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An ER is a 4-connected set of pixels with all its grey-level values smaller than the values in its outer boundary. A class-specific ER is selected (using a classifier) from all the ER's in the component tree of the image. : */ struct CV_EXPORTS ERStat { public: //! Constructor explicit ERStat(int level = 256, int pixel = 0, int x = 0, int y = 0); //! Destructor ~ERStat() { } //! seed point and the threshold (max grey-level value) int pixel; int level; //! incrementally computable features int area; int perimeter; int euler; //!< euler number Rect rect; double raw_moments[2]; //!< order 1 raw moments to derive the centroid double central_moments[3]; //!< order 2 central moments to construct the covariance matrix std::deque *crossings;//!< horizontal crossings float med_crossings; //!< median of the crossings at three different height levels //! 2nd stage features float hole_area_ratio; float convex_hull_ratio; float num_inflexion_points; // TODO Other features can be added (average color, standard deviation, and such) // TODO shall we include the pixel list whenever available (i.e. after 2nd stage) ? std::vector *pixels; //! probability that the ER belongs to the class we are looking for double probability; //! pointers preserving the tree structure of the component tree ERStat* parent; ERStat* child; ERStat* next; ERStat* prev; //! wenever the regions is a local maxima of the probability bool local_maxima; ERStat* max_probability_ancestor; ERStat* min_probability_ancestor; }; /** @brief Base class for 1st and 2nd stages of Neumann and Matas scene text detection algorithm [Neumann12]. : Extracts the component tree (if needed) and filter the extremal regions (ER's) by using a given classifier. */ class CV_EXPORTS_W ERFilter : public Algorithm { public: /** @brief Callback with the classifier is made a class. By doing it we hide SVM, Boost etc. Developers can provide their own classifiers to the ERFilter algorithm. */ class CV_EXPORTS_W Callback { public: virtual ~Callback() { } /** @brief The classifier must return probability measure for the region. @param stat : The region to be classified */ virtual double eval(const ERStat& stat) = 0; //const = 0; //TODO why cannot use const = 0 here? }; /** @brief The key method of ERFilter algorithm. Takes image on input and returns the selected regions in a vector of ERStat only distinctive ERs which correspond to characters are selected by a sequential classifier @param image Single channel image CV_8UC1 @param regions Output for the 1st stage and Input/Output for the 2nd. The selected Extremal Regions are stored here. Extracts the component tree (if needed) and filter the extremal regions (ER's) by using a given classifier. */ virtual void run( InputArray image, std::vector& regions ) = 0; //! set/get methods to set the algorithm properties, virtual void setCallback(const Ptr& cb) = 0; virtual void setThresholdDelta(int thresholdDelta) = 0; virtual void setMinArea(float minArea) = 0; virtual void setMaxArea(float maxArea) = 0; virtual void setMinProbability(float minProbability) = 0; virtual void setMinProbabilityDiff(float minProbabilityDiff) = 0; virtual void setNonMaxSuppression(bool nonMaxSuppression) = 0; virtual int getNumRejected() = 0; }; /*! Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012 The component tree of the image is extracted by a threshold increased step by step from 0 to 255, incrementally computable descriptors (aspect_ratio, compactness, number of holes, and number of horizontal crossings) are computed for each ER and used as features for a classifier which estimates the class-conditional probability P(er|character). The value of P(er|character) is tracked using the inclusion relation of ER across all thresholds and only the ERs which correspond to local maximum of the probability P(er|character) are selected (if the local maximum of the probability is above a global limit pmin and the difference between local maximum and local minimum is greater than minProbabilityDiff). @param cb 每 Callback with the classifier. Default classifier can be implicitly load with function loadClassifierNM1(), e.g. from file in samples/cpp/trained_classifierNM1.xml @param thresholdDelta 每 Threshold step in subsequent thresholds when extracting the component tree @param minArea 每 The minimum area (% of image size) allowed for retreived ER＊s @param maxArea 每 The maximum area (% of image size) allowed for retreived ER＊s @param minProbability 每 The minimum probability P(er|character) allowed for retreived ER＊s @param nonMaxSuppression 每 Whenever non-maximum suppression is done over the branch probabilities @param minProbabilityDiff 每 The minimum probability difference between local maxima and local minima ERs */ /** @brief Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm [Neumann12]. @param cb : Callback with the classifier. Default classifier can be implicitly load with function loadClassifierNM1, e.g. from file in samples/cpp/trained_classifierNM1.xml @param thresholdDelta : Threshold step in subsequent thresholds when extracting the component tree @param minArea : The minimum area (% of image size) allowed for retreived ER's @param minArea : The maximum area (% of image size) allowed for retreived ER's @param minProbability : The minimum probability P(er|character) allowed for retreived ER's @param nonMaxSuppression : Whenever non-maximum suppression is done over the branch probabilities @param minProbability : The minimum probability difference between local maxima and local minima ERs The component tree of the image is extracted by a threshold increased step by step from 0 to 255, incrementally computable descriptors (aspect_ratio, compactness, number of holes, and number of horizontal crossings) are computed for each ER and used as features for a classifier which estimates the class-conditional probability P(er|character). The value of P(er|character) is tracked using the inclusion relation of ER across all thresholds and only the ERs which correspond to local maximum of the probability P(er|character) are selected (if the local maximum of the probability is above a global limit pmin and the difference between local maximum and local minimum is greater than minProbabilityDiff). */ CV_EXPORTS_W Ptr createERFilterNM1(const Ptr& cb, int thresholdDelta = 1, float minArea = (float)0.00025, float maxArea = (float)0.13, float minProbability = (float)0.4, bool nonMaxSuppression = true, float minProbabilityDiff = (float)0.1); /** @brief Create an Extremal Region Filter for the 2nd stage classifier of N&M algorithm [Neumann12]. @param cb : Callback with the classifier. Default classifier can be implicitly load with function loadClassifierNM2, e.g. from file in samples/cpp/trained_classifierNM2.xml @param minProbability : The minimum probability P(er|character) allowed for retreived ER's In the second stage, the ERs that passed the first stage are classified into character and non-character classes using more informative but also more computationally expensive features. The classifier uses all the features calculated in the first stage and the following additional features: hole area ratio, convex hull ratio, and number of outer inflexion points. */ CV_EXPORTS_W Ptr createERFilterNM2(const Ptr& cb, float minProbability = (float)0.3); /** @brief Allow to implicitly load the default classifier when creating an ERFilter object. @param filename The XML or YAML file with the classifier model (e.g. trained_classifierNM1.xml) returns a pointer to ERFilter::Callback. */ CV_EXPORTS_W Ptr loadClassifierNM1(const String& filename); /** @brief Allow to implicitly load the default classifier when creating an ERFilter object. @param filename The XML or YAML file with the classifier model (e.g. trained_classifierNM2.xml) returns a pointer to ERFilter::Callback. */ CV_EXPORTS_W Ptr loadClassifierNM2(const String& filename); //! computeNMChannels operation modes enum { ERFILTER_NM_RGBLGrad, ERFILTER_NM_IHSGrad }; /** @brief Compute the different channels to be processed independently in the N&M algorithm [Neumann12]. @param _src Source image. Must be RGB CV_8UC3. @param _channels Output vector\ where computed channels are stored. @param _mode Mode of operation. Currently the only available options are: **ERFILTER_NM_RGBLGrad** (used by default) and **ERFILTER_NM_IHSGrad**. In N&M algorithm, the combination of intensity (I), hue (H), saturation (S), and gradient magnitude channels (Grad) are used in order to obtain high localization recall. This implementation also provides an alternative combination of red (R), green (G), blue (B), lightness (L), and gradient magnitude (Grad). */ CV_EXPORTS_W void computeNMChannels(InputArray _src, CV_OUT OutputArrayOfArrays _channels, int _mode = ERFILTER_NM_RGBLGrad); //! text::erGrouping operation modes enum erGrouping_Modes { /** Exhaustive Search algorithm proposed in [Neumann11] for grouping horizontally aligned text. The algorithm models a verification function for all the possible ER sequences. The verification fuction for ER pairs consists in a set of threshold-based pairwise rules which compare measurements of two regions (height ratio, centroid angle, and region distance). The verification function for ER triplets creates a word text line estimate using Least Median-Squares fitting for a given triplet and then verifies that the estimate is valid (based on thresholds created during training). Verification functions for sequences larger than 3 are approximated by verifying that the text line parameters of all (sub)sequences of length 3 are consistent. */ ERGROUPING_ORIENTATION_HORIZ, /** Text grouping method proposed in [Gomez13][Gomez14] for grouping arbitrary oriented text. Regions are agglomerated by Single Linkage Clustering in a weighted feature space that combines proximity (x,y coordinates) and similarity measures (color, size, gradient magnitude, stroke width, etc.). SLC provides a dendrogram where each node represents a text group hypothesis. Then the algorithm finds the branches corresponding to text groups by traversing this dendrogram with a stopping rule that combines the output of a rotation invariant text group classifier and a probabilistic measure for hierarchical clustering validity assessment. */ ERGROUPING_ORIENTATION_ANY }; /** @brief Find groups of Extremal Regions that are organized as text blocks. @param img Original RGB or Greyscale image from wich the regions were extracted. @param channels Vector of single channel images CV_8UC1 from wich the regions were extracted. @param regions Vector of ER's retreived from the ERFilter algorithm from each channel. @param groups The output of the algorithm is stored in this parameter as set of lists of indexes to provided regions. @param groups_rects The output of the algorithm are stored in this parameter as list of rectangles. @param method Grouping method (see text::erGrouping_Modes). Can be one of ERGROUPING_ORIENTATION_HORIZ, ERGROUPING_ORIENTATION_ANY. @param filename The XML or YAML file with the classifier model (e.g. samples/trained_classifier_erGrouping.xml). Only to use when grouping method is ERGROUPING_ORIENTATION_ANY. @param minProbablity The minimum probability for accepting a group. Only to use when grouping method is ERGROUPING_ORIENTATION_ANY. */ CV_EXPORTS void erGrouping(InputArray img, InputArrayOfArrays channels, std::vector > ®ions, std::vector > &groups, std::vector &groups_rects, int method = ERGROUPING_ORIENTATION_HORIZ, const std::string& filename = std::string(), float minProbablity = 0.5); CV_EXPORTS_W void erGrouping(InputArray image, InputArray channel, std::vector > regions, CV_OUT std::vector &groups_rects, int method = ERGROUPING_ORIENTATION_HORIZ, const String& filename = String(), float minProbablity = (float)0.5); /** @brief Converts MSER contours (vector\) to ERStat regions. @param image Source image CV_8UC1 from which the MSERs where extracted. @param contours Intput vector with all the contours (vector\). @param regions Output where the ERStat regions are stored. It takes as input the contours provided by the OpenCV MSER feature detector and returns as output two vectors of ERStats. This is because MSER() output contains both MSER+ and MSER- regions in a single vector\, the function separates them in two different vectors (this is as if the ERStats where extracted from two different channels). An example of MSERsToERStats in use can be found in the text detection webcam_demo: */ CV_EXPORTS void MSERsToERStats(InputArray image, std::vector > &contours, std::vector > ®ions); // Utility funtion for scripting CV_EXPORTS_W void detectRegions(InputArray image, const Ptr& er_filter1, const Ptr& er_filter2, CV_OUT std::vector< std::vector >& regions); //! @} } } #endif // _OPENCV_TEXT_ERFILTER_HPP_