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Merge branch 4.x

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This commit is contained in:
Alexander Alekhin
2021-10-15 15:59:36 +00:00
parent 91a7a4523a 0e86e292e4
commit 7ba26ada12
537 changed files with 39768 additions and 10712 deletions
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modules/python/src2/cv2.cpp
+393 -371
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@@ -49,6 +49,8 @@
static PyObject* opencv_error = NULL;
static PyTypeObject* pyopencv_Mat_TypePtr = nullptr;
class ArgInfo
{
public:
@@ -496,6 +498,33 @@ bool parseSequence(PyObject* obj, RefWrapper<T> (&value)[N], const ArgInfo& info
}
} // namespace
namespace traits {
template <bool Value>
struct BooleanConstant
{
static const bool value = Value;
typedef BooleanConstant<Value> type;
};
typedef BooleanConstant<true> TrueType;
typedef BooleanConstant<false> FalseType;
template <class T>
struct VoidType {
typedef void type;
};
template <class T, class DType = void>
struct IsRepresentableAsMatDataType : FalseType
{
};
template <class T>
struct IsRepresentableAsMatDataType<T, typename VoidType<typename DataType<T>::channel_type>::type> : TrueType
{
};
} // namespace traits
typedef std::vector<uchar> vector_uchar;
typedef std::vector<char> vector_char;
typedef std::vector<int> vector_int;
@@ -611,10 +640,20 @@ static bool isBool(PyObject* obj) CV_NOEXCEPT
return PyArray_IsScalar(obj, Bool) || PyBool_Check(obj);
}
template <typename T>
static std::string pycv_dumpArray(const T* arr, int n)
{
std::ostringstream out;
out << "[";
for (int i = 0; i < n; ++i)
out << " " << arr[i];
out << " ]";
return out.str();
}
// special case, when the converter 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 )
@@ -700,12 +739,29 @@ static bool pyopencv_to(PyObject* o, Mat& m, const ArgInfo& info)
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);
CV_LOG_DEBUG(NULL, "Incoming ndarray '" << info.name << "': ndims=" << ndims << " _sizes=" << pycv_dumpArray(_sizes, ndims) << " _strides=" << pycv_dumpArray(_strides, ndims));
bool ismultichannel = ndims == 3 && _sizes[2] <= CV_CN_MAX;
if (pyopencv_Mat_TypePtr && PyObject_TypeCheck(o, pyopencv_Mat_TypePtr))
{
bool wrapChannels = false;
PyObject* pyobj_wrap_channels = PyObject_GetAttrString(o, "wrap_channels");
if (pyobj_wrap_channels)
{
if (!pyopencv_to_safe(pyobj_wrap_channels, wrapChannels, ArgInfo("cv.Mat.wrap_channels", 0)))
{
// TODO extra message
Py_DECREF(pyobj_wrap_channels);
return false;
}
Py_DECREF(pyobj_wrap_channels);
}
ismultichannel = wrapChannels && ndims >= 1;
}
for( int i = ndims-1; i >= 0 && !needcopy; i-- )
{
@@ -719,14 +775,26 @@ static bool pyopencv_to(PyObject* o, Mat& m, const ArgInfo& info)
needcopy = true;
}
if( ismultichannel && _strides[1] != (npy_intp)elemsize*_sizes[2] )
needcopy = true;
if (ismultichannel)
{
int channels = ndims >= 1 ? (int)_sizes[ndims - 1] : 1;
if (channels > CV_CN_MAX)
{
failmsg("%s unable to wrap channels, too high (%d > CV_CN_MAX=%d)", info.name, (int)channels, (int)CV_CN_MAX);
return false;
}
ndims--;
type |= CV_MAKETYPE(0, channels);
if (ndims >= 1 && _strides[ndims - 1] != (npy_intp)elemsize*_sizes[ndims])
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);
failmsg("Layout of the output array %s is incompatible with cv::Mat", info.name);
return false;
}
@@ -742,6 +810,9 @@ static bool pyopencv_to(PyObject* o, Mat& m, const ArgInfo& info)
_strides = PyArray_STRIDES(oarr);
}
int size[CV_MAX_DIM+1] = {};
size_t step[CV_MAX_DIM+1] = {};
// Normalize strides in case NPY_RELAXED_STRIDES is set
size_t default_step = elemsize;
for ( int i = ndims - 1; i >= 0; --i )
@@ -760,23 +831,16 @@ static bool pyopencv_to(PyObject* o, Mat& m, const ArgInfo& info)
}
// handle degenerate case
// FIXIT: Don't force 1D for Scalars
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;
}
#if 1
CV_LOG_DEBUG(NULL, "Construct Mat: ndims=" << ndims << " size=" << pycv_dumpArray(size, ndims) << " step=" << pycv_dumpArray(step, ndims) << " type=" << cv::typeToString(type));
#endif
m = Mat(ndims, size, type, PyArray_DATA(oarr), step);
m.u = g_numpyAllocator.allocate(o, ndims, size, type, step);
@@ -1072,6 +1136,30 @@ bool pyopencv_to(PyObject* obj, uchar& value, const ArgInfo& info)
return ivalue != -1 || !PyErr_Occurred();
}
template<>
bool pyopencv_to(PyObject* obj, char& value, const ArgInfo& info)
{
if (!obj || obj == Py_None)
{
return true;
}
if (isBool(obj))
{
failmsg("Argument '%s' must be an integer, not bool", info.name);
return false;
}
if (PyArray_IsIntegerScalar(obj))
{
value = saturate_cast<char>(PyArray_PyIntAsInt(obj));
}
else
{
failmsg("Argument '%s' is required to be an integer", info.name);
return false;
}
return !CV_HAS_CONVERSION_ERROR(value);
}
template<>
PyObject* pyopencv_from(const double& value)
{
@@ -1484,357 +1572,12 @@ PyObject* pyopencv_from(const Point3d& p)
return Py_BuildValue("(ddd)", p.x, p.y, p.z);
}
template<typename _Tp> struct pyopencvVecConverter
{
typedef typename DataType<_Tp>::channel_type _Cp;
static inline bool copyOneItem(PyObject *obj, size_t start, int channels, _Cp * data)
{
for(size_t j = 0; (int)j < channels; j++ )
{
SafeSeqItem sub_item_wrap(obj, start + j);
PyObject* item_ij = sub_item_wrap.item;
if( PyInt_Check(item_ij))
{
int v = (int)PyInt_AsLong(item_ij);
if( v == -1 && PyErr_Occurred() )
return false;
data[j] = saturate_cast<_Cp>(v);
}
else if( PyLong_Check(item_ij))
{
int v = (int)PyLong_AsLong(item_ij);
if( v == -1 && PyErr_Occurred() )
return false;
data[j] = saturate_cast<_Cp>(v);
}
else if( PyFloat_Check(item_ij))
{
double v = PyFloat_AsDouble(item_ij);
if( PyErr_Occurred() )
return false;
data[j] = saturate_cast<_Cp>(v);
}
else
return false;
}
return true;
}
static bool to(PyObject* obj, std::vector<_Tp>& value, const ArgInfo& info)
{
if(!obj || obj == Py_None)
return true;
if (PyArray_Check(obj))
{
Mat m;
pyopencv_to(obj, m, info);
m.copyTo(value);
return true;
}
else if (PySequence_Check(obj))
{
const int type = traits::Type<_Tp>::value;
const int depth = CV_MAT_DEPTH(type), channels = CV_MAT_CN(type);
size_t i, n = PySequence_Size(obj);
value.resize(n);
for (i = 0; i < n; i++ )
{
SafeSeqItem item_wrap(obj, i);
PyObject* item = item_wrap.item;
_Cp* data = (_Cp*)&value[i];
if( channels == 2 && PyComplex_Check(item) )
{
data[0] = saturate_cast<_Cp>(PyComplex_RealAsDouble(item));
data[1] = saturate_cast<_Cp>(PyComplex_ImagAsDouble(item));
}
else if( channels > 1 )
{
if( PyArray_Check(item))
{
Mat src;
pyopencv_to(item, src, info);
if( src.dims != 2 || src.channels() != 1 ||
((src.cols != 1 || src.rows != channels) &&
(src.cols != channels || src.rows != 1)))
break;
Mat dst(src.rows, src.cols, depth, data);
src.convertTo(dst, type);
if( dst.data != (uchar*)data )
break;
}
else if (PySequence_Check(item))
{
if (!copyOneItem(item, 0, channels, data))
break;
}
else
{
break;
}
}
else if (channels == 1)
{
if (!copyOneItem(obj, i, channels, data))
break;
}
else
{
break;
}
}
if (i != n)
{
failmsg("Can't convert vector element for '%s', index=%d", info.name, i);
}
return i == n;
}
failmsg("Can't convert object to vector for '%s', unsupported type", info.name);
return false;
}
static PyObject* from(const std::vector<_Tp>& value)
{
if(value.empty())
return PyTuple_New(0);
int type = traits::Type<_Tp>::value;
int depth = CV_MAT_DEPTH(type), channels = CV_MAT_CN(type);
Mat src((int)value.size(), channels, depth, (uchar*)&value[0]);
return pyopencv_from(src);
}
};
template<typename _Tp>
bool pyopencv_to(PyObject* obj, std::vector<_Tp>& value, const ArgInfo& info)
{
return pyopencvVecConverter<_Tp>::to(obj, value, info);
}
template<typename _Tp>
PyObject* pyopencv_from(const std::vector<_Tp>& value)
{
return pyopencvVecConverter<_Tp>::from(value);
}
template<typename _Tp> static inline bool pyopencv_to_generic_vec(PyObject* obj, std::vector<_Tp>& value, const ArgInfo& info)
{
if(!obj || obj == Py_None)
return true;
if (!PySequence_Check(obj))
return false;
size_t n = PySequence_Size(obj);
value.resize(n);
for(size_t i = 0; i < n; i++ )
{
SafeSeqItem item_wrap(obj, i);
if(!pyopencv_to(item_wrap.item, value[i], info))
return false;
}
return true;
}
template<> inline bool pyopencv_to_generic_vec(PyObject* obj, std::vector<bool>& value, const ArgInfo& info)
{
if(!obj || obj == Py_None)
return true;
if (!PySequence_Check(obj))
return false;
size_t n = PySequence_Size(obj);
value.resize(n);
for(size_t i = 0; i < n; i++ )
{
SafeSeqItem item_wrap(obj, i);
bool elem{};
if(!pyopencv_to(item_wrap.item, elem, info))
return false;
value[i] = elem;
}
return true;
}
template<typename _Tp> static inline PyObject* pyopencv_from_generic_vec(const std::vector<_Tp>& value)
{
int i, n = (int)value.size();
PyObject* seq = PyList_New(n);
for( i = 0; i < n; i++ )
{
_Tp elem = value[i];
PyObject* item = pyopencv_from(elem);
if(!item)
break;
PyList_SetItem(seq, i, item);
}
if( i < n )
{
Py_DECREF(seq);
return 0;
}
return seq;
}
template<> inline PyObject* pyopencv_from_generic_vec(const std::vector<bool>& value)
{
int i, n = (int)value.size();
PyObject* seq = PyList_New(n);
for( i = 0; i < n; i++ )
{
bool elem = value[i];
PyObject* item = pyopencv_from(elem);
if(!item)
break;
PyList_SetItem(seq, i, item);
}
if( i < n )
{
Py_DECREF(seq);
return 0;
}
return seq;
}
template<std::size_t I = 0, typename... Tp>
inline typename std::enable_if<I == sizeof...(Tp), void>::type
convert_to_python_tuple(const std::tuple<Tp...>&, PyObject*) { }
template<std::size_t I = 0, typename... Tp>
inline typename std::enable_if<I < sizeof...(Tp), void>::type
convert_to_python_tuple(const std::tuple<Tp...>& cpp_tuple, PyObject* py_tuple)
{
PyObject* item = pyopencv_from(std::get<I>(cpp_tuple));
if (!item)
return;
PyTuple_SetItem(py_tuple, I, item);
convert_to_python_tuple<I + 1, Tp...>(cpp_tuple, py_tuple);
}
template<typename... Ts>
PyObject* pyopencv_from(const std::tuple<Ts...>& cpp_tuple)
{
size_t size = sizeof...(Ts);
PyObject* py_tuple = PyTuple_New(size);
convert_to_python_tuple(cpp_tuple, py_tuple);
size_t actual_size = PyTuple_Size(py_tuple);
if (actual_size < size)
{
Py_DECREF(py_tuple);
return NULL;
}
return py_tuple;
}
template<>
PyObject* pyopencv_from(const std::pair<int, double>& src)
{
return Py_BuildValue("(id)", src.first, src.second);
}
template<typename _Tp, typename _Tr> struct pyopencvVecConverter<std::pair<_Tp, _Tr> >
{
static bool to(PyObject* obj, std::vector<std::pair<_Tp, _Tr> >& value, const ArgInfo& info)
{
return pyopencv_to_generic_vec(obj, value, info);
}
static PyObject* from(const std::vector<std::pair<_Tp, _Tr> >& value)
{
return pyopencv_from_generic_vec(value);
}
};
template<typename _Tp> struct pyopencvVecConverter<std::vector<_Tp> >
{
static bool to(PyObject* obj, std::vector<std::vector<_Tp> >& value, const ArgInfo& info)
{
return pyopencv_to_generic_vec(obj, value, info);
}
static PyObject* from(const std::vector<std::vector<_Tp> >& value)
{
return pyopencv_from_generic_vec(value);
}
};
template<> struct pyopencvVecConverter<Mat>
{
static bool to(PyObject* obj, std::vector<Mat>& value, const ArgInfo& info)
{
return pyopencv_to_generic_vec(obj, value, info);
}
static PyObject* from(const std::vector<Mat>& value)
{
return pyopencv_from_generic_vec(value);
}
};
template<> struct pyopencvVecConverter<UMat>
{
static bool to(PyObject* obj, std::vector<UMat>& value, const ArgInfo& info)
{
return pyopencv_to_generic_vec(obj, value, info);
}
static PyObject* from(const std::vector<UMat>& value)
{
return pyopencv_from_generic_vec(value);
}
};
template<> struct pyopencvVecConverter<KeyPoint>
{
static bool to(PyObject* obj, std::vector<KeyPoint>& value, const ArgInfo& info)
{
return pyopencv_to_generic_vec(obj, value, info);
}
static PyObject* from(const std::vector<KeyPoint>& value)
{
return pyopencv_from_generic_vec(value);
}
};
template<> struct pyopencvVecConverter<DMatch>
{
static bool to(PyObject* obj, std::vector<DMatch>& value, const ArgInfo& info)
{
return pyopencv_to_generic_vec(obj, value, info);
}
static PyObject* from(const std::vector<DMatch>& value)
{
return pyopencv_from_generic_vec(value);
}
};
template<> struct pyopencvVecConverter<String>
{
static bool to(PyObject* obj, std::vector<String>& value, const ArgInfo& info)
{
return pyopencv_to_generic_vec(obj, value, info);
}
static PyObject* from(const std::vector<String>& value)
{
return pyopencv_from_generic_vec(value);
}
};
template<> struct pyopencvVecConverter<RotatedRect>
{
static bool to(PyObject* obj, std::vector<RotatedRect>& value, const ArgInfo& info)
{
return pyopencv_to_generic_vec(obj, value, info);
}
static PyObject* from(const std::vector<RotatedRect>& value)
{
return pyopencv_from_generic_vec(value);
}
};
template<>
bool pyopencv_to(PyObject* obj, TermCriteria& dst, const ArgInfo& info)
{
@@ -1962,6 +1705,266 @@ PyObject* pyopencv_from(const Moments& m)
"nu30", m.nu30, "nu21", m.nu21, "nu12", m.nu12, "nu03", m.nu03);
}
template <typename Tp>
struct pyopencvVecConverter;
template <typename Tp>
bool pyopencv_to(PyObject* obj, std::vector<Tp>& value, const ArgInfo& info)
{
if (!obj || obj == Py_None)
{
return true;
}
return pyopencvVecConverter<Tp>::to(obj, value, info);
}
template <typename Tp>
PyObject* pyopencv_from(const std::vector<Tp>& value)
{
return pyopencvVecConverter<Tp>::from(value);
}
template <typename Tp>
static bool pyopencv_to_generic_vec(PyObject* obj, std::vector<Tp>& value, const ArgInfo& info)
{
if (!obj || obj == Py_None)
{
return true;
}
if (!PySequence_Check(obj))
{
failmsg("Can't parse '%s'. Input argument doesn't provide sequence protocol", info.name);
return false;
}
const size_t n = static_cast<size_t>(PySequence_Size(obj));
value.resize(n);
for (size_t i = 0; i < n; i++)
{
SafeSeqItem item_wrap(obj, i);
if (!pyopencv_to(item_wrap.item, value[i], info))
{
failmsg("Can't parse '%s'. Sequence item with index %lu has a wrong type", info.name, i);
return false;
}
}
return true;
}
template<> inline bool pyopencv_to_generic_vec(PyObject* obj, std::vector<bool>& value, const ArgInfo& info)
{
if (!obj || obj == Py_None)
{
return true;
}
if (!PySequence_Check(obj))
{
failmsg("Can't parse '%s'. Input argument doesn't provide sequence protocol", info.name);
return false;
}
const size_t n = static_cast<size_t>(PySequence_Size(obj));
value.resize(n);
for (size_t i = 0; i < n; i++)
{
SafeSeqItem item_wrap(obj, i);
bool elem{};
if (!pyopencv_to(item_wrap.item, elem, info))
{
failmsg("Can't parse '%s'. Sequence item with index %lu has a wrong type", info.name, i);
return false;
}
value[i] = elem;
}
return true;
}
template <typename Tp>
static PyObject* pyopencv_from_generic_vec(const std::vector<Tp>& value)
{
Py_ssize_t n = static_cast<Py_ssize_t>(value.size());
PySafeObject seq(PyTuple_New(n));
for (Py_ssize_t i = 0; i < n; i++)
{
PyObject* item = pyopencv_from(value[i]);
// If item can't be assigned - PyTuple_SetItem raises exception and returns -1.
if (!item || PyTuple_SetItem(seq, i, item) == -1)
{
return NULL;
}
}
return seq.release();
}
template<> inline PyObject* pyopencv_from_generic_vec(const std::vector<bool>& value)
{
Py_ssize_t n = static_cast<Py_ssize_t>(value.size());
PySafeObject seq(PyTuple_New(n));
for (Py_ssize_t i = 0; i < n; i++)
{
bool elem = value[i];
PyObject* item = pyopencv_from(elem);
// If item can't be assigned - PyTuple_SetItem raises exception and returns -1.
if (!item || PyTuple_SetItem(seq, i, item) == -1)
{
return NULL;
}
}
return seq.release();
}
template<std::size_t I = 0, typename... Tp>
inline typename std::enable_if<I == sizeof...(Tp), void>::type
convert_to_python_tuple(const std::tuple<Tp...>&, PyObject*) { }
template<std::size_t I = 0, typename... Tp>
inline typename std::enable_if<I < sizeof...(Tp), void>::type
convert_to_python_tuple(const std::tuple<Tp...>& cpp_tuple, PyObject* py_tuple)
{
PyObject* item = pyopencv_from(std::get<I>(cpp_tuple));
if (!item)
return;
PyTuple_SetItem(py_tuple, I, item);
convert_to_python_tuple<I + 1, Tp...>(cpp_tuple, py_tuple);
}
template<typename... Ts>
PyObject* pyopencv_from(const std::tuple<Ts...>& cpp_tuple)
{
size_t size = sizeof...(Ts);
PyObject* py_tuple = PyTuple_New(size);
convert_to_python_tuple(cpp_tuple, py_tuple);
size_t actual_size = PyTuple_Size(py_tuple);
if (actual_size < size)
{
Py_DECREF(py_tuple);
return NULL;
}
return py_tuple;
}
template <typename Tp>
struct pyopencvVecConverter
{
typedef typename std::vector<Tp>::iterator VecIt;
static bool to(PyObject* obj, std::vector<Tp>& value, const ArgInfo& info)
{
if (!PyArray_Check(obj))
{
return pyopencv_to_generic_vec(obj, value, info);
}
// If user passed an array it is possible to make faster conversions in several cases
PyArrayObject* array_obj = reinterpret_cast<PyArrayObject*>(obj);
const NPY_TYPES target_type = asNumpyType<Tp>();
const NPY_TYPES source_type = static_cast<NPY_TYPES>(PyArray_TYPE(array_obj));
if (target_type == NPY_OBJECT)
{
// Non-planar arrays representing objects (e.g. array of N Rect is an array of shape Nx4) have NPY_OBJECT
// as their target type.
return pyopencv_to_generic_vec(obj, value, info);
}
if (PyArray_NDIM(array_obj) > 1)
{
failmsg("Can't parse %dD array as '%s' vector argument", PyArray_NDIM(array_obj), info.name);
return false;
}
if (target_type != source_type)
{
// Source type requires conversion
// Allowed conversions for target type is handled in the corresponding pyopencv_to function
return pyopencv_to_generic_vec(obj, value, info);
}
// For all other cases, all array data can be directly copied to std::vector data
// Simple `memcpy` is not possible because NumPy array can reference a slice of the bigger array:
// ```
// arr = np.ones((8, 4, 5), dtype=np.int32)
// convertible_to_vector_of_int = arr[:, 0, 1]
// ```
value.resize(static_cast<size_t>(PyArray_SIZE(array_obj)));
const npy_intp item_step = PyArray_STRIDE(array_obj, 0) / PyArray_ITEMSIZE(array_obj);
const Tp* data_ptr = static_cast<Tp*>(PyArray_DATA(array_obj));
for (VecIt it = value.begin(); it != value.end(); ++it, data_ptr += item_step) {
*it = *data_ptr;
}
return true;
}
static PyObject* from(const std::vector<Tp>& value)
{
if (value.empty())
{
return PyTuple_New(0);
}
return from(value, ::traits::IsRepresentableAsMatDataType<Tp>());
}
private:
static PyObject* from(const std::vector<Tp>& value, ::traits::FalseType)
{
// Underlying type is not representable as Mat Data Type
return pyopencv_from_generic_vec(value);
}
static PyObject* from(const std::vector<Tp>& value, ::traits::TrueType)
{
// Underlying type is representable as Mat Data Type, so faster return type is available
typedef DataType<Tp> DType;
typedef typename DType::channel_type UnderlyingArrayType;
// If Mat is always exposed as NumPy array this code path can be reduced to the following snipped:
// Mat src(value);
// PyObject* array = pyopencv_from(src);
// return PyArray_Squeeze(reinterpret_cast<PyArrayObject*>(array));
// This puts unnecessary restrictions on Mat object those might be avoided without losing the performance.
// Moreover, this version is a bit faster, because it doesn't create temporary objects with reference counting.
const NPY_TYPES target_type = asNumpyType<UnderlyingArrayType>();
const int cols = DType::channels;
PyObject* array = NULL;
if (cols == 1)
{
npy_intp dims = static_cast<npy_intp>(value.size());
array = PyArray_SimpleNew(1, &dims, target_type);
}
else
{
npy_intp dims[2] = {static_cast<npy_intp>(value.size()), cols};
array = PyArray_SimpleNew(2, dims, target_type);
}
if(!array)
{
// NumPy arrays with shape (N, 1) and (N) are not equal, so correct error message should distinguish
// them too.
String shape;
if (cols > 1)
{
shape = format("(%d x %d)", static_cast<int>(value.size()), cols);
}
else
{
shape = format("(%d)", static_cast<int>(value.size()));
}
const String error_message = format("Can't allocate NumPy array for vector with dtype=%d and shape=%s",
static_cast<int>(target_type), shape.c_str());
emit_failmsg(PyExc_MemoryError, error_message.c_str());
return array;
}
// Fill the array
PyArrayObject* array_obj = reinterpret_cast<PyArrayObject*>(array);
UnderlyingArrayType* array_data = static_cast<UnderlyingArrayType*>(PyArray_DATA(array_obj));
// if Tp is representable as Mat DataType, so the following cast is pretty safe...
const UnderlyingArrayType* value_data = reinterpret_cast<const UnderlyingArrayType*>(value.data());
memcpy(array_data, value_data, sizeof(UnderlyingArrayType) * value.size() * static_cast<size_t>(cols));
return array;
}
};
static int OnError(int status, const char *func_name, const char *err_msg, const char *file_name, int line, void *userdata)
{
PyGILState_STATE gstate;
@@ -2081,15 +2084,23 @@ static void OnChange(int pos, void *param)
}
#ifdef HAVE_OPENCV_HIGHGUI
// workaround for #20408, use nullptr, set value later
static int _createTrackbar(const String &trackbar_name, const String &window_name, int value, int count,
TrackbarCallback onChange, PyObject* py_callback_info)
{
int n = createTrackbar(trackbar_name, window_name, NULL, count, onChange, py_callback_info);
setTrackbarPos(trackbar_name, window_name, value);
return n;
}
static PyObject *pycvCreateTrackbar(PyObject*, PyObject *args)
{
PyObject *on_change;
char* trackbar_name;
char* window_name;
int *value = new int;
int value;
int count;
if (!PyArg_ParseTuple(args, "ssiiO", &trackbar_name, &window_name, value, &count, &on_change))
if (!PyArg_ParseTuple(args, "ssiiO", &trackbar_name, &window_name, &value, &count, &on_change))
return NULL;
if (!PyCallable_Check(on_change)) {
PyErr_SetString(PyExc_TypeError, "on_change must be callable");
@@ -2108,7 +2119,7 @@ static PyObject *pycvCreateTrackbar(PyObject*, PyObject *args)
{
registered_callbacks.insert(std::pair<std::string, PyObject*>(name, py_callback_info));
}
ERRWRAP2(createTrackbar(trackbar_name, window_name, value, count, OnChange, py_callback_info));
ERRWRAP2(_createTrackbar(trackbar_name, window_name, value, count, OnChange, py_callback_info));
Py_RETURN_NONE;
}
@@ -2209,7 +2220,24 @@ static int convert_to_char(PyObject *o, char *dst, const ArgInfo& info)
#include "pyopencv_generated_types_content.h"
#include "pyopencv_generated_funcs.h"
static PyObject* pycvRegisterMatType(PyObject *self, PyObject *value)
{
CV_LOG_DEBUG(NULL, cv::format("pycvRegisterMatType %p %p\n", self, value));
if (0 == PyType_Check(value))
{
PyErr_SetString(PyExc_TypeError, "Type argument is expected");
return NULL;
}
Py_INCREF(value);
pyopencv_Mat_TypePtr = (PyTypeObject*)value;
Py_RETURN_NONE;
}
static PyMethodDef special_methods[] = {
{"_registerMatType", (PyCFunction)(pycvRegisterMatType), METH_O, "_registerMatType(cv.Mat) -> None (Internal)"},
{"redirectError", CV_PY_FN_WITH_KW(pycvRedirectError), "redirectError(onError) -> None"},
#ifdef HAVE_OPENCV_HIGHGUI
{"createTrackbar", (PyCFunction)pycvCreateTrackbar, METH_VARARGS, "createTrackbar(trackbarName, windowName, value, count, onChange) -> None"},
@@ -2219,12 +2247,6 @@ static PyMethodDef special_methods[] = {
#ifdef HAVE_OPENCV_DNN
{"dnn_registerLayer", CV_PY_FN_WITH_KW(pyopencv_cv_dnn_registerLayer), "registerLayer(type, class) -> None"},
{"dnn_unregisterLayer", CV_PY_FN_WITH_KW(pyopencv_cv_dnn_unregisterLayer), "unregisterLayer(type) -> None"},
#endif
#ifdef HAVE_OPENCV_GAPI
{"GIn", CV_PY_FN_WITH_KW(pyopencv_cv_GIn), "GIn(...) -> GInputProtoArgs"},
{"GOut", CV_PY_FN_WITH_KW(pyopencv_cv_GOut), "GOut(...) -> GOutputProtoArgs"},
{"gin", CV_PY_FN_WITH_KW(pyopencv_cv_gin), "gin(...) -> ExtractArgsCallback"},
{"descr_of", CV_PY_FN_WITH_KW(pyopencv_cv_descr_of), "descr_of(...) -> ExtractMetaCallback"},
#endif
{NULL, NULL},
};
modules/python/src2/gen2.py
+12
View File
@@ -214,6 +214,16 @@ simple_argtype_mapping = {
"Stream": ArgTypeInfo("Stream", FormatStrings.object, 'Stream::Null()', True),
}
# Set of reserved keywords for Python. Can be acquired via the following call
# $ python -c "help('keywords')"
# Keywords that are reserved in C/C++ are excluded because they can not be
# used as variables identifiers
python_reserved_keywords = {
"True", "None", "False", "as", "assert", "def", "del", "elif", "except", "exec",
"finally", "from", "global", "import", "in", "is", "lambda", "nonlocal",
"pass", "print", "raise", "with", "yield"
}
def normalize_class_name(name):
return re.sub(r"^cv\.", "", name).replace(".", "_")
@@ -387,6 +397,8 @@ class ArgInfo(object):
def __init__(self, arg_tuple):
self.tp = handle_ptr(arg_tuple[0])
self.name = arg_tuple[1]
if self.name in python_reserved_keywords:
self.name += "_"
self.defval = arg_tuple[2]
self.isarray = False
self.arraylen = 0
modules/python/src2/hdr_parser.py
+21 -11
View File
@@ -55,13 +55,13 @@ class CppHeaderParser(object):
def get_macro_arg(self, arg_str, npos):
npos2 = npos3 = arg_str.find("(", npos)
if npos2 < 0:
print("Error: no arguments for the macro at %d" % (self.lineno,))
print("Error: no arguments for the macro at %s:%d" % (self.hname, self.lineno))
sys.exit(-1)
balance = 1
while 1:
t, npos3 = self.find_next_token(arg_str, ['(', ')'], npos3+1)
if npos3 < 0:
print("Error: no matching ')' in the macro call at %d" % (self.lineno,))
print("Error: no matching ')' in the macro call at %s:%d" % (self.hname, self.lineno))
sys.exit(-1)
if t == '(':
balance += 1
@@ -168,7 +168,7 @@ class CppHeaderParser(object):
angle_stack.append(0)
elif w == "," or w == '>':
if not angle_stack:
print("Error at %d: argument contains ',' or '>' not within template arguments" % (self.lineno,))
print("Error at %s:%d: argument contains ',' or '>' not within template arguments" % (self.hname, self.lineno))
sys.exit(-1)
if w == ",":
arg_type += "_and_"
@@ -198,7 +198,7 @@ class CppHeaderParser(object):
p1 = arg_name.find("[")
p2 = arg_name.find("]",p1+1)
if p2 < 0:
print("Error at %d: no closing ]" % (self.lineno,))
print("Error at %s:%d: no closing ]" % (self.hname, self.lineno))
sys.exit(-1)
counter_str = arg_name[p1+1:p2].strip()
if counter_str == "":
@@ -443,11 +443,18 @@ class CppHeaderParser(object):
# filter off some common prefixes, which are meaningless for Python wrappers.
# note that we do not strip "static" prefix, which does matter;
# it means class methods, not instance methods
decl_str = self.batch_replace(decl_str, [("static inline", ""), ("inline", ""), ("explicit ", ""),
("CV_EXPORTS_W", ""), ("CV_EXPORTS", ""), ("CV_CDECL", ""),
("CV_WRAP ", " "), ("CV_INLINE", ""),
("CV_DEPRECATED", ""), ("CV_DEPRECATED_EXTERNAL", "")]).strip()
decl_str = self.batch_replace(decl_str, [("static inline", ""),
("inline", ""),
("explicit ", ""),
("CV_EXPORTS_W", ""),
("CV_EXPORTS", ""),
("CV_CDECL", ""),
("CV_WRAP ", " "),
("CV_INLINE", ""),
("CV_DEPRECATED", ""),
("CV_DEPRECATED_EXTERNAL", ""),
("CV_NODISCARD_STD", ""),
("CV_NODISCARD", "")]).strip()
if decl_str.strip().startswith('virtual'):
virtual_method = True
@@ -843,6 +850,7 @@ class CppHeaderParser(object):
("GAPI_EXPORTS_W_SIMPLE","CV_EXPORTS_W_SIMPLE"),
("GAPI_WRAP", "CV_WRAP"),
("GAPI_PROP", "CV_PROP"),
("GAPI_PROP_RW", "CV_PROP_RW"),
('defined(GAPI_STANDALONE)', '0'),
])
@@ -989,7 +997,8 @@ class CppHeaderParser(object):
has_mat = len(list(filter(lambda x: x[0] in {"Mat", "vector_Mat"}, args))) > 0
if has_mat:
_, _, _, gpumat_decl = self.parse_stmt(stmt, token, mat="cuda::GpuMat", docstring=docstring)
decls.append(gpumat_decl)
if gpumat_decl != decl:
decls.append(gpumat_decl)
if self._generate_umat_decls:
# If function takes as one of arguments Mat or vector<Mat> - we want to create the
@@ -998,7 +1007,8 @@ class CppHeaderParser(object):
has_mat = len(list(filter(lambda x: x[0] in {"Mat", "vector_Mat"}, args))) > 0
if has_mat:
_, _, _, umat_decl = self.parse_stmt(stmt, token, mat="UMat", docstring=docstring)
decls.append(umat_decl)
if umat_decl != decl:
decls.append(umat_decl)
docstring = ""
if stmt_type == "namespace":