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) 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 (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.

	Model (Model &&)=default

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 &	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

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

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

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

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

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

opencv2/dnn/dnn.hpp

Public Member Functions

Protected Member Functions

Additional Inherited Members