Add heatmap inspired by python's matplotlib and implement a library for running it from python through CFFI
This commit is contained in:
Ruben van de Ven
parent
92ec2a4256
commit
9621bdc934
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@ -14,6 +14,11 @@ path = "src/main.rs"
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name = "visual_haarcascades_test"
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path = "src/test.rs"
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[lib]
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name = "visual_haarcascades_lib"
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path = "src/lib.rs"
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crate-type = ["dylib"]
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[dependencies]
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nannou = "0.14"
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# clang-sys = "0.29.3"
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775
src/heatmap.rs
775
src/heatmap.rs
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@ -3,53 +3,39 @@ use image;
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pub enum ColorMaps{
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Binary,
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/// matplotlib
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NipySpectral,
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TraficLight,
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/// matplotlib
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Viridis,
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/// matplotlib
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Plasma,
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}
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trait Colormap{
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fn get_lut(&self, N: usize) -> Vec<[u8; 3]>;
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}
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#[derive(Debug)]
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pub struct ColorMap{
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struct LinearSegmentedColormap{
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pub red: Vec<(f64, f64, f64)>,
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pub green: Vec<(f64, f64, f64)>,
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pub blue: Vec<(f64, f64, f64)>,
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}
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#[derive(Debug)]
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pub struct Heatmap{
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pub cm: ColorMap
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struct ListedColormap{
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pub lut: Vec<(f64, f64, f64)>,
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}
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pub struct Heatmap{
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pub lut: Vec<[u8; 3]>
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}
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impl Heatmap{
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pub fn new(cm: ColorMaps) -> Self{
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Self{
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cm: ColorMap::new(cm)
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}
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}
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pub fn convert_image(&self, img: image::DynamicImage) -> image::RgbImage {
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let gray_img: image::GrayImage = match img {
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image::DynamicImage::ImageLuma8(gray_image) => {
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gray_image
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}
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_ => {
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img.to_luma()
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}
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};
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let mut heatmap_img = image::RgbImage::new(gray_img.width(), gray_img.height());
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let lut_size = 256;// * 256 * 256;
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let lut = self.cm.generate_lut(lut_size);
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// info!("LUT: {:?}", lut);
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for pixel in gray_img.enumerate_pixels() {
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let l = pixel.2;
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let p = image::Rgb(lut[l.0[0] as usize]);
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heatmap_img.put_pixel(pixel.0, pixel.1, p);
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}
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return heatmap_img;
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impl Colormap for LinearSegmentedColormap{
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fn get_lut(&self, N: usize) -> Vec<[u8; 3]>{
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return self.generate_lut(N);
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}
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}
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@ -67,79 +53,7 @@ impl Heatmap{
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// }
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// }
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impl ColorMap{
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pub fn new(m: ColorMaps) -> Self {
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let cm = match m {
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ColorMaps::Binary => {
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Self{
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red: vec![
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(0., 0., 0.), (1., 1., 1.)
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],
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green: vec![
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(0., 0., 0.), (1., 1., 1.)
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],
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blue: vec![
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(0., 0., 0.), (1., 1., 1.)
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],
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}
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}
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ColorMaps::TraficLight => {
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Self{
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red: vec![
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(0., 0., 0.), (0.5, 1., 1.), (1., 1., 1.)
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],
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green: vec![
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(0., 0., 0.), (0.5, 1., 1.), (1., 0., 0.)
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],
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blue: vec![
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(0., 0., 1.), (0.5, 0., 0.), (1., 0., 0.)
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],
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}
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}
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ColorMaps::NipySpectral => {
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Self{
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red: vec![(0.0, 0.0, 0.0), (0.05, 0.4667, 0.4667),
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(0.10, 0.5333, 0.5333), (0.15, 0.0, 0.0),
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(0.20, 0.0, 0.0), (0.25, 0.0, 0.0),
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(0.30, 0.0, 0.0), (0.35, 0.0, 0.0),
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(0.40, 0.0, 0.0), (0.45, 0.0, 0.0),
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(0.50, 0.0, 0.0), (0.55, 0.0, 0.0),
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(0.60, 0.0, 0.0), (0.65, 0.7333, 0.7333),
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(0.70, 0.9333, 0.9333), (0.75, 1.0, 1.0),
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(0.80, 1.0, 1.0), (0.85, 1.0, 1.0),
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(0.90, 0.8667, 0.8667), (0.95, 0.80, 0.80),
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(1.0, 0.80, 0.80)],
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green: vec![(0.0, 0.0, 0.0), (0.05, 0.0, 0.0),
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(0.10, 0.0, 0.0), (0.15, 0.0, 0.0),
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(0.20, 0.0, 0.0), (0.25, 0.4667, 0.4667),
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(0.30, 0.6000, 0.6000), (0.35, 0.6667, 0.6667),
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(0.40, 0.6667, 0.6667), (0.45, 0.6000, 0.6000),
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(0.50, 0.7333, 0.7333), (0.55, 0.8667, 0.8667),
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(0.60, 1.0, 1.0), (0.65, 1.0, 1.0),
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(0.70, 0.9333, 0.9333), (0.75, 0.8000, 0.8000),
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(0.80, 0.6000, 0.6000), (0.85, 0.0, 0.0),
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(0.90, 0.0, 0.0), (0.95, 0.0, 0.0),
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(1.0, 0.80, 0.80)],
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blue: vec![(0.0, 0.0, 0.0), (0.05, 0.5333, 0.5333),
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(0.10, 0.6000, 0.6000), (0.15, 0.6667, 0.6667),
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(0.20, 0.8667, 0.8667), (0.25, 0.8667, 0.8667),
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(0.30, 0.8667, 0.8667), (0.35, 0.6667, 0.6667),
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(0.40, 0.5333, 0.5333), (0.45, 0.0, 0.0),
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(0.5, 0.0, 0.0), (0.55, 0.0, 0.0),
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(0.60, 0.0, 0.0), (0.65, 0.0, 0.0),
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(0.70, 0.0, 0.0), (0.75, 0.0, 0.0),
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(0.80, 0.0, 0.0), (0.85, 0.0, 0.0),
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(0.90, 0.0, 0.0), (0.95, 0.0, 0.0),
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(1.0, 0.80, 0.80)],
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}
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}
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};
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return cm;
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}
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impl LinearSegmentedColormap{
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/// Similar to MatplotLib LinearSegmentedColormap
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/// @see https://github.com/matplotlib/matplotlib/blob/13e3573b721210d84865d148aab7f63cc2fc95a6/lib/matplotlib/colors.py
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/// """
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@ -222,4 +136,649 @@ impl ColorMap{
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lut
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}
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}
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}
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impl Colormap for ListedColormap{
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fn get_lut(&self, N: usize) -> Vec<[u8; 3]> {
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let mut lut = Vec::<[u8;3]>::new();
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// TODO: handle variable length of N
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for d in &self.lut{
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lut.push([
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(d.0 * 256.) as u8,
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(d.1 * 256.) as u8,
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(d.2 * 256.) as u8
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]);
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}
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lut
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}
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}
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impl Heatmap{
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pub fn convert_image(&self, img: image::DynamicImage) -> image::RgbImage {
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let gray_img: image::GrayImage = match img {
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image::DynamicImage::ImageLuma8(gray_image) => {
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gray_image
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}
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_ => {
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img.to_luma()
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}
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};
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let mut heatmap_img = image::RgbImage::new(gray_img.width(), gray_img.height());
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let lut_size = 256;// * 256 * 256;
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// let lut = self.cm.get_lut(lut_size);
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// info!("LUT: {:?}", lut);
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for pixel in gray_img.enumerate_pixels() {
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let l = pixel.2; //0: x, 1: y, 2: value
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let p = image::Rgb(self.lut[l.0[0] as usize]);
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heatmap_img.put_pixel(pixel.0, pixel.1, p);
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}
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return heatmap_img;
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}
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pub fn new(m: ColorMaps) -> Self {
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let N = 256;
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let lut = match m {
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ColorMaps::Binary => {
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LinearSegmentedColormap {
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red: vec![
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(0., 0., 0.), (1., 1., 1.)
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],
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green: vec![
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(0., 0., 0.), (1., 1., 1.)
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],
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blue: vec![
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(0., 0., 0.), (1., 1., 1.)
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],
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}.get_lut(N)
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}
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// ColorMaps::TraficLight => {
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// Self{
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// red: vec![
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// (0., 0., 0.), (0.5, 1., 1.), (1., 1., 1.)
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// ],
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// green: vec![
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// (0., 0., 0.), (0.5, 1., 1.), (1., 0., 0.)
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// ],
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// blue: vec![
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// (0., 0., 1.), (0.5, 0., 0.), (1., 0., 0.)
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// ],
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// }
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// }
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ColorMaps::NipySpectral => {
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LinearSegmentedColormap{
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red: vec![(0.0, 0.0, 0.0), (0.05, 0.4667, 0.4667),
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(0.10, 0.5333, 0.5333), (0.15, 0.0, 0.0),
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(0.20, 0.0, 0.0), (0.25, 0.0, 0.0),
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(0.30, 0.0, 0.0), (0.35, 0.0, 0.0),
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(0.40, 0.0, 0.0), (0.45, 0.0, 0.0),
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(0.50, 0.0, 0.0), (0.55, 0.0, 0.0),
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(0.60, 0.0, 0.0), (0.65, 0.7333, 0.7333),
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(0.70, 0.9333, 0.9333), (0.75, 1.0, 1.0),
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(0.80, 1.0, 1.0), (0.85, 1.0, 1.0),
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(0.90, 0.8667, 0.8667), (0.95, 0.80, 0.80),
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(1.0, 0.80, 0.80)],
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green: vec![(0.0, 0.0, 0.0), (0.05, 0.0, 0.0),
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(0.10, 0.0, 0.0), (0.15, 0.0, 0.0),
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(0.20, 0.0, 0.0), (0.25, 0.4667, 0.4667),
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(0.30, 0.6000, 0.6000), (0.35, 0.6667, 0.6667),
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(0.40, 0.6667, 0.6667), (0.45, 0.6000, 0.6000),
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(0.50, 0.7333, 0.7333), (0.55, 0.8667, 0.8667),
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(0.60, 1.0, 1.0), (0.65, 1.0, 1.0),
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(0.70, 0.9333, 0.9333), (0.75, 0.8000, 0.8000),
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(0.80, 0.6000, 0.6000), (0.85, 0.0, 0.0),
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(0.90, 0.0, 0.0), (0.95, 0.0, 0.0),
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(1.0, 0.80, 0.80)],
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blue: vec![(0.0, 0.0, 0.0), (0.05, 0.5333, 0.5333),
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(0.10, 0.6000, 0.6000), (0.15, 0.6667, 0.6667),
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(0.20, 0.8667, 0.8667), (0.25, 0.8667, 0.8667),
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(0.30, 0.8667, 0.8667), (0.35, 0.6667, 0.6667),
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(0.40, 0.5333, 0.5333), (0.45, 0.0, 0.0),
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(0.5, 0.0, 0.0), (0.55, 0.0, 0.0),
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(0.60, 0.0, 0.0), (0.65, 0.0, 0.0),
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(0.70, 0.0, 0.0), (0.75, 0.0, 0.0),
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(0.80, 0.0, 0.0), (0.85, 0.0, 0.0),
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(0.90, 0.0, 0.0), (0.95, 0.0, 0.0),
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(1.0, 0.80, 0.80)],
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}.get_lut(N)
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}
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ColorMaps::Viridis => {
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ListedColormap{
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lut: vec![
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(0.267004, 0.004874, 0.329415),
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(0.268510, 0.009605, 0.335427),
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(0.269944, 0.014625, 0.341379),
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(0.271305, 0.019942, 0.347269),
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(0.272594, 0.025563, 0.353093),
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(0.273809, 0.031497, 0.358853),
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(0.274952, 0.037752, 0.364543),
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(0.276022, 0.044167, 0.370164),
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(0.277018, 0.050344, 0.375715),
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(0.277941, 0.056324, 0.381191),
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(0.278791, 0.062145, 0.386592),
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(0.279566, 0.067836, 0.391917),
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(0.280267, 0.073417, 0.397163),
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(0.280894, 0.078907, 0.402329),
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(0.281446, 0.084320, 0.407414),
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(0.281924, 0.089666, 0.412415),
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(0.282327, 0.094955, 0.417331),
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(0.282656, 0.100196, 0.422160),
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(0.282910, 0.105393, 0.426902),
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(0.283091, 0.110553, 0.431554),
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(0.283197, 0.115680, 0.436115),
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(0.283229, 0.120777, 0.440584),
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(0.283187, 0.125848, 0.444960),
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(0.283072, 0.130895, 0.449241),
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(0.282884, 0.135920, 0.453427),
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(0.282623, 0.140926, 0.457517),
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(0.282290, 0.145912, 0.461510),
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(0.281887, 0.150881, 0.465405),
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(0.281412, 0.155834, 0.469201),
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(0.280868, 0.160771, 0.472899),
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(0.280255, 0.165693, 0.476498),
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(0.279574, 0.170599, 0.479997),
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(0.278826, 0.175490, 0.483397),
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(0.278012, 0.180367, 0.486697),
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(0.277134, 0.185228, 0.489898),
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(0.276194, 0.190074, 0.493001),
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(0.275191, 0.194905, 0.496005),
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(0.274128, 0.199721, 0.498911),
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(0.273006, 0.204520, 0.501721),
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(0.271828, 0.209303, 0.504434),
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(0.270595, 0.214069, 0.507052),
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(0.269308, 0.218818, 0.509577),
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(0.267968, 0.223549, 0.512008),
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(0.266580, 0.228262, 0.514349),
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(0.265145, 0.232956, 0.516599),
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(0.263663, 0.237631, 0.518762),
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(0.262138, 0.242286, 0.520837),
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(0.260571, 0.246922, 0.522828),
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(0.258965, 0.251537, 0.524736),
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(0.257322, 0.256130, 0.526563),
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(0.255645, 0.260703, 0.528312),
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(0.253935, 0.265254, 0.529983),
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(0.252194, 0.269783, 0.531579),
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(0.250425, 0.274290, 0.533103),
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(0.248629, 0.278775, 0.534556),
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(0.246811, 0.283237, 0.535941),
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(0.244972, 0.287675, 0.537260),
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(0.243113, 0.292092, 0.538516),
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(0.241237, 0.296485, 0.539709),
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(0.239346, 0.300855, 0.540844),
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(0.237441, 0.305202, 0.541921),
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(0.235526, 0.309527, 0.542944),
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(0.233603, 0.313828, 0.543914),
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(0.231674, 0.318106, 0.544834),
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(0.229739, 0.322361, 0.545706),
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(0.227802, 0.326594, 0.546532),
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(0.225863, 0.330805, 0.547314),
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(0.223925, 0.334994, 0.548053),
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(0.221989, 0.339161, 0.548752),
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(0.220057, 0.343307, 0.549413),
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(0.218130, 0.347432, 0.550038),
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(0.216210, 0.351535, 0.550627),
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(0.214298, 0.355619, 0.551184),
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(0.212395, 0.359683, 0.551710),
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(0.210503, 0.363727, 0.552206),
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(0.208623, 0.367752, 0.552675),
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(0.206756, 0.371758, 0.553117),
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(0.204903, 0.375746, 0.553533),
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(0.203063, 0.379716, 0.553925),
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(0.201239, 0.383670, 0.554294),
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(0.199430, 0.387607, 0.554642),
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(0.197636, 0.391528, 0.554969),
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(0.195860, 0.395433, 0.555276),
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(0.194100, 0.399323, 0.555565),
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(0.192357, 0.403199, 0.555836),
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(0.190631, 0.407061, 0.556089),
|
||||
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|
||||
(0.819651, 0.306812, 0.448306),
|
||||
(0.823132, 0.311261, 0.444806),
|
||||
(0.826588, 0.315714, 0.441316),
|
||||
(0.830018, 0.320172, 0.437836),
|
||||
(0.833422, 0.324635, 0.434366),
|
||||
(0.836801, 0.329105, 0.430905),
|
||||
(0.840155, 0.333580, 0.427455),
|
||||
(0.843484, 0.338062, 0.424013),
|
||||
(0.846788, 0.342551, 0.420579),
|
||||
(0.850066, 0.347048, 0.417153),
|
||||
(0.853319, 0.351553, 0.413734),
|
||||
(0.856547, 0.356066, 0.410322),
|
||||
(0.859750, 0.360588, 0.406917),
|
||||
(0.862927, 0.365119, 0.403519),
|
||||
(0.866078, 0.369660, 0.400126),
|
||||
(0.869203, 0.374212, 0.396738),
|
||||
(0.872303, 0.378774, 0.393355),
|
||||
(0.875376, 0.383347, 0.389976),
|
||||
(0.878423, 0.387932, 0.386600),
|
||||
(0.881443, 0.392529, 0.383229),
|
||||
(0.884436, 0.397139, 0.379860),
|
||||
(0.887402, 0.401762, 0.376494),
|
||||
(0.890340, 0.406398, 0.373130),
|
||||
(0.893250, 0.411048, 0.369768),
|
||||
(0.896131, 0.415712, 0.366407),
|
||||
(0.898984, 0.420392, 0.363047),
|
||||
(0.901807, 0.425087, 0.359688),
|
||||
(0.904601, 0.429797, 0.356329),
|
||||
(0.907365, 0.434524, 0.352970),
|
||||
(0.910098, 0.439268, 0.349610),
|
||||
(0.912800, 0.444029, 0.346251),
|
||||
(0.915471, 0.448807, 0.342890),
|
||||
(0.918109, 0.453603, 0.339529),
|
||||
(0.920714, 0.458417, 0.336166),
|
||||
(0.923287, 0.463251, 0.332801),
|
||||
(0.925825, 0.468103, 0.329435),
|
||||
(0.928329, 0.472975, 0.326067),
|
||||
(0.930798, 0.477867, 0.322697),
|
||||
(0.933232, 0.482780, 0.319325),
|
||||
(0.935630, 0.487712, 0.315952),
|
||||
(0.937990, 0.492667, 0.312575),
|
||||
(0.940313, 0.497642, 0.309197),
|
||||
(0.942598, 0.502639, 0.305816),
|
||||
(0.944844, 0.507658, 0.302433),
|
||||
(0.947051, 0.512699, 0.299049),
|
||||
(0.949217, 0.517763, 0.295662),
|
||||
(0.951344, 0.522850, 0.292275),
|
||||
(0.953428, 0.527960, 0.288883),
|
||||
(0.955470, 0.533093, 0.285490),
|
||||
(0.957469, 0.538250, 0.282096),
|
||||
(0.959424, 0.543431, 0.278701),
|
||||
(0.961336, 0.548636, 0.275305),
|
||||
(0.963203, 0.553865, 0.271909),
|
||||
(0.965024, 0.559118, 0.268513),
|
||||
(0.966798, 0.564396, 0.265118),
|
||||
(0.968526, 0.569700, 0.261721),
|
||||
(0.970205, 0.575028, 0.258325),
|
||||
(0.971835, 0.580382, 0.254931),
|
||||
(0.973416, 0.585761, 0.251540),
|
||||
(0.974947, 0.591165, 0.248151),
|
||||
(0.976428, 0.596595, 0.244767),
|
||||
(0.977856, 0.602051, 0.241387),
|
||||
(0.979233, 0.607532, 0.238013),
|
||||
(0.980556, 0.613039, 0.234646),
|
||||
(0.981826, 0.618572, 0.231287),
|
||||
(0.983041, 0.624131, 0.227937),
|
||||
(0.984199, 0.629718, 0.224595),
|
||||
(0.985301, 0.635330, 0.221265),
|
||||
(0.986345, 0.640969, 0.217948),
|
||||
(0.987332, 0.646633, 0.214648),
|
||||
(0.988260, 0.652325, 0.211364),
|
||||
(0.989128, 0.658043, 0.208100),
|
||||
(0.989935, 0.663787, 0.204859),
|
||||
(0.990681, 0.669558, 0.201642),
|
||||
(0.991365, 0.675355, 0.198453),
|
||||
(0.991985, 0.681179, 0.195295),
|
||||
(0.992541, 0.687030, 0.192170),
|
||||
(0.993032, 0.692907, 0.189084),
|
||||
(0.993456, 0.698810, 0.186041),
|
||||
(0.993814, 0.704741, 0.183043),
|
||||
(0.994103, 0.710698, 0.180097),
|
||||
(0.994324, 0.716681, 0.177208),
|
||||
(0.994474, 0.722691, 0.174381),
|
||||
(0.994553, 0.728728, 0.171622),
|
||||
(0.994561, 0.734791, 0.168938),
|
||||
(0.994495, 0.740880, 0.166335),
|
||||
(0.994355, 0.746995, 0.163821),
|
||||
(0.994141, 0.753137, 0.161404),
|
||||
(0.993851, 0.759304, 0.159092),
|
||||
(0.993482, 0.765499, 0.156891),
|
||||
(0.993033, 0.771720, 0.154808),
|
||||
(0.992505, 0.777967, 0.152855),
|
||||
(0.991897, 0.784239, 0.151042),
|
||||
(0.991209, 0.790537, 0.149377),
|
||||
(0.990439, 0.796859, 0.147870),
|
||||
(0.989587, 0.803205, 0.146529),
|
||||
(0.988648, 0.809579, 0.145357),
|
||||
(0.987621, 0.815978, 0.144363),
|
||||
(0.986509, 0.822401, 0.143557),
|
||||
(0.985314, 0.828846, 0.142945),
|
||||
(0.984031, 0.835315, 0.142528),
|
||||
(0.982653, 0.841812, 0.142303),
|
||||
(0.981190, 0.848329, 0.142279),
|
||||
(0.979644, 0.854866, 0.142453),
|
||||
(0.977995, 0.861432, 0.142808),
|
||||
(0.976265, 0.868016, 0.143351),
|
||||
(0.974443, 0.874622, 0.144061),
|
||||
(0.972530, 0.881250, 0.144923),
|
||||
(0.970533, 0.887896, 0.145919),
|
||||
(0.968443, 0.894564, 0.147014),
|
||||
(0.966271, 0.901249, 0.148180),
|
||||
(0.964021, 0.907950, 0.149370),
|
||||
(0.961681, 0.914672, 0.150520),
|
||||
(0.959276, 0.921407, 0.151566),
|
||||
(0.956808, 0.928152, 0.152409),
|
||||
(0.954287, 0.934908, 0.152921),
|
||||
(0.951726, 0.941671, 0.152925),
|
||||
(0.949151, 0.948435, 0.152178),
|
||||
(0.946602, 0.955190, 0.150328),
|
||||
(0.944152, 0.961916, 0.146861),
|
||||
(0.941896, 0.968590, 0.140956),
|
||||
(0.940015, 0.975158, 0.131326)]
|
||||
}.get_lut(N)
|
||||
}
|
||||
};
|
||||
|
||||
Heatmap{
|
||||
lut: lut
|
||||
}
|
||||
|
||||
// return cm;
|
||||
}
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,83 @@
|
|||
#[macro_use] extern crate log;
|
||||
#[macro_use(s)] extern crate ndarray;
|
||||
mod visualhaar;
|
||||
mod heatmap;
|
||||
|
||||
|
||||
use std::slice;
|
||||
use image;
|
||||
|
||||
static mut IMAGENR: i32 = 0;
|
||||
|
||||
#[no_mangle]
|
||||
pub extern "C" fn test(x: i32) -> i32 {
|
||||
x * 2
|
||||
}
|
||||
|
||||
|
||||
/// partly inspired by https://bheisler.github.io/post/calling-rust-in-python/
|
||||
#[no_mangle]
|
||||
pub extern "C" fn classifier_new()
|
||||
-> *mut visualhaar::HaarClassifier {
|
||||
let haar = visualhaar::HaarClassifier::from_xml("/home/ruben/Documents/Projecten/2020/rust/testproject/haarcascade_frontalface_alt2.xml").unwrap();
|
||||
let boxed_haar = Box::new(haar);
|
||||
Box::into_raw(boxed_haar)
|
||||
}
|
||||
|
||||
|
||||
#[no_mangle]
|
||||
pub extern "C" fn scan_image(haar: *mut visualhaar::HaarClassifier,
|
||||
width: usize, height: usize,
|
||||
input: *const u8,
|
||||
buffer: *mut u8,
|
||||
length: usize,
|
||||
debug: bool) {
|
||||
if haar.is_null() || input.is_null() || buffer.is_null() {
|
||||
return;
|
||||
}
|
||||
let haar = unsafe { Box::from_raw(haar) };
|
||||
// let input = unsafe { slice::from_raw_parts_mut(input, length) };
|
||||
let buffer = unsafe { slice::from_raw_parts_mut(buffer, length) };
|
||||
let input = unsafe { slice::from_raw_parts(input, length) };
|
||||
let input = Vec::from(input);
|
||||
|
||||
|
||||
let mut buf_img: image::ImageBuffer<image::Rgb<u8>, &mut [u8]> = image::ImageBuffer::from_raw(width as u32, height as u32, buffer).unwrap();
|
||||
let input_frame: image::ImageBuffer<image::Rgb<u8>, Vec<u8>> = image::ImageBuffer::from_raw(width as u32, height as u32, input).unwrap();
|
||||
|
||||
// let frame = image::open("/home/ruben/Documents/Projecten/2020/rust/lena_orig.png").unwrap();
|
||||
// let input_frame = frame.as_rgb8().unwrap().clone();
|
||||
|
||||
let hm = Some(heatmap::Heatmap::new(heatmap::ColorMaps::Plasma));
|
||||
// let hm = None;
|
||||
|
||||
if debug {
|
||||
unsafe{
|
||||
IMAGENR+=1;
|
||||
let filename = format!("/tmp/last_frame{}.png",IMAGENR);
|
||||
println!("Saving debug! {}", filename);
|
||||
input_frame.save(filename);
|
||||
}
|
||||
}
|
||||
|
||||
let image = haar.scan_image(input_frame, &hm).unwrap().dynamic_img;
|
||||
let rgb_img = image.to_rgb();
|
||||
// image.save("/home/ruben/Documents/Projecten/2020/rust/lena_orig-output-lib.png");
|
||||
info!("Scanning for faces took done");
|
||||
|
||||
for x in 0..(width as u32){
|
||||
for y in 0..(height as u32){
|
||||
buf_img.put_pixel(x, y, rgb_img.get_pixel(x,y).clone());
|
||||
}
|
||||
}
|
||||
|
||||
// buffer =6 image.to_rgb().into_raw();
|
||||
|
||||
|
||||
// haar.scan_image(input_frame, &hm);
|
||||
// raytracer::render_into(block, &*scene, &mut image);
|
||||
|
||||
|
||||
//Don't free the haar
|
||||
Box::into_raw(haar);
|
||||
}
|
||||
49
src/main.rs
49
src/main.rs
|
|
@ -7,6 +7,7 @@ use nannou::prelude::*;
|
|||
use v4l::{Buffer, CaptureDevice, MappedBufferStream};
|
||||
use image;
|
||||
mod visualhaar;
|
||||
mod heatmap;
|
||||
|
||||
// use std::fs::File;
|
||||
|
||||
|
|
@ -22,16 +23,37 @@ fn main() {
|
|||
|
||||
warn!("test");
|
||||
|
||||
unsafe{
|
||||
CAMERA = Some(CaptureDevice::new(2)
|
||||
.expect("Failed to open device")
|
||||
// .format(640, 480, b"RGB3")
|
||||
.format(424, 240, b"RGB3")
|
||||
// .format(320, 240, b"RGB3")
|
||||
.expect("Failed to set format")
|
||||
.fps(30)
|
||||
.expect("Failed to set frame interval"));
|
||||
// unsafe{
|
||||
|
||||
let device_id = 0;
|
||||
|
||||
if let Ok(dev) = CaptureDevice::new(device_id) {
|
||||
|
||||
let formats = dev.enumerate_formats();
|
||||
if let Ok(formats) = formats {
|
||||
info!("Supported camera formats");
|
||||
for fmt in formats {
|
||||
info!("{}", fmt);
|
||||
}
|
||||
}
|
||||
|
||||
unsafe{
|
||||
CAMERA = Some(dev.format(424, 240, b"RGB3")
|
||||
.expect("Failed to set format"));
|
||||
}
|
||||
} else {
|
||||
println!("Failed to open camera device {}", device_id);
|
||||
return;
|
||||
}
|
||||
// CAMERA = Some(CaptureDevice::new(3)
|
||||
// .expect("Failed to open device")
|
||||
// // .format(640, 480, b"RGB3")
|
||||
// .format(424, 240, b"RGB3")
|
||||
// // .format(320, 240, b"RGB3")
|
||||
// .expect("Failed to set format")()
|
||||
// .fps(30)
|
||||
// .expect("Failed to set frame interval"));
|
||||
// }
|
||||
|
||||
nannou::app(model)
|
||||
.event(event)
|
||||
|
|
@ -45,6 +67,7 @@ struct Model<'a> {
|
|||
_window: window::Id,
|
||||
image: Option<nannou::image::DynamicImage>,
|
||||
haar: visualhaar::HaarClassifier,
|
||||
heatmap: Option<heatmap::Heatmap>,
|
||||
haar_outcome: Option<visualhaar::Outcome>,
|
||||
}
|
||||
|
||||
|
|
@ -82,7 +105,7 @@ fn model<'a>(app: &App) -> Model<'a> {
|
|||
|
||||
let haar = visualhaar::HaarClassifier::from_xml("haarcascade_frontalface_alt2.xml").unwrap();
|
||||
|
||||
println!("Haar: {:?}", haar);
|
||||
// println!("Haar: {:?}", haar);
|
||||
|
||||
|
||||
Model {
|
||||
|
|
@ -90,6 +113,7 @@ fn model<'a>(app: &App) -> Model<'a> {
|
|||
_window: _window,
|
||||
image: None,
|
||||
haar: haar,
|
||||
heatmap: Some(heatmap::Heatmap::new(heatmap::ColorMaps::Plasma)),
|
||||
haar_outcome: None,
|
||||
}
|
||||
}
|
||||
|
|
@ -134,7 +158,8 @@ fn update(_app: &App, _model: &mut Model, _update: Update) {
|
|||
// ib.map( nannou::image::DynamicImage::ImageRgb8);
|
||||
// let ib_bw = nannou::image::imageops::grayscale(&ib);
|
||||
// _model.image = Some(nannou::image::DynamicImage::ImageLuma8(ib_bw));
|
||||
let outcome = _model.haar.scan_image(ib).unwrap();
|
||||
let outcome = _model.haar.scan_image(ib, &_model.heatmap).unwrap();
|
||||
// let image_hm = _model.heatmap.convert_image(outcome.dynamic_img);
|
||||
_model.haar_outcome = Some(outcome);
|
||||
// _model.image = Some(nannou::image::DynamicImage::ImageRgb8(ib));
|
||||
|
||||
|
|
@ -164,6 +189,8 @@ fn view(_app: &App, _model: &Model, frame: Frame){
|
|||
Some(outcome) => {
|
||||
// let i = outcome.dyn(/);
|
||||
// let img // ::from(&outcome.dynamic_img);
|
||||
// let hm = heatmap::Heatmap::new(heatmap::ColorMaps::Plasma);
|
||||
// let image_hm = hm.convert_image(image);
|
||||
let img = image::DynamicImage::ImageRgb8(outcome.dynamic_img.to_rgb()).resize(1000, 1000, image::imageops::FilterType::Triangle);
|
||||
|
||||
let texture = wgpu::Texture::from_image(_app, &img);
|
||||
|
|
|
|||
17
src/test.rs
17
src/test.rs
|
|
@ -29,7 +29,7 @@ fn main() {
|
|||
// println!("Haar: {:?}", haar);
|
||||
|
||||
|
||||
let sw = Stopwatch::start_new();
|
||||
let mut sw = Stopwatch::start_new();
|
||||
|
||||
let frame = image::open("/home/ruben/Documents/Projecten/2020/rust/lena_orig-s.png");
|
||||
|
||||
|
|
@ -49,15 +49,18 @@ fn main() {
|
|||
// let ib_bw = nannou::image::imageops::grayscale(&ib);
|
||||
// _model.image = Some(nannou::image::DynamicImage::ImageLuma8(ib_bw));
|
||||
let i = ib.as_rgb8().unwrap().clone();
|
||||
let image = haar.scan_image(i).unwrap().dynamic_img;
|
||||
let hm = Some(heatmap::Heatmap::new(heatmap::ColorMaps::Plasma));
|
||||
let image = haar.scan_image(i, &hm).unwrap().dynamic_img;
|
||||
image.save("/home/ruben/Documents/Projecten/2020/rust/lena_orig-output.png");
|
||||
|
||||
// let hm = heatmap::Heatmap::new(heatmap::ColorMaps::NipySpectral);
|
||||
let hm = heatmap::Heatmap::new(heatmap::ColorMaps::TraficLight);
|
||||
// let hm = heatmap::Heatmap::new(heatmap::ColorMaps::Binary);
|
||||
let image = hm.convert_image(image);
|
||||
|
||||
image.save("/home/ruben/Documents/Projecten/2020/rust/lena_orig-output.png");
|
||||
// let hm = heatmap::Heatmap::new(heatmap::ColorMaps::TraficLight);
|
||||
info!("Scanning for faces took {}ms", sw.elapsed_ms());
|
||||
// sw.restart();
|
||||
// let hm = h;
|
||||
// let image_hm = hm.convert_image(image);
|
||||
// image_hm.save("/home/ruben/Documents/Projecten/2020/rust/lena_orig-output-hm.png");
|
||||
// info!("Generating Heatmap {}ms", sw.elapsed_ms());
|
||||
// _model.image = Some(nannou::image::DynamicImage::ImageRgb8(ib));
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ use std::{convert::TryInto, error::Error};
|
|||
|
||||
use stopwatch::{Stopwatch};
|
||||
use ndarray as nd;
|
||||
use super::heatmap as heatmap;
|
||||
|
||||
/// A haarclasifier based on opencv cascade XML files
|
||||
/// Structure info from https://answers.opencv.org/question/8418/explanation-of-cascadexml-in-a-haar-classifier/
|
||||
|
|
@ -108,7 +109,7 @@ impl HaarClassifierFeatureRect{
|
|||
// info!("Draw {} {} {} {} ({:?}),", x1, y1, x2, y2,self);
|
||||
let mut rect = draw_window.slice_mut(s![y1..y2, x1..x2]); // semi slow (initially 500ms)
|
||||
rect += self.weight; // super slow (initially 10.000 ms)
|
||||
|
||||
// info!("add")
|
||||
// for x in x1..x2{
|
||||
// for y in y1..y2{
|
||||
// draw_window[[y, x]] = draw_window[[y, x]] as f64 + self.weight;
|
||||
|
|
@ -139,9 +140,9 @@ impl HaarClassifier {
|
|||
|
||||
// root: <opencv_storage>
|
||||
let root_el = doc.root().first_element_child().unwrap();
|
||||
println!("{:?}", root_el);
|
||||
// println!("{:?}", root_el);
|
||||
let cascade = root_el.first_element_child().unwrap();
|
||||
println!("{:?}", cascade);
|
||||
// println!("{:?}", cascade);
|
||||
|
||||
let features_el = cascade.children().find(|n| n.is_element() && n.has_tag_name("features")).unwrap();
|
||||
let stages_el = cascade.children().find(|n| n.is_element() && n.has_tag_name("stages")).unwrap();
|
||||
|
|
@ -330,7 +331,7 @@ impl HaarClassifier {
|
|||
// }
|
||||
|
||||
/// take an ImageBuffer and scan it for faces.
|
||||
pub fn scan_image(&self, frame: image::ImageBuffer<image::Rgb<u8>, Vec<u8>>) -> Result<Outcome, String> {
|
||||
pub fn scan_image(&self, frame: image::ImageBuffer<image::Rgb<u8>, Vec<u8>>, heatmap: &Option<heatmap::Heatmap>) -> Result<Outcome, String> {
|
||||
let img_bw = image::imageops::grayscale(&frame);
|
||||
// let mut output_image = image::GrayImage::new(frame.width(), frame.height());
|
||||
|
||||
|
|
@ -341,7 +342,7 @@ impl HaarClassifier {
|
|||
img_bw.dimensions().0 as usize,
|
||||
));
|
||||
|
||||
info!("Frame: {:?} {:?}", integral[[0,0]], integral[[integral.dim().0-1,integral.dim().1-1]]);
|
||||
// info!("Frame: {:?} {:?}", integral[[0,0]], integral[[integral.dim().0-1,integral.dim().1-1]]);
|
||||
|
||||
// let rect = integral.slice(s![3..5, 2..4]);
|
||||
|
||||
|
|
@ -412,6 +413,16 @@ impl HaarClassifier {
|
|||
|
||||
// let dynamic = image::DynamicImage::ImageLuma8(img_bw);
|
||||
let dynamic = image::DynamicImage::ImageLuma8(final_img);
|
||||
let dynamic = match heatmap {
|
||||
Some(hm) => {
|
||||
// TODO remove intermediate DynamicImage conversin
|
||||
image::DynamicImage::ImageRgb8(hm.convert_image(dynamic))
|
||||
}
|
||||
None => {
|
||||
// no changes needed
|
||||
dynamic
|
||||
}
|
||||
};
|
||||
Ok(Outcome{
|
||||
// frame: img_bw,
|
||||
dynamic_img: dynamic,
|
||||
|
|
@ -439,6 +450,7 @@ impl HaarClassifier {
|
|||
classifier.right
|
||||
};
|
||||
|
||||
// TODO remove to use all stages (we need to speed up somewhere else)
|
||||
// if i > 2{
|
||||
// break;
|
||||
// }
|
||||
|
|
|
|||
Loading…
Reference in New Issue