[ VIGRA Homepage | Function Index | Class Index | Namespaces | File List | Main Page ]

vigra/numpy_array_traits.hxx |

    00001 /************************************************************************/
    00002 /*                                                                      */
    00003 /*       Copyright 2009 by Ullrich Koethe and Hans Meine                */
    00004 /*                                                                      */
    00005 /*    This file is part of the VIGRA computer vision library.           */
    00006 /*    The VIGRA Website is                                              */
    00007 /*        http://hci.iwr.uni-heidelberg.de/vigra/                       */
    00008 /*    Please direct questions, bug reports, and contributions to        */
    00009 /*        ullrich.koethe@iwr.uni-heidelberg.de    or                    */
    00010 /*        vigra@informatik.uni-hamburg.de                               */
    00011 /*                                                                      */
    00012 /*    Permission is hereby granted, free of charge, to any person       */
    00013 /*    obtaining a copy of this software and associated documentation    */
    00014 /*    files (the "Software"), to deal in the Software without           */
    00015 /*    restriction, including without limitation the rights to use,      */
    00016 /*    copy, modify, merge, publish, distribute, sublicense, and/or      */
    00017 /*    sell copies of the Software, and to permit persons to whom the    */
    00018 /*    Software is furnished to do so, subject to the following          */
    00019 /*    conditions:                                                       */
    00020 /*                                                                      */
    00021 /*    The above copyright notice and this permission notice shall be    */
    00022 /*    included in all copies or substantial portions of the             */
    00023 /*    Software.                                                         */
    00024 /*                                                                      */
    00025 /*    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND    */
    00026 /*    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES   */
    00027 /*    OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND          */
    00028 /*    NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT       */
    00029 /*    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,      */
    00030 /*    WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING      */
    00031 /*    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR     */
    00032 /*    OTHER DEALINGS IN THE SOFTWARE.                                   */
    00033 /*                                                                      */
    00034 /************************************************************************/
    00035
    00036 #ifndef VIGRA_NUMPY_ARRAY_TRAITS_HXX
    00037 #define VIGRA_NUMPY_ARRAY_TRAITS_HXX
    00038
    00039 #include "numerictraits.hxx"
    00040 #include "multi_array.hxx"
    00041 #include "numpy_array_taggedshape.hxx"
    00042
    00043 namespace vigra {
    00044
    00045 /********************************************************/
    00046 /*                                                      */
    00047 /*              Singleband and Multiband                */
    00048 /*                                                      */
    00049 /********************************************************/
    00050
    00051 template <class T>
    00052 struct Singleband  // the resulting NumpyArray has no explicit channel axis
    00053                    // (i.e. the number of channels is implicitly one)
    00054 {
    00055     typedef T value_type;
    00056 };
    00057
    00058 template <class T>
    00059 struct Multiband  // the last axis is explicitly designated as channel axis
    00060 {
    00061     typedef T value_type;
    00062 };
    00063
    00064 template<class T>
    00065 struct NumericTraits<Singleband<T> >
    00066 : public NumericTraits<T>
    00067 {};
    00068
    00069 template<class T>
    00070 struct NumericTraits<Multiband<T> >
    00071 {
    00072     typedef Multiband<T> Type;
    00073 /*
    00074     typedef int Promote;
    00075     typedef unsigned int UnsignedPromote;
    00076     typedef double RealPromote;
    00077     typedef std::complex<RealPromote> ComplexPromote;
    00078 */
    00079     typedef Type ValueType;
    00080
    00081     typedef typename NumericTraits<T>::isIntegral isIntegral;
    00082     typedef VigraFalseType isScalar;
    00083     typedef typename NumericTraits<T>::isSigned isSigned;
    00084     typedef typename NumericTraits<T>::isSigned isOrdered;
    00085     typedef typename NumericTraits<T>::isSigned isComplex;
    00086 /*
    00087     static signed char zero() { return 0; }
    00088     static signed char one() { return 1; }
    00089     static signed char nonZero() { return 1; }
    00090     static signed char min() { return SCHAR_MIN; }
    00091     static signed char max() { return SCHAR_MAX; }
    00092
    00093 #ifdef NO_INLINE_STATIC_CONST_DEFINITION
    00094     enum { minConst = SCHAR_MIN, maxConst = SCHAR_MIN };
    00095 #else
    00096     static const signed char minConst = SCHAR_MIN;
    00097     static const signed char maxConst = SCHAR_MIN;
    00098 #endif
    00099
    00100     static Promote toPromote(signed char v) { return v; }
    00101     static RealPromote toRealPromote(signed char v) { return v; }
    00102     static signed char fromPromote(Promote v) {
    00103         return ((v < SCHAR_MIN) ? SCHAR_MIN : (v > SCHAR_MAX) ? SCHAR_MAX : v);
    00104     }
    00105     static signed char fromRealPromote(RealPromote v) {
    00106         return ((v < 0.0)
    00107                    ? ((v < (RealPromote)SCHAR_MIN)
    00108                        ? SCHAR_MIN
    00109                        : static_cast<signed char>(v - 0.5))
    00110                    : (v > (RealPromote)SCHAR_MAX)
    00111                        ? SCHAR_MAX
    00112                        : static_cast<signed char>(v + 0.5));
    00113     }
    00114 */
    00115 };
    00116
    00117 /********************************************************/
    00118 /*                                                      */
    00119 /*               NumpyArrayValuetypeTraits              */
    00120 /*                                                      */
    00121 /********************************************************/
    00122
    00123 template<class ValueType>
    00124 struct ERROR_NumpyArrayValuetypeTraits_not_specialized_for_ { };
    00125
    00126 template<class ValueType>
    00127 struct NumpyArrayValuetypeTraits
    00128 {
    00129     static bool isValuetypeCompatible(PyArrayObject const * obj)
    00130     {
    00131         return ERROR_NumpyArrayValuetypeTraits_not_specialized_for_<ValueType>();
    00132     }
    00133
    00134     static ERROR_NumpyArrayValuetypeTraits_not_specialized_for_<ValueType> typeCode;
    00135
    00136     static std::string typeName()
    00137     {
    00138         return std::string("ERROR: NumpyArrayValuetypeTraits not specialized for this case");
    00139     }
    00140
    00141     static std::string typeNameImpex()
    00142     {
    00143         return std::string("ERROR: NumpyArrayValuetypeTraits not specialized for this case");
    00144     }
    00145
    00146     static PyObject * typeObject()
    00147     {
    00148         return (PyObject *)0;
    00149     }
    00150 };
    00151
    00152 template<class ValueType>
    00153 ERROR_NumpyArrayValuetypeTraits_not_specialized_for_<ValueType> NumpyArrayValuetypeTraits<ValueType>::typeCode;
    00154
    00155 #define VIGRA_NUMPY_VALUETYPE_TRAITS(type, typeID, numpyTypeName, impexTypeName) \
    00156 template <> \
    00157 struct NumpyArrayValuetypeTraits<type > \
    00158 { \
    00159     static bool isValuetypeCompatible(PyArrayObject const * obj) /* obj must not be NULL */ \
    00160     { \
    00161         return PyArray_EquivTypenums(typeID, PyArray_DESCR((PyObject *)obj)->type_num) && \
    00162                PyArray_ITEMSIZE((PyObject *)obj) == sizeof(type); \
    00163     } \
    00164     \
    00165     static NPY_TYPES const typeCode = typeID; \
    00166     \
    00167     static std::string typeName() \
    00168     { \
    00169         return #numpyTypeName; \
    00170     } \
    00171     \
    00172     static std::string typeNameImpex() \
    00173     { \
    00174         return impexTypeName; \
    00175     } \
    00176     \
    00177     static PyObject * typeObject() \
    00178     { \
    00179         return PyArray_TypeObjectFromType(typeID); \
    00180     } \
    00181 };
    00182
    00183 VIGRA_NUMPY_VALUETYPE_TRAITS(bool,           NPY_BOOL, bool, "UINT8")
    00184 VIGRA_NUMPY_VALUETYPE_TRAITS(signed char,    NPY_INT8, int8, "INT16")
    00185 VIGRA_NUMPY_VALUETYPE_TRAITS(unsigned char,  NPY_UINT8, uint8, "UINT8")
    00186 VIGRA_NUMPY_VALUETYPE_TRAITS(short,          NPY_INT16, int16, "INT16")
    00187 VIGRA_NUMPY_VALUETYPE_TRAITS(unsigned short, NPY_UINT16, uint16, "UINT16")
    00188
    00189 #if VIGRA_BITSOF_LONG == 32
    00190 VIGRA_NUMPY_VALUETYPE_TRAITS(long,           NPY_INT32, int32, "INT32")
    00191 VIGRA_NUMPY_VALUETYPE_TRAITS(unsigned long,  NPY_UINT32, uint32, "UINT32")
    00192 #elif VIGRA_BITSOF_LONG == 64
    00193 VIGRA_NUMPY_VALUETYPE_TRAITS(long,           NPY_INT64, int64, "DOUBLE")
    00194 VIGRA_NUMPY_VALUETYPE_TRAITS(unsigned long,  NPY_UINT64, uint64, "DOUBLE")
    00195 #endif
    00196
    00197 #if VIGRA_BITSOF_INT == 32
    00198 VIGRA_NUMPY_VALUETYPE_TRAITS(int,            NPY_INT32, int32, "INT32")
    00199 VIGRA_NUMPY_VALUETYPE_TRAITS(unsigned int,   NPY_UINT32, uint32, "UINT32")
    00200 #elif VIGRA_BITSOF_INT == 64
    00201 VIGRA_NUMPY_VALUETYPE_TRAITS(int,            NPY_INT64, int64, "DOUBLE")
    00202 VIGRA_NUMPY_VALUETYPE_TRAITS(unsigned int,   NPY_UINT64, uint64, "DOUBLE")
    00203 #endif
    00204
    00205 #ifdef PY_LONG_LONG
    00206 # if VIGRA_BITSOF_LONG_LONG == 32
    00207 VIGRA_NUMPY_VALUETYPE_TRAITS(long long,            NPY_INT32, int32, "INT32")
    00208 VIGRA_NUMPY_VALUETYPE_TRAITS(unsigned long long,   NPY_UINT32, uint32, "UINT32")
    00209 # elif VIGRA_BITSOF_LONG_LONG == 64
    00210 VIGRA_NUMPY_VALUETYPE_TRAITS(long long,          NPY_INT64, int64, "DOUBLE")
    00211 VIGRA_NUMPY_VALUETYPE_TRAITS(unsigned long long, NPY_UINT64, uint64, "DOUBLE")
    00212 # endif
    00213 #endif
    00214
    00215 VIGRA_NUMPY_VALUETYPE_TRAITS(npy_float32, NPY_FLOAT32, float32, "FLOAT")
    00216 VIGRA_NUMPY_VALUETYPE_TRAITS(npy_float64, NPY_FLOAT64, float64, "DOUBLE")
    00217 #if NPY_SIZEOF_LONGDOUBLE != NPY_SIZEOF_DOUBLE
    00218 VIGRA_NUMPY_VALUETYPE_TRAITS(npy_longdouble, NPY_LONGDOUBLE, longdouble, "")
    00219 #endif
    00220 VIGRA_NUMPY_VALUETYPE_TRAITS(npy_cfloat, NPY_CFLOAT, complex64, "")
    00221 VIGRA_NUMPY_VALUETYPE_TRAITS(std::complex<npy_float>, NPY_CFLOAT, complex64, "")
    00222 VIGRA_NUMPY_VALUETYPE_TRAITS(npy_cdouble, NPY_CDOUBLE, complex128, "")
    00223 VIGRA_NUMPY_VALUETYPE_TRAITS(std::complex<npy_double>, NPY_CDOUBLE, complex128, "")
    00224 VIGRA_NUMPY_VALUETYPE_TRAITS(npy_clongdouble, NPY_CLONGDOUBLE, clongdouble, "")
    00225 #if NPY_SIZEOF_LONGDOUBLE != NPY_SIZEOF_DOUBLE
    00226 VIGRA_NUMPY_VALUETYPE_TRAITS(std::complex<npy_longdouble>, NPY_CLONGDOUBLE, clongdouble, "")
    00227 #endif
    00228
    00229 #undef VIGRA_NUMPY_VALUETYPE_TRAITS
    00230
    00231 /********************************************************/
    00232 /*                                                      */
    00233 /*                  NumpyArrayTraits                    */
    00234 /*                                                      */
    00235 /********************************************************/
    00236
    00237 template<unsigned int N, class T, class Stride>
    00238 struct NumpyArrayTraits;
    00239
    00240 /********************************************************/
    00241
    00242 template<unsigned int N, class T>
    00243 struct NumpyArrayTraits<N, T, StridedArrayTag>
    00244 {
    00245     typedef T dtype;
    00246     typedef T value_type;
    00247     typedef NumpyArrayValuetypeTraits<T> ValuetypeTraits;
    00248     static NPY_TYPES const typeCode = ValuetypeTraits::typeCode;
    00249
    00250     static bool isArray(PyObject * obj)
    00251     {
    00252         return obj && PyArray_Check(obj);
    00253     }
    00254
    00255     static bool isValuetypeCompatible(PyArrayObject * obj)  /* obj must not be NULL */
    00256     {
    00257         return ValuetypeTraits::isValuetypeCompatible(obj);
    00258     }
    00259
    00260     static bool isShapeCompatible(PyArrayObject * array) /* array must not be NULL */
    00261     {
    00262         PyObject * obj = (PyObject *)array;
    00263         int ndim = PyArray_NDIM(obj);
    00264
    00265         return ndim == N;
    00266     }
    00267
    00268     // The '*Compatible' functions are called whenever a NumpyArray is to be constructed
    00269     // from a Python numpy.ndarray to check whether types and memory layout are
    00270     // compatible. During overload resolution, boost::python iterates through the list
    00271     // of overloads and invokes the first function where all arguments pass this check.
    00272     static bool isPropertyCompatible(PyArrayObject * obj) /* obj must not be NULL */
    00273     {
    00274         return isShapeCompatible(obj) && isValuetypeCompatible(obj);
    00275     }
    00276
    00277     // Construct a tagged shape from a 'shape - axistags' pair (called in
    00278     // NumpyArray::taggedShape()).
    00279     template <class U>
    00280     static TaggedShape taggedShape(TinyVector<U, N> const & shape, PyAxisTags axistags)
    00281     {
    00282         return TaggedShape(shape, axistags);
    00283     }
    00284
    00285     // Construct a tagged shape from a 'shape - order' pair by creating
    00286     // the appropriate axistags object for that order and NumpyArray type.
    00287     // (called in NumpyArray constructors via NumpyArray::init())
    00288     template <class U>
    00289     static TaggedShape taggedShape(TinyVector<U, N> const & shape,
    00290                                    std::string const & /* order */ = "")
    00291     {
    00292         // We ignore the 'order' parameter, because we don't know the axis meaning
    00293         // in a plain array (use Singleband, Multiband, TinyVector etc. instead).
    00294         // Since we also have no useful axistags in this case, we enforce
    00295         // the result array to be a plain numpy.ndarray by passing empty axistags.
    00296         return TaggedShape(shape, PyAxisTags());
    00297     }
    00298
    00299     // Adjust a TaggedShape that was created by another array to the properties of
    00300     // the present NumpyArray type (called in NumpyArray::reshapeIfEmpty()).
    00301     static void finalizeTaggedShape(TaggedShape & tagged_shape)
    00302     {
    00303         vigra_precondition(tagged_shape.size() == N,
    00304                   "reshapeIfEmpty(): tagged_shape has wrong size.");
    00305     }
    00306
    00307     // This function is used to synchronize the axis re-ordering of 'data'
    00308     // with that of 'array'. For example, when we want to apply Gaussian smoothing
    00309     // with a different scale for each axis, 'data' would contains those scales,
    00310     // and permuteLikewise() would make sure that the scales are applied to the right
    00311     // axes, regardless of axis re-ordering.
    00312     template <class ARRAY>
    00313     static void permuteLikewise(python_ptr array, ARRAY const & data, ARRAY & res)
    00314     {
    00315         vigra_precondition((int)data.size() == N,
    00316             "NumpyArray::permuteLikewise(): size mismatch.");
    00317
    00318         ArrayVector<npy_intp> permute;
    00319         detail::getAxisPermutationImpl(permute, array, "permutationToNormalOrder",
    00320                                        AxisInfo::AllAxes, true);
    00321
    00322         if(permute.size() != 0)
    00323         {
    00324             applyPermutation(permute.begin(), permute.end(), data.begin(), res.begin());
    00325         }
    00326     }
    00327
    00328     // This function is called in NumpyArray::setupArrayView() to determine the
    00329     // desired axis re-ordering.
    00330     template <class U>
    00331     static void permutationToSetupOrder(python_ptr array, ArrayVector<U> & permute)
    00332     {
    00333         detail::getAxisPermutationImpl(permute, array, "permutationToNormalOrder",
    00334                                        AxisInfo::AllAxes, true);
    00335
    00336         if(permute.size() == 0)
    00337         {
    00338             permute.resize(N);
    00339             linearSequence(permute.begin(), permute.end());
    00340         }
    00341     }
    00342
    00343     // This function is called in NumpyArray::makeUnsafeReference() to create
    00344     // a numpy.ndarray view for a block of memory managed by C++.
    00345     // The term 'unsafe' should remind you that memory management cannot be done
    00346     // automatically, bu must be done explicitly by the programmer.
    00347     template <class U>
    00348     static python_ptr unsafeConstructorFromData(TinyVector<U, N> const & shape,
    00349                                                 T *data, TinyVector<U, N> const & stride)
    00350     {
    00351         TinyVector<npy_intp, N> npyStride(stride * sizeof(T));
    00352         return constructNumpyArrayFromData(shape, npyStride.begin(),
    00353                                                     ValuetypeTraits::typeCode, data);
    00354     }
    00355 };
    00356
    00357 /********************************************************/
    00358
    00359 template<unsigned int N, class T>
    00360 struct NumpyArrayTraits<N, T, UnstridedArrayTag>
    00361 : public NumpyArrayTraits<N, T, StridedArrayTag>
    00362 {
    00363     typedef NumpyArrayTraits<N, T, StridedArrayTag> BaseType;
    00364     typedef typename BaseType::ValuetypeTraits ValuetypeTraits;
    00365
    00366     static bool isShapeCompatible(PyArrayObject * array) /* obj must not be NULL */
    00367     {
    00368         PyObject * obj = (PyObject *)array;
    00369         int ndim = PyArray_NDIM(obj);
    00370         long channelIndex = pythonGetAttr(obj, "channelIndex", ndim);
    00371         long majorIndex = pythonGetAttr(obj, "innerNonchannelIndex", ndim);
    00372         npy_intp * strides = PyArray_STRIDES(obj);
    00373
    00374         if(channelIndex < ndim)
    00375         {
    00376             // When we have a channel axis, it will become the innermost dimension
    00377             return (ndim == N && strides[channelIndex] == sizeof(T));
    00378         }
    00379         else if(majorIndex < ndim)
    00380         {
    00381             // When we have axistags, but no channel axis, the major spatial
    00382             // axis will be the innermost dimension
    00383             return (ndim == N && strides[majorIndex] == sizeof(T));
    00384         }
    00385         else
    00386         {
    00387             // When we have no axistags, the first axis will be the innermost dimension
    00388             return (ndim == N && strides[0] == sizeof(T));
    00389         }
    00390     }
    00391
    00392     static bool isPropertyCompatible(PyArrayObject * obj) /* obj must not be NULL */
    00393     {
    00394         return isShapeCompatible(obj) && BaseType::isValuetypeCompatible(obj);
    00395     }
    00396 };
    00397
    00398 /********************************************************/
    00399
    00400 template<unsigned int N, class T>
    00401 struct NumpyArrayTraits<N, Singleband<T>, StridedArrayTag>
    00402 : public NumpyArrayTraits<N, T, StridedArrayTag>
    00403 {
    00404     typedef NumpyArrayTraits<N, T, StridedArrayTag> BaseType;
    00405     typedef typename BaseType::ValuetypeTraits ValuetypeTraits;
    00406
    00407     static bool isShapeCompatible(PyArrayObject * array) /* array must not be NULL */
    00408     {
    00409         PyObject * obj = (PyObject *)array;
    00410         int ndim = PyArray_NDIM(obj);
    00411         long channelIndex = pythonGetAttr(obj, "channelIndex", ndim);
    00412
    00413         // If we have no channel axis (because either we don't have axistags,
    00414         // or the tags do not contain a channel axis), ndim must match.
    00415         if(channelIndex == ndim)
    00416             return ndim == N;
    00417
    00418         // Otherwise, the channel axis must be a singleton axis that we can drop.
    00419         return ndim == N+1 && PyArray_DIM(obj, channelIndex) == 1;
    00420     }
    00421
    00422     static bool isPropertyCompatible(PyArrayObject * obj) /* obj must not be NULL */
    00423     {
    00424         return isShapeCompatible(obj) && BaseType::isValuetypeCompatible(obj);
    00425     }
    00426
    00427     template <class U>
    00428     static TaggedShape taggedShape(TinyVector<U, N> const & shape, PyAxisTags axistags)
    00429     {
    00430         return TaggedShape(shape, axistags).setChannelCount(1);
    00431     }
    00432
    00433     template <class U>
    00434     static TaggedShape taggedShape(TinyVector<U, N> const & shape, std::string const & order = "")
    00435     {
    00436         return TaggedShape(shape,
    00437                   PyAxisTags(detail::defaultAxistags(shape.size()+1, order))).setChannelCount(1);
    00438     }
    00439
    00440     static void finalizeTaggedShape(TaggedShape & tagged_shape)
    00441     {
    00442         if(tagged_shape.axistags.hasChannelAxis())
    00443         {
    00444             tagged_shape.setChannelCount(1);
    00445             vigra_precondition(tagged_shape.size() == N+1,
    00446                      "reshapeIfEmpty(): tagged_shape has wrong size.");
    00447         }
    00448         else
    00449         {
    00450             tagged_shape.setChannelCount(0);
    00451             vigra_precondition(tagged_shape.size() == N,
    00452                      "reshapeIfEmpty(): tagged_shape has wrong size.");
    00453         }
    00454     }
    00455
    00456     template <class ARRAY>
    00457     static void permuteLikewise(python_ptr array, ARRAY const & data, ARRAY & res)
    00458     {
    00459         vigra_precondition((int)data.size() == N,
    00460             "NumpyArray::permuteLikewise(): size mismatch.");
    00461
    00462         ArrayVector<npy_intp> permute;
    00463         detail::getAxisPermutationImpl(permute, array, "permutationToNormalOrder",
    00464                                        AxisInfo::NonChannel, true);
    00465
    00466         if(permute.size() == 0)
    00467         {
    00468             permute.resize(N);
    00469             linearSequence(permute.begin(), permute.end());
    00470         }
    00471
    00472         applyPermutation(permute.begin(), permute.end(), data.begin(), res.begin());
    00473     }
    00474
    00475     template <class U>
    00476     static void permutationToSetupOrder(python_ptr array, ArrayVector<U> & permute)
    00477     {
    00478         detail::getAxisPermutationImpl(permute, array, "permutationToNormalOrder",
    00479                                        AxisInfo::AllAxes, true);
    00480         if(permute.size() == 0)
    00481         {
    00482             permute.resize(N);
    00483             linearSequence(permute.begin(), permute.end());
    00484         }
    00485         else if(permute.size() == N+1)
    00486         {
    00487             permute.erase(permute.begin());
    00488         }
    00489     }
    00490 };
    00491
    00492 /********************************************************/
    00493
    00494 template<unsigned int N, class T>
    00495 struct NumpyArrayTraits<N, Singleband<T>, UnstridedArrayTag>
    00496 : public NumpyArrayTraits<N, Singleband<T>, StridedArrayTag>
    00497 {
    00498     typedef NumpyArrayTraits<N, T, UnstridedArrayTag> UnstridedTraits;
    00499     typedef NumpyArrayTraits<N, Singleband<T>, StridedArrayTag> BaseType;
    00500     typedef typename BaseType::ValuetypeTraits ValuetypeTraits;
    00501
    00502     static bool isShapeCompatible(PyArrayObject * array) /* obj must not be NULL */
    00503     {
    00504         PyObject * obj = (PyObject *)array;
    00505         int ndim = PyArray_NDIM(obj);
    00506         long channelIndex = pythonGetAttr(obj, "channelIndex", ndim);
    00507         long majorIndex = pythonGetAttr(obj, "innerNonchannelIndex", ndim);
    00508         npy_intp * strides = PyArray_STRIDES(obj);
    00509
    00510         // If we have no axistags, ndim must match, and axis 0 must be unstrided.
    00511         if(majorIndex == ndim)
    00512             return N == ndim && strides[0] == sizeof(T);
    00513
    00514         // If we have axistags, but no channel axis, ndim must match,
    00515         // and the major non-channel axis must be unstrided.
    00516         if(channelIndex == ndim)
    00517             return N == ndim && strides[majorIndex] == sizeof(T);
    00518
    00519         // Otherwise, the channel axis must be a singleton axis that we can drop,
    00520         // and the major non-channel axis must be unstrided.
    00521         return ndim == N+1 && PyArray_DIM(obj, channelIndex) == 1 &&
    00522                 strides[majorIndex] == sizeof(T);
    00523     }
    00524
    00525     static bool isPropertyCompatible(PyArrayObject * obj) /* obj must not be NULL */
    00526     {
    00527         return isShapeCompatible(obj) && BaseType::isValuetypeCompatible(obj);
    00528     }
    00529 };
    00530
    00531 /********************************************************/
    00532
    00533 template<unsigned int N, class T>
    00534 struct NumpyArrayTraits<N, Multiband<T>, StridedArrayTag>
    00535 : public NumpyArrayTraits<N, T, StridedArrayTag>
    00536 {
    00537     typedef NumpyArrayTraits<N, T, StridedArrayTag> BaseType;
    00538     typedef typename BaseType::ValuetypeTraits ValuetypeTraits;
    00539
    00540     static bool isShapeCompatible(PyArrayObject * array) /* array must not be NULL */
    00541     {
    00542         PyObject * obj = (PyObject*)array;
    00543         int ndim = PyArray_NDIM(obj);
    00544         long channelIndex = pythonGetAttr(obj, "channelIndex", ndim);
    00545         long majorIndex = pythonGetAttr(obj, "innerNonchannelIndex", ndim);
    00546
    00547         if(channelIndex < ndim)
    00548         {
    00549             // When we have a channel axis, ndim must match.
    00550             return ndim == N;
    00551         }
    00552         else if(majorIndex < ndim)
    00553         {
    00554             // When we have axistags, but no channel axis, we must add a singleton axis.
    00555             return ndim == N-1;
    00556         }
    00557         else
    00558         {
    00559             // When we have no axistags, we may add a singleton dimension.
    00560             return ndim == N || ndim == N-1;
    00561         }
    00562     }
    00563
    00564     static bool isPropertyCompatible(PyArrayObject * obj) /* obj must not be NULL */
    00565     {
    00566         return isShapeCompatible(obj) && ValuetypeTraits::isValuetypeCompatible(obj);
    00567     }
    00568
    00569     template <class U>
    00570     static TaggedShape taggedShape(TinyVector<U, N> const & shape, PyAxisTags axistags)
    00571     {
    00572         return TaggedShape(shape, axistags).setChannelIndexLast();
    00573     }
    00574
    00575     template <class U>
    00576     static TaggedShape taggedShape(TinyVector<U, N> const & shape, std::string const & order = "")
    00577     {
    00578         return TaggedShape(shape,
    00579                     PyAxisTags(detail::defaultAxistags(shape.size(), order))).setChannelIndexLast();
    00580     }
    00581
    00582     static void finalizeTaggedShape(TaggedShape & tagged_shape)
    00583     {
    00584         // When there is only one channel, and the axistags don't enforce an
    00585         // explicit channel axis, we return an array without explicit channel axis.
    00586         if(tagged_shape.channelCount() == 1 && !tagged_shape.axistags.hasChannelAxis())
    00587         {
    00588             tagged_shape.setChannelCount(0);
    00589             vigra_precondition(tagged_shape.size() == N-1,
    00590                   "reshapeIfEmpty(): tagged_shape has wrong size.");
    00591         }
    00592         else
    00593         {
    00594             vigra_precondition(tagged_shape.size() == N,
    00595                   "reshapeIfEmpty(): tagged_shape has wrong size.");
    00596         }
    00597     }
    00598
    00599     template <class ARRAY>
    00600     static void permuteLikewise(python_ptr array, ARRAY const & data, ARRAY & res)
    00601     {
    00602         ArrayVector<npy_intp> permute;
    00603
    00604         if((int)data.size() == N)
    00605         {
    00606             vigra_precondition(PyArray_NDIM((PyArrayObject*)array.get()) == N,
    00607                 "NumpyArray::permuteLikewise(): input array has no channel axis.");
    00608
    00609             detail::getAxisPermutationImpl(permute, array, "permutationToNormalOrder",
    00610                                            AxisInfo::AllAxes, true);
    00611
    00612             if(permute.size() == 0)
    00613             {
    00614                 permute.resize(N);
    00615                 linearSequence(permute.begin(), permute.end());
    00616             }
    00617             else
    00618             {
    00619                 // rotate channel axis to last position
    00620                 int channelIndex = permute[0];
    00621                 for(int k=1; k<N; ++k)
    00622                     permute[k-1] = permute[k];
    00623                 permute[N-1] = channelIndex;
    00624             }
    00625         }
    00626         else
    00627         {
    00628             vigra_precondition((int)data.size() == N-1,
    00629                 "NumpyArray::permuteLikewise(): size mismatch.");
    00630
    00631             detail::getAxisPermutationImpl(permute, array, "permutationToNormalOrder",
    00632                                            AxisInfo::NonChannel, true);
    00633
    00634             if(permute.size() == 0)
    00635             {
    00636                 permute.resize(N-1);
    00637                 linearSequence(permute.begin(), permute.end());
    00638             }
    00639         }
    00640
    00641         applyPermutation(permute.begin(), permute.end(), data.begin(), res.begin());
    00642     }
    00643
    00644     template <class U>
    00645     static void permutationToSetupOrder(python_ptr array, ArrayVector<U> & permute)
    00646     {
    00647         detail::getAxisPermutationImpl(permute, array, "permutationToNormalOrder",
    00648                                        AxisInfo::AllAxes, true);
    00649
    00650         if(permute.size() == 0)
    00651         {
    00652             permute.resize(PyArray_NDIM((PyArrayObject*)array.get()));
    00653             linearSequence(permute.begin(), permute.end());
    00654         }
    00655         else if(permute.size() == N)
    00656         {
    00657             // if we have a channel axis, rotate it to last position
    00658             int channelIndex = permute[0];
    00659             for(int k=1; k<N; ++k)
    00660                 permute[k-1] = permute[k];
    00661             permute[N-1] = channelIndex;
    00662         }
    00663     }
    00664 };
    00665
    00666 /********************************************************/
    00667
    00668 template<unsigned int N, class T>
    00669 struct NumpyArrayTraits<N, Multiband<T>, UnstridedArrayTag>
    00670 : public NumpyArrayTraits<N, Multiband<T>, StridedArrayTag>
    00671 {
    00672     typedef NumpyArrayTraits<N, Multiband<T>, StridedArrayTag> BaseType;
    00673     typedef typename BaseType::ValuetypeTraits ValuetypeTraits;
    00674
    00675     static bool isShapeCompatible(PyArrayObject * array) /* obj must not be NULL */
    00676     {
    00677         PyObject * obj = (PyObject *)array;
    00678         int ndim = PyArray_NDIM(obj);
    00679         long channelIndex = pythonGetAttr(obj, "channelIndex", ndim);
    00680         long majorIndex = pythonGetAttr(obj, "innerNonchannelIndex", ndim);
    00681         npy_intp * strides = PyArray_STRIDES(obj);
    00682
    00683         if(channelIndex < ndim)
    00684         {
    00685             // When we have a channel axis, ndim must match, and the major non-channel
    00686             // axis must be unstrided.
    00687             return ndim == N && strides[majorIndex] == sizeof(T);
    00688         }
    00689         else if(majorIndex < ndim)
    00690         {
    00691             // When we have axistags, but no channel axis, we will add a
    00692             // singleton channel axis, and the major non-channel axis must be unstrided.
    00693             return ndim == N-1 && strides[majorIndex] == sizeof(T);
    00694         }
    00695         else
    00696         {
    00697             // When we have no axistags, axis 0 must be unstrided, but we
    00698             // may add a singleton dimension at the end.
    00699             return (ndim == N || ndim == N-1) && strides[0] == sizeof(T);
    00700         }
    00701     }
    00702
    00703     static bool isPropertyCompatible(PyArrayObject * obj) /* obj must not be NULL */
    00704     {
    00705         return isShapeCompatible(obj) && BaseType::isValuetypeCompatible(obj);
    00706     }
    00707 };
    00708
    00709 /********************************************************/
    00710
    00711 template<unsigned int N, int M, class T>
    00712 struct NumpyArrayTraits<N, TinyVector<T, M>, StridedArrayTag>
    00713 {
    00714     typedef T dtype;
    00715     typedef TinyVector<T, M> value_type;
    00716     typedef NumpyArrayValuetypeTraits<T> ValuetypeTraits;
    00717     static NPY_TYPES const typeCode = ValuetypeTraits::typeCode;
    00718
    00719     static bool isArray(PyObject * obj)
    00720     {
    00721         return obj && PyArray_Check(obj);
    00722     }
    00723
    00724     static bool isValuetypeCompatible(PyArrayObject * obj)  /* obj must not be NULL */
    00725     {
    00726         return ValuetypeTraits::isValuetypeCompatible(obj);
    00727     }
    00728
    00729     static bool isShapeCompatible(PyArrayObject * array) /* array must not be NULL */
    00730     {
    00731         PyObject * obj = (PyObject *)array;
    00732
    00733          // We need an extra channel axis.
    00734          if(PyArray_NDIM(obj) != N+1)
    00735             return false;
    00736
    00737         // When there are no axistags, we assume that the last axis represents the channels.
    00738         long channelIndex = pythonGetAttr(obj, "channelIndex", N);
    00739         npy_intp * strides = PyArray_STRIDES(obj);
    00740
    00741         return PyArray_DIM(obj, channelIndex) == M && strides[channelIndex] == sizeof(T);
    00742     }
    00743
    00744     static bool isPropertyCompatible(PyArrayObject * obj) /* obj must not be NULL */
    00745     {
    00746         return isShapeCompatible(obj) && ValuetypeTraits::isValuetypeCompatible(obj);
    00747     }
    00748
    00749     template <class U>
    00750     static TaggedShape taggedShape(TinyVector<U, N> const & shape, PyAxisTags axistags)
    00751     {
    00752         return TaggedShape(shape, axistags).setChannelCount(M);
    00753     }
    00754
    00755     template <class U>
    00756     static TaggedShape taggedShape(TinyVector<U, N> const & shape, std::string const & order = "")
    00757     {
    00758         return TaggedShape(shape,
    00759                      PyAxisTags(detail::defaultAxistags(shape.size()+1, order))).setChannelCount(M);
    00760     }
    00761
    00762     static void finalizeTaggedShape(TaggedShape & tagged_shape)
    00763     {
    00764         tagged_shape.setChannelCount(M);
    00765         vigra_precondition(tagged_shape.size() == N+1,
    00766               "reshapeIfEmpty(): tagged_shape has wrong size.");
    00767     }
    00768
    00769     template <class ARRAY>
    00770     static void permuteLikewise(python_ptr array, ARRAY const & data, ARRAY & res)
    00771     {
    00772         vigra_precondition((int)data.size() == N,
    00773             "NumpyArray::permuteLikewise(): size mismatch.");
    00774
    00775         ArrayVector<npy_intp> permute;
    00776         detail::getAxisPermutationImpl(permute, array, "permutationToNormalOrder",
    00777                                        AxisInfo::NonChannel, true);
    00778
    00779         if(permute.size() == 0)
    00780         {
    00781             permute.resize(N);
    00782             linearSequence(permute.begin(), permute.end());
    00783         }
    00784
    00785         applyPermutation(permute.begin(), permute.end(), data.begin(), res.begin());
    00786     }
    00787
    00788     template <class U>
    00789     static void permutationToSetupOrder(python_ptr array, ArrayVector<U> & permute)
    00790     {
    00791         detail::getAxisPermutationImpl(permute, array, "permutationToNormalOrder",
    00792                                        AxisInfo::AllAxes, true);
    00793         if(permute.size() == 0)
    00794         {
    00795             permute.resize(N);
    00796             linearSequence(permute.begin(), permute.end());
    00797         }
    00798         else if(permute.size() == N+1)
    00799         {
    00800             permute.erase(permute.begin());
    00801         }
    00802     }
    00803
    00804     template <class U>
    00805     static python_ptr unsafeConstructorFromData(TinyVector<U, N> const & shape,
    00806                                                 value_type *data, TinyVector<U, N> const & stride)
    00807     {
    00808         TinyVector<npy_intp, N+1> npyShape;
    00809         std::copy(shape.begin(), shape.end(), npyShape.begin());
    00810         npyShape[N] = M;
    00811
    00812         TinyVector<npy_intp, N+1> npyStride;
    00813         std::transform(
    00814             stride.begin(), stride.end(), npyStride.begin(),
    00815             std::bind2nd(std::multiplies<npy_intp>(), sizeof(value_type)));
    00816         npyStride[N] = sizeof(T);
    00817
    00818         return constructNumpyArrayFromData(npyShape, npyStride.begin(),
    00819                                                     ValuetypeTraits::typeCode, data);
    00820     }
    00821 };
    00822
    00823 /********************************************************/
    00824
    00825 template<unsigned int N, int M, class T>
    00826 struct NumpyArrayTraits<N, TinyVector<T, M>, UnstridedArrayTag>
    00827 : public NumpyArrayTraits<N, TinyVector<T, M>, StridedArrayTag>
    00828 {
    00829     typedef NumpyArrayTraits<N, TinyVector<T, M>, StridedArrayTag> BaseType;
    00830     typedef typename BaseType::value_type value_type;
    00831     typedef typename BaseType::ValuetypeTraits ValuetypeTraits;
    00832
    00833     static bool isShapeCompatible(PyArrayObject * array) /* obj must not be NULL */
    00834     {
    00835         PyObject * obj = (PyObject *)array;
    00836         int ndim = PyArray_NDIM(obj);
    00837
    00838          // We need an extra channel axis.
    00839         if(ndim != N+1)
    00840             return false;
    00841
    00842         long channelIndex = pythonGetAttr(obj, "channelIndex", ndim);
    00843         long majorIndex = pythonGetAttr(obj, "innerNonchannelIndex", ndim);
    00844         npy_intp * strides = PyArray_STRIDES(obj);
    00845
    00846         if(majorIndex < ndim)
    00847         {
    00848             // We have axistags, but no channel axis => cannot be a TinyVector image
    00849             if(channelIndex == ndim)
    00850                 return false;
    00851
    00852             // We have an explicit channel axis => shapes and strides must match
    00853             return PyArray_DIM(obj, channelIndex) == M &&
    00854                    strides[channelIndex] == sizeof(T) &&
    00855                    strides[majorIndex] == sizeof(TinyVector<T, M>);
    00856
    00857
    00858         }
    00859         else
    00860         {
    00861             // we have no axistags => we assume that the channel axis is last
    00862             return PyArray_DIM(obj, N) == M &&
    00863                    strides[N] == sizeof(T) &&
    00864                    strides[0] == sizeof(TinyVector<T, M>);
    00865         }
    00866     }
    00867
    00868     static bool isPropertyCompatible(PyArrayObject * obj) /* obj must not be NULL */
    00869     {
    00870         return isShapeCompatible(obj) && BaseType::isValuetypeCompatible(obj);
    00871     }
    00872 };
    00873
    00874 /********************************************************/
    00875
    00876 template<unsigned int N, class T>
    00877 struct NumpyArrayTraits<N, RGBValue<T>, StridedArrayTag>
    00878 : public NumpyArrayTraits<N, TinyVector<T, 3>, StridedArrayTag>
    00879 {
    00880     typedef T dtype;
    00881     typedef RGBValue<T> value_type;
    00882     typedef NumpyArrayValuetypeTraits<T> ValuetypeTraits;
    00883 };
    00884
    00885 /********************************************************/
    00886
    00887 template<unsigned int N, class T>
    00888 struct NumpyArrayTraits<N, RGBValue<T>, UnstridedArrayTag>
    00889 : public NumpyArrayTraits<N, RGBValue<T>, StridedArrayTag>
    00890 {
    00891     typedef NumpyArrayTraits<N, TinyVector<T, 3>, UnstridedArrayTag> UnstridedTraits;
    00892     typedef NumpyArrayTraits<N, RGBValue<T>, StridedArrayTag> BaseType;
    00893     typedef typename BaseType::value_type value_type;
    00894     typedef typename BaseType::ValuetypeTraits ValuetypeTraits;
    00895
    00896     static bool isShapeCompatible(PyArrayObject * obj) /* obj must not be NULL */
    00897     {
    00898         return UnstridedTraits::isShapeCompatible(obj);
    00899     }
    00900
    00901     static bool isPropertyCompatible(PyArrayObject * obj) /* obj must not be NULL */
    00902     {
    00903         return UnstridedTraits::isPropertyCompatible(obj);
    00904     }
    00905 };
    00906
    00907 } // namespace vigra
    00908
    00909 #endif // VIGRA_NUMPY_ARRAY_TRAITS_HXX

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.9.0 (Tue Nov 6 2012)