def test_pertrurb_shape():
fitter = LucasKanadeAAMFitter(aam)
s = fitter.perturb_shape(training_images[0].landmarks['PTS'].lms,
noise_std=0.08, rotation=False)
assert (s.n_dims == 2)
assert (s.n_landmark_groups == 0)
assert (s.n_points == 68)
def test_obtain_shape_from_bb():
fitter = LucasKanadeAAMFitter(aam)
s = fitter.obtain_shape_from_bb(np.array([[26, 49], [350, 400]]))
assert ((np.around(s.points) == np.around(initial_shape[3].points)).all())
assert (s.n_dims == 2)
assert (s.n_landmark_groups == 0)
assert (s.n_points == 68)
def test_obtain_shape_from_bb():
fitter = LucasKanadeAAMFitter(aam)
s = fitter.obtain_shape_from_bb(np.array([[26, 49], [350, 400]]))
assert ((np.around(s.points) == np.around(initial_shape[3].points)).
all())
assert (s.n_dims == 2)
assert (s.n_landmark_groups == 0)
assert (s.n_points == 68)
def test_str_mock(mock_stdout):
print(aam)
fitter = LucasKanadeAAMFitter(aam,
algorithm=AlternatingInverseCompositional)
print(fitter)
print(aam2)
fitter = LucasKanadeAAMFitter(aam2, algorithm=ProbabilisticForwardAdditive)
print(fitter)
def test_pertrurb_shape():
fitter = LucasKanadeAAMFitter(aam)
s = fitter.perturb_shape(training_images[0].landmarks['PTS'].lms,
noise_std=0.08,
rotation=False)
assert (s.n_dims == 2)
assert (s.n_landmark_groups == 0)
assert (s.n_points == 68)
def aam_helper(aam=aam, algorithm=AlternatingInverseCompositional, im_number=0,
max_iters=2, initial_error=0.08605, final_error=0.06605,
error_type='me_norm'):
fitter = LucasKanadeAAMFitter(aam, algorithm=algorithm)
fitting_result = fitter.fit(
training_images[im_number], initial_shape[im_number],
gt_shape=training_images[im_number].landmarks['PTS'].lms,
max_iters=max_iters, error_type=error_type)
assert (np.around(fitting_result.initial_error, 5) == initial_error)
assert (np.around(fitting_result.final_error, 5) == final_error)
def aam_helper(aam=aam,
algorithm=AlternatingInverseCompositional,
im_number=0,
max_iters=2,
initial_error=0.08605,
final_error=0.06605,
error_type='me_norm'):
fitter = LucasKanadeAAMFitter(aam, algorithm=algorithm)
fitting_result = fitter.fit(
training_images[im_number],
initial_shape[im_number],
gt_shape=training_images[im_number].landmarks['PTS'].lms,
max_iters=max_iters,
error_type=error_type)
assert (np.around(fitting_result.initial_error, 5) == initial_error)
assert (np.around(fitting_result.final_error, 5) == final_error)
def aam_fit_benchmark(fitting_images, aam, fitting_options=None,
perturb_options=None, verbose=False):
r"""
Fits a trained AAM model to a database.
Parameters
----------
fitting_images: list of :class:MaskedImage objects
A list of the fitting images.
aam: :class:menpo.fitmultilevel.aam.AAM object
The trained AAM object. It can be generated from the
aam_build_benchmark() method.
fitting_options: dictionary, optional
A dictionary with the parameters that will be passed in the
LucasKanadeAAMFitter (:class:menpo.fitmultilevel.aam.base).
If None, the default options will be used.
This is an example of the dictionary with the default options:
fitting_options = {'algorithm': AlternatingInverseCompositional,
'md_transform': OrthoMDTransform,
'global_transform': AlignmentSimilarity,
'n_shape': None,
'n_appearance': None,
'max_iters': 50,
'error_type': 'me_norm'
}
For an explanation of the options, please refer to the
LucasKanadeAAMFitter documentation.
Default: None
bounding_boxes: list of (2, 2) ndarray, optional
If provided, fits will be initialized from a bounding box. If
None, perturbation of ground truth will be used instead.
can be provided). Interpreted as [[min_x, min_y], [max_x, max_y]].
perturb_options: dictionary, optional
A dictionary with parameters that control the perturbation on the
ground truth shape with noise of specified std. Note that if
bounding_box is provided perturb_options is ignored and not used.
If None, the default options will be used.
This is an example of the dictionary with the default options:
initialization_options = {'noise_std': 0.04,
'rotation': False
}
For an explanation of the options, please refer to the perturb_shape()
method documentation of :map:`MultilevelFitter`.
verbose: bool, optional
If True, it prints information regarding the AAM fitting including
progress bar, current image error and percentage of images with errors
less or equal than a value.
Default: False
Returns
-------
fitting_results: :map:`FittingResultList`
A list with the :map:`FittingResult` object per image.
"""
if verbose:
print('AAM Fitting:')
perc1 = 0.
perc2 = 0.
# parse options
if fitting_options is None:
fitting_options = {}
if perturb_options is None:
perturb_options = {}
# extract some options
group = fitting_options.pop('gt_group', 'PTS')
max_iters = fitting_options.pop('max_iters', 50)
error_type = fitting_options.pop('error_type', 'me_norm')
# create fitter
fitter = LucasKanadeAAMFitter(aam, **fitting_options)
# fit images
n_images = len(fitting_images)
fitting_results = []
for j, i in enumerate(fitting_images):
# perturb shape
gt_s = i.landmarks[group].lms
if 'bbox' in i.landmarks:
# shape from bounding box
s = fitter.obtain_shape_from_bb(i.landmarks['bbox'].lms.points)
else:
# shape from perturbation
s = fitter.perturb_shape(gt_s, **perturb_options)
# fit
fr = fitter.fit(i, s, gt_shape=gt_s, max_iters=max_iters,
error_type=error_type, verbose=False)
fitting_results.append(fr)
# print
if verbose:
if error_type == 'me_norm':
if fr.final_error <= 0.03:
perc1 += 1.
if fr.final_error <= 0.04:
perc2 += 1.
elif error_type == 'rmse':
if fr.final_error <= 0.05:
perc1 += 1.
if fr.final_error <= 0.06:
perc2 += 1.
print_dynamic('- {0} - [<=0.03: {1:.1f}%, <=0.04: {2:.1f}%] - '
'Image {3}/{4} (error: {5:.3f} --> {6:.3f})'.format(
progress_bar_str(float(j + 1) / n_images, show_bar=False),
perc1 * 100. / n_images, perc2 * 100. / n_images, j + 1,
n_images, fr.initial_error, fr.final_error))
if verbose:
print_dynamic('- Fitting completed: [<=0.03: {0:.1f}%, <=0.04: '
'{1:.1f}%]\n'.format(perc1 * 100. / n_images,
perc2 * 100. / n_images))
# fit images
fitting_results_list = FittingResultList(fitting_results)
return fitting_results_list
def aam_fit_benchmark(fitting_images,
aam,
fitting_options=None,
perturb_options=None,
verbose=False):
r"""
Fits a trained AAM model to a database.
Parameters
----------
fitting_images: list of :class:MaskedImage objects
A list of the fitting images.
aam: :class:menpo.fitmultilevel.aam.AAM object
The trained AAM object. It can be generated from the
aam_build_benchmark() method.
fitting_options: dictionary, optional
A dictionary with the parameters that will be passed in the
LucasKanadeAAMFitter (:class:menpo.fitmultilevel.aam.base).
If None, the default options will be used.
This is an example of the dictionary with the default options:
fitting_options = {'algorithm': AlternatingInverseCompositional,
'md_transform': OrthoMDTransform,
'global_transform': AlignmentSimilarity,
'n_shape': None,
'n_appearance': None,
'max_iters': 50,
'error_type': 'me_norm'
}
For an explanation of the options, please refer to the
LucasKanadeAAMFitter documentation.
Default: None
bounding_boxes: list of (2, 2) ndarray, optional
If provided, fits will be initialized from a bounding box. If
None, perturbation of ground truth will be used instead.
can be provided). Interpreted as [[min_x, min_y], [max_x, max_y]].
perturb_options: dictionary, optional
A dictionary with parameters that control the perturbation on the
ground truth shape with noise of specified std. Note that if
bounding_box is provided perturb_options is ignored and not used.
If None, the default options will be used.
This is an example of the dictionary with the default options:
initialization_options = {'noise_std': 0.04,
'rotation': False
}
For an explanation of the options, please refer to the perturb_shape()
method documentation of :map:`MultilevelFitter`.
verbose: bool, optional
If True, it prints information regarding the AAM fitting including
progress bar, current image error and percentage of images with errors
less or equal than a value.
Default: False
Returns
-------
fitting_results: :map:`FittingResultList`
A list with the :map:`FittingResult` object per image.
"""
if verbose:
print('AAM Fitting:')
perc1 = 0.
perc2 = 0.
# parse options
if fitting_options is None:
fitting_options = {}
if perturb_options is None:
perturb_options = {}
# extract some options
group = fitting_options.pop('gt_group', 'PTS')
max_iters = fitting_options.pop('max_iters', 50)
error_type = fitting_options.pop('error_type', 'me_norm')
# create fitter
fitter = LucasKanadeAAMFitter(aam, **fitting_options)
# fit images
n_images = len(fitting_images)
fitting_results = []
for j, i in enumerate(fitting_images):
# perturb shape
gt_s = i.landmarks[group].lms
if 'bbox' in i.landmarks:
# shape from bounding box
s = fitter.obtain_shape_from_bb(i.landmarks['bbox'].lms.points)
else:
# shape from perturbation
s = fitter.perturb_shape(gt_s, **perturb_options)
# fit
fr = fitter.fit(i,
s,
gt_shape=gt_s,
max_iters=max_iters,
error_type=error_type,
verbose=False)
fitting_results.append(fr)
# print
if verbose:
if error_type == 'me_norm':
if fr.final_error <= 0.03:
perc1 += 1.
if fr.final_error <= 0.04:
perc2 += 1.
elif error_type == 'rmse':
if fr.final_error <= 0.05:
perc1 += 1.
if fr.final_error <= 0.06:
perc2 += 1.
print_dynamic('- {0} - [<=0.03: {1:.1f}%, <=0.04: {2:.1f}%] - '
'Image {3}/{4} (error: {5:.3f} --> {6:.3f})'.format(
progress_bar_str(float(j + 1) / n_images,
show_bar=False),
perc1 * 100. / n_images, perc2 * 100. / n_images,
j + 1, n_images, fr.initial_error,
fr.final_error))
if verbose:
print_dynamic('- Fitting completed: [<=0.03: {0:.1f}%, <=0.04: '
'{1:.1f}%]\n'.format(perc1 * 100. / n_images,
perc2 * 100. / n_images))
# fit images
fitting_results_list = FittingResultList(fitting_results)
return fitting_results_list
def test_max_iters_exception():
fitter = LucasKanadeAAMFitter(aam,
algorithm=AlternatingInverseCompositional)
fitter.fit(training_images[0], initial_shape[0],
max_iters=[10, 20, 30, 40])
def test_max_iters_exception():
fitter = LucasKanadeAAMFitter(aam,
algorithm=AlternatingInverseCompositional)
fitter.fit(training_images[0],
initial_shape[0],
max_iters=[10, 20, 30, 40])
def test_n_appearance_exception():
fitter = LucasKanadeAAMFitter(aam, n_appearance=[10, 20])
def test_n_shape_exception():
fitter = LucasKanadeAAMFitter(aam, n_shape=[3, 6, 'a'])
