Package org.opencv.dnn
Class TextDetectionModel
java.lang.Object
org.opencv.dnn.Model
org.opencv.dnn.TextDetectionModel
- Direct Known Subclasses:
TextDetectionModel_DB
,TextDetectionModel_EAST
Base class for text detection networks
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Field Summary
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionstatic TextDetectionModel
__fromPtr__
(long addr) void
detect
(Mat frame, List<MatOfPoint> detections) void
detect
(Mat frame, 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()
Methods inherited from class org.opencv.dnn.Model
getNativeObjAddr, predict, setInputCrop, setInputMean, setInputParams, setInputParams, setInputParams, setInputParams, setInputParams, setInputParams, setInputScale, setInputSize, setInputSize, setInputSwapRB, setPreferableBackend, setPreferableTarget
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Constructor Details
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TextDetectionModel
protected TextDetectionModel(long addr)
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Method Details
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__fromPtr__
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detect
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
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detectTextRectangles
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
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finalize
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