Merge pull request #876 from alalek:fix_contrib_872

modules/text/samples/detect_er_chars.py

@@ -5,7 +5,6 @@ import os
 
 import cv2
 import numpy as np
-from matplotlib import pyplot as plt
 
 print('\ndetect_er_chars.py')
 print('       A simple demo script using the Extremal Region Filter algorithm described in:')
@@ -32,8 +31,8 @@ regions = cv2.text.detectRegions(gray,er1,er2)
 #Visualization
 rects = [cv2.boundingRect(p.reshape(-1, 1, 2)) for p in regions]
 for rect in rects:
-  cv2.rectangle(img, rect[0:2], (rect[0]+rect[2],rect[1]+rect[3]), (0, 0, 255), 2)
-img = img[:,:,::-1] #flip the colors dimension from BGR to RGB
-plt.imshow(img)
-plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis
-plt.show()
+  cv2.rectangle(img, rect[0:2], (rect[0]+rect[2],rect[1]+rect[3]), (0, 0, 0), 2)
+for rect in rects:
+  cv2.rectangle(img, rect[0:2], (rect[0]+rect[2],rect[1]+rect[3]), (255, 255, 255), 1)
+cv2.imshow("Text detection result", img)
+cv2.waitKey(0)

modules/text/samples/textdetection.cpp

@@ -32,7 +32,6 @@ int main(int argc, const char * argv[])
 
     if (argc < 2) show_help_and_exit(argv[0]);
 
-    namedWindow("grouping",WINDOW_NORMAL);
     Mat src = imread(argv[1]);
 
     // Extract channels to be processed individually
@@ -70,8 +69,8 @@ int main(int argc, const char * argv[])
     imshow("grouping",src);
 
     cout << "Done!" << endl << endl;
-    cout << "Press 'e' to show the extracted Extremal Regions, any other key to exit." << endl << endl;
-    if( waitKey (-1) == 101)
+    cout << "Press 'space' to show the extracted Extremal Regions, any other key to exit." << endl << endl;
+    if ((waitKey()&0xff) == ' ')
         er_show(channels,regions);
 
     // memory clean-up

modules/text/samples/textdetection.py

@@ -5,7 +5,6 @@ import os
 
 import cv2
 import numpy as np
-from matplotlib import pyplot as plt
 
 print('\ntextdetection.py')
 print('       A demo script of the Extremal Region Filter algorithm described in:')
@@ -50,11 +49,10 @@ for channel in channels:
   #Visualization
   for r in range(0,np.shape(rects)[0]):
     rect = rects[r]
-    cv2.rectangle(vis, (rect[0],rect[1]), (rect[0]+rect[2],rect[1]+rect[3]), (0, 255, 255), 2)
+    cv2.rectangle(vis, (rect[0],rect[1]), (rect[0]+rect[2],rect[1]+rect[3]), (0, 0, 0), 2)
+    cv2.rectangle(vis, (rect[0],rect[1]), (rect[0]+rect[2],rect[1]+rect[3]), (255, 255, 255), 1)
 
 
 #Visualization
-vis = vis[:,:,::-1] #flip the colors dimension from BGR to RGB
-plt.imshow(vis)
-plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis
-plt.show()
+cv2.imshow("Text detection result", vis)
+cv2.waitKey(0)

modules/text/src/erfilter.cpp

@@ -275,24 +275,18 @@ void ERFilterNM::er_tree_extract( InputArray image )
     // the component stack
     vector<ERStat*> er_stack;
 
-    //the quads for euler number calculation
-    unsigned char quads[3][4];
-    quads[0][0] = 1 << 3;
-    quads[0][1] = 1 << 2;
-    quads[0][2] = 1 << 1;
-    quads[0][3] = 1;
-    quads[1][0] = (1<<2)|(1<<1)|(1);
-    quads[1][1] = (1<<3)|(1<<1)|(1);
-    quads[1][2] = (1<<3)|(1<<2)|(1);
-    quads[1][3] = (1<<3)|(1<<2)|(1<<1);
-    quads[2][0] = (1<<2)|(1<<1);
-    quads[2][1] = (1<<3)|(1);
-    // quads[2][2] and quads[2][3] are never used so no need to initialize them.
+    // the quads for euler number calculation
+    // quads[2][2] and quads[2][3] are never used.
     // The four lowest bits in each quads[i][j] correspond to the 2x2 binary patterns 
     // Q_1, Q_2, Q_3 in the Neumann and Matas CVPR 2012 paper 
     // (see in page 4 at the end of first column). 
     // Q_1 and Q_2 have four patterns, while Q_3 has only two.
-
+    const int quads[3][4] =
+    {
+        { 1<<3                 ,          1<<2          ,                 1<<1   ,                       1<<0 },
+        {     (1<<2)|(1<<1)|(1),   (1<<3)|    (1<<1)|(1),   (1<<3)|(1<<2)|    (1),   (1<<3)|(1<<2)|(1<<1)     },
+        {     (1<<2)|(1<<1)    ,   (1<<3)|           (1),            /*unused*/-1,               /*unused*/-1 }
+    };
 
     // masks to know if a pixel is accessible and if it has been already added to some region
     vector<bool> accessible_pixel_mask(width * height);
@@ -392,8 +386,8 @@ void ERFilterNM::er_tree_extract( InputArray image )
         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};
+        int quad_before[4] = {0,0,0,0};
+        int quad_after[4] = {0,0,0,0};
         quad_after[0] = 1<<1;
         quad_after[1] = 1<<3;
         quad_after[2] = 1<<2;
@@ -542,9 +536,9 @@ void ERFilterNM::er_tree_extract( InputArray image )
         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++;
-
+        for (; threshold_level < (255/thresholdDelta)+1; threshold_level++)
+            if (!boundary_pixes[threshold_level].empty())
+                break;
 
         int new_level = image_data[current_pixel];
 
@@ -784,28 +778,27 @@ ERStat* ERFilterNM::er_save( ERStat *er, ERStat *parent, ERStat *prev )
 // recursively walk the tree and filter (remove) regions using the callback classifier
 ERStat* ERFilterNM::er_tree_filter ( InputArray image, ERStat * stat, ERStat *parent, ERStat *prev )
 {
-    Mat src = image.getMat();
     // assert correct image type
-    CV_Assert( src.type() == CV_8UC1 );
+    CV_Assert( image.type() == CV_8UC1 );
+
+    Mat src = image.getMat();
 
     //Fill the region and calculate 2nd stage features
-    Mat region = region_mask(Rect(Point(stat->rect.x,stat->rect.y),Point(stat->rect.br().x+2,stat->rect.br().y+2)));
+    Mat region = region_mask(Rect(stat->rect.tl(), stat->rect.br() + Point(2,2)));
     region = Scalar(0);
     int newMaskVal = 255;
     int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY;
     Rect rect;
 
-    floodFill( src(Rect(Point(stat->rect.x,stat->rect.y),Point(stat->rect.br().x,stat->rect.br().y))),
-               region, Point(stat->pixel%src.cols - stat->rect.x, stat->pixel/src.cols - stat->rect.y),
+    floodFill( src(stat->rect),
+               region, Point(stat->pixel%src.cols, stat->pixel/src.cols) - stat->rect.tl(),
                Scalar(255), &rect, Scalar(stat->level), Scalar(0), flags );
-    rect.width += 2;
-    rect.height += 2;
-    region = region(rect);
+    region = region(Rect(1, 1, rect.width, rect.height));
 
     vector<vector<Point> > contours;
     vector<Point> contour_poly;
     vector<Vec4i> hierarchy;
-    findContours( region(Rect(1, 1, region.cols - 2, region.rows - 2)), contours, hierarchy, RETR_TREE, CHAIN_APPROX_NONE, Point(1, 1) );
+    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, (float)min(rect.width,rect.height)/17, true );
@@ -2859,9 +2852,7 @@ bool guo_hall_thinning(const Mat1b & img, Mat& skeleton)
 }
 
 
-float extract_features(Mat &grey, Mat& channel, vector<ERStat> &regions, vector<ERFeatures> &features);
-
-float extract_features(Mat &grey, Mat& channel, vector<ERStat> &regions, vector<ERFeatures> &features)
+static float extract_features(Mat &grey, Mat& channel, vector<ERStat> &regions, vector<ERFeatures> &features)
 {
     // assert correct image type
     CV_Assert(( channel.type() == CV_8UC1 ) && ( grey.type() == CV_8UC1 ));
@@ -2890,18 +2881,15 @@ float extract_features(Mat &grey, Mat& channel, vector<ERStat> &regions, vector<
         {
 
             //Fill the region and calculate features
-            Mat region = region_mask(Rect(Point(stat->rect.x,stat->rect.y),
-                                          Point(stat->rect.br().x+2,stat->rect.br().y+2)));
+            Mat region = region_mask(Rect(stat->rect.tl(),
+                                          stat->rect.br() + Point(2,2)));
             region = Scalar(0);
             int newMaskVal = 255;
             int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY;
-            Rect rect;
 
-            floodFill( channel(Rect(Point(stat->rect.x,stat->rect.y),Point(stat->rect.br().x,stat->rect.br().y))),
+            floodFill( channel(stat->rect),
                        region, Point(stat->pixel%channel.cols - stat->rect.x, stat->pixel/channel.cols - stat->rect.y),
-                       Scalar(255), &rect, Scalar(stat->level), Scalar(0), flags );
-            rect.width += 2;
-            rect.height += 2;
+                       Scalar(255), NULL, Scalar(stat->level), Scalar(0), flags );
             Mat rect_mask = region_mask(Rect(stat->rect.x+1,stat->rect.y+1,stat->rect.width,stat->rect.height));
 
 
@@ -2911,7 +2899,7 @@ float extract_features(Mat &grey, Mat& channel, vector<ERStat> &regions, vector<
             f.intensity_std  = (float)std[0];
 
             Mat tmp,bw;
-            region_mask(Rect(stat->rect.x+1,stat->rect.y+1,stat->rect.width,stat->rect.height)).copyTo(bw);
+            rect_mask.copyTo(bw);
             distanceTransform(bw, tmp, DIST_L1,3); //L1 gives distance in round integers while L2 floats
 
             // Add border because if region span all the image size skeleton will crash
@@ -3513,19 +3501,16 @@ bool isValidPair(Mat &grey, Mat &lab, Mat &mask, vector<Mat> &channels, vector<
     i = &regions[idx1[0]][idx1[1]];
     j = &regions[idx2[0]][idx2[1]];
 
-    Mat region = mask(Rect(Point(i->rect.x,i->rect.y),
-                           Point(i->rect.br().x+2,i->rect.br().y+2)));
+    Mat region = mask(Rect(i->rect.tl(),
+                           i->rect.br()+ Point(2,2)));
     region = Scalar(0);
 
     int newMaskVal = 255;
     int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY;
-    Rect rect;
 
-    floodFill( channels[idx1[0]](Rect(Point(i->rect.x,i->rect.y),Point(i->rect.br().x,i->rect.br().y))),
-               region, Point(i->pixel%grey.cols - i->rect.x, i->pixel/grey.cols - i->rect.y),
-               Scalar(255), &rect, Scalar(i->level), Scalar(0), flags);
-    rect.width += 2;
-    rect.height += 2;
+    floodFill( channels[idx1[0]](i->rect),
+               region, Point(i->pixel%grey.cols, i->pixel/grey.cols) - i->rect.tl(),
+               Scalar(255), NULL, Scalar(i->level), Scalar(0), flags);
     Mat rect_mask = mask(Rect(i->rect.x+1,i->rect.y+1,i->rect.width,i->rect.height));
 
     Scalar mean,std;
@@ -3535,15 +3520,12 @@ bool isValidPair(Mat &grey, Mat &lab, Mat &mask, vector<Mat> &channels, vector<
     float a_mean1 = (float)mean[1];
     float b_mean1 = (float)mean[2];
 
-    region = mask(Rect(Point(j->rect.x,j->rect.y),
-                           Point(j->rect.br().x+2,j->rect.br().y+2)));
+    region = mask(Rect(j->rect.tl(), j->rect.br()+ Point(2,2)));
     region = Scalar(0);
 
-    floodFill( channels[idx2[0]](Rect(Point(j->rect.x,j->rect.y),Point(j->rect.br().x,j->rect.br().y))),
-               region, Point(j->pixel%grey.cols - j->rect.x, j->pixel/grey.cols - j->rect.y),
-               Scalar(255), &rect, Scalar(j->level), Scalar(0), flags);
-    rect.width += 2;
-    rect.height += 2;
+    floodFill( channels[idx2[0]](j->rect),
+               region, Point(j->pixel%grey.cols, j->pixel/grey.cols) - j->rect.tl(),
+               Scalar(255), NULL, Scalar(j->level), Scalar(0), flags);
     rect_mask = mask(Rect(j->rect.x+1,j->rect.y+1,j->rect.width,j->rect.height));
 
     meanStdDev(grey(j->rect),mean,std,rect_mask);
@@ -4181,7 +4163,7 @@ void MSERsToERStats(InputArray image, vector<vector<Point> > &contours, vector<v
 void detectRegions(InputArray image, const Ptr<ERFilter>& er_filter1, const Ptr<ERFilter>& er_filter2, CV_OUT vector< vector<Point> >& regions)
 {
     // assert correct image type
-    CV_Assert( image.getMat().type() == CV_8UC1 );
+    CV_Assert( image.type() == CV_8UC1 );
     // at least one ERFilter must be passed
     CV_Assert( !er_filter1.empty() );
 
@@ -4195,36 +4177,33 @@ void detectRegions(InputArray image, const Ptr<ERFilter>& er_filter1, const Ptr<
     }
 
     //Convert each ER to vector<Point> and push it to output regions
-    Mat src = image.getMat();
-    Mat region_mask = Mat::zeros(src.rows+2, src.cols+2, CV_8UC1);
+    const Mat src = image.getMat();
     for (size_t i=1; i < ers.size(); i++) //start from 1 to deprecate root region
     {
       ERStat* stat = &ers[i];
 
       //Fill the region and calculate 2nd stage features
-      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);
+      Mat region_mask(Size(stat->rect.width + 2, stat->rect.height + 2), CV_8UC1, Scalar(0));
+      Mat region = region_mask(Rect(1, 1, stat->rect.width, stat->rect.height));
+
       int newMaskVal = 255;
       int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY;
-      Rect rect;
 
-      floodFill( src(Rect(Point(stat->rect.x,stat->rect.y),Point(stat->rect.br().x,stat->rect.br().y))),
-                 region, Point(stat->pixel%src.cols - stat->rect.x, stat->pixel/src.cols - stat->rect.y),
-                 Scalar(255), &rect, Scalar(stat->level), Scalar(0), flags );
-      rect.width += 2;
-      rect.height += 2;
-      region = region(rect);
+      const Point seed_pt(stat->pixel%src.cols, stat->pixel/src.cols);
+      uchar seed_v = src.at<uchar>(seed_pt);
+      CV_Assert((int)seed_v <= stat->level);
+
+      floodFill( src(stat->rect),
+                 region_mask,
+                 seed_pt - stat->rect.tl(),
+                 Scalar(255), NULL, Scalar(/*stat->level*/255), Scalar(0), flags );
 
       vector<vector<Point> > contours;
       vector<Vec4i> hierarchy;
-      findContours( region, contours, hierarchy, RETR_TREE, CHAIN_APPROX_NONE, Point(0, 0) );
-
-      for (size_t j=0; j < contours[0].size(); j++)
-        contours[0][j] += (stat->rect.tl()-Point(1,1));
+      findContours( region, contours, hierarchy, RETR_TREE, CHAIN_APPROX_NONE, stat->rect.tl() );
 
       regions.push_back(contours[0]);
     }
-
 }
 
 }