From 047b568f6da05ad4c64f4bb3f6d0a890dfc582d7 Mon Sep 17 00:00:00 2001 From: lluis Date: Fri, 27 Sep 2013 16:28:47 +0200 Subject: [PATCH] erGrouping now uses a classifier for group validation instead of a set of heuristical ifos. Updated documentation and sample to use the new function API --- modules/objdetect/doc/erfilter.rst | 6 +- .../include/opencv2/objdetect/erfilter.hpp | 7 +- modules/objdetect/src/erfilter.cpp | 348 +- samples/cpp/scenetext.jpg | Bin 84697 -> 0 bytes samples/cpp/scenetext01.jpg | Bin 0 -> 97100 bytes samples/cpp/scenetext02.jpg | Bin 0 -> 95135 bytes samples/cpp/scenetext03.jpg | Bin 0 -> 60751 bytes samples/cpp/scenetext04.jpg | Bin 0 -> 99487 bytes samples/cpp/scenetext05.jpg | Bin 0 -> 113689 bytes samples/cpp/scenetext06.jpg | Bin 0 -> 70430 bytes samples/cpp/scenetext_GT.png | Bin 3160 -> 0 bytes samples/cpp/{erfilter.cpp => textdetection.cpp} | 62 +- samples/cpp/trained_classifier_erGrouping.xml | 20046 +++++++++++++++++++ 13 files changed, 20267 insertions(+), 202 deletions(-) delete mode 100644 samples/cpp/scenetext.jpg create mode 100644 samples/cpp/scenetext01.jpg create mode 100644 samples/cpp/scenetext02.jpg create mode 100644 samples/cpp/scenetext03.jpg create mode 100644 samples/cpp/scenetext04.jpg create mode 100644 samples/cpp/scenetext05.jpg create mode 100644 samples/cpp/scenetext06.jpg delete mode 100644 samples/cpp/scenetext_GT.png rename samples/cpp/{erfilter.cpp => textdetection.cpp} (56%) create mode 100644 samples/cpp/trained_classifier_erGrouping.xml diff --git a/modules/objdetect/doc/erfilter.rst b/modules/objdetect/doc/erfilter.rst index 684b9b7..a8976fb 100644 --- a/modules/objdetect/doc/erfilter.rst +++ b/modules/objdetect/doc/erfilter.rst @@ -198,12 +198,14 @@ erGrouping ---------- Find groups of Extremal Regions that are organized as text blocks. -.. ocv:function:: void erGrouping( InputArrayOfArrays src, std::vector > ®ions, std::vector &groups ) +.. ocv:function:: void erGrouping( InputArrayOfArrays src, std::vector > ®ions, const std::string& filename, float minProbablity, std::vector &groups) :param src: Vector of sinle channel images CV_8UC1 from wich the regions were extracted :param regions: Vector of ER's retreived from the ERFilter algorithm from each channel + :param filename: The XML or YAML file with the classifier model (e.g. trained_classifier_erGrouping.xml) + :param minProbability: The minimum probability for accepting a group :param groups: The output of the algorithm are stored in this parameter as list of rectangles. This function implements the grouping algorithm described in [Gomez13]. Notice that this implementation constrains the results to horizontally-aligned text and latin script (since ERFilter classifiers are trained only for latin script detection). -The algorithm combines two different clustering techniques in a single parameter-free procedure to detect groups of regions organized as text. The maximally meaningful groups are fist detected in several feature spaces, where each feature space is a combination of proximity information (x,y coordinates) and a similarity measure (intensity, color, size, gradient magnitude, etc.), thus providing a set of hypotheses of text groups. Evidence Accumulation framework is used to combine all these hypotheses to get the final estimate. Each of the resulting groups are finally heuristically validated in order to assess if they form a valid horizontally-aligned text block. +The algorithm combines two different clustering techniques in a single parameter-free procedure to detect groups of regions organized as text. The maximally meaningful groups are fist detected in several feature spaces, where each feature space is a combination of proximity information (x,y coordinates) and a similarity measure (intensity, color, size, gradient magnitude, etc.), thus providing a set of hypotheses of text groups. Evidence Accumulation framework is used to combine all these hypotheses to get the final estimate. Each of the resulting groups are finally validated using a classifier in order to assess if they form a valid horizontally-aligned text block. diff --git a/modules/objdetect/include/opencv2/objdetect/erfilter.hpp b/modules/objdetect/include/opencv2/objdetect/erfilter.hpp index bbf1b54..9dc919a 100644 --- a/modules/objdetect/include/opencv2/objdetect/erfilter.hpp +++ b/modules/objdetect/include/opencv2/objdetect/erfilter.hpp @@ -250,14 +250,17 @@ CV_EXPORTS void computeNMChannels(InputArray _src, OutputArrayOfArrays _channels (x,y coordinates) and a similarity measure (intensity, color, size, gradient magnitude, etc.), thus providing a set of hypotheses of text groups. Evidence Accumulation framework is used to combine all these hypotheses to get the final estimate. Each of the resulting groups are finally - heuristically validated in order to assest if they form a valid horizontally-aligned text block. + validated using a classifier in order to assest if they form a valid horizontally-aligned text block. \param src Vector of sinle channel images CV_8UC1 from wich the regions were extracted. \param regions Vector of ER's retreived from the ERFilter algorithm from each channel + \param filename The XML or YAML file with the classifier model (e.g. trained_classifier_erGrouping.xml) + \param minProbability The minimum probability for accepting a group \param groups The output of the algorithm are stored in this parameter as list of rectangles. */ CV_EXPORTS void erGrouping(InputArrayOfArrays src, std::vector > ®ions, - std::vector &groups); + const std::string& filename, float minProbablity, + std::vector &groups); } #endif // _OPENCV_ERFILTER_HPP_ diff --git a/modules/objdetect/src/erfilter.cpp b/modules/objdetect/src/erfilter.cpp index ce07443..55bfe6f 100644 --- a/modules/objdetect/src/erfilter.cpp +++ b/modules/objdetect/src/erfilter.cpp @@ -235,10 +235,7 @@ void ERFilterNM::er_tree_extract( InputArray image ) if (thresholdDelta > 1) { - Mat tmp; - src.copyTo(tmp); - src.release(); - src = (image.getMat() / thresholdDelta) -1; + src = (src / thresholdDelta) -1; } const unsigned char * image_data = src.data; @@ -2721,6 +2718,28 @@ float extract_features(InputOutputArray src, vector ®ions, vector &vertex, Point &p) +{ + for (int i=0; i<(int)vertex.size(); i++) + { + if (vertex.at(i) == p) + return true; + } + return false; +} + + /*! Find groups of Extremal Regions that are organized as text blocks. This function implements the grouping algorithm described in: @@ -2734,16 +2753,25 @@ float extract_features(InputOutputArray src, vector ®ions, vector > ®ions, std::vector &text_boxes) +void erGrouping(InputArrayOfArrays _src, vector > ®ions, const std::string& filename, float minProbability, std::vector &text_boxes) { + // TODO assert correct vector + CvBoost group_boost; + if (ifstream(filename.c_str())) + group_boost.load( filename.c_str(), "boost" ); + else + CV_Error(CV_StsBadArg, "erGrouping: Default classifier file not found!"); + std::vector src; _src.getMatVector(src); @@ -2868,18 +2896,22 @@ void erGrouping(InputArrayOfArrays _src, vector > ®ions, std:: free(D); + /* --------------------------------- Groups Validation --------------------------------*/ /* Examine each of the clusters in order to assest if they are valid text lines or not */ /* ------------------------------------------------------------------------------------*/ - // remove non-horizontally-aligned groups - vector groups_rects; + vector > data_arrays(meaningful_clusters.size()); + vector groups_rects(meaningful_clusters.size()); + + // Collect group level features and classify the group for (int i=(int)meaningful_clusters.size()-1; i>=0; i--) { Rect group_rect; float sumx=0, sumy=0, sumxy=0, sumx2=0; + // linear regression slope helps discriminating horizontal aligned groups for (int j=0; j<(int)meaningful_clusters.at(i).size();j++) { if (j==0) @@ -2907,105 +2939,69 @@ void erGrouping(InputArrayOfArrays _src, vector > ®ions, std:: float a1=((int)meaningful_clusters.at(i).size()*sumxy-sumx*sumy) / ((int)meaningful_clusters.at(i).size()*sumx2-sumx*sumx); - if (abs(a1) > 0.13) - meaningful_clusters.erase(meaningful_clusters.begin()+i); + vector data; + if (a1 != a1) + data_arrays.at(i).push_back(1.f); else - groups_rects.insert(groups_rects.begin(), group_rect); - - } + data_arrays.at(i).push_back(a1); - //TODO if group has less than 5 chars we can infer that is a single line so an additional rule can - // be added here in order to reject non-colinear small groups + groups_rects.at(i) = group_rect; + // group probability mean + double group_probability_mean = 0; + // number of non-overlapping regions + vector individual_components; - /* TODO this is better code for detecting non-horizontally-aligned groups but only works for - * single lines so we need here a way to split the rejected groups into several line hypothesis - * and recursively apply the text line validation test until no more splits can be done - - vector groups_rects; - - for (int i=meaningful_clusters.size()-1; i>=0; i--) - { - - Rect group_rect; + // The variance of several similarity features is also helpful + vector strokes; + vector grad_magnitudes; + vector intensities; + vector bg_intensities; - for (int j=0; j<(int)meaningful_clusters.at(i).size();j++) - { - if (j==0) - { - group_rect = regions.at(c).at(meaningful_clusters.at(i).at(j)).rect; - } else { - group_rect = group_rect | regions.at(c).at(meaningful_clusters.at(i).at(j)).rect; - } - } - - float group_y_center = 0; - for (int j=0; j<(int)meaningful_clusters.at(i).size();j++) - { - int region_y_center = regions.at(c).at(meaningful_clusters.at(i).at(j)).rect.y + - regions.at(c).at(meaningful_clusters.at(i).at(j)).rect.height/2; - group_y_center += region_y_center; - } - group_y_center = group_y_center / (int)meaningful_clusters.at(i).size(); + // We'll try to remove groups with repetitive patterns using averaged SAD + // SAD = Sum of Absolute Differences + Mat grey = img; + Mat sad = Mat::zeros(regions.at(c).at(meaningful_clusters.at(i).at(0)).rect.size() , CV_8UC1); + Mat region_mask = Mat::zeros(grey.rows+2, grey.cols+2, CV_8UC1); + float sad_value = 0; + Mat ratios = Mat::zeros(1, (int)meaningful_clusters.at(i).size(), CV_32FC1); + //Mat holes = Mat::zeros(1, (int)meaningful_clusters.at(i).size(), CV_32FC1); - float err = 0; for (int j=0; j<(int)meaningful_clusters.at(i).size();j++) { - int region_y_center = regions.at(c).at(meaningful_clusters.at(i).at(j)).rect.y + - regions.at(c).at(meaningful_clusters.at(i).at(j)).rect.height/2; - err += pow(group_y_center-region_y_center, 2); - } - err = sqrt(err / (int)meaningful_clusters.at(i).size()); - err = err / group_rect.height; - - if (err > 0.17) - meaningful_clusters.erase(meaningful_clusters.begin()+i); - else - groups_rects.insert(groups_rects.begin(), group_rect); + ERStat *stat = ®ions.at(c).at(meaningful_clusters.at(i).at(j)); - } */ - - // check for colinear groups that can be merged - for (int i=0; i<(int)meaningful_clusters.size(); i++) - { - int ay1 = groups_rects.at(i).y; - int ay2 = groups_rects.at(i).y + groups_rects.at(i).height; - int ax1 = groups_rects.at(i).x; - int ax2 = groups_rects.at(i).x + groups_rects.at(i).width; - for (int j=(int)meaningful_clusters.size()-1; j>i; j--) - { - int by1 = groups_rects.at(j).y; - int by2 = groups_rects.at(j).y + groups_rects.at(j).height; - int bx1 = groups_rects.at(j).x; - int bx2 = groups_rects.at(j).x + groups_rects.at(j).width; + //Fill the region + Mat region = region_mask(Rect(Point(stat->rect.x,stat->rect.y), + Point(stat->rect.br().x+2,stat->rect.br().y+2))); + region = Scalar(0); + int newMaskVal = 255; + int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY; + Rect rect; - int y_intersection = min(ay2,by2) - max(ay1,by1); + floodFill( grey(Rect(Point(stat->rect.x,stat->rect.y),Point(stat->rect.br().x,stat->rect.br().y))), + region, Point(stat->pixel%grey.cols - stat->rect.x, stat->pixel/grey.cols - stat->rect.y), + Scalar(255), &rect, Scalar(stat->level), Scalar(0), flags ); - if (y_intersection > 0.75*(max(groups_rects.at(i).height,groups_rects.at(j).height))) + Mat mask = Mat::zeros(regions.at(c).at(meaningful_clusters.at(i).at(0)).rect.size() , CV_8UC1); + resize(region, mask, mask.size()); + mask = mask - 254; + if (j!=0) { - int xdist = min(abs(ax2-bx1),abs(bx2-ax1)); - if (xdist < 0.75*(max(groups_rects.at(i).height,groups_rects.at(j).height))) - { - for (int r=0; r<(int)meaningful_clusters.at(j).size(); r++) - meaningful_clusters.at(i).push_back(meaningful_clusters.at(j).at(r)); - meaningful_clusters.erase(meaningful_clusters.begin()+j); - groups_rects.erase(groups_rects.begin()+j); - } + // accumulate Sum of Absolute Differences + absdiff(sad, mask, sad); + Scalar s = sum(sad); + sad_value += (float)s[0]/(sad.rows*sad.cols); } + mask.copyTo(sad); + ratios.at(0,j) = (float)min(stat->rect.width, stat->rect.height) / + max(stat->rect.width, stat->rect.height); + //holes.at(0,j) = (float)stat->hole_area_ratio; - } - } - - // remove groups with less than 3 non-overlapping regions - for (int i=(int)meaningful_clusters.size()-1; i>=0; i--) - { - - double group_probability_mean = 0; - Rect group_rect; - vector individual_components; - - for (int j=0; j<(int)meaningful_clusters.at(i).size();j++) - { + strokes.push_back((float)features.at(meaningful_clusters.at(i).at(j)).stroke_mean); + grad_magnitudes.push_back((float)features.at(meaningful_clusters.at(i).at(j)).gradient_mean); + intensities.push_back(features.at(meaningful_clusters.at(i).at(j)).intensity_mean); + bg_intensities.push_back(features.at(meaningful_clusters.at(i).at(j)).boundary_intensity_mean); group_probability_mean += regions.at(c).at(meaningful_clusters.at(i).at(j)).probability; if (j==0) @@ -3036,112 +3032,108 @@ void erGrouping(InputArrayOfArrays _src, vector > ®ions, std:: } group_probability_mean = group_probability_mean / meaningful_clusters.at(i).size(); + data_arrays.at(i).insert(data_arrays.at(i).begin(),(float)individual_components.size()); - if (individual_components.size()<3) // || (group_probability_mean < 0.5) + // variance of widths and heights help to discriminate groups with high height variability + vector widths; + vector heights; + // the MST edge orientations histogram may be dominated by the horizontal axis orientation + Subdiv2D subdiv(Rect(0,0,src.at(0).cols,src.at(0).rows)); + + for (int r=0; r < (int)individual_components.size(); r++) { - meaningful_clusters.erase(meaningful_clusters.begin()+i); - groups_rects.erase(groups_rects.begin()+i); - continue; + widths.push_back(individual_components.at(r).width); + heights.push_back(individual_components.at(r).height); + + Point2f fp( (float)individual_components.at(r).x + individual_components.at(r).width/2, + (float)individual_components.at(r).y + individual_components.at(r).height/2 ); + subdiv.insert(fp); } - } + Scalar mean, std; + meanStdDev(Mat(widths), mean, std); + data_arrays.at(i).push_back((float)(std[0]/mean[0])); + data_arrays.at(i).push_back((float)mean[0]); + meanStdDev(Mat(heights), mean, std); + data_arrays.at(i).push_back((float)(std[0]/mean[0])); - // TODO remove groups with high height variability + vector edgeList; + subdiv.getEdgeList(edgeList); + std::sort (edgeList.begin(), edgeList.end(), edge_comp); + vector mst_vertices; - // Try to remove groups with repetitive patterns - for (int i=(int)meaningful_clusters.size()-1; i>=0; i--) - { - Mat grey = img; - Mat sad = Mat::zeros(regions.at(c).at(meaningful_clusters.at(i).at(0)).rect.size() , CV_8UC1); - Mat region_mask = Mat::zeros(grey.rows+2, grey.cols+2, CV_8UC1); - float sad_value = 0; - Mat ratios = Mat::zeros(1, (int)meaningful_clusters.at(i).size(), CV_32FC1); - Mat holes = Mat::zeros(1, (int)meaningful_clusters.at(i).size(), CV_32FC1); + int horiz_edges = 0, non_horiz_edges = 0; + vector edge_distances; - for (int r=0; r<(int)meaningful_clusters.at(i).size(); r++) + for( size_t k = 0; k < edgeList.size(); k++ ) { - ERStat *stat = ®ions.at(c).at(meaningful_clusters.at(i).at(r)); - - //Fill the region - Mat region = region_mask(Rect(Point(stat->rect.x,stat->rect.y), - Point(stat->rect.br().x+2,stat->rect.br().y+2))); - region = Scalar(0); - int newMaskVal = 255; - int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY; - Rect rect; - - floodFill( grey(Rect(Point(stat->rect.x,stat->rect.y),Point(stat->rect.br().x,stat->rect.br().y))), - region, Point(stat->pixel%grey.cols - stat->rect.x, stat->pixel/grey.cols - stat->rect.y), - Scalar(255), &rect, Scalar(stat->level), Scalar(0), flags ); - - Mat mask = Mat::zeros(regions.at(c).at(meaningful_clusters.at(i).at(0)).rect.size() , CV_8UC1); - resize(region, mask, mask.size()); - mask = mask - 254; - if (r!=0) + Vec4f e = edgeList[k]; + Point pt0 = Point(cvRound(e[0]), cvRound(e[1])); + Point pt1 = Point(cvRound(e[2]), cvRound(e[3])); + if (((pt0.x>0)&&(pt0.x0)&&(pt0.y0)&&(pt1.x0)&&(pt1.y 5.93) || ((abs(angle) > 2.79)&&(abs(angle) < 3.49)) ) + if ( (abs(angle) < 0.25) || (abs(angle) > 6.03) || ((abs(angle) > 2.88)&&(abs(angle) < 3.4)) ) + { + horiz_edges++; + edge_distances.push_back((float)norm(pt0-pt1)); + } + else + non_horiz_edges++; + mst_vertices.push_back(pt0); + mst_vertices.push_back(pt1); } - mask.copyTo(sad); - ratios.at(0,r) = (float)min(stat->rect.width, stat->rect.height) / - max(stat->rect.width, stat->rect.height); - holes.at(0,r) = (float)stat->hole_area_ratio; - - } - Scalar mean,std; - meanStdDev( holes, mean, std); - float holes_mean = (float)mean[0]; - meanStdDev( ratios, mean, std); - - // Set empirically - if (((float)sad_value / ((int)meaningful_clusters.at(i).size()-1) < 0.12) || - (((float)sad_value / ((int)meaningful_clusters.at(i).size()-1) < 0.175)&&(holes_mean < 0.015))|| - //TODO this must be num of non-overlapping regions.at(c) and probably 7 is ok! - ((holes_mean < 0.005)&&((int)meaningful_clusters.at(i).size()>10))) - { - meaningful_clusters.erase(meaningful_clusters.begin()+i); - groups_rects.erase(groups_rects.begin()+i); } - } - // remove small groups inside others - vector groups_to_remove; - for (int i=0; i<(int)meaningful_clusters.size()-1; i++) - { - for (int j=i+1; j<(int)meaningful_clusters.size(); j++) - { + if (horiz_edges == 0) + data_arrays.at(i).push_back(0.f); + else + data_arrays.at(i).push_back((float)horiz_edges/(horiz_edges+non_horiz_edges)); - Rect intersection = groups_rects.at(i) & groups_rects.at(j); + // remove groups where objects are not equidistant enough + Scalar dist_mean, dist_std; + meanStdDev(Mat(edge_distances),dist_mean, dist_std); + if (dist_std[0] == 0) + data_arrays.at(i).push_back(0.f); + else + data_arrays.at(i).push_back((float)(dist_std[0]/dist_mean[0])); - if (intersection == groups_rects.at(i)) - groups_to_remove.push_back(i); - if (intersection == groups_rects.at(j)) - groups_to_remove.push_back(j); + if (dist_mean[0] == 0) + data_arrays.at(i).push_back(0.f); + else + data_arrays.at(i).push_back((float)dist_mean[0]/data_arrays.at(i).at(3)); - } - } + //meanStdDev( holes, mean, std); + //float holes_mean = (float)mean[0]; + meanStdDev( ratios, mean, std); - if (!groups_to_remove.empty()) - { - int last_removed = -1; - std::sort(groups_to_remove.begin(), groups_to_remove.end()); - for (int i=(int)groups_to_remove.size()-1; i>=0; i--) + data_arrays.at(i).push_back((float)sad_value / ((int)meaningful_clusters.at(i).size()-1)); + meanStdDev( Mat(strokes), mean, std); + data_arrays.at(i).push_back((float)(std[0]/mean[0])); + meanStdDev( Mat(grad_magnitudes), mean, std); + data_arrays.at(i).push_back((float)(std[0]/mean[0])); + meanStdDev( Mat(intensities), mean, std); + data_arrays.at(i).push_back((float)std[0]); + meanStdDev( Mat(bg_intensities), mean, std); + data_arrays.at(i).push_back((float)std[0]); + + // Validate only groups with more than 2 non-overlapping regions + if (data_arrays.at(i).at(0) > 2) { - if (groups_to_remove.at(i) == last_removed) - continue; - else - last_removed = groups_to_remove.at(i); + data_arrays.at(i).insert(data_arrays.at(i).begin(),0.f); + float votes = group_boost.predict( Mat(data_arrays.at(i)), Mat(), Range::all(), false, true ); + // Logistic Correction returns a probability value (in the range(0,1)) + double probability = (double)1-(double)1/(1+exp(-2*votes)); - meaningful_clusters.erase(meaningful_clusters.begin()+groups_to_remove.at(i)); - groups_rects.erase(groups_rects.begin()+groups_to_remove.at(i)); + if (probability > minProbability) + text_boxes.push_back(groups_rects.at(i)); } } - groups_to_remove.clear(); - for (int i=0; i<(int)groups_rects.size(); i++) - text_boxes.push_back(groups_rects.at(i)); } // check for colinear groups that can be merged diff --git a/samples/cpp/scenetext.jpg b/samples/cpp/scenetext.jpg deleted file mode 100644 index 6a9c6bf5e236816fdc81bc403bd9563649d29bcd..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 84697 zcmbTdcQ_n>^glW*R_~&%5-VClbh}tBA2nii(Go!-YOs1QLCESQ!CFL*Xwf4QBCHZ6 zY!EHjW%UwWM1H>a-sg9p=l*r?eb3Ba^PYLmYu+>Gyv{kV`8WM<5nwjZ(boaU$jE@8 zs}1-!3)}%f~RT z&cVS!&&0`fjh%~?orC>*l{r~g$*9EXplT(uyfXIXZauzZW z3)#P~fB*oHQC?a5KL-C_gN*#jMk+8h4K3Z(gl1-doD2jarvUwr)mO8_ug(J$ER?L* zWi+YSOdf%SeAs29(~GI0S`FPCrr-C3x5d6PKs1ZYV$hfWV;Okl5#O@d+;ylQJ^1vU6U&&dqyMQd(AC 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endl << endl; + 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; if (argc < 2) show_help_and_exit(argv[0]); @@ -37,11 +44,13 @@ int main(int argc, const char * argv[]) channels.push_back(255-channels[c]); // Create ERFilter objects with the 1st and 2nd stage default classifiers - Ptr er_filter1 = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"),8,0.00025,0.13,0.4,true,0.1); - Ptr er_filter2 = createERFilterNM2(loadClassifierNM2("trained_classifierNM2.xml"),0.3); + Ptr er_filter1 = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"),16,0.00015,0.13,0.2,true,0.1); + Ptr er_filter2 = createERFilterNM2(loadClassifierNM2("trained_classifierNM2.xml"),0.5); vector > regions(channels.size()); // Apply the default cascade classifier to each independent channel (could be done in parallel) + cout << "Extracting Class Specific Extremal Regions from " << (int)channels.size() << " channels ..." << endl; + cout << " (...) this may take a while (...)" << endl << endl; for (int c=0; c<(int)channels.size(); c++) { er_filter1->run(channels[c], regions[c]); @@ -49,13 +58,18 @@ int main(int argc, const char * argv[]) } // Detect character groups + cout << "Grouping extracted ERs ... "; vector groups; - erGrouping(channels, regions, groups); + erGrouping(channels, regions, "trained_classifier_erGrouping.xml", 0.5, groups); // draw groups groups_draw(src, groups); imshow("grouping",src); - waitKey(-1); + + cout << "Done!" << endl << endl; + cout << "Press 'e' to show the extracted Extremal Regions, any other key to exit." << endl << endl; + if( waitKey (-1) == 101) + er_show(channels,regions); // memory clean-up er_filter1.release(); @@ -73,9 +87,6 @@ int main(int argc, const char * argv[]) void show_help_and_exit(const char *cmd) { - cout << endl << cmd << endl << endl; - 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: " << cmd << " " << endl; cout << " Default classifier files (trained_classifierNM*.xml) must be in current directory" << endl << endl; exit(-1); @@ -92,14 +103,25 @@ void groups_draw(Mat &src, vector &groups) } } -void er_draw(Mat &src, Mat &dst, ERStat& er) +void er_show(vector &channels, vector > ®ions) { - - if (er.parent != NULL) // deprecate the root region + for (int c=0; c<(int)channels.size(); c++) { - 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); + Mat dst = Mat::zeros(channels[0].rows+2,channels[0].cols+2,CV_8UC1); + for (int r=0; r<(int)regions[c].size(); r++) + { + ERStat er = regions[c][r]; + if (er.parent != NULL) // deprecate the root region + { + int newMaskVal = 255; + int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY; + floodFill(channels[c],dst,Point(er.pixel%channels[c].cols,er.pixel/channels[c].cols), + Scalar(255),0,Scalar(er.level),Scalar(0),flags); + } + } + char buff[10]; char *buff_ptr = buff; + sprintf(buff, "channel %d", c); + imshow(buff_ptr, dst); } - + waitKey(-1); } diff --git a/samples/cpp/trained_classifier_erGrouping.xml b/samples/cpp/trained_classifier_erGrouping.xml new file mode 100644 index 0000000..2c20941 --- /dev/null +++ b/samples/cpp/trained_classifier_erGrouping.xml @@ -0,0 +1,20046 @@ + + + + RealAdaboost + Gini + 500 + 1. + 1 + 14 + 13 + 13 + 0 + + 0 + 10 + 1 + 10 + 0 + + 1 + 2 +

d
+ + 1. 1. + + 1 + 13 +
i
+ + 1 2 3 4 5 6 7 8 9 10 11 12 13 + + 0 0 0 0 0 0 0 0 0 0 0 0 0 + + 1 + 1 +
i
+ + 2 + + 1 + 2 +
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