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617 lines
16 KiB
C++
617 lines
16 KiB
C++
/* -----------------------------------------------------------------------------
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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 "ClusterFit.h"
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#include "nvmath/Fitting.h"
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#include "nvimage/ColorBlock.h"
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#include <float.h> // FLT_MAX
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using namespace nv;
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ClusterFit::ClusterFit()
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{
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}
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void ClusterFit::setColourSet(const ColorSet * set)
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{
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// initialise the best error
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#if NVTT_USE_SIMD
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m_besterror = SimdVector( FLT_MAX );
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Vector3 metric = m_metric.toVector3();
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#else
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m_besterror = FLT_MAX;
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Vector3 metric = m_metric;
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#endif
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// cache some values
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count = set->count;
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Vector3 values[16];
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for (uint i = 0; i < count; i++)
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{
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values[i] = set->colors[i].xyz();
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}
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Vector3 principle = Fit::computePrincipalComponent(count, values, set->weights, metric);
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// build the list of values
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float dps[16];
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for (uint 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 (uint i = 0; i < count; ++i)
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{
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for (uint j = i; j > 0 && dps[j] < dps[j - 1]; --j)
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{
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swap( dps[j], dps[j - 1] );
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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 NVTT_USE_SIMD
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SimdVector const* unweighted = set->GetPointsSimd();
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SimdVector const* weights = set->GetWeightsSimd();
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m_xxsum = SimdVector( 0.0f );
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m_xsum = SimdVector( 0.0f );
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#else
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Vector3 const* unweighted = values;
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float const* weights = set->weights;
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m_xxsum = Vector3(0.0f);
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m_xsum = Vector3(0.0f);
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m_wsum = 0.0f;
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#endif
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for (uint i = 0; i < count; ++i)
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{
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int p = m_order[i];
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m_weighted[i] = weights[p] * unweighted[p];
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m_xxsum += m_weighted[i] * m_weighted[i];
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m_xsum += m_weighted[i];
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#if !NVTT_USE_SIMD
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m_weights[i] = weights[p];
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m_wsum += m_weights[i];
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#endif
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}
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}
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void ClusterFit::setMetric(Vector4::Arg w)
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{
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#if NVTT_USE_SIMD
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m_metric = SimdVector(w);
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#else
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m_metric = w.xyz();
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#endif
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m_metricSqr = m_metric * m_metric;
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}
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float ClusterFit::bestError() const
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{
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#if NVTT_USE_SIMD
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SimdVector x = m_xxsum * m_metricSqr;
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SimdVector error = m_besterror + x.splatX() + x.splatY() + x.splatZ();
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return error.toFloat();
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#else
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return m_besterror + dot(m_xxsum, m_metricSqr);
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#endif
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}
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#if NVTT_USE_SIMD
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bool ClusterFit::compress3( Vector3 * start, Vector3 * end )
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{
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int const count = m_colours->count;
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SimdVector const one = SimdVector(1.0f);
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SimdVector const zero = SimdVector(0.0f);
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SimdVector const half(0.5f, 0.5f, 0.5f, 0.25f);
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SimdVector const two = SimdVector(2.0);
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SimdVector const grid( 31.0f, 63.0f, 31.0f, 0.0f );
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SimdVector const gridrcp( 1.0f/31.0f, 1.0f/63.0f, 1.0f/31.0f, 0.0f );
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// declare variables
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SimdVector beststart = SimdVector( 0.0f );
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SimdVector bestend = SimdVector( 0.0f );
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SimdVector besterror = SimdVector( FLT_MAX );
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SimdVector x0 = zero;
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int b0 = 0, b1 = 0;
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// check all possible clusters for this total order
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for( int c0 = 0; c0 <= count; c0++)
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{
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SimdVector x1 = zero;
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for( int c1 = 0; c1 <= count-c0; c1++)
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{
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SimdVector const x2 = m_xsum - x1 - x0;
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//Vector3 const alphax_sum = x0 + x1 * 0.5f;
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//float const alpha2_sum = w0 + w1 * 0.25f;
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SimdVector const alphax_sum = multiplyAdd(x1, half, x0); // alphax_sum, alpha2_sum
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SimdVector const alpha2_sum = alphax_sum.splatW();
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//Vector3 const betax_sum = x2 + x1 * 0.5f;
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//float const beta2_sum = w2 + w1 * 0.25f;
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SimdVector const betax_sum = multiplyAdd(x1, half, x2); // betax_sum, beta2_sum
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SimdVector const beta2_sum = betax_sum.splatW();
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//float const alphabeta_sum = w1 * 0.25f;
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SimdVector const alphabeta_sum = (x1 * half).splatW(); // alphabeta_sum
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// float const factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum);
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SimdVector const factor = reciprocal( negativeMultiplySubtract(alphabeta_sum, alphabeta_sum, alpha2_sum*beta2_sum) );
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SimdVector a = negativeMultiplySubtract(betax_sum, alphabeta_sum, alphax_sum*beta2_sum) * factor;
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SimdVector b = negativeMultiplySubtract(alphax_sum, alphabeta_sum, betax_sum*alpha2_sum) * factor;
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// clamp to the grid
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a = min( one, max( zero, a ) );
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b = min( one, max( zero, b ) );
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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 (we skip the constant xxsum)
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SimdVector e1 = multiplyAdd( a*a, alpha2_sum, b*b*beta2_sum );
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SimdVector e2 = negativeMultiplySubtract( a, alphax_sum, a*b*alphabeta_sum );
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SimdVector e3 = negativeMultiplySubtract( b, betax_sum, e2 );
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SimdVector e4 = multiplyAdd( two, e3, e1 );
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// apply the metric to the error term
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SimdVector e5 = e4 * m_metricSqr;
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SimdVector error = e5.splatX() + e5.splatY() + e5.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_weighted[c0+c1];
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}
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x0 += m_weighted[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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/*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 < count; 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 < count; ++i )
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ordered[m_order[i]] = bestindices[i];
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m_colours->RemapIndices( ordered, bestindices );
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// save the block
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WriteColourBlock3( beststart.toVector3(), bestend.toVector3(), bestindices, block );*/
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*start = beststart.toVector3();
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*end = bestend.toVector3();
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// save the error
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m_besterror = besterror;
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return true;
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}
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return false;
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}
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bool ClusterFit::compress4( Vector3 * start, Vector3 * end )
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{
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int const count = m_colours->count;
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SimdVector const one = SimdVector(1.0f);
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SimdVector const zero = SimdVector(0.0f);
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SimdVector const half = SimdVector(0.5f);
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SimdVector const two = SimdVector(2.0);
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SimdVector const onethird( 1.0f/3.0f, 1.0f/3.0f, 1.0f/3.0f, 1.0f/9.0f );
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SimdVector const twothirds( 2.0f/3.0f, 2.0f/3.0f, 2.0f/3.0f, 4.0f/9.0f );
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SimdVector const twonineths = SimdVector( 2.0f/9.0f );
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SimdVector const grid( 31.0f, 63.0f, 31.0f, 0.0f );
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SimdVector const gridrcp( 1.0f/31.0f, 1.0f/63.0f, 1.0f/31.0f, 0.0f );
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// declare variables
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SimdVector beststart = SimdVector( 0.0f );
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SimdVector bestend = SimdVector( 0.0f );
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SimdVector besterror = SimdVector( FLT_MAX );
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SimdVector x0 = zero;
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int b0 = 0, b1 = 0, b2 = 0;
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// check all possible clusters for this total order
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for( int c0 = 0; c0 <= count; c0++)
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{
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SimdVector x1 = zero;
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for( int c1 = 0; c1 <= count-c0; c1++)
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{
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SimdVector x2 = zero;
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for( int c2 = 0; c2 <= count-c0-c1; c2++)
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{
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SimdVector const x3 = m_xsum - x2 - x1 - x0;
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//Vector3 const alphax_sum = x0 + x1 * (2.0f / 3.0f) + x2 * (1.0f / 3.0f);
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//float const alpha2_sum = w0 + w1 * (4.0f/9.0f) + w2 * (1.0f/9.0f);
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SimdVector const alphax_sum = multiplyAdd(x2, onethird, multiplyAdd(x1, twothirds, x0)); // alphax_sum, alpha2_sum
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SimdVector const alpha2_sum = alphax_sum.splatW();
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//Vector3 const betax_sum = x3 + x2 * (2.0f / 3.0f) + x1 * (1.0f / 3.0f);
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//float const beta2_sum = w3 + w2 * (4.0f/9.0f) + w1 * (1.0f/9.0f);
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SimdVector const betax_sum = multiplyAdd(x2, twothirds, multiplyAdd(x1, onethird, x3)); // betax_sum, beta2_sum
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SimdVector const beta2_sum = betax_sum.splatW();
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//float const alphabeta_sum = (w1 + w2) * (2.0f/9.0f);
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SimdVector const alphabeta_sum = twonineths*( x1 + x2 ).splatW(); // alphabeta_sum
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// float const factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum);
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SimdVector const factor = reciprocal( negativeMultiplySubtract(alphabeta_sum, alphabeta_sum, alpha2_sum*beta2_sum) );
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SimdVector a = negativeMultiplySubtract(betax_sum, alphabeta_sum, alphax_sum*beta2_sum) * factor;
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SimdVector b = negativeMultiplySubtract(alphax_sum, alphabeta_sum, betax_sum*alpha2_sum) * factor;
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// clamp to the grid
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a = min( one, max( zero, a ) );
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b = min( one, max( zero, b ) );
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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 (we skip the constant xxsum)
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SimdVector e1 = multiplyAdd( a*a, alpha2_sum, b*b*beta2_sum );
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SimdVector e2 = negativeMultiplySubtract( a, alphax_sum, a*b*alphabeta_sum );
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SimdVector e3 = negativeMultiplySubtract( b, betax_sum, e2 );
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SimdVector e4 = multiplyAdd( two, e3, e1 );
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// apply the metric to the error term
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SimdVector e5 = e4 * m_metricSqr;
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SimdVector error = e5.splatX() + e5.splatY() + e5.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_weighted[c0+c1+c2];
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}
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x1 += m_weighted[c0+c1];
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}
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x0 += m_weighted[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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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 < b0+b1+b2; i++) {
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bestindices[i] = 3;
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}
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for(; i < count; 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 < count; ++i )
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ordered[m_order[i]] = bestindices[i];
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m_colours->RemapIndices( ordered, bestindices );
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// save the block
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WriteColourBlock4( beststart.toVector3(), bestend.toVector3(), bestindices, block );*/
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*start = beststart.toVector3();
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*end = bestend.toVector3();
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// save the error
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m_besterror = besterror;
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return true;
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}
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return false;
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}
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#else
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bool ClusterFit::compress3(Vector3 * start, Vector3 * end)
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{
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const Vector3 one( 1.0f );
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const Vector3 zero( 0.0f );
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const Vector3 half( 0.5f );
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const Vector3 grid( 31.0f, 63.0f, 31.0f );
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const Vector3 gridrcp( 1.0f/31.0f, 1.0f/63.0f, 1.0f/31.0f );
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// declare variables
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Vector3 beststart( 0.0f );
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Vector3 bestend( 0.0f );
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float besterror = FLT_MAX;
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Vector3 x0(0.0f);
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float w0 = 0.0f;
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int b0 = 0, b1 = 0;
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// check all possible clusters for this total order
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for (uint c0 = 0; c0 <= count; c0++)
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{
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Vector3 x1(0.0f);
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float w1 = 0.0f;
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for (uint c1 = 0; c1 <= count-c0; c1++)
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{
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float w2 = m_wsum - w0 - w1;
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// These factors could be entirely precomputed.
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float const alpha2_sum = w0 + w1 * 0.25f;
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float const beta2_sum = w2 + w1 * 0.25f;
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float const alphabeta_sum = w1 * 0.25f;
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float const factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum);
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Vector3 const alphax_sum = x0 + x1 * 0.5f;
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Vector3 const betax_sum = m_xsum - alphax_sum;
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Vector3 a = (alphax_sum*beta2_sum - betax_sum*alphabeta_sum) * factor;
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Vector3 b = (betax_sum*alpha2_sum - alphax_sum*alphabeta_sum) * factor;
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// clamp to the grid
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a = min(one, max(zero, a));
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b = min(one, max(zero, b));
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a = floor(grid * a + half) * gridrcp;
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b = floor(grid * b + half) * gridrcp;
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// compute the error
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Vector3 e1 = a*a*alpha2_sum + b*b*beta2_sum + 2.0f*( a*b*alphabeta_sum - a*alphax_sum - b*betax_sum );
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// apply the metric to the error term
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float error = dot(e1, m_metricSqr);
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// keep the solution if it wins
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if (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_weighted[c0+c1];
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w1 += m_weights[c0+c1];
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}
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x0 += m_weighted[c0];
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w0 += m_weights[c0];
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}
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// save the block if necessary
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if( besterror < m_besterror )
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{
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/*// compute indices from cluster sizes.
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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 < count; 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 < count; ++i )
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ordered[m_order[i]] = bestindices[i];
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|
|
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m_colours->RemapIndices( ordered, bestindices );
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|
|
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// save the block
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WriteColourBlock3( beststart, bestend, bestindices, block );*/
|
|
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*start = beststart;
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*end = bestend;
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|
|
|
// save the error
|
|
m_besterror = besterror;
|
|
|
|
return true;
|
|
}
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|
|
|
return false;
|
|
}
|
|
|
|
bool ClusterFit::compress4(Vector3 * start, Vector3 * end)
|
|
{
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Vector3 const one( 1.0f );
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Vector3 const zero( 0.0f );
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Vector3 const half( 0.5f );
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Vector3 const grid( 31.0f, 63.0f, 31.0f );
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Vector3 const gridrcp( 1.0f/31.0f, 1.0f/63.0f, 1.0f/31.0f );
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|
|
|
// 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 = min( one, max( zero, a ) );
|
|
b = min( one, max( zero, b ) );
|
|
a = floor( grid*a + half )*gridrcp;
|
|
b = floor( grid*b + half )*gridrcp;
|
|
|
|
// 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 )
|
|
{
|
|
/*// compute indices from cluster sizes.
|
|
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 < count; i++) {
|
|
bestindices[i] = 1;
|
|
}
|
|
}
|
|
|
|
// remap the indices
|
|
u8 ordered[16];
|
|
for( int i = 0; i < count; ++i )
|
|
ordered[m_order[i]] = bestindices[i];
|
|
|
|
m_colours->RemapIndices( ordered, bestindices );
|
|
|
|
// save the block
|
|
WriteColourBlock4( beststart, bestend, bestindices, block );*/
|
|
|
|
*start = beststart;
|
|
*end = bestend;
|
|
|
|
// save the error
|
|
m_besterror = besterror;
|
|
|
|
return true;
|
|
}
|
|
|
|
return false;
|
|
}
|
|
|
|
#endif // NVTT_USE_SIMD
|