public ClusterFit(ColourSet colours, SquishFlags flags, float?metric)
: base(colours, flags)
{
// set the iteration count
_mIterationCount = (MFlags & SquishFlags.KColourIterativeClusterFit) != 0 ? 8 : 1;
// initialise the metric (old perceptual = 0.2126f, 0.7152f, 0.0722f)
if (metric != null)
{
//m_metric = Vec4( metric[0], metric[1], metric[2], 1.0f );
}
else
{
_mMetric = new Vector4(1.0f);
}
// initialise the best error
_mBesterror = new Vector4(float.MaxValue);
// cache some values
var count = MColours.Count;
var values = MColours.Points;
// get the covariance matrix
var covariance = Sym3X3.ComputeWeightedCovariance(count, values, MColours.Weights);
// compute the principle component
_mPrinciple = Sym3X3.ComputePrincipleComponent(covariance);
}
public static Sym3X3 ComputeWeightedCovariance(int n, Vector3[] points, float[] weights)
{
// compute the centroid
var total = 0.0f;
var centroid = new Vector3(0.0f);
for (var i = 0; i < n; ++i)
{
total += weights[i];
centroid += weights[i] * points[i];
}
if (total > float.Epsilon)
{
centroid /= total;
}
// accumulate the covariance matrix
var covariance = new Sym3X3(0.0f);
for (var i = 0; i < n; ++i)
{
var a = points[i] - centroid;
var b = weights[i] * a;
covariance[0] += a.X * b.X;
covariance[1] += a.X * b.Y;
covariance[2] += a.X * b.Z;
covariance[3] += a.Y * b.Y;
covariance[4] += a.Y * b.Z;
covariance[5] += a.Z * b.Z;
}
// return it
return(covariance);
}
public static Vector3 ComputePrincipleComponent(Sym3X3 matrix)
{
var row0 = new Vector4(matrix[0], matrix[1], matrix[2], 0.0f);
var row1 = new Vector4(matrix[1], matrix[3], matrix[4], 0.0f);
var row2 = new Vector4(matrix[2], matrix[4], matrix[5], 0.0f);
var v = new Vector4(1.0f);
for (var i = 0; i < 8; ++i)
{
// matrix multiply
var w = row0 * v.SplatX();
w = Helpers.MultiplyAdd(row1, v.SplatY(), w);
w = Helpers.MultiplyAdd(row2, v.SplatZ(), w);
// get max component from xyz in all channels
var a = Vector4.Max(w.SplatX(), Vector4.Max(w.SplatY(), w.SplatZ()));
// divide through and advance
v = w * Helpers.Reciprocal(a);
}
return(v.ToVector3());
}
public RangeFit(ColourSet colours, SquishFlags flags, float?metric)
: base(colours, flags)
{
// initialise the metric (old perceptual = 0.2126f, 0.7152f, 0.0722f)
if (metric != null)
{
//m_metric = new Vector3( metric[0], metric[1], metric[2] );
}
else
{
_mMetric = new Vector3(1.0f);
}
// initialise the best error
_mBesterror = float.MaxValue;
// cache some values
var count = MColours.Count;
var values = MColours.Points;
var weights = MColours.Weights;
// get the covariance matrix
var covariance = Sym3X3.ComputeWeightedCovariance(count, values, weights);
// compute the principle component
var principle = Sym3X3.ComputePrincipleComponent(covariance);
// get the min and max range as the codebook endpoints
var start = new Vector3(0.0f);
var end = new Vector3(0.0f);
if (count > 0)
{
float max;
// compute the range
start = end = values[0];
var min = max = Vector3.Dot(values[0], principle);
for (var i = 1; i < count; ++i)
{
var val = Vector3.Dot(values[i], principle);
if (val < min)
{
start = values[i];
min = val;
}
else if (val > max)
{
end = values[i];
max = val;
}
}
}
// clamp the output to [0, 1]
var one = new Vector3(1.0f);
var zero = new Vector3(0.0f);
start = Vector3.Min(one, Vector3.Max(zero, start));
end = Vector3.Min(one, Vector3.Max(zero, end));
// clamp to the grid and save
var grid = new Vector3(31.0f, 63.0f, 31.0f);
var gridrcp = new Vector3(1.0f / 31.0f, 1.0f / 63.0f, 1.0f / 31.0f);
var half = new Vector3(0.5f);
_mStart = Helpers.Truncate(grid * start + half) * gridrcp;
_mEnd = Helpers.Truncate(grid * end + half) * gridrcp;
}
