Jetson Inference
DNN Vision Library

Mono depth estimation from monocular images, using TensorRT. More...

#include <depthNet.h>

Inheritance diagram for depthNet:
tensorNet

Public Types

enum  NetworkType { CUSTOM, FCN_MOBILENET, FCN_RESNET18, FCN_RESNET50 }
 Network choice enumeration. More...
 
enum  VisualizationFlags { VISUALIZE_INPUT = (1 << 0), VISUALIZE_DEPTH = (1 << 1) }
 Visualization flags. More...
 

Public Member Functions

virtual ~depthNet ()
 Destroy. More...
 
template<typename T >
bool Process (T *image, uint32_t width, uint32_t height)
 Compute the depth field from a monocular RGB/RGBA image. More...
 
bool Process (void *input, uint32_t width, uint32_t height, imageFormat format)
 Compute the depth field from a monocular RGB/RGBA image. More...
 
template<typename T1 , typename T2 >
bool Process (T1 *input, T2 *output, uint32_t width, uint32_t height, cudaColormapType colormap=COLORMAP_VIRIDIS_INVERTED, cudaFilterMode filter=FILTER_LINEAR)
 Process an RGB/RGBA image and map the depth image with the specified colormap. More...
 
bool Process (void *input, imageFormat input_format, void *output, imageFormat output_format, uint32_t width, uint32_t height, cudaColormapType colormap=COLORMAP_VIRIDIS_INVERTED, cudaFilterMode filter=FILTER_LINEAR)
 Process an RGB/RGBA image and map the depth image with the specified colormap. More...
 
template<typename T1 , typename T2 >
bool Process (T1 *input, uint32_t input_width, uint32_t input_height, T2 *output, uint32_t output_width, uint32_t output_height, cudaColormapType colormap=COLORMAP_DEFAULT, cudaFilterMode filter=FILTER_LINEAR)
 Process an RGB/RGBA image and map the depth image with the specified colormap. More...
 
bool Process (void *input, uint32_t input_width, uint32_t input_height, imageFormat input_format, void *output, uint32_t output_width, uint32_t output_height, imageFormat output_format, cudaColormapType colormap=COLORMAP_DEFAULT, cudaFilterMode filter=FILTER_LINEAR)
 Process an RGB/RGBA image and map the depth image with the specified colormap. More...
 
template<typename T >
bool Visualize (T *output, uint32_t width, uint32_t height, cudaColormapType colormap=COLORMAP_DEFAULT, cudaFilterMode filter=FILTER_LINEAR)
 Visualize the raw depth field into a colorized RGB/RGBA depth map. More...
 
bool Visualize (void *output, uint32_t width, uint32_t height, imageFormat format, cudaColormapType colormap=COLORMAP_DEFAULT, cudaFilterMode filter=FILTER_LINEAR)
 Visualize the raw depth field into a colorized RGB/RGBA depth map. More...
 
float * GetDepthField () const
 Return the raw depth field. More...
 
uint32_t GetDepthFieldWidth () const
 Return the width of the depth field. More...
 
uint32_t GetDepthFieldHeight () const
 Return the height of the depth field. More...
 
NetworkType GetNetworkType () const
 Retrieve the network type (alexnet or googlenet) More...
 
const char * GetNetworkName () const
 Retrieve a string describing the network name. More...
 
bool SavePointCloud (const char *filename)
 Extract and save the point cloud to a PCD file (depth only). More...
 
bool SavePointCloud (const char *filename, float *rgba, uint32_t width, uint32_t height)
 Extract and save the point cloud to a PCD file (depth + RGB). More...
 
bool SavePointCloud (const char *filename, float *rgba, uint32_t width, uint32_t height, const float2 &focalLength, const float2 &principalPoint)
 Extract and save the point cloud to a PCD file (depth + RGB). More...
 
bool SavePointCloud (const char *filename, float *rgba, uint32_t width, uint32_t height, const float intrinsicCalibration[3][3])
 Extract and save the point cloud to a PCD file (depth + RGB). More...
 
bool SavePointCloud (const char *filename, float *rgba, uint32_t width, uint32_t height, const char *intrinsicCalibrationPath)
 Extract and save the point cloud to a PCD file (depth + RGB). More...
 
- Public Member Functions inherited from tensorNet
virtual ~tensorNet ()
 Destory. More...
 
bool LoadNetwork (const char *prototxt, const char *model, const char *mean=NULL, const char *input_blob="data", const char *output_blob="prob", uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)
 Load a new network instance. More...
 
bool LoadNetwork (const char *prototxt, const char *model, const char *mean, const char *input_blob, const std::vector< std::string > &output_blobs, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)
 Load a new network instance with multiple output layers. More...
 
bool LoadNetwork (const char *prototxt, const char *model, const char *mean, const std::vector< std::string > &input_blobs, const std::vector< std::string > &output_blobs, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)
 Load a new network instance with multiple input layers. More...
 
bool LoadNetwork (const char *prototxt, const char *model, const char *mean, const char *input_blob, const Dims3 &input_dims, const std::vector< std::string > &output_blobs, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)
 Load a new network instance (this variant is used for UFF models) More...
 
bool LoadNetwork (const char *prototxt, const char *model, const char *mean, const std::vector< std::string > &input_blobs, const std::vector< Dims3 > &input_dims, const std::vector< std::string > &output_blobs, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)
 Load a new network instance with multiple input layers (used for UFF models) More...
 
bool LoadEngine (const char *engine_filename, const std::vector< std::string > &input_blobs, const std::vector< std::string > &output_blobs, nvinfer1::IPluginFactory *pluginFactory=NULL, deviceType device=DEVICE_GPU, cudaStream_t stream=NULL)
 Load a network instance from a serialized engine plan file. More...
 
bool LoadEngine (char *engine_stream, size_t engine_size, const std::vector< std::string > &input_blobs, const std::vector< std::string > &output_blobs, nvinfer1::IPluginFactory *pluginFactory=NULL, deviceType device=DEVICE_GPU, cudaStream_t stream=NULL)
 Load a network instance from a serialized engine plan file. More...
 
bool LoadEngine (nvinfer1::ICudaEngine *engine, const std::vector< std::string > &input_blobs, const std::vector< std::string > &output_blobs, deviceType device=DEVICE_GPU, cudaStream_t stream=NULL)
 Load network resources from an existing TensorRT engine instance. More...
 
bool LoadEngine (const char *filename, char **stream, size_t *size)
 Load a serialized engine plan file into memory. More...
 
void EnableLayerProfiler ()
 Manually enable layer profiling times. More...
 
void EnableDebug ()
 Manually enable debug messages and synchronization. More...
 
bool AllowGPUFallback () const
 Return true if GPU fallback is enabled. More...
 
deviceType GetDevice () const
 Retrieve the device being used for execution. More...
 
precisionType GetPrecision () const
 Retrieve the type of precision being used. More...
 
bool IsPrecision (precisionType type) const
 Check if a particular precision is being used. More...
 
cudaStream_t GetStream () const
 Retrieve the stream that the device is operating on. More...
 
cudaStream_t CreateStream (bool nonBlocking=true)
 Create and use a new stream for execution. More...
 
void SetStream (cudaStream_t stream)
 Set the stream that the device is operating on. More...
 
const char * GetPrototxtPath () const
 Retrieve the path to the network prototxt file. More...
 
const char * GetModelPath () const
 Retrieve the path to the network model file. More...
 
modelType GetModelType () const
 Retrieve the format of the network model. More...
 
bool IsModelType (modelType type) const
 Return true if the model is of the specified format. More...
 
uint32_t GetInputLayers () const
 Retrieve the number of input layers to the network. More...
 
uint32_t GetOutputLayers () const
 Retrieve the number of output layers to the network. More...
 
Dims3 GetInputDims (uint32_t layer=0) const
 Retrieve the dimensions of network input layer. More...
 
uint32_t GetInputWidth (uint32_t layer=0) const
 Retrieve the width of network input layer. More...
 
uint32_t GetInputHeight (uint32_t layer=0) const
 Retrieve the height of network input layer. More...
 
uint32_t GetInputSize (uint32_t layer=0) const
 Retrieve the size (in bytes) of network input layer. More...
 
Dims3 GetOutputDims (uint32_t layer=0) const
 Retrieve the dimensions of network output layer. More...
 
uint32_t GetOutputWidth (uint32_t layer=0) const
 Retrieve the width of network output layer. More...
 
uint32_t GetOutputHeight (uint32_t layer=0) const
 Retrieve the height of network output layer. More...
 
uint32_t GetOutputSize (uint32_t layer=0) const
 Retrieve the size (in bytes) of network output layer. More...
 
float GetNetworkFPS ()
 Retrieve the network frames per second (FPS). More...
 
float GetNetworkTime ()
 Retrieve the network runtime (in milliseconds). More...
 
float2 GetProfilerTime (profilerQuery query)
 Retrieve the profiler runtime (in milliseconds). More...
 
float GetProfilerTime (profilerQuery query, profilerDevice device)
 Retrieve the profiler runtime (in milliseconds). More...
 
void PrintProfilerTimes ()
 Print the profiler times (in millseconds). More...
 

Static Public Member Functions

static uint32_t VisualizationFlagsFromStr (const char *str, uint32_t default_value=VISUALIZE_INPUT|VISUALIZE_DEPTH)
 Parse a string of one of more VisualizationMode values. More...
 
static NetworkType NetworkTypeFromStr (const char *model_name)
 Parse a string to one of the built-in pretrained models. More...
 
static const char * NetworkTypeToStr (NetworkType network)
 Convert a NetworkType enum to a string. More...
 
static depthNetCreate (NetworkType networkType=FCN_MOBILENET, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true)
 Load a new network instance. More...
 
static depthNetCreate (const char *model_path, const char *input=DEPTHNET_DEFAULT_INPUT, const char *output=DEPTHNET_DEFAULT_OUTPUT, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true)
 Load a new network instance. More...
 
static depthNetCreate (const char *model_path, const char *input, const Dims3 &inputDims, const char *output, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true)
 Load a custom network instance of a UFF model. More...
 
static depthNetCreate (int argc, char **argv)
 Load a new network instance by parsing the command line. More...
 
static depthNetCreate (const commandLine &cmdLine)
 Load a new network instance by parsing the command line. More...
 
static const char * Usage ()
 Usage string for command line arguments to Create() More...
 
- Static Public Member Functions inherited from tensorNet
static precisionType SelectPrecision (precisionType precision, deviceType device=DEVICE_GPU, bool allowInt8=true)
 Resolve a desired precision to a specific one that's available. More...
 
static precisionType FindFastestPrecision (deviceType device=DEVICE_GPU, bool allowInt8=true)
 Determine the fastest native precision on a device. More...
 
static std::vector< precisionTypeDetectNativePrecisions (deviceType device=DEVICE_GPU)
 Detect the precisions supported natively on a device. More...
 
static bool DetectNativePrecision (const std::vector< precisionType > &nativeTypes, precisionType type)
 Detect if a particular precision is supported natively. More...
 
static bool DetectNativePrecision (precisionType precision, deviceType device=DEVICE_GPU)
 Detect if a particular precision is supported natively. More...
 

Protected Member Functions

 depthNet ()
 
bool allocHistogramBuffers ()
 
bool histogramEqualization ()
 
bool histogramEqualizationCUDA ()
 
- Protected Member Functions inherited from tensorNet
 tensorNet ()
 Constructor. More...
 
bool ProcessNetwork (bool sync=true)
 Execute processing of the network. More...
 
bool ProfileModel (const std::string &deployFile, const std::string &modelFile, const std::vector< std::string > &inputs, const std::vector< Dims3 > &inputDims, const std::vector< std::string > &outputs, uint32_t maxBatchSize, precisionType precision, deviceType device, bool allowGPUFallback, nvinfer1::IInt8Calibrator *calibrator, char **engineStream, size_t *engineSize)
 Create and output an optimized network model. More...
 
bool ConfigureBuilder (nvinfer1::IBuilder *builder, uint32_t maxBatchSize, uint32_t workspaceSize, precisionType precision, deviceType device, bool allowGPUFallback, nvinfer1::IInt8Calibrator *calibrator)
 Configure builder options. More...
 
void PROFILER_BEGIN (profilerQuery query)
 Begin a profiling query, before network is run. More...
 
void PROFILER_END (profilerQuery query)
 End a profiling query, after the network is run. More...
 
bool PROFILER_QUERY (profilerQuery query)
 Query the CUDA part of a profiler query. More...
 

Protected Attributes

NetworkType mNetworkType
 
int2 * mDepthRange
 
float * mDepthEqualized
 
uint32_t * mHistogram
 
float * mHistogramPDF
 
float * mHistogramCDF
 
uint32_t * mHistogramEDU
 
- Protected Attributes inherited from tensorNet
tensorNet::Logger gLogger
 
tensorNet::Profiler gProfiler
 
std::string mPrototxtPath
 
std::string mModelPath
 
std::string mMeanPath
 
std::string mCacheEnginePath
 
std::string mCacheCalibrationPath
 
deviceType mDevice
 
precisionType mPrecision
 
modelType mModelType
 
cudaStream_t mStream
 
cudaEvent_t mEventsGPU [PROFILER_TOTAL *2]
 
timespec mEventsCPU [PROFILER_TOTAL *2]
 
nvinfer1::IRuntime * mInfer
 
nvinfer1::ICudaEngine * mEngine
 
nvinfer1::IExecutionContext * mContext
 
float2 mProfilerTimes [PROFILER_TOTAL+1]
 
uint32_t mProfilerQueriesUsed
 
uint32_t mProfilerQueriesDone
 
uint32_t mWorkspaceSize
 
uint32_t mMaxBatchSize
 
bool mEnableProfiler
 
bool mEnableDebug
 
bool mAllowGPUFallback
 
void ** mBindings
 
std::vector< layerInfomInputs
 
std::vector< layerInfomOutputs
 

Detailed Description

Mono depth estimation from monocular images, using TensorRT.

Member Enumeration Documentation

◆ NetworkType

Network choice enumeration.

Enumerator
CUSTOM 

Custom model provided by the user.

FCN_MOBILENET 

MobileNet backbone.

FCN_RESNET18 

ResNet-18 backbone.

FCN_RESNET50 

ResNet-50 backbone.

◆ VisualizationFlags

Visualization flags.

Enumerator
VISUALIZE_INPUT 

Display the original input image.

VISUALIZE_DEPTH 

Display the colorized depth field.

Constructor & Destructor Documentation

◆ ~depthNet()

virtual depthNet::~depthNet ( )
virtual

Destroy.

◆ depthNet()

depthNet::depthNet ( )
protected

Member Function Documentation

◆ allocHistogramBuffers()

bool depthNet::allocHistogramBuffers ( )
protected

◆ Create() [1/5]

static depthNet* depthNet::Create ( NetworkType  networkType = FCN_MOBILENET,
uint32_t  maxBatchSize = DEFAULT_MAX_BATCH_SIZE,
precisionType  precision = TYPE_FASTEST,
deviceType  device = DEVICE_GPU,
bool  allowGPUFallback = true 
)
static

Load a new network instance.

◆ Create() [2/5]

static depthNet* depthNet::Create ( const char *  model_path,
const char *  input = DEPTHNET_DEFAULT_INPUT,
const char *  output = DEPTHNET_DEFAULT_OUTPUT,
uint32_t  maxBatchSize = DEFAULT_MAX_BATCH_SIZE,
precisionType  precision = TYPE_FASTEST,
deviceType  device = DEVICE_GPU,
bool  allowGPUFallback = true 
)
static

Load a new network instance.

Parameters
model_pathFile path to the caffemodel
mean_binaryFile path to the mean value binary proto (can be NULL)
class_labelsFile path to list of class name labels
inputName of the input layer blob.
outputName of the output layer blob.
maxBatchSizeThe maximum batch size that the network will support and be optimized for.

◆ Create() [3/5]

static depthNet* depthNet::Create ( const char *  model_path,
const char *  input,
const Dims3 inputDims,
const char *  output,
uint32_t  maxBatchSize = DEFAULT_MAX_BATCH_SIZE,
precisionType  precision = TYPE_FASTEST,
deviceType  device = DEVICE_GPU,
bool  allowGPUFallback = true 
)
static

Load a custom network instance of a UFF model.

Parameters
model_pathFile path to the UFF model
inputName of the input layer blob.
inputDimsDimensions of the input layer blob.
outputName of the output layer blob containing the bounding boxes, ect.
maxBatchSizeThe maximum batch size that the network will support and be optimized for.

◆ Create() [4/5]

static depthNet* depthNet::Create ( int  argc,
char **  argv 
)
static

Load a new network instance by parsing the command line.

◆ Create() [5/5]

static depthNet* depthNet::Create ( const commandLine cmdLine)
static

Load a new network instance by parsing the command line.

◆ GetDepthField()

float* depthNet::GetDepthField ( ) const
inline

Return the raw depth field.

◆ GetDepthFieldHeight()

uint32_t depthNet::GetDepthFieldHeight ( ) const
inline

Return the height of the depth field.

◆ GetDepthFieldWidth()

uint32_t depthNet::GetDepthFieldWidth ( ) const
inline

Return the width of the depth field.

◆ GetNetworkName()

const char* depthNet::GetNetworkName ( ) const
inline

Retrieve a string describing the network name.

◆ GetNetworkType()

NetworkType depthNet::GetNetworkType ( ) const
inline

Retrieve the network type (alexnet or googlenet)

◆ histogramEqualization()

bool depthNet::histogramEqualization ( )
protected

◆ histogramEqualizationCUDA()

bool depthNet::histogramEqualizationCUDA ( )
protected

◆ NetworkTypeFromStr()

static NetworkType depthNet::NetworkTypeFromStr ( const char *  model_name)
static

Parse a string to one of the built-in pretrained models.

Valid names are "mobilenet", "resnet-18", or "resnet-50", ect.

Returns
one of the depthNet::NetworkType enums, or depthNet::CUSTOM on invalid string.

◆ NetworkTypeToStr()

static const char* depthNet::NetworkTypeToStr ( NetworkType  network)
static

Convert a NetworkType enum to a string.

◆ Process() [1/6]

template<typename T >
bool depthNet::Process ( T *  image,
uint32_t  width,
uint32_t  height 
)
inline

Compute the depth field from a monocular RGB/RGBA image.

Note
the raw depth field can be retrieved with GetDepthField().

◆ Process() [2/6]

bool depthNet::Process ( void *  input,
uint32_t  width,
uint32_t  height,
imageFormat  format 
)

Compute the depth field from a monocular RGB/RGBA image.

Note
the raw depth field can be retrieved with GetDepthField().

◆ Process() [3/6]

template<typename T1 , typename T2 >
bool depthNet::Process ( T1 *  input,
T2 *  output,
uint32_t  width,
uint32_t  height,
cudaColormapType  colormap = COLORMAP_VIRIDIS_INVERTED,
cudaFilterMode  filter = FILTER_LINEAR 
)
inline

Process an RGB/RGBA image and map the depth image with the specified colormap.

Note
this function calls Process() followed by Visualize().

◆ Process() [4/6]

bool depthNet::Process ( void *  input,
imageFormat  input_format,
void *  output,
imageFormat  output_format,
uint32_t  width,
uint32_t  height,
cudaColormapType  colormap = COLORMAP_VIRIDIS_INVERTED,
cudaFilterMode  filter = FILTER_LINEAR 
)

Process an RGB/RGBA image and map the depth image with the specified colormap.

Note
this function calls Process() followed by Visualize().

◆ Process() [5/6]

template<typename T1 , typename T2 >
bool depthNet::Process ( T1 *  input,
uint32_t  input_width,
uint32_t  input_height,
T2 *  output,
uint32_t  output_width,
uint32_t  output_height,
cudaColormapType  colormap = COLORMAP_DEFAULT,
cudaFilterMode  filter = FILTER_LINEAR 
)
inline

Process an RGB/RGBA image and map the depth image with the specified colormap.

Note
this function calls Process() followed by Visualize().

◆ Process() [6/6]

bool depthNet::Process ( void *  input,
uint32_t  input_width,
uint32_t  input_height,
imageFormat  input_format,
void *  output,
uint32_t  output_width,
uint32_t  output_height,
imageFormat  output_format,
cudaColormapType  colormap = COLORMAP_DEFAULT,
cudaFilterMode  filter = FILTER_LINEAR 
)

Process an RGB/RGBA image and map the depth image with the specified colormap.

Note
this function calls Process() followed by Visualize().

◆ SavePointCloud() [1/5]

bool depthNet::SavePointCloud ( const char *  filename)

Extract and save the point cloud to a PCD file (depth only).

Note
SavePointCloud() should only be called after Process()

◆ SavePointCloud() [2/5]

bool depthNet::SavePointCloud ( const char *  filename,
float *  rgba,
uint32_t  width,
uint32_t  height 
)

Extract and save the point cloud to a PCD file (depth + RGB).

Note
SavePointCloud() should only be called after Process()

◆ SavePointCloud() [3/5]

bool depthNet::SavePointCloud ( const char *  filename,
float *  rgba,
uint32_t  width,
uint32_t  height,
const float2 &  focalLength,
const float2 &  principalPoint 
)

Extract and save the point cloud to a PCD file (depth + RGB).

Note
SavePointCloud() should only be called after Process()

◆ SavePointCloud() [4/5]

bool depthNet::SavePointCloud ( const char *  filename,
float *  rgba,
uint32_t  width,
uint32_t  height,
const float  intrinsicCalibration[3][3] 
)

Extract and save the point cloud to a PCD file (depth + RGB).

Note
SavePointCloud() should only be called after Process()

◆ SavePointCloud() [5/5]

bool depthNet::SavePointCloud ( const char *  filename,
float *  rgba,
uint32_t  width,
uint32_t  height,
const char *  intrinsicCalibrationPath 
)

Extract and save the point cloud to a PCD file (depth + RGB).

Note
SavePointCloud() should only be called after Process()

◆ Usage()

static const char* depthNet::Usage ( )
inlinestatic

Usage string for command line arguments to Create()

◆ VisualizationFlagsFromStr()

static uint32_t depthNet::VisualizationFlagsFromStr ( const char *  str,
uint32_t  default_value = VISUALIZE_INPUT|VISUALIZE_DEPTH 
)
static

Parse a string of one of more VisualizationMode values.

Valid strings are "depth" "input" "input|depth" "input,depth" ect.

◆ Visualize() [1/2]

template<typename T >
bool depthNet::Visualize ( T *  output,
uint32_t  width,
uint32_t  height,
cudaColormapType  colormap = COLORMAP_DEFAULT,
cudaFilterMode  filter = FILTER_LINEAR 
)
inline

Visualize the raw depth field into a colorized RGB/RGBA depth map.

Note
Visualize() should only be called after Process()

◆ Visualize() [2/2]

bool depthNet::Visualize ( void *  output,
uint32_t  width,
uint32_t  height,
imageFormat  format,
cudaColormapType  colormap = COLORMAP_DEFAULT,
cudaFilterMode  filter = FILTER_LINEAR 
)

Visualize the raw depth field into a colorized RGB/RGBA depth map.

Note
Visualize() should only be called after Process()

Member Data Documentation

◆ mDepthEqualized

float* depthNet::mDepthEqualized
protected

◆ mDepthRange

int2* depthNet::mDepthRange
protected

◆ mHistogram

uint32_t* depthNet::mHistogram
protected

◆ mHistogramCDF

float* depthNet::mHistogramCDF
protected

◆ mHistogramEDU

uint32_t* depthNet::mHistogramEDU
protected

◆ mHistogramPDF

float* depthNet::mHistogramPDF
protected

◆ mNetworkType

NetworkType depthNet::mNetworkType
protected

The documentation for this class was generated from the following file: