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Generate convolution kernel at compile time
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| Original file line number | Diff line number | Diff line change |
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| [package] | ||
| name = "sond-has-macros" | ||
| version = "0.1.0" | ||
| edition = "2021" | ||
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| [lib] | ||
| proc-macro = true | ||
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| [dependencies] | ||
| proc-macro2 = "1" | ||
| syn = { version = "2", features = ["full"] } | ||
| quote = "1" | ||
| proc-macro-crate = "2" |
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| use proc_macro::TokenStream; | ||
| use proc_macro2::Ident; | ||
| use quote::quote; | ||
| use std::f64::consts::{PI, TAU}; | ||
| use syn::parse::{Parse, ParseStream}; | ||
| use syn::spanned::Spanned; | ||
| use syn::{ | ||
| braced, parse_macro_input, parse_quote, Error, Expr, ExprLit, FieldValue, Lit, LitInt, Member, | ||
| Path, Token, | ||
| }; | ||
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| /// Generates a kernel for 2-dimensional convolutional blurring of type `[[f64; SIZE]; SIZE]`. | ||
| /// This allows generating the coefficients at compile-time for any size of kernel. The sum of | ||
| /// the kernel is guaranteed to be as close to 1.0 as possible, so that blurring does not alter | ||
| /// unblurred values. | ||
| /// | ||
| /// ## Usage: | ||
| /// `convolution_kernel!(<KIND>, <SIZE>)` | ||
| /// | ||
| /// where `<KIND>` is of the following type: | ||
| /// ``` | ||
| /// enum KernelKind { | ||
| /// /// Mean blur | ||
| /// /// All cells have the same value: | ||
| /// /// `1.0 / (SIZE * SIZE)` | ||
| /// Mean, | ||
| /// /// Gaussian blur | ||
| /// /// Cells are generated by the circular Gaussian | ||
| /// /// function with the given `sigma` value. | ||
| /// Gaussian { | ||
| /// sigma: f64, | ||
| /// }, | ||
| /// /// Cosine blur | ||
| /// /// Cells are generated as a function of the | ||
| /// /// cosine of their distance from the center. | ||
| /// Cosine, | ||
| /// } | ||
| /// ``` | ||
| /// and `<SIZE>` is a `usize` | ||
| /// | ||
| /// # Example: | ||
| /// ``` | ||
| /// # fn main () { | ||
| /// # use sond_has_macros::convolution_kernel; | ||
| /// const KERNEL: [[f64; 7]; 7] = convolution_kernel!(Gaussian { sigma: 3.0 }, 7); | ||
| /// | ||
| /// let sum: f64 = KERNEL.iter().flatten().sum(); | ||
| /// let diff = (sum - 1.0).abs(); | ||
| /// assert!(diff < 1.0e-10); | ||
| /// # } | ||
| /// ``` | ||
| #[proc_macro] | ||
| pub fn convolution_kernel(input: TokenStream) -> TokenStream { | ||
| let input = parse_macro_input!(input as Kernel); | ||
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| let mut kernel = input.get_baseline_curve(); | ||
| let sum: f64 = kernel.iter().sum(); | ||
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| // Correct for inaccuracies to guarantee an integral of 1.0 | ||
| let scale = 1.0 / sum; | ||
| for cell in &mut kernel { | ||
| *cell *= scale | ||
| } | ||
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| let columns = kernel.chunks_exact(input.size).map(|chunk| { | ||
| let elements = chunk.iter(); | ||
| quote! { [ #(#elements,)* ] } | ||
| }); | ||
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| quote! { | ||
| [ #(#columns,)* ] | ||
| } | ||
| .into() | ||
| } | ||
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| #[derive(Copy, Clone)] | ||
| struct Kernel { | ||
| kind: KernelKind, | ||
| size: usize, | ||
| } | ||
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| #[derive(Copy, Clone)] | ||
| enum KernelKind { | ||
| Mean, | ||
| Gaussian { sigma: f64 }, | ||
| Cosine, | ||
| } | ||
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| impl Kernel { | ||
| fn get_baseline_curve(self) -> Vec<f64> { | ||
| match self.kind { | ||
| KernelKind::Mean => std::iter::repeat(1.0 / (self.size * self.size) as f64) | ||
| .take(self.size * self.size) | ||
| .collect(), | ||
| KernelKind::Gaussian { sigma } => { | ||
| let mut ret = Vec::with_capacity(self.size * self.size); | ||
| let r = (self.size - 1) as f64 * 0.5; | ||
| for x in 0..self.size { | ||
| for y in 0..self.size { | ||
| let x = x as f64 - r; | ||
| let y = y as f64 - r; | ||
| let s2 = sigma * sigma; | ||
| ret.push(1. / (TAU * s2) * (-(x * x + y * y) / (2. * s2)).exp()) | ||
| } | ||
| } | ||
| ret | ||
| } | ||
| KernelKind::Cosine => { | ||
| let mut ret = Vec::with_capacity(self.size * self.size); | ||
| let r = (self.size - 1) as f64 * 0.5; | ||
| for x in 0..self.size { | ||
| for y in 0..self.size { | ||
| let x = x as f64 - r; | ||
| let y = y as f64 - r; | ||
| let len = ((x * x) + (y * y)).sqrt(); | ||
| let t = f64::min(len / r, 1.0); | ||
| ret.push(((t * PI).cos() + 1.0) / (r * r * ((PI * PI) - 4.0) / PI)) | ||
| } | ||
| } | ||
| ret | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| impl Parse for Kernel { | ||
| fn parse(input: ParseStream) -> syn::Result<Self> { | ||
| use KernelKind::*; | ||
| let kind = input.parse::<Path>()?; | ||
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| let mean: Ident = parse_quote!(Mean); | ||
| let gaussian: Ident = parse_quote!(Gaussian); | ||
| let cosine: Ident = parse_quote!(Cosine); | ||
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| let kind = match &kind.segments.last().as_ref().unwrap().ident { | ||
| ident if ident == &mean => Mean, | ||
| ident if ident == &gaussian => { | ||
| let fields; | ||
| braced!(fields in input); | ||
| let fields_span = fields.span(); | ||
| let mut fields = fields | ||
| .parse_terminated(FieldValue::parse, Token![,])? | ||
| .into_iter(); | ||
| let Some(field) = fields.next() else { | ||
| return Err(Error::new(fields_span, "Expected field `sigma: f64`")); | ||
| }; | ||
| if let Some(field) = fields.next() { | ||
| return Err(Error::new( | ||
| field.span(), | ||
| "Wasn't expecting more than one field for Gaussian definition", | ||
| )); | ||
| } | ||
| let sigma_ident: Ident = parse_quote!(sigma); | ||
| match &field.member { | ||
| Member::Named(ident) if ident == &sigma_ident => {} | ||
| other => return Err(Error::new(other.span(), "Expected field `sigma: f64`")), | ||
| } | ||
| let Expr::Lit(ExprLit { | ||
| lit: Lit::Float(val), | ||
| .. | ||
| }) = &field.expr | ||
| else { | ||
| return Err(Error::new(field.expr.span(), "Expected a literal f64")); | ||
| }; | ||
| let sigma = val.base10_parse()?; | ||
| Gaussian { sigma } | ||
| } | ||
| ident if ident == &cosine => Cosine, | ||
| other => { | ||
| return Err(input.error(format!( | ||
| "Expected {}, got {other}", | ||
| std::any::type_name::<KernelKind>() | ||
| ))) | ||
| } | ||
| }; | ||
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| input.parse::<Token![,]>()?; | ||
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| let size = input.parse::<LitInt>()?.base10_parse()?; | ||
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| Ok(Self { kind, size }) | ||
| } | ||
| } |