logtopolar

modules/adas/tools/fcw_train/fcw_train.cpp

@@ -106,7 +106,7 @@ int main(int argc, char *argv[])
         cerr << "Error reading model size from `" << model_size << "`" << endl;
         return 1;
     }
-
+    
     if( params.feature_count <= 0 )
     {
         cerr << "feature_count must be positive number" << endl;
@@ -159,15 +159,17 @@ int main(int argc, char *argv[])
     int features_size = params.feature_count;
     int max_features_allowed = (int)(INT_MAX/(sizeof(int)* samples_size));
     int max_samples_allowed = (int)(INT_MAX/(sizeof(int)* features_size));
+    int total_samples = (int)((params.bg_per_image * bg_filenames.size()) + pos_filenames.size());
+    
     
-    if((int)((params.bg_per_image * bg_filenames.size()) + pos_filenames.size()) >max_samples_allowed)
+    if(total_samples >max_samples_allowed)
     {
-      CV_Error_(1, ("exceeded maximum number of samples. Maximum number of samples with %d features is %d\n",features_size,max_samples_allowed ));
+      CV_Error_(1, ("exceeded maximum number of samples. Maximum number of samples with %d features is %d, you have %d (%d positive samples + (%d bg * %d bg_per_image))\n",features_size,max_samples_allowed,total_samples,pos_filenames.size(),bg_filenames.size(),params.bg_per_image ));
     }
     
     if(params.feature_count >max_features_allowed)
     {
-      CV_Error_(1, ("exceeded maximum number of features. Maximum number of features with %d samples is %d\n",samples_size,max_features_allowed ));
+      CV_Error_(1, ("exceeded maximum number of features. Maximum number of features with %d samples is %d, you have %d\n",samples_size,max_features_allowed, features_size ));
     }
     
     std::cout<<pos_filenames.size()<<std::endl;

modules/text/src/erfilter.cpp

@@ -3749,7 +3749,7 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
     _src.getMatVector(src);
 
     CV_Assert ( !src.empty() );
-    CV_Assert ( src.size() == regions.size() );
+    //CV_Assert ( src.size() == regions.size() );
     size_t num_channels = src.size();
 
     Mat img = _img.getMat();

modules/xobjdetect/src/acffeature.cpp

@@ -276,6 +276,7 @@ vector<vector<int> > generateFeatures(Size window_size, const std::string& type,
 void computeChannels(InputArray image, vector<Mat>& channels)
 {
     Mat_<float> grad;
+    Mat_<float> angles;
     Mat luv, gray, src;
     
     if(image.getMat().channels() > 1)
@@ -297,13 +298,15 @@ void computeChannels(InputArray image, vector<Mat>& channels)
     Sobel(gray, row_der, CV_32F, 0, 1);
     Sobel(gray, col_der, CV_32F, 1, 0);
 
-    magnitude(row_der, col_der, grad);
+    cartToPolar(col_der, row_der, grad, angles, true);
+    //magnitude(row_der, col_der, grad);
 
     Mat_<Vec6f> hist = Mat_<Vec6f>::zeros(grad.rows, grad.cols);
-    const float to_deg = 180 / 3.1415926f;
+    //const float to_deg = 180 / 3.1415926f;
     for (int row = 0; row < grad.rows; ++row) {
         for (int col = 0; col < grad.cols; ++col) {
-            float angle = atan2(row_der(row, col), col_der(row, col)) * to_deg;
+            //float angle = atan2(row_der(row, col), col_der(row, col)) * to_deg;
+            float angle = angles(row, col);
             if (angle < 0)
                 angle += 180;
             int ind = (int)(angle / 30);