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Remove unused files.

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pull/322/head
Ignacio Castano 4 years ago
parent e5b93bbfe8
commit c87706f2a4
3 changed files with 0 additions and 704 deletions
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src/nvtt/CMakeLists.txt
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@ -5,7 +5,6 @@ ADD_SUBDIRECTORY(squish)
SET(NVTT_SRCS
nvtt.h nvtt.cpp
nvtt_wrapper.h nvtt_wrapper.cpp
ClusterFit.h ClusterFit.cpp
Compressor.h
BlockCompressor.h BlockCompressor.cpp
CompressorDX9.h CompressorDX9.cpp

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src/nvtt/ClusterFit.cpp
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// MIT license see full LICENSE text at end of file
#include "ClusterFit.h"
#include "nvmath/Vector.inl"
#include <float.h> // FLT_MAX
using namespace nv;
static Vector3 computeCentroid(int n, const Vector3 *__restrict points, const float *__restrict weights, Vector3::Arg metric)
{
Vector3 centroid(0.0f);
float total = 0.0f;
for (int i = 0; i < n; i++)
{
total += weights[i];
centroid += weights[i] * points[i];
}
centroid *= (1.0f / total);
return centroid;
}
static Vector3 computeCovariance(int n, const Vector3 *__restrict points, const float *__restrict weights, Vector3::Arg metric, float *__restrict covariance)
{
// compute the centroid
Vector3 centroid = computeCentroid(n, points, weights, metric);
// compute covariance matrix
for (int i = 0; i < 6; i++)
{
covariance[i] = 0.0f;
}
for (int i = 0; i < n; i++)
{
Vector3 a = (points[i] - centroid) * metric; // @@ I think weight should be squared, but that seems to increase the error slightly.
Vector3 b = weights[i] * a;
covariance[0] += a.x * b.x;
covariance[1] += a.x * b.y;
covariance[2] += a.x * b.z;
covariance[3] += a.y * b.y;
covariance[4] += a.y * b.z;
covariance[5] += a.z * b.z;
}
return centroid;
}
// @@ We should be able to do something cheaper...
static Vector3 estimatePrincipalComponent(const float * __restrict matrix)
{
const Vector3 row0(matrix[0], matrix[1], matrix[2]);
const Vector3 row1(matrix[1], matrix[3], matrix[4]);
const Vector3 row2(matrix[2], matrix[4], matrix[5]);
float r0 = lengthSquared(row0);
float r1 = lengthSquared(row1);
float r2 = lengthSquared(row2);
if (r0 > r1 && r0 > r2) return row0;
if (r1 > r2) return row1;
return row2;
}
static inline Vector3 firstEigenVector_PowerMethod(const float *__restrict matrix)
{
if (matrix[0] == 0 && matrix[3] == 0 && matrix[5] == 0)
{
return Vector3(0.0f);
}
Vector3 v = estimatePrincipalComponent(matrix);
const int NUM = 8;
for (int i = 0; i < NUM; i++)
{
float x = v.x * matrix[0] + v.y * matrix[1] + v.z * matrix[2];
float y = v.x * matrix[1] + v.y * matrix[3] + v.z * matrix[4];
float z = v.x * matrix[2] + v.y * matrix[4] + v.z * matrix[5];
float norm = max(max(x, y), z);
v = Vector3(x, y, z) * (1.0f / norm);
}
return v;
}
static Vector3 computePrincipalComponent_PowerMethod(int n, const Vector3 *__restrict points, const float *__restrict weights, Vector3::Arg metric)
{
float matrix[6];
computeCovariance(n, points, weights, metric, matrix);
return firstEigenVector_PowerMethod(matrix);
}
void ClusterFit::setColorSet(const Vector3 * colors, const float * weights, int count)
{
// initialise the best error
#if NVTT_USE_SIMD
m_besterror = SimdVector( FLT_MAX );
Vector3 metric = m_metric.toVector3();
#else
m_besterror = FLT_MAX;
Vector3 metric = m_metric;
#endif
m_count = count;
// I've tried using a lower quality approximation of the principal direction, but the best fit line seems to produce best results.
Vector3 principal = computePrincipalComponent_PowerMethod(count, colors, weights, metric);
// build the list of values
int order[16];
float dps[16];
for (uint i = 0; i < m_count; ++i)
{
dps[i] = dot(colors[i], principal);
order[i] = i;
}
// stable sort
for (uint i = 0; i < m_count; ++i)
{
for (uint j = i; j > 0 && dps[j] < dps[j - 1]; --j)
{
swap(dps[j], dps[j - 1]);
swap(order[j], order[j - 1]);
}
}
// weight all the points
#if NVTT_USE_SIMD
m_xxsum = SimdVector( 0.0f );
m_xsum = SimdVector( 0.0f );
#else
m_xxsum = Vector3(0.0f);
m_xsum = Vector3(0.0f);
m_wsum = 0.0f;
#endif
for (uint i = 0; i < m_count; ++i)
{
int p = order[i];
#if NVTT_USE_SIMD
NV_ALIGN_16 Vector4 tmp(colors[p], 1);
m_weighted[i] = SimdVector(tmp.component) * SimdVector(weights[p]);
m_xxsum += m_weighted[i] * m_weighted[i];
m_xsum += m_weighted[i];
#else
m_weighted[i] = colors[p] * weights[p];
m_xxsum += m_weighted[i] * m_weighted[i];
m_xsum += m_weighted[i];
m_weights[i] = weights[p];
m_wsum += m_weights[i];
#endif
}
}
void ClusterFit::setColorWeights(Vector4::Arg w)
{
#if NVTT_USE_SIMD
NV_ALIGN_16 Vector4 tmp(w.xyz(), 1);
m_metric = SimdVector(tmp.component);
#else
m_metric = w.xyz();
#endif
m_metricSqr = m_metric * m_metric;
}
float ClusterFit::bestError() const
{
#if NVTT_USE_SIMD
SimdVector x = m_xxsum * m_metricSqr;
SimdVector error = m_besterror + x.splatX() + x.splatY() + x.splatZ();
return error.toFloat();
#else
return m_besterror + dot(m_xxsum, m_metricSqr);
#endif
}
#if NVTT_USE_SIMD
bool ClusterFit::compress3( Vector3 * start, Vector3 * end )
{
const int count = m_count;
const SimdVector one = SimdVector(1.0f);
const SimdVector zero = SimdVector(0.0f);
const SimdVector half(0.5f, 0.5f, 0.5f, 0.25f);
const SimdVector two = SimdVector(2.0);
const SimdVector grid( 31.0f, 63.0f, 31.0f, 0.0f );
const SimdVector gridrcp( 1.0f/31.0f, 1.0f/63.0f, 1.0f/31.0f, 0.0f );
// declare variables
SimdVector beststart = SimdVector( 0.0f );
SimdVector bestend = SimdVector( 0.0f );
SimdVector besterror = SimdVector( FLT_MAX );
SimdVector x0 = zero;
// check all possible clusters for this total order
for (int c0 = 0; c0 <= count; c0++)
{
SimdVector x1 = zero;
for (int c1 = 0; c1 <= count-c0; c1++)
{
const SimdVector x2 = m_xsum - x1 - x0;
//Vector3 alphax_sum = x0 + x1 * 0.5f;
//float alpha2_sum = w0 + w1 * 0.25f;
const SimdVector alphax_sum = multiplyAdd(x1, half, x0); // alphax_sum, alpha2_sum
const SimdVector alpha2_sum = alphax_sum.splatW();
//const Vector3 betax_sum = x2 + x1 * 0.5f;
//const float beta2_sum = w2 + w1 * 0.25f;
const SimdVector betax_sum = multiplyAdd(x1, half, x2); // betax_sum, beta2_sum
const SimdVector beta2_sum = betax_sum.splatW();
//const float alphabeta_sum = w1 * 0.25f;
const SimdVector alphabeta_sum = (x1 * half).splatW(); // alphabeta_sum
// const float factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum);
const SimdVector factor = reciprocal( negativeMultiplySubtract(alphabeta_sum, alphabeta_sum, alpha2_sum*beta2_sum) );
SimdVector a = negativeMultiplySubtract(betax_sum, alphabeta_sum, alphax_sum*beta2_sum) * factor;
SimdVector b = negativeMultiplySubtract(alphax_sum, alphabeta_sum, betax_sum*alpha2_sum) * factor;
// clamp to the grid
a = min( one, max( zero, a ) );
b = min( one, max( zero, b ) );
a = truncate( multiplyAdd( grid, a, half ) ) * gridrcp;
b = truncate( multiplyAdd( grid, b, half ) ) * gridrcp;
// compute the error (we skip the constant xxsum)
SimdVector e1 = multiplyAdd( a*a, alpha2_sum, b*b*beta2_sum );
SimdVector e2 = negativeMultiplySubtract( a, alphax_sum, a*b*alphabeta_sum );
SimdVector e3 = negativeMultiplySubtract( b, betax_sum, e2 );
SimdVector e4 = multiplyAdd( two, e3, e1 );
// apply the metric to the error term
SimdVector e5 = e4 * m_metricSqr;
SimdVector error = e5.splatX() + e5.splatY() + e5.splatZ();
// keep the solution if it wins
if (compareAnyLessThan(error, besterror))
{
besterror = error;
beststart = a;
bestend = b;
}
x1 += m_weighted[c0+c1];
}
x0 += m_weighted[c0];
}
// save the block if necessary
if (compareAnyLessThan(besterror, m_besterror))
{
*start = beststart.toVector3();
*end = bestend.toVector3();
// save the error
m_besterror = besterror;
return true;
}
return false;
}
bool ClusterFit::compress4( Vector3 * start, Vector3 * end )
{
const int count = m_count;
const SimdVector one = SimdVector(1.0f);
const SimdVector zero = SimdVector(0.0f);
const SimdVector half = SimdVector(0.5f);
const SimdVector two = SimdVector(2.0);
const SimdVector onethird( 1.0f/3.0f, 1.0f/3.0f, 1.0f/3.0f, 1.0f/9.0f );
const SimdVector twothirds( 2.0f/3.0f, 2.0f/3.0f, 2.0f/3.0f, 4.0f/9.0f );
const SimdVector twonineths = SimdVector( 2.0f/9.0f );
const SimdVector grid( 31.0f, 63.0f, 31.0f, 0.0f );
const SimdVector gridrcp( 1.0f/31.0f, 1.0f/63.0f, 1.0f/31.0f, 0.0f );
// declare variables
SimdVector beststart = SimdVector( 0.0f );
SimdVector bestend = SimdVector( 0.0f );
SimdVector besterror = SimdVector( FLT_MAX );
SimdVector x0 = zero;
// check all possible clusters for this total order
for (int c0 = 0; c0 <= count; c0++)
{
SimdVector x1 = zero;
for (int c1 = 0; c1 <= count-c0; c1++)
{
SimdVector x2 = zero;
for (int c2 = 0; c2 <= count-c0-c1; c2++)
{
const SimdVector x3 = m_xsum - x2 - x1 - x0;
//const Vector3 alphax_sum = x0 + x1 * (2.0f / 3.0f) + x2 * (1.0f / 3.0f);
//const float alpha2_sum = w0 + w1 * (4.0f/9.0f) + w2 * (1.0f/9.0f);
const SimdVector alphax_sum = multiplyAdd(x2, onethird, multiplyAdd(x1, twothirds, x0)); // alphax_sum, alpha2_sum
const SimdVector alpha2_sum = alphax_sum.splatW();
//const Vector3 betax_sum = x3 + x2 * (2.0f / 3.0f) + x1 * (1.0f / 3.0f);
//const float beta2_sum = w3 + w2 * (4.0f/9.0f) + w1 * (1.0f/9.0f);
const SimdVector betax_sum = multiplyAdd(x2, twothirds, multiplyAdd(x1, onethird, x3)); // betax_sum, beta2_sum
const SimdVector beta2_sum = betax_sum.splatW();
//const float alphabeta_sum = (w1 + w2) * (2.0f/9.0f);
const SimdVector alphabeta_sum = twonineths*( x1 + x2 ).splatW(); // alphabeta_sum
//const float factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum);
const SimdVector factor = reciprocal( negativeMultiplySubtract(alphabeta_sum, alphabeta_sum, alpha2_sum*beta2_sum) );
SimdVector a = negativeMultiplySubtract(betax_sum, alphabeta_sum, alphax_sum*beta2_sum) * factor;
SimdVector b = negativeMultiplySubtract(alphax_sum, alphabeta_sum, betax_sum*alpha2_sum) * factor;
// clamp to the grid
a = min( one, max( zero, a ) );
b = min( one, max( zero, b ) );
a = truncate( multiplyAdd( grid, a, half ) ) * gridrcp;
b = truncate( multiplyAdd( grid, b, half ) ) * gridrcp;
// compute the error (we skip the constant xxsum)
// error = a*a*alpha2_sum + b*b*beta2_sum + 2.0f*( a*b*alphabeta_sum - a*alphax_sum - b*betax_sum );
SimdVector e1 = multiplyAdd( a*a, alpha2_sum, b*b*beta2_sum );
SimdVector e2 = negativeMultiplySubtract( a, alphax_sum, a*b*alphabeta_sum );
SimdVector e3 = negativeMultiplySubtract( b, betax_sum, e2 );
SimdVector e4 = multiplyAdd( two, e3, e1 );
// apply the metric to the error term
SimdVector e5 = e4 * m_metricSqr;
SimdVector error = e5.splatX() + e5.splatY() + e5.splatZ();
// keep the solution if it wins
if (compareAnyLessThan(error, besterror))
{
besterror = error;
beststart = a;
bestend = b;
}
x2 += m_weighted[c0+c1+c2];
}
x1 += m_weighted[c0+c1];
}
x0 += m_weighted[c0];
}
// save the block if necessary
if (compareAnyLessThan(besterror, m_besterror))
{
*start = beststart.toVector3();
*end = bestend.toVector3();
// save the error
m_besterror = besterror;
return true;
}
return false;
}
#else
static const float midpoints5[32] = {
0.015686f, 0.047059f, 0.078431f, 0.111765f, 0.145098f, 0.176471f, 0.207843f, 0.241176f, 0.274510f, 0.305882f, 0.337255f, 0.370588f, 0.403922f, 0.435294f, 0.466667f, 0.5f,
0.533333f, 0.564706f, 0.596078f, 0.629412f, 0.662745f, 0.694118f, 0.725490f, 0.758824f, 0.792157f, 0.823529f, 0.854902f, 0.888235f, 0.921569f, 0.952941f, 0.984314f, 1.0f
};
static const float midpoints6[64] = {
0.007843f, 0.023529f, 0.039216f, 0.054902f, 0.070588f, 0.086275f, 0.101961f, 0.117647f, 0.133333f, 0.149020f, 0.164706f, 0.180392f, 0.196078f, 0.211765f, 0.227451f, 0.245098f,
0.262745f, 0.278431f, 0.294118f, 0.309804f, 0.325490f, 0.341176f, 0.356863f, 0.372549f, 0.388235f, 0.403922f, 0.419608f, 0.435294f, 0.450980f, 0.466667f, 0.482353f, 0.500000f,
0.517647f, 0.533333f, 0.549020f, 0.564706f, 0.580392f, 0.596078f, 0.611765f, 0.627451f, 0.643137f, 0.658824f, 0.674510f, 0.690196f, 0.705882f, 0.721569f, 0.737255f, 0.754902f,
0.772549f, 0.788235f, 0.803922f, 0.819608f, 0.835294f, 0.850980f, 0.866667f, 0.882353f, 0.898039f, 0.913725f, 0.929412f, 0.945098f, 0.960784f, 0.976471f, 0.992157f, 1.0f
};
// This is the ideal way to round, but it's too expensive to do this in the inner loop.
inline Vector3 round565(const Vector3 & v) {
const Vector3 grid(31.0f, 63.0f, 31.0f);
const Vector3 gridrcp(1.0f / 31.0f, 1.0f / 63.0f, 1.0f / 31.0f);
Vector3 q = floor(grid * v);
q.x += (v.x > midpoints5[int(q.x)]);
q.y += (v.y > midpoints6[int(q.y)]);
q.z += (v.z > midpoints5[int(q.z)]);
q *= gridrcp;
return q;
}
bool ClusterFit::compress3(Vector3 * start, Vector3 * end)
{
const uint count = m_count;
const Vector3 grid( 31.0f, 63.0f, 31.0f );
const Vector3 gridrcp( 1.0f/31.0f, 1.0f/63.0f, 1.0f/31.0f );
// declare variables
Vector3 beststart( 0.0f );
Vector3 bestend( 0.0f );
float besterror = FLT_MAX;
Vector3 x0(0.0f);
float w0 = 0.0f;
int b0 = 0, b1 = 0;
// check all possible clusters for this total order
for (uint c0 = 0; c0 <= count; c0++)
{
Vector3 x1(0.0f);
float w1 = 0.0f;
for (uint c1 = 0; c1 <= count-c0; c1++)
{
float w2 = m_wsum - w0 - w1;
// These factors could be entirely precomputed.
float const alpha2_sum = w0 + w1 * 0.25f;
float const beta2_sum = w2 + w1 * 0.25f;
float const alphabeta_sum = w1 * 0.25f;
float const factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum);
Vector3 const alphax_sum = x0 + x1 * 0.5f;
Vector3 const betax_sum = m_xsum - alphax_sum;
Vector3 a = (alphax_sum*beta2_sum - betax_sum*alphabeta_sum) * factor;
Vector3 b = (betax_sum*alpha2_sum - alphax_sum*alphabeta_sum) * factor;
// clamp to the grid
a = clamp(a, 0, 1);
b = clamp(b, 0, 1);
#if 1
a = floor(grid * a + 0.5f) * gridrcp;
b = floor(grid * b + 0.5f) * gridrcp;
#else
a = round565(a);
b = round565(b);
#endif
// compute the error
Vector3 e1 = a*a*alpha2_sum + b*b*beta2_sum + 2.0f*( a*b*alphabeta_sum - a*alphax_sum - b*betax_sum );
// apply the metric to the error term
float error = dot(e1, m_metricSqr);
// keep the solution if it wins
if (error < besterror)
{
besterror = error;
beststart = a;
bestend = b;
b0 = c0;
b1 = c1;
}
x1 += m_weighted[c0+c1];
w1 += m_weights[c0+c1];
}
x0 += m_weighted[c0];
w0 += m_weights[c0];
}
// save the block if necessary
if (besterror < m_besterror)
{
*start = beststart;
*end = bestend;
// save the error
m_besterror = besterror;
return true;
}
return false;
}
bool ClusterFit::compress4(Vector3 * start, Vector3 * end)
{
const uint count = m_count;
const Vector3 grid( 31.0f, 63.0f, 31.0f );
const Vector3 gridrcp( 1.0f/31.0f, 1.0f/63.0f, 1.0f/31.0f );
// declare variables
Vector3 beststart( 0.0f );
Vector3 bestend( 0.0f );
float besterror = FLT_MAX;
Vector3 x0(0.0f);
float w0 = 0.0f;
int b0 = 0, b1 = 0, b2 = 0;
// check all possible clusters for this total order
for (uint c0 = 0; c0 <= count; c0++)
{
Vector3 x1(0.0f);
float w1 = 0.0f;
for (uint c1 = 0; c1 <= count-c0; c1++)
{
Vector3 x2(0.0f);
float w2 = 0.0f;
for (uint c2 = 0; c2 <= count-c0-c1; c2++)
{
float w3 = m_wsum - w0 - w1 - w2;
float const alpha2_sum = w0 + w1 * (4.0f/9.0f) + w2 * (1.0f/9.0f);
float const beta2_sum = w3 + w2 * (4.0f/9.0f) + w1 * (1.0f/9.0f);
float const alphabeta_sum = (w1 + w2) * (2.0f/9.0f);
float const factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum);
Vector3 const alphax_sum = x0 + x1 * (2.0f / 3.0f) + x2 * (1.0f / 3.0f);
Vector3 const betax_sum = m_xsum - alphax_sum;
Vector3 a = ( alphax_sum*beta2_sum - betax_sum*alphabeta_sum )*factor;
Vector3 b = ( betax_sum*alpha2_sum - alphax_sum*alphabeta_sum )*factor;
// clamp to the grid
a = clamp(a, 0, 1);
b = clamp(b, 0, 1);
#if 1
a = floor(a * grid + 0.5f) * gridrcp;
b = floor(b * grid + 0.5f) * gridrcp;
#else
a = round565(a);
b = round565(b);
#endif
// @@ It would be much more accurate to evaluate the error exactly.
// compute the error
Vector3 e1 = a*a*alpha2_sum + b*b*beta2_sum + 2.0f*( a*b*alphabeta_sum - a*alphax_sum - b*betax_sum );
// apply the metric to the error term
float error = dot( e1, m_metricSqr );
// keep the solution if it wins
if (error < besterror)
{
besterror = error;
beststart = a;
bestend = b;
b0 = c0;
b1 = c1;
b2 = c2;
}
x2 += m_weighted[c0+c1+c2];
w2 += m_weights[c0+c1+c2];
}
x1 += m_weighted[c0+c1];
w1 += m_weights[c0+c1];
}
x0 += m_weighted[c0];
w0 += m_weights[c0];
}
// save the block if necessary
if (besterror < m_besterror)
{
*start = beststart;
*end = bestend;
// save the error
m_besterror = besterror;
return true;
}
return false;
}
#endif // NVTT_USE_SIMD
// Copyright (c) 2006-2020 Ignacio Castano icastano@nvidia.com
// Copyright (c) 2006 Simon Brown si@sjbrown.co.uk
//
// 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.

86
src/nvtt/ClusterFit.h
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// MIT license see full LICENSE text at end of file
#pragma once
#include "nvmath/SimdVector.h"
#include "nvmath/Vector.h"
// Use SIMD version if altivec or SSE are available.
#define NVTT_USE_SIMD (NV_USE_ALTIVEC || NV_USE_SSE)
//#define NVTT_USE_SIMD 0
#include <xmmintrin.h>
#if (NV_USE_SSE > 1)
#include <emmintrin.h>
#endif
#ifndef NV_ALIGN_16
#if NV_CC_GNUC
# define NV_ALIGN_16 __attribute__ ((__aligned__ (16)))
#else
# define NV_ALIGN_16 __declspec(align(16))
#endif
#endif
namespace nv {
class ClusterFit
{
public:
ClusterFit() {}
void setColorSet(const Vector3 * colors, const float * weights, int count);
void setColorWeights(const Vector4 & w);
float bestError() const;
bool compress3(Vector3 * start, Vector3 * end);
bool compress4(Vector3 * start, Vector3 * end);
private:
uint m_count;
// IC: Color and weight arrays are larger than necessary to avoid compiler warning.
#if NVTT_USE_SIMD
NV_ALIGN_16 SimdVector m_weighted[17]; // color | weight
SimdVector m_metric; // vec3
SimdVector m_metricSqr; // vec3
SimdVector m_xxsum; // color | weight
SimdVector m_xsum; // color | weight (wsum)
SimdVector m_besterror; // scalar
#else
Vector3 m_weighted[17];
float m_weights[17];
Vector3 m_metric;
Vector3 m_metricSqr;
Vector3 m_xxsum;
Vector3 m_xsum;
float m_wsum;
float m_besterror;
#endif
};
} // nv namespace
// Copyright (c) 2006-2020 Ignacio Castano icastano@nvidia.com
// Copyright (c) 2006 Simon Brown si@sjbrown.co.uk
//
// 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.
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