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Split MxArray out of bridge into standalone file. Does not depend on bridge. Working to make it API complete with respect to mxArray, but with better calling syntax

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This commit is contained in:
hbristow 2013-07-02 16:42:37 -07:00
parent 504558c0be
commit baa946c390
7 changed files with 643 additions and 499 deletions
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6
modules/matlab/generator/gen_matlab.py
View File

@ -58,9 +58,9 @@ class MatlabWrapperGenerator(object):
# populate templates
for namespace in parse_tree.namespaces:
# functions
for function in namespace.functions:
populated = tfunction.render(fun=function, time=time, includes=namespace.name)
with open(output_source_dir+'/'+function.name+'.cpp', 'wb') as f:
for method in namespace.methods:
populated = tfunction.render(fun=method, time=time, includes=namespace.name)
with open(output_source_dir+'/'+method.name+'.cpp', 'wb') as f:
f.write(populated)
# classes
for clss in namespace.classes:

33
modules/matlab/generator/parse_tree.py
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@ -13,7 +13,7 @@ class ParseTree(object):
babel = Translator()
for name, definitions in namespaces.items():
class_tree = {}
functions = []
methods = []
constants = []
for defn in definitions:
obj = babel.translate(defn)
@ -21,13 +21,13 @@ class ParseTree(object):
continue
if type(obj) is Class or obj.clss:
self.insertIntoClassTree(obj, class_tree)
elif type(obj) is Function:
functions.append(obj)
elif type(obj) is Method:
methods.append(obj)
elif type(obj) is Constant:
constants.append(obj)
else:
raise TypeError('Unexpected object type: '+str(type(obj)))
self.namespaces.append(Namespace(name, constants, class_tree.values(), functions))
self.namespaces.append(Namespace(name, constants, class_tree.values(), methods))
def insertIntoClassTree(self, obj, class_tree):
cname = obj.name if type(obj) is Class else obj.clss
@ -38,8 +38,8 @@ class ParseTree(object):
class_tree[cname] = Class(cname)
# insert the definition into the class
val = class_tree[cname]
if type(obj) is Function:
val.functions.append(obj)
if type(obj) is Method:
val.methods.append(obj)
elif type(obj) is Constant:
val.constants.append(obj)
else:
@ -63,7 +63,7 @@ class Translator(object):
# --- function ---
# functions either need to have input arguments, or not uppercase names
elif defn[3] or not self.translateName(defn[0]).split('_')[0].isupper():
return self.translateFunction(defn)
return self.translateMethod(defn)
# --- constant ---
else:
return self.translateConstant(defn)
@ -71,7 +71,7 @@ class Translator(object):
def translateClass(self, defn):
return Class()
def translateFunction(self, defn, class_tree=None):
def translateMethod(self, defn, class_tree=None):
name = self.translateName(defn[0])
clss = self.translateClassName(defn[0])
rtp = defn[1]
@ -83,7 +83,7 @@ class Translator(object):
if arg:
a = self.translateArgument(arg)
opt.append(a) if a.default else req.append(a)
return Function(name, clss, static, '', rtp, False, req, opt)
return Method(name, clss, static, '', rtp, False, req, opt)
def translateConstant(self, defn):
const = True if 'const' in defn[0] else False
@ -116,34 +116,35 @@ class Translator(object):
class Namespace(object):
def __init__(self, name='', constants=None, classes=None, functions=None):
def __init__(self, name='', constants=None, classes=None, methods=None):
self.name = name
self.constants = constants if constants else []
self.classes = classes if classes else []
self.functions = functions if functions else []
self.methods = methods if methods else []
def __str__(self):
return 'namespace '+self.name+' {\n\n'+\
(join((c.__str__() for c in self.constants), '\n')+'\n\n' if self.constants else '')+\
(join((f.__str__() for f in self.functions), '\n')+'\n\n' if self.functions else '')+\
(join((f.__str__() for f in self.methods), '\n')+'\n\n' if self.methods else '')+\
(join((o.__str__() for o in self.classes), '\n\n') if self.classes else '')+'\n};'
class Class(object):
def __init__(self, name='', namespace='', constants=None, functions=None):
def __init__(self, name='', namespace='', constants=None, methods=None):
self.name = name
self.namespace = namespace
self.constants = constants if constants else []
self.functions = functions if functions else []
self.methods = methods if methods else []
def __str__(self):
return 'class '+self.name+' {\n\t'+\
(join((c.__str__() for c in self.constants), '\n\t')+'\n\n\t' if self.constants else '')+\
(join((f.__str__() for f in self.functions), '\n\t') if self.functions else '')+'\n};'
(join((f.__str__() for f in self.methods), '\n\t') if self.methods else '')+'\n};'
class Function(object):
class Method(object):
def __init__(self, name='', clss='', static=False, namespace='', rtp='', const=False, req=None, opt=None):
self.name = name
self.clss = clss
self.constructor = True if name == clss else False
self.static = static
self.const = const
self.namespace = namespace

70
modules/matlab/generator/templates/functional.cpp
View File

@ -1,14 +1,14 @@
/*
* compose
* compose a function call
* This macro takes as input a Function object and composes
* This macro takes as input a Method object and composes
* a function call by inspecting the types and argument names
*/
/
{% macro compose(fun) %}
{# ----------- Return type ------------- #}
{%- if not fun.rtp|void -%} retval = {% endif -%}
{%- if fun.clss -%}inst.{%- else -%} cv:: {%- endif -%}
{%- if not fun.rtp|void and not fun.constructor -%} retval = {% endif -%}
{%- if fun.constructor -%}{{fun.clss}} obj = {% endif -%}
{%- if fun.clss and not fun.constructor -%}inst.{%- else -%} cv:: {%- endif -%}
{{fun.name}}(
{#- ----------- Required ------------- -#}
{%- for arg in fun.req -%}
@ -26,10 +26,39 @@
);
{%- endmacro %}
// create a full function invocation
{%- macro generate(fun) -%}
{% if fun|ninputs or fun|noutputs %}
/*
* composeWithExceptionHandler
* compose a function call wrapped in exception traps
* This macro takes an input a Method object and composes a function
* call through the compose() macro, then wraps the return in traps
* for cv::Exceptions, std::exceptions, and all generic exceptions
* and returns a useful error message to the Matlab interpreter
*/
{%- macro composeWithExceptionHandler(fun) -%}
// call the opencv function
// [out =] namespace.fun(src1, ..., srcn, dst1, ..., dstn, opt1, ..., optn);
try {
{{ compose(fun) }}
} catch(cv::Exception& e) {
error(std::string("cv::exception caught: ").append(e.what()).c_str());
} catch(std::exception& e) {
error(std::string("std::exception caught: ").append(e.what()).c_str());
} catch(...) {
error("Uncaught exception occurred in {{fun.name}}");
}
{%- endmacro %}
/*
* handleInputs
* unpack input arguments from the Bridge
* Given an input Bridge object, this unpacks the object from the Bridge and
* casts them into the correct type
*/
{%- macro handleInputs(fun) %}
{% if fun|ninputs or (fun|noutputs and not fun.constructor) %}
// unpack the arguments
{# ----------- Inputs ------------- #}
{% for arg in fun.req|inputs %}
@ -45,26 +74,24 @@
{% for opt in fun.opt|only|outputs %}
{{opt.tp}} {{opt.name}};
{% endfor %}
{% if not fun.rtp|void %}
{% if not fun.rtp|void and not fun.constructor %}
{{fun.rtp}} retval;
{% endif %}
{% endif %}
// call the opencv function
// [out =] namespace.fun(src1, ..., srcn, dst1, ..., dstn, opt1, ..., optn);
try {
{{ compose(fun) }}
} catch(cv::Exception& e) {
mexErrMsgTxt(std::string("cv::exception caught: ").append(e.what()).c_str());
} catch(std::exception& e) {
mexErrMsgTxt(std::string("std::exception caught: ").append(e.what()).c_str());
} catch(...) {
mexErrMsgTxt("Uncaught exception occurred in {{fun.name}}");
}
{%- endmacro %}
/*
* handleOutputs
* pack outputs into the bridge
* Given a set of outputs, this methods assigns them into the bridge for
* return to the calling method
*/
{%- macro handleOutputs(fun) %}
{% if fun|noutputs %}
// assign the outputs into the bridge
{% if not fun.rtp|void %}
{% if not fun.rtp|void and not fun.constructor %}
outputs[0] = retval;
{% endif %}
{% for arg in fun.req|outputs %}
@ -74,5 +101,4 @@
outputs[{{loop.index0 + fun.rtp|void|not + fun.req|outputs|length}}] = {{opt.name}};
{% endfor %}
{% endif %}
{% endmacro %}
{%- endmacro %}

33
modules/matlab/generator/templates/template_class_base.cpp
View File

@ -9,30 +9,41 @@
* Copyright {{time.strftime("%Y", time.localtime())}} The OpenCV Foundation
*/
#include "mex.h"
#include "map.hpp"
#include "bridge.hpp"
#include <vector>
//TODO: Standard C++ does not have an unordered_map (only C++11 and Boost)
#include <unordered_map>
#include <string>
#include <opencv2/core.hpp>
using namespace cv;
namespace {
typedef std::unordered_map Map;
typedef std::vector<Bridge> (*)({{clss.name}}&, const std::vector<Bridge>&) MethodSignature;
{% for function in clss.functions %}
// wrapper for {{function.name}}() method
std::vector<Bridge> {{function.name}}({{clss.name}}& inst, const std::vector<Bridge>& args) {
{{ functional.generate(function) }}
}
{% for function in clss.methods %}
{% if function.constructor %}
// wrapper for {{function.name}}() constructor
{{ function.clss }} {{function.name}}(const std::vector<Bridge>& inputs) {
{{ functional.handleInputs(function) }}
{{ functional.compose(function) }}
return obj;
}
{% else %}
// wrapper for {{function.name}}() method
std::vector<Bridge> {{function.name}}({{clss.name}}& inst, const std::vector<Bridge>& inputs) {
std::vector<Bridge> outputs{% if function|noutputs %}({{function|noutputs}}){% endif %};
{{ functional.handleInputs(function) }}
{{ functional.composeWithExceptionHandler(function) }}
{{ functional.handleOutputs(function) }}
return outputs;
}
{% endif %}
{% endfor %}
map<std::string, MethodSignature> createMethodMap() {
Map<std::string, MethodSignature> createMethodMap() {
Map<std::string, MethodSignature> m;
{% for function in clss.functions -%}
{% for function in clss.methods %}
m["{{function.name}}"] = &{{function.name}};
{% endfor %}
@ -82,4 +93,4 @@ void mexFunction(int nlhs, mxArray* plhs[],
}
}; // end namespace
} // end namespace

6
modules/matlab/generator/templates/template_function_base.cpp
View File

@ -44,9 +44,11 @@ void mexFunction(int nlhs, mxArray*{% if fun|noutputs %} plhs[]{% else %}*{% end
{% endif %}
{% endif %}
{{ functional.generate(fun) }}
{{ functional.handleInputs(fun) }}
{{ functional.composeWithExceptionHandler(fun) }}
{{ functional.handleOutputs(fun) }}
{%- if fun|noutputs %}
{% if fun|noutputs %}
// push the outputs back to matlab
for (size_t n = 0; n < static_cast<size_t>(nlhs); ++n) {
plhs[n] = outputs[n].toMxArray().releaseOwnership();

447
modules/matlab/include/bridge.hpp
View File

@ -1,34 +1,12 @@
#ifndef OPENCV_BRIDGE_HPP_
#define OPENCV_BRIDGE_HPP_
#include "mex.h"
#include "map.hpp"
#include "mxarray.hpp"
#include <vector>
#include <string>
#include <opencv2/core.hpp>
#include <opencv2/calib3d.hpp>
/*
* All recent versions of Matlab ship with the MKL library which contains
* a blas extension called mkl_?omatcopy(). This defines an out-of-place
* copy and transpose operation.
*
* The mkl library is in ${MATLAB_ROOT}/bin/${MATLAB_MEXEXT}/libmkl...
* Matlab does not ship headers for the mkl functions, so we define them
* here.
*
* This operation is used extensively to copy between Matlab's column-major
* format and OpenCV's row-major format.
*/
#ifdef __cplusplus
extern "C" {
#endif
void mkl_somatcopy(char, char, size_t, size_t, const float, const float*, size_t, float*, size_t);
void mkl_domatcopy(char, char, size_t, size_t, const double, const double*, size_t, double*, size_t);
#ifdef __cplusplus
}
#endif
/*
* Custom typedefs
* Parsed names from the hdr_parser
@ -46,427 +24,6 @@ typedef cv::Ptr<cv::StereoSGBM> Ptr_StereoSGBM;
typedef cv::Ptr<cv::FeatureDetector> Ptr_FeatureDetector;
/*!
* @brief raise error if condition fails
*
* This is a conditional wrapper for mexErrMsgTxt. If the conditional
* expression fails, an error is raised and the mex function returns
* to Matlab, otherwise this function does nothing
*/
void conditionalError(bool expr, const std::string& str) {
if (!expr) mexErrMsgTxt(std::string("condition failed: ").append(str).c_str());
}
/*!
* @brief raise an error
*
* This function is a wrapper around mexErrMsgTxt
*/
void error(const std::string& str) {
mexErrMsgTxt(str.c_str());
}
// ----------------------------------------------------------------------------
// PREDECLARATIONS
// ----------------------------------------------------------------------------
class MxArray;
class Bridge;
template <typename InputScalar, typename OutputScalar>
void deepCopyAndTranspose(const cv::Mat& src, MxArray& dst);
template <typename InputScalar, typename OutputScalar>
void deepCopyAndTranspose(const MxArray& src, cv::Mat& dst);
// ----------------------------------------------------------------------------
// MATLAB TRAITS
// ----------------------------------------------------------------------------
namespace Matlab {
class DefaultTraits {};
class InheritType {};
static const int Dynamic = -1;
template<typename _Tp = DefaultTraits> class Traits {
public:
static const mxClassID ScalarType = mxUNKNOWN_CLASS;
static const mxComplexity Complex = mxCOMPLEX;
static const mxComplexity Real = mxCOMPLEX;
static std::string ToString() { return "Unknown/Unsupported"; }
};
// bool
template<> class Traits<bool> {
public:
static const mxClassID ScalarType = mxLOGICAL_CLASS;
static std::string ToString() { return "boolean"; }
};
// uint8_t
template<> class Traits<uint8_t> {
public:
static const mxClassID ScalarType = mxUINT8_CLASS;
static std::string ToString() { return "uint8_t"; }
};
// int8_t
template<> class Traits<int8_t> {
public:
static const mxClassID ScalarType = mxINT8_CLASS;
static std::string ToString() { return "int8_t"; }
};
// uint16_t
template<> class Traits<uint16_t> {
public:
static const mxClassID ScalarType = mxUINT16_CLASS;
static std::string ToString() { return "uint16_t"; }
};
// int16_t
template<> class Traits<int16_t> {
public:
static const mxClassID ScalarType = mxINT16_CLASS;
static std::string ToString() { return "int16_t"; }
};
// uint32_t
template<> class Traits<uint32_t> {
public:
static const mxClassID ScalarType = mxUINT32_CLASS;
static std::string ToString() { return "uint32_t"; }
};
// int32_t
template<> class Traits<int32_t> {
public:
static const mxClassID ScalarType = mxINT32_CLASS;
static std::string ToString() { return "int32_t"; }
};
// uint64_t
template<> class Traits<uint64_t> {
public:
static const mxClassID ScalarType = mxUINT64_CLASS;
static std::string ToString() { return "uint64_t"; }
};
// int64_t
template<> class Traits<int64_t> {
public:
static const mxClassID ScalarType = mxINT64_CLASS;
static std::string ToString() { return "int64_t"; }
};
// float
template<> class Traits<float> {
public:
static const mxClassID ScalarType = mxSINGLE_CLASS;
static std::string ToString() { return "float"; }
};
// double
template<> class Traits<double> {
public:
static const mxClassID ScalarType = mxDOUBLE_CLASS;
static std::string ToString() { return "double"; }
};
// size_t
template<> class Traits<size_t> {
public:
static const mxClassID ScalarType = (sizeof(size_t) == 4) ? mxUINT32_CLASS : mxUINT64_CLASS;
static std::string ToString() { return "size_t"; }
};
// char
template<> class Traits<char> {
public:
static const mxClassID ScalarType = mxCHAR_CLASS;
static std::string ToString() { return "char"; }
};
// char
template<> class Traits<Matlab::InheritType> {
public:
static std::string ToString() { return "Inherited type"; }
};
}
// ----------------------------------------------------------------------------
// MXARRAY
// ----------------------------------------------------------------------------
/*!
* @class MxArray
* @brief A thin wrapper around Matlab's mxArray types
*
* MxArray provides a thin object oriented wrapper around Matlab's
* native mxArray type which exposes most of the functionality of the
* Matlab interface, but in a more C++ manner. MxArray objects are scoped,
* so you can freely create and destroy them without worrying about memory
* management. If you wish to pass the underlying mxArray* representation
* back to Matlab as an lvalue, see the releaseOwnership() method
*/
class MxArray {
private:
mxArray* ptr_;
bool owns_;
void dealloc() {
if (owns_ && ptr_) { mxDestroyArray(ptr_); ptr_ = NULL; owns_ = false; }
}
public:
// constructors and destructor
MxArray() : ptr_(mxCreateDoubleMatrix(1, 1, Matlab::Traits<>::Real)), owns_(true) {}
MxArray(const mxArray* ptr) : ptr_(const_cast<mxArray *>(ptr)), owns_(false) {}
virtual ~MxArray() {
dealloc();
}
// copy constructor
// all copies are deep copies
MxArray(const MxArray& other) : ptr_(mxDuplicateArray(other.ptr_)), owns_(true) {}
// swap
friend void swap(MxArray& first, MxArray& second) {
using std::swap;
swap(first.ptr_, second.ptr_);
swap(first.owns_, second.owns_);
}
// assignment operator
// copy-and-swap idiom
// all copies are deep copies
MxArray& operator=(MxArray other) {
swap(*this, other);
return *this;
}
#if __cplusplus >= 201103L
// move constructor, if C++11
// default construct and swap
MxArray(MxArray&& other) : MxArray() {
swap(*this, other);
}
#endif
/*
* @brief release ownership to allow return into Matlab workspace
*
* MxArray is not directly convertible back to mxArray types through assignment
* because the MxArray may have been allocated on the free store, making it impossible
* to know whether the returned pointer will be released by someone else or not.
*
* Since Matlab requires mxArrays be passed back into the workspace, the only way
* to achieve that is through this function, which explicitly releases ownership
* of the object, assuming the Matlab interpreter receving the object will delete
* it at a later time
*
* e.g.
* {
* MxArray A<double>(5, 5); // allocates memory
* MxArray B<double>(5, 5); // ditto
* plhs[0] = A; // not allowed!!
* plhs[0] = A.releaseOwnership(); // makes explicit that ownership is being released
* } // end of scope. B is released, A isn't
*
*/
mxArray* releaseOwnership() {
owns_ = false;
return ptr_;
}
template <typename Scalar>
explicit MxArray(size_t m, size_t n, size_t k=1) : owns_(true) {
mwSize dims[] = { static_cast<mwSize>(m), static_cast<mwSize>(n), static_cast<mwSize>(k) };
ptr_ = mxCreateNumericArray(3, dims, Matlab::Traits<Scalar>::ScalarType, Matlab::Traits<>::Real);
}
// this function is called exclusively from constructors!!
template <typename Scalar>
MxArray& fromMat(const cv::Mat& mat) {
// dealloc any existing storage before reallocating
dealloc();
mwSize dims[] = { static_cast<mwSize>(mat.rows), static_cast<mwSize>(mat.cols), static_cast<mwSize>(mat.channels()) };
ptr_ = mxCreateNumericArray(3, dims, Matlab::Traits<Scalar>::ScalarType, Matlab::Traits<>::Real);
owns_ = true;
switch (mat.depth()) {
case CV_8U: deepCopyAndTranspose<uint8_t, Scalar>(mat, *this); break;
case CV_8S: deepCopyAndTranspose<int8_t, Scalar>(mat, *this); break;
case CV_16U: deepCopyAndTranspose<uint16_t, Scalar>(mat, *this); break;
case CV_16S: deepCopyAndTranspose<int16_t, Scalar>(mat, *this); break;
case CV_32S: deepCopyAndTranspose<int32_t, Scalar>(mat, *this); break;
case CV_32F: deepCopyAndTranspose<float, Scalar>(mat, *this); break;
case CV_64F: deepCopyAndTranspose<double, Scalar>(mat, *this); break;
default: error("Attempted to convert from unknown class");
}
return *this;
}
template <typename Scalar>
cv::Mat toMat() const {
cv::Mat mat(cols(), rows(), CV_MAKETYPE(cv::DataType<Scalar>::type, channels()));
switch (ID()) {
case mxINT8_CLASS: deepCopyAndTranspose<int8_t, Scalar>(*this, mat); break;
case mxUINT8_CLASS: deepCopyAndTranspose<uint8_t, Scalar>(*this, mat); break;
case mxINT16_CLASS: deepCopyAndTranspose<int16_t, Scalar>(*this, mat); break;
case mxUINT16_CLASS: deepCopyAndTranspose<uint16_t, Scalar>(*this, mat); break;
case mxINT32_CLASS: deepCopyAndTranspose<int32_t, Scalar>(*this, mat); break;
case mxUINT32_CLASS: deepCopyAndTranspose<uint32_t, Scalar>(*this, mat); break;
case mxINT64_CLASS: deepCopyAndTranspose<int64_t, Scalar>(*this, mat); break;
case mxUINT64_CLASS: deepCopyAndTranspose<uint64_t, Scalar>(*this, mat); break;
case mxSINGLE_CLASS: deepCopyAndTranspose<float, Scalar>(*this, mat); break;
case mxDOUBLE_CLASS: deepCopyAndTranspose<double, Scalar>(*this, mat); break;
case mxCHAR_CLASS: deepCopyAndTranspose<char, Scalar>(*this, mat); break;
case mxLOGICAL_CLASS: deepCopyAndTranspose<int8_t, Scalar>(*this, mat); break;
default: error("Attempted to convert from unknown class");
}
return mat;
}
MxArray field(const std::string& name) { return MxArray(mxGetField(ptr_, 0, name.c_str())); }
template <typename Scalar>
Scalar* real() { return static_cast<Scalar *>(mxGetData(ptr_)); }
template <typename Scalar>
Scalar* imag() { return static_cast<Scalar *>(mxGetData(ptr_)); }
template <typename Scalar>
const Scalar* real() const { return static_cast<const Scalar *>(mxGetData(ptr_)); }
template <typename Scalar>
const Scalar* imag() const { return static_cast<const Scalar *>(mxGetData(ptr_)); }
template <typename Scalar>
Scalar scalar() const { return static_cast<Scalar *>(mxGetData(ptr_))[0]; }
std::string toString() const {
conditionalError(isString(), "Attempted to convert non-string type to string");
std::string str;
str.reserve(size()+1);
mxGetString(ptr_, const_cast<char *>(str.data()), str.size());
return str;
}
size_t size() const { return mxGetNumberOfElements(ptr_); }
size_t rows() const { return mxGetM(ptr_); }
size_t cols() const { return mxGetN(ptr_); }
size_t channels() const { return (mxGetNumberOfDimensions(ptr_) > 2) ? mxGetDimensions(ptr_)[2] : 1; }
bool isComplex() const { return mxIsComplex(ptr_); }
bool isNumeric() const { return mxIsNumeric(ptr_); }
bool isLogical() const { return mxIsLogical(ptr_); }
bool isString() const { return mxIsChar(ptr_); }
bool isCell() const { return mxIsCell(ptr_); }
bool isStructure() const { return mxIsStruct(ptr_); }
bool isClass(const std::string& name) const { return mxIsClass(ptr_, name.c_str()); }
std::string className() const { return std::string(mxGetClassName(ptr_)); }
mxClassID ID() const { return mxGetClassID(ptr_); }
};
/*!
* @brief template specialization for inheriting types
*
* This template specialization attempts to preserve the best mapping
* between OpenCV and Matlab types. Matlab uses double types almost universally, so
* all floating float types are converted to doubles.
* Unfortunately OpenCV does not have a native logical type, so
* that gets mapped to an unsigned 8-bit value
*/
template <>
MxArray& MxArray::fromMat<Matlab::InheritType>(const cv::Mat& mat) {
switch (mat.depth()) {
case CV_8U: return fromMat<uint8_t>(mat); break;
case CV_8S: return fromMat<int8_t>(mat); break;
case CV_16U: return fromMat<uint16_t>(mat); break;
case CV_16S: return fromMat<int16_t>(mat); break;
case CV_32S: return fromMat<int32_t>(mat); break;
case CV_32F: return fromMat<double>(mat); break; //NOTE: Matlab uses double as native type!
case CV_64F: return fromMat<double>(mat); break;
default: error("Attempted to convert from unknown class");
}
return *this;
}
/*!
* @brief template specialization for inheriting types
*
* This template specialization attempts to preserve the best mapping
* between Matlab and OpenCV types. OpenCV has poor support for double precision
* types, so all floating point types are cast to float. Logicals get cast
* to unsignd 8-bit value.
*/
template <>
cv::Mat MxArray::toMat<Matlab::InheritType>() const {
switch (ID()) {
case mxINT8_CLASS: return toMat<int8_t>();
case mxUINT8_CLASS: return toMat<uint8_t>();;
case mxINT16_CLASS: return toMat<int16_t>();
case mxUINT16_CLASS: return toMat<uint16_t>();
case mxINT32_CLASS: return toMat<int32_t>();
case mxUINT32_CLASS: return toMat<int32_t>();
case mxINT64_CLASS: return toMat<int64_t>();
case mxUINT64_CLASS: return toMat<int64_t>();
case mxSINGLE_CLASS: return toMat<float>();
case mxDOUBLE_CLASS: return toMat<float>(); //NOTE: OpenCV uses float as native type!
case mxCHAR_CLASS: return toMat<int8_t>();
case mxLOGICAL_CLASS: return toMat<int8_t>();
default: error("Attempted to convert from unknown class");
}
return cv::Mat();
}
// ----------------------------------------------------------------------------
// MATRIX TRANSPOSE
// ----------------------------------------------------------------------------
template <typename InputScalar, typename OutputScalar>
void deepCopyAndTranspose(const cv::Mat& in, MxArray& out) {
conditionalError(static_cast<size_t>(in.rows) == out.rows(), "Matrices must have the same number of rows");
conditionalError(static_cast<size_t>(in.cols) == out.cols(), "Matrices must have the same number of cols");
conditionalError(static_cast<size_t>(in.channels()) == out.channels(), "Matrices must have the same number of channels");
OutputScalar* outp = out.real<OutputScalar>();
const size_t M = out.rows();
const size_t N = out.cols();
for (size_t m = 0; m < M; ++m) {
const InputScalar* inp = in.ptr<InputScalar>(m);
for (size_t n = 0; n < N; ++n) {
// copy and transpose
outp[m + n*M] = inp[n];
}
}
}
template <typename InputScalar, typename OutputScalar>
void deepCopyAndTranspose(const MxArray& in, cv::Mat& out) {
conditionalError(in.rows() == static_cast<size_t>(out.rows), "Matrices must have the same number of rows");
conditionalError(in.cols() == static_cast<size_t>(out.cols), "Matrices must have the same number of cols");
conditionalError(in.channels() == static_cast<size_t>(out.channels()), "Matrices must have the same number of channels");
const InputScalar* inp = in.real<InputScalar>();
const size_t M = in.rows();
const size_t N = in.cols();
for (size_t m = 0; m < M; ++m) {
OutputScalar* outp = out.ptr<OutputScalar>(m);
for (size_t n = 0; n < N; ++n) {
// copy and transpose
outp[n] = inp[m + n*M];
}
}
}
template <>
void deepCopyAndTranspose<float, float>(const cv::Mat&, MxArray&) {
}
template <>
void deepCopyAndTranspose<double, double>(const cv::Mat&, MxArray&) {
}
template <>
void deepCopyAndTranspose<float, float>(const MxArray&, cv::Mat&) {
// use mkl
}
template <>
void deepCopyAndTranspose<double, double>(const MxArray&, cv::Mat& ) {
// use mkl
}
// ----------------------------------------------------------------------------
@ -613,7 +170,7 @@ public:
// --------------------------------------------------------------------------
// --------------------------- cv::Mat --------------------------------------
Bridge& operator=(const cv::Mat& mat) { ptr_ = MxArray().fromMat<Matlab::InheritType>(mat); return *this; }
Bridge& operator=(const cv::Mat& mat) { ptr_ = MxArray::FromMat<Matlab::InheritType>(mat); return *this; }
cv::Mat toMat() const { return ptr_.toMat<Matlab::InheritType>(); }
operator cv::Mat() const { return toMat(); }

547
modules/matlab/include/mxarray.hpp Normal file
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@ -0,0 +1,547 @@
#ifndef OPENCV_MXARRAY_HPP_
#define OPENCV_MXARRAY_HPP_
#include "mex.h"
#include <vector>
#include <string>
#include <opencv2/core.hpp>
/*
* All recent versions of Matlab ship with the MKL library which contains
* a blas extension called mkl_?omatcopy(). This defines an out-of-place
* copy and transpose operation.
*
* The mkl library is in ${MATLAB_ROOT}/bin/${MATLAB_MEXEXT}/libmkl...
* Matlab does not ship headers for the mkl functions, so we define them
* here.
*
* This operation is used extensively to copy between Matlab's column-major
* format and OpenCV's row-major format.
*/
#ifdef __cplusplus
extern "C" {
#endif
void mkl_somatcopy(char, char, size_t, size_t, const float, const float*, size_t, float*, size_t);
void mkl_domatcopy(char, char, size_t, size_t, const double, const double*, size_t, double*, size_t);
#ifdef __cplusplus
}
#endif
/*!
* @brief raise error if condition fails
*
* This is a conditional wrapper for mexErrMsgTxt. If the conditional
* expression fails, an error is raised and the mex function returns
* to Matlab, otherwise this function does nothing
*/
void conditionalError(bool expr, const std::string& str) {
if (!expr) mexErrMsgTxt(std::string("condition failed: ").append(str).c_str());
}
/*!
* @brief raise an error
*
* This function is a wrapper around mexErrMsgTxt
*/
void error(const std::string& str) {
mexErrMsgTxt(str.c_str());
}
// ----------------------------------------------------------------------------
// PREDECLARATIONS
// ----------------------------------------------------------------------------
class MxArray;
template <typename InputScalar, typename OutputScalar>
void deepCopyAndTranspose(const cv::Mat& src, MxArray& dst);
template <typename InputScalar, typename OutputScalar>
void deepCopyAndTranspose(const MxArray& src, cv::Mat& dst);
// ----------------------------------------------------------------------------
// MATLAB TRAITS
// ----------------------------------------------------------------------------
namespace Matlab {
class DefaultTraits {};
class InheritType {};
static const int Dynamic = -1;
template<typename _Tp = DefaultTraits> class Traits {
public:
static const mxClassID ScalarType = mxUNKNOWN_CLASS;
static const mxComplexity Complex = mxCOMPLEX;
static const mxComplexity Real = mxCOMPLEX;
static std::string ToString() { return "Unknown/Unsupported"; }
};
// bool
template<> class Traits<bool> {
public:
static const mxClassID ScalarType = mxLOGICAL_CLASS;
static std::string ToString() { return "boolean"; }
};
// uint8_t
template<> class Traits<uint8_t> {
public:
static const mxClassID ScalarType = mxUINT8_CLASS;
static std::string ToString() { return "uint8_t"; }
};
// int8_t
template<> class Traits<int8_t> {
public:
static const mxClassID ScalarType = mxINT8_CLASS;
static std::string ToString() { return "int8_t"; }
};
// uint16_t
template<> class Traits<uint16_t> {
public:
static const mxClassID ScalarType = mxUINT16_CLASS;
static std::string ToString() { return "uint16_t"; }
};
// int16_t
template<> class Traits<int16_t> {
public:
static const mxClassID ScalarType = mxINT16_CLASS;
static std::string ToString() { return "int16_t"; }
};
// uint32_t
template<> class Traits<uint32_t> {
public:
static const mxClassID ScalarType = mxUINT32_CLASS;
static std::string ToString() { return "uint32_t"; }
};
// int32_t
template<> class Traits<int32_t> {
public:
static const mxClassID ScalarType = mxINT32_CLASS;
static std::string ToString() { return "int32_t"; }
};
// uint64_t
template<> class Traits<uint64_t> {
public:
static const mxClassID ScalarType = mxUINT64_CLASS;
static std::string ToString() { return "uint64_t"; }
};
// int64_t
template<> class Traits<int64_t> {
public:
static const mxClassID ScalarType = mxINT64_CLASS;
static std::string ToString() { return "int64_t"; }
};
// float
template<> class Traits<float> {
public:
static const mxClassID ScalarType = mxSINGLE_CLASS;
static std::string ToString() { return "float"; }
};
// double
template<> class Traits<double> {
public:
static const mxClassID ScalarType = mxDOUBLE_CLASS;
static std::string ToString() { return "double"; }
};
// size_t
template<> class Traits<size_t> {
public:
static const mxClassID ScalarType = (sizeof(size_t) == 4) ? mxUINT32_CLASS : mxUINT64_CLASS;
static std::string ToString() { return "size_t"; }
};
// char
template<> class Traits<char> {
public:
static const mxClassID ScalarType = mxCHAR_CLASS;
static std::string ToString() { return "char"; }
};
// char
template<> class Traits<Matlab::InheritType> {
public:
static std::string ToString() { return "Inherited type"; }
};
}
// ----------------------------------------------------------------------------
// MXARRAY
// ----------------------------------------------------------------------------
/*!
* @class MxArray
* @brief A thin wrapper around Matlab's mxArray types
*
* MxArray provides a thin object oriented wrapper around Matlab's
* native mxArray type which exposes most of the functionality of the
* Matlab interface, but in a more C++ manner. MxArray objects are scoped,
* so you can freely create and destroy them without worrying about memory
* management. If you wish to pass the underlying mxArray* representation
* back to Matlab as an lvalue, see the releaseOwnership() method
*
* MxArrays can be directly converted into OpenCV mat objects and std::string
* objects, since there is a natural mapping between these types. More
* complex types are mapped through the Bridge which does custom conversions
* such as MxArray --> cv::Keypoints, etc
*/
class MxArray {
private:
mxArray* ptr_;
bool owns_;
/*!
* @brief swap all members of this and other
*
* the swap method is used by the assignment and move constructors
* to swap the members of two MxArrays, leaving both in destructible states
*/
friend void swap(MxArray& first, MxArray& second) {
using std::swap;
swap(first.ptr_, second.ptr_);
swap(first.owns_, second.owns_);
}
void dealloc() {
if (owns_ && ptr_) { mxDestroyArray(ptr_); ptr_ = NULL; owns_ = false; }
}
public:
// --------------------------------------------------------------------------
// CONSTRUCTORS
// --------------------------------------------------------------------------
/*!
* @brief default constructor
*
* Construct a valid 0x0 matrix (so all other methods do not need validity checks
*/
MxArray() : ptr_(mxCreateDoubleMatrix(1, 1, Matlab::Traits<>::Real)), owns_(true) {}
/*!
* @brief inheriting constructor
*
* Inherit an mxArray from Matlab. Don't claim ownership of the array,
* just encapsulate it
*/
MxArray(const mxArray* ptr) : ptr_(const_cast<mxArray *>(ptr)), owns_(false) {}
/*!
* @brief explicit typed constructor
*
* This constructor explicitly creates an MxArray of the given size and type.
*/
MxArray(size_t m, size_t n, size_t k, mxClassID id, mxComplexity com = Matlab::Traits<>::Real) : owns_(true) {
mwSize dims[] = { static_cast<mwSize>(m), static_cast<mwSize>(n), static_cast<mwSize>(k) };
ptr_ = mxCreateNumericArray(3, dims, id, com);
}
/*!
* @brief explicit tensor constructor
*
* Explicitly construct a tensor of given size and type. Since constructors cannot
* be explicitly templated, this is a static factory method
*/
template <typename Scalar>
static MxArray Tensor(size_t m, size_t n, size_t k=1) {
return MxArray(m, n, k, Matlab::Traits<Scalar>::ScalarType);
}
/*!
* @brief explicit matrix constructor
*
* Explicitly construct a matrix of given size and type. Since constructors cannot
* be explicitly templated, this is a static factory method
*/
template <typename Scalar>
static MxArray Matrix(size_t m, size_t n) {
return MxArray(m, n, 1, Matlab::Traits<Scalar>::ScalarType);
}
/*!
* @brief explicit vector constructor
*
* Explicitly construct a vector of given size and type. Since constructors cannot
* be explicitly templated, this is a static factory method
*/
template <typename Scalar>
static MxArray Vector(size_t m) {
return MxArray(m, 1, 1, Matlab::Traits<Scalar>::ScalarType);
}
/*!
* @brief explicit scalar constructor
*
* Explicitly construct a scalar of given type. Since constructors cannot
* be explicitly templated, this is a static factory method
*/
template <typename Scalar>
static MxArray Scalar(Scalar value = 0) {
MxArray s(1, 1, 1, Matlab::Traits<Scalar>::ScalarType);
s.real<Scalar>()[0] = value;
return s;
}
/*!
* @brief destructor
*
* The destructor deallocates any data allocated by mxCreate* methods only
* if the object is owned
*/
virtual ~MxArray() {
dealloc();
}
/*!
* @brief copy constructor
*
* All copies are deep copies. If you have a C++11 compatible compiler, prefer
* move construction to copy construction
*/
MxArray(const MxArray& other) : ptr_(mxDuplicateArray(other.ptr_)), owns_(true) {}
/*!
* @brief copy-and-swap assignment
*
* This assignment operator uses the copy and swap idiom to provide a strong
* exception guarantee when swapping two objects.
*
* Note in particular that the other MxArray is passed by value, thus invoking
* the copy constructor which performs a deep copy of the input. The members of
* this and other are then swapped
*/
MxArray& operator=(MxArray other) {
swap(*this, other);
return *this;
}
#if __cplusplus >= 201103L
/*
* @brief C++11 move constructor
*
* When C++11 support is available, move construction is used to move returns
* out of functions, etc. This is much fast than copy construction, since the
* move constructed object replaced itself with a default constructed MxArray,
* which is of size 0 x 0.
*/
MxArray(MxArray&& other) : MxArray() {
swap(*this, other);
}
#endif
/*
* @brief release ownership to allow return into Matlab workspace
*
* MxArray is not directly convertible back to mxArray types through assignment
* because the MxArray may have been allocated on the free store, making it impossible
* to know whether the returned pointer will be released by someone else or not.
*
* Since Matlab requires mxArrays be passed back into the workspace, the only way
* to achieve that is through this function, which explicitly releases ownership
* of the object, assuming the Matlab interpreter receving the object will delete
* it at a later time
*
* e.g.
* {
* MxArray A<double>(5, 5); // allocates memory
* MxArray B<double>(5, 5); // ditto
* plhs[0] = A; // not allowed!!
* plhs[0] = A.releaseOwnership(); // makes explicit that ownership is being released
* } // end of scope. B is released, A isn't
*
*/
mxArray* releaseOwnership() {
owns_ = false;
return ptr_;
}
template <typename Scalar>
static MxArray FromMat(const cv::Mat& mat) {
MxArray arr(mat.rows, mat.cols, mat.channels(), Matlab::Traits<Scalar>::ScalarType);
switch (mat.depth()) {
case CV_8U: deepCopyAndTranspose<uint8_t, Scalar>(mat, arr); break;
case CV_8S: deepCopyAndTranspose<int8_t, Scalar>(mat, arr); break;
case CV_16U: deepCopyAndTranspose<uint16_t, Scalar>(mat, arr); break;
case CV_16S: deepCopyAndTranspose<int16_t, Scalar>(mat, arr); break;
case CV_32S: deepCopyAndTranspose<int32_t, Scalar>(mat, arr); break;
case CV_32F: deepCopyAndTranspose<float, Scalar>(mat, arr); break;
case CV_64F: deepCopyAndTranspose<double, Scalar>(mat, arr); break;
default: error("Attempted to convert from unknown class");
}
return arr;
}
template <typename Scalar>
cv::Mat toMat() const {
cv::Mat mat(cols(), rows(), CV_MAKETYPE(cv::DataType<Scalar>::type, channels()));
switch (ID()) {
case mxINT8_CLASS: deepCopyAndTranspose<int8_t, Scalar>(*this, mat); break;
case mxUINT8_CLASS: deepCopyAndTranspose<uint8_t, Scalar>(*this, mat); break;
case mxINT16_CLASS: deepCopyAndTranspose<int16_t, Scalar>(*this, mat); break;
case mxUINT16_CLASS: deepCopyAndTranspose<uint16_t, Scalar>(*this, mat); break;
case mxINT32_CLASS: deepCopyAndTranspose<int32_t, Scalar>(*this, mat); break;
case mxUINT32_CLASS: deepCopyAndTranspose<uint32_t, Scalar>(*this, mat); break;
case mxINT64_CLASS: deepCopyAndTranspose<int64_t, Scalar>(*this, mat); break;
case mxUINT64_CLASS: deepCopyAndTranspose<uint64_t, Scalar>(*this, mat); break;
case mxSINGLE_CLASS: deepCopyAndTranspose<float, Scalar>(*this, mat); break;
case mxDOUBLE_CLASS: deepCopyAndTranspose<double, Scalar>(*this, mat); break;
case mxCHAR_CLASS: deepCopyAndTranspose<char, Scalar>(*this, mat); break;
case mxLOGICAL_CLASS: deepCopyAndTranspose<int8_t, Scalar>(*this, mat); break;
default: error("Attempted to convert from unknown class");
}
return mat;
}
MxArray field(const std::string& name) { return MxArray(mxGetField(ptr_, 0, name.c_str())); }
template <typename Scalar>
Scalar* real() { return static_cast<Scalar *>(mxGetData(ptr_)); }
template <typename Scalar>
Scalar* imag() { return static_cast<Scalar *>(mxGetData(ptr_)); }
template <typename Scalar>
const Scalar* real() const { return static_cast<const Scalar *>(mxGetData(ptr_)); }
template <typename Scalar>
const Scalar* imag() const { return static_cast<const Scalar *>(mxGetData(ptr_)); }
template <typename Scalar>
Scalar scalar() const { return static_cast<Scalar *>(mxGetData(ptr_))[0]; }
std::string toString() const {
conditionalError(isString(), "Attempted to convert non-string type to string");
std::string str;
str.reserve(size()+1);
mxGetString(ptr_, const_cast<char *>(str.data()), str.size());
return str;
}
size_t size() const { return mxGetNumberOfElements(ptr_); }
size_t rows() const { return mxGetM(ptr_); }
size_t cols() const { return mxGetN(ptr_); }
size_t channels() const { return (mxGetNumberOfDimensions(ptr_) > 2) ? mxGetDimensions(ptr_)[2] : 1; }
bool isComplex() const { return mxIsComplex(ptr_); }
bool isNumeric() const { return mxIsNumeric(ptr_); }
bool isLogical() const { return mxIsLogical(ptr_); }
bool isString() const { return mxIsChar(ptr_); }
bool isCell() const { return mxIsCell(ptr_); }
bool isStructure() const { return mxIsStruct(ptr_); }
bool isClass(const std::string& name) const { return mxIsClass(ptr_, name.c_str()); }
std::string className() const { return std::string(mxGetClassName(ptr_)); }
mxClassID ID() const { return mxGetClassID(ptr_); }
};
/*!
* @brief template specialization for inheriting types
*
* This template specialization attempts to preserve the best mapping
* between OpenCV and Matlab types. Matlab uses double types almost universally, so
* all floating float types are converted to doubles.
* Unfortunately OpenCV does not have a native logical type, so
* that gets mapped to an unsigned 8-bit value
*/
template <>
MxArray MxArray::FromMat<Matlab::InheritType>(const cv::Mat& mat) {
switch (mat.depth()) {
case CV_8U: return FromMat<uint8_t>(mat);
case CV_8S: return FromMat<int8_t>(mat);
case CV_16U: return FromMat<uint16_t>(mat);
case CV_16S: return FromMat<int16_t>(mat);
case CV_32S: return FromMat<int32_t>(mat);
case CV_32F: return FromMat<double>(mat); //NOTE: Matlab uses double as native type!
case CV_64F: return FromMat<double>(mat);
default: error("Attempted to convert from unknown class");
}
return MxArray();
}
/*!
* @brief template specialization for inheriting types
*
* This template specialization attempts to preserve the best mapping
* between Matlab and OpenCV types. OpenCV has poor support for double precision
* types, so all floating point types are cast to float. Logicals get cast
* to unsignd 8-bit value.
*/
template <>
cv::Mat MxArray::toMat<Matlab::InheritType>() const {
switch (ID()) {
case mxINT8_CLASS: return toMat<int8_t>();
case mxUINT8_CLASS: return toMat<uint8_t>();;
case mxINT16_CLASS: return toMat<int16_t>();
case mxUINT16_CLASS: return toMat<uint16_t>();
case mxINT32_CLASS: return toMat<int32_t>();
case mxUINT32_CLASS: return toMat<int32_t>();
case mxINT64_CLASS: return toMat<int64_t>();
case mxUINT64_CLASS: return toMat<int64_t>();
case mxSINGLE_CLASS: return toMat<float>();
case mxDOUBLE_CLASS: return toMat<float>(); //NOTE: OpenCV uses float as native type!
case mxCHAR_CLASS: return toMat<int8_t>();
case mxLOGICAL_CLASS: return toMat<int8_t>();
default: error("Attempted to convert from unknown class");
}
return cv::Mat();
}
// ----------------------------------------------------------------------------
// MATRIX TRANSPOSE
// ----------------------------------------------------------------------------
template <typename InputScalar, typename OutputScalar>
void deepCopyAndTranspose(const cv::Mat& in, MxArray& out) {
conditionalError(static_cast<size_t>(in.rows) == out.rows(), "Matrices must have the same number of rows");
conditionalError(static_cast<size_t>(in.cols) == out.cols(), "Matrices must have the same number of cols");
conditionalError(static_cast<size_t>(in.channels()) == out.channels(), "Matrices must have the same number of channels");
OutputScalar* outp = out.real<OutputScalar>();
const size_t M = out.rows();
const size_t N = out.cols();
for (size_t m = 0; m < M; ++m) {
const InputScalar* inp = in.ptr<InputScalar>(m);
for (size_t n = 0; n < N; ++n) {
// copy and transpose
outp[m + n*M] = inp[n];
}
}
}
template <typename InputScalar, typename OutputScalar>
void deepCopyAndTranspose(const MxArray& in, cv::Mat& out) {
conditionalError(in.rows() == static_cast<size_t>(out.rows), "Matrices must have the same number of rows");
conditionalError(in.cols() == static_cast<size_t>(out.cols), "Matrices must have the same number of cols");
conditionalError(in.channels() == static_cast<size_t>(out.channels()), "Matrices must have the same number of channels");
const InputScalar* inp = in.real<InputScalar>();
const size_t M = in.rows();
const size_t N = in.cols();
for (size_t m = 0; m < M; ++m) {
OutputScalar* outp = out.ptr<OutputScalar>(m);
for (size_t n = 0; n < N; ++n) {
// copy and transpose
outp[n] = inp[m + n*M];
}
}
}
template <>
void deepCopyAndTranspose<float, float>(const cv::Mat&, MxArray&) {
}
template <>
void deepCopyAndTranspose<double, double>(const cv::Mat&, MxArray&) {
}
template <>
void deepCopyAndTranspose<float, float>(const MxArray&, cv::Mat&) {
// use mkl
}
template <>
void deepCopyAndTranspose<double, double>(const MxArray&, cv::Mat& ) {
// use mkl
}
#endif