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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
View file

@ -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();
}
}
}

2
ext/opencv/dnn.hpp
View file

@ -6,7 +6,7 @@
*/
namespace rubyopencv {
namespace Dnn {
void init(VALUE opencv);
void init();
}
}

20
ext/opencv/dnn_layer.cpp
View file

@ -3,6 +3,9 @@
#include "opencv.hpp"
#include "error.hpp"
/*
* Document-class: Cv::Dnn::Layer
*/
namespace rubyopencv {
namespace Dnn {
namespace Layer {
@ -39,18 +42,31 @@ namespace rubyopencv {
return self;
}
/*
* Returns the layer name
* @overload name
* @return [String] Layer name
*/
VALUE rb_name(VALUE self) {
cv::dnn::Layer* selfptr = obj2layer(self);
return rb_str_new_cstr(selfptr->name.c_str());
}
/*
* Returns the layer type
*
* @overload type
* @return [String] Layer type
*/
VALUE rb_type(VALUE self) {
cv::dnn::Layer* selfptr = obj2layer(self);
return rb_str_new_cstr(selfptr->type.c_str());
}
void init(VALUE rb_module) {
rb_klass = rb_define_class_under(rb_module, "Layer", rb_cData);
void init() {
VALUE opencv = rb_define_module("Cv");
VALUE dnn = rb_define_module_under(opencv, "Dnn");
rb_klass = rb_define_class_under(dnn, "Layer", rb_cData);
rb_define_alloc_func(rb_klass, rb_allocate);
rb_define_private_method(rb_klass, "initialize", RUBY_METHOD_FUNC(rb_initialize), 0);

2
ext/opencv/dnn_layer.hpp
View file

@ -7,7 +7,7 @@
namespace rubyopencv {
namespace Dnn {
namespace Layer {
void init(VALUE rb_module);
void init();
VALUE layer2obj(cv::dnn::Layer* ptr);
}
}

144
ext/opencv/dnn_net.cpp
View file

@ -5,6 +5,9 @@
#include "error.hpp"
#include "dnn_layer.hpp"
/*
* Document-class: Cv::Dnn::Net
*/
namespace rubyopencv {
namespace Dnn {
namespace Net {
@ -51,6 +54,19 @@ namespace rubyopencv {
return dataptr;
}
/*
* Creates or reads a deep learning network
*
* @overload new(model = nil, config = nil, framework = nil)
* @param model [String] Binary file contains trained weights
* @param config [String] Text file contains network configuration
* @param framework [String] Explicit framework name tag to determine a format
* @return [Net] Network object
* @opencv_func cv::dnn::Net
* @example
* net1 = Dnn::Net.new
* net2 = Dnn::Net.new("bvlc_googlenet.caffemodel", "bvlc_googlenet.prototxt")
*/
VALUE rb_initialize(int argc, VALUE *argv, VALUE self) {
VALUE model, config, framework;
rb_scan_args(argc, argv, "03", &model, &config, &framework);
@ -62,13 +78,16 @@ namespace rubyopencv {
return self;
}
VALUE rb_read_net(int argc, VALUE *argv, VALUE self) {
VALUE model, config, framework;
rb_scan_args(argc, argv, "12", &model, &config, &framework);
return net2obj(rb_read_net_internal(model, config, framework));
}
// void setInput(const Mat &blob, const String& name = "")
/*
* Sets the new input value for the network
*
* @overload input=(blob)
* @param blob [Mat] A blob of CV_32F or CV_8U depth
* @overload input(blob, name = nil)
* @param blob [Mat] A blob of CV_32F or CV_8U depth
* @param name [String] Name of an input layer
* @return [void]
*/
VALUE rb_set_input(int argc, VALUE *argv, VALUE self) {
VALUE blob, name;
rb_scan_args(argc, argv, "11", &blob, &name);
@ -84,7 +103,13 @@ namespace rubyopencv {
return Qnil;
}
// Mat forward(const String& outputName = String())
/*
* Runs forward pass
*
* @overload forward(output_name = nil)
* @param output_name [String] Name of the layer for which output is needed
* @return [Mat] Blob for first output
*/
VALUE rb_forward(int argc, VALUE *argv, VALUE self) {
VALUE output_name;
rb_scan_args(argc, argv, "01", &output_name);
@ -102,12 +127,23 @@ namespace rubyopencv {
return Mat::rb_clone(Mat::mat2obj(m));
}
// bool empty() const
/*
* Returns whether or not the network is empty
*
* @overload empty?
* @return [Boolean] Whether or not the network is empty
*/
VALUE rb_empty(VALUE self) {
cv::dnn::Net* selfptr = obj2net(self);
return selfptr->empty() ? Qtrue : Qfalse;
}
/*
* Returns an array of layers loaded in this model
*
* @overload layers
* @return [Array<Layer>] Loaded layers
*/
VALUE rb_get_layers(VALUE self) {
cv::dnn::Net* selfptr = obj2net(self);
@ -123,25 +159,68 @@ namespace rubyopencv {
return layers;
}
/*
* Enables or disables layer fusion in the network
*
* @overload fusion=(fusion)
* @param fusion [Boolean] Whether or not fusion should be enabled
* @return [Net] The current network
*/
VALUE rb_enable_fusion(VALUE self, VALUE fusion) {
cv::dnn::Net* selfptr = obj2net(self);
selfptr->enableFusion(RTEST(fusion) ? true : false);
return self;
}
/*
* Ask network to use specific computation backend where it supported
*
* @overload preferable_backend=(backend_id)
* @param backend_id [Integer] The preferable backend identifier
* @return [Net] The current network
*/
VALUE rb_set_preferable_backend(VALUE self, VALUE backend_id) {
cv::dnn::Net* selfptr = obj2net(self);
selfptr->setPreferableBackend(NUM2INT(backend_id));
return self;
}
/*
* Ask network to make computations on specific target device
*
* @overload preferable_target=(target_id)
* @param target_id [Integer] The preferable target identifier
* @return [Net] The current network
*/
VALUE rb_set_preferable_target(VALUE self, VALUE target_id) {
cv::dnn::Net* selfptr = obj2net(self);
selfptr->setPreferableTarget(NUM2INT(target_id));
return self;
}
// Net readNetFromCaffe(const String &prototxt, const String &caffeModel = String());
/*
* Read deep learning network represented in one of the supported formats.
*
* @overload read_net(model = nil, config = nil, framework = nil)
* @param model [String] Binary file contains trained weights
* @param config [String] Text file contains network configuration
* @param framework [String] Explicit framework name tag to determine a format
* @return [Net] Network object
*/
VALUE rb_read_net(int argc, VALUE *argv, VALUE self) {
VALUE model, config, framework;
rb_scan_args(argc, argv, "12", &model, &config, &framework);
return net2obj(rb_read_net_internal(model, config, framework));
}
/*
* Reads a network model stored in Caffe framework's format
*
* @overload read_net_from_caffe(prototxt, caffe_model)
* @param prototxt [String] Path to the .prototxt file with text description of the network architecture
* @param caffe_model [String] Path to the .caffemodel file with learned network
* @return [Net] Network object
*/
VALUE rb_read_net_from_caffe(VALUE self, VALUE prototxt, VALUE caffe_model) {
cv::dnn::Net *net = NULL;
@ -155,12 +234,19 @@ namespace rubyopencv {
return net2obj(net);
}
// Net readNetFromTorch(const String &model, bool isBinary)
VALUE rb_read_net_from_tensorflow(VALUE self, VALUE model) {
/*
* Reads a network model stored in TensorFlow framework's format
*
* @overload read_net_from_tensorflow(model, config)
* @param model [String] Path to the .pb file with binary protobuf description of the network architecture
* @param config [String] Path to the .pbtxt file that contains text graph definition in protobuf format
* @return [Net] Network object
*/
VALUE rb_read_net_from_tensorflow(VALUE self, VALUE model, VALUE config) {
cv::dnn::Net *net = NULL;
try {
net = new cv::dnn::Net(cv::dnn::readNetFromTensorflow(StringValueCStr(model)));
net = new cv::dnn::Net(cv::dnn::readNetFromTensorflow(StringValueCStr(model), StringValueCStr(config)));
} catch(cv::Exception& e) {
delete net;
Error::raise(e);
@ -169,12 +255,21 @@ namespace rubyopencv {
return net2obj(net);
}
// Net readNetFromTorch(const String &model, bool isBinary)
VALUE rb_read_net_from_torch(VALUE self, VALUE model) {
/*
* Reads a network model stored in Torch7 framework's format
*
* @overload read_net_from_torch(model, binary = true)
* @param model [String] Path to the file, dumped from Torch by using torch.save() function
* @param binary [Boolean] Specifies whether the network was serialized in ascii mode or binary
* @return [Net] Network object
*/
VALUE rb_read_net_from_torch(int argc, VALUE *argv, VALUE self) {
VALUE model, binary;
rb_scan_args(argc, argv, "11", &model, &binary);
cv::dnn::Net *net = NULL;
try {
net = new cv::dnn::Net(cv::dnn::readNetFromTorch(StringValueCStr(model)));
net = new cv::dnn::Net(cv::dnn::readNetFromTorch(StringValueCStr(model), RTEST_DEFAULT(binary, true)));
} catch(cv::Exception& e) {
delete net;
Error::raise(e);
@ -183,7 +278,14 @@ namespace rubyopencv {
return net2obj(net);
}
// Net readNetFromDarknet(const String &cfgFile, const String &darknetModel /*= String()*/)
/*
* Reads a network model stored in Darknet model files
*
* @overload read_net_from_darknet(cfg_file, darknet_model)
* @param cfg_file [String] Path to the .cfg file with text description of the network architecture
* @param darknet_model [String] Path to the .weights file with learned network
* @return [Net] Network object
*/
VALUE rb_read_net_from_darknet(VALUE self, VALUE cfg_file, VALUE darknet_model) {
cv::dnn::Net *net = NULL;
@ -197,11 +299,13 @@ namespace rubyopencv {
return net2obj(net);
}
void init(VALUE rb_module) {
rb_klass = rb_define_class_under(rb_module, "Net", rb_cData);
void init() {
VALUE opencv = rb_define_module("Cv");
VALUE dnn = rb_define_module_under(opencv, "Dnn");
rb_klass = rb_define_class_under(dnn, "Net", rb_cData);
rb_define_alloc_func(rb_klass, rb_allocate);
rb_define_private_method(rb_klass, "initialize", RUBY_METHOD_FUNC(rb_initialize), -1);
rb_define_method(rb_klass, "initialize", RUBY_METHOD_FUNC(rb_initialize), -1);
rb_define_method(rb_klass, "input=", RUBY_METHOD_FUNC(rb_set_input), -1);
rb_define_alias(rb_klass, "input", "input=");

4
ext/opencv/dnn_net.hpp
View file

@ -7,11 +7,11 @@
namespace rubyopencv {
namespace Dnn {
namespace Net {
void init(VALUE rb_module);
void init();
VALUE net2obj(cv::dnn::Net* ptr);
VALUE rb_read_net(int argc, VALUE *argv, VALUE self);
VALUE rb_read_net_from_caffe(VALUE self, VALUE prototxt, VALUE caffe_model);
VALUE rb_read_net_from_tensorflow(VALUE self, VALUE model);
VALUE rb_read_net_from_tensorflow(VALUE self, VALUE model, VALUE config);
VALUE rb_read_net_from_torch(VALUE self, VALUE model);
VALUE rb_read_net_from_darknet(VALUE self, VALUE cfg_file, VALUE darknet_model);
}

2
ext/opencv/mat.cpp
View file

@ -235,7 +235,7 @@ namespace rubyopencv {
/*
* Loads an image from a file.
*
* @overload imread(filename, flags)
* @overload imread(filename, flags = IMREAD_UNCHANGED)
* @param filename [String] Name of file to be loaded.
* @param flags [Integer] Flags specifying the color type of a loaded image:
* - CV_LOAD_IMAGE_ANYDEPTH - If set, return 16-bit/32-bit image when the input has the corresponding depth, otherwise convert it to 8-bit.

2
ext/opencv/opencv.cpp
View file

@ -93,7 +93,7 @@ namespace rubyopencv {
Size::init();
Scalar::init();
CascadeClassifier::init();
Dnn::init(rb_module);
Dnn::init();
VideoCapture::init();
Window::init();
Trackbar::init();