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void | detect (InputArray frame, std::vector< std::vector< Point > > &detections) const |
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void | detect (InputArray frame, std::vector< std::vector< Point > > &detections, std::vector< float > &confidences) const |
| Performs detection.
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void | detectTextRectangles (InputArray frame, std::vector< cv::RotatedRect > &detections) const |
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void | detectTextRectangles (InputArray frame, std::vector< cv::RotatedRect > &detections, std::vector< float > &confidences) const |
| Performs detection.
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| Model () |
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| Model (const Model &)=default |
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| Model (const Net &network) |
| Create model from deep learning network.
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| 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.
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| Model (Model &&)=default |
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Model & | enableWinograd (bool useWinograd) |
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Impl * | getImpl () const |
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Impl & | getImplRef () const |
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Net & | getNetwork_ () |
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Net & | getNetwork_ () const |
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| operator Net & () const |
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Model & | operator= (const Model &)=default |
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Model & | operator= (Model &&)=default |
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void | predict (InputArray frame, OutputArrayOfArrays outs) const |
| Given the input frame, create input blob, run net and return the output blobs .
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Model & | setInputCrop (bool crop) |
| Set flag crop for frame.
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Model & | setInputMean (const Scalar &mean) |
| Set mean value for frame.
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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.
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Model & | setInputScale (const Scalar &scale) |
| Set scalefactor value for frame.
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Model & | setInputSize (const Size &size) |
| Set input size for frame.
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Model & | setInputSize (int width, int height) |
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Model & | setInputSwapRB (bool swapRB) |
| Set flag swapRB for frame.
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Model & | setOutputNames (const std::vector< String > &outNames) |
| Set output names for frame.
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Model & | setPreferableBackend (dnn::Backend backendId) |
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Model & | setPreferableTarget (dnn::Target targetId) |
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Base class for text detection networks.
void cv::dnn::TextDetectionModel::detect |
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InputArray |
frame, |
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std::vector< std::vector< Point > > & |
detections, |
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std::vector< float > & |
confidences |
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Python: |
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| 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
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[in] | frame | The input image |
[out] | detections | array with detections' quadrangles (4 points per result) |
[out] | confidences | array with detection confidences |