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Jetson Inference: backgroundNet Class Reference

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backgroundNet Class Reference DNN Vision Library (jetson-inference) » backgroundNet

Background subtraction/removal with DNNs, using TensorRT. More...

#include <backgroundNet.h>

Inheritance diagram for backgroundNet:

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Public Member Functions

| | virtual | ~backgroundNet () | | | Destroy. More...
| | | | template<typename T > | | int | Process (T *image, uint32_t width, uint32_t height, cudaFilterMode filter=FILTER_LINEAR, bool maskAlpha=true) | | | Perform background subtraction/removal on the image (in-place). More...
| | | | template<typename T > | | int | Process (T *input, T *output, uint32_t width, uint32_t height, cudaFilterMode filter=FILTER_LINEAR, bool maskAlpha=true) | | | Perform background subtraction/removal on the image. More...
| | | | bool | Process (void *image, uint32_t width, uint32_t height, imageFormat format, cudaFilterMode filter=FILTER_LINEAR, bool maskAlpha=true) | | | Perform background subtraction/removal on the image (in-place). More...
| | | | bool | Process (void *input, void *output, uint32_t width, uint32_t height, imageFormat format, cudaFilterMode filter=FILTER_LINEAR, bool maskAlpha=true) | | | Perform background subtraction/removal on the image. 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 full path to model file, including the filename. More...
| | | | const char * | GetModelFilename () const | | | Retrieve the filename of the file, excluding the directory. 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...
| | | | float * | GetInputPtr (uint32_t layer=0) const | | | Get the CUDA pointer to the input layer's memory. 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 * | GetOutputPtr (uint32_t layer=0) const | | | Get the CUDA pointer to the output memory. More...
| | | | float | GetNetworkFPS () | | | Retrieve the network frames per second (FPS). More...
| | | | float | GetNetworkTime () | | | Retrieve the network runtime (in milliseconds). More...
| | | | const char * | GetNetworkName () const | | | Retrieve the network name (it's filename). 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...
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Static Public Member Functions

| | static backgroundNet * | Create (const char *network="u2net", uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true) | | | Load a pre-trained model. More...
| | | | static backgroundNet * | Create (const char *model_path, const char *input=BACKGROUNDNET_DEFAULT_INPUT, const char *output=BACKGROUNDNET_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 backgroundNet * | Create (int argc, char **argv) | | | Load a new network instance by parsing the command line. More...
| | | | static backgroundNet * | Create (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 bool | LoadClassLabels (const char *filename, std::vector< std::string > &descriptions, int expectedClasses=-1) | | | Load class descriptions from a label file. More...
| | | | static bool | LoadClassLabels (const char *filename, std::vector< std::string > &descriptions, std::vector< std::string > &synsets, int expectedClasses=-1) | | | Load class descriptions and synset strings from a label file. More...
| | | | static bool | LoadClassColors (const char *filename, float4 *colors, int expectedClasses, float defaultAlpha=255.0f) | | | Load class colors from a text file. More...
| | | | static bool | LoadClassColors (const char *filename, float4 **colors, int expectedClasses, float defaultAlpha=255.0f) | | | Load class colors from a text file. More...
| | | | static float4 | GenerateColor (uint32_t classID, float alpha=255.0f) | | | Procedurally generate a color for a given class index with the specified alpha value. More...
| | | | 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< precisionType > | DetectNativePrecisions (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...
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Protected Member Functions

| | | backgroundNet () | | | | bool | init (const char *model_path, const char *input, const char *output, uint32_t maxBatchSize, precisionType precision, deviceType device, bool allowGPUFallback) | | | | 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...
| | | | bool | ValidateEngine (const char *model_path, const char *cache_path, const char *checksum_path) | | | Validate that the model already has a built TensorRT engine that exists and doesn't need updating. 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...
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Additional Inherited Members

| | Protected Attributes inherited from tensorNet | | tensorNet::Logger | gLogger | | | | tensorNet::Profiler | gProfiler | | | | std::string | mPrototxtPath | | | | std::string | mModelPath | | | | std::string | mModelFile | | | | std::string | mMeanPath | | | | std::string | mCacheEnginePath | | | | std::string | mCacheCalibrationPath | | | | std::string | mChecksumPath | | | | 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< layerInfo > | mInputs | | | | std::vector< layerInfo > | mOutputs | | |

Detailed Description

Background subtraction/removal with DNNs, using TensorRT.

Constructor & Destructor Documentation

◆~backgroundNet()

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| virtual backgroundNet::~backgroundNet | ( | | ) | |

| virtual |

Destroy.

◆backgroundNet()

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| backgroundNet::backgroundNet | ( | | ) | |

| protected |

Member Function Documentation

◆Create() [1/4]

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| static backgroundNet* backgroundNet::Create | ( | const char * | model_path, | | | | const char * | input = BACKGROUNDNET_DEFAULT_INPUT, | | | | const char * | output = BACKGROUNDNET_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_path | File path to the caffemodel | | input | Name of the input layer blob. | | output | Name of the output layer blob. | | maxBatchSize | The maximum batch size that the network will support and be optimized for. |

◆Create() [2/4]

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| static backgroundNet* backgroundNet::Create | ( | const char * | network = "u2net", | | | | uint32_t | maxBatchSize = DEFAULT_MAX_BATCH_SIZE, | | | | precisionType | precision = TYPE_FASTEST, | | | | deviceType | device = DEVICE_GPU, | | | | bool | allowGPUFallback = true | | | ) | | |

| static |

Load a pre-trained model.

◆Create() [3/4]

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| static backgroundNet* backgroundNet::Create | ( | const commandLine & | cmdLine | ) | |

| static |

Load a new network instance by parsing the command line.

◆Create() [4/4]

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| static backgroundNet* backgroundNet::Create | ( | int | argc, | | | | char ** | argv | | | ) | | |

| static |

Load a new network instance by parsing the command line.

◆init()

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| bool backgroundNet::init | ( | const char * | model_path, | | | | const char * | input, | | | | const char * | output, | | | | uint32_t | maxBatchSize, | | | | precisionType | precision, | | | | deviceType | device, | | | | bool | allowGPUFallback | | | ) | | |

| protected |

◆Process() [1/4]

template<typename T >

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| int backgroundNet::Process | ( | T * | image, | | | | uint32_t | width, | | | | uint32_t | height, | | | | cudaFilterMode | filter = FILTER_LINEAR, | | | | bool | maskAlpha = true | | | ) | | |

| inline |

Perform background subtraction/removal on the image (in-place).

Parameters

| image | input/output image in CUDA device memory. | | width | width of the image in pixels. | | height | height of the output image in pixels. | | filter | the upsampling mode used to resize the DNN mask (FILTER_LINEAR or FILTER_POINT) | | maskAlpha | if true (default), the mask will be applied to the alpha channel in addition to the color channels. |

Returnstrue on success and false if an error occurred.

◆Process() [2/4]

template<typename T >

|

| int backgroundNet::Process | ( | T * | input, | | | | T * | output, | | | | uint32_t | width, | | | | uint32_t | height, | | | | cudaFilterMode | filter = FILTER_LINEAR, | | | | bool | maskAlpha = true | | | ) | | |

| inline |

Perform background subtraction/removal on the image.

Parameters

| input | input image in CUDA device memory. | | output | output image in CUDA device memory. | | width | width of the image in pixels. | | height | height of the output image in pixels. | | filter | the upsampling mode used to resize the DNN mask (FILTER_LINEAR or FILTER_POINT) | | maskAlpha | if true (default), the mask will be applied to the alpha channel in addition to the color channels. |

Returnstrue on success and false if an error occurred.

◆Process() [3/4]

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| bool backgroundNet::Process | ( | void * | image, | | | | uint32_t | width, | | | | uint32_t | height, | | | | imageFormat | format, | | | | cudaFilterMode | filter = FILTER_LINEAR, | | | | bool | maskAlpha = true | | | ) | | |

| inline |

Perform background subtraction/removal on the image (in-place).

Parameters

| image | input/output image in CUDA device memory. | | width | width of the image in pixels. | | height | height of the output image in pixels. | | filter | the upsampling mode used to resize the DNN mask (FILTER_LINEAR or FILTER_POINT) | | maskAlpha | if true (default), the mask will be applied to the alpha channel as well. |

Returnstrue on success and false if an error occurred.

◆Process() [4/4]

| bool backgroundNet::Process | ( | void * | input, | | | | void * | output, | | | | uint32_t | width, | | | | uint32_t | height, | | | | imageFormat | format, | | | | cudaFilterMode | filter = FILTER_LINEAR, | | | | bool | maskAlpha = true | | | ) | | |

Perform background subtraction/removal on the image.

Parameters

| input | input image in CUDA device memory. | | output | output image in CUDA device memory. | | width | width of the image in pixels. | | height | height of the output image in pixels. | | filter | the upsampling mode used to resize the DNN mask (FILTER_LINEAR or FILTER_POINT) | | maskAlpha | if true (default), the mask will be applied to the alpha channel as well. |

Returnstrue on success and false if an error occurred.

◆Usage()

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| static const char* backgroundNet::Usage | ( | | ) | |

| inlinestatic |

Usage string for command line arguments to Create()

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