import afield.graphics.render.neural.*
import java.nio.file.Path

// Run as a child of the standard renderer box. Edit this path to use your own image.
val image = Path.of("public/docs/assets/opendlssnr/ink-input.png").toAbsolutePath()
prop<OpenDlssNrImage>(study)
val runScope = requireNotNull(RunScope.current()) { "Run this example in a Field box." }
val available = OpenDlssNrSupport.availability(renderer)
println(available.reason)
if (available.ready) {
    val ownedStudy = runScope.own(OpenDlssNrImage(renderer, image.toString()).install())
    study = ownedStudy
    var style = 1
    var tone = 0f
    var structure = 10f
    var skin = -1f
    var autoMask = false
    var intensity = 1f
    var red = 1f
    var green = 1f
    var blue = 1f
    controlGroup("neural image") {
        slider("Network style", min = 0.0, max = 2.0, step = 1.0, initialValue = 1.0) { style = it.toInt() }
        slider("Tone", min = -10.0, max = 30.0, initialValue = 0.0) { tone = it.toFloat() }
        slider("Structure", min = -10.0, max = 30.0, initialValue = 10.0) { structure = it.toFloat() }
        checkbox("Automatic skin mask", initialValue = false) { autoMask = it }
        slider("Skin (active with mask; -1 follows structure)", min = -1.0, max = 30.0, initialValue = -1.0) {
            skin = it.toFloat()
        }
        slider("NR intensity", min = 0.0, max = 1.0, initialValue = 1.0) { intensity = it.toFloat() }
        slider("Grade red", min = 0.0, max = 2.0, initialValue = red.toDouble()) { red = it.toFloat() }
        slider("Grade green", min = 0.0, max = 2.0, initialValue = green.toDouble()) { green = it.toFloat() }
        slider("Grade blue", min = 0.0, max = 2.0, initialValue = blue.toDouble()) { blue = it.toFloat() }
    }
    displayOpen("neural image")
    frames {
        try {
            while (true) {
                ownedStudy.controls = OpenDlssNrControls(
                    style = style,
                    tone = tone,
                    structure = structure,
                    skin = skin,
                    autoMask = autoMask,
                    intensity = intensity,
                )
                ownedStudy.exposure = Vector4f(red, green, blue, 1f)
                frame()
            }
        } finally {
            ownedStudy.close()
        }
    }
} else frames { println("Image study unavailable: ${available.reason}") }