diff --git a/modules/objdetect/CMakeLists.txt b/modules/objdetect/CMakeLists.txt index 78aa4aa234..5739ee9e38 100644 --- a/modules/objdetect/CMakeLists.txt +++ b/modules/objdetect/CMakeLists.txt @@ -1,2 +1,2 @@ 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) diff --git a/modules/objdetect/include/opencv2/objdetect.hpp b/modules/objdetect/include/opencv2/objdetect.hpp index 3ccb057e33..39cf4654d0 100644 --- a/modules/objdetect/include/opencv2/objdetect.hpp +++ b/modules/objdetect/include/opencv2/objdetect.hpp @@ -394,5 +394,6 @@ CV_EXPORTS_W void drawDataMatrixCodes(InputOutputArray image, } #include "opencv2/objdetect/linemod.hpp" +#include "opencv2/objdetect/erfilter.hpp" #endif diff --git a/modules/objdetect/include/opencv2/objdetect/erfilter.hpp b/modules/objdetect/include/opencv2/objdetect/erfilter.hpp new file mode 100644 index 0000000000..c03d49f94e --- /dev/null +++ b/modules/objdetect/include/opencv2/objdetect/erfilter.hpp @@ -0,0 +1,202 @@ +/*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 +#include + +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 *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 *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& regions ) = 0; + + + //! set/get methods to set the algorithm properties, + virtual void setCallback(const cv::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 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 createERFilterNM1(const cv::Ptr& 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 createERFilterNM2(const cv::Ptr& cb = NULL, + float minProbability = 0.85); + +} +#endif // _OPENCV_ERFILTER_HPP_ diff --git a/modules/objdetect/src/erfilter.cpp b/modules/objdetect/src/erfilter.cpp new file mode 100644 index 0000000000..002aa5247a --- /dev/null +++ b/modules/objdetect/src/erfilter.cpp @@ -0,0 +1,1124 @@ +/*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 + +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(); + 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& regions ); + +protected: + int thresholdDelta; + float maxArea; + float minArea; + + cv::Ptr 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& 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 *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& _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 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 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 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 accessible_pixel_mask(width * height); + vector accumulated_pixel_mask(width * height); + + // heap of boundary pixels + vector boundary_pixes[(255/thresholdDelta)+1]; + vector 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 (ybbox[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; ibbox[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 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 > contours; + vector contour_poly; + vector 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 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& 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 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 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 createERFilterNM1(const cv::Ptr& 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 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)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 createERFilterNM2(const cv::Ptr& cb, float minProbability) +{ + + CV_Assert( (minProbability >= 0.) && (minProbability <= 1.) ); + + Ptr filter = new ERFilterNM(); + + + if (cb == NULL) + filter->setCallback(new ERClassifierNM2()); + else + filter->setCallback(cb); + + filter->setMinProbability(minProbability); + return (Ptr)filter; +} + +} diff --git a/samples/cpp/erfilter.cpp b/samples/cpp/erfilter.cpp new file mode 100644 index 0000000000..f318371d70 --- /dev/null +++ b/samples/cpp/erfilter.cpp @@ -0,0 +1,120 @@ + +//-------------------------------------------------------------------------------------------------- +// 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 +#include +#include + +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 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 " << 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 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 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(); + +} diff --git a/samples/cpp/scenetext.jpg b/samples/cpp/scenetext.jpg new file mode 100644 index 0000000000..6a9c6bf5e2 Binary files /dev/null and b/samples/cpp/scenetext.jpg differ diff --git a/samples/cpp/scenetext_GT.png b/samples/cpp/scenetext_GT.png new file mode 100644 index 0000000000..a5c1905572 Binary files /dev/null and b/samples/cpp/scenetext_GT.png differ diff --git a/samples/cpp/trained_classifierNM1.xml b/samples/cpp/trained_classifierNM1.xml new file mode 100644 index 0000000000..afce7951c6 --- /dev/null +++ b/samples/cpp/trained_classifierNM1.xml @@ -0,0 +1,4046 @@ + + + + RealAdaboost + Gini + 100 + 1. + 1 + 5 + 4 + 4 + 0 + + 0 + 10 + 1 + 10 + 0 + + 1 + 2 +
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