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[orx-hash-grid] Add generated and verified documentation, improve DemoHashGrid01.kt

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This commit is contained in:
Edwin Jakobs
2025-01-24 18:54:39 +01:00
parent 3442c95208
commit a1b4070b51
5 changed files with 216 additions and 5 deletions
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7
orx-hash-grid/src/jvmDemo/kotlin/DemoFilter01.kt
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@@ -3,6 +3,13 @@ import org.openrndr.extra.hashgrid.filter
import org.openrndr.extra.noise.shapes.uniform
import kotlin.random.Random
/** A demo to generate and display filtered random points.
*
* The program performs the following steps:
* - Generates 10,000 random points uniformly distributed within the drawable bounds.
* - Filters the generated points to enforce a minimum distance of 20.0 units between them.
* - Visualizes the filtered points as circles with a radius of 10.0 units on the canvas.
*/
fun main() {
application {
configure {

10
orx-hash-grid/src/jvmDemo/kotlin/DemoFilter3D01.kt
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@@ -10,6 +10,16 @@ import org.openrndr.extra.noise.uniformRing
import org.openrndr.math.Vector3
import kotlin.random.Random
/**
* This demo sets up and renders a 3D visualization of filtered random points displayed as small spheres.
*
* The program performs the following key steps:
* - Generates 10,000 random 3D points within a ring defined by a minimum and maximum radius.
* - Filters the points to ensure a minimum distance between any two points using a spatial hash grid.
* - Creates a small sphere mesh that will be instanced for each filtered point.
* - Sets up an orbital camera to allow viewing the 3D scene interactively.
* - Renders the filtered points by translating the sphere mesh to each point's position and applying a shader that modifies the fragment color based on the view normal.
*/
fun main() = application {
configure {
width = 720

26
orx-hash-grid/src/jvmDemo/kotlin/DemoHashGrid01.kt
View File

@@ -4,6 +4,15 @@ import org.openrndr.extra.hashgrid.HashGrid
import org.openrndr.extra.noise.shapes.uniform
import kotlin.random.Random
/**
* This demo sets up an interactive graphics application with a configurable
* display window and visualization logic. It uses a `HashGrid` to manage points
* in a 2D space and randomly generates points within the drawable area. These
* points are then inserted into the grid if they satisfy certain spatial conditions.
* The visual output includes:
* - Rectangles representing the bounds of the cells in the grid.
* - Circles representing the generated points.
*/
fun main() {
application {
configure {
@@ -12,16 +21,23 @@ fun main() {
}
program {
val r = Random(0)
val hashGrid = HashGrid(20.0)
val hashGrid = HashGrid(72.0)
extend {
val p = drawer.bounds.uniform(random = r)
if (hashGrid.isFree(p)) {
hashGrid.insert(p)
for (i in 0 until 100) {
val p = drawer.bounds.uniform(random = r)
if (hashGrid.isFree(p)) {
hashGrid.insert(p)
}
}
drawer.circles(hashGrid.points().map { it.first }.toList(), 4.0)
drawer.fill = null
drawer.stroke = ColorRGBa.WHITE
drawer.rectangles(hashGrid.cells().map { it.bounds }.toList())
drawer.fill = null
drawer.stroke = ColorRGBa.PINK
drawer.circles(hashGrid.points().map { it.first }.toList(), 36.0)
}
}
}