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Updated to support 3.3.1+.

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This commit is contained in:
Francois Deschenes 2018-07-25 22:20:20 -07:00
parent fe3493f47c
commit 256df73e7b
4 changed files with 33 additions and 18 deletions
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2
README.md
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@ -3,7 +3,7 @@
An OpenCV wrapper for Ruby.
* Web site: <https://github.com/ruby-opencv/ruby-opencv>
* Ruby 2.x and OpenCV 3.2.0 are supported.
* Ruby 2.x and OpenCV 3.3.1 are supported.
## Requirement

27
ext/opencv/dnn.cpp
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@ -48,10 +48,10 @@ namespace rubyopencv {
// Net readNetFromCaffe(const String &prototxt, const String &caffeModel = String());
VALUE rb_read_net_from_caffe(VALUE self, VALUE prototxt, VALUE caffe_model) {
cv::dnn::experimental_dnn_v1::Net *net = NULL;
cv::dnn::Net *net = NULL;
try {
net = new cv::dnn::experimental_dnn_v1::Net(cv::dnn::readNetFromCaffe(StringValueCStr(prototxt), StringValueCStr(caffe_model)));
net = new cv::dnn::Net(cv::dnn::readNetFromCaffe(StringValueCStr(prototxt), StringValueCStr(caffe_model)));
} catch(cv::Exception& e) {
delete net;
Error::raise(e);
@ -62,10 +62,10 @@ namespace rubyopencv {
// Net readNetFromTorch(const String &model, bool isBinary)
VALUE rb_read_net_from_tensorflow(VALUE self, VALUE model) {
cv::dnn::experimental_dnn_v1::Net *net = NULL;
cv::dnn::Net *net = NULL;
try {
net = new cv::dnn::experimental_dnn_v1::Net(cv::dnn::readNetFromTensorflow(StringValueCStr(model)));
net = new cv::dnn::Net(cv::dnn::readNetFromTensorflow(StringValueCStr(model)));
} catch(cv::Exception& e) {
delete net;
Error::raise(e);
@ -76,10 +76,24 @@ namespace rubyopencv {
// Net readNetFromTorch(const String &model, bool isBinary)
VALUE rb_read_net_from_torch(VALUE self, VALUE model) {
cv::dnn::experimental_dnn_v1::Net *net = NULL;
cv::dnn::Net *net = NULL;
try {
net = new cv::dnn::experimental_dnn_v1::Net(cv::dnn::readNetFromTorch(StringValueCStr(model)));
net = new cv::dnn::Net(cv::dnn::readNetFromTorch(StringValueCStr(model)));
} catch(cv::Exception& e) {
delete net;
Error::raise(e);
}
return Dnn::Net::net2obj(net);
}
// Net readNetFromDarknet(const String &cfgFile, const String &darknetModel /*= String()*/)
VALUE rb_read_net_from_darknet(VALUE self, VALUE cfg_file, VALUE darknet_model) {
cv::dnn::Net *net = NULL;
try {
net = new cv::dnn::Net(cv::dnn::readNetFromDarknet(StringValueCStr(cfg_file), StringValueCStr(darknet_model)));
} catch(cv::Exception& e) {
delete net;
Error::raise(e);
@ -97,6 +111,7 @@ namespace rubyopencv {
rb_define_singleton_method(rb_module, "read_net_from_caffe", RUBY_METHOD_FUNC(rb_read_net_from_caffe), 2);
rb_define_singleton_method(rb_module, "read_net_from_tensorflow", RUBY_METHOD_FUNC(rb_read_net_from_tensorflow), 1);
rb_define_singleton_method(rb_module, "read_net_from_torch", RUBY_METHOD_FUNC(rb_read_net_from_torch), 1);
rb_define_singleton_method(rb_module, "read_net_from_darknet", RUBY_METHOD_FUNC(rb_read_net_from_darknet), 2);
Dnn::Net::init(rb_module);
}

20
ext/opencv/dnn_net.cpp
View file

@ -10,29 +10,29 @@ namespace rubyopencv {
VALUE rb_klass = Qnil;
void free_net(void* ptr) {
delete (cv::dnn::experimental_dnn_v1::Net*)ptr;
delete (cv::dnn::Net*)ptr;
}
size_t memsize_net(const void* ptr) {
return sizeof(cv::dnn::experimental_dnn_v1::Net);
return sizeof(cv::dnn::Net);
}
rb_data_type_t opencv_net_type = {
"Dnn::Net", { 0, free_net, memsize_net, }, 0, 0, 0
};
VALUE net2obj(cv::dnn::experimental_dnn_v1::Net* ptr) {
VALUE net2obj(cv::dnn::Net* ptr) {
return TypedData_Wrap_Struct(rb_klass, &opencv_net_type, ptr);
}
cv::dnn::experimental_dnn_v1::Net* obj2net(VALUE obj) {
cv::dnn::experimental_dnn_v1::Net* ptr = NULL;
TypedData_Get_Struct(obj, cv::dnn::experimental_dnn_v1::Net, &opencv_net_type, ptr);
cv::dnn::Net* obj2net(VALUE obj) {
cv::dnn::Net* ptr = NULL;
TypedData_Get_Struct(obj, cv::dnn::Net, &opencv_net_type, ptr);
return ptr;
}
VALUE rb_allocate(VALUE klass) {
cv::dnn::experimental_dnn_v1::Net* ptr = new cv::dnn::experimental_dnn_v1::Net();
cv::dnn::Net* ptr = new cv::dnn::Net();
return TypedData_Wrap_Struct(klass, &opencv_net_type, ptr);
}
@ -45,7 +45,7 @@ namespace rubyopencv {
VALUE blob, name;
rb_scan_args(argc, argv, "11", &blob, &name);
cv::dnn::experimental_dnn_v1::Net* selfptr = obj2net(self);
cv::dnn::Net* selfptr = obj2net(self);
cv::Mat *m = Mat::obj2mat(blob);
@ -68,7 +68,7 @@ namespace rubyopencv {
VALUE output_name;
rb_scan_args(argc, argv, "01", &output_name);
cv::dnn::experimental_dnn_v1::Net* selfptr = obj2net(self);
cv::dnn::Net* selfptr = obj2net(self);
cv::Mat* m = NULL;
@ -92,7 +92,7 @@ namespace rubyopencv {
// bool empty() const
VALUE rb_empty(VALUE self) {
cv::dnn::experimental_dnn_v1::Net* selfptr = obj2net(self);
cv::dnn::Net* selfptr = obj2net(self);
return selfptr->empty() ? Qtrue : Qfalse;
}

2
ext/opencv/dnn_net.hpp
View file

@ -8,7 +8,7 @@ namespace rubyopencv {
namespace Dnn {
namespace Net {
void init(VALUE rb_module);
VALUE net2obj(cv::dnn::experimental_dnn_v1::Net* ptr);
VALUE net2obj(cv::dnn::Net* ptr);
}
}
}