Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012.
High-level C++ interface and implementation of algorithm is in the objdetect module.
C++ example, a test image, and the default classifiers in xml files.
set(the_description "Object Detection")
-ocv_define_module(objdetect opencv_core opencv_imgproc OPTIONAL opencv_highgui)
+ocv_define_module(objdetect opencv_core opencv_imgproc opencv_ml OPTIONAL opencv_highgui)
}
#include "opencv2/objdetect/linemod.hpp"
+#include "opencv2/objdetect/erfilter.hpp"
#endif
--- /dev/null
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+// By downloading, copying, installing or using the software you agree to this license.
+// If you do not agree to this license, do not download, install,
+// copy or use the software.
+//
+//
+// License Agreement
+// For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+// * Redistribution's of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+//
+// * Redistribution's in binary form must reproduce the above copyright notice,
+// this list of conditions and the following disclaimer in the documentation
+// and/or other materials provided with the distribution.
+//
+// * The name of the copyright holders may not be used to endorse or promote products
+// derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_OBJDETECT_ERFILTER_HPP__
+#define __OPENCV_OBJDETECT_ERFILTER_HPP__
+
+#include "opencv2/core.hpp"
+#include <vector>
+#include <deque>
+
+namespace cv
+{
+
+/*!
+ Extremal Region Stat structure
+
+ The ERStat structure represents a class-specific Extremal Region (ER).
+
+ 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
+ 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
+ int bbox[4];
+ 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<int> *crossings;//!< horizontal crossings
+
+ //! 1st stage features
+ float aspect_ratio;
+ float compactness;
+ float num_holes;
+ float med_crossings;
+
+ //! 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<int> *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;
+};
+
+/*!
+ Base class for 1st and 2nd stages of Neumann and Matas scene text detection algorithms
+ Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
+
+ Extracts the component tree (if needed) and filter the extremal regions (ER's) by using a given classifier.
+*/
+class CV_EXPORTS ERFilter : public cv::Algorithm
+{
+public:
+
+ //! callback with the classifier is made a class. By doing it we hide SVM, Boost etc.
+ class CV_EXPORTS Callback
+ {
+ public:
+ virtual ~Callback(){};
+ //! The classifier must return probability measure for the region.
+ virtual double eval(const ERStat& stat) = 0; //const = 0; //TODO why cannot use const = 0 here?
+ };
+
+ /*!
+ the key method. 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 is the input image
+ \param regions is output for the first stage, input/output for the second one.
+ */
+ virtual void run( cv::InputArray image, std::vector<ERStat>& regions ) = 0;
+
+
+ //! set/get methods to set the algorithm properties,
+ virtual void setCallback(const cv::Ptr<ERFilter::Callback>& 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 Callback with the classifier.
+ if omitted tries to load a default classifier from file 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
+*/
+CV_EXPORTS cv::Ptr<ERFilter> createERFilterNM1(const cv::Ptr<ERFilter::Callback>& cb = NULL,
+ int thresholdDelta = 1, float minArea = 0.000025,
+ float maxArea = 0.13, float minProbability = 0.2,
+ bool nonMaxSuppression = true,
+ float minProbabilityDiff = 0.1);
+
+/*!
+ Create an Extremal Region Filter for the 2nd stage classifier of N&M algorithm
+ Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
+
+ 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.
+
+ \param cb Callback with the classifier
+ if omitted tries to load a default classifier from file trained_classifierNM2.xml
+ \param minProbability The minimum probability P(er|character) allowed for retreived ER's
+*/
+CV_EXPORTS cv::Ptr<ERFilter> createERFilterNM2(const cv::Ptr<ERFilter::Callback>& cb = NULL,
+ float minProbability = 0.85);
+
+}
+#endif // _OPENCV_ERFILTER_HPP_
--- /dev/null
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+// By downloading, copying, installing or using the software you agree to this license.
+// If you do not agree to this license, do not download, install,
+// copy or use the software.
+//
+//
+// License Agreement
+// For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+// * Redistribution's of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+//
+// * Redistribution's in binary form must reproduce the above copyright notice,
+// this list of conditions and the following disclaimer in the documentation
+// and/or other materials provided with the distribution.
+//
+// * The name of the copyright holders may not be used to endorse or promote products
+// derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"
+#include <fstream>
+
+using namespace std;
+
+namespace cv
+{
+
+ERStat::ERStat(int init_level, int init_pixel, int init_x, int init_y) : pixel(init_pixel),
+ level(init_level), area(0), perimeter(0), euler(0), probability(1.0),
+ parent(0), child(0), next(0), prev(0), local_maxima(0),
+ max_probability_ancestor(0), min_probability_ancestor(0)
+{
+ bbox[0] = init_x;
+ bbox[1] = init_y;
+ bbox[2] = init_x;
+ bbox[3] = init_y;
+ raw_moments[0] = 0.0;
+ raw_moments[1] = 0.0;
+ central_moments[0] = 0.0;
+ central_moments[1] = 0.0;
+ central_moments[2] = 0.0;
+ crossings = new std::deque<int>();
+ crossings->push_back(0);
+}
+
+
+// derivative classes
+
+
+// the classe implementing the interface for the 1st and 2nd stages of Neumann and Matas algorithm
+class CV_EXPORTS ERFilterNM : public ERFilter
+{
+public:
+ //Constructor
+ ERFilterNM();
+ //Destructor
+ ~ERFilterNM() {};
+
+ float minProbability;
+ bool nonMaxSuppression;
+ float minProbabilityDiff;
+
+ // the key method. Takes image on input, vector of ERStat is output for the first stage,
+ // input/output - for the second one.
+ void run( cv::InputArray image, std::vector<ERStat>& regions );
+
+protected:
+ int thresholdDelta;
+ float maxArea;
+ float minArea;
+
+ cv::Ptr<ERFilter::Callback> classifier;
+
+ // count of the rejected/accepted regions
+ int num_rejected_regions;
+ int num_accepted_regions;
+
+public:
+
+ // set/get methods to set the algorithm properties,
+ void setCallback(const cv::Ptr<ERFilter::Callback>& cb);
+ void setThresholdDelta(int thresholdDelta);
+ void setMinArea(float minArea);
+ void setMaxArea(float maxArea);
+ void setMinProbability(float minProbability);
+ void setMinProbabilityDiff(float minProbabilityDiff);
+ void setNonMaxSuppression(bool nonMaxSuppression);
+ int getNumRejected();
+
+private:
+ // pointer to the input/output regions vector
+ std::vector<ERStat> *regions;
+ // image mask used for feature calculations
+ cv::Mat region_mask;
+
+ // extract the component tree and store all the ER regions
+ void er_tree_extract( cv::InputArray image );
+ // accumulate a pixel into an ER
+ void er_add_pixel( ERStat *parent, int x, int y, int non_boundary_neighbours,
+ int non_boundary_neighbours_horiz,
+ int d_C1, int d_C2, int d_C3 );
+ // merge an ER with its nested parent
+ void er_merge( ERStat *parent, ERStat *child );
+ // recursively walk the tree and clean memory
+ void er_tree_clean( ERStat *er );
+ // copy extracted regions into the output vector
+ ERStat* er_save( ERStat *er, ERStat *parent, ERStat *prev );
+ // recursively walk the tree and filter (remove) regions using the callback classifier
+ ERStat* er_tree_filter( cv::InputArray image, ERStat *stat, ERStat *parent, ERStat *prev );
+ // recursively walk the tree selecting only regions with local maxima probability
+ ERStat* er_tree_nonmax_suppression( ERStat *er, ERStat *parent, ERStat *prev );
+};
+
+
+// default 1st stage classifier
+class CV_EXPORTS ERClassifierNM1 : public ERFilter::Callback
+{
+public:
+ //Constructor
+ ERClassifierNM1();
+ // Destructor
+ ~ERClassifierNM1() {};
+
+ // The classifier must return probability measure for the region.
+ double eval(const ERStat& stat);
+
+private:
+ CvBoost boost;
+};
+
+// default 2nd stage classifier
+class CV_EXPORTS ERClassifierNM2 : public ERFilter::Callback
+{
+public:
+ //constructor
+ ERClassifierNM2();
+ // Destructor
+ ~ERClassifierNM2() {};
+
+ // The classifier must return probability measure for the region.
+ double eval(const ERStat& stat);
+
+private:
+ CvBoost boost;
+};
+
+
+
+
+
+// default constructor
+ERFilterNM::ERFilterNM()
+{
+ thresholdDelta = 1;
+ minArea = 0.;
+ maxArea = 1.;
+ minProbability = 0.;
+ nonMaxSuppression = false;
+ minProbabilityDiff = 1.;
+ num_accepted_regions = 0;
+ num_rejected_regions = 0;
+ classifier = NULL;
+}
+
+// the key method. Takes image on input, vector of ERStat is output for the first stage,
+// input/output for the second one.
+void ERFilterNM::run( cv::InputArray image, std::vector<ERStat>& _regions )
+{
+
+ // assert correct image type
+ CV_Assert( image.getMat().type() == CV_8UC1 );
+
+ regions = &_regions;
+ region_mask = Mat::zeros(image.getMat().rows+2, image.getMat().cols+2, CV_8UC1);
+
+ // if regions vector is empty we must extract the entire component tree
+ if ( regions->size() == 0 )
+ {
+ er_tree_extract( image );
+ if (nonMaxSuppression)
+ {
+ vector<ERStat> aux_regions;
+ regions->swap(aux_regions);
+ regions->reserve(aux_regions.size());
+ er_tree_nonmax_suppression( &aux_regions.front(), NULL, NULL );
+ aux_regions.clear();
+ }
+ }
+ else // if regions vector is already filled we'll just filter the current regions
+ {
+ // the tree root must have no parent
+ CV_Assert( regions->front().parent == NULL );
+
+ vector<ERStat> aux_regions;
+ regions->swap(aux_regions);
+ regions->reserve(aux_regions.size());
+ er_tree_filter( image, &aux_regions.front(), NULL, NULL );
+ aux_regions.clear();
+ }
+}
+
+// extract the component tree and store all the ER regions
+// uses the algorithm described in
+// Linear time maximally stable extremal regions, D Nistér, H Stewénius – ECCV 2008
+void ERFilterNM::er_tree_extract( cv::InputArray image )
+{
+
+ Mat src = image.getMat();
+ // assert correct image type
+ CV_Assert( src.type() == CV_8UC1 );
+
+ if (thresholdDelta > 1)
+ {
+ Mat tmp;
+ src.copyTo(tmp);
+ src.release();
+ src = (image.getMat() / thresholdDelta) -1;
+ }
+
+ const unsigned char * image_data = src.data;
+ int width = src.cols, height = src.rows;
+
+ // the component stack
+ vector<ERStat*> er_stack;
+
+ //the quads for euler number calculation
+ unsigned char quads[3][4];
+ quads[0][0] = 1 << 3;
+ quads[0][1] = 1 << 2;
+ quads[0][2] = 1 << 1;
+ quads[0][3] = 1;
+ quads[1][0] = (1<<2)|(1<<1)|(1);
+ quads[1][1] = (1<<3)|(1<<1)|(1);
+ quads[1][2] = (1<<3)|(1<<2)|(1);
+ quads[1][3] = (1<<3)|(1<<2)|(1<<1);
+ quads[2][0] = (1<<2)|(1<<1);
+ quads[2][1] = (1<<3)|(1);
+ quads[2][3] = 255;
+ quads[2][4] = 255;
+
+
+ // masks to know if a pixel is accessible and if it has been already added to some region
+ vector<bool> accessible_pixel_mask(width * height);
+ vector<bool> accumulated_pixel_mask(width * height);
+
+ // heap of boundary pixels
+ vector<int> boundary_pixes[(255/thresholdDelta)+1];
+ vector<int> boundary_edges[(255/thresholdDelta)+1];
+
+ // add a dummy-component before start
+ er_stack.push_back(new ERStat);
+
+ // we'll look initially for all pixels with grey-level lower than a grey-level higher than any allowed in the image
+ int threshold_level = (255/thresholdDelta)+1;
+
+ // starting from the first pixel (0,0)
+ int current_pixel = 0;
+ int current_edge = 0;
+ int current_level = image_data[0];
+ accessible_pixel_mask[0] = true;
+
+ bool push_new_component = true;
+
+ for (;;) {
+
+ int x = current_pixel % width;
+ int y = current_pixel / width;
+
+ // push a component with current level in the component stack
+ if (push_new_component)
+ er_stack.push_back(new ERStat(current_level, current_pixel, x, y));
+ push_new_component = false;
+
+ // explore the (remaining) edges to the neighbors to the current pixel
+ for (current_edge = current_edge; current_edge < 4; current_edge++)
+ {
+
+ int neighbour_pixel = current_pixel;
+
+ switch (current_edge)
+ {
+ case 0: if (x < width - 1) neighbour_pixel = current_pixel + 1; break;
+ case 1: if (y < height - 1) neighbour_pixel = current_pixel + width; break;
+ case 2: if (x > 0) neighbour_pixel = current_pixel - 1; break;
+ default: if (y > 0) neighbour_pixel = current_pixel - width; break;
+ }
+
+ // if neighbour is not accessible, mark it accessible and retreive its grey-level value
+ if ( !accessible_pixel_mask[neighbour_pixel] && (neighbour_pixel != current_pixel) )
+ {
+
+ int neighbour_level = image_data[neighbour_pixel];
+ accessible_pixel_mask[neighbour_pixel] = true;
+
+ // if neighbour level is not lower than current level add neighbour to the boundary heap
+ if (neighbour_level >= current_level)
+ {
+
+ boundary_pixes[neighbour_level].push_back(neighbour_pixel);
+ boundary_edges[neighbour_level].push_back(0);
+
+ // if neighbour level is lower than our threshold_level set threshold_level to neighbour level
+ if (neighbour_level < threshold_level)
+ threshold_level = neighbour_level;
+
+ }
+ else // if neighbour level is lower than current add current_pixel (and next edge)
+ // to the boundary heap for later processing
+ {
+
+ boundary_pixes[current_level].push_back(current_pixel);
+ boundary_edges[current_level].push_back(current_edge + 1);
+
+ // if neighbour level is lower than threshold_level set threshold_level to neighbour level
+ if (current_level < threshold_level)
+ threshold_level = current_level;
+
+ // consider the new pixel and its grey-level as current pixel
+ current_pixel = neighbour_pixel;
+ current_edge = 0;
+ current_level = neighbour_level;
+
+ // and push a new component
+ push_new_component = true;
+ break;
+ }
+ }
+
+ } // else neigbor was already accessible
+
+ if (push_new_component) continue;
+
+
+ // once here we can add the current pixel to the component at the top of the stack
+ // but first we find how many of its neighbours are part of the region boundary (needed for
+ // perimeter and crossings calc.) and the increment in quads counts for euler number calc.
+ int non_boundary_neighbours = 0;
+ int non_boundary_neighbours_horiz = 0;
+
+ unsigned char quad_before[4] = {0,0,0,0};
+ unsigned char quad_after[4] = {0,0,0,0};
+ quad_after[0] = 1<<1;
+ quad_after[1] = 1<<3;
+ quad_after[2] = 1<<2;
+ quad_after[3] = 1;
+
+ for (int edge = 0; edge < 8; edge++)
+ {
+ int neighbour4 = -1;
+ int neighbour8 = -1;
+ int cell = 0;
+ switch (edge)
+ {
+ case 0: if (x < width - 1) { neighbour4 = neighbour8 = current_pixel + 1;} cell = 5; break;
+ case 1: if ((x < width - 1)&&(y < height - 1)) { neighbour8 = current_pixel + 1 + width;} cell = 8; break;
+ case 2: if (y < height - 1) { neighbour4 = neighbour8 = current_pixel + width;} cell = 7; break;
+ case 3: if ((x > 0)&&(y < height - 1)) { neighbour8 = current_pixel - 1 + width;} cell = 6; break;
+ case 4: if (x > 0) { neighbour4 = neighbour8 = current_pixel - 1;} cell = 3; break;
+ case 5: if ((x > 0)&&(y > 0)) { neighbour8 = current_pixel - 1 - width;} cell = 0; break;
+ case 6: if (y > 0) { neighbour4 = neighbour8 = current_pixel - width;} cell = 1; break;
+ default: if ((x < width - 1)&&(y > 0)) { neighbour8 = current_pixel + 1 - width;} cell = 2; break;
+ }
+ if ((neighbour4 != -1)&&(accumulated_pixel_mask[neighbour4])&&(image_data[neighbour4]<=image_data[current_pixel]))
+ {
+ non_boundary_neighbours++;
+ if ((edge == 0) || (edge == 4))
+ non_boundary_neighbours_horiz++;
+ }
+
+ int pix_value = image_data[current_pixel] + 1;
+ if (neighbour8 != -1)
+ {
+ if (accumulated_pixel_mask[neighbour8])
+ pix_value = image_data[neighbour8];
+ }
+
+ if (pix_value<=image_data[current_pixel])
+ {
+ switch(cell)
+ {
+ case 0:
+ quad_before[3] = quad_before[3] | (1<<3);
+ quad_after[3] = quad_after[3] | (1<<3);
+ break;
+ case 1:
+ quad_before[3] = quad_before[3] | (1<<2);
+ quad_after[3] = quad_after[3] | (1<<2);
+ quad_before[0] = quad_before[0] | (1<<3);
+ quad_after[0] = quad_after[0] | (1<<3);
+ break;
+ case 2:
+ quad_before[0] = quad_before[0] | (1<<2);
+ quad_after[0] = quad_after[0] | (1<<2);
+ break;
+ case 3:
+ quad_before[3] = quad_before[3] | (1<<1);
+ quad_after[3] = quad_after[3] | (1<<1);
+ quad_before[2] = quad_before[2] | (1<<3);
+ quad_after[2] = quad_after[2] | (1<<3);
+ break;
+ case 5:
+ quad_before[0] = quad_before[0] | (1);
+ quad_after[0] = quad_after[0] | (1);
+ quad_before[1] = quad_before[1] | (1<<2);
+ quad_after[1] = quad_after[1] | (1<<2);
+ break;
+ case 6:
+ quad_before[2] = quad_before[2] | (1<<1);
+ quad_after[2] = quad_after[2] | (1<<1);
+ break;
+ case 7:
+ quad_before[2] = quad_before[2] | (1);
+ quad_after[2] = quad_after[2] | (1);
+ quad_before[1] = quad_before[1] | (1<<1);
+ quad_after[1] = quad_after[1] | (1<<1);
+ break;
+ default:
+ quad_before[1] = quad_before[1] | (1);
+ quad_after[1] = quad_after[1] | (1);
+ break;
+ }
+ }
+
+ }
+
+ int C_before[3] = {0, 0, 0};
+ int C_after[3] = {0, 0, 0};
+
+ for (int p=0; p<3; p++)
+ {
+ for (int q=0; q<4; q++)
+ {
+ if ( (quad_before[0] == quads[p][q]) )
+ if ((p<2)||(q<2)) C_before[p]++;
+ if ( (quad_before[1] == quads[p][q]) )
+ if ((p<2)||(q<2)) C_before[p]++;
+ if ( (quad_before[2] == quads[p][q]) )
+ if ((p<2)||(q<2)) C_before[p]++;
+ if ( (quad_before[3] == quads[p][q]) )
+ if ((p<2)||(q<2)) C_before[p]++;
+
+ if ( (quad_after[0] == quads[p][q]) )
+ if ((p<2)||(q<2)) C_after[p]++;
+ if ( (quad_after[1] == quads[p][q]) )
+ if ((p<2)||(q<2)) C_after[p]++;
+ if ( (quad_after[2] == quads[p][q]) )
+ if ((p<2)||(q<2)) C_after[p]++;
+ if ( (quad_after[3] == quads[p][q]) )
+ if ((p<2)||(q<2)) C_after[p]++;
+ }
+ }
+
+ int d_C1 = C_after[0]-C_before[0];
+ int d_C2 = C_after[1]-C_before[1];
+ int d_C3 = C_after[2]-C_before[2];
+
+ er_add_pixel(er_stack.back(), x, y, non_boundary_neighbours, non_boundary_neighbours_horiz, d_C1, d_C2, d_C3);
+ accumulated_pixel_mask[current_pixel] = true;
+
+ // if we have processed all the possible threshold levels (the hea is empty) we are done!
+ if (threshold_level == (255/thresholdDelta)+1)
+ {
+
+ // save the extracted regions into the output vector
+ regions->reserve(num_accepted_regions+1);
+ er_save(er_stack.back(), NULL, NULL);
+
+ // clean memory
+ er_tree_clean(er_stack.back());
+ er_stack.clear();
+
+ return;
+ }
+
+
+ // pop the heap of boundary pixels
+ current_pixel = boundary_pixes[threshold_level].back();
+ boundary_pixes[threshold_level].erase(boundary_pixes[threshold_level].end()-1);
+ current_edge = boundary_edges[threshold_level].back();
+ boundary_edges[threshold_level].erase(boundary_edges[threshold_level].end()-1);
+
+ while (boundary_pixes[threshold_level].empty() && (threshold_level < (255/thresholdDelta)+1))
+ threshold_level++;
+
+
+ int new_level = image_data[current_pixel];
+
+ // if the new pixel has higher grey value than the current one
+ if (new_level != current_level) {
+
+ current_level = new_level;
+
+ // process components on the top of the stack until we reach the higher grey-level
+ while (er_stack.back()->level < new_level)
+ {
+ ERStat* er = er_stack.back();
+ er_stack.erase(er_stack.end()-1);
+
+ if (new_level < er_stack.back()->level)
+ {
+ er_stack.push_back(new ERStat(new_level, current_pixel, current_pixel%width, current_pixel/width));
+ er_merge(er_stack.back(), er);
+ break;
+ }
+
+ er_merge(er_stack.back(), er);
+ }
+
+ }
+
+ }
+}
+
+// accumulate a pixel into an ER
+void ERFilterNM::er_add_pixel(ERStat *parent, int x, int y, int non_border_neighbours,
+ int non_border_neighbours_horiz,
+ int d_C1, int d_C2, int d_C3)
+{
+ parent->area++;
+ parent->perimeter += 4 - 2*non_border_neighbours;
+
+ if (parent->crossings->size()>0)
+ {
+ if (y<parent->bbox[1]) parent->crossings->push_front(2);
+ else if (y>parent->bbox[3]) parent->crossings->push_back(2);
+ else {
+ parent->crossings->at(y - parent->bbox[1]) += 2-2*non_border_neighbours_horiz;
+ }
+ } else {
+ parent->crossings->push_back(2);
+ }
+
+ parent->euler += (d_C1 - d_C2 + 2*d_C3) / 4;
+
+ parent->bbox[0] = min(parent->bbox[0],x);
+ parent->bbox[1] = min(parent->bbox[1],y);
+ parent->bbox[2] = max(parent->bbox[2],x);
+ parent->bbox[3] = max(parent->bbox[3],y);
+
+ parent->raw_moments[0] += x;
+ parent->raw_moments[1] += y;
+
+ parent->central_moments[0] += x * x;
+ parent->central_moments[1] += x * y;
+ parent->central_moments[2] += y * y;
+}
+
+// merge an ER with its nested parent
+void ERFilterNM::er_merge(ERStat *parent, ERStat *child)
+{
+
+ parent->area += child->area;
+
+ parent->perimeter += child->perimeter;
+
+ for (int i=parent->bbox[1]; i<=min(parent->bbox[3],child->bbox[3]); i++)
+ if (i-child->bbox[1] >= 0)
+ parent->crossings->at(i-parent->bbox[1]) += child->crossings->at(i-child->bbox[1]);
+
+ for (int i=parent->bbox[1]-1; i>=child->bbox[1]; i--)
+ if (i-child->bbox[1] < (int)child->crossings->size())
+ parent->crossings->push_front(child->crossings->at(i-child->bbox[1]));
+ else
+ parent->crossings->push_front(0);
+
+ for (int i=parent->bbox[3]+1; i<child->bbox[1]; i++)
+ parent->crossings->push_back(0);
+
+ for (int i=max(parent->bbox[3]+1,child->bbox[1]); i<=child->bbox[3]; i++)
+ parent->crossings->push_back(child->crossings->at(i-child->bbox[1]));
+
+ parent->euler += child->euler;
+
+ parent->bbox[0] = min(parent->bbox[0],child->bbox[0]);
+ parent->bbox[1] = min(parent->bbox[1],child->bbox[1]);
+ parent->bbox[2] = max(parent->bbox[2],child->bbox[2]);
+ parent->bbox[3] = max(parent->bbox[3],child->bbox[3]);
+
+ parent->raw_moments[0] += child->raw_moments[0];
+ parent->raw_moments[1] += child->raw_moments[1];
+
+ parent->central_moments[0] += child->central_moments[0];
+ parent->central_moments[1] += child->central_moments[1];
+ parent->central_moments[2] += child->central_moments[2];
+
+ // child region done, we can calculate 1st stage features from the incrementally computable descriptors
+ child->aspect_ratio = (float)(child->bbox[2]-child->bbox[0]+1)/(child->bbox[3]-child->bbox[1]+1);
+ child->compactness = sqrt(child->area)/child->perimeter;
+ child->num_holes = (float)(1-child->euler);
+
+ vector<int> m_crossings;
+ m_crossings.push_back(child->crossings->at((int)(child->bbox[3]-child->bbox[1]+1)/6));
+ m_crossings.push_back(child->crossings->at((int)3*(child->bbox[3]-child->bbox[1]+1)/6));
+ m_crossings.push_back(child->crossings->at((int)5*(child->bbox[3]-child->bbox[1]+1)/6));
+ sort(m_crossings.begin(), m_crossings.end());
+ child->med_crossings = (float)m_crossings.at(1);
+
+ // free unnecessary mem
+ child->crossings->clear();
+ delete(child->crossings);
+ child->crossings = NULL;
+
+ // recover the original grey-level
+ child->level = child->level*thresholdDelta;
+
+ // before saving calculate P(child|character) and filter if possible
+ if (classifier != NULL)
+ {
+ child->probability = classifier->eval(*child);
+ }
+
+ if ( ((classifier!=NULL)?(child->probability >= minProbability):true) &&
+ ((child->area >= (minArea*region_mask.rows*region_mask.cols)) &&
+ (child->area <= (maxArea*region_mask.rows*region_mask.cols))) )
+ {
+
+ num_accepted_regions++;
+
+ child->next = parent->child;
+ if (parent->child)
+ parent->child->prev = child;
+ parent->child = child;
+ child->parent = parent;
+
+ } else {
+
+ num_rejected_regions++;
+
+ if (child->prev !=NULL)
+ child->prev->next = child->next;
+
+ ERStat *new_child = child->child;
+ if (new_child != NULL)
+ {
+ while (new_child->next != NULL)
+ new_child = new_child->next;
+ new_child->next = parent->child;
+ if (parent->child)
+ parent->child->prev = new_child;
+ parent->child = child->child;
+ child->child->parent = parent;
+ }
+
+ // free mem
+ if(child->crossings)
+ {
+ child->crossings->clear();
+ delete(child->crossings);
+ child->crossings = NULL;
+ }
+ delete(child);
+ }
+
+}
+
+// recursively walk the tree and clean memory
+void ERFilterNM::er_tree_clean( ERStat *stat )
+{
+ for (ERStat * child = stat->child; child; child = child->next)
+ {
+ er_tree_clean(child);
+ }
+ if (stat->crossings)
+ {
+ stat->crossings->clear();
+ delete(stat->crossings);
+ stat->crossings = NULL;
+ }
+ delete stat;
+}
+
+// copy extracted regions into the output vector
+ERStat* ERFilterNM::er_save( ERStat *er, ERStat *parent, ERStat *prev )
+{
+
+ regions->push_back(*er);
+
+ regions->back().parent = parent;
+ if (prev != NULL)
+ prev->next = &(regions->back());
+ else if (parent != NULL)
+ parent->child = &(regions->back());
+
+ ERStat *old_prev = NULL;
+ ERStat *this_er = ®ions->back();
+
+ if (nonMaxSuppression)
+ {
+ if (this_er->parent == NULL)
+ {
+ this_er->probability = 0; //TODO this makes sense in order to select at least one region in short tree's but is it really necessary?
+ this_er->max_probability_ancestor = this_er;
+ this_er->min_probability_ancestor = this_er;
+ }
+ else
+ {
+ this_er->max_probability_ancestor = (this_er->probability > parent->max_probability_ancestor->probability)? this_er : parent->max_probability_ancestor;
+
+ this_er->min_probability_ancestor = (this_er->probability < parent->min_probability_ancestor->probability)? this_er : parent->min_probability_ancestor;
+
+ if ( (this_er->max_probability_ancestor->probability > minProbability) && (this_er->max_probability_ancestor->probability - this_er->min_probability_ancestor->probability > minProbabilityDiff))
+ {
+
+ this_er->max_probability_ancestor->local_maxima = true;
+ //TODO check here if the last local_maxima can be also suppressed, is the following correct?
+ //if (this_er->min_probability_ancestor->local_maxima)
+ // this_er->min_probability_ancestor->local_maxima = false;
+
+ this_er->max_probability_ancestor = this_er;
+ this_er->min_probability_ancestor = this_er;
+ }
+ }
+ }
+
+ for (ERStat * child = er->child; child; child = child->next)
+ {
+ old_prev = er_save(child, this_er, old_prev);
+ }
+
+ return this_er;
+}
+
+// recursively walk the tree and filter (remove) regions using the callback classifier
+ERStat* ERFilterNM::er_tree_filter ( cv::InputArray image, ERStat * stat, ERStat *parent, ERStat *prev )
+{
+ Mat src = image.getMat();
+ // assert correct image type
+ CV_Assert( src.type() == CV_8UC1 );
+
+ //Fill the region and calculate 2nd stage features
+ Mat region = region_mask(Rect(Point(stat->bbox[0],stat->bbox[1]),Point(stat->bbox[2]+3,stat->bbox[3]+3)));
+ region = Scalar(0);
+ int newMaskVal = 255;
+ int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY;
+ Rect rect;
+
+ floodFill( src(Rect(Point(stat->bbox[0],stat->bbox[1]),Point(stat->bbox[2]+1,stat->bbox[3]+1))),
+ region, Point(stat->pixel%src.cols - stat->bbox[0], stat->pixel/src.cols - stat->bbox[1]),
+ Scalar(255), &rect, Scalar(stat->level), Scalar(0), flags );
+ rect.width += 2;
+ rect.height += 2;
+ region = region(rect);
+
+ vector<vector<Point> > contours;
+ vector<Point> contour_poly;
+ vector<Vec4i> hierarchy;
+ findContours( region, contours, hierarchy, RETR_TREE, CHAIN_APPROX_NONE, Point(0, 0) );
+ //TODO check epsilon parameter of approxPolyDP (set empirically) : we want more precission
+ // if the region is very small because otherwise we'll loose all the convexities
+ approxPolyDP( Mat(contours[0]), contour_poly, max(rect.width,rect.height)/25, true );
+
+
+ bool was_convex = false;
+ int num_inflexion_points = 0;
+
+ for (int p = 0 ; p<(int)contour_poly.size(); p++)
+ {
+ int p_prev = p-1;
+ int p_next = p+1;
+ if (p_prev == -1)
+ p_prev = contour_poly.size()-1;
+ if (p_next == (int)contour_poly.size())
+ p_next = 0;
+
+ double angle_next = atan2((contour_poly[p_next].y-contour_poly[p].y),(contour_poly[p_next].x-contour_poly[p].x));
+ double angle_prev = atan2((contour_poly[p_prev].y-contour_poly[p].y),(contour_poly[p_prev].x-contour_poly[p].x));
+ if ( angle_next < 0 )
+ angle_next = 2.*CV_PI + angle_next;
+
+ double angle = (angle_next - angle_prev);
+ if (angle > 2.*CV_PI)
+ angle = angle - 2.*CV_PI;
+ else if (angle < 0)
+ angle = 2.*CV_PI + std::abs(angle);
+
+ if (p>0)
+ {
+ if ( ((angle > CV_PI)&&(!was_convex)) || ((angle < CV_PI)&&(was_convex)) )
+ num_inflexion_points++;
+ }
+ was_convex = (angle > CV_PI);
+
+ }
+
+ floodFill(region, Point(0,0), Scalar(255), 0);
+ int holes_area = region.cols*region.rows-countNonZero(region);
+
+ int hull_area = 0;
+
+ {
+
+ vector<Point> hull;
+ cv::convexHull(contours[0], hull, false);
+ hull_area = contourArea(hull);
+ }
+
+
+ stat->hole_area_ratio = (float)holes_area / stat->area;
+ stat->convex_hull_ratio = (float)hull_area / contourArea(contours[0]);
+ stat->num_inflexion_points = (float)num_inflexion_points;
+
+
+ // calculate P(child|character) and filter if possible
+ if ( (classifier != NULL) && (stat->parent != NULL) )
+ {
+ stat->probability = classifier->eval(*stat);
+ }
+
+ if ( ( ((classifier != NULL)?(stat->probability >= minProbability):true) &&
+ ((stat->area >= minArea*region_mask.rows*region_mask.cols) &&
+ (stat->area <= maxArea*region_mask.rows*region_mask.cols)) ) ||
+ (stat->parent == NULL) )
+ {
+
+ num_accepted_regions++;
+ regions->push_back(*stat);
+
+ regions->back().parent = parent;
+ regions->back().next = NULL;
+ regions->back().child = NULL;
+
+ if (prev != NULL)
+ prev->next = &(regions->back());
+ else if (parent != NULL)
+ parent->child = &(regions->back());
+
+ ERStat *old_prev = NULL;
+ ERStat *this_er = ®ions->back();
+
+ for (ERStat * child = stat->child; child; child = child->next)
+ {
+ old_prev = er_tree_filter(image, child, this_er, old_prev);
+ }
+
+ return this_er;
+
+ } else {
+
+ num_rejected_regions++;
+
+ ERStat *old_prev = prev;
+
+ for (ERStat * child = stat->child; child; child = child->next)
+ {
+ old_prev = er_tree_filter(image, child, parent, old_prev);
+ }
+
+ return old_prev;
+ }
+
+}
+
+// recursively walk the tree selecting only regions with local maxima probability
+ERStat* ERFilterNM::er_tree_nonmax_suppression ( ERStat * stat, ERStat *parent, ERStat *prev )
+{
+
+ if ( ( stat->local_maxima ) || ( stat->parent == NULL ) )
+ {
+
+ regions->push_back(*stat);
+
+ regions->back().parent = parent;
+ regions->back().next = NULL;
+ regions->back().child = NULL;
+
+ if (prev != NULL)
+ prev->next = &(regions->back());
+ else if (parent != NULL)
+ parent->child = &(regions->back());
+
+ ERStat *old_prev = NULL;
+ ERStat *this_er = ®ions->back();
+
+ for (ERStat * child = stat->child; child; child = child->next)
+ {
+ old_prev = er_tree_nonmax_suppression( child, this_er, old_prev );
+ }
+
+ return this_er;
+
+ } else {
+
+ num_rejected_regions++;
+ num_accepted_regions--;
+
+ ERStat *old_prev = prev;
+
+ for (ERStat * child = stat->child; child; child = child->next)
+ {
+ old_prev = er_tree_nonmax_suppression( child, parent, old_prev );
+ }
+
+ return old_prev;
+ }
+
+}
+
+void ERFilterNM::setCallback(const Ptr<ERFilter::Callback>& cb)
+{
+ classifier = cb;
+};
+
+void ERFilterNM::setMinArea(float _minArea)
+{
+ CV_Assert( (_minArea >= 0) && (_minArea < maxArea) );
+ minArea = _minArea;
+ return;
+};
+
+void ERFilterNM::setMaxArea(float _maxArea)
+{
+ CV_Assert(_maxArea <= 1);
+ CV_Assert(minArea < _maxArea);
+ maxArea = _maxArea;
+ return;
+};
+
+void ERFilterNM::setThresholdDelta(int _thresholdDelta)
+{
+ CV_Assert( (_thresholdDelta > 0) && (_thresholdDelta <= 128) );
+ thresholdDelta = _thresholdDelta;
+ return;
+};
+
+void ERFilterNM::setMinProbability(float _minProbability)
+{
+ CV_Assert( (_minProbability >= 0.0) && (_minProbability <= 1.0) );
+ minProbability = _minProbability;
+ return;
+};
+
+void ERFilterNM::setMinProbabilityDiff(float _minProbabilityDiff)
+{
+ CV_Assert( (_minProbabilityDiff >= 0.0) && (_minProbabilityDiff <= 1.0) );
+ minProbabilityDiff = _minProbabilityDiff;
+ return;
+};
+
+void ERFilterNM::setNonMaxSuppression(bool _nonMaxSuppression)
+{
+ nonMaxSuppression = _nonMaxSuppression;
+ return;
+};
+
+int ERFilterNM::getNumRejected()
+{
+ return num_rejected_regions;
+};
+
+
+
+
+// load default 1st stage classifier if found
+ERClassifierNM1::ERClassifierNM1()
+{
+
+ if (ifstream("./trained_classifierNM1.xml"))
+ {
+ // The file with default classifier exists
+ boost.load("./trained_classifierNM1.xml", "boost");
+ }
+ else if (ifstream("./training/trained_classifierNM1.xml"))
+ {
+ // The file with default classifier exists
+ boost.load("./training/trained_classifierNM1.xml", "boost");
+ }
+ else
+ {
+ // File not found
+ CV_Error(CV_StsBadArg, "Default classifier ./trained_classifierNM1.xml not found!");
+ }
+};
+
+double ERClassifierNM1::eval(const ERStat& stat)
+{
+ //Classify
+ float arr[] = {0,stat.aspect_ratio, stat.compactness, stat.num_holes, stat.med_crossings};
+ vector<float> sample (arr, arr + sizeof(arr) / sizeof(arr[0]) );
+
+ float votes = boost.predict( Mat(sample), Mat(), Range::all(), false, true );
+
+ // Logistic Correction returns a probability value (in the range(0,1))
+ return (double)1-(double)1/(1+exp(-2*votes));
+};
+
+
+// load default 2nd stage classifier if found
+ERClassifierNM2::ERClassifierNM2()
+{
+
+ if (ifstream("./trained_classifierNM2.xml"))
+ {
+ // The file with default classifier exists
+ boost.load("./trained_classifierNM2.xml", "boost");
+ }
+ else if (ifstream("./training/trained_classifierNM2.xml"))
+ {
+ // The file with default classifier exists
+ boost.load("./training/trained_classifierNM2.xml", "boost");
+ }
+ else
+ {
+ // File not found
+ CV_Error(CV_StsBadArg, "Default classifier ./trained_classifierNM2.xml not found!");
+ }
+};
+
+double ERClassifierNM2::eval(const ERStat& stat)
+{
+ //Classify
+ float arr[] = {0,stat.aspect_ratio, stat.compactness, stat.num_holes, stat.med_crossings,
+ stat.hole_area_ratio, stat.convex_hull_ratio, stat.num_inflexion_points};
+ vector<float> sample (arr, arr + sizeof(arr) / sizeof(arr[0]) );
+
+ float votes = boost.predict( Mat(sample), Mat(), Range::all(), false, true );
+
+ // Logistic Correction returns a probability value (in the range(0,1))
+ return (double)1-(double)1/(1+exp(-2*votes));
+};
+
+
+/*!
+ 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 Callback with the classifier.
+ if omitted tries to load a default classifier from file 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
+*/
+Ptr<ERFilter> createERFilterNM1(const cv::Ptr<ERFilter::Callback>& cb, int thresholdDelta,
+ float minArea, float maxArea, float minProbability,
+ bool nonMaxSuppression, float minProbabilityDiff)
+{
+
+ CV_Assert( (minProbability >= 0.) && (minProbability <= 1.) );
+ CV_Assert( (minArea < maxArea) && (minArea >=0.) && (maxArea <= 1.) );
+ CV_Assert( (thresholdDelta >= 0) && (thresholdDelta <= 128) );
+ CV_Assert( (minProbabilityDiff >= 0.) && (minProbabilityDiff <= 1.) );
+
+ Ptr<ERFilterNM> filter = new ERFilterNM();
+
+ if (cb == NULL)
+ filter->setCallback(new ERClassifierNM1());
+ else
+ filter->setCallback(cb);
+
+ filter->setThresholdDelta(thresholdDelta);
+ filter->setMinArea(minArea);
+ filter->setMaxArea(maxArea);
+ filter->setMinProbability(minProbability);
+ filter->setNonMaxSuppression(nonMaxSuppression);
+ filter->setMinProbabilityDiff(minProbabilityDiff);
+ return (Ptr<ERFilter>)filter;
+}
+
+/*!
+ Create an Extremal Region Filter for the 2nd stage classifier of N&M algorithm
+ Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
+
+ 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.
+
+ \param cb Callback with the classifier
+ if omitted tries to load a default classifier from file trained_classifierNM2.xml
+ \param minProbability The minimum probability P(er|character) allowed for retreived ER's
+*/
+Ptr<ERFilter> createERFilterNM2(const cv::Ptr<ERFilter::Callback>& cb, float minProbability)
+{
+
+ CV_Assert( (minProbability >= 0.) && (minProbability <= 1.) );
+
+ Ptr<ERFilterNM> filter = new ERFilterNM();
+
+
+ if (cb == NULL)
+ filter->setCallback(new ERClassifierNM2());
+ else
+ filter->setCallback(cb);
+
+ filter->setMinProbability(minProbability);
+ return (Ptr<ERFilter>)filter;
+}
+
+}
--- /dev/null
+
+//--------------------------------------------------------------------------------------------------
+// A demo program of the Extremal Region Filter algorithm described in
+// Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
+//--------------------------------------------------------------------------------------------------
+
+#include "opencv2/opencv.hpp"
+#include "opencv2/objdetect.hpp"
+#include "opencv2/highgui.hpp"
+#include "opencv2/imgproc.hpp"
+
+#include <vector>
+#include <iostream>
+#include <iomanip>
+
+using namespace std;
+using namespace cv;
+
+void er_draw(Mat &src, Mat &dst, ERStat& er);
+
+void er_draw(Mat &src, Mat &dst, ERStat& er)
+{
+
+ if (er.parent != NULL) // deprecate the root region
+ {
+ int newMaskVal = 255;
+ int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY;
+ floodFill(src,dst,Point(er.pixel%src.cols,er.pixel/src.cols),Scalar(255),0,Scalar(er.level),Scalar(0),flags);
+ }
+
+}
+
+int main(int argc, const char * argv[])
+{
+
+
+ vector<ERStat> regions;
+
+ if (argc < 2) {
+ cout << "Demo program of the Extremal Region Filter algorithm described in " << endl;
+ cout << "Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012" << endl << endl;
+ cout << " Usage: " << argv[0] << " input_image <optional_groundtruth_image>" << endl;
+ cout << " Default classifier files (trained_classifierNM*.xml) should be in ./" << endl;
+ return -1;
+ }
+
+ Mat original = imread(argv[1]);
+ Mat gt;
+ if (argc > 2)
+ {
+ gt = imread(argv[2]);
+ cvtColor(gt, gt, COLOR_RGB2GRAY);
+ threshold(gt, gt, 254, 255, THRESH_BINARY);
+ }
+ Mat grey(original.size(),CV_8UC1);
+ cvtColor(original,grey,COLOR_RGB2GRAY);
+
+ double t = (double)getTickCount();
+
+ // Build ER tree and filter with the 1st stage default classifier
+ Ptr<ERFilter> er_filter1 = createERFilterNM1();
+
+ er_filter1->run(grey, regions);
+
+ t = (double)getTickCount() - t;
+ cout << " --------------------------------------------------------------------------------------------------" << endl;
+ cout << "\t FIRST STAGE CLASSIFIER done in " << t * 1000. / getTickFrequency() << " ms." << endl;
+ cout << " --------------------------------------------------------------------------------------------------" << endl;
+ cout << setw(9) << regions.size()+er_filter1->getNumRejected() << "\t Extremal Regions extracted " << endl;
+ cout << setw(9) << regions.size() << "\t Extremal Regions selected by the first stage of the sequential classifier." << endl;
+ cout << "\t \t (saving into out_second_stage.jpg)" << endl;
+ cout << " --------------------------------------------------------------------------------------------------" << endl;
+
+ er_filter1.release();
+
+ // draw regions
+ Mat mask = Mat::zeros(grey.rows+2,grey.cols+2,CV_8UC1);
+ for (int r=0; r<(int)regions.size(); r++)
+ er_draw(grey, mask, regions.at(r));
+ mask = 255-mask;
+ imwrite("out_first_stage.jpg", mask);
+
+ if (argc > 2)
+ {
+ Mat tmp_mask = (255-gt) & (255-mask(Rect(Point(1,1),Size(mask.cols-2,mask.rows-2))));
+ cout << "Recall for the 1st stage filter = " << (float)countNonZero(tmp_mask) / countNonZero(255-gt) << endl;
+ }
+
+ t = (double)getTickCount();
+
+ // Default second stage classifier
+ Ptr<ERFilter> er_filter2 = createERFilterNM2();
+ er_filter2->run(grey, regions);
+
+ t = (double)getTickCount() - t;
+ cout << " --------------------------------------------------------------------------------------------------" << endl;
+ cout << "\t SECOND STAGE CLASSIFIER done in " << t * 1000. / getTickFrequency() << " ms." << endl;
+ cout << " --------------------------------------------------------------------------------------------------" << endl;
+ cout << setw(9) << regions.size() << "\t Extremal Regions selected by the second stage of the sequential classifier." << endl;
+ cout << "\t \t (saving into out_second_stage.jpg)" << endl;
+ cout << " --------------------------------------------------------------------------------------------------" << endl;
+
+ er_filter2.release();
+
+ // draw regions
+ mask = mask*0;
+ for (int r=0; r<(int)regions.size(); r++)
+ er_draw(grey, mask, regions.at(r));
+ mask = 255-mask;
+ imwrite("out_second_stage.jpg", mask);
+
+ if (argc > 2)
+ {
+ Mat tmp_mask = (255-gt) & (255-mask(Rect(Point(1,1),Size(mask.cols-2,mask.rows-2))));
+ cout << "Recall for the 2nd stage filter = " << (float)countNonZero(tmp_mask) / countNonZero(255-gt) << endl;
+ }
+
+ regions.clear();
+
+}
--- /dev/null
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+ <boosting_type>RealAdaboost</boosting_type>
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+ <ntrees>100</ntrees>
+ <weight_trimming_rate>1.</weight_trimming_rate>
+ <is_classifier>1</is_classifier>
+ <var_all>5</var_all>
+ <var_count>4</var_count>
+ <ord_var_count>4</ord_var_count>
+ <cat_var_count>0</cat_var_count>
+ <training_params>
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+ <max_categories>10</max_categories>
+ <max_depth>1</max_depth>
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+ <rows>1</rows>
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--- /dev/null
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projects / platform / upstream / opencv.git / commitdiff
