[orx-fft] Add orx-fft for a simple fast fourier transform routine
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109
orx-fft/src/jvmDemo/kotlin/DemoFFTShape01.kt
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109
orx-fft/src/jvmDemo/kotlin/DemoFFTShape01.kt
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import org.openrndr.application
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import org.openrndr.color.ColorRGBa
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import org.openrndr.extra.fft.FFT
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import org.openrndr.extra.noise.scatter
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import org.openrndr.extra.shapes.hobbycurve.hobbyCurve
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import org.openrndr.math.Vector2
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import org.openrndr.extra.shapes.splines.catmullRom
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import org.openrndr.extra.shapes.splines.toContour
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import org.openrndr.math.smoothstep
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import org.openrndr.math.transforms.buildTransform
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import kotlin.math.*
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import kotlin.random.Random
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/**
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* Demonstration of using FFT to filter a two-dimensional shape. Mouse xy-position is mapped
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* to lowpass and highpass settings of the filter.
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*/
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fun main() {
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application {
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configure {
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width = 720
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height = 720
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}
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program {
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val fftSize = 512
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val fft = FFT(fftSize)
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fun List<Vector2>.toFloatArrays(x: FloatArray, y: FloatArray) {
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for ((index, segment) in this.withIndex()) {
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x[index] = segment.x.toFloat()
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y[index] = segment.y.toFloat()
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}
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}
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fun vectorsFromFloatArrays(x: FloatArray, y: FloatArray): List<Vector2> {
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val n = x.size
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val result = mutableListOf<Vector2>()
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for (i in 0 until n) {
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result.add(Vector2(x[i].toDouble(), y[i].toDouble()))
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}
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return result
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}
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fun lp(t: Double, c: Double): Double {
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return smoothstep(c, c - 0.1, t)
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}
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fun hp(t: Double, c: Double): Double {
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return smoothstep(c, c + 0.1, t)
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}
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val c = hobbyCurve(
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drawer.bounds.scatter(40.0, distanceToEdge = 100.0, random = Random(0)),
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true
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).transform(buildTransform { translate(-drawer.bounds.center) })
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val x = FloatArray(fftSize)
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val y = FloatArray(fftSize)
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val xFiltered = FloatArray(fftSize)
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val yFiltered = FloatArray(fftSize)
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extend {
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c.equidistantPositions(fftSize).take(fftSize).toFloatArrays(x, y)
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// process x-component
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fft.forward(x)
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val xpower = fft.magnitudeSum()
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val lpc = mouse.position.x / width
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val hpc = mouse.position.y / height
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for (i in 1..fftSize / 2) {
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val t = i.toDouble() / (fftSize / 2 - 1)
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val f = max(lp(t, lpc), hp(t, hpc))
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fft.scaleBand(i, f.toFloat())
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}
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val xfpower = fft.magnitudeSum().coerceAtLeast(1.0)
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fft.scaleAll((xpower / xfpower).toFloat())
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fft.inverse(xFiltered)
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// process y-component
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fft.forward(y)
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val ypower = fft.magnitudeSum()
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for (i in 1..fftSize / 2) {
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val t = i.toDouble() / (fftSize / 2 - 1)
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val f = max(lp(t, lpc), hp(t, hpc))
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fft.scaleBand(i, f.toFloat())
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}
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val yfpower = fft.magnitudeSum().coerceAtLeast(1.0)
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fft.scaleAll((ypower / yfpower).toFloat())
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fft.inverse(yFiltered)
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val cr = vectorsFromFloatArrays(xFiltered, yFiltered).catmullRom(closed = true).toContour()
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//val cr = ShapeContour.fromPoints(vectorsFromFloatArrays(xr, yr), closed=true)
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val recenteredShape = cr.transform(buildTransform {
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translate(drawer.bounds.center)
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})
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drawer.fill = null
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drawer.stroke = ColorRGBa.WHITE
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drawer.contour(recenteredShape)
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}
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}
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}
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}
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