That is if anyone has done this before...Sourcecode and simple forumas will suffice as an explanation as mine all produce simple noise and not the products seen here:
http://www.cs.utah.edu/~budge/Graphics/ ... /ass5.html
Perlin noise source:
Code: Select all
#define P_B 0x100
#define P_BM 0xff
#define P_N 0x1000
#define P_NP 12 /* 2^N */
#define P_NM 0xfff
#define s_curve(t) ( t * t * (3. - 2. * t) )
#define lerp(t, a, b) ( a + t * (b - a) )
#define setup(i,b0,b1,r0,r1)\
t = vec[i] + P_N;\
b0 = ((int)t) & P_BM;\
b1 = (b0+1) & P_BM;\
r0 = t - (int)t;\
r1 = r0 - 1.;
#define at2(rx,ry) ( rx * q[0] + ry * q[1] )
#define at3(rx,ry,rz) ( rx * q[0] + ry * q[1] + rz * q[2] )
static int p[P_B + P_B + 2];
static double g3[P_B + P_B + 2][3];
static double g2[P_B + P_B + 2][2];
static double g1[P_B + P_B + 2];
static int start = 1;
void normalize2(double v[2])
{
double s;
s = sqrt(v[0] * v[0] + v[1] * v[1]);
v[0] = v[0] / s;
v[1] = v[1] / s;
}
void normalize3(double v[3])
{
double s;
s = sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);
v[0] = v[0] / s;
v[1] = v[1] / s;
v[2] = v[2] / s;
}
void initP(void)
{
int i, j, k;
for (i = 0 ; i < P_B ; i++) {
p[i] = i;
g1[i] = (double)((rand() % (P_B + P_B)) - P_B) / P_B;
for (j = 0 ; j < 2 ; j++)
g2[i][j] = (double)((rand() % (P_B + P_B)) - P_B) / P_B;
normalize2(g2[i]);
for (j = 0 ; j < 3 ; j++)
g3[i][j] = (double)((rand() % (P_B + P_B)) - P_B) / P_B;
normalize3(g3[i]);
}
while (--i) {
k = p[i];
p[i] = p[j = rand() % P_B];
p[j] = k;
}
for (i = 0 ; i < P_B + 2 ; i++) {
p[P_B + i] = p[i];
g1[P_B + i] = g1[i];
for (j = 0 ; j < 2 ; j++)
g2[P_B + i][j] = g2[i][j];
for (j = 0 ; j < 3 ; j++)
g3[P_B + i][j] = g3[i][j];
}
}
double noise1(double arg)
{
int bx0, bx1;
double rx0, rx1, sx, t, u, v, vec[1];
vec[0] = arg;
if (start) {
start = 0;
initP();
}
setup(0,bx0,bx1,rx0,rx1);
sx = s_curve(rx0);
u = rx0 * g1[ p[ bx0 ] ];
v = rx1 * g1[ p[ bx1 ] ];
return(lerp(sx, u, v));
}
double noise2(double vec[2])
{
int bx0, bx1, by0, by1, b00, b10, b01, b11;
double rx0, rx1, ry0, ry1, *q, sx, sy, a, b, t, u, v;
int i, j;
if (start) {
start = 0;
initP();
}
setup(0, bx0,bx1, rx0,rx1);
setup(1, by0,by1, ry0,ry1);
i = p[ bx0 ];
j = p[ bx1 ];
b00 = p[ i + by0 ];
b10 = p[ j + by0 ];
b01 = p[ i + by1 ];
b11 = p[ j + by1 ];
sx = s_curve(rx0);
sy = s_curve(ry0);
q = g2[ b00 ] ; u = at2(rx0,ry0);
q = g2[ b10 ] ; v = at2(rx1,ry0);
a = lerp(sx, u, v);
q = g2[ b01 ] ; u = at2(rx0,ry1);
q = g2[ b11 ] ; v = at2(rx1,ry1);
b = lerp(sx, u, v);
return lerp(sy, a, b);
}
double noise3(double vec[3])
{
int bx0, bx1, by0, by1, bz0, bz1, b00, b10, b01, b11;
double rx0, rx1, ry0, ry1, rz0, rz1, *q, sy, sz, a, b, c, d, t, u, v;
int i, j;
if (start) {
start = 0;
initP();
}
setup(0, bx0,bx1, rx0,rx1);
setup(1, by0,by1, ry0,ry1);
setup(2, bz0,bz1, rz0,rz1);
i = p[ bx0 ];
j = p[ bx1 ];
b00 = p[ i + by0 ];
b10 = p[ j + by0 ];
b01 = p[ i + by1 ];
b11 = p[ j + by1 ];
t = s_curve(rx0);
sy = s_curve(ry0);
sz = s_curve(rz0);
q = g3[ b00 + bz0 ] ; u = at3(rx0,ry0,rz0);
q = g3[ b10 + bz0 ] ; v = at3(rx1,ry0,rz0);
a = lerp(t, u, v);
q = g3[ b01 + bz0 ] ; u = at3(rx0,ry1,rz0);
q = g3[ b11 + bz0 ] ; v = at3(rx1,ry1,rz0);
b = lerp(t, u, v);
c = lerp(sy, a, b);
q = g3[ b00 + bz1 ] ; u = at3(rx0,ry0,rz1);
q = g3[ b10 + bz1 ] ; v = at3(rx1,ry0,rz1);
a = lerp(t, u, v);
q = g3[ b01 + bz1 ] ; u = at3(rx0,ry1,rz1);
q = g3[ b11 + bz1 ] ; v = at3(rx1,ry1,rz1);
b = lerp(t, u, v);
d = lerp(sy, a, b);
return lerp(sz, c, d);
}
/* --- My harmonic summing functions - PDB --------------------------*/
/*
In what follows "alpha" is the weight when the sum is formed.
Typically it is 2, As this approaches 1 the function is noisier.
"beta" is the harmonic scaling/spacing, typically 2.
*/
double PerlinNoise1D(double x,double alpha,double beta,int n)
{
int i;
double val,sum = 0;
double p,scale = 1;
p = x;
for (i=0;i<n;i++) {
val = noise1(p);
sum += val / scale;
scale *= alpha;
p *= beta;
}
return(sum);
}
double PerlinNoise2D(double x,double y,double alpha,double beta,int n)
{
int i;
double val,sum = 0;
double p[2],scale = 1;
p[0] = x;
p[1] = y;
for (i=0;i<n;i++) {
val = noise2(p);
sum += val / scale;
scale *= alpha;
p[0] *= beta;
p[1] *= beta;
}
return(sum);
}
double PerlinNoise3D(double x,double y,double z,double alpha,double beta,int n)
{
int i;
double val,sum = 0;
double p[3],scale = 1;
p[0] = x;
p[1] = y;
p[2] = z;
for (i=0;i<n;i++) {
val = noise3(p);
sum += val / scale;
scale *= alpha;
p[0] *= beta;
p[1] *= beta;
p[2] *= beta;
}
return(sum);
}
Now how does one use these functions correctly if not pixel by pixel how else are they computed? Using matrices? I would think that somehow the last pixel set would have to be remembered and the value next up would be a shade lighter or darker to clump them into a pattern more than just random pixels...