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Sector & Loadout Generation Fixes

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Adjusted sector generation to improve sample elimination performance and better preserve density map
Added unit tests for item shape rotation and fit functions + fixed those (required for correct loadout generation)!
Fixed faction hostility by marking player ship
Added faction to entity serialization
Generating starting loadout for player ship
This commit is contained in:
Reinier Vens 2021-03-18 22:48:33 +00:00
commit 351cb9f2af
23 changed files with 879 additions and 128 deletions
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73
Assets/Scripts/ServerShared/SampleElimination.cs
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@ -37,7 +37,7 @@ public static class WeightedSampleElimination
// of a sample point based on the placement of its neighbors within d_max radius. The weight function
// must have the following form:
//
// float weightFunction( float2 p0, float2 p1, float dist2, float dmax )
// float weightFunction( float2 p0, float2 p1, float dist2, float dmax, float density )
//
// The arguments p0 and p1 are the two neighboring points, dist2 is the square of the Euclidean distance
// between these two points, and d_max is the current radius for the weight function.
@ -45,44 +45,46 @@ public static class WeightedSampleElimination
// different than the d_max value passed to this method.
public static void Eliminate (
float2[] inputPoints,
float2[] outputPoints,
Func<float2, float2, float, float, float> weightFunction,
float2[] outputPoints,
Func<float2, float2, float, float, float, float> weightFunction,
Func<float2, float> densityFunction,
float d_max = 0)
{
if ( d_max < .001f ) d_max = 2 * GetMaxPoissonDiskRadius( outputPoints.Length );
DoEliminate( inputPoints, outputPoints, d_max, weightFunction );
DoEliminate( inputPoints, outputPoints, d_max, weightFunction, densityFunction );
}
public static void Eliminate(
float2[] inputPoints,
float2[] outputPoints,
float d_max = 0,
float alpha = 8,
float beta = 0.65f,
float gamma = 1.5f)
{
if ( d_max < .001f ) d_max = 2 * GetMaxPoissonDiskRadius( outputPoints.Length );
float d_min = d_max * GetWeightLimitFraction( inputPoints.Length, outputPoints.Length, beta, gamma );
Eliminate( inputPoints, outputPoints, (p0, p1, d2, dmax) =>
{
float d = sqrt(d2);
if ( d < d_min ) d = d_min;
return pow( 1f / (d / dmax + .01f), alpha );
}, d_max);
}
// public static void Eliminate(
// float2[] inputPoints,
// float2[] outputPoints,
// float d_max = 0,
// float alpha = 8,
// float beta = 0.65f,
// float gamma = 1.5f)
// {
// if ( d_max < .001f ) d_max = 2 * GetMaxPoissonDiskRadius( outputPoints.Length );
// float d_min = d_max * GetWeightLimitFraction( inputPoints.Length, outputPoints.Length, beta, gamma );
// Eliminate( inputPoints, outputPoints, (p0, p1, d2, dmax, density) =>
// {
// float d = sqrt(d2);
// if ( d < d_min ) d = d_min;
// return pow( 1f / (d / dmax + .01f), alpha );
// }, v => 1, d_max);
// }
public static void Eliminate(
float2[] inputPoints,
float2[] outputPoints,
Func<float2, float> density = null,
Func<float2, float> densityFunc = null,
float d_max = 0)
{
if (densityFunc == null) densityFunc = v => 1;
if ( d_max < .001f ) d_max = 2 * GetMaxPoissonDiskRadius( outputPoints.Length );
Eliminate( inputPoints, outputPoints, (p0, p1, d2, dmax) =>
Eliminate( inputPoints, outputPoints, (p0, p1, d2, dmax, density) =>
{
float d = sqrt(d2);
return pow( 1 - (d / dmax), 5 + 6 * saturate((density ?? (v => .5f))((p0 + p1) / 2)) );
}, d_max);
return pow( 1 - d / dmax, 5 + 6 * density );
}, densityFunc, d_max);
}
public static float2[] GeneratePoints(int count, Func<float2, float> density = null, Func<float2, float> envelope = null, Action<string> progressCallback = null, uint seed = 1337)
@ -127,7 +129,8 @@ public static class WeightedSampleElimination
float2[] inputPoints,
float2[] outputPoints,
float d_max,
Func<float2, float2, float, float, float> weightFunction
Func<float2, float2, float, float, float, float> weightFunction,
Func<float2, float> density
)
{
// Build a k-d tree for samples
@ -137,13 +140,15 @@ public static class WeightedSampleElimination
var dmax2 = d_max * d_max;
// Assign weights to each sample
float[] weights = new float[inputPoints.Length];
float[] densities = new float[inputPoints.Length];
for (int i = 0; i < inputPoints.Length; i++)
{
var p = inputPoints[i];
var point = inputPoints[i];
densities[i] += density(point);
foreach (var (qi, d2) in query.Radius(kdtree, inputPoints[i], d_max))
{
var p2 = inputPoints[qi];
weights[i] += weightFunction(p, p2, d2, d_max);
var otherPoint = inputPoints[qi];
weights[i] += weightFunction(point, otherPoint, d2, d_max, densities[i]);
}
}
@ -157,13 +162,13 @@ public static class WeightedSampleElimination
while (sampleCount > outputPoints.Length)
{
// Pull the top sample from heap
var i = heap.PopObj();
var p = inputPoints[i];
var elim = heap.PopObj();
var pElim = inputPoints[elim];
// For each sample around it, remove its weight contribution and update the heap
foreach (var (qi, d2) in query.Radius(kdtree, p, d_max))
foreach (var (qi, d2) in query.Radius(kdtree, pElim, d_max))
{
var p2 = inputPoints[qi];
weights[qi] -= weightFunction(p, p2, d2, d_max);
var point = inputPoints[qi];
weights[qi] -= weightFunction(point, pElim, d2, d_max, densities[qi]);
heap.SetValue(qi, weights[qi]);
}
sampleCount--;