This class represents high-level API for text detection DL networks compatible with DB model. More...

#include <opencv2/dnn/dnn.hpp>

Collaboration diagram for cv::dnn::TextDetectionModel_DB:

Public Member Functions
	TextDetectionModel_DB ()

	TextDetectionModel_DB (const Net &network)
	Create text detection algorithm from deep learning network.

	TextDetectionModel_DB (CV_WRAP_FILE_PATH const std::string &model, CV_WRAP_FILE_PATH const std::string &config="")
	Create text detection model from network represented in one of the supported formats. An order of `model` and `config` arguments does not matter.

float	getBinaryThreshold () const

int	getMaxCandidates () const

float	getPolygonThreshold () const

double	getUnclipRatio () const

TextDetectionModel_DB &	setBinaryThreshold (float binaryThreshold)

TextDetectionModel_DB &	setMaxCandidates (int maxCandidates)

TextDetectionModel_DB &	setPolygonThreshold (float polygonThreshold)

TextDetectionModel_DB &	setUnclipRatio (double unclipRatio)

Public Member Functions inherited from cv::dnn::TextDetectionModel
void	detect (InputArray frame, std::vector< std::vector< Point > > &detections) const

void	detect (InputArray frame, std::vector< std::vector< Point > > &detections, std::vector< float > &confidences) const
	Performs detection.

void	detectTextRectangles (InputArray frame, std::vector< cv::RotatedRect > &detections) const

void	detectTextRectangles (InputArray frame, std::vector< cv::RotatedRect > &detections, std::vector< float > &confidences) const
	Performs detection.

Public Member Functions inherited from cv::dnn::Model
	Model ()

	Model (const Model &)=default

	Model (const Net &network)
	Create model from deep learning network.

	Model (CV_WRAP_FILE_PATH const String &model, CV_WRAP_FILE_PATH 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.

	Model (Model &&)=default

Model &	enableWinograd (bool useWinograd)

Impl *	getImpl () const

Impl &	getImplRef () const

Net &	getNetwork_ ()

Net &	getNetwork_ () const

	operator Net & () const

Model &	operator= (const Model &)=default

Model &	operator= (Model &&)=default

void	predict (InputArray frame, OutputArrayOfArrays outs) const
	Given the `input` frame, create input blob, run net and return the output `blobs`.

Model &	setInputCrop (bool crop)
	Set flag crop for frame.

Model &	setInputMean (const Scalar &mean)
	Set mean value for frame.

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.

Model &	setInputScale (const Scalar &scale)
	Set scalefactor value for frame.

Model &	setInputSize (const Size &size)
	Set input size for frame.

Model &	setInputSize (int width, int height)

Model &	setInputSwapRB (bool swapRB)
	Set flag swapRB for frame.

Model &	setOutputNames (const std::vector< String > &outNames)
	Set output names for frame.

Model &	setPreferableBackend (dnn::Backend backendId)

Model &	setPreferableTarget (dnn::Target targetId)

Additional Inherited Members
Protected Member Functions inherited from cv::dnn::TextDetectionModel
	TextDetectionModel ()

Protected Attributes inherited from cv::dnn::Model
Ptr< Impl >	impl

Detailed Description

This class represents high-level API for text detection DL networks compatible with DB model.

Related publications: [167] Paper: https://arxiv.org/abs/1911.08947 For more information about the hyper-parameters setting, please refer to https://github.com/MhLiao/DB

Configurable parameters:

(float) binaryThreshold - The threshold of the binary map. It is usually set to 0.3.
(float) polygonThreshold - The threshold of text polygons. It is usually set to 0.5, 0.6, and 0.7. Default is 0.5f
(double) unclipRatio - The unclip ratio of the detected text region, which determines the output size. It is usually set to 2.0.
(int) maxCandidates - The max number of the output results.

Constructor & Destructor Documentation

◆ TextDetectionModel_DB() [1/3]

cv::dnn::TextDetectionModel_DB::TextDetectionModel_DB ( )

Python:
	cv.dnn.TextDetectionModel_DB(	network	) ->	<dnn_TextDetectionModel_DB object>
	cv.dnn.TextDetectionModel_DB(	model[, config]	) ->	<dnn_TextDetectionModel_DB object>

◆ TextDetectionModel_DB() [2/3]

cv::dnn::TextDetectionModel_DB::TextDetectionModel_DB ( const Net & network )

Python:
	cv.dnn.TextDetectionModel_DB(	network	) ->	<dnn_TextDetectionModel_DB object>
	cv.dnn.TextDetectionModel_DB(	model[, config]	) ->	<dnn_TextDetectionModel_DB object>

Create text detection algorithm from deep learning network.

Parameters

[in] network Net object.

◆ TextDetectionModel_DB() [3/3]

cv::dnn::TextDetectionModel_DB::TextDetectionModel_DB	(	CV_WRAP_FILE_PATH const std::string &	model,
		CV_WRAP_FILE_PATH const std::string &	config = "" )

inline

Python:
	cv.dnn.TextDetectionModel_DB(	network	) ->	<dnn_TextDetectionModel_DB object>
	cv.dnn.TextDetectionModel_DB(	model[, config]	) ->	<dnn_TextDetectionModel_DB object>

Create text detection model from 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.

Here is the call graph for this function:

Member Function Documentation

◆ getBinaryThreshold()

float cv::dnn::TextDetectionModel_DB::getBinaryThreshold ( ) const

Python:
	cv.dnn.TextDetectionModel_DB.getBinaryThreshold(		) ->	retval

◆ getMaxCandidates()

int cv::dnn::TextDetectionModel_DB::getMaxCandidates ( ) const

Python:
	cv.dnn.TextDetectionModel_DB.getMaxCandidates(		) ->	retval

◆ getPolygonThreshold()

float cv::dnn::TextDetectionModel_DB::getPolygonThreshold ( ) const

Python:
	cv.dnn.TextDetectionModel_DB.getPolygonThreshold(		) ->	retval

◆ getUnclipRatio()

double cv::dnn::TextDetectionModel_DB::getUnclipRatio ( ) const

Python:
	cv.dnn.TextDetectionModel_DB.getUnclipRatio(		) ->	retval

◆ setBinaryThreshold()

TextDetectionModel_DB & cv::dnn::TextDetectionModel_DB::setBinaryThreshold ( float binaryThreshold )

Python:
	cv.dnn.TextDetectionModel_DB.setBinaryThreshold(	binaryThreshold	) ->	retval

◆ setMaxCandidates()

TextDetectionModel_DB & cv::dnn::TextDetectionModel_DB::setMaxCandidates ( int maxCandidates )

Python:
	cv.dnn.TextDetectionModel_DB.setMaxCandidates(	maxCandidates	) ->	retval

◆ setPolygonThreshold()

TextDetectionModel_DB & cv::dnn::TextDetectionModel_DB::setPolygonThreshold ( float polygonThreshold )

Python:
	cv.dnn.TextDetectionModel_DB.setPolygonThreshold(	polygonThreshold	) ->	retval

◆ setUnclipRatio()

TextDetectionModel_DB & cv::dnn::TextDetectionModel_DB::setUnclipRatio ( double unclipRatio )

Python:
	cv.dnn.TextDetectionModel_DB.setUnclipRatio(	unclipRatio	) ->	retval

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

opencv2/dnn/dnn.hpp

Public Member Functions

Additional Inherited Members