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tidy for tag support

This commit is contained in:
stelzo 2024-05-17 14:28:14 +02:00
parent 91a81436c7
commit 1654cdedfd
13 changed files with 3 additions and 1799 deletions
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11
Cargo.toml
View File

@ -35,8 +35,8 @@ rosrust = { version = "0.9.11", optional = true }
r2r = { version = "0.8.4", optional = true }
rayon = { version = "1", optional = true }
nalgebra = { version = "0.32.5", optional = true, default-features = false }
rpcl2_derive = { path = "rpcl2_derive", optional = true }
type-layout = { path = "type-layout", optional = true }
rpcl2-derive = { version = "0.1", optional = true }
type-layout = { version = "0.2", package = "type-layout-syn2", optional = true }
sensor_msgs = { version = "*", optional = true }
std_msgs = { version = "*", optional = true }
@ -46,16 +46,12 @@ builtin_interfaces = { version = "*", optional = true }
rand = "0.8"
criterion = { version = "0.5", features = ["html_reports"] }
[[bench]]
name = "roundtrip"
harness = false
[features]
rclrs_msg = ["dep:sensor_msgs", "dep:std_msgs", "dep:builtin_interfaces"]
rosrust_msg = ["dep:rosrust_msg", "dep:rosrust"]
r2r_msg = ["dep:r2r"]
rayon = ["dep:rayon"]
derive = ["dep:rpcl2_derive", "dep:type-layout"]
derive = ["dep:rpcl2-derive", "dep:type-layout"]
nalgebra = ["dep:nalgebra"]
std = ["nalgebra/std"]
@ -65,4 +61,3 @@ default = ["std", "derive", "rclrs_msg"]
features = ["derive", "nalgebra", "rayon"]
default-target = "x86_64-unknown-linux-gnu"
rustdoc-args = ["--cfg", "docsrs"]

695
benches/roundtrip.rs
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@ -1,695 +0,0 @@
use criterion::{black_box, criterion_group, criterion_main, Criterion};
use ros_pointcloud2::prelude::*;
use rand::Rng;
pub type PointXYZB = PointXYZINormal;
pub fn distance_to_origin(point: &PointXYZ) -> f32 {
((point.x.powi(2)) + (point.y.powi(2)) + (point.z.powi(2))).sqrt()
}
pub fn dot_product(point1: &PointXYZ, point2: &PointXYZ) -> f32 {
point1.x * point2.x + point1.y * point2.y + point1.z * point2.z
}
pub fn cross_product(point1: &PointXYZ, point2: &PointXYZ) -> PointXYZ {
PointXYZ {
x: point1.y * point2.z - point1.z * point2.y,
y: point1.z * point2.x - point1.x * point2.z,
z: point1.x * point2.y - point1.y * point2.x,
}
}
pub fn scalar_multiply(point: &PointXYZ, scalar: f32) -> PointXYZ {
PointXYZ {
x: point.x * scalar,
y: point.y * scalar,
z: point.z * scalar,
}
}
pub fn magnitude_squared(point: &PointXYZ) -> f32 {
(point.x.powi(2)) + (point.y.powi(2)) + (point.z.powi(2))
}
pub fn reflection_through_plane(
point: &PointXYZ,
normal: &PointXYZ,
point_on_plane: &PointXYZ,
) -> PointXYZ {
PointXYZ {
x: point.x
- 2.0
* ((point.x - point_on_plane.x) * normal.x
+ (point.y - point_on_plane.y) * normal.y
+ (point.z - point_on_plane.z) * normal.z),
y: point.y
- 2.0
* ((point.x - point_on_plane.x) * normal.x
+ (point.y - point_on_plane.y) * normal.y
+ (point.z - point_on_plane.z) * normal.z),
z: point.z
- 2.0
* ((point.x - point_on_plane.x) * normal.x
+ (point.y - point_on_plane.y) * normal.y
+ (point.z - point_on_plane.z) * normal.z),
}
}
pub fn rotation_about_x(point: &PointXYZ, angle: f32) -> PointXYZ {
let c = f32::cos(angle);
let s = f32::sin(angle);
PointXYZ {
x: point.x,
y: point.y * c - point.z * s,
z: point.y * s + point.z * c,
}
}
pub fn closest_point_on_line(
point: &PointXYZ,
line_point: &PointXYZ,
line_direction: &PointXYZ,
) -> PointXYZ {
PointXYZ {
x: line_point.x
+ (line_point.x - point.x) * ((line_point.x - point.x).powi(2))
/ ((line_direction.x * 2.0).powi(2))
+ (line_direction.y * 2.0) * (point.z - line_point.z)
/ ((line_direction.z * 2.0).powi(2)),
y: line_point.y
+ (line_point.y - point.y) * ((line_point.y - point.y).powi(2))
/ ((line_direction.y * 2.0).powi(2))
+ (line_direction.x * 2.0) * (point.x - line_point.x)
/ ((line_direction.x * 2.0).powi(2)),
z: line_point.z
+ (line_point.z - point.z) * ((line_point.z - point.z).powi(2))
/ ((line_direction.z * 2.0).powi(2))
+ (line_direction.y * 2.0) * (point.y - line_point.y)
/ ((line_direction.y * 2.0).powi(2)),
}
}
fn minus(point1: &PointXYZ, point2: &PointXYZ) -> PointXYZ {
PointXYZ {
x: point1.x - point2.x,
y: point1.y - point2.y,
z: point1.z - point2.z,
}
}
pub fn generate_random_pointcloud(num_points: usize, min: f32, max: f32) -> Vec<PointXYZB> {
let mut rng = rand::thread_rng();
let mut pointcloud = Vec::with_capacity(num_points);
for _ in 0..num_points {
let point = PointXYZB {
x: rng.gen_range(min..max),
y: rng.gen_range(min..max),
z: rng.gen_range(min..max),
..Default::default()
};
pointcloud.push(point);
}
pointcloud
}
pub fn heavy_computing(point: &PointXYZ, iterations: u32) -> f32 {
let mut result = distance_to_origin(point);
for _ in 0..iterations {
result += dot_product(
point,
&PointXYZ {
x: 1.0,
y: 2.0,
z: 3.0,
},
);
result += cross_product(
point,
&PointXYZ {
x: 4.0,
y: 5.0,
z: 6.0,
},
)
.x;
result = result + (result * 10.0).sqrt();
let reflected_point = reflection_through_plane(
point,
&PointXYZ {
x: 7.0,
y: 8.0,
z: 9.0,
},
&PointXYZ {
x: 3.0,
y: 4.0,
z: 5.0,
},
);
let rotated_point = rotation_about_x(
&PointXYZ {
x: 10.0,
y: 11.0,
z: 12.0,
},
std::f32::consts::PI / 2.0,
);
result += magnitude_squared(&minus(&reflected_point, &rotated_point));
}
result
}
#[cfg(feature = "derive")]
fn roundtrip_vec(cloud: Vec<PointXYZB>) -> bool {
let orig_len = cloud.len();
let internal_msg = PointCloud2Msg::try_from_vec(cloud).unwrap();
let total: Vec<PointXYZ> = internal_msg.try_into_vec().unwrap();
orig_len == total.len()
}
fn roundtrip(cloud: Vec<PointXYZB>) -> bool {
let orig_len = cloud.len();
let internal_msg = PointCloud2Msg::try_from_iter(cloud).unwrap();
let total = internal_msg
.try_into_iter()
.unwrap()
.collect::<Vec<PointXYZ>>();
orig_len == total.len()
}
#[cfg(feature = "derive")]
fn roundtrip_filter_vec(cloud: Vec<PointXYZB>) -> bool {
let orig_len = cloud.len();
let internal_msg = PointCloud2Msg::try_from_vec(cloud).unwrap();
let total = internal_msg
.try_into_iter()
.unwrap()
.filter(|point: &PointXYZ| distance_to_origin(point) < 69.9)
.fold(PointXYZ::default(), |acc, point| PointXYZ {
x: acc.x + point.x,
y: acc.y + point.y,
z: acc.z + point.z,
});
orig_len == total.x as usize
}
fn roundtrip_filter(cloud: Vec<PointXYZB>) -> bool {
let orig_len = cloud.len();
let internal_msg = PointCloud2Msg::try_from_iter(cloud).unwrap();
let total = internal_msg
.try_into_iter()
.unwrap()
.filter(|point: &PointXYZ| distance_to_origin(point) < 69.9)
.fold(PointXYZ::default(), |acc, point| PointXYZ {
x: acc.x + point.x,
y: acc.y + point.y,
z: acc.z + point.z,
});
orig_len == total.x as usize
}
fn roundtrip_computing(cloud: Vec<PointXYZB>) -> bool {
let internal_msg = PointCloud2Msg::try_from_iter(cloud).unwrap();
let total = internal_msg
.try_into_iter()
.unwrap()
.map(|point: PointXYZ| heavy_computing(&point, 100))
.sum::<f32>();
total > 0.0
}
#[cfg(feature = "rayon")]
fn roundtrip_computing_par(cloud: Vec<PointXYZB>) -> bool {
let internal_msg = PointCloud2Msg::try_from_iter(cloud).unwrap();
let total = internal_msg
.try_into_par_iter()
.unwrap()
.map(|point: PointXYZ| heavy_computing(&point, 100))
.sum::<f32>();
total > 0.0
}
#[cfg(feature = "rayon")]
fn roundtrip_computing_par_par(cloud: Vec<PointXYZB>) -> bool {
let internal_msg = PointCloud2Msg::try_from_par_iter(cloud.into_par_iter()).unwrap();
let total = internal_msg
.try_into_par_iter()
.unwrap()
.map(|point: PointXYZ| heavy_computing(&point, 100))
.sum::<f32>();
total > 0.0
}
#[cfg(feature = "derive")]
fn roundtrip_computing_vec(cloud: Vec<PointXYZB>) -> bool {
let internal_msg = PointCloud2Msg::try_from_vec(cloud).unwrap();
let total: f32 = internal_msg
.try_into_vec()
.unwrap()
.into_iter()
.map(|point: PointXYZ| heavy_computing(&point, 100))
.sum();
total > 0.0
}
#[cfg(feature = "rayon")]
fn roundtrip_par(cloud: Vec<PointXYZB>) -> bool {
let orig_len = cloud.len();
let internal_msg = PointCloud2Msg::try_from_iter(cloud).unwrap();
let total = internal_msg
.try_into_par_iter()
.unwrap()
.collect::<Vec<PointXYZ>>();
orig_len != total.len()
}
#[cfg(feature = "rayon")]
fn roundtrip_par_par(cloud: Vec<PointXYZB>) -> bool {
let orig_len = cloud.len();
let internal_msg = PointCloud2Msg::try_from_par_iter(cloud.into_par_iter()).unwrap();
let total = internal_msg
.try_into_par_iter()
.unwrap()
.collect::<Vec<PointXYZ>>();
orig_len != total.len()
}
#[cfg(feature = "rayon")]
fn roundtrip_filter_par(cloud: Vec<PointXYZB>) -> bool {
let orig_len: usize = cloud.len();
let internal_msg = PointCloud2Msg::try_from_iter(cloud).unwrap();
let total = internal_msg
.try_into_par_iter()
.unwrap()
.filter(|point: &PointXYZ| distance_to_origin(point) < 69.9)
.reduce(PointXYZ::default, |acc, point| PointXYZ {
x: acc.x + point.x,
y: acc.y + point.y,
z: acc.z + point.z,
});
orig_len == total.x as usize
}
#[cfg(feature = "rayon")]
fn roundtrip_filter_par_par(cloud: Vec<PointXYZB>) -> bool {
let orig_len: usize = cloud.len();
let internal_msg = PointCloud2Msg::try_from_par_iter(cloud.into_par_iter()).unwrap();
let total = internal_msg
.try_into_par_iter()
.unwrap()
.filter(|point: &PointXYZ| distance_to_origin(point) < 69.9)
.reduce(PointXYZ::default, |acc, point| PointXYZ {
x: acc.x + point.x,
y: acc.y + point.y,
z: acc.z + point.z,
});
orig_len == total.x as usize
}
fn roundtrip_benchmark(c: &mut Criterion) {
let cloud_points_16k = generate_random_pointcloud(16_000, f32::MIN / 2.0, f32::MAX / 2.0);
let cloud_points_60k = generate_random_pointcloud(60_000, f32::MIN / 2.0, f32::MAX / 2.0);
let cloud_points_120k = generate_random_pointcloud(120_000, f32::MIN / 2.0, f32::MAX / 2.0);
let cloud_points_500k = generate_random_pointcloud(500_000, f32::MIN / 2.0, f32::MAX / 2.0);
let cloud_points_1_5m = generate_random_pointcloud(1_500_000, f32::MIN / 2.0, f32::MAX / 2.0);
// 16k points (Velodyne with 16 beams)
// Moving memory
c.bench_function("16k iter", |b| {
b.iter(|| {
black_box(roundtrip(cloud_points_16k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("16k iter_par", |b| {
b.iter(|| {
black_box(roundtrip_par(cloud_points_16k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("16k iter_par_par", |b| {
b.iter(|| {
black_box(roundtrip_par_par(cloud_points_16k.clone()));
})
});
#[cfg(feature = "derive")]
c.bench_function("16k vec", |b| {
b.iter(|| {
black_box(roundtrip_vec(cloud_points_16k.clone()));
})
});
// Simple distance filter
c.bench_function("16k iter_filter", |b| {
b.iter(|| {
roundtrip_filter(black_box(cloud_points_16k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("16k filter_par", |b| {
b.iter(|| {
roundtrip_filter_par(black_box(cloud_points_16k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("16k filter_par_par", |b| {
b.iter(|| {
black_box(roundtrip_filter_par_par(cloud_points_16k.clone()));
})
});
#[cfg(feature = "derive")]
c.bench_function("16k vec_filter", |b| {
b.iter(|| {
roundtrip_filter_vec(black_box(cloud_points_16k.clone()));
})
});
// Heavy computing
c.bench_function("16k iter_compute", |b| {
b.iter(|| {
roundtrip_computing(black_box(cloud_points_16k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("16k iter_compute_par", |b| {
b.iter(|| {
roundtrip_computing_par(black_box(cloud_points_16k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("16k iter_compute_par_par", |b| {
b.iter(|| {
roundtrip_computing_par_par(black_box(cloud_points_16k.clone()));
})
});
#[cfg(feature = "derive")]
c.bench_function("16k vec_compute", |b| {
b.iter(|| {
roundtrip_computing_vec(black_box(cloud_points_16k.clone()));
})
});
// 60k points (Ouster with 64 beams)
// Moving memory
c.bench_function("60k iter", |b| {
b.iter(|| {
black_box(roundtrip(cloud_points_60k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("60k iter_par", |b| {
b.iter(|| {
black_box(roundtrip_par(cloud_points_60k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("60k iter_par_par", |b| {
b.iter(|| {
black_box(roundtrip_par_par(cloud_points_60k.clone()));
})
});
#[cfg(feature = "derive")]
c.bench_function("60k vec", |b| {
b.iter(|| {
black_box(roundtrip_vec(cloud_points_60k.clone()));
})
});
// 120k points (Ouster with 128 beams)
// Moving memory
c.bench_function("120k iter", |b| {
b.iter(|| {
black_box(roundtrip(cloud_points_120k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("120k iter_par", |b| {
b.iter(|| {
black_box(roundtrip_par(cloud_points_120k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("120k iter_par_par", |b| {
b.iter(|| {
black_box(roundtrip_par_par(cloud_points_120k.clone()));
})
});
#[cfg(feature = "derive")]
c.bench_function("120k vec", |b| {
b.iter(|| {
black_box(roundtrip_vec(cloud_points_120k.clone()));
})
});
// Simple distance filter
c.bench_function("120k iter_filter", |b| {
b.iter(|| {
roundtrip_filter(black_box(cloud_points_120k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("120k filter_par", |b| {
b.iter(|| {
roundtrip_filter_par(black_box(cloud_points_120k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("120k filter_par_par", |b| {
b.iter(|| {
black_box(roundtrip_filter_par_par(cloud_points_120k.clone()));
})
});
#[cfg(feature = "derive")]
c.bench_function("120k vec_filter", |b| {
b.iter(|| {
roundtrip_filter_vec(black_box(cloud_points_120k.clone()));
})
});
// Heavy computing
c.bench_function("120k iter_compute", |b| {
b.iter(|| {
roundtrip_computing(black_box(cloud_points_120k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("120k iter_compute_par", |b| {
b.iter(|| {
roundtrip_computing_par(black_box(cloud_points_120k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("120k iter_compute_par_par", |b| {
b.iter(|| {
roundtrip_computing_par_par(black_box(cloud_points_120k.clone()));
})
});
#[cfg(feature = "derive")]
c.bench_function("120k vec_compute", |b| {
b.iter(|| {
roundtrip_computing_vec(black_box(cloud_points_120k.clone()));
})
});
// 500k points (just to show how it scales)
// Moving memory
c.bench_function("500k iter", |b| {
b.iter(|| {
black_box(roundtrip(cloud_points_500k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("500k iter_par", |b| {
b.iter(|| {
black_box(roundtrip_par(cloud_points_500k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("500k iter_par_par", |b| {
b.iter(|| {
black_box(roundtrip_par_par(cloud_points_500k.clone()));
})
});
#[cfg(feature = "derive")]
c.bench_function("500k vec", |b| {
b.iter(|| {
black_box(roundtrip_vec(cloud_points_500k.clone()));
})
});
// Simple distance filter
c.bench_function("500k iter_filter", |b| {
b.iter(|| {
roundtrip_filter(black_box(cloud_points_500k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("500k filter_par", |b| {
b.iter(|| {
roundtrip_filter_par(black_box(cloud_points_500k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("500k filter_par_par", |b| {
b.iter(|| {
black_box(roundtrip_filter_par_par(cloud_points_500k.clone()));
})
});
#[cfg(feature = "derive")]
c.bench_function("500k vec_filter", |b| {
b.iter(|| {
roundtrip_filter_vec(black_box(cloud_points_500k.clone()));
})
});
// Heavy computing
c.bench_function("500k iter_compute", |b| {
b.iter(|| {
roundtrip_computing(black_box(cloud_points_500k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("500k iter_compute_par", |b| {
b.iter(|| {
roundtrip_computing_par(black_box(cloud_points_500k.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("500k iter_compute_par_par", |b| {
b.iter(|| {
roundtrip_computing_par_par(black_box(cloud_points_500k.clone()));
})
});
#[cfg(feature = "derive")]
c.bench_function("500k vec_compute", |b| {
b.iter(|| {
roundtrip_computing_vec(black_box(cloud_points_500k.clone()));
})
});
// 1.5m points (scale of small localmaps in SLAM)
// Moving memory
c.bench_function("1.5m iter", |b| {
b.iter(|| {
black_box(roundtrip(cloud_points_1_5m.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("1.5m iter_par", |b| {
b.iter(|| {
black_box(roundtrip_par(cloud_points_1_5m.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("1.5m iter_par_par", |b| {
b.iter(|| {
black_box(roundtrip_par_par(cloud_points_1_5m.clone()));
})
});
#[cfg(feature = "derive")]
c.bench_function("1.5m vec", |b| {
b.iter(|| {
black_box(roundtrip_vec(cloud_points_1_5m.clone()));
})
});
// Simple distance filter
c.bench_function("1.5m iter_filter", |b| {
b.iter(|| {
roundtrip_filter(black_box(cloud_points_1_5m.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("1.5m iter_par_filter", |b| {
b.iter(|| {
black_box(roundtrip_filter_par(cloud_points_1_5m.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("1.5m iter_par_par_filter", |b| {
b.iter(|| {
black_box(roundtrip_filter_par_par(cloud_points_1_5m.clone()));
})
});
#[cfg(feature = "derive")]
c.bench_function("1.5m vec_filter", |b| {
b.iter(|| {
roundtrip_filter_vec(black_box(cloud_points_1_5m.clone()));
})
});
// Heavy computing
c.bench_function("1.5m iter_compute", |b| {
b.iter(|| {
roundtrip_computing(black_box(cloud_points_1_5m.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("1.5m iter_compute_par", |b| {
b.iter(|| {
roundtrip_computing_par(black_box(cloud_points_1_5m.clone()));
})
});
#[cfg(feature = "rayon")]
c.bench_function("1.5m iter_compute_par_par", |b| {
b.iter(|| {
roundtrip_computing_par_par(black_box(cloud_points_1_5m.clone()));
})
});
#[cfg(feature = "derive")]
c.bench_function("1.5m vec_compute", |b| {
b.iter(|| {
roundtrip_computing_vec(black_box(cloud_points_1_5m.clone()));
})
});
}
criterion_group!(benches, roundtrip_benchmark);
criterion_main!(benches);

159
examples/custom_enum_field_filter.rs
View File

@ -1,159 +0,0 @@
/// This example demonstrates how to use a custom point with encoded metadata.
/// The use case is a segmentation point cloud where each point holds a label and we want to filter by it.
/// Since the datatypes for the PointCloud2 message are very limited,
/// we need to encode the enum into a supported type.
/// This needs some manual work to tell the library how to encode and decode the enum.
///
/// Important Note: This example is only possible with disabled `derive` feature,
/// because the library (currently) does not know the size of your chosen supported type at compile time.
/// This makes direct copies impossible.
use ros_pointcloud2::prelude::*;
#[derive(Debug, PartialEq, Clone, Default, Copy)]
enum Label {
#[default]
Human,
Deer,
Car,
}
// Define a custom point with an enum.
// This is normally not supported by PointCloud2 but we will explain the library how to handle it.
#[derive(Debug, PartialEq, Clone, Default)]
struct CustomPoint {
x: f32,
y: f32,
z: f32,
intensity: f32,
my_custom_label: Label,
}
// Some convenience functions to convert between the enum and u8.
impl From<Label> for u8 {
fn from(label: Label) -> Self {
match label {
Label::Human => 0,
Label::Deer => 1,
Label::Car => 2,
}
}
}
impl From<u8> for Label {
fn from(label: u8) -> Self {
match label {
0 => Label::Human,
1 => Label::Deer,
2 => Label::Car,
_ => panic!("Invalid label"),
}
}
}
impl CustomPoint {
fn new(x: f32, y: f32, z: f32, intensity: f32, my_custom_label: Label) -> Self {
Self {
x,
y,
z,
intensity,
my_custom_label,
}
}
}
// We implement the PointConvertible trait (needed for every custom point).
// RPCL2Point is the internal representation. It takes the amount of fields as generic arguments.
impl From<CustomPoint> for RPCL2Point<5> {
fn from(point: CustomPoint) -> Self {
[
point.x.into(),
point.y.into(),
point.z.into(),
point.intensity.into(),
u8::from(point.my_custom_label).into(),
]
.into()
}
}
impl From<RPCL2Point<5>> for CustomPoint {
fn from(point: RPCL2Point<5>) -> Self {
Self::new(
point[0].get(),
point[1].get(),
point[2].get(),
point[3].get(),
point[4].get(),
)
}
}
// Define wow we want to name the fields in the message.
impl Fields<5> for CustomPoint {
fn field_names_ordered() -> [&'static str; 5] {
["x", "y", "z", "intensity", "my_custom_label"]
}
}
// We implemented everything that is needed for PointConvertible so we declare it as a done.
#[cfg(not(feature = "derive"))]
impl PointConvertible<5> for CustomPoint {}
// Now we tell the library how to encode and decode the label.
// You don't need to do this if your CustomPoint has a field that is already supported by PointCloud2.
impl GetFieldDatatype for Label {
fn field_datatype() -> FieldDatatype {
FieldDatatype::U8 // Declare that we want to use u8 as the datatype for the label.
}
}
// Again, you don't need this with only supported field types.
// u8 -> Label
impl FromBytes for Label {
// Technically, PointCloud2 supports big and little endian even though it is rarely used.
// 'be' stands for big endian and 'le' for little endian.
fn from_be_bytes(bytes: PointDataBuffer) -> Self {
u8::from_be_bytes([bytes[0]]).into()
}
fn from_le_bytes(bytes: PointDataBuffer) -> Self {
u8::from_le_bytes([bytes[0]]).into()
}
}
// Label -> u8
impl From<Label> for PointDataBuffer {
fn from(label: Label) -> Self {
[u8::from(label)].into()
}
}
fn main() {
#[cfg(not(feature = "derive"))]
{
let cloud = vec![
CustomPoint::new(1.0, 2.0, 3.0, 4.0, Label::Deer),
CustomPoint::new(4.0, 5.0, 6.0, 7.0, Label::Car),
CustomPoint::new(7.0, 8.0, 9.0, 10.0, Label::Human),
];
println!("Original cloud: {:?}", cloud);
let msg = PointCloud2Msg::try_from_iter(cloud).unwrap();
println!("filtering by label == Deer");
let out = msg
.try_into_iter()
.unwrap()
.filter(|point: &CustomPoint| point.my_custom_label == Label::Deer)
.collect::<Vec<_>>();
println!("Filtered cloud: {:?}", out);
assert_eq!(
vec![CustomPoint::new(1.0, 2.0, 3.0, 4.0, Label::Deer),],
out
);
}
}

135
examples/rayon_bench.rs
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@ -1,135 +0,0 @@
use rand::Rng;
/// This example implements a naive benchmark for the library so you can evaluate the use of rayon for parallel processing.
/// It generates a random point cloud and measures the time it takes to iterate over it.
/// The code works mainly as a showcase. For actual benchmarks, check the `benches` directory or run `cargo bench`.
use std::time::Duration;
use ros_pointcloud2::prelude::*;
pub fn generate_random_pointcloud(num_points: usize, min: f32, max: f32) -> Vec<PointXYZ> {
let mut rng = rand::thread_rng();
let mut pointcloud = Vec::with_capacity(num_points);
for _ in 0..num_points {
let point = PointXYZ {
x: rng.gen_range(min..max),
y: rng.gen_range(min..max),
z: rng.gen_range(min..max),
};
pointcloud.push(point);
}
pointcloud
}
fn roundtrip(cloud: Vec<PointXYZ>) -> bool {
let orig_len = cloud.len();
let internal_msg = PointCloud2Msg::try_from_iter(cloud).unwrap();
let total = internal_msg
.try_into_iter()
.unwrap()
.collect::<Vec<PointXYZ>>();
orig_len == total.len()
}
fn roundtrip_filter(cloud: Vec<PointXYZ>) -> bool {
let orig_len = cloud.len();
let internal_msg = PointCloud2Msg::try_from_iter(cloud).unwrap();
let total = internal_msg
.try_into_iter()
.unwrap()
.filter(|point: &PointXYZ| {
(point.x.powi(2) + point.y.powi(2) + point.z.powi(2)).sqrt() < 1.9
})
.fold(PointXYZ::default(), |acc, point| PointXYZ {
x: acc.x + point.x,
y: acc.y + point.y,
z: acc.z + point.z,
});
orig_len == total.x as usize
}
#[cfg(feature = "rayon")]
fn roundtrip_par(cloud: Vec<PointXYZ>) -> bool {
let orig_len = cloud.len();
let internal_msg = PointCloud2Msg::try_from_iter(cloud).unwrap();
let total = internal_msg
.try_into_par_iter()
.unwrap()
.collect::<Vec<PointXYZ>>();
orig_len != total.len()
}
#[cfg(feature = "rayon")]
fn roundtrip_filter_par(cloud: Vec<PointXYZ>) -> bool {
let orig_len: usize = cloud.len();
let internal_msg = PointCloud2Msg::try_from_iter(cloud).unwrap();
let total = internal_msg
.try_into_par_iter()
.unwrap()
.filter(|point: &PointXYZ| {
(point.x.powi(2) + point.y.powi(2) + point.z.powi(2)).sqrt() < 1.9
})
.reduce(PointXYZ::default, |acc, point| PointXYZ {
x: acc.x + point.x,
y: acc.y + point.y,
z: acc.z + point.z,
});
orig_len == total.x as usize
}
// call measure_func X times and print the average time
fn measure_func_avg(
num_iterations: u32,
pcl_size: usize,
func: fn(Vec<PointXYZ>) -> bool,
) -> Duration {
let mut total_time = Duration::new(0, 0);
for _ in 0..num_iterations {
total_time += measure_func(pcl_size, func);
}
total_time / num_iterations
}
fn measure_func<F>(pcl_size: usize, func: F) -> Duration
where
F: Fn(Vec<PointXYZ>) -> bool,
{
let cloud_points = generate_random_pointcloud(pcl_size, f32::MIN / 2.0, f32::MAX / 2.0);
let start = std::time::Instant::now();
let _ = func(cloud_points);
start.elapsed()
}
fn main() {
println!("100k");
let how_many = 10_000;
let how_often = 1_000;
let dur = measure_func_avg(how_often, how_many, roundtrip);
println!("roundtrip: {:?}", dur);
#[cfg(feature = "rayon")]
let dur = measure_func_avg(how_often, how_many, roundtrip_par);
println!("roundtrip_par: {:?}", dur);
println!("200k");
let how_many = 200_000;
let how_often = 100;
let dur = measure_func_avg(how_often, how_many, roundtrip_filter);
println!("roundtrip_filter: {:?}", dur);
#[cfg(feature = "rayon")]
let dur = measure_func_avg(how_often, how_many, roundtrip_filter_par);
println!("roundtrip_filter_par: {:?}", dur);
println!("10m");
let how_many = 10_000_000;
let how_often = 10;
let dur = measure_func_avg(how_often, how_many, roundtrip_filter);
println!("roundtrip_filter: {:?}", dur);
#[cfg(feature = "rayon")]
let dur = measure_func_avg(how_often, how_many, roundtrip_filter_par);
println!("roundtrip_filter_par: {:?}", dur);
}

31
examples/simple_distance_filter.rs
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@ -1,31 +0,0 @@
/// This example demonstrates a very simple distance filter with predefined point types.
/// Note that this example is a simplified version of the custom_enum_field_filter.rs example.
/// Also, it effectively demonstrates a typesafe byte-to-byte buffer filter with a single iteration.
///
/// It also works without any dependencies, making it a good "hello world" example.
use ros_pointcloud2::prelude::*;
fn main() {
let cloud = vec![
PointXYZ::new(1.0, 1.0, 1.0),
PointXYZ::new(2.0, 2.0, 2.0),
PointXYZ::new(3.0, 3.0, 3.0),
];
println!("Original cloud: {:?}", cloud);
let msg = PointCloud2Msg::try_from_iter(cloud).unwrap();
println!("filtering by distance < 1.9m");
let out = msg
.try_into_iter()
.unwrap()
.filter(|point: &PointXYZ| {
(point.x.powi(2) + point.y.powi(2) + point.z.powi(2)).sqrt() < 1.9
})
.collect::<Vec<_>>();
println!("Filtered cloud: {:?}", out);
assert_eq!(vec![PointXYZ::new(1.0, 1.0, 1.0),], out);
}

12
rpcl2_derive/Cargo.toml
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@ -1,12 +0,0 @@
[package]
name = "rpcl2_derive"
version = "0.1.0"
edition = "2021"
[lib]
proc-macro = true
[dependencies]
syn = "2.0"
quote = "1.0"
proc-macro2 = "1.0"

197
rpcl2_derive/src/lib.rs
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@ -1,197 +0,0 @@
extern crate proc_macro;
use std::collections::HashMap;
use proc_macro::TokenStream;
use quote::{quote, ToTokens};
use syn::{parenthesized, parse_macro_input, DeriveInput, LitStr};
fn get_allowed_types() -> HashMap<&'static str, usize> {
let mut allowed_datatypes = HashMap::<&'static str, usize>::new();
allowed_datatypes.insert("f32", std::mem::size_of::<f32>());
allowed_datatypes.insert("f64", std::mem::size_of::<f64>());
allowed_datatypes.insert("i32", std::mem::size_of::<i32>());
allowed_datatypes.insert("u8", std::mem::size_of::<u8>());
allowed_datatypes.insert("u16", std::mem::size_of::<u16>());
allowed_datatypes.insert("u32", std::mem::size_of::<u32>());
allowed_datatypes.insert("i8", std::mem::size_of::<i8>());
allowed_datatypes.insert("i16", std::mem::size_of::<i16>());
allowed_datatypes
}
fn struct_field_rename_array(input: &DeriveInput) -> Vec<String> {
let fields = match input.data {
syn::Data::Struct(ref data) => match data.fields {
syn::Fields::Named(ref fields) => &fields.named,
_ => panic!("StructNames can only be derived for structs with named fields"),
},
_ => panic!("StructNames can only be derived for structs"),
};
let mut field_names = Vec::with_capacity(fields.len());
for f in fields.iter() {
if f.attrs.len() == 0 {
field_names.push(f.ident.as_ref().unwrap().to_token_stream().to_string());
} else {
f.attrs.iter().for_each(|attr| {
if attr.path().is_ident("rpcl2") {
let res = attr.parse_nested_meta(|meta| {
if meta.path.is_ident("rename") {
let new_name;
parenthesized!(new_name in meta.input);
let lit: LitStr = new_name.parse()?;
field_names.push(lit.value());
Ok(())
} else {
panic!("expected `name` attribute");
}
});
if let Err(err) = res {
panic!("Error parsing attribute: {}", err);
}
}
});
}
}
field_names
}
/// This macro implements the `Fields` trait which is a subset of the `PointConvertible` trait.
/// It is useful for points that convert the `From` trait themselves but want to use this macro for not repeating the field names.
///
/// You can rename the fields with the `rename` attribute.
///
/// Use the rename attribute if your struct field name should be different to the ROS field name.
#[proc_macro_derive(Fields, attributes(rpcl2))]
pub fn ros_point_fields_derive(input: TokenStream) -> TokenStream {
let input = parse_macro_input!(input as DeriveInput);
let struct_name = &input.ident;
let field_names = struct_field_rename_array(&input)
.into_iter()
.map(|field_name| {
quote! { #field_name }
});
let field_names_len = field_names.len();
let expanded = quote! {
impl Fields<#field_names_len> for #struct_name {
fn field_names_ordered() -> [&'static str; #field_names_len] {
[
#(#field_names,)*
]
}
}
};
// Return the generated implementation
expanded.into()
}
/// This macro implements the `PointConvertible` trait for your struct so you can use your point for the PointCloud2 conversion.
///
/// The struct field names are used in the message if you do not use the `rename` attribute for a custom name.
///
/// Note that the repr(C) attribute is required for the struct to work efficiently with C++ PCL.
/// With Rust layout optimizations, the struct might not work with the PCL library but the message still conforms to the description of PointCloud2.
/// Furthermore, Rust layout can lead to smaller messages to be send over the network.
///
#[proc_macro_derive(PointConvertible, attributes(rpcl2))]
pub fn ros_point_derive(input: TokenStream) -> TokenStream {
let input = parse_macro_input!(input as DeriveInput);
let name = input.clone().ident;
let fields = match input.data {
syn::Data::Struct(ref data) => data.fields.clone(),
_ => {
return syn::Error::new_spanned(input, "Only structs are supported")
.to_compile_error()
.into()
}
};
let allowed_datatypes = get_allowed_types();
if fields.is_empty() {
return syn::Error::new_spanned(input, "No fields found")
.to_compile_error()
.into();
}
for field in fields.iter() {
let ty = field.ty.to_token_stream().to_string();
if !allowed_datatypes.contains_key(&ty.as_str()) {
return syn::Error::new_spanned(field, "Field type not allowed")
.to_compile_error()
.into();
}
}
let field_len_token: usize = fields.len();
let field_names = struct_field_rename_array(&input)
.into_iter()
.map(|field_name| {
quote! { #field_name }
});
let field_impl = quote! {
impl ros_pointcloud2::Fields<#field_len_token> for #name {
fn field_names_ordered() -> [&'static str; #field_len_token] {
[
#(#field_names,)*
]
}
}
};
let field_names_get = fields
.iter()
.enumerate()
.map(|(idx, f)| {
let field_name = f.ident.as_ref().unwrap();
quote! { #field_name: point[#idx].get() }
})
.collect::<Vec<_>>();
let from_my_point = quote! {
impl From<ros_pointcloud2::RPCL2Point<#field_len_token>> for #name {
fn from(point: ros_pointcloud2::RPCL2Point<#field_len_token>) -> Self {
Self {
#(#field_names_get,)*
}
}
}
};
let field_names_into = fields
.iter()
.map(|f| {
let field_name = f.ident.as_ref().unwrap();
quote! { point.#field_name.into() }
})
.collect::<Vec<_>>();
let from_custom_point = quote! {
impl From<#name> for ros_pointcloud2::RPCL2Point<#field_len_token> {
fn from(point: #name) -> Self {
[ #(#field_names_into,)* ].into()
}
}
};
let convertible = quote! {
impl ros_pointcloud2::PointConvertible<#field_len_token> for #name {}
};
let out = TokenStream::from(quote! {
#field_impl
#from_my_point
#from_custom_point
#convertible
});
TokenStream::from(out)
}

27
type-layout/Cargo.toml
View File

@ -1,27 +0,0 @@
[package]
name = "type-layout"
description = "Derivable trait to view the layout of a struct, useful for debugging."
version = "0.2.0"
edition = "2018"
authors = ["Lucien Greathouse <me@lpghatguy.com>"]
documentation = "https://docs.rs/type-layout"
homepage = "https://github.com/LPGhatguy/type-layout"
repository = "https://github.com/LPGhatguy/type-layout"
readme = "README.md"
keywords = ["layout", "struct", "type"]
license = "MIT OR Apache-2.0"
rust-version = "1.60.0"
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[features]
serde1 = ["serde"]
[workspace]
members = ["type-layout-derive", "try-crate"]
[dependencies]
type-layout-derive = { version = "0.2.0", path = "type-layout-derive" }
memoffset = "0.5"
serde = { version = "1.0.116", features = ["derive"], optional = true }

201
type-layout/LICENSE-APACHE
View File

@ -1,201 +0,0 @@
i Apache License
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19
type-layout/LICENSE-MIT
View File

@ -1,19 +0,0 @@
Copyright (c) 2020 Lucien Greathouse
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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type-layout/src/lib.rs
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/*!
[![GitHub CI Status](https://github.com/LPGhatguy/type-layout/workflows/CI/badge.svg)](https://github.com/LPGhatguy/type-layout/actions)
[![type-layout on crates.io](https://img.shields.io/crates/v/type-layout.svg)](https://crates.io/crates/type-layout)
[![type-layout docs](https://img.shields.io/badge/docs-docs.rs-orange.svg)](https://docs.rs/type-layout)
type-layout is a type layout debugging aid, providing a `#[derive]`able trait
that reports:
- The type's name, size, and minimum alignment
- Each field's name, type, offset, and size
- Padding due to alignment requirements
**type-layout currently only functions on structs with named fields.** This is a
temporary limitation.
## Examples
The layout of types is only defined if they're `#[repr(C)]`. This crate works on
non-`#[repr(C)]` types, but their layout is unpredictable.
```rust
use type_layout::TypeLayout;
#[derive(TypeLayout)]
#[repr(C)]
struct Foo {
a: u8,
b: u32,
}
println!("{}", Foo::type_layout());
// prints:
// Foo (size 8, alignment 4)
// | Offset | Name | Size |
// | ------ | --------- | ---- |
// | 0 | a | 1 |
// | 1 | [padding] | 3 |
// | 4 | b | 4 |
```
Over-aligned types have trailing padding, which can be a source of bugs in some
FFI scenarios:
```rust
use type_layout::TypeLayout;
#[derive(TypeLayout)]
#[repr(C, align(128))]
struct OverAligned {
value: u8,
}
println!("{}", OverAligned::type_layout());
// prints:
// OverAligned (size 128, alignment 128)
// | Offset | Name | Size |
// | ------ | --------- | ---- |
// | 0 | value | 1 |
// | 1 | [padding] | 127 |
```
## Minimum Supported Rust Version (MSRV)
type-layout supports Rust 1.34.1 and newer. Until type-layout reaches 1.0,
changes to the MSRV will require major version bumps. After 1.0, MSRV changes
will only require minor version bumps, but will need significant justification.
*/
use std::borrow::Cow;
use std::fmt::{self, Display};
use std::str;
pub use type_layout_derive::TypeLayout;
#[doc(hidden)]
pub use memoffset;
pub trait TypeLayout {
fn type_layout() -> TypeLayoutInfo;
}
#[derive(Debug)]
#[cfg_attr(feature = "serde1", derive(serde::Serialize, serde::Deserialize))]
pub struct TypeLayoutInfo {
pub name: Cow<'static, str>,
pub size: usize,
pub alignment: usize,
pub fields: Vec<Field>,
}
#[derive(Debug)]
#[cfg_attr(feature = "serde1", derive(serde::Serialize, serde::Deserialize))]
pub enum Field {
Field {
name: Cow<'static, str>,
ty: Cow<'static, str>,
size: usize,
},
Padding {
size: usize,
},
}
impl fmt::Display for TypeLayoutInfo {
fn fmt(&self, formatter: &mut fmt::Formatter) -> fmt::Result {
writeln!(
formatter,
"{} (size {}, alignment {})",
self.name, self.size, self.alignment
)?;
let longest_name = self
.fields
.iter()
.map(|field| match field {
Field::Field { name, .. } => name.len(),
Field::Padding { .. } => "[padding]".len(),
})
.max()
.unwrap_or(1);
let widths = RowWidths {
offset: "Offset".len(),
name: longest_name,
size: "Size".len(),
};
write_row(
formatter,
widths,
Row {
offset: "Offset",
name: "Name",
size: "Size",
},
)?;
write_row(
formatter,
widths,
Row {
offset: "------",
name: str::repeat("-", longest_name),
size: "----",
},
)?;
let mut offset = 0;
for field in &self.fields {
match field {
Field::Field { name, size, .. } => {
write_row(formatter, widths, Row { offset, name, size })?;
offset += size;
}
Field::Padding { size } => {
write_row(
formatter,
widths,
Row {
offset,
name: "[padding]",
size,
},
)?;
offset += size;
}
}
}
Ok(())
}
}
#[derive(Clone, Copy)]
struct RowWidths {
offset: usize,
name: usize,
size: usize,
}
struct Row<O, N, S> {
offset: O,
name: N,
size: S,
}
fn write_row<O: Display, N: Display, S: Display>(
formatter: &mut fmt::Formatter,
widths: RowWidths,
row: Row<O, N, S>,
) -> fmt::Result {
writeln!(
formatter,
"| {:<offset_width$} | {:<name_width$} | {:<size_width$} |",
row.offset,
row.name,
row.size,
offset_width = widths.offset,
name_width = widths.name,
size_width = widths.size
)
}

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type-layout/type-layout-derive/Cargo.toml
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[package]
name = "type-layout-derive"
description = "Derive macro implementation for type-layout crate"
version = "0.2.0"
edition = "2018"
authors = ["Lucien Greathouse <me@lpghatguy.com>"]
homepage = "https://github.com/LPGhatguy/type-layout"
license = "MIT OR Apache-2.0"
[lib]
proc-macro = true
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[dependencies]
syn = "2"
quote = "1.0.7"
proc-macro2 = "1.0.21"

93
type-layout/type-layout-derive/src/lib.rs
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extern crate proc_macro;
use proc_macro::TokenStream;
use proc_macro2::{Ident, Literal};
use quote::{quote, quote_spanned, ToTokens};
use syn::{parse_macro_input, spanned::Spanned, Data, DeriveInput, Fields};
#[proc_macro_derive(TypeLayout)]
pub fn derive_type_layout(input: TokenStream) -> TokenStream {
// Parse the input tokens into a syntax tree
let input = parse_macro_input!(input as DeriveInput);
// Used in the quasi-quotation below as `#name`.
let name = input.ident;
let name_str = Literal::string(&name.to_string());
let (impl_generics, ty_generics, where_clause) = input.generics.split_for_impl();
let layout = layout_of_type(&name, &input.data);
// Build the output, possibly using quasi-quotation
let expanded = quote! {
impl #impl_generics ::type_layout::TypeLayout for #name #ty_generics #where_clause {
fn type_layout() -> ::type_layout::TypeLayoutInfo {
let mut last_field_end = 0;
let mut fields = Vec::new();
#layout
::type_layout::TypeLayoutInfo {
name: ::std::borrow::Cow::Borrowed(#name_str),
size: std::mem::size_of::<#name>(),
alignment: ::std::mem::align_of::<#name>(),
fields,
}
}
}
};
// Hand the output tokens back to the compiler
TokenStream::from(expanded)
}
fn layout_of_type(struct_name: &Ident, data: &Data) -> proc_macro2::TokenStream {
match data {
Data::Struct(data) => match &data.fields {
Fields::Named(fields) => {
let values = fields.named.iter().map(|field| {
let field_name = field.ident.as_ref().unwrap();
let field_name_str = Literal::string(&field_name.to_string());
let field_ty = &field.ty;
let field_ty_str = Literal::string(&field_ty.to_token_stream().to_string());
quote_spanned! { field.span() =>
#[allow(unused_assignments)]
{
let size = ::std::mem::size_of::<#field_ty>();
let offset = ::type_layout::memoffset::offset_of!(#struct_name, #field_name);
if offset > last_field_end {
fields.push(::type_layout::Field::Padding {
size: offset - last_field_end
});
}
fields.push(::type_layout::Field::Field {
name: ::std::borrow::Cow::Borrowed(#field_name_str),
ty: ::std::borrow::Cow::Borrowed(#field_ty_str),
size,
});
last_field_end = offset + size;
}
}
});
quote! {
#(#values)*
let struct_size = ::std::mem::size_of::<#struct_name>();
if struct_size > last_field_end {
fields.push(::type_layout::Field::Padding {
size: struct_size - last_field_end,
});
}
}
}
Fields::Unnamed(_) => unimplemented!(),
Fields::Unit => unimplemented!(),
},
Data::Enum(_) | Data::Union(_) => unimplemented!("type-layout only supports structs"),
}
}