mirror of https://github.com/Relintai/pmlpp.git
88 lines
3.3 KiB
C++
88 lines
3.3 KiB
C++
/*************************************************************************/
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/* transforms.cpp */
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/*************************************************************************/
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/* This file is part of: */
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/* PMLPP Machine Learning Library */
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/* https://github.com/Relintai/pmlpp */
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/*************************************************************************/
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/* Copyright (c) 2023-present Péter Magyar. */
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/* Copyright (c) 2022-2023 Marc Melikyan */
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/* */
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/* Permission is hereby granted, free of charge, to any person obtaining */
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/* a copy of this software and associated documentation files (the */
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/* "Software"), to deal in the Software without restriction, including */
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/* without limitation the rights to use, copy, modify, merge, publish, */
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/* distribute, sublicense, and/or sell copies of the Software, and to */
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/* permit persons to whom the Software is furnished to do so, subject to */
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/* the following conditions: */
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/* */
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/* The above copyright notice and this permission notice shall be */
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/* included in all copies or substantial portions of the Software. */
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/* */
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/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
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/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
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/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
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/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
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/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
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/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
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/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
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/*************************************************************************/
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#include "transforms.h"
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#include "../lin_alg/lin_alg.h"
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#ifdef USING_SFW
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#include "sfw.h"
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#else
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#include "core/math/math_funcs.h"
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#endif
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// DCT ii.
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// https://www.mathworks.com/help/images/discrete-cosine-transform.html
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Ref<MLPPMatrix> MLPPTransforms::discrete_cosine_transform(const Ref<MLPPMatrix> &p_A) {
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Ref<MLPPMatrix> A = p_A->scalar_addn(-128); // Center around 0.
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Size2i size = A->size();
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Ref<MLPPMatrix> B;
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B.instance();
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B->resize(size);
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real_t M = size.y;
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real_t inv_sqrt_M = 1 / Math::sqrt(M);
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real_t s2M = Math::sqrt(real_t(2) / real_t(M));
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for (int i = 0; i < size.y; i++) {
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for (int j = 0; j < size.x; j++) {
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real_t sum = 0;
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real_t alphaI;
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if (i == 0) {
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alphaI = inv_sqrt_M;
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} else {
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alphaI = s2M;
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}
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real_t alphaJ;
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if (j == 0) {
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alphaJ = inv_sqrt_M;
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} else {
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alphaJ = s2M;
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}
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for (int k = 0; k < size.y; k++) {
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for (int f = 0; f < size.x; f++) {
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sum += A->element_get(k, f) * Math::cos((Math_PI * i * (2 * k + 1)) / (2 * M)) * Math::cos((Math_PI * j * (2 * f + 1)) / (2 * M));
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}
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}
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B->element_set(i, j, sum * alphaI * alphaJ);
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}
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}
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return B;
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}
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void MLPPTransforms::_bind_methods() {
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}
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