cv::dnn::Model Class Reference

This class is presented high-level API for neural networks. More...

#include <opencv2/dnn/dnn.hpp>

Inheritance diagram for cv::dnn::Model:

Public Member Functions
	Model ()
	Default constructor. More...
	Model (const String &model, const String &config="")
	Create model from deep learning network represented in one of the supported formats. An order of model and config arguments does not matter. More...
	Model (const Net &network)
	Create model from deep learning network. More...
void	predict (InputArray frame, OutputArrayOfArrays outs)
	Given the input frame, create input blob, run net and return the output blobs. More...
Model &	setInputCrop (bool crop)
	Set flag crop for frame. More...
Model &	setInputMean (const Scalar &mean)
	Set mean value for frame. More...
void	setInputParams (double scale=1.0, const Size &size=Size(), const Scalar &mean=Scalar(), bool swapRB=false, bool crop=false)
	Set preprocessing parameters for frame. More...
Model &	setInputScale (double scale)
	Set scalefactor value for frame. More...
Model &	setInputSize (const Size &size)
	Set input size for frame. More...
Model &	setInputSize (int width, int height)
	Set input size for frame. More...
Model &	setInputSwapRB (bool swapRB)
	Set flag swapRB for frame. More...
Public Member Functions inherited from cv::dnn::Net
	Net ()
	Default constructor. More...
	~Net ()
	Destructor frees the net only if there aren't references to the net anymore. More...
int	addLayer (const String &name, const String &type, LayerParams &params)
	Adds new layer to the net. More...
int	addLayerToPrev (const String &name, const String &type, LayerParams &params)
	Adds new layer and connects its first input to the first output of previously added layer. More...
void	connect (String outPin, String inpPin)
	Connects output of the first layer to input of the second layer. More...
void	connect (int outLayerId, int outNum, int inpLayerId, int inpNum)
	Connects #outNum output of the first layer to #inNum input of the second layer. More...
String	dump ()
	Dump net to String. More...
void	dumpToFile (const String &path)
	Dump net structure, hyperparameters, backend, target and fusion to dot file. More...
bool	empty () const
void	enableFusion (bool fusion)
	Enables or disables layer fusion in the network. More...
Mat	forward (const String &outputName=String())
	Runs forward pass to compute output of layer with name outputName. More...
void	forward (OutputArrayOfArrays outputBlobs, const String &outputName=String())
	Runs forward pass to compute output of layer with name outputName. More...
void	forward (OutputArrayOfArrays outputBlobs, const std::vector< String > &outBlobNames)
	Runs forward pass to compute outputs of layers listed in outBlobNames. More...
void	forward (std::vector< std::vector< Mat > > &outputBlobs, const std::vector< String > &outBlobNames)
	Runs forward pass to compute outputs of layers listed in outBlobNames. More...
AsyncArray	forwardAsync (const String &outputName=String())
	Runs forward pass to compute output of layer with name outputName. More...
int64	getFLOPS (const std::vector< MatShape > &netInputShapes) const
	Computes FLOP for whole loaded model with specified input shapes. More...
int64	getFLOPS (const MatShape &netInputShape) const
int64	getFLOPS (const int layerId, const std::vector< MatShape > &netInputShapes) const
int64	getFLOPS (const int layerId, const MatShape &netInputShape) const
Ptr< Layer >	getLayer (LayerId layerId)
	Returns pointer to layer with specified id or name which the network use. More...
int	getLayerId (const String &layer)
	Converts string name of the layer to the integer identifier. More...
std::vector< Ptr< Layer > >	getLayerInputs (LayerId layerId)
	Returns pointers to input layers of specific layer. More...
std::vector< String >	getLayerNames () const
int	getLayersCount (const String &layerType) const
	Returns count of layers of specified type. More...
void	getLayerShapes (const MatShape &netInputShape, const int layerId, std::vector< MatShape > &inLayerShapes, std::vector< MatShape > &outLayerShapes) const
	Returns input and output shapes for layer with specified id in loaded model; preliminary inferencing isn't necessary. More...
void	getLayerShapes (const std::vector< MatShape > &netInputShapes, const int layerId, std::vector< MatShape > &inLayerShapes, std::vector< MatShape > &outLayerShapes) const
void	getLayersShapes (const std::vector< MatShape > &netInputShapes, std::vector< int > &layersIds, std::vector< std::vector< MatShape > > &inLayersShapes, std::vector< std::vector< MatShape > > &outLayersShapes) const
	Returns input and output shapes for all layers in loaded model; preliminary inferencing isn't necessary. More...
void	getLayersShapes (const MatShape &netInputShape, std::vector< int > &layersIds, std::vector< std::vector< MatShape > > &inLayersShapes, std::vector< std::vector< MatShape > > &outLayersShapes) const
void	getLayerTypes (std::vector< String > &layersTypes) const
	Returns list of types for layer used in model. More...
void	getMemoryConsumption (const std::vector< MatShape > &netInputShapes, size_t &weights, size_t &blobs) const
	Computes bytes number which are required to store all weights and intermediate blobs for model. More...
void	getMemoryConsumption (const MatShape &netInputShape, size_t &weights, size_t &blobs) const
void	getMemoryConsumption (const int layerId, const std::vector< MatShape > &netInputShapes, size_t &weights, size_t &blobs) const
void	getMemoryConsumption (const int layerId, const MatShape &netInputShape, size_t &weights, size_t &blobs) const
void	getMemoryConsumption (const std::vector< MatShape > &netInputShapes, std::vector< int > &layerIds, std::vector< size_t > &weights, std::vector< size_t > &blobs) const
	Computes bytes number which are required to store all weights and intermediate blobs for each layer. More...
void	getMemoryConsumption (const MatShape &netInputShape, std::vector< int > &layerIds, std::vector< size_t > &weights, std::vector< size_t > &blobs) const
Mat	getParam (LayerId layer, int numParam=0)
	Returns parameter blob of the layer. More...
int64	getPerfProfile (std::vector< double > &timings)
	Returns overall time for inference and timings (in ticks) for layers. Indexes in returned vector correspond to layers ids. Some layers can be fused with others, in this case zero ticks count will be return for that skipped layers. More...
std::vector< int >	getUnconnectedOutLayers () const
	Returns indexes of layers with unconnected outputs. More...
std::vector< String >	getUnconnectedOutLayersNames () const
	Returns names of layers with unconnected outputs. More...
void	setHalideScheduler (const String &scheduler)
	Compile Halide layers. More...
void	setInput (InputArray blob, const String &name="", double scalefactor=1.0, const Scalar &mean=Scalar())
	Sets the new input value for the network. More...
void	setInputsNames (const std::vector< String > &inputBlobNames)
	Sets outputs names of the network input pseudo layer. More...
void	setParam (LayerId layer, int numParam, const Mat &blob)
	Sets the new value for the learned param of the layer. More...
void	setPreferableBackend (int backendId)
	Ask network to use specific computation backend where it supported. More...
void	setPreferableTarget (int targetId)
	Ask network to make computations on specific target device. More...

Protected Attributes
Ptr< Impl >	impl

Additional Inherited Members
Public Types inherited from cv::dnn::Net
typedef DictValue	LayerId
	Container for strings and integers. More...
Static Public Member Functions inherited from cv::dnn::Net
static Net	readFromModelOptimizer (const String &xml, const String &bin)
	Create a network from Intel's Model Optimizer intermediate representation. More...

Detailed Description

This class is presented high-level API for neural networks.

Model allows to set params for preprocessing input image. Model creates net from file with trained weights and config, sets preprocessing input and runs forward pass.

Constructor & Destructor Documentation

Model() [1/3]

cv::dnn::Model::Model

(

)

Python:
	<dnn_Model object>	=	cv.dnn_Model(	model[, config]	)
	<dnn_Model object>	=	cv.dnn_Model(	network	)

Default constructor.

Model() [2/3]

cv::dnn::Model::Model	(	const String &	model,
		const String &	config = ""
	)

Python:
	<dnn_Model object>	=	cv.dnn_Model(	model[, config]	)
	<dnn_Model object>	=	cv.dnn_Model(	network	)

Create model from deep learning network represented in one of the supported formats. An order of model and config arguments does not matter.

Parameters

[in]	model	Binary file contains trained weights.
[in]	config	Text file contains network configuration.

Model() [3/3]

cv::dnn::Model::Model

(

const Net &

network

)

Python:
	<dnn_Model object>	=	cv.dnn_Model(	model[, config]	)
	<dnn_Model object>	=	cv.dnn_Model(	network	)

Create model from deep learning network.

Parameters

[in]

network

Net object.

Member Function Documentation

predict()

void cv::dnn::Model::predict	(	InputArray	frame,
		OutputArrayOfArrays	outs
	)

Python:
	outs	=	cv.dnn_Model.predict(	frame[, outs]	)

Given the input frame, create input blob, run net and return the output blobs.

Parameters

[in]	frame	The input image.
[out]	outs	Allocated output blobs, which will store results of the computation.

setInputCrop()

Model& cv::dnn::Model::setInputCrop

(

bool

crop

)

Python:
	retval	=	cv.dnn_Model.setInputCrop(	crop	)

Set flag crop for frame.

Parameters

[in]

crop

Flag which indicates whether image will be cropped after resize or not.

setInputMean()

Model& cv::dnn::Model::setInputMean

(

const Scalar &

mean

)

Python:
	retval	=	cv.dnn_Model.setInputMean(	mean	)

Set mean value for frame.

Parameters

[in]

mean

Scalar with mean values which are subtracted from channels.

setInputParams()

void cv::dnn::Model::setInputParams	(	double	scale = 1.0,
		const Size &	size = Size(),
		const Scalar &	mean = Scalar(),
		bool	swapRB = false,
		bool	crop = false
	)

Python:
	None	=	cv.dnn_Model.setInputParams(	[, scale[, size[, mean[, swapRB[, crop]]]]]	)

Set preprocessing parameters for frame.

Parameters

[in]	size	New input size.
[in]	mean	Scalar with mean values which are subtracted from channels.
[in]	scale	Multiplier for frame values.
[in]	swapRB	Flag which indicates that swap first and last channels.
[in]	crop	Flag which indicates whether image will be cropped after resize or not. blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )

setInputScale()

Model& cv::dnn::Model::setInputScale

(

double

scale

)

Python:
	retval	=	cv.dnn_Model.setInputScale(	scale	)

Set scalefactor value for frame.

Parameters

[in]

scale

Multiplier for frame values.

setInputSize() [1/2]

Model& cv::dnn::Model::setInputSize

(

const Size &

size

)

Python:
	retval	=	cv.dnn_Model.setInputSize(	size	)
	retval	=	cv.dnn_Model.setInputSize(	width, height	)

Set input size for frame.

Parameters

[in]

size

New input size.

Note

If shape of the new blob less than 0, then frame size not change.

setInputSize() [2/2]

Model& cv::dnn::Model::setInputSize	(	int	width,
		int	height
	)

Python:
	retval	=	cv.dnn_Model.setInputSize(	size	)
	retval	=	cv.dnn_Model.setInputSize(	width, height	)

Set input size for frame.

Parameters

[in]	width	New input width.
[in]	height	New input height.

Note

If shape of the new blob less than 0, then frame size not change.

setInputSwapRB()

Model& cv::dnn::Model::setInputSwapRB

(

bool

swapRB

)

Python:
	retval	=	cv.dnn_Model.setInputSwapRB(	swapRB	)

Set flag swapRB for frame.

Parameters

[in]

swapRB

Flag which indicates that swap first and last channels.

Member Data Documentation

impl

Ptr<Impl> cv::dnn::Model::impl

protected

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

• opencv2/dnn/dnn.hpp