[orx-triangulation] Add smoothScatter

orx-triangulation/build.gradle.kts

@@ -33,6 +33,7 @@ kotlin {
             dependencies {
                 api(libs.openrndr.math)
                 api(libs.openrndr.shape)
+                implementation(project(":orx-noise"))
             }
         }
 

orx-triangulation/src/commonMain/kotlin/Delaunay.kt

@@ -2,10 +2,6 @@ package org.openrndr.extra.triangulation
 
 import org.openrndr.math.Vector2
 import org.openrndr.shape.Rectangle
-import org.openrndr.shape.Triangle
-import org.openrndr.shape.contour
-import org.openrndr.shape.contours
-import kotlin.js.JsName
 import kotlin.math.cos
 import kotlin.math.pow
 import kotlin.math.sin
@@ -77,13 +73,13 @@ class Delaunay(val points: DoubleArray) {
         init()
     }
 
-    fun neighbors(i:Int) = sequence<Int> {
+    fun neighbors(i: Int) = sequence {
         val e0 = inedges.getOrNull(i) ?: return@sequence
         if (e0 != -1) {
             var e = e0
-            var p0 = -1
+            var p0 : Int
 
-            loop@do {
+            loop@ do {
                 p0 = triangles[e]
                 yield(p0)
                 e = if (e % 3 == 2) e - 2 else e + 1
@@ -109,29 +105,31 @@ class Delaunay(val points: DoubleArray) {
     }
 
     fun collinear(): Boolean {
-        for (i in 0 until triangles.size step 3) {
+        for (i in triangles.indices step 3) {
             val a = 2 * triangles[i]
             val b = 2 * triangles[i + 1]
             val c = 2 * triangles[i + 2]
             val coords = points
             val cross = (coords[c] - coords[a]) * (coords[b + 1] - coords[a + 1])
-            - (coords[b] - coords[a]) * (coords[c + 1] - coords[a + 1])
+            -(coords[b] - coords[a]) * (coords[c + 1] - coords[a + 1])
-            if (cross > 1e-10) return false;
+            if (cross > 1e-10) return false
         }
         return true
     }
-    private fun jitter(x:Double, y:Double, r:Double): DoubleArray {
+
-        return doubleArrayOf(x + sin(x+y) * r, y + cos(x-y)*r)
+    private fun jitter(x: Double, y: Double, r: Double): DoubleArray {
+        return doubleArrayOf(x + sin(x + y) * r, y + cos(x - y) * r)
     }
+
     fun init() {
 
         if (hull.size > 2 && collinear()) {
             println("warning: triangulation is collinear")
             val r = 1E-8
-            for (i in 0 until points.size step 2) {
+            for (i in points.indices step 2) {
-                val p = jitter(points[i], points[i+1], r)
+                val p = jitter(points[i], points[i + 1], r)
                 points[i] = p[0]
-                points[i+1] = p[1]
+                points[i + 1] = p[1]
             }
 
             delaunator = Delaunator(points)

orx-triangulation/src/commonMain/kotlin/DelaunayTriangulation.kt

@@ -14,29 +14,44 @@ class DelaunayTriangulation(val points: List<Vector2>) {
 
     fun voronoiDiagram(bounds: Rectangle) = VoronoiDiagram(this, bounds)
 
-    fun neighbors(pointIndex: Int) : Sequence<Int> {
+    fun neighbors(pointIndex: Int): Sequence<Int> {
         return delaunay.neighbors(pointIndex)
     }
 
-    fun neighborPoints(pointIndex: Int) : List<Vector2> {
+    fun neighborPoints(pointIndex: Int): List<Vector2> {
         return neighbors(pointIndex).map { points[it] }.toList()
     }
 
-    fun triangles(): List<Triangle> {
+    fun triangleIndices(): List<IntArray> {
+        val list = mutableListOf<IntArray>()
+        for (i in delaunay.triangles.indices step 3) {
+            list.add(
+                intArrayOf(
+                    delaunay.triangles[i],
+                    delaunay.triangles[i + 1],
+                    delaunay.triangles[i + 2]
+                )
+            )
+        }
+        return list
+    }
+
+    fun triangles(filterPredicate: (Int, Int, Int) -> Boolean = { _, _, _ -> true }): List<Triangle> {
         val list = mutableListOf<Triangle>()
 
-        for (i in delaunay.triangles.indices step 3 ) {
+        for (i in delaunay.triangles.indices step 3) {
             val t0 = delaunay.triangles[i]
             val t1 = delaunay.triangles[i + 1]
             val t2 = delaunay.triangles[i + 2]
 
+            // originally they are defined *counterclockwise*
+            if (filterPredicate(t2, t1, t0)) {
                 val p1 = points[t0]
                 val p2 = points[t1]
                 val p3 = points[t2]
-
-            // originally they are defined *counterclockwise*
                 list.add(Triangle(p3, p2, p1))
             }
+        }
         return list
     }
 
@@ -61,11 +76,11 @@ class DelaunayTriangulation(val points: List<Vector2>) {
         close()
     }
 
-    fun nearest(query: Vector2) : Int = delaunay.find(query.x, query.y)
+    fun nearest(query: Vector2): Int = delaunay.find(query.x, query.y)
 
-    fun nearestPoint(query: Vector2) : Vector2 = points[nearest(query)]
+    fun nearestPoint(query: Vector2): Vector2 = points[nearest(query)]
 }
 
-fun List<Vector2>.delaunayTriangulation() : DelaunayTriangulation {
+fun List<Vector2>.delaunayTriangulation(): DelaunayTriangulation {
     return DelaunayTriangulation(this)
 }

orx-triangulation/src/commonMain/kotlin/DoubleDouble.kt

@@ -17,8 +17,8 @@ import kotlin.math.pow
  * See https://people.eecs.berkeley.edu/~jrs/papers/robustr.pdf
  */
 internal fun fastTwoDiff(a: Double, b: Double): DoubleArray {
-    val x = a - b;
+    val x = a - b
-    val y = (a - x) - b;
+    val y = (a - x) - b
 
     return doubleArrayOf(y, x)
 }
@@ -60,11 +60,11 @@ internal fun reduceSignificand(
     bits: Int
 ): Double {
 
-    val s = 53 - bits;
+    val s = 53 - bits
-    val f = 2.0.pow(s) + 1;
+    val f = 2.0.pow(s) + 1
 
-    val c = f * a;
+    val c = f * a
-    val r = c - (c - a);
+    val r = c - (c - a)
 
     return r;
 }
@@ -74,7 +74,7 @@ internal fun reduceSignificand(
  * === 2^Math.ceil(p/2) + 1 where p is the # of significand bits in a double === 53.
  * @internal
  */
-private const val f = 134217729;  // 2**27 + 1;
+private const val f = 134217729  // 2**27 + 1;
 
 
 /**
@@ -90,9 +90,9 @@ private const val f = 134217729;  // 2**27 + 1;
  * @param a A double floating point number
  */
 private fun split(a: Double): DoubleArray {
-    val c = f * a;
+    val c = f * a
-    val a_h = c - (c - a);
+    val a_h = c - (c - a)
-    val a_l = a - a_h;
+    val a_l = a - a_h
 
     return doubleArrayOf(a_h, a_l)
 }
@@ -104,9 +104,9 @@ private fun split(a: Double): DoubleArray {
  * @param b subtrahend - a double-double precision floating point number
  */
 internal fun twoDiff(a: Double, b: Double): DoubleArray {
-    val x = a - b;
+    val x = a - b
-    val bvirt = a - x;
+    val bvirt = a - x
-    val y = (a - (x + bvirt)) + (bvirt - b);
+    val y = (a - (x + bvirt)) + (bvirt - b)
 
     return doubleArrayOf(y, x)
 }
@@ -130,15 +130,15 @@ internal fun twoProduct(a: Double, b: Double): DoubleArray {
     val x = a * b;
 
     //const [ah, al] = split(a);
-    val c = f * a;
+    val c = f * a
-    val ah = c - (c - a);
+    val ah = c - (c - a)
-    val al = a - ah;
+    val al = a - ah
     //const [bh, bl] = split(b);
-    val d = f * b;
+    val d = f * b
-    val bh = d - (d - b);
+    val bh = d - (d - b)
-    val bl = b - bh;
+    val bl = b - bh
 
-    val y = (al * bl) - ((x - (ah * bh)) - (al * bh) - (ah * bl));
+    val y = (al * bl) - ((x - (ah * bh)) - (al * bh) - (ah * bl))
 
     //const err1 = x - (ah * bh);
     //const err2 = err1 - (al * bh);
@@ -149,14 +149,14 @@ internal fun twoProduct(a: Double, b: Double): DoubleArray {
 }
 
 internal fun twoSquare(a: Double): DoubleArray {
-    val x = a * a;
+    val x = a * a
 
     //const [ah, al] = split(a);
-    val c = f * a;
+    val c = f * a
-    val ah = c - (c - a);
+    val ah = c - (c - a)
-    val al = a - ah;
+    val al = a - ah
 
-    val y = (al * al) - ((x - (ah * ah)) - 2 * (ah * al));
+    val y = (al * al) - ((x - (ah * ah)) - 2 * (ah * al))
 
     return doubleArrayOf(y, x)
 }
@@ -174,8 +174,8 @@ internal fun twoSquare(a: Double): DoubleArray {
  * See https://people.eecs.berkeley.edu/~jrs/papers/robustr.pdf
  */
 internal fun twoSum(a: Double, b: Double): DoubleArray {
-    val x = a + b;
+    val x = a + b
-    val bv = x - a;
+    val bv = x - a
 
     return doubleArrayOf((a - (x - bv)) + (b - bv), x)
 }
@@ -194,21 +194,21 @@ internal fun twoSum(a: Double, b: Double): DoubleArray {
  * @param y another double-double precision floating point number
  */
 internal fun ddDiffDd(x: DoubleArray, y: DoubleArray): DoubleArray {
-    val xl = x[0];
+    val xl = x[0]
-    val xh = x[1];
+    val xh = x[1]
-    val yl = y[0];
+    val yl = y[0]
-    val yh = y[1];
+    val yh = y[1]
 
     //const [sl,sh] = twoSum(xh,yh);
-    val sh = xh - yh; val _1 = sh - xh; val sl = (xh - (sh - _1)) + (-yh - _1);
+    val sh = xh - yh; val _1 = sh - xh; val sl = (xh - (sh - _1)) + (-yh - _1)
     //const [tl,th] = twoSum(xl,yl);
-    val th = xl - yl; val _2 = th - xl; val tl = (xl - (th - _2)) + (-yl - _2);
+    val th = xl - yl; val _2 = th - xl; val tl = (xl - (th - _2)) + (-yl - _2)
-    val c = sl + th;
+    val c = sl + th
     //const [vl,vh] = fastTwoSum(sh,c)
-    val vh = sh + c; val vl = c - (vh - sh);
+    val vh = sh + c; val vl = c - (vh - sh)
     val w = tl + vl
     //const [zl,zh] = fastTwoSum(vh,w)
-    val zh = vh + w; val zl = w - (zh - vh);
+    val zh = vh + w; val zl = w - (zh - vh)
 
     return doubleArrayOf(zl, zh)
 }
@@ -229,19 +229,19 @@ internal fun ddMultDd(x: DoubleArray, y: DoubleArray): DoubleArray {
 
 
     //const xl = x[0];
-    val xh = x[1];
+    val xh = x[1]
     //const yl = y[0];
-    val yh = y[1];
+    val yh = y[1]
 
     //const [cl1,ch] = twoProduct(xh,yh);
-    val ch = xh*yh;
+    val ch = xh*yh
-    val c = f * xh; val ah = c - (c - xh); val al = xh - ah;
+    val c = f * xh; val ah = c - (c - xh); val al = xh - ah
-    val d = f * yh; val bh = d - (d - yh); val bl = yh - bh;
+    val d = f * yh; val bh = d - (d - yh); val bl = yh - bh
-    val cl1 = (al*bl) - ((ch - (ah*bh)) - (al*bh) - (ah*bl));
+    val cl1 = (al*bl) - ((ch - (ah*bh)) - (al*bh) - (ah*bl))
 
     //return fastTwoSum(ch,cl1 + (xh*yl + xl*yh));
-    val b = cl1 + (xh*y[0] + x[0]*yh);
+    val b = cl1 + (xh*y[0] + x[0]*yh)
-    val xx = ch + b;
+    val xx = ch + b
 
     return doubleArrayOf(b - (xx - ch), xx)
 }
@@ -261,21 +261,21 @@ internal fun ddMultDd(x: DoubleArray, y: DoubleArray): DoubleArray {
  * @param y another double-double precision floating point number
  */
 internal fun ddAddDd(x: DoubleArray, y: DoubleArray): DoubleArray {
-    val xl = x[0];
+    val xl = x[0]
-    val xh = x[1];
+    val xh = x[1]
-    val yl = y[0];
+    val yl = y[0]
-    val yh = y[1];
+    val yh = y[1]
 
     //const [sl,sh] = twoSum(xh,yh);
-    val sh = xh + yh; val _1 = sh - xh; val sl = (xh - (sh - _1)) + (yh - _1);
+    val sh = xh + yh; val _1 = sh - xh; val sl = (xh - (sh - _1)) + (yh - _1)
     //val [tl,th] = twoSum(xl,yl);
-    val th = xl + yl; val _2 = th - xl; val tl = (xl - (th - _2)) + (yl - _2);
+    val th = xl + yl; val _2 = th - xl; val tl = (xl - (th - _2)) + (yl - _2)
-    val c = sl + th;
+    val c = sl + th
     //val [vl,vh] = fastTwoSum(sh,c)
-    val vh = sh + c; val vl = c - (vh - sh);
+    val vh = sh + c; val vl = c - (vh - sh)
     val w = tl + vl
     //val [zl,zh] = fastTwoSum(vh,w)
-    val zh = vh + w; val zl = w - (zh - vh);
+    val zh = vh + w; val zl = w - (zh - vh)
 
     return doubleArrayOf(zl, zh)
 }
@@ -297,24 +297,24 @@ internal fun ddAddDd(x: DoubleArray, y: DoubleArray): DoubleArray {
  * @param x a double-double precision floating point number
  */
 internal fun ddMultDouble1(y: Double, x: DoubleArray): DoubleArray {
-    val xl = x[0];
+    val xl = x[0]
-    val xh = x[1];
+    val xh = x[1]
 
     //val [cl1,ch] = twoProduct(xh,y);
-    val ch = xh*y;
+    val ch = xh*y
-    val c = f * xh; val ah = c - (c - xh); val al = xh - ah;
+    val c = f * xh; val ah = c - (c - xh); val al = xh - ah
-    val d = f * y; val bh = d - (d - y); val bl = y - bh;
+    val d = f * y; val bh = d - (d - y); val bl = y - bh
-    val cl1 = (al*bl) - ((ch - (ah*bh)) - (al*bh) - (ah*bl));
+    val cl1 = (al*bl) - ((ch - (ah*bh)) - (al*bh) - (ah*bl))
 
-    val cl2 = xl*y;
+    val cl2 = xl*y
     //val [tl1,th] = fastTwoSum(ch,cl2);
-    val th = ch + cl2;
+    val th = ch + cl2
-    val tl1 = cl2 - (th - ch);
+    val tl1 = cl2 - (th - ch)
 
-    val tl2 = tl1 + cl1;
+    val tl2 = tl1 + cl1
     //val [zl,zh] = fastTwoSum(th,tl2);
-    val zh = th + tl2;
+    val zh = th + tl2
-    val zl = tl2 - (zh - th);
+    val zl = tl2 - (zh - th)
 
-    return doubleArrayOf(zl,zh);
+    return doubleArrayOf(zl,zh)
 }

orx-triangulation/src/commonMain/kotlin/Predicates.kt

@@ -1,6 +1,6 @@
 package org.openrndr.extra.triangulation
 
-internal fun orient2d(bx: Double, by: Double, ax: Double, ay: Double, cx: Double, cy: Double): Double {
+fun orient2d(bx: Double, by: Double, ax: Double, ay: Double, cx: Double, cy: Double): Double {
     // (ax,ay) (bx,by) are swapped such that the sign of the determinant is flipped. which is what Delaunator.kt expects.
 
     /*
@@ -8,12 +8,12 @@ internal fun orient2d(bx: Double, by: Double, ax: Double, ay: Double, cx: Double
     | c  d |   | bx - cx  by - cy |
     */
 
-    val a = ax - cx
+    val a = twoDiff(ax, cx)
-    val b = ay - cy
+    val b = twoDiff(ay, cy)
-    val c = bx - cx
+    val c = twoDiff(bx, cx)
-    val d = by - cy
+    val d = twoDiff(by, cy)
 
-    val determinant = ddDiffDd(twoProduct(a, d), twoProduct(b, c))
+    val determinant = ddDiffDd(ddMultDd(a, d), ddMultDd(b, c))
 
     return determinant[1]
 }

orx-triangulation/src/commonMain/kotlin/SmoothScatter.kt

@@ -0,0 +1,120 @@
+package org.openrndr.extra.triangulation
+
+import org.openrndr.extra.noise.scatter
+import org.openrndr.math.Vector2
+import org.openrndr.shape.ShapeProvider
+import org.openrndr.shape.bounds
+import kotlin.random.Random
+
+fun ShapeProvider.smoothScatterSeq(
+    placementRadius: Double,
+    distanceToEdge: Double = placementRadius * 2.0,
+    smoothing: Double = 0.5,
+    random: Random = Random.Default
+) = sequence {
+    val boundaryPointSets = this@smoothScatterSeq.shape.contours.map {
+        it.equidistantPositions((it.length / placementRadius).toInt())
+    }
+
+    val boundaryPoints = boundaryPointSets.flatten()
+    val interiorPoints = this@smoothScatterSeq.shape.scatter(
+        placementRadius = placementRadius, distanceToEdge = distanceToEdge, random = random
+    )
+
+    val bounds = interiorPoints.bounds.offsetEdges(100.0)
+    var relaxedPoints = interiorPoints
+
+    while (true) {
+        val dt = (relaxedPoints + boundaryPoints)
+        val v = dt.voronoiDiagram(bounds)
+
+        relaxedPoints = relaxedPoints.mapIndexed { index, it ->
+            val c = v.cellCentroid(index)
+            if (c.x == c.x && c.y == c.y) {
+                it * smoothing + c * (1.0 - smoothing)
+            } else {
+                it
+            }
+        }
+        yield(relaxedPoints)
+    }
+}
+
+fun ShapeProvider.smoothScatterWeightedSeq(
+    placementRadius: Double,
+    distanceToEdge: Double = placementRadius * 2.0,
+    smoothing: Double = 0.5,
+    random: Random = Random.Default
+) = sequence {
+    val boundaryPointSets = this@smoothScatterWeightedSeq.shape.contours.map {
+        it.equidistantPositions((it.length / placementRadius).toInt())
+    }
+
+    val boundaryPoints = boundaryPointSets.flatten()
+    val interiorPoints = this@smoothScatterWeightedSeq.shape.scatter(
+        placementRadius = placementRadius, distanceToEdge = distanceToEdge, random = random
+    )
+
+    val bounds = interiorPoints.bounds.offsetEdges(100.0)
+    var relaxedPoints = interiorPoints
+
+    fun isBoundaryPoint(i: Int) = i >= interiorPoints.size
+
+    val targetAreas = interiorPoints.map { if (random.nextDouble() < 0.1) 450.0 else null }
+
+    while (true) {
+        val dt = (relaxedPoints + boundaryPoints)
+        val v = dt.voronoiDiagram(bounds)
+
+
+        relaxedPoints = relaxedPoints.mapIndexed { index, it ->
+            val c = v.cellCentroid(index)
+            if (c.x == c.x && c.y == c.y) {
+                it * smoothing + c * (1.0 - smoothing)
+            } else {
+                it
+            }
+        }
+        val resolvedPoints = relaxedPoints.map { it }.toMutableList()
+
+
+        for (i in interiorPoints.indices) {
+
+            if (targetAreas[i] != null) {
+
+                val targetArea = targetAreas[i]!!
+                val cellArea = v.cellArea(i)
+                val cellCentroid = v.cellCentroid(i)
+                val areaDiff = targetArea - cellArea
+
+                val ns = v.neighbors(i).filter { !isBoundaryPoint(it) }.toList()
+
+                var force: Vector2
+                val scale = 1.0 / ns.size
+                for (n in ns) {
+                    force = v.cellCentroid(n) - cellCentroid
+                    resolvedPoints[n] += force.normalized * (areaDiff * 0.01) * scale
+                }
+            }
+            relaxedPoints = resolvedPoints
+        }
+        yield(relaxedPoints)
+    }
+}
+
+
+fun ShapeProvider.smoothScatter(
+    placementRadius: Double,
+    distanceToEdge: Double = placementRadius * 2.0,
+    iterations: Int = 10,
+    smoothing: Double = 0.5,
+    random: Random = Random.Default
+): List<Vector2> {
+
+    val seq = smoothScatterSeq(placementRadius, distanceToEdge, smoothing, random).iterator()
+
+    for (i in 0 until iterations - 1) {
+        seq.next()
+    }
+    return seq.next()
+}

orx-triangulation/src/commonMain/kotlin/Voronoi.kt

@@ -154,11 +154,7 @@ class Voronoi(val delaunay: Delaunay, val bounds: Rectangle) {
     }
 
 
-
     private fun cell(i: Int): MutableList<Double>? {
-
-
-
         val inedges = delaunay.inedges
         val halfedges = delaunay.halfedges
         val triangles = delaunay.triangles
@@ -195,8 +191,8 @@ class Voronoi(val delaunay: Delaunay, val bounds: Rectangle) {
                 if (cj != null) {
                     val li = ci.size
                     val lj = cj.size
-                    loop@ for (ai in 0 until ci.size step 2) {
+                    loop@ for (ai in ci.indices step 2) {
-                        for (aj in 0 until cj.size step 2) {
+                        for (aj in cj.indices step 2) {
                             if (ci[ai] == cj[aj]
                                 && ci[ai + 1] == cj[aj + 1]
                                 && ci[(ai + 2) % li] == cj[(aj + lj - 2) % lj]
@@ -256,10 +252,10 @@ class Voronoi(val delaunay: Delaunay, val bounds: Rectangle) {
         project(P[0], P[1], vx0, vy0)?.let { p -> P!!.add(0, p[1]); P!!.add(0, p[0]) }
         project(P[P.size - 2], P[P.size - 1], vxn, vyn)?.let { p -> P!!.add(p[0]); P!!.add(p[1]) }
 
-        P = this.clipFinite(i, P!!)
+        P = this.clipFinite(i, P)
         var n = 0
         if (P != null) {
-            n = P!!.size
+            n = P.size
             var c0 = -1
             var c1 = edgeCode(P[n - 2], P[n - 1])
             var j = 0
@@ -298,7 +294,7 @@ class Voronoi(val delaunay: Delaunay, val bounds: Rectangle) {
         var y1 = points[n - 1]
         var c0: Int
         var c1: Int = regionCode(x1, y1)
-        var e0: Int? = null
+        var e0: Int?
         var e1: Int? = 0
 
         for (j in 0 until n step 2) {
@@ -360,7 +356,7 @@ class Voronoi(val delaunay: Delaunay, val bounds: Rectangle) {
             e0 = e1
             e1 = this.edgeCode(P[0], P[1])
 
-            if (e0.isTruthy() && e1.isTruthy()) this.edge(i, e0!!, e1!!, P, P.size);
+            if (e0.isTruthy() && e1.isTruthy()) this.edge(i, e0!!, e1, P, P.size);
         } else if (this.contains(i, (bounds.xmin + bounds.xmax) / 2, (bounds.ymin + bounds.ymax) / 2)) {
             return mutableListOf(
                 bounds.xmax,
@@ -398,19 +394,22 @@ class Voronoi(val delaunay: Delaunay, val bounds: Rectangle) {
             when {
                 (c and 0b1000) != 0 -> {
                     x = nx0 + (nx1 - nx0) * (bounds.ymax - ny0) / (ny1 - ny0)
-                    y = bounds.ymax;
+                    y = bounds.ymax
                 }
+
                 (c and 0b0100) != 0 -> {
                     x = nx0 + (nx1 - nx0) * (bounds.ymin - ny0) / (ny1 - ny0)
                     y = bounds.ymin
                 }
+
                 (c and 0b0010) != 0 -> {
                     y = ny0 + (ny1 - ny0) * (bounds.xmax - nx0) / (nx1 - nx0)
                     x = bounds.xmax
                 }
+
                 else -> {
                     y = ny0 + (ny1 - ny0) * (bounds.xmin - nx0) / (nx1 - nx0)
-                    x = bounds.xmin;
+                    x = bounds.xmin
                 }
             }
 
@@ -458,33 +457,40 @@ class Voronoi(val delaunay: Delaunay, val bounds: Rectangle) {
                     e = 0b0100
                     continue@loop
                 }
+
                 0b0100 -> { // top
                     e = 0b0110
                     x = bounds.xmax
                     y = bounds.ymin
                 }
+
                 0b0110 -> { // top-right
                     e = 0b0010
                     continue@loop
                 }
+
                 0b0010 -> { // right
                     e = 0b1010
                     x = bounds.xmax
                     y = bounds.ymax
                 }
+
                 0b1010 -> { // bottom-right
                     e = 0b1000
                     continue@loop
                 }
+
                 0b1000 -> { // bottom
                     e = 0b1001
                     x = bounds.xmin
                     y = bounds.ymax
                 }
+
                 0b1001 -> { // bottom-left
                     e = 0b0001
                     continue@loop
                 }
+
                 0b0001 -> { // left
                     e = 0b0101
                     x = bounds.xmin

orx-triangulation/src/commonMain/kotlin/VoronoiDiagram.kt

@@ -20,6 +20,37 @@ class VoronoiDiagram(val delaunayTriangulation: DelaunayTriangulation, val bound
         }
     }
 
+    fun cellArea(i: Int, contour: ShapeContour = cellPolygon(i)): Double {
+        val segments = contour.segments
+        var sum = 0.0
+        for (j in segments.indices) {
+            val v0 = segments[j].start
+            val v1 = segments[(j + 1).mod(segments.size)].start
+            sum += v0.x * v1.y - v1.x * v0.y
+        }
+        return sum / 2.0
+    }
+
+    fun cellCentroid(i: Int, contour: ShapeContour = cellPolygon(i)): Vector2 {
+        val segments = cellPolygon(i).segments
+        var cx = 0.0
+        var cy = 0.0
+        for (j in segments.indices) {
+            val v0 = segments[j].start
+            val v1 = segments[(j + 1).mod(segments.size)].start
+            cx += (v0.x + v1.x) * (v0.x * v1.y - v1.x * v0.y)
+            cy += (v0.y + v1.y) * (v0.x * v1.y - v1.x * v0.y)
+        }
+        val a = cellArea(i, contour) * 6.0
+        cx /= a
+        cy /= a
+        return Vector2(cx, cy)
+    }
+
+    fun cellCentroids() = (delaunayTriangulation.points.indices).map {
+        cellCentroid(it)
+    }
+
     fun cellPolygon(i: Int): ShapeContour {
         val points = voronoi.clip(i)