forked from FWGS/Paranoia2
117 lines
3.6 KiB
C++
117 lines
3.6 KiB
C++
/* -----------------------------------------------------------------------------
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Copyright (c) 2006 Simon Brown si@sjbrown.co.uk
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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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/*! @file
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The symmetric eigensystem solver algorithm is from
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http://www.geometrictools.com/Documentation/EigenSymmetric3x3.pdf
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*/
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#include "maths.h"
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#include "simd.h"
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#include <cfloat>
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namespace squish {
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Sym3x3 ComputeWeightedCovariance( int n, Vec3 const* points, float const* weights, Vec3::Arg metric )
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{
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// compute the centroid
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float total = 0.0f;
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Vec3 centroid( 0.0f );
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int i;
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for( i = 0; i < n; ++i )
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{
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total += weights[i];
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centroid += weights[i]*points[i];
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}
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if( total > FLT_EPSILON )
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centroid /= total;
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// accumulate the covariance matrix
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Sym3x3 covariance( 0.0f );
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for( i = 0; i < n; ++i )
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{
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Vec3 a = (points[i] - centroid) * metric;
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Vec3 b = weights[i]*a;
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covariance[0] += a.X()*b.X();
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covariance[1] += a.X()*b.Y();
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covariance[2] += a.X()*b.Z();
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covariance[3] += a.Y()*b.Y();
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covariance[4] += a.Y()*b.Z();
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covariance[5] += a.Z()*b.Z();
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}
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// return it
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return covariance;
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}
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static Vec3 EstimatePrincipleComponent( Sym3x3 const& matrix )
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{
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Vec3 const row0(matrix[0], matrix[1], matrix[2]);
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Vec3 const row1(matrix[1], matrix[3], matrix[4]);
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Vec3 const row2(matrix[2], matrix[4], matrix[5]);
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float r0 = Dot(row0, row0);
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float r1 = Dot(row1, row1);
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float r2 = Dot(row2, row2);
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if (r0 > r1 && r0 > r2) return row0;
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if (r1 > r2) return row1;
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return row2;
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}
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#define POWER_ITERATION_COUNT 8
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Vec3 ComputePrincipleComponent( Sym3x3 const& matrix )
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{
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Vec4 const row0( matrix[0], matrix[1], matrix[2], 0.0f );
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Vec4 const row1( matrix[1], matrix[3], matrix[4], 0.0f );
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Vec4 const row2( matrix[2], matrix[4], matrix[5], 0.0f );
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#if 1
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Vec3 v3 = EstimatePrincipleComponent( matrix );
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Vec4 v( v3.X(), v3.Y(), v3.Z(), 0.0f );
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#else
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Vec4 v = VEC4_CONST( 1.0f );
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#endif
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for( int i = 0; i < POWER_ITERATION_COUNT; ++i )
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{
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// matrix multiply
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Vec4 w = row0*v.SplatX();
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w = MultiplyAdd(row1, v.SplatY(), w);
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w = MultiplyAdd(row2, v.SplatZ(), w);
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// get max component from xyz in all channels
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Vec4 a = Max(w.SplatX(), Max(w.SplatY(), w.SplatZ()));
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// divide through and advance
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v = w*Reciprocal(a);
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}
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return v.GetVec3();
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}
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} // namespace squish
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