segmentation_image.hpp

// DO NOT EDIT! This file was auto-generated by crates/build/re_types_builder/src/codegen/cpp/mod.rs
// Based on "crates/store/re_types/definitions/rerun/archetypes/segmentation_image.fbs".
 
#pragma once
 
#include "../collection.hpp"
#include "../component_batch.hpp"
#include "../component_column.hpp"
#include "../components/draw_order.hpp"
#include "../components/image_buffer.hpp"
#include "../components/image_format.hpp"
#include "../components/opacity.hpp"
#include "../image_utils.hpp"
#include "../indicator_component.hpp"
#include "../result.hpp"
 
#include <cstdint>
#include <optional>
#include <utility>
#include <vector>
 
namespace rerun::archetypes {
    /// **Archetype**: An image made up of integer `components::ClassId`s.
    ///
    /// Each pixel corresponds to a `components::ClassId` that will be mapped to a color based on `archetypes::AnnotationContext`.
    ///
    /// In the case of floating point images, the label will be looked up based on rounding to the nearest
    /// integer value.
    ///
    /// Use `archetypes::AnnotationContext` to associate each class with a color and a label.
    ///
    /// Since the underlying `rerun::datatypes::TensorData` uses `rerun::Collection` internally,
    /// data can be passed in without a copy from raw pointers or by reference from `std::vector`/`std::array`/c-arrays.
    /// If needed, this "borrow-behavior" can be extended by defining your own `rerun::CollectionAdapter`.
    ///
    /// ## Example
    ///
    /// ### Simple segmentation image
    /// ![image](https://static.rerun.io/segmentation_image_simple/f8aac62abcf4c59c5d62f9ebc2d86fd0285c1736/full.png)
    ///
    /// ```cpp
    /// #include <rerun.hpp>
    ///
    /// #include <algorithm> // std::fill_n
    /// #include <vector>
    ///
    /// int main() {
    ///     const auto rec = rerun::RecordingStream("rerun_example_segmentation_image");
    ///     rec.spawn().exit_on_failure();
    ///
    ///     // Create a segmentation image
    ///     const int HEIGHT = 8;
    ///     const int WIDTH = 12;
    ///     std::vector<uint8_t> data(WIDTH * HEIGHT, 0);
    ///     for (auto y = 0; y <4; ++y) {                                         // top half
    ///         std::fill_n(data.begin() + y * WIDTH, 6, static_cast<uint8_t>(1)); // left half
    ///     }
    ///     for (auto y = 4; y <8; ++y) {                                             // bottom half
    ///         std::fill_n(data.begin() + y * WIDTH + 6, 6, static_cast<uint8_t>(2)); // right half
    ///     }
    ///
    ///     // create an annotation context to describe the classes
    ///     rec.log_static(
    ///         "/",
    ///         rerun::AnnotationContext({
    ///             rerun::AnnotationInfo(1, "red", rerun::Rgba32(255, 0, 0)),
    ///             rerun::AnnotationInfo(2, "green", rerun::Rgba32(0, 255, 0)),
    ///         })
    ///     );
    ///
    ///     rec.log("image", rerun::SegmentationImage(data.data(), {WIDTH, HEIGHT}));
    /// }
    /// ```
    struct SegmentationImage {
        /// The raw image data.
        std::optional<ComponentBatch> buffer;
 
        /// The format of the image.
        std::optional<ComponentBatch> format;
 
        /// Opacity of the image, useful for layering the segmentation image on top of another image.
        ///
        /// Defaults to 0.5 if there's any other images in the scene, otherwise 1.0.
        std::optional<ComponentBatch> opacity;
 
        /// An optional floating point value that specifies the 2D drawing order.
        ///
        /// Objects with higher values are drawn on top of those with lower values.
        /// Defaults to `0.0`.
        std::optional<ComponentBatch> draw_order;
 
      public:
        static constexpr const char IndicatorComponentName[] =
            "rerun.components.SegmentationImageIndicator";
 
        /// Indicator component, used to identify the archetype when converting to a list of components.
        using IndicatorComponent = rerun::components::IndicatorComponent<IndicatorComponentName>;
        /// The name of the archetype as used in `ComponentDescriptor`s.
        static constexpr const char ArchetypeName[] = "rerun.archetypes.SegmentationImage";
 
        /// `ComponentDescriptor` for the `buffer` field.
        static constexpr auto Descriptor_buffer = ComponentDescriptor(
            ArchetypeName, "buffer",
            Loggable<rerun::components::ImageBuffer>::Descriptor.component_name
        );
        /// `ComponentDescriptor` for the `format` field.
        static constexpr auto Descriptor_format = ComponentDescriptor(
            ArchetypeName, "format",
            Loggable<rerun::components::ImageFormat>::Descriptor.component_name
        );
        /// `ComponentDescriptor` for the `opacity` field.
        static constexpr auto Descriptor_opacity = ComponentDescriptor(
            ArchetypeName, "opacity",
            Loggable<rerun::components::Opacity>::Descriptor.component_name
        );
        /// `ComponentDescriptor` for the `draw_order` field.
        static constexpr auto Descriptor_draw_order = ComponentDescriptor(
            ArchetypeName, "draw_order",
            Loggable<rerun::components::DrawOrder>::Descriptor.component_name
        );
 
      public: // START of extensions from segmentation_image_ext.cpp:
        /// Constructs image from pointer + resolution, inferring the datatype from the pointer type.
        ///
        /// @param pixels The raw image data.
        /// ⚠️ Does not take ownership of the data, the caller must ensure the data outlives the image.
        /// The number of elements is assumed to be `W * H`.
        /// @param resolution The resolution of the image as {width, height}.
        template <typename TElement>
        SegmentationImage(const TElement* pixels, WidthHeight resolution)
            : SegmentationImage{
                  reinterpret_cast<const uint8_t*>(pixels), resolution, get_datatype(pixels)} {}
 
        /// Constructs image from pixel data + resolution with datatype inferred from the passed collection.
        ///
        /// @param pixels The raw image data.
        /// If the data does not outlive the image, use `std::move` or create the `rerun::Collection`
        /// explicitly ahead of time with `rerun::Collection::take_ownership`.
        /// The length of the data should be `W * H`.
        /// @param resolution The resolution of the image as {width, height}.
        template <typename TElement>
        SegmentationImage(Collection<TElement> pixels, WidthHeight resolution)
            : SegmentationImage{pixels.to_uint8(), resolution, get_datatype(pixels.data())} {}
 
        /// Constructs image from pixel data + resolution with explicit datatype. Borrows data from a pointer (i.e. data must outlive the image!).
        ///
        /// @param bytes The raw image data.
        /// ⚠️ Does not take ownership of the data, the caller must ensure the data outlives the image.
        /// The byte size of the data is assumed to be `W * H * datatype.size`
        /// @param resolution The resolution of the image as {width, height}.
        /// @param datatype How the data should be interpreted.
        SegmentationImage(
            const void* bytes, WidthHeight resolution, datatypes::ChannelDatatype datatype
        )
            : SegmentationImage{
                  Collection<uint8_t>::borrow(bytes, num_bytes(resolution, datatype)),
                  resolution,
                  datatype} {}
 
        /// Constructs image from pixel data + resolution + datatype.
        ///
        /// @param bytes The raw image data as bytes.
        /// If the data does not outlive the image, use `std::move` or create the `rerun::Collection`
        /// explicitly ahead of time with `rerun::Collection::take_ownership`.
        /// The length of the data should be `W * H`.
        /// @param resolution The resolution of the image as {width, height}.
        /// @param datatype How the data should be interpreted.
        SegmentationImage(
            Collection<uint8_t> bytes, WidthHeight resolution, datatypes::ChannelDatatype datatype
        ) {
            auto image_format = datatypes::ImageFormat{resolution, datatype};
            if (bytes.size() != image_format.num_bytes()) {
                Error(
                    ErrorCode::InvalidTensorDimension,
                    "SegmentationImage buffer has the wrong size. Got " +
                        std::to_string(bytes.size()) + " bytes, expected " +
                        std::to_string(image_format.num_bytes())
                )
                    .handle();
            }
            *this = std::move(*this).with_buffer(bytes).with_format(image_format);
        }
 
        // END of extensions from segmentation_image_ext.cpp, start of generated code:
 
      public:
        SegmentationImage() = default;
        SegmentationImage(SegmentationImage&& other) = default;
        SegmentationImage(const SegmentationImage& other) = default;
        SegmentationImage& operator=(const SegmentationImage& other) = default;
        SegmentationImage& operator=(SegmentationImage&& other) = default;
 
        /// Update only some specific fields of a `SegmentationImage`.
        static SegmentationImage update_fields() {
            return SegmentationImage();
        }
 
        /// Clear all the fields of a `SegmentationImage`.
        static SegmentationImage clear_fields();
 
        /// The raw image data.
        SegmentationImage with_buffer(const rerun::components::ImageBuffer& _buffer) && {
            buffer = ComponentBatch::from_loggable(_buffer, Descriptor_buffer).value_or_throw();
            return std::move(*this);
        }
 
        /// This method makes it possible to pack multiple `buffer` in a single component batch.
        ///
        /// This only makes sense when used in conjunction with `columns`. `with_buffer` should
        /// be used when logging a single row's worth of data.
        SegmentationImage with_many_buffer(const Collection<rerun::components::ImageBuffer>& _buffer
        ) && {
            buffer = ComponentBatch::from_loggable(_buffer, Descriptor_buffer).value_or_throw();
            return std::move(*this);
        }
 
        /// The format of the image.
        SegmentationImage with_format(const rerun::components::ImageFormat& _format) && {
            format = ComponentBatch::from_loggable(_format, Descriptor_format).value_or_throw();
            return std::move(*this);
        }
 
        /// This method makes it possible to pack multiple `format` in a single component batch.
        ///
        /// This only makes sense when used in conjunction with `columns`. `with_format` should
        /// be used when logging a single row's worth of data.
        SegmentationImage with_many_format(const Collection<rerun::components::ImageFormat>& _format
        ) && {
            format = ComponentBatch::from_loggable(_format, Descriptor_format).value_or_throw();
            return std::move(*this);
        }
 
        /// Opacity of the image, useful for layering the segmentation image on top of another image.
        ///
        /// Defaults to 0.5 if there's any other images in the scene, otherwise 1.0.
        SegmentationImage with_opacity(const rerun::components::Opacity& _opacity) && {
            opacity = ComponentBatch::from_loggable(_opacity, Descriptor_opacity).value_or_throw();
            return std::move(*this);
        }
 
        /// This method makes it possible to pack multiple `opacity` in a single component batch.
        ///
        /// This only makes sense when used in conjunction with `columns`. `with_opacity` should
        /// be used when logging a single row's worth of data.
        SegmentationImage with_many_opacity(const Collection<rerun::components::Opacity>& _opacity
        ) && {
            opacity = ComponentBatch::from_loggable(_opacity, Descriptor_opacity).value_or_throw();
            return std::move(*this);
        }
 
        /// An optional floating point value that specifies the 2D drawing order.
        ///
        /// Objects with higher values are drawn on top of those with lower values.
        /// Defaults to `0.0`.
        SegmentationImage with_draw_order(const rerun::components::DrawOrder& _draw_order) && {
            draw_order =
                ComponentBatch::from_loggable(_draw_order, Descriptor_draw_order).value_or_throw();
            return std::move(*this);
        }
 
        /// This method makes it possible to pack multiple `draw_order` in a single component batch.
        ///
        /// This only makes sense when used in conjunction with `columns`. `with_draw_order` should
        /// be used when logging a single row's worth of data.
        SegmentationImage with_many_draw_order(
            const Collection<rerun::components::DrawOrder>& _draw_order
        ) && {
            draw_order =
                ComponentBatch::from_loggable(_draw_order, Descriptor_draw_order).value_or_throw();
            return std::move(*this);
        }
 
        /// Partitions the component data into multiple sub-batches.
        ///
        /// Specifically, this transforms the existing `ComponentBatch` data into `ComponentColumn`s
        /// instead, via `ComponentBatch::partitioned`.
        ///
        /// This makes it possible to use `RecordingStream::send_columns` to send columnar data directly into Rerun.
        ///
        /// The specified `lengths` must sum to the total length of the component batch.
        Collection<ComponentColumn> columns(const Collection<uint32_t>& lengths_);
 
        /// Partitions the component data into unit-length sub-batches.
        ///
        /// This is semantically similar to calling `columns` with `std::vector<uint32_t>(n, 1)`,
        /// where `n` is automatically guessed.
        Collection<ComponentColumn> columns();
    };
 
} // namespace rerun::archetypes
 
namespace rerun {
    /// \private
    template <typename T>
    struct AsComponents;
 
    /// \private
    template <>
    struct AsComponents<archetypes::SegmentationImage> {
        /// Serialize all set component batches.
        static Result<Collection<ComponentBatch>> as_batches(
            const archetypes::SegmentationImage& archetype
        );
    };
} // namespace rerun