def _to_xml(self):
"""
Convert object to xml.
Return:
Element whose tag is "object" with appropriate sub-elements
(id, category, bounds, and pose)
"""
base_elem = ET.Element("element")
id_elem = ET.SubElement(base_elem, "id")
id_elem.text = self.id
category_elem = ET.SubElement(base_elem, "category")
category_elem.text = self.category
if self.project_type == "meshes":
compute_bbox_alg = ComputeBbox()
bounds = compute_bbox_alg.from_meshes(
self.meshes.primitive_meshes())
elif self.project_type == "voxels":
bounds = self.voxels.bounds()
elif self.project_type == "bounding_box":
bounds = self.bounds
bounds_xml = bounds.to_xml()
bounds_xml.tag = "bounds"
base_elem.append(bounds_xml)
base_elem.append(self.pose.to_xml())
base_elem.append(self.symmetry.to_xml())
score_elem = ET.SubElement(base_elem, "detectionScore")
score_elem.text = str(self.score)
return base_elem
def __init__(self, submission, ground_truth, settings=None):
"""
Constructor. Computes similarity between all elements in the
submission and ground_truth and also computes
data association caches.
Inputs:
submission (ProjectScene) - Submitted scene to be evaluated
ground_truth (ProjectScene) - The ground truth scene
settings (dict) - configuration for the evaluator. See
Evaluator.py for recognized keys and values.
"""
# sample submission and GT and store in points field
# also compute bounding box of each posed object
for e in submission.elements.values():
e.posed_points = sample_element(e, settings["density"])
e.posed_bbox = ComputeBbox().from_point_cloud(
e.posed_points[:, 0:3].T)
for e in ground_truth.elements.values():
e.posed_points = sample_element(e, settings["density"])
e.posed_bbox = ComputeBbox().from_point_cloud(
e.posed_points[:, 0:3].T)
super(MeshEvaluator, self).__init__(submission, ground_truth, settings)
def test_from_mesh(self):
"""Make a mesh and verify the bounding box's corners are correct."""
indices = np.array([0, 1, 2, 1, 3, 2, 2, 3, 4], dtype=np.uint32)
vertices = np.column_stack(
[
Vector3f(0, 0, 0),
Vector3f(1, 0, 0),
Vector3f(0, 1, 0),
Vector3f(1, 1, 0),
Vector3f(1, 1, 1),
]
)
normals = np.column_stack([Vector3f(0, 0, 1)] * 5)
self.assertEqual(vertices.shape, normals.shape)
self.assertEqual(indices.shape, (9,))
mesh = Mesh(indices, vertices, normals)
box = ComputeBbox().from_mesh(mesh)
corners = box.corners()
corners_target = np.column_stack(
[
[0.0, 0.0, 1.0],
[1.0, 0.0, 1.0],
[1.0, 1.0, 1.0],
[0.0, 1.0, 1.0],
[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
[1.0, 1.0, 0.0],
[0.0, 1.0, 0.0],
]
)
np.testing.assert_array_equal(corners, corners_target)
def test_from_gltf_model(self):
"""Verify bounding box computed from a GltfModel."""
object = GltfModel.example()
box = ComputeBbox().from_gltf_object(object)
np.testing.assert_array_equal(box.min_corner, Vector3(-1, -1, -1))
np.testing.assert_array_equal(box.max_corner, Vector3(1, 1, 1))
def test_empty(self):
"""
Test computing bbox for an empty point cloud. This should produce a
default bbox with corners at (0,0,0).
"""
points = np.ndarray(shape=(3, 0))
box = ComputeBbox().from_point_cloud(points)
np.testing.assert_array_equal(box.min_corner, np.array([0, 0, 0]))
np.testing.assert_array_equal(box.max_corner, np.array([0, 0, 0]))
def test_random_points(self):
""" Make three random points and verify the bounding box's corners are
correct
"""
point1 = Vector3f(-1, -2, -3)
point2 = Vector3f(0, 2, 1)
point3 = Vector3f(1, 2, 3)
points = np.column_stack([point1, point2, point3])
box = ComputeBbox().from_point_cloud(points)
corners = box.corners()
corners_target = np.column_stack([
[-1.0, -2.0, 3.0],
[1.0, -2.0, 3.0],
[1.0, 2.0, 3.0],
[-1.0, 2.0, 3.0],
[-1.0, -2.0, -3.0],
[1.0, -2.0, -3.0],
[1.0, 2.0, -3.0],
[-1.0, 2.0, -3.0],
])
np.testing.assert_array_equal(corners, corners_target)
def test_unit_cube(self):
""" Make three points inside a unit cube and verify the bounding box's
corners are correct
"""
point1 = Vector3(1, 0, 0)
point2 = Vector3(0, 1, 0)
point3 = Vector3(0, 0, 1)
points = np.column_stack([point1, point2, point3])
box = ComputeBbox().from_point_cloud(points)
corners = box.corners()
corners_target = np.column_stack([
[0.0, 0.0, 1.0],
[1.0, 0.0, 1.0],
[1.0, 1.0, 1.0],
[0.0, 1.0, 1.0],
[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
[1.0, 1.0, 0.0],
[0.0, 1.0, 0.0],
])
np.testing.assert_array_equal(corners, corners_target)
def convert_element(self, element, target_type):
"""
Convert <element> to <target_type> track. Makes a copy of the element.
Inputs:
element (ProjectObject) - element to convert
target_type (string) - destination project type
Return
new_element (ProjectObject) - converted element
See above for allowed conversions.
"""
if (element.project_type, target_type) not in self.allowed_conversions:
raise ValueError("Invalid target_type ({}) for element with type \
{}".format(target_type, element.project_type))
source_type = element.project_type
if target_type == "bounding_box":
if source_type == "voxels":
bounds = element.voxels.bounds()
elif source_type == "meshes":
bounds = ComputeBbox().from_gltf_object(element.meshes)
else:
raise ValueError(
"Invalid target type") # this should not be possible
new_element = ProjectObject.gen_bounding_box_object(
id=element.id,
bounds=bounds,
pose=deepcopy(element.pose),
category=element.category,
symmetry=element.symmetry,
score=element.score,
evaluated=element.evaluated)
elif target_type == "voxels":
voxelizer = Voxelizer()
voxels = voxelizer.run(element.meshes)
new_element = ProjectObject.gen_voxels_object(
id=element.id,
bounds=voxels.bounds(),
voxels=voxels,
pose=deepcopy(element.pose),
category=element.category,
symmetry=element.symmetry,
score=element.score,
evaluated=element.evaluated)
else:
raise ValueError(
"Invalid target type") # this should not be possible
return new_element
def __init__(self, submission, ground_truth, settings=None):
"""
Constructor. Computes similarity between all elements in the
submission and ground_truth and also computes
data association caches.
Inputs:
submission (ProjectScene) - Submitted scene to be evaluated
ground_truth (ProjectScene) - The ground truth scene
settings (dict) - configuration for the evaluator. See
Evaluator.py for recognized keys and values.
"""
# extract posed bounds and save
# (used for IoU calcs)
for e in submission.elements.values():
posed_corners = e.pose.transform_all_from(e.bounds.corners())
e.posed_bbox = ComputeBbox().from_point_cloud(posed_corners)
for e in ground_truth.elements.values():
posed_corners = e.pose.transform_all_from(e.bounds.corners())
e.posed_bbox = ComputeBbox().from_point_cloud(posed_corners)
super(BBEvaluator, self).__init__(submission, ground_truth, settings)