def test_LabelledPointUndirectedGraph_get_label():
pcloud = LabelledPointUndirectedGraph(points, adjacency_matrix,
mask_dict_2)
assert isinstance(pcloud, PointUndirectedGraph)
assert pcloud.get_label('lower').n_points == 6
assert pcloud.get_label('upper').n_points == 4
def test_LabelledPointUndirectedGraph_remove_label():
lgroup = LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict_3)
new_lgroup = lgroup.remove_label('lower')
assert 'all' in new_lgroup.labels
assert 'lower' not in new_lgroup.labels
assert 'all' in lgroup.labels
def test_LabelledPointUndirectedGraph_add_label():
lgroup = LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict_2)
new_lgroup = lgroup.add_label('lower2', [0, 1])
assert not is_same_array(new_lgroup.points, lgroup.points)
lower_pcloud = new_lgroup.get_label('lower2')
assert lower_pcloud.n_points == 2
assert_allclose(lower_pcloud.points[0, :], [1., 1., 1.])
assert_allclose(lower_pcloud.points[1, :], [1., 1., 1.])
def test_LabelledPointUndirectedGraph_copy_method():
lgroup = LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict)
lgroup_copy = lgroup.copy()
assert not is_same_array(lgroup_copy.points, lgroup.points)
# Check the mask dictionary is deepcopied properly
assert lgroup._labels_to_masks is not lgroup_copy._labels_to_masks
masks = zip(lgroup_copy._labels_to_masks.values(), lgroup._labels_to_masks.values())
for ms in masks:
assert ms[0] is not ms[1]
def test_LabelledPointUndirectedGraph_add_ordered_labels():
lgroup = LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict_2)
labels = lgroup.labels
assert labels[0] == 'lower'
assert labels[1] == 'upper'
new_lgroup = lgroup.add_label('A', [0, 1])
new_labels = new_lgroup.labels
assert new_labels[2] == 'A'
def test_LabelledPointUndirectedGraph_copy_method():
lgroup = LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict)
lgroup_copy = lgroup.copy()
assert not is_same_array(lgroup_copy.points, lgroup.points)
# Check the mask dictionary is deepcopied properly
assert lgroup._labels_to_masks is not lgroup_copy._labels_to_masks
masks = zip(lgroup_copy._labels_to_masks.values(),
lgroup._labels_to_masks.values())
for ms in masks:
assert ms[0] is not ms[1]
def test_LabelledPointUndirectedGraph_with_labels():
lgroup = LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict_2)
new_lgroup = lgroup.with_labels('lower')
assert new_lgroup.n_labels == 1
assert new_lgroup.n_points == 6
assert 'lower' in new_lgroup.labels
new_lgroup = lgroup.with_labels(['lower'])
assert new_lgroup.n_labels == 1
assert new_lgroup.n_points == 6
assert 'lower' in new_lgroup.labels
def test_LabelledPointUndirectedGraph_create_incorrect_shape():
points = np.array([[0, 1], [2, 3], [4, 5]])
adjacency_matrix = csr_matrix((3, 3))
mask_dict = OrderedDict([("all", np.ones(5, dtype=bool))])
with raises(ValueError):
LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict)
def test_LabelledPointUndirectedGraph_pass_non_ordered_dict():
points = np.array([[0, 1], [2, 3], [4, 5]])
adjacency_matrix = csr_matrix((3, 3))
mask_dict = {"all": np.ones(3, dtype=bool)}
with raises(ValueError):
LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict)
def test_LabelledPointUndirectedGraph_create_no_mask():
points = np.array([[0, 1], [2, 3], [4, 5]])
adjacency_matrix = csr_matrix((3, 3))
mask_dict = None
with raises(ValueError):
LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict)
def test_LabelledPointUndirectedGraph_create_unlabelled():
points = np.array([[0, 1], [2, 3], [4, 5]])
adjacency_matrix = csr_matrix((3, 3))
mask_dict = OrderedDict([('all', np.zeros(3, dtype=np.bool))])
with raises(ValueError):
LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict)
def test_LandmarkManager_set_None_key():
lgroup = LabelledPointUndirectedGraph.init_with_all_label(
points, adjacency_matrix)
man = LandmarkManager()
with raises(ValueError):
man[None] = lgroup
def __new__(cls, *args, **kwargs):
# This is a crazy hack that means when old style LandmarkGroup
# objects are imported it is actually new-style
# LabelledPointUndirectedGraph objects that are created.
from menpo.shape import LabelledPointUndirectedGraph
return LabelledPointUndirectedGraph.__new__(
LabelledPointUndirectedGraph, *args, **kwargs)
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 _parse_ljson_v1(lms_dict):
all_points = []
labels = [] # label per group
labels_slices = [] # slices into the full pointcloud per label
offset = 0
connectivity = []
for group in lms_dict["groups"]:
lms = group["landmarks"]
labels.append(group["label"])
labels_slices.append(slice(offset, len(lms) + offset))
# Create the connectivity if it exists
conn = group.get("connectivity", [])
if conn:
# Offset relative connectivity according to the current index
conn = offset + np.asarray(conn)
connectivity += conn.tolist()
for p in lms:
all_points.append(p["point"])
offset += len(lms)
# Don't create a PointUndirectedGraph with no connectivity
points = _ljson_parse_null_values(all_points)
n_points = points.shape[0]
labels_to_masks = OrderedDict()
# go through each label and build the appropriate boolean array
for label, l_slice in zip(labels, labels_slices):
mask = np.zeros(n_points, dtype=np.bool)
mask[l_slice] = True
labels_to_masks[label] = mask
lmarks = LabelledPointUndirectedGraph.init_from_edges(
points, connectivity, labels_to_masks
)
return {"LJSON": lmarks}
def test_LandmarkManager_set_LabelledPointUndirectedGraph():
lgroup = LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict)
man = LandmarkManager()
man['test_set'] = lgroup
assert not is_same_array(man['test_set'].points, lgroup.points)
assert man['test_set']._labels_to_masks is not lgroup._labels_to_masks
def test_LabelledPointUndirectedGraph_without_labels():
lgroup = LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict_2)
new_lgroup = lgroup.without_labels("upper")
assert new_lgroup.n_labels == 1
assert new_lgroup.n_points == 6
assert "lower" in new_lgroup.labels
assert "upper" not in new_lgroup.labels
new_lgroup = lgroup.without_labels(["upper"])
assert new_lgroup.n_labels == 1
assert new_lgroup.n_points == 6
assert "lower" in new_lgroup.labels
assert "upper" not in new_lgroup.labels
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 test_LabelledPointUndirectedGraph_copy_false():
lgroup = LabelledPointUndirectedGraph(
points, adjacency_matrix, mask_dict, copy=False
)
assert is_same_array(lgroup.points, points)
assert lgroup._labels_to_masks is mask_dict
assert lgroup.adjacency_matrix is adjacency_matrix
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 _parse_ljson_v1(lms_dict):
all_points = []
labels = [] # label per group
labels_slices = [] # slices into the full pointcloud per label
offset = 0
connectivity = []
for group in lms_dict['groups']:
lms = group['landmarks']
labels.append(group['label'])
labels_slices.append(slice(offset, len(lms) + offset))
# Create the connectivity if it exists
conn = group.get('connectivity', [])
if conn:
# Offset relative connectivity according to the current index
conn = offset + np.asarray(conn)
connectivity += conn.tolist()
for p in lms:
all_points.append(p['point'])
offset += len(lms)
# Don't create a PointUndirectedGraph with no connectivity
points = _ljson_parse_null_values(all_points)
n_points = points.shape[0]
labels_to_masks = OrderedDict()
# go through each label and build the appropriate boolean array
for label, l_slice in zip(labels, labels_slices):
mask = np.zeros(n_points, dtype=np.bool)
mask[l_slice] = True
labels_to_masks[label] = mask
lmarks = LabelledPointUndirectedGraph.init_from_edges(points, connectivity,
labels_to_masks)
return {'LJSON': lmarks}
def landmark3d_viewer_test():
adj_matrix = csr_matrix((3, 3))
LabelledPointUndirectedGraph(fake_triangle,
adj_matrix,
OrderedDict([('all',
np.ones(3, dtype=np.bool))]),
copy=False).view()
def test_LandmarkManager_set():
lgroup = LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict)
man = LandmarkManager()
man['test_set'] = lgroup
assert_allclose(man._landmark_groups['test_set'].points, lgroup.points)
assert man._landmark_groups['test_set'].n_labels == 1
def test_LandmarkManager_get():
lgroup = LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict)
man = LandmarkManager()
man._landmark_groups['test_set'] = lgroup
assert (man['test_set'] is lgroup)
assert is_same_array(man['test_set'].points, lgroup.points)
def test_landmark3d_viewer():
adj_matrix = csr_matrix((3, 3))
labels_map = OrderedDict([('all', np.ones(3, dtype=np.bool))])
with raises(Menpo3dMissingError):
LabelledPointUndirectedGraph(fake_triangle,
adj_matrix,
labels_map,
copy=False).view()
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_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 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 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 _parse_ljson_v2(lms_dict):
points = _ljson_parse_null_values(lms_dict['landmarks']['points'])
connectivity = lms_dict['landmarks'].get('connectivity')
if connectivity is None and len(lms_dict['labels']) == 0:
return PointCloud(points)
else:
labels_to_mask = OrderedDict() # masks into the pointcloud per label
n_points = points.shape[0]
for label in lms_dict['labels']:
mask = np.zeros(n_points, dtype=np.bool)
mask[label['mask']] = True
labels_to_mask[label['label']] = mask
# Note that we can pass connectivity as None here and the edges will be
# empty.
return LabelledPointUndirectedGraph.init_from_edges(
points, connectivity, labels_to_mask)
def _parse_ljson_v2(lms_dict):
points = _ljson_parse_null_values(lms_dict['landmarks']['points'])
connectivity = lms_dict['landmarks'].get('connectivity')
if connectivity is None and len(lms_dict['labels']) == 0:
return PointCloud(points)
else:
labels_to_mask = OrderedDict() # masks into the pointcloud per label
n_points = points.shape[0]
for label in lms_dict['labels']:
mask = np.zeros(n_points, dtype=np.bool)
mask[label['mask']] = True
labels_to_mask[label['label']] = mask
# Note that we can pass connectivity as None here and the edges will be
# empty.
return LabelledPointUndirectedGraph.init_from_edges(
points, connectivity, labels_to_mask)
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 _parse_ljson_v3(lms_dict):
all_lms = {}
for key, lms_dict_group in lms_dict['groups'].items():
points = _ljson_parse_null_values(lms_dict_group['landmarks']['points'])
connectivity = lms_dict_group['landmarks'].get('connectivity')
# TODO: create the metadata label!
if connectivity is None and len(lms_dict_group['labels']) == 0:
all_lms[key] = PointCloud(points)
else:
# masks into the pointcloud per label
labels_to_mask = OrderedDict()
n_points = points.shape[0]
for label in lms_dict_group['labels']:
mask = np.zeros(n_points, dtype=np.bool)
mask[label['mask']] = True
labels_to_mask[label['label']] = mask
# Note that we can pass connectivity as None here and the edges
# will be empty.
all_lms[key] = LabelledPointUndirectedGraph.init_from_edges(
points, connectivity, labels_to_mask)
return all_lms
def _parse_ljson_v1(lms_dict):
from menpo.base import MenpoDeprecationWarning
warnings.warn('LJSON v1 is deprecated. export_landmark_file{s}() will '
'only save out LJSON v2 files. Please convert all LJSON '
'files to v2 by importing into Menpo and re-exporting to '
'overwrite the files.', MenpoDeprecationWarning)
all_points = []
labels = [] # label per group
labels_slices = [] # slices into the full pointcloud per label
offset = 0
connectivity = []
for group in lms_dict['groups']:
lms = group['landmarks']
labels.append(group['label'])
labels_slices.append(slice(offset, len(lms) + offset))
# Create the connectivity if it exists
conn = group.get('connectivity', [])
if conn:
# Offset relative connectivity according to the current index
conn = offset + np.asarray(conn)
connectivity += conn.tolist()
for p in lms:
all_points.append(p['point'])
offset += len(lms)
# Don't create a PointUndirectedGraph with no connectivity
points = _ljson_parse_null_values(all_points)
n_points = points.shape[0]
labels_to_masks = OrderedDict()
# go through each label and build the appropriate boolean array
for label, l_slice in zip(labels, labels_slices):
mask = np.zeros(n_points, dtype=np.bool)
mask[l_slice] = True
labels_to_masks[label] = mask
return LabelledPointUndirectedGraph.init_from_edges(points, connectivity, labels_to_masks)
def test_LabelledPointUndirectedGraph_str():
lgroup = LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict_2)
out_str = lgroup.__str__()
assert len(out_str) > 0
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 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 labelledpointundirectedgraph_view_widget_test():
adj_matrix = csr_matrix((1, 1))
LabelledPointUndirectedGraph.init_with_all_label(
pcloud2d, adj_matrix).view_widget()
def test_LabelledPointUndirectedGraph_get_label():
pcloud = LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict_2)
assert isinstance(pcloud, PointUndirectedGraph)
assert pcloud.get_label('lower').n_points == 6
assert pcloud.get_label('upper').n_points == 4
def asf_importer(filepath, asset=None, **kwargs):
r"""
Importer for the ASF file format.
For images, the `x` and `y` axes are flipped such that the first axis is
`y` (height in the image domain).
Currently only open and closed path types are supported.
Landmark labels:
+---------+
| label |
+=========+
| all |
+---------+
Parameters
----------
filepath : `Path`
Absolute filepath of the file.
asset : `object`, optional
An optional asset that may help with loading. This is unused for this
implementation.
\**kwargs : `dict`, optional
Any other keyword arguments.
Returns
-------
landmarks : :map:`LabelledPointUndirectedGraph`
The landmarks including appropriate labels if available.
References
----------
.. [1] http://www2.imm.dtu.dk/~aam/datasets/datasets.html
"""
with open(str(filepath), 'r') as f:
landmarks = f.read()
# Remove comments and blank lines
landmarks = [l for l in landmarks.splitlines()
if (l.rstrip() and not '#' in l)]
# Pop the front of the list for the number of landmarks
count = int(landmarks.pop(0))
# Pop the last element of the list for the image_name
image_name = landmarks.pop()
xs = np.empty([count, 1])
ys = np.empty([count, 1])
connectivity = []
# Only unpack the first 7 (the last 3 are always 0)
split_landmarks = [ASFPath(*landmarks[i].split()[:7])
for i in range(count)]
paths = [list(g)
for k, g in itertools.groupby(split_landmarks, lambda x: x[0])]
vert_index = 0
for path in paths:
if path:
path_type = path[0].path_type
for vertex in path:
# Relative coordinates, will be scaled by the image size
xs[vert_index, ...] = float(vertex.xpos)
ys[vert_index, ...] = float(vertex.ypos)
vert_index += 1
# If True, isolated point
if not (vertex.connects_from == vertex.connects_to and
vertex.connects_to == vertex.point_num):
# Connectivity is defined by connects_from and connects_to
# as well as the path_type:
# Bit 1: Outer edge point/Inside point
# Bit 2: Original annotated point/Artificial point
# Bit 3: Closed path point/Open path point
# Bit 4: Non-hole/Hole point
# For now we only parse cases 0 and 4 (closed or open)
connectivity.append((int(vertex.point_num),
int(vertex.connects_to)))
if path_type == '0':
connectivity.append((int(path[-1].point_num),
int(path[0].point_num)))
connectivity = np.vstack(connectivity)
points = np.hstack([ys, xs])
if asset is not None:
# we've been given an asset. As ASF files are normalized,
# fix that here
points = Scale(np.array(asset.shape)).apply(points)
labels_to_masks = OrderedDict(
[('all', np.ones(points.shape[0], dtype=np.bool))])
return LabelledPointUndirectedGraph.init_from_edges(points, connectivity,
labels_to_masks)
def test_LabelledPointUndirectedGraph_has_nan_values():
points2 = points.copy()
points2[0, 0] = np.nan
lgroup = LabelledPointUndirectedGraph.init_with_all_label(points2,
adjacency_matrix)
assert lgroup.has_nan_values()
def test_LabelledPointUndirectedGraph_remove_label_unlabelled():
lgroup = LabelledPointUndirectedGraph(points, adjacency_matrix, mask_dict_2)
lgroup.remove_label('lower')
def test_LandmarkManager_set_None_key():
lgroup = LabelledPointUndirectedGraph.init_with_all_label(points, adjacency_matrix)
man = LandmarkManager()
man[None] = lgroup
def test_LabelledPointUndirectedGraph_create_with_all_label():
lgroup = LabelledPointUndirectedGraph.init_with_all_label(points,
adjacency_matrix)
assert lgroup.n_labels == 1
assert 'all' in lgroup.labels
def test_labelledpointundirectedgraph_view_widget():
adj_matrix = csr_matrix((1, 1))
with raises(MenpowidgetsMissingError):
LabelledPointUndirectedGraph.init_with_all_label(
pcloud2d, adj_matrix).view_widget()
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
