node-graph/graster-nodes/src/std_nodes.rs

use crate::adjustments::{CellularDistanceFunction, CellularReturnType, DomainWarpType, FractalType, NoiseType};
use dyn_any::DynAny;
use fastnoise_lite;
use glam::{DAffine2, DVec2, Vec2};
use graphene_core::blending::AlphaBlending;
use graphene_core::color::Color;
use graphene_core::color::{Alpha, AlphaMut, Channel, LinearChannel, Luminance, RGBMut};
use graphene_core::context::{Ctx, ExtractFootprint};
use graphene_core::instances::Instance;
use graphene_core::math::bbox::Bbox;
use graphene_core::raster::image::Image;
use graphene_core::raster::{Bitmap, BitmapMut};
use graphene_core::raster_types::{CPU, Raster, RasterDataTable};
use graphene_core::transform::Transform;
use rand::prelude::*;
use rand_chacha::ChaCha8Rng;
use std::fmt::Debug;
use std::hash::Hash;

#[derive(Debug, DynAny)]
pub enum Error {
	IO(std::io::Error),
	Image(::image::ImageError),
}

impl From<std::io::Error> for Error {
	fn from(e: std::io::Error) -> Self {
		Error::IO(e)
	}
}

#[node_macro::node(category("Debug: Raster"))]
pub fn sample_image(ctx: impl ExtractFootprint + Clone + Send, image_frame: RasterDataTable<CPU>) -> RasterDataTable<CPU> {
	let mut result_table = RasterDataTable::default();

	for mut image_frame_instance in image_frame.instance_iter() {
		let image_frame_transform = image_frame_instance.transform;
		let image = image_frame_instance.instance;

		// Resize the image using the image crate
		let data = bytemuck::cast_vec(image.data.clone());

		let footprint = ctx.footprint();
		let viewport_bounds = footprint.viewport_bounds_in_local_space();
		let image_bounds = Bbox::from_transform(image_frame_transform).to_axis_aligned_bbox();
		let intersection = viewport_bounds.intersect(&image_bounds);
		let image_size = DAffine2::from_scale(DVec2::new(image.width as f64, image.height as f64));
		let size = intersection.size();
		let size_px = image_size.transform_vector2(size).as_uvec2();

		// If the image would not be visible, add nothing.
		if size.x <= 0. || size.y <= 0. {
			continue;
		}

		let image_buffer = ::image::Rgba32FImage::from_raw(image.width, image.height, data).expect("Failed to convert internal image format into image-rs data type.");

		let dynamic_image: ::image::DynamicImage = image_buffer.into();
		let offset = (intersection.start - image_bounds.start).max(DVec2::ZERO);
		let offset_px = image_size.transform_vector2(offset).as_uvec2();
		let cropped = dynamic_image.crop_imm(offset_px.x, offset_px.y, size_px.x, size_px.y);

		let viewport_resolution_x = footprint.transform.transform_vector2(DVec2::X * size.x).length();
		let viewport_resolution_y = footprint.transform.transform_vector2(DVec2::Y * size.y).length();
		let mut new_width = size_px.x;
		let mut new_height = size_px.y;

		// Only downscale the image for now
		let resized = if new_width < image.width || new_height < image.height {
			new_width = viewport_resolution_x as u32;
			new_height = viewport_resolution_y as u32;
			// TODO: choose filter based on quality requirements
			cropped.resize_exact(new_width, new_height, ::image::imageops::Triangle)
		} else {
			cropped
		};
		let buffer = resized.to_rgba32f();
		let buffer = buffer.into_raw();
		let vec = bytemuck::cast_vec(buffer);
		let image = Image {
			width: new_width,
			height: new_height,
			data: vec,
			base64_string: None,
		};
		// we need to adjust the offset if we truncate the offset calculation

		let new_transform = image_frame_transform * DAffine2::from_translation(offset) * DAffine2::from_scale(size);

		image_frame_instance.transform = new_transform;
		image_frame_instance.source_node_id = None;
		image_frame_instance.instance = Raster::new_cpu(image);
		result_table.push(image_frame_instance)
	}

	result_table
}

#[node_macro::node(category("Raster: Channels"))]
pub fn combine_channels(
	_: impl Ctx,
	_primary: (),
	#[expose] red: RasterDataTable<CPU>,
	#[expose] green: RasterDataTable<CPU>,
	#[expose] blue: RasterDataTable<CPU>,
	#[expose] alpha: RasterDataTable<CPU>,
) -> RasterDataTable<CPU> {
	let mut result_table = RasterDataTable::default();

	let max_len = red.len().max(green.len()).max(blue.len()).max(alpha.len());
	let red = red.instance_iter().map(Some).chain(std::iter::repeat(None)).take(max_len);
	let green = green.instance_iter().map(Some).chain(std::iter::repeat(None)).take(max_len);
	let blue = blue.instance_iter().map(Some).chain(std::iter::repeat(None)).take(max_len);
	let alpha = alpha.instance_iter().map(Some).chain(std::iter::repeat(None)).take(max_len);

	for (((red, green), blue), alpha) in red.zip(green).zip(blue).zip(alpha) {
		// Turn any default zero-sized image instances into None
		let red = red.filter(|i| i.instance.width > 0 && i.instance.height > 0);
		let green = green.filter(|i| i.instance.width > 0 && i.instance.height > 0);
		let blue = blue.filter(|i| i.instance.width > 0 && i.instance.height > 0);
		let alpha = alpha.filter(|i| i.instance.width > 0 && i.instance.height > 0);

		// Get this instance's transform and alpha blending mode from the first non-empty channel
		let Some((transform, alpha_blending)) = [&red, &green, &blue, &alpha].iter().find_map(|i| i.as_ref()).map(|i| (i.transform, i.alpha_blending)) else {
			continue;
		};

		// Get the common width and height of the channels, which must have equal dimensions
		let channel_dimensions = [
			red.as_ref().map(|r| (r.instance.width, r.instance.height)),
			green.as_ref().map(|g| (g.instance.width, g.instance.height)),
			blue.as_ref().map(|b| (b.instance.width, b.instance.height)),
			alpha.as_ref().map(|a| (a.instance.width, a.instance.height)),
		];
		if channel_dimensions.iter().all(Option::is_none)
			|| channel_dimensions
				.iter()
				.flatten()
				.any(|&(x, y)| channel_dimensions.iter().flatten().any(|&(other_x, other_y)| x != other_x || y != other_y))
		{
			continue;
		}
		let Some(&(width, height)) = channel_dimensions.iter().flatten().next() else { continue };

		// Create a new image for this instance output
		let mut image = Image::new(width, height, Color::TRANSPARENT);

		// Iterate over all pixels in the image and set the color channels
		for y in 0..image.height() {
			for x in 0..image.width() {
				let image_pixel = image.get_pixel_mut(x, y).unwrap();

				if let Some(r) = red.as_ref().and_then(|r| r.instance.get_pixel(x, y)) {
					image_pixel.set_red(r.l().cast_linear_channel());
				} else {
					image_pixel.set_red(Channel::from_linear(0.));
				}
				if let Some(g) = green.as_ref().and_then(|g| g.instance.get_pixel(x, y)) {
					image_pixel.set_green(g.l().cast_linear_channel());
				} else {
					image_pixel.set_green(Channel::from_linear(0.));
				}
				if let Some(b) = blue.as_ref().and_then(|b| b.instance.get_pixel(x, y)) {
					image_pixel.set_blue(b.l().cast_linear_channel());
				} else {
					image_pixel.set_blue(Channel::from_linear(0.));
				}
				if let Some(a) = alpha.as_ref().and_then(|a| a.instance.get_pixel(x, y)) {
					image_pixel.set_alpha(a.l().cast_linear_channel());
				} else {
					image_pixel.set_alpha(Channel::from_linear(1.));
				}
			}
		}

		// Add this instance to the result table
		result_table.push(Instance {
			instance: Raster::new_cpu(image),
			transform,
			alpha_blending,
			source_node_id: None,
		});
	}

	result_table
}

#[node_macro::node(category("Raster"))]
pub fn mask(
	_: impl Ctx,
	/// The image to be masked.
	image: RasterDataTable<CPU>,
	/// The stencil to be used for masking.
	#[expose]
	stencil: RasterDataTable<CPU>,
) -> RasterDataTable<CPU> {
	// TODO: Support multiple stencil instances
	let Some(stencil_instance) = stencil.instance_iter().next() else {
		// No stencil provided so we return the original image
		return image;
	};
	let stencil_size = DVec2::new(stencil_instance.instance.width as f64, stencil_instance.instance.height as f64);

	let mut result_table = RasterDataTable::default();

	for mut image_instance in image.instance_iter() {
		let image_size = DVec2::new(image_instance.instance.width as f64, image_instance.instance.height as f64);
		let mask_size = stencil_instance.transform.decompose_scale();

		if mask_size == DVec2::ZERO {
			continue;
		}

		// Transforms a point from the background image to the foreground image
		let bg_to_fg = image_instance.transform * DAffine2::from_scale(1. / image_size);
		let stencil_transform_inverse = stencil_instance.transform.inverse();

		for y in 0..image_instance.instance.height {
			for x in 0..image_instance.instance.width {
				let image_point = DVec2::new(x as f64, y as f64);
				let mask_point = bg_to_fg.transform_point2(image_point);
				let local_mask_point = stencil_transform_inverse.transform_point2(mask_point);
				let mask_point = stencil_instance.transform.transform_point2(local_mask_point.clamp(DVec2::ZERO, DVec2::ONE));
				let mask_point = (DAffine2::from_scale(stencil_size) * stencil_instance.transform.inverse()).transform_point2(mask_point);

				let image_pixel = image_instance.instance.data_mut().get_pixel_mut(x, y).unwrap();
				let mask_pixel = stencil_instance.instance.sample(mask_point);
				*image_pixel = image_pixel.multiplied_alpha(mask_pixel.l().cast_linear_channel());
			}
		}

		result_table.push(image_instance);
	}

	result_table
}

#[node_macro::node(category(""))]
pub fn extend_image_to_bounds(_: impl Ctx, image: RasterDataTable<CPU>, bounds: DAffine2) -> RasterDataTable<CPU> {
	let mut result_table = RasterDataTable::default();

	for mut image_instance in image.instance_iter() {
		let image_aabb = Bbox::unit().affine_transform(image_instance.transform).to_axis_aligned_bbox();
		let bounds_aabb = Bbox::unit().affine_transform(bounds.transform()).to_axis_aligned_bbox();
		if image_aabb.contains(bounds_aabb.start) && image_aabb.contains(bounds_aabb.end) {
			result_table.push(image_instance);
			continue;
		}

		let image_data = &image_instance.instance.data;
		let (image_width, image_height) = (image_instance.instance.width, image_instance.instance.height);
		if image_width == 0 || image_height == 0 {
			for image_instance in empty_image((), bounds, Color::TRANSPARENT).instance_iter() {
				result_table.push(image_instance);
			}
			continue;
		}

		let orig_image_scale = DVec2::new(image_width as f64, image_height as f64);
		let layer_to_image_space = DAffine2::from_scale(orig_image_scale) * image_instance.transform.inverse();
		let bounds_in_image_space = Bbox::unit().affine_transform(layer_to_image_space * bounds).to_axis_aligned_bbox();

		let new_start = bounds_in_image_space.start.floor().min(DVec2::ZERO);
		let new_end = bounds_in_image_space.end.ceil().max(orig_image_scale);
		let new_scale = new_end - new_start;

		// Copy over original image into enlarged image.
		let mut new_image = Image::new(new_scale.x as u32, new_scale.y as u32, Color::TRANSPARENT);
		let offset_in_new_image = (-new_start).as_uvec2();
		for y in 0..image_height {
			let old_start = y * image_width;
			let new_start = (y + offset_in_new_image.y) * new_image.width + offset_in_new_image.x;
			let old_row = &image_data[old_start as usize..(old_start + image_width) as usize];
			let new_row = &mut new_image.data[new_start as usize..(new_start + image_width) as usize];
			new_row.copy_from_slice(old_row);
		}

		// Compute new transform.
		// let layer_to_new_texture_space = (DAffine2::from_scale(1. / new_scale) * DAffine2::from_translation(new_start) * layer_to_image_space).inverse();
		let new_texture_to_layer_space = image_instance.transform * DAffine2::from_scale(1. / orig_image_scale) * DAffine2::from_translation(new_start) * DAffine2::from_scale(new_scale);

		image_instance.instance = Raster::new_cpu(new_image);
		image_instance.transform = new_texture_to_layer_space;
		image_instance.source_node_id = None;
		result_table.push(image_instance);
	}

	result_table
}

#[node_macro::node(category("Debug: Raster"))]
pub fn empty_image(_: impl Ctx, transform: DAffine2, color: Color) -> RasterDataTable<CPU> {
	let width = transform.transform_vector2(DVec2::new(1., 0.)).length() as u32;
	let height = transform.transform_vector2(DVec2::new(0., 1.)).length() as u32;

	let image = Image::new(width, height, color);

	let mut result_table = RasterDataTable::new(Raster::new_cpu(image));
	let image_instance = result_table.get_mut(0).unwrap();
	*image_instance.transform = transform;
	*image_instance.alpha_blending = AlphaBlending::default();

	// Callers of empty_image can safely unwrap on returned table
	result_table
}

/// Constructs a raster image.
#[node_macro::node(category(""))]
pub fn image_value(_: impl Ctx, _primary: (), image: RasterDataTable<CPU>) -> RasterDataTable<CPU> {
	image
}

#[node_macro::node(category("Raster: Pattern"))]
#[allow(clippy::too_many_arguments)]
pub fn noise_pattern(
	ctx: impl ExtractFootprint + Ctx,
	_primary: (),
	clip: bool,
	seed: u32,
	scale: f64,
	noise_type: NoiseType,
	domain_warp_type: DomainWarpType,
	domain_warp_amplitude: f64,
	fractal_type: FractalType,
	fractal_octaves: u32,
	fractal_lacunarity: f64,
	fractal_gain: f64,
	fractal_weighted_strength: f64,
	fractal_ping_pong_strength: f64,
	cellular_distance_function: CellularDistanceFunction,
	cellular_return_type: CellularReturnType,
	cellular_jitter: f64,
) -> RasterDataTable<CPU> {
	let footprint = ctx.footprint();
	let viewport_bounds = footprint.viewport_bounds_in_local_space();

	let mut size = viewport_bounds.size();
	let mut offset = viewport_bounds.start;
	if clip {
		// TODO: Remove "clip" entirely (and its arbitrary 100x100 clipping square) once we have proper resolution-aware layer clipping
		const CLIPPING_SQUARE_SIZE: f64 = 100.;
		let image_bounds = Bbox::from_transform(DAffine2::from_scale(DVec2::splat(CLIPPING_SQUARE_SIZE))).to_axis_aligned_bbox();
		let intersection = viewport_bounds.intersect(&image_bounds);

		offset = (intersection.start - image_bounds.start).max(DVec2::ZERO);
		size = intersection.size();
	}

	// If the image would not be visible, return an empty image
	if size.x <= 0. || size.y <= 0. {
		return RasterDataTable::default();
	}

	let footprint_scale = footprint.scale();
	let width = (size.x * footprint_scale.x) as u32;
	let height = (size.y * footprint_scale.y) as u32;

	// All
	let mut image = Image::new(width, height, Color::from_luminance(0.5));
	let mut noise = fastnoise_lite::FastNoiseLite::with_seed(seed as i32);
	noise.set_frequency(Some(1. / (scale as f32).max(f32::EPSILON)));

	// Domain Warp
	let domain_warp_type = match domain_warp_type {
		DomainWarpType::None => None,
		DomainWarpType::OpenSimplex2 => Some(fastnoise_lite::DomainWarpType::OpenSimplex2),
		DomainWarpType::OpenSimplex2Reduced => Some(fastnoise_lite::DomainWarpType::OpenSimplex2Reduced),
		DomainWarpType::BasicGrid => Some(fastnoise_lite::DomainWarpType::BasicGrid),
	};
	let domain_warp_active = domain_warp_type.is_some();
	noise.set_domain_warp_type(domain_warp_type);
	noise.set_domain_warp_amp(Some(domain_warp_amplitude as f32));

	// Fractal
	let noise_type = match noise_type {
		NoiseType::Perlin => fastnoise_lite::NoiseType::Perlin,
		NoiseType::OpenSimplex2 => fastnoise_lite::NoiseType::OpenSimplex2,
		NoiseType::OpenSimplex2S => fastnoise_lite::NoiseType::OpenSimplex2S,
		NoiseType::Cellular => fastnoise_lite::NoiseType::Cellular,
		NoiseType::ValueCubic => fastnoise_lite::NoiseType::ValueCubic,
		NoiseType::Value => fastnoise_lite::NoiseType::Value,
		NoiseType::WhiteNoise => {
			// TODO: Generate in layer space, not viewport space

			let mut rng = ChaCha8Rng::seed_from_u64(seed as u64);

			for y in 0..height {
				for x in 0..width {
					let pixel = image.get_pixel_mut(x, y).unwrap();
					let luminance = rng.random_range(0.0..1.) as f32;
					*pixel = Color::from_luminance(luminance);
				}
			}

			let mut result = RasterDataTable::default();
			result.push(Instance {
				instance: Raster::new_cpu(image),
				transform: DAffine2::from_translation(offset) * DAffine2::from_scale(size),
				..Default::default()
			});

			return result;
		}
	};
	noise.set_noise_type(Some(noise_type));
	let fractal_type = match fractal_type {
		FractalType::None => fastnoise_lite::FractalType::None,
		FractalType::FBm => fastnoise_lite::FractalType::FBm,
		FractalType::Ridged => fastnoise_lite::FractalType::Ridged,
		FractalType::PingPong => fastnoise_lite::FractalType::PingPong,
		FractalType::DomainWarpProgressive => fastnoise_lite::FractalType::DomainWarpProgressive,
		FractalType::DomainWarpIndependent => fastnoise_lite::FractalType::DomainWarpIndependent,
	};
	noise.set_fractal_type(Some(fractal_type));
	noise.set_fractal_octaves(Some(fractal_octaves as i32));
	noise.set_fractal_lacunarity(Some(fractal_lacunarity as f32));
	noise.set_fractal_gain(Some(fractal_gain as f32));
	noise.set_fractal_weighted_strength(Some(fractal_weighted_strength as f32));
	noise.set_fractal_ping_pong_strength(Some(fractal_ping_pong_strength as f32));

	// Cellular
	let cellular_distance_function = match cellular_distance_function {
		CellularDistanceFunction::Euclidean => fastnoise_lite::CellularDistanceFunction::Euclidean,
		CellularDistanceFunction::EuclideanSq => fastnoise_lite::CellularDistanceFunction::EuclideanSq,
		CellularDistanceFunction::Manhattan => fastnoise_lite::CellularDistanceFunction::Manhattan,
		CellularDistanceFunction::Hybrid => fastnoise_lite::CellularDistanceFunction::Hybrid,
	};
	let cellular_return_type = match cellular_return_type {
		CellularReturnType::CellValue => fastnoise_lite::CellularReturnType::CellValue,
		CellularReturnType::Nearest => fastnoise_lite::CellularReturnType::Distance,
		CellularReturnType::NextNearest => fastnoise_lite::CellularReturnType::Distance2,
		CellularReturnType::Average => fastnoise_lite::CellularReturnType::Distance2Add,
		CellularReturnType::Difference => fastnoise_lite::CellularReturnType::Distance2Sub,
		CellularReturnType::Product => fastnoise_lite::CellularReturnType::Distance2Mul,
		CellularReturnType::Division => fastnoise_lite::CellularReturnType::Distance2Div,
	};
	noise.set_cellular_distance_function(Some(cellular_distance_function));
	noise.set_cellular_return_type(Some(cellular_return_type));
	noise.set_cellular_jitter(Some(cellular_jitter as f32));

	let coordinate_offset = offset.as_vec2();
	let scale = size.as_vec2() / Vec2::new(width as f32, height as f32);
	// Calculate the noise for every pixel
	for y in 0..height {
		for x in 0..width {
			let pixel = image.get_pixel_mut(x, y).unwrap();
			let pos = Vec2::new(x as f32, y as f32);
			let vec = pos * scale + coordinate_offset;

			let (mut x, mut y) = (vec.x, vec.y);
			if domain_warp_active && domain_warp_amplitude > 0. {
				(x, y) = noise.domain_warp_2d(x, y);
			}

			let luminance = (noise.get_noise_2d(x, y) + 1.) * 0.5;
			*pixel = Color::from_luminance(luminance);
		}
	}

	let mut result = RasterDataTable::default();
	result.push(Instance {
		instance: Raster::new_cpu(image),
		transform: DAffine2::from_translation(offset) * DAffine2::from_scale(size),
		..Default::default()
	});

	result
}

#[node_macro::node(category("Raster: Pattern"))]
pub fn mandelbrot(ctx: impl ExtractFootprint + Send) -> RasterDataTable<CPU> {
	let footprint = ctx.footprint();
	let viewport_bounds = footprint.viewport_bounds_in_local_space();

	let image_bounds = Bbox::from_transform(DAffine2::IDENTITY).to_axis_aligned_bbox();
	let intersection = viewport_bounds.intersect(&image_bounds);
	let size = intersection.size();

	let offset = (intersection.start - image_bounds.start).max(DVec2::ZERO);

	// If the image would not be visible, return an empty image
	if size.x <= 0. || size.y <= 0. {
		return RasterDataTable::default();
	}

	let scale = footprint.scale();
	let width = (size.x * scale.x) as u32;
	let height = (size.y * scale.y) as u32;

	let mut data = Vec::with_capacity(width as usize * height as usize);
	let max_iter = 255;

	let scale = 3. * size.as_vec2() / Vec2::new(width as f32, height as f32);
	let coordinate_offset = offset.as_vec2() * 3. - Vec2::new(2., 1.5);
	for y in 0..height {
		for x in 0..width {
			let pos = Vec2::new(x as f32, y as f32);
			let c = pos * scale + coordinate_offset;

			let iter = mandelbrot_impl(c, max_iter);
			data.push(map_color(iter, max_iter));
		}
	}

	let image = Image {
		width,
		height,
		data,
		..Default::default()
	};
	let mut result = RasterDataTable::default();
	result.push(Instance {
		instance: Raster::new_cpu(image),
		transform: DAffine2::from_translation(offset) * DAffine2::from_scale(size),
		..Default::default()
	});

	result
}

#[inline(always)]
fn mandelbrot_impl(c: Vec2, max_iter: usize) -> usize {
	let mut z = Vec2::new(0., 0.);
	for i in 0..max_iter {
		z = Vec2::new(z.x * z.x - z.y * z.y, 2. * z.x * z.y) + c;
		if z.length_squared() > 4. {
			return i;
		}
	}
	max_iter
}

fn map_color(iter: usize, max_iter: usize) -> Color {
	let v = iter as f32 / max_iter as f32;
	Color::from_rgbaf32_unchecked(v, v, v, 1.)
}