Class cv::dnn::TextDetectionModel#

Base class for text detection networks.

#include <opencv2/dnn/dnn.hpp>

Collaboration diagram for cv::dnn::TextDetectionModel:

Public Member Functions#

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

Return	Name	Description
	`Model()`
	`Model(const Model &)`
	`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 &&)`
`Model &`	`enableWinograd(bool useWinograd)`
`Impl *`	`getImpl()`
`Impl &`	`getImplRef()`
`Net &`	`getNetwork_()`
`Net &`	`getNetwork_()`
	`operator Net &()`
`Model &`	`operator=(const Model &)`
`Model &`	`operator=(Model &&)`
`void`	`predict(` `InputArray frame,` `OutputArrayOfArrays outs )`	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#

Detailed Description#

Base class for text detection networks.

Constructor & Destructor Documentation#

TextDetectionModel()#

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

Member Function Documentation#

detect()#

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

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()#

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

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

frame — The input image
detections — array with detections’ quadrangles (4 points per result)
confidences — array with detection confidences

detectTextRectangles()#

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

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()#

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

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

frame — the input image
detections — array with detections’ RotationRect results
confidences — array with detection confidences

Source file#

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

opencv2/dnn/dnn.hpp