def test_obtain_shape_from_bb():
fitter = LucasKanadeAAMFitter(aam)
s = fitter.obtain_shape_from_bb(
np.array([[53.916, 1.853], [408.469, 339.471]]))
assert ((np.around(s.points) == np.around(initial_shape[0].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([[53.916, 1.853],
[408.469, 339.471]]))
assert ((np.around(s.points) == np.around(initial_shape[0].points)).all())
assert (s.n_dims == 2)
assert (s.n_landmark_groups == 0)
assert (s.n_points == 68)
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)
fitting_results.append(fr)
# print
final_error = fr.final_error(error_type=error_type)
initial_error = fr.initial_error(error_type=error_type)
if verbose:
if error_type == 'me_norm':
if final_error <= 0.03:
perc1 += 1.
if final_error <= 0.04:
perc2 += 1.
elif error_type == 'rmse':
if final_error <= 0.05:
perc1 += 1.
if 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, initial_error, 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))
return fitting_results
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)
fitting_results.append(fr)
# print
final_error = fr.final_error(error_type=error_type)
initial_error = fr.initial_error(error_type=error_type)
if verbose:
if error_type == 'me_norm':
if final_error <= 0.03:
perc1 += 1.
if final_error <= 0.04:
perc2 += 1.
elif error_type == 'rmse':
if final_error <= 0.05:
perc1 += 1.
if 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, initial_error, 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))
return fitting_results
