Base class for text detection networks. More...

#include <opencv2/dnn/dnn.hpp>

Inheritance diagram for cv::dnn::TextDetectionModel:

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

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

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

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

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

	Model (const Model &)=default

	Model (Model &&)=default

	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...

Impl *	getImpl () const

Impl &	getImplRef () const

Net &	getNetwork_ () const

Net &	getNetwork_ ()

	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`. 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 (const Scalar &scale)
	Set scalefactor value for frame. More...

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

Model &	setInputSize (int width, int height)

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

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,
		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

◆ detect() [2/2]

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.

◆ detectTextRectangles() [1/2]

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

◆ detectTextRectangles() [2/2]

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.

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

opencv2/dnn/dnn.hpp

Public Member Functions

Protected Member Functions

Additional Inherited Members