Class FormatConverterOp
Defined in File format_converter.hpp
Base Type
public holoscan::Operator
(Class Operator)
-
class FormatConverterOp : public holoscan::Operator
Operator class to convert the data format of the input data.
==Named Inputs==
source_video :
nvidia::gxf::Tensor
ornvidia::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==
tensor :
nvidia::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
.
==Parameters==
pool: Memory pool allocator (holoscan::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"
Optional (default:"rgb888"
).
in_tensor_name: The name of the input tensor. Optional (default:
""
).out_tensor_name: The name of the output tensor. Optional (default:
""
).scale_min: Output will be clipped to this minimum value. Optional (default:
0.0
).scale_max: Output will be clipped to this maximum value. Optional (default:
1.0
).alpha_value: Unsigned integer in range [0, 255], indicating the alpha channel value to use when converting from RGB to RGBA. Optional (default:
255
).resize_height: Desired height for the (resized) output. Height will be unchanged if
resize_height
is0
. Optional (default:0
).resize_width: Desired width for the (resized) output. Width will be unchanged if
resize_width
is0
. Optional (default: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.
Optional (default:
0
). The default value0
(NPPI_INTER_UNDEFINED) which would be equivalent to4
(NPPI_INTER_CUBIC).channel_order: Sequence of integers describing how channel values are permuted. Optional (default:
[0, 1, 2]
for 3-channel images and[0, 1, 2, 3]
for 4-channel images).cuda_stream_pool:
<a class="reference internal" href="classholoscan_1_1CudaStreamPool.html#classholoscan_1_1CudaStreamPool" target="_self">holoscan::CudaStreamPool</a>
instance to allocate CUDA streams. Optional (default:nullptr
).
==Device Memory Requirements==
When using this operator with a
<a class="reference internal" href="classholoscan_1_1BlockMemoryPool.html#classholoscan_1_1BlockMemoryPool" target="_self">BlockMemoryPool</a>
, 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
should be between 1-3 andblock_size
should be set to the maximum of the individual blocks sizes described above.Public Functions
- HOLOSCAN_OPERATOR_FORWARD_ARGS (FormatConverterOp) FormatConverterOp()=default
-
virtual void setup(OperatorSpec &spec) override
Define the operator specification.
- Parameters
spec – The reference to the operator specification.
-
virtual void initialize() override
Initialize the operator.
This function is called when the fragment is initialized by Executor::initialize_fragment().
-
virtual void start() override
Implement the startup logic of the operator.
This method is called multiple times over the lifecycle of the operator according to the order defined in the lifecycle, and used for heavy initialization tasks such as allocating memory resources.
-
virtual void compute(InputContext &op_input, OutputContext &op_output, ExecutionContext &context) override
Implement the compute method.
This method is called by the runtime multiple times. The runtime calls this method until the operator is stopped.
- Parameters
op_input – The input context of the operator.
op_output – The output context of the operator.
context – The execution context of the operator.
-
virtual void stop() override
Implement the shutdown logic of the operator.
This method is called multiple times over the lifecycle of the operator according to the order defined in the lifecycle, and used for heavy deinitialization tasks such as deallocation of all resources previously assigned in start.
-
nvidia::gxf::Expected<void*> resizeImage(const void *in_tensor_data, const std::vector<nvidia::gxf::ColorPlane> &in_color_planes, const int32_t rows, const int32_t columns, const int16_t channels, const nvidia::gxf::PrimitiveType primitive_type, const int32_t resize_width, const int32_t resize_height)
-
void convertTensorFormat(const void *in_tensor_data, const std::vector<nvidia::gxf::ColorPlane> &in_color_planes, void *out_tensor_data, const int32_t rows, const int32_t columns, const int16_t out_channels)