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ros2 working with image inject

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This commit is contained in:
Timo Schneider
2025-05-11 16:59:38 +02:00
parent 8ec107cf29
commit 74414934c4
6 changed files with 199 additions and 191 deletions
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226
src/main.rs
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@@ -1,180 +1,19 @@
mod pipeline; mod pipeline;
use hdbscan::{Hdbscan, HdbscanHyperParams};
use rand::Rng; use rand::Rng;
use rayon::prelude::*; use rayon::prelude::*;
use show_image::{create_window, ImageInfo, ImageView, PixelFormat}; use std::collections::VecDeque;
use std::collections::{HashMap, VecDeque};
use std::iter::zip;
use std::sync::mpsc::Receiver;
use std::sync::{mpsc, Arc, Mutex}; use std::sync::{mpsc, Arc, Mutex};
use std::thread; use std::thread;
use std::time::{Duration, Instant}; use std::time::{Duration, Instant};
use indicatif::{MultiProgress, ProgressBar, ProgressStyle}; use indicatif::{MultiProgress, ProgressBar, ProgressStyle};
use nalgebra::{DMatrix};
use ndarray::{Array1, Array2};
use r2r::QosProfile; use r2r::QosProfile;
use futures::{executor::LocalPool, future, stream::StreamExt, task::LocalSpawnExt}; use futures::{executor::LocalPool, future, stream::StreamExt, task::LocalSpawnExt};
use image::{ImageReader, Rgb, RgbImage};
use show_image::{create_window, ImageInfo, ImageView, PixelFormat};
use crate::pipeline::hdb_clusterer::create_pointcloud_labels_thread;
use crate::pipeline::highlight_extractor::{create_extract_thread, Pointcloud}; use crate::pipeline::highlight_extractor::{create_extract_thread, Pointcloud};
use crate::pipeline::m_estimator::create_mestimator_thread;
fn create_pointcloud_labels_thread(pointcloud_rx: Receiver<Arc<Vec<Vec<f64>>>>) -> (Receiver<(Arc<Vec<Vec<f64>>>, Arc<Vec<i32>>)>, Arc<Mutex<(Duration, Duration)>>) {
let (pointcloud_labels_tx, pointcloud_labels_rx) = mpsc::sync_channel::<(Arc<Vec<Vec<f64>>>, Arc<Vec<i32>>)>(1);
let usage = Arc::new(Mutex::new((Duration::ZERO, Duration::ZERO)));
let local_usage = usage.clone();
thread::spawn(move || {
let mut t_loop = Instant::now();
for pointcloud in pointcloud_rx {
if pointcloud.len() < 2 {continue;}
let start = Instant::now();
let params = HdbscanHyperParams::builder()
.epsilon(1000.0)
.min_samples(3)
.allow_single_cluster(false)
.min_cluster_size(2)
.build();
let labels = Arc::new(Hdbscan::new(&pointcloud, params).cluster().unwrap());
let end = Instant::now();
pointcloud_labels_tx.send((pointcloud, labels)).unwrap();
*local_usage.lock().unwrap() = (end - start, t_loop.elapsed());
t_loop = Instant::now();
}
});
(pointcloud_labels_rx, usage)
}
fn create_cluster_separator_thread(cluster_labels: Receiver<(Arc<Vec<Vec<f64>>>, Arc<Vec<i32>>)>) -> (Receiver<Arc<HashMap<i32, Vec<Vec<f64>>>>>, Arc<Mutex<(Duration, Duration)>>) {
let (clusters_tx, clusters_rx) = mpsc::sync_channel::<Arc<HashMap<i32, Vec<Vec<f64>>>>>(1);
let usage = Arc::new(Mutex::new((Duration::ZERO, Duration::ZERO)));
let local_usage = usage.clone();
thread::spawn(move || {
let mut t_loop = Instant::now();
for (pointcloud, labels) in cluster_labels {
let start = Instant::now();
let mut clusters = HashMap::<i32, Vec<Vec<f64>>>::with_capacity(5);
let _ = zip(pointcloud.iter(), labels.iter()).for_each(|(point, label)| {
if !clusters.contains_key(label) { clusters.insert(*label, Vec::new()); }
clusters.get_mut(label).unwrap().push(point.clone());
});
let end = Instant::now();
clusters_tx.send(Arc::new(clusters)).unwrap();
*local_usage.lock().unwrap() = (end - start, t_loop.elapsed());
t_loop = Instant::now();
}
});
(clusters_rx, usage)
}
fn create_isolate_lane_thread(clusters_rx: Receiver<Arc<HashMap<i32, Vec<Vec<f64>>>>>) -> (Receiver<Arc<[Vec<Vec<f64>>; 2]>>, Arc<Mutex<(Duration, Duration)>>) {
let (lanes_tx, lanes_rx) = mpsc::sync_channel::<Arc<[Vec<Vec<f64>>; 2]>>(1);
let usage = Arc::new(Mutex::new((Duration::ZERO, Duration::ZERO)));
let local_usage = usage.clone();
thread::spawn(move || {
let mut t_loop = Instant::now();
for clusters in clusters_rx {
let start = Instant::now();
let mut averages = clusters.iter().filter(|(label, _)| **label != -1).map(|(label, cluster)| {
(*label, cluster.iter().map(|point| point[0]).sum::<f64>() / (cluster.len() as f64))
}).collect::<Vec<(i32, f64)>>();
averages.sort_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap());
if averages.len() < 2 {continue;}
let end = Instant::now();
lanes_tx.send(Arc::new([clusters.get(&averages[0].0).unwrap().clone(), clusters.get(&averages[1].0 ).unwrap().clone()])).unwrap() ;
*local_usage.lock().unwrap() = (end - start, t_loop.elapsed());
t_loop = Instant::now();
}
});
(lanes_rx, usage)
}
fn create_mestimator_thread(lanes_rx: Receiver<Arc<[Vec<Vec<f64>>; 2]>>) -> (Receiver<(Arc<Vec<f64>>, Arc<[Vec<Vec<f64>>; 2]>)>, Arc<Mutex<(Duration, Duration)>>) {
let (z_tx, z_rx) = mpsc::sync_channel::<(Arc<Vec<f64>>, Arc<[Vec<Vec<f64>>; 2]>)>(1);
let usage = Arc::new(Mutex::new((Duration::ZERO, Duration::ZERO)));
let local_usage = usage.clone();
let c = 0.1 * 2.3849;
thread::spawn(move || {
let mut z = vec![4.0, -2.0, 0.0, 0.0];
let mut t_loop = Instant::now();
for lanes in lanes_rx {
let start = Instant::now();
let mut p = Vec::<Vec<f64>>::new();
p.append(&mut lanes[0].clone());
p.append(&mut lanes[1].clone());
let y = Array1::from_vec(p.iter().map(|point| point[0]).collect());
let mut H: Array2<f64> = Array2::from_shape_vec((0, 4), Vec::<f64>::new()).unwrap();
lanes[0].clone().into_iter().map(|point| point[1]).for_each(|x| {
let a = vec![0.5, -1.0, -x, 0.5*x.powi(2)];
let b: Array1<f64> = Array1::from_vec(a);
H.push_row(b.view()).unwrap()
});
lanes[1].clone().into_iter().map(|point| point[1]).for_each(|x| {
let a = vec![-0.5, -1.0, -x, 0.5*x.powi(2)];
let b: Array1<f64> = Array1::from_vec(a);
H.push_row(b.view()).unwrap()
});
let H_t = H.t();
for _ in 0..3 {
let res_l = lanes[0]
.iter()
.map(|point| point[0] - (0.5 * z[0] - z[1] - point[1] * z[2] + 0.5 * z[3] * point[1].powi(2)))
.map(|r| 1.0/(1.0 + (r/c).powi(2)))
.collect::<Vec<_>>();
let res_r = lanes[1]
.iter()
.map(|point| point[0] - (-0.5 * z[0] - z[1] - point[0] * z[2] + 0.5 * z[3] * point[1].powi(2)))
.map(|r| 1.0/(1.0 + (r/c).powi(2)))
.collect::<Vec<_>>();
let mut v = Vec::<f64>::new();
v.append(&mut res_l.clone());
v.append(&mut res_r.clone());
let w = Array2::from_diag(&Array1::from_vec(v));
let first_part: Array2<f64> = H_t.dot(&w).dot(&H);
let a_nalgebra = DMatrix::from_row_slice(4, 4, first_part.as_slice().unwrap());
// Perform the matrix inversion using nalgebra
let inv = a_nalgebra.pseudo_inverse(0.0).unwrap();
// Convert the nalgebra matrix back to Array2<f64>
let second_part: Array2<f64> = Array2::from_shape_vec((4, 4), inv.as_slice().to_vec()).unwrap();
z = second_part.dot(&H_t).dot(&w).dot(&y).to_vec();
}
let end = Instant::now();
z_tx.send((Arc::new(z.clone()), lanes)).unwrap();
*local_usage.lock().unwrap() = (end - start, t_loop.elapsed());
t_loop = Instant::now();
}
});
(z_rx, usage)
}
#[show_image::main] #[show_image::main]
fn main() { fn main() {
@@ -189,32 +28,29 @@ fn main() {
let mut pool = LocalPool::new(); let mut pool = LocalPool::new();
let spawner = pool.spawner(); let spawner = pool.spawner();
let (pointcloud_rx, usage_extract) = create_extract_thread(img_rx, 160, 0.025); let (pointcloud_rx, usage_extract) = create_extract_thread(img_rx, 196, 0.025);
let (cluster_labels_rx, usage_cluster) = create_pointcloud_labels_thread(pointcloud_rx); let (cluster_labels_rx, usage_cluster) = create_pointcloud_labels_thread(pointcloud_rx);
let (clusters_rx, usage_split) = create_cluster_separator_thread(cluster_labels_rx); let (z_rx, usage_detect) = create_mestimator_thread(cluster_labels_rx);
let (lanes_rx, usage_isolate) = create_isolate_lane_thread(clusters_rx); /*
let (z_rx, usage_detect) = create_mestimator_thread(lanes_rx);
let multi_stats = MultiProgress::new(); let multi_stats = MultiProgress::new();
let p_style = ProgressStyle::default_bar().template("{prefix:15} {bar:50.gray/white} {pos:>6} us | {len:>6} us").unwrap(); let p_style = ProgressStyle::default_bar().template("{prefix:15} {bar:50.gray/white} {pos:>6} us | {len:>6} us").unwrap();
let p_extract = multi_stats.add(ProgressBar::new(100).with_prefix("Extract").with_style(p_style.clone())); let p_extract = multi_stats.add(ProgressBar::new(100).with_prefix("Extract").with_style(p_style.clone()));
let p_cluster = multi_stats.add(ProgressBar::new(100).with_prefix("Cluster").with_style(p_style.clone())); let p_cluster = multi_stats.add(ProgressBar::new(100).with_prefix("Cluster").with_style(p_style.clone()));
let p_split = multi_stats.add(ProgressBar::new(100).with_prefix("Split").with_style(p_style.clone()));
let p_isolate = multi_stats.add(ProgressBar::new(100).with_prefix("Isolate").with_style(p_style.clone()));
let p_detect = multi_stats.add(ProgressBar::new(100).with_prefix("Detect").with_style(p_style.clone())); let p_detect = multi_stats.add(ProgressBar::new(100).with_prefix("Detect").with_style(p_style.clone()));
let p_max = multi_stats.add(ProgressBar::new(100).with_prefix("Pipeline load").with_style(ProgressStyle::default_bar().template("{prefix:15} {bar:50.red/white} {percent:>3}%").unwrap())); let p_max = multi_stats.add(ProgressBar::new(100).with_prefix("Pipeline load").with_style(ProgressStyle::default_bar().template("{prefix:15} {bar:50.red/white} {percent:>3}%").unwrap()));
let p_avg_ms = multi_stats.add(ProgressBar::new(100_000).with_prefix("Pipeline delay").with_style(ProgressStyle::default_bar().template("{prefix:15} {bar:50.cyan/white} {pos:>6} us").unwrap())); let p_avg_ms = multi_stats.add(ProgressBar::new(100_000).with_prefix("Pipeline delay").with_style(ProgressStyle::default_bar().template("{prefix:15} {bar:50.cyan/white} {pos:>6} us").unwrap()));
let p_framerate = multi_stats.add(ProgressBar::new(150).with_prefix("Framerate").with_style(ProgressStyle::default_bar().template("{prefix:15} {bar:50.blue/white} {pos:>3} FPS").unwrap())); let p_framerate = multi_stats.add(ProgressBar::new(150).with_prefix("Framerate").with_style(ProgressStyle::default_bar().template("{prefix:15} {bar:50.blue/white} {pos:>3} FPS").unwrap()));
*/
let window = create_window("image", Default::default()).unwrap();
let mut t: VecDeque<Duration> = VecDeque::with_capacity(25); let mut t: VecDeque<Duration> = VecDeque::with_capacity(25);
let mut now: Instant = Instant::now(); let mut now: Instant = Instant::now();
let img = ImageReader::open("./images/lane_detection_loop_80.png").unwrap().decode().unwrap();
// Run the subscriber in one task, printing the messages // Run the subscriber in one task, printing the messages
spawner.spawn_local(async move { spawner.spawn_local(async move {
subscriber subscriber
.for_each(|msg| { .for_each(|mut msg| {
msg.data = img.clone().into_bytes();
img_tx.send(Arc::new(msg)).unwrap(); img_tx.send(Arc::new(msg)).unwrap();
future::ready(()) future::ready(())
}) })
@@ -222,7 +58,30 @@ fn main() {
}).unwrap(); }).unwrap();
thread::spawn(move || { thread::spawn(move || {
for _ in z_rx {} let window = create_window("image", Default::default()).unwrap();
for z in z_rx {
let mut rgb_image = RgbImage::new(800, 800);
let colors = vec![Rgb([0, 255, 0]), Rgb([255, 0, 0])];
(0..rgb_image.height()).for_each(|x| {
let x_m = x as f64 * (45.0 / 800.0);
let const_part = - z[1] - x_m * z[2] + 0.5 * z[3] * x_m.powi(2);
let l = (0.5 * z[0] + const_part) * (800.0/45.0);;
let r = (-0.5 * z[0] + const_part) * (800.0/45.0);;
if l.abs() < rgb_image.width() as f64 / 2.0 - 1.0 {
rgb_image.put_pixel((rgb_image.width() as f64 /2.0 + l) as u32, rgb_image.height() - 1 - x, colors[0]);
}
if r.abs() < rgb_image.width() as f64 / 2.0 - 1.0 {
rgb_image.put_pixel((rgb_image.width() as f64/2.0 + r) as u32, rgb_image.height() - 1 - x, colors[1]);
}
});
let image = ImageView::new(ImageInfo::new(PixelFormat::Rgb8, 800, 800), rgb_image.iter().as_slice());
window.set_image("camera", image).unwrap();
}
}); });
loop { loop {
@@ -231,28 +90,21 @@ fn main() {
let u_extract = *usage_extract.lock().unwrap(); let u_extract = *usage_extract.lock().unwrap();
let u_cluster = *usage_cluster.lock().unwrap(); let u_cluster = *usage_cluster.lock().unwrap();
let u_split = *usage_split.lock().unwrap();
let u_isolate = *usage_isolate.lock().unwrap();
let u_detect = *usage_detect.lock().unwrap(); let u_detect = *usage_detect.lock().unwrap();
let u_vec = vec![u_extract, u_cluster, u_split, u_isolate, u_detect]; let u_vec = vec![u_extract, u_cluster, u_detect];
/*
p_extract.set_length(u_extract.1.as_micros() as u64); p_extract.set_length(u_extract.1.as_micros() as u64);
p_extract.set_position(u_extract.0.as_micros() as u64); p_extract.set_position(u_extract.0.as_micros() as u64);
p_cluster.set_length(u_cluster.1.as_micros() as u64); p_cluster.set_length(u_cluster.1.as_micros() as u64);
p_cluster.set_position(u_cluster.0.as_micros() as u64); p_cluster.set_position(u_cluster.0.as_micros() as u64);
p_split.set_length(u_split.1.as_micros() as u64);
p_split.set_position(u_split.0.as_micros() as u64);
p_isolate.set_length(u_isolate.1.as_micros() as u64);
p_isolate.set_position(u_isolate.0.as_micros() as u64);
p_detect.set_length(u_detect.1.as_micros() as u64); p_detect.set_length(u_detect.1.as_micros() as u64);
p_detect.set_position(u_detect.0.as_micros() as u64); p_detect.set_position(u_detect.0.as_micros() as u64);
p_max.set_position(u_vec.iter().map(|(t, d)| ((t.as_secs_f64() / d.as_secs_f64()) * 100.0) as u64).max().unwrap()); p_max.set_position(u_vec.iter().map(|(t, d)| ((t.as_secs_f64() / d.as_secs_f64()) * 100.0) as u64).max().unwrap());
p_avg_ms.set_position(u_vec.iter().map(|(t, _)| t).sum::<Duration>().as_micros() as u64); p_avg_ms.set_position(u_vec.iter().map(|(t, _)| t).sum::<Duration>().as_micros() as u64);
p_framerate.set_position((1.0 / (t.iter().map(|d| d.as_secs_f64()).sum::<f64>() / t.len() as f64)) as u64); p_framerate.set_position((1.0 / (t.iter().map(|d| d.as_secs_f64()).sum::<f64>() / t.len() as f64)) as u64);
*/
if t.len() == 25 { if t.len() == 25 {
t.pop_front(); t.pop_front();
} }

4
src/pipeline.rs
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@@ -1 +1,3 @@
pub mod highlight_extractor; pub mod highlight_extractor;
pub mod hdb_clusterer;
pub mod m_estimator;

89
src/pipeline/hdb_clusterer.rs Normal file
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@@ -0,0 +1,89 @@
use std::collections::HashMap;
use std::iter::zip;
use std::sync::{mpsc, Arc, Mutex};
use std::sync::mpsc::Receiver;
use std::thread;
use std::time::{Duration, Instant};
use hdbscan::{Hdbscan, HdbscanHyperParams};
use r2r::indexmap::IndexMap;
use crate::pipeline::highlight_extractor::Pointcloud;
type PointcloudLabels = Arc<Vec<i32>>;
pub type PointcloudLabeled = Arc<Vec<(Vec<f64>, i32)>>;
trait Label {
fn label(&self) -> Option<PointcloudLabeled>;
}
trait SortLabeled {
fn sort_horizontally(&self) -> PointcloudLabeled;
}
impl SortLabeled for PointcloudLabeled {
fn sort_horizontally(&self) -> PointcloudLabeled {
let mut averages: HashMap<i32, Vec<f64>> = HashMap::new();
self.iter().filter(|(_, label)| *label >= 0).for_each(|(point, label)| {
if !averages.contains_key(label) {
averages.insert(*label, Vec::new());
}
averages.get_mut(label).unwrap().push(point[1]);
});
let mut average: Vec<(i32, f64)> = averages.into_iter().map(|(label, y_values)| (label, y_values.iter().sum::<f64>() / y_values.len() as f64)).collect();
average.sort_by(|(_, a), (_, b)| a.partial_cmp(&b).unwrap());
println!("{:?}", average);
let sorted_map: IndexMap<_, _> = average.iter().enumerate().map(|(new_label, (old_label, _))| (old_label, new_label)).collect();
Arc::new(self.iter()
.filter(|(_, label)| *label >= 0)
.map(|(point, label)| (point.clone(), *sorted_map.get(label).unwrap() as i32))
.filter(|(_, label)| *label == 0 || *label == average.len() as i32 - 1)
.collect())
}
}
impl Label for Pointcloud {
fn label(&self) -> Option<PointcloudLabeled> {
if self.len() < 75 { return None }
let params = HdbscanHyperParams::builder()
.epsilon(10000.0)
.min_samples(3)
.allow_single_cluster(false)
.min_cluster_size(20)
.build();
let labels = Hdbscan::new(self, params).cluster().unwrap();
let pointcloud_labeled: PointcloudLabeled = Arc::new(zip(self.iter(), labels)
.map(|(point, label)| (point.clone(), label))
.collect());
Some(pointcloud_labeled)
}
}
pub fn create_pointcloud_labels_thread(pointcloud_rx: Receiver<Pointcloud>) -> (Receiver<PointcloudLabeled>, Arc<Mutex<(Duration, Duration)>>) {
let (pointcloud_labels_tx, pointcloud_labels_rx) = mpsc::sync_channel::<PointcloudLabeled>(1);
let usage = Arc::new(Mutex::new((Duration::ZERO, Duration::ZERO)));
let local_usage = usage.clone();
thread::spawn(move || {
let mut t_loop = Instant::now();
for pointcloud in pointcloud_rx {
let start = Instant::now();
if let Some(labeled_pointcloud) = pointcloud.label() {
let clusters = labeled_pointcloud.sort_horizontally();
let end = Instant::now();
pointcloud_labels_tx.send(clusters).unwrap();
*local_usage.lock().unwrap() = (end - start, t_loop.elapsed());
t_loop = Instant::now();
}
}
});
(pointcloud_labels_rx, usage)
}

5
src/pipeline/highlight_extractor.rs
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@@ -4,7 +4,6 @@ use std::thread;
use std::time::{Duration, Instant}; use std::time::{Duration, Instant};
use rand::Rng; use rand::Rng;
use r2r::sensor_msgs::msg::Image; use r2r::sensor_msgs::msg::Image;
use rayon::prelude::IntoParallelRefIterator;
pub type Pointcloud = Arc<Vec<Vec<f64>>>; pub type Pointcloud = Arc<Vec<Vec<f64>>>;
@@ -42,8 +41,8 @@ impl ExtractHighlights for Image {
let v_norm = v / w; let v_norm = v / w;
vec![u_norm, v_norm] vec![u_norm, v_norm]
}) })
.filter(|point| point[0].abs() <= 5.0) //.filter(|point| point[0].abs() <= 5.0)
.collect()) .collect())
} }
} }

66
src/pipeline/m_estimator.rs
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@@ -0,0 +1,66 @@
use std::sync::{mpsc, Arc, Mutex};
use std::sync::mpsc::Receiver;
use std::thread;
use std::time::{Duration, Instant};
use nalgebra::DMatrix;
use ndarray::{Array1, Array2};
use crate::pipeline::hdb_clusterer::PointcloudLabeled;
pub type LaneEstimation = Arc<Vec<f64>>;
pub fn create_mestimator_thread(lanes_rx: Receiver<PointcloudLabeled>) -> (Receiver<LaneEstimation>, Arc<Mutex<(Duration, Duration)>>) {
let (z_tx, z_rx) = mpsc::sync_channel::<LaneEstimation>(1);
let usage = Arc::new(Mutex::new((Duration::ZERO, Duration::ZERO)));
let local_usage = usage.clone();
let c = 0.1 * 2.3849;
thread::spawn(move || {
let mut z = vec![4.0, -2.0, 0.0, 0.0];
let mut t_loop = Instant::now();
for lanes in lanes_rx {
let start = Instant::now();
let y = Array1::from_vec(lanes.iter().map(|(point, label)| point[0]).collect());
let mut H: Array2<f64> = Array2::from_shape_vec((0, 4), Vec::<f64>::new()).unwrap();
lanes.iter().for_each(|(point, label)| {
let a= vec![if *label == 0 {0.5} else {-0.5}, -1.0, -point[1], 0.5*point[1].powi(2)];
let b: Array1<f64> = Array1::from_vec(a);
H.push_row(b.view()).unwrap()
});
let H_t = H.t();
for _ in 0..3 {
let res = lanes
.iter()
.map(|(point, label)| point[0] - (if *label == 0 {0.5} else {-0.5} * z[0] - z[1] - point[1] * z[2] + 0.5 * z[3] * point[1].powi(2)))
.map(|r| 1.0/(1.0 + (r/c).powi(2)))
.collect::<Vec<_>>();
let w = Array2::from_diag(&Array1::from_vec(res));
let first_part: Array2<f64> = H_t.dot(&w).dot(&H);
let a_nalgebra = DMatrix::from_row_slice(4, 4, first_part.as_slice().unwrap());
// Perform the matrix inversion using nalgebra
let inv = a_nalgebra.pseudo_inverse(0.0).unwrap();
// Convert the nalgebra matrix back to Array2<f64>
let second_part: Array2<f64> = Array2::from_shape_vec((4, 4), inv.as_slice().to_vec()).unwrap();
z = second_part.dot(&H_t).dot(&w).dot(&y).to_vec();
}
let end = Instant::now();
z_tx.send(Arc::new(z.clone())).unwrap();
*local_usage.lock().unwrap() = (end - start, t_loop.elapsed());
t_loop = Instant::now();
}
});
(z_rx, usage)
}

0
src/pipeline/pointcloud_clusterer.rs
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