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Added documentation.

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This commit is contained in:
Francois Deschenes 2018-07-27 00:10:39 -07:00
parent 99c8eec7fc
commit ac88971aff
8 changed files with 170 additions and 34 deletions
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28
ext/opencv/dnn.cpp
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@ -8,11 +8,26 @@
#include "dnn_layer.hpp"
#include "error.hpp"
/*
* Document-class: Cv::Dnn
*/
namespace rubyopencv {
namespace Dnn {
VALUE rb_module = Qnil;
// Mat blobFromImage(const Mat& image, double scalefactor=1.0, const Size& size = Size(), const Scalar& mean = Scalar(), bool swapRB=true)
/*
* Creates 4-dimensional blob from image. Optionally resizes and crops image from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.
*
* @overload blob_from_image(image, options = {})
* @param image [Mat] Input image (with 1-, 3- or 4-channels)
* @param options [Hash] Options
* @option options [Number] :scale_factor (1.0) Multiplier for image values
* @option options [Mat] :size Spatial size for output image
* @option options [Scalar] :mean Scalar with mean values which are subtracted from channels – values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swap_rb is true
* @option options [Boolean] :swap_rb (true) Indicates that swap first and last channels in 3-channel image is necessary
* @option options [Boolean] :crop (true) Indicates whether image will be cropped after resize or not
* @return [Mat] 4-dimensional Mat with NCHW dimensions order
*/
VALUE rb_blob_from_image(int argc, VALUE *argv, VALUE self) {
VALUE image, options;
rb_scan_args(argc, argv, "11", &image, &options);
@ -54,19 +69,20 @@ namespace rubyopencv {
return Mat::mat2obj(b);
}
void init(VALUE opencv) {
void init() {
VALUE opencv = rb_define_module("Cv");
rb_module = rb_define_module_under(opencv, "Dnn");
rb_define_singleton_method(rb_module, "blob_from_image", RUBY_METHOD_FUNC(rb_blob_from_image), -1);
rb_define_singleton_method(rb_module, "read_net", RUBY_METHOD_FUNC(Dnn::Net::rb_read_net), -1); // in ext/opencv/dnn_net.cpp
rb_define_singleton_method(rb_module, "read_net_from_caffe", RUBY_METHOD_FUNC(Dnn::Net::rb_read_net_from_caffe), 2); // in ext/opencv/dnn_net.cpp
rb_define_singleton_method(rb_module, "read_net_from_tensorflow", RUBY_METHOD_FUNC(Dnn::Net::rb_read_net_from_tensorflow), 1); // in ext/opencv/dnn_net.cpp
rb_define_singleton_method(rb_module, "read_net_from_torch", RUBY_METHOD_FUNC(Dnn::Net::rb_read_net_from_torch), 1); // in ext/opencv/dnn_net.cpp
rb_define_singleton_method(rb_module, "read_net_from_tensorflow", RUBY_METHOD_FUNC(Dnn::Net::rb_read_net_from_tensorflow), 2); // in ext/opencv/dnn_net.cpp
rb_define_singleton_method(rb_module, "read_net_from_torch", RUBY_METHOD_FUNC(Dnn::Net::rb_read_net_from_torch), -1); // in ext/opencv/dnn_net.cpp
rb_define_singleton_method(rb_module, "read_net_from_darknet", RUBY_METHOD_FUNC(Dnn::Net::rb_read_net_from_darknet), 2); // in ext/opencv/dnn_net.cpp
Dnn::Net::init(rb_module);
Dnn::Layer::init(rb_module);
Dnn::Net::init();
Dnn::Layer::init();
}
}
}