def test_verbose_mock(mock_stdout):
sdm = SDMTrainer(regression_type=mlr_svd,
regression_features=sparse_hog,
patch_shape=(16, 16),
features=no_op,
normalization_diagonal=150,
n_levels=1,
downscale=1.3,
noise_std=0.04,
rotation=False,
n_perturbations=2).train(training_images, verbose=True)
def sdm_build_benchmark(training_images, training_options=None, verbose=False):
r"""
Builds an SDM model.
Parameters
----------
training_images: list of :class:MaskedImage objects
A list of the training images.
training_options: dictionary, optional
A dictionary with the parameters that will be passed in the AAMBuilder
(:class:menpo.fitmultilevel.aam.AAMBuilder).
If None, the default options will be used.
This is an example of the dictionary with the default options:
training_options = {'group': 'PTS',
'features': 'igo',
'transform': PiecewiseAffine,
'trilist': None,
'normalization_diagonal': None,
'n_levels': 3,
'downscale': 2,
'scaled_shape_models': True,
'max_shape_components': None,
'max_appearance_components': None,
'boundary': 3
}
For an explanation of the options, please refer to the AAMBuilder
documentation.
Default: None
verbose: boolean, optional
If True, it prints information regarding the AAM training.
Default: False
Returns
-------
aam: :class:menpo.fitmultilevel.aam.AAM object
The trained AAM model.
"""
if verbose:
print('SDM Training:')
# parse options
if training_options is None:
training_options = {}
# group option
group = training_options.pop('group', None)
# build sdm
sdm = SDMTrainer(**training_options).train(training_images,
group=group,
verbose=verbose)
return sdm
def train(img_generator):
# clean up the images with the standard menpo pre-processing
images = [menpo_img_process(img) for img in img_generator]
fitter = SDMTrainer(normalization_diagonal=150,
downscale=1.1,
n_perturbations=15).train(images,
group='gt',
verbose=True)
# return a callable that wraps the menpo fitter in order to integrate with
# menpobench
return MenpoFitWrapper(fitter)
def test_downscale_exception():
sdm = SDMTrainer(downscale=0).train(training_images)
def test_n_levels_exception():
sdm = SDMTrainer(n_levels=0).train(training_images)
def test_regression_features_sdmtrainer_exception_2():
sdm = SDMTrainer(n_levels=3, regression_features=[no_op, sparse_hog, 1]).\
train(training_images)
def test_regression_features_sdmtrainer_exception_1():
sdm = SDMTrainer(n_levels=2, regression_features=[no_op, no_op, no_op]).\
train(training_images)
def test_features_exception():
sdm = SDMTrainer(features=[igo, sparse_hog],
n_levels=3).train(training_images)