Refined the scan_image_pyramid interface a little. In particular, I split the

get_feature_vector() method into two separate functions so the interface
is a little simpler and more flexible.

dlib/image_processing/scan_image_pyramid_abstract.h

@@ -289,10 +289,22 @@ namespace dlib
                   been reached).
         !*/
 
+        const rectangle get_best_matching_rect (
+            const rectangle& rect
+        ) const;
+        /*!
+            requires
+                - is_loaded_with_image() == true
+                - get_num_detection_templates() > 0
+            ensures
+                - Since scan_image_pyramid is a sliding window classifier system, not all possible rectangles 
+                  can be represented.  Therefore, this function allows you to supply a rectangle and obtain the
+                  nearest possible sliding window rectangle.
+        !*/
+
         void get_feature_vector (
             const std::vector<rectangle>& rects,
-            feature_vector_type& psi,
-            std::vector<rectangle>& mapped_rects
+            feature_vector_type& psi
         ) const;
         /*!
             requires
@@ -305,7 +317,6 @@ namespace dlib
                 - if (rects was produced by a call to detect(), i.e. rects contains the contents of dets) then
                     - #psi == the sum of feature vectors corresponding to the sliding window locations contained
                       in rects.
-                    - #mapped_rects == rects
                     - Let w denote the w vector given to detect(), then we have:
                         - dot(w,#psi) == sum of scores of the dets produced by detect()
                 - else
@@ -313,8 +324,8 @@ namespace dlib
                       be output by detect().  So in the case where rects contains rectangles which could not arise
                       from a call to detect(), this function will map the rectangles in rects to the nearest possible 
                       object boxes and then store the sum of feature vectors for the mapped rectangles into #psi.
-                    - for all valid i: #mapped_rects[i] == the rectangle rects[i] gets mapped to for feature extraction.
-                - #mapped_rects.size() == rects.size()
+                    - for all valid i: get_best_matching_rect(rects[i]) == the rectangle rects[i] gets mapped to for 
+                      feature extraction.
         !*/
 
     };

dlib/svm/structural_svm_object_detection_problem.h

@@ -156,7 +156,11 @@ namespace dlib
             scanner.load(images[idx]);
             psi.set_size(get_num_dimensions());
             std::vector<rectangle> mapped_rects;
-            scanner.get_feature_vector(truth_rects[idx], psi, mapped_rects);
+            scanner.get_feature_vector(truth_rects[idx], psi);
+            for (unsigned long i = 0; i < truth_rects[idx].size(); ++i)
+            {
+                mapped_rects.push_back(scanner.get_best_matching_rect(truth_rects[idx][i]));
+            }
             psi(scanner.get_num_dimensions()) = -1.0*truth_rects[idx].size();
 
             // check if any of the boxes overlap.  If they do then it is impossible for
@@ -328,8 +332,7 @@ namespace dlib
 
             psi.set_size(get_num_dimensions());
             psi = 0;
-            std::vector<rectangle> mapped_rects;
-            scanner.get_feature_vector(final_dets, psi, mapped_rects);
+            scanner.get_feature_vector(final_dets, psi);
 
             psi(scanner.get_num_dimensions()) = -1.0*final_dets.size();
         }