Added the translation jittering option to the python API.

tools/python/src/shape_predictor.cpp

@@ -189,6 +189,8 @@ void bind_shape_predictors(py::module &m)
                        cause overfitting.  The value must be in the range (0, 1].")
         .def_readwrite("oversampling_amount", &type::oversampling_amount,
                       "The number of randomly selected initial starting points sampled for each training example")
+        .def_readwrite("oversampling_translation_jitter", &type::oversampling_translation_jitter,
+                      "The amount of translation jittering to apply to bounding boxes, a good value is in in the range [0 0.5].")
         .def_readwrite("feature_pool_size", &type::feature_pool_size,
                       "Number of pixels used to generate features for the random trees.")
         .def_readwrite("lambda_param", &type::lambda_param,

tools/python/src/shape_predictor.h

@@ -25,6 +25,7 @@ namespace dlib
             num_trees_per_cascade_level = 500;
             nu = 0.1;
             oversampling_amount = 20;
+            oversampling_translation_jitter = 0;
             feature_pool_size = 400;
             lambda_param = 0.1;
             num_test_splits = 20;
@@ -39,6 +40,7 @@ namespace dlib
         unsigned long num_trees_per_cascade_level;
         double nu;
         unsigned long oversampling_amount;
+        double oversampling_translation_jitter;
         unsigned long feature_pool_size;
         double lambda_param;
         unsigned long num_test_splits;
@@ -56,12 +58,14 @@ namespace dlib
     {
         try
         {
+            serialize("shape_predictor_training_options", out);
             serialize(item.be_verbose,out);
             serialize(item.cascade_depth,out);
             serialize(item.tree_depth,out);
             serialize(item.num_trees_per_cascade_level,out);
             serialize(item.nu,out);
             serialize(item.oversampling_amount,out);
+            serialize(item.oversampling_translation_jitter,out);
             serialize(item.feature_pool_size,out);
             serialize(item.lambda_param,out);
             serialize(item.num_test_splits,out);
@@ -81,12 +85,14 @@ namespace dlib
     {
         try
         {
+            check_serialized_version("shape_predictor_training_options", in);
             deserialize(item.be_verbose,in);
             deserialize(item.cascade_depth,in);
             deserialize(item.tree_depth,in);
             deserialize(item.num_trees_per_cascade_level,in);
             deserialize(item.nu,in);
             deserialize(item.oversampling_amount,in);
+            deserialize(item.oversampling_translation_jitter,in);
             deserialize(item.feature_pool_size,in);
             deserialize(item.lambda_param,in);
             deserialize(item.num_test_splits,in);
@@ -109,6 +115,7 @@ namespace dlib
             << "num_trees_per_cascade_level=" << o.num_trees_per_cascade_level << ","
             << "nu=" << o.nu << ","
             << "oversampling_amount=" << o.oversampling_amount << ","
+            << "oversampling_translation_jitter=" << o.oversampling_translation_jitter << ","
             << "feature_pool_size=" << o.feature_pool_size << ","
             << "lambda_param=" << o.lambda_param << ","
             << "num_test_splits=" << o.num_test_splits << ","
@@ -166,6 +173,7 @@ namespace dlib
         trainer.set_nu(options.nu);
         trainer.set_random_seed(options.random_seed);
         trainer.set_oversampling_amount(options.oversampling_amount);
+        trainer.set_oversampling_translation_jitter(options.oversampling_translation_jitter);
         trainer.set_feature_pool_size(options.feature_pool_size);
         trainer.set_feature_pool_region_padding(options.feature_pool_region_padding);
         trainer.set_lambda(options.lambda_param);
@@ -180,6 +188,7 @@ namespace dlib
             std::cout << "Training with nu: " << options.nu << std::endl;
             std::cout << "Training with random seed: " << options.random_seed << std::endl;
             std::cout << "Training with oversampling amount: " << options.oversampling_amount << std::endl;
+            std::cout << "Training with oversampling translation jitter: " << options.oversampling_translation_jitter << std::endl;
             std::cout << "Training with feature pool size: " << options.feature_pool_size << std::endl;
             std::cout << "Training with feature pool region padding: " << options.feature_pool_region_padding << std::endl;
             std::cout << "Training with " << options.num_threads << " threads." << std::endl;