/usr/include/vigra/numpy_array_taggedshape.hxx is in libvigraimpex-dev 1.10.0+dfsg-3ubuntu2.
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/* */
/* Copyright 2009 by Ullrich Koethe and Hans Meine */
/* */
/* This file is part of the VIGRA computer vision library. */
/* The VIGRA Website is */
/* http://hci.iwr.uni-heidelberg.de/vigra/ */
/* Please direct questions, bug reports, and contributions to */
/* ullrich.koethe@iwr.uni-heidelberg.de or */
/* vigra@informatik.uni-hamburg.de */
/* */
/* Permission is hereby granted, free of charge, to any person */
/* obtaining a copy of this software and associated documentation */
/* files (the "Software"), to deal in the Software without */
/* restriction, including without limitation the rights to use, */
/* copy, modify, merge, publish, distribute, sublicense, and/or */
/* sell copies of the Software, and to permit persons to whom the */
/* Software is furnished to do so, subject to the following */
/* conditions: */
/* */
/* The above copyright notice and this permission notice shall be */
/* included in all copies or substantial portions of the */
/* Software. */
/* */
/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND */
/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES */
/* OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND */
/* NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT */
/* HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, */
/* WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING */
/* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR */
/* OTHER DEALINGS IN THE SOFTWARE. */
/* */
/************************************************************************/
#ifndef VIGRA_NUMPY_ARRAY_TAGGEDSHAPE_HXX
#define VIGRA_NUMPY_ARRAY_TAGGEDSHAPE_HXX
#ifndef NPY_NO_DEPRECATED_API
# define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#endif
#include <string>
#include "array_vector.hxx"
#include "python_utility.hxx"
#include "axistags.hxx"
namespace vigra {
namespace detail {
inline
python_ptr getArrayTypeObject()
{
python_ptr arraytype((PyObject*)&PyArray_Type);
python_ptr vigra(PyImport_ImportModule("vigra"));
if(!vigra)
PyErr_Clear();
return pythonGetAttr(vigra, "standardArrayType", arraytype);
}
inline
std::string defaultOrder(std::string defaultValue = "C")
{
python_ptr arraytype = getArrayTypeObject();
return pythonGetAttr(arraytype, "defaultOrder", defaultValue);
}
inline
python_ptr defaultAxistags(int ndim, std::string order = "")
{
if(order == "")
order = defaultOrder();
python_ptr arraytype = getArrayTypeObject();
python_ptr func(PyString_FromString("defaultAxistags"), python_ptr::keep_count);
python_ptr d(PyInt_FromLong(ndim), python_ptr::keep_count);
python_ptr o(PyString_FromString(order.c_str()), python_ptr::keep_count);
python_ptr axistags(PyObject_CallMethodObjArgs(arraytype, func.get(), d.get(), o.get(), NULL),
python_ptr::keep_count);
if(axistags)
return axistags;
PyErr_Clear();
return python_ptr();
}
inline
python_ptr emptyAxistags(int ndim)
{
python_ptr arraytype = getArrayTypeObject();
python_ptr func(PyString_FromString("_empty_axistags"), python_ptr::keep_count);
python_ptr d(PyInt_FromLong(ndim), python_ptr::keep_count);
python_ptr axistags(PyObject_CallMethodObjArgs(arraytype, func.get(), d.get(), NULL),
python_ptr::keep_count);
if(axistags)
return axistags;
PyErr_Clear();
return python_ptr();
}
inline
void
getAxisPermutationImpl(ArrayVector<npy_intp> & permute,
python_ptr object, const char * name,
AxisInfo::AxisType type, bool ignoreErrors)
{
python_ptr func(PyString_FromString(name), python_ptr::keep_count);
python_ptr t(PyInt_FromLong((long)type), python_ptr::keep_count);
python_ptr permutation(PyObject_CallMethodObjArgs(object, func.get(), t.get(), NULL),
python_ptr::keep_count);
if(!permutation && ignoreErrors)
{
PyErr_Clear();
return;
}
pythonToCppException(permutation);
if(!PySequence_Check(permutation))
{
if(ignoreErrors)
return;
std::string message = std::string(name) + "() did not return a sequence.";
PyErr_SetString(PyExc_ValueError, message.c_str());
pythonToCppException(false);
}
ArrayVector<npy_intp> res(PySequence_Length(permutation));
for(int k=0; k<(int)res.size(); ++k)
{
python_ptr i(PySequence_GetItem(permutation, k), python_ptr::keep_count);
if(!PyInt_Check(i))
{
if(ignoreErrors)
return;
std::string message = std::string(name) + "() did not return a sequence of int.";
PyErr_SetString(PyExc_ValueError, message.c_str());
pythonToCppException(false);
}
res[k] = PyInt_AsLong(i);
}
res.swap(permute);
}
inline
void
getAxisPermutationImpl(ArrayVector<npy_intp> & permute,
python_ptr object, const char * name, bool ignoreErrors)
{
getAxisPermutationImpl(permute, object, name, AxisInfo::AllAxes, ignoreErrors);
}
} // namespace detail
/********************************************************/
/* */
/* PyAxisTags */
/* */
/********************************************************/
// FIXME: right now, we implement this class using the standard
// Python C-API only. It would be easier and more efficient
// to use boost::python here, but it would cause NumpyArray
// to depend on boost, making it more difficult to use
// NumpyArray in connection with other glue code generators.
class PyAxisTags
{
public:
typedef PyObject * pointer;
python_ptr axistags;
PyAxisTags(python_ptr tags = python_ptr(), bool createCopy = false)
{
if(!tags)
return;
// FIXME: do a more elaborate type check here?
if(!PySequence_Check(tags))
{
PyErr_SetString(PyExc_TypeError,
"PyAxisTags(tags): tags argument must have type 'AxisTags'.");
pythonToCppException(false);
}
else if(PySequence_Length(tags) == 0)
{
return;
}
if(createCopy)
{
python_ptr func(PyString_FromString("__copy__"), python_ptr::keep_count);
axistags = python_ptr(PyObject_CallMethodObjArgs(tags, func.get(), NULL),
python_ptr::keep_count);
}
else
{
axistags = tags;
}
}
PyAxisTags(PyAxisTags const & other, bool createCopy = false)
{
if(!other.axistags)
return;
if(createCopy)
{
python_ptr func(PyString_FromString("__copy__"), python_ptr::keep_count);
axistags = python_ptr(PyObject_CallMethodObjArgs(other.axistags, func.get(), NULL),
python_ptr::keep_count);
}
else
{
axistags = other.axistags;
}
}
PyAxisTags(int ndim, std::string const & order = "")
{
if(order != "")
axistags = detail::defaultAxistags(ndim, order);
else
axistags = detail::emptyAxistags(ndim);
}
long size() const
{
return axistags
? PySequence_Length(axistags)
: 0;
}
long channelIndex(long defaultVal) const
{
return pythonGetAttr(axistags, "channelIndex", defaultVal);
}
long channelIndex() const
{
return channelIndex(size());
}
bool hasChannelAxis() const
{
return channelIndex() != size();
}
long innerNonchannelIndex(long defaultVal) const
{
return pythonGetAttr(axistags, "innerNonchannelIndex", defaultVal);
}
long innerNonchannelIndex() const
{
return innerNonchannelIndex(size());
}
void setChannelDescription(std::string const & description)
{
if(!axistags)
return;
python_ptr d(PyString_FromString(description.c_str()), python_ptr::keep_count);
python_ptr func(PyString_FromString("setChannelDescription"), python_ptr::keep_count);
python_ptr res(PyObject_CallMethodObjArgs(axistags, func.get(), d.get(), NULL),
python_ptr::keep_count);
pythonToCppException(res);
}
double resolution(long index)
{
if(!axistags)
return 0.0;
python_ptr func(PyString_FromString("resolution"), python_ptr::keep_count);
python_ptr i(PyInt_FromLong(index), python_ptr::keep_count);
python_ptr res(PyObject_CallMethodObjArgs(axistags, func.get(), i.get(), NULL),
python_ptr::keep_count);
pythonToCppException(res);
if(!PyFloat_Check(res))
{
PyErr_SetString(PyExc_TypeError, "AxisTags.resolution() did not return float.");
pythonToCppException(false);
}
return PyFloat_AsDouble(res);
}
void setResolution(long index, double resolution)
{
if(!axistags)
return;
python_ptr func(PyString_FromString("setResolution"), python_ptr::keep_count);
python_ptr i(PyInt_FromLong(index), python_ptr::keep_count);
python_ptr r(PyFloat_FromDouble(resolution), python_ptr::keep_count);
python_ptr res(PyObject_CallMethodObjArgs(axistags, func.get(), i.get(), r.get(), NULL),
python_ptr::keep_count);
pythonToCppException(res);
}
void scaleResolution(long index, double factor)
{
if(!axistags)
return;
python_ptr func(PyString_FromString("scaleResolution"), python_ptr::keep_count);
python_ptr i(PyInt_FromLong(index), python_ptr::keep_count);
python_ptr f(PyFloat_FromDouble(factor), python_ptr::keep_count);
python_ptr res(PyObject_CallMethodObjArgs(axistags, func.get(), i.get(), f.get(), NULL),
python_ptr::keep_count);
pythonToCppException(res);
}
void toFrequencyDomain(long index, int size, int sign = 1)
{
if(!axistags)
return;
python_ptr func(sign == 1
? PyString_FromString("toFrequencyDomain")
: PyString_FromString("fromFrequencyDomain"),
python_ptr::keep_count);
python_ptr i(PyInt_FromLong(index), python_ptr::keep_count);
python_ptr s(PyInt_FromLong(size), python_ptr::keep_count);
python_ptr res(PyObject_CallMethodObjArgs(axistags, func.get(), i.get(), s.get(), NULL),
python_ptr::keep_count);
pythonToCppException(res);
}
void fromFrequencyDomain(long index, int size)
{
toFrequencyDomain(index, size, -1);
}
ArrayVector<npy_intp>
permutationToNormalOrder(bool ignoreErrors = false) const
{
ArrayVector<npy_intp> permute;
detail::getAxisPermutationImpl(permute, axistags, "permutationToNormalOrder", ignoreErrors);
return permute;
}
ArrayVector<npy_intp>
permutationToNormalOrder(AxisInfo::AxisType types, bool ignoreErrors = false) const
{
ArrayVector<npy_intp> permute;
detail::getAxisPermutationImpl(permute, axistags,
"permutationToNormalOrder", types, ignoreErrors);
return permute;
}
ArrayVector<npy_intp>
permutationFromNormalOrder(bool ignoreErrors = false) const
{
ArrayVector<npy_intp> permute;
detail::getAxisPermutationImpl(permute, axistags,
"permutationFromNormalOrder", ignoreErrors);
return permute;
}
ArrayVector<npy_intp>
permutationFromNormalOrder(AxisInfo::AxisType types, bool ignoreErrors = false) const
{
ArrayVector<npy_intp> permute;
detail::getAxisPermutationImpl(permute, axistags,
"permutationFromNormalOrder", types, ignoreErrors);
return permute;
}
void dropChannelAxis()
{
if(!axistags)
return;
python_ptr func(PyString_FromString("dropChannelAxis"),
python_ptr::keep_count);
python_ptr res(PyObject_CallMethodObjArgs(axistags, func.get(), NULL),
python_ptr::keep_count);
pythonToCppException(res);
}
void insertChannelAxis()
{
if(!axistags)
return;
python_ptr func(PyString_FromString("insertChannelAxis"),
python_ptr::keep_count);
python_ptr res(PyObject_CallMethodObjArgs(axistags, func.get(), NULL),
python_ptr::keep_count);
pythonToCppException(res);
}
operator pointer()
{
return axistags.get();
}
bool operator!() const
{
return !axistags;
}
};
/********************************************************/
/* */
/* TaggedShape */
/* */
/********************************************************/
class TaggedShape
{
public:
enum ChannelAxis { first, last, none };
ArrayVector<npy_intp> shape, original_shape;
PyAxisTags axistags;
ChannelAxis channelAxis;
std::string channelDescription;
explicit TaggedShape(MultiArrayIndex size)
: shape(size),
axistags(size),
channelAxis(none)
{}
template <class U, int N>
TaggedShape(TinyVector<U, N> const & sh, PyAxisTags tags)
: shape(sh.begin(), sh.end()),
original_shape(sh.begin(), sh.end()),
axistags(tags),
channelAxis(none)
{}
template <class T>
TaggedShape(ArrayVector<T> const & sh, PyAxisTags tags)
: shape(sh.begin(), sh.end()),
original_shape(sh.begin(), sh.end()),
axistags(tags),
channelAxis(none)
{}
template <class U, int N>
explicit TaggedShape(TinyVector<U, N> const & sh)
: shape(sh.begin(), sh.end()),
original_shape(sh.begin(), sh.end()),
channelAxis(none)
{}
template <class T>
explicit TaggedShape(ArrayVector<T> const & sh)
: shape(sh.begin(), sh.end()),
original_shape(sh.begin(), sh.end()),
channelAxis(none)
{}
template <class U, int N>
TaggedShape & resize(TinyVector<U, N> const & sh)
{
int start = channelAxis == first
? 1
: 0,
stop = channelAxis == last
? (int)size()-1
: (int)size();
vigra_precondition(N == stop - start || size() == 0,
"TaggedShape.resize(): size mismatch.");
if(size() == 0)
shape.resize(N);
for(int k=0; k<N; ++k)
shape[k+start] = sh[k];
return *this;
}
TaggedShape & resize(MultiArrayIndex v1)
{
return resize(TinyVector<MultiArrayIndex, 1>(v1));
}
TaggedShape & resize(MultiArrayIndex v1, MultiArrayIndex v2)
{
return resize(TinyVector<MultiArrayIndex, 2>(v1, v2));
}
TaggedShape & resize(MultiArrayIndex v1, MultiArrayIndex v2, MultiArrayIndex v3)
{
return resize(TinyVector<MultiArrayIndex, 3>(v1, v2, v3));
}
TaggedShape & resize(MultiArrayIndex v1, MultiArrayIndex v2,
MultiArrayIndex v3, MultiArrayIndex v4)
{
return resize(TinyVector<MultiArrayIndex, 4>(v1, v2, v3, v4));
}
npy_intp & operator[](int i)
{
return shape[i];
}
npy_intp operator[](int i) const
{
return shape[i];
}
unsigned int size() const
{
return shape.size();
}
TaggedShape & operator+=(int v)
{
int start = channelAxis == first
? 1
: 0,
stop = channelAxis == last
? (int)size()-1
: (int)size();
for(int k=start; k<stop; ++k)
shape[k] += v;
return *this;
}
TaggedShape & operator-=(int v)
{
return operator+=(-v);
}
TaggedShape & operator*=(int factor)
{
int start = channelAxis == first
? 1
: 0,
stop = channelAxis == last
? (int)size()-1
: (int)size();
for(int k=start; k<stop; ++k)
shape[k] *= factor;
return *this;
}
void rotateToNormalOrder()
{
if(axistags && channelAxis == last)
{
int ndim = (int)size();
npy_intp channelCount = shape[ndim-1];
for(int k=ndim-1; k>0; --k)
shape[k] = shape[k-1];
shape[0] = channelCount;
channelCount = original_shape[ndim-1];
for(int k=ndim-1; k>0; --k)
original_shape[k] = original_shape[k-1];
original_shape[0] = channelCount;
channelAxis = first;
}
}
TaggedShape & setChannelDescription(std::string const & description)
{
// we only remember the description here, and will actually set
// it in the finalize function
channelDescription = description;
return *this;
}
TaggedShape & setChannelIndexLast()
{
// FIXME: add some checks?
channelAxis = last;
return *this;
}
// transposeShape() means: only shape and resolution are transposed, not the axis keys
template <class U, int N>
TaggedShape & transposeShape(TinyVector<U, N> const & p)
{
int ntags = axistags.size();
ArrayVector<npy_intp> permute = axistags.permutationToNormalOrder();
int tstart = (axistags.channelIndex(ntags) < ntags)
? 1
: 0;
int sstart = (channelAxis == first)
? 1
: 0;
int ndim = ntags - tstart;
vigra_precondition(N == ndim,
"TaggedShape.transposeShape(): size mismatch.");
PyAxisTags newAxistags(axistags.axistags); // force copy
for(int k=0; k<ndim; ++k)
{
original_shape[k+sstart] = shape[p[k]+sstart];
newAxistags.setResolution(permute[k+tstart], axistags.resolution(permute[p[k]+tstart]));
}
shape = original_shape;
axistags = newAxistags;
return *this;
}
TaggedShape & toFrequencyDomain(int sign = 1)
{
int ntags = axistags.size();
ArrayVector<npy_intp> permute = axistags.permutationToNormalOrder();
int tstart = (axistags.channelIndex(ntags) < ntags)
? 1
: 0;
int sstart = (channelAxis == first)
? 1
: 0;
int send = (channelAxis == last)
? (int)size()-1
: (int)size();
int size = send - sstart;
for(int k=0; k<size; ++k)
{
axistags.toFrequencyDomain(permute[k+tstart], shape[k+sstart], sign);
}
return *this;
}
TaggedShape & fromFrequencyDomain()
{
return toFrequencyDomain(-1);
}
bool compatible(TaggedShape const & other) const
{
if(channelCount() != other.channelCount())
return false;
int start = channelAxis == first
? 1
: 0,
stop = channelAxis == last
? (int)size()-1
: (int)size();
int ostart = other.channelAxis == first
? 1
: 0,
ostop = other.channelAxis == last
? (int)other.size()-1
: (int)other.size();
int len = stop - start;
if(len != ostop - ostart)
return false;
for(int k=0; k<len; ++k)
if(shape[k+start] != other.shape[k+ostart])
return false;
return true;
}
TaggedShape & setChannelCount(int count)
{
switch(channelAxis)
{
case first:
if(count > 0)
{
shape[0] = count;
}
else
{
shape.erase(shape.begin());
original_shape.erase(original_shape.begin());
channelAxis = none;
}
break;
case last:
if(count > 0)
{
shape[size()-1] = count;
}
else
{
shape.pop_back();
original_shape.pop_back();
channelAxis = none;
}
break;
case none:
if(count > 0)
{
shape.push_back(count);
original_shape.push_back(count);
channelAxis = last;
}
break;
}
return *this;
}
int channelCount() const
{
switch(channelAxis)
{
case first:
return shape[0];
case last:
return shape[size()-1];
default:
return 1;
}
}
};
inline
void scaleAxisResolution(TaggedShape & tagged_shape)
{
if(tagged_shape.size() != tagged_shape.original_shape.size())
return;
int ntags = tagged_shape.axistags.size();
ArrayVector<npy_intp> permute = tagged_shape.axistags.permutationToNormalOrder();
int tstart = (tagged_shape.axistags.channelIndex(ntags) < ntags)
? 1
: 0;
int sstart = (tagged_shape.channelAxis == TaggedShape::first)
? 1
: 0;
int size = (int)tagged_shape.size() - sstart;
for(int k=0; k<size; ++k)
{
int sk = k + sstart;
if(tagged_shape.shape[sk] == tagged_shape.original_shape[sk])
continue;
double factor = (tagged_shape.original_shape[sk] - 1.0) / (tagged_shape.shape[sk] - 1.0);
tagged_shape.axistags.scaleResolution(permute[k+tstart], factor);
}
}
inline
void unifyTaggedShapeSize(TaggedShape & tagged_shape)
{
PyAxisTags axistags = tagged_shape.axistags;
ArrayVector<npy_intp> & shape = tagged_shape.shape;
int ndim = (int)shape.size();
int ntags = axistags.size();
long channelIndex = axistags.channelIndex();
if(tagged_shape.channelAxis == TaggedShape::none)
{
// shape has no channel axis
if(channelIndex == ntags)
{
// std::cerr << "branch (shape, axitags) 0 0\n";
// axistags have no channel axis either => sizes should match
vigra_precondition(ndim == ntags,
"constructArray(): size mismatch between shape and axistags.");
}
else
{
// std::cerr << "branch (shape, axitags) 0 1\n";
if(ndim+1 == ntags)
{
// std::cerr << " drop channel axis\n";
// axistags have one additional element => drop the channel tag
// FIXME: would it be cleaner to make this an error ?
axistags.dropChannelAxis();
}
else
{
vigra_precondition(ndim == ntags,
"constructArray(): size mismatch between shape and axistags.");
}
}
}
else
{
// shape has a channel axis
if(channelIndex == ntags)
{
// std::cerr << "branch (shape, axitags) 1 0\n";
// axistags have no channel axis => should be one element shorter
vigra_precondition(ndim == ntags+1,
"constructArray(): size mismatch between shape and axistags.");
if(shape[0] == 1)
{
// std::cerr << " drop channel axis\n";
// we have a singleband image => drop the channel axis
shape.erase(shape.begin());
ndim -= 1;
}
else
{
// std::cerr << " insert channel axis\n";
// we have a multiband image => add a channel tag
axistags.insertChannelAxis();
}
}
else
{
// std::cerr << "branch (shape, axitags) 1 1\n";
// axistags have channel axis => sizes should match
vigra_precondition(ndim == ntags,
"constructArray(): size mismatch between shape and axistags.");
}
}
}
inline
ArrayVector<npy_intp> finalizeTaggedShape(TaggedShape & tagged_shape)
{
if(tagged_shape.axistags)
{
tagged_shape.rotateToNormalOrder();
// we assume here that the axistag object belongs to the array to be created
// so that we can freely edit it
scaleAxisResolution(tagged_shape);
// this must be after scaleAxisResolution(), because the latter requires
// shape and original_shape to be still in sync
unifyTaggedShapeSize(tagged_shape);
if(tagged_shape.channelDescription != "")
tagged_shape.axistags.setChannelDescription(tagged_shape.channelDescription);
}
return tagged_shape.shape;
}
} // namespace vigra
#endif // VIGRA_NUMPY_ARRAY_TAGGEDSHAPE_HXX
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