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Merge pull request #17570 from HannibalAPE:text_det_recog_demo
[GSoC] High Level API and Samples for Scene Text Detection and Recognition * APIs and samples for scene text detection and recognition * update APIs and tutorial for Text Detection and Recognition * API updates: (1) put decodeType into struct Voc (2) optimize the post-processing of DB * sample update: (1) add transformation into scene_text_spotting.cpp (2) modify text_detection.cpp with API update * update tutorial * simplify text recognition API update tutorial * update impl usage in recognize() and detect() * dnn: refactoring public API of TextRecognitionModel/TextDetectionModel * update provided models update opencv.bib * dnn: adjust text rectangle angle * remove points ordering operation in model.cpp * update gts of DB test in test_model.cpp * dnn: ensure to keep text rectangle angle - avoid 90/180 degree turns * dnn(text): use quadrangle result in TextDetectionModel API * dnn: update Text Detection API (1) keep points' order consistent with (bl, tl, tr, br) in unclip (2) update contourScore with boundingRect
This commit is contained in:
Wenqing Zhang
GitHub
@@ -1326,6 +1326,255 @@ CV__DNN_INLINE_NS_BEGIN
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float confThreshold = 0.5f, float nmsThreshold = 0.0f);
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};
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/** @brief This class represents high-level API for text recognition networks.
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*
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* TextRecognitionModel allows to set params for preprocessing input image.
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* TextRecognitionModel creates net from file with trained weights and config,
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* sets preprocessing input, runs forward pass and return recognition result.
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* For TextRecognitionModel, CRNN-CTC is supported.
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*/
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class CV_EXPORTS_W_SIMPLE TextRecognitionModel : public Model
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{
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public:
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CV_DEPRECATED_EXTERNAL // avoid using in C++ code, will be moved to "protected" (need to fix bindings first)
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TextRecognitionModel();
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/**
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* @brief Create Text Recognition model from deep learning network
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* Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method
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* @param[in] network Net object
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*/
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CV_WRAP TextRecognitionModel(const Net& network);
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/**
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* @brief Create text recognition model from network represented in one of the supported formats
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* Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method
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* @param[in] model Binary file contains trained weights
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* @param[in] config Text file contains network configuration
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*/
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CV_WRAP inline
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TextRecognitionModel(const std::string& model, const std::string& config = "")
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: TextRecognitionModel(readNet(model, config)) { /* nothing */ }
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/**
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* @brief Set the decoding method of translating the network output into string
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* @param[in] decodeType The decoding method of translating the network output into string: {'CTC-greedy': greedy decoding for the output of CTC-based methods}
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*/
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CV_WRAP
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TextRecognitionModel& setDecodeType(const std::string& decodeType);
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/**
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* @brief Get the decoding method
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* @return the decoding method
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*/
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CV_WRAP
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const std::string& getDecodeType() const;
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/**
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* @brief Set the vocabulary for recognition.
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* @param[in] vocabulary the associated vocabulary of the network.
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*/
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CV_WRAP
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TextRecognitionModel& setVocabulary(const std::vector<std::string>& vocabulary);
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/**
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* @brief Get the vocabulary for recognition.
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* @return vocabulary the associated vocabulary
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*/
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CV_WRAP
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const std::vector<std::string>& getVocabulary() const;
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/**
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* @brief Given the @p input frame, create input blob, run net and return recognition result
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* @param[in] frame The input image
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* @return The text recognition result
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*/
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CV_WRAP
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std::string recognize(InputArray frame) const;
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/**
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* @brief Given the @p input frame, create input blob, run net and return recognition result
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* @param[in] frame The input image
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* @param[in] roiRects List of text detection regions of interest (cv::Rect, CV_32SC4). ROIs is be cropped as the network inputs
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* @param[out] results A set of text recognition results.
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*/
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CV_WRAP
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void recognize(InputArray frame, InputArrayOfArrays roiRects, CV_OUT std::vector<std::string>& results) const;
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};
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/** @brief Base class for text detection networks
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*/
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class CV_EXPORTS_W TextDetectionModel : public Model
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{
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protected:
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CV_DEPRECATED_EXTERNAL // avoid using in C++ code, will be moved to "protected" (need to fix bindings first)
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TextDetectionModel();
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public:
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/** @brief Performs detection
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*
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* Given the input @p frame, prepare network input, run network inference, post-process network output and return result detections.
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*
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* Each result is quadrangle's 4 points in this order:
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* - bottom-left
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* - top-left
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* - top-right
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* - bottom-right
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*
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* Use cv::getPerspectiveTransform function to retrive image region without perspective transformations.
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*
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* @note If DL model doesn't support that kind of output then result may be derived from detectTextRectangles() output.
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*
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* @param[in] frame The input image
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* @param[out] detections array with detections' quadrangles (4 points per result)
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* @param[out] confidences array with detection confidences
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*/
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CV_WRAP
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void detect(
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InputArray frame,
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CV_OUT std::vector< std::vector<Point> >& detections,
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CV_OUT std::vector<float>& confidences
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) const;
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/** @overload */
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CV_WRAP
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void detect(
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InputArray frame,
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CV_OUT std::vector< std::vector<Point> >& detections
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) const;
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/** @brief Performs detection
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*
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* Given the input @p frame, prepare network input, run network inference, post-process network output and return result detections.
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*
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* Each result is rotated rectangle.
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*
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* @note Result may be inaccurate in case of strong perspective transformations.
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*
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* @param[in] frame the input image
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* @param[out] detections array with detections' RotationRect results
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* @param[out] confidences array with detection confidences
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*/
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CV_WRAP
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void detectTextRectangles(
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InputArray frame,
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CV_OUT std::vector<cv::RotatedRect>& detections,
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CV_OUT std::vector<float>& confidences
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) const;
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/** @overload */
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CV_WRAP
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void detectTextRectangles(
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InputArray frame,
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CV_OUT std::vector<cv::RotatedRect>& detections
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) const;
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};
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/** @brief This class represents high-level API for text detection DL networks compatible with EAST model.
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*
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* Configurable parameters:
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* - (float) confThreshold - used to filter boxes by confidences, default: 0.5f
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* - (float) nmsThreshold - used in non maximum suppression, default: 0.0f
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*/
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class CV_EXPORTS_W_SIMPLE TextDetectionModel_EAST : public TextDetectionModel
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{
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public:
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CV_DEPRECATED_EXTERNAL // avoid using in C++ code, will be moved to "protected" (need to fix bindings first)
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TextDetectionModel_EAST();
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/**
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* @brief Create text detection algorithm from deep learning network
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* @param[in] network Net object
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*/
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CV_WRAP TextDetectionModel_EAST(const Net& network);
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/**
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* @brief Create text detection model from network represented in one of the supported formats.
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* An order of @p model and @p config arguments does not matter.
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* @param[in] model Binary file contains trained weights.
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* @param[in] config Text file contains network configuration.
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*/
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CV_WRAP inline
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TextDetectionModel_EAST(const std::string& model, const std::string& config = "")
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: TextDetectionModel_EAST(readNet(model, config)) { /* nothing */ }
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/**
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* @brief Set the detection confidence threshold
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* @param[in] confThreshold A threshold used to filter boxes by confidences
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*/
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CV_WRAP
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TextDetectionModel_EAST& setConfidenceThreshold(float confThreshold);
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/**
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* @brief Get the detection confidence threshold
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*/
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CV_WRAP
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float getConfidenceThreshold() const;
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/**
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* @brief Set the detection NMS filter threshold
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* @param[in] nmsThreshold A threshold used in non maximum suppression
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*/
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CV_WRAP
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TextDetectionModel_EAST& setNMSThreshold(float nmsThreshold);
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/**
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* @brief Get the detection confidence threshold
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*/
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CV_WRAP
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float getNMSThreshold() const;
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};
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/** @brief This class represents high-level API for text detection DL networks compatible with DB model.
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*
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* Related publications: @cite liao2020real
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* Paper: https://arxiv.org/abs/1911.08947
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* For more information about the hyper-parameters setting, please refer to https://github.com/MhLiao/DB
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*
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* Configurable parameters:
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* - (float) binaryThreshold - The threshold of the binary map. It is usually set to 0.3.
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* - (float) polygonThreshold - The threshold of text polygons. It is usually set to 0.5, 0.6, and 0.7. Default is 0.5f
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* - (double) unclipRatio - The unclip ratio of the detected text region, which determines the output size. It is usually set to 2.0.
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* - (int) maxCandidates - The max number of the output results.
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*/
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class CV_EXPORTS_W_SIMPLE TextDetectionModel_DB : public TextDetectionModel
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{
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public:
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CV_DEPRECATED_EXTERNAL // avoid using in C++ code, will be moved to "protected" (need to fix bindings first)
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TextDetectionModel_DB();
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/**
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* @brief Create text detection algorithm from deep learning network.
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* @param[in] network Net object.
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*/
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CV_WRAP TextDetectionModel_DB(const Net& network);
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/**
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* @brief Create text detection model from network represented in one of the supported formats.
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* An order of @p model and @p config arguments does not matter.
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* @param[in] model Binary file contains trained weights.
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* @param[in] config Text file contains network configuration.
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*/
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CV_WRAP inline
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TextDetectionModel_DB(const std::string& model, const std::string& config = "")
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: TextDetectionModel_DB(readNet(model, config)) { /* nothing */ }
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CV_WRAP TextDetectionModel_DB& setBinaryThreshold(float binaryThreshold);
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CV_WRAP float getBinaryThreshold() const;
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CV_WRAP TextDetectionModel_DB& setPolygonThreshold(float polygonThreshold);
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CV_WRAP float getPolygonThreshold() const;
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CV_WRAP TextDetectionModel_DB& setUnclipRatio(double unclipRatio);
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CV_WRAP double getUnclipRatio() const;
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CV_WRAP TextDetectionModel_DB& setMaxCandidates(int maxCandidates);
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CV_WRAP int getMaxCandidates() const;
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};
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//! @}
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CV__DNN_INLINE_NS_END
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}
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