Estimate a log-linear formula for the extracted transfer function LUT.
This commit is contained in:
parent
22db31fe5e
commit
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40
Cargo.lock
generated
40
Cargo.lock
generated
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@ -211,6 +211,7 @@ dependencies = [
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"clap",
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"colorbox",
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"exr",
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"simplers_optimization",
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]
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[[package]]
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@ -228,6 +229,15 @@ dependencies = [
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"getrandom",
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]
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[[package]]
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name = "num-traits"
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version = "0.2.14"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "9a64b1ec5cda2586e284722486d802acf1f7dbdc623e2bfc57e65ca1cd099290"
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dependencies = [
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"autocfg",
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]
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[[package]]
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name = "num_cpus"
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version = "1.13.1"
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@ -238,6 +248,15 @@ dependencies = [
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"libc",
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]
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[[package]]
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name = "ordered-float"
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version = "2.10.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "7940cf2ca942593318d07fcf2596cdca60a85c9e7fab408a5e21a4f9dcd40d87"
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dependencies = [
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"num-traits",
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]
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[[package]]
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name = "os_str_bytes"
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version = "6.0.0"
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@ -267,6 +286,16 @@ dependencies = [
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"syn",
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]
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[[package]]
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name = "priority-queue"
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version = "1.2.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "00ba480ac08d3cfc40dea10fd466fd2c14dee3ea6fc7873bc4079eda2727caf0"
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dependencies = [
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"autocfg",
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"indexmap",
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]
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[[package]]
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name = "proc-macro2"
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version = "1.0.37"
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@ -291,6 +320,17 @@ version = "1.1.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "d29ab0c6d3fc0ee92fe66e2d99f700eab17a8d57d1c1d3b748380fb20baa78cd"
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[[package]]
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name = "simplers_optimization"
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version = "0.4.3"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "2cd97912bb2a16575a2c632c2a2f2bac8a527827ceaddd73e0ccc12d86adec43"
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dependencies = [
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"num-traits",
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"ordered-float",
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"priority-queue",
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]
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[[package]]
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name = "smallvec"
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version = "1.8.0"
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@ -7,3 +7,4 @@ edition = "2021"
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exr = "1.4.1"
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clap = { version = "3.1.8", default-features = false, features=["std"] }
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colorbox = { git = "https://github.com/cessen/colorbox", branch = "master" }
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simplers_optimization = "0.4.3"
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107
src/linear_log.rs
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107
src/linear_log.rs
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@ -0,0 +1,107 @@
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/// A composite linear-log function.
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///
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/// `slope` is the slope of the linear segment.
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/// `base` is the log base.
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/// The offsets shift the linear and log parts of the curve along
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/// the linear color axis.
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pub fn linear_to_log(x: f64, line_offset: f64, slope: f64, log_offset: f64, base: f64) -> f64 {
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// Transition point between log and linear.
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let transition = 1.0 / (slope * base.ln());
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let k = transition + log_offset;
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let l = (transition - line_offset + log_offset) * slope - transition.log(base);
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if x <= k {
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(x - line_offset) * slope
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} else {
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(x - log_offset).log(base) + l
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}
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}
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/// A composite linear-log function.
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///
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/// `slope` is the slope of the linear segment.
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/// `base` is the log base.
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/// The offsets shift the linear and log parts of the curve along
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/// the linear color axis.
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pub fn log_to_linear(x: f64, line_offset: f64, slope: f64, log_offset: f64, base: f64) -> f64 {
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// Transition point between log and linear.
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let transition = 1.0 / (slope * base.ln());
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let k = (transition - line_offset + log_offset) * slope;
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let l = (transition - line_offset + log_offset) * slope - transition.log(base);
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if x <= k {
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(x / slope) + line_offset
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} else {
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base.powf(x - l) + log_offset
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}
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}
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//-------------------------------------------------------------
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/// Generates Rust code for a linear-to-log transfer function with the
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/// given parameters.
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pub fn generate_linear_to_log(line_offset: f64, slope: f64, log_offset: f64, base: f64) -> String {
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let transition = 1.0 / (slope * base.ln());
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let k = transition + log_offset;
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let l = (transition - line_offset + log_offset) * slope - transition.log(base);
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format!(
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r#"
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pub fn linear_to_log(x: f32) -> f32 {{
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const A: f32 = {};
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const B: f32 = {};
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const C: f32 = {};
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const D: f32 = {};
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const E: f32 = {};
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const F: f32 = {};
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if x <= A {{
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(x - B) * C
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}} else {{
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(x - D).log2() * (1.0 / E) + F
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}}
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}}
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"#,
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k,
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line_offset,
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slope,
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log_offset,
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base.log2(),
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l,
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)
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}
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/// Generates Rust code for a log-to-linear transfer function with the
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/// given parameters.
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pub fn generate_log_to_linear(line_offset: f64, slope: f64, log_offset: f64, base: f64) -> String {
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let transition = 1.0 / (slope * base.ln());
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let k = (transition - line_offset + log_offset) * slope;
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let l = (transition - line_offset + log_offset) * slope - transition.log(base);
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format!(
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r#"
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pub fn log_to_linear(x: f32) -> f32 {{
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const A: f32 = {};
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const B: f32 = {};
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const C: f32 = {};
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const D: f32 = {};
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const E: f32 = {};
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const F: f32 = {};
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if x <= A {{
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(x * (1.0 / C)) + B
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}} else {{
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((x - F) * E).exp2() + D
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}}
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}}
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"#,
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k,
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line_offset,
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slope,
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log_offset,
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base.log2(),
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l,
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)
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}
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@ -1,9 +1,11 @@
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mod linear_log;
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mod optimize_log;
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mod test_image;
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use std::{fs::File, io::BufWriter, path::Path};
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use clap::{Arg, Command};
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use colorbox::transfer_functions::srgb;
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// use colorbox::transfer_functions::srgb;
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use test_image::{GRADIENT_LEN, RES_X, RES_Y};
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@ -46,7 +48,7 @@ fn main() {
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} else {
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let input_path = args.value_of("input").unwrap();
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let base_image = test_image::build();
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// let base_image = test_image::build();
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let mut input_image = {
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let (image, res_x, res_y) = read_rgb_exr(input_path).unwrap();
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assert_eq!(
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@ -87,6 +89,8 @@ fn main() {
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gray_b.push(rgb[2]);
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}
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optimize_log::find_parameters(&gray_r);
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// Write the LUT.
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colorbox::formats::cube::write_1d(
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BufWriter::new(File::create("test.cube").unwrap()),
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57
src/optimize_log.rs
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57
src/optimize_log.rs
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@ -0,0 +1,57 @@
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use crate::linear_log::log_to_linear as log_to_lin;
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pub fn find_parameters(lut: &[f32]) {
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let lin_norm = 1.0 / (lut.len() - 1) as f64;
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// Compute the stuff that we can without estimation.
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let offset = lut[0] as f64;
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let slope = lin_norm / (lut[1] as f64 - lut[0] as f64);
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// Select a range of points from the lookup table to fit to.
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let idxs: Vec<_> = (0..lut.len()).step_by(lut.len() / 256).collect();
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let coords: Vec<(f64, f64)> = idxs
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.iter()
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.map(|i| (*i as f64 * lin_norm, lut[*i] as f64))
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.collect();
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// Do the fitting.
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let f = |v: &[f64]| {
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let mut avg_sqr_err = 0.0f64;
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for (x, y) in coords.iter().copied() {
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let e = (log_to_lin(x, offset, slope, v[0], v[1]) - y).abs() / y.abs();
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avg_sqr_err += e * e;
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}
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let last_y = lut[lut.len() - 1] as f64;
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let e = (log_to_lin(1.0, offset, slope, v[0], v[1]) - last_y).abs() / last_y.abs();
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avg_sqr_err += e * e;
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avg_sqr_err
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};
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let input_interval = vec![(-0.2, 0.2), (1.1, 1000.0)];
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let (_, params) = simplers_optimization::Optimizer::minimize(&f, &input_interval, 1000000);
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// Calculate the error of our model.
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let mut max_err = 0.0f64;
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let mut avg_err = 0.0f64;
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let mut avg_samples = 0usize;
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for (i, y) in lut.iter().map(|y| *y as f64).enumerate() {
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// We only record error for values that aren't crazy tiny, since
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// their relative error isn't representative.
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if y.abs() > 0.0001 {
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let x = i as f64 * lin_norm;
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let e = (log_to_lin(x, offset, slope, params[0], params[1]) - y).abs()
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/ y.abs();
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max_err = max_err.max(e);
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avg_err += e;
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avg_samples += 1;
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}
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}
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avg_err /= avg_samples as f64;
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println!("Max Err: {:.4}%\nAvg Err: {:.4}%", max_err * 100.0, avg_err * 100.0);
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println!(
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"{}{}",
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crate::linear_log::generate_linear_to_log(offset, slope, params[0], params[1],),
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crate::linear_log::generate_log_to_linear(offset, slope, params[0], params[1],),
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);
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}
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