updated examples

examples/object_detector_advanced_ex.cpp

@@ -199,7 +199,8 @@ int main()
 
         typedef scan_image_pyramid<pyramid_down_5_4, very_simple_feature_extractor> image_scanner_type;
         image_scanner_type scanner;
-        // Setup the sliding window box.  Lets use a window with the same shape as the white boxes we
+        // Instead of using setup_grid_detection_templates() like in object_detector_ex.cpp, lets manually
+        // setup the sliding window box.  We use a window with the same shape as the white boxes we
         // are trying to detect.
         const rectangle object_box = compute_box_dimensions(1,    // width/height ratio
                                                             70*70 // box area
@@ -234,10 +235,6 @@ int main()
         structural_object_detection_trainer<image_scanner_type> trainer(scanner);
         trainer.set_num_threads(4); // Set this to the number of processing cores on your machine. 
 
-        // This line tells the algorithm that it is never OK for two detections to overlap.  So
-        // this controls how the non-max suppression is performed and in general you can set this up
-        // any way you like. 
-        trainer.set_overlap_tester(test_box_overlap(0));
 
         // The trainer will try and find the detector which minimizes the number of detection mistakes.
         // This function controls how it decides if a detection output is a mistake or not.  The bigger
@@ -246,7 +243,7 @@ int main()
         // see that the detections aren't exactly on top of the white squares anymore.  See the documentation
         // for the structural_object_detection_trainer and structural_svm_object_detection_problem objects
         // for a more detailed discussion of this parameter.  
-        trainer.set_overlap_eps(0.95);
+        trainer.set_match_eps(0.95);
 
 
         object_detector<image_scanner_type> detector = trainer.train(images, object_locations);

examples/object_detector_ex.cpp

@@ -78,6 +78,20 @@ void make_simple_test_data (
     temp.push_back(centered_rect(point(123,121), 70,70));
     fill_rect(images[2],temp.back(),255); // Paint the square white
     object_locations.push_back(temp);
+
+    // corrupt each image with random noise just to make this a little more 
+    // challenging 
+    dlib::rand rnd;
+    for (unsigned long i = 0; i < images.size(); ++i)
+    {
+        for (long r = 0; r < images[i].nr(); ++r)
+        {
+            for (long c = 0; c < images[i].nc(); ++c)
+            {
+                images[i][r][c] = put_in_range(0,255,images[i][r][c] + 50*rnd.get_random_gaussian());
+            }
+        }
+    }
 }
 
 // ----------------------------------------------------------------------------------------
@@ -140,18 +154,22 @@ int main()
         typedef hashed_feature_image<hog_image<3,3,1,4,hog_signed_gradient,hog_full_interpolation> > feature_extractor_type;
         typedef scan_image_pyramid<pyramid_down, feature_extractor_type> image_scanner_type;
         image_scanner_type scanner;
-        // Setup the sliding window box.  Lets use a window with the same shape as the white boxes we
-        // are trying to detect.
-        const rectangle object_box = compute_box_dimensions(1,    // width/height ratio
-                                                            70*70 // box area 
-                                                            );
-        // Setup the detection template so it contains 4 feature extraction zones inside the object_box.  These
-        // are the upper left, upper right, lower left, and lower right quadrants of object_box.  (Note that
-        // in general we can add more than one detection template.  But in this case one is enough.)
-        scanner.add_detection_template(object_box, create_grid_detection_template(object_box,2,2));
-
 
+        // The hashed_feature_image feature extractor needs to be supplied with a hash function capable 
+        // of hashing the outputs of the hog_image.  Calling this function will set it up for us.  The 10
+        // here indicates that the hash will hash hog vectors into the range [0, pow(2,10)).  Therefore,
+        // the feature vectors output by the hashed_feature_image will have dimension pow(2,10).
+        setup_hashed_features(scanner, images, 10);
 
+        // We also need to setup the detection templates the scanner will use.  It is important that 
+        // we add detection templates which are capable of matching all the output boxes we want to learn.
+        // For example, if object_locations contained a rectangle with a height to width ratio of 10 but
+        // we only added square detection templates then it would be impossible to detect this non-square
+        // rectangle.  The setup_grid_detection_templates() routine will take care of this for us by looking
+        // at the contents of object_locations and automatically picking an appropriate set.  Also, the final 
+        // arguments indicate that we want our detection templates to have 4 enveloping rectangles laid out 
+        // in a 2x2 regular grid inside each sliding window.
+        setup_grid_detection_templates(scanner, object_locations, 2, 2);
 
 
         // Now that we have defined the kind of sliding window classifier system we want and stored 
@@ -160,14 +178,10 @@ int main()
         structural_object_detection_trainer<image_scanner_type> trainer(scanner);
         trainer.set_num_threads(4); // Set this to the number of processing cores on your machine. 
 
-        // This line tells the algorithm that it is never OK for two detections to overlap.  So
-        // this controls how the non-max suppression is performed and in general you can set this up
-        // any way you like. 
-        trainer.set_overlap_tester(test_box_overlap(0));
 
         // There are a variety of other useful parameters to the structural_object_detection_trainer.  
         // Examples of the ones you are most likely to use follow (see dlib documentation for what they do):
-        //trainer.set_overlap_eps(0.80);
+        //trainer.set_match_eps(0.80);
         //trainer.set_c(1.0);
         //trainer.set_loss_per_missed_target(1);
         //trainer.set_loss_per_false_alarm(1);