Add bg_per_image param

modules/adas/tools/fcw_train/fcw_train.cpp

@@ -52,6 +52,7 @@ int main(int argc, char *argv[])
         "{help           |           | print this message}"
         "{pos_path       |       pos | path to training object samples}"
         "{bg_path        |        bg | path to background images}"
+        "{bg_per_image   |         5 | number of windows to sample per bg image}"
         "{feature_count  |     10000 | number of features to generate}"
         "{weak_count     |       100 | number of weak classifiers in cascade}"
         "{model_size     |     40x40 | model size in pixels}"
@@ -71,12 +72,10 @@ int main(int argc, char *argv[])
     string bg_path = parser.get<string>("bg_path");
     string model_filename = parser.get<string>("model_filename");
 
-    cerr << pos_path << endl;
-    cerr << bg_path << endl;
-
     ICFDetectorParams params;
     params.feature_count = parser.get<size_t>("feature_count");
     params.weak_count = parser.get<size_t>("weak_count");
+    params.bg_per_image = parser.get<size_t>("bg_per_image");
 
     string model_size = parser.get<string>("model_size");
     if( !read_model_size(model_size.c_str(), &params.model_n_rows,

modules/xobjdetect/include/opencv2/xobjdetect.hpp

@@ -159,9 +159,10 @@ struct CV_EXPORTS ICFDetectorParams
     int weak_count;
     int model_n_rows;
     int model_n_cols;
+    int bg_per_image;
 
     ICFDetectorParams(): feature_count(UINT_MAX), weak_count(100),
-        model_n_rows(40), model_n_cols(40)
+        model_n_rows(56), model_n_cols(56), bg_per_image(5)
     {}
 };
 

modules/xobjdetect/src/icfdetector.cpp

@@ -97,11 +97,11 @@ void ICFDetector::train(const String& pos_path,
     {
         cout << setw(6) << (i + 1) << "/" << bg_filenames.size() << "\r";
         Mat img = imread(bg_filenames[i]);
-        for( int j = 0; j < 50;
-             ++j, ++neg_count)
+        for( int j = 0; j < params.bg_per_image; ++j, ++neg_count)
         {
             Rect r;
-            r.x = rng.uniform(0, img.cols); r.width = rng.uniform(r.x + 1, img.cols);
+            r.x = rng.uniform(0, img.cols);
+            r.width = rng.uniform(r.x + 1, img.cols);
             r.y = rng.uniform(0, img.rows);
             r.height = rng.uniform(r.y + 1, img.rows);