Program Listing for File format_converter.cpp
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/*
* Copyright (c) 2022, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "format_converter.hpp"
#include <cuda_runtime.h>
#include <npp.h>
#include <string>
#include <utility>
#include <vector>
#include "gxf/core/entity.hpp"
#include "gxf/core/expected.hpp"
#include "gxf/multimedia/video.hpp"
#include "gxf/std/tensor.hpp"
#define CUDA_TRY(stmt) \
({ \
cudaError_t _holoscan_cuda_err = stmt; \
if (cudaSuccess != _holoscan_cuda_err) { \
GXF_LOG_ERROR("CUDA Runtime call %s in line %d of file %s failed with '%s' (%d).\n", #stmt, \
__LINE__, __FILE__, cudaGetErrorString(_holoscan_cuda_err), \
_holoscan_cuda_err); \
} \
_holoscan_cuda_err; \
})
namespace nvidia::holoscan::formatconverter {
static FormatDType toFormatDType(const std::string& str) {
if (str == "rgb888") {
return FormatDType::kRGB888;
} else if (str == "uint8") {
return FormatDType::kUnsigned8;
} else if (str == "float32") {
return FormatDType::kFloat32;
} else if (str == "rgba8888") {
return FormatDType::kRGBA8888;
} else {
return FormatDType::kUnknown;
}
}
static constexpr FormatConversionType getFormatConversionType(FormatDType from, FormatDType to) {
if (from != FormatDType::kUnknown && to != FormatDType::kUnknown && from == to) {
return FormatConversionType::kNone;
} else if (from == FormatDType::kUnsigned8 && to == FormatDType::kFloat32) {
return FormatConversionType::kUnsigned8ToFloat32;
} else if (from == FormatDType::kFloat32 && to == FormatDType::kUnsigned8) {
return FormatConversionType::kFloat32ToUnsigned8;
} else if (from == FormatDType::kUnsigned8 && to == FormatDType::kRGBA8888) {
return FormatConversionType::kRGB888ToRGBA8888;
} else if (from == FormatDType::kRGBA8888 && to == FormatDType::kUnsigned8) {
return FormatConversionType::kRGBA8888ToRGB888;
} else if (from == FormatDType::kRGBA8888 && to == FormatDType::kFloat32) {
return FormatConversionType::kRGBA8888ToFloat32;
} else {
return FormatConversionType::kUnknown;
}
}
static constexpr FormatDType normalizeFormatDType(FormatDType dtype) {
switch (dtype) {
case FormatDType::kRGB888:
return FormatDType::kUnsigned8;
default:
return dtype;
}
}
static constexpr gxf::PrimitiveType primitiveTypeFromFormatDType(FormatDType dtype) {
switch (dtype) {
case FormatDType::kRGB888:
case FormatDType::kRGBA8888:
case FormatDType::kUnsigned8:
return gxf::PrimitiveType::kUnsigned8;
case FormatDType::kFloat32:
return gxf::PrimitiveType::kFloat32;
default:
return gxf::PrimitiveType::kCustom;
}
}
static constexpr FormatDType FormatDTypeFromPrimitiveType(gxf::PrimitiveType type) {
switch (type) {
case gxf::PrimitiveType::kUnsigned8:
return FormatDType::kUnsigned8;
case gxf::PrimitiveType::kFloat32:
return FormatDType::kFloat32;
default:
return FormatDType::kUnknown;
}
}
static gxf_result_t verifyFormatDTypeChannels(FormatDType dtype, int channel_count) {
switch (dtype) {
case FormatDType::kRGB888:
if (channel_count != 3) {
GXF_LOG_ERROR("Invalid channel count for RGB888 %d != 3\n", channel_count);
return GXF_FAILURE;
}
break;
case FormatDType::kRGBA8888:
if (channel_count != 4) {
GXF_LOG_ERROR("Invalid channel count for RGBA8888 %d != 4\n", channel_count);
return GXF_FAILURE;
}
break;
default:
break;
}
return GXF_SUCCESS;
}
gxf_result_t FormatConverter::start() {
out_dtype_ = toFormatDType(out_dtype_str_.get());
if (out_dtype_ == FormatDType::kUnknown) {
GXF_LOG_ERROR("Unsupported output format dtype: %s\n", out_dtype_str_.get().c_str());
return GXF_FAILURE;
}
out_primitive_type_ = primitiveTypeFromFormatDType(out_dtype_);
if (out_primitive_type_ == gxf::PrimitiveType::kCustom) {
GXF_LOG_ERROR("Unsupported output format dtype: %s\n", out_dtype_str_.get().c_str());
return GXF_FAILURE;
}
if (!in_dtype_str_.get().empty()) {
in_dtype_ = toFormatDType(in_dtype_str_.get());
if (in_dtype_ == FormatDType::kUnknown) {
GXF_LOG_ERROR("Unsupported input format dtype: %s\n", in_dtype_str_.get().c_str());
return GXF_FAILURE;
}
format_conversion_type_ =
getFormatConversionType(normalizeFormatDType(in_dtype_), normalizeFormatDType(out_dtype_));
in_primitive_type_ = primitiveTypeFromFormatDType(in_dtype_);
}
switch (resize_mode_) {
case 0:
resize_mode_.set(NPPI_INTER_CUBIC);
break;
case 1: // NPPI_INTER_NN
case 2: // NPPI_INTER_LINEAR
case 4: // NPPI_INTER_CUBIC
case 5: // NPPI_INTER_CUBIC2P_BSPLINE
case 6: // NPPI_INTER_CUBIC2P_CATMULLROM
case 7: // NPPI_INTER_CUBIC2P_B05C03
case 8: // NPPI_INTER_SUPER
case 16: // NPPI_INTER_LANCZOS
case 17: // NPPI_INTER_LANCZOS3_ADVANCED
case static_cast<int32_t>(0x8000000): // NPPI_SMOOTH_EDGE
break;
default:
GXF_LOG_ERROR("Unsupported resize mode: %d\n", resize_mode_.get());
return GXF_FAILURE;
}
return GXF_SUCCESS;
}
gxf_result_t FormatConverter::stop() {
resize_buffer_.freeBuffer();
channel_buffer_.freeBuffer();
device_scratch_buffer_.freeBuffer();
return GXF_SUCCESS;
}
gxf_result_t FormatConverter::tick() {
// Process input message
const auto in_message = in_->receive();
if (!in_message || in_message.value().is_null()) { return GXF_CONTRACT_MESSAGE_NOT_AVAILABLE; }
gxf::Shape out_shape{0, 0, 0};
void* in_tensor_data = nullptr;
gxf::PrimitiveType in_primitive_type = gxf::PrimitiveType::kCustom;
gxf::MemoryStorageType in_memory_storage_type = gxf::MemoryStorageType::kHost;
int32_t rows = 0;
int32_t columns = 0;
int16_t in_channels = 0;
int16_t out_channels = 0;
// Get tensor attached to the message
auto maybe_video = in_message.value().get<gxf::VideoBuffer>();
gxf::Handle<gxf::Tensor> in_tensor;
if (maybe_video) {
// Convert VideoBuffer to Tensor
auto frame = maybe_video.value();
// NOTE: VideoBuffer::moveToTensor() converts VideoBuffer instance to the Tensor instance
// with an unexpected shape: [width, height] or [width, height, num_planes].
// And, if we use moveToTensor() to convert VideoBuffer to Tensor, we may lose the the original
// video buffer when the VideoBuffer instance is used in other places. For that reason, we
// directly access internal data of VideoBuffer instance to access Tensor data.
const auto& buffer_info = frame->video_frame_info();
switch (buffer_info.color_format) {
case gxf::VideoFormat::GXF_VIDEO_FORMAT_RGBA:
in_primitive_type = gxf::PrimitiveType::kUnsigned8;
break;
default:
GXF_LOG_ERROR("Unsupported input format: %d\n", buffer_info.color_format);
return GXF_FAILURE;
}
// Get needed information from the tensor
in_memory_storage_type = frame->storage_type();
out_shape = gxf::Shape{static_cast<int32_t>(buffer_info.height),
static_cast<int32_t>(buffer_info.width), 4};
in_tensor_data = frame->pointer();
rows = buffer_info.height;
columns = buffer_info.width;
in_channels = 4; // RGBA
out_channels = in_channels;
// If the buffer is in host memory, copy it to a device (GPU) buffer
// as needed for the NPP resize/convert operations.
if (in_memory_storage_type == gxf::MemoryStorageType::kHost) {
size_t buffer_size = rows * columns * in_channels;
if (buffer_size > device_scratch_buffer_.size()) {
device_scratch_buffer_.resize(pool_, buffer_size, gxf::MemoryStorageType::kDevice);
if (!device_scratch_buffer_.pointer()) {
GXF_LOG_ERROR("Failed to allocate device scratch buffer (%d bytes)", buffer_size);
return GXF_FAILURE;
}
}
CUDA_TRY(cudaMemcpy(device_scratch_buffer_.pointer(), frame->pointer(), buffer_size,
cudaMemcpyHostToDevice));
in_tensor_data = device_scratch_buffer_.pointer();
in_memory_storage_type = gxf::MemoryStorageType::kDevice;
}
} else {
const auto maybe_tensor = in_message.value().get<gxf::Tensor>(in_tensor_name_.get().c_str());
if (!maybe_tensor) {
GXF_LOG_ERROR("Tensor '%s' not found in message.\n", in_tensor_name_.get().c_str());
return GXF_FAILURE;
}
in_tensor = maybe_tensor.value();
// Get needed information from the tensor
out_shape = in_tensor->shape();
in_tensor_data = in_tensor->pointer();
in_primitive_type = in_tensor->element_type();
in_memory_storage_type = in_tensor->storage_type();
rows = in_tensor->shape().dimension(0);
columns = in_tensor->shape().dimension(1);
in_channels = in_tensor->shape().dimension(2);
out_channels = in_channels;
}
if (in_memory_storage_type != gxf::MemoryStorageType::kDevice) {
GXF_LOG_ERROR("Tensor('%s') or VideoBuffer is not allocated on device.\n",
in_tensor_name_.get().c_str());
return GXF_MEMORY_INVALID_STORAGE_MODE;
}
if (in_dtype_ == FormatDType::kUnknown) {
in_primitive_type_ = in_primitive_type;
in_dtype_ = FormatDTypeFromPrimitiveType(in_primitive_type_);
format_conversion_type_ =
getFormatConversionType(normalizeFormatDType(in_dtype_), normalizeFormatDType(out_dtype_));
}
// Check if input tensor is consistent over all the frames
if (in_primitive_type != in_primitive_type_) {
GXF_LOG_ERROR("Input tensor element type is inconsistent over all the frames.\n");
return GXF_FAILURE;
}
// Check if the input/output tensor is compatible with the format conversion
if (format_conversion_type_ == FormatConversionType::kUnknown) {
GXF_LOG_ERROR("Unsupported format conversion: %s (%" PRIu32 ") -> %s\n",
in_dtype_str_.get().c_str(), static_cast<uint32_t>(in_dtype_),
out_dtype_str_.get().c_str());
return GXF_FAILURE;
}
// Check that if the format requires a specific number of channels they are consistent.
// Some formats (e.g. float32) are agnostic to channel count, while others (e.g. RGB888) have a
// specific channel count.
if (GXF_SUCCESS != verifyFormatDTypeChannels(in_dtype_, in_channels)) {
GXF_LOG_ERROR("Failed to verify the channels for the expected input dtype [%d]: %d.", in_dtype_,
in_channels);
return GXF_FAILURE;
}
// Resize the input image before converting data type
if (resize_width_ > 0 && resize_height_ > 0) {
auto resize_result = resizeImage(in_tensor_data, rows, columns, in_channels, in_primitive_type,
resize_width_, resize_height_);
if (!resize_result) {
GXF_LOG_ERROR("Failed to resize image.\n");
return resize_result.error();
}
// Update the tensor pointer and shape
out_shape = gxf::Shape{resize_height_, resize_width_, in_channels};
in_tensor_data = resize_result.value();
rows = resize_height_;
columns = resize_width_;
}
// Create output message
const uint32_t dst_typesize = gxf::PrimitiveTypeSize(out_primitive_type_);
// Adjust output shape if the conversion involves the change in the channel dimension
switch (format_conversion_type_) {
case FormatConversionType::kRGB888ToRGBA8888: {
out_channels = 4;
out_shape = gxf::Shape{out_shape.dimension(0), out_shape.dimension(1), out_channels};
break;
}
case FormatConversionType::kRGBA8888ToRGB888:
case FormatConversionType::kRGBA8888ToFloat32: {
out_channels = 3;
out_shape = gxf::Shape{out_shape.dimension(0), out_shape.dimension(1), out_channels};
break;
}
default:
break;
}
// Check that if the format requires a specific number of channels they are consistent.
// Some formats (e.g. float32) are agnostic to channel count, while others (e.g. RGB888) have a
// specific channel count.
if (GXF_SUCCESS != verifyFormatDTypeChannels(out_dtype_, out_channels)) {
GXF_LOG_ERROR("Failed to verify the channels for the expected output dtype [%d]: %d.",
out_dtype_, out_channels);
return GXF_FAILURE;
}
gxf::Expected<gxf::Entity> out_message = CreateTensorMap(
context(), pool_,
{{out_tensor_name_.get(), gxf::MemoryStorageType::kDevice, out_shape, out_primitive_type_, 0,
gxf::ComputeTrivialStrides(out_shape, dst_typesize)}});
if (!out_message) return out_message.error();
const auto out_tensor = out_message.value().get<gxf::Tensor>();
if (!out_tensor) { return out_tensor.error(); }
// Set tensor to constant using NPP
if (in_channels == 3 || in_channels == 4) {
gxf_result_t convert_result = convertTensorFormat(in_tensor_data, out_tensor.value()->pointer(),
rows, columns, in_channels, out_channels);
if (convert_result != GXF_SUCCESS) {
GXF_LOG_ERROR("Failed to convert tensor format (conversion type:%d)",
static_cast<int>(format_conversion_type_));
return GXF_FAILURE;
}
} else {
GXF_LOG_ERROR("Only support 3 or 4 channel input tensor");
return GXF_NOT_IMPLEMENTED;
}
const auto result = out_->publish(out_message.value());
// Publish output message
return gxf::ToResultCode(result);
}
gxf::Expected<void*> FormatConverter::resizeImage(const void* in_tensor_data, const int32_t rows,
const int32_t columns, const int16_t channels,
const gxf::PrimitiveType primitive_type,
const int32_t resize_width,
const int32_t resize_height) {
if (resize_buffer_.size() == 0) {
uint64_t buffer_size = resize_width * resize_height * channels;
resize_buffer_.resize(pool_, buffer_size, gxf::MemoryStorageType::kDevice);
}
const auto converted_tensor_ptr = resize_buffer_.pointer();
if (converted_tensor_ptr == nullptr) {
GXF_LOG_ERROR("Failed to allocate memory for the resizing image");
return gxf::ExpectedOrCode(GXF_FAILURE, nullptr);
}
// Resize image
NppStatus status = NPP_ERROR;
const NppiSize src_size = {static_cast<int>(columns), static_cast<int>(rows)};
const NppiRect src_roi = {0, 0, static_cast<int>(columns), static_cast<int>(rows)};
const NppiSize dst_size = {static_cast<int>(resize_width), static_cast<int>(resize_height)};
const NppiRect dst_roi = {0, 0, static_cast<int>(resize_width), static_cast<int>(resize_height)};
switch (channels) {
case 3:
switch (primitive_type) {
case gxf::PrimitiveType::kUnsigned8:
status = nppiResize_8u_C3R(static_cast<const Npp8u*>(in_tensor_data), columns * channels,
src_size, src_roi, converted_tensor_ptr,
resize_width * channels, dst_size, dst_roi, resize_mode_);
break;
default:
GXF_LOG_ERROR("Unsupported input primitive type for resizing image");
return gxf::ExpectedOrCode(GXF_FAILURE, nullptr);
}
break;
case 4:
switch (primitive_type) {
case gxf::PrimitiveType::kUnsigned8:
status = nppiResize_8u_C4R(static_cast<const Npp8u*>(in_tensor_data), columns * channels,
src_size, src_roi, converted_tensor_ptr,
resize_width * channels, dst_size, dst_roi, resize_mode_);
break;
default:
GXF_LOG_ERROR("Unsupported input primitive type for resizing image");
return gxf::ExpectedOrCode(GXF_FAILURE, nullptr);
}
break;
default:
GXF_LOG_ERROR("Unsupported input primitive type for resizing image (%d, %d)", channels,
static_cast<int32_t>(primitive_type));
return gxf::ExpectedOrCode(GXF_FAILURE, nullptr);
break;
}
if (status != NPP_SUCCESS) { return gxf::ExpectedOrCode(GXF_FAILURE, nullptr); }
return gxf::ExpectedOrCode(GXF_SUCCESS, converted_tensor_ptr);
}
gxf_result_t FormatConverter::convertTensorFormat(const void* in_tensor_data, void* out_tensor_data,
const int32_t rows, const int32_t columns,
const int16_t in_channels,
const int16_t out_channels) {
const uint32_t src_typesize = gxf::PrimitiveTypeSize(in_primitive_type_);
const uint32_t dst_typesize = gxf::PrimitiveTypeSize(out_primitive_type_);
const int32_t src_step = columns * in_channels * src_typesize;
const int32_t dst_step = columns * out_channels * dst_typesize;
const auto& out_channel_order = out_channel_order_.get();
NppStatus status = NPP_ERROR;
const NppiSize roi = {static_cast<int>(columns), static_cast<int>(rows)};
switch (format_conversion_type_) {
case FormatConversionType::kNone: {
const auto in_tensor_ptr = static_cast<const uint8_t*>(in_tensor_data);
auto out_tensor_ptr = static_cast<uint8_t*>(out_tensor_data);
cudaError_t cuda_status = CUDA_TRY(
cudaMemcpy(out_tensor_ptr, in_tensor_ptr, src_step * rows, cudaMemcpyDeviceToDevice));
if (cuda_status) {
GXF_LOG_ERROR("Failed to copy GPU data to GPU memory.");
return GXF_FAILURE;
}
status = NPP_SUCCESS;
break;
}
case FormatConversionType::kUnsigned8ToFloat32: {
const auto in_tensor_ptr = static_cast<const uint8_t*>(in_tensor_data);
const auto out_tensor_ptr = static_cast<float*>(out_tensor_data);
status = nppiScale_8u32f_C3R(in_tensor_ptr, src_step, out_tensor_ptr, dst_step, roi,
scale_min_, scale_max_);
break;
}
case FormatConversionType::kFloat32ToUnsigned8: {
const auto in_tensor_ptr = static_cast<const float*>(in_tensor_data);
const auto out_tensor_ptr = static_cast<uint8_t*>(out_tensor_data);
status = nppiScale_32f8u_C3R(in_tensor_ptr, src_step, out_tensor_ptr, dst_step, roi,
scale_min_, scale_max_);
break;
}
case FormatConversionType::kRGB888ToRGBA8888: {
const auto in_tensor_ptr = static_cast<const uint8_t*>(in_tensor_data);
const auto out_tensor_ptr = static_cast<uint8_t*>(out_tensor_data);
// Convert RGB888 to RGBA8888 (3 channels -> 4 channels, uint8_t)
int dst_order[4]{0, 1, 2, 3};
if (!out_channel_order.empty()) {
if (out_channel_order.size() != 4) {
GXF_LOG_ERROR("Invalid channel order for RGBA8888");
return GXF_FAILURE;
}
for (int i = 0; i < 4; i++) { dst_order[i] = out_channel_order[i]; }
}
status = nppiSwapChannels_8u_C3C4R(in_tensor_ptr, src_step, out_tensor_ptr,
out_channels * dst_typesize * columns, roi, dst_order,
alpha_value_.get());
break;
}
case FormatConversionType::kRGBA8888ToRGB888: {
const auto in_tensor_ptr = static_cast<const uint8_t*>(in_tensor_data);
const auto out_tensor_ptr = static_cast<uint8_t*>(out_tensor_data);
// Convert RGBA8888 to RGB888 (4 channels -> 3 channels, uint8_t)
int dst_order[3]{0, 1, 2};
if (!out_channel_order.empty()) {
if (out_channel_order.size() != 3) {
GXF_LOG_ERROR("Invalid channel order for RGB888");
return GXF_FAILURE;
}
for (int i = 0; i < 3; i++) { dst_order[i] = out_channel_order[i]; }
}
status = nppiSwapChannels_8u_C4C3R(in_tensor_ptr, src_step, out_tensor_ptr,
out_channels * dst_typesize * columns, roi, dst_order);
break;
}
case FormatConversionType::kRGBA8888ToFloat32: {
const auto in_tensor_ptr = static_cast<const uint8_t*>(in_tensor_data);
const auto out_tensor_ptr = static_cast<float*>(out_tensor_data);
if (channel_buffer_.size() == 0) {
uint64_t buffer_size = rows * columns * 3; // 4 channels -> 3 channels
channel_buffer_.resize(pool_, buffer_size, gxf::MemoryStorageType::kDevice);
}
const auto converted_tensor_ptr = channel_buffer_.pointer();
if (converted_tensor_ptr == nullptr) {
GXF_LOG_ERROR("Failed to allocate memory for the channel conversion");
return GXF_FAILURE;
}
int dst_order[3]{0, 1, 2};
if (!out_channel_order.empty()) {
if (out_channel_order.size() != 3) {
GXF_LOG_ERROR("Invalid channel order for RGB888");
return GXF_FAILURE;
}
for (int i = 0; i < 3; i++) { dst_order[i] = out_channel_order[i]; }
}
status = nppiSwapChannels_8u_C4C3R(in_tensor_ptr, src_step, converted_tensor_ptr,
out_channels * src_typesize * columns, roi, dst_order);
if (status == NPP_SUCCESS) {
const int32_t new_src_step = columns * out_channels * src_typesize;
status = nppiScale_8u32f_C3R(converted_tensor_ptr, new_src_step, out_tensor_ptr, dst_step,
roi, scale_min_, scale_max_);
} else {
GXF_LOG_ERROR("Failed to convert channel order (NPP error code: %d)", status);
return GXF_FAILURE;
}
break;
}
default:
GXF_LOG_ERROR("Unsupported format conversion: %s (%" PRIu32 ") -> %s\n",
in_dtype_str_.get().c_str(), static_cast<uint32_t>(in_dtype_),
out_dtype_str_.get().c_str());
}
// Reorder channels in the output tensor (inplace) if needed.
switch (format_conversion_type_) {
case FormatConversionType::kNone:
case FormatConversionType::kUnsigned8ToFloat32:
case FormatConversionType::kFloat32ToUnsigned8: {
if (!out_channel_order.empty()) {
switch (out_channels) {
case 3: {
int dst_order[3]{0, 1, 2};
if (out_channel_order.size() != 3) {
GXF_LOG_ERROR("Invalid channel order for %s", out_dtype_str_.get().c_str());
return GXF_FAILURE;
}
for (int i = 0; i < 3; i++) { dst_order[i] = out_channel_order[i]; }
switch (out_primitive_type_) {
case gxf::PrimitiveType::kUnsigned8: {
auto out_tensor_ptr = static_cast<uint8_t*>(out_tensor_data);
status = nppiSwapChannels_8u_C3IR(out_tensor_ptr, dst_step, roi, dst_order);
break;
}
case gxf::PrimitiveType::kFloat32: {
auto out_tensor_ptr = static_cast<float*>(out_tensor_data);
status = nppiSwapChannels_32f_C3IR(out_tensor_ptr, dst_step, roi, dst_order);
break;
}
default:
GXF_LOG_ERROR("Unsupported output data type for reordering channels: %s",
out_dtype_str_.get().c_str());
}
break;
}
case 4: {
int dst_order[4]{0, 1, 2, 3};
if (out_channel_order.size() != 4) {
GXF_LOG_ERROR("Invalid channel order for %s", out_dtype_str_.get().c_str());
return GXF_FAILURE;
}
for (int i = 0; i < 4; i++) { dst_order[i] = out_channel_order[i]; }
switch (out_primitive_type_) {
case gxf::PrimitiveType::kUnsigned8: {
auto out_tensor_ptr = static_cast<uint8_t*>(out_tensor_data);
status = nppiSwapChannels_8u_C4IR(out_tensor_ptr, dst_step, roi, dst_order);
break;
}
case gxf::PrimitiveType::kFloat32: {
auto out_tensor_ptr = static_cast<float*>(out_tensor_data);
status = nppiSwapChannels_32f_C4IR(out_tensor_ptr, dst_step, roi, dst_order);
break;
}
default:
GXF_LOG_ERROR("Unsupported output data type for reordering channels: %s\n",
out_dtype_str_.get().c_str());
}
break;
}
}
if (status != NPP_SUCCESS) {
GXF_LOG_ERROR("Failed to convert channel order");
return GXF_FAILURE;
}
}
}
default:
break;
}
if (status != NPP_SUCCESS) { return GXF_FAILURE; }
return GXF_SUCCESS;
}
gxf_result_t FormatConverter::registerInterface(gxf::Registrar* registrar) {
gxf::Expected<void> result;
result &= registrar->parameter(in_, "in", "Input", "Input channel.");
result &= registrar->parameter(in_tensor_name_, "in_tensor_name", "InputTensorName",
"Name of the input tensor.", std::string(""));
result &= registrar->parameter(in_dtype_str_, "in_dtype", "InputDataType", "Source data type.",
std::string(""));
result &= registrar->parameter(out_, "out", "Output", "Output channel.");
result &= registrar->parameter(out_tensor_name_, "out_tensor_name", "OutputTensorName",
"Name of the output tensor.", std::string(""));
result &=
registrar->parameter(out_dtype_str_, "out_dtype", "OutputDataType", "Destination data type.");
result &= registrar->parameter(scale_min_, "scale_min", "Scale min",
"Minimum value of the scale.", 0.f);
result &= registrar->parameter(scale_max_, "scale_max", "Scale max",
"Maximum value of the scale.", 1.f);
result &= registrar->parameter(alpha_value_, "alpha_value", "Alpha value",
"Alpha value that can be used to fill the alpha channel when "
"converting RGB888 to RGBA8888.",
static_cast<uint8_t>(255));
result &= registrar->parameter(resize_width_, "resize_width", "Resize width",
"Width for resize. No actions if this value is zero.", 0);
result &= registrar->parameter(resize_height_, "resize_height", "Resize height",
"Height for resize. No actions if this value is zero.", 0);
result &= registrar->parameter(resize_mode_, "resize_mode", "Resize mode",
"Mode for resize. 4 (NPPI_INTER_CUBIC) if this value is zero.", 0);
result &= registrar->parameter(
out_channel_order_, "out_channel_order", "Output channel order",
"Host memory integer array describing how channel values are permutated.",
std::vector<int>{});
result &= registrar->parameter(pool_, "pool", "Pool", "Pool to allocate the output message.");
return gxf::ToResultCode(result);
}
} // namespace nvidia::holoscan::formatconverter