erGrouping now uses a classifier for group validation instead of a set of heuristical...

Updated documentation and sample to use the new function API

diff --git a/modules/objdetect/doc/erfilter.rst b/modules/objdetect/doc/erfilter.rst
index 684b9b7..a8976fb 100644 (file)
--- 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<std::vector<ERStat> > &regions, std::vector<Rect> &groups )
+.. ocv:function:: void erGrouping( InputArrayOfArrays src, std::vector<std::vector<ERStat> > &regions, const std::string& filename, float minProbablity, std::vector<Rect > &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 (file)
--- 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<std::vector<ERStat> > &regions,
-                                                   std::vector<Rect> &groups);
+                                                   const std::string& filename, float minProbablity,
+                                                   std::vector<Rect > &groups);
 
 }
 #endif // _OPENCV_ERFILTER_HPP_

diff --git a/modules/objdetect/src/erfilter.cpp b/modules/objdetect/src/erfilter.cpp
index ce07443..55bfe6f 100644 (file)
--- 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<ERStat> &regions, vector<ERF
     return max_stroke;
 }
 
+static bool edge_comp (Vec4f i,Vec4f j)
+{
+    Point a = Point(cvRound(i[0]), cvRound(i[1]));
+    Point b = Point(cvRound(i[2]), cvRound(i[3]));
+    double edist_i = cv::norm(a-b);
+    a = Point(cvRound(j[0]), cvRound(j[1]));
+    b = Point(cvRound(j[2]), cvRound(j[3]));
+    double edist_j = cv::norm(a-b);
+    return (edist_i<edist_j);
+}
+
+static bool find_vertex(vector<Point> &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<ERStat> &regions, vector<ERF
     (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 by 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.
 */
-void erGrouping(InputArrayOfArrays _src, vector<vector<ERStat> > &regions, std::vector<Rect > &text_boxes)
+void erGrouping(InputArrayOfArrays _src, vector<vector<ERStat> > &regions, const std::string& filename, float minProbability, std::vector<Rect > &text_boxes)
 {
+
     // TODO assert correct vector<Mat>
 
+    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<Mat> src;
     _src.getMatVector(src);
 
@@ -2868,18 +2896,22 @@ void erGrouping(InputArrayOfArrays _src, vector<vector<ERStat> > &regions, 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<Rect> groups_rects;
+        vector<vector<float> > data_arrays(meaningful_clusters.size());
+        vector<Rect> 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<vector<ERStat> > &regions, 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<float> 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<Rect> 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<Rect> 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<float> strokes;
+            vector<float> grad_magnitudes;
+            vector<float> intensities;
+            vector<float> 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 = &regions.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<float>(0,j) = (float)min(stat->rect.width, stat->rect.height) /
+                                               max(stat->rect.width, stat->rect.height);
+                //holes.at<float>(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<Rect> 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<vector<ERStat> > &regions, 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<int> widths;
+            vector<int> 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<Vec4f> edgeList;
+            subdiv.getEdgeList(edgeList);
+            std::sort (edgeList.begin(), edgeList.end(), edge_comp);
+            vector<Point> 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<float> 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 = &regions.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.x<src.at(0).cols)&&(pt0.y>0)&&(pt0.y<src.at(0).rows) &&
+                     (pt1.x>0)&&(pt1.x<src.at(0).cols)&&(pt1.y>0)&&(pt1.y<src.at(0).rows)) &&
+                    ((!find_vertex(mst_vertices,pt0)) ||
+                     (!find_vertex(mst_vertices,pt1))))
                 {
-                    //  using Sum of Absolute Differences
-                    absdiff(sad, mask, sad);
-                    Scalar s = sum(sad);
-                    sad_value += (float)s[0]/(sad.rows*sad.cols);
+                    double angle = atan2((double)(pt0.y-pt1.y),(double)(pt0.x-pt1.x));
+                    //if ( (abs(angle) < 0.35) || (abs(angle) > 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<float>(0,r) = (float)min(stat->rect.width, stat->rect.height) /
-                                               max(stat->rect.width, stat->rect.height);
-                holes.at<float>(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<int> 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 (file)
index 6a9c6bf..0000000
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diff --git a/samples/cpp/scenetext01.jpg b/samples/cpp/scenetext01.jpg
new file mode 100644 (file)
index 0000000..f77e60a
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diff --git a/samples/cpp/scenetext02.jpg b/samples/cpp/scenetext02.jpg
new file mode 100644 (file)
index 0000000..7c89866
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diff --git a/samples/cpp/scenetext03.jpg b/samples/cpp/scenetext03.jpg
new file mode 100644 (file)
index 0000000..3950013
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diff --git a/samples/cpp/scenetext04.jpg b/samples/cpp/scenetext04.jpg
new file mode 100644 (file)
index 0000000..05dda0f
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diff --git a/samples/cpp/scenetext05.jpg b/samples/cpp/scenetext05.jpg
new file mode 100644 (file)
index 0000000..1d090f0
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diff --git a/samples/cpp/scenetext06.jpg b/samples/cpp/scenetext06.jpg
new file mode 100644 (file)
index 0000000..05de82f
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diff --git a/samples/cpp/scenetext_GT.png b/samples/cpp/scenetext_GT.png
deleted file mode 100644 (file)
index a5c1905..0000000
Binary files a/samples/cpp/scenetext_GT.png and /dev/null differ

diff --git a/samples/cpp/erfilter.cpp b/samples/cpp/textdetection.cpp
similarity index 56%
rename from samples/cpp/erfilter.cpp
rename to samples/cpp/textdetection.cpp
index 389cb48..9de33fa 100644 (file)
--- a/samples/cpp/erfilter.cpp
+++ b/samples/cpp/textdetection.cpp
@@ -1,8 +1,12 @@
-
-//--------------------------------------------------------------------------------------------------
-//  A demo program of the Extremal Region Filter algorithm described in
-//  Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
-//--------------------------------------------------------------------------------------------------
+/*
+ * textdetection.cpp
+ *
+ * A demo program of the Extremal Region Filter algorithm described in
+ * Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
+ *
+ * Created on: Sep 23, 2013
+ *     Author: Lluis Gomez i Bigorda <lgomez AT cvc.uab.es>
+ */
 
 #include  "opencv2/opencv.hpp"
 #include  "opencv2/objdetect.hpp"
@@ -18,10 +22,13 @@ using  namespace cv;
 
 void show_help_and_exit(const char *cmd);
 void groups_draw(Mat &src, vector<Rect> &groups);
-void er_draw(Mat &src, Mat &dst, ERStat& er);
+void er_show(vector<Mat> &channels, vector<vector<ERStat> > &regions);
 
 int  main(int argc, const char * argv[])
 {
+    cout << endl << argv[0] << 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<ERFilter> er_filter1 = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"),8,0.00025,0.13,0.4,true,0.1);
-    Ptr<ERFilter> er_filter2 = createERFilterNM2(loadClassifierNM2("trained_classifierNM2.xml"),0.3);
+    Ptr<ERFilter> er_filter1 = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"),16,0.00015,0.13,0.2,true,0.1);
+    Ptr<ERFilter> er_filter2 = createERFilterNM2(loadClassifierNM2("trained_classifierNM2.xml"),0.5);
 
     vector<vector<ERStat> > 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<Rect> 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 << " <input_image> " << 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<Rect> &groups)
     }
 }
 
-void er_draw(Mat &src, Mat &dst, ERStat& er)
+void er_show(vector<Mat> &channels, vector<vector<ERStat> > &regions)
 {
-
-    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 (file)
index 0000000..2c20941
--- /dev/null
+++ b/samples/cpp/trained_classifier_erGrouping.xml
@@ -0,0 +1,20046 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<boost type_id="opencv-ml-boost-tree">
+  <boosting_type>RealAdaboost</boosting_type>
+  <splitting_criteria>Gini</splitting_criteria>
+  <ntrees>500</ntrees>
+  <weight_trimming_rate>1.</weight_trimming_rate>
+  <is_classifier>1</is_classifier>
+  <var_all>14</var_all>
+  <var_count>13</var_count>
+  <ord_var_count>13</ord_var_count>
+  <cat_var_count>0</cat_var_count>
+  <training_params>
+    <use_surrogates>0</use_surrogates>
+    <max_categories>10</max_categories>
+    <max_depth>1</max_depth>
+    <min_sample_count>10</min_sample_count>
+    <cross_validation_folds>0</cross_validation_folds>
+    <priors type_id="opencv-matrix">
+      <rows>1</rows>
+      <cols>2</cols>
+      <dt>d</dt>
+      <data>
+        1. 1.</data></priors></training_params>
+  <var_idx type_id="opencv-matrix">
+    <rows>1</rows>
+    <cols>13</cols>
+    <dt>i</dt>
+    <data>
+      1 2 3 4 5 6 7 8 9 10 11 12 13</data></var_idx>
+  <var_type>
+    0 0 0 0 0 0 0 0 0 0 0 0 0</var_type>
+  <cat_count type_id="opencv-matrix">
+    <rows>1</rows>
+    <cols>1</cols>
+    <dt>i</dt>
+    <data>
+      2</data></cat_count>
+  <cat_map type_id="opencv-matrix">
+    <rows>1</rows>
+    <cols>2</cols>
+    <dt>i</dt>
+    <data>
+      1 2</data></cat_map>
+  <trees>
+    <_>
+      <best_tree_idx>-1</best_tree_idx>
+      <nodes>
+        <_>
+          <depth>0</depth>
+          <sample_count>4743</sample_count>
+          <value>4.9283312788368594e-01</value>
+          <norm_class_idx>1</norm_class_idx>
+          <Tn>0</Tn>
+          <complexity>0</complexity>
+          <alpha>0.</alpha>
+          <node_risk>0.</node_risk>
+          <tree_risk>0.</tree_risk>
+          <tree_error>0.</tree_error>
+          <splits>
+            <_><var>5</var>
+              <quality>7.8683781623840332e-01</quality>
+              <le>6.9047653675079346e-01</le></_></splits></_>
+        <_>
+          <depth>1</depth>
+          <sample_count>2922</sample_count>
+          <value>1.6857763325385804e+00</value>
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+          <alpha>0.</alpha>
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+          <tree_risk>0.</tree_risk>
+          <tree_error>0.</tree_error></_>
+        <_>
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+          <value>-3.1962829546244120e-01</value>
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+          <complexity>0</complexity>
+          <alpha>0.</alpha>
+          <node_risk>0.</node_risk>
+          <tree_risk>0.</tree_risk>
+          <tree_error>0.</tree_error></_></nodes></_>
+    <_>
+      <best_tree_idx>-1</best_tree_idx>
+      <nodes>
+        <_>
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+          <value>2.2204460492502639e-14</value>
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+          <complexity>0</complexity>
+          <alpha>0.</alpha>
+          <node_risk>0.</node_risk>
+          <tree_risk>0.</tree_risk>
+          <tree_error>0.</tree_error>
+          <splits>
+            <_><var>3</var>
+              <quality>6.7457795143127441e-01</quality>
+              <le>1.6023799896240234e+01</le></_></splits></_>
+        <_>
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+          <sample_count>2515</sample_count>
+          <value>1.3206782471212661e+00</value>
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+          <complexity>0</complexity>
+          <alpha>0.</alpha>
+          <node_risk>0.</node_risk>
+          <tree_risk>0.</tree_risk>
+          <tree_error>0.</tree_error></_></nodes></_>
+    <_>
+      <best_tree_idx>-1</best_tree_idx>
+      <nodes>
+        <_>
+          <depth>0</depth>
+          <sample_count>4743</sample_count>
+          <value>4.4408920985004293e-14</value>
+          <norm_class_idx>1</norm_class_idx>
+          <Tn>0</Tn>
+          <complexity>0</complexity>
+          <alpha>0.</alpha>
+          <node_risk>0.</node_risk>
+          <tree_risk>0.</tree_risk>
+          <tree_error>0.</tree_error>
+          <splits>
+            <_><var>8</var>
+              <quality>5.7657080888748169e-01</quality>
+              <le>1.6375650465488434e-01</le></_></splits></_>
+        <_>
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+          <value>1.0082101731479634e+00</value>
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+          <alpha>0.</alpha>
+          <node_risk>0.</node_risk>
+          <tree_risk>0.</tree_risk>
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+        <_>
+          <depth>1</depth>
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+          <value>-2.0291975151863534e-01</value>
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+          <alpha>0.</alpha>
+          <node_risk>0.</node_risk>
+          <tree_risk>0.</tree_risk>
+          <tree_error>0.</tree_error></_></nodes></_>
+    <_>
+      <best_tree_idx>-1</best_tree_idx>
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+          <tree_risk>0.</tree_risk>
+          <tree_error>0.</tree_error>
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+            <_><var>0</var>
+              <quality>5.7874917984008789e-01</quality>
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+        <_>
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+        <_>
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+          <alpha>0.</alpha>
+          <node_risk>0.</node_risk>
+          <tree_risk>0.</tree_risk>
+          <tree_error>0.</tree_error></_></nodes></_>
+    <_>
+      <best_tree_idx>-1</best_tree_idx>
+      <nodes>
+        <_>
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+          <alpha>0.</alpha>
+          <node_risk>0.</node_risk>
+          <tree_risk>0.</tree_risk>
+          <tree_error>0.</tree_error>
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+            <_><var>4</var>
+              <quality>5.7350283861160278e-01</quality>
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+        <_>
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+          <tree_risk>0.</tree_risk>
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+        <_>
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+          <value>6.3213741437159332e-01</value>
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+          <alpha>0.</alpha>
+          <node_risk>0.</node_risk>
+          <tree_risk>0.</tree_risk>
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+    <_>
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+        <_>
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+          <sample_count>4743</sample_count>
+          <value>1.5987211554601999e-14</value>
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+          <alpha>0.</alpha>
+          <node_risk>0.</node_risk>
+          <tree_risk>0.</tree_risk>
+          <tree_error>0.</tree_error>
+          <splits>
+            <_><var>9</var>
+              <quality>5.3986549377441406e-01</quality>
+              <le>1.8935400247573853e-01</le></_></splits></_>
+        <_>
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+          <value>-2.1807445008848766e-01</value>
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+          <tree_risk>0.</tree_risk>
+          <tree_error>0.</tree_error></_>
+        <_>
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+          <node_risk>0.</node_risk>
+          <tree_risk>0.</tree_risk>
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+    <_>
+      <best_tree_idx>-1</best_tree_idx>
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+        <_>
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+          <sample_count>4743</sample_count>
+          <value>-1.6209256159527547e-14</value>
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+          <node_risk>0.</node_risk>
+          <tree_risk>0.</tree_risk>
+          <tree_error>0.</tree_error>
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+            <_><var>7</var>
+              <quality>5.4257267713546753e-01</quality>
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+          <node_risk>0.</node_risk>
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+</opencv_storage>