from collections import OrderedDict
import numpy as np
from menpo.landmark.base import LandmarkGroup
from menpo.landmark.exceptions import LabellingError
def _connectivity_from_array(array, close_loop=False):
r"""
Build the connectivity over a given array. For example, given ::
array = [(0, 3, 1, 2 )]
Generate the connectivity of ::
[(0, 3), (3, 1), (1, 2)]
If ``close_loop`` is true, add an extra connection from the last point to
the first.
"""
# zip is a generator - need a list in this case
conn = list(zip(array, array[1:]))
if close_loop:
conn.append((array[-1], array[0]))
return np.asarray(conn)
def _connectivity_from_range(range_tuple, close_loop=False):
r"""
Build the connectivity over a range. For example, given ::
range_array = np.arange(3)
Generate the connectivity of ::
[(0, 1), (1, 2), (2, 3)]
If ``close_loop`` is true, add an extra connection from the last point to
the first.
"""
return _connectivity_from_array(
np.arange(*range_tuple), close_loop=close_loop)
def _mask_from_range(range_tuple, n_points):
r"""
Generate a mask over the range. The mask will be true inside the range.
"""
mask = np.zeros(n_points, dtype=np.bool)
range_slice = slice(*range_tuple)
mask[range_slice] = True
return mask
def _build_labelling_error_msg(group, n_expected_points,
n_actual_points):
return '{} mark-up expects exactly {} ' \
'points. However, the given landmark group ' \
'has {} points'.format(group, n_expected_points,
n_actual_points)
def _validate_input(landmark_group, n_expected_points, group):
r"""
Ensure that the input matches the number of expected points.
Parameters
----------
landmark_group : :map:`LandmarkGroup`
Landmark group to validate
n_expected_points : `int`
Number of expected points
group : `str`
Group label for error message
Raises
------
LabellingError
If the number of expected points doesn't match the number of given
points
"""
n_points = landmark_group.lms.n_points
if n_points != n_expected_points:
raise LabellingError(_build_labelling_error_msg(group,
n_expected_points,
n_points))
def _relabel_group_from_dict(pointcloud, labels_to_ranges):
"""
Label the given pointcloud according to the given ordered dictionary
of labels to ranges. This assumes that you can semantically label the group
by using ranges in to the existing points e.g ::
labels_to_ranges = {'jaw': (0, 17, False)}
The third element of the range tuple is whether the range is a closed loop
or not. For example, for an eye landmark this would be ``True``, as you
do want to create a closed loop for an eye.
Parameters
----------
pointcloud : :map:`PointCloud`
The pointcloud to apply semantic labels to.
labels_to_ranges : `OrderedDict`
Ordered dictionary of string labels to range tuples.
Returns
-------
landmark_group: :map:`LandmarkGroup`
New landmark group
Raises
------
:class:`menpo.landmark.exceptions.LabellingError`
If the given pointcloud contains less than ``n_expected_points``
points.
"""
from menpo.shape import PointUndirectedGraph
n_points = pointcloud.n_points
masks = OrderedDict()
adjacency_lists = []
for label, tup in labels_to_ranges.items():
range_tuple = tup[:-1]
close_loop = tup[-1]
adjacency_lists.append(_connectivity_from_range(
range_tuple, close_loop=close_loop))
masks[label] = _mask_from_range(range_tuple, n_points)
adjacency_array = np.vstack(adjacency_lists)
new_landmark_group = LandmarkGroup(
PointUndirectedGraph.init_from_edges(pointcloud.points,
adjacency_array), masks)
return new_landmark_group
[docs]def ibug_face_68(landmark_group):
"""
Apply the ibug's "standard" 68 point semantic labels (based on the
original semantic labels of multiPIE) to the landmark group.
The group label will be ``ibug_face_68``.
The semantic labels applied are as follows:
- jaw
- left_eyebrow
- right_eyebrow
- nose
- left_eye
- right_eye
- mouth
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``ibug_face_68``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 68 points
References
----------
.. [1] http://www.multipie.org/
"""
from menpo.shape import PointUndirectedGraph
group = 'ibug_face_68'
_validate_input(landmark_group, 68, group)
jaw_indices = np.arange(0, 17)
lbrow_indices = np.arange(17, 22)
rbrow_indices = np.arange(22, 27)
upper_nose_indices = np.arange(27, 31)
lower_nose_indices = np.arange(31, 36)
leye_indices = np.arange(36, 42)
reye_indices = np.arange(42, 48)
outer_mouth_indices = np.arange(48, 60)
inner_mouth_indices = np.arange(60, 68)
jaw_connectivity = _connectivity_from_array(jaw_indices)
lbrow_connectivity = _connectivity_from_array(lbrow_indices)
rbrow_connectivity = _connectivity_from_array(rbrow_indices)
nose_connectivity = np.vstack([
_connectivity_from_array(upper_nose_indices),
_connectivity_from_array(lower_nose_indices)])
leye_connectivity = _connectivity_from_array(leye_indices, close_loop=True)
reye_connectivity = _connectivity_from_array(reye_indices, close_loop=True)
mouth_connectivity = np.vstack([
_connectivity_from_array(outer_mouth_indices, close_loop=True),
_connectivity_from_array(inner_mouth_indices, close_loop=True)])
total_conn = np.vstack([
jaw_connectivity, lbrow_connectivity, rbrow_connectivity,
nose_connectivity, leye_connectivity, reye_connectivity,
mouth_connectivity
])
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points,
total_conn))
new_landmark_group['jaw'] = jaw_indices
new_landmark_group['left_eyebrow'] = lbrow_indices
new_landmark_group['right_eyebrow'] = rbrow_indices
new_landmark_group['nose'] = np.hstack((upper_nose_indices,
lower_nose_indices))
new_landmark_group['left_eye'] = leye_indices
new_landmark_group['right_eye'] = reye_indices
new_landmark_group['mouth'] = np.hstack((outer_mouth_indices,
inner_mouth_indices))
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def ibug_face_66(landmark_group):
"""
Apply the ibug's "standard" 66 point semantic labels (based on the
original semantic labels of multiPIE but ignoring the 2 points
describing the inner mouth corners) to the landmark group.
The group label will be ``ibug_face_66``.
The semantic labels applied are as follows:
- jaw
- left_eyebrow
- right_eyebrow
- nose
- left_eye
- right_eye
- mouth
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``ibug_face_66``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 68 points
References
----------
.. [1] http://www.multipie.org/
"""
from menpo.shape import PointUndirectedGraph
group = 'ibug_face_66'
_validate_input(landmark_group, 68, group)
jaw_indices = np.arange(0, 17)
lbrow_indices = np.arange(17, 22)
rbrow_indices = np.arange(22, 27)
upper_nose_indices = np.arange(27, 31)
lower_nose_indices = np.arange(31, 36)
leye_indices = np.arange(36, 42)
reye_indices = np.arange(42, 48)
outer_mouth_indices = np.arange(48, 60)
inner_mouth_indices = np.hstack((48, np.arange(60, 63),
54, np.arange(63, 66)))
jaw_connectivity = _connectivity_from_array(jaw_indices)
lbrow_connectivity = _connectivity_from_array(lbrow_indices)
rbrow_connectivity = _connectivity_from_array(rbrow_indices)
nose_connectivity = np.vstack([
_connectivity_from_array(upper_nose_indices),
_connectivity_from_array(lower_nose_indices)])
leye_connectivity = _connectivity_from_array(leye_indices, close_loop=True)
reye_connectivity = _connectivity_from_array(reye_indices, close_loop=True)
mouth_connectivity = np.vstack([
_connectivity_from_array(outer_mouth_indices, close_loop=True),
_connectivity_from_array(inner_mouth_indices, close_loop=True)])
total_conn = np.vstack([
jaw_connectivity, lbrow_connectivity, rbrow_connectivity,
nose_connectivity, leye_connectivity, reye_connectivity,
mouth_connectivity])
# Ignore the two inner mouth points
ind = np.hstack((np.arange(60), np.arange(61, 64), np.arange(65, 68)))
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points[ind],
total_conn))
new_landmark_group['jaw'] = jaw_indices
new_landmark_group['left_eyebrow'] = lbrow_indices
new_landmark_group['right_eyebrow'] = rbrow_indices
new_landmark_group['nose'] = np.hstack([upper_nose_indices,
lower_nose_indices])
new_landmark_group['left_eye'] = leye_indices
new_landmark_group['right_eye'] = reye_indices
new_landmark_group['mouth'] = np.hstack([outer_mouth_indices,
inner_mouth_indices])
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def ibug_face_51(landmark_group):
"""
Apply the ibug's "standard" 51 point semantic labels (based on the
original semantic labels of multiPIE but removing the annotations
corresponding to the jaw region) to the landmark group.
The group label will be ``ibug_face_51``.
The semantic labels applied are as follows:
- left_eyebrow
- right_eyebrow
- nose
- left_eye
- right_eye
- mouth
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``ibug_face_51``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 68 points
References
----------
.. [1] http://www.multipie.org/
"""
from menpo.shape import PointUndirectedGraph
group = 'ibug_face_51'
_validate_input(landmark_group, 68, group)
lbrow_indices = np.arange(0, 5)
rbrow_indices = np.arange(5, 10)
upper_nose_indices = np.arange(10, 14)
lower_nose_indices = np.arange(14, 19)
leye_indices = np.arange(19, 25)
reye_indices = np.arange(25, 31)
outer_mouth_indices = np.arange(31, 43)
inner_mouth_indices = np.arange(43, 51)
lbrow_connectivity = _connectivity_from_array(lbrow_indices)
rbrow_connectivity = _connectivity_from_array(rbrow_indices)
nose_connectivity = np.vstack([
_connectivity_from_array(upper_nose_indices),
_connectivity_from_array(lower_nose_indices)])
leye_connectivity = _connectivity_from_array(leye_indices, close_loop=True)
reye_connectivity = _connectivity_from_array(reye_indices, close_loop=True)
mouth_connectivity = np.vstack([
_connectivity_from_array(outer_mouth_indices, close_loop=True),
_connectivity_from_array(inner_mouth_indices, close_loop=True)])
total_conn = np.vstack([
lbrow_connectivity, rbrow_connectivity, nose_connectivity,
leye_connectivity, reye_connectivity, mouth_connectivity])
# Ignore the two inner mouth points
ind = np.arange(17, 68)
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points[ind],
total_conn))
new_landmark_group['left_eyebrow'] = lbrow_indices
new_landmark_group['right_eyebrow'] = rbrow_indices
new_landmark_group['nose'] = np.hstack([upper_nose_indices,
lower_nose_indices])
new_landmark_group['left_eye'] = leye_indices
new_landmark_group['right_eye'] = reye_indices
new_landmark_group['mouth'] = np.hstack([outer_mouth_indices,
inner_mouth_indices])
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def ibug_face_49(landmark_group):
"""
Apply the ibug's "standard" 49 point semantic labels (based on the
original semantic labels of multiPIE but removing the annotations
corresponding to the jaw region and the 2 describing the inner mouth
corners) to the landmark group.
The group label will be ``ibug_face_49``.
The semantic labels applied are as follows:
- left_eyebrow
- right_eyebrow
- nose
- left_eye
- right_eye
- mouth
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``ibug_face_49``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 68 points
References
----------
.. [1] http://www.multipie.org/
"""
from menpo.shape import PointUndirectedGraph
group = 'ibug_face_49'
_validate_input(landmark_group, 68, group)
lbrow_indices = np.arange(0, 5)
rbrow_indices = np.arange(5, 10)
upper_nose_indices = np.arange(10, 14)
lower_nose_indices = np.arange(14, 19)
leye_indices = np.arange(19, 25)
reye_indices = np.arange(25, 31)
outer_mouth_indices = np.arange(31, 43)
inner_mouth_indices = np.hstack((31, np.arange(43, 46),
37, np.arange(46, 49)))
lbrow_connectivity = _connectivity_from_array(lbrow_indices)
rbrow_connectivity = _connectivity_from_array(rbrow_indices)
nose_connectivity = np.vstack([
_connectivity_from_array(upper_nose_indices),
_connectivity_from_array(lower_nose_indices)])
leye_connectivity = _connectivity_from_array(leye_indices, close_loop=True)
reye_connectivity = _connectivity_from_array(reye_indices, close_loop=True)
mouth_connectivity = np.vstack([
_connectivity_from_array(outer_mouth_indices, close_loop=True),
_connectivity_from_array(inner_mouth_indices, close_loop=True)])
total_conn = np.vstack([
lbrow_connectivity, rbrow_connectivity, nose_connectivity,
leye_connectivity, reye_connectivity, mouth_connectivity])
# Ignore the two inner mouth points
ind = np.hstack((np.arange(17, 60), np.arange(61, 64), np.arange(65, 68)))
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points[ind],
total_conn))
new_landmark_group['left_eyebrow'] = lbrow_indices
new_landmark_group['right_eyebrow'] = rbrow_indices
new_landmark_group['nose'] = np.hstack([upper_nose_indices,
lower_nose_indices])
new_landmark_group['left_eye'] = leye_indices
new_landmark_group['right_eye'] = reye_indices
new_landmark_group['mouth'] = np.hstack([outer_mouth_indices,
inner_mouth_indices])
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def ibug_face_68_trimesh(landmark_group):
"""
Apply the ibug's "standard" 68 point triangulation to the landmarks in
the given landmark group.
The group label will be ``ibug_face_68_trimesh``.
The semantic labels applied are as follows:
- tri
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``ibug_face_68_trimesh``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 68 points
References
----------
.. [1] http://www.multipie.org/
"""
from menpo.shape import TriMesh
group = 'ibug_face_68_trimesh'
n_expected_points = 68
n_points = landmark_group.lms.n_points
_validate_input(landmark_group, n_expected_points, group)
tri_list = np.array([[47, 29, 28], [44, 43, 23], [38, 20, 21],
[47, 28, 42], [49, 61, 60], [40, 41, 37],
[37, 19, 20], [28, 40, 39], [38, 21, 39],
[36, 1, 0], [48, 59, 4], [49, 60, 48],
[67, 59, 60], [13, 53, 14], [61, 51, 62],
[57, 8, 7], [52, 51, 33], [61, 67, 60],
[52, 63, 51], [66, 56, 57], [35, 30, 29],
[53, 52, 35], [37, 36, 17], [18, 37, 17],
[37, 38, 40], [38, 37, 20], [19, 37, 18],
[38, 39, 40], [28, 29, 40], [41, 36, 37],
[27, 39, 21], [41, 31, 1], [30, 32, 31],
[33, 51, 50], [33, 30, 34], [31, 40, 29],
[36, 0, 17], [31, 2, 1], [31, 41, 40],
[ 1, 36, 41], [31, 49, 2], [ 2, 49, 3],
[60, 59, 48], [ 3, 49, 48], [31, 32, 50],
[48, 4, 3], [59, 5, 4], [58, 67, 66],
[ 5, 59, 58], [58, 59, 67], [ 7, 6, 58],
[66, 57, 58], [13, 54, 53], [ 7, 58, 57],
[ 6, 5, 58], [50, 61, 49], [62, 67, 61],
[31, 50, 49], [32, 33, 50], [30, 33, 32],
[34, 52, 33], [35, 52, 34], [53, 63, 52],
[62, 63, 65], [62, 51, 63], [66, 65, 56],
[63, 53, 64], [62, 66, 67], [62, 65, 66],
[57, 56, 9], [65, 63, 64], [ 8, 57, 9],
[ 9, 56, 10], [10, 56, 11], [11, 56, 55],
[11, 55, 12], [56, 65, 55], [55, 64, 54],
[55, 65, 64], [55, 54, 12], [64, 53, 54],
[12, 54, 13], [45, 46, 44], [35, 34, 30],
[14, 53, 35], [15, 46, 45], [27, 28, 39],
[27, 42, 28], [35, 29, 47], [30, 31, 29],
[15, 35, 46], [15, 14, 35], [43, 22, 23],
[27, 21, 22], [24, 44, 23], [44, 47, 43],
[43, 47, 42], [46, 35, 47], [26, 45, 44],
[46, 47, 44], [25, 44, 24], [25, 26, 44],
[16, 15, 45], [16, 45, 26], [22, 42, 43],
[50, 51, 61], [27, 22, 42]])
new_landmark_group = LandmarkGroup(
TriMesh(landmark_group.lms.points, tri_list, copy=False),
OrderedDict([('tri', np.ones(n_points, dtype=np.bool))]))
return group, new_landmark_group
def ibug_face_66_trimesh(landmark_group):
"""
Apply the ibug's "standard" 66 point triangulation to the landmarks in
the given landmark group.
The group label will be 'ibug_face_66_trimesh'.
The semantic labels applied are as follows:
- tri
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: 'ibug_face_66_trimesh'
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
:class:`menpo.landmark.exceptions.LabellingError`
If the given landmark group contains less than 66 points
References
----------
.. [1] http://www.multipie.org/
"""
from menpo.shape import TriMesh
# apply ibug_face_66
_, landmark_group = ibug_face_66(landmark_group)
group = 'ibug_face_66_trimesh'
n_expected_points = 66
n_points = landmark_group.lms.n_points
_validate_input(landmark_group, n_expected_points, group)
tri_list = np.array([[47, 29, 28], [44, 43, 23], [38, 20, 21],
[47, 28, 42], [40, 41, 37], [51, 62, 61],
[37, 19, 20], [28, 40, 39], [38, 21, 39],
[36, 1, 0], [48, 59, 4], [49, 60, 48],
[13, 53, 14], [60, 51, 61], [51, 51, 62],
[52, 51, 33], [49, 50, 60], [57, 7, 8],
[64, 56, 57], [35, 30, 29], [52, 62, 53],
[53, 52, 35], [37, 36, 17], [18, 37, 17],
[37, 38, 40], [38, 37, 20], [19, 37, 18],
[38, 39, 40], [28, 29, 40], [41, 36, 37],
[27, 39, 21], [41, 31, 1], [30, 32, 31],
[33, 51, 50], [33, 30, 34], [31, 40, 29],
[36, 0, 17], [31, 2, 1], [31, 41, 40],
[ 1, 36, 41], [31, 49, 2], [ 2, 49, 3],
[ 3, 49, 48], [31, 32, 50], [62, 53, 54],
[48, 4, 3], [59, 5, 4], [58, 65, 64],
[ 5, 59, 58], [58, 59, 65], [ 7, 6, 58],
[64, 57, 58], [13, 54, 53], [ 7, 58, 57],
[ 6, 5, 58], [63, 55, 54], [65, 59, 48],
[31, 50, 49], [32, 33, 50], [30, 33, 32],
[34, 52, 33], [35, 52, 34], [48, 60, 65],
[64, 63, 56], [60, 65, 61], [65, 64, 61],
[57, 56, 9], [ 8, 57, 9], [64, 63, 61],
[ 9, 56, 10], [10, 56, 11], [11, 56, 55],
[11, 55, 12], [56, 63, 55], [51, 52, 62],
[55, 54, 12], [63, 54, 62], [61, 62, 63],
[12, 54, 13], [45, 46, 44], [35, 34, 30],
[14, 53, 35], [15, 46, 45], [27, 28, 39],
[27, 42, 28], [35, 29, 47], [30, 31, 29],
[15, 35, 46], [15, 14, 35], [43, 22, 23],
[27, 21, 22], [24, 44, 23], [44, 47, 43],
[43, 47, 42], [46, 35, 47], [26, 45, 44],
[46, 47, 44], [25, 44, 24], [25, 26, 44],
[16, 15, 45], [16, 45, 26], [22, 42, 43],
[50, 60, 51], [27, 22, 42]])
new_landmark_group = LandmarkGroup(
TriMesh(landmark_group.lms.points, tri_list, copy=False),
OrderedDict([('tri', np.ones(n_points, dtype=np.bool))]))
return group, new_landmark_group
def ibug_face_51_trimesh(landmark_group):
"""
Apply the ibug's "standard" 51 point triangulation to the landmarks in
the given landmark group.
The group label will be 'ibug_face_51_trimesh'.
The semantic labels applied are as follows:
- tri
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: 'ibug_face_51_trimesh'
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
:class:`menpo.landmark.exceptions.LabellingError`
If the given landmark group contains less than 51 points
References
----------
.. [1] http://www.multipie.org/
"""
from menpo.shape import TriMesh
# apply ibug_face_51
_, landmark_group = ibug_face_51(landmark_group)
group = 'ibug_face_51_trimesh'
n_expected_points = 51
n_points = landmark_group.lms.n_points
_validate_input(landmark_group, n_expected_points, group)
tri_list = np.array([[30, 12, 11], [27, 26, 6], [21, 3, 4],
[30, 11, 25], [32, 44, 43], [23, 24, 20],
[20, 2, 3], [11, 23, 22], [21, 4, 22],
[32, 43, 31], [50, 42, 43], [44, 34, 45],
[35, 34, 16], [44, 50, 43], [35, 46, 34],
[49, 39, 40], [18, 13, 12], [36, 35, 18],
[20, 19, 0], [ 1, 20, 0], [20, 21, 23],
[21, 20, 3], [ 2, 20, 1], [21, 22, 23],
[11, 12, 23], [24, 19, 20], [10, 22, 4],
[13, 15, 14], [16, 34, 33], [16, 13, 17],
[14, 23, 12], [14, 24, 23], [43, 42, 31],
[14, 15, 33], [41, 50, 49], [41, 42, 50],
[49, 40, 41], [33, 44, 32], [45, 50, 44],
[14, 33, 32], [15, 16, 33], [13, 16, 15],
[17, 35, 16], [18, 35, 17], [36, 46, 35],
[45, 46, 48], [45, 34, 46], [49, 48, 39],
[46, 36, 47], [45, 49, 50], [45, 48, 49],
[48, 46, 47], [39, 48, 38], [38, 47, 37],
[38, 48, 47], [47, 36, 37], [28, 29, 27],
[18, 17, 13], [10, 11, 22], [10, 25, 11],
[18, 12, 30], [13, 14, 12], [26, 5, 6],
[10, 4, 5], [ 7, 27, 6], [27, 30, 26],
[26, 30, 25], [29, 18, 30], [ 9, 28, 27],
[29, 30, 27], [ 8, 27, 7], [ 8, 9, 27],
[ 5, 25, 26], [33, 34, 44], [10, 5, 25]])
new_landmark_group = LandmarkGroup(
TriMesh(landmark_group.lms.points, tri_list, copy=False),
OrderedDict([('tri', np.ones(n_points, dtype=np.bool))]))
return group, new_landmark_group
def ibug_face_49_trimesh(landmark_group):
"""
Apply the ibug's "standard" 49 point triangulation to the landmarks in
the given landmark group.
The group label will be 'ibug_face_49_trimesh'.
The semantic labels applied are as follows:
- tri
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: 'ibug_face_49_trimesh'
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
:class:`menpo.landmark.exceptions.LabellingError`
If the given landmark group contains less than 49 points
References
----------
.. [1] http://www.multipie.org/
"""
from menpo.shape import TriMesh
# apply ibug_face_49
_, landmark_group = ibug_face_49(landmark_group)
group = 'ibug_face_49_trimesh'
n_expected_points = 49
n_points = landmark_group.lms.n_points
_validate_input(landmark_group, n_expected_points, group)
tri_list = np.array([[30, 12, 11], [27, 26, 6], [21, 3, 4],
[30, 11, 25], [23, 24, 20], [34, 45, 44],
[20, 2, 3], [11, 23, 22], [21, 4, 22],
[32, 43, 31], [43, 34, 44], [34, 34, 45],
[35, 34, 16], [32, 33, 43], [47, 39, 40],
[18, 13, 12], [35, 45, 36], [36, 35, 18],
[20, 19, 0], [ 1, 20, 0], [20, 21, 23],
[21, 20, 3], [ 2, 20, 1], [21, 22, 23],
[11, 12, 23], [24, 19, 20], [10, 22, 4],
[13, 15, 14], [16, 34, 33], [16, 13, 17],
[14, 23, 12], [14, 24, 23], [14, 15, 33],
[45, 36, 37], [41, 48, 47], [41, 42, 48],
[47, 40, 41], [46, 38, 37], [48, 42, 31],
[14, 33, 32], [15, 16, 33], [13, 16, 15],
[17, 35, 16], [18, 35, 17], [31, 43, 48],
[47, 46, 39], [43, 48, 44], [48, 47, 44],
[47, 46, 44], [39, 46, 38], [46, 37, 45],
[28, 29, 27], [18, 17, 13], [10, 11, 22],
[10, 25, 11], [18, 12, 30], [13, 14, 12],
[26, 5, 6], [10, 4, 5], [ 7, 27, 6],
[27, 30, 26], [26, 30, 25], [29, 18, 30],
[ 9, 28, 27], [29, 30, 27], [ 8, 27, 7],
[ 8, 9, 27], [ 5, 25, 26], [33, 43, 34],
[10, 5, 25], [34, 35, 45], [44, 45, 46]])
new_landmark_group = LandmarkGroup(
TriMesh(landmark_group.lms.points, tri_list, copy=False),
OrderedDict([('tri', np.ones(n_points, dtype=np.bool))]))
return group, new_landmark_group
[docs]def ibug_face_65_closed_mouth(landmark_group):
"""
Apply the ibug's "standard" 68 point semantic labels (based on the
original semantic labels of multiPIE) to the landmarks in
the given landmark group - but ignore the 3 points that are coincident for
a closed mouth. Therefore, there only 65 points are returned.
The group label will be ``ibug_face_65_closed_mouth``.
The semantic labels applied are as follows:
- jaw
- left_eyebrow
- right_eyebrow
- nose
- left_eye
- right_eye
- mouth
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``ibug_face_65_closed_mouth``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 68 points
References
----------
.. [1] http://www.multipie.org/
"""
from menpo.shape import PointUndirectedGraph
group = 'ibug_face_65_closed_mouth'
_validate_input(landmark_group, 68, group)
jaw_indices = np.arange(0, 17)
lbrow_indices = np.arange(17, 22)
rbrow_indices = np.arange(22, 27)
upper_nose_indices = np.arange(27, 31)
lower_nose_indices = np.arange(31, 36)
leye_indices = np.arange(36, 42)
reye_indices = np.arange(42, 48)
outer_mouth_indices = np.arange(48, 60)
inner_mouth_indices = np.arange(60, 65)
jaw_connectivity = _connectivity_from_array(jaw_indices)
lbrow_connectivity = _connectivity_from_array(lbrow_indices)
rbrow_connectivity = _connectivity_from_array(rbrow_indices)
nose_connectivity = np.vstack([
_connectivity_from_array(upper_nose_indices),
_connectivity_from_array(lower_nose_indices)])
leye_connectivity = _connectivity_from_array(leye_indices, close_loop=True)
reye_connectivity = _connectivity_from_array(reye_indices, close_loop=True)
mouth_connectivity = np.vstack([
_connectivity_from_array(outer_mouth_indices, close_loop=True),
_connectivity_from_array(inner_mouth_indices)])
total_conn = np.vstack([
jaw_connectivity, lbrow_connectivity, rbrow_connectivity,
nose_connectivity, leye_connectivity, reye_connectivity,
mouth_connectivity])
# Ignore the two inner mouth points
ind = np.arange(65)
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points[ind],
total_conn))
new_landmark_group['jaw'] = jaw_indices
new_landmark_group['left_eyebrow'] = lbrow_indices
new_landmark_group['right_eyebrow'] = rbrow_indices
new_landmark_group['nose'] = np.hstack([upper_nose_indices,
lower_nose_indices])
new_landmark_group['left_eye'] = leye_indices
new_landmark_group['right_eye'] = reye_indices
new_landmark_group['mouth'] = np.hstack([outer_mouth_indices,
inner_mouth_indices])
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def imm_face(landmark_group):
"""
Apply the 58 point semantic labels from the IMM dataset to the
landmarks in the given landmark group.
The group label will be ``imm_face``.
The semantic labels applied are as follows:
- jaw
- left_eye
- right_eye
- left_eyebrow
- right_eyebrow
- mouth
- nose
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``imm_face``
landmark_group : :map:`LandmarkGroup`
New landmark group
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 58 points
References
----------
.. [1] http://www2.imm.dtu.dk/~aam/
"""
group = 'imm_face'
_validate_input(landmark_group, 58, group)
labels = OrderedDict([
('jaw', (0, 13, False)),
('left_eye', (13, 21, True)),
('right_eye', (21, 29, True)),
('left _eyebrow', (29, 34, False)),
('right_eyebrow', (34, 39, False)),
('mouth', (39, 47, True)),
('nose', (47, 58, False))
])
return group, _relabel_group_from_dict(landmark_group.lms, labels)
[docs]def lfpw_face(landmark_group):
"""
Apply the 29 point semantic labels from the LFPW dataset to the
landmarks in the given landmark group.
The group label will be ``lfpw_face``.
The semantic labels applied are as follows:
- chin
- left_eye
- right_eye
- left_eyebrow
- right_eyebrow
- mouth
- nose
Parameters
----------
landmark_group: :class:`menpo.landmark.base.LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``lfpw_face``
landmark_group: :map:`LandmarkGroup`
New landmark group
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 29 points
References
----------
.. [1] http://homes.cs.washington.edu/~neeraj/databases/lfpw/
"""
from menpo.shape import PointUndirectedGraph
group = 'lfpw_face'
_validate_input(landmark_group, 29, group)
chin_indices = np.array([28])
outer_leye_indices = np.array([8, 12, 10, 13])
pupil_leye_indices = np.array([16])
outer_reye_indices = np.array([11, 14, 9, 15])
pupil_reye_indices = np.array([17])
lbrow_indices = np.array([0, 4, 2, 5])
rbrow_indices = np.array([3, 6, 1, 7])
outer_mouth_indices = np.array([22, 24, 23, 27])
inner_mouth_indices = np.array([22, 25, 23, 26])
nose_indices = np.array([18, 20, 19, 21])
# TODO: Not sure this makes a lot of sense...
chin_connectivity = _connectivity_from_array(chin_indices, close_loop=True)
leye_connectivity = _connectivity_from_array(outer_leye_indices,
close_loop=True)
reye_connectivity = _connectivity_from_array(outer_reye_indices,
close_loop=True)
lbrow_connectivity = _connectivity_from_array(lbrow_indices,
close_loop=True)
rbrow_connectivity = _connectivity_from_array(rbrow_indices,
close_loop=True)
mouth_connectivity = np.vstack(
(_connectivity_from_array(outer_mouth_indices, close_loop=True),
_connectivity_from_array(inner_mouth_indices)))
nose_connectivity = _connectivity_from_array(nose_indices, close_loop=True)
total_conn = np.vstack(
(chin_connectivity, leye_connectivity, reye_connectivity,
lbrow_connectivity, rbrow_connectivity, mouth_connectivity,
nose_connectivity))
# Ignore the two inner mouth points
ind = np.arange(29)
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points[ind],
total_conn))
new_landmark_group['chin'] = chin_indices
new_landmark_group['left_eye'] = np.hstack((outer_leye_indices,
pupil_leye_indices))
new_landmark_group['right_eye'] = np.hstack((outer_reye_indices,
pupil_reye_indices))
new_landmark_group['left_eyebrow'] = lbrow_indices
new_landmark_group['right_eyebrow'] = rbrow_indices
new_landmark_group['mouth'] = np.hstack((outer_mouth_indices,
inner_mouth_indices))
new_landmark_group['nose'] = nose_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
def _build_upper_eyelid():
top_indices = np.arange(0, 7)
middle_indices = np.arange(12, 17)
upper_eyelid_indices = np.hstack((top_indices, middle_indices))
upper_eyelid_connectivity = zip(top_indices, top_indices[1:])
upper_eyelid_connectivity += [(0, 12)]
upper_eyelid_connectivity += zip(middle_indices, middle_indices[1:])
upper_eyelid_connectivity += [(16, 6)]
return upper_eyelid_indices, upper_eyelid_connectivity
[docs]def ibug_open_eye(landmark_group):
"""
Apply the ibug's "standard" open eye semantic labels to the
landmarks in the given landmark group.
The group label will be ``ibug_open_eye``.
The semantic labels applied are as follows:
- upper_eyelid
- lower_eyelid
- iris
- pupil
- sclera
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``ibug_open_eye``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 38 points
"""
from menpo.shape import PointUndirectedGraph
group = 'ibug_open_eye'
n_expected_points = 38
_validate_input(landmark_group, n_expected_points, group)
upper_el_indices, upper_el_connectivity = _build_upper_eyelid()
iris_range = (22, 30)
pupil_range = (30, 38)
sclera_top = np.arange(12, 17)
sclera_bottom = np.arange(17, 22)
sclera_indices = np.hstack((0, sclera_top, 6, sclera_bottom))
lower_el_top = np.arange(17, 22)
lower_el_bottom = np.arange(7, 12)
lower_el_indices = np.hstack((6, lower_el_top, 0, lower_el_bottom))
iris_connectivity = _connectivity_from_range(iris_range, close_loop=True)
pupil_connectivity = _connectivity_from_range(pupil_range, close_loop=True)
sclera_connectivity = zip(sclera_top, sclera_top[1:])
sclera_connectivity += [(0, 21)]
sclera_connectivity += zip(sclera_bottom, sclera_bottom[1:])
sclera_connectivity += [(6, 17)]
lower_el_connectivity = zip(lower_el_top, lower_el_top[1:])
lower_el_connectivity += [(6, 7)]
lower_el_connectivity += zip(lower_el_bottom, lower_el_bottom[1:])
lower_el_connectivity += [(11, 0)]
total_conn = np.asarray(upper_el_connectivity +
lower_el_connectivity +
iris_connectivity.tolist() +
pupil_connectivity.tolist() +
sclera_connectivity)
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points,
total_conn))
new_landmark_group['upper_eyelid'] = upper_el_indices
new_landmark_group['lower_eyelid'] = lower_el_indices
new_landmark_group['pupil'] = np.arange(*pupil_range)
new_landmark_group['iris'] = np.arange(*iris_range)
new_landmark_group['sclera'] = sclera_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def ibug_close_eye_points(landmark_group):
"""
Apply the ibug's "standard" close eye semantic labels to the
landmarks in the given landmark group.
The group label will be ``ibug_close_eye``.
The semantic labels applied are as follows:
- upper_eyelid
- lower_eyelid
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``ibug_close_eye``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 17 points
"""
from menpo.shape import PointUndirectedGraph
group = 'ibug_close_eye'
n_expected_points = 17
_validate_input(landmark_group, n_expected_points, group)
upper_indices, upper_connectivity = _build_upper_eyelid()
middle_indices = np.arange(12, 17)
bottom_indices = np.arange(6, 12)
lower_indices = np.hstack((bottom_indices, 0, middle_indices))
lower_connectivity = zip(bottom_indices, bottom_indices[1:])
lower_connectivity += [(0, 12)]
lower_connectivity += zip(middle_indices, middle_indices[1:])
lower_connectivity += [(11, 0)]
total_conn = np.asarray(upper_connectivity + lower_connectivity)
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points,
total_conn))
new_landmark_group['upper_eyelid'] = upper_indices
new_landmark_group['lower_eyelid'] = lower_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def ibug_open_eye_trimesh(landmark_group):
"""
Apply the ibug's "standard" open eye semantic labels to the
landmarks in the given landmark group.
The group label will be ``ibug_open_eye_trimesh``.
The semantic labels applied are as follows:
- tri
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``ibug_open_eye_trimesh``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 38 points
"""
from menpo.shape import TriMesh
group = 'ibug_open_eye_trimesh'
n_expected_points = 38
n_points = landmark_group.lms.n_points
_validate_input(landmark_group, n_expected_points, group)
tri_list = np.array([[29, 36, 28], [22, 13, 23], [12, 1, 2],
[29, 30, 37], [13, 3, 14], [13, 12, 2],
[19, 8, 9], [25, 33, 24], [36, 37, 33],
[24, 32, 31], [33, 37, 31], [35, 34, 27],
[35, 36, 33], [ 3, 13, 2], [14, 24, 23],
[33, 32, 24], [15, 25, 14], [25, 26, 34],
[22, 30, 29], [31, 37, 30], [24, 31, 23],
[32, 33, 31], [22, 12, 13], [ 0, 1, 12],
[14, 23, 13], [31, 30, 23], [28, 19, 20],
[21, 11, 0], [12, 21, 0], [20, 11, 21],
[20, 10, 11], [21, 29, 20], [21, 12, 22],
[30, 22, 23], [29, 21, 22], [27, 19, 28],
[29, 37, 36], [29, 28, 20], [36, 35, 28],
[20, 19, 10], [10, 19, 9], [28, 35, 27],
[19, 19, 8], [17, 16, 6], [18, 7, 8],
[25, 34, 33], [18, 27, 17], [18, 19, 27],
[18, 17, 7], [27, 26, 17], [17, 6, 7],
[14, 25, 24], [34, 35, 33], [17, 26, 16],
[27, 34, 26], [ 3, 15, 14], [15, 26, 25],
[ 4, 15, 3], [16, 26, 15], [16, 4, 5],
[16, 15, 4], [16, 5, 6], [8, 18, 19]])
new_landmark_group = LandmarkGroup(
TriMesh(landmark_group.lms.points, tri_list, copy=False),
OrderedDict([('tri', np.ones(n_points, dtype=np.bool))]))
return group, new_landmark_group
[docs]def ibug_close_eye_trimesh(landmark_group):
"""
Apply the ibug's "standard" close eye semantic labels to the
landmarks in the given landmark group.
The group label will be ``ibug_close_eye_trimesh``.
The semantic labels applied are as follows:
- tri
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``ibug_close_eye_trimesh``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 38 points
"""
from menpo.shape import TriMesh
group = 'ibug_close_eye_trimesh'
n_expected_points = 17
n_points = landmark_group.lms.n_points
_validate_input(landmark_group, n_expected_points, group)
tri_list = np.array([[10, 11, 13], [ 3, 13, 2], [ 4, 14, 3],
[15, 5, 16], [12, 11, 0], [13, 14, 10],
[13, 12, 2], [14, 13, 3], [ 0, 1, 12],
[ 2, 12, 1], [13, 11, 12], [ 9, 10, 14],
[15, 9, 14], [ 7, 8, 15], [ 5, 6, 16],
[15, 14, 4], [ 7, 15, 16], [ 8, 9, 15],
[15, 4, 5], [16, 6, 7]])
new_landmark_group = LandmarkGroup(
TriMesh(landmark_group.lms.points, tri_list, copy=False),
OrderedDict([('tri', np.ones(n_points, dtype=np.bool))]))
return group, new_landmark_group
[docs]def ibug_tongue(landmark_group):
"""
Apply the ibug's "standard" tongue semantic labels to the landmarks in the
given landmark group.
The group label will be ``ibug_tongue``.
The semantic labels applied are as follows:
- outline
- bisector
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``ibug_tongue``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 19 points
"""
group = 'ibug_tongue'
_validate_input(landmark_group, 19, group)
labels = OrderedDict([
('outline', (0, 13, False)),
('bisector', (13, 19, False))
])
return group, _relabel_group_from_dict(landmark_group.lms, labels)
[docs]def ibug_hand(landmark_group):
"""
Apply the ibug's "standard" 39 point semantic labels to the landmark group.
The group label will be ``ibug_hand``.
The semantic labels applied are as follows:
- thumb
- index
- middle
- ring
- pinky
- palm
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``ibug_hand``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 39 points
"""
from menpo.shape import PointUndirectedGraph
group = 'ibug_hand'
_validate_input(landmark_group, 39, group)
thumb_indices = np.arange(0, 5)
index_indices = np.arange(5, 12)
middle_indices = np.arange(12, 19)
ring_indices = np.arange(19, 26)
pinky_indices = np.arange(26, 33)
palm_indices = np.hstack((np.array([32, 25, 18, 11, 33, 34, 4]),
np.arange(35, 39)))
thumb_connectivity = _connectivity_from_array(thumb_indices,
close_loop=False)
index_connectivity = _connectivity_from_array(index_indices,
close_loop=False)
middle_connectivity = _connectivity_from_array(middle_indices,
close_loop=False)
ring_connectivity = _connectivity_from_array(ring_indices,
close_loop=False)
pinky_connectivity = _connectivity_from_array(pinky_indices,
close_loop=False)
palm_connectivity = _connectivity_from_array(palm_indices,
close_loop=True)
total_conn = np.vstack((thumb_connectivity, index_connectivity,
middle_connectivity, ring_connectivity,
pinky_connectivity, palm_connectivity))
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points,
total_conn))
new_landmark_group['thumb'] = thumb_indices
new_landmark_group['index'] = index_indices
new_landmark_group['middle'] = middle_indices
new_landmark_group['ring'] = ring_indices
new_landmark_group['pinky'] = pinky_indices
new_landmark_group['palm'] = palm_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def stickmen_pose(landmark_group):
"""
Apply the stickmen "standard" 12 point semantic labels to the landmark
group.
The group label will be ``stickmen_pose``.
The semantic labels applied are as follows:
- torso
- right_upper_arm
- left_upper_arm
- right_lower_arm
- left_lower_arm
- head
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``stickmen_pose``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 12 points
References
----------
.. [1] http://www.robots.ox.ac.uk/~vgg/data/stickmen/
"""
group = 'stickmen_pose'
_validate_input(landmark_group, 12, group)
labels = OrderedDict([
('torso', (0, 2, False)),
('right_upper arm', (2, 4, False)),
('left_upper arm', (4, 6, False)),
('right_lower_arm', (6, 8, False)),
('left_lower_arm', (8, 10, False)),
('head', (10, 12, False))
])
return group, _relabel_group_from_dict(landmark_group.lms, labels)
[docs]def lsp_pose(landmark_group):
"""
Apply the lsp "standard" 14 point semantic labels to the landmark
group.
The group label will be ``lsp_pose``.
The semantic labels applied are as follows:
- left_leg
- right_leg
- left_arm
- right_arm
- head
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``lsp_pose``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 14 points
References
----------
.. [1] http://www.comp.leeds.ac.uk/mat4saj/lsp.html
"""
from menpo.shape import PointUndirectedGraph
group = 'lsp_pose'
_validate_input(landmark_group, 14, group)
left_leg_indices = np.arange(0, 3)
right_leg_indices = np.arange(3, 6)
left_arm_indices = np.arange(6, 9)
right_arm_indices = np.arange(9, 12)
head_indices = np.arange(12, 14)
left_leg_connectivity = _connectivity_from_array(left_leg_indices)
right_leg_connectivity = _connectivity_from_array(right_leg_indices)
left_arm_connectivity = _connectivity_from_array(left_arm_indices)
right_arm_connectivity = _connectivity_from_array(right_arm_indices)
head_connectivity = _connectivity_from_array(head_indices)
total_conn = np.vstack([left_leg_connectivity,
right_leg_connectivity,
left_arm_connectivity,
right_arm_connectivity,
head_connectivity])
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points,
total_conn))
new_landmark_group['left_leg'] = left_leg_indices
new_landmark_group['right_leg'] = right_leg_indices
new_landmark_group['left_arm'] = left_arm_indices
new_landmark_group['right_arm'] = right_arm_indices
new_landmark_group['head'] = head_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def flic_pose(landmark_group):
"""
Apply the flic "standard" 11 point semantic labels to the landmark
group.
The group label will be ``flic_pose``.
The semantic labels applied are as follows:
- left_arm
- right_arm
- hips
- face
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``flic_pose``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 11 points
References
----------
.. [1] http://vision.grasp.upenn.edu/cgi-bin/index.php?n=VideoLearning.FLIC
"""
group = 'flic_pose'
_validate_input(landmark_group, 11, group)
labels = OrderedDict([
('left_arm', (0, 3, False)),
('right_arm', (3, 6, False)),
('hips', (6, 8, False)),
('face', (8, 11, True))])
return group, _relabel_group_from_dict(landmark_group.lms, labels)
def human36M_pose_32(landmark_group):
"""
Apply the human3.6M "standard" 32 point semantic labels to the landmark
group.
The group label will be ``human36M_pose_32``.
The semantic labels applied are as follows:
- pelvis
- right_leg
- left_leg
- spine
- head
- left_arm
- left_hand
- right_arm
- right_hand
- torso
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``human36M_pose_32``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 32 points
References
----------
.. [1] http://vision.imar.ro/human3.6m/
"""
from menpo.shape import PointUndirectedGraph
group = 'human36M_pose_32'
_validate_input(landmark_group, 32, group)
pelvis_indices = np.array([1, 0, 6])
right_leg_indices = np.array(range(1, 6))
left_leg_indices = np.array(range(6, 11))
spine_indices = np.array([11, 12, 13])
head_indices = np.array([13, 14, 15])
left_arm_indices = np.array([16, 17, 18, 19, 23])
left_hand_indices = np.array([20, 21, 22])
right_arm_indices = np.array([24, 25, 26, 27, 29, 31])
right_hand_indices = np.array([28, 29, 30])
torso_indices = np.array([0, 1, 25, 13, 17, 6])
pelvis_connectivity = _connectivity_from_array(pelvis_indices)
right_leg_connectivity = _connectivity_from_array(right_leg_indices)
left_leg_connectivity = _connectivity_from_array(left_leg_indices)
spine_connectivity = _connectivity_from_array(spine_indices)
head_connectivity = _connectivity_from_array(head_indices)
left_arm_connectivity = _connectivity_from_array(left_arm_indices)
left_hand_connectivity = _connectivity_from_array(left_hand_indices)
right_arm_connectivity = _connectivity_from_array(right_arm_indices)
right_hand_connectivity = _connectivity_from_array(right_hand_indices)
torso_connectivity = _connectivity_from_array(torso_indices,
close_loop=True)
total_conn = np.vstack([
pelvis_connectivity, right_leg_connectivity, left_leg_connectivity,
spine_connectivity, head_connectivity, left_arm_connectivity,
left_hand_connectivity, right_arm_connectivity,
right_hand_connectivity, torso_connectivity])
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points,
total_conn))
new_landmark_group['pelvis'] = pelvis_indices
new_landmark_group['right_leg'] = right_leg_indices
new_landmark_group['left_leg'] = left_leg_indices
new_landmark_group['spine'] = spine_indices
new_landmark_group['head'] = head_indices
new_landmark_group['left_arm'] = left_arm_indices
new_landmark_group['left_hand'] = left_hand_indices
new_landmark_group['right_arm'] = right_arm_indices
new_landmark_group['right_hand'] = right_hand_indices
new_landmark_group['torso'] = torso_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
def human36M_pose_17(landmark_group):
"""
Apply the human3.6M "standard" 17 point semantic labels (based on the
original semantic labels of Human3.6 but removing the annotations
corresponding to duplicate points, soles and palms) to the landmark group.
The group label will be ``human36M_pose_17``.
The semantic labels applied are as follows:
- pelvis
- right_leg
- left_leg
- spine
- head
- left_arm
- right_arm
- torso
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``human36M_pose_17``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 32 points
References
----------
.. [1] http://vision.imar.ro/human3.6m/
"""
from menpo.shape import PointUndirectedGraph
group = 'human36M_pose_17'
_validate_input(landmark_group, 32, group)
pelvis_indices = np.array([1, 0, 4])
right_leg_indices = np.arange(1, 4)
left_leg_indices = np.arange(4, 7)
spine_indices = np.array([0, 7, 8])
head_indices = np.array([8, 9, 10])
left_arm_indices = np.array([8, 11, 12, 13])
right_arm_indices = np.array([8, 14, 15, 16])
torso_indices = np.array([0, 1, 14, 8, 11, 4])
pelvis_connectivity = _connectivity_from_array(pelvis_indices)
right_leg_connectivity = _connectivity_from_array(right_leg_indices)
left_leg_connectivity = _connectivity_from_array(left_leg_indices)
spine_connectivity = _connectivity_from_array(spine_indices)
head_connectivity = _connectivity_from_array(head_indices)
left_arm_connectivity = _connectivity_from_array(left_arm_indices)
right_arm_connectivity = _connectivity_from_array(right_arm_indices)
torso_connectivity = _connectivity_from_array(torso_indices,
close_loop=True)
total_conn = np.vstack([
pelvis_connectivity, right_leg_connectivity, left_leg_connectivity,
spine_connectivity, head_connectivity, left_arm_connectivity,
right_arm_connectivity, torso_connectivity])
# Ignore duplicate points, sole and palms
ind = np.hstack((np.arange(0, 4), np.arange(6, 9), np.arange(12, 16),
np.arange(17, 20), np.arange(25, 28)))
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points[ind],
total_conn))
new_landmark_group['pelvis'] = pelvis_indices
new_landmark_group['right_leg'] = right_leg_indices
new_landmark_group['left_leg'] = left_leg_indices
new_landmark_group['spine'] = spine_indices
new_landmark_group['head'] = head_indices
new_landmark_group['left_arm'] = left_arm_indices
new_landmark_group['right_arm'] = right_arm_indices
new_landmark_group['torso'] = torso_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def streetscene_car_view_0(landmark_group):
"""
Apply the 8 point semantic labels of the view 0 of the MIT Street Scene
Car dataset to the landmark group.
The group label will be ``streetscene_car_view_0``.
The semantic labels applied are as follows:
- front
- bonnet
- windshield
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``streetscene_car_view_0``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 20 points
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import PointUndirectedGraph
group = 'streetscene_car_view_0'
_validate_input(landmark_group, 20, group)
front_indices = np.array([0, 1, 3, 2])
bonnet_indices = np.array([2, 3, 5, 4])
windshield_indices = np.array([4, 5, 7, 6])
front_connectivity = _connectivity_from_array(front_indices,
close_loop=True)
bonnet_connectivity = _connectivity_from_array(bonnet_indices,
close_loop=True)
windshield_connectivity = _connectivity_from_array(windshield_indices,
close_loop=True)
total_conn = np.vstack((front_connectivity, bonnet_connectivity,
windshield_connectivity))
ind = np.arange(8)
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points[ind],
total_conn))
new_landmark_group['front'] = front_indices
new_landmark_group['bonnet'] = bonnet_indices
new_landmark_group['windshield'] = windshield_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def streetscene_car_view_1(landmark_group):
"""
Apply the 14 point semantic labels of the view 1 of the MIT Street Scene
Car dataset to the landmark group.
The group label will be ``streetscene_car_view_1``.
The semantic labels applied are as follows:
- front
- bonnet
- windshield
- left_side
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``streetscene_car_view_1``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 20 points
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import PointUndirectedGraph
group = 'streetscene_car_view_1'
_validate_input(landmark_group, 20, group)
front_indices = np.array([0, 1, 3, 2])
bonnet_indices = np.array([2, 3, 5, 4])
windshield_indices = np.array([4, 5, 7, 6])
left_side_indices = np.array([0, 2, 4, 6, 8, 9, 10, 11, 13, 12])
front_connectivity = _connectivity_from_array(front_indices,
close_loop=True)
bonnet_connectivity = _connectivity_from_array(bonnet_indices,
close_loop=True)
windshield_connectivity = _connectivity_from_array(windshield_indices,
close_loop=True)
left_side_connectivity = _connectivity_from_array(left_side_indices,
close_loop=True)
total_conn = np.vstack((front_connectivity, bonnet_connectivity,
windshield_connectivity, left_side_connectivity))
ind = np.hstack((np.arange(9), np.array([10, 12, 14, 16, 18])))
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points[ind],
total_conn))
new_landmark_group['front'] = front_indices
new_landmark_group['bonnet'] = bonnet_indices
new_landmark_group['windshield'] = windshield_indices
new_landmark_group['left_side'] = left_side_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def streetscene_car_view_2(landmark_group):
"""
Apply the 10 point semantic labels of the view 2 of the MIT Street Scene
Car dataset to the landmark group.
The group label will be ``streetscene_car_view_2``.
The semantic labels applied are as follows:
- left_side
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: 'streetscene_car_view_2'
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 20 points
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import PointUndirectedGraph
group = 'streetscene_car_view_2'
_validate_input(landmark_group, 20, group)
left_side_indices = np.array([0, 1, 2, 3, 4, 5, 6, 7, 9, 8])
left_side_connectivity = _connectivity_from_array(left_side_indices,
close_loop=True)
total_conn = left_side_connectivity
ind = np.array([0, 2, 4, 6, 8, 10, 12, 14, 16, 18])
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points[ind],
total_conn))
new_landmark_group['left_side'] = left_side_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def streetscene_car_view_3(landmark_group):
"""
Apply the 14 point semantic labels of the view 3 of the MIT Street Scene
Car dataset to the landmark group.
The group label will be ``streetscene_car_view_2``.
The semantic labels applied are as follows:
- left_side
- rear windshield
- trunk
- rear
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``streetscene_car_view_3``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 20 points
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import PointUndirectedGraph
group = 'streetscene_car_view_3'
_validate_input(landmark_group, 20, group)
left_side_indices = np.array([0, 1, 2, 3, 4, 6, 8, 10, 13, 12])
rear_windshield_indices = np.array([4, 5, 7, 6])
trunk_indices = np.array([6, 7, 9, 8])
rear_indices = np.array([8, 9, 11, 10])
left_side_connectivity = _connectivity_from_array(left_side_indices,
close_loop=True)
rear_windshield_connectivity = _connectivity_from_array(
rear_windshield_indices, close_loop=True)
trunk_connectivity = _connectivity_from_array(trunk_indices,
close_loop=True)
rear_connectivity = _connectivity_from_array(rear_indices, close_loop=True)
total_conn = np.vstack((left_side_connectivity,
rear_windshield_connectivity,
trunk_connectivity, rear_connectivity))
ind = np.array([0, 2, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18])
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points[ind],
total_conn))
new_landmark_group['left_side'] = left_side_indices
new_landmark_group['rear_windshield'] = rear_windshield_indices
new_landmark_group['trunk'] = trunk_indices
new_landmark_group['rear'] = rear_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def streetscene_car_view_4(landmark_group):
"""
Apply the 14 point semantic labels of the view 4 of the MIT Street Scene
Car dataset to the landmark group.
The group label will be ``streetscene_car_view_4``.
The semantic labels applied are as follows:
- front
- bonnet
- windshield
- right_side
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: 'streetscene_car_view_4'
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 20 points
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import PointUndirectedGraph
group = 'streetscene_car_view_4'
_validate_input(landmark_group, 20, group)
front_indices = np.array([0, 1, 3, 2])
bonnet_indices = np.array([2, 3, 5, 4])
windshield_indices = np.array([4, 5, 7, 6])
right_side_indices = np.array([8, 9, 10, 11, 13, 12, 1, 3, 5, 7])
front_connectivity = _connectivity_from_array(front_indices,
close_loop=True)
bonnet_connectivity = _connectivity_from_array(bonnet_indices,
close_loop=True)
windshield_connectivity = _connectivity_from_array(windshield_indices,
close_loop=True)
right_side_connectivity = _connectivity_from_array(right_side_indices,
close_loop=True)
total_conn = np.vstack((front_connectivity, bonnet_connectivity,
windshield_connectivity,
right_side_connectivity))
ind = np.hstack((np.arange(8), np.array([9, 11, 13, 15, 17, 19])))
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points[ind],
total_conn))
new_landmark_group['front'] = front_indices
new_landmark_group['bonnet'] = bonnet_indices
new_landmark_group['windshield'] = windshield_indices
new_landmark_group['right_side'] = right_side_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def streetscene_car_view_5(landmark_group):
"""
Apply the 10 point semantic labels of the view 5 of the MIT Street Scene
Car dataset to the landmark group.
The group label will be ``streetscene_car_view_5``.
The semantic labels applied are as follows:
- right_side
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``streetscene_car_view_5``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 20 points
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import PointUndirectedGraph
group = 'streetscene_car_view_5'
_validate_input(landmark_group, 20, group)
right_side_indices = np.array([0, 1, 2, 3, 4, 5, 6, 7, 9, 8])
right_side_connectivity = _connectivity_from_array(right_side_indices,
close_loop=True)
total_conn = right_side_connectivity
ind = np.array([1, 3, 5, 7, 9, 11, 13, 15, 17, 19])
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points[ind],
total_conn))
new_landmark_group['right_side'] = right_side_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def streetscene_car_view_6(landmark_group):
"""
Apply the 14 point semantic labels of the view 6 of the MIT Street Scene
Car dataset to the landmark group.
The group label will be ``streetscene_car_view_6``.
The semantic labels applied are as follows:
- right_side
- rear_windshield
- trunk
- rear
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``streetscene_car_view_3``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 20 points
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import PointUndirectedGraph
group = 'streetscene_car_view_6'
_validate_input(landmark_group, 20, group)
right_side_indices = np.array([0, 1, 2, 3, 5, 7, 9, 11, 13, 12])
rear_windshield_indices = np.array([4, 5, 7, 6])
trunk_indices = np.array([6, 7, 9, 8])
rear_indices = np.array([8, 9, 11, 10])
right_side_connectivity = _connectivity_from_array(right_side_indices,
close_loop=True)
rear_windshield_connectivity = _connectivity_from_array(
rear_windshield_indices, close_loop=True)
trunk_connectivity = _connectivity_from_array(trunk_indices,
close_loop=True)
rear_connectivity = _connectivity_from_array(rear_indices, close_loop=True)
total_conn = np.vstack((right_side_connectivity,
rear_windshield_connectivity,
trunk_connectivity, rear_connectivity))
ind = np.array([1, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 19])
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points[ind],
total_conn))
new_landmark_group['right_side'] = right_side_indices
new_landmark_group['rear_windshield'] = rear_windshield_indices
new_landmark_group['trunk'] = trunk_indices
new_landmark_group['rear'] = rear_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def streetscene_car_view_7(landmark_group):
"""
Apply the 8 point semantic labels of the view 0 of the MIT Street Scene
Car dataset to the landmark group.
The group label will be ``streetscene_car_view_7``.
The semantic labels applied are as follows:
- rear_windshield
- trunk
- rear
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``streetscene_car_view_7``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
error : :map:`LabellingError`
If the given landmark group contains less than 20 points
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import PointUndirectedGraph
group = 'streetscene_car_view_7'
_validate_input(landmark_group, 20, group)
rear_windshield_indices = np.array([0, 1, 3, 2])
trunk_indices = np.array([2, 3, 5, 4])
rear_indices = np.array([4, 5, 7, 6])
rear_windshield_connectivity = _connectivity_from_array(
rear_windshield_indices, close_loop=True)
trunk_connectivity = _connectivity_from_array(trunk_indices,
close_loop=True)
rear_connectivity = _connectivity_from_array(rear_indices, close_loop=True)
total_conn = np.vstack((rear_windshield_connectivity,
trunk_connectivity, rear_connectivity))
ind = np.arange(8, 16)
new_landmark_group = LandmarkGroup.init_with_all_label(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points[ind],
total_conn))
new_landmark_group['rear_windshield'] = rear_windshield_indices
new_landmark_group['trunk'] = trunk_indices
new_landmark_group['rear'] = rear_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def bu3dfe_83(landmark_group):
"""
Apply the BU-3DFE (Binghamton University 3D Facial Expression)
Database 83 point facial annotation markup to this landmark group.
The group label will be ``bu3dfe_83``.
The semantic labels applied are as follows:
- right_eye
- left_eye
- right_eyebrow
- left_eyebrow
- right_nose
- left_nose
- nostrils
- outer_mouth
- inner_mouth
- jaw
Parameters
----------
landmark_group : :map:`LandmarkGroup`
The landmark group to apply semantic labels to.
Returns
-------
group : `str`
The group label: ``bu3dfe_83``
landmark_group : :map:`LandmarkGroup`
New landmark group.
Raises
------
:class:`menpo.landmark.exceptions.LabellingError`
If the given landmark group contains less than 83 points
References
----------
.. [1] http://www.cs.binghamton.edu/~lijun/Research/3DFE/3DFE_Analysis.html
"""
from menpo.shape import PointUndirectedGraph
group = 'bu3dfe_83'
n_points = 83
_validate_input(landmark_group, n_points, group)
reye_indices = np.arange(0, 8)
leye_indices = np.arange(8, 16)
rbrow_indices = np.arange(16, 26)
lbrow_indices = np.arange(26, 36)
rnose_indicies = np.arange(36, 39)
lnose_indicies = np.arange(39, 42)
nostril_indices = np.arange(42, 48)
outermouth_indices = np.arange(48, 60)
innermouth_indices = np.arange(60, 68)
jaw_indices = np.arange(68, 83)
reye_connectivity = _connectivity_from_array(reye_indices, close_loop=True)
leye_connectivity = _connectivity_from_array(leye_indices, close_loop=True)
rbrow_connectivity = _connectivity_from_array(rbrow_indices,
close_loop=True)
lbrow_connectivity = _connectivity_from_array(lbrow_indices,
close_loop=True)
rnose_connectivity = _connectivity_from_array(rnose_indicies)
nostril_connectivity = _connectivity_from_array(nostril_indices)
lnose_connectivity = _connectivity_from_array(lnose_indicies)
outermouth_connectivity = _connectivity_from_array(outermouth_indices,
close_loop=True)
innermouth_connectivity = _connectivity_from_array(innermouth_indices,
close_loop=True)
jaw_connectivity = _connectivity_from_array(jaw_indices)
total_conn = np.vstack([
reye_connectivity, leye_connectivity,
rbrow_connectivity, lbrow_connectivity,
rnose_connectivity, nostril_connectivity, lnose_connectivity,
outermouth_connectivity, innermouth_connectivity,
jaw_connectivity
])
new_landmark_group = LandmarkGroup(
PointUndirectedGraph.init_from_edges(landmark_group.lms.points,
total_conn),
OrderedDict([('all', np.ones(n_points, dtype=np.bool))]))
new_landmark_group['right_eye'] = reye_indices
new_landmark_group['left_eye'] = leye_indices
new_landmark_group['right_eyebrow'] = rbrow_indices
new_landmark_group['left_eyebrow'] = lbrow_indices
new_landmark_group['right_nose'] = rnose_indicies
new_landmark_group['left_nose'] = lnose_indicies
new_landmark_group['nostrils'] = nostril_indices
new_landmark_group['outer_mouth'] = outermouth_indices
new_landmark_group['inner_mouth'] = innermouth_indices
new_landmark_group['jaw'] = jaw_indices
del new_landmark_group['all'] # Remove pointless all group
return group, new_landmark_group
[docs]def labeller(landmarkable, group, label_func):
"""
Re-label an existing landmark group on a :map:`Landmarkable` object with a
new label set.
Parameters
----------
landmarkable: :map:`Landmarkable`
:map:`Landmarkable` that will have it's :map:`LandmarkManager`
augmented with a new :map:`LandmarkGroup`
group: `str`
The group label of the existing landmark group that should be
re-labelled. A copy of this group will be attached to it's landmark
manager with new labels. The group label of this new group and the
labels it will have is determined by ``label_func``
label_func: `func` -> `(str, LandmarkGroup)`
A labelling function taken from this module, Takes as input a
:map:`LandmarkGroup` and returns a tuple of
(new group label, new LandmarkGroup with semantic labels applied).
Returns
-------
landmarkable : :map:`Landmarkable`
Augmented ``landmarkable`` (this is just for convenience,
the object will actually be modified in place)
"""
new_group, lmark_group = label_func(landmarkable.landmarks[group])
landmarkable.landmarks[new_group] = lmark_group
return landmarkable