forked from bartvdbraak/blender
58c88bcf76
Not working: a) rendering (since volumterics branch is not merged yet) b) moving collision objects of any kind c) saving of collision objects (because that's what I am working on) d) pointcache e) A bunch of other things I already know of So please do not report any bugs on this one yet :-)
457 lines
15 KiB
C++
457 lines
15 KiB
C++
//////////////////////////////////////////////////////////////////////
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// This file is part of Wavelet Turbulence.
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//
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// Wavelet Turbulence is free software: you can redistribute it and/or modify
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// it under the terms of the GNU General Public License as published by
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// the Free Software Foundation, either version 3 of the License, or
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// (at your option) any later version.
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//
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// Wavelet Turbulence is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU General Public License
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// along with Wavelet Turbulence. If not, see <http://www.gnu.org/licenses/>.
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//
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// Copyright 2008 Theodore Kim and Nils Thuerey
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//
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//////////////////////////////////////////////////////////////////////////////////////////
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// Wavelet noise functions
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//
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// This code is based on the C code provided in the appendices of:
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//
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// @article{1073264,
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// author = {Robert L. Cook and Tony DeRose},
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// title = {Wavelet noise},
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// journal = {ACM Trans. Graph.},
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// volume = {24},
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// number = {3},
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// year = {2005},
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// issn = {0730-0301},
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// pages = {803--811},
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// doi = {http://doi.acm.org/10.1145/1073204.1073264},
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// publisher = {ACM},
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// address = {New York, NY, USA},
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// }
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//
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//////////////////////////////////////////////////////////////////////////////////////////
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#ifndef WAVELET_NOISE_H
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#define WAVELET_NOISE_H
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#include <MERSENNETWISTER.h>
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#define NOISE_TILE_SIZE 128
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static const int noiseTileSize = NOISE_TILE_SIZE;
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// warning - noiseTileSize has to be 128^3!
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#define modFast128(x) ((x) & 127)
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#define modFast64(x) ((x) & 63)
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#define DOWNCOEFFS 0.000334f,-0.001528f, 0.000410f, 0.003545f,-0.000938f,-0.008233f, 0.002172f, 0.019120f, \
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-0.005040f,-0.044412f, 0.011655f, 0.103311f,-0.025936f,-0.243780f, 0.033979f, 0.655340f, \
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0.655340f, 0.033979f,-0.243780f,-0.025936f, 0.103311f, 0.011655f,-0.044412f,-0.005040f, \
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0.019120f, 0.002172f,-0.008233f,-0.000938f, 0.003546f, 0.000410f,-0.001528f, 0.000334f
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//////////////////////////////////////////////////////////////////////////////////////////
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// Wavelet downsampling -- periodic boundary conditions
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//////////////////////////////////////////////////////////////////////////////////////////
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static void downsampleX(float *from, float *to, int n){
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// if these values are not local incorrect results are generated
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float downCoeffs[32] = { DOWNCOEFFS };
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const float *a = &downCoeffs[16];
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for (int i = 0; i < n / 2; i++) {
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to[i] = 0;
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for (int k = 2 * i - 16; k <= 2 * i + 16; k++)
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to[i] += a[k - 2 * i] * from[modFast128(k)];
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}
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}
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static void downsampleY(float *from, float *to, int n){
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// if these values are not local incorrect results are generated
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float downCoeffs[32] = { DOWNCOEFFS };
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const float *a = &downCoeffs[16];
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for (int i = 0; i < n / 2; i++) {
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to[i * n] = 0;
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for (int k = 2 * i - 16; k <= 2 * i + 16; k++)
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to[i * n] += a[k - 2 * i] * from[modFast128(k) * n];
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}
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}
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static void downsampleZ(float *from, float *to, int n){
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// if these values are not local incorrect results are generated
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float downCoeffs[32] = { DOWNCOEFFS };
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const float *a = &downCoeffs[16];
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for (int i = 0; i < n / 2; i++) {
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to[i * n * n] = 0;
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for (int k = 2 * i - 16; k <= 2 * i + 16; k++)
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to[i * n * n] += a[k - 2 * i] * from[modFast128(k) * n * n];
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////
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// Wavelet downsampling -- Neumann boundary conditions
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//////////////////////////////////////////////////////////////////////////////////////////
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static void downsampleNeumann(const float *from, float *to, int n, int stride)
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{
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// if these values are not local incorrect results are generated
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float downCoeffs[32] = { DOWNCOEFFS };
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static const float *const aCoCenter= &downCoeffs[16];
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for (int i = 0; i < n / 2; i++) {
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to[i * stride] = 0;
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for (int k = 2 * i - 16; k < 2 * i + 16; k++) {
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// handle boundary
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float fromval;
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if (k < 0) {
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fromval = from[0];
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} else if(k > n - 1) {
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fromval = from[(n - 1) * stride];
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} else {
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fromval = from[k * stride];
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}
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to[i * stride] += aCoCenter[k - 2 * i] * fromval;
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}
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}
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}
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static void downsampleXNeumann(float* to, const float* from, int sx,int sy, int sz) {
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for (int iy = 0; iy < sy; iy++)
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for (int iz = 0; iz < sz; iz++) {
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const int i = iy * sx + iz*sx*sy;
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downsampleNeumann(&from[i], &to[i], sx, 1);
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}
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}
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static void downsampleYNeumann(float* to, const float* from, int sx,int sy, int sz) {
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for (int ix = 0; ix < sx; ix++)
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for (int iz = 0; iz < sz; iz++) {
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const int i = ix + iz*sx*sy;
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downsampleNeumann(&from[i], &to[i], sy, sx);
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}
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}
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static void downsampleZNeumann(float* to, const float* from, int sx,int sy, int sz) {
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for (int ix = 0; ix < sx; ix++)
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for (int iy = 0; iy < sy; iy++) {
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const int i = ix + iy*sx;
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downsampleNeumann(&from[i], &to[i], sz, sx*sy);
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////
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// Wavelet upsampling - periodic boundary conditions
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//////////////////////////////////////////////////////////////////////////////////////////
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static float _upCoeffs[4] = {0.25f, 0.75f, 0.75f, 0.25f};
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static void upsampleX(float *from, float *to, int n) {
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const float *p = &_upCoeffs[2];
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for (int i = 0; i < n; i++) {
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to[i] = 0;
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for (int k = i / 2; k <= i / 2 + 1; k++)
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to[i] += p[i - 2 * k] * from[modFast64(k)];
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}
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}
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static void upsampleY(float *from, float *to, int n) {
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const float *p = &_upCoeffs[2];
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for (int i = 0; i < n; i++) {
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to[i * n] = 0;
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for (int k = i / 2; k <= i / 2 + 1; k++)
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to[i * n] += p[i - 2 * k] * from[modFast64(k) * n];
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}
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}
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static void upsampleZ(float *from, float *to, int n) {
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const float *p = &_upCoeffs[2];
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for (int i = 0; i < n; i++) {
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to[i * n * n] = 0;
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for (int k = i / 2; k <= i / 2 + 1; k++)
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to[i * n * n] += p[i - 2 * k] * from[modFast64(k) * n * n];
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////
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// Wavelet upsampling - Neumann boundary conditions
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//////////////////////////////////////////////////////////////////////////////////////////
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static void upsampleNeumann(const float *from, float *to, int n, int stride) {
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static const float *const pCoCenter = &_upCoeffs[2];
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for (int i = 0; i < n; i++) {
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to[i * stride] = 0;
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for (int k = i / 2; k <= i / 2 + 1; k++) {
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float fromval;
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if(k>n/2) {
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fromval = from[(n/2) * stride];
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} else {
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fromval = from[k * stride];
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}
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to[i * stride] += pCoCenter[i - 2 * k] * fromval;
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}
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}
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}
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static void upsampleXNeumann(float* to, const float* from, int sx, int sy, int sz) {
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for (int iy = 0; iy < sy; iy++)
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for (int iz = 0; iz < sz; iz++) {
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const int i = iy * sx + iz*sx*sy;
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upsampleNeumann(&from[i], &to[i], sx, 1);
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}
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}
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static void upsampleYNeumann(float* to, const float* from, int sx, int sy, int sz) {
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for (int ix = 0; ix < sx; ix++)
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for (int iz = 0; iz < sz; iz++) {
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const int i = ix + iz*sx*sy;
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upsampleNeumann(&from[i], &to[i], sy, sx);
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}
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}
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static void upsampleZNeumann(float* to, const float* from, int sx, int sy, int sz) {
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for (int ix = 0; ix < sx; ix++)
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for (int iy = 0; iy < sy; iy++) {
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const int i = ix + iy*sx;
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upsampleNeumann(&from[i], &to[i], sz, sx*sy);
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////
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// load in an existing noise tile
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//////////////////////////////////////////////////////////////////////////////////////////
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static bool loadTile(float* const noiseTileData, std::string filename)
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{
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FILE* file;
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file = fopen(filename.c_str(), "rb");
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if (file == NULL) {
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printf("loadTile: No noise tile '%s' found.\n", filename.c_str());
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return false;
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}
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// dimensions
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int gridSize = noiseTileSize * noiseTileSize * noiseTileSize;
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// noiseTileData memory is managed by caller
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int bread = fread((void*)noiseTileData, sizeof(float), gridSize, file);
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fclose(file);
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printf("Noise tile file '%s' loaded.\n", filename.c_str());
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if (bread != gridSize) {
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printf("loadTile: Noise tile '%s' is wrong size %d.\n", filename.c_str(), bread);
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return false;
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}
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return true;
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}
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//////////////////////////////////////////////////////////////////////////////////////////
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// write out an existing noise tile
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//////////////////////////////////////////////////////////////////////////////////////////
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static void saveTile(float* const noiseTileData, std::string filename)
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{
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FILE* file;
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file = fopen(filename.c_str(), "wb");
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if (file == NULL) {
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printf("saveTile: Noise tile '%s' could not be saved.\n", filename.c_str());
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return;
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}
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fwrite((void*)noiseTileData, sizeof(float), noiseTileSize * noiseTileSize * noiseTileSize, file);
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fclose(file);
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printf("saveTile: Noise tile file '%s' saved.\n", filename.c_str());
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}
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//////////////////////////////////////////////////////////////////////////////////////////
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// create a new noise tile if necessary
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//////////////////////////////////////////////////////////////////////////////////////////
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static void generateTile_WAVELET(float* const noiseTileData, std::string filename) {
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// if a tile already exists, just use that
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if (loadTile(noiseTileData, filename)) return;
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const int n = noiseTileSize;
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const int n3 = n*n*n;
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std::cout <<"Generating new 3d noise tile size="<<n<<"^3 \n";
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MTRand twister;
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float *temp13 = new float[n3];
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float *temp23 = new float[n3];
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float *noise3 = new float[n3];
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// initialize
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for (int i = 0; i < n3; i++) {
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temp13[i] = temp23[i] = noise3[i] = 0.;
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}
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// Step 1. Fill the tile with random numbers in the range -1 to 1.
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for (int i = 0; i < n3; i++)
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noise3[i] = twister.randNorm();
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// Steps 2 and 3. Downsample and upsample the tile
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for (int iy = 0; iy < n; iy++)
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for (int iz = 0; iz < n; iz++) {
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const int i = iy * n + iz*n*n;
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downsampleX(&noise3[i], &temp13[i], n);
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upsampleX (&temp13[i], &temp23[i], n);
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}
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for (int ix = 0; ix < n; ix++)
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for (int iz = 0; iz < n; iz++) {
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const int i = ix + iz*n*n;
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downsampleY(&temp23[i], &temp13[i], n);
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upsampleY (&temp13[i], &temp23[i], n);
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}
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for (int ix = 0; ix < n; ix++)
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for (int iy = 0; iy < n; iy++) {
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const int i = ix + iy*n;
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downsampleZ(&temp23[i], &temp13[i], n);
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upsampleZ (&temp13[i], &temp23[i], n);
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}
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// Step 4. Subtract out the coarse-scale contribution
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for (int i = 0; i < n3; i++)
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noise3[i] -= temp23[i];
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// Avoid even/odd variance difference by adding odd-offset version of noise to itself.
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int offset = n / 2;
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if (offset % 2 == 0) offset++;
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int icnt=0;
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for (int ix = 0; ix < n; ix++)
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for (int iy = 0; iy < n; iy++)
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for (int iz = 0; iz < n; iz++) {
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temp13[icnt] = noise3[modFast128(ix+offset) + modFast128(iy+offset)*n + modFast128(iz+offset)*n*n];
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icnt++;
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}
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for (int i = 0; i < n3; i++)
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noise3[i] += temp13[i];
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for (int i = 0; i < n3; i++)
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noiseTileData[i] = noise3[i];
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saveTile(noise3, filename);
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delete[] temp13;
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delete[] temp23;
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std::cout <<"Generating new 3d noise done\n";
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}
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//////////////////////////////////////////////////////////////////////////////////////////
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// x derivative of noise
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//////////////////////////////////////////////////////////////////////////////////////////
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static inline float WNoiseDx(Vec3 p, float* data) {
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int i, f[3], c[3], mid[3], n = noiseTileSize;
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float w[3][3], t, result = 0;
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mid[0] = ceil(p[0] - 0.5);
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t = mid[0] - (p[0] - 0.5);
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w[0][0] = -t;
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w[0][2] = (1.f - t);
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w[0][1] = 2.0f * t - 1.0f;
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mid[1] = ceil(p[1] - 0.5);
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t = mid[1] - (p[1] - 0.5);
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w[1][0] = t * t / 2;
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w[1][2] = (1 - t) * (1 - t) / 2;
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w[1][1] = 1 - w[1][0] - w[1][2];
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mid[2] = ceil(p[2] - 0.5);
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t = mid[2] - (p[2] - 0.5);
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w[2][0] = t * t / 2;
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w[2][2] = (1 - t) * (1 - t)/2;
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w[2][1] = 1 - w[2][0] - w[2][2];
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// to optimize, explicitly unroll this loop
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for (int z = -1; z <=1; z++)
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for (int y = -1; y <=1; y++)
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for (int x = -1; x <=1; x++)
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{
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float weight = 1.0f;
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c[0] = modFast128(mid[0] + x);
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weight *= w[0][x+1];
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c[1] = modFast128(mid[1] + y);
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weight *= w[1][y+1];
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c[2] = modFast128(mid[2] + z);
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weight *= w[2][z+1];
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result += weight * data[c[2]*n*n+c[1]*n+c[0]];
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}
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return result;
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}
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//////////////////////////////////////////////////////////////////////////////////////////
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// y derivative of noise
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//////////////////////////////////////////////////////////////////////////////////////////
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static inline float WNoiseDy(Vec3 p, float* data) {
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int i, f[3], c[3], mid[3], n=noiseTileSize;
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float w[3][3], t, result =0;
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mid[0] = ceil(p[0] - 0.5);
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t = mid[0]-(p[0] - 0.5);
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w[0][0] = t * t / 2;
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w[0][2] = (1 - t) * (1 - t) / 2;
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w[0][1] = 1 - w[0][0] - w[0][2];
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mid[1] = ceil(p[1] - 0.5);
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t = mid[1]-(p[1] - 0.5);
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w[1][0] = -t;
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w[1][2] = (1.f - t);
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w[1][1] = 2.0f * t - 1.0f;
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mid[2] = ceil(p[2] - 0.5);
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t = mid[2] - (p[2] - 0.5);
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w[2][0] = t * t / 2;
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w[2][2] = (1 - t) * (1 - t)/2;
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w[2][1] = 1 - w[2][0] - w[2][2];
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// to optimize, explicitly unroll this loop
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for (int z = -1; z <=1; z++)
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for (int y = -1; y <=1; y++)
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for (int x = -1; x <=1; x++)
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{
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float weight = 1.0f;
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c[0] = modFast128(mid[0] + x);
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weight *= w[0][x+1];
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c[1] = modFast128(mid[1] + y);
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weight *= w[1][y+1];
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c[2] = modFast128(mid[2] + z);
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weight *= w[2][z+1];
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result += weight * data[c[2]*n*n+c[1]*n+c[0]];
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}
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return result;
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}
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//////////////////////////////////////////////////////////////////////////////////////////
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// z derivative of noise
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//////////////////////////////////////////////////////////////////////////////////////////
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static inline float WNoiseDz(Vec3 p, float* data) {
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int i, f[3], c[3], mid[3], n=noiseTileSize;
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float w[3][3], t, result =0;
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mid[0] = ceil(p[0] - 0.5);
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t = mid[0]-(p[0] - 0.5);
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w[0][0] = t * t / 2;
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w[0][2] = (1 - t) * (1 - t) / 2;
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w[0][1] = 1 - w[0][0] - w[0][2];
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mid[1] = ceil(p[1] - 0.5);
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t = mid[1]-(p[1] - 0.5);
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w[1][0] = t * t / 2;
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w[1][2] = (1 - t) * (1 - t) / 2;
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w[1][1] = 1 - w[1][0] - w[1][2];
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mid[2] = ceil(p[2] - 0.5);
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t = mid[2] - (p[2] - 0.5);
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w[2][0] = -t;
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w[2][2] = (1.f - t);
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w[2][1] = 2.0f * t - 1.0f;
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|
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// to optimize, explicitly unroll this loop
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for (int z = -1; z <=1; z++)
|
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for (int y = -1; y <=1; y++)
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for (int x = -1; x <=1; x++)
|
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{
|
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float weight = 1.0f;
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c[0] = modFast128(mid[0] + x);
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weight *= w[0][x+1];
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c[1] = modFast128(mid[1] + y);
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weight *= w[1][y+1];
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c[2] = modFast128(mid[2] + z);
|
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weight *= w[2][z+1];
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result += weight * data[c[2]*n*n+c[1]*n+c[0]];
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}
|
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return result;
|
|
}
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#endif
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