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52dc51a62c
opencv/modules/matlab/include/mxarray.hpp
hbristow 52dc51a62c Added custom OpenCV mex compiler
2013-08-28 12:01:34 +10:00

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#ifndef OPENCV_MXARRAY_HPP_
#define OPENCV_MXARRAY_HPP_
#include <stdint.h>
#include <cstdarg>
#include <string>
#include <vector>
#include <algorithm>
#include <sstream>
#include <opencv2/core.hpp>
#if __cplusplus > 201103
#include <unordered_set>
typedef std::unordered_set<std::string> StringSet;
#else
#include <set>
typedef std::set<std::string> StringSet;
#endif
#include "mex.h"
#include "transpose.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
#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
*/
static 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
*/
static 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"; }
};
// char
template<> class Traits<char> {
public:
static const mxClassID ScalarType = mxCHAR_CLASS;
static std::string ToString() { return "char"; }
};
// inherited type
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) {}
MxArray& operator=(const mxArray* ptr) {
dealloc();
ptr_ = const_cast<mxArray *>(ptr);
owns_ = false;
return *this;
}
/*!
* @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 ScalarType>
static MxArray Scalar(ScalarType value = 0) {
MxArray s(1, 1, 1, Matlab::Traits<ScalarType>::ScalarType);
s.real<ScalarType>()[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(rows(), cols(), 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 *>(mxGetImagData(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(size(), '\0');
mxGetString(ptr_, const_cast<char *>(str.data()), str.size()+1);
return str;
}
size_t size() const { return mxGetNumberOfElements(ptr_); }
size_t rows() const { return mxGetDimensions(ptr_)[0]; }
size_t cols() const { return mxGetDimensions(ptr_)[1]; }
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_); }
};
/*! @class ArgumentParser
* @brief parses inputs to a method and resolves the argument names.
*
* The ArgumentParser resolves the inputs to a method. It checks that all
* required arguments are specified and also allows named optional arguments.
* For example, the C++ function:
* void randn(Mat& mat, Mat& mean=Mat(), Mat& std=Mat());
* could be called in Matlab using any of the following signatures:
* \code
* out = randn(in);
* out = randn(in, 0, 1);
* out = randn(in, 'mean', 0, 'std', 1);
* \endcode
*
* ArgumentParser also enables function overloading by allowing users
* to add variants to a method. For example, there may be two C++ sum() methods:
* \code
* double sum(Mat& mat); % sum elements of a matrix
* Mat sum(Mat& A, Mat& B); % add two matrices
* \endcode
*
* by adding two variants to ArgumentParser, the correct underlying sum
* method can be called. If the function call is ambiguous, the
* ArgumentParser will fail with an error message.
*
* The previous example could be parsed as:
* \code
* // set up the Argument parser
* ArgumentParser arguments;
* arguments.addVariant("elementwise", 1);
* arguments.addVariant("matrix", 2);
*
* // parse the arguments
* std::vector<MxArray> inputs;
* inputs = arguments.parse(std::vector<MxArray>(prhs, prhs+nrhs));
*
* // if we get here, one unique variant is valid
* if (arguments.variantIs("elementwise")) {
* // call elementwise sum()
* }
*/
class ArgumentParser {
private:
struct Variant;
typedef std::string String;
typedef std::vector<std::string> StringVector;
typedef std::vector<MxArray> MxArrayVector;
typedef std::vector<Variant> VariantVector;
/* @class Variant
* @brief Describes a variant of arguments to a method
*
* When addVariant() is called on an instance to ArgumentParser, this class
* holds the the information that decribes that variant. The parse() method
* of ArgumentParser then attempts to match a Variant, given a set of
* inputs for a method invocation.
*/
class Variant {
public:
Variant(const String& _name, size_t _nreq, size_t _nopt, const StringVector& _keys)
: name(_name), nreq(_nreq), nopt(_nopt), keys(_keys), using_named(false) {}
String name;
size_t nreq;
size_t nopt;
StringVector keys;
bool using_named;
/*! @brief return true if the named-argument is in the Variant */
bool count(const String& key) { return std::find(keys.begin(), keys.end(), key) != keys.end(); }
/*! @brief remove a key by index from the Variant */
void erase(const size_t idx) { keys.erase(keys.begin()+idx); }
/*! @brief remove a key by name from the Variant */
void erase(const String& key) { keys.erase(std::find(keys.begin(), keys.end(), key)); }
/*! @brief convert a Variant to a string representation */
String toString(const String& method_name=String("f")) const {
std::ostringstream s;
s << method_name << "(";
for (size_t n = 0; n < nreq; ++n) {
s << "src" << n+1; if (n != nreq-1) s << ", ";
}
if (nreq && nopt) s << ", ";
for (size_t n = 0; n < keys.size(); ++n) {
s << "'" << keys[n] << "', " << keys[n];
if (n != keys.size()-1) s << ", ";
}
s << ");";
return s.str();
}
};
MxArrayVector filled_;
VariantVector variants_;
String valid_;
String method_name_;
public:
ArgumentParser(const String& method_name) : method_name_(method_name) {}
/*! @brief add a function call variant to the parser
*
* Adds a function-call signature to the parser. The function call *must* be
* unique either in its number of arguments, or in the named-syntax.
* Currently this function does not check whether that invariant stands true.
*
* This function is variadic. If should be called as follows:
* addVariant(2, 2, 'opt_1_name', 'opt_2_name');
*/
void addVariant(const String& name, size_t nreq, size_t nopt = 0, ...) {
StringVector keys;
va_list opt;
va_start(opt, nopt);
for (size_t n = 0; n < nopt; ++n) keys.push_back(va_arg(opt, const char*));
addVariant(name, nreq, nopt, keys);
}
void addVariant(const String& name, size_t nreq, size_t nopt, StringVector keys) {
variants_.push_back(Variant(name, nreq, nopt, keys));
}
/*! @brief check if the valid variant is the key name */
bool variantIs(const String& name) {
return name.compare(valid_) == 0;
}
/*! @brief parse a vector of input arguments
*
* This method parses a vector of input arguments, attempting to match them
* to a Variant spec. For each input, the method attempts to cull any
* Variants which don't match the given inputs so far.
*
* Once all inputs have been parsed, if there is one unique spec remaining,
* the output MxArray vector gets populated with the arguments, with named
* arguments removed. Any optional arguments that have not been encountered
* are set to an empty array.
*
* If multiple variants or no variants match the given call, an error
* message is emitted
*/
MxArrayVector parse(const MxArrayVector& inputs) {
// allocate the outputs
MxArrayVector outputs;
VariantVector candidates = variants_;
// iterate over the inputs, attempting to match a variant
for (MxArrayVector::const_iterator input = inputs.begin(); input != inputs.end(); ++input) {
String name = input->isString() ? input->toString() : String();
for (VariantVector::iterator candidate = candidates.begin(); candidate < candidates.end(); ++candidate) {
// check if the input is a key
bool key = candidate->count(name);
/*
* FAILURE CASES
* 1. too many inputs, or
* 2. name is not a key and we're expecting a key
* 3. name is a key, and
* we're still expecting required arguments, or
* we're expecting an argument for a previous key
*/
if ((!candidate->nreq && !candidate->nopt) ||
(!key && !candidate->nreq && candidate->keys.size() == candidate->nopt && candidate->using_named) ||
(key && (candidate->nreq || candidate->keys.size() < candidate->nopt))) {
candidate = candidates.erase(candidate)--;
}
// VALID CASES
// Still parsing required argments (input is not a key)
else if (!key && candidate->nreq) {
candidate->nreq--;
}
// Parsing optional arguments and a named argument is encountered
else if (key && !candidate->nreq && candidate->nopt > 0 && candidate->keys.size() == candidate->nopt) {
candidate->erase(name);
candidate->using_named = true;
}
// Parsing input for a named argument
else if (!key && candidate->keys.size() < candidate->nopt) {
candidate->nopt--;
}
// Parsing un-named optional arguments
else if (!key && !candidate->nreq && candidate->nopt && candidate->keys.size() && !candidate->using_named) {
candidate->erase(0);
candidate->nopt--;
}
}
}
// if any candidates remain, check that they have been fully parsed
for (VariantVector::iterator candidate = candidates.begin(); candidate < candidates.end(); ++candidate) {
if (candidate->nreq || candidate->keys.size() < candidate->nopt) {
candidate = candidates.erase(candidate)--;
}
}
// if there is not a unique candidate, throw an error
String variant_string;
for (VariantVector::iterator variant = variants_.begin(); variant != variants_.end(); ++variant) {
variant_string += "\n" + variant->toString(method_name_);
}
// if there is not a unique candidate, throw an error
if (candidates.size() > 1) {
error(String("Call to method is ambiguous. Valid variants are:")
.append(variant_string).append("\nUse named arguments to disambiguate call"));
}
if (candidates.size() == 0) {
error(String("No matching method signatures for given arguments. Valid variants are:").append(variant_string));
}
return outputs;
}
};
/*!
* @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");
std::vector<cv::Mat> channels;
cv::split(in, channels);
for (size_t c = 0; c < out.channels(); ++c) {
cv::transpose(channels[c], channels[c]);
cv::Mat outmat(out.cols(), out.rows(), cv::DataType<OutputScalar>::type,
static_cast<void *>(out.real<OutputScalar>() + out.cols()*out.rows()*c));
channels[c].convertTo(outmat, cv::DataType<OutputScalar>::type);
}
//const InputScalar* inp = in.ptr<InputScalar>(0);
//OutputScalar* outp = out.real<OutputScalar>();
//gemt('R', out.rows(), out.cols(), inp, in.step1(), outp, out.rows());
}
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");
std::vector<cv::Mat> channels;
for (size_t c = 0; c < in.channels(); ++c) {
cv::Mat outmat;
cv::Mat inmat(in.cols(), in.rows(), cv::DataType<InputScalar>::type,
static_cast<void *>(const_cast<InputScalar *>(in.real<InputScalar>() + in.cols()*in.rows()*c)));
inmat.convertTo(outmat, cv::DataType<OutputScalar>::type);
cv::transpose(outmat, outmat);
channels.push_back(outmat);
}
cv::merge(channels, out);
//const InputScalar* inp = in.real<InputScalar>();
//OutputScalar* outp = out.ptr<OutputScalar>(0);
//gemt('C', in.rows(), in.cols(), inp, in.rows(), outp, out.step1());
}
#endif
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