def car_streetscene_20_to_car_streetscene_view_5_10(pcloud):
r"""
Apply the 10-point semantic labels of "view 5" from the MIT Street Scene
Car dataset (originally a 20-point markup).
The semantic labels applied are as follows:
- right_side
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 20
validate_input(pcloud, n_expected_points)
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)
all_connectivity = right_side_connectivity
mapping = OrderedDict()
mapping['right_side'] = right_side_indices
ind = np.array([1, 3, 5, 7, 9, 11, 13, 15, 17, 19])
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points[ind], all_connectivity, mapping)
return new_pcloud, mapping
def eye_ibug_close_17_to_eye_ibug_close_17(pcloud):
r"""
Apply the IBUG 17-point close eye semantic labels.
The semantic labels applied are as follows:
- upper_eyelid
- lower_eyelid
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 17
validate_input(pcloud, n_expected_points)
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 = list(zip(bottom_indices, bottom_indices[1:]))
lower_connectivity += [(0, 12)]
lower_connectivity += list(zip(middle_indices, middle_indices[1:]))
lower_connectivity += [(11, 0)]
all_connectivity = np.asarray(upper_connectivity + lower_connectivity)
mapping = OrderedDict()
mapping["upper_eyelid"] = upper_indices
mapping["lower_eyelid"] = lower_indices
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points, all_connectivity, mapping)
return new_pcloud, mapping
def eye_ibug_close_17_to_eye_ibug_close_17(pcloud):
r"""
Apply the IBUG 17-point close eye semantic labels.
The semantic labels applied are as follows:
- upper_eyelid
- lower_eyelid
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 17
validate_input(pcloud, n_expected_points)
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 = list(zip(bottom_indices, bottom_indices[1:]))
lower_connectivity += [(0, 12)]
lower_connectivity += list(zip(middle_indices, middle_indices[1:]))
lower_connectivity += [(11, 0)]
all_connectivity = np.asarray(upper_connectivity + lower_connectivity)
mapping = OrderedDict()
mapping['upper_eyelid'] = upper_indices
mapping['lower_eyelid'] = lower_indices
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points, all_connectivity, mapping)
return new_pcloud, mapping
def car_streetscene_20_to_car_streetscene_view_5_10(pcloud):
r"""
Apply the 10-point semantic labels of "view 5" from the MIT Street Scene
Car dataset (originally a 20-point markup).
The semantic labels applied are as follows:
- right_side
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 20
validate_input(pcloud, n_expected_points)
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)
all_connectivity = right_side_connectivity
mapping = OrderedDict()
mapping['right_side'] = right_side_indices
ind = np.array([1, 3, 5, 7, 9, 11, 13, 15, 17, 19])
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points[ind], all_connectivity, mapping)
return new_pcloud, mapping
def eye_ibug_open_38_to_eye_ibug_open_38(pcloud):
r"""
Apply the IBUG 38-point open eye semantic labels.
The semantic labels applied are as follows:
- upper_eyelid
- lower_eyelid
- iris
- pupil
- sclera
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 38
validate_input(pcloud, n_expected_points)
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 = list(zip(sclera_top, sclera_top[1:]))
sclera_connectivity += [(0, 21)]
sclera_connectivity += list(zip(sclera_bottom, sclera_bottom[1:]))
sclera_connectivity += [(6, 17)]
lower_el_connectivity = list(zip(lower_el_top, lower_el_top[1:]))
lower_el_connectivity += [(6, 7)]
lower_el_connectivity += list(zip(lower_el_bottom, lower_el_bottom[1:]))
lower_el_connectivity += [(11, 0)]
all_connectivity = np.asarray(upper_el_connectivity +
lower_el_connectivity +
iris_connectivity.tolist() +
pupil_connectivity.tolist() +
sclera_connectivity)
mapping = OrderedDict()
mapping["upper_eyelid"] = upper_el_indices
mapping["lower_eyelid"] = lower_el_indices
mapping["pupil"] = np.arange(*pupil_range)
mapping["iris"] = np.arange(*iris_range)
mapping["sclera"] = sclera_indices
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points, all_connectivity, mapping)
return new_pcloud, mapping
def eye_ibug_open_38_to_eye_ibug_open_38(pcloud):
r"""
Apply the IBUG 38-point open eye semantic labels.
The semantic labels applied are as follows:
- upper_eyelid
- lower_eyelid
- iris
- pupil
- sclera
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 38
validate_input(pcloud, n_expected_points)
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 = list(zip(sclera_top, sclera_top[1:]))
sclera_connectivity += [(0, 21)]
sclera_connectivity += list(zip(sclera_bottom, sclera_bottom[1:]))
sclera_connectivity += [(6, 17)]
lower_el_connectivity = list(zip(lower_el_top, lower_el_top[1:]))
lower_el_connectivity += [(6, 7)]
lower_el_connectivity += list(zip(lower_el_bottom, lower_el_bottom[1:]))
lower_el_connectivity += [(11, 0)]
all_connectivity = np.asarray(upper_el_connectivity +
lower_el_connectivity +
iris_connectivity.tolist() +
pupil_connectivity.tolist() +
sclera_connectivity)
mapping = OrderedDict()
mapping['upper_eyelid'] = upper_el_indices
mapping['lower_eyelid'] = lower_el_indices
mapping['pupil'] = np.arange(*pupil_range)
mapping['iris'] = np.arange(*iris_range)
mapping['sclera'] = sclera_indices
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points, all_connectivity, mapping)
return new_pcloud, mapping
def hand_ibug_39_to_hand_ibug_39(pcloud):
r"""
Apply the IBUG 39-point semantic labels.
The semantic labels applied are as follows:
- thumb
- index
- middle
- ring
- pinky
- palm
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 39
validate_input(pcloud, n_expected_points)
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)
all_connectivity = np.vstack(
[
thumb_connectivity,
index_connectivity,
middle_connectivity,
ring_connectivity,
pinky_connectivity,
palm_connectivity,
]
)
mapping = OrderedDict()
mapping["thumb"] = thumb_indices
mapping["index"] = index_indices
mapping["middle"] = middle_indices
mapping["ring"] = ring_indices
mapping["pinky"] = pinky_indices
mapping["palm"] = palm_indices
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points, all_connectivity, mapping
)
return new_pcloud, mapping
def car_streetscene_20_to_car_streetscene_view_6_14(pcloud):
r"""
Apply the 14-point semantic labels of "view 6" from the MIT Street Scene
Car dataset (originally a 20-point markup).
The semantic labels applied are as follows:
- right_side
- rear_windshield
- trunk
- rear
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 20
validate_input(pcloud, n_expected_points)
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)
all_connectivity = np.vstack(
[
right_side_connectivity,
rear_windshield_connectivity,
trunk_connectivity,
rear_connectivity,
]
)
mapping = OrderedDict()
mapping["right_side"] = right_side_indices
mapping["rear_windshield"] = rear_windshield_indices
mapping["trunk"] = trunk_indices
mapping["rear"] = rear_indices
ind = np.array([1, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 19])
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points[ind], all_connectivity, mapping
)
return new_pcloud, mapping
def car_streetscene_20_to_car_streetscene_view_4_14(pcloud):
r"""
Apply the 14-point semantic labels of "view 4" from the MIT Street Scene
Car dataset (originally a 20-point markup).
The semantic labels applied are as follows:
- front
- bonnet
- windshield
- right_side
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 20
validate_input(pcloud, n_expected_points)
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
)
all_connectivity = np.vstack(
[
front_connectivity,
bonnet_connectivity,
windshield_connectivity,
right_side_connectivity,
]
)
mapping = OrderedDict()
mapping["front"] = front_indices
mapping["bonnet"] = bonnet_indices
mapping["windshield"] = windshield_indices
mapping["right_side"] = right_side_indices
ind = np.hstack([np.arange(8), np.array([9, 11, 13, 15, 17, 19])])
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points[ind], all_connectivity, mapping
)
return new_pcloud, mapping
def hand_ibug_39_to_hand_ibug_39(pcloud):
r"""
Apply the IBUG 39-point semantic labels.
The semantic labels applied are as follows:
- thumb
- index
- middle
- ring
- pinky
- palm
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 39
validate_input(pcloud, n_expected_points)
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)
all_connectivity = np.vstack([thumb_connectivity, index_connectivity,
middle_connectivity, ring_connectivity,
pinky_connectivity, palm_connectivity])
mapping = OrderedDict()
mapping['thumb'] = thumb_indices
mapping['index'] = index_indices
mapping['middle'] = middle_indices
mapping['ring'] = ring_indices
mapping['pinky'] = pinky_indices
mapping['palm'] = palm_indices
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points, all_connectivity, mapping)
return new_pcloud, mapping
def pose_lsp_14_to_pose_lsp_14(pcloud):
r"""
Apply the lsp 14-point semantic labels.
The semantic labels applied are as follows:
- left_leg
- right_leg
- left_arm
- right_arm
- head
References
----------
.. [1] http://www.comp.leeds.ac.uk/mat4saj/lsp.html
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 14
validate_input(pcloud, n_expected_points)
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)
all_connectivity = np.vstack([
left_leg_connectivity,
right_leg_connectivity,
left_arm_connectivity,
right_arm_connectivity,
head_connectivity,
])
mapping = OrderedDict()
mapping["left_leg"] = left_leg_indices
mapping["right_leg"] = right_leg_indices
mapping["left_arm"] = left_arm_indices
mapping["right_arm"] = right_arm_indices
mapping["head"] = head_indices
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points, all_connectivity, mapping)
return new_pcloud, mapping
def car_streetscene_20_to_car_streetscene_view_1_14(pcloud):
"""
Apply the 14-point semantic labels of "view 1" from the MIT Street Scene
Car dataset (originally a 20-point markup).
The semantic labels applied are as follows:
- front
- bonnet
- windshield
- left_side
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 20
validate_input(pcloud, n_expected_points)
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)
all_connectivity = np.vstack([
front_connectivity, bonnet_connectivity, windshield_connectivity,
left_side_connectivity
])
mapping = OrderedDict()
mapping['front'] = front_indices
mapping['bonnet'] = bonnet_indices
mapping['windshield'] = windshield_indices
mapping['left_side'] = left_side_indices
ind = np.hstack((np.arange(9), np.array([10, 12, 14, 16, 18])))
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points[ind], all_connectivity, mapping)
return new_pcloud, mapping
def car_streetscene_20_to_car_streetscene_view_1_14(pcloud):
"""
Apply the 14-point semantic labels of "view 1" from the MIT Street Scene
Car dataset (originally a 20-point markup).
The semantic labels applied are as follows:
- front
- bonnet
- windshield
- left_side
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 20
validate_input(pcloud, n_expected_points)
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)
all_connectivity = np.vstack([
front_connectivity, bonnet_connectivity, windshield_connectivity,
left_side_connectivity
])
mapping = OrderedDict()
mapping['front'] = front_indices
mapping['bonnet'] = bonnet_indices
mapping['windshield'] = windshield_indices
mapping['left_side'] = left_side_indices
ind = np.hstack((np.arange(9), np.array([10, 12, 14, 16, 18])))
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points[ind], all_connectivity, mapping)
return new_pcloud, mapping
def pose_lsp_14_to_pose_lsp_14(pcloud):
r"""
Apply the lsp 14-point semantic labels.
The semantic labels applied are as follows:
- left_leg
- right_leg
- left_arm
- right_arm
- head
References
----------
.. [1] http://www.comp.leeds.ac.uk/mat4saj/lsp.html
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 14
validate_input(pcloud, n_expected_points)
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)
all_connectivity = np.vstack([
left_leg_connectivity, right_leg_connectivity,
left_arm_connectivity, right_arm_connectivity,
head_connectivity
])
mapping = OrderedDict()
mapping['left_leg'] = left_leg_indices
mapping['right_leg'] = right_leg_indices
mapping['left_arm'] = left_arm_indices
mapping['right_arm'] = right_arm_indices
mapping['head'] = head_indices
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points, all_connectivity, mapping)
return new_pcloud, mapping
def car_streetscene_20_to_car_streetscene_view_6_14(pcloud):
r"""
Apply the 14-point semantic labels of "view 6" from the MIT Street Scene
Car dataset (originally a 20-point markup).
The semantic labels applied are as follows:
- right_side
- rear_windshield
- trunk
- rear
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 20
validate_input(pcloud, n_expected_points)
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)
all_connectivity = np.vstack([
right_side_connectivity, rear_windshield_connectivity,
trunk_connectivity, rear_connectivity
])
mapping = OrderedDict()
mapping['right_side'] = right_side_indices
mapping['rear_windshield'] = rear_windshield_indices
mapping['trunk'] = trunk_indices
mapping['rear'] = rear_indices
ind = np.array([1, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 19])
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points[ind], all_connectivity, mapping)
return new_pcloud, mapping
def pcloud_and_lgroup_from_ranges(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` {`str` -> (`int`, `int`, `bool`)}
Ordered dictionary of string labels to range tuples.
Returns
-------
new_pcloud : :map:`PointCloud`
New pointcloud with specific connectivity information applied.
mapping : `ordereddict` {`str` -> `int ndarray`}
For each label, the indices in to the pointcloud that belong to the
label.
"""
from menpo.shape import LabelledPointUndirectedGraph
mapping = OrderedDict()
all_connectivity = []
for label, tup in labels_to_ranges.items():
range_tuple = tup[:-1]
close_loop = tup[-1]
connectivity = connectivity_from_range(range_tuple,
close_loop=close_loop)
all_connectivity.append(connectivity)
mapping[label] = np.arange(*range_tuple)
all_connectivity = np.vstack(all_connectivity)
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pointcloud.points, all_connectivity, mapping)
return new_pcloud, mapping
def pcloud_and_lgroup_from_ranges(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` {`str` -> (`int`, `int`, `bool`)}
Ordered dictionary of string labels to range tuples.
Returns
-------
new_pcloud : :map:`PointCloud`
New pointcloud with specific connectivity information applied.
mapping : `ordereddict` {`str` -> `int ndarray`}
For each label, the indices in to the pointcloud that belong to the
label.
"""
from menpo.shape import LabelledPointUndirectedGraph
mapping = OrderedDict()
all_connectivity = []
for label, tup in labels_to_ranges.items():
range_tuple = tup[:-1]
close_loop = tup[-1]
connectivity = connectivity_from_range(range_tuple,
close_loop=close_loop)
all_connectivity.append(connectivity)
mapping[label] = np.arange(*range_tuple)
all_connectivity = np.vstack(all_connectivity)
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pointcloud.points, all_connectivity, mapping)
return new_pcloud, mapping
def face_ibug_68_to_face_ibug_65(pcloud):
r"""
Apply the IBUG 68 point semantic labels, but ignore the 3 points that are
coincident for a closed mouth (bottom of the inner mouth).
The semantic labels applied are as follows:
- jaw
- left_eyebrow
- right_eyebrow
- nose
- left_eye
- right_eye
- mouth
References
----------
.. [1] http://www.multipie.org/
.. [2] http://ibug.doc.ic.ac.uk/resources/300-W/
"""
from menpo.shape import LabelledPointUndirectedGraph
# Apply face_ibug_68_to_face_ibug_68
new_pcloud, mapping = face_ibug_68_to_face_ibug_68(pcloud,
return_mapping=True)
# The coincident points are considered the final 3 landmarks (bottom of
# the inner mouth points). We remove all the edges for the inner mouth
# which are the last 8.
edges = new_pcloud.edges[:-8]
# Re-add the inner mouth without the bottom 3 points
edges = np.vstack([edges,
connectivity_from_range((60, 65), close_loop=True)])
# Luckily, OrderedDict maintains the original ordering despite updates
outer_mouth_indices = np.arange(48, 60)
inner_mouth_indices = np.arange(60, 65)
mapping['mouth'] = np.hstack([outer_mouth_indices, inner_mouth_indices])
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
new_pcloud.points[:-3], edges, mapping)
return new_pcloud, mapping
def car_streetscene_20_to_car_streetscene_view_7_8(pcloud):
r"""
Apply the 8-point semantic labels of "view 7" from the MIT Street Scene
Car dataset (originally a 20-point markup).
The semantic labels applied are as follows:
- rear_windshield
- trunk
- rear
References
----------
.. [1] http://www.cs.cmu.edu/~vboddeti/alignment.html
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 20
validate_input(pcloud, n_expected_points)
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)
all_connectivity = np.vstack(
[rear_windshield_connectivity, trunk_connectivity, rear_connectivity])
mapping = OrderedDict()
mapping['rear_windshield'] = rear_windshield_indices
mapping['trunk'] = trunk_indices
mapping['rear'] = rear_indices
ind = np.arange(8, 16)
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points[ind], all_connectivity, mapping)
return new_pcloud, mapping
def face_ibug_68_to_face_ibug_65(pcloud):
r"""
Apply the IBUG 68 point semantic labels, but ignore the 3 points that are
coincident for a closed mouth (bottom of the inner mouth).
The semantic labels applied are as follows:
- jaw
- left_eyebrow
- right_eyebrow
- nose
- left_eye
- right_eye
- mouth
References
----------
.. [1] http://www.multipie.org/
.. [2] http://ibug.doc.ic.ac.uk/resources/300-W/
"""
from menpo.shape import LabelledPointUndirectedGraph
# Apply face_ibug_68_to_face_ibug_68
new_pcloud, mapping = face_ibug_68_to_face_ibug_68(pcloud,
return_mapping=True)
# The coincident points are considered the final 3 landmarks (bottom of
# the inner mouth points). We remove all the edges for the inner mouth
# which are the last 8.
edges = new_pcloud.edges[:-8]
# Re-add the inner mouth without the bottom 3 points
edges = np.vstack(
[edges, connectivity_from_range((60, 65), close_loop=True)])
# Luckily, OrderedDict maintains the original ordering despite updates
outer_mouth_indices = np.arange(48, 60)
inner_mouth_indices = np.arange(60, 65)
mapping["mouth"] = np.hstack([outer_mouth_indices, inner_mouth_indices])
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
new_pcloud.points[:-3], edges, mapping)
return new_pcloud, mapping
def face_bu3dfe_83_to_face_bu3dfe_83(pcloud):
r"""
Apply the BU-3DFE (Binghamton University 3D Facial Expression)
Database 83-point facial semantic labels.
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
References
----------
.. [1] http://www.cs.binghamton.edu/~lijun/Research/3DFE/3DFE_Analysis.html
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 83
validate_input(pcloud, n_expected_points)
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_indices = np.arange(36, 39)
lnose_indices = 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_indices)
nostril_connectivity = connectivity_from_array(nostril_indices)
lnose_connectivity = connectivity_from_array(lnose_indices)
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)
all_connectivity = np.vstack([
reye_connectivity, leye_connectivity, rbrow_connectivity,
lbrow_connectivity, rnose_connectivity, nostril_connectivity,
lnose_connectivity, outermouth_connectivity, innermouth_connectivity,
jaw_connectivity
])
mapping = OrderedDict()
mapping['right_eye'] = reye_indices
mapping['left_eye'] = leye_indices
mapping['right_eyebrow'] = rbrow_indices
mapping['left_eyebrow'] = lbrow_indices
mapping['right_nose'] = rnose_indices
mapping['left_nose'] = lnose_indices
mapping['nostrils'] = nostril_indices
mapping['outer_mouth'] = outermouth_indices
mapping['inner_mouth'] = innermouth_indices
mapping['jaw'] = jaw_indices
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points, all_connectivity, mapping)
return new_pcloud, mapping
def face_lfpw_29_to_face_lfpw_29(pcloud):
r"""
Apply the 29-point semantic labels from the original LFPW dataset.
The semantic labels applied are as follows:
- chin
- left_eye
- right_eye
- left_eyebrow
- right_eyebrow
- mouth
- nose
References
----------
.. [1] http://homes.cs.washington.edu/~neeraj/databases/lfpw/
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 29
validate_input(pcloud, n_expected_points)
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])
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, close_loop=True)])
nose_connectivity = connectivity_from_array(nose_indices, close_loop=True)
all_connectivity = np.vstack([
chin_connectivity, leye_connectivity, reye_connectivity,
lbrow_connectivity, rbrow_connectivity, mouth_connectivity,
nose_connectivity])
mapping = OrderedDict()
mapping['chin'] = chin_indices
mapping['left_eye'] = np.hstack((outer_leye_indices, pupil_leye_indices))
mapping['right_eye'] = np.hstack((outer_reye_indices, pupil_reye_indices))
mapping['left_eyebrow'] = lbrow_indices
mapping['right_eyebrow'] = rbrow_indices
mapping['mouth'] = np.hstack((outer_mouth_indices, inner_mouth_indices))
mapping['nose'] = nose_indices
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points, all_connectivity, mapping)
return new_pcloud, mapping
def face_ibug_68_to_face_ibug_68(pcloud):
r"""
Apply the IBUG 68-point semantic labels.
The semantic labels are as follows:
- jaw
- left_eyebrow
- right_eyebrow
- nose
- left_eye
- right_eye
- mouth
References
----------
.. [1] http://www.multipie.org/
.. [2] http://ibug.doc.ic.ac.uk/resources/300-W/
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 68
validate_input(pcloud, n_expected_points)
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)])
all_connectivity = np.vstack([
jaw_connectivity, lbrow_connectivity, rbrow_connectivity,
nose_connectivity, leye_connectivity, reye_connectivity,
mouth_connectivity
])
mapping = OrderedDict()
mapping['jaw'] = jaw_indices
mapping['left_eyebrow'] = lbrow_indices
mapping['right_eyebrow'] = rbrow_indices
mapping['nose'] = np.hstack((upper_nose_indices, lower_nose_indices))
mapping['left_eye'] = leye_indices
mapping['right_eye'] = reye_indices
mapping['mouth'] = np.hstack((outer_mouth_indices, inner_mouth_indices))
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points, all_connectivity, mapping)
return new_pcloud, mapping
def face_ibug_68_to_face_ibug_49(pcloud):
r"""
Apply the IBUG 49-point semantic labels, but removing the annotations
corresponding to the jaw region and the 2 describing the inner mouth
corners.
The semantic labels applied are as follows:
- left_eyebrow
- right_eyebrow
- nose
- left_eye
- right_eye
- mouth
References
----------
.. [1] http://www.multipie.org/
.. [2] http://ibug.doc.ic.ac.uk/resources/300-W/
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 68
validate_input(pcloud, n_expected_points)
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),
])
all_connectivity = np.vstack([
lbrow_connectivity,
rbrow_connectivity,
nose_connectivity,
leye_connectivity,
reye_connectivity,
mouth_connectivity,
])
mapping = OrderedDict()
mapping["left_eyebrow"] = lbrow_indices
mapping["right_eyebrow"] = rbrow_indices
mapping["nose"] = np.hstack([upper_nose_indices, lower_nose_indices])
mapping["left_eye"] = leye_indices
mapping["right_eye"] = reye_indices
mapping["mouth"] = np.hstack([outer_mouth_indices, inner_mouth_indices])
# Ignore the two inner mouth points
ind = np.hstack((np.arange(17, 60), np.arange(61, 64), np.arange(65, 68)))
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points[ind], all_connectivity, mapping)
return new_pcloud, mapping
def face_ibug_68_to_face_ibug_66(pcloud):
r"""
Apply the IBUG 66-point semantic labels, but ignoring the 2 points
describing the inner mouth corners).
The semantic labels applied are as follows:
- jaw
- left_eyebrow
- right_eyebrow
- nose
- left_eye
- right_eye
- mouth
References
----------
.. [1] http://www.multipie.org/
.. [2] http://ibug.doc.ic.ac.uk/resources/300-W/
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 68
validate_input(pcloud, n_expected_points)
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)
])
all_connectivity = np.vstack([
jaw_connectivity, lbrow_connectivity, rbrow_connectivity,
nose_connectivity, leye_connectivity, reye_connectivity,
mouth_connectivity
])
mapping = OrderedDict()
mapping['jaw'] = jaw_indices
mapping['left_eyebrow'] = lbrow_indices
mapping['right_eyebrow'] = rbrow_indices
mapping['nose'] = np.hstack([upper_nose_indices, lower_nose_indices])
mapping['left_eye'] = leye_indices
mapping['right_eye'] = reye_indices
mapping['mouth'] = np.hstack([outer_mouth_indices, inner_mouth_indices])
# Ignore the two inner mouth points
ind = np.hstack((np.arange(60), np.arange(61, 64), np.arange(65, 68)))
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points[ind], all_connectivity, mapping)
return new_pcloud, mapping
def face_ibug_68_to_face_ibug_68(pcloud):
r"""
Apply the IBUG 68-point semantic labels.
The semantic labels are as follows:
- jaw
- left_eyebrow
- right_eyebrow
- nose
- left_eye
- right_eye
- mouth
References
----------
.. [1] http://www.multipie.org/
.. [2] http://ibug.doc.ic.ac.uk/resources/300-W/
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 68
validate_input(pcloud, n_expected_points)
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),
])
all_connectivity = np.vstack([
jaw_connectivity,
lbrow_connectivity,
rbrow_connectivity,
nose_connectivity,
leye_connectivity,
reye_connectivity,
mouth_connectivity,
])
mapping = OrderedDict()
mapping["jaw"] = jaw_indices
mapping["left_eyebrow"] = lbrow_indices
mapping["right_eyebrow"] = rbrow_indices
mapping["nose"] = np.hstack((upper_nose_indices, lower_nose_indices))
mapping["left_eye"] = leye_indices
mapping["right_eye"] = reye_indices
mapping["mouth"] = np.hstack((outer_mouth_indices, inner_mouth_indices))
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points, all_connectivity, mapping)
return new_pcloud, mapping
def face_ibug_49_to_face_ibug_49(pcloud):
r"""
Apply the IBUG 49-point semantic labels.
The semantic labels applied are as follows:
- left_eyebrow
- right_eyebrow
- nose
- left_eye
- right_eye
- mouth
References
----------
.. [1] http://www.multipie.org/
.. [2] http://ibug.doc.ic.ac.uk/resources/300-W/
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 49
validate_input(pcloud, n_expected_points)
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)
])
all_connectivity = np.vstack([
lbrow_connectivity, rbrow_connectivity, nose_connectivity,
leye_connectivity, reye_connectivity, mouth_connectivity
])
mapping = OrderedDict()
mapping['left_eyebrow'] = lbrow_indices
mapping['right_eyebrow'] = rbrow_indices
mapping['nose'] = np.hstack([upper_nose_indices, lower_nose_indices])
mapping['left_eye'] = leye_indices
mapping['right_eye'] = reye_indices
mapping['mouth'] = np.hstack([outer_mouth_indices, inner_mouth_indices])
# Ignore the two inner mouth points
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points, all_connectivity, mapping)
return new_pcloud, mapping
def face_lfpw_29_to_face_lfpw_29(pcloud):
r"""
Apply the 29-point semantic labels from the original LFPW dataset.
The semantic labels applied are as follows:
- chin
- left_eye
- right_eye
- left_eyebrow
- right_eyebrow
- mouth
- nose
References
----------
.. [1] http://homes.cs.washington.edu/~neeraj/databases/lfpw/
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 29
validate_input(pcloud, n_expected_points)
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])
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, close_loop=True),
])
nose_connectivity = connectivity_from_array(nose_indices, close_loop=True)
all_connectivity = np.vstack([
chin_connectivity,
leye_connectivity,
reye_connectivity,
lbrow_connectivity,
rbrow_connectivity,
mouth_connectivity,
nose_connectivity,
])
mapping = OrderedDict()
mapping["chin"] = chin_indices
mapping["left_eye"] = np.hstack((outer_leye_indices, pupil_leye_indices))
mapping["right_eye"] = np.hstack((outer_reye_indices, pupil_reye_indices))
mapping["left_eyebrow"] = lbrow_indices
mapping["right_eyebrow"] = rbrow_indices
mapping["mouth"] = np.hstack((outer_mouth_indices, inner_mouth_indices))
mapping["nose"] = nose_indices
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points, all_connectivity, mapping)
return new_pcloud, mapping
def face_ibug_68_to_face_ibug_49(pcloud):
r"""
Apply the IBUG 49-point semantic labels, but removing the annotations
corresponding to the jaw region and the 2 describing the inner mouth
corners.
The semantic labels applied are as follows:
- left_eyebrow
- right_eyebrow
- nose
- left_eye
- right_eye
- mouth
References
----------
.. [1] http://www.multipie.org/
.. [2] http://ibug.doc.ic.ac.uk/resources/300-W/
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 68
validate_input(pcloud, n_expected_points)
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)])
all_connectivity = np.vstack([
lbrow_connectivity, rbrow_connectivity, nose_connectivity,
leye_connectivity, reye_connectivity, mouth_connectivity])
mapping = OrderedDict()
mapping['left_eyebrow'] = lbrow_indices
mapping['right_eyebrow'] = rbrow_indices
mapping['nose'] = np.hstack([upper_nose_indices, lower_nose_indices])
mapping['left_eye'] = leye_indices
mapping['right_eye'] = reye_indices
mapping['mouth'] = np.hstack([outer_mouth_indices, inner_mouth_indices])
# Ignore the two inner mouth points
ind = np.hstack((np.arange(17, 60), np.arange(61, 64), np.arange(65, 68)))
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points[ind], all_connectivity, mapping)
return new_pcloud, mapping
def pose_human36M_32_to_pose_human36M_17(pcloud):
r"""
Apply the human3.6M 17-point semantic labels (based on the
original semantic labels of Human3.6 but removing the annotations
corresponding to duplicate points, soles and palms), originally 32-points.
The semantic labels applied are as follows:
- pelvis
- right_leg
- left_leg
- spine
- head
- left_arm
- right_arm
- torso
References
----------
.. [1] http://vision.imar.ro/human3.6m/
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 32
validate_input(pcloud, n_expected_points)
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)
all_connectivity = np.vstack([
pelvis_connectivity, right_leg_connectivity, left_leg_connectivity,
spine_connectivity, head_connectivity, left_arm_connectivity,
right_arm_connectivity, torso_connectivity
])
mapping = OrderedDict()
mapping['pelvis'] = pelvis_indices
mapping['right_leg'] = right_leg_indices
mapping['left_leg'] = left_leg_indices
mapping['spine'] = spine_indices
mapping['head'] = head_indices
mapping['left_arm'] = left_arm_indices
mapping['right_arm'] = right_arm_indices
mapping['torso'] = torso_indices
# 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_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points[ind], all_connectivity, mapping)
return new_pcloud, mapping
def pose_human36M_32_to_pose_human36M_32(pcloud):
r"""
Apply the human3.6M 32-point semantic labels.
The semantic labels applied are as follows:
- pelvis
- right_leg
- left_leg
- spine
- head
- left_arm
- left_hand
- right_arm
- right_hand
- torso
References
----------
.. [1] http://vision.imar.ro/human3.6m/
"""
from menpo.shape import LabelledPointUndirectedGraph
n_expected_points = 32
validate_input(pcloud, n_expected_points)
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)
all_connectivity = 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
])
mapping = OrderedDict()
mapping['pelvis'] = pelvis_indices
mapping['right_leg'] = right_leg_indices
mapping['left_leg'] = left_leg_indices
mapping['spine'] = spine_indices
mapping['head'] = head_indices
mapping['left_arm'] = left_arm_indices
mapping['left_hand'] = left_hand_indices
mapping['right_arm'] = right_arm_indices
mapping['right_hand'] = right_hand_indices
mapping['torso'] = torso_indices
new_pcloud = LabelledPointUndirectedGraph.init_from_indices_mapping(
pcloud.points, all_connectivity, mapping)
return new_pcloud, mapping