def test_init_from_edges():
g = PointDirectedGraph.init_from_edges(
points,
np.array([[1, 0], [2, 0], [1, 2], [2, 1], [1, 3], [2, 4], [3, 4],
[3, 5]]))
assert (pg_directed.adjacency_matrix - g.adjacency_matrix).nnz == 0
g = PointUndirectedGraph.init_from_edges(
points,
np.array([[0, 1], [0, 2], [1, 2], [1, 3], [2, 4], [3, 4], [3, 5]]))
assert (pg_undirected.adjacency_matrix - g.adjacency_matrix).nnz == 0
g = PointUndirectedGraph.init_from_edges(
points,
np.array([[0, 1], [1, 0], [0, 2], [2, 0], [1, 2], [2, 1], [1, 3],
[3, 1], [2, 4], [4, 2], [3, 4], [4, 3], [3, 5], [5, 3]]))
assert (pg_undirected.adjacency_matrix - g.adjacency_matrix).nnz == 0
g = PointTree.init_from_edges(points2,
np.array([[0, 1], [0, 2], [1, 3], [1, 4],
[2, 5], [3, 6], [4, 7], [5, 8]]),
root_vertex=0)
assert (pg_tree.adjacency_matrix - g.adjacency_matrix).nnz == 0
g = PointUndirectedGraph.init_from_edges(
points, np.array([[0, 2], [2, 4], [3, 4]]))
assert (pg_isolated.adjacency_matrix - g.adjacency_matrix).nnz == 0
g = PointDirectedGraph.init_from_edges(point, np.array([]))
assert (pg_single.adjacency_matrix - g.adjacency_matrix).nnz == 0
def __init__(self,
points,
adjacency_matrix,
labels_to_masks,
copy=True,
skip_checks=False):
PointUndirectedGraph.__init__(self,
points,
adjacency_matrix,
copy=copy,
skip_checks=skip_checks)
if not labels_to_masks:
raise ValueError("Labelled point graphs are designed to be "
"immutable. Empty label sets are not permitted.")
if np.vstack(list(
labels_to_masks.values())).shape[1] != points.shape[0]:
raise ValueError("Each mask must have the same number of points "
"as the given points.")
if not isinstance(labels_to_masks, OrderedDict):
raise ValueError("Must provide an OrderedDict to maintain the "
"semantic meaning of the labels.")
# Another sanity check
self._labels_to_masks = labels_to_masks
self._verify_all_labels_masked()
if copy:
self._labels_to_masks = OrderedDict([
(l, m.copy()) for l, m in labels_to_masks.items()
])
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 PointUndirectedGraph
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
ind = np.array([1, 3, 5, 7, 9, 11, 13, 15, 17, 19])
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points[ind],
all_connectivity)
mapping = OrderedDict()
mapping['right_side'] = right_side_indices
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 PointUndirectedGraph
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)
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points,
all_connectivity)
mapping = OrderedDict()
mapping['upper_eyelid'] = upper_indices
mapping['lower_eyelid'] = lower_indices
return new_pcloud, mapping
def view_appearance_graph_widget(self,
scale_index=-1,
figure_size=(10, 8)):
r"""
Visualize the appearance graph using an interactive widget.
Parameters
----------
scale_index : `int`, optional
The scale to be used.
figure_size : (`int`, `int`), optional
The size of the rendered figure.
Raises
------
ValueError
Scale level {scale_index} uses a PCA appearance model, so there
is no graph
"""
if self.appearance_graph[scale_index] is not None:
PointUndirectedGraph(
self.shape_models[scale_index].model.mean().points,
self.appearance_graph[scale_index].adjacency_matrix).\
view_widget(figure_size=figure_size)
else:
raise ValueError("Scale level {} uses a PCA appearance model, "
"so there is no graph".format(scale_index))
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 PointUndirectedGraph
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
ind = np.array([1, 3, 5, 7, 9, 11, 13, 15, 17, 19])
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points[ind],
all_connectivity)
mapping = OrderedDict()
mapping['right_side'] = right_side_indices
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 PointUndirectedGraph
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])
# Ignore the two inner mouth points
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points,
all_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])
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 PointUndirectedGraph
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)
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points,
all_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
return new_pcloud, mapping
def test_init_2d_grid():
g = PointTree.init_2d_grid((5, 5))
assert g.adjacency_matrix.nnz == 24
assert g.n_points == 25
g = PointUndirectedGraph.init_2d_grid((5, 5))
assert g.adjacency_matrix.nnz == 80
assert g.n_points == 25
g = PointDirectedGraph.init_2d_grid((5, 5))
assert g.adjacency_matrix.nnz == 80
assert g.n_points == 25
def plot_shape_graph(aps, level):
mean_shape = aps.shape_models[level].mean().points
# plot mean shape points
plt.scatter(aps.shape_models[level].mean().points[:, 0],
aps.shape_models[level].mean().points[:, 1])
# create and plot edge connections
PointUndirectedGraph(mean_shape, aps.graph_shape.adjacency_array).view_on(
plt.gcf().number)
def test_init_2d_grid():
g = PointTree.init_2d_grid((5, 5))
assert g.adjacency_matrix.nnz == 24
assert g.n_points == 25
g = PointUndirectedGraph.init_2d_grid((5, 5))
assert g.adjacency_matrix.nnz == 80
assert g.n_points == 25
g = PointDirectedGraph.init_2d_grid((5, 5))
assert g.adjacency_matrix.nnz == 80
assert g.n_points == 25
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 PointUndirectedGraph
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])
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points,
all_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
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 PointUndirectedGraph
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
])
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points,
all_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
return new_pcloud, mapping
def __init__(self, points, adjacency_matrix, labels_to_masks, copy=True,
skip_checks=False):
PointUndirectedGraph.__init__(self, points, adjacency_matrix, copy=copy,
skip_checks=skip_checks)
if not labels_to_masks:
raise ValueError('Labelled point graphs are designed to be '
'immutable. Empty label sets are not permitted.')
if np.vstack(labels_to_masks.values()).shape[1] != points.shape[0]:
raise ValueError('Each mask must have the same number of points '
'as the given points.')
if not isinstance(labels_to_masks, OrderedDict):
raise ValueError('Must provide an OrderedDict to maintain the '
'semantic meaning of the labels.')
# Another sanity check
self._labels_to_masks = labels_to_masks
self._verify_all_labels_masked()
if copy:
self._labels_to_masks = OrderedDict([(l, m.copy()) for l, m in
labels_to_masks.items()])
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 PointUndirectedGraph
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
])
ind = np.hstack((np.arange(9), np.array([10, 12, 14, 16, 18])))
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points[ind],
all_connectivity)
mapping = OrderedDict()
mapping['front'] = front_indices
mapping['bonnet'] = bonnet_indices
mapping['windshield'] = windshield_indices
mapping['left_side'] = left_side_indices
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 PointUndirectedGraph
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
])
ind = np.hstack((np.arange(9), np.array([10, 12, 14, 16, 18])))
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points[ind],
all_connectivity)
mapping = OrderedDict()
mapping['front'] = front_indices
mapping['bonnet'] = bonnet_indices
mapping['windshield'] = windshield_indices
mapping['left_side'] = left_side_indices
return new_pcloud, mapping
def test_init_2d_grid_custom_adjacency():
tree_adj = np.array([[0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 1, 0]])
g = PointTree.init_2d_grid((2, 2), root_vertex=0, adjacency_matrix=tree_adj)
assert g.adjacency_matrix.nnz == 3
assert g.n_points == 4
single_edge = lil_matrix((25, 25))
single_edge[[0, 1], [1, 0]] = 1
single_edge = single_edge.tocsr()
g = PointUndirectedGraph.init_2d_grid((5, 5), adjacency_matrix=single_edge)
assert g.adjacency_matrix.nnz == 2
assert g.n_points == 25
g = PointDirectedGraph.init_2d_grid((5, 5), adjacency_matrix=single_edge)
assert g.adjacency_matrix.nnz == 2
assert g.n_points == 25
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 PointUndirectedGraph
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
])
ind = np.array([1, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 19])
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points[ind],
all_connectivity)
mapping = OrderedDict()
mapping['right_side'] = right_side_indices
mapping['rear_windshield'] = rear_windshield_indices
mapping['trunk'] = trunk_indices
mapping['rear'] = rear_indices
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 PointUndirectedGraph
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
])
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points,
all_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
return new_pcloud, mapping
def test_init_from_edges():
g = PointDirectedGraph.init_from_edges(
points, np.array([[1, 0], [2, 0], [1, 2], [2, 1], [1, 3], [2, 4],
[3, 4], [3, 5]]))
assert (pg_directed.adjacency_matrix - g.adjacency_matrix).nnz == 0
g = PointUndirectedGraph.init_from_edges(
points, np.array([[0, 1], [0, 2], [1, 2], [1, 3], [2, 4], [3, 4],
[3, 5]]))
assert (pg_undirected.adjacency_matrix - g.adjacency_matrix).nnz == 0
g = PointUndirectedGraph.init_from_edges(
points, np.array([[0, 1], [1, 0], [0, 2], [2, 0], [1, 2], [2, 1],
[1, 3], [3, 1], [2, 4], [4, 2], [3, 4], [4, 3],
[3, 5], [5, 3]]))
assert (pg_undirected.adjacency_matrix - g.adjacency_matrix).nnz == 0
g = PointTree.init_from_edges(
points2, np.array([[0, 1], [0, 2], [1, 3], [1, 4], [2, 5], [3, 6],
[4, 7], [5, 8]]), root_vertex=0)
assert (pg_tree.adjacency_matrix - g.adjacency_matrix).nnz == 0
g = PointUndirectedGraph.init_from_edges(
points, np.array([[0, 2], [2, 4], [3, 4]]))
assert (pg_isolated.adjacency_matrix - g.adjacency_matrix).nnz == 0
g = PointDirectedGraph.init_from_edges(point, np.array([]))
assert (pg_single.adjacency_matrix - g.adjacency_matrix).nnz == 0
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 PointUndirectedGraph
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
])
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points,
all_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
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 PointUndirectedGraph
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
])
ind = np.array([1, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 19])
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points[ind],
all_connectivity)
mapping = OrderedDict()
mapping['right_side'] = right_side_indices
mapping['rear_windshield'] = rear_windshield_indices
mapping['trunk'] = trunk_indices
mapping['rear'] = rear_indices
return new_pcloud, mapping
def test_init_from_depth_image():
fake_z = np.random.uniform(size=(10, 10))
g = PointTree.init_from_depth_image(Image(fake_z))
assert g.n_dims == 3
assert g.root_vertex == 55
assert g.adjacency_matrix.nnz == 99
assert g.n_points == 100
g = PointUndirectedGraph.init_from_depth_image(Image(fake_z))
assert g.n_dims == 3
assert g.adjacency_matrix.nnz == 360
assert g.n_points == 100
g = PointDirectedGraph.init_from_depth_image(Image(fake_z))
assert g.n_dims == 3
assert g.adjacency_matrix.nnz == 360
assert g.n_points == 100
def test_init_from_depth_image():
fake_z = np.random.uniform(size=(10, 10))
g = PointTree.init_from_depth_image(Image(fake_z))
assert g.n_dims == 3
assert g.root_vertex == 55
assert g.adjacency_matrix.nnz == 99
assert g.n_points == 100
g = PointUndirectedGraph.init_from_depth_image(Image(fake_z))
assert g.n_dims == 3
assert g.adjacency_matrix.nnz == 360
assert g.n_points == 100
g = PointDirectedGraph.init_from_depth_image(Image(fake_z))
assert g.n_dims == 3
assert g.adjacency_matrix.nnz == 360
assert g.n_points == 100
def tojson(self):
r"""
Convert this `LabelledPointUndirectedGraph` to a dictionary JSON
representation.
Returns
-------
json : ``dict``
Dictionary conforming to the LJSON v2 specification.
"""
labels = [{'mask': mask.nonzero()[0].tolist(),
'label': label}
for label, mask in self._labels_to_masks.items()]
lms_dict = PointUndirectedGraph.tojson(self)
lms_dict['labels'] = labels
return lms_dict
def tojson(self):
r"""
Convert this `LabelledPointUndirectedGraph` to a dictionary JSON
representation.
Returns
-------
json : ``dict``
Dictionary conforming to the LJSON v2 specification.
"""
labels = [{'mask': mask.nonzero()[0].tolist(),
'label': label}
for label, mask in self._labels_to_masks.items()]
lms_dict = PointUndirectedGraph.tojson(self)
lms_dict['labels'] = labels
return lms_dict
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 PointUndirectedGraph
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 = PointUndirectedGraph.init_from_edges(pointcloud.points,
all_connectivity)
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 PointUndirectedGraph
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 = PointUndirectedGraph.init_from_edges(pointcloud.points,
all_connectivity)
return new_pcloud, mapping
def _parse_ljson_v2(lms_dict):
labels_to_mask = OrderedDict() # masks into the full pointcloud per label
points = _ljson_parse_null_values(lms_dict['landmarks']['points'])
connectivity = lms_dict['landmarks'].get('connectivity')
# Don't create a PointUndirectedGraph with no connectivity
if connectivity is None or len(connectivity) == 0:
pcloud = PointCloud(points)
else:
pcloud = PointUndirectedGraph.init_from_edges(points, connectivity)
for label in lms_dict['labels']:
mask = np.zeros(pcloud.n_points, dtype=np.bool)
mask[label['mask']] = True
labels_to_mask[label['label']] = mask
return pcloud, labels_to_mask
def get_label(self, label):
"""
Returns a new :map:`PointUndirectedGraph` that contains the subset of
points that this label represents.
Parameters
----------
label : `string`
Label to filter on.
Returns
-------
graph : :map:`PointUndirectedGraph`
The PointUndirectedGraph containing the subset of points that this
label masks. Will be a subset of the entire group's points.
"""
mask = self._labels_to_masks[label]
return PointUndirectedGraph.from_mask(self, mask)
def test_init_from_depth_image_masked():
fake_z = np.random.uniform(size=(10, 10))
mask = np.zeros(fake_z.shape, dtype=np.bool)
mask[2:6, 2:6] = True
im = MaskedImage(fake_z, mask=mask)
g = PointTree.init_from_depth_image(im)
assert g.n_dims == 3
assert g.root_vertex == 0
assert g.adjacency_matrix.nnz == 15
assert g.n_points == 16
g = PointUndirectedGraph.init_from_depth_image(im)
assert g.n_dims == 3
assert g.adjacency_matrix.nnz == 48
assert g.n_points == 16
g = PointDirectedGraph.init_from_depth_image(im)
assert g.n_dims == 3
assert g.adjacency_matrix.nnz == 48
assert g.n_points == 16
def test_init_from_depth_image_masked():
fake_z = np.random.uniform(size=(10, 10))
mask = np.zeros(fake_z.shape, dtype=np.bool)
mask[2:6, 2:6] = True
im = MaskedImage(fake_z, mask=mask)
g = PointTree.init_from_depth_image(im)
assert g.n_dims == 3
assert g.root_vertex == 0
assert g.adjacency_matrix.nnz == 15
assert g.n_points == 16
g = PointUndirectedGraph.init_from_depth_image(im)
assert g.n_dims == 3
assert g.adjacency_matrix.nnz == 48
assert g.n_points == 16
g = PointDirectedGraph.init_from_depth_image(im)
assert g.n_dims == 3
assert g.adjacency_matrix.nnz == 48
assert g.n_points == 16
def _parse_ljson_v2(lms_dict):
labels_to_mask = OrderedDict() # masks into the full pointcloud per label
points = _ljson_parse_null_values(lms_dict['landmarks']['points'])
connectivity = lms_dict['landmarks'].get('connectivity')
# Don't create a PointUndirectedGraph with no connectivity
if connectivity is None or len(connectivity) == 0:
pcloud = PointCloud(points)
else:
pcloud = PointUndirectedGraph.init_from_edges(points, connectivity)
for label in lms_dict['labels']:
mask = np.zeros(pcloud.n_points, dtype=np.bool)
mask[label['mask']] = True
labels_to_mask[label['label']] = mask
return pcloud, labels_to_mask
def get_label(self, label):
"""
Returns a new :map:`PointUndirectedGraph` that contains the subset of
points that this label represents.
Parameters
----------
label : `string`
Label to filter on.
Returns
-------
graph : :map:`PointUndirectedGraph`
The PointUndirectedGraph containing the subset of points that this
label masks. Will be a subset of the entire group's points.
"""
mask = self._labels_to_masks[label]
return PointUndirectedGraph.from_mask(self, mask)
def test_init_2d_grid_custom_adjacency():
tree_adj = np.array([[0, 1, 0, 0],
[0, 0, 0, 1],
[0, 0, 0, 0],
[0, 0, 1, 0]])
g = PointTree.init_2d_grid((2, 2), root_vertex=0, adjacency_matrix=tree_adj)
assert g.adjacency_matrix.nnz == 3
assert g.n_points == 4
single_edge = lil_matrix((25, 25))
single_edge[[0, 1], [1, 0]] = 1
single_edge = single_edge.tocsr()
g = PointUndirectedGraph.init_2d_grid(
(5, 5), adjacency_matrix=single_edge)
assert g.adjacency_matrix.nnz == 2
assert g.n_points == 25
g = PointDirectedGraph.init_2d_grid(
(5, 5), adjacency_matrix=single_edge)
assert g.adjacency_matrix.nnz == 2
assert g.n_points == 25
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 PointUndirectedGraph
# 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)])
new_pcloud = PointUndirectedGraph.init_from_edges(
new_pcloud.points[:-3], edges)
# 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])
return new_pcloud, mapping
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)
if len(connectivity) == 0:
pcloud = PointCloud(points)
else:
pcloud = PointUndirectedGraph.init_from_edges(points, connectivity)
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(pcloud.n_points, dtype=np.bool)
mask[l_slice] = True
labels_to_masks[label] = mask
return pcloud, labels_to_masks
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)
if len(connectivity) == 0:
pcloud = PointCloud(points)
else:
pcloud = PointUndirectedGraph.init_from_edges(points, connectivity)
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(pcloud.n_points, dtype=np.bool)
mask[l_slice] = True
labels_to_masks[label] = mask
return pcloud, labels_to_masks
def _parse_format(self, asset=None):
with open(self.filepath, 'rb') as f:
# lms_dict is now a dict rep of the JSON
lms_dict = json.load(f, object_pairs_hook=OrderedDict)
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.append(conn)
for p in lms:
all_points.append(p['point'])
offset += len(lms)
# Don't create a PointUndirectedGraph with no connectivity
points = np.array(all_points)
if len(connectivity) == 0:
self.pointcloud = PointCloud(points)
else:
self.pointcloud = PointUndirectedGraph(points,
np.vstack(connectivity))
self.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(self.pointcloud.n_points, dtype=np.bool)
mask[l_slice] = True
self.labels_to_masks[label] = mask
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 PointUndirectedGraph
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
])
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points,
all_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
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 PointUndirectedGraph
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
])
# 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 = PointUndirectedGraph.init_from_edges(pcloud.points[ind],
all_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
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 PointUndirectedGraph
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
])
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points,
all_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
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 PointUndirectedGraph
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
])
# 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 = PointUndirectedGraph.init_from_edges(
pcloud.points[ind], all_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
return new_pcloud, mapping
)
point = np.array([[10, 10]])
# Define undirected graph and pointgraph
adj_undirected = np.array(
[
[0, 1, 1, 0, 0, 0],
[1, 0, 1, 1, 0, 0],
[1, 1, 0, 0, 1, 0],
[0, 1, 0, 0, 1, 1],
[0, 0, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 0],
]
)
g_undirected = UndirectedGraph(adj_undirected)
pg_undirected = PointUndirectedGraph(points, adj_undirected)
# Define directed graph and pointgraph
adj_directed = csr_matrix(
([1] * 8, ([1, 2, 1, 2, 1, 2, 3, 3], [0, 0, 2, 1, 3, 4, 4, 5])), shape=(6, 6)
)
g_directed = DirectedGraph(adj_directed)
pg_directed = PointDirectedGraph(points, adj_directed)
# Define tree and pointtree
adj_tree = np.array(
[
[0, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 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 PointUndirectedGraph
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
])
new_pcloud = PointUndirectedGraph.init_from_edges(
pcloud.points, all_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))
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 PointUndirectedGraph
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
])
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points,
all_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
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 PointUndirectedGraph
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
])
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points,
all_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
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 PointUndirectedGraph
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])
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points,
all_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
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 PointUndirectedGraph
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])
# Ignore the two inner mouth points
ind = np.hstack((np.arange(17, 60), np.arange(61, 64), np.arange(65, 68)))
new_pcloud = PointUndirectedGraph.init_from_edges(pcloud.points[ind],
all_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])
return new_pcloud, mapping
import numpy as np
from numpy.testing import assert_allclose
from menpo.image import Image
from menpo.image.rasterize import rasterize_landmarks_2d
from menpo.shape import PointCloud, PointUndirectedGraph
centre = PointCloud([[4.5, 4.5]])
line = PointUndirectedGraph(np.array([[2, 4.5], [8, 4.5]]),
adjacency_matrix=np.array([[0, 1], [1, 0]]))
def test_rasterize_matplotlib_basic():
im = Image.init_blank([11, 11], fill=0, n_channels=1)
im.landmarks["test"] = centre
new_im = rasterize_landmarks_2d(
im,
group="test",
render_lines=False,
marker_style=".",
marker_face_colour="r",
marker_size=2,
marker_edge_width=0,
backend="matplotlib",
)
assert new_im.n_channels == 3
assert new_im.shape == (11, 11)
assert_allclose(new_im.pixels[:, 5, 5], [255, 0, 0])
def test_rasterize_pillow_basic():