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Add heatmap inspired by python's matplotlib and implement a library for running it from python through CFFI

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This commit is contained in:
Ruben van de Ven 2020-09-23 12:41:24 +02:00
parent 92ec2a4256
commit 9621bdc934
6 changed files with 820 additions and 131 deletions
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5
Cargo.toml
View file

@ -14,6 +14,11 @@ path = "src/main.rs"
name = "visual_haarcascades_test" name = "visual_haarcascades_test"
path = "src/test.rs" path = "src/test.rs"
[lib]
name = "visual_haarcascades_lib"
path = "src/lib.rs"
crate-type = ["dylib"]
[dependencies] [dependencies]
nannou = "0.14" nannou = "0.14"
# clang-sys = "0.29.3" # clang-sys = "0.29.3"

775
src/heatmap.rs
View file

@ -3,53 +3,39 @@ use image;
pub enum ColorMaps{ pub enum ColorMaps{
Binary, Binary,
/// matplotlib
NipySpectral, NipySpectral,
TraficLight, /// matplotlib
Viridis,
/// matplotlib
Plasma,
}
trait Colormap{
fn get_lut(&self, N: usize) -> Vec<[u8; 3]>;
} }
#[derive(Debug)] #[derive(Debug)]
pub struct ColorMap{ struct LinearSegmentedColormap{
pub red: Vec<(f64, f64, f64)>, pub red: Vec<(f64, f64, f64)>,
pub green: Vec<(f64, f64, f64)>, pub green: Vec<(f64, f64, f64)>,
pub blue: Vec<(f64, f64, f64)>, pub blue: Vec<(f64, f64, f64)>,
} }
#[derive(Debug)] #[derive(Debug)]
pub struct Heatmap{ struct ListedColormap{
pub cm: ColorMap pub lut: Vec<(f64, f64, f64)>,
} }
pub struct Heatmap{
pub lut: Vec<[u8; 3]>
}
impl Heatmap{ impl Colormap for LinearSegmentedColormap{
pub fn new(cm: ColorMaps) -> Self{ fn get_lut(&self, N: usize) -> Vec<[u8; 3]>{
Self{ return self.generate_lut(N);
cm: ColorMap::new(cm)
}
}
pub fn convert_image(&self, img: image::DynamicImage) -> image::RgbImage {
let gray_img: image::GrayImage = match img {
image::DynamicImage::ImageLuma8(gray_image) => {
gray_image
}
_ => {
img.to_luma()
}
};
let mut heatmap_img = image::RgbImage::new(gray_img.width(), gray_img.height());
let lut_size = 256;// * 256 * 256;
let lut = self.cm.generate_lut(lut_size);
// info!("LUT: {:?}", lut);
for pixel in gray_img.enumerate_pixels() {
let l = pixel.2;
let p = image::Rgb(lut[l.0[0] as usize]);
heatmap_img.put_pixel(pixel.0, pixel.1, p);
}
return heatmap_img;
} }
} }
@ -67,79 +53,7 @@ impl Heatmap{
// } // }
// } // }
impl ColorMap{ impl LinearSegmentedColormap{
pub fn new(m: ColorMaps) -> Self {
let cm = match m {
ColorMaps::Binary => {
Self{
red: vec![
(0., 0., 0.), (1., 1., 1.)
],
green: vec![
(0., 0., 0.), (1., 1., 1.)
],
blue: vec![
(0., 0., 0.), (1., 1., 1.)
],
}
}
ColorMaps::TraficLight => {
Self{
red: vec![
(0., 0., 0.), (0.5, 1., 1.), (1., 1., 1.)
],
green: vec![
(0., 0., 0.), (0.5, 1., 1.), (1., 0., 0.)
],
blue: vec![
(0., 0., 1.), (0.5, 0., 0.), (1., 0., 0.)
],
}
}
ColorMaps::NipySpectral => {
Self{
red: vec![(0.0, 0.0, 0.0), (0.05, 0.4667, 0.4667),
(0.10, 0.5333, 0.5333), (0.15, 0.0, 0.0),
(0.20, 0.0, 0.0), (0.25, 0.0, 0.0),
(0.30, 0.0, 0.0), (0.35, 0.0, 0.0),
(0.40, 0.0, 0.0), (0.45, 0.0, 0.0),
(0.50, 0.0, 0.0), (0.55, 0.0, 0.0),
(0.60, 0.0, 0.0), (0.65, 0.7333, 0.7333),
(0.70, 0.9333, 0.9333), (0.75, 1.0, 1.0),
(0.80, 1.0, 1.0), (0.85, 1.0, 1.0),
(0.90, 0.8667, 0.8667), (0.95, 0.80, 0.80),
(1.0, 0.80, 0.80)],
green: vec![(0.0, 0.0, 0.0), (0.05, 0.0, 0.0),
(0.10, 0.0, 0.0), (0.15, 0.0, 0.0),
(0.20, 0.0, 0.0), (0.25, 0.4667, 0.4667),
(0.30, 0.6000, 0.6000), (0.35, 0.6667, 0.6667),
(0.40, 0.6667, 0.6667), (0.45, 0.6000, 0.6000),
(0.50, 0.7333, 0.7333), (0.55, 0.8667, 0.8667),
(0.60, 1.0, 1.0), (0.65, 1.0, 1.0),
(0.70, 0.9333, 0.9333), (0.75, 0.8000, 0.8000),
(0.80, 0.6000, 0.6000), (0.85, 0.0, 0.0),
(0.90, 0.0, 0.0), (0.95, 0.0, 0.0),
(1.0, 0.80, 0.80)],
blue: vec![(0.0, 0.0, 0.0), (0.05, 0.5333, 0.5333),
(0.10, 0.6000, 0.6000), (0.15, 0.6667, 0.6667),
(0.20, 0.8667, 0.8667), (0.25, 0.8667, 0.8667),
(0.30, 0.8667, 0.8667), (0.35, 0.6667, 0.6667),
(0.40, 0.5333, 0.5333), (0.45, 0.0, 0.0),
(0.5, 0.0, 0.0), (0.55, 0.0, 0.0),
(0.60, 0.0, 0.0), (0.65, 0.0, 0.0),
(0.70, 0.0, 0.0), (0.75, 0.0, 0.0),
(0.80, 0.0, 0.0), (0.85, 0.0, 0.0),
(0.90, 0.0, 0.0), (0.95, 0.0, 0.0),
(1.0, 0.80, 0.80)],
}
}
};
return cm;
}
/// Similar to MatplotLib LinearSegmentedColormap /// Similar to MatplotLib LinearSegmentedColormap
/// @see https://github.com/matplotlib/matplotlib/blob/13e3573b721210d84865d148aab7f63cc2fc95a6/lib/matplotlib/colors.py /// @see https://github.com/matplotlib/matplotlib/blob/13e3573b721210d84865d148aab7f63cc2fc95a6/lib/matplotlib/colors.py
/// """ /// """
@ -222,4 +136,649 @@ impl ColorMap{
lut lut
} }
} }
impl Colormap for ListedColormap{
fn get_lut(&self, N: usize) -> Vec<[u8; 3]> {
let mut lut = Vec::<[u8;3]>::new();
// TODO: handle variable length of N
for d in &self.lut{
lut.push([
(d.0 * 256.) as u8,
(d.1 * 256.) as u8,
(d.2 * 256.) as u8
]);
}
lut
}
}
impl Heatmap{
pub fn convert_image(&self, img: image::DynamicImage) -> image::RgbImage {
let gray_img: image::GrayImage = match img {
image::DynamicImage::ImageLuma8(gray_image) => {
gray_image
}
_ => {
img.to_luma()
}
};
let mut heatmap_img = image::RgbImage::new(gray_img.width(), gray_img.height());
let lut_size = 256;// * 256 * 256;
// let lut = self.cm.get_lut(lut_size);
// info!("LUT: {:?}", lut);
for pixel in gray_img.enumerate_pixels() {
let l = pixel.2; //0: x, 1: y, 2: value
let p = image::Rgb(self.lut[l.0[0] as usize]);
heatmap_img.put_pixel(pixel.0, pixel.1, p);
}
return heatmap_img;
}
pub fn new(m: ColorMaps) -> Self {
let N = 256;
let lut = match m {
ColorMaps::Binary => {
LinearSegmentedColormap {
red: vec![
(0., 0., 0.), (1., 1., 1.)
],
green: vec![
(0., 0., 0.), (1., 1., 1.)
],
blue: vec![
(0., 0., 0.), (1., 1., 1.)
],
}.get_lut(N)
}
// ColorMaps::TraficLight => {
// Self{
// red: vec![
// (0., 0., 0.), (0.5, 1., 1.), (1., 1., 1.)
// ],
// green: vec![
// (0., 0., 0.), (0.5, 1., 1.), (1., 0., 0.)
// ],
// blue: vec![
// (0., 0., 1.), (0.5, 0., 0.), (1., 0., 0.)
// ],
// }
// }
ColorMaps::NipySpectral => {
LinearSegmentedColormap{
red: vec![(0.0, 0.0, 0.0), (0.05, 0.4667, 0.4667),
(0.10, 0.5333, 0.5333), (0.15, 0.0, 0.0),
(0.20, 0.0, 0.0), (0.25, 0.0, 0.0),
(0.30, 0.0, 0.0), (0.35, 0.0, 0.0),
(0.40, 0.0, 0.0), (0.45, 0.0, 0.0),
(0.50, 0.0, 0.0), (0.55, 0.0, 0.0),
(0.60, 0.0, 0.0), (0.65, 0.7333, 0.7333),
(0.70, 0.9333, 0.9333), (0.75, 1.0, 1.0),
(0.80, 1.0, 1.0), (0.85, 1.0, 1.0),
(0.90, 0.8667, 0.8667), (0.95, 0.80, 0.80),
(1.0, 0.80, 0.80)],
green: vec![(0.0, 0.0, 0.0), (0.05, 0.0, 0.0),
(0.10, 0.0, 0.0), (0.15, 0.0, 0.0),
(0.20, 0.0, 0.0), (0.25, 0.4667, 0.4667),
(0.30, 0.6000, 0.6000), (0.35, 0.6667, 0.6667),
(0.40, 0.6667, 0.6667), (0.45, 0.6000, 0.6000),
(0.50, 0.7333, 0.7333), (0.55, 0.8667, 0.8667),
(0.60, 1.0, 1.0), (0.65, 1.0, 1.0),
(0.70, 0.9333, 0.9333), (0.75, 0.8000, 0.8000),
(0.80, 0.6000, 0.6000), (0.85, 0.0, 0.0),
(0.90, 0.0, 0.0), (0.95, 0.0, 0.0),
(1.0, 0.80, 0.80)],
blue: vec![(0.0, 0.0, 0.0), (0.05, 0.5333, 0.5333),
(0.10, 0.6000, 0.6000), (0.15, 0.6667, 0.6667),
(0.20, 0.8667, 0.8667), (0.25, 0.8667, 0.8667),
(0.30, 0.8667, 0.8667), (0.35, 0.6667, 0.6667),
(0.40, 0.5333, 0.5333), (0.45, 0.0, 0.0),
(0.5, 0.0, 0.0), (0.55, 0.0, 0.0),
(0.60, 0.0, 0.0), (0.65, 0.0, 0.0),
(0.70, 0.0, 0.0), (0.75, 0.0, 0.0),
(0.80, 0.0, 0.0), (0.85, 0.0, 0.0),
(0.90, 0.0, 0.0), (0.95, 0.0, 0.0),
(1.0, 0.80, 0.80)],
}.get_lut(N)
}
ColorMaps::Viridis => {
ListedColormap{
lut: vec![
(0.267004, 0.004874, 0.329415),
(0.268510, 0.009605, 0.335427),
(0.269944, 0.014625, 0.341379),
(0.271305, 0.019942, 0.347269),
(0.272594, 0.025563, 0.353093),
(0.273809, 0.031497, 0.358853),
(0.274952, 0.037752, 0.364543),
(0.276022, 0.044167, 0.370164),
(0.277018, 0.050344, 0.375715),
(0.277941, 0.056324, 0.381191),
(0.278791, 0.062145, 0.386592),
(0.279566, 0.067836, 0.391917),
(0.280267, 0.073417, 0.397163),
(0.280894, 0.078907, 0.402329),
(0.281446, 0.084320, 0.407414),
(0.281924, 0.089666, 0.412415),
(0.282327, 0.094955, 0.417331),
(0.282656, 0.100196, 0.422160),
(0.282910, 0.105393, 0.426902),
(0.283091, 0.110553, 0.431554),
(0.283197, 0.115680, 0.436115),
(0.283229, 0.120777, 0.440584),
(0.283187, 0.125848, 0.444960),
(0.283072, 0.130895, 0.449241),
(0.282884, 0.135920, 0.453427),
(0.282623, 0.140926, 0.457517),
(0.282290, 0.145912, 0.461510),
(0.281887, 0.150881, 0.465405),
(0.281412, 0.155834, 0.469201),
(0.280868, 0.160771, 0.472899),
(0.280255, 0.165693, 0.476498),
(0.279574, 0.170599, 0.479997),
(0.278826, 0.175490, 0.483397),
(0.278012, 0.180367, 0.486697),
(0.277134, 0.185228, 0.489898),
(0.276194, 0.190074, 0.493001),
(0.275191, 0.194905, 0.496005),
(0.274128, 0.199721, 0.498911),
(0.273006, 0.204520, 0.501721),
(0.271828, 0.209303, 0.504434),
(0.270595, 0.214069, 0.507052),
(0.269308, 0.218818, 0.509577),
(0.267968, 0.223549, 0.512008),
(0.266580, 0.228262, 0.514349),
(0.265145, 0.232956, 0.516599),
(0.263663, 0.237631, 0.518762),
(0.262138, 0.242286, 0.520837),
(0.260571, 0.246922, 0.522828),
(0.258965, 0.251537, 0.524736),
(0.257322, 0.256130, 0.526563),
(0.255645, 0.260703, 0.528312),
(0.253935, 0.265254, 0.529983),
(0.252194, 0.269783, 0.531579),
(0.250425, 0.274290, 0.533103),
(0.248629, 0.278775, 0.534556),
(0.246811, 0.283237, 0.535941),
(0.244972, 0.287675, 0.537260),
(0.243113, 0.292092, 0.538516),
(0.241237, 0.296485, 0.539709),
(0.239346, 0.300855, 0.540844),
(0.237441, 0.305202, 0.541921),
(0.235526, 0.309527, 0.542944),
(0.233603, 0.313828, 0.543914),
(0.231674, 0.318106, 0.544834),
(0.229739, 0.322361, 0.545706),
(0.227802, 0.326594, 0.546532),
(0.225863, 0.330805, 0.547314),
(0.223925, 0.334994, 0.548053),
(0.221989, 0.339161, 0.548752),
(0.220057, 0.343307, 0.549413),
(0.218130, 0.347432, 0.550038),
(0.216210, 0.351535, 0.550627),
(0.214298, 0.355619, 0.551184),
(0.212395, 0.359683, 0.551710),
(0.210503, 0.363727, 0.552206),
(0.208623, 0.367752, 0.552675),
(0.206756, 0.371758, 0.553117),
(0.204903, 0.375746, 0.553533),
(0.203063, 0.379716, 0.553925),
(0.201239, 0.383670, 0.554294),
(0.199430, 0.387607, 0.554642),
(0.197636, 0.391528, 0.554969),
(0.195860, 0.395433, 0.555276),
(0.194100, 0.399323, 0.555565),
(0.192357, 0.403199, 0.555836),
(0.190631, 0.407061, 0.556089),
(0.188923, 0.410910, 0.556326),
(0.187231, 0.414746, 0.556547),
(0.185556, 0.418570, 0.556753),
(0.183898, 0.422383, 0.556944),
(0.182256, 0.426184, 0.557120),
(0.180629, 0.429975, 0.557282),
(0.179019, 0.433756, 0.557430),
(0.177423, 0.437527, 0.557565),
(0.175841, 0.441290, 0.557685),
(0.174274, 0.445044, 0.557792),
(0.172719, 0.448791, 0.557885),
(0.171176, 0.452530, 0.557965),
(0.169646, 0.456262, 0.558030),
(0.168126, 0.459988, 0.558082),
(0.166617, 0.463708, 0.558119),
(0.165117, 0.467423, 0.558141),
(0.163625, 0.471133, 0.558148),
(0.162142, 0.474838, 0.558140),
(0.160665, 0.478540, 0.558115),
(0.159194, 0.482237, 0.558073),
(0.157729, 0.485932, 0.558013),
(0.156270, 0.489624, 0.557936),
(0.154815, 0.493313, 0.557840),
(0.153364, 0.497000, 0.557724),
(0.151918, 0.500685, 0.557587),
(0.150476, 0.504369, 0.557430),
(0.149039, 0.508051, 0.557250),
(0.147607, 0.511733, 0.557049),
(0.146180, 0.515413, 0.556823),
(0.144759, 0.519093, 0.556572),
(0.143343, 0.522773, 0.556295),
(0.141935, 0.526453, 0.555991),
(0.140536, 0.530132, 0.555659),
(0.139147, 0.533812, 0.555298),
(0.137770, 0.537492, 0.554906),
(0.136408, 0.541173, 0.554483),
(0.135066, 0.544853, 0.554029),
(0.133743, 0.548535, 0.553541),
(0.132444, 0.552216, 0.553018),
(0.131172, 0.555899, 0.552459),
(0.129933, 0.559582, 0.551864),
(0.128729, 0.563265, 0.551229),
(0.127568, 0.566949, 0.550556),
(0.126453, 0.570633, 0.549841),
(0.125394, 0.574318, 0.549086),
(0.124395, 0.578002, 0.548287),
(0.123463, 0.581687, 0.547445),
(0.122606, 0.585371, 0.546557),
(0.121831, 0.589055, 0.545623),
(0.121148, 0.592739, 0.544641),
(0.120565, 0.596422, 0.543611),
(0.120092, 0.600104, 0.542530),
(0.119738, 0.603785, 0.541400),
(0.119512, 0.607464, 0.540218),
(0.119423, 0.611141, 0.538982),
(0.119483, 0.614817, 0.537692),
(0.119699, 0.618490, 0.536347),
(0.120081, 0.622161, 0.534946),
(0.120638, 0.625828, 0.533488),
(0.121380, 0.629492, 0.531973),
(0.122312, 0.633153, 0.530398),
(0.123444, 0.636809, 0.528763),
(0.124780, 0.640461, 0.527068),
(0.126326, 0.644107, 0.525311),
(0.128087, 0.647749, 0.523491),
(0.130067, 0.651384, 0.521608),
(0.132268, 0.655014, 0.519661),
(0.134692, 0.658636, 0.517649),
(0.137339, 0.662252, 0.515571),
(0.140210, 0.665859, 0.513427),
(0.143303, 0.669459, 0.511215),
(0.146616, 0.673050, 0.508936),
(0.150148, 0.676631, 0.506589),
(0.153894, 0.680203, 0.504172),
(0.157851, 0.683765, 0.501686),
(0.162016, 0.687316, 0.499129),
(0.166383, 0.690856, 0.496502),
(0.170948, 0.694384, 0.493803),
(0.175707, 0.697900, 0.491033),
(0.180653, 0.701402, 0.488189),
(0.185783, 0.704891, 0.485273),
(0.191090, 0.708366, 0.482284),
(0.196571, 0.711827, 0.479221),
(0.202219, 0.715272, 0.476084),
(0.208030, 0.718701, 0.472873),
(0.214000, 0.722114, 0.469588),
(0.220124, 0.725509, 0.466226),
(0.226397, 0.728888, 0.462789),
(0.232815, 0.732247, 0.459277),
(0.239374, 0.735588, 0.455688),
(0.246070, 0.738910, 0.452024),
(0.252899, 0.742211, 0.448284),
(0.259857, 0.745492, 0.444467),
(0.266941, 0.748751, 0.440573),
(0.274149, 0.751988, 0.436601),
(0.281477, 0.755203, 0.432552),
(0.288921, 0.758394, 0.428426),
(0.296479, 0.761561, 0.424223),
(0.304148, 0.764704, 0.419943),
(0.311925, 0.767822, 0.415586),
(0.319809, 0.770914, 0.411152),
(0.327796, 0.773980, 0.406640),
(0.335885, 0.777018, 0.402049),
(0.344074, 0.780029, 0.397381),
(0.352360, 0.783011, 0.392636),
(0.360741, 0.785964, 0.387814),
(0.369214, 0.788888, 0.382914),
(0.377779, 0.791781, 0.377939),
(0.386433, 0.794644, 0.372886),
(0.395174, 0.797475, 0.367757),
(0.404001, 0.800275, 0.362552),
(0.412913, 0.803041, 0.357269),
(0.421908, 0.805774, 0.351910),
(0.430983, 0.808473, 0.346476),
(0.440137, 0.811138, 0.340967),
(0.449368, 0.813768, 0.335384),
(0.458674, 0.816363, 0.329727),
(0.468053, 0.818921, 0.323998),
(0.477504, 0.821444, 0.318195),
(0.487026, 0.823929, 0.312321),
(0.496615, 0.826376, 0.306377),
(0.506271, 0.828786, 0.300362),
(0.515992, 0.831158, 0.294279),
(0.525776, 0.833491, 0.288127),
(0.535621, 0.835785, 0.281908),
(0.545524, 0.838039, 0.275626),
(0.555484, 0.840254, 0.269281),
(0.565498, 0.842430, 0.262877),
(0.575563, 0.844566, 0.256415),
(0.585678, 0.846661, 0.249897),
(0.595839, 0.848717, 0.243329),
(0.606045, 0.850733, 0.236712),
(0.616293, 0.852709, 0.230052),
(0.626579, 0.854645, 0.223353),
(0.636902, 0.856542, 0.216620),
(0.647257, 0.858400, 0.209861),
(0.657642, 0.860219, 0.203082),
(0.668054, 0.861999, 0.196293),
(0.678489, 0.863742, 0.189503),
(0.688944, 0.865448, 0.182725),
(0.699415, 0.867117, 0.175971),
(0.709898, 0.868751, 0.169257),
(0.720391, 0.870350, 0.162603),
(0.730889, 0.871916, 0.156029),
(0.741388, 0.873449, 0.149561),
(0.751884, 0.874951, 0.143228),
(0.762373, 0.876424, 0.137064),
(0.772852, 0.877868, 0.131109),
(0.783315, 0.879285, 0.125405),
(0.793760, 0.880678, 0.120005),
(0.804182, 0.882046, 0.114965),
(0.814576, 0.883393, 0.110347),
(0.824940, 0.884720, 0.106217),
(0.835270, 0.886029, 0.102646),
(0.845561, 0.887322, 0.099702),
(0.855810, 0.888601, 0.097452),
(0.866013, 0.889868, 0.095953),
(0.876168, 0.891125, 0.095250),
(0.886271, 0.892374, 0.095374),
(0.896320, 0.893616, 0.096335),
(0.906311, 0.894855, 0.098125),
(0.916242, 0.896091, 0.100717),
(0.926106, 0.897330, 0.104071),
(0.935904, 0.898570, 0.108131),
(0.945636, 0.899815, 0.112838),
(0.955300, 0.901065, 0.118128),
(0.964894, 0.902323, 0.123941),
(0.974417, 0.903590, 0.130215),
(0.983868, 0.904867, 0.136897),
(0.993248, 0.906157, 0.143936)]
}.get_lut(N)
}
ColorMaps::Plasma => {
ListedColormap{
lut: vec![(0.050383, 0.029803, 0.527975),
(0.063536, 0.028426, 0.533124),
(0.075353, 0.027206, 0.538007),
(0.086222, 0.026125, 0.542658),
(0.096379, 0.025165, 0.547103),
(0.105980, 0.024309, 0.551368),
(0.115124, 0.023556, 0.555468),
(0.123903, 0.022878, 0.559423),
(0.132381, 0.022258, 0.563250),
(0.140603, 0.021687, 0.566959),
(0.148607, 0.021154, 0.570562),
(0.156421, 0.020651, 0.574065),
(0.164070, 0.020171, 0.577478),
(0.171574, 0.019706, 0.580806),
(0.178950, 0.019252, 0.584054),
(0.186213, 0.018803, 0.587228),
(0.193374, 0.018354, 0.590330),
(0.200445, 0.017902, 0.593364),
(0.207435, 0.017442, 0.596333),
(0.214350, 0.016973, 0.599239),
(0.221197, 0.016497, 0.602083),
(0.227983, 0.016007, 0.604867),
(0.234715, 0.015502, 0.607592),
(0.241396, 0.014979, 0.610259),
(0.248032, 0.014439, 0.612868),
(0.254627, 0.013882, 0.615419),
(0.261183, 0.013308, 0.617911),
(0.267703, 0.012716, 0.620346),
(0.274191, 0.012109, 0.622722),
(0.280648, 0.011488, 0.625038),
(0.287076, 0.010855, 0.627295),
(0.293478, 0.010213, 0.629490),
(0.299855, 0.009561, 0.631624),
(0.306210, 0.008902, 0.633694),
(0.312543, 0.008239, 0.635700),
(0.318856, 0.007576, 0.637640),
(0.325150, 0.006915, 0.639512),
(0.331426, 0.006261, 0.641316),
(0.337683, 0.005618, 0.643049),
(0.343925, 0.004991, 0.644710),
(0.350150, 0.004382, 0.646298),
(0.356359, 0.003798, 0.647810),
(0.362553, 0.003243, 0.649245),
(0.368733, 0.002724, 0.650601),
(0.374897, 0.002245, 0.651876),
(0.381047, 0.001814, 0.653068),
(0.387183, 0.001434, 0.654177),
(0.393304, 0.001114, 0.655199),
(0.399411, 0.000859, 0.656133),
(0.405503, 0.000678, 0.656977),
(0.411580, 0.000577, 0.657730),
(0.417642, 0.000564, 0.658390),
(0.423689, 0.000646, 0.658956),
(0.429719, 0.000831, 0.659425),
(0.435734, 0.001127, 0.659797),
(0.441732, 0.001540, 0.660069),
(0.447714, 0.002080, 0.660240),
(0.453677, 0.002755, 0.660310),
(0.459623, 0.003574, 0.660277),
(0.465550, 0.004545, 0.660139),
(0.471457, 0.005678, 0.659897),
(0.477344, 0.006980, 0.659549),
(0.483210, 0.008460, 0.659095),
(0.489055, 0.010127, 0.658534),
(0.494877, 0.011990, 0.657865),
(0.500678, 0.014055, 0.657088),
(0.506454, 0.016333, 0.656202),
(0.512206, 0.018833, 0.655209),
(0.517933, 0.021563, 0.654109),
(0.523633, 0.024532, 0.652901),
(0.529306, 0.027747, 0.651586),
(0.534952, 0.031217, 0.650165),
(0.540570, 0.034950, 0.648640),
(0.546157, 0.038954, 0.647010),
(0.551715, 0.043136, 0.645277),
(0.557243, 0.047331, 0.643443),
(0.562738, 0.051545, 0.641509),
(0.568201, 0.055778, 0.639477),
(0.573632, 0.060028, 0.637349),
(0.579029, 0.064296, 0.635126),
(0.584391, 0.068579, 0.632812),
(0.589719, 0.072878, 0.630408),
(0.595011, 0.077190, 0.627917),
(0.600266, 0.081516, 0.625342),
(0.605485, 0.085854, 0.622686),
(0.610667, 0.090204, 0.619951),
(0.615812, 0.094564, 0.617140),
(0.620919, 0.098934, 0.614257),
(0.625987, 0.103312, 0.611305),
(0.631017, 0.107699, 0.608287),
(0.636008, 0.112092, 0.605205),
(0.640959, 0.116492, 0.602065),
(0.645872, 0.120898, 0.598867),
(0.650746, 0.125309, 0.595617),
(0.655580, 0.129725, 0.592317),
(0.660374, 0.134144, 0.588971),
(0.665129, 0.138566, 0.585582),
(0.669845, 0.142992, 0.582154),
(0.674522, 0.147419, 0.578688),
(0.679160, 0.151848, 0.575189),
(0.683758, 0.156278, 0.571660),
(0.688318, 0.160709, 0.568103),
(0.692840, 0.165141, 0.564522),
(0.697324, 0.169573, 0.560919),
(0.701769, 0.174005, 0.557296),
(0.706178, 0.178437, 0.553657),
(0.710549, 0.182868, 0.550004),
(0.714883, 0.187299, 0.546338),
(0.719181, 0.191729, 0.542663),
(0.723444, 0.196158, 0.538981),
(0.727670, 0.200586, 0.535293),
(0.731862, 0.205013, 0.531601),
(0.736019, 0.209439, 0.527908),
(0.740143, 0.213864, 0.524216),
(0.744232, 0.218288, 0.520524),
(0.748289, 0.222711, 0.516834),
(0.752312, 0.227133, 0.513149),
(0.756304, 0.231555, 0.509468),
(0.760264, 0.235976, 0.505794),
(0.764193, 0.240396, 0.502126),
(0.768090, 0.244817, 0.498465),
(0.771958, 0.249237, 0.494813),
(0.775796, 0.253658, 0.491171),
(0.779604, 0.258078, 0.487539),
(0.783383, 0.262500, 0.483918),
(0.787133, 0.266922, 0.480307),
(0.790855, 0.271345, 0.476706),
(0.794549, 0.275770, 0.473117),
(0.798216, 0.280197, 0.469538),
(0.801855, 0.284626, 0.465971),
(0.805467, 0.289057, 0.462415),
(0.809052, 0.293491, 0.458870),
(0.812612, 0.297928, 0.455338),
(0.816144, 0.302368, 0.451816),
(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;
}
}

83
src/lib.rs Normal file
View file

@ -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
View file

@ -7,6 +7,7 @@ use nannou::prelude::*;
use v4l::{Buffer, CaptureDevice, MappedBufferStream}; use v4l::{Buffer, CaptureDevice, MappedBufferStream};
use image; use image;
mod visualhaar; mod visualhaar;
mod heatmap;
// use std::fs::File; // use std::fs::File;
@ -22,16 +23,37 @@ fn main() {
warn!("test"); warn!("test");
unsafe{ // unsafe{
CAMERA = Some(CaptureDevice::new(2)
.expect("Failed to open device") let device_id = 0;
// .format(640, 480, b"RGB3")
.format(424, 240, b"RGB3") if let Ok(dev) = CaptureDevice::new(device_id) {
// .format(320, 240, b"RGB3")
.expect("Failed to set format") let formats = dev.enumerate_formats();
.fps(30) if let Ok(formats) = formats {
.expect("Failed to set frame interval")); 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) nannou::app(model)
.event(event) .event(event)
@ -45,6 +67,7 @@ struct Model<'a> {
_window: window::Id, _window: window::Id,
image: Option<nannou::image::DynamicImage>, image: Option<nannou::image::DynamicImage>,
haar: visualhaar::HaarClassifier, haar: visualhaar::HaarClassifier,
heatmap: Option<heatmap::Heatmap>,
haar_outcome: Option<visualhaar::Outcome>, 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(); let haar = visualhaar::HaarClassifier::from_xml("haarcascade_frontalface_alt2.xml").unwrap();
println!("Haar: {:?}", haar); // println!("Haar: {:?}", haar);
Model { Model {
@ -90,6 +113,7 @@ fn model<'a>(app: &App) -> Model<'a> {
_window: _window, _window: _window,
image: None, image: None,
haar: haar, haar: haar,
heatmap: Some(heatmap::Heatmap::new(heatmap::ColorMaps::Plasma)),
haar_outcome: None, haar_outcome: None,
} }
} }
@ -134,7 +158,8 @@ fn update(_app: &App, _model: &mut Model, _update: Update) {
// ib.map( nannou::image::DynamicImage::ImageRgb8); // ib.map( nannou::image::DynamicImage::ImageRgb8);
// let ib_bw = nannou::image::imageops::grayscale(&ib); // let ib_bw = nannou::image::imageops::grayscale(&ib);
// _model.image = Some(nannou::image::DynamicImage::ImageLuma8(ib_bw)); // _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.haar_outcome = Some(outcome);
// _model.image = Some(nannou::image::DynamicImage::ImageRgb8(ib)); // _model.image = Some(nannou::image::DynamicImage::ImageRgb8(ib));
@ -164,6 +189,8 @@ fn view(_app: &App, _model: &Model, frame: Frame){
Some(outcome) => { Some(outcome) => {
// let i = outcome.dyn(/); // let i = outcome.dyn(/);
// let img // ::from(&outcome.dynamic_img); // 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 img = image::DynamicImage::ImageRgb8(outcome.dynamic_img.to_rgb()).resize(1000, 1000, image::imageops::FilterType::Triangle);
let texture = wgpu::Texture::from_image(_app, &img); let texture = wgpu::Texture::from_image(_app, &img);

17
src/test.rs
View file

@ -29,7 +29,7 @@ fn main() {
// println!("Haar: {:?}", haar); // 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"); 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); // let ib_bw = nannou::image::imageops::grayscale(&ib);
// _model.image = Some(nannou::image::DynamicImage::ImageLuma8(ib_bw)); // _model.image = Some(nannou::image::DynamicImage::ImageLuma8(ib_bw));
let i = ib.as_rgb8().unwrap().clone(); 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::NipySpectral);
let hm = heatmap::Heatmap::new(heatmap::ColorMaps::TraficLight); // 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");
info!("Scanning for faces took {}ms", sw.elapsed_ms()); 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)); // _model.image = Some(nannou::image::DynamicImage::ImageRgb8(ib));
} }

22
src/visualhaar.rs
View file

@ -6,6 +6,7 @@ use std::{convert::TryInto, error::Error};
use stopwatch::{Stopwatch}; use stopwatch::{Stopwatch};
use ndarray as nd; use ndarray as nd;
use super::heatmap as heatmap;
/// A haarclasifier based on opencv cascade XML files /// A haarclasifier based on opencv cascade XML files
/// Structure info from https://answers.opencv.org/question/8418/explanation-of-cascadexml-in-a-haar-classifier/ /// 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); // info!("Draw {} {} {} {} ({:?}),", x1, y1, x2, y2,self);
let mut rect = draw_window.slice_mut(s![y1..y2, x1..x2]); // semi slow (initially 500ms) 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) rect += self.weight; // super slow (initially 10.000 ms)
// info!("add")
// for x in x1..x2{ // for x in x1..x2{
// for y in y1..y2{ // for y in y1..y2{
// draw_window[[y, x]] = draw_window[[y, x]] as f64 + self.weight; // draw_window[[y, x]] = draw_window[[y, x]] as f64 + self.weight;
@ -139,9 +140,9 @@ impl HaarClassifier {
// root: <opencv_storage> // root: <opencv_storage>
let root_el = doc.root().first_element_child().unwrap(); let root_el = doc.root().first_element_child().unwrap();
println!("{:?}", root_el); // println!("{:?}", root_el);
let cascade = root_el.first_element_child().unwrap(); 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 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(); 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. /// 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 img_bw = image::imageops::grayscale(&frame);
// let mut output_image = image::GrayImage::new(frame.width(), frame.height()); // let mut output_image = image::GrayImage::new(frame.width(), frame.height());
@ -341,7 +342,7 @@ impl HaarClassifier {
img_bw.dimensions().0 as usize, 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]); // 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(img_bw);
let dynamic = image::DynamicImage::ImageLuma8(final_img); 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{ Ok(Outcome{
// frame: img_bw, // frame: img_bw,
dynamic_img: dynamic, dynamic_img: dynamic,
@ -439,6 +450,7 @@ impl HaarClassifier {
classifier.right classifier.right
}; };
// TODO remove to use all stages (we need to speed up somewhere else)
// if i > 2{ // if i > 2{
// break; // break;
// } // }