diff --git a/Cargo.toml b/Cargo.toml index 3a34d6f..4dd50a0 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -14,6 +14,11 @@ path = "src/main.rs" name = "visual_haarcascades_test" path = "src/test.rs" +[lib] +name = "visual_haarcascades_lib" +path = "src/lib.rs" +crate-type = ["dylib"] + [dependencies] nannou = "0.14" # clang-sys = "0.29.3" diff --git a/src/heatmap.rs b/src/heatmap.rs index 522cd5b..52e5db6 100644 --- a/src/heatmap.rs +++ b/src/heatmap.rs @@ -3,53 +3,39 @@ use image; pub enum ColorMaps{ Binary, + /// matplotlib NipySpectral, - TraficLight, + /// matplotlib + Viridis, + /// matplotlib + Plasma, +} + +trait Colormap{ + fn get_lut(&self, N: usize) -> Vec<[u8; 3]>; + } #[derive(Debug)] -pub struct ColorMap{ +struct LinearSegmentedColormap{ pub red: Vec<(f64, f64, f64)>, pub green: Vec<(f64, f64, f64)>, pub blue: Vec<(f64, f64, f64)>, } + #[derive(Debug)] -pub struct Heatmap{ - pub cm: ColorMap +struct ListedColormap{ + pub lut: Vec<(f64, f64, f64)>, } +pub struct Heatmap{ + pub lut: Vec<[u8; 3]> +} -impl Heatmap{ - pub fn new(cm: ColorMaps) -> Self{ - Self{ - 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; +impl Colormap for LinearSegmentedColormap{ + fn get_lut(&self, N: usize) -> Vec<[u8; 3]>{ + return self.generate_lut(N); } } @@ -67,79 +53,7 @@ impl Heatmap{ // } // } -impl ColorMap{ - 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; - } - - +impl LinearSegmentedColormap{ /// Similar to MatplotLib LinearSegmentedColormap /// @see https://github.com/matplotlib/matplotlib/blob/13e3573b721210d84865d148aab7f63cc2fc95a6/lib/matplotlib/colors.py /// """ @@ -222,4 +136,649 @@ impl ColorMap{ lut } -} \ No newline at end of file +} + +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, 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(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; + } + +} diff --git a/src/lib.rs b/src/lib.rs new file mode 100644 index 0000000..325d9a6 --- /dev/null +++ b/src/lib.rs @@ -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, &mut [u8]> = image::ImageBuffer::from_raw(width as u32, height as u32, buffer).unwrap(); + let input_frame: image::ImageBuffer, Vec> = 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); +} \ No newline at end of file diff --git a/src/main.rs b/src/main.rs index 44c8dfe..ac59a60 100644 --- a/src/main.rs +++ b/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, haar: visualhaar::HaarClassifier, + heatmap: Option, haar_outcome: Option, } @@ -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); diff --git a/src/test.rs b/src/test.rs index 2c9a50b..c532c96 100644 --- a/src/test.rs +++ b/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)); } diff --git a/src/visualhaar.rs b/src/visualhaar.rs index 6f4c0c2..735b7d7 100644 --- a/src/visualhaar.rs +++ b/src/visualhaar.rs @@ -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: 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, Vec>) -> Result { + pub fn scan_image(&self, frame: image::ImageBuffer, Vec>, heatmap: &Option) -> Result { 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; // }