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Diffstat (limited to 'thirdparty/linux/include/opencv2/dnn/blob.inl.hpp')
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1 files changed, 533 insertions, 0 deletions
diff --git a/thirdparty/linux/include/opencv2/dnn/blob.inl.hpp b/thirdparty/linux/include/opencv2/dnn/blob.inl.hpp new file mode 100644 index 0000000..b7f741e --- /dev/null +++ b/thirdparty/linux/include/opencv2/dnn/blob.inl.hpp @@ -0,0 +1,533 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_DNN_DNN_BLOB_INL_HPP__ +#define __OPENCV_DNN_DNN_BLOB_INL_HPP__ +#include "blob.hpp" + +namespace cv +{ +namespace dnn +{ + +inline BlobShape::BlobShape() +{ + sz.allocate(4); + for (size_t i = 0; i < sz.size(); i++) + sz[i] = 1; +} + +inline BlobShape BlobShape::all(int ndims, int fill) +{ + CV_Assert(ndims >= 0); + BlobShape res; + res.sz.allocate(ndims); + for (int i = 0; i < ndims; i++) + res.sz[i] = fill; + return res; +} + +inline BlobShape::BlobShape(int ndims, const int *sizes) : sz( (size_t)std::max(ndims, 0) ) +{ + CV_Assert(ndims >= 0); + if (!sizes) + return; + for (int i = 0; i < ndims; i++) + sz[i] = sizes[i]; +} + +inline BlobShape::BlobShape(int s0) : sz(1) +{ + sz[0] = s0; +} + +inline BlobShape::BlobShape(int s0, int s1) : sz(2) +{ + sz[0] = s0; + sz[1] = s1; +} + +inline BlobShape::BlobShape(int s0, int s1, int s2) : sz(3) +{ + sz[0] = s0; + sz[1] = s1; + sz[2] = s2; +} + +inline BlobShape::BlobShape(int num, int cn, int rows, int cols) : sz(4) +{ + sz[0] = num; + sz[1] = cn; + sz[2] = rows; + sz[3] = cols; +} + +inline BlobShape::BlobShape(const std::vector<int> &sizes) : sz( sizes.size() ) +{ + for (int i = 0; i < (int)sizes.size(); i++) + sz[i] = sizes[i]; +} + +template<int n> +inline BlobShape::BlobShape(const Vec<int, n> &shape) : sz(n) +{ + for (int i = 0; i < n; i++) + sz[i] = shape[i]; +} + +inline int BlobShape::dims() const +{ + return (int)sz.size(); +} + +inline int BlobShape::xsize(int axis) const +{ + if (axis < -dims() || axis >= dims()) + return 1; + + return sz[(axis < 0) ? axis + dims() : axis]; +} + +inline int BlobShape::size(int axis) const +{ + CV_Assert(-dims() <= axis && axis < dims()); + return sz[(axis < 0) ? axis + dims() : axis]; +} + +inline int &BlobShape::size(int axis) +{ + CV_Assert(-dims() <= axis && axis < dims()); + return sz[(axis < 0) ? axis + dims() : axis]; +} + +inline int BlobShape::operator[] (int axis) const +{ + CV_Assert(-dims() <= axis && axis < dims()); + return sz[(axis < 0) ? axis + dims() : axis]; +} + +inline int &BlobShape::operator[] (int axis) +{ + CV_Assert(-dims() <= axis && axis < dims()); + return sz[(axis < 0) ? axis + dims() : axis]; +} + +inline int BlobShape::canonicalAxis(int axis) const +{ + CV_Assert(-dims() <= axis && axis < dims()); + return (axis < 0) ? axis + dims() : axis; +} + +inline ptrdiff_t BlobShape::total() const +{ + if (dims() == 0) + return 0; + + ptrdiff_t res = 1; + for (int i = 0; i < dims(); i++) + res *= sz[i]; + return res; +} + +inline ptrdiff_t BlobShape::total(int startAxis, int endAxis) const +{ + if (isEmpty()) + return 0; + + if (endAxis == INT_MAX) + endAxis = dims(); + else if (endAxis < 0) + endAxis += dims(); + startAxis = (startAxis < 0) ? startAxis + dims() : startAxis; + CV_Assert(0 <= startAxis && startAxis <= endAxis && endAxis <= dims()); + + ptrdiff_t res = 1; + for (int i = startAxis; i < endAxis; i++) + res *= sz[i]; + return res; +} + +inline BlobShape BlobShape::slice(int startAxis, int endAxis) const +{ + if (isEmpty()) + return BlobShape::empty(); + + if (endAxis == INT_MAX) + endAxis = dims(); + else if (endAxis < 0) + endAxis += dims(); + startAxis = (startAxis < 0) ? startAxis + dims() : startAxis; + CV_Assert(0 <= startAxis && startAxis <= endAxis && endAxis <= dims()); + + BlobShape res(endAxis - startAxis, (const int*)NULL); + for (int i = startAxis; i < endAxis; i++) + res[i - startAxis] = sz[i]; + return res; +} + +inline const int *BlobShape::ptr() const +{ + return sz; +} + +inline int *BlobShape::ptr() +{ + return sz; +} + +inline bool BlobShape::equal(const BlobShape &other) const +{ + if (this->dims() != other.dims()) + return false; + + for (int i = 0; i < other.dims(); i++) + { + if (sz[i] != other.sz[i]) + return false; + } + + return true; +} + +inline bool BlobShape::operator==(const BlobShape &r) const +{ + return this->equal(r); +} + +inline BlobShape BlobShape::like(const Mat &m) +{ + return BlobShape(m.dims, (const int*)m.size); +} + +inline BlobShape BlobShape::like(const UMat &m) +{ + return BlobShape(m.dims, (const int*)m.size); +} + +inline BlobShape BlobShape::empty() +{ + return BlobShape(0, (const int*)NULL); +} + +inline bool BlobShape::isEmpty() const +{ + return dims() == 0; +} + +inline BlobShape BlobShape::operator+(const BlobShape &r) const +{ + BlobShape newShape(this->dims() + r.dims(), (int*)NULL); + for (int i = 0; i < this->dims(); i++) + newShape[i] = (*this)[i]; + for (int i = 0; i < r.dims(); i++) + newShape[this->dims() + i] = r[i]; + return newShape; +} + +CV_EXPORTS std::ostream &operator<< (std::ostream &stream, const BlobShape &shape); + +///////////////////////////////////////////////////////////////////// + +#ifndef CV_DNN_UMAT +# define CV_DNN_SWITCH_MU(cpu_expr, gpu_expr) (cpu_expr) +#else +# define CV_DNN_SWITCH_MU(cpu_expr, gpu_expr) ((state == HEAD_AT_UMAT) ? (gpu_expr) : (cpu_expr)) +#endif + + +inline int Blob::dims() const +{ + return CV_DNN_SWITCH_MU(m.dims, um.dims); +} + +inline const int * Blob::sizes() const +{ + return CV_DNN_SWITCH_MU((const int*)m.size, (const int*)um.size); +} + +inline int Blob::type() const +{ + return CV_DNN_SWITCH_MU(m.type(), um.type()); +} + +template<int n> +inline size_t Blob::offset(const Vec<int, n> &pos) const +{ + const MatStep &step = CV_DNN_SWITCH_MU(m.step, um.step); + size_t ofs = 0; + int i; + for (i = 0; i < std::min(n, dims()); i++) + { + CV_DbgAssert(pos[i] >= 0 && pos[i] < size(i)); + ofs += step[i] * pos[i]; + } + for (; i < dims(); i++) + CV_DbgAssert(pos[i] == 0); + CV_DbgAssert(ofs % elemSize() == 0); + return ofs / elemSize(); +} + +inline int Blob::canonicalAxis(int axis) const +{ + CV_Assert(-dims() <= axis && axis < dims()); + return (axis < 0) ? axis + dims() : axis; +} + +inline int Blob::xsize(int axis) const +{ + if (axis < -dims() || axis >= dims()) + return 1; + + return sizes()[(axis < 0) ? axis + dims() : axis]; +} + +inline int Blob::size(int axis) const +{ + CV_Assert(-dims() <= axis && axis < dims()); + return sizes()[(axis < 0) ? axis + dims() : axis]; +} + +inline size_t Blob::total(int startAxis, int endAxis) const +{ + if (startAxis < 0) + startAxis += dims(); + + if (endAxis == INT_MAX) + endAxis = dims(); + else if (endAxis < 0) + endAxis += dims(); + + CV_Assert(0 <= startAxis && startAxis <= endAxis && endAxis <= dims()); + + size_t cnt = 1; //fix: assume that slice isn't empty + for (int i = startAxis; i < endAxis; i++) + cnt *= (size_t)sizes()[i]; + + return cnt; +} + +inline size_t Blob::offset(int n, int cn, int row, int col) const +{ + return offset(Vec4i(n, cn, row, col)); +} + +inline float *Blob::ptrf(int n, int cn, int row, int col) +{ + return matRef(false).ptr<float>() + offset(n, cn, row, col); +} + +inline uchar *Blob::ptr(int n, int cn, int row, int col) +{ + Mat &mat = matRef(false); + return mat.ptr() + mat.elemSize() * offset(n, cn, row, col); +} + +template<typename Dtype> +inline Dtype* Blob::ptr(int n, int cn, int row, int col) +{ + CV_Assert(type() == cv::DataDepth<Dtype>::value); + return (Dtype*) ptr(n, cn, row, col); +} + +inline BlobShape Blob::shape() const +{ + return BlobShape(dims(), sizes()); +} + +inline bool Blob::equalShape(const Blob &other) const +{ + if (this->dims() != other.dims()) + return false; + + for (int i = 0; i < dims(); i++) + { + if (this->sizes()[i] != other.sizes()[i]) + return false; + } + return true; +} + +inline Mat& Blob::matRef(bool writeOnly) +{ +#ifdef CV_DNN_UMAT + updateMat(!writeOnly); + state = HEAD_AT_MAT; +#else + (void)writeOnly; +#endif + return m; +} + +inline const Mat& Blob::matRefConst() const +{ + CV_DNN_UMAT_ONLY( updateMat() ); + return m; +} + +inline UMat &Blob::umatRef(bool writeOnly) +{ +#ifndef CV_DNN_UMAT + CV_Error(Error::GpuNotSupported, ""); + (void)writeOnly; + return *(new UMat()); +#else + updateUMat(!writeOnly); + state = HEAD_AT_UMAT; + return um; +#endif +} + +inline const UMat &Blob::umatRefConst() const +{ +#ifndef CV_DNN_UMAT + CV_Error(Error::GpuNotSupported, ""); + return *(new UMat()); +#else + updateUMat(); + return um; +#endif +} + +template<> +inline Mat &Blob::getRef<Mat>(bool writeOnly) +{ + return matRef(writeOnly); +} + +template<> +inline UMat &Blob::getRef<UMat>(bool writeOnly) +{ + return umatRef(writeOnly); +} + +template<> +inline const Mat &Blob::getRefConst<Mat>() const +{ + return matRefConst(); +} + +template<> +inline const UMat &Blob::getRefConst<UMat>() const +{ + return umatRefConst(); +} + +inline Mat Blob::getPlane(int n, int cn) +{ + CV_Assert(dims() > 2); + return Mat(dims() - 2, sizes() + 2, type(), ptr(n, cn)); +} + +inline Mat Blob::getPlanes(int n) +{ + CV_Assert(dims() > 3); + return Mat(dims() - 1, sizes() + 1, type(), ptr(n)); +} + +inline int Blob::cols() const +{ + return xsize(3); +} + +inline int Blob::rows() const +{ + return xsize(2); +} + +inline int Blob::channels() const +{ + return xsize(1); +} + +inline int Blob::num() const +{ + return xsize(0); +} + +inline Size Blob::size2() const +{ + return Size(cols(), rows()); +} + +inline Blob &Blob::shareFrom(const Blob &blob) +{ + this->m = blob.m; +#ifdef CV_DNN_UMAT + this->um = blob.um; + this->state = blob.state; +#endif + return *this; +} + +inline Blob &Blob::reshape(const BlobShape &newShape) +{ + if (!m.empty()) m = m.reshape(1, newShape.dims(), newShape.ptr()); +#ifdef CV_DNN_UMAT + if (!um.empty()) um = um.reshape(1, newShape.dims(), newShape.ptr()); +#endif + return *this; +} + +inline Blob Blob::reshaped(const BlobShape &newShape) const +{ + Blob res(*this); //also, res.shareFrom(*this) could be used + res.reshape(newShape); + return res; +} + +inline int Blob::elemSize() const +{ + return CV_ELEM_SIZE(type()); +} + +inline int Blob::getState() const +{ +#ifdef CV_DNN_UMAT + return this->state; +#else + return m.empty() ? UNINITIALIZED : HEAD_AT_MAT; +#endif +} + +} +} + +#endif |