74 +85,72 @@ rb_load(VALUE klass, VALUE path)
/*
* call-seq:
* detect_objects(image[,scale_factor = 1.1, min_neighbor = 3, min_size = CvSize.new(0,0)]) -> cvseq(include CvAvgComp object)
* detect_objects(image[,scale_factor = 1.1, min_neighbor = 3, min_size = CvSize.new(0,0)]){|cmp| ... } -> cvseq(include CvAvgComp object)
* 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.
*
* * scale_factor (should be > 1.0)
* The factor by which the search window is scaled between the subsequent scans, for example, 1.1 mean increasing window by 10%.
* * 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.
* * <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
VALUE
rb_detect_objects(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), 0, 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;
}
VALUE image, options;
rb_scan_args(argc, argv, "11", &image, &options);
/*
* 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);
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));
for(int i = 0; i < seq->total; ++i)
rb_yield(REFER_OBJECT(cCvAvgComp::rb_class(), cvGetSeqElem(seq, i), storage_val));
}
return result;
}