1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
|
/* Copyright 1994, 2002 by Steven Worley
Permission is hereby granted, free of charge, to any person
obtaining a copy of this software and associated documentation files
(the "Software"), to deal in the Software without restriction,
including without limitation the rights to use, copy, modify, merge,
publish, distribute, sublicense, and/or sell copies of the Software,
and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
A detailed description and application examples can be found in the
1996 SIGGRAPH paper "A Cellular Texture Basis Function" and
especially in the 2002 book "Texturing and Modeling, a Procedural
Approach, 3rd edition." There is also extra information on the web
site http://www.worley.com/cellular.html .
If you do find interesting uses for this tool, and especially if
you enhance it, please drop me an email at steve@worley.com.
*/
/* Worley()
An implementation of the key cellular texturing basis
function. This function is hardwired to return an average F_1 value
of 1.0. It returns the <n> most closest feature point distances
F_1, F_2, .. F_n the vector delta to those points, and a 32 bit
seed for each of the feature points. This function is not
difficult to extend to compute alternative information such as
higher order F values, to use the Manhattan distance metric, or
other fun perversions.
<at> The input sample location.
<max_order> Smaller values compute faster. < 5, read the book to extend it.
<F> The output values of F_1, F_2, ..F[n] in F[0], F[1], F[n-1]
<delta> The output vector difference between the sample point and the n-th
closest feature point. Thus, the feature point's location is the
hit point minus this value. The DERIVATIVE of F is the unit
normalized version of this vector.
<ID> The output 32 bit ID number which labels the feature point. This
is useful for domain partitions, especially for coloring flagstone
patterns.
This implementation is tuned for speed in a way that any order > 5
will likely have discontinuous artefacts in its computation of F5+.
This can be fixed by increasing the internal points-per-cube
density in the source code, at the expense of slower
computation. The book lists the details of this tuning. */
#pragma once
namespace worley
{
struct noise_datum
{
double distance[2];
uint32_t id[2];
double pos[2][3];
};
noise_datum noise(double x, double y, double z);
}
|
