cv::dnn::TextDetectionModel Class Reference

Base class for text detection networks. More...

#include <opencv2/dnn/dnn.hpp>

Public Member Functions
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)

Protected Member Functions
	TextDetectionModel ()

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

Detailed Description

Base class for text detection networks.

Constructor & Destructor Documentation

TextDetectionModel()

cv::dnn::TextDetectionModel::TextDetectionModel ( )

protected

Member Function Documentation

detect() [1/2]

void cv::dnn::TextDetectionModel::detect	(	InputArray	frame,
		std::vector< std::vector< Point > > &	detections ) const

Python:
	cv.dnn.TextDetectionModel.detect(	frame	) ->	detections, confidences
	cv.dnn.TextDetectionModel.detect(	frame	) ->	detections

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

detect() [2/2]

void cv::dnn::TextDetectionModel::detect	(	InputArray	frame,
		std::vector< std::vector< Point > > &	detections,
		std::vector< float > &	confidences ) const

Python:
	cv.dnn.TextDetectionModel.detect(	frame	) ->	detections, confidences
	cv.dnn.TextDetectionModel.detect(	frame	) ->	detections

Performs detection.

Given the input frame, prepare network input, run network inference, post-process network output and return result detections.

Each result is quadrangle's 4 points in this order:

• bottom-left
• top-left
• top-right
• bottom-right

Use cv::getPerspectiveTransform function to retrieve image region without perspective transformations.

Note

If DL model doesn't support that kind of output then result may be derived from detectTextRectangles() output.

Parameters

[in]	frame	The input image
[out]	detections	array with detections' quadrangles (4 points per result)
[out]	confidences	array with detection confidences

detectTextRectangles() [1/2]

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

Python:
	cv.dnn.TextDetectionModel.detectTextRectangles(	frame	) ->	detections, confidences
	cv.dnn.TextDetectionModel.detectTextRectangles(	frame	) ->	detections

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

detectTextRectangles() [2/2]

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

Python:
	cv.dnn.TextDetectionModel.detectTextRectangles(	frame	) ->	detections, confidences
	cv.dnn.TextDetectionModel.detectTextRectangles(	frame	) ->	detections

Performs detection.

Given the input frame, prepare network input, run network inference, post-process network output and return result detections.

Each result is rotated rectangle.

Note

Result may be inaccurate in case of strong perspective transformations.

Parameters

[in]	frame	the input image
[out]	detections	array with detections' RotationRect results
[out]	confidences	array with detection confidences

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

• opencv2/dnn/dnn.hpp