Package org.opencv.dnn
Class TextDetectionModel
- java.lang.Object
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- org.opencv.dnn.Model
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- org.opencv.dnn.TextDetectionModel
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- Direct Known Subclasses:
TextDetectionModel_DB
,TextDetectionModel_EAST
public class TextDetectionModel extends Model
Base class for text detection networks
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Constructor Summary
Constructors Modifier Constructor Description protected
TextDetectionModel(long addr)
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description static TextDetectionModel
__fromPtr__(long addr)
void
detect(Mat frame, java.util.List<MatOfPoint> detections)
void
detect(Mat frame, java.util.List<MatOfPoint> detections, MatOfFloat confidences)
Performs detection Given the inputframe
, prepare network input, run network inference, post-process network output and return result detections.void
detectTextRectangles(Mat frame, MatOfRotatedRect detections)
void
detectTextRectangles(Mat frame, MatOfRotatedRect detections, MatOfFloat confidences)
Performs detection Given the inputframe
, prepare network input, run network inference, post-process network output and return result detections.protected void
finalize()
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Methods inherited from class org.opencv.dnn.Model
enableWinograd, getNativeObjAddr, predict, setInputCrop, setInputMean, setInputParams, setInputParams, setInputParams, setInputParams, setInputParams, setInputParams, setInputScale, setInputSize, setInputSize, setInputSwapRB, setOutputNames, setPreferableBackend, setPreferableTarget
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Method Detail
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__fromPtr__
public static TextDetectionModel __fromPtr__(long addr)
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detect
public void detect(Mat frame, java.util.List<MatOfPoint> detections, MatOfFloat confidences)
Performs detection Given the inputframe
, 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 imagedetections
- array with detections' quadrangles (4 points per result)confidences
- array with detection confidences
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detect
public void detect(Mat frame, java.util.List<MatOfPoint> detections)
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detectTextRectangles
public void detectTextRectangles(Mat frame, MatOfRotatedRect detections, MatOfFloat confidences)
Performs detection Given the inputframe
, 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 imagedetections
- array with detections' RotationRect resultsconfidences
- array with detection confidences
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detectTextRectangles
public void detectTextRectangles(Mat frame, MatOfRotatedRect detections)
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