Esempio n. 1
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File: base.py Progetto: yymath/menpo
def clm_build_benchmark(training_images, training_options=None, verbose=False):
    r"""
    Builds an CLM 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 CLMBuilder
        (:class:menpo.fitmultilevel.clm.CLMBuilder).
        If None, the default options will be used.
        This is an example of the dictionary with the default options:
            training_options = {'group': 'PTS',
                                'classifier_type': linear_svm_lr,
                                'patch_shape': (5, 5),
                                'feature_type': sparse_hog,
                                'normalization_diagonal': None,
                                'n_levels': 3,
                                'downscale': 1.1,
                                'scaled_shape_models': True,
                                'pyramid_on_features': True,
                                'max_shape_components': None,
                                'boundary': 3,
                                'interpolator': 'scipy'
                                }
        For an explanation of the options, please refer to the CLMBuilder
        documentation.

        Default: None
    verbose: boolean, optional
        If True, it prints information regarding the CLM training.

        Default: False

    Returns
    -------
    clm: :class:menpo.fitmultilevel.clm.CLM object
        The trained CLM model.
    """
    if verbose:
        print('CLM Training:')

    # parse options
    if training_options is None:
        training_options = {}

    # group option
    group = training_options.pop('group', None)

    # build aam
    aam = CLMBuilder(**training_options).build(training_images,
                                               group=group,
                                               verbose=verbose)

    return aam
Esempio n. 2
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                    regression_features=weights,
                    noise_std=0.04,
                    rotation=False,
                    n_perturbations=1,
                    update='compositional',
                    md_transform=OrthoMDTransform,
                    global_transform=AlignmentSimilarity,
                    n_shape=25,
                    n_appearance=None).train(training_images, group='PTS')

clm = CLMBuilder(classifier_type=linear_svm_lr,
                 feature_type=[sparse_hog],
                 normalization_diagonal=100,
                 patch_shape=(5, 5),
                 n_levels=1,
                 downscale=1.1,
                 scaled_shape_models=True,
                 pyramid_on_features=True,
                 max_shape_components=25,
                 boundary=3,
                 interpolator='scipy').build(training_images, group='PTS')

sdm3 = SDCLMTrainer(clm,
                    regression_type=mlr,
                    noise_std=0.04,
                    rotation=False,
                    n_perturbations=1,
                    pdm_transform=OrthoPDM,
                    global_transform=AlignmentSimilarity,
                    n_shape=None).train(training_images, group='PTS')
Esempio n. 3
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def test_feature_type_with_pyramid_on_features_exception():
    clm = CLMBuilder(feature_type=['igo', sparse_hog]).build(training_images,
                                                             group='PTS')
Esempio n. 4
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def test_classifier_type_2_exception():
    clm = CLMBuilder(classifier_type=['linear_svm_lr']).build(
        training_images, group='PTS')
Esempio n. 5
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def test_patch_shape_2_exception():
    clm = CLMBuilder(patch_shape=(5, 6, 7)).build(training_images, group='PTS')
Esempio n. 6
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def test_boundary_exception():
    clm = CLMBuilder(boundary=-1).build(training_images, group='PTS')
Esempio n. 7
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def test_verbose_mock(mock_stdout):
    clm = CLMBuilder().build(training_images, group='PTS', verbose=True)
Esempio n. 8
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def test_max_shape_components_2_exception():
    clm = CLMBuilder(max_shape_components=[1, 2]).build(training_images,
                                                        group='PTS')
Esempio n. 9
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def test_normalization_diagonal_exception():
    clm = CLMBuilder(normalization_diagonal=10).build(training_images,
                                                      group='PTS')
Esempio n. 10
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def test_downscale_exception():
    clm = CLMBuilder(downscale=1).build(training_images,
                                        group='PTS')
    assert (clm.downscale == 1)
    clm = CLMBuilder(downscale=0).build(training_images,
                                        group='PTS')
Esempio n. 11
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def test_n_levels_exception():
    clm = CLMBuilder(n_levels=0).build(training_images,
                                       group='PTS')
Esempio n. 12
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training_images = []
for i in range(4):
    im = mio.import_builtin_asset(filenames[i])
    im.crop_to_landmarks_proportion_inplace(0.1)
    labeller(im, 'PTS', ibug_68_trimesh)
    if im.n_channels == 3:
        im = im.as_greyscale(mode='luminosity')
    training_images.append(im)

# build clms
clm1 = CLMBuilder(classifier_type=[linear_svm_lr],
                  patch_shape=(5, 5),
                  feature_type=['igo', sparse_hog, None],
                  normalization_diagonal=150,
                  n_levels=3,
                  downscale=2,
                  scaled_shape_models=False,
                  pyramid_on_features=False,
                  max_shape_components=[1, 2, 3],
                  boundary=3,
                  interpolator='scipy').build(training_images, group='PTS')

clm2 = CLMBuilder(classifier_type=[random_forest, linear_svm_lr],
                  patch_shape=(3, 10),
                  feature_type=None,
                  normalization_diagonal=None,
                  n_levels=2,
                  downscale=1.2,
                  scaled_shape_models=True,
                  pyramid_on_features=False,
                  max_shape_components=None,