bounding_box_2d_tight_fast

Outputs tight 2d bounding box of each entity with semantics in the camera’s viewport. Tight bounding boxes bound only the visible pixels of entities. Completely occluded entities are ommited.

Initialization Parameters

• semanticTypes: List of allowed semantic types the types. For example, if semantic_types is [“class”], only the bounding boxes for prims with semantics of type “class” will be retrieved.

Output Format

The bounding box annotator returns a dictionary with the bounds and semantic id found under the “data” key, while other information is under the “info” key: “idToLabels”, “bboxIds” and “primPaths”.

{
    "data": np.dtype(
                [
                    ("semanticId", "<u4"),
                    ("x_min", "<i4"),
                    ("y_min", "<i4"),
                    ("x_max", "<i4"),
                    ("y_max", "<i4"),
                ],
    "info": {
        "idToLabels": {<semanticId>: <semantic_labels>},    # mapping from integer semantic ID to a comma delimited list of associated semantics
        "bboxIds": [<bbox_id_0>, ..., <bbox_id_n>],         # ID specific to bounding box annotators allowing easy mapping between different bounding box annotators.
        "primPaths": [<prim_path_0>, ... <prim_path_n>],    # prim path tied to each bounding box
    }
}

Note

bounding_box_2d_tight_fast bounds only visible pixels.

Example

import omni.replicator.core as rep

async def test_bbox_2d_tight_fast():
    cone = rep.create.cone(semantics=[("prim", "cone")], position=(100, 0, 0))
    sphere = rep.create.sphere(semantics=[("prim", "sphere")], position=(-100, 0, 0))
    invalid_type = rep.create.cube(semantics=[("shape", "boxy")], position=(0, 100, 0))

    cam = rep.create.camera(position=(500,500,500), look_at=cone)
    rp = rep.create.render_product(cam, (1024, 512))

    bbox_2d_tight_fast = rep.AnnotatorRegistry.get_annotator("bounding_box_2d_tight_fast", init_params={"semanticTypes": ["prim"]})
    bbox_2d_tight_fast.attach(rp)

    await rep.orchestrator.step_async()
    data = bbox_2d_tight_fast.get_data()
    print(data)

    # {
    #   'data': array([
    #       (0, 442, 198, 581, 357, 0.),
    #       (1, 245,  94, 368, 220, 0.38),
    #       dtype=[('semanticId', '<u4'),
    #              ('x_min', '<i4'),
    #              ('y_min', '<i4'),
    #              ('x_max', '<i4'),
    #              ('y_max', '<i4'),
    #              ('occlusionRatio', '<f4')]),
    #   'info': {
    #       'bboxIds': array([0, 1], dtype=uint32),
    #       'idToLabels': {'0': {'prim': 'cone'}, '1': {'prim': 'sphere'}},
    #       'primPaths': ['/Replicator/Cone_Xform', '/Replicator/Sphere_Xform']}
    #   }
    # }

import asyncio
asyncio.ensure_future(test_bbox_2d_tight_fast())

bounding_box_2d_tight

Outputs tight 2d bounding box of each entity with semantics in the camera’s viewport. Tight bounding boxes bound only the visible pixels of entities. Completely occluded entities are ommited.

Initialization Parameters

• semanticTypes: List of allowed semantic types the types. For example, if semantic_types is [“class”], only the bounding boxes for prims with semantics of type “class” will be retrieved.

Output Format

The bounding box annotator returns a dictionary with the bounds and semantic id found under the “data” key, while other information is under the “info” key: “idToLabels”, “bboxIds” and “primPaths”.

{
    "data": np.dtype(
                [
                    ("semanticId", "<u4"),
                    ("x_min", "<i4"),
                    ("y_min", "<i4"),
                    ("x_max", "<i4"),
                    ("y_max", "<i4"),
                    ("occlusionRatio", "<f4"),
                ],
    "info": {
        "idToLabels": {<semanticId>: <semantic_labels>},    # mapping from integer semantic ID to a comma delimited list of associated semantics
        "bboxIds": [<bbox_id_0>, ..., <bbox_id_n>],         # ID specific to bounding box annotators allowing easy mapping between different bounding box annotators.
        "primPaths": [<prim_path_0>, ... <prim_path_n>],    # prim path tied to each bounding box
    }
}

Note

bounding_box_2d_tight bounds only visible pixels.

Example

import omni.replicator.core as rep

async def test_bbox_2d_tight():
    cone = rep.create.cone(semantics=[("prim", "cone")], position=(100, 0, 0))
    sphere = rep.create.sphere(semantics=[("prim", "sphere")], position=(-100, 0, 0))
    invalid_type = rep.create.cube(semantics=[("shape", "boxy")], position=(0, 100, 0))

    cam = rep.create.camera(position=(500,500,500), look_at=cone)
    rp = rep.create.render_product(cam, (1024, 512))

    bbox_2d_tight = rep.AnnotatorRegistry.get_annotator("bounding_box_2d_tight", init_params={"semanticTypes": ["prim"]})
    bbox_2d_tight.attach(rp)

    await rep.orchestrator.step_async()
    data = bbox_2d_tight.get_data()
    print(data)
    # {
    #   'data': array([
    #       (0, 442, 198, 581, 357, 0.),
    #       (1, 245,  94, 368, 220, 0.38),
    #       dtype=[('semanticId', '<u4'),
    #              ('x_min', '<i4'),
    #              ('y_min', '<i4'),
    #              ('x_max', '<i4'),
    #              ('y_max', '<i4')]),
    #              ("occlusionRatio", "<f4"),
    #   'info': {
    #       'bboxIds': array([0, 1], dtype=uint32),
    #       'idToLabels': {'0': {'prim': 'cone'}, '1': {'prim': 'sphere'}},
    #       'primPaths': ['/Replicator/Cone_Xform', '/Replicator/Sphere_Xform']}
    #   }
    # }

import asyncio
asyncio.ensure_future(test_bbox_2d_tight())

bounding_box_2d_loose_fast

Outputs loose 2d bounding box of each entity with semantics in the camera’s field of view. Loose bounding boxes bound the entire entity regardless of occlusions.

Initialization Parameters

• semanticTypes: List of allowed semantic types the types. For example, if semantic_types is [“class”], only the bounding boxes for prims with semantics of type “class” will be retrieved.

Output Format

The bounding box annotator returns a dictionary with the bounds and semantic id found under the “data” key, while other information is under the “info” key: “idToLabels”, “bboxIds” and “primPaths”.

{
    "data": np.dtype(
                [
                    ("semanticId", "<u4"),
                    ("x_min", "<i4"),
                    ("y_min", "<i4"),
                    ("x_max", "<i4"),
                    ("y_max", "<i4"),
                    ("occlusionRatio", "<f4"),
                ],
    "info": {
        "idToLabels": {<semanticId>: <semantic_labels>},    # mapping from integer semantic ID to a comma delimited list of associated semantics
        "bboxIds": [<bbox_id_0>, ..., <bbox_id_n>],         # ID specific to bounding box annotators allowing easy mapping between different bounding box annotators.
        "primPaths": [<prim_path_0>, ... <prim_path_n>],    # prim path tied to each bounding box
    }
}

Note

bounding_box_2d_loose will produce the loose 2d bounding box of any prim in the viewport, no matter if is partially occluded or fully occluded.

Example

import omni.replicator.core as rep

async def test_bbox_2d_loose_fast():
    cone = rep.create.cone(semantics=[("prim", "cone")], position=(100, 0, 0))
    sphere = rep.create.sphere(semantics=[("prim", "sphere")], position=(-100, 0, 0))
    invalid_type = rep.create.cube(semantics=[("shape", "boxy")], position=(0, 100, 0))

    cam = rep.create.camera(position=(500,500,500), look_at=cone)
    rp = rep.create.render_product(cam, (1024, 512))

    bbox_2d_loose_fast = rep.AnnotatorRegistry.get_annotator("bounding_box_2d_loose_fast", init_params={"semanticTypes": ["prim"]})
    bbox_2d_loose_fast.attach(rp)

    await rep.orchestrator.step_async()
    data = bbox_2d_loose_fast.get_data()
    print(data)
    # {
    #   'data': array([
    #       (0, 442, 198, 581, 357, 0.),
    #       (1, 245,  92, 375, 220, 0.38),
    #       dtype=[('semanticId', '<u4'),
    #              ('x_min', '<i4'),
    #              ('y_min', '<i4'),
    #              ('x_max', '<i4'),
    #              ('y_max', '<i4')]),
    #              ("occlusionRatio", "<f4"),
    #   'info': {
    #       'bboxIds': array([0, 1], dtype=uint32),
    #       'idToLabels': {'0': {'prim': 'cone'}, '1': {'prim': 'sphere'}},
    #       'primPaths': ['/Replicator/Cone_Xform', '/Replicator/Sphere_Xform']}
    #   }
    # }

import asyncio
asyncio.ensure_future(test_bbox_2d_loose_fast())

bounding_box_2d_loose

Outputs loose 2d bounding box of each entity with semantics in the camera’s field of view. Loose bounding boxes bound the entire entity regardless of occlusions.

Initialization Parameters

• semanticTypes: List of allowed semantic types the types. For example, if semantic_types is [“class”], only the bounding boxes for prims with semantics of type “class” will be retrieved.

Output Format

The bounding box annotator returns a dictionary with the bounds and semantic id found under the “data” key, while other information is under the “info” key: “idToLabels”, “bboxIds” and “primPaths”.

{
    "data": np.dtype(
                [
                    ("semanticId", "<u4"),
                    ("x_min", "<i4"),
                    ("y_min", "<i4"),
                    ("x_max", "<i4"),
                    ("y_max", "<i4"),
                    ("occlusionRatio", "<f4"),
                ],
    "info": {
        "idToLabels": {<semanticId>: <semantic_labels>},    # mapping from integer semantic ID to a comma delimited list of associated semantics
        "bboxIds": [<bbox_id_0>, ..., <bbox_id_n>],         # ID specific to bounding box annotators allowing easy mapping between different bounding box annotators.
        "primPaths": [<prim_path_0>, ... <prim_path_n>],    # prim path tied to each bounding box
    }
}

Note

bounding_box_2d_loose will produce the loose 2d bounding box of any prim in the viewport, no matter if is partially occluded or fully occluded.

Example

import omni.replicator.core as rep

async def test_bbox_2d_loose():
    cone = rep.create.cone(semantics=[("prim", "cone")], position=(100, 0, 0))
    sphere = rep.create.sphere(semantics=[("prim", "sphere")], position=(-100, 0, 0))
    invalid_type = rep.create.cube(semantics=[("shape", "boxy")], position=(0, 100, 0))

    cam = rep.create.camera(position=(500,500,500), look_at=cone)
    rp = rep.create.render_product(cam, (1024, 512))

    bbox_2d_loose = rep.AnnotatorRegistry.get_annotator("bounding_box_2d_loose", init_params={"semanticTypes": ["prim"]})
    bbox_2d_loose.attach(rp)

    await rep.orchestrator.step_async()
    data = bbox_2d_loose.get_data()
    print(data)
    # {
    #   'data': array([
    #       (0, 442, 198, 581, 357, 0.),
    #       (1, 245,  92, 375, 220, 0.38),
    #       dtype=[('semanticId', '<u4'),
    #              ('x_min', '<i4'),
    #              ('y_min', '<i4'),
    #              ('x_max', '<i4'),
    #              ('y_max', '<i4')]),
    #              ("occlusionRatio", "<f4"),
    #   'info': {
    #       'bboxIds': array([0, 1], dtype=uint32),
    #       'idToLabels': {'0': {'prim': 'cone'}, '1': {'prim': 'sphere'}},
    #       'primPaths': ['/Replicator/Cone_Xform', '/Replicator/Sphere_Xform']}
    #   }
    # }

import asyncio
asyncio.ensure_future(test_bbox_2d_loose())

bounding_box_3d_360

Outputs 3D bounding box of each entity with semantics for the entire world including outside the sensor’s field of view

Initialization Parameters

• None

Output Format

The bounding box annotator returns a dictionary with the bounds and semantic id found under the “data” key, while other information is under the “info” key: “idToLabels”, “bboxIds” and “primPaths”.

{
    "data": np.dtype(
                [
                    ('x_min', '<f4'),               # Minimum bound in x axis in local reference frame (in world units)
                    ('y_min', '<f4'),               # Minimum bound in y axis in local reference frame (in world units)
                    ('z_min', '<f4'),               # Minimum bound in z axis in local reference frame (in world units)
                    ('x_max', '<f4'),               # Maximum bound in x axis in local reference frame (in world units)
                    ('y_max', '<f4'),               # Maximum bound in y axis in local reference frame (in world units)
                    ('z_max', '<f4'),               # Maximum bound in z axis in local reference frame (in world units)
                    ('transform', '<f4', (4, 4)),   # Local to world transformation matrix (transforms the bounds from local frame to world frame)
                    ('occlusionRatio', '<f4')]),    # Occlusion (visible pixels / total pixels), where `0.0` is fully visible and `1.0` is fully occluded. See additional notes below.
                ],
    "info": {
        "idToLabels": {<semanticId>: <semantic_labels>},    # mapping from integer semantic ID to a comma delimited list of associated semantics
        "bboxIds": [<bbox_id_0>, ..., <bbox_id_n>],         # ID specific to bounding box annotators allowing easy mapping between different bounding box annotators.
        "primPaths": [<prim_path_0>, ... <prim_path_n>],    # prim path tied to each bounding box
    }
}

Note

• bounding boxes are retrieved regardless of occlusion.

• bounding box dimensions (<axis>_min, <axis>_max) are expressed in stage units.

• occlusionRatio can only provide valid values for prims composed of a single mesh. Multi-mesh labelled prims will return a value of -1 indicating that no occlusion value is available.

Example

import omni.replicator.core as rep

async def test_bbox_3d_360():
    cone = rep.create.cone(semantics=[("prim", "cone")], position=(100, 0, 0))
    sphere = rep.create.sphere(semantics=[("prim", "sphere")], position=(-100, 0, 0))
    cube = rep.create.cube(semantics=[("prim", "cube")], position=(1000, 1000, 1000))

    cam = rep.create.camera(position=(500,500,500), look_at=cone)
    rp = rep.create.render_product(cam, (1024, 512))

    bbox_3d_360 = rep.AnnotatorRegistry.get_annotator("bounding_box_3d_360")
    bbox_3d_360.attach(rp)

    await rep.orchestrator.step_async()
    data = bbox_3d_360.get_data()
    print(data)
    #   {
    #       'data': array([
    #           (0, -50., -50., -50., 50., 50., 50., [[   1.,    0.,    0.,    0.], [   0.,    1.,    0.,    0.], [   0.,    0.,    1.,    0.], [ 100.,    0.,    0.,    1.]], 0.  ),
    #           (1, -50., -50., -50., 50., 50., 50., [[   1.,    0.,    0.,    0.], [   0.,    1.,    0.,    0.], [   0.,    0.,    1.,    0.], [-100.,    0.,    0.,    1.]], 0.38)],
    #           (2, -50., -50., -50., 50., 50., 50., [[   1.,    0.,    0.,    0.], [   0.,    1.,    0.,    0.], [   0.,    0.,    1.,    0.], [1000., 1000., 1000.,    1.]], nan)],
    #           dtype=[
    #               ('semanticId', '<u4'),
    #               ('x_min', '<f4'),
    #               ('y_min', '<f4'),
    #               ('z_min', '<f4'),
    #               ('x_max', '<f4'),
    #               ('y_max', '<f4'),
    #               ('z_max', '<f4'),
    #               ('transform', '<f4', (4, 4)),
    #               ('occlusionRatio', '<f4')]),
    #       'info': {
    #           'bboxIds': array([0, 1, 2], dtype=uint32),
    #           'idToLabels': {0: {'prim': 'cone'}, 1: {'prim': 'sphere'}, 2: {'prim': 'cube'}},
    #           'primPaths': ['/Replicator/Cone_Xform', '/Replicator/Sphere_Xform', '/Replicator/Cube_Xform']
    #       }
    #   }

import asyncio
asyncio.ensure_future(test_bbox_3d_360())

bounding_box_3d_fast

Outputs 3D bounding box of each entity with semantics for entities within the sensor’s field of view.

Initialization Parameters

• None

Output Format

The bounding box annotator returns a dictionary with the bounds and semantic id found under the “data” key, while other information is under the “info” key: “idToLabels”, “bboxIds” and “primPaths”.

{
    "data": np.dtype(
                [
                    ("semanticId", "<u4"),
                    ("x_min", "<i4"),
                    ("y_min", "<i4"),
                    ("x_max", "<i4"),
                    ("y_max", "<i4"),
                    ("z_min", "<i4"),
                    ("z_max", "<i4"),
                    ("transform", "<i4"),
                ],
    "info": {
        "idToLabels": {<semanticId>: <semantic_labels>},    # mapping from integer semantic ID to a comma delimited list of associated semantics
        "bboxIds": [<bbox_id_0>, ..., <bbox_id_n>],         # ID specific to bounding box annotators allowing easy mapping between different bounding box annotators.
        "primPaths": [<prim_path_0>, ... <prim_path_n>],    # prim path tied to each bounding box
    }
}

Note

• bounding boxes are retrieved regardless of occlusion.

• bounding box dimensions (<axis>_min, <axis>_max) are expressed in stage units.

Example

import omni.replicator.core as rep

async def test_bbox_3d_fast():
    cone = rep.create.cone(semantics=[("prim", "cone")], position=(100, 0, 0))
    sphere = rep.create.sphere(semantics=[("prim", "sphere")], position=(-100, 0, 0))
    cube = rep.create.cube(semantics=[("prim", "cube")], position=(1000, 1000, 1000))

    cam = rep.create.camera(position=(500,500,500), look_at=cone)
    rp = rep.create.render_product(cam, (1024, 512))

    bbox_3d_fast = rep.AnnotatorRegistry.get_annotator("bounding_box_3d_fast")
    bbox_3d_fast.attach(rp)

    await rep.orchestrator.step_async()
    data = bbox_3d_fast.get_data()
    print(data)
    #   {
    #       'data': array([
    #           (0, -50., -50., -50., 50., 50., 50., [[   1.,    0.,    0.,    0.], [   0.,    1.,    0.,    0.], [   0.,    0.,    1.,    0.], [ 100.,    0.,    0.,    1.]], 0.  ),
    #           (1, -50., -50., -50., 50., 50., 50., [[   1.,    0.,    0.,    0.], [   0.,    1.,    0.,    0.], [   0.,    0.,    1.,    0.], [-100.,    0.,    0.,    1.]], 0.38)],
    #           dtype=[
    #               ('semanticId', '<u4'),
    #               ('x_min', '<f4'),
    #               ('y_min', '<f4'),
    #               ('z_min', '<f4'),
    #               ('x_max', '<f4'),
    #               ('y_max', '<f4'),
    #               ('z_max', '<f4'),
    #               ('transform', '<f4', (4, 4)),
    #               ('occlusionRatio', '<f4')]),
    #       'info': {
    #           'bboxIds': array([0, 1, 2], dtype=uint32),
    #           'idToLabels': {0: {'prim': 'cone'}, 1: {'prim': 'sphere'}}},
    #           'primPaths': ['/Replicator/Cone_Xform', '/Replicator/Sphere_Xform']
    #       }
    #   }

import asyncio
asyncio.ensure_future(test_bbox_3d_fast())

bounding_box_3d

Outputs 3D bounding box of each entity with semantics for entities within the sensor’s field of view.

Initialization Parameters

• None

Output Format

The bounding box annotator returns a dictionary with the bounds and semantic id found under the “data” key, while other information is under the “info” key: “idToLabels”, “bboxIds” and “primPaths”.

{
    "data": np.dtype(
                [
                    ("semanticId", "<u4"),
                    ("x_min", "<i4"),
                    ("y_min", "<i4"),
                    ("x_max", "<i4"),
                    ("y_max", "<i4"),
                    ("z_min", "<i4"),
                    ("z_max", "<i4"),
                    ("transform", "<i4"),
                ],
    "info": {
        "idToLabels": {<semanticId>: <semantic_labels>},    # mapping from integer semantic ID to a comma delimited list of associated semantics
        "bboxIds": [<bbox_id_0>, ..., <bbox_id_n>],         # ID specific to bounding box annotators allowing easy mapping between different bounding box annotators.
        "primPaths": [<prim_path_0>, ... <prim_path_n>],    # prim path tied to each bounding box
    }
}

Note

• bounding boxes are retrieved regardless of occlusion.

• bounding box dimensions (<axis>_min, <axis>_max) are expressed in stage units.

Example

import omni.replicator.core as rep

async def test_bbox_3d():
    cone = rep.create.cone(semantics=[("prim", "cone")], position=(100, 0, 0))
    sphere = rep.create.sphere(semantics=[("prim", "sphere")], position=(-100, 0, 0))
    cube = rep.create.cube(semantics=[("prim", "cube")], position=(1000, 1000, 1000))

    cam = rep.create.camera(position=(500,500,500), look_at=cone)
    rp = rep.create.render_product(cam, (1024, 512))

    bbox_3d = rep.AnnotatorRegistry.get_annotator("bounding_box_3d")
    bbox_3d.attach(rp)

    await rep.orchestrator.step_async()
    data = bbox_3d.get_data()
    print(data)
    #   {
    #       'data': array([
    #           (0, -50., -50., -50., 50., 50., 50., [[   1.,    0.,    0.,    0.], [   0.,    1.,    0.,    0.], [   0.,    0.,    1.,    0.], [ 100.,    0.,    0.,    1.]], 0.  ),
    #           (1, -50., -50., -50., 50., 50., 50., [[   1.,    0.,    0.,    0.], [   0.,    1.,    0.,    0.], [   0.,    0.,    1.,    0.], [-100.,    0.,    0.,    1.]], 0.38)],
    #           dtype=[
    #               ('semanticId', '<u4'),
    #               ('x_min', '<f4'),
    #               ('y_min', '<f4'),
    #               ('z_min', '<f4'),
    #               ('x_max', '<f4'),
    #               ('y_max', '<f4'),
    #               ('z_max', '<f4'),
    #               ('transform', '<f4', (4, 4)),
    #               ('occlusionRatio', '<f4')]),
    #       'info': {
    #           'bboxIds': array([0, 1, 2], dtype=uint32),
    #           'idToLabels': {0: {'prim': 'cone'}, 1: {'prim': 'sphere'}}},
    #           'primPaths': ['/Replicator/Cone_Xform', '/Replicator/Sphere_Xform']
    #       }
    #   }

import asyncio
asyncio.ensure_future(test_bbox_3d())

semantic_segmentation

Outputs semantic segmentation of each entity in the camera’s field of view that has semantic labels.

Initialization Parameters

• Colorize (bool): whether to output colorized semantic segmentation or non-colorized one.

Output Format

{
    "data": array((height, width), dtype=<np.uint32>),

    "info": {
        "idToLabels": {<semanticId>: <semantic_labels>},    # mapping from semantic ID to a comma delimited list of associated semantics
    }
}

• data (semantic segmentation array):

  • If colorize is set to True, the image will be a 2d array of types np.uint8 with 4 channels. The uint32 array can be converted using semantic_seg_data[“data”].view(np.uint8).reshape(height, width, -1)

  • Different colors represent different semantic labels.

  • If colorize is set to False, the image will be a 2d array of types np.uint32 with 1 channel, which is the semantic id of the entities.

• info:

  • idToLabels

  • If colorize is set to True, it will be the mapping from color to semantic labels.

  • If colorize is set to False, it will be the mapping from semantic id to semantic labels.

Note

The semantic labels of an entity will be the semantic labels of itself, plus all the semantic labels it inherit from its parent and semantic labels with same type will be concatenated, separated by comma. For example, if an entity has a semantic label of [{“class”: “cube”}], and its parent has [{“class”: “rectangle”}]. Then the final semantic labels of that entity will be [{“class”: “rectangle, cube”}].

import omni.replicator.core as rep

async def test_semantic_segmentation():
    cone = rep.create.cone(semantics=[("prim", "cone")], position=(100, 0, 0))
    sphere = rep.create.sphere(semantics=[("prim", "sphere")], position=(-100, 0, 0))
    invalid_type = rep.create.cube(semantics=[("shape", "boxy")], position=(0, 100, 0))

    cam = rep.create.camera(position=(500,500,500), look_at=cone)
    rp = rep.create.render_product(cam, (1024, 512))

    semantic_seg = rep.AnnotatorRegistry.get_annotator("semantic_segmentation")
    semantic_seg.attach(rp)

    await rep.orchestrator.step_async()
    data = semantic_seg.get_data()
    print(data)
    # {
    #   'data': array([[0, 0, 0, ..., 0, 0, 0],
    #       [0, 0, 0, ..., 0, 0, 0],
    #       [0, 0, 0, ..., 0, 0, 0],
    #       ...,
    #       [0, 0, 0, ..., 0, 0, 0],
    #       [0, 0, 0, ..., 0, 0, 0],
    #       [0, 0, 0, ..., 0, 0, 0]],
    #   'info': {
    #       'idToLabels': {'0': {'class': 'BACKGROUND'}, '2': {'prim': 'cone'}, '3': {'shape': 'boxy'}, '4': {'prim': 'sphere'}}
    #   }
    # }

import asyncio
asyncio.ensure_future(test_semantic_segmentation())

instance_id_segmentation_fast

Development segmentation node Instance segmentation that returns the renderer instance ID - used for debugging

instance_id_segmentation

Development segmentation node

Instance segmentation that returns the renderer instance ID - used for debugging

instance_segmentation_fast

Outputs instance segmentation of each entity in the camera’s viewport. Only semantically labelled entities are returned.

Initialization Parameters

• Colorize (bool): whether to output colorized instance segmentation or non-colorized one.

Output Format

{
    "data": array((height, width), dtype=<np.uint32>),

    "info": {
        "idToLabels": {<semanticId>: <prim_path>},    # mapping from instance ID to the instance's prim path
        "idToSemantic":{<instanceId>: <semantic_labels>},    # mapping from instance ID to a comma delimited list of associated semantics
    }
}

Note

• Two prims with same semantic labels but live in different USD path will have different ids.

• If two prims have no semantic labels, and they have a same parent which has semantic labels, they will be classified as the same instance.

• The semantic labels of an entity will be the semantic labels of itself, plus all the semantic labels it inherit from its parent and semantic labels with same type will be concatenated, separated by comma. For example, if an entity has a semantic label of [{“class”: “cube”}], and its parent has [{“class”: “rectangle”}]. Then the final semantic labels of that entity will be [{“class”: “rectangle, cube”}].

import omni.replicator.core as rep

async def test_instance_segmentation_fast():
    cone = rep.create.cone(semantics=[("prim", "cone")], position=(100, 0, 0))
    sphere = rep.create.sphere(semantics=[("prim", "sphere")], position=(-100, 0, 0))
    invalid_type = rep.create.cube(semantics=[("shape", "boxy")], position=(0, 100, 0))

    cam = rep.create.camera(position=(500,500,500), look_at=cone)
    rp = rep.create.render_product(cam, (1024, 512))

    instance_seg = rep.AnnotatorRegistry.get_annotator("instance_segmentation_fast")
    instance_seg.attach(rp)

    await rep.orchestrator.step_async()
    data = instance_seg.get_data()
    print(data)
    # {
    #   'data': array([[0, 0, 0, ..., 0, 0, 0],
    #       [0, 0, 0, ..., 0, 0, 0],
    #       [0, 0, 0, ..., 0, 0, 0],
    #       ...,
    #       [0, 0, 0, ..., 0, 0, 0],
    #       [0, 0, 0, ..., 0, 0, 0],
    #       [0, 0, 0, ..., 0, 0, 0]],
    #   'info': {
    #       'idToLabels': {'idToLabels': {0: 'BACKGROUND', 1: 'UNLABELLED', 3: '/Replicator/Sphere_Xform', 2: '/Replicator/Cone_Xform', 4: '/Replicator/Cube_Xform'},
    #       'idToSemantics': {0: {'class': 'BACKGROUND'}, 1: {'class': 'UNLABELLED'}, 3: {'prim': 'sphere'}, 2: {'prim': 'cone'}, 4: {'shape': 'boxy'}}
    #   }
    # }

import asyncio
asyncio.ensure_future(test_instance_segmentation_fast())

instance_segmentation

Outputs instance segmentation of each entity in the camera’s viewport. Only semantically labelled entities are returned.

Initialization Parameters

• Colorize (bool): whether to output colorized instance segmentation or non-colorized one.

Output Format

{
    "data": array((height, width), dtype=<np.uint32>),

    "info": {
        "idToLabels": {<semanticId>: <prim_path>},    # mapping from instance ID to the instance's prim path
        "idToSemantic":{<instanceId>: <semantic_labels>},    # mapping from instance ID to a comma delimited list of associated semantics
    }
}

Note

• Two prims with same semantic labels but live in different USD path will have different ids.

• If two prims have no semantic labels, and they have a same parent which has semantic labels, they will be classified as the same instance.

• The semantic labels of an entity will be the semantic labels of itself, plus all the semantic labels it inherit from its parent and semantic labels with same type will be concatenated, separated by comma. For example, if an entity has a semantic label of [{“class”: “cube”}], and its parent has [{“class”: “rectangle”}]. Then the final semantic labels of that entity will be [{“class”: “rectangle, cube”}].

import omni.replicator.core as rep

async def test_instance_segmentation():
    cone = rep.create.cone(semantics=[("prim", "cone")], position=(100, 0, 0))
    sphere = rep.create.sphere(semantics=[("prim", "sphere")], position=(-100, 0, 0))
    invalid_type = rep.create.cube(semantics=[("shape", "boxy")], position=(0, 100, 0))

    cam = rep.create.camera(position=(500,500,500), look_at=cone)
    rp = rep.create.render_product(cam, (1024, 512))

    instance_seg = rep.AnnotatorRegistry.get_annotator("instance_segmentation")
    instance_seg.attach(rp)

    await rep.orchestrator.step_async()
    data = instance_seg.get_data()
    print(data)
    # {
    #   'data': array([[0, 0, 0, ..., 0, 0, 0],
    #       [0, 0, 0, ..., 0, 0, 0],
    #       [0, 0, 0, ..., 0, 0, 0],
    #       ...,
    #       [0, 0, 0, ..., 0, 0, 0],
    #       [0, 0, 0, ..., 0, 0, 0],
    #       [0, 0, 0, ..., 0, 0, 0]],
    #   'info': {
    #       'idToLabels': {'idToLabels': {'0': 'BACKGROUND', '1': 'UNLABELLED', '3': '/Replicator/Sphere_Xform', '2': '/Replicator/Cone_Xform', '4': '/Replicator/Cube_Xform'},
    #       'idToSemantics': {'0': {'class': 'BACKGROUND'}, '1': {'class': 'UNLABELLED'}, '3': {'prim': 'sphere'}, '2': {'prim': 'cone'}, '4': {'shape': 'boxy'}}
    #   }
    # }

import asyncio
asyncio.ensure_future(test_instance_segmentation())

CameraParams

The Camera Parameters annotator returns the camera details for the camera corresponding to the render product to which the annotator is attached.

Data Details

• cameraFocalLength: Camera focal length

• cameraFocusDistance: Camera focus distance

• cameraFStop: Camera fStop value

• cameraAperture: Camera horizontal and vertical aperture

• cameraApertureOffset: Camera horizontal and vertical aperture offset

• renderProductResolution: RenderProduct resolution

• cameraModel: Camera model name

• cameraViewTransform: Camera to world transformation matrix

• cameraProjection: Camera projection matrix

• cameraFisheyeNominalWidth: Camera fisheye nominal width

• cameraFisheyeNominalHeight: Camera fisheye nominal height

• cameraFisheyeOpticalCentre: Camera fisheye optical centre

• cameraFisheyeMaxFOV: Camera fisheye maximum field of view

• cameraFisheyePolynomial: Camera fisheye polynomial

• cameraNearFar: Camera near/far clipping range

Example

import asyncio
import omni.replicator.core as rep

async def test_camera_params():
    camera_1 = rep.create.camera()
    camera_2 = rep.create.camera(
        position=(100, 0, 0),
        projection_type="fisheye_polynomial"
    )

    render_product_1 = rep.create.render_product(camera_1, (1024, 512))
    render_product_2 = rep.create.render_product(camera_2, (800, 600))


    anno_1 = rep.annotators.get("CameraParams").attach(render_product_1)
    anno_2 = rep.annotators.get("CameraParams").attach(render_product_2)

    await rep.orchestrator.step_async()

    print(anno_1.get_data())
    # {'cameraAperture': array([20.95 , 15.29], dtype=float32),
    #     'cameraApertureOffset': array([0., 0.], dtype=float32),
    #     'cameraFisheyeLensP': array([], dtype=float32),
    #     'cameraFisheyeLensS': array([], dtype=float32),
    #     'cameraFisheyeMaxFOV': 0.0,
    #     'cameraFisheyeNominalHeight': 0,
    #     'cameraFisheyeNominalWidth': 0,
    #     'cameraFisheyeOpticalCentre': array([0., 0.], dtype=float32),
    #     'cameraFisheyePolynomial': array([0., 0., 0., 0., 0.], dtype=float32),
    #     'cameraFocalLength': 24.0,
    #     'cameraFocusDistance': 400.0,
    #     'cameraFStop': 0.0,
    #     'cameraModel': 'pinhole',
    #     'cameraNearFar': array([1., 1000000.], dtype=float32),
    #     'cameraProjection': array([ 2.29,  0.  ,  0.  ,  0.  ,
    #                                 0.  ,  4.58,  0.  ,  0.  ,
    #                                 0.  ,  0.  ,  0.  , -1.  ,
    #                                 0.  ,  0.  ,  1.  ,  0.  ]),
    #     'cameraViewTransform': array([1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1.]),
    #     'metersPerSceneUnit': 0.009999999776482582,
    #     'renderProductResolution': array([1024, 512], dtype=int32)
    # }

    print(anno_2.get_data())
    # {
    #     'cameraAperture': array([20.955 , 15.291], dtype=float32),
    #     'cameraApertureOffset': array([0., 0.], dtype=float32),
    #     'cameraFisheyeLensP': array([-0., -0.], dtype=float32),
    #     'cameraFisheyeLensS': array([-0., -0.,  0., -0.], dtype=float32),
    #     'cameraFisheyeMaxFOV': 200.0,
    #     'cameraFisheyeNominalHeight': 1216,
    #     'cameraFisheyeNominalWidth': 1936,
    #     'cameraFisheyeOpticalCentre': array([970.9424, 600.375 ], dtype=float32),
    #     'cameraFisheyePolynomial': array([0.     , 0.002, 0.     , 0.     , 0.     ], dtype=float32),
    #     'cameraFocalLength': 24.0,
    #     'cameraFocusDistance': 400.0,
    #     'cameraFStop': 0.0,
    #     'cameraModel': 'fisheyePolynomial',
    #     'cameraNearFar': array([1., 1000000.], dtype=float32),
    #     'cameraProjection': array([ 2.29,  0.  ,  0.  ,  0.  ,
    #                                 0.  ,  3.05,  0.  ,  0.  ,
    #                                 0.  ,  0.  ,  0.  , -1.  ,
    #                                 0.  ,  0.  ,  1.  ,  0.  ]),
    #     'cameraViewTransform': array([1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., -100., 0., 0., 1.]),
    #   'metersPerSceneUnit': 0.009999999776482582,
    #   'renderProductResolution': array([800, 600], dtype=int32)
    #  }

asyncio.ensure_future(test_camera_params())

skeleton_data

The skeleton data annotator outputs pose information about the skeletons in the scene view.

Output Format

Parameter	Data Type	Description
animationVariant	list(<num_skeletons>, dtype=str)	Animation variant name for each skeleton
assetPath	list(<num_skeletons>, dtype=str)	Asset path for each skeleton
globalTranslations	array((<num_joints>, 3), dtype=float32)	Global translation of each joint
globalTranslationsSizes	array(<num_skeletons>, dtype=int32)	Size of each set of joints per skeleton
inView	array(<num_skeletons>, dtype=bool)	If the skeleton is in view of the camera
jointOcclusions	array(<num_joints>, dtype=bool)	For each joint, True if joint is occluded, otherwise False
jointOcclusionsSizes	array(<num_skeletons>, dtype=int32)	Size of each set of joints per skeleton
localRotations	array((<num_joints>, 4), dtype=float32)	Local rotation of each joint
localRotationsSizes	array(<num_skeletons>, dtype=int32)	Size of each set of joints per skeleton
numSkeletons	<num_skeletons>	Number of skeletons in scene
occlusionTypes	list(str)	For each joint, the type of occlusion
occlusionTypesSizes	array([<num_skeletons>], dtype=int32)	Size of each set of joints per skeleton
restGlobalTranslations	array((<num_joints>, 3), dtype=float32)	Global translation for each joint at rest
restGlobalTranslationsSizes	array([<num_skeletons>], dtype=int32)	Size of each set of joints per skeleton
restLocalRotations	array((<num_joints>, 4), dtype=float32)	Local rotation of each join at rest
restLocalRotationsSizes	array(<num_skeletons>, dtype=int32)	Size of each set of joints per skeleton
restLocalTranslations	array((<num_joints>, 3), dtype=float32)	Local translation of each join at rest
restLocalTranslationsSizes	array(<num_skeletons>, dtype=int32)	Size of each set of joints per skeleton
skeletonJoints	list(str)	List of skeleton joints, encoded as a string
skeletonParents	array(<num_joints>, dtype=int32)	Which joint is the parent of the index, -1 is root
skeletonParentsSizes	array(<num_skeletons>, dtype=int32)	Size of each set of joints per skeleton
skelName	list(<num_skeletons>, dtype=str)	Name of each skeleton
skelPath	list(<num_skeletons>, dtype=str)	Path of each skeleton prim
translations2d	array((<num_joints>, 2), dtype=float32)	Screen space joint position in pixels
translations2dSizes	array(<num_skeletons>, dtype=int32)	Size of each set of joints per skeleton

This annotator returns additional data as a single string held in a dictionary with the key skeleton_data for backwards compatibility with the original implementation of this annotator. Use eval(data["skeleton_data"]) to extract the attributes from this string.

Example

Below is an example script that outputs 10 images with skeleton pose annotation.

import asyncio
import omni.replicator.core as rep

# Define paths for the character
PERSON_SRC = 'omniverse://localhost/NVIDIA/Assets/Characters/Reallusion/Worker/Worker.usd'

async def test_skeleton_data():
    # Human Model
    person = rep.create.from_usd(PERSON_SRC, semantics=[('class', 'person')])
    # Area to scatter cubes in
    area = rep.create.cube(scale=2, position=(0.0, 0.0, 100.0), visible=False)

    # Create the camera and render product
    camera = rep.create.camera(position=(25, -421.0, 182.0), rotation=(77.0, 0.0, 3.5))
    render_product = rep.create.render_product(camera, (1024, 1024))

    def randomize_spheres():
        spheres = rep.create.sphere(scale=0.1, count=100)
        with spheres:
            rep.randomizer.scatter_3d(area)
        return spheres.node

    rep.randomizer.register(randomize_spheres)

    with rep.trigger.on_frame(interval=10, max_execs=5):
        rep.randomizer.randomize_spheres()

    # Attach annotator
    skeleton_anno = rep.annotators.get("skeleton_data")
    skeleton_anno.attach(render_product)

    await rep.orchestrator.step_async()

    data = skeleton_anno.get_data()
    print(data)
    # {
    #     'animationVariant': ['None'],
    #     'assetPath': ['Bones/Worker.StandingDiscussion_LookingDown_M.usd'],
    #     'globalTranslations': array([[  0.  ,   0.  ,   0.  ], ..., [-21.64,   2.58, 129.8 ]], dtype=float32),
    #     'globalTranslationsSizes': array([101], dtype=int32),
    #     'inView': array([ True]),
    #     'jointOcclusions': array([ True, False, ..., False, False]),
    #     'jointOcclusionsSizes': array([101], dtype=int32),
    #     'localRotations': array([[ 1.  ,  0.  ,  0.  ,  0.  ], ..., [ 1.  ,  0.  , -0.09, -0.  ]], dtype=float32),
    #     'localRotationsSizes': array([101], dtype=int32),
    #     'numSkeletons': 1,
    #     'occlusionTypes': ["['BACKGROUND', 'None', ..., 'None', 'None']"],
    #     'occlusionTypesSizes': array([101], dtype=int32),
    #     'restGlobalTranslations': array([[  0.  ,   0.  ,   0.  ], ..., [-31.86,   8.96, 147.72]], dtype=float32),
    #     'restGlobalTranslationsSizes': array([101], dtype=int32),
    #     'restLocalRotations': array([[ 1.  ,  0.  ,  0.  ,  0.  ], ..., [ 1.  ,  0.  ,  0.  , -0.  ]], dtype=float32),
    #     'restLocalRotationsSizes': array([101], dtype=int32),
    #     'restLocalTranslations': array([[  0.  ,   0.  ,   0.  ], ..., [ -0.  ,  12.92,   0.01]], dtype=float32),
    #     'restLocalTranslationsSizes': array([101], dtype=int32),
    #     'skeletonJoints': ["['RL_BoneRoot', 'RL_BoneRoot/Hip', ..., 'RL_BoneRoot/Hip/Waist/Spine01/Spine02/R_Clavicle/R_Upperarm/R_UpperarmTwist01/R_UpperarmTwist02']"],
    #     'skeletonParents': array([-1,  0,  1, ..., 97, 78, 99], dtype=int32),
    #     'skeletonParentsSizes': array([101], dtype=int32),
    #     'skelName': ['Worker'],
    #     'skelPath': ['/Replicator/Ref_Xform/Ref/ManRoot/Worker/Worker'],
    #     'translations2d': array([[513.94, 726.03],
    #                              [514.42, 480.42],
    #                              [514.42, 480.42],
    #                              ...,
    #                              [499.45, 450.9 ],
    #                              [466.3 , 354.6 ],
    #                              [455.09, 388.56]], dtype=float32),
    #    'translations2dSizes': array([101], dtype=int32),
    #    'skeletonData': ...   # string data representation for backward compatibility
    # }

asyncio.ensure_future(test_skeleton_data())

pointcloud

Outputs a 2D array of shape (N, 3) representing the points sampled on the surface of the prims in the viewport, where N is the number of point.

Output Format

The pointcloud annotator returns positions of the points found under the “data” key, while other information is under the “info” key: “pointRgb”, “pointNormals”, “pointSemantic” and “pointInstance”.

{
    'data': array([...], shape=(<num_points>, 3), dtype=float32),
    'info': {
        'pointNormals': array([...], shape=(<num_points> * 4), dtype=float32),
        'pointRgb': array([...], shape=(<num_points> * 4), dtype=uint8),
        'pointSemantic': array([...], shape=(<num_points>), dtype=uint8),
        'pointInstance': array([...], shape=(<num_points>), dtype=uint8),
    }
}

Data Details

• Point positions are in the world space.

• Sample resolution is determined by the resolution of the render product.

Note

To get the mapping from semantic id to semantic labels, pointcloud annotator is better used with semantic segmentation annotator, and users can extract the idToLabels data from the semantic segmentation annotator.

Example 1

Pointcloud annotator captures prims seen in the camera, and sampled the points on the surface of the prims, based on the resolution of the render product attached to the camera. Additional to the points sampled, it also outputs rgb, normals and semantic id values associated to the prim where that point belongs to. For prims without any valid semantic labels, pointcloud annotator will ignore it.

import asyncio
import omni.replicator.core as rep

async def test_pointcloud():
    # Pointcloud only capture prims with valid semantics
    W, H = (1024, 512)
    cube = rep.create.cube(position=(0, 0, 0), semantics=[("class", "cube")])
    camera = rep.create.camera(position=(200., 200., 200.), look_at=cube)
    render_product = rep.create.render_product(camera, (W, H))

    pointcloud_anno = rep.annotators.get("pointcloud")
    pointcloud_anno.attach(render_product)

    await rep.orchestrator.step_async()

    pc_data = pointcloud_anno.get_data()
    print(pc_data)
    # {
    #   'data': array([[-49.96,  50.  , -49.28],
    #                  [-49.74,  50.  , -49.51],
    #                  [-49.51,  50.  , -49.74],
    #                  ...,
    #                  [ 50.  , -49.33,  27.51],
    #                  [ 50.  , -49.67,  27.08],
    #                  [ 50.  , -50.  ,  26.65]], dtype=float32),
    #   'info': {
    #       'pointNormals': array([ 0.,  1., -0., ...,  0., -0.,  1.], dtype=float32),
    #       'pointRgb': array([154, 154, 154, ...,  24,  24, 255], dtype=uint8),
    #       'pointSemantic': array([2, 2, 2, ..., 2, 2, 2], dtype=uint8)},
    #       'pointInstance': array([1, 1, 1, ..., 1, 1, 1], dtype=uint8)}
    # }

asyncio.ensure_future(test_pointcloud())

Example 2

In this example, we demonstrate a scenario where multiple camera captures are taken to produce a more complete pointcloud, utilizing the excellent open3d library to export a coloured ply file.

import os
import asyncio
import omni.replicator.core as rep
import open3d as o3d
import numpy as np

async def test_pointcloud():
    # Pointcloud only capture prims with valid semantics
    cube = rep.create.cube(semantics=[("class", "cube")])
    camera = rep.create.camera()
    render_product = rep.create.render_product(camera, (1024, 512))

    pointcloud_anno = rep.annotators.get("pointcloud")
    pointcloud_anno.attach(render_product)

    # Camera positions to capture the cube
    camera_positions = [(500, 500, 0), (-500, -500, 0), (500, 0, 500), (-500, 0, -500)]

    with rep.trigger.on_frame(max_execs=len(camera_positions)):
        with camera:
            rep.modify.pose(position=rep.distribution.sequence(camera_positions), look_at=cube)  # make the camera look at the cube

    # Accumulate points
    points = []
    points_rgb = []
    for _ in range(len(camera_positions)):
        await rep.orchestrator.step_async()

        pc_data = pointcloud_anno.get_data()
        points.append(pc_data["data"])
        points_rgb.append(pc_data["info"]["pointRgb"].reshape(-1, 4)[:, :3])

    # Output pointcloud as .ply file
    ply_out_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "out")
    os.makedirs(ply_out_dir, exist_ok=True)

    pc_data = np.concatenate(points)
    pc_rgb = np.concatenate(points_rgb)

    pcd = o3d.geometry.PointCloud()
    pcd.points = o3d.utility.Vector3dVector(pc_data)
    pcd.colors = o3d.utility.Vector3dVector(pc_rgb)
    o3d.io.write_point_cloud(os.path.join(ply_out_dir, "pointcloud.ply"), pcd)

asyncio.ensure_future(test_pointcloud())

ReferenceTime

Outputs the reference time corresponding to the render and associated annotations.

Output Format

The reference time annotator returns a numerator and denominator representing the time corresponding to the render and associated annotations.

{
    'referenceTimeNumerator': int,
    'referenceTimeDenominator': int,
}

Example

import asyncio
import omni.replicator.core as rep

async def test_reference_time():
    W, H = (1024, 512)
    camera = rep.create.camera()
    render_product = rep.create.render_product(camera, (W, H))

    ref_time_anno = rep.annotators.get("ReferenceTime")
    ref_time_anno.attach(render_product)

    await rep.orchestrator.step_async()

    ref_time_data = ref_time_anno.get_data()
    print(ref_time_data)
    # {
    #   'referenceTimeNumerator': <numerator>,
    #   'referenceTimeDenominator': <denominator>,
    # }

asyncio.ensure_future(test_reference_time())

CrossCorrespondence

The cross correspondence annotator outputs a 2D array representing the camera optical flow map of the camera’s viewport against a reference viewport.

To enable the cross correspondance annotation, the camera attached to the render product annotated with cross correspondance must have the attribute crossCameraReferenceName set to the (unique) name (not path) of a second camera (itself attached to a second render product). The Projection Type of the two cameras needs to be of type fisheyePolynomial (Camera –> Fisheye Lens –> Projection Type –> fisheyePolynomial).

Output Format

The Cross Correspondence annotator produces the cross correspondence between pixels seen from two cameras.

The components of each entry in the 2D array represent for different values encoded as floating point values:

• x: dx - difference to the x value of of the corresponding pixel in the reference viewport. This value is normalized to [-1.0, 1.0]

• y: dy - difference to the y value of of the corresponding pixel in the reference viewport. This value is normalized to [-1.0, 1.0]

• z: occlusion mask - boolean signifying that the pixel is occluded or truncated in one of the cross referenced viewports. Floating point value represents a boolean (1.0 = True, 0.0 = False)

• w: geometric occlusion calculated - boolean signifying that the pixel can or cannot be tested as having occluded geometry (e.g. no occlusion testing is performed on missed rays) (1.0 = True, 0.0 = False)

array((height, width, 4), dtype=<np.float32>)

Example

import asyncio
import omni.replicator.core as rep
from pxr import Sdf

async def test_cross_correspondence():
    # Add an object to look at
    rep.create.cone()

    # Add stereo camera pair
    stereo = rep.create.stereo_camera(position=(20, 0, 300), projection_type="fisheye_polynomial", stereo_baseline=20)

    # Add cross correspondence attribute
    stereo_L_prim = stereo.get_output_prims()["prims"][0].GetChildren()[0].GetChildren()[0]
    stereo_L_prim.CreateAttribute("crossCameraReferenceName", Sdf.ValueTypeNames.String)

    # Set attribute to refer to second camera name - beware of scenes with multiple cameras that share the same name!
    stereo_L_prim.GetAttribute("crossCameraReferenceName").Set("StereoCam_R")

    render_products = rep.create.render_product(stereo, (512, 512))

    # Add annotator to left render product
    anno = rep.annotators.get("cross_correspondence")
    anno.attach(render_products[0])

    await rep.orchestrator.step_async()
    data = anno.get_data()
    print(data.shape, data.dtype)
    # (512, 512, 4), float32

asyncio.ensure_future(test_cross_correspondence())

Note

• Both cameras must have the cameraProjectionType attribute set to fisheyePolynomial

• The annotated camera must have the crossCameraReferenceName attribute set to the name of the second camera

• To avoid unexpected results, ensure that the referenced camera has a unique name

MotionVectors

Outputs a 2D array of motion vectors representing the relative motion of a pixel in the camera’s viewport between frames.

The MotionVectors annotator returns the per-pixel motion vectors in in image space.

Output Format

array((height, width, 4), dtype=<np.float32>)

The components of each entry in the 2D array represent for different values encoded as floating point values:

• x: motion distance in the horizontal axis (image width) with movement to the left of the image being positive and movement to the right being negative.

• y: motion distance in the vertical axis (image height) with movement towards the top of the image being positive and movement to the bottom being negative.

• z: unused

• w: unused

Example

import asyncio
import omni.replicator.core as rep

async def test_motion_vectors():
    # Add an object to look at
    cone = rep.create.cone()

    # Add motion to object
    cone_prim = cone.get_output_prims()["prims"][0]
    cone_prim.GetAttribute("xformOp:translate").Set((-100, 0, 0), time=0.0)
    cone_prim.GetAttribute("xformOp:translate").Set((100, 50, 0), time=10.0)

    camera = rep.create.camera()
    render_product = rep.create.render_product(camera, (512, 512))

    motion_vectors_anno = rep.annotators.get("MotionVectors")
    motion_vectors_anno.attach(render_product)

    # Take a step to render the initial state (no movement yet)
    await rep.orchestrator.step_async()

    # Capture second frame (now the timeline is playing)
    await rep.orchestrator.step_async()
    data = motion_vectors_anno.get_data()
    print(data.shape, data.dtype, data.reshape(-1, 4).min(axis=0), data.reshape(-1, 4).max(axis=0))
    # (1024, 512, 4), float32,  [-93.80073  -1.       -1.       -1.     ] [ 0.       23.450201  1.        1.      ]

asyncio.ensure_future(test_motion_vectors())

Note

The values represent motion relative to camera space.

Attribute

Outputs the value of an attribute attached to one of more prims.

The Attribute annotator retrieves the attribute value(s) of one or more prims at the time of render. On attach, the attribute specified will be automatically pushed to Fabric to ensure it can be retrieved. Note that the output type of the attribute must be identical in all specified prims.

Output Format

array((attribute_size x number_of_prims, 1))

The Attribute annotator retrieves the data from the attribute and flattens them, creating a 1D array of shape (attribute_size x number_of_prims, 1).

Currently it can retrieve the attribute with following Sdf data types:

• Int, IntArray, Int2, Int2Array, Int3, Int3Array

• Float, FloatArray, Float2, Float2Array, Float3, Float3Array

• Double, DoubleArray, Double2, Double2Array, Double3, Double3Array

Example

import asyncio
import omni.replicator.core as rep
from pxr import Sdf

async def test_attribute_anno():
    cube1 = rep.create.cube(as_mesh=False)
    cube2 = rep.create.cube(as_mesh=False)
    cube_prim_path = "/Replicator/Cube_Xform/Cube"
    cube_prim_path_2 = "/Replicator/Cube_Xform_01/Cube"

    for path in [cube_prim_path, cube_prim_path_2]:
        stage = omni.usd.get_context().get_stage()
        cube_prim = stage.GetPrimAtPath(path)
        cube_prim.CreateAttribute("float2Arr", Sdf.ValueTypeNames.Float2Array).Set([(12.34, 56.78), (56.78, 12.34)])

    await omni.kit.app.get_app().next_update_async()

    rp = rep.create.render_product("/OmniverseKit_Persp", (1024, 1024))

    fabric_reader_anno = rep.annotators.get(
        "Attribute",
        init_params={
            "prims": [cube_prim_path, cube_prim_path_2],
            "attribute": "float2Arr",
        },
    )
    fabric_reader_anno.attach(rp)

    await rep.orchestrator.step_async()

    data = fabric_reader_anno.get_data()
    print(data, data.shape, data.dtype)
    # [12.34 56.78 56.78 12.34 12.34 56.78 56.78 12.34] (8,) float32

asyncio.ensure_future(test_attribute_anno())