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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistribution's in binary form must reproduce the above copyright notice,
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//
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//M*/
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#ifndef __OPENCV_TEXT_ERFILTER_HPP__
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#define __OPENCV_TEXT_ERFILTER_HPP__
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#include "opencv2/core.hpp"
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#include <vector>
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#include <deque>
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#include <string>
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namespace cv
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{
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namespace text
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{
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//! @addtogroup text_detect
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//! @{
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/** @brief The ERStat structure represents a class-specific Extremal Region (ER).
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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
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outer boundary. A class-specific ER is selected (using a classifier) from all the ER's in the
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component tree of the image. :
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*/
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struct CV_EXPORTS ERStat
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{
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public:
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//! Constructor
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explicit ERStat(int level = 256, int pixel = 0, int x = 0, int y = 0);
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//! Destructor
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~ERStat() { }
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//! seed point and the threshold (max grey-level value)
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int pixel;
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int level;
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//! incrementally computable features
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int area;
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int perimeter;
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int euler; //!< euler number
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Rect rect;
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double raw_moments[2]; //!< order 1 raw moments to derive the centroid
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double central_moments[3]; //!< order 2 central moments to construct the covariance matrix
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std::deque<int> *crossings;//!< horizontal crossings
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float med_crossings; //!< median of the crossings at three different height levels
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//! 2nd stage features
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float hole_area_ratio;
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float convex_hull_ratio;
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float num_inflexion_points;
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// TODO Other features can be added (average color, standard deviation, and such)
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// TODO shall we include the pixel list whenever available (i.e. after 2nd stage) ?
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std::vector<int> *pixels;
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//! probability that the ER belongs to the class we are looking for
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double probability;
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//! pointers preserving the tree structure of the component tree
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ERStat* parent;
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ERStat* child;
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ERStat* next;
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ERStat* prev;
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//! wenever the regions is a local maxima of the probability
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bool local_maxima;
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ERStat* max_probability_ancestor;
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ERStat* min_probability_ancestor;
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};
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/** @brief Base class for 1st and 2nd stages of Neumann and Matas scene text detection algorithm [Neumann12]. :
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Extracts the component tree (if needed) and filter the extremal regions (ER's) by using a given classifier.
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*/
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class CV_EXPORTS_W ERFilter : public Algorithm
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{
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public:
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/** @brief Callback with the classifier is made a class.
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By doing it we hide SVM, Boost etc. Developers can provide their own classifiers to the
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ERFilter algorithm.
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*/
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class CV_EXPORTS_W Callback
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{
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public:
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virtual ~Callback() { }
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/** @brief The classifier must return probability measure for the region.
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@param stat : The region to be classified
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*/
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virtual double eval(const ERStat& stat) = 0; //const = 0; //TODO why cannot use const = 0 here?
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};
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/** @brief The key method of ERFilter algorithm.
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Takes image on input and returns the selected regions in a vector of ERStat only distinctive
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ERs which correspond to characters are selected by a sequential classifier
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@param image Single channel image CV_8UC1
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@param regions Output for the 1st stage and Input/Output for the 2nd. The selected Extremal Regions
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are stored here.
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Extracts the component tree (if needed) and filter the extremal regions (ER's) by using a given
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classifier.
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*/
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virtual void run( InputArray image, std::vector<ERStat>& regions ) = 0;
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//! set/get methods to set the algorithm properties,
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virtual void setCallback(const Ptr<ERFilter::Callback>& cb) = 0;
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virtual void setThresholdDelta(int thresholdDelta) = 0;
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virtual void setMinArea(float minArea) = 0;
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virtual void setMaxArea(float maxArea) = 0;
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virtual void setMinProbability(float minProbability) = 0;
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virtual void setMinProbabilityDiff(float minProbabilityDiff) = 0;
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virtual void setNonMaxSuppression(bool nonMaxSuppression) = 0;
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virtual int getNumRejected() = 0;
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};
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/*!
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Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm
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Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
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The component tree of the image is extracted by a threshold increased step by step
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from 0 to 255, incrementally computable descriptors (aspect_ratio, compactness,
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number of holes, and number of horizontal crossings) are computed for each ER
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and used as features for a classifier which estimates the class-conditional
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probability P(er|character). The value of P(er|character) is tracked using the inclusion
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relation of ER across all thresholds and only the ERs which correspond to local maximum
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of the probability P(er|character) are selected (if the local maximum of the
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probability is above a global limit pmin and the difference between local maximum and
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local minimum is greater than minProbabilityDiff).
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@param cb 每 Callback with the classifier. Default classifier can be implicitly load with function
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loadClassifierNM1(), e.g. from file in samples/cpp/trained_classifierNM1.xml
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@param thresholdDelta 每 Threshold step in subsequent thresholds when extracting the component tree
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@param minArea 每 The minimum area (% of image size) allowed for retreived ER＊s
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@param maxArea 每 The maximum area (% of image size) allowed for retreived ER＊s
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@param minProbability 每 The minimum probability P(er|character) allowed for retreived ER＊s
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@param nonMaxSuppression 每 Whenever non-maximum suppression is done over the branch probabilities
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@param minProbabilityDiff 每 The minimum probability difference between local maxima and local minima ERs
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*/
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/** @brief Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm [Neumann12].
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@param cb : Callback with the classifier. Default classifier can be implicitly load with function
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loadClassifierNM1, e.g. from file in samples/cpp/trained_classifierNM1.xml
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@param thresholdDelta : Threshold step in subsequent thresholds when extracting the component tree
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@param minArea : The minimum area (% of image size) allowed for retreived ER's
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@param minArea : The maximum area (% of image size) allowed for retreived ER's
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@param minProbability : The minimum probability P(er|character) allowed for retreived ER's
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@param nonMaxSuppression : Whenever non-maximum suppression is done over the branch probabilities
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@param minProbability : The minimum probability difference between local maxima and local minima ERs
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The component tree of the image is extracted by a threshold increased step by step from 0 to 255,
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incrementally computable descriptors (aspect_ratio, compactness, number of holes, and number of
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horizontal crossings) are computed for each ER and used as features for a classifier which estimates
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the class-conditional probability P(er|character). The value of P(er|character) is tracked using the
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inclusion relation of ER across all thresholds and only the ERs which correspond to local maximum of
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the probability P(er|character) are selected (if the local maximum of the probability is above a
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global limit pmin and the difference between local maximum and local minimum is greater than
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minProbabilityDiff).
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*/
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CV_EXPORTS_W Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb,
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int thresholdDelta = 1, float minArea = (float)0.00025,
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float maxArea = (float)0.13, float minProbability = (float)0.4,
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bool nonMaxSuppression = true,
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float minProbabilityDiff = (float)0.1);
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/** @brief Create an Extremal Region Filter for the 2nd stage classifier of N&M algorithm [Neumann12].
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@param cb : Callback with the classifier. Default classifier can be implicitly load with function
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loadClassifierNM2, e.g. from file in samples/cpp/trained_classifierNM2.xml
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@param minProbability : The minimum probability P(er|character) allowed for retreived ER's
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In the second stage, the ERs that passed the first stage are classified into character and
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non-character classes using more informative but also more computationally expensive features. The
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classifier uses all the features calculated in the first stage and the following additional
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features: hole area ratio, convex hull ratio, and number of outer inflexion points.
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*/
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CV_EXPORTS_W Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb,
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float minProbability = (float)0.3);
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/** @brief Allow to implicitly load the default classifier when creating an ERFilter object.
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@param filename The XML or YAML file with the classifier model (e.g. trained_classifierNM1.xml)
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returns a pointer to ERFilter::Callback.
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*/
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CV_EXPORTS_W Ptr<ERFilter::Callback> loadClassifierNM1(const String& filename);
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/** @brief Allow to implicitly load the default classifier when creating an ERFilter object.
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@param filename The XML or YAML file with the classifier model (e.g. trained_classifierNM2.xml)
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returns a pointer to ERFilter::Callback.
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*/
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CV_EXPORTS_W Ptr<ERFilter::Callback> loadClassifierNM2(const String& filename);
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//! computeNMChannels operation modes
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enum { ERFILTER_NM_RGBLGrad,
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ERFILTER_NM_IHSGrad
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};
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/** @brief Compute the different channels to be processed independently in the N&M algorithm [Neumann12].
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@param _src Source image. Must be RGB CV_8UC3.
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@param _channels Output vector\<Mat\> where computed channels are stored.
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@param _mode Mode of operation. Currently the only available options are:
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**ERFILTER_NM_RGBLGrad** (used by default) and **ERFILTER_NM_IHSGrad**.
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In N&M algorithm, the combination of intensity (I), hue (H), saturation (S), and gradient magnitude
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channels (Grad) are used in order to obtain high localization recall. This implementation also
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provides an alternative combination of red (R), green (G), blue (B), lightness (L), and gradient
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magnitude (Grad).
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*/
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CV_EXPORTS_W void computeNMChannels(InputArray _src, CV_OUT OutputArrayOfArrays _channels, int _mode = ERFILTER_NM_RGBLGrad);
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//! text::erGrouping operation modes
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enum erGrouping_Modes {
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/** Exhaustive Search algorithm proposed in [Neumann11] for grouping horizontally aligned text.
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The algorithm models a verification function for all the possible ER sequences. The
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verification fuction for ER pairs consists in a set of threshold-based pairwise rules which
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compare measurements of two regions (height ratio, centroid angle, and region distance). The
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verification function for ER triplets creates a word text line estimate using Least
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Median-Squares fitting for a given triplet and then verifies that the estimate is valid (based
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on thresholds created during training). Verification functions for sequences larger than 3 are
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approximated by verifying that the text line parameters of all (sub)sequences of length 3 are
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consistent.
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*/
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ERGROUPING_ORIENTATION_HORIZ,
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/** Text grouping method proposed in [Gomez13][Gomez14] for grouping arbitrary oriented text. Regions
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are agglomerated by Single Linkage Clustering in a weighted feature space that combines proximity
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(x,y coordinates) and similarity measures (color, size, gradient magnitude, stroke width, etc.).
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SLC provides a dendrogram where each node represents a text group hypothesis. Then the algorithm
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finds the branches corresponding to text groups by traversing this dendrogram with a stopping rule
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that combines the output of a rotation invariant text group classifier and a probabilistic measure
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for hierarchical clustering validity assessment.
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*/
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ERGROUPING_ORIENTATION_ANY
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};
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/** @brief Find groups of Extremal Regions that are organized as text blocks.
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@param img Original RGB or Greyscale image from wich the regions were extracted.
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@param channels Vector of single channel images CV_8UC1 from wich the regions were extracted.
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@param regions Vector of ER's retreived from the ERFilter algorithm from each channel.
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@param groups The output of the algorithm is stored in this parameter as set of lists of indexes to
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provided regions.
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@param groups_rects The output of the algorithm are stored in this parameter as list of rectangles.
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@param method Grouping method (see text::erGrouping_Modes). Can be one of ERGROUPING_ORIENTATION_HORIZ,
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ERGROUPING_ORIENTATION_ANY.
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@param filename The XML or YAML file with the classifier model (e.g.
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samples/trained_classifier_erGrouping.xml). Only to use when grouping method is
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ERGROUPING_ORIENTATION_ANY.
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@param minProbablity The minimum probability for accepting a group. Only to use when grouping
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method is ERGROUPING_ORIENTATION_ANY.
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*/
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CV_EXPORTS void erGrouping(InputArray img, InputArrayOfArrays channels,
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std::vector<std::vector<ERStat> > ®ions,
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std::vector<std::vector<Vec2i> > &groups,
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std::vector<Rect> &groups_rects,
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int method = ERGROUPING_ORIENTATION_HORIZ,
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const std::string& filename = std::string(),
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float minProbablity = 0.5);
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CV_EXPORTS_W void erGrouping(InputArray image, InputArray channel,
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std::vector<std::vector<Point> > regions,
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CV_OUT std::vector<Rect> &groups_rects,
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int method = ERGROUPING_ORIENTATION_HORIZ,
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const String& filename = String(),
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float minProbablity = (float)0.5);
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/** @brief Converts MSER contours (vector\<Point\>) to ERStat regions.
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@param image Source image CV_8UC1 from which the MSERs where extracted.
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@param contours Intput vector with all the contours (vector\<Point\>).
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@param regions Output where the ERStat regions are stored.
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It takes as input the contours provided by the OpenCV MSER feature detector and returns as output
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two vectors of ERStats. This is because MSER() output contains both MSER+ and MSER- regions in a
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single vector\<Point\>, the function separates them in two different vectors (this is as if the
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ERStats where extracted from two different channels).
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An example of MSERsToERStats in use can be found in the text detection webcam_demo:
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<https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/webcam_demo.cpp>
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*/
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CV_EXPORTS void MSERsToERStats(InputArray image, std::vector<std::vector<Point> > &contours,
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std::vector<std::vector<ERStat> > ®ions);
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// Utility funtion for scripting
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CV_EXPORTS_W void detectRegions(InputArray image, const Ptr<ERFilter>& er_filter1, const Ptr<ERFilter>& er_filter2, CV_OUT std::vector< std::vector<Point> >& regions);
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//! @}
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}
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}
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#endif // _OPENCV_TEXT_ERFILTER_HPP_
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