Class cv::dnn::TextDetectionModel_DB#

This class represents high-level API for text detection DL networks compatible with DB model. View details

#include <opencv2/dnn/dnn.hpp>

Collaboration diagram for cv::dnn::TextDetectionModel_DB:

Public Member Functions#

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

Return	Name	Description
`void`	`detect(` `InputArray frame,` `std::vector< std::vector< Point > > & detections )`
`void`	`detect(` `InputArray frame,` `std::vector< std::vector< Point > > & detections,` `std::vector< float > & confidences )`	Performs detection.
`void`	`detectTextRectangles(` `InputArray frame,` `std::vector< cv::RotatedRect > & detections )`
`void`	`detectTextRectangles(` `InputArray frame,` `std::vector< cv::RotatedRect > & detections,` `std::vector< float > & confidences )`	Performs detection.

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#

This class represents high-level API for text detection DL networks compatible with DB model.

Related publications: liao2020real Paper: https://arxiv.org/abs/1911.08947 For more information about the hyper-parameters setting, please refer to MhLiao/DB

Configurable parameters:

(float) binaryThreshold - The threshold of the binary map. It is usually set to 0.3.
(float) polygonThreshold - The threshold of text polygons. It is usually set to 0.5, 0.6, and 0.7. Default is 0.5f
(double) unclipRatio - The unclip ratio of the detected text region, which determines the output size. It is usually set to 2.0.
(int) maxCandidates - The max number of the output results.

Examples: samples/dnn/text_detection.cpp.

Constructor & Destructor Documentation#

TextDetectionModel_DB()#

cv::dnn::TextDetectionModel_DB::TextDetectionModel_DB()

Python:

cv.dnn.TextDetectionModel_DB(network) -> <dnn_TextDetectionModel_DB object>

cv.dnn.TextDetectionModel_DB(model[, config]) -> <dnn_TextDetectionModel_DB object>

TextDetectionModel_DB()#

cv::dnn::TextDetectionModel_DB::TextDetectionModel_DB(const Net & network)

Python:

cv.dnn.TextDetectionModel_DB(network) -> <dnn_TextDetectionModel_DB object>

cv.dnn.TextDetectionModel_DB(model[, config]) -> <dnn_TextDetectionModel_DB object>

Create text detection algorithm from deep learning network.

Parameters

network — Net object.

TextDetectionModel_DB()#

cv::dnn::TextDetectionModel_DB::TextDetectionModel_DB(
CV_WRAP_FILE_PATH const std::string & model,
CV_WRAP_FILE_PATH const std::string & config = “” )

Python:

cv.dnn.TextDetectionModel_DB(network) -> <dnn_TextDetectionModel_DB object>

cv.dnn.TextDetectionModel_DB(model[, config]) -> <dnn_TextDetectionModel_DB object>

Create text detection model from network represented in one of the supported formats. An order of model and config arguments does not matter.

Parameters

model — Binary file contains trained weights.
config — Text file contains network configuration.

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Member Function Documentation#

getBinaryThreshold()#

float cv::dnn::TextDetectionModel_DB::getBinaryThreshold()

Python:

cv.dnn.TextDetectionModel_DB.getBinaryThreshold() -> retval

getMaxCandidates()#

int cv::dnn::TextDetectionModel_DB::getMaxCandidates()

Python:

cv.dnn.TextDetectionModel_DB.getMaxCandidates() -> retval

getPolygonThreshold()#

float cv::dnn::TextDetectionModel_DB::getPolygonThreshold()

Python:

cv.dnn.TextDetectionModel_DB.getPolygonThreshold() -> retval

getUnclipRatio()#

double cv::dnn::TextDetectionModel_DB::getUnclipRatio()

Python:

cv.dnn.TextDetectionModel_DB.getUnclipRatio() -> retval

setBinaryThreshold()#

TextDetectionModel_DB & cv::dnn::TextDetectionModel_DB::setBinaryThreshold(float binaryThreshold)

Python:

cv.dnn.TextDetectionModel_DB.setBinaryThreshold(binaryThreshold) -> retval

setMaxCandidates()#

TextDetectionModel_DB & cv::dnn::TextDetectionModel_DB::setMaxCandidates(int maxCandidates)

Python:

cv.dnn.TextDetectionModel_DB.setMaxCandidates(maxCandidates) -> retval

setPolygonThreshold()#

TextDetectionModel_DB & cv::dnn::TextDetectionModel_DB::setPolygonThreshold(float polygonThreshold)

Python:

cv.dnn.TextDetectionModel_DB.setPolygonThreshold(polygonThreshold) -> retval

setUnclipRatio()#

TextDetectionModel_DB & cv::dnn::TextDetectionModel_DB::setUnclipRatio(double unclipRatio)

Python:

cv.dnn.TextDetectionModel_DB.setUnclipRatio(unclipRatio) -> retval

Source file#

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

opencv2/dnn/dnn.hpp