Cyd
2b8ca921cb
Complete disaster-recovery snapshot: engine/game source, game data assets, VC6 toolchain + DX SDKs, build outputs, deployed game, and _UNUSED archive. Large binaries in Git LFS; text preserved byte-for-byte (core.autocrlf=false, no eol attributes). See RECOVERY.md for the one-clone rebuild procedure.
321 lines
6.2 KiB
C++
321 lines
6.2 KiB
C++
//
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// From "Texturing and Modeling A Procedural Approach"
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//
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// Chapter 6 by Ken Perlin
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//
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#include <stdlib.h>
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#include <stdio.h>
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#include <math.h>
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#include "noise.h"
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#define random() rand()
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float bias(float a, float b)
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{
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return pow((double)a, log((double)b) / log(0.5));
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}
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float gain(float a, float b)
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{
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float p = log(1. - b) / log(0.5);
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if (a < .001)
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return 0.;
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else if (a > .999)
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return 1.;
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if (a < 0.5)
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return pow(2 * a, p) / 2;
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else
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return 1. - pow(2.0 * (1. - a), (double)p) / 2;
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}
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float noise1(float arg);
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float noise2(float vec[]);
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float noise3(float vec[]);
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float noise(float vec[], int len)
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{
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switch (len) {
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case 0:
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return 0.;
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case 1:
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return noise1(vec[0]);
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case 2:
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return noise2(vec);
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default:
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return noise3(vec);
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}
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}
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float turbulence(float *v, float freq)
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{
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float t, vec[3];
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for (t = 0. ; freq >= 1. ; freq /= 2) {
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vec[0] = freq * v[0];
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vec[1] = freq * v[1];
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vec[2] = freq * v[2];
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t += fabs(noise3(vec)) / freq;
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}
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return t;
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}
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/* noise functions over 1, 2, and 3 dimensions */
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#define B 0x100
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#define BM 0xff
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#define N 0x1000
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#define NP 12 /* 2^N */
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#define NM 0xfff
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static p[B + B + 2];
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static float g3[B + B + 2][3];
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static float g2[B + B + 2][2];
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static float g1[B + B + 2];
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static start = 1;
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static void init(void);
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int Perm(int v)
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{
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return p[v&BM];
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}
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#define s_curve(t) ( t * t * (3. - 2. * t) )
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#define lerp(t, a, b) ( a + t * (b - a) )
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#define setup(i,b0,b1,r0,r1)\
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t = vec[i] + N;\
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b0 = ((int)t) & BM;\
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b1 = (b0+1) & BM;\
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r0 = t - (int)t;\
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r1 = r0 - 1.;
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float noise1(float arg)
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{
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int bx0, bx1;
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float rx0, rx1, sx, t, u, v, vec[1];
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vec[0] = arg;
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if (start) {
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start = 0;
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init();
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}
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setup(0, bx0,bx1, rx0,rx1);
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sx = s_curve(rx0);
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u = rx0 * g1[ p[ bx0 ] ];
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v = rx1 * g1[ p[ bx1 ] ];
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return lerp(sx, u, v);
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}
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float noise2(float vec[2])
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{
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int bx0, bx1, by0, by1, b00, b10, b01, b11;
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float rx0, rx1, ry0, ry1, *q, sx, sy, a, b, t, u, v;
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register i, j;
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if (start) {
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start = 0;
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init();
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}
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setup(0, bx0,bx1, rx0,rx1);
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setup(1, by0,by1, ry0,ry1);
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i = p[ bx0 ];
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j = p[ bx1 ];
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b00 = p[ i + by0 ];
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b10 = p[ j + by0 ];
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b01 = p[ i + by1 ];
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b11 = p[ j + by1 ];
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sx = s_curve(rx0);
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sy = s_curve(ry0);
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#define at2(rx,ry) ( rx * q[0] + ry * q[1] )
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q = g2[ b00 ] ; u = at2(rx0,ry0);
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q = g2[ b10 ] ; v = at2(rx1,ry0);
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a = lerp(sx, u, v);
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q = g2[ b01 ] ; u = at2(rx0,ry1);
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q = g2[ b11 ] ; v = at2(rx1,ry1);
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b = lerp(sx, u, v);
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return lerp(sy, a, b);
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}
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float noise3(float vec[3])
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{
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int bx0, bx1, by0, by1, bz0, bz1, b00, b10, b01, b11;
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float rx0, rx1, ry0, ry1, rz0, rz1, *q, sy, sz, a, b, c, d, t, u, v;
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register i, j;
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if (start) {
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start = 0;
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init();
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}
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setup(0, bx0,bx1, rx0,rx1);
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setup(1, by0,by1, ry0,ry1);
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setup(2, bz0,bz1, rz0,rz1);
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i = p[ bx0 ];
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j = p[ bx1 ];
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b00 = p[ i + by0 ];
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b10 = p[ j + by0 ];
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b01 = p[ i + by1 ];
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b11 = p[ j + by1 ];
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t = s_curve(rx0);
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sy = s_curve(ry0);
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sz = s_curve(rz0);
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#define at3(rx,ry,rz) ( rx * q[0] + ry * q[1] + rz * q[2] )
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q = g3[ b00 + bz0 ] ; u = at3(rx0,ry0,rz0);
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q = g3[ b10 + bz0 ] ; v = at3(rx1,ry0,rz0);
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a = lerp(t, u, v);
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q = g3[ b01 + bz0 ] ; u = at3(rx0,ry1,rz0);
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q = g3[ b11 + bz0 ] ; v = at3(rx1,ry1,rz0);
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b = lerp(t, u, v);
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c = lerp(sy, a, b);
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q = g3[ b00 + bz1 ] ; u = at3(rx0,ry0,rz1);
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q = g3[ b10 + bz1 ] ; v = at3(rx1,ry0,rz1);
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a = lerp(t, u, v);
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q = g3[ b01 + bz1 ] ; u = at3(rx0,ry1,rz1);
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q = g3[ b11 + bz1 ] ; v = at3(rx1,ry1,rz1);
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b = lerp(t, u, v);
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d = lerp(sy, a, b);
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return lerp(sz, c, d);
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}
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static void normalize2(float v[2])
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{
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float s;
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s = sqrt(v[0] * v[0] + v[1] * v[1]);
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v[0] = v[0] / s;
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v[1] = v[1] / s;
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}
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static void normalize3(float v[3])
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{
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float s;
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s = sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);
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v[0] = v[0] / s;
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v[1] = v[1] / s;
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v[2] = v[2] / s;
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}
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static void init(void)
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{
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int i, j, k;
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srand(0);
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for (i = 0 ; i < B ; i++) {
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p[i] = i;
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g1[i] = (float)((random() % (B + B)) - B) / B;
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for (j = 0 ; j < 2 ; j++)
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g2[i][j] = (float)((random() % (B + B)) - B) / B;
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normalize2(g2[i]);
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for (j = 0 ; j < 3 ; j++)
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g3[i][j] = (float)((random() % (B + B)) - B) / B;
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normalize3(g3[i]);
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}
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while (--i) {
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k = p[i];
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p[i] = p[j = random() % B];
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p[j] = k;
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}
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for (i = 0 ; i < B + 2 ; i++) {
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p[B + i] = p[i];
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g1[B + i] = g1[i];
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for (j = 0 ; j < 2 ; j++)
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g2[B + i][j] = g2[i][j];
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for (j = 0 ; j < 3 ; j++)
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g3[B + i][j] = g3[i][j];
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}
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}
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/*
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* Procedural fBm evaluated at "point"; returns value stored in "value".
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*
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* Copyright 1994 F. Kenton Musgrave
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*
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* Parameters:
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* ``H'' is the fractal increment parameter
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* ``lacunarity'' is the gap between successive frequencies
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* ``octaves'' is the number of frequencies in the fBm
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*/
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// RB:
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// Modified to be evaluated with a scalar.
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#define TRUE 1
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#define FALSE 0
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double
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fBm1( double point, double H, double lacunarity, double octaves )
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{
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static double exponent_array[MAX_OCTAVES+1];
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static double lastH;
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double value, frequency, remainder, Noise3();
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int i;
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static int first = TRUE;
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/* precompute and store spectral weights */
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if (first || H!= lastH) {
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lastH = H;
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frequency = 1.0;
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for (i=0; i<=octaves; i++) {
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/* compute weight for each frequency */
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exponent_array[i] = pow( frequency, -H );
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frequency *= lacunarity;
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}
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first = FALSE;
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}
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value = 0.0; /* initialize vars to proper values */
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frequency = 1.0;
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/* inner loop of spectral construction */
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for (i=0; i<octaves; i++) {
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value += noise1( point ) * exponent_array[i];
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point *= lacunarity;
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} /* for */
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remainder = octaves - (int)octaves;
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if ( remainder ) /* add in ``octaves'' remainder */
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/* ``i'' and spatial freq. are preset in loop above */
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value += remainder * noise1( point ) * exponent_array[i];
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return( value );
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} /* fBm() */
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