potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-31 00:03:03 +04:00
Code Packages Releases Wiki Activity

add java wrappers to dnn module

Browse Source
This commit is contained in:
abratchik
2017-06-27 10:52:44 +04:00
parent e5aa213554
commit 8f7181429f
11 changed files with 347 additions and 32 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/include/opencv2/dnn/dict.hpp
+14 -10
View File
@@ -39,15 +39,15 @@
//
//M*/
#ifndef OPENCV_DNN_DNN_DICT_HPP
#define OPENCV_DNN_DNN_DICT_HPP
#include <opencv2/core.hpp>
#include <map>
#include <ostream>
#include <opencv2/dnn/dnn.hpp>
#ifndef OPENCV_DNN_DNN_DICT_HPP
#define OPENCV_DNN_DNN_DICT_HPP
namespace cv {
namespace dnn {
CV__DNN_EXPERIMENTAL_NS_BEGIN
@@ -57,14 +57,14 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
/** @brief This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64.
* @todo Maybe int64 is useless because double type exactly stores at least 2^52 integers.
*/
struct DictValue
struct CV_EXPORTS_W DictValue
{
DictValue(const DictValue &r);
DictValue(int64 i = 0) : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i; } //!< Constructs integer scalar
DictValue(int i) : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i; } //!< Constructs integer scalar
CV_WRAP DictValue(int i) : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i; } //!< Constructs integer scalar
DictValue(unsigned p) : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = p; } //!< Constructs integer scalar
DictValue(double p) : type(Param::REAL), pd(new AutoBuffer<double,1>) { (*pd)[0] = p; } //!< Constructs floating point scalar
DictValue(const String &s) : type(Param::STRING), ps(new AutoBuffer<String,1>) { (*ps)[0] = s; } //!< Constructs string scalar
CV_WRAP DictValue(double p) : type(Param::REAL), pd(new AutoBuffer<double,1>) { (*pd)[0] = p; } //!< Constructs floating point scalar
CV_WRAP DictValue(const String &s) : type(Param::STRING), ps(new AutoBuffer<String,1>) { (*ps)[0] = s; } //!< Constructs string scalar
DictValue(const char *s) : type(Param::STRING), ps(new AutoBuffer<String,1>) { (*ps)[0] = s; } //!< @overload
template<typename TypeIter>
@@ -79,9 +79,13 @@ struct DictValue
int size() const;
bool isInt() const;
bool isString() const;
bool isReal() const;
CV_WRAP bool isInt() const;
CV_WRAP bool isString() const;
CV_WRAP bool isReal() const;
CV_WRAP int getIntValue(int idx = -1) const;
CV_WRAP double getRealValue(int idx = -1) const;
CV_WRAP String getStringValue(int idx = -1) const;
DictValue &operator=(const DictValue &r);
modules/dnn/include/opencv2/dnn/dnn.hpp
+15 -17
View File
@@ -46,11 +46,10 @@
#include <opencv2/core.hpp>
#if !defined CV_DOXYGEN && !defined CV_DNN_DONT_ADD_EXPERIMENTAL_NS
#define CV__DNN_EXPERIMENTAL_NS_USE using namespace experimental_dnn_v1;
#define CV__DNN_EXPERIMENTAL_NS_BEGIN namespace experimental_dnn_v1 {
#define CV__DNN_EXPERIMENTAL_NS_END }
namespace cv { namespace dnn { namespace experimental_dnn_v1 { } using namespace experimental_dnn_v1; }}
#else
#define CV__DNN_EXPERIMENTAL_NS_USE
#define CV__DNN_EXPERIMENTAL_NS_BEGIN
#define CV__DNN_EXPERIMENTAL_NS_END
#endif
@@ -59,7 +58,6 @@
namespace cv {
namespace dnn {
CV__DNN_EXPERIMENTAL_NS_USE
CV__DNN_EXPERIMENTAL_NS_BEGIN
//! @addtogroup dnn
//! @{
@@ -160,7 +158,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* Each class, derived from Layer, must implement allocate() methods to declare own outputs and forward() to compute outputs.
* Also before using the new layer into networks you must register your layer by using one of @ref dnnLayerFactory "LayerFactory" macros.
*/
class CV_EXPORTS_W Layer
class CV_EXPORTS_W Layer : public Algorithm
{
public:
@@ -329,7 +327,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
/** @brief Container for strings and integers. */
typedef DictValue LayerId;
/** @brief Returns pointer to layer with specified name which the network use. */
/** @brief Returns pointer to layer with specified id or name which the network use. */
CV_WRAP Ptr<Layer> getLayer(LayerId layerId);
/** @brief Returns pointers to input layers of specific layer. */
@@ -517,7 +515,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
/** @brief Returns list of types for layer used in model.
* @param layersTypes output parameter for returning types.
*/
CV_WRAP void getLayerTypes(std::vector<String>& layersTypes) const;
CV_WRAP void getLayerTypes(CV_OUT std::vector<String>& layersTypes) const;
/** @brief Returns count of layers of specified type.
* @param layerType type.
@@ -532,18 +530,18 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* @param blobs output parameter to store resulting bytes for intermediate blobs.
*/
CV_WRAP void getMemoryConsumption(const std::vector<MatShape>& netInputShapes,
size_t& weights, size_t& blobs) const;
CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
/** @overload */
CV_WRAP void getMemoryConsumption(const MatShape& netInputShape,
size_t& weights, size_t& blobs) const;
CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
/** @overload */
CV_WRAP void getMemoryConsumption(const int layerId,
const std::vector<MatShape>& netInputShapes,
size_t& weights, size_t& blobs) const;
CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
/** @overload */
CV_WRAP void getMemoryConsumption(const int layerId,
const MatShape& netInputShape,
size_t& weights, size_t& blobs) const;
CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
/** @brief Computes bytes number which are requered to store
* all weights and intermediate blobs for each layer.
@@ -553,12 +551,12 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* @param blobs output parameter to store resulting bytes for intermediate blobs.
*/
CV_WRAP void getMemoryConsumption(const std::vector<MatShape>& netInputShapes,
std::vector<int>& layerIds, std::vector<size_t>& weights,
std::vector<size_t>& blobs) const;
CV_OUT std::vector<int>& layerIds, CV_OUT std::vector<size_t>& weights,
CV_OUT std::vector<size_t>& blobs) const;
/** @overload */
CV_WRAP void getMemoryConsumption(const MatShape& netInputShape,
std::vector<int>& layerIds, std::vector<size_t>& weights,
std::vector<size_t>& blobs) const;
CV_OUT std::vector<int>& layerIds, CV_OUT std::vector<size_t>& weights,
CV_OUT std::vector<size_t>& blobs) const;
private:
struct Impl;
@@ -566,7 +564,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
};
/** @brief Small interface class for loading trained serialized models of different dnn-frameworks. */
class CV_EXPORTS_W Importer
class CV_EXPORTS_W Importer : public Algorithm
{
public:
@@ -602,7 +600,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* @param model path to the .pb file with binary protobuf description of the network architecture.
* @returns Pointer to the created importer, NULL in failure cases.
*/
CV_EXPORTS Ptr<Importer> createTensorflowImporter(const String &model);
CV_EXPORTS_W Ptr<Importer> createTensorflowImporter(const String &model);
/** @brief Creates the importer of <a href="http://torch.ch">Torch7</a> framework network.
* @param filename path to the file, dumped from Torch by using torch.save() function.
@@ -676,4 +674,4 @@ CV__DNN_EXPERIMENTAL_NS_END
#include <opencv2/dnn/layer.hpp>
#include <opencv2/dnn/dnn.inl.hpp>
#endif /* __OPENCV_DNN_DNN_HPP__ */
#endif /* OPENCV_DNN_DNN_HPP */
modules/dnn/include/opencv2/dnn/dnn.inl.hpp
+16
View File
@@ -115,6 +115,11 @@ inline int DictValue::get<int>(int idx) const
return (int)get<int64>(idx);
}
inline int DictValue::getIntValue(int idx) const
{
return (int)get<int64>(idx);
}
template<>
inline unsigned DictValue::get<unsigned>(int idx) const
{
@@ -148,6 +153,11 @@ inline double DictValue::get<double>(int idx) const
}
}
inline double DictValue::getRealValue(int idx) const
{
return get<double>(idx);
}
template<>
inline float DictValue::get<float>(int idx) const
{
@@ -162,6 +172,12 @@ inline String DictValue::get<String>(int idx) const
return (*ps)[(idx == -1) ? 0 : idx];
}
inline String DictValue::getStringValue(int idx) const
{
return get<String>(idx);
}
inline void DictValue::release()
{
switch (type)