holoscan.operators

Holoscan v2.2.0

This module provides a Python API to underlying C++ API Operators.

holoscan.operators.AJASourceOp Operator to get a video stream from an AJA capture card.
holoscan.operators.BayerDemosaicOp Bayer Demosaic operator.
holoscan.operators.FormatConverterOp Format conversion operator.
holoscan.operators.GXFCodeletOp(fragment, ...) GXF Codelet wrapper operator.
holoscan.operators.HolovizOp(fragment, *args) Holoviz visualization operator using Holoviz module.
holoscan.operators.InferenceOp Inference operator.
holoscan.operators.InferenceProcessorOp Holoinfer Processing operator.
holoscan.operators.PingRxOp(fragment, *args, ...) Simple receiver operator.
holoscan.operators.PingTxOp(fragment, *args, ...) Simple transmitter operator.
holoscan.operators.SegmentationPostprocessorOp Operator carrying out post-processing operations on segmentation outputs.
holoscan.operators.V4L2VideoCaptureOp Operator to get a video stream from a V4L2 source.
holoscan.operators.VideoStreamRecorderOp Operator class to record a video stream to a file.
holoscan.operators.VideoStreamReplayerOp Operator class to replay a video stream from a file.
class holoscan.operators.AJASourceOp

Bases: holoscan.core._core.Operator

Operator to get a video stream from an AJA capture card.

==Named Inputs==

overlay_buffer_inputnvidia::gxf::VideoBuffer (optional)

The operator does not require a message on this input port in order for compute to be called. If a message is found, and enable_overlay is True, the image will be mixed with the image captured by the AJA card. If enable_overlay is False, any message on this port will be ignored.

==Named Outputs==

video_buffer_outputnvidia::gxf::VideoBuffer

The output video frame from the AJA capture card. If overlay_rdma is True, this video buffer will be on the device, otherwise it will be in pinned host memory.

overlay_buffer_outputnvidia::gxf::VideoBuffer (optional)

This output port will only emit a video buffer when enable_overlay is True. If overlay_rdma is True, this video buffer will be on the device, otherwise it will be in pinned host memory.

Parameters
fragment

The fragment that the operator belongs to.

device

The device to target (e.g., “0” for device 0). Default value is "0".

channel

The camera NTV2Channel to use for output (e.g., NTV2Channel.NTV2_CHANNEL1 (0) or “NTV2_CHANNEL1” (in YAML) for the first channel). Default value is NTV2Channel.NTV2_CHANNEL1 ("NTV2_CHANNEL1" in YAML).

width

Width of the video stream. Default value is 1920.

height

Height of the video stream. Default value is 1080.

framerate

Frame rate of the video stream. Default value is 60.

rdma

Boolean indicating whether RDMA is enabled. Default value is False ("false" in YAML).

enable_overlay

Boolean indicating whether a separate overlay channel is enabled. Default value is False ("false" in YAML).

overlay_channel

The camera NTV2Channel to use for overlay output. Default value is NTV2Channel.NTV2_CHANNEL2 ("NTV2_CHANNEL2" in YAML).

overlay_rdma

Boolean indicating whether RDMA is enabled for the overlay. Default value is False ("false" in YAML).

name

The name of the operator. Default value is "aja_source".

Attributes

args The list of arguments associated with the component.
conditions Conditions associated with the operator.
description YAML formatted string describing the operator.
fragment The fragment (holoscan.core.Fragment) that the operator belongs to.
id The identifier of the component.
name The name of the operator.
operator_type The operator type.
resources Resources associated with the operator.
spec The operator spec (holoscan.core.OperatorSpec) associated with the operator.

Methods

add_arg(*args, **kwargs) Overloaded function.
compute(self, arg0, arg1, arg2) Operator compute method.
initialize(self) Initialize the operator.
resource(self, name) Resources associated with the operator.
setup(self, spec) Define the operator specification.
start(self) Operator start method.
stop(self) Operator stop method.
OperatorType
class OperatorType

Bases: pybind11_builtins.pybind11_object

Enum class for operator types used by the executor.

  • NATIVE: Native operator.

  • GXF: GXF operator.

  • VIRTUAL: Virtual operator. (for internal use, not intended for use by application authors)

Members:

NATIVE

GXF

VIRTUAL

Attributes

name

value
GXF = <OperatorType.GXF: 1>

NATIVE = <OperatorType.NATIVE: 0>

VIRTUAL = <OperatorType.VIRTUAL: 2>

__init__(self: holoscan.core._core.Operator.OperatorType, value: int) → None

property name

property value

__init__(self: holoscan.operators.aja_source._aja_source.AJASourceOp, fragment: holoscan.core._core.Fragment, *args, device: str = '0', channel: Union[str, holoscan.operators.aja_source._aja_source.NTV2Channel] = <NTV2Channel.NTV2_CHANNEL1: 0>, width: int = 1920, height: int = 1080, framerate: int = 60, rdma: bool = False, enable_overlay: bool = False, overlay_channel: Union[str, holoscan.operators.aja_source._aja_source.NTV2Channel] = <NTV2Channel.NTV2_CHANNEL2: 1>, overlay_rdma: bool = True, name: str = 'aja_source') → None

Operator to get a video stream from an AJA capture card.

==Named Inputs==

overlay_buffer_inputnvidia::gxf::VideoBuffer (optional)

The operator does not require a message on this input port in order for compute to be called. If a message is found, and enable_overlay is True, the image will be mixed with the image captured by the AJA card. If enable_overlay is False, any message on this port will be ignored.

==Named Outputs==

video_buffer_outputnvidia::gxf::VideoBuffer

The output video frame from the AJA capture card. If overlay_rdma is True, this video buffer will be on the device, otherwise it will be in pinned host memory.

overlay_buffer_outputnvidia::gxf::VideoBuffer (optional)

This output port will only emit a video buffer when enable_overlay is True. If overlay_rdma is True, this video buffer will be on the device, otherwise it will be in pinned host memory.

Parameters
fragment

The fragment that the operator belongs to.

device

The device to target (e.g., “0” for device 0). Default value is "0".

channel

The camera NTV2Channel to use for output (e.g., NTV2Channel.NTV2_CHANNEL1 (0) or “NTV2_CHANNEL1” (in YAML) for the first channel). Default value is NTV2Channel.NTV2_CHANNEL1 ("NTV2_CHANNEL1" in YAML).

width

Width of the video stream. Default value is 1920.

height

Height of the video stream. Default value is 1080.

framerate

Frame rate of the video stream. Default value is 60.

rdma

Boolean indicating whether RDMA is enabled. Default value is False ("false" in YAML).

enable_overlay

Boolean indicating whether a separate overlay channel is enabled. Default value is False ("false" in YAML).

overlay_channel

The camera NTV2Channel to use for overlay output. Default value is NTV2Channel.NTV2_CHANNEL2 ("NTV2_CHANNEL2" in YAML).

overlay_rdma

Boolean indicating whether RDMA is enabled for the overlay. Default value is False ("false" in YAML).

name

The name of the operator. Default value is "aja_source".

add_arg(*args, **kwargs)

Overloaded function.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Arg) -> None

Add an argument to the component.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.ArgList) -> None

Add a list of arguments to the component.

  1. add_arg(self: holoscan.core._core.Operator, **kwargs) -> None

Add arguments to the component via Python kwargs.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Condition) -> None

  2. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Resource) -> None

Add a condition or resource to the Operator.

This can be used to add a condition or resource to an operator after it has already been constructed.

Parameters
arg

The condition or resource to add.

property args

The list of arguments associated with the component.

Returns
arglist

compute(self: holoscan.core._core.Operator, arg0: holoscan.core._core.InputContext, arg1: holoscan.core._core.OutputContext, arg2: holoscan.core._core.ExecutionContext) → None

Operator compute method. This method defines the primary computation to be executed by the operator.

property conditions

Conditions associated with the operator.

property description

YAML formatted string describing the operator.

property fragment

The fragment (holoscan.core.Fragment) that the operator belongs to.

property id

The identifier of the component.

The identifier is initially set to -1, and will become a valid value when the component is initialized.

With the default executor (holoscan.gxf.GXFExecutor), the identifier is set to the GXF component ID.

Returns
id

initialize(self: holoscan.operators.aja_source._aja_source.AJASourceOp) → None

Initialize the operator.

This method is called only once when the operator is created for the first time, and uses a light-weight initialization.

property name

The name of the operator.

property operator_type

The operator type.

holoscan.core.Operator.OperatorType enum representing the type of the operator. The two types currently implemented are native and GXF.

resource(self: holoscan.core._core.Operator, name: str) → Optional[object]

Resources associated with the operator.

Parameters
name

The name of the resource to retrieve

Returns
holoscan.core.Resource or None

The resource with the given name. If no resource with the given name is found, None is returned.

property resources

Resources associated with the operator.

setup(self: holoscan.operators.aja_source._aja_source.AJASourceOp, spec: holoscan.core._core.OperatorSpec) → None

Define the operator specification.

Parameters
spec

The operator specification.

property spec

The operator spec (holoscan.core.OperatorSpec) associated with the operator.

start(self: holoscan.core._core.Operator) → None

Operator start method.

stop(self: holoscan.core._core.Operator) → None

Operator stop method.

class holoscan.operators.BayerDemosaicOp

Bases: holoscan.core._core.Operator

Bayer Demosaic operator.

==Named Inputs==

receivernvidia::gxf::Tensor or nvidia::gxf::VideoBuffer

The input video frame to process. If the input is a VideoBuffer it must be an 8-bit unsigned grayscale video (nvidia::gxf::VideoFormat::GXF_VIDEO_FORMAT_GRAY). If a video buffer is not found, the input port message is searched for a device tensor with the name specified by in_tensor_name. The tensor must have either 8-bit or 16-bit unsigned integer format. The tensor or video buffer may be in either host or device memory (a host->device copy is performed if needed).

==Named Outputs==

transmitternvidia::gxf::Tensor

The output video frame after demosaicing. This will be a 3-channel RGB image if alpha_value is True, otherwise it will be a 4-channel RGBA image. The data type will be either 8-bit or 16-bit unsigned integer (matching the bit depth of the input). The name of the tensor that is output is controlled by out_tensor_name.

==Device Memory Requirements==

When using this operator with a holoscan.resources.BlockMemoryPool, the minimum block_size is (rows * columns * output_channels * element_size_bytes) where output_channels is 4 when generate_alpha is True and 3 otherwise. If the input tensor or video buffer is already on the device, only a single memory block is needed. However, if the input is on the host, a second memory block will also be needed in order to make an internal copy of the input to the device. The memory buffer must be on device (storage_type=1).

Parameters
fragment

The fragment that the operator belongs to.

pool

Memory pool allocator used by the operator.

cuda_stream_pool

holoscan.resources.CudaStreamPool instance to allocate CUDA streams. Default value is None.

in_tensor_name

The name of the input tensor. Default value is "" (empty string).

out_tensor_name

The name of the output tensor. Default value is "" (empty string).

interpolation_mode

The interpolation model to be used for demosaicing. Values available at: https://docs.nvidia.com/cuda/npp/nppdefs.html?highlight=Two%20parameter%20cubic%20filter#c.NppiInterpolationMode

  • NPPI_INTER_UNDEFINED (0): Undefined filtering interpolation mode.

  • NPPI_INTER_NN (1): Nearest neighbor filtering.

  • NPPI_INTER_LINEAR (2): Linear interpolation.

  • NPPI_INTER_CUBIC (4): Cubic interpolation.

  • NPPI_INTER_CUBIC2P_BSPLINE (5): Two-parameter cubic filter (B=1, C=0)

  • NPPI_INTER_CUBIC2P_CATMULLROM (6): Two-parameter cubic filter (B=0, C=1/2)

  • NPPI_INTER_CUBIC2P_B05C03 (7): Two-parameter cubic filter (B=1/2, C=3/10)

  • NPPI_INTER_SUPER (8): Super sampling.

  • NPPI_INTER_LANCZOS (16): Lanczos filtering.

  • NPPI_INTER_LANCZOS3_ADVANCED (17): Generic Lanczos filtering with order 3.

  • NPPI_SMOOTH_EDGE (0x8000000): Smooth edge filtering.

Default value is 0 (NPPI_INTER_UNDEFINED).

bayer_grid_pos

The Bayer grid position. Values available at: https://docs.nvidia.com/cuda/npp/nppdefs.html?highlight=Two%20parameter%20cubic%20filter#c.NppiBayerGridPosition

  • NPPI_BAYER_BGGR (0): Default registration position BGGR.

  • NPPI_BAYER_RGGB (1): Registration position RGGB.

  • NPPI_BAYER_GBRG (2): Registration position GBRG.

  • NPPI_BAYER_GRBG (3): Registration position GRBG.

Default value is 2 (NPPI_BAYER_GBRG).

generate_alpha

Generate alpha channel. Default value is False.

alpha_value

Alpha value to be generated if generate_alpha is set to True. Default value is 255.

name

The name of the operator. Default value is "bayer_demosaic".

Attributes

args The list of arguments associated with the component.
conditions Conditions associated with the operator.
description YAML formatted string describing the operator.
fragment The fragment (holoscan.core.Fragment) that the operator belongs to.
id The identifier of the component.
name The name of the operator.
operator_type The operator type.
resources Resources associated with the operator.
spec The operator spec (holoscan.core.OperatorSpec) associated with the operator.

Methods

add_arg(*args, **kwargs) Overloaded function.
compute(self, arg0, arg1, arg2) Operator compute method.
initialize(self) Initialize the operator.
resource(self, name) Resources associated with the operator.
setup(self, spec) Define the operator specification.
start(self) Operator start method.
stop(self) Operator stop method.
OperatorType
class OperatorType

Bases: pybind11_builtins.pybind11_object

Enum class for operator types used by the executor.

  • NATIVE: Native operator.

  • GXF: GXF operator.

  • VIRTUAL: Virtual operator. (for internal use, not intended for use by application authors)

Members:

NATIVE

GXF

VIRTUAL

Attributes

name

value
GXF = <OperatorType.GXF: 1>

NATIVE = <OperatorType.NATIVE: 0>

VIRTUAL = <OperatorType.VIRTUAL: 2>

__init__(self: holoscan.core._core.Operator.OperatorType, value: int) → None

property name

property value

__init__(*args, **kwargs)

Overloaded function.

  1. __init__(self: holoscan.operators.bayer_demosaic._bayer_demosaic.BayerDemosaicOp) -> None

  2. __init__(self: holoscan.operators.bayer_demosaic._bayer_demosaic.BayerDemosaicOp, fragment: holoscan.core._core.Fragment, *args, pool: holoscan.resources._resources.Allocator, cuda_stream_pool: holoscan.resources._resources.CudaStreamPool = None, in_tensor_name: str = ‘’, out_tensor_name: str = ‘’, interpolation_mode: int = 0, bayer_grid_pos: int = 2, generate_alpha: bool = False, alpha_value: int = 255, name: str = ‘bayer_demosaic’) -> None

Bayer Demosaic operator.

==Named Inputs==

receivernvidia::gxf::Tensor or nvidia::gxf::VideoBuffer

The input video frame to process. If the input is a VideoBuffer it must be an 8-bit unsigned grayscale video (nvidia::gxf::VideoFormat::GXF_VIDEO_FORMAT_GRAY). If a video buffer is not found, the input port message is searched for a device tensor with the name specified by in_tensor_name. The tensor must have either 8-bit or 16-bit unsigned integer format. The tensor or video buffer may be in either host or device memory (a host->device copy is performed if needed).

==Named Outputs==

transmitternvidia::gxf::Tensor

The output video frame after demosaicing. This will be a 3-channel RGB image if alpha_value is True, otherwise it will be a 4-channel RGBA image. The data type will be either 8-bit or 16-bit unsigned integer (matching the bit depth of the input). The name of the tensor that is output is controlled by out_tensor_name.

==Device Memory Requirements==

When using this operator with a holoscan.resources.BlockMemoryPool, the minimum block_size is (rows * columns * output_channels * element_size_bytes) where output_channels is 4 when generate_alpha is True and 3 otherwise. If the input tensor or video buffer is already on the device, only a single memory block is needed. However, if the input is on the host, a second memory block will also be needed in order to make an internal copy of the input to the device. The memory buffer must be on device (storage_type=1).

Parameters
fragment

The fragment that the operator belongs to.

pool

Memory pool allocator used by the operator.

cuda_stream_pool

holoscan.resources.CudaStreamPool instance to allocate CUDA streams. Default value is None.

in_tensor_name

The name of the input tensor. Default value is "" (empty string).

out_tensor_name

The name of the output tensor. Default value is "" (empty string).

interpolation_mode

The interpolation model to be used for demosaicing. Values available at: https://docs.nvidia.com/cuda/npp/nppdefs.html?highlight=Two%20parameter%20cubic%20filter#c.NppiInterpolationMode

  • NPPI_INTER_UNDEFINED (0): Undefined filtering interpolation mode.

  • NPPI_INTER_NN (1): Nearest neighbor filtering.

  • NPPI_INTER_LINEAR (2): Linear interpolation.

  • NPPI_INTER_CUBIC (4): Cubic interpolation.

  • NPPI_INTER_CUBIC2P_BSPLINE (5): Two-parameter cubic filter (B=1, C=0)

  • NPPI_INTER_CUBIC2P_CATMULLROM (6): Two-parameter cubic filter (B=0, C=1/2)

  • NPPI_INTER_CUBIC2P_B05C03 (7): Two-parameter cubic filter (B=1/2, C=3/10)

  • NPPI_INTER_SUPER (8): Super sampling.

  • NPPI_INTER_LANCZOS (16): Lanczos filtering.

  • NPPI_INTER_LANCZOS3_ADVANCED (17): Generic Lanczos filtering with order 3.

  • NPPI_SMOOTH_EDGE (0x8000000): Smooth edge filtering.

Default value is 0 (NPPI_INTER_UNDEFINED).

bayer_grid_pos

The Bayer grid position. Values available at: https://docs.nvidia.com/cuda/npp/nppdefs.html?highlight=Two%20parameter%20cubic%20filter#c.NppiBayerGridPosition

  • NPPI_BAYER_BGGR (0): Default registration position BGGR.

  • NPPI_BAYER_RGGB (1): Registration position RGGB.

  • NPPI_BAYER_GBRG (2): Registration position GBRG.

  • NPPI_BAYER_GRBG (3): Registration position GRBG.

Default value is 2 (NPPI_BAYER_GBRG).

generate_alpha

Generate alpha channel. Default value is False.

alpha_value

Alpha value to be generated if generate_alpha is set to True. Default value is 255.

name

The name of the operator. Default value is "bayer_demosaic".

add_arg(*args, **kwargs)

Overloaded function.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Arg) -> None

Add an argument to the component.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.ArgList) -> None

Add a list of arguments to the component.

  1. add_arg(self: holoscan.core._core.Operator, **kwargs) -> None

Add arguments to the component via Python kwargs.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Condition) -> None

  2. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Resource) -> None

Add a condition or resource to the Operator.

This can be used to add a condition or resource to an operator after it has already been constructed.

Parameters
arg

The condition or resource to add.

property args

The list of arguments associated with the component.

Returns
arglist

compute(self: holoscan.core._core.Operator, arg0: holoscan.core._core.InputContext, arg1: holoscan.core._core.OutputContext, arg2: holoscan.core._core.ExecutionContext) → None

Operator compute method. This method defines the primary computation to be executed by the operator.

property conditions

Conditions associated with the operator.

property description

YAML formatted string describing the operator.

property fragment

The fragment (holoscan.core.Fragment) that the operator belongs to.

property id

The identifier of the component.

The identifier is initially set to -1, and will become a valid value when the component is initialized.

With the default executor (holoscan.gxf.GXFExecutor), the identifier is set to the GXF component ID.

Returns
id

initialize(self: holoscan.operators.bayer_demosaic._bayer_demosaic.BayerDemosaicOp) → None

Initialize the operator.

This method is called only once when the operator is created for the first time, and uses a light-weight initialization.

property name

The name of the operator.

property operator_type

The operator type.

holoscan.core.Operator.OperatorType enum representing the type of the operator. The two types currently implemented are native and GXF.

resource(self: holoscan.core._core.Operator, name: str) → Optional[object]

Resources associated with the operator.

Parameters
name

The name of the resource to retrieve

Returns
holoscan.core.Resource or None

The resource with the given name. If no resource with the given name is found, None is returned.

property resources

Resources associated with the operator.

setup(self: holoscan.operators.bayer_demosaic._bayer_demosaic.BayerDemosaicOp, spec: holoscan.core._core.OperatorSpec) → None

Define the operator specification.

Parameters
spec

The operator specification.

property spec

The operator spec (holoscan.core.OperatorSpec) associated with the operator.

start(self: holoscan.core._core.Operator) → None

Operator start method.

stop(self: holoscan.core._core.Operator) → None

Operator stop method.

class holoscan.operators.FormatConverterOp

Bases: holoscan.core._core.Operator

Format conversion operator.

==Named Inputs==

source_videonvidia::gxf::Tensor or nvidia::gxf::VideoBuffer

The input video frame to process. If the input is a VideoBuffer it must be in format GXF_VIDEO_FORMAT_RGBA, GXF_VIDEO_FORMAT_RGB or GXF_VIDEO_FORMAT_NV12. If a video buffer is not found, the input port message is searched for a tensor with the name specified by in_tensor_name. This must be a tensor in one of several supported formats (unsigned 8-bit int or float32 graycale, unsigned 8-bit int RGB or RGBA YUV420 or NV12). The tensor or video buffer may be in either host or device memory (a host->device copy is performed if needed).

==Named Outputs==

tensornvidia::gxf::Tensor

The output video frame after processing. The shape, data type and number of channels of this output tensor will depend on the specific parameters that were set for this operator. The name of the Tensor transmitted on this port is determined by out_tensor_name.

==Device Memory Requirements==

When using this operator with a holoscan.resources.BlockMemoryPool, between 1 and 3 device memory blocks (storage_type=1) will be required based on the input tensors and parameters:

  • 1.) In all cases there is a memory block needed for the output tensor. The size of this

    block will be out_height * out_width * out_channels * out_element_size_bytes where (out_height, out_width) will either be (in_height, in_width) (or (resize_height, resize_width) a resize was specified). out_element_size is the element size in bytes (e.g. 1 for RGB888 or 4 for Float32).

  • 2.) If a resize is being done, another memory block is required for this. This block will

    have size resize_height * resize_width * in_channels * in_element_size_bytes.

  • 3.) If the input tensor will be in host memory, a memory block is needed to copy the input

    to the device. This block will have size in_height * in_width * in_channels * in_element_size_bytes.

Thus when declaring the memory pool, num_blocks will be between 1 and 3 and block_size must be greater or equal to the largest of the individual blocks sizes described above.

Parameters
fragment

The fragment that the operator belongs to.

pool

Memory pool allocator used by the operator.

out_dtype

Destination data type. The available options are:

  • "rgb888"

  • "uint8"

  • "float32"

  • "rgba8888"

  • "yuv420"

  • "nv12"

in_dtype

Source data type. The available options are:

  • "rgb888"

  • "uint8"

  • "float32"

  • "rgba8888"

  • "yuv420"

  • "nv12"

in_tensor_name

The name of the input tensor. Default value is "" (empty string).

out_tensor_name

The name of the output tensor. Default value is "" (empty string).

scale_min

Output will be clipped to this minimum value. Default value is 0.0.

scale_max

Output will be clipped to this maximum value. Default value is 1.0.

alpha_value

Unsigned integer in range [0, 255], indicating the alpha channel value to use when converting from RGB to RGBA. Default value is 255.

resize_height

Desired height for the (resized) output. Height will be unchanged if resize_height is 0. Default value is 0.

resize_width

Desired width for the (resized) output. Width will be unchanged if resize_width is 0. Default value is 0.

resize_mode

Resize mode enum value corresponding to NPP’s NppiInterpolationMode. Values available at: https://docs.nvidia.com/cuda/npp/nppdefs.html?highlight=Two%20parameter%20cubic%20filter#c.NppiInterpolationMode

  • NPPI_INTER_UNDEFINED (0): Undefined filtering interpolation mode.

  • NPPI_INTER_NN (1): Nearest neighbor filtering.

  • NPPI_INTER_LINEAR (2): Linear interpolation.

  • NPPI_INTER_CUBIC (4): Cubic interpolation.

  • NPPI_INTER_CUBIC2P_BSPLINE (5): Two-parameter cubic filter (B=1, C=0)

  • NPPI_INTER_CUBIC2P_CATMULLROM (6): Two-parameter cubic filter (B=0, C=1/2)

  • NPPI_INTER_CUBIC2P_B05C03 (7): Two-parameter cubic filter (B=1/2, C=3/10)

  • NPPI_INTER_SUPER (8): Super sampling.

  • NPPI_INTER_LANCZOS (16): Lanczos filtering.

  • NPPI_INTER_LANCZOS3_ADVANCED (17): Generic Lanczos filtering with order 3.

  • NPPI_SMOOTH_EDGE (0x8000000): Smooth edge filtering.

Default value is 0 (NPPI_INTER_UNDEFINED) which would be equivalent to 4 (NPPI_INTER_CUBIC).

channel_order

Sequence of integers describing how channel values are permuted. Default value is [0, 1, 2] for 3-channel images and [0, 1, 2, 3] for 4-channel images.

cuda_stream_pool

holoscan.resources.CudaStreamPool instance to allocate CUDA streams. Default value is None.

name

The name of the operator. Default value is "format_converter".

Attributes

args The list of arguments associated with the component.
conditions Conditions associated with the operator.
description YAML formatted string describing the operator.
fragment The fragment (holoscan.core.Fragment) that the operator belongs to.
id The identifier of the component.
name The name of the operator.
operator_type The operator type.
resources Resources associated with the operator.
spec The operator spec (holoscan.core.OperatorSpec) associated with the operator.

Methods

add_arg(*args, **kwargs) Overloaded function.
compute(self, arg0, arg1, arg2) Operator compute method.
initialize(self) Initialize the operator.
resource(self, name) Resources associated with the operator.
setup(self, spec) Define the operator specification.
start(self) Operator start method.
stop(self) Operator stop method.
OperatorType
class OperatorType

Bases: pybind11_builtins.pybind11_object

Enum class for operator types used by the executor.

  • NATIVE: Native operator.

  • GXF: GXF operator.

  • VIRTUAL: Virtual operator. (for internal use, not intended for use by application authors)

Members:

NATIVE

GXF

VIRTUAL

Attributes

name

value
GXF = <OperatorType.GXF: 1>

NATIVE = <OperatorType.NATIVE: 0>

VIRTUAL = <OperatorType.VIRTUAL: 2>

__init__(self: holoscan.core._core.Operator.OperatorType, value: int) → None

property name

property value

__init__(self: holoscan.operators.format_converter._format_converter.FormatConverterOp, fragment: holoscan.core._core.Fragment, *args, pool: holoscan.resources._resources.Allocator, out_dtype: str, in_dtype: str = '', in_tensor_name: str = '', out_tensor_name: str = '', scale_min: float = 0.0, scale_max: float = 1.0, alpha_value: int = 255, resize_height: int = 0, resize_width: int = 0, resize_mode: int = 0, out_channel_order: List[int] = [], cuda_stream_pool: holoscan.resources._resources.CudaStreamPool = None, name: str = 'format_converter') → None

Format conversion operator.

==Named Inputs==

source_videonvidia::gxf::Tensor or nvidia::gxf::VideoBuffer

The input video frame to process. If the input is a VideoBuffer it must be in format GXF_VIDEO_FORMAT_RGBA, GXF_VIDEO_FORMAT_RGB or GXF_VIDEO_FORMAT_NV12. If a video buffer is not found, the input port message is searched for a tensor with the name specified by in_tensor_name. This must be a tensor in one of several supported formats (unsigned 8-bit int or float32 graycale, unsigned 8-bit int RGB or RGBA YUV420 or NV12). The tensor or video buffer may be in either host or device memory (a host->device copy is performed if needed).

==Named Outputs==

tensornvidia::gxf::Tensor

The output video frame after processing. The shape, data type and number of channels of this output tensor will depend on the specific parameters that were set for this operator. The name of the Tensor transmitted on this port is determined by out_tensor_name.

==Device Memory Requirements==

When using this operator with a holoscan.resources.BlockMemoryPool, between 1 and 3 device memory blocks (storage_type=1) will be required based on the input tensors and parameters:

  • 1.) In all cases there is a memory block needed for the output tensor. The size of this

    block will be out_height * out_width * out_channels * out_element_size_bytes where (out_height, out_width) will either be (in_height, in_width) (or (resize_height, resize_width) a resize was specified). out_element_size is the element size in bytes (e.g. 1 for RGB888 or 4 for Float32).

  • 2.) If a resize is being done, another memory block is required for this. This block will

    have size resize_height * resize_width * in_channels * in_element_size_bytes.

  • 3.) If the input tensor will be in host memory, a memory block is needed to copy the input

    to the device. This block will have size in_height * in_width * in_channels * in_element_size_bytes.

Thus when declaring the memory pool, num_blocks will be between 1 and 3 and block_size must be greater or equal to the largest of the individual blocks sizes described above.

Parameters
fragment

The fragment that the operator belongs to.

pool

Memory pool allocator used by the operator.

out_dtype

Destination data type. The available options are:

  • "rgb888"

  • "uint8"

  • "float32"

  • "rgba8888"

  • "yuv420"

  • "nv12"

in_dtype

Source data type. The available options are:

  • "rgb888"

  • "uint8"

  • "float32"

  • "rgba8888"

  • "yuv420"

  • "nv12"

in_tensor_name

The name of the input tensor. Default value is "" (empty string).

out_tensor_name

The name of the output tensor. Default value is "" (empty string).

scale_min

Output will be clipped to this minimum value. Default value is 0.0.

scale_max

Output will be clipped to this maximum value. Default value is 1.0.

alpha_value

Unsigned integer in range [0, 255], indicating the alpha channel value to use when converting from RGB to RGBA. Default value is 255.

resize_height

Desired height for the (resized) output. Height will be unchanged if resize_height is 0. Default value is 0.

resize_width

Desired width for the (resized) output. Width will be unchanged if resize_width is 0. Default value is 0.

resize_mode

Resize mode enum value corresponding to NPP’s NppiInterpolationMode. Values available at: https://docs.nvidia.com/cuda/npp/nppdefs.html?highlight=Two%20parameter%20cubic%20filter#c.NppiInterpolationMode

  • NPPI_INTER_UNDEFINED (0): Undefined filtering interpolation mode.

  • NPPI_INTER_NN (1): Nearest neighbor filtering.

  • NPPI_INTER_LINEAR (2): Linear interpolation.

  • NPPI_INTER_CUBIC (4): Cubic interpolation.

  • NPPI_INTER_CUBIC2P_BSPLINE (5): Two-parameter cubic filter (B=1, C=0)

  • NPPI_INTER_CUBIC2P_CATMULLROM (6): Two-parameter cubic filter (B=0, C=1/2)

  • NPPI_INTER_CUBIC2P_B05C03 (7): Two-parameter cubic filter (B=1/2, C=3/10)

  • NPPI_INTER_SUPER (8): Super sampling.

  • NPPI_INTER_LANCZOS (16): Lanczos filtering.

  • NPPI_INTER_LANCZOS3_ADVANCED (17): Generic Lanczos filtering with order 3.

  • NPPI_SMOOTH_EDGE (0x8000000): Smooth edge filtering.

Default value is 0 (NPPI_INTER_UNDEFINED) which would be equivalent to 4 (NPPI_INTER_CUBIC).

channel_order

Sequence of integers describing how channel values are permuted. Default value is [0, 1, 2] for 3-channel images and [0, 1, 2, 3] for 4-channel images.

cuda_stream_pool

holoscan.resources.CudaStreamPool instance to allocate CUDA streams. Default value is None.

name

The name of the operator. Default value is "format_converter".

add_arg(*args, **kwargs)

Overloaded function.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Arg) -> None

Add an argument to the component.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.ArgList) -> None

Add a list of arguments to the component.

  1. add_arg(self: holoscan.core._core.Operator, **kwargs) -> None

Add arguments to the component via Python kwargs.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Condition) -> None

  2. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Resource) -> None

Add a condition or resource to the Operator.

This can be used to add a condition or resource to an operator after it has already been constructed.

Parameters
arg

The condition or resource to add.

property args

The list of arguments associated with the component.

Returns
arglist

compute(self: holoscan.core._core.Operator, arg0: holoscan.core._core.InputContext, arg1: holoscan.core._core.OutputContext, arg2: holoscan.core._core.ExecutionContext) → None

Operator compute method. This method defines the primary computation to be executed by the operator.

property conditions

Conditions associated with the operator.

property description

YAML formatted string describing the operator.

property fragment

The fragment (holoscan.core.Fragment) that the operator belongs to.

property id

The identifier of the component.

The identifier is initially set to -1, and will become a valid value when the component is initialized.

With the default executor (holoscan.gxf.GXFExecutor), the identifier is set to the GXF component ID.

Returns
id

initialize(self: holoscan.operators.format_converter._format_converter.FormatConverterOp) → None

Initialize the operator.

This method is called only once when the operator is created for the first time, and uses a light-weight initialization.

property name

The name of the operator.

property operator_type

The operator type.

holoscan.core.Operator.OperatorType enum representing the type of the operator. The two types currently implemented are native and GXF.

resource(self: holoscan.core._core.Operator, name: str) → Optional[object]

Resources associated with the operator.

Parameters
name

The name of the resource to retrieve

Returns
holoscan.core.Resource or None

The resource with the given name. If no resource with the given name is found, None is returned.

property resources

Resources associated with the operator.

setup(self: holoscan.operators.format_converter._format_converter.FormatConverterOp, spec: holoscan.core._core.OperatorSpec) → None

Define the operator specification.

Parameters
spec

The operator specification.

property spec

The operator spec (holoscan.core.OperatorSpec) associated with the operator.

start(self: holoscan.core._core.Operator) → None

Operator start method.

stop(self: holoscan.core._core.Operator) → None

Operator stop method.

class holoscan.operators.GXFCodeletOp(fragment, *args, **kwargs)

Bases: holoscan.operators.gxf_codelet._gxf_codelet.GXFCodeletOp

GXF Codelet wrapper operator.

==Named Inputs==

Input ports are automatically defined based on the parameters of the underlying GXF Codelet that include the nvidia::gxf::Receiver component handle.

To view the information about the operator, refer to the description property of this object.

==Named Outputs==

Output ports are automatically defined based on the parameters of the underlying GXF Codelet that include the nvidia::gxf::Transmitter component handle.

To view the information about the operator, refer to the description property of this object.

Parameters
fragment

The fragment that the operator belongs to.

gxf_typename

The GXF type name that identifies the specific GXF Codelet being wrapped.

*args

Additional positional arguments (holoscan.core.Condition or holoscan.core.Resource).

name

The name of the operator. Default value is "gxf_codelet".

**kwargs

The additional keyword arguments that can be passed depend on the underlying GXF Codelet. The additional parameters are the parameters of the underlying GXF Codelet that are neither specifically part of the nvidia::gxf::Receiver nor the nvidia::gxf::Transmitter components. These parameters can provide further customization and functionality to the operator.

Attributes

args The list of arguments associated with the component.
conditions Conditions associated with the operator.
description YAML formatted string describing the operator.
fragment The fragment (holoscan.core.Fragment) that the operator belongs to.
gxf_typename The GXF type name of the resource.
id The identifier of the component.
name The name of the operator.
operator_type The operator type.
resources Resources associated with the operator.
spec The operator spec (holoscan.core.OperatorSpec) associated with the operator.

Methods

add_arg(*args, **kwargs) Overloaded function.
compute(self, arg0, arg1, arg2) Operator compute method.
initialize(self) Initialize the operator.
resource(self, name) Resources associated with the operator.
setup(self, arg0) Define the operator specification.
start(self) Operator start method.
stop(self) Operator stop method.
OperatorType
class OperatorType

Bases: pybind11_builtins.pybind11_object

Enum class for operator types used by the executor.

  • NATIVE: Native operator.

  • GXF: GXF operator.

  • VIRTUAL: Virtual operator. (for internal use, not intended for use by application authors)

Members:

NATIVE

GXF

VIRTUAL

Attributes

name

value
GXF = <OperatorType.GXF: 1>

NATIVE = <OperatorType.NATIVE: 0>

VIRTUAL = <OperatorType.VIRTUAL: 2>

__init__(self: holoscan.core._core.Operator.OperatorType, value: int) → None

property name

property value

__init__(*args, **kwargs)

Overloaded function.

  1. __init__(self: holoscan.operators.gxf_codelet._gxf_codelet.GXFCodeletOp) -> None

  2. __init__(self: holoscan.operators.gxf_codelet._gxf_codelet.GXFCodeletOp, op: object, fragment: holoscan.core._core.Fragment, gxf_typename: str, *args, name: str = ‘gxf_codelet’, **kwargs) -> None

GXF Codelet wrapper operator.

==Named Inputs==

Input ports are automatically defined based on the parameters of the underlying GXF Codelet that include the nvidia::gxf::Receiver component handle.

To view the information about the operator, refer to the description property of this object.

==Named Outputs==

Output ports are automatically defined based on the parameters of the underlying GXF Codelet that include the nvidia::gxf::Transmitter component handle.

To view the information about the operator, refer to the description property of this object.

Parameters
fragment

The fragment that the operator belongs to.

gxf_typename

The GXF type name that identifies the specific GXF Codelet being wrapped.

*args

Additional positional arguments (holoscan.core.Condition or holoscan.core.Resource).

name

The name of the operator. Default value is "gxf_codelet".

**kwargs

The additional keyword arguments that can be passed depend on the underlying GXF Codelet. The additional parameters are the parameters of the underlying GXF Codelet that are neither specifically part of the nvidia::gxf::Receiver nor the nvidia::gxf::Transmitter components. These parameters can provide further customization and functionality to the operator.

add_arg(*args, **kwargs)

Overloaded function.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Arg) -> None

Add an argument to the component.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.ArgList) -> None

Add a list of arguments to the component.

  1. add_arg(self: holoscan.core._core.Operator, **kwargs) -> None

Add arguments to the component via Python kwargs.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Condition) -> None

  2. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Resource) -> None

Add a condition or resource to the Operator.

This can be used to add a condition or resource to an operator after it has already been constructed.

Parameters
arg

The condition or resource to add.

property args

The list of arguments associated with the component.

Returns
arglist

compute(self: holoscan.core._core.Operator, arg0: holoscan.core._core.InputContext, arg1: holoscan.core._core.OutputContext, arg2: holoscan.core._core.ExecutionContext) → None

Operator compute method. This method defines the primary computation to be executed by the operator.

property conditions

Conditions associated with the operator.

property description

YAML formatted string describing the operator.

property fragment

The fragment (holoscan.core.Fragment) that the operator belongs to.

property gxf_typename

The GXF type name of the resource.

Returns
str

The GXF type name of the resource

property id

The identifier of the component.

The identifier is initially set to -1, and will become a valid value when the component is initialized.

With the default executor (holoscan.gxf.GXFExecutor), the identifier is set to the GXF component ID.

Returns
id

initialize(self: holoscan.operators.gxf_codelet._gxf_codelet.GXFCodeletOp) → None

Initialize the operator.

This method is called only once when the operator is created for the first time, and uses a light-weight initialization.

property name

The name of the operator.

property operator_type

The operator type.

holoscan.core.Operator.OperatorType enum representing the type of the operator. The two types currently implemented are native and GXF.

resource(self: holoscan.core._core.Operator, name: str) → Optional[object]

Resources associated with the operator.

Parameters
name

The name of the resource to retrieve

Returns
holoscan.core.Resource or None

The resource with the given name. If no resource with the given name is found, None is returned.

property resources

Resources associated with the operator.

setup(self: holoscan.operators.gxf_codelet._gxf_codelet.GXFCodeletOp, arg0: holoscan.core._core.OperatorSpec) → None

Define the operator specification.

Parameters
spec

The operator specification.

property spec

The operator spec (holoscan.core.OperatorSpec) associated with the operator.

start(self: holoscan.core._core.Operator) → None

Operator start method.

stop(self: holoscan.core._core.Operator) → None

Operator stop method.

class holoscan.operators.HolovizOp(fragment, *args, allocator=None, receivers=(), tensors=(), color_lut=(), window_title='Holoviz', display_name='DP-0', width=1920, height=1080, framerate=60, use_exclusive_display=False, fullscreen=False, headless=False, enable_render_buffer_input=False, enable_render_buffer_output=False, enable_camera_pose_output=False, camera_pose_output_type='projection_matrix', camera_eye=(0.0, 0.0, 1.0), camera_look_at=(0.0, 0.0, 0.0), camera_up=(0.0, 1.0, 0.0), font_path='', cuda_stream_pool=None, name='holoviz_op')

Bases: holoscan.operators.holoviz._holoviz.HolovizOp

Holoviz visualization operator using Holoviz module.

This is a Vulkan-based visualizer.

==Named Inputs==

receiversmulti-receiver accepting nvidia::gxf::Tensor and/or nvidia::gxf::VideoBuffer

Any number of upstream ports may be connected to this receivers port. This port can accept either VideoBuffers or Tensors. These inputs can be in either host or device memory. Each tensor or video buffer will result in a layer. The operator autodetects the layer type for certain input types (e.g. a video buffer will result in an image layer). For other input types or more complex use cases, input specifications can be provided either at initialization time as a parameter or dynamically at run time (via input_specs). On each call to compute, tensors corresponding to all names specified in the tensors parameter must be found or an exception will be raised. Any extra, named tensors not present in the tensors parameter specification (or optional, dynamic input_specs input) will be ignored.

input_specslist[holoscan.operators.HolovizOp.InputSpec], optional

A list of InputSpec objects. This port can be used to dynamically update the overlay specification at run time. No inputs are required on this port in order for the operator to compute.

render_buffer_inputnvidia::gxf::VideoBuffer, optional

An empty render buffer can optionally be provided. The video buffer must have format GXF_VIDEO_FORMAT_RGBA and be in device memory. This input port only exists if enable_render_buffer_input was set to True, in which case compute will only be called when a message arrives on this input.

==Named Outputs==

render_buffer_outputnvidia::gxf::VideoBuffer, optional

Output for a filled render buffer. If an input render buffer is specified, it is using that one, else it allocates a new buffer. The video buffer will have format GXF_VIDEO_FORMAT_RGBA and will be in device memory. This output is useful for offline rendering or headless mode. This output port only exists if enable_render_buffer_output was set to True.

camera_pose_outputstd::array<float, 16> or nvidia::gxf::Pose3D, optional

The camera pose. Depending on the value of camera_pose_output_type this outputs a 4x4 row major projection matrix (type std::array<float, 16>) or the camera extrinsics model (type nvidia::gxf::Pose3D). This output port only exists if enable_camera_pose_output was set to True.

==Device Memory Requirements==

If render_buffer_input is enabled, the provided buffer is used and no memory block will be allocated. Otherwise, when using this operator with a holoscan.resources.BlockMemoryPool, a single device memory block is needed (storage_type=1). The size of this memory block can be determined by rounding the width and height up to the nearest even size and then padding the rows as needed so that the row stride is a multiple of 256 bytes. C++ code to calculate the block size is as follows

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def get_block_size(height, width): height_even = height + (height & 1) width_even = width + (width & 1) row_bytes = width_even * 4; # 4 bytes per pixel for 8-bit RGBA row_stride = (row_bytes % 256 == 0) ? row_bytes : ((row_bytes // 256 + 1) * 256) return height_even * row_stride

Parameters
fragment

The fragment that the operator belongs to.

allocator

Allocator used to allocate render buffer output. If None, will default to holoscan.core.UnboundedAllocator.

receivers

List of input receivers.

tensors

List of input tensors. Each tensor is defined by a dictionary where the "name" key must correspond to a tensor sent to the operator’s input. See the notes section below for further details on how the tensor dictionary is defined.

color_lut

Color lookup table for tensors of type color_lut. Should be shape (n_colors, 4).

window_title

Title on window canvas. Default value is "Holoviz".

display_name

In exclusive mode, name of display to use as shown with xrandr or hwinfo –monitor. Default value is "DP-0".

width

Window width or display resolution width if in exclusive or fullscreen mode. Default value is 1920.

height

Window height or display resolution width if in exclusive or fullscreen mode. Default value is 1080.

framerate

Display framerate in Hz if in exclusive mode. Default value is 60.0.

use_exclusive_display

Enable exclusive display. Default value is False.

fullscreen

Enable fullscreen window. Default value is False.

headless

Enable headless mode. No window is opened, the render buffer is output to port render_buffer_output. Default value is False.

enable_render_buffer_input

If True, an additional input port, named "render_buffer_input" is added to the operator. Default value is False.

enable_render_buffer_output

If True, an additional output port, named "render_buffer_output" is added to the operator. Default value is False.

enable_camera_pose_output

If True, an additional output port, named "camera_pose_output" is added to the operator. Default value is False.

camera_pose_output_type

Type of data output at "camera_pose_output". Supported values are projection_matrix and extrinsics_model. Default value is projection_matrix.

camera_eye

Initial camera eye position. Default value is (0.0, 0.0, 1.0).

camera_look_at

Initial camera look at position. Default value is (0.0, 0.0, 0.0).

camera_up

Initial camera up vector. Default value is (0.0, 1.0, 0.0).

font_path

File path for the font used for rendering text. Default value is "".

cuda_stream_pool

holoscan.resources.CudaStreamPool instance to allocate CUDA streams. Default value is None.

name

The name of the operator. Default value is "holoviz_op".

Notes

The tensors argument is used to specify the tensors to display. Each tensor is defined using a dictionary, that must, at minimum include a ‘name’ key that corresponds to a tensor found on the operator’s input. A ‘type’ key should also be provided to indicate the type of entry to display. The ‘type’ key will be one of {"color", "color_lut", "crosses", "lines", "lines_3d", "line_strip", "line_strip_3d", "ovals", "points", "points_3d", "rectangles", "text", "triangles", "triangles_3d", "depth_map", "depth_map_color", "unknown"}. The default type is "unknown" which will attempt to guess the corresponding type based on the tensor dimensions. Concrete examples are given below.

To show a single 2D RGB or RGBA image, use a list containing a single tensor of type "color".

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tensors = [dict(name="video", type="color", opacity=1.0, priority=0)]

Here, the optional key opacity is used to scale the opacity of the tensor. The priority key is used to specify the render priority for layers. Layers with a higher priority will be rendered on top of those with a lower priority.

If we also had a "boxes"` tensor representing rectangular bounding boxes, we could display them on top of the image like this.

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tensors = [ dict(name="video", type="color", priority=0), dict(name="boxes", type="rectangles", color=[1.0, 0.0, 0.0], line_width=2, priority=1), ]

where the color and line_width keys specify the color and line width of the bounding box.

The details of the dictionary is as follows:

  • name: name of the tensor containing the input data to display

    • type: str

  • type: input type (default "unknown")

    • type: str

    • possible values:

      • unknown: unknown type, the operator tries to guess the type by inspecting the tensor.

      • color: RGB or RGBA color 2d image.

      • color_lut: single channel 2d image, color is looked up.

      • points: point primitives, one coordinate (x, y) per primitive.

      • lines: line primitives, two coordinates (x0, y0) and (x1, y1) per primitive.

      • line_strip: line strip primitive, a line primitive i is defined by each coordinate (xi, yi) and the following (xi+1, yi+1).

      • triangles: triangle primitive, three coordinates (x0, y0), (x1, y1) and (x2, y2) per primitive.

      • crosses: cross primitive, a cross is defined by the center coordinate and the size (xi, yi, si).

      • rectangles: axis aligned rectangle primitive, each rectangle is defined by two coordinates (xi, yi) and (xi+1, yi+1).

      • ovals: oval primitive, an oval primitive is defined by the center coordinate and the axis sizes (xi, yi, sxi, syi).

      • text: text is defined by the top left coordinate and the size (x, y, s) per string, text strings are defined by InputSpec member text.

      • depth_map: single channel 2d array where each element represents a depth value. The data is rendered as a 3d object using points, lines or triangles. The color for the elements can be specified through depth_map_color. Supported format: 8-bit unsigned normalized format that has a single 8-bit depth component.

      • depth_map_color: RGBA 2d image, same size as the depth map. One color value for each element of the depth map grid. Supported format: 32-bit unsigned normalized format that has an 8-bit R component in byte 0, an 8-bit G component in byte 1, an 8-bit B component in byte 2, and an 8-bit A component in byte 3.

  • opacity: layer opacity, 1.0 is fully opaque, 0.0 is fully transparent (default: 1.0)

    • type: float

  • priority: layer priority, determines the render order, layers with higher priority

    values are rendered on top of layers with lower priority values (default: 0)

    • type: int

  • color: RGBA color of rendered geometry (default: [1.f, 1.f, 1.f, 1.f])

    • type: List[float]

  • line_width: line width for geometry made of lines (default: 1.0)

    • type: float

  • point_size: point size for geometry made of points (default: 1.0)

    • type: float

  • text: array of text strings, used when type is text (default: [])

    • type: List[str]

  • depth_map_render_mode: depth map render mode (default: points)

    • type: str

    • possible values:

      • points: render as points

      • lines: render as lines

      • triangles: render as triangles

  1. Displaying Color Images

    Image data can either be on host or device (GPU). Multiple image formats are supported

    • R 8 bit unsigned

    • R 16 bit unsigned

    • R 16 bit float

    • R 32 bit unsigned

    • R 32 bit float

    • RGB 8 bit unsigned

    • BGR 8 bit unsigned

    • RGBA 8 bit unsigned

    • BGRA 8 bit unsigned

    • RGBA 16 bit unsigned

    • RGBA 16 bit float

    • RGBA 32 bit float

    When the type parameter is set to color_lut the final color is looked up using the values from the color_lut parameter. For color lookups these image formats are supported

    • R 8 bit unsigned

    • R 16 bit unsigned

    • R 32 bit unsigned

  2. Drawing Geometry

    In all cases, x and y are normalized coordinates in the range [0, 1]. The x and y correspond to the horizontal and vertical axes of the display, respectively. The origin (0, 0) is at the top left of the display. Geometric primitives outside of the visible area are clipped. Coordinate arrays are expected to have the shape (N, C) where N is the coordinate count and C is the component count for each coordinate.

    • Points are defined by a (x, y) coordinate pair.

    • Lines are defined by a set of two (x, y) coordinate pairs.

    • Lines strips are defined by a sequence of (x, y) coordinate pairs. The first two coordinates define the first line, each additional coordinate adds a line connecting to the previous coordinate.

    • Triangles are defined by a set of three (x, y) coordinate pairs.

    • Crosses are defined by (x, y, size) tuples. size specifies the size of the cross in the x direction and is optional, if omitted it’s set to 0.05. The size in the y direction is calculated using the aspect ratio of the window to make the crosses square.

    • Rectangles (bounding boxes) are defined by a pair of 2-tuples defining the upper-left and lower-right coordinates of a box: (x1, y1), (x2, y2).

    • Ovals are defined by (x, y, size_x, size_y) tuples. size_x and size_y are optional, if omitted they are set to 0.05.

    • Texts are defined by (x, y, size) tuples. size specifies the size of the text in y direction and is optional, if omitted it’s set to 0.05. The size in the x direction is calculated using the aspect ratio of the window. The index of each coordinate references a text string from the text parameter and the index is clamped to the size of the text array. For example, if there is one item set for the text parameter, e.g. text=["my_text"] and three coordinates, then my_text is rendered three times. If text=["first text", "second text"] and three coordinates are specified, then first text is rendered at the first coordinate, second text at the second coordinate and then second text again at the third coordinate. The text string array is fixed and can’t be changed after initialization. To hide text which should not be displayed, specify coordinates greater than (1.0, 1.0) for the text item, the text is then clipped away.

    • 3D Points are defined by a (x, y, z) coordinate tuple.

    • 3D Lines are defined by a set of two (x, y, z) coordinate tuples.

    • 3D Lines strips are defined by a sequence of (x, y, z) coordinate tuples. The first two coordinates define the first line, each additional coordinate adds a line connecting to the previous coordinate.

    • 3D Triangles are defined by a set of three (x, y, z) coordinate tuples.

  3. Displaying Depth Maps

    When type is depth_map the provided data is interpreted as a rectangular array of depth values. Additionally a 2d array with a color value for each point in the grid can be specified by setting type to depth_map_color.

    The type of geometry drawn can be selected by setting depth_map_render_mode.

    Depth maps are rendered in 3D and support camera movement. The camera is controlled using the mouse:

    • Orbit (LMB)

    • Pan (LMB + CTRL | MMB)

    • Dolly (LMB + SHIFT | RMB | Mouse wheel)

    • Look Around (LMB + ALT | LMB + CTRL + SHIFT)

    • Zoom (Mouse wheel + SHIFT)

  4. Output

    By default a window is opened to display the rendering, but the extension can also be run in headless mode with the headless parameter.

    Using a display in exclusive mode is also supported with the use_exclusive_display parameter. This reduces the latency by avoiding the desktop compositor.

    The rendered framebuffer can be output to render_buffer_output.

Attributes

args The list of arguments associated with the component.
conditions Conditions associated with the operator.
description YAML formatted string describing the operator.
fragment The fragment (holoscan.core.Fragment) that the operator belongs to.
id The identifier of the component.
name The name of the operator.
operator_type The operator type.
resources Resources associated with the operator.
spec The operator spec (holoscan.core.OperatorSpec) associated with the operator.

Methods

InputSpec InputSpec for the HolovizOp operator.
add_arg(*args, **kwargs) Overloaded function.
compute(self, arg0, arg1, arg2) Operator compute method.
initialize(self) Initialize the operator.
resource(self, name) Resources associated with the operator.
setup(self, spec) Define the operator specification.
start(self) Operator start method.
stop(self) Operator stop method.
DepthMapRenderMode
InputType
OperatorType
class DepthMapRenderMode

Bases: pybind11_builtins.pybind11_object

Members:

POINTS

LINES

TRIANGLES

Attributes

name

value
LINES = <DepthMapRenderMode.LINES: 1>

POINTS = <DepthMapRenderMode.POINTS: 0>

TRIANGLES = <DepthMapRenderMode.TRIANGLES: 2>

__init__(self: holoscan.operators.holoviz._holoviz.HolovizOp.DepthMapRenderMode, value: int) → None

property name

property value

class InputSpec

Bases: pybind11_builtins.pybind11_object

InputSpec for the HolovizOp operator.

Parameters
tensor_name

The tensor name for this input.

type

The type of data that this tensor represents.

Attributes

type (holoscan.operators.HolovizOp.InputType) The type of data that this tensor represents.
opacity (float) The opacity of the object. Must be in range [0.0, 1.0] where 1.0 is fully opaque.
priority (int) Layer priority, determines the render order. Layers with higher priority values are rendered on top of layers with lower priority.
color (4-tuple of float) RGBA values in range [0.0, 1.0] for rendered geometry.
line_width (float) Line width for geometry made of lines.
point_size (float) Point size for geometry made of points.
text (sequence of str) Sequence of strings used when type is HolovizOp.InputType.TEXT.
depth_map_render_mode (holoscan.operators.HolovizOp.DepthMapRenderMode) The depth map render mode. Used only if type is HolovizOp.InputType.DEPTH_MAP or HolovizOp.InputType.DEPTH_MAP_COLOR.
views (list of HolovizOp.InputSpec.View) Sequence of layer views. By default a layer will fill the whole window. When using a view, the layer can be placed freely within the window. When multiple views are specified, the layer is drawn multiple times using the specified layer views.

Methods

View View for the InputSpec of a HolovizOp operator.
description(self)

Returns

class View

Bases: pybind11_builtins.pybind11_object

View for the InputSpec of a HolovizOp operator.

Notes

Layers can also be placed in 3D space by specifying a 3D transformation matrix. Note that for geometry layers there is a default matrix which allows coordinates in the range of [0 … 1] instead of the Vulkan [-1 … 1] range. When specifying a matrix for a geometry layer, this default matrix is overwritten.

When multiple views are specified, the layer is drawn multiple times using the specified layer views.

It’s possible to specify a negative term for height, which flips the image. When using a negative height, one should also adjust the y value to point to the lower left corner of the viewport instead of the upper left corner.

Attributes

offset_x, offset_y (float) Offset of top-left corner of the view. (0, 0) is the upper left and (1, 1) is the lower right.
width (float) Normalized width (range [0.0, 1.0]).
height (float) Normalized height (range [0.0, 1.0]).
matrix (sequence of float) 16-elements representing a 4x4 transformation matrix.
__init__(self: holoscan.operators.holoviz._holoviz.HolovizOp.InputSpec.View) → None

View for the InputSpec of a HolovizOp operator.

Notes

Layers can also be placed in 3D space by specifying a 3D transformation matrix. Note that for geometry layers there is a default matrix which allows coordinates in the range of [0 … 1] instead of the Vulkan [-1 … 1] range. When specifying a matrix for a geometry layer, this default matrix is overwritten.

When multiple views are specified, the layer is drawn multiple times using the specified layer views.

It’s possible to specify a negative term for height, which flips the image. When using a negative height, one should also adjust the y value to point to the lower left corner of the viewport instead of the upper left corner.

Attributes

offset_x, offset_y (float) Offset of top-left corner of the view. (0, 0) is the upper left and (1, 1) is the lower right.
width (float) Normalized width (range [0.0, 1.0]).
height (float) Normalized height (range [0.0, 1.0]).
matrix (sequence of float) 16-elements representing a 4x4 transformation matrix.
property height

property matrix

property offset_x

property offset_y

property width

__init__(*args, **kwargs)

Overloaded function.

  1. __init__(self: holoscan.operators.holoviz._holoviz.HolovizOp.InputSpec, arg0: str, arg1: holoscan.operators.holoviz._holoviz.HolovizOp.InputType) -> None

InputSpec for the HolovizOp operator.

Parameters
tensor_name

The tensor name for this input.

type

The type of data that this tensor represents.

Attributes

type (holoscan.operators.HolovizOp.InputType) The type of data that this tensor represents.
opacity (float) The opacity of the object. Must be in range [0.0, 1.0] where 1.0 is fully opaque.
priority (int) Layer priority, determines the render order. Layers with higher priority values are rendered on top of layers with lower priority.
color (4-tuple of float) RGBA values in range [0.0, 1.0] for rendered geometry.
line_width (float) Line width for geometry made of lines.
point_size (float) Point size for geometry made of points.
text (sequence of str) Sequence of strings used when type is HolovizOp.InputType.TEXT.
depth_map_render_mode (holoscan.operators.HolovizOp.DepthMapRenderMode) The depth map render mode. Used only if type is HolovizOp.InputType.DEPTH_MAP or HolovizOp.InputType.DEPTH_MAP_COLOR.
views (list of HolovizOp.InputSpec.View) Sequence of layer views. By default a layer will fill the whole window. When using a view, the layer can be placed freely within the window. When multiple views are specified, the layer is drawn multiple times using the specified layer views.
2. __init__(self: holoscan.operators.holoviz._holoviz.HolovizOp.InputSpec, arg0: str, arg1: str) -> None
property color

property depth_map_render_mode

description(self: holoscan.operators.holoviz._holoviz.HolovizOp.InputSpec) → str
Returns
description

YAML string representation of the InputSpec class.

property line_width

property opacity

property point_size

property priority

property text

property type

property views

class InputType

Bases: pybind11_builtins.pybind11_object

Members:

UNKNOWN

COLOR

COLOR_LUT

POINTS

LINES

LINE_STRIP

TRIANGLES

CROSSES

RECTANGLES

OVALS

TEXT

DEPTH_MAP

DEPTH_MAP_COLOR

POINTS_3D

LINES_3D

LINE_STRIP_3D

TRIANGLES_3D

Attributes

name

value
COLOR = <InputType.COLOR: 1>

COLOR_LUT = <InputType.COLOR_LUT: 2>

CROSSES = <InputType.CROSSES: 7>

DEPTH_MAP = <InputType.DEPTH_MAP: 11>

DEPTH_MAP_COLOR = <InputType.DEPTH_MAP_COLOR: 12>

LINES = <InputType.LINES: 4>

LINES_3D = <InputType.LINES_3D: 14>

LINE_STRIP = <InputType.LINE_STRIP: 5>

LINE_STRIP_3D = <InputType.LINE_STRIP_3D: 15>

OVALS = <InputType.OVALS: 9>

POINTS = <InputType.POINTS: 3>

POINTS_3D = <InputType.POINTS_3D: 13>

RECTANGLES = <InputType.RECTANGLES: 8>

TEXT = <InputType.TEXT: 10>

TRIANGLES = <InputType.TRIANGLES: 6>

TRIANGLES_3D = <InputType.TRIANGLES_3D: 16>

UNKNOWN = <InputType.UNKNOWN: 0>

__init__(self: holoscan.operators.holoviz._holoviz.HolovizOp.InputType, value: int) → None

property name

property value

class OperatorType

Bases: pybind11_builtins.pybind11_object

Enum class for operator types used by the executor.

  • NATIVE: Native operator.

  • GXF: GXF operator.

  • VIRTUAL: Virtual operator. (for internal use, not intended for use by application authors)

Members:

NATIVE

GXF

VIRTUAL

Attributes

name

value
GXF = <OperatorType.GXF: 1>

NATIVE = <OperatorType.NATIVE: 0>

VIRTUAL = <OperatorType.VIRTUAL: 2>

__init__(self: holoscan.core._core.Operator.OperatorType, value: int) → None

property name

property value

__init__(self: holoscan.operators.holoviz._holoviz.HolovizOp, fragment: holoscan.core._core.Fragment, *args, allocator: holoscan.resources._resources.Allocator, receivers: List[holoscan.core._core.IOSpec] = [], tensors: List[holoscan::ops::HolovizOp::InputSpec] = [], color_lut: List[List[float]] = [], window_title: str = 'Holoviz', display_name: str = 'DP-0', width: int = 1920, height: int = 1080, framerate: int = 60, use_exclusive_display: bool = False, fullscreen: bool = False, headless: bool = False, enable_render_buffer_input: bool = False, enable_render_buffer_output: bool = False, enable_camera_pose_output: bool = False, camera_pose_output_type: str = 'projection_matrix', camera_eye: Annotated[List[float], FixedSize(3)] = [0.0, 0.0, 1.0], camera_look_at: Annotated[List[float], FixedSize(3)] = [0.0, 0.0, 0.0], camera_up: Annotated[List[float], FixedSize(3)] = [0.0, 1.0, 1.0], font_path: str = '', cuda_stream_pool: holoscan.resources._resources.CudaStreamPool = None, name: str = 'holoviz_op') → None

Holoviz visualization operator using Holoviz module.

This is a Vulkan-based visualizer.

==Named Inputs==

receiversmulti-receiver accepting nvidia::gxf::Tensor and/or nvidia::gxf::VideoBuffer

Any number of upstream ports may be connected to this receivers port. This port can accept either VideoBuffers or Tensors. These inputs can be in either host or device memory. Each tensor or video buffer will result in a layer. The operator autodetects the layer type for certain input types (e.g. a video buffer will result in an image layer). For other input types or more complex use cases, input specifications can be provided either at initialization time as a parameter or dynamically at run time (via input_specs). On each call to compute, tensors corresponding to all names specified in the tensors parameter must be found or an exception will be raised. Any extra, named tensors not present in the tensors parameter specification (or optional, dynamic input_specs input) will be ignored.

input_specslist[holoscan.operators.HolovizOp.InputSpec], optional

A list of InputSpec objects. This port can be used to dynamically update the overlay specification at run time. No inputs are required on this port in order for the operator to compute.

render_buffer_inputnvidia::gxf::VideoBuffer, optional

An empty render buffer can optionally be provided. The video buffer must have format GXF_VIDEO_FORMAT_RGBA and be in device memory. This input port only exists if enable_render_buffer_input was set to True, in which case compute will only be called when a message arrives on this input.

==Named Outputs==

render_buffer_outputnvidia::gxf::VideoBuffer, optional

Output for a filled render buffer. If an input render buffer is specified, it is using that one, else it allocates a new buffer. The video buffer will have format GXF_VIDEO_FORMAT_RGBA and will be in device memory. This output is useful for offline rendering or headless mode. This output port only exists if enable_render_buffer_output was set to True.

camera_pose_outputstd::array<float, 16> or nvidia::gxf::Pose3D, optional

The camera pose. Depending on the value of camera_pose_output_type this outputs a 4x4 row major projection matrix (type std::array<float, 16>) or the camera extrinsics model (type nvidia::gxf::Pose3D). This output port only exists if enable_camera_pose_output was set to True.

==Device Memory Requirements==

If render_buffer_input is enabled, the provided buffer is used and no memory block will be allocated. Otherwise, when using this operator with a holoscan.resources.BlockMemoryPool, a single device memory block is needed (storage_type=1). The size of this memory block can be determined by rounding the width and height up to the nearest even size and then padding the rows as needed so that the row stride is a multiple of 256 bytes. C++ code to calculate the block size is as follows

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def get_block_size(height, width): height_even = height + (height & 1) width_even = width + (width & 1) row_bytes = width_even * 4; # 4 bytes per pixel for 8-bit RGBA row_stride = (row_bytes % 256 == 0) ? row_bytes : ((row_bytes // 256 + 1) * 256) return height_even * row_stride

Parameters
fragment

The fragment that the operator belongs to.

allocator

Allocator used to allocate render buffer output. If None, will default to holoscan.core.UnboundedAllocator.

receivers

List of input receivers.

tensors

List of input tensors. Each tensor is defined by a dictionary where the "name" key must correspond to a tensor sent to the operator’s input. See the notes section below for further details on how the tensor dictionary is defined.

color_lut

Color lookup table for tensors of type color_lut. Should be shape (n_colors, 4).

window_title

Title on window canvas. Default value is "Holoviz".

display_name

In exclusive mode, name of display to use as shown with xrandr or hwinfo –monitor. Default value is "DP-0".

width

Window width or display resolution width if in exclusive or fullscreen mode. Default value is 1920.

height

Window height or display resolution width if in exclusive or fullscreen mode. Default value is 1080.

framerate

Display framerate in Hz if in exclusive mode. Default value is 60.0.

use_exclusive_display

Enable exclusive display. Default value is False.

fullscreen

Enable fullscreen window. Default value is False.

headless

Enable headless mode. No window is opened, the render buffer is output to port render_buffer_output. Default value is False.

enable_render_buffer_input

If True, an additional input port, named "render_buffer_input" is added to the operator. Default value is False.

enable_render_buffer_output

If True, an additional output port, named "render_buffer_output" is added to the operator. Default value is False.

enable_camera_pose_output

If True, an additional output port, named "camera_pose_output" is added to the operator. Default value is False.

camera_pose_output_type

Type of data output at "camera_pose_output". Supported values are projection_matrix and extrinsics_model. Default value is projection_matrix.

camera_eye

Initial camera eye position. Default value is (0.0, 0.0, 1.0).

camera_look_at

Initial camera look at position. Default value is (0.0, 0.0, 0.0).

camera_up

Initial camera up vector. Default value is (0.0, 1.0, 0.0).

font_path

File path for the font used for rendering text. Default value is "".

cuda_stream_pool

holoscan.resources.CudaStreamPool instance to allocate CUDA streams. Default value is None.

name

The name of the operator. Default value is "holoviz_op".

Notes

The tensors argument is used to specify the tensors to display. Each tensor is defined using a dictionary, that must, at minimum include a ‘name’ key that corresponds to a tensor found on the operator’s input. A ‘type’ key should also be provided to indicate the type of entry to display. The ‘type’ key will be one of {"color", "color_lut", "crosses", "lines", "lines_3d", "line_strip", "line_strip_3d", "ovals", "points", "points_3d", "rectangles", "text", "triangles", "triangles_3d", "depth_map", "depth_map_color", "unknown"}. The default type is "unknown" which will attempt to guess the corresponding type based on the tensor dimensions. Concrete examples are given below.

To show a single 2D RGB or RGBA image, use a list containing a single tensor of type "color".

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tensors = [dict(name="video", type="color", opacity=1.0, priority=0)]

Here, the optional key opacity is used to scale the opacity of the tensor. The priority key is used to specify the render priority for layers. Layers with a higher priority will be rendered on top of those with a lower priority.

If we also had a "boxes"` tensor representing rectangular bounding boxes, we could display them on top of the image like this.

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tensors = [ dict(name="video", type="color", priority=0), dict(name="boxes", type="rectangles", color=[1.0, 0.0, 0.0], line_width=2, priority=1), ]

where the color and line_width keys specify the color and line width of the bounding box.

The details of the dictionary is as follows:

  • name: name of the tensor containing the input data to display

    • type: str

  • type: input type (default "unknown")

    • type: str

    • possible values:

      • unknown: unknown type, the operator tries to guess the type by inspecting the tensor.

      • color: RGB or RGBA color 2d image.

      • color_lut: single channel 2d image, color is looked up.

      • points: point primitives, one coordinate (x, y) per primitive.

      • lines: line primitives, two coordinates (x0, y0) and (x1, y1) per primitive.

      • line_strip: line strip primitive, a line primitive i is defined by each coordinate (xi, yi) and the following (xi+1, yi+1).

      • triangles: triangle primitive, three coordinates (x0, y0), (x1, y1) and (x2, y2) per primitive.

      • crosses: cross primitive, a cross is defined by the center coordinate and the size (xi, yi, si).

      • rectangles: axis aligned rectangle primitive, each rectangle is defined by two coordinates (xi, yi) and (xi+1, yi+1).

      • ovals: oval primitive, an oval primitive is defined by the center coordinate and the axis sizes (xi, yi, sxi, syi).

      • text: text is defined by the top left coordinate and the size (x, y, s) per string, text strings are defined by InputSpec member text.

      • depth_map: single channel 2d array where each element represents a depth value. The data is rendered as a 3d object using points, lines or triangles. The color for the elements can be specified through depth_map_color. Supported format: 8-bit unsigned normalized format that has a single 8-bit depth component.

      • depth_map_color: RGBA 2d image, same size as the depth map. One color value for each element of the depth map grid. Supported format: 32-bit unsigned normalized format that has an 8-bit R component in byte 0, an 8-bit G component in byte 1, an 8-bit B component in byte 2, and an 8-bit A component in byte 3.

  • opacity: layer opacity, 1.0 is fully opaque, 0.0 is fully transparent (default: 1.0)

    • type: float

  • priority: layer priority, determines the render order, layers with higher priority

    values are rendered on top of layers with lower priority values (default: 0)

    • type: int

  • color: RGBA color of rendered geometry (default: [1.f, 1.f, 1.f, 1.f])

    • type: List[float]

  • line_width: line width for geometry made of lines (default: 1.0)

    • type: float

  • point_size: point size for geometry made of points (default: 1.0)

    • type: float

  • text: array of text strings, used when type is text (default: [])

    • type: List[str]

  • depth_map_render_mode: depth map render mode (default: points)

    • type: str

    • possible values:

      • points: render as points

      • lines: render as lines

      • triangles: render as triangles

  1. Displaying Color Images

    Image data can either be on host or device (GPU). Multiple image formats are supported

    • R 8 bit unsigned

    • R 16 bit unsigned

    • R 16 bit float

    • R 32 bit unsigned

    • R 32 bit float

    • RGB 8 bit unsigned

    • BGR 8 bit unsigned

    • RGBA 8 bit unsigned

    • BGRA 8 bit unsigned

    • RGBA 16 bit unsigned

    • RGBA 16 bit float

    • RGBA 32 bit float

    When the type parameter is set to color_lut the final color is looked up using the values from the color_lut parameter. For color lookups these image formats are supported

    • R 8 bit unsigned

    • R 16 bit unsigned

    • R 32 bit unsigned

  2. Drawing Geometry

    In all cases, x and y are normalized coordinates in the range [0, 1]. The x and y correspond to the horizontal and vertical axes of the display, respectively. The origin (0, 0) is at the top left of the display. Geometric primitives outside of the visible area are clipped. Coordinate arrays are expected to have the shape (N, C) where N is the coordinate count and C is the component count for each coordinate.

    • Points are defined by a (x, y) coordinate pair.

    • Lines are defined by a set of two (x, y) coordinate pairs.

    • Lines strips are defined by a sequence of (x, y) coordinate pairs. The first two coordinates define the first line, each additional coordinate adds a line connecting to the previous coordinate.

    • Triangles are defined by a set of three (x, y) coordinate pairs.

    • Crosses are defined by (x, y, size) tuples. size specifies the size of the cross in the x direction and is optional, if omitted it’s set to 0.05. The size in the y direction is calculated using the aspect ratio of the window to make the crosses square.

    • Rectangles (bounding boxes) are defined by a pair of 2-tuples defining the upper-left and lower-right coordinates of a box: (x1, y1), (x2, y2).

    • Ovals are defined by (x, y, size_x, size_y) tuples. size_x and size_y are optional, if omitted they are set to 0.05.

    • Texts are defined by (x, y, size) tuples. size specifies the size of the text in y direction and is optional, if omitted it’s set to 0.05. The size in the x direction is calculated using the aspect ratio of the window. The index of each coordinate references a text string from the text parameter and the index is clamped to the size of the text array. For example, if there is one item set for the text parameter, e.g. text=["my_text"] and three coordinates, then my_text is rendered three times. If text=["first text", "second text"] and three coordinates are specified, then first text is rendered at the first coordinate, second text at the second coordinate and then second text again at the third coordinate. The text string array is fixed and can’t be changed after initialization. To hide text which should not be displayed, specify coordinates greater than (1.0, 1.0) for the text item, the text is then clipped away.

    • 3D Points are defined by a (x, y, z) coordinate tuple.

    • 3D Lines are defined by a set of two (x, y, z) coordinate tuples.

    • 3D Lines strips are defined by a sequence of (x, y, z) coordinate tuples. The first two coordinates define the first line, each additional coordinate adds a line connecting to the previous coordinate.

    • 3D Triangles are defined by a set of three (x, y, z) coordinate tuples.

  3. Displaying Depth Maps

    When type is depth_map the provided data is interpreted as a rectangular array of depth values. Additionally a 2d array with a color value for each point in the grid can be specified by setting type to depth_map_color.

    The type of geometry drawn can be selected by setting depth_map_render_mode.

    Depth maps are rendered in 3D and support camera movement. The camera is controlled using the mouse:

    • Orbit (LMB)

    • Pan (LMB + CTRL | MMB)

    • Dolly (LMB + SHIFT | RMB | Mouse wheel)

    • Look Around (LMB + ALT | LMB + CTRL + SHIFT)

    • Zoom (Mouse wheel + SHIFT)

  4. Output

    By default a window is opened to display the rendering, but the extension can also be run in headless mode with the headless parameter.

    Using a display in exclusive mode is also supported with the use_exclusive_display parameter. This reduces the latency by avoiding the desktop compositor.

    The rendered framebuffer can be output to render_buffer_output.

add_arg(*args, **kwargs)

Overloaded function.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Arg) -> None

Add an argument to the component.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.ArgList) -> None

Add a list of arguments to the component.

  1. add_arg(self: holoscan.core._core.Operator, **kwargs) -> None

Add arguments to the component via Python kwargs.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Condition) -> None

  2. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Resource) -> None

Add a condition or resource to the Operator.

This can be used to add a condition or resource to an operator after it has already been constructed.

Parameters
arg

The condition or resource to add.

property args

The list of arguments associated with the component.

Returns
arglist

compute(self: holoscan.core._core.Operator, arg0: holoscan.core._core.InputContext, arg1: holoscan.core._core.OutputContext, arg2: holoscan.core._core.ExecutionContext) → None

Operator compute method. This method defines the primary computation to be executed by the operator.

property conditions

Conditions associated with the operator.

property description

YAML formatted string describing the operator.

property fragment

The fragment (holoscan.core.Fragment) that the operator belongs to.

property id

The identifier of the component.

The identifier is initially set to -1, and will become a valid value when the component is initialized.

With the default executor (holoscan.gxf.GXFExecutor), the identifier is set to the GXF component ID.

Returns
id

initialize(self: holoscan.operators.holoviz._holoviz.HolovizOp) → None

Initialize the operator.

This method is called only once when the operator is created for the first time, and uses a light-weight initialization.

property name

The name of the operator.

property operator_type

The operator type.

holoscan.core.Operator.OperatorType enum representing the type of the operator. The two types currently implemented are native and GXF.

resource(self: holoscan.core._core.Operator, name: str) → Optional[object]

Resources associated with the operator.

Parameters
name

The name of the resource to retrieve

Returns
holoscan.core.Resource or None

The resource with the given name. If no resource with the given name is found, None is returned.

property resources

Resources associated with the operator.

setup(self: holoscan.operators.holoviz._holoviz.HolovizOp, spec: holoscan.core._core.OperatorSpec) → None

Define the operator specification.

Parameters
spec

The operator specification.

property spec

The operator spec (holoscan.core.OperatorSpec) associated with the operator.

start(self: holoscan.core._core.Operator) → None

Operator start method.

stop(self: holoscan.core._core.Operator) → None

Operator stop method.

class holoscan.operators.InferenceOp

Bases: holoscan.core._core.Operator

Inference operator.

==Named Inputs==

receiversmulti-receiver accepting nvidia::gxf::Tensor(s)

Any number of upstream ports may be connected to this receivers port. The operator will search across all messages for tensors matching those specified in in_tensor_names. These are the set of input tensors used by the models in inference_map.

==Named Outputs==

transmitternvidia::gxf::Tensor(s)

A message containing tensors corresponding to the inference results from all models will be emitted. The names of the tensors transmitted correspond to those in out_tensor_names.

==Device Memory Requirements==

When using this operator with a holoscan.resources.BlockMemoryPool, num_blocks must be greater than or equal to the number of output tensors that will be produced. The block_size in bytes must be greater than or equal to the largest output tensor (in bytes). If output_on_cuda is True, the blocks should be in device memory (storage_type=1), otherwise they should be CUDA pinned host memory (storage_type=0).

For more details on InferenceOp parameters, see [Customizing the Inference Operator](https://docs.nvidia.com/holoscan/sdk-user-guide/examples/byom.html#customizing-the-inference-operator) or refer to [Inference](https://docs.nvidia.com/holoscan/sdk-user-guide/inference.html).

Parameters
fragment

The fragment that the operator belongs to.

backend

Backend to use for inference. Set "trt" for TensorRT, "torch" for LibTorch and "onnxrt" for the ONNX runtime.

allocator

Memory allocator to use for the output.

inference_map

Tensor to model map.

model_path_map

Path to the ONNX model to be loaded.

pre_processor_map

Pre processed data to model map.

device_map

Mapping of model to GPU ID for inference.

temporal_map

Mapping of model to frame delay for inference.

activation_map

Mapping of model to activation state for inference.

backend_map

Mapping of model to backend type for inference. Backend options: "trt" or "torch"

in_tensor_names

Input tensors.

out_tensor_names

Output tensors.

infer_on_cpu

Whether to run the computation on the CPU instead of GPU. Default value is False.

parallel_inference

Whether to enable parallel execution. Default value is True.

input_on_cuda

Whether the input buffer is on the GPU. Default value is True.

output_on_cuda

Whether the output buffer is on the GPU. Default value is True.

transmit_on_cuda

Whether to transmit the message on the GPU. Default value is True.

enable_fp16

Use 16-bit floating point computations. Default value is False.

is_engine_path

Whether the input model path mapping is for trt engine files. Default value is False.

cuda_stream_pool

holoscan.resources.CudaStreamPool instance to allocate CUDA streams. Default value is None.

name

The name of the operator. Default value is "inference".

Attributes

args The list of arguments associated with the component.
conditions Conditions associated with the operator.
description YAML formatted string describing the operator.
fragment The fragment (holoscan.core.Fragment) that the operator belongs to.
id The identifier of the component.
name The name of the operator.
operator_type The operator type.
resources Resources associated with the operator.
spec The operator spec (holoscan.core.OperatorSpec) associated with the operator.

Methods

add_arg(*args, **kwargs) Overloaded function.
compute(self, arg0, arg1, arg2) Operator compute method.
initialize(self) Initialize the operator.
resource(self, name) Resources associated with the operator.
setup(self, spec) Define the operator specification.
start(self) Operator start method.
stop(self) Operator stop method.
DataMap
DataVecMap
OperatorType
class DataMap

Bases: pybind11_builtins.pybind11_object

Methods

get_map(self)

insert(self)

__init__(self: holoscan.operators.inference._inference.InferenceOp.DataMap) → None

get_map(self: holoscan.operators.inference._inference.InferenceOp.DataMap) → Dict[str, str]

insert(self: holoscan.operators.inference._inference.InferenceOp.DataMap) → Dict[str, str]

class DataVecMap

Bases: pybind11_builtins.pybind11_object

Methods

get_map(self)

insert(self)

__init__(self: holoscan.operators.inference._inference.InferenceOp.DataVecMap) → None

get_map(self: holoscan.operators.inference._inference.InferenceOp.DataVecMap) → Dict[str, List[str]]

insert(self: holoscan.operators.inference._inference.InferenceOp.DataVecMap) → Dict[str, List[str]]

class OperatorType

Bases: pybind11_builtins.pybind11_object

Enum class for operator types used by the executor.

  • NATIVE: Native operator.

  • GXF: GXF operator.

  • VIRTUAL: Virtual operator. (for internal use, not intended for use by application authors)

Members:

NATIVE

GXF

VIRTUAL

Attributes

name

value
GXF = <OperatorType.GXF: 1>

NATIVE = <OperatorType.NATIVE: 0>

VIRTUAL = <OperatorType.VIRTUAL: 2>

__init__(self: holoscan.core._core.Operator.OperatorType, value: int) → None

property name

property value

__init__(self: holoscan.operators.inference._inference.InferenceOp, fragment: holoscan.core._core.Fragment, *args, backend: str, allocator: holoscan.resources._resources.Allocator, inference_map: dict, model_path_map: dict, pre_processor_map: dict, device_map: dict = {}, temporal_map: dict = {}, activation_map: dict = {}, backend_map: dict = {}, in_tensor_names: List[str] = [], out_tensor_names: List[str] = [], infer_on_cpu: bool = False, parallel_inference: bool = True, input_on_cuda: bool = True, output_on_cuda: bool = True, transmit_on_cuda: bool = True, enable_fp16: bool = False, is_engine_path: bool = False, cuda_stream_pool: holoscan.resources._resources.CudaStreamPool = None, name: str = 'inference') → None

Inference operator.

==Named Inputs==

receiversmulti-receiver accepting nvidia::gxf::Tensor(s)

Any number of upstream ports may be connected to this receivers port. The operator will search across all messages for tensors matching those specified in in_tensor_names. These are the set of input tensors used by the models in inference_map.

==Named Outputs==

transmitternvidia::gxf::Tensor(s)

A message containing tensors corresponding to the inference results from all models will be emitted. The names of the tensors transmitted correspond to those in out_tensor_names.

==Device Memory Requirements==

When using this operator with a holoscan.resources.BlockMemoryPool, num_blocks must be greater than or equal to the number of output tensors that will be produced. The block_size in bytes must be greater than or equal to the largest output tensor (in bytes). If output_on_cuda is True, the blocks should be in device memory (storage_type=1), otherwise they should be CUDA pinned host memory (storage_type=0).

For more details on InferenceOp parameters, see [Customizing the Inference Operator](https://docs.nvidia.com/holoscan/sdk-user-guide/examples/byom.html#customizing-the-inference-operator) or refer to [Inference](https://docs.nvidia.com/holoscan/sdk-user-guide/inference.html).

Parameters
fragment

The fragment that the operator belongs to.

backend

Backend to use for inference. Set "trt" for TensorRT, "torch" for LibTorch and "onnxrt" for the ONNX runtime.

allocator

Memory allocator to use for the output.

inference_map

Tensor to model map.

model_path_map

Path to the ONNX model to be loaded.

pre_processor_map

Pre processed data to model map.

device_map

Mapping of model to GPU ID for inference.

temporal_map

Mapping of model to frame delay for inference.

activation_map

Mapping of model to activation state for inference.

backend_map

Mapping of model to backend type for inference. Backend options: "trt" or "torch"

in_tensor_names

Input tensors.

out_tensor_names

Output tensors.

infer_on_cpu

Whether to run the computation on the CPU instead of GPU. Default value is False.

parallel_inference

Whether to enable parallel execution. Default value is True.

input_on_cuda

Whether the input buffer is on the GPU. Default value is True.

output_on_cuda

Whether the output buffer is on the GPU. Default value is True.

transmit_on_cuda

Whether to transmit the message on the GPU. Default value is True.

enable_fp16

Use 16-bit floating point computations. Default value is False.

is_engine_path

Whether the input model path mapping is for trt engine files. Default value is False.

cuda_stream_pool

holoscan.resources.CudaStreamPool instance to allocate CUDA streams. Default value is None.

name

The name of the operator. Default value is "inference".

add_arg(*args, **kwargs)

Overloaded function.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Arg) -> None

Add an argument to the component.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.ArgList) -> None

Add a list of arguments to the component.

  1. add_arg(self: holoscan.core._core.Operator, **kwargs) -> None

Add arguments to the component via Python kwargs.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Condition) -> None

  2. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Resource) -> None

Add a condition or resource to the Operator.

This can be used to add a condition or resource to an operator after it has already been constructed.

Parameters
arg

The condition or resource to add.

property args

The list of arguments associated with the component.

Returns
arglist

compute(self: holoscan.core._core.Operator, arg0: holoscan.core._core.InputContext, arg1: holoscan.core._core.OutputContext, arg2: holoscan.core._core.ExecutionContext) → None

Operator compute method. This method defines the primary computation to be executed by the operator.

property conditions

Conditions associated with the operator.

property description

YAML formatted string describing the operator.

property fragment

The fragment (holoscan.core.Fragment) that the operator belongs to.

property id

The identifier of the component.

The identifier is initially set to -1, and will become a valid value when the component is initialized.

With the default executor (holoscan.gxf.GXFExecutor), the identifier is set to the GXF component ID.

Returns
id

initialize(self: holoscan.operators.inference._inference.InferenceOp) → None

Initialize the operator.

This method is called only once when the operator is created for the first time, and uses a light-weight initialization.

property name

The name of the operator.

property operator_type

The operator type.

holoscan.core.Operator.OperatorType enum representing the type of the operator. The two types currently implemented are native and GXF.

resource(self: holoscan.core._core.Operator, name: str) → Optional[object]

Resources associated with the operator.

Parameters
name

The name of the resource to retrieve

Returns
holoscan.core.Resource or None

The resource with the given name. If no resource with the given name is found, None is returned.

property resources

Resources associated with the operator.

setup(self: holoscan.operators.inference._inference.InferenceOp, spec: holoscan.core._core.OperatorSpec) → None

Define the operator specification.

Parameters
spec

The operator specification.

property spec

The operator spec (holoscan.core.OperatorSpec) associated with the operator.

start(self: holoscan.core._core.Operator) → None

Operator start method.

stop(self: holoscan.core._core.Operator) → None

Operator stop method.

class holoscan.operators.InferenceProcessorOp

Bases: holoscan.core._core.Operator

Holoinfer Processing operator.

==Named Inputs==

receiversmulti-receiver accepting nvidia::gxf::Tensor(s)

Any number of upstream ports may be connected to this receivers port. The operator will search across all messages for tensors matching those specified in in_tensor_names. These are the set of input tensors used by the processing operations specified in process_map.

==Named Outputs==

transmitternvidia::gxf::Tensor(s)

A message containing tensors corresponding to the processed results from operations will be emitted. The names of the tensors transmitted correspond to those in out_tensor_names.

==Device Memory Requirements==

When using this operator with a holoscan.resources.BlockMemoryPool, num_blocks must be greater than or equal to the number of output tensors that will be produced. The block_size in bytes must be greater than or equal to the largest output tensor (in bytes). If output_on_cuda is True, the blocks should be in device memory (storage_type=1), otherwise they should be CUDA pinned host memory (storage_type=0).

Parameters
fragment

The fragment that the operator belongs to.

allocator

Memory allocator to use for the output.

process_operations

Operations in sequence on tensors.

processed_map

Input-output tensor mapping.

in_tensor_names

Names of input tensors in the order to be fed into the operator.

out_tensor_names

Names of output tensors in the order to be fed into the operator.

input_on_cuda

Whether the input buffer is on the GPU. Default value is False.

output_on_cuda

Whether the output buffer is on the GPU. Default value is False.

transmit_on_cuda

Whether to transmit the message on the GPU. Default value is False.

cuda_stream_pool

holoscan.resources.CudaStreamPool instance to allocate CUDA streams. Default value is None.

config_path

File path to the config file. Default value is "".

disable_transmitter

If True, disable the transmitter output port of the operator. Default value is False.

name

The name of the operator. Default value is "postprocessor".

Attributes

args The list of arguments associated with the component.
conditions Conditions associated with the operator.
description YAML formatted string describing the operator.
fragment The fragment (holoscan.core.Fragment) that the operator belongs to.
id The identifier of the component.
name The name of the operator.
operator_type The operator type.
resources Resources associated with the operator.
spec The operator spec (holoscan.core.OperatorSpec) associated with the operator.

Methods

add_arg(*args, **kwargs) Overloaded function.
compute(self, arg0, arg1, arg2) Operator compute method.
initialize(self) Initialize the operator.
resource(self, name) Resources associated with the operator.
setup(self, spec) Define the operator specification.
start(self) Operator start method.
stop(self) Operator stop method.
DataMap
DataVecMap
OperatorType
class DataMap

Bases: pybind11_builtins.pybind11_object

Methods

get_map(self)

insert(self)

__init__(self: holoscan.operators.inference_processor._inference_processor.InferenceProcessorOp.DataMap) → None

get_map(self: holoscan.operators.inference_processor._inference_processor.InferenceProcessorOp.DataMap) → Dict[str, str]

insert(self: holoscan.operators.inference_processor._inference_processor.InferenceProcessorOp.DataMap) → Dict[str, str]

class DataVecMap

Bases: pybind11_builtins.pybind11_object

Methods

get_map(self)

insert(self)

__init__(self: holoscan.operators.inference_processor._inference_processor.InferenceProcessorOp.DataVecMap) → None

get_map(self: holoscan.operators.inference_processor._inference_processor.InferenceProcessorOp.DataVecMap) → Dict[str, List[str]]

insert(self: holoscan.operators.inference_processor._inference_processor.InferenceProcessorOp.DataVecMap) → Dict[str, List[str]]

class OperatorType

Bases: pybind11_builtins.pybind11_object

Enum class for operator types used by the executor.

  • NATIVE: Native operator.

  • GXF: GXF operator.

  • VIRTUAL: Virtual operator. (for internal use, not intended for use by application authors)

Members:

NATIVE

GXF

VIRTUAL

Attributes

name

value
GXF = <OperatorType.GXF: 1>

NATIVE = <OperatorType.NATIVE: 0>

VIRTUAL = <OperatorType.VIRTUAL: 2>

__init__(self: holoscan.core._core.Operator.OperatorType, value: int) → None

property name

property value

__init__(self: holoscan.operators.inference_processor._inference_processor.InferenceProcessorOp, fragment: holoscan.core._core.Fragment, *args, allocator: holoscan.resources._resources.Allocator, process_operations: dict = {}, processed_map: dict = {}, in_tensor_names: List[str] = [], out_tensor_names: List[str] = [], input_on_cuda: bool = False, output_on_cuda: bool = False, transmit_on_cuda: bool = False, disable_transmitter: bool = False, cuda_stream_pool: holoscan.resources._resources.CudaStreamPool = None, config_path: str = '', name: str = 'postprocessor') → None

Holoinfer Processing operator.

==Named Inputs==

receiversmulti-receiver accepting nvidia::gxf::Tensor(s)

Any number of upstream ports may be connected to this receivers port. The operator will search across all messages for tensors matching those specified in in_tensor_names. These are the set of input tensors used by the processing operations specified in process_map.

==Named Outputs==

transmitternvidia::gxf::Tensor(s)

A message containing tensors corresponding to the processed results from operations will be emitted. The names of the tensors transmitted correspond to those in out_tensor_names.

==Device Memory Requirements==

When using this operator with a holoscan.resources.BlockMemoryPool, num_blocks must be greater than or equal to the number of output tensors that will be produced. The block_size in bytes must be greater than or equal to the largest output tensor (in bytes). If output_on_cuda is True, the blocks should be in device memory (storage_type=1), otherwise they should be CUDA pinned host memory (storage_type=0).

Parameters
fragment

The fragment that the operator belongs to.

allocator

Memory allocator to use for the output.

process_operations

Operations in sequence on tensors.

processed_map

Input-output tensor mapping.

in_tensor_names

Names of input tensors in the order to be fed into the operator.

out_tensor_names

Names of output tensors in the order to be fed into the operator.

input_on_cuda

Whether the input buffer is on the GPU. Default value is False.

output_on_cuda

Whether the output buffer is on the GPU. Default value is False.

transmit_on_cuda

Whether to transmit the message on the GPU. Default value is False.

cuda_stream_pool

holoscan.resources.CudaStreamPool instance to allocate CUDA streams. Default value is None.

config_path

File path to the config file. Default value is "".

disable_transmitter

If True, disable the transmitter output port of the operator. Default value is False.

name

The name of the operator. Default value is "postprocessor".

add_arg(*args, **kwargs)

Overloaded function.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Arg) -> None

Add an argument to the component.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.ArgList) -> None

Add a list of arguments to the component.

  1. add_arg(self: holoscan.core._core.Operator, **kwargs) -> None

Add arguments to the component via Python kwargs.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Condition) -> None

  2. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Resource) -> None

Add a condition or resource to the Operator.

This can be used to add a condition or resource to an operator after it has already been constructed.

Parameters
arg

The condition or resource to add.

property args

The list of arguments associated with the component.

Returns
arglist

compute(self: holoscan.core._core.Operator, arg0: holoscan.core._core.InputContext, arg1: holoscan.core._core.OutputContext, arg2: holoscan.core._core.ExecutionContext) → None

Operator compute method. This method defines the primary computation to be executed by the operator.

property conditions

Conditions associated with the operator.

property description

YAML formatted string describing the operator.

property fragment

The fragment (holoscan.core.Fragment) that the operator belongs to.

property id

The identifier of the component.

The identifier is initially set to -1, and will become a valid value when the component is initialized.

With the default executor (holoscan.gxf.GXFExecutor), the identifier is set to the GXF component ID.

Returns
id

initialize(self: holoscan.operators.inference_processor._inference_processor.InferenceProcessorOp) → None

Initialize the operator.

This method is called only once when the operator is created for the first time, and uses a light-weight initialization.

property name

The name of the operator.

property operator_type

The operator type.

holoscan.core.Operator.OperatorType enum representing the type of the operator. The two types currently implemented are native and GXF.

resource(self: holoscan.core._core.Operator, name: str) → Optional[object]

Resources associated with the operator.

Parameters
name

The name of the resource to retrieve

Returns
holoscan.core.Resource or None

The resource with the given name. If no resource with the given name is found, None is returned.

property resources

Resources associated with the operator.

setup(self: holoscan.operators.inference_processor._inference_processor.InferenceProcessorOp, spec: holoscan.core._core.OperatorSpec) → None

Define the operator specification.

Parameters
spec

The operator specification.

property spec

The operator spec (holoscan.core.OperatorSpec) associated with the operator.

start(self: holoscan.core._core.Operator) → None

Operator start method.

stop(self: holoscan.core._core.Operator) → None

Operator stop method.

class holoscan.operators.NTV2Channel

Bases: pybind11_builtins.pybind11_object

Members:

NTV2_CHANNEL1

NTV2_CHANNEL2

NTV2_CHANNEL3

NTV2_CHANNEL4

NTV2_CHANNEL5

NTV2_CHANNEL6

NTV2_CHANNEL7

NTV2_CHANNEL8

NTV2_MAX_NUM_CHANNELS

NTV2_CHANNEL_INVALID

Attributes

name

value
NTV2_CHANNEL1 = <NTV2Channel.NTV2_CHANNEL1: 0>

NTV2_CHANNEL2 = <NTV2Channel.NTV2_CHANNEL2: 1>

NTV2_CHANNEL3 = <NTV2Channel.NTV2_CHANNEL3: 2>

NTV2_CHANNEL4 = <NTV2Channel.NTV2_CHANNEL4: 3>

NTV2_CHANNEL5 = <NTV2Channel.NTV2_CHANNEL5: 4>

NTV2_CHANNEL6 = <NTV2Channel.NTV2_CHANNEL6: 5>

NTV2_CHANNEL7 = <NTV2Channel.NTV2_CHANNEL7: 6>

NTV2_CHANNEL8 = <NTV2Channel.NTV2_CHANNEL8: 7>

NTV2_CHANNEL_INVALID = <NTV2Channel.NTV2_MAX_NUM_CHANNELS: 8>

NTV2_MAX_NUM_CHANNELS = <NTV2Channel.NTV2_MAX_NUM_CHANNELS: 8>

__init__(self: holoscan.operators.aja_source._aja_source.NTV2Channel, value: int) → None

property name

property value

class holoscan.operators.PingRxOp(fragment, *args, **kwargs)

Bases: holoscan.core.Operator

Simple receiver operator.

This is an example of a native operator with one input port. On each tick, it receives an integer from the “in” port.

==Named Inputs==

inany

A received value.

Attributes

args The list of arguments associated with the component.
conditions Conditions associated with the operator.
description YAML formatted string describing the operator.
fragment The fragment (holoscan.core.Fragment) that the operator belongs to.
id The identifier of the component.
name The name of the operator.
operator_type The operator type.
resources Resources associated with the operator.
spec The operator spec (holoscan.core.OperatorSpec) associated with the operator.

Methods

add_arg(*args, **kwargs) Overloaded function.
compute(op_input, op_output, context) Default implementation of compute
initialize() Default implementation of initialize
resource(self, name) Resources associated with the operator.
setup(spec) Default implementation of setup method.
start() Default implementation of start
stop() Default implementation of stop
OperatorType
class OperatorType

Bases: pybind11_builtins.pybind11_object

Enum class for operator types used by the executor.

  • NATIVE: Native operator.

  • GXF: GXF operator.

  • VIRTUAL: Virtual operator. (for internal use, not intended for use by application authors)

Members:

NATIVE

GXF

VIRTUAL

Attributes

name

value
GXF = <OperatorType.GXF: 1>

NATIVE = <OperatorType.NATIVE: 0>

VIRTUAL = <OperatorType.VIRTUAL: 2>

__init__(self: holoscan.core._core.Operator.OperatorType, value: int) → None

property name

property value

__init__(self: holoscan.core._core.Operator, arg0: object, arg1: holoscan::Fragment, *args, **kwargs) → None

Operator class.

Can be initialized with any number of Python positional and keyword arguments.

If a name keyword argument is provided, it must be a str and will be used to set the name of the operator.

Condition classes will be added to self.conditions, Resource classes will be added to self.resources, and any other arguments will be cast from a Python argument type to a C++ Arg and stored in self.args. (For details on how the casting is done, see the py_object_to_arg utility). When a Condition or Resource is provided via a kwarg, it’s name will be automatically be updated to the name of the kwarg.

Parameters
fragment

The holoscan.core.Fragment (or holoscan.core.Application) to which this Operator will belong.

*args

Positional arguments.

**kwargs

Keyword arguments.

Raises
RuntimeError

If name kwarg is provided, but is not of str type. If multiple arguments of type Fragment are provided. If any other arguments cannot be converted to Arg type via py_object_to_arg.

add_arg(*args, **kwargs)

Overloaded function.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Arg) -> None

Add an argument to the component.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.ArgList) -> None

Add a list of arguments to the component.

  1. add_arg(self: holoscan.core._core.Operator, **kwargs) -> None

Add arguments to the component via Python kwargs.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Condition) -> None

  2. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Resource) -> None

Add a condition or resource to the Operator.

This can be used to add a condition or resource to an operator after it has already been constructed.

Parameters
arg

The condition or resource to add.

property args

The list of arguments associated with the component.

Returns
arglist

compute(op_input, op_output, context)

Default implementation of compute

property conditions

Conditions associated with the operator.

property description

YAML formatted string describing the operator.

property fragment

The fragment (holoscan.core.Fragment) that the operator belongs to.

property id

The identifier of the component.

The identifier is initially set to -1, and will become a valid value when the component is initialized.

With the default executor (holoscan.gxf.GXFExecutor), the identifier is set to the GXF component ID.

Returns
id

initialize()

Default implementation of initialize

property name

The name of the operator.

property operator_type

The operator type.

holoscan.core.Operator.OperatorType enum representing the type of the operator. The two types currently implemented are native and GXF.

resource(self: holoscan.core._core.Operator, name: str) → Optional[object]

Resources associated with the operator.

Parameters
name

The name of the resource to retrieve

Returns
holoscan.core.Resource or None

The resource with the given name. If no resource with the given name is found, None is returned.

property resources

Resources associated with the operator.

setup(spec: holoscan.core._core.PyOperatorSpec)

Default implementation of setup method.

property spec

The operator spec (holoscan.core.OperatorSpec) associated with the operator.

start()

Default implementation of start

stop()

Default implementation of stop

class holoscan.operators.PingTxOp(fragment, *args, **kwargs)

Bases: holoscan.core.Operator

Simple transmitter operator.

On each tick, it transmits an integer to the “out” port.

==Named Outputs==

outint

An index value that increments by one on each call to compute. The starting value is 1.

Attributes

args The list of arguments associated with the component.
conditions Conditions associated with the operator.
description YAML formatted string describing the operator.
fragment The fragment (holoscan.core.Fragment) that the operator belongs to.
id The identifier of the component.
name The name of the operator.
operator_type The operator type.
resources Resources associated with the operator.
spec The operator spec (holoscan.core.OperatorSpec) associated with the operator.

Methods

add_arg(*args, **kwargs) Overloaded function.
compute(op_input, op_output, context) Default implementation of compute
initialize() Default implementation of initialize
resource(self, name) Resources associated with the operator.
setup(spec) Default implementation of setup method.
start() Default implementation of start
stop() Default implementation of stop
OperatorType
class OperatorType

Bases: pybind11_builtins.pybind11_object

Enum class for operator types used by the executor.

  • NATIVE: Native operator.

  • GXF: GXF operator.

  • VIRTUAL: Virtual operator. (for internal use, not intended for use by application authors)

Members:

NATIVE

GXF

VIRTUAL

Attributes

name

value
GXF = <OperatorType.GXF: 1>

NATIVE = <OperatorType.NATIVE: 0>

VIRTUAL = <OperatorType.VIRTUAL: 2>

__init__(self: holoscan.core._core.Operator.OperatorType, value: int) → None

property name

property value

__init__(self: holoscan.core._core.Operator, arg0: object, arg1: holoscan::Fragment, *args, **kwargs) → None

Operator class.

Can be initialized with any number of Python positional and keyword arguments.

If a name keyword argument is provided, it must be a str and will be used to set the name of the operator.

Condition classes will be added to self.conditions, Resource classes will be added to self.resources, and any other arguments will be cast from a Python argument type to a C++ Arg and stored in self.args. (For details on how the casting is done, see the py_object_to_arg utility). When a Condition or Resource is provided via a kwarg, it’s name will be automatically be updated to the name of the kwarg.

Parameters
fragment

The holoscan.core.Fragment (or holoscan.core.Application) to which this Operator will belong.

*args

Positional arguments.

**kwargs

Keyword arguments.

Raises
RuntimeError

If name kwarg is provided, but is not of str type. If multiple arguments of type Fragment are provided. If any other arguments cannot be converted to Arg type via py_object_to_arg.

add_arg(*args, **kwargs)

Overloaded function.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Arg) -> None

Add an argument to the component.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.ArgList) -> None

Add a list of arguments to the component.

  1. add_arg(self: holoscan.core._core.Operator, **kwargs) -> None

Add arguments to the component via Python kwargs.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Condition) -> None

  2. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Resource) -> None

Add a condition or resource to the Operator.

This can be used to add a condition or resource to an operator after it has already been constructed.

Parameters
arg

The condition or resource to add.

property args

The list of arguments associated with the component.

Returns
arglist

compute(op_input, op_output, context)

Default implementation of compute

property conditions

Conditions associated with the operator.

property description

YAML formatted string describing the operator.

property fragment

The fragment (holoscan.core.Fragment) that the operator belongs to.

property id

The identifier of the component.

The identifier is initially set to -1, and will become a valid value when the component is initialized.

With the default executor (holoscan.gxf.GXFExecutor), the identifier is set to the GXF component ID.

Returns
id

initialize()

Default implementation of initialize

property name

The name of the operator.

property operator_type

The operator type.

holoscan.core.Operator.OperatorType enum representing the type of the operator. The two types currently implemented are native and GXF.

resource(self: holoscan.core._core.Operator, name: str) → Optional[object]

Resources associated with the operator.

Parameters
name

The name of the resource to retrieve

Returns
holoscan.core.Resource or None

The resource with the given name. If no resource with the given name is found, None is returned.

property resources

Resources associated with the operator.

setup(spec: holoscan.core._core.PyOperatorSpec)

Default implementation of setup method.

property spec

The operator spec (holoscan.core.OperatorSpec) associated with the operator.

start()

Default implementation of start

stop()

Default implementation of stop

class holoscan.operators.SegmentationPostprocessorOp

Bases: holoscan.core._core.Operator

Operator carrying out post-processing operations on segmentation outputs.

==Named Inputs==

in_tensornvidia::gxf::Tensor

Expects a message containing a 32-bit floating point device tensor with name in_tensor_name. The expected data layout of this tensor is HWC, NCHW or NHWC format as specified via data_format.

==Named Outputs==

out_tensornvidia::gxf::Tensor

Emits a message containing a device tensor named “out_tensor” that contains the segmentation labels. This tensor will have unsigned 8-bit integer data type and shape (H, W, 1).

==Device Memory Requirements==

When used with a holoscan.resources.BlockMemoryPool, this operator requires only a single device memory block (storage_type=1) of size height * width bytes.

Parameters
fragment

The fragment that the operator belongs to.

allocator

Memory allocator to use for the output.

in_tensor_name

Name of the input tensor. Default value is "".

network_output_type

Network output type (e.g. ‘softmax’). Default value is "softmax".

data_format

Data format of network output. Default value is "hwc".

cuda_stream_pool

holoscan.resources.CudaStreamPool instance to allocate CUDA streams. Default value is None.

name

The name of the operator. Default value is "segmentation_postprocessor".

Attributes

args The list of arguments associated with the component.
conditions Conditions associated with the operator.
description YAML formatted string describing the operator.
fragment The fragment (holoscan.core.Fragment) that the operator belongs to.
id The identifier of the component.
name The name of the operator.
operator_type The operator type.
resources Resources associated with the operator.
spec The operator spec (holoscan.core.OperatorSpec) associated with the operator.

Methods

add_arg(*args, **kwargs) Overloaded function.
compute(self, arg0, arg1, arg2) Operator compute method.
initialize(self) Operator initialization method.
resource(self, name) Resources associated with the operator.
setup(self, spec) Define the operator specification.
start(self) Operator start method.
stop(self) Operator stop method.
OperatorType
class OperatorType

Bases: pybind11_builtins.pybind11_object

Enum class for operator types used by the executor.

  • NATIVE: Native operator.

  • GXF: GXF operator.

  • VIRTUAL: Virtual operator. (for internal use, not intended for use by application authors)

Members:

NATIVE

GXF

VIRTUAL

Attributes

name

value
GXF = <OperatorType.GXF: 1>

NATIVE = <OperatorType.NATIVE: 0>

VIRTUAL = <OperatorType.VIRTUAL: 2>

__init__(self: holoscan.core._core.Operator.OperatorType, value: int) → None

property name

property value

__init__(self: holoscan.operators.segmentation_postprocessor._segmentation_postprocessor.SegmentationPostprocessorOp, fragment: holoscan.core._core.Fragment, *args, allocator: holoscan.resources._resources.Allocator, in_tensor_name: str = '', network_output_type: str = 'softmax', data_format: str = 'hwc', cuda_stream_pool: holoscan.resources._resources.CudaStreamPool = None, name: str = 'segmentation_postprocessor') → None

Operator carrying out post-processing operations on segmentation outputs.

==Named Inputs==

in_tensornvidia::gxf::Tensor

Expects a message containing a 32-bit floating point device tensor with name in_tensor_name. The expected data layout of this tensor is HWC, NCHW or NHWC format as specified via data_format.

==Named Outputs==

out_tensornvidia::gxf::Tensor

Emits a message containing a device tensor named “out_tensor” that contains the segmentation labels. This tensor will have unsigned 8-bit integer data type and shape (H, W, 1).

==Device Memory Requirements==

When used with a holoscan.resources.BlockMemoryPool, this operator requires only a single device memory block (storage_type=1) of size height * width bytes.

Parameters
fragment

The fragment that the operator belongs to.

allocator

Memory allocator to use for the output.

in_tensor_name

Name of the input tensor. Default value is "".

network_output_type

Network output type (e.g. ‘softmax’). Default value is "softmax".

data_format

Data format of network output. Default value is "hwc".

cuda_stream_pool

holoscan.resources.CudaStreamPool instance to allocate CUDA streams. Default value is None.

name

The name of the operator. Default value is "segmentation_postprocessor".

add_arg(*args, **kwargs)

Overloaded function.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Arg) -> None

Add an argument to the component.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.ArgList) -> None

Add a list of arguments to the component.

  1. add_arg(self: holoscan.core._core.Operator, **kwargs) -> None

Add arguments to the component via Python kwargs.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Condition) -> None

  2. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Resource) -> None

Add a condition or resource to the Operator.

This can be used to add a condition or resource to an operator after it has already been constructed.

Parameters
arg

The condition or resource to add.

property args

The list of arguments associated with the component.

Returns
arglist

compute(self: holoscan.core._core.Operator, arg0: holoscan.core._core.InputContext, arg1: holoscan.core._core.OutputContext, arg2: holoscan.core._core.ExecutionContext) → None

Operator compute method. This method defines the primary computation to be executed by the operator.

property conditions

Conditions associated with the operator.

property description

YAML formatted string describing the operator.

property fragment

The fragment (holoscan.core.Fragment) that the operator belongs to.

property id

The identifier of the component.

The identifier is initially set to -1, and will become a valid value when the component is initialized.

With the default executor (holoscan.gxf.GXFExecutor), the identifier is set to the GXF component ID.

Returns
id

initialize(self: holoscan.core._core.Operator) → None

Operator initialization method.

property name

The name of the operator.

property operator_type

The operator type.

holoscan.core.Operator.OperatorType enum representing the type of the operator. The two types currently implemented are native and GXF.

resource(self: holoscan.core._core.Operator, name: str) → Optional[object]

Resources associated with the operator.

Parameters
name

The name of the resource to retrieve

Returns
holoscan.core.Resource or None

The resource with the given name. If no resource with the given name is found, None is returned.

property resources

Resources associated with the operator.

setup(self: holoscan.operators.segmentation_postprocessor._segmentation_postprocessor.SegmentationPostprocessorOp, spec: holoscan.core._core.OperatorSpec) → None

Define the operator specification.

Parameters
spec

The operator specification.

property spec

The operator spec (holoscan.core.OperatorSpec) associated with the operator.

start(self: holoscan.core._core.Operator) → None

Operator start method.

stop(self: holoscan.core._core.Operator) → None

Operator stop method.

class holoscan.operators.V4L2VideoCaptureOp

Bases: holoscan.core._core.Operator

Operator to get a video stream from a V4L2 source.

https://www.kernel.org/doc/html/v4.9/media/uapi/v4l/v4l2.html

Inputs a video stream from a V4L2 node, including USB cameras and HDMI IN.

  • Input stream is on host. If no pixel format is specified in the yaml configuration file, the pixel format will be automatically selected. However, only AB24, YUYV, and MJPG are then supported. If a pixel format is specified in the yaml file, then this format will be used. However, note that the operator then expects that this format can be encoded as RGBA32. If not, the behavior is undefined.

  • Output stream is on host. Always RGBA32 at this time.

Use holoscan.operators.FormatConverterOp to move data from the host to a GPU device.

==Named Outputs==

signalnvidia::gxf::VideoBuffer

A message containing a video buffer on the host with format GXF_VIDEO_FORMAT_RGBA.

==Device Memory Requirements==

When using this operator with a holoscan.resources.BlockMemoryPool, a single device memory block is needed (storage_type=1). The size of this memory block can be determined by rounding the width and height up to the nearest even size and then padding the rows as needed so that the row stride is a multiple of 256 bytes. C++ code to calculate the block size is as follows:

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def get_block_size(height, width): height_even = height + (height & 1) width_even = width + (width & 1) row_bytes = width_even * 4; # 4 bytes per pixel for 8-bit RGBA row_stride = (row_bytes % 256 == 0) ? row_bytes : ((row_bytes // 256 + 1) * 256) return height_even * row_stride

Parameters
fragment

The fragment that the operator belongs to.

allocator

Memory allocator to use for the output.

device

The device to target (e.g. “/dev/video0” for device 0). Default value is "/dev/video0".

width

Width of the video stream. Default value is 0.

height

Height of the video stream. Default value is 0.

num_buffers

Number of V4L2 buffers to use. Default value is 4.

pixel_format

Video stream pixel format (little endian four character code (fourcc)). Default value is "auto".

name

The name of the operator. Default value is "v4l2_video_capture".

exposure_time

Exposure time of the camera sensor in multiples of 100 μs (e.g. setting exposure_time to 100 is 10 ms). Default: auto exposure, or camera sensor default. Use v4l2-ctl -d /dev/<your_device> -L for a range of values supported by your device. When not set by the user, V4L2_CID_EXPOSURE_AUTO is set to V4L2_EXPOSURE_AUTO, or to V4L2_EXPOSURE_APERTURE_PRIORITY if the former is not supported. When set by the user, V4L2_CID_EXPOSURE_AUTO is set to V4L2_EXPOSURE_SHUTTER_PRIORITY, or to V4L2_EXPOSURE_MANUAL if the former is not supported. The provided value is then used to set V4L2_CID_EXPOSURE_ABSOLUTE.

gain

Gain of the camera sensor. Default: auto gain, or camera sensor default. Use v4l2-ctl -d /dev/<your_device> -L for a range of values supported by your device. When not set by the user, V4L2_CID_AUTOGAIN is set to true (if supported). When set by the user, V4L2_CID_AUTOGAIN is set to false (if supported). The provided value is then used to set V4L2_CID_GAIN.

Attributes

args The list of arguments associated with the component.
conditions Conditions associated with the operator.
description YAML formatted string describing the operator.
fragment The fragment (holoscan.core.Fragment) that the operator belongs to.
id The identifier of the component.
name The name of the operator.
operator_type The operator type.
resources Resources associated with the operator.
spec The operator spec (holoscan.core.OperatorSpec) associated with the operator.

Methods

add_arg(*args, **kwargs) Overloaded function.
compute(self, arg0, arg1, arg2) Operator compute method.
initialize(self) Initialize the operator.
resource(self, name) Resources associated with the operator.
setup(self, spec) Define the operator specification.
start(self) Operator start method.
stop(self) Operator stop method.
OperatorType
class OperatorType

Bases: pybind11_builtins.pybind11_object

Enum class for operator types used by the executor.

  • NATIVE: Native operator.

  • GXF: GXF operator.

  • VIRTUAL: Virtual operator. (for internal use, not intended for use by application authors)

Members:

NATIVE

GXF

VIRTUAL

Attributes

name

value
GXF = <OperatorType.GXF: 1>

NATIVE = <OperatorType.NATIVE: 0>

VIRTUAL = <OperatorType.VIRTUAL: 2>

__init__(self: holoscan.core._core.Operator.OperatorType, value: int) → None

property name

property value

__init__(self: holoscan.operators.v4l2_video_capture._v4l2_video_capture.V4L2VideoCaptureOp, fragment: holoscan.core._core.Fragment, *args, allocator: holoscan.resources._resources.Allocator, device: str = '0', width: int = 0, height: int = 0, num_buffers: int = 4, pixel_format: str = 'auto', name: str = 'v4l2_video_capture', exposure_time: Optional[int] = None, gain: Optional[int] = None) → None

Operator to get a video stream from a V4L2 source.

https://www.kernel.org/doc/html/v4.9/media/uapi/v4l/v4l2.html

Inputs a video stream from a V4L2 node, including USB cameras and HDMI IN.

  • Input stream is on host. If no pixel format is specified in the yaml configuration file, the pixel format will be automatically selected. However, only AB24, YUYV, and MJPG are then supported. If a pixel format is specified in the yaml file, then this format will be used. However, note that the operator then expects that this format can be encoded as RGBA32. If not, the behavior is undefined.

  • Output stream is on host. Always RGBA32 at this time.

Use holoscan.operators.FormatConverterOp to move data from the host to a GPU device.

==Named Outputs==

signalnvidia::gxf::VideoBuffer

A message containing a video buffer on the host with format GXF_VIDEO_FORMAT_RGBA.

==Device Memory Requirements==

When using this operator with a holoscan.resources.BlockMemoryPool, a single device memory block is needed (storage_type=1). The size of this memory block can be determined by rounding the width and height up to the nearest even size and then padding the rows as needed so that the row stride is a multiple of 256 bytes. C++ code to calculate the block size is as follows:

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Copied!
            

def get_block_size(height, width): height_even = height + (height & 1) width_even = width + (width & 1) row_bytes = width_even * 4; # 4 bytes per pixel for 8-bit RGBA row_stride = (row_bytes % 256 == 0) ? row_bytes : ((row_bytes // 256 + 1) * 256) return height_even * row_stride

Parameters
fragment

The fragment that the operator belongs to.

allocator

Memory allocator to use for the output.

device

The device to target (e.g. “/dev/video0” for device 0). Default value is "/dev/video0".

width

Width of the video stream. Default value is 0.

height

Height of the video stream. Default value is 0.

num_buffers

Number of V4L2 buffers to use. Default value is 4.

pixel_format

Video stream pixel format (little endian four character code (fourcc)). Default value is "auto".

name

The name of the operator. Default value is "v4l2_video_capture".

exposure_time

Exposure time of the camera sensor in multiples of 100 μs (e.g. setting exposure_time to 100 is 10 ms). Default: auto exposure, or camera sensor default. Use v4l2-ctl -d /dev/<your_device> -L for a range of values supported by your device. When not set by the user, V4L2_CID_EXPOSURE_AUTO is set to V4L2_EXPOSURE_AUTO, or to V4L2_EXPOSURE_APERTURE_PRIORITY if the former is not supported. When set by the user, V4L2_CID_EXPOSURE_AUTO is set to V4L2_EXPOSURE_SHUTTER_PRIORITY, or to V4L2_EXPOSURE_MANUAL if the former is not supported. The provided value is then used to set V4L2_CID_EXPOSURE_ABSOLUTE.

gain

Gain of the camera sensor. Default: auto gain, or camera sensor default. Use v4l2-ctl -d /dev/<your_device> -L for a range of values supported by your device. When not set by the user, V4L2_CID_AUTOGAIN is set to true (if supported). When set by the user, V4L2_CID_AUTOGAIN is set to false (if supported). The provided value is then used to set V4L2_CID_GAIN.

add_arg(*args, **kwargs)

Overloaded function.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Arg) -> None

Add an argument to the component.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.ArgList) -> None

Add a list of arguments to the component.

  1. add_arg(self: holoscan.core._core.Operator, **kwargs) -> None

Add arguments to the component via Python kwargs.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Condition) -> None

  2. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Resource) -> None

Add a condition or resource to the Operator.

This can be used to add a condition or resource to an operator after it has already been constructed.

Parameters
arg

The condition or resource to add.

property args

The list of arguments associated with the component.

Returns
arglist

compute(self: holoscan.core._core.Operator, arg0: holoscan.core._core.InputContext, arg1: holoscan.core._core.OutputContext, arg2: holoscan.core._core.ExecutionContext) → None

Operator compute method. This method defines the primary computation to be executed by the operator.

property conditions

Conditions associated with the operator.

property description

YAML formatted string describing the operator.

property fragment

The fragment (holoscan.core.Fragment) that the operator belongs to.

property id

The identifier of the component.

The identifier is initially set to -1, and will become a valid value when the component is initialized.

With the default executor (holoscan.gxf.GXFExecutor), the identifier is set to the GXF component ID.

Returns
id

initialize(self: holoscan.operators.v4l2_video_capture._v4l2_video_capture.V4L2VideoCaptureOp) → None

Initialize the operator.

This method is called only once when the operator is created for the first time, and uses a light-weight initialization.

property name

The name of the operator.

property operator_type

The operator type.

holoscan.core.Operator.OperatorType enum representing the type of the operator. The two types currently implemented are native and GXF.

resource(self: holoscan.core._core.Operator, name: str) → Optional[object]

Resources associated with the operator.

Parameters
name

The name of the resource to retrieve

Returns
holoscan.core.Resource or None

The resource with the given name. If no resource with the given name is found, None is returned.

property resources

Resources associated with the operator.

setup(self: holoscan.operators.v4l2_video_capture._v4l2_video_capture.V4L2VideoCaptureOp, spec: holoscan.core._core.OperatorSpec) → None

Define the operator specification.

Parameters
spec : holoscan.core.OperatorSpec

The operator specification.

property spec

The operator spec (holoscan.core.OperatorSpec) associated with the operator.

start(self: holoscan.core._core.Operator) → None

Operator start method.

stop(self: holoscan.core._core.Operator) → None

Operator stop method.

class holoscan.operators.VideoStreamRecorderOp

Bases: holoscan.core._core.Operator

Operator class to record a video stream to a file.

==Named Inputs==

inputnvidia::gxf::Tensor

A message containing a video frame to serialize to disk. The input tensor can be on either the CPU or GPU. This data location will be recorded as part of the metadata serialized to disk and if the data is later read back in via VideoStreamReplayerOp, the tensor output of that operator will be on the same device (CPU or GPU).

Parameters
fragment

The fragment that the operator belongs to.

directory

Directory path for storing files.

basename

User specified file name without extension.

flush_on_tick

Flushes output buffer on every tick when True. Default value is False.

name

The name of the operator. Default value is "video_stream_recorder".

Attributes

args The list of arguments associated with the component.
conditions Conditions associated with the operator.
description YAML formatted string describing the operator.
fragment The fragment (holoscan.core.Fragment) that the operator belongs to.
id The identifier of the component.
name The name of the operator.
operator_type The operator type.
resources Resources associated with the operator.
spec The operator spec (holoscan.core.OperatorSpec) associated with the operator.

Methods

add_arg(*args, **kwargs) Overloaded function.
compute(self, arg0, arg1, arg2) Operator compute method.
initialize(self) Initialize the operator.
resource(self, name) Resources associated with the operator.
setup(self, spec) Define the operator specification.
start(self) Operator start method.
stop(self) Operator stop method.
OperatorType
class OperatorType

Bases: pybind11_builtins.pybind11_object

Enum class for operator types used by the executor.

  • NATIVE: Native operator.

  • GXF: GXF operator.

  • VIRTUAL: Virtual operator. (for internal use, not intended for use by application authors)

Members:

NATIVE

GXF

VIRTUAL

Attributes

name

value
GXF = <OperatorType.GXF: 1>

NATIVE = <OperatorType.NATIVE: 0>

VIRTUAL = <OperatorType.VIRTUAL: 2>

__init__(self: holoscan.core._core.Operator.OperatorType, value: int) → None

property name

property value

__init__(self: holoscan.operators.video_stream_recorder._video_stream_recorder.VideoStreamRecorderOp, fragment: holoscan.core._core.Fragment, *args, directory: str, basename: str, flush_on_tick: bool = False, name: str = 'recorder') → None

Operator class to record a video stream to a file.

==Named Inputs==

inputnvidia::gxf::Tensor

A message containing a video frame to serialize to disk. The input tensor can be on either the CPU or GPU. This data location will be recorded as part of the metadata serialized to disk and if the data is later read back in via VideoStreamReplayerOp, the tensor output of that operator will be on the same device (CPU or GPU).

Parameters
fragment

The fragment that the operator belongs to.

directory

Directory path for storing files.

basename

User specified file name without extension.

flush_on_tick

Flushes output buffer on every tick when True. Default value is False.

name

The name of the operator. Default value is "video_stream_recorder".

add_arg(*args, **kwargs)

Overloaded function.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Arg) -> None

Add an argument to the component.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.ArgList) -> None

Add a list of arguments to the component.

  1. add_arg(self: holoscan.core._core.Operator, **kwargs) -> None

Add arguments to the component via Python kwargs.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Condition) -> None

  2. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Resource) -> None

Add a condition or resource to the Operator.

This can be used to add a condition or resource to an operator after it has already been constructed.

Parameters
arg

The condition or resource to add.

property args

The list of arguments associated with the component.

Returns
arglist

compute(self: holoscan.core._core.Operator, arg0: holoscan.core._core.InputContext, arg1: holoscan.core._core.OutputContext, arg2: holoscan.core._core.ExecutionContext) → None

Operator compute method. This method defines the primary computation to be executed by the operator.

property conditions

Conditions associated with the operator.

property description

YAML formatted string describing the operator.

property fragment

The fragment (holoscan.core.Fragment) that the operator belongs to.

property id

The identifier of the component.

The identifier is initially set to -1, and will become a valid value when the component is initialized.

With the default executor (holoscan.gxf.GXFExecutor), the identifier is set to the GXF component ID.

Returns
id

initialize(self: holoscan.operators.video_stream_recorder._video_stream_recorder.VideoStreamRecorderOp) → None

Initialize the operator.

This method is called only once when the operator is created for the first time, and uses a light-weight initialization.

property name

The name of the operator.

property operator_type

The operator type.

holoscan.core.Operator.OperatorType enum representing the type of the operator. The two types currently implemented are native and GXF.

resource(self: holoscan.core._core.Operator, name: str) → Optional[object]

Resources associated with the operator.

Parameters
name

The name of the resource to retrieve

Returns
holoscan.core.Resource or None

The resource with the given name. If no resource with the given name is found, None is returned.

property resources

Resources associated with the operator.

setup(self: holoscan.operators.video_stream_recorder._video_stream_recorder.VideoStreamRecorderOp, spec: holoscan.core._core.OperatorSpec) → None

Define the operator specification.

Parameters
spec

The operator specification.

property spec

The operator spec (holoscan.core.OperatorSpec) associated with the operator.

start(self: holoscan.core._core.Operator) → None

Operator start method.

stop(self: holoscan.core._core.Operator) → None

Operator stop method.

class holoscan.operators.VideoStreamReplayerOp

Bases: holoscan.core._core.Operator

Operator class to replay a video stream from a file.

==Named Outputs==

outputnvidia::gxf::Tensor

A message containing a video frame deserialized from disk. Depending on the metadata in the file being read, this tensor could be on either CPU or GPU. For the data used in examples distributed with the SDK, the tensor will be an unnamed GPU tensor (name == “”).

Parameters
fragment

The fragment that the operator belongs to.

directory

Directory path for reading files from.

basename

User specified file name without extension.

batch_size

Number of entities to read and publish for one tick. Default value is 1.

ignore_corrupted_entities

If an entity could not be deserialized, it is ignored by default; otherwise a failure is generated. Default value is True.

frame_rate

Frame rate to replay. If zero value is specified, it follows timings in timestamps. Default value is 0.0.

realtime

Playback video in realtime, based on frame_rate or timestamps. Default value is True.

repeat

Repeat video stream in a loop. Default value is False.

count

Number of frame counts to playback. If zero value is specified, it is ignored. If the count is less than the number of frames in the video, it would finish early. Default value is 0.

name

The name of the operator. Default value is "video_stream_replayer".

Attributes

args The list of arguments associated with the component.
conditions Conditions associated with the operator.
description YAML formatted string describing the operator.
fragment The fragment (holoscan.core.Fragment) that the operator belongs to.
id The identifier of the component.
name The name of the operator.
operator_type The operator type.
resources Resources associated with the operator.
spec The operator spec (holoscan.core.OperatorSpec) associated with the operator.

Methods

add_arg(*args, **kwargs) Overloaded function.
compute(self, arg0, arg1, arg2) Operator compute method.
initialize(self) Initialize the operator.
resource(self, name) Resources associated with the operator.
setup(self, spec) Define the operator specification.
start(self) Operator start method.
stop(self) Operator stop method.
OperatorType
class OperatorType

Bases: pybind11_builtins.pybind11_object

Enum class for operator types used by the executor.

  • NATIVE: Native operator.

  • GXF: GXF operator.

  • VIRTUAL: Virtual operator. (for internal use, not intended for use by application authors)

Members:

NATIVE

GXF

VIRTUAL

Attributes

name

value
GXF = <OperatorType.GXF: 1>

NATIVE = <OperatorType.NATIVE: 0>

VIRTUAL = <OperatorType.VIRTUAL: 2>

__init__(self: holoscan.core._core.Operator.OperatorType, value: int) → None

property name

property value

__init__(self: holoscan.operators.video_stream_replayer._video_stream_replayer.VideoStreamReplayerOp, fragment: holoscan.core._core.Fragment, *args, directory: str, basename: str, batch_size: int = 1, ignore_corrupted_entities: bool = True, frame_rate: float = 1.0, realtime: bool = True, repeat: bool = False, count: int = 0, name: str = 'video_stream_replayer') → None

Operator class to replay a video stream from a file.

==Named Outputs==

outputnvidia::gxf::Tensor

A message containing a video frame deserialized from disk. Depending on the metadata in the file being read, this tensor could be on either CPU or GPU. For the data used in examples distributed with the SDK, the tensor will be an unnamed GPU tensor (name == “”).

Parameters
fragment

The fragment that the operator belongs to.

directory

Directory path for reading files from.

basename

User specified file name without extension.

batch_size

Number of entities to read and publish for one tick. Default value is 1.

ignore_corrupted_entities

If an entity could not be deserialized, it is ignored by default; otherwise a failure is generated. Default value is True.

frame_rate

Frame rate to replay. If zero value is specified, it follows timings in timestamps. Default value is 0.0.

realtime

Playback video in realtime, based on frame_rate or timestamps. Default value is True.

repeat

Repeat video stream in a loop. Default value is False.

count

Number of frame counts to playback. If zero value is specified, it is ignored. If the count is less than the number of frames in the video, it would finish early. Default value is 0.

name

The name of the operator. Default value is "video_stream_replayer".

add_arg(*args, **kwargs)

Overloaded function.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Arg) -> None

Add an argument to the component.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.ArgList) -> None

Add a list of arguments to the component.

  1. add_arg(self: holoscan.core._core.Operator, **kwargs) -> None

Add arguments to the component via Python kwargs.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Condition) -> None

  2. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Resource) -> None

Add a condition or resource to the Operator.

This can be used to add a condition or resource to an operator after it has already been constructed.

Parameters
arg

The condition or resource to add.

property args

The list of arguments associated with the component.

Returns
arglist

compute(self: holoscan.core._core.Operator, arg0: holoscan.core._core.InputContext, arg1: holoscan.core._core.OutputContext, arg2: holoscan.core._core.ExecutionContext) → None

Operator compute method. This method defines the primary computation to be executed by the operator.

property conditions

Conditions associated with the operator.

property description

YAML formatted string describing the operator.

property fragment

The fragment (holoscan.core.Fragment) that the operator belongs to.

property id

The identifier of the component.

The identifier is initially set to -1, and will become a valid value when the component is initialized.

With the default executor (holoscan.gxf.GXFExecutor), the identifier is set to the GXF component ID.

Returns
id

initialize(self: holoscan.operators.video_stream_replayer._video_stream_replayer.VideoStreamReplayerOp) → None

Initialize the operator.

This method is called only once when the operator is created for the first time, and uses a light-weight initialization.

property name

The name of the operator.

property operator_type

The operator type.

holoscan.core.Operator.OperatorType enum representing the type of the operator. The two types currently implemented are native and GXF.

resource(self: holoscan.core._core.Operator, name: str) → Optional[object]

Resources associated with the operator.

Parameters
name

The name of the resource to retrieve

Returns
holoscan.core.Resource or None

The resource with the given name. If no resource with the given name is found, None is returned.

property resources

Resources associated with the operator.

setup(self: holoscan.operators.video_stream_replayer._video_stream_replayer.VideoStreamReplayerOp, spec: holoscan.core._core.OperatorSpec) → None

Define the operator specification.

Parameters
spec

The operator specification.

property spec

The operator spec (holoscan.core.OperatorSpec) associated with the operator.

start(self: holoscan.core._core.Operator) → None

Operator start method.

stop(self: holoscan.core._core.Operator) → None

Operator stop method.

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