#ifndef MLPP_CONVOLUTIONS_H #define MLPP_CONVOLUTIONS_H /*************************************************************************/ /* convolutions.h */ /*************************************************************************/ /* This file is part of: */ /* PMLPP Machine Learning Library */ /* https://github.com/Relintai/pmlpp */ /*************************************************************************/ /* Copyright (c) 2023-present Péter Magyar. */ /* Copyright (c) 2022-2023 Marc Melikyan */ /* */ /* Permission is hereby granted, free of charge, to any person obtaining */ /* a copy of this software and associated documentation files (the */ /* "Software"), to deal in the Software without restriction, including */ /* without limitation the rights to use, copy, modify, merge, publish, */ /* distribute, sublicense, and/or sell copies of the Software, and to */ /* permit persons to whom the Software is furnished to do so, subject to */ /* the following conditions: */ /* */ /* The above copyright notice and this permission notice shall be */ /* included in all copies or substantial portions of the Software. */ /* */ /* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */ /* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */ /* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/ /* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */ /* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */ /* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */ /* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ /*************************************************************************/ #include "core/containers/vector.h" #include "core/string/ustring.h" #include "core/math/math_defs.h" #include "../lin_alg/mlpp_matrix.h" #include "../lin_alg/mlpp_tensor3.h" #include "../lin_alg/mlpp_vector.h" #include "core/object/reference.h" class MLPPConvolutions : public Reference { GDCLASS(MLPPConvolutions, Reference); public: enum PoolType { POOL_TYPE_AVERAGE = 0, POOL_TYPE_MIN, POOL_TYPE_MAX, }; Ref convolve_2d(const Ref &input, const Ref &filter, const int S, const int P = 0); Ref convolve_3d(const Ref &input, const Ref &filter, const int S, const int P = 0); Ref pool_2d(const Ref &input, const int F, const int S, const PoolType type); Ref pool_3d(const Ref &input, const int F, const int S, const PoolType type); real_t global_pool_2d(const Ref &input, const PoolType type); Ref global_pool_3d(const Ref &input, const PoolType type); real_t gaussian_2d(const real_t x, const real_t y, const real_t std); Ref gaussian_filter_2d(const int size, const real_t std); Ref dx(const Ref &input); Ref dy(const Ref &input); Ref grad_magnitude(const Ref &input); Ref grad_orientation(const Ref &input); Ref compute_m(const Ref &input); Vector> compute_mv(const Ref &input); //TODO better data srtucture for this. Maybe IntMatrix? Vector> harris_corner_detection(const Ref &input); Ref get_prewitt_horizontal() const; Ref get_prewitt_vertical() const; Ref get_sobel_horizontal() const; Ref get_sobel_vertical() const; Ref get_scharr_horizontal() const; Ref get_scharr_vertical() const; Ref get_roberts_horizontal() const; Ref get_roberts_vertical() const; MLPPConvolutions(); protected: static void _bind_methods(); Ref _prewitt_horizontal; Ref _prewitt_vertical; Ref _sobel_horizontal; Ref _sobel_vertical; Ref _scharr_horizontal; Ref _scharr_vertical; Ref _roberts_horizontal; Ref _roberts_vertical; }; #endif // Convolutions_hpp