post-rebase cleanup

docs/code-examples/annotation_context_segmentation.py

@@ -12,4 +12,4 @@
 # Log an annotation context to assign a label and color to each class
 rr2.log("segmentation", rr2.AnnotationContext([(1, "red", (255, 0, 0)), (2, "green", (0, 255, 0))]))
 
-rr.log_segmentation_image("segmentation/image", np.array(image), tensor_id=np.repeat(0, 16).astype(np.uint8))
+rr.log_segmentation_image("segmentation/image", np.array(image))

docs/code-examples/annotation_context_segmentation.rs

@@ -5,7 +5,6 @@ use rerun::{
     archetypes::AnnotationContext,
     components::{Tensor, TensorDataMeaning},
     datatypes::Color,
-    external::uuid,
     RecordingStreamBuilder,
 };
 
@@ -20,7 +19,6 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
 
     let mut image = Tensor::try_from(data.as_standard_layout().view())?;
     image.meaning = TensorDataMeaning::ClassId;
-    image.tensor_id = uuid::Uuid::nil().into();
 
     // create an annotation context to describe the classes
     rec.log(

docs/code-examples/depth_image_3d.py

@@ -20,4 +20,4 @@
 )
 
 # Log the tensor, assigning names to each dimension
-rr.log_depth_image("world/camera/depth", image, tensor_id=np.repeat(0, 16).astype(np.uint8), meter=10000.0)
+rr.log_depth_image("world/camera/depth", image, meter=10000.0)

docs/code-examples/depth_image_3d.rs

@@ -3,7 +3,6 @@ use ndarray::{s, Array, ShapeBuilder};
 use rerun::{
     components::{Pinhole, Tensor, TensorDataMeaning},
     datatypes::Mat3x3,
-    external::uuid,
     RecordingStreamBuilder,
 };
 
@@ -18,7 +17,6 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
     let mut tensor = Tensor::try_from(image.as_standard_layout().view())?;
     tensor.meaning = TensorDataMeaning::Depth;
     tensor.meter = Some(10000.);
-    tensor.tensor_id = uuid::Uuid::nil().into();
 
     // If we log a pinhole camera model, the depth gets automatically back-projected to 3D
     // TODO(#2816): Pinhole archetype

docs/code-examples/depth_image_simple.py

@@ -11,4 +11,4 @@
 rr.init("rerun_example_depth_image", spawn=True)
 
 # Log the tensor, assigning names to each dimension
-rr.log_depth_image("depth", image, tensor_id=np.repeat(0, 16).astype(np.uint8), meter=10000.0)
+rr.log_depth_image("depth", image, meter=10000.0)

docs/code-examples/depth_image_simple.rs

@@ -3,7 +3,6 @@
 use ndarray::{s, Array, ShapeBuilder};
 use rerun::{
     components::{Tensor, TensorDataMeaning},
-    external::uuid,
     RecordingStreamBuilder,
 };
 
@@ -17,7 +16,6 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
     let mut tensor = Tensor::try_from(image.as_standard_layout().view())?;
     tensor.meaning = TensorDataMeaning::Depth;
     tensor.meter = Some(10000.);
-    tensor.tensor_id = uuid::Uuid::nil().into();
 
     // TODO(#2792): Image archetype
     rec.log_component_lists("depth", false, 1, [&tensor as _])?;

docs/code-examples/image_simple.rs

@@ -1,7 +1,7 @@
 //! Create and log an image
 
 use ndarray::{s, Array, ShapeBuilder};
-use rerun::{archetypes::Image, external::uuid, RecordingStreamBuilder};
+use rerun::{archetypes::Image, RecordingStreamBuilder};
 
 fn main() -> Result<(), Box<dyn std::error::Error>> {
     let (rec, storage) = RecordingStreamBuilder::new("rerun_example_image_simple").memory()?;
@@ -11,7 +11,7 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
     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", &Image::try_from(image)?.with_id(uuid::Uuid::nil()))?;
+    rec.log("image", &Image::try_from(image)?)?;
 
     rerun::native_viewer::show(storage.take())?;
     Ok(())

docs/code-examples/mesh_simple.py

@@ -22,5 +22,4 @@
         [0, 255, 0],
         [0, 0, 255],
     ],
-    mesh_id=np.repeat(0, 16).astype(np.uint8),
 )

docs/code-examples/segmentation_image_simple.py

@@ -15,4 +15,4 @@
 rr2.log("/", rr2.AnnotationContext([(1, "red", (255, 0, 0)), (2, "green", (0, 255, 0))]))
 
 # TODO(#2792): SegmentationImage archetype
-rr.log_segmentation_image("image", np.array(image), tensor_id=np.repeat(0, 16).astype(np.uint8))
+rr.log_segmentation_image("image", np.array(image))

docs/code-examples/segmentation_image_simple.rs

@@ -5,7 +5,6 @@ use rerun::{
     archetypes::AnnotationContext,
     components::{Tensor, TensorDataMeaning},
     datatypes::Color,
-    external::uuid,
     RecordingStreamBuilder,
 };
 
@@ -21,7 +20,6 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
     // TODO(#2792): SegmentationImage archetype
     let mut tensor = Tensor::try_from(image.as_standard_layout().view())?;
     tensor.meaning = TensorDataMeaning::ClassId;
-    tensor.tensor_id = uuid::Uuid::nil().into();
 
     // create an annotation context to describe the classes
     let annotation = AnnotationContext::new([

rerun_py/rerun_sdk/rerun/log/image.py

@@ -130,7 +130,6 @@ def log_depth_image(
     *,
     draw_order: float | None = None,
     meter: float | None = None,
-    tensor_id: npt.NDArray[np.uint8] | None = None,
     ext: dict[str, Any] | None = None,
     timeless: bool = False,
     recording: RecordingStream | None = None,
@@ -158,9 +157,6 @@ def log_depth_image(
         How long is a meter in the given dtype?
         For instance: with uint16, perhaps meter=1000 which would mean
         you have millimeter precision and a range of up to ~65 meters (2^16 / 1000).
-    tensor_id:
-        A unique UUID for this tensor.
-        Leave empty to autogenerate a random one.
     ext:
         Optional dictionary of extension components. See [rerun.log_extension_components][]
     timeless:
@@ -199,7 +195,6 @@ def log_depth_image(
             image,
             draw_order=draw_order,
             meter=meter,
-            tensor_id=tensor_id,
             ext=ext,
             timeless=timeless,
             meaning=bindings.TensorDataMeaning.Depth,
@@ -213,7 +208,6 @@ def log_segmentation_image(
     image: npt.ArrayLike,
     *,
     draw_order: float | None = None,
-    tensor_id: npt.NDArray[np.uint8] | None = None,
     ext: dict[str, Any] | None = None,
     timeless: bool = False,
     recording: RecordingStream | None = None,
@@ -240,9 +234,6 @@ def log_segmentation_image(
         An optional floating point value that specifies the 2D drawing order.
         Objects with higher values are drawn on top of those with lower values.
         The default for images is -10.0.
-    tensor_id:
-        A unique UUID for this tensor.
-        Leave empty to autogenerate a random one.
     ext:
         Optional dictionary of extension components. See [rerun.log_extension_components][]
     timeless:
@@ -272,7 +263,6 @@ def log_segmentation_image(
             entity_path,
             tensor=image,
             draw_order=draw_order,
-            tensor_id=tensor_id,
             ext=ext,
             timeless=timeless,
             recording=recording,
@@ -286,7 +276,6 @@ def log_segmentation_image(
             tensor=image,
             draw_order=draw_order,
             meaning=bindings.TensorDataMeaning.ClassId,
-            tensor_id=tensor_id,
             ext=ext,
             timeless=timeless,
             recording=recording,

rerun_py/rerun_sdk/rerun/log/mesh.py

@@ -25,7 +25,6 @@ def log_mesh(
     normals: Any | None = None,
     albedo_factor: Any | None = None,
     vertex_colors: Colors | None = None,
-    mesh_id: npt.NDArray[np.uint8] | None = None,
     timeless: bool = False,
     recording: RecordingStream | None = None,
 ) -> None:
@@ -74,9 +73,6 @@ def log_mesh(
     vertex_colors:
         Optional array of RGB(A) vertex colors, in sRGB gamma space, either as 0-1 floats or 0-255 integers.
         If specified, the alpha is considered separate (unmultiplied).
-    mesh_id:
-        A unique UUID for this mesh.
-        Leave empty to autogenerate a random one.
     timeless:
         If true, the mesh will be timeless (default: False)
     recording:
@@ -97,8 +93,6 @@ def log_mesh(
         albedo_factor = np.asarray(albedo_factor, dtype=np.float32).flatten()
     if vertex_colors is not None:
         vertex_colors = _normalize_colors(vertex_colors)
-    if mesh_id is not None:
-        mesh_id = np.asarray(mesh_id, dtype=np.uint8)
 
     # Mesh arrow handling happens inside the python bridge
     bindings.log_meshes(
@@ -108,7 +102,6 @@ def log_mesh(
         index_buffers=[indices],
         normal_buffers=[normals],
         albedo_factors=[albedo_factor],
-        mesh_ids=[mesh_id],
         timeless=timeless,
         recording=recording,
     )

rerun_py/rerun_sdk/rerun/log/tensor.py

@@ -94,7 +94,6 @@ def _log_tensor(
     names: Iterable[str | None] | None = None,
     meter: float | None = None,
     meaning: bindings.TensorDataMeaning = None,
-    tensor_id: npt.NDArray[np.uint8] | None = None,
     ext: dict[str, Any] | None = None,
     timeless: bool = False,
     recording: RecordingStream | None = None,
@@ -140,7 +139,7 @@ def _log_tensor(
     instanced: dict[str, Any] = {}
     splats: dict[str, Any] = {}
 
-    instanced["rerun.tensor"] = TensorArray.from_numpy(tensor, names, meaning, meter, tensor_id=tensor_id)
+    instanced["rerun.tensor"] = TensorArray.from_numpy(tensor, names, meaning, meter)
 
     if draw_order is not None:
         instanced["rerun.draw_order"] = DrawOrderArray.splat(draw_order)

rerun_py/src/python_bridge.rs

@@ -806,7 +806,6 @@ fn log_meshes(
     index_buffers: Vec<Option<numpy::PyReadonlyArray1<'_, u32>>>,
     normal_buffers: Vec<Option<numpy::PyReadonlyArray1<'_, f32>>>,
     albedo_factors: Vec<Option<numpy::PyReadonlyArray1<'_, f32>>>,
-    mesh_ids: Vec<Option<numpy::PyReadonlyArray1<'_, u8>>>,
     timeless: bool,
     recording: Option<&PyRecordingStream>,
 ) -> PyResult<()> {
@@ -824,25 +823,23 @@ fn log_meshes(
     {
         return Err(PyTypeError::new_err(format!(
             "Top-level position/index/normal/albedo/id buffer arrays must be same the length, \
-                got positions={}, vertex_colors={}, indices={}, normals={}, albedo={}, mesh_id={} instead",
+                got positions={}, vertex_colors={}, indices={}, normals={}, albedo={} instead",
             position_buffers.len(),
             vertex_color_buffers.len(),
             index_buffers.len(),
             normal_buffers.len(),
             albedo_factors.len(),
-            mesh_ids.len(),
         )));
     }
 
     let mut meshes = Vec::with_capacity(position_buffers.len());
 
-    for (vertex_positions, vertex_colors, indices, normals, albedo_factor, mesh_id) in izip!(
+    for (vertex_positions, vertex_colors, indices, normals, albedo_factor) in izip!(
         position_buffers,
         vertex_color_buffers,
         index_buffers,
         normal_buffers,
         albedo_factors,
-        mesh_ids,
     ) {
         let albedo_factor =
             if let Some(v) = albedo_factor.map(|albedo_factor| albedo_factor.as_array().to_vec()) {