From 0bb3011f7fba06f5ec36e48d7d473d8c71faf75e Mon Sep 17 00:00:00 2001 From: castano Date: Fri, 12 Nov 2010 03:35:28 +0000 Subject: [PATCH] Add cluster fit implementation based on squish. --- src/nvtt/ClusterFit.cpp | 614 ++++++++++++++++++++++++++++++++++++++++ src/nvtt/ClusterFit.h | 82 ++++++ 2 files changed, 696 insertions(+) create mode 100644 src/nvtt/ClusterFit.cpp create mode 100644 src/nvtt/ClusterFit.h diff --git a/src/nvtt/ClusterFit.cpp b/src/nvtt/ClusterFit.cpp new file mode 100644 index 0000000..7f21813 --- /dev/null +++ b/src/nvtt/ClusterFit.cpp @@ -0,0 +1,614 @@ +/* ----------------------------------------------------------------------------- + + Copyright (c) 2006 Simon Brown si@sjbrown.co.uk + Copyright (c) 2006 Ignacio Castano icastano@nvidia.com + + 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. + + -------------------------------------------------------------------------- */ + +#include "ClusterFit.h" +#include "nvmath/Fitting.h" +#include "nvimage/ColorBlock.h" + +using namespace nv; + +ClusterFit::ClusterFit() +{ +} + +void ClusterFit::setColourSet(const ColorSet * set) +{ + // 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 + + // cache some values + count = set->count; + + Vector3 values[16]; + for (uint i = 0; i < count; i++) + { + values[i] = set->colors[i].xyz(); + } + + Vector3 principle = Fit::computePrincipalComponent(count, values, set->weights, metric); + + + // build the list of values + float dps[16]; + for (uint i = 0; i < count; ++i) + { + dps[i] = dot(values[i], principle); + m_order[i] = i; + } + + // stable sort + for (uint i = 0; i < count; ++i) + { + for (uint j = i; j > 0 && dps[j] < dps[j - 1]; --j) + { + swap( dps[j], dps[j - 1] ); + swap( m_order[j], m_order[j - 1] ); + } + } + + // weight all the points +#if NVTT_USE_SIMD + SimdVector const* unweighted = set->GetPointsSimd(); + SimdVector const* weights = set->GetWeightsSimd(); + m_xxsum = SimdVector( 0.0f ); + m_xsum = SimdVector( 0.0f ); +#else + Vector3 const* unweighted = values; + float const* weights = set->weights; + m_xxsum = Vector3(0.0f); + m_xsum = Vector3(0.0f); + m_wsum = 0.0f; +#endif + + for (uint i = 0; i < count; ++i) + { + int p = m_order[i]; + m_weighted[i] = weights[p] * unweighted[p]; + m_xxsum += m_weighted[i] * m_weighted[i]; + m_xsum += m_weighted[i]; +#if !NVTT_USE_SIMD + m_weights[i] = weights[p]; + m_wsum += m_weights[i]; +#endif + } +} + + +void ClusterFit::setMetric(Vector4::Arg w) +{ +#if NVTT_USE_SIMD + m_metric = SimdVector(w); +#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 ) +{ + int const count = m_colours->count; + SimdVector const one = SimdVector(1.0f); + SimdVector const zero = SimdVector(0.0f); + SimdVector const half(0.5f, 0.5f, 0.5f, 0.25f); + SimdVector const two = SimdVector(2.0); + SimdVector const grid( 31.0f, 63.0f, 31.0f, 0.0f ); + SimdVector const 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; + + int b0 = 0, b1 = 0; + + // 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 const x2 = m_xsum - x1 - x0; + + //Vector3 const alphax_sum = x0 + x1 * 0.5f; + //float const alpha2_sum = w0 + w1 * 0.25f; + SimdVector const alphax_sum = multiplyAdd(x1, half, x0); // alphax_sum, alpha2_sum + SimdVector const alpha2_sum = alphax_sum.splatW(); + + //Vector3 const betax_sum = x2 + x1 * 0.5f; + //float const beta2_sum = w2 + w1 * 0.25f; + SimdVector const betax_sum = multiplyAdd(x1, half, x2); // betax_sum, beta2_sum + SimdVector const beta2_sum = betax_sum.splatW(); + + //float const alphabeta_sum = w1 * 0.25f; + SimdVector const alphabeta_sum = (x1 * half).splatW(); // alphabeta_sum + + // float const factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum); + SimdVector const 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; + b0 = c0; + b1 = c1; + } + + x1 += m_weighted[c0+c1]; + } + + x0 += m_weighted[c0]; + } + + // save the block if necessary + if( compareAnyLessThan( 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 < 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 + WriteColourBlock3( beststart.toVector3(), bestend.toVector3(), bestindices, block );*/ + + *start = beststart.toVector3(); + *end = bestend.toVector3(); + + // save the error + m_besterror = besterror; + + return true; + } + + return false; +} + +bool ClusterFit::compress4( Vector3 * start, Vector3 * end ) +{ + int const count = m_colours->count; + SimdVector const one = SimdVector(1.0f); + SimdVector const zero = SimdVector(0.0f); + SimdVector const half = SimdVector(0.5f); + SimdVector const two = SimdVector(2.0); + SimdVector const onethird( 1.0f/3.0f, 1.0f/3.0f, 1.0f/3.0f, 1.0f/9.0f ); + SimdVector const twothirds( 2.0f/3.0f, 2.0f/3.0f, 2.0f/3.0f, 4.0f/9.0f ); + SimdVector const twonineths = SimdVector( 2.0f/9.0f ); + SimdVector const grid( 31.0f, 63.0f, 31.0f, 0.0f ); + SimdVector const 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; + int b0 = 0, b1 = 0, b2 = 0; + + // 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++) + { + SimdVector const x3 = m_xsum - x2 - x1 - x0; + + //Vector3 const alphax_sum = x0 + x1 * (2.0f / 3.0f) + x2 * (1.0f / 3.0f); + //float const alpha2_sum = w0 + w1 * (4.0f/9.0f) + w2 * (1.0f/9.0f); + SimdVector const alphax_sum = multiplyAdd(x2, onethird, multiplyAdd(x1, twothirds, x0)); // alphax_sum, alpha2_sum + SimdVector const alpha2_sum = alphax_sum.splatW(); + + //Vector3 const betax_sum = x3 + x2 * (2.0f / 3.0f) + x1 * (1.0f / 3.0f); + //float const beta2_sum = w3 + w2 * (4.0f/9.0f) + w1 * (1.0f/9.0f); + SimdVector const betax_sum = multiplyAdd(x2, twothirds, multiplyAdd(x1, onethird, x3)); // betax_sum, beta2_sum + SimdVector const beta2_sum = betax_sum.splatW(); + + //float const alphabeta_sum = (w1 + w2) * (2.0f/9.0f); + SimdVector const alphabeta_sum = twonineths*( x1 + x2 ).splatW(); // alphabeta_sum + + // float const factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum); + SimdVector const 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; + b0 = c0; + b1 = c1; + b2 = c2; + } + + 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 ) ) + { + /*// 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.toVector3(), bestend.toVector3(), bestindices, block );*/ + + *start = beststart.toVector3(); + *end = bestend.toVector3(); + + // save the error + m_besterror = besterror; + + return true; + } + + return false; +} + +#else + +bool ClusterFit::compress3(Vector3 * start, Vector3 * end) +{ + const Vector3 one( 1.0f ); + const Vector3 zero( 0.0f ); + const Vector3 half( 0.5f ); + 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 = 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; + } + + 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 < 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 + WriteColourBlock3( beststart, bestend, bestindices, block );*/ + + *start = beststart; + *end = bestend; + + // save the error + m_besterror = besterror; + + return true; + } + + return false; +} + +bool ClusterFit::compress4(Vector3 * start, Vector3 * end) +{ + Vector3 const one( 1.0f ); + Vector3 const zero( 0.0f ); + Vector3 const half( 0.5f ); + Vector3 const grid( 31.0f, 63.0f, 31.0f ); + Vector3 const 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 = 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 diff --git a/src/nvtt/ClusterFit.h b/src/nvtt/ClusterFit.h new file mode 100644 index 0000000..af3f84d --- /dev/null +++ b/src/nvtt/ClusterFit.h @@ -0,0 +1,82 @@ +/* ----------------------------------------------------------------------------- + + Copyright (c) 2006 Simon Brown si@sjbrown.co.uk + Copyright (c) 2006 Ignacio Castano icastano@nvidia.com + + 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. + + -------------------------------------------------------------------------- */ + +#ifndef NVTT_CLUSTERFIT_H +#define NVTT_CLUSTERFIT_H + +#define NVTT_USE_SIMD 0 + +#include "nvmath/SimdVector.h" +#include "nvmath/Vector.h" + +namespace nv { + + struct ColorSet; + + class ClusterFit + { + public: + ClusterFit(); + + void setColourSet(const ColorSet * set); + + void setMetric(Vector4::Arg w); + float bestError() const; + + bool compress3(Vector3 * start, Vector3 * end); + bool compress4(Vector3 * start, Vector3 * end); + + private: + + uint count; + //ColorSet const* m_colours; + + Vector3 m_principle; + + #if NVTT_USE_SIMD + SimdVector m_weighted[16]; + SimdVector m_metric; + SimdVector m_metricSqr; + SimdVector m_xxsum; + SimdVector m_xsum; + SimdVector m_besterror; + #else + Vector3 m_weighted[16]; + float m_weights[16]; + Vector3 m_metric; + Vector3 m_metricSqr; + Vector3 m_xxsum; + Vector3 m_xsum; + float m_wsum; + float m_besterror; + #endif + + int m_order[16]; + }; + +} // nv namespace + +#endif // NVTT_CLUSTERFIT_H