2021-12-05 20:49:36 +08:00
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#ifndef CV2_NUMPY_HPP
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#define CV2_NUMPY_HPP
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#include "cv2.hpp"
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#include "opencv2/core.hpp"
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class NumpyAllocator : public cv::MatAllocator
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{
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public:
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NumpyAllocator() { stdAllocator = cv::Mat::getStdAllocator(); }
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~NumpyAllocator() {}
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cv::UMatData* allocate(PyObject* o, int dims, const int* sizes, int type, size_t* step) const;
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cv::UMatData* allocate(int dims0, const int* sizes, int type, void* data, size_t* step, cv::AccessFlag flags, cv::UMatUsageFlags usageFlags) const CV_OVERRIDE;
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bool allocate(cv::UMatData* u, cv::AccessFlag accessFlags, cv::UMatUsageFlags usageFlags) const CV_OVERRIDE;
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void deallocate(cv::UMatData* u) const CV_OVERRIDE;
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const cv::MatAllocator* stdAllocator;
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};
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extern NumpyAllocator g_numpyAllocator;
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//======================================================================================================================
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// HACK(?): function from cv2_util.hpp
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extern int failmsg(const char *fmt, ...);
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namespace {
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template<class T>
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NPY_TYPES asNumpyType()
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{
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return NPY_OBJECT;
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}
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template<>
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NPY_TYPES asNumpyType<bool>()
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{
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return NPY_BOOL;
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}
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#define CV_GENERATE_INTEGRAL_TYPE_NPY_CONVERSION(src, dst) \
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template<> \
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NPY_TYPES asNumpyType<src>() \
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{ \
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return NPY_##dst; \
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} \
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template<> \
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NPY_TYPES asNumpyType<u##src>() \
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{ \
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return NPY_U##dst; \
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}
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2022-09-28 06:06:46 +08:00
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CV_GENERATE_INTEGRAL_TYPE_NPY_CONVERSION(int8_t, INT8)
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2021-12-05 20:49:36 +08:00
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2022-09-28 06:06:46 +08:00
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CV_GENERATE_INTEGRAL_TYPE_NPY_CONVERSION(int16_t, INT16)
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2021-12-05 20:49:36 +08:00
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2022-09-28 06:06:46 +08:00
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CV_GENERATE_INTEGRAL_TYPE_NPY_CONVERSION(int32_t, INT32)
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2021-12-05 20:49:36 +08:00
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2022-09-28 06:06:46 +08:00
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CV_GENERATE_INTEGRAL_TYPE_NPY_CONVERSION(int64_t, INT64)
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2021-12-05 20:49:36 +08:00
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#undef CV_GENERATE_INTEGRAL_TYPE_NPY_CONVERSION
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template<>
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NPY_TYPES asNumpyType<float>()
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{
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return NPY_FLOAT;
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}
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template<>
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NPY_TYPES asNumpyType<double>()
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{
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return NPY_DOUBLE;
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}
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template <class T>
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PyArray_Descr* getNumpyTypeDescriptor()
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{
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return PyArray_DescrFromType(asNumpyType<T>());
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}
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template <>
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PyArray_Descr* getNumpyTypeDescriptor<size_t>()
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{
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#if SIZE_MAX == ULONG_MAX
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return PyArray_DescrFromType(NPY_ULONG);
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#elif SIZE_MAX == ULLONG_MAX
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return PyArray_DescrFromType(NPY_ULONGLONG);
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#else
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return PyArray_DescrFromType(NPY_UINT);
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#endif
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}
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template <class T, class U>
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bool isRepresentable(U value) {
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return (std::numeric_limits<T>::min() <= value) && (value <= std::numeric_limits<T>::max());
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}
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template<class T>
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bool canBeSafelyCasted(PyObject* obj, PyArray_Descr* to)
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{
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return PyArray_CanCastTo(PyArray_DescrFromScalar(obj), to) != 0;
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}
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template<>
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bool canBeSafelyCasted<size_t>(PyObject* obj, PyArray_Descr* to)
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{
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PyArray_Descr* from = PyArray_DescrFromScalar(obj);
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if (PyArray_CanCastTo(from, to))
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{
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return true;
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}
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else
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{
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// False negative scenarios:
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// - Signed input is positive so it can be safely cast to unsigned output
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// - Input has wider limits but value is representable within output limits
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// - All the above
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if (PyDataType_ISSIGNED(from))
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{
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int64_t input = 0;
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PyArray_CastScalarToCtype(obj, &input, getNumpyTypeDescriptor<int64_t>());
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return (input >= 0) && isRepresentable<size_t>(static_cast<uint64_t>(input));
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}
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else
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{
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uint64_t input = 0;
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PyArray_CastScalarToCtype(obj, &input, getNumpyTypeDescriptor<uint64_t>());
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return isRepresentable<size_t>(input);
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}
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return false;
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}
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}
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template<class T>
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bool parseNumpyScalar(PyObject* obj, T& value)
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{
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if (PyArray_CheckScalar(obj))
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{
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// According to the numpy documentation:
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// There are 21 statically-defined PyArray_Descr objects for the built-in data-types
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// So descriptor pointer is not owning.
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PyArray_Descr* to = getNumpyTypeDescriptor<T>();
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if (canBeSafelyCasted<T>(obj, to))
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{
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PyArray_CastScalarToCtype(obj, &value, to);
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return true;
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}
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}
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return false;
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}
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struct SafeSeqItem
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{
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PyObject * item;
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SafeSeqItem(PyObject *obj, size_t idx) { item = PySequence_GetItem(obj, idx); }
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~SafeSeqItem() { Py_XDECREF(item); }
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private:
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SafeSeqItem(const SafeSeqItem&); // = delete
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SafeSeqItem& operator=(const SafeSeqItem&); // = delete
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};
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template <class T>
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class RefWrapper
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{
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public:
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RefWrapper(T& item) : item_(item) {}
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T& get() CV_NOEXCEPT { return item_; }
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private:
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T& item_;
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};
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// In order to support this conversion on 3.x branch - use custom reference_wrapper
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// and C-style array instead of std::array<T, N>
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template <class T, std::size_t N>
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bool parseSequence(PyObject* obj, RefWrapper<T> (&value)[N], const ArgInfo& info)
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{
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if (!obj || obj == Py_None)
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{
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return true;
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}
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if (!PySequence_Check(obj))
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{
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failmsg("Can't parse '%s'. Input argument doesn't provide sequence "
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"protocol", info.name);
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return false;
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}
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const std::size_t sequenceSize = PySequence_Size(obj);
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if (sequenceSize != N)
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{
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failmsg("Can't parse '%s'. Expected sequence length %lu, got %lu",
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info.name, N, sequenceSize);
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return false;
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}
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for (std::size_t i = 0; i < N; ++i)
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{
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SafeSeqItem seqItem(obj, i);
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if (!pyopencv_to(seqItem.item, value[i].get(), info))
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{
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failmsg("Can't parse '%s'. Sequence item with index %lu has a "
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"wrong type", info.name, i);
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return false;
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}
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}
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return true;
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}
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} // namespace
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#endif // CV2_NUMPY_HPP
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