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// DO NOT EDIT! This file was auto-generated by crates/re_types_builder/src/codegen/rust/api.rs
// Based on "crates/re_types/definitions/rerun/archetypes/image.fbs".
#![allow(trivial_numeric_casts)]
#![allow(unused_imports)]
#![allow(unused_parens)]
#![allow(clippy::clone_on_copy)]
#![allow(clippy::cloned_instead_of_copied)]
#![allow(clippy::iter_on_single_items)]
#![allow(clippy::map_flatten)]
#![allow(clippy::match_wildcard_for_single_variants)]
#![allow(clippy::needless_question_mark)]
#![allow(clippy::new_without_default)]
#![allow(clippy::redundant_closure)]
#![allow(clippy::too_many_arguments)]
#![allow(clippy::too_many_lines)]
#![allow(clippy::unnecessary_cast)]
use ::re_types_core::external::arrow2;
use ::re_types_core::ComponentName;
use ::re_types_core::SerializationResult;
use ::re_types_core::{ComponentBatch, MaybeOwnedComponentBatch};
use ::re_types_core::{DeserializationError, DeserializationResult};
/// **Archetype**: A monochrome or color image.
///
/// The order of dimensions in the underlying `TensorData` follows the typical
/// row-major, interleaved-pixel image format. Additionally, Rerun orders the
/// `TensorDimension`s within the shape description from outer-most to inner-most.
///
/// As such, the shape of the `TensorData` must be mappable to:
/// - A `HxW` tensor, treated as a grayscale image.
/// - A `HxWx3` tensor, treated as an RGB image.
/// - A `HxWx4` tensor, treated as an RGBA image.
///
/// Leading and trailing unit-dimensions are ignored, so that
/// `1x480x640x3x1` is treated as a `480x640x3` RGB image.
///
/// Rerun also supports compressed image encoded as JPEG, N12, and YUY2.
/// Using these formats can save a lot of bandwidth and memory.
/// See [`crate::components::TensorData`] for more.
///
/// ## Example
///
/// ### `image_simple`:
/// ```ignore
/// use ndarray::{s, Array, ShapeBuilder};
///
/// fn main() -> Result<(), Box<dyn std::error::Error>> {
/// let rec = rerun::RecordingStreamBuilder::new("rerun_example_image").spawn()?;
///
/// let mut image = Array::<u8, _>::zeros((200, 300, 3).f());
/// image.slice_mut(s![.., .., 0]).fill(255);
/// image.slice_mut(s![50..150, 50..150, 0]).fill(0);
/// image.slice_mut(s![50..150, 50..150, 1]).fill(255);
///
/// rec.log("image", &rerun::Image::try_from(image)?)?;
///
/// Ok(())
/// }
/// ```
/// <center>
/// <picture>
/// <source media="(max-width: 480px)" srcset="https://static.rerun.io/image_simple/06ba7f8582acc1ffb42a7fd0006fad7816f3e4e4/480w.png">
/// <source media="(max-width: 768px)" srcset="https://static.rerun.io/image_simple/06ba7f8582acc1ffb42a7fd0006fad7816f3e4e4/768w.png">
/// <source media="(max-width: 1024px)" srcset="https://static.rerun.io/image_simple/06ba7f8582acc1ffb42a7fd0006fad7816f3e4e4/1024w.png">
/// <source media="(max-width: 1200px)" srcset="https://static.rerun.io/image_simple/06ba7f8582acc1ffb42a7fd0006fad7816f3e4e4/1200w.png">
/// <img src="https://static.rerun.io/image_simple/06ba7f8582acc1ffb42a7fd0006fad7816f3e4e4/full.png" width="640">
/// </picture>
/// </center>
#[derive(Clone, Debug, PartialEq)]
pub struct Image {
/// The image data. Should always be a rank-2 or rank-3 tensor.
pub data: crate::components::TensorData,
/// An optional floating point value that specifies the 2D drawing order.
///
/// Objects with higher values are drawn on top of those with lower values.
pub draw_order: Option<crate::components::DrawOrder>,
}
impl ::re_types_core::SizeBytes for Image {
#[inline]
fn heap_size_bytes(&self) -> u64 {
self.data.heap_size_bytes() + self.draw_order.heap_size_bytes()
}
#[inline]
fn is_pod() -> bool {
<crate::components::TensorData>::is_pod()
&& <Option<crate::components::DrawOrder>>::is_pod()
}
}
static REQUIRED_COMPONENTS: once_cell::sync::Lazy<[ComponentName; 1usize]> =
once_cell::sync::Lazy::new(|| ["rerun.components.TensorData".into()]);
static RECOMMENDED_COMPONENTS: once_cell::sync::Lazy<[ComponentName; 1usize]> =
once_cell::sync::Lazy::new(|| ["rerun.components.ImageIndicator".into()]);
static OPTIONAL_COMPONENTS: once_cell::sync::Lazy<[ComponentName; 1usize]> =
once_cell::sync::Lazy::new(|| ["rerun.components.DrawOrder".into()]);
static ALL_COMPONENTS: once_cell::sync::Lazy<[ComponentName; 3usize]> =
once_cell::sync::Lazy::new(|| {
[
"rerun.components.TensorData".into(),
"rerun.components.ImageIndicator".into(),
"rerun.components.DrawOrder".into(),
]
});
impl Image {
/// The total number of components in the archetype: 1 required, 1 recommended, 1 optional
pub const NUM_COMPONENTS: usize = 3usize;
}
/// Indicator component for the [`Image`] [`::re_types_core::Archetype`]
pub type ImageIndicator = ::re_types_core::GenericIndicatorComponent<Image>;
impl ::re_types_core::Archetype for Image {
type Indicator = ImageIndicator;
#[inline]
fn name() -> ::re_types_core::ArchetypeName {
"rerun.archetypes.Image".into()
}
#[inline]
fn indicator() -> MaybeOwnedComponentBatch<'static> {
static INDICATOR: ImageIndicator = ImageIndicator::DEFAULT;
MaybeOwnedComponentBatch::Ref(&INDICATOR)
}
#[inline]
fn required_components() -> ::std::borrow::Cow<'static, [ComponentName]> {
REQUIRED_COMPONENTS.as_slice().into()
}
#[inline]
fn recommended_components() -> ::std::borrow::Cow<'static, [ComponentName]> {
RECOMMENDED_COMPONENTS.as_slice().into()
}
#[inline]
fn optional_components() -> ::std::borrow::Cow<'static, [ComponentName]> {
OPTIONAL_COMPONENTS.as_slice().into()
}
#[inline]
fn all_components() -> ::std::borrow::Cow<'static, [ComponentName]> {
ALL_COMPONENTS.as_slice().into()
}
#[inline]
fn from_arrow_components(
arrow_data: impl IntoIterator<Item = (ComponentName, Box<dyn arrow2::array::Array>)>,
) -> DeserializationResult<Self> {
re_tracing::profile_function!();
use ::re_types_core::{Loggable as _, ResultExt as _};
let arrays_by_name: ::std::collections::HashMap<_, _> = arrow_data
.into_iter()
.map(|(name, array)| (name.full_name(), array))
.collect();
let data = {
let array = arrays_by_name
.get("rerun.components.TensorData")
.ok_or_else(DeserializationError::missing_data)
.with_context("rerun.archetypes.Image#data")?;
<crate::components::TensorData>::from_arrow_opt(&**array)
.with_context("rerun.archetypes.Image#data")?
.into_iter()
.next()
.flatten()
.ok_or_else(DeserializationError::missing_data)
.with_context("rerun.archetypes.Image#data")?
};
let draw_order = if let Some(array) = arrays_by_name.get("rerun.components.DrawOrder") {
<crate::components::DrawOrder>::from_arrow_opt(&**array)
.with_context("rerun.archetypes.Image#draw_order")?
.into_iter()
.next()
.flatten()
} else {
None
};
Ok(Self { data, draw_order })
}
}
impl ::re_types_core::AsComponents for Image {
fn as_component_batches(&self) -> Vec<MaybeOwnedComponentBatch<'_>> {
re_tracing::profile_function!();
use ::re_types_core::Archetype as _;
[
Some(Self::indicator()),
Some((&self.data as &dyn ComponentBatch).into()),
self.draw_order
.as_ref()
.map(|comp| (comp as &dyn ComponentBatch).into()),
]
.into_iter()
.flatten()
.collect()
}
}
impl Image {
/// Create a new `Image`.
#[inline]
pub fn new(data: impl Into<crate::components::TensorData>) -> Self {
Self {
data: data.into(),
draw_order: None,
}
}
/// An optional floating point value that specifies the 2D drawing order.
///
/// Objects with higher values are drawn on top of those with lower values.
#[inline]
pub fn with_draw_order(mut self, draw_order: impl Into<crate::components::DrawOrder>) -> Self {
self.draw_order = Some(draw_order.into());
self
}
}