Jetson Inference
DNN Vision Library

Image segmentation with FCN-Alexnet or custom models, using TensorRT. More...

#include <segNet.h>

Inheritance diagram for segNet:
tensorNet

Public Types

enum  NetworkType {
  FCN_RESNET18_CITYSCAPES_512x256, FCN_RESNET18_CITYSCAPES_1024x512, FCN_RESNET18_CITYSCAPES_2048x1024, FCN_RESNET18_DEEPSCENE_576x320,
  FCN_RESNET18_DEEPSCENE_864x480, FCN_RESNET18_MHP_512x320, FCN_RESNET18_MHP_640x360, FCN_RESNET18_VOC_320x320,
  FCN_RESNET18_VOC_512x320, FCN_RESNET18_SUNRGB_512x400, FCN_RESNET18_SUNRGB_640x512, FCN_ALEXNET_PASCAL_VOC,
  FCN_ALEXNET_SYNTHIA_CVPR16, FCN_ALEXNET_SYNTHIA_SUMMER_HD, FCN_ALEXNET_SYNTHIA_SUMMER_SD, FCN_ALEXNET_CITYSCAPES_HD,
  FCN_ALEXNET_CITYSCAPES_SD, FCN_ALEXNET_AERIAL_FPV_720p, SEGNET_CUSTOM
}
 Enumeration of pretrained/built-in network models. More...
 
enum  FilterMode { FILTER_POINT = 0, FILTER_LINEAR }
 Enumeration of mask/overlay filtering modes. More...
 
enum  VisualizationFlags { VISUALIZE_OVERLAY = (1 << 0), VISUALIZE_MASK = (1 << 1) }
 Visualization flags. More...
 

Public Member Functions

virtual ~segNet ()
 Destroy. More...
 
template<typename T >
bool Process (T *input, uint32_t width, uint32_t height, const char *ignore_class="void")
 Perform the initial inferencing processing portion of the segmentation. More...
 
bool Process (void *input, uint32_t width, uint32_t height, imageFormat format, const char *ignore_class="void")
 Perform the initial inferencing processing portion of the segmentation. More...
 
bool Process (float *input, uint32_t width, uint32_t height, const char *ignore_class="void")
 Perform the initial inferencing processing portion of the segmentation. More...
 
template<typename T >
bool Mask (T *output, uint32_t width, uint32_t height, FilterMode filter=FILTER_LINEAR)
 Produce a colorized segmentation mask. More...
 
bool Mask (void *output, uint32_t width, uint32_t height, imageFormat format, FilterMode filter=FILTER_LINEAR)
 Produce a colorized segmentation mask. More...
 
bool Mask (float *output, uint32_t width, uint32_t height, FilterMode filter=FILTER_LINEAR)
 Produce a colorized RGBA segmentation mask. More...
 
bool Mask (uint8_t *output, uint32_t width, uint32_t height)
 Produce a grayscale binary segmentation mask, where the pixel values correspond to the class ID of the corresponding class type. More...
 
template<typename T >
bool Overlay (T *output, uint32_t width, uint32_t height, FilterMode filter=FILTER_LINEAR)
 Produce the segmentation overlay alpha blended on top of the original image. More...
 
bool Overlay (void *output, uint32_t width, uint32_t height, imageFormat format, FilterMode filter=FILTER_LINEAR)
 Produce the segmentation overlay alpha blended on top of the original image. More...
 
bool Overlay (float *output, uint32_t width, uint32_t height, FilterMode filter=FILTER_LINEAR)
 Produce the segmentation overlay alpha blended on top of the original image. More...
 
int FindClassID (const char *label_name)
 Find the ID of a particular class (by label name). More...
 
uint32_t GetNumClasses () const
 Retrieve the number of object classes supported in the detector. More...
 
const char * GetClassDesc (uint32_t id) const
 Retrieve the description of a particular class. More...
 
float * GetClassColor (uint32_t id) const
 Retrieve the RGBA visualization color a particular class. More...
 
void SetClassColor (uint32_t classIndex, float r, float g, float b, float a=255.0f)
 Set the visualization color of a particular class of object. More...
 
void SetOverlayAlpha (float alpha, bool explicit_exempt=true)
 Set overlay alpha blending value for all classes (between 0-255), (optionally except for those that have been explicitly set). More...
 
const char * GetClassPath () const
 Retrieve the path to the file containing the class label descriptions. More...
 
uint32_t GetGridWidth () const
 Retrieve the number of columns in the classification grid. More...
 
uint32_t GetGridHeight () const
 Retrieve the number of rows in the classification grid. More...
 
NetworkType GetNetworkType () const
 Retrieve the network type (alexnet or googlenet) More...
 
const char * GetNetworkName () const
 Retrieve a string describing the network name. 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_OVERLAY)
 Parse a string of one of more VisualizationMode values. More...
 
static FilterMode FilterModeFromStr (const char *str, FilterMode default_value=FILTER_LINEAR)
 Parse a string from one of the FilterMode values. More...
 
static NetworkType NetworkTypeFromStr (const char *model_name)
 Parse a string from one of the built-in pretrained models. More...
 
static const char * NetworkTypeToStr (NetworkType networkType)
 Convert a NetworkType enum to a human-readable string. More...
 
static segNetCreate (NetworkType networkType=FCN_ALEXNET_CITYSCAPES_SD, 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 segNetCreate (const char *prototxt_path, const char *model_path, const char *class_labels, const char *class_colors=NULL, const char *input=SEGNET_DEFAULT_INPUT, const char *output=SEGNET_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 segNetCreate (int argc, char **argv)
 Load a new network instance by parsing the command line. More...
 
static segNetCreate (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

 segNet ()
 
bool classify (const char *ignore_class)
 
bool overlayPoint (void *input, uint32_t in_width, uint32_t in_height, imageFormat in_format, void *output, uint32_t out_width, uint32_t out_height, imageFormat out_format, bool mask_only)
 
bool overlayLinear (void *input, uint32_t in_width, uint32_t in_height, imageFormat in_format, void *output, uint32_t out_width, uint32_t out_height, imageFormat out_format, bool mask_only)
 
bool loadClassColors (const char *filename)
 
bool loadClassLabels (const char *filename)
 
bool saveClassLegend (const char *filename)
 
- 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

std::vector< std::string > mClassLabels
 
std::string mClassPath
 
bool * mColorsAlphaSet
 true if class color had been explicitly set from file or user More...
 
float * mClassColors
 array of overlay colors in shared CPU/GPU memory More...
 
uint8_t * mClassMap
 runtime buffer for the argmax-classified class index of each tile More...
 
void * mLastInputImg
 last input image to be processed, stored for overlay More...
 
uint32_t mLastInputWidth
 width in pixels of last input image to be processed More...
 
uint32_t mLastInputHeight
 height in pixels of last input image to be processed More...
 
imageFormat mLastInputFormat
 pixel format of last input image More...
 
NetworkType mNetworkType
 Pretrained built-in model type enumeration. More...
 
- 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

Image segmentation with FCN-Alexnet or custom models, using TensorRT.

Member Enumeration Documentation

◆ FilterMode

Enumeration of mask/overlay filtering modes.

Enumerator
FILTER_POINT 

Nearest point sampling.

FILTER_LINEAR 

Bilinear filtering.

◆ NetworkType

Enumeration of pretrained/built-in network models.

Enumerator
FCN_RESNET18_CITYSCAPES_512x256 

FCN-ResNet18 trained on Cityscapes dataset (512x256)

FCN_RESNET18_CITYSCAPES_1024x512 

FCN-ResNet18 trained on Cityscapes dataset (1024x512)

FCN_RESNET18_CITYSCAPES_2048x1024 

FCN-ResNet18 trained on Cityscapes dataset (2048x1024)

FCN_RESNET18_DEEPSCENE_576x320 

FCN-ResNet18 trained on DeepScene Forest dataset (576x320)

FCN_RESNET18_DEEPSCENE_864x480 

FCN-ResNet18 trained on DeepScene Forest dataset (864x480)

FCN_RESNET18_MHP_512x320 

FCN-ResNet18 trained on Multi-Human Parsing dataset (512x320)

FCN_RESNET18_MHP_640x360 

FCN-ResNet18 trained on Multi-Human Parsing dataset (640x360)

FCN_RESNET18_VOC_320x320 

FCN-ResNet18 trained on Pascal VOC dataset (320x320)

FCN_RESNET18_VOC_512x320 

FCN-ResNet18 trained on Pascal VOC dataset (512x320)

FCN_RESNET18_SUNRGB_512x400 

FCN-ResNet18 trained on SUN RGB-D dataset (512x400)

FCN_RESNET18_SUNRGB_640x512 

FCN-ResNet18 trained on SUN RGB-D dataset (640x512)

FCN_ALEXNET_PASCAL_VOC 

FCN-Alexnet trained on Pascal VOC dataset.

FCN_ALEXNET_SYNTHIA_CVPR16 

FCN-Alexnet trained on SYNTHIA CVPR16 dataset.

Note
To save disk space, this model isn't downloaded by default. Enable it in CMakePreBuild.sh
FCN_ALEXNET_SYNTHIA_SUMMER_HD 

FCN-Alexnet trained on SYNTHIA SEQS summer datasets.

Note
To save disk space, this model isn't downloaded by default. Enable it in CMakePreBuild.sh
FCN_ALEXNET_SYNTHIA_SUMMER_SD 

FCN-Alexnet trained on SYNTHIA SEQS summer datasets.

Note
To save disk space, this model isn't downloaded by default. Enable it in CMakePreBuild.sh
FCN_ALEXNET_CITYSCAPES_HD 

FCN-Alexnet trained on Cityscapes dataset with 21 classes.

FCN_ALEXNET_CITYSCAPES_SD 

FCN-Alexnet trained on Cityscapes dataset with 21 classes.

Note
To save disk space, this model isn't downloaded by default. Enable it in CMakePreBuild.sh
FCN_ALEXNET_AERIAL_FPV_720p 

FCN-Alexnet trained on aerial first-person view of the horizon line for drones, 1280x720 and 21 output classes.

SEGNET_CUSTOM 

◆ VisualizationFlags

Visualization flags.

Enumerator
VISUALIZE_OVERLAY 
VISUALIZE_MASK 

Constructor & Destructor Documentation

◆ ~segNet()

virtual segNet::~segNet ( )
virtual

Destroy.

◆ segNet()

segNet::segNet ( )
protected

Member Function Documentation

◆ classify()

bool segNet::classify ( const char *  ignore_class)
protected

◆ Create() [1/4]

static segNet* segNet::Create ( NetworkType  networkType = FCN_ALEXNET_CITYSCAPES_SD,
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() [2/4]

static segNet* segNet::Create ( const char *  prototxt_path,
const char *  model_path,
const char *  class_labels,
const char *  class_colors = NULL,
const char *  input = SEGNET_DEFAULT_INPUT,
const char *  output = SEGNET_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
prototxt_pathFile path to the deployable network prototxt
model_pathFile path to the caffemodel
class_labelsFile path to list of class name labels
class_colorsFile path to list of class colors
inputName of the input layer blob.
See also
SEGNET_DEFAULT_INPUT
Parameters
outputName of the output layer blob.
See also
SEGNET_DEFAULT_OUTPUT
Parameters
maxBatchSizeThe maximum batch size that the network will support and be optimized for.

◆ Create() [3/4]

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

Load a new network instance by parsing the command line.

◆ Create() [4/4]

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

Load a new network instance by parsing the command line.

◆ FilterModeFromStr()

static FilterMode segNet::FilterModeFromStr ( const char *  str,
FilterMode  default_value = FILTER_LINEAR 
)
static

Parse a string from one of the FilterMode values.

Valid strings are "point", and "linear"

Returns
one of the segNet::FilterMode enums, or default segNet::FILTER_LINEAR on an error.

◆ FindClassID()

int segNet::FindClassID ( const char *  label_name)

Find the ID of a particular class (by label name).

◆ GetClassColor()

float* segNet::GetClassColor ( uint32_t  id) const
inline

Retrieve the RGBA visualization color a particular class.

◆ GetClassDesc()

const char* segNet::GetClassDesc ( uint32_t  id) const
inline

Retrieve the description of a particular class.

◆ GetClassPath()

const char* segNet::GetClassPath ( ) const
inline

Retrieve the path to the file containing the class label descriptions.

◆ GetGridHeight()

uint32_t segNet::GetGridHeight ( ) const
inline

Retrieve the number of rows in the classification grid.

This indicates the resolution of the raw segmentation output.

◆ GetGridWidth()

uint32_t segNet::GetGridWidth ( ) const
inline

Retrieve the number of columns in the classification grid.

This indicates the resolution of the raw segmentation output.

◆ GetNetworkName()

const char* segNet::GetNetworkName ( ) const
inline

Retrieve a string describing the network name.

◆ GetNetworkType()

NetworkType segNet::GetNetworkType ( ) const
inline

Retrieve the network type (alexnet or googlenet)

◆ GetNumClasses()

uint32_t segNet::GetNumClasses ( ) const
inline

Retrieve the number of object classes supported in the detector.

◆ loadClassColors()

bool segNet::loadClassColors ( const char *  filename)
protected

◆ loadClassLabels()

bool segNet::loadClassLabels ( const char *  filename)
protected

◆ Mask() [1/4]

template<typename T >
bool segNet::Mask ( T *  output,
uint32_t  width,
uint32_t  height,
FilterMode  filter = FILTER_LINEAR 
)
inline

Produce a colorized segmentation mask.

◆ Mask() [2/4]

bool segNet::Mask ( void *  output,
uint32_t  width,
uint32_t  height,
imageFormat  format,
FilterMode  filter = FILTER_LINEAR 
)

Produce a colorized segmentation mask.

◆ Mask() [3/4]

bool segNet::Mask ( float *  output,
uint32_t  width,
uint32_t  height,
FilterMode  filter = FILTER_LINEAR 
)

Produce a colorized RGBA segmentation mask.

Deprecated:
this overload is for legacy compatibility. It expects float4 RGBA image.

◆ Mask() [4/4]

bool segNet::Mask ( uint8_t *  output,
uint32_t  width,
uint32_t  height 
)

Produce a grayscale binary segmentation mask, where the pixel values correspond to the class ID of the corresponding class type.

◆ NetworkTypeFromStr()

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

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

Valid names are "cityscapes-hd", "cityscapes-sd", "pascal-voc", ect.

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

◆ NetworkTypeToStr()

static const char* segNet::NetworkTypeToStr ( NetworkType  networkType)
static

Convert a NetworkType enum to a human-readable string.

Returns
stringized version of the provided NetworkType enum.

◆ Overlay() [1/3]

template<typename T >
bool segNet::Overlay ( T *  output,
uint32_t  width,
uint32_t  height,
FilterMode  filter = FILTER_LINEAR 
)
inline

Produce the segmentation overlay alpha blended on top of the original image.

Parameters
outputoutput image in CUDA device memory, RGB/RGBA colorspace with values 0-255.
widthwidth of the input image in pixels.
heightheight of the input image in pixels.
ignore_classlabel name of class to ignore in the classification (or NULL to process all).
typeoverlay visualization options
Returns
true on success, false on error.

◆ Overlay() [2/3]

bool segNet::Overlay ( void *  output,
uint32_t  width,
uint32_t  height,
imageFormat  format,
FilterMode  filter = FILTER_LINEAR 
)

Produce the segmentation overlay alpha blended on top of the original image.

Parameters
outputoutput image in CUDA device memory, RGB/RGBA colorspace with values 0-255.
widthwidth of the input image in pixels.
heightheight of the input image in pixels.
ignore_classlabel name of class to ignore in the classification (or NULL to process all).
typeoverlay visualization options
Returns
true on success, false on error.

◆ Overlay() [3/3]

bool segNet::Overlay ( float *  output,
uint32_t  width,
uint32_t  height,
FilterMode  filter = FILTER_LINEAR 
)

Produce the segmentation overlay alpha blended on top of the original image.

Deprecated:
this overload is for legacy compatibility. It expects float4 RGBA image.
Parameters
inputfloat4 input image in CUDA device memory, RGBA colorspace with values 0-255.
outputfloat4 output image in CUDA device memory, RGBA colorspace with values 0-255.
widthwidth of the input image in pixels.
heightheight of the input image in pixels.
ignore_classlabel name of class to ignore in the classification (or NULL to process all).
typeoverlay visualization options
Returns
true on success, false on error.

◆ overlayLinear()

bool segNet::overlayLinear ( void *  input,
uint32_t  in_width,
uint32_t  in_height,
imageFormat  in_format,
void *  output,
uint32_t  out_width,
uint32_t  out_height,
imageFormat  out_format,
bool  mask_only 
)
protected

◆ overlayPoint()

bool segNet::overlayPoint ( void *  input,
uint32_t  in_width,
uint32_t  in_height,
imageFormat  in_format,
void *  output,
uint32_t  out_width,
uint32_t  out_height,
imageFormat  out_format,
bool  mask_only 
)
protected

◆ Process() [1/3]

template<typename T >
bool segNet::Process ( T *  input,
uint32_t  width,
uint32_t  height,
const char *  ignore_class = "void" 
)
inline

Perform the initial inferencing processing portion of the segmentation.

The results can then be visualized using the Overlay() and Mask() functions.

Parameters
inputthe input image in CUDA device memory, with pixel values 0-255.
widthwidth of the input image in pixels.
heightheight of the input image in pixels.
ignore_classlabel name of class to ignore in the classification (or NULL to process all).

◆ Process() [2/3]

bool segNet::Process ( void *  input,
uint32_t  width,
uint32_t  height,
imageFormat  format,
const char *  ignore_class = "void" 
)

Perform the initial inferencing processing portion of the segmentation.

The results can then be visualized using the Overlay() and Mask() functions.

Parameters
inputthe input image in CUDA device memory, with pixel values 0-255.
widthwidth of the input image in pixels.
heightheight of the input image in pixels.
ignore_classlabel name of class to ignore in the classification (or NULL to process all).

◆ Process() [3/3]

bool segNet::Process ( float *  input,
uint32_t  width,
uint32_t  height,
const char *  ignore_class = "void" 
)

Perform the initial inferencing processing portion of the segmentation.

The results can then be visualized using the Overlay() and Mask() functions.

Deprecated:
this overload is for legacy compatibility. It expects float4 RGBA image.
Parameters
inputfloat4 input image in CUDA device memory, RGBA colorspace with values 0-255.
widthwidth of the input image in pixels.
heightheight of the input image in pixels.
ignore_classlabel name of class to ignore in the classification (or NULL to process all).

◆ saveClassLegend()

bool segNet::saveClassLegend ( const char *  filename)
protected

◆ SetClassColor()

void segNet::SetClassColor ( uint32_t  classIndex,
float  r,
float  g,
float  b,
float  a = 255.0f 
)

Set the visualization color of a particular class of object.

◆ SetOverlayAlpha()

void segNet::SetOverlayAlpha ( float  alpha,
bool  explicit_exempt = true 
)

Set overlay alpha blending value for all classes (between 0-255), (optionally except for those that have been explicitly set).

◆ Usage()

static const char* segNet::Usage ( )
inlinestatic

Usage string for command line arguments to Create()

◆ VisualizationFlagsFromStr()

static uint32_t segNet::VisualizationFlagsFromStr ( const char *  str,
uint32_t  default_value = VISUALIZE_OVERLAY 
)
static

Parse a string of one of more VisualizationMode values.

Valid strings are "overlay" "mask" "overlay|mask" "overlay,mask" ect.

Member Data Documentation

◆ mClassColors

float* segNet::mClassColors
protected

array of overlay colors in shared CPU/GPU memory

◆ mClassLabels

std::vector<std::string> segNet::mClassLabels
protected

◆ mClassMap

uint8_t* segNet::mClassMap
protected

runtime buffer for the argmax-classified class index of each tile

◆ mClassPath

std::string segNet::mClassPath
protected

◆ mColorsAlphaSet

bool* segNet::mColorsAlphaSet
protected

true if class color had been explicitly set from file or user

◆ mLastInputFormat

imageFormat segNet::mLastInputFormat
protected

pixel format of last input image

◆ mLastInputHeight

uint32_t segNet::mLastInputHeight
protected

height in pixels of last input image to be processed

◆ mLastInputImg

void* segNet::mLastInputImg
protected

last input image to be processed, stored for overlay

◆ mLastInputWidth

uint32_t segNet::mLastInputWidth
protected

width in pixels of last input image to be processed

◆ mNetworkType

NetworkType segNet::mNetworkType
protected

Pretrained built-in model type enumeration.


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