Add a save method to detectors and predictors

Also, removed the saving of the upsample which I missed from
before (since I'm not using the struct now). I understand why
the upsample was being saved, but I don't necessarily agree it
is particularly useful as you should really be upsampling on
a case by case basis at test time.

tools/python/src/object_detection.cpp

@@ -108,6 +108,14 @@ std::vector<rectangle> run_detector_with_upscale (
     }
 }
 
+void save_simple_object_detector(const simple_object_detector& detector, const std::string& detector_output_filename)
+{
+    std::ofstream fout(detector_output_filename.c_str(), std::ios::binary);
+    int version = 1;
+    serialize(detector, fout);
+    serialize(version, fout);
+}
+
 // ----------------------------------------------------------------------------------------
 
 inline void train_simple_object_detector_on_images_py (
@@ -384,6 +392,7 @@ ensures \n\
                       default will be used.
             !*/
             )
+        .def("save", save_simple_object_detector, (arg("detector_output_filename")), "Save a simple_object_detector to the provided path.")
         .def_pickle(serialize_pickle<type>());
     }
     {

tools/python/src/shape_predictor.cpp

@@ -35,6 +35,14 @@ full_object_detection run_predictor (
     }
 }
 
+void save_shape_predictor(const shape_predictor& predictor, const std::string& predictor_output_filename)
+{
+    std::ofstream fout(predictor_output_filename.c_str(), std::ios::binary);
+    int version = 1;
+    serialize(predictor, fout);
+    serialize(version, fout);
+}
+
 // ----------------------------------------------------------------------------------------
 
 rectangle full_obj_det_get_rect (const full_object_detection& detection)
@@ -226,6 +234,7 @@ train_shape_predictor() routine.")
 ensures \n\
     - This function runs the shape predictor on the input image and returns \n\
       a single full object detection.")
+        .def("save", save_shape_predictor, (arg("predictor_output_filename")), "Save a shape_predictor to the provided path.")
         .def_pickle(serialize_pickle<type>());
     }
     {

tools/python/src/simple_object_detector.h

@@ -176,24 +176,15 @@ namespace dlib
         // upsample the images at most two times to help make the boxes obtainable.
         std::vector<std::vector<rectangle> > temp(boxes), removed;
         removed = remove_unobtainable_rectangles(trainer, images, temp);
-        if (impl::contains_any_boxes(removed))
-        {
-            ++upsample_amount;
-            if (options.be_verbose)
-                std::cout << "upsample images..." << std::endl;
-            upsample_image_dataset<pyramid_down<2> >(images, boxes, ignore);
-            temp = boxes;
-            removed = remove_unobtainable_rectangles(trainer, images, temp);
-            if (impl::contains_any_boxes(removed))
+        while (impl::contains_any_boxes(removed) && upsample_amount < 2)
         {
             ++upsample_amount;
             if (options.be_verbose)
-                    std::cout << "upsample images..." << std::endl;
+                std::cout << "Upsample images..." << std::endl;
             upsample_image_dataset<pyramid_down<2> >(images, boxes, ignore);
             temp = boxes;
             removed = remove_unobtainable_rectangles(trainer, images, temp);
         }
-        }
         // if we weren't able to get all the boxes to match then throw an error 
         if (impl::contains_any_boxes(removed))
             impl::throw_invalid_box_error_message(dataset_filename, removed, options);
@@ -207,7 +198,6 @@ namespace dlib
         int version = 1;
         serialize(detector, fout);
         serialize(version, fout);
-        serialize(upsample_amount, fout);
 
         if (options.be_verbose)
         {
@@ -218,10 +208,10 @@ namespace dlib
             std::cout << "Trained with sliding window " << width << " pixels wide by " << height << " pixels tall." << std::endl;
             if (upsample_amount != 0)
             {
-                if (upsample_amount == 1)
-                    std::cout << "Upsampled images " << upsample_amount << " time to allow detection of small boxes." << std::endl;
-                else
-                    std::cout << "Upsampled images " << upsample_amount << " times to allow detection of small boxes." << std::endl;
+                // Unsampled images # time(s) to allow detection of small boxes
+                std::cout << "Upsampled images " << upsample_amount;
+                std::cout << (upsample_amount == 1) ? " time" : " times";
+                std::cout << " to allow detection of small boxes." << std::endl;
             }
             if (options.add_left_right_image_flips)
                 std::cout << "Trained on both left and right flipped versions of images." << std::endl;
@@ -270,10 +260,6 @@ namespace dlib
         deserialize(version, fin);
         if (version != 1)
             throw error("Unknown simple_object_detector format.");
-        deserialize(upsample_amount, fin);
-
-        for (unsigned int i = 0; i < upsample_amount; ++i)
-            upsample_image_dataset<pyramid_down<2> >(images, boxes);
 
         matrix<double,1,3> res = test_object_detection_function(detector, images, boxes, ignore);
         simple_test_results ret;