OpenCV  4.10.0-dev
Open Source Computer Vision
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Public Member Functions | Protected Member Functions | List of all members
cv::dnn::TextDetectionModel Class Reference

Base class for text detection networks. More...

#include <opencv2/dnn/dnn.hpp>

Collaboration 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 (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
 
ModelenableWinograd (bool useWinograd)
 
Impl * getImpl () const
 
Impl & getImplRef () const
 
NetgetNetwork_ ()
 
NetgetNetwork_ () const
 
 operator Net & () const
 
Modeloperator= (const Model &)=default
 
Modeloperator= (Model &&)=default
 
void predict (InputArray frame, OutputArrayOfArrays outs) const
 Given the input frame, create input blob, run net and return the output blobs.
 
ModelsetInputCrop (bool crop)
 Set flag crop for frame.
 
ModelsetInputMean (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.
 
ModelsetInputScale (const Scalar &scale)
 Set scalefactor value for frame.
 
ModelsetInputSize (const Size &size)
 Set input size for frame.
 
ModelsetInputSize (int width, int height)
 
ModelsetInputSwapRB (bool swapRB)
 Set flag swapRB for frame.
 
ModelsetOutputNames (const std::vector< String > &outNames)
 Set output names for frame.
 
ModelsetPreferableBackend (dnn::Backend backendId)
 
ModelsetPreferableTarget (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]frameThe input image
[out]detectionsarray with detections' quadrangles (4 points per result)
[out]confidencesarray 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]framethe input image
[out]detectionsarray with detections' RotationRect results
[out]confidencesarray with detection confidences

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