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o3de/Gems/GradientSignal/Code/Source/PerlinImprovedNoise.cpp

149 lines
6.1 KiB
C++

/*
* Copyright (c) Contributors to the Open 3D Engine Project.
* For complete copyright and license terms please see the LICENSE at the root of this distribution.
*
* SPDX-License-Identifier: Apache-2.0 OR MIT
*
*/
#include <GradientSignal/PerlinImprovedNoise.h>
#include <numeric>
#include <random> // std::mt19937 std::random_device
namespace GradientSignal
{
//////////////////////////////////////////////////////////////////////////
// http://flafla2.github.io/2014/08/09/perlinnoise.html
// https://gist.github.com/Flafla2/f0260a861be0ebdeef76
// MIT License, http://www.opensource.org/licenses/mit-license.php
// Source: http://riven8192.blogspot.com/2010/08/calculate-perlinnoise-twice-as-fast.html
namespace PerlinImprovedNoiseDetails
{
AZ_INLINE float Gradient(int hash, float x, float y, float z)
{
switch (hash & 0xF)
{
case 0x0: return x + y;
case 0x1: return -x + y;
case 0x2: return x - y;
case 0x3: return -x - y;
case 0x4: return x + z;
case 0x5: return -x + z;
case 0x6: return x - z;
case 0x7: return -x - z;
case 0x8: return y + z;
case 0x9: return -y + z;
case 0xA: return y - z;
case 0xB: return -y - z;
case 0xC: return y + x;
case 0xD: return -y + z;
case 0xE: return y - x;
case 0xF: return -y - z;
default: return 0; // never happens
}
}
AZ_FORCE_INLINE float Fade(float t)
{
// Fade function as defined by Ken Perlin. This eases coordinate values
// so that they will "ease" towards integral values.
// This ends up smoothing the final output.
return t * t * t * (t * (t * 6 - 15) + 10); // 6t^5 - 15t^4 + 10t^3
}
AZ_FORCE_INLINE float Lerp(float a, float b, float x)
{
return a + x * (b - a);
}
}
PerlinImprovedNoise::PerlinImprovedNoise(int seed)
{
PrepareTable(seed);
}
float PerlinImprovedNoise::GenerateOctaveNoise(float x, float y, float z, int octaves, float persistence, float initialFrequency)
{
float total = 0.0f;
float frequency = initialFrequency;
float amplitude = 1.0f;
float maxValue = 0.0f; // Used for normalizing result to 0.0 - 1.0
for (int i = 0; i < octaves; ++i)
{
total += GenerateNoise(x * frequency, y * frequency, z * frequency) * amplitude;
maxValue += amplitude;
amplitude *= persistence;
frequency *= 2.0f;
}
if (maxValue <= 0.0f)
{
return 0.0f;
}
return total / maxValue;
}
float PerlinImprovedNoise::GenerateNoise(float x, float y, float z)
{
const int fx = (int)std::floor(x);
const int fy = (int)std::floor(y);
const int fz = (int)std::floor(z);
const float xf = x - fx; // We also fade the location to smooth the result.
const float yf = y - fy;
const float zf = z - fz;
const int xi0 = fx & 255; // Calculate the "unit cube" that the point asked will be located in
const int yi0 = fy & 255; // The left bound is ( |_x_|,|_y_|,|_z_| ) and the right bound is that
const int zi0 = fz & 255; // plus 1. Next we calculate the location (from 0.0 to 1.0) in that cube.
const int xi1 = (xi0 + 1) /*% 255*/;
const int yi1 = (yi0 + 1) /*% 255*/;
const int zi1 = (zi0 + 1) /*% 255*/;
const float u = PerlinImprovedNoiseDetails::Fade(xf);
const float v = PerlinImprovedNoiseDetails::Fade(yf);
const float w = PerlinImprovedNoiseDetails::Fade(zf);
const AZStd::array<int, 512>& p = m_permutationTable;
const int aaa = p[p[p[xi0] + yi0] + zi0];
const int aba = p[p[p[xi0] + yi1] + zi0];
const int aab = p[p[p[xi0] + yi0] + zi1];
const int abb = p[p[p[xi0] + yi1] + zi1];
const int baa = p[p[p[xi1] + yi0] + zi0];
const int bba = p[p[p[xi1] + yi1] + zi0];
const int bab = p[p[p[xi1] + yi0] + zi1];
const int bbb = p[p[p[xi1] + yi1] + zi1];
// The gradient function calculates the dot product between a pseudorandom
// gradient vector and the vector from the input coordinate to the 8
// surrounding points in its unit cube. This is all then lerped together as a sort of
// weighted average based on the faded (u,v,w) values we made earlier.
float x1, x2, y1, y2;
x1 = PerlinImprovedNoiseDetails::Lerp(PerlinImprovedNoiseDetails::Gradient(aaa, xf, yf, zf), PerlinImprovedNoiseDetails::Gradient(baa, xf - 1.0f, yf, zf), u);
x2 = PerlinImprovedNoiseDetails::Lerp(PerlinImprovedNoiseDetails::Gradient(aba, xf, yf - 1.0f, zf), PerlinImprovedNoiseDetails::Gradient(bba, xf - 1.0f, yf - 1.0f, zf), u);
y1 = PerlinImprovedNoiseDetails::Lerp(x1, x2, v);
x1 = PerlinImprovedNoiseDetails::Lerp(PerlinImprovedNoiseDetails::Gradient(aab, xf, yf, zf - 1.0f), PerlinImprovedNoiseDetails::Gradient(bab, xf - 1.0f, yf, zf - 1.0f), u);
x2 = PerlinImprovedNoiseDetails::Lerp(PerlinImprovedNoiseDetails::Gradient(abb, xf, yf - 1.0f, zf - 1.0f), PerlinImprovedNoiseDetails::Gradient(bbb, xf - 1.0f, yf - 1.0f, zf - 1.0f), u);
y2 = PerlinImprovedNoiseDetails::Lerp(x1, x2, v);
// For convenience we bound it to 0 - 1 (theoretical min/max before is -1 - 1)
return (PerlinImprovedNoiseDetails::Lerp(y1, y2, w) + 1.0f) / 2.0f;
}
void PerlinImprovedNoise::PrepareTable(int seed)
{
AZStd::array<int, 256> randtable;
std::iota(randtable.begin(), randtable.end(), 0);
std::shuffle(randtable.begin(), randtable.end(), std::mt19937(seed));
for (int x = 0; x < 256; ++x)
{
m_permutationTable[x] = randtable[x];
m_permutationTable[x + 256] = randtable[x];
}
}
}