// special case, when the convertor needs full ArgInfo structure
static bool pyopencv_to(PyObject* o, Mat& m, const ArgInfo info)
{
bool allowND = true;
if(!o || o == Py_None)
{
if( !m.data )
m.allocator = &g_numpyAllocator;
return true;
}
if( PyInt_Check(o) )
{
double v[] = {(double)PyInt_AsLong((PyObject*)o), 0., 0., 0.};
m = Mat(4, 1, CV_64F, v).clone();
return true;
}
if( PyFloat_Check(o) )
{
double v[] = {PyFloat_AsDouble((PyObject*)o), 0., 0., 0.};
m = Mat(4, 1, CV_64F, v).clone();
return true;
}
if( PyTuple_Check(o) )
{
int i, sz = (int)PyTuple_Size((PyObject*)o);
m = Mat(sz, 1, CV_64F);
for( i = 0; i < sz; i++ )
{
PyObject* oi = PyTuple_GET_ITEM(o, i);
if( PyInt_Check(oi) )
m.at<double>(i) = (double)PyInt_AsLong(oi);
else if( PyFloat_Check(oi) )
m.at<double>(i) = (double)PyFloat_AsDouble(oi);
else
{
failmsg("%s is not a numerical tuple", info.name);
m.release();
return false;
}
}
return true;
}
if( !PyArray_Check(o) )
{
failmsg("%s is not a numpy array, neither a scalar", info.name);
return false;
}
PyArrayObject* oarr = (PyArrayObject*) o;
bool needcopy = false, needcast = false;
int typenum = PyArray_TYPE(oarr), new_typenum = typenum;
int type = typenum == NPY_UBYTE ? CV_8U :
typenum == NPY_BYTE ? CV_8S :
typenum == NPY_USHORT ? CV_16U :
typenum == NPY_SHORT ? CV_16S :
typenum == NPY_INT ? CV_32S :
typenum == NPY_INT32 ? CV_32S :
typenum == NPY_FLOAT ? CV_32F :
typenum == NPY_DOUBLE ? CV_64F : -1;
if( type < 0 )
{
if( typenum == NPY_INT64 || typenum == NPY_UINT64 || type == NPY_LONG )
{
needcopy = needcast = true;
new_typenum = NPY_INT;
type = CV_32S;
}
else
{
failmsg("%s data type = %d is not supported", info.name, typenum);
return false;
}
}
#ifndef CV_MAX_DIM
const int CV_MAX_DIM = 32;
#endif
int ndims = PyArray_NDIM(oarr);
if(ndims >= CV_MAX_DIM)
{
failmsg("%s dimensionality (=%d) is too high", info.name, ndims);
return false;
}
int size[CV_MAX_DIM+1];
size_t step[CV_MAX_DIM+1];
size_t elemsize = CV_ELEM_SIZE1(type);
const npy_intp* _sizes = PyArray_DIMS(oarr);
const npy_intp* _strides = PyArray_STRIDES(oarr);
bool ismultichannel = ndims == 3 && _sizes[2] <= CV_CN_MAX;
for( int i = ndims-1; i >= 0 && !needcopy; i-- )
{
// these checks handle cases of
// a) multi-dimensional (ndims > 2) arrays, as well as simpler 1- and 2-dimensional cases
// b) transposed arrays, where _strides[] elements go in non-descending order
// c) flipped arrays, where some of _strides[] elements are negative
if( (i == ndims-1 && (size_t)_strides[i] != elemsize) ||
(i < ndims-1 && _strides[i] < _strides[i+1]) )
needcopy = true;
}
if( ismultichannel && _strides[1] != (npy_intp)elemsize*_sizes[2] )
needcopy = true;
if (needcopy)
{
if (info.outputarg)
{
failmsg("Layout of the output array %s is incompatible with cv::Mat (step[ndims-1] != elemsize or step[1] != elemsize*nchannels)", info.name);
return false;
}
if( needcast ) {
o = PyArray_Cast(oarr, new_typenum);
oarr = (PyArrayObject*) o;
}
else {
oarr = PyArray_GETCONTIGUOUS(oarr);
o = (PyObject*) oarr;
}
_strides = PyArray_STRIDES(oarr);
}
for(int i = 0; i < ndims; i++)
{
size[i] = (int)_sizes[i];
step[i] = (size_t)_strides[i];
}
// handle degenerate case
if( ndims == 0) {
size[ndims] = 1;
step[ndims] = elemsize;
ndims++;
}
if( ismultichannel )
{
ndims--;
type |= CV_MAKETYPE(0, size[2]);
}
if( ndims > 2 && !allowND )
{
failmsg("%s has more than 2 dimensions", info.name);
return false;
}
m = Mat(ndims, size, type, PyArray_DATA(oarr), step);
m.u = g_numpyAllocator.allocate(o, ndims, size, type, step);
m.addref();
if( !needcopy )
{
Py_INCREF(o);
}
m.allocator = &g_numpyAllocator;
return true;
}
/// @brief Construct a Mat from an NDArray object.
void matFromNDArrayBoostConverter::construct(PyObject* object,
boost::python::converter::rvalue_from_python_stage1_data* data) {
namespace python = boost::python;
// Object is a borrowed reference, so create a handle indicting it is
// borrowed for proper reference counting.
python::handle<> handle(python::borrowed(object));
// Obtain a handle to the memory block that the converter has allocated
// for the C++ type.
typedef python::converter::rvalue_from_python_storage<Mat> storage_type;
void* storage = reinterpret_cast<storage_type*>(data)->storage.bytes;
// Allocate the C++ type into the converter's memory block, and assign
// its handle to the converter's convertible variable. The C++
// container is populated by passing the begin and end iterators of
// the python object to the container's constructor.
PyArrayObject* oarr = (PyArrayObject*) object;
bool needcopy = false, needcast = false;
int typenum = PyArray_TYPE(oarr), new_typenum = typenum;
int type = typenum == NPY_UBYTE ? CV_8U : typenum == NPY_BYTE ? CV_8S :
typenum == NPY_USHORT ? CV_16U :
typenum == NPY_SHORT ? CV_16S :
typenum == NPY_INT ? CV_32S :
typenum == NPY_INT32 ? CV_32S :
typenum == NPY_FLOAT ? CV_32F :
typenum == NPY_DOUBLE ? CV_64F : -1;
if (type < 0) {
needcopy = needcast = true;
new_typenum = NPY_INT;
type = CV_32S;
}
#ifndef CV_MAX_DIM
const int CV_MAX_DIM = 32;
#endif
int ndims = PyArray_NDIM(oarr);
int size[CV_MAX_DIM + 1];
size_t step[CV_MAX_DIM + 1];
size_t elemsize = CV_ELEM_SIZE1(type);
const npy_intp* _sizes = PyArray_DIMS(oarr);
const npy_intp* _strides = PyArray_STRIDES(oarr);
bool ismultichannel = ndims == 3 && _sizes[2] <= CV_CN_MAX;
for (int i = ndims - 1; i >= 0 && !needcopy; i--) {
// these checks handle cases of
// a) multi-dimensional (ndims > 2) arrays, as well as simpler 1- and 2-dimensional cases
// b) transposed arrays, where _strides[] elements go in non-descending order
// c) flipped arrays, where some of _strides[] elements are negative
if ((i == ndims - 1 && (size_t) _strides[i] != elemsize)
|| (i < ndims - 1 && _strides[i] < _strides[i + 1]))
needcopy = true;
}
if (ismultichannel && _strides[1] != (npy_intp) elemsize * _sizes[2])
needcopy = true;
if (needcopy) {
if (needcast) {
object = PyArray_Cast(oarr, new_typenum);
oarr = (PyArrayObject*) object;
} else {
oarr = PyArray_GETCONTIGUOUS(oarr);
object = (PyObject*) oarr;
}
_strides = PyArray_STRIDES(oarr);
}
for (int i = 0; i < ndims; i++) {
size[i] = (int) _sizes[i];
step[i] = (size_t) _strides[i];
}
// handle degenerate case
if (ndims == 0) {
size[ndims] = 1;
step[ndims] = elemsize;
ndims++;
}
if (ismultichannel) {
ndims--;
type |= CV_MAKETYPE(0, size[2]);
}
if (!needcopy) {
Py_INCREF(object);
}
cv::Mat* m = new (storage) cv::Mat(ndims, size, type, PyArray_DATA(oarr), step);
m->u = g_numpyAllocator.allocate(object, ndims, size, type, step);
m->allocator = &g_numpyAllocator;
m->addref();
data->convertible = storage;
}
namespace pbcvt {
using namespace cv;
//=================== ERROR HANDLING =========================================================
static int failmsg(const char *fmt, ...) {
char str[1000];
va_list ap;
va_start(ap, fmt);
vsnprintf(str, sizeof(str), fmt, ap);
va_end(ap);
PyErr_SetString(PyExc_TypeError, str);
return 0;
}
//=================== THREADING ==============================================================
class PyAllowThreads {
public:
PyAllowThreads() :
_state(PyEval_SaveThread()) {
}
~PyAllowThreads() {
PyEval_RestoreThread(_state);
}
private:
PyThreadState* _state;
};
class PyEnsureGIL {
public:
PyEnsureGIL() :
_state(PyGILState_Ensure()) {
}
~PyEnsureGIL() {
PyGILState_Release(_state);
}
private:
PyGILState_STATE _state;
};
enum {
ARG_NONE = 0, ARG_MAT = 1, ARG_SCALAR = 2
};
class NumpyAllocator:
public MatAllocator {
public:
NumpyAllocator() {
stdAllocator = Mat::getStdAllocator();
}
~NumpyAllocator() {
}
UMatData* allocate(PyObject* o, int dims, const int* sizes, int type,
size_t* step) const {
UMatData* u = new UMatData(this);
u->data = u->origdata = (uchar*) PyArray_DATA((PyArrayObject*) o);
npy_intp* _strides = PyArray_STRIDES((PyArrayObject*) o);
for (int i = 0; i < dims - 1; i++)
step[i] = (size_t) _strides[i];
step[dims - 1] = CV_ELEM_SIZE(type);
u->size = sizes[0] * step[0];
u->userdata = o;
return u;
}
UMatData* allocate(int dims0, const int* sizes, int type, void* data,
size_t* step, cv::AccessFlag flags, UMatUsageFlags usageFlags) const {
if (data != 0) {
CV_Error(Error::StsAssert, "The data should normally be NULL!");
// probably this is safe to do in such extreme case
return stdAllocator->allocate(dims0, sizes, type, data, step, flags,
usageFlags);
}
PyEnsureGIL gil;
int depth = CV_MAT_DEPTH(type);
int cn = CV_MAT_CN(type);
const int f = (int) (sizeof(size_t) / 8);
int typenum =
depth == CV_8U ? NPY_UBYTE :
depth == CV_8S ? NPY_BYTE :
depth == CV_16U ? NPY_USHORT :
depth == CV_16S ? NPY_SHORT :
depth == CV_32S ? NPY_INT :
depth == CV_32F ? NPY_FLOAT :
depth == CV_64F ?
NPY_DOUBLE :
f * NPY_ULONGLONG + (f ^ 1) * NPY_UINT;
int i, dims = dims0;
cv::AutoBuffer<npy_intp> _sizes(dims + 1);
for (i = 0; i < dims; i++)
_sizes[i] = sizes[i];
if (cn > 1)
_sizes[dims++] = cn;
PyObject* o = PyArray_SimpleNew(dims, _sizes, typenum);
if (!o)
CV_Error_(Error::StsError,
("The numpy array of typenum=%d, ndims=%d can not be created", typenum, dims));
return allocate(o, dims0, sizes, type, step);
}
bool allocate(UMatData* u, cv::AccessFlag accessFlags,
UMatUsageFlags usageFlags) const {
return stdAllocator->allocate(u, accessFlags, usageFlags);
}
void deallocate(UMatData* u) const {
if (u) {
PyEnsureGIL gil;
PyObject* o = (PyObject*) u->userdata;
Py_XDECREF(o);
delete u;
}
}
const MatAllocator* stdAllocator;
};
//=================== ALLOCATOR INITIALIZTION ==================================================
NumpyAllocator g_numpyAllocator;
//=================== STANDALONE CONVERTER FUNCTIONS =========================================
PyObject* fromMatToNDArray(const Mat& m) {
if (!m.data)
Py_RETURN_NONE;
Mat temp,
*p = (Mat*) &m;
if (!p->u || p->allocator != &g_numpyAllocator) {
temp.allocator = &g_numpyAllocator;
ERRWRAP2(m.copyTo(temp));
p = &temp;
}
PyObject* o = (PyObject*) p->u->userdata;
Py_INCREF(o);
return o;
}
Mat fromNDArrayToMat(PyObject* o) {
cv::Mat m;
bool allowND = true;
if (!PyArray_Check(o)) {
failmsg("argument is not a numpy array");
if (!m.data)
m.allocator = &g_numpyAllocator;
} else {
PyArrayObject* oarr = (PyArrayObject*) o;
bool needcopy = false, needcast = false;
int typenum = PyArray_TYPE(oarr), new_typenum = typenum;
int type = typenum == NPY_UBYTE ? CV_8U : typenum == NPY_BYTE ? CV_8S :
typenum == NPY_USHORT ? CV_16U :
typenum == NPY_SHORT ? CV_16S :
typenum == NPY_INT ? CV_32S :
typenum == NPY_INT32 ? CV_32S :
typenum == NPY_FLOAT ? CV_32F :
typenum == NPY_DOUBLE ? CV_64F : -1;
if (type < 0) {
if (typenum == NPY_INT64 || typenum == NPY_UINT64
|| type == NPY_LONG) {
needcopy = needcast = true;
new_typenum = NPY_INT;
type = CV_32S;
} else {
failmsg("Argument data type is not supported");
m.allocator = &g_numpyAllocator;
return m;
}
}
#ifndef CV_MAX_DIM
const int CV_MAX_DIM = 32;
#endif
int ndims = PyArray_NDIM(oarr);
if (ndims >= CV_MAX_DIM) {
failmsg("Dimensionality of argument is too high");
if (!m.data)
m.allocator = &g_numpyAllocator;
return m;
}
int size[CV_MAX_DIM + 1];
size_t step[CV_MAX_DIM + 1];
size_t elemsize = CV_ELEM_SIZE1(type);
const npy_intp* _sizes = PyArray_DIMS(oarr);
const npy_intp* _strides = PyArray_STRIDES(oarr);
bool ismultichannel = ndims == 3 && _sizes[2] <= CV_CN_MAX;
for (int i = ndims - 1; i >= 0 && !needcopy; i--) {
// these checks handle cases of
// a) multi-dimensional (ndims > 2) arrays, as well as simpler 1- and 2-dimensional cases
// b) transposed arrays, where _strides[] elements go in non-descending order
// c) flipped arrays, where some of _strides[] elements are negative
if ((i == ndims - 1 && (size_t) _strides[i] != elemsize)
|| (i < ndims - 1 && _strides[i] < _strides[i + 1]))
needcopy = true;
}
if (ismultichannel && _strides[1] != (npy_intp) elemsize * _sizes[2])
needcopy = true;
if (needcopy) {
if (needcast) {
o = PyArray_Cast(oarr, new_typenum);
oarr = (PyArrayObject*) o;
} else {
oarr = PyArray_GETCONTIGUOUS(oarr);
o = (PyObject*) oarr;
}
_strides = PyArray_STRIDES(oarr);
}
for (int i = 0; i < ndims; i++) {
size[i] = (int) _sizes[i];
step[i] = (size_t) _strides[i];
}
// handle degenerate case
if (ndims == 0) {
size[ndims] = 1;
step[ndims] = elemsize;
ndims++;
}
if (ismultichannel) {
ndims--;
type |= CV_MAKETYPE(0, size[2]);
}
if (ndims > 2 && !allowND) {
failmsg("%s has more than 2 dimensions");
} else {
m = Mat(ndims, size, type, PyArray_DATA(oarr), step);
m.u = g_numpyAllocator.allocate(o, ndims, size, type, step);
m.addref();
if (!needcopy) {
Py_INCREF(o);
}
}
m.allocator = &g_numpyAllocator;
}
return m;
}
//=================== BOOST CONVERTERS =======================================================
PyObject* matToNDArrayBoostConverter::convert(Mat const& m) {
if (!m.data)
Py_RETURN_NONE;
Mat temp,
*p = (Mat*) &m;
if (!p->u || p->allocator != &g_numpyAllocator)
{
temp.allocator = &g_numpyAllocator;
ERRWRAP2(m.copyTo(temp));
p = &temp;
}
PyObject* o = (PyObject*) p->u->userdata;
Py_INCREF(o);
return o;
}
matFromNDArrayBoostConverter::matFromNDArrayBoostConverter() {
boost::python::converter::registry::push_back(convertible, construct,
boost::python::type_id<Mat>());
}
/// @brief Check if PyObject is an array and can be converted to OpenCV matrix.
void* matFromNDArrayBoostConverter::convertible(PyObject* object) {
if (!PyArray_Check(object)) {
return NULL;
}
#ifndef CV_MAX_DIM
const int CV_MAX_DIM = 32;
#endif
PyArrayObject* oarr = (PyArrayObject*) object;
int typenum = PyArray_TYPE(oarr);
if (typenum != NPY_INT64 && typenum != NPY_UINT64 && typenum != NPY_LONG
&& typenum != NPY_UBYTE && typenum != NPY_BYTE
&& typenum != NPY_USHORT && typenum != NPY_SHORT
&& typenum != NPY_INT && typenum != NPY_INT32
&& typenum != NPY_FLOAT && typenum != NPY_DOUBLE) {
return NULL;
}
int ndims = PyArray_NDIM(oarr); //data type not supported
if (ndims >= CV_MAX_DIM) {
return NULL; //too many dimensions
}
return object;
}
/// @brief Construct a Mat from an NDArray object.
void matFromNDArrayBoostConverter::construct(PyObject* object,
boost::python::converter::rvalue_from_python_stage1_data* data) {
namespace python = boost::python;
// Object is a borrowed reference, so create a handle indicting it is
// borrowed for proper reference counting.
python::handle<> handle(python::borrowed(object));
// Obtain a handle to the memory block that the converter has allocated
// for the C++ type.
typedef python::converter::rvalue_from_python_storage<Mat> storage_type;
void* storage = reinterpret_cast<storage_type*>(data)->storage.bytes;
// Allocate the C++ type into the converter's memory block, and assign
// its handle to the converter's convertible variable. The C++
// container is populated by passing the begin and end iterators of
// the python object to the container's constructor.
PyArrayObject* oarr = (PyArrayObject*) object;
bool needcopy = false, needcast = false;
int typenum = PyArray_TYPE(oarr), new_typenum = typenum;
int type = typenum == NPY_UBYTE ? CV_8U : typenum == NPY_BYTE ? CV_8S :
typenum == NPY_USHORT ? CV_16U :
typenum == NPY_SHORT ? CV_16S :
typenum == NPY_INT ? CV_32S :
typenum == NPY_INT32 ? CV_32S :
typenum == NPY_FLOAT ? CV_32F :
typenum == NPY_DOUBLE ? CV_64F : -1;
if (type < 0) {
needcopy = needcast = true;
new_typenum = NPY_INT;
type = CV_32S;
}
#ifndef CV_MAX_DIM
const int CV_MAX_DIM = 32;
#endif
int ndims = PyArray_NDIM(oarr);
int size[CV_MAX_DIM + 1];
size_t step[CV_MAX_DIM + 1];
size_t elemsize = CV_ELEM_SIZE1(type);
const npy_intp* _sizes = PyArray_DIMS(oarr);
const npy_intp* _strides = PyArray_STRIDES(oarr);
bool ismultichannel = ndims == 3 && _sizes[2] <= CV_CN_MAX;
for (int i = ndims - 1; i >= 0 && !needcopy; i--) {
// these checks handle cases of
// a) multi-dimensional (ndims > 2) arrays, as well as simpler 1- and 2-dimensional cases
// b) transposed arrays, where _strides[] elements go in non-descending order
// c) flipped arrays, where some of _strides[] elements are negative
if ((i == ndims - 1 && (size_t) _strides[i] != elemsize)
|| (i < ndims - 1 && _strides[i] < _strides[i + 1]))
needcopy = true;
}
if (ismultichannel && _strides[1] != (npy_intp) elemsize * _sizes[2])
needcopy = true;
if (needcopy) {
if (needcast) {
object = PyArray_Cast(oarr, new_typenum);
oarr = (PyArrayObject*) object;
} else {
oarr = PyArray_GETCONTIGUOUS(oarr);
object = (PyObject*) oarr;
}
_strides = PyArray_STRIDES(oarr);
}
for (int i = 0; i < ndims; i++) {
size[i] = (int) _sizes[i];
step[i] = (size_t) _strides[i];
}
// handle degenerate case
if (ndims == 0) {
size[ndims] = 1;
step[ndims] = elemsize;
ndims++;
}
if (ismultichannel) {
ndims--;
type |= CV_MAKETYPE(0, size[2]);
}
if (!needcopy) {
Py_INCREF(object);
}
cv::Mat* m = new (storage) cv::Mat(ndims, size, type, PyArray_DATA(oarr), step);
m->u = g_numpyAllocator.allocate(object, ndims, size, type, step);
m->allocator = &g_numpyAllocator;
m->addref();
data->convertible = storage;
}
} //end namespace pbcvt
Mat fromNDArrayToMat(PyObject* o) {
cv::Mat m;
bool allowND = true;
if (!PyArray_Check(o)) {
failmsg("argument is not a numpy array");
if (!m.data)
m.allocator = &g_numpyAllocator;
} else {
PyArrayObject* oarr = (PyArrayObject*) o;
bool needcopy = false, needcast = false;
int typenum = PyArray_TYPE(oarr), new_typenum = typenum;
int type = typenum == NPY_UBYTE ? CV_8U : typenum == NPY_BYTE ? CV_8S :
typenum == NPY_USHORT ? CV_16U :
typenum == NPY_SHORT ? CV_16S :
typenum == NPY_INT ? CV_32S :
typenum == NPY_INT32 ? CV_32S :
typenum == NPY_FLOAT ? CV_32F :
typenum == NPY_DOUBLE ? CV_64F : -1;
if (type < 0) {
if (typenum == NPY_INT64 || typenum == NPY_UINT64
|| type == NPY_LONG) {
needcopy = needcast = true;
new_typenum = NPY_INT;
type = CV_32S;
} else {
failmsg("Argument data type is not supported");
m.allocator = &g_numpyAllocator;
return m;
}
}
#ifndef CV_MAX_DIM
const int CV_MAX_DIM = 32;
#endif
int ndims = PyArray_NDIM(oarr);
if (ndims >= CV_MAX_DIM) {
failmsg("Dimensionality of argument is too high");
if (!m.data)
m.allocator = &g_numpyAllocator;
return m;
}
int size[CV_MAX_DIM + 1];
size_t step[CV_MAX_DIM + 1];
size_t elemsize = CV_ELEM_SIZE1(type);
const npy_intp* _sizes = PyArray_DIMS(oarr);
const npy_intp* _strides = PyArray_STRIDES(oarr);
bool ismultichannel = ndims == 3 && _sizes[2] <= CV_CN_MAX;
for (int i = ndims - 1; i >= 0 && !needcopy; i--) {
// these checks handle cases of
// a) multi-dimensional (ndims > 2) arrays, as well as simpler 1- and 2-dimensional cases
// b) transposed arrays, where _strides[] elements go in non-descending order
// c) flipped arrays, where some of _strides[] elements are negative
if ((i == ndims - 1 && (size_t) _strides[i] != elemsize)
|| (i < ndims - 1 && _strides[i] < _strides[i + 1]))
needcopy = true;
}
if (ismultichannel && _strides[1] != (npy_intp) elemsize * _sizes[2])
needcopy = true;
if (needcopy) {
if (needcast) {
o = PyArray_Cast(oarr, new_typenum);
oarr = (PyArrayObject*) o;
} else {
oarr = PyArray_GETCONTIGUOUS(oarr);
o = (PyObject*) oarr;
}
_strides = PyArray_STRIDES(oarr);
}
for (int i = 0; i < ndims; i++) {
size[i] = (int) _sizes[i];
step[i] = (size_t) _strides[i];
}
// handle degenerate case
if (ndims == 0) {
size[ndims] = 1;
step[ndims] = elemsize;
ndims++;
}
if (ismultichannel) {
ndims--;
type |= CV_MAKETYPE(0, size[2]);
}
if (ndims > 2 && !allowND) {
failmsg("%s has more than 2 dimensions");
} else {
m = Mat(ndims, size, type, PyArray_DATA(oarr), step);
m.u = g_numpyAllocator.allocate(o, ndims, size, type, step);
m.addref();
if (!needcopy) {
Py_INCREF(o);
}
}
m.allocator = &g_numpyAllocator;
}
return m;
}
