cv::dnn::Model クラス

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

#include <dnn.hpp>

cv::dnn::ClassificationModel, cv::dnn::DetectionModel, cv::dnn::KeypointsModel, cv::dnn::SegmentationModel, cv::dnn::TextDetectionModel, cv::dnn::TextRecognitionModelに継承されています。

公開メンバ関数
	Model (const Model &)=default
	Model (Model &&)=default
Model &	operator= (const Model &)=default
Model &	operator= (Model &&)=default
CV_WRAP	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. [詳解]
CV_WRAP	Model (const Net &network)
	Create model from deep learning network. [詳解]
CV_WRAP Model &	setInputSize (const Size &size)
	Set input size for frame. [詳解]
CV_WRAP Model &	setInputSize (int width, int height)
CV_WRAP Model &	setInputMean (const Scalar &mean)
	Set mean value for frame. [詳解]
CV_WRAP Model &	setInputScale (double scale)
	Set scalefactor value for frame. [詳解]
CV_WRAP Model &	setInputCrop (bool crop)
	Set flag crop for frame. [詳解]
CV_WRAP Model &	setInputSwapRB (bool swapRB)
	Set flag swapRB for frame. [詳解]
CV_WRAP 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. [詳解]
CV_WRAP void	predict (InputArray frame, OutputArrayOfArrays outs) const
	Given the input frame, create input blob, run net and return the output blobs. [詳解]
CV_WRAP Model &	setPreferableBackend (dnn::Backend backendId)
CV_WRAP Model &	setPreferableTarget (dnn::Target targetId)
CV_DEPRECATED_EXTERNAL	operator Net & () const
Net &	getNetwork_ () const
Net &	getNetwork_ ()
Impl *	getImpl () const
Impl &	getImplRef () const

限定公開変数類
Ptr< Impl >	impl

詳解

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.

構築子と解体子

Model() [1/2]

CV_WRAP cv::dnn::Model::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.

引数

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

Model() [2/2]

CV_WRAP cv::dnn::Model::Model

(

const Net &

network

)

Create model from deep learning network.

引数

[in]

network

Net object.

関数詳解

predict()

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

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

引数

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

setInputCrop()

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

(

bool

crop

)

Set flag crop for frame.

引数

[in]

crop

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

setInputMean()

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

(

const Scalar &

mean

)

Set mean value for frame.

引数

[in]

mean

Scalar with mean values which are subtracted from channels.

setInputParams()

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

Set preprocessing parameters for frame.

引数

[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()

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

(

double

scale

)

Set scalefactor value for frame.

引数

[in]

scale

Multiplier for frame values.

setInputSize() [1/2]

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

(

const Size &

size

)

Set input size for frame.

引数

[in]

size

New input size.

覚え書き

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

setInputSize() [2/2]

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

inline

これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。

引数

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

setInputSwapRB()

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

(

bool

swapRB

)

Set flag swapRB for frame.

引数

[in]

swapRB

Flag which indicates that swap first and last channels.

setPreferableBackend()

CV_WRAP Model & cv::dnn::Model::setPreferableBackend

(

dnn::Backend

backendId

)

参照

Net::setPreferableBackend

setPreferableTarget()

CV_WRAP Model & cv::dnn::Model::setPreferableTarget

(

dnn::Target

targetId

)

参照

Net::setPreferableTarget

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このクラス詳解は次のファイルから抽出されました:

• dnn/dnn.hpp