674 lines
17 KiB
C++
674 lines
17 KiB
C++
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/* -----------------------------------------------------------------------------
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Copyright (c) 2006 Simon Brown si@sjbrown.co.uk
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Copyright (c) 2006 Ignacio Castano icastano@nvidia.com
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Permission is hereby granted, free of charge, to any person obtaining
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a copy of this software and associated documentation files (the
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"Software"), to deal in the Software without restriction, including
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without limitation the rights to use, copy, modify, merge, publish,
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distribute, sublicense, and/or sell copies of the Software, and to
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permit persons to whom the Software is furnished to do so, subject to
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the following conditions:
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The above copyright notice and this permission notice shall be included
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in all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
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OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
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CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
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TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
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SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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-------------------------------------------------------------------------- */
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#include "fastclusterfit.h"
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#include "colourset.h"
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#include "colourblock.h"
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#include <cfloat>
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namespace squish {
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FastClusterFit::FastClusterFit( ColourSet const* colours, int flags ) :
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ColourFit( colours, flags )
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{
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// initialise the best error
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#if SQUISH_USE_SIMD
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m_besterror = VEC4_CONST( FLT_MAX );
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#else
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m_besterror = FLT_MAX;
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#endif
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// cache some values
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int const count = m_colours->GetCount();
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Vec3 const* values = m_colours->GetPoints();
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// get the covariance matrix
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Sym3x3 covariance = ComputeWeightedCovariance( count, values, m_colours->GetWeights() );
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// compute the principle component
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Vec3 principle = ComputePrincipleComponent( covariance );
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// build the list of values
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float dps[16];
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for( int i = 0; i < count; ++i )
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{
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dps[i] = Dot( values[i], principle );
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m_order[i] = i;
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}
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// stable sort
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for( int i = 0; i < count; ++i )
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{
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for( int j = i; j > 0 && dps[j] < dps[j - 1]; --j )
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{
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std::swap( dps[j], dps[j - 1] );
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std::swap( m_order[j], m_order[j - 1] );
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}
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}
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// weight all the points
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#if SQUISH_USE_SIMD
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Vec4 const* unweighted = m_colours->GetPointsSimd();
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m_xxsum = VEC4_CONST( 0.0f );
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m_xsum = VEC4_CONST( 0.0f );
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#else
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Vec3 const* unweighted = m_colours->GetPoints();
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m_xxsum = Vec3( 0.0f );
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m_xsum = Vec3( 0.0f );
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#endif
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for( int i = 0; i < count; ++i )
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{
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int p = m_order[i];
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m_unweighted[i] = unweighted[p];
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m_xxsum += m_unweighted[i]*m_unweighted[i];
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m_xsum += m_unweighted[i];
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}
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}
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struct Precomp {
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float alpha2_sum;
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float beta2_sum;
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float alphabeta_sum;
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float factor;
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};
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static Precomp s_threeElement[153];
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static Precomp s_fourElement[969];
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void FastClusterFit::doPrecomputation()
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{
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int i = 0;
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// Three element clusters:
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for( int c0 = 0; c0 <= 16; c0++) // At least two clusters.
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{
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for( int c1 = 0; c1 <= 16-c0; c1++)
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{
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int c2 = 16 - c0 - c1;
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/*if (c2 == 16) {
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// a = b = x2 / 16
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s_threeElement[i].alpha2_sum = 0;
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s_threeElement[i].beta2_sum = 16;
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s_threeElement[i].alphabeta_sum = -16;
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s_threeElement[i].factor = 1.0f / 256.0f;
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}
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else if (c0 == 16) {
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// a = b = x0 / 16
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s_threeElement[i].alpha2_sum = 16;
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s_threeElement[i].beta2_sum = 0;
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s_threeElement[i].alphabeta_sum = -16;
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s_threeElement[i].factor = 1.0f / 256.0f;
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}
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else*/ {
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s_threeElement[i].alpha2_sum = c0 + c1 * 0.25f;
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s_threeElement[i].beta2_sum = c2 + c1 * 0.25f;
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s_threeElement[i].alphabeta_sum = c1 * 0.25f;
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s_threeElement[i].factor = 1.0f / (s_threeElement[i].alpha2_sum * s_threeElement[i].beta2_sum - s_threeElement[i].alphabeta_sum * s_threeElement[i].alphabeta_sum);
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}
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i++;
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}
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}
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//printf("%d three cluster elements\n", i);
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// Four element clusters:
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i = 0;
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for( int c0 = 0; c0 <= 16; c0++)
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{
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for( int c1 = 0; c1 <= 16-c0; c1++)
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{
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for( int c2 = 0; c2 <= 16-c0-c1; c2++)
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{
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int c3 = 16 - c0 - c1 - c2;
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/*if (c3 == 16) {
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// a = b = x3 / 16
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s_fourElement[i].alpha2_sum = 16.0f;
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s_fourElement[i].beta2_sum = 0.0f;
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s_fourElement[i].alphabeta_sum = -16.0f;
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s_fourElement[i].factor = 1.0f / 256.0f;
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}
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else if (c0 == 16) {
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// a = b = x0 / 16
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s_fourElement[i].alpha2_sum = 0.0f;
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s_fourElement[i].beta2_sum = 16.0f;
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s_fourElement[i].alphabeta_sum = -16.0f;
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s_fourElement[i].factor = 1.0f / 256.0f;
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}
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else*/ {
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s_fourElement[i].alpha2_sum = c0 + c1 * (4.0f/9.0f) + c2 * (1.0f/9.0f);
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s_fourElement[i].beta2_sum = c3 + c2 * (4.0f/9.0f) + c1 * (1.0f/9.0f);
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s_fourElement[i].alphabeta_sum = (c1 + c2) * (2.0f/9.0f);
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s_fourElement[i].factor = 1.0f / (s_fourElement[i].alpha2_sum * s_fourElement[i].beta2_sum - s_fourElement[i].alphabeta_sum * s_fourElement[i].alphabeta_sum);
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}
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i++;
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}
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}
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}
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//printf("%d four cluster elements\n", i);
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}
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void FastClusterFit::setMetric(float r, float g, float b)
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{
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#if SQUISH_USE_SIMD
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m_metric = Vec4(r, g, b, 0);
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#else
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m_metric = Vec3(r, g, b);
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#endif
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}
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float FastClusterFit::bestError() const
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{
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#if SQUISH_USE_SIMD
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Vec4 x = m_xxsum * m_metric;
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Vec4 error = m_besterror + x.SplatX() + x.SplatY() + x.SplatZ();
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return error.GetVec3().X();
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#else
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return m_besterror + Dot(m_xxsum, m_metric);
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#endif
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}
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#if SQUISH_USE_SIMD
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void FastClusterFit::Compress3( void* block )
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{
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Vec4 const one = VEC4_CONST(1.0f);
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Vec4 const zero = VEC4_CONST(0.0f);
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Vec4 const half = VEC4_CONST(0.5f);
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Vec4 const two = VEC4_CONST(2.0);
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// declare variables
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Vec4 beststart = VEC4_CONST( 0.0f );
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Vec4 bestend = VEC4_CONST( 0.0f );
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Vec4 besterror = VEC4_CONST( FLT_MAX );
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Vec4 x0 = zero;
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Vec4 x1;
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int b0 = 0, b1 = 0;
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int i = 0;
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// check all possible clusters for this total order
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for( int c0 = 0; c0 <= 16; c0++)
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{
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x1 = zero;
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for( int c1 = 0; c1 <= 16-c0; c1++)
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{
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Vec4 const alpha2_sum = VEC4_CONST(s_threeElement[i].alpha2_sum);
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Vec4 const beta2_sum = VEC4_CONST(s_threeElement[i].beta2_sum);
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Vec4 const alphabeta_sum = VEC4_CONST(s_threeElement[i].alphabeta_sum);
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Vec4 const factor = VEC4_CONST(s_threeElement[i].factor);
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i++;
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Vec4 const alphax_sum = MultiplyAdd(half, x1, x0);
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Vec4 const betax_sum = m_xsum - alphax_sum;
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Vec4 a = NegativeMultiplySubtract(betax_sum, alphabeta_sum, alphax_sum*beta2_sum) * factor;
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Vec4 b = NegativeMultiplySubtract(alphax_sum, alphabeta_sum, betax_sum*alpha2_sum) * factor;
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// clamp the output to [0, 1]
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a = Min( one, Max( zero, a ) );
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b = Min( one, Max( zero, b ) );
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// clamp to the grid
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Vec4 const grid( 31.0f, 63.0f, 31.0f, 0.0f );
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Vec4 const gridrcp( 0.03227752766457f, 0.01583151765563f, 0.03227752766457f, 0.0f );
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a = Truncate( MultiplyAdd( grid, a, half ) ) * gridrcp;
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b = Truncate( MultiplyAdd( grid, b, half ) ) * gridrcp;
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// compute the error
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Vec4 e1 = MultiplyAdd( a, alphax_sum, b*betax_sum );
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Vec4 e2 = MultiplyAdd( a*a, alpha2_sum, b*b*beta2_sum );
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Vec4 e3 = MultiplyAdd( a*b*alphabeta_sum - e1, two, e2 );
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// apply the metric to the error term
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Vec4 e4 = e3 * m_metric;
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Vec4 error = e4.SplatX() + e4.SplatY() + e4.SplatZ();
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// keep the solution if it wins
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if( CompareAnyLessThan( error, besterror ) )
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{
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besterror = error;
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beststart = a;
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bestend = b;
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b0 = c0;
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b1 = c1;
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}
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x1 += m_unweighted[c0+c1];
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}
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x0 += m_unweighted[c0];
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}
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// save the block if necessary
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if( CompareAnyLessThan( besterror, m_besterror ) )
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{
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// compute indices from cluster sizes.
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/*uint bestindices = 0;
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{
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int i = b0;
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for(; i < b0+b1; i++) {
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bestindices |= 2 << (2 * i);
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}
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for(; i < 16; i++) {
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bestindices |= 1 << (2 * i);
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}
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}*/
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u8 bestindices[16];
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{
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int i = 0;
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for(; i < b0; i++) {
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bestindices[i] = 0;
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}
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for(; i < b0+b1; i++) {
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bestindices[i] = 2;
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}
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for(; i < 16; i++) {
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bestindices[i] = 1;
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}
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}
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// remap the indices
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u8 ordered[16];
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for( int i = 0; i < 16; ++i )
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ordered[m_order[i]] = bestindices[i];
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// save the block
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WriteColourBlock3( beststart.GetVec3(), bestend.GetVec3(), ordered, block );
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// save the error
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m_besterror = besterror;
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}
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}
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void FastClusterFit::Compress4( void* block )
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{
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Vec4 const one = VEC4_CONST(1.0f);
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Vec4 const zero = VEC4_CONST(0.0f);
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Vec4 const half = VEC4_CONST(0.5f);
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Vec4 const two = VEC4_CONST(2.0);
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Vec4 const onethird = VEC4_CONST( 1.0f/3.0f );
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Vec4 const twothirds = VEC4_CONST( 2.0f/3.0f );
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// declare variables
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Vec4 beststart = VEC4_CONST( 0.0f );
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Vec4 bestend = VEC4_CONST( 0.0f );
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Vec4 besterror = VEC4_CONST( FLT_MAX );
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Vec4 x0 = zero;
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int b0 = 0, b1 = 0, b2 = 0;
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int i = 0;
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// check all possible clusters for this total order
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for( int c0 = 0; c0 <= 16; c0++)
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{
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Vec4 x1 = zero;
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for( int c1 = 0; c1 <= 16-c0; c1++)
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{
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Vec4 x2 = zero;
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for( int c2 = 0; c2 <= 16-c0-c1; c2++)
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{
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Vec4 const alpha2_sum = VEC4_CONST(s_fourElement[i].alpha2_sum);
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Vec4 const beta2_sum = VEC4_CONST(s_fourElement[i].beta2_sum);
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Vec4 const alphabeta_sum = VEC4_CONST(s_fourElement[i].alphabeta_sum);
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Vec4 const factor = VEC4_CONST(s_fourElement[i].factor);
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i++;
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Vec4 const alphax_sum = x0 + MultiplyAdd(x1, twothirds, x2 * onethird);
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Vec4 const betax_sum = m_xsum - alphax_sum;
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Vec4 a = NegativeMultiplySubtract(betax_sum, alphabeta_sum, alphax_sum*beta2_sum) * factor;
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Vec4 b = NegativeMultiplySubtract(alphax_sum, alphabeta_sum, betax_sum*alpha2_sum) * factor;
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// clamp the output to [0, 1]
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a = Min( one, Max( zero, a ) );
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b = Min( one, Max( zero, b ) );
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// clamp to the grid
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Vec4 const grid( 31.0f, 63.0f, 31.0f, 0.0f );
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Vec4 const gridrcp( 0.03227752766457f, 0.01583151765563f, 0.03227752766457f, 0.0f );
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a = Truncate( MultiplyAdd( grid, a, half ) ) * gridrcp;
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b = Truncate( MultiplyAdd( grid, b, half ) ) * gridrcp;
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// compute the error
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Vec4 e1 = MultiplyAdd( a, alphax_sum, b*betax_sum );
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Vec4 e2 = MultiplyAdd( a*a, alpha2_sum, b*b*beta2_sum );
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Vec4 e3 = MultiplyAdd( a*b*alphabeta_sum - e1, two, e2 );
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// apply the metric to the error term
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Vec4 e4 = e3 * m_metric;
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Vec4 error = e4.SplatX() + e4.SplatY() + e4.SplatZ();
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// keep the solution if it wins
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if( CompareAnyLessThan( error, besterror ) )
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{
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besterror = error;
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beststart = a;
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bestend = b;
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b0 = c0;
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b1 = c1;
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b2 = c2;
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}
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x2 += m_unweighted[c0+c1+c2];
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}
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x1 += m_unweighted[c0+c1];
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}
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x0 += m_unweighted[c0];
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}
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// save the block if necessary
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if( CompareAnyLessThan( besterror, m_besterror ) )
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{
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// compute indices from cluster sizes.
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/*uint bestindices = 0;
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{
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int i = b0;
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for(; i < b0+b1; i++) {
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bestindices = 2 << (2 * m_order[i]);
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}
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for(; i < b0+b1+b2; i++) {
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bestindices = 3 << (2 * m_order[i]);
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}
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|
for(; i < 16; i++) {
|
||
|
bestindices = 1 << (2 * m_order[i]);
|
||
|
}
|
||
|
}*/
|
||
|
u8 bestindices[16];
|
||
|
{
|
||
|
int i = 0;
|
||
|
for(; i < b0; i++) {
|
||
|
bestindices[i] = 0;
|
||
|
}
|
||
|
for(; i < b0+b1; i++) {
|
||
|
bestindices[i] = 2;
|
||
|
}
|
||
|
for(; i < b0+b1+b2; i++) {
|
||
|
bestindices[i] = 3;
|
||
|
}
|
||
|
for(; i < 16; i++) {
|
||
|
bestindices[i] = 1;
|
||
|
}
|
||
|
}
|
||
|
|
||
|
// remap the indices
|
||
|
u8 ordered[16];
|
||
|
for( int i = 0; i < 16; ++i )
|
||
|
ordered[m_order[i]] = bestindices[i];
|
||
|
|
||
|
// save the block
|
||
|
WriteColourBlock4( beststart.GetVec3(), bestend.GetVec3(), ordered, block );
|
||
|
|
||
|
// save the error
|
||
|
m_besterror = besterror;
|
||
|
}
|
||
|
}
|
||
|
|
||
|
#else
|
||
|
|
||
|
void FastClusterFit::Compress3( void* block )
|
||
|
{
|
||
|
// declare variables
|
||
|
Vec3 beststart( 0.0f );
|
||
|
Vec3 bestend( 0.0f );
|
||
|
float besterror = FLT_MAX;
|
||
|
|
||
|
Vec3 x0(0.0f);
|
||
|
Vec3 x1;
|
||
|
int b0 = 0, b1 = 0;
|
||
|
int i = 0;
|
||
|
|
||
|
// check all possible clusters for this total order
|
||
|
for( int c0 = 0; c0 <= 16; c0++)
|
||
|
{
|
||
|
x1 = Vec3(0);
|
||
|
|
||
|
for( int c1 = 0; c1 <= 16-c0; c1++)
|
||
|
{
|
||
|
float const alpha2_sum = s_threeElement[i].alpha2_sum;
|
||
|
float const beta2_sum = s_threeElement[i].beta2_sum;
|
||
|
float const alphabeta_sum = s_threeElement[i].alphabeta_sum;
|
||
|
float const factor = s_threeElement[i].factor;
|
||
|
i++;
|
||
|
|
||
|
Vec3 const alphax_sum = x0 + x1 * 0.5f;
|
||
|
Vec3 const betax_sum = m_xsum - alphax_sum;
|
||
|
|
||
|
Vec3 a = (alphax_sum*beta2_sum - betax_sum*alphabeta_sum) * factor;
|
||
|
Vec3 b = (betax_sum*alpha2_sum - alphax_sum*alphabeta_sum) * factor;
|
||
|
|
||
|
// clamp the output to [0, 1]
|
||
|
Vec3 const one( 1.0f );
|
||
|
Vec3 const zero( 0.0f );
|
||
|
a = Min( one, Max( zero, a ) );
|
||
|
b = Min( one, Max( zero, b ) );
|
||
|
|
||
|
// clamp to the grid
|
||
|
Vec3 const grid( 31.0f, 63.0f, 31.0f );
|
||
|
Vec3 const gridrcp( 0.03227752766457f, 0.01583151765563f, 0.03227752766457f );
|
||
|
Vec3 const half( 0.5f );
|
||
|
a = Floor( grid*a + half )*gridrcp;
|
||
|
b = Floor( grid*b + half )*gridrcp;
|
||
|
|
||
|
// compute the error
|
||
|
Vec3 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_metric );
|
||
|
|
||
|
// keep the solution if it wins
|
||
|
if( error < besterror )
|
||
|
{
|
||
|
besterror = error;
|
||
|
beststart = a;
|
||
|
bestend = b;
|
||
|
b0 = c0;
|
||
|
b1 = c1;
|
||
|
}
|
||
|
|
||
|
x1 += m_unweighted[c0+c1];
|
||
|
}
|
||
|
|
||
|
x0 += m_unweighted[c0];
|
||
|
}
|
||
|
|
||
|
// save the block if necessary
|
||
|
if( besterror < m_besterror )
|
||
|
{
|
||
|
// compute indices from cluster sizes.
|
||
|
/*uint bestindices = 0;
|
||
|
{
|
||
|
int i = b0;
|
||
|
for(; i < b0+b1; i++) {
|
||
|
bestindices |= 2 << (2 * m_order[i]);
|
||
|
}
|
||
|
for(; i < 16; i++) {
|
||
|
bestindices |= 1 << (2 * m_order[i]);
|
||
|
}
|
||
|
}*/
|
||
|
u8 bestindices[16];
|
||
|
{
|
||
|
int i = 0;
|
||
|
for(; i < b0; i++) {
|
||
|
bestindices[i] = 0;
|
||
|
}
|
||
|
for(; i < b0+b1; i++) {
|
||
|
bestindices[i] = 2;
|
||
|
}
|
||
|
for(; i < 16; i++) {
|
||
|
bestindices[i] = 1;
|
||
|
}
|
||
|
}
|
||
|
|
||
|
// remap the indices
|
||
|
u8 ordered[16];
|
||
|
for( int i = 0; i < 16; ++i )
|
||
|
ordered[m_order[i]] = bestindices[i];
|
||
|
|
||
|
// save the block
|
||
|
WriteColourBlock3( beststart, bestend, ordered, block );
|
||
|
|
||
|
// save the error
|
||
|
m_besterror = besterror;
|
||
|
}
|
||
|
}
|
||
|
|
||
|
void FastClusterFit::Compress4( void* block )
|
||
|
{
|
||
|
// declare variables
|
||
|
Vec3 beststart( 0.0f );
|
||
|
Vec3 bestend( 0.0f );
|
||
|
float besterror = FLT_MAX;
|
||
|
|
||
|
Vec3 x0(0.0f);
|
||
|
Vec3 x1;
|
||
|
Vec3 x2;
|
||
|
int b0 = 0, b1 = 0, b2 = 0;
|
||
|
int i = 0;
|
||
|
|
||
|
// check all possible clusters for this total order
|
||
|
for( int c0 = 0; c0 <= 16; c0++)
|
||
|
{
|
||
|
x1 = Vec3(0.0f);
|
||
|
|
||
|
for( int c1 = 0; c1 <= 16-c0; c1++)
|
||
|
{
|
||
|
x2 = Vec3(0.0f);
|
||
|
|
||
|
for( int c2 = 0; c2 <= 16-c0-c1; c2++)
|
||
|
{
|
||
|
float const alpha2_sum = s_fourElement[i].alpha2_sum;
|
||
|
float const beta2_sum = s_fourElement[i].beta2_sum;
|
||
|
float const alphabeta_sum = s_fourElement[i].alphabeta_sum;
|
||
|
float const factor = s_fourElement[i].factor;
|
||
|
i++;
|
||
|
|
||
|
Vec3 const alphax_sum = x0 + x1 * (2.0f / 3.0f) + x2 * (1.0f / 3.0f);
|
||
|
Vec3 const betax_sum = m_xsum - alphax_sum;
|
||
|
|
||
|
Vec3 a = ( alphax_sum*beta2_sum - betax_sum*alphabeta_sum )*factor;
|
||
|
Vec3 b = ( betax_sum*alpha2_sum - alphax_sum*alphabeta_sum )*factor;
|
||
|
|
||
|
// clamp the output to [0, 1]
|
||
|
Vec3 const one( 1.0f );
|
||
|
Vec3 const zero( 0.0f );
|
||
|
a = Min( one, Max( zero, a ) );
|
||
|
b = Min( one, Max( zero, b ) );
|
||
|
|
||
|
// clamp to the grid
|
||
|
Vec3 const grid( 31.0f, 63.0f, 31.0f );
|
||
|
Vec3 const gridrcp( 0.03227752766457f, 0.01583151765563f, 0.03227752766457f );
|
||
|
Vec3 const half( 0.5f );
|
||
|
a = Floor( grid*a + half )*gridrcp;
|
||
|
b = Floor( grid*b + half )*gridrcp;
|
||
|
|
||
|
// compute the error
|
||
|
Vec3 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_metric );
|
||
|
|
||
|
// keep the solution if it wins
|
||
|
if( error < besterror )
|
||
|
{
|
||
|
besterror = error;
|
||
|
beststart = a;
|
||
|
bestend = b;
|
||
|
b0 = c0;
|
||
|
b1 = c1;
|
||
|
b2 = c2;
|
||
|
}
|
||
|
|
||
|
x2 += m_unweighted[c0+c1+c2];
|
||
|
}
|
||
|
|
||
|
x1 += m_unweighted[c0+c1];
|
||
|
}
|
||
|
|
||
|
x0 += m_unweighted[c0];
|
||
|
}
|
||
|
|
||
|
// save the block if necessary
|
||
|
if( besterror < m_besterror )
|
||
|
{
|
||
|
// compute indices from cluster sizes.
|
||
|
/*uint bestindices = 0;
|
||
|
{
|
||
|
int i = b0;
|
||
|
for(; i < b0+b1; i++) {
|
||
|
bestindices = 2 << (2 * m_order[i]);
|
||
|
}
|
||
|
for(; i < b0+b1+b2; i++) {
|
||
|
bestindices = 3 << (2 * m_order[i]);
|
||
|
}
|
||
|
for(; i < 16; i++) {
|
||
|
bestindices = 1 << (2 * m_order[i]);
|
||
|
}
|
||
|
}*/
|
||
|
u8 bestindices[16];
|
||
|
{
|
||
|
int i = 0;
|
||
|
for(; i < b0; i++) {
|
||
|
bestindices[i] = 0;
|
||
|
}
|
||
|
for(; i < b0+b1; i++) {
|
||
|
bestindices[i] = 2;
|
||
|
}
|
||
|
for(; i < b0+b1+b2; i++) {
|
||
|
bestindices[i] = 3;
|
||
|
}
|
||
|
for(; i < 16; i++) {
|
||
|
bestindices[i] = 1;
|
||
|
}
|
||
|
}
|
||
|
|
||
|
// remap the indices
|
||
|
u8 ordered[16];
|
||
|
for( int i = 0; i < 16; ++i )
|
||
|
ordered[m_order[i]] = bestindices[i];
|
||
|
|
||
|
// save the block
|
||
|
WriteColourBlock4( beststart, bestend, ordered, block );
|
||
|
|
||
|
// save the error
|
||
|
m_besterror = besterror;
|
||
|
}
|
||
|
}
|
||
|
|
||
|
#endif
|
||
|
|
||
|
} // namespace squish
|