How to use the dlib.shape_predictor_training_options function in dlib

# In this example we are going to train a face detector based on the small
# faces dataset in the examples/faces directory.  This means you need to supply
# the path to this faces folder as a command line argument so we will know
# where it is.
if len(sys.argv) != 2:
    print(
        "Give the path to the examples/faces directory as the argument to this "
        "program. For example, if you are in the python_examples folder then "
        "execute this program by running:\n"
        "    ./train_shape_predictor.py ../examples/faces")
    exit()
faces_folder = sys.argv[1]

options = dlib.shape_predictor_training_options()
# Now make the object responsible for training the model.
# This algorithm has a bunch of parameters you can mess with.  The
# documentation for the shape_predictor_trainer explains all of them.
# You should also read Kazemi's paper which explains all the parameters
# in great detail.  However, here I'm just setting three of them
# differently than their default values.  I'm doing this because we
# have a very small dataset.  In particular, setting the oversampling
# to a high amount (300) effectively boosts the training set size, so
# that helps this example.
options.oversampling_amount = 300
# I'm also reducing the capacity of the model by explicitly increasing
# the regularization (making nu smaller) and by using trees with
# smaller depths.
options.nu = 0.05
options.tree_depth = 2
options.be_verbose = True

# In this example we are going to train a face detector based on the small
# faces dataset in the examples/faces directory.  This means you need to supply
# the path to this faces folder as a command line argument so we will know
# where it is.
if len(sys.argv) != 2:
    print(
        "Give the path to the examples/faces directory as the argument to this "
        "program. For example, if you are in the python_examples folder then "
        "execute this program by running:\n"
        "    ./train_shape_predictor.py ../examples/faces")
    exit()
faces_folder = sys.argv[1]

options = dlib.shape_predictor_training_options()
# Now make the object responsible for training the model.
# This algorithm has a bunch of parameters you can mess with.  The
# documentation for the shape_predictor_trainer explains all of them.
# You should also read Kazemi's paper which explains all the parameters
# in great detail.  However, here I'm just setting three of them
# differently than their default values.  I'm doing this because we
# have a very small dataset.  In particular, setting the oversampling
# to a high amount (300) effectively boosts the training set size, so
# that helps this example.
options.oversampling_amount = 300
# I'm also reducing the capacity of the model by explicitly increasing
# the regularization (making nu smaller) and by using trees with
# smaller depths.
options.nu = 0.05
options.tree_depth = 2
options.be_verbose = True

check_float = partial(checks.check_multi_scale_param, self.n_scales,
                              (float,))
        feature_padding = check_int('feature_padding', feature_padding)
        n_pixel_pairs = check_int('n_pixel_pairs', n_pixel_pairs)
        distance_prior_weighting = check_float('distance_prior_weighting',
                                               distance_prior_weighting)
        regularisation_weight = check_float('regularisation_weight',
                                            regularisation_weight)
        n_split_tests = check_int('n_split_tests', n_split_tests)
        n_trees = check_int('n_trees', n_trees)
        n_dlib_perturbations = check_int('n_dlib_perturbations',
                                         n_dlib_perturbations)
        n_tree_levels = check_int('n_tree_levels', n_tree_levels)
        self._dlib_options_templates = []
        for j in range(self.n_scales):
            new_opts = dlib.shape_predictor_training_options()

            # Size of region within which to sample features for the feature
            # pool, e.g a padding of 0.5 would cause the algorithm to sample
            # pixels from a box that was 2x2 pixels
            new_opts.feature_pool_region_padding = feature_padding[j]
            # P parameter from Kazemi paper
            new_opts.feature_pool_size = n_pixel_pairs[j]
            # Controls how tight the feature sampling should be. Lower values
            # enforce closer features. Opposite of explanation from Kazemi
            # paper, lambda
            new_opts.lambda_param = distance_prior_weighting[j]
            # Boosting regularization parameter - nu from Kazemi paper, larger
            # values may cause overfitting but improve performance on training
            # data
            new_opts.nu = regularisation_weight[j]
            # S from Kazemi paper - Number of split features at each node to

def copy_dlib_options(options):
    new_options = dlib.shape_predictor_training_options()
    for p in sorted(filter(lambda x: '__' not in x, dir(options))):
        setattr(new_options, p, getattr(options, p))
    return new_options

def __init__(self, model):
        if isinstance(model, STRING_TYPES) or isinstance(model, Path):
            m_path = Path(model)
            if not Path(m_path).exists():
                raise ValueError('Model {} does not exist.'.format(m_path))
            model = dlib.shape_predictor(str(m_path))

        # Dlib doesn't expose any information about how the model was built,
        # so we just create dummy options
        self.algorithm = DlibAlgorithm(dlib.shape_predictor_training_options(),
                                       n_iterations=0)
        self.algorithm.dlib_model = model
        self.scales = [1]

def train_shape_predictor(self):
        self.__print_training_message('shape predictor')
        opt = dlib.shape_predictor_training_options()
        opt.oversampling_amount = 300
        opt.nu = 0.05
        opt.tree_depth = 2
        opt.num_threads = self.cpu_cores
        opt.be_verbose = True
        dlib.train_shape_predictor(self.xml, PREDICTOR_DAT, opt)