class TestSegmentMapper(TestCase):
def setUp(self):
with zipfile.ZipFile("./Ressources/MidAir.zip", "r") as zip_ref:
zip_ref.extractall("./Ressources")
self.processor = MidAirDataPreprocessor(
"./Ressources/MidAir/Kite_test/")
self.processor.clean()
data_segmenter = MidAirDataSegmenter("./Ressources/MidAir/Kite_test/")
# segment so that 1 trajectory equals 1 segment
data_segmenter.segment()
self.sensor_record = h5py.File(
"./Ressources/MidAir/Kite_test/cloudy/sensor_records.hdf5", "r+")
def tearDown(self):
shutil.rmtree("./Ressources/MidAir")
def test_given_sensor_record_when_no_trajectories_specified_should_map_all_segments(
self):
mapper = SegmentMapper()
segments = mapper.map_all(
"./Ressources/MidAir_segment_mapper_tests/MidAir/Kite_test/cloudy/",
self.sensor_record)
self.assertEqual(len(segments), 5)
def test_given_sensor_record_when_trajectories_specified_should_map_segments_of_specified_trajectories_only(
self):
mapper = SegmentMapper()
segments = mapper.map_all(
"./Ressources/MidAir_segment_mapper_tests/MidAir/Kite_test/cloudy/",
self.sensor_record, ["trajectory_3000", "trajectory_3001"])
self.assertEqual(len(segments), 2)
for segments in segments:
self.assertTrue(
segments.trajectory in ["trajectory_3000", "trajectory_3001"])
class TestMidAirImageSequenceDatasetDeepVO(TestCase):
def setUp(self):
with zipfile.ZipFile("./Ressources/MidAir.zip", "r") as zip_ref:
zip_ref.extractall("./Ressources")
self.processor = MidAirDataPreprocessor("./Ressources/MidAir")
self.processor.clean()
data_segmenter = MidAirDataSegmenter("./Ressources/MidAir/Kite_test")
data_segmenter.segment((4, ), 0)
def tearDown(self):
shutil.rmtree("./Ressources/MidAir")
def test_givenDataset_whenCallingItem_ShouldReturnTheTupleOfTheCorrespondingSegment(
self):
dataset = MidAirImageSequenceDatasetEulerDifferences(
"./Ressources/MidAir/Kite_test",
new_size=(512, 512),
img_mean=[1, 1, 1],
img_std=[1, 1, 1],
trajectories=["trajectory_3000"])
first_segment = dataset.__getitem__(1)
# First item should be the segment's images as image tensors
self.assertEqual(len(first_segment[0]), 4)
# verify the image size and its type
for image in first_segment[0]:
self.assertTrue(isinstance(image, torch.FloatTensor))
self.assertEqual(image.shape, (3, 512, 512))
# Third item should be the image's pose
self.assertEqual(first_segment[1].shape, (3, 6))
def test_givenDataset_whenCallingItem_ShouldReturnTheTupleWithProperAttitude(
self):
dataset = MidAirImageSequenceDatasetEulerDifferences(
"./Ressources/MidAir/Kite_test",
new_size=(512, 512),
img_mean=[1, 1, 1],
img_std=[1, 1, 1],
trajectories=["trajectory_3000"])
relative_attitude = dataset.__getitem__(1)[1][:, :3]
with dataset.HDF5["./Ressources/MidAir/Kite_test/cloudy"] as hdf5:
expected_attitude = hdf5["trajectory_3000"]["groundtruth"][
"attitude"][4:8]
initial_rotation = Quaternion(expected_attitude[0])
relative_attitude = Geometry.tait_bryan_rotations_to_quaternions(
relative_attitude)
actual_attitudes = Geometry.assemble_delta_quaternion_rotations(
relative_attitude)
actual_attitudes = self._rotate_quaternions_to_world_frame(
actual_attitudes)
actual_attitudes = Geometry.reset_orientations_to_origin(
initial_rotation, actual_attitudes)
actual_attitudes = [att.elements for att in actual_attitudes]
self.assertTrue(numpy.allclose(actual_attitudes, expected_attitude))
def test_givenDataset_whenCallingItem_ShouldReturnTheTupleWithProperPosition(
self):
dataset = MidAirImageSequenceDatasetEulerDifferences(
"./Ressources/MidAir/Kite_test",
new_size=(512, 512),
img_mean=[1, 1, 1],
img_std=[1, 1, 1],
trajectories=["trajectory_3000"])
with dataset.HDF5["./Ressources/MidAir/Kite_test/cloudy"] as hdf5:
expected_position = hdf5["trajectory_3000"]["groundtruth"][
"position"][4:8]
initial_rotation = Quaternion(
hdf5["trajectory_3000"]["groundtruth"]["attitude"][4])
initial_position = expected_position[0]
#Reverse position differences
actual_position_differences = dataset.__getitem__(1)[1][:, 3:].numpy()
self.assertEqual(actual_position_differences.shape, (3, 3))
actual_positions = Geometry.assemble_delta_translations(
actual_position_differences)
actual_positions = self._rotate_camera_frame_to_world_frame(
torch.Tensor(actual_positions)).numpy()
#remap axis to original absolute frame
actual_positions = Geometry.remap_position_axes(
initial_rotation, actual_positions)
actual_positions = Geometry.reset_positions_to_origin(
initial_position, actual_positions)
self.assertEqual(actual_positions.shape, (4, 3))
self.assertTrue(numpy.allclose(actual_positions, expected_position))
def _rotate_camera_frame_to_world_frame(
self, position: torch.Tensor) -> torch.Tensor:
return torch.mm(
position, torch.Tensor(self._get_to_world_frame_rotation_matrix()))
def _rotate_quaternions_to_world_frame(self, quatertions):
# Rotate the i,j,k (x,y,z) component of the quaternion to the camera frame using the rotation matrix
remapped_quaternions = []
for quaternion in quatertions:
world_frame_quat = numpy.append(
quaternion.elements[:1], quaternion.elements[1:].dot(
self._get_to_world_frame_rotation_matrix()))
remapped_quaternions.append(Quaternion(world_frame_quat))
return remapped_quaternions
def _get_to_world_frame_rotation_matrix(self) -> numpy.ndarray:
"""
Rotation matrix describing the tranformation to the world frame of midair (x: forward, y:right, z:down)
from the camera frame (x: right, y:down, z:forward)
@return:
"""
to_camera_frame_rotation_matrix = numpy.asarray([[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0],
[1.0, 0.0, 0.0]])
return to_camera_frame_rotation_matrix
class TestSegment(TestCase):
def setUp(self):
with zipfile.ZipFile("./Ressources/MidAir.zip", "r") as zip_ref:
zip_ref.extractall("./Ressources")
self.processor = MidAirDataPreprocessor("./Ressources/MidAir")
self.processor.clean()
data_segmenter = MidAirDataSegmenter("./Ressources/MidAir/Kite_test")
data_segmenter.segment((4, ), 0)
self.dataset: MidAirImageSequenceDataset = MidAirImageSequenceDataset(
"./Ressources/MidAir/Kite_test",
new_size=(512, 512),
img_mean=(1, 1, 1))
self.second_segment: Segment = self.dataset._get_segment(1)
def tearDown(self):
shutil.rmtree("./Ressources/MidAir")
def test_givenDataset_whenCallingGetPosition_shouldReturnProperPosition(
self):
with self.dataset.HDF5["./Ressources/MidAir/Kite_test/cloudy"] as hdf5:
expected_position = hdf5["trajectory_3000"]["groundtruth"][
"position"][4:8]
initial_rotation = Quaternion(
hdf5["trajectory_3000"]["groundtruth"]["attitude"][4])
initial_position = expected_position[0]
actual_positions_camera_frame = self.second_segment.get_positions(
).numpy()
self.assertEqual(actual_positions_camera_frame.shape, (4, 3))
numpy.testing.assert_array_equal(actual_positions_camera_frame[0],
numpy.asarray([0.0, 0.0, 0.0]))
#remap axis to original absolute frame
actual_position = self._rotate_camera_frame_to_world_frame(
torch.Tensor(actual_positions_camera_frame)).numpy()
for i, pos in enumerate(actual_positions_camera_frame):
#assert that the positions were switch to the KITTI camera coordinate systen
numpy.testing.assert_array_equal(actual_position[i], pos[[2, 0,
1]])
actual_position = Geometry.remap_position_axes(initial_rotation,
actual_position)
actual_position = Geometry.reset_positions_to_origin(
initial_position, actual_position)
self.assertTrue(numpy.allclose(actual_position, expected_position))
def test_givenDataset_whenCallingGetPositionDifferences_shouldReturnProperPositionDifferences(
self):
with self.dataset.HDF5["./Ressources/MidAir/Kite_test/cloudy"] as hdf5:
expected_position = hdf5["trajectory_3000"]["groundtruth"][
"position"][4:8]
initial_rotation = Quaternion(
hdf5["trajectory_3000"]["groundtruth"]["attitude"][4])
initial_position = expected_position[0]
#Reverse position differences
actual_position_differences = self.second_segment.get_position_differences(
).numpy()
self.assertEqual(actual_position_differences.shape, (3, 3))
actual_positions_camera_frame = Geometry.assemble_delta_translations(
actual_position_differences)
#remap axis to original absolute frame
actual_positions = self._rotate_camera_frame_to_world_frame(
torch.Tensor(actual_positions_camera_frame)).numpy()
for i, pos in enumerate(actual_positions_camera_frame):
#assert that the positions were switch to the KITTI camera coordinate systen
numpy.testing.assert_array_equal(actual_positions[i],
pos[[2, 0, 1]])
actual_positions = Geometry.remap_position_axes(
initial_rotation, actual_positions)
actual_positions = Geometry.reset_positions_to_origin(
initial_position, actual_positions)
self.assertEqual(actual_positions.shape, (4, 3))
self.assertTrue(numpy.allclose(actual_positions, expected_position))
def test_givenDataset_whenCallingGetAttitudes_shouldReturnProperAttitude(
self):
with self.dataset.HDF5["./Ressources/MidAir/Kite_test/cloudy"] as hdf5:
expected_attitude = hdf5["trajectory_3000"]["groundtruth"][
"attitude"][4:8]
initial_attitude = expected_attitude[0]
actual_attitude = self.second_segment.get_attitudes()
self.assertEqual(len(actual_attitude), 4)
self.assertEqual(
None,
numpy.testing.assert_array_almost_equal(
actual_attitude[0].elements,
numpy.asarray([1.0, 0.0, 0.0, 0.0])))
actual_attitude_camera_frame = Geometry.quaternion_elements_to_quaternions(
actual_attitude)
actual_attitude = self._rotate_quaternions_to_world_frame(
actual_attitude_camera_frame)
for i, quat in enumerate(actual_attitude_camera_frame):
imaginaries = quat.elements[1:]
#assert that the quaternions were switch to the KITTI camera coordinate systen
numpy.testing.assert_array_equal(actual_attitude[i].elements[1:],
imaginaries[[2, 0, 1]])
actual_attitude = Geometry.reset_orientations_to_origin(
Quaternion(initial_attitude), actual_attitude)
self.assertTrue(
numpy.allclose(
numpy.asarray([att.elements for att in actual_attitude]),
expected_attitude))
def test_givenDataset_whenCallingGetAttitudeDifferences_shouldReturnProperAttitudeDifference(
self):
with self.dataset.HDF5["./Ressources/MidAir/Kite_test/cloudy"] as hdf5:
expected_attitude = hdf5["trajectory_3000"]["groundtruth"][
"attitude"][4:8]
initial_attitude = expected_attitude[0]
actual_attitude_differences = self.second_segment.get_attitude_differences(
)
self.assertEqual(len(actual_attitude_differences), 3)
self.assertFalse(
numpy.allclose(actual_attitude_differences[0].elements,
numpy.asarray([1.0, 0.0, 0.0, 0.0])))
actual_attitude = Geometry.assemble_delta_quaternion_rotations(
actual_attitude_differences)
self.assertEqual(len(actual_attitude), 4)
self.assertEqual(
None,
numpy.testing.assert_array_almost_equal(
actual_attitude[0].elements,
numpy.asarray([1.0, 0.0, 0.0, 0.0])))
actual_attitude_camera_frame = Geometry.quaternion_elements_to_quaternions(
actual_attitude)
actual_attitude = self._rotate_quaternions_to_world_frame(
actual_attitude_camera_frame)
for i, quat in enumerate(actual_attitude_camera_frame):
imaginaries = quat.elements[1:]
#assert that the quaternions were switch to the KITTI camera coordinate systen
numpy.testing.assert_array_equal(actual_attitude[i].elements[1:],
imaginaries[[2, 0, 1]])
actual_attitude = Geometry.reset_orientations_to_origin(
Quaternion(initial_attitude), actual_attitude)
self.assertTrue(
numpy.allclose(
numpy.asarray([att.elements for att in actual_attitude]),
expected_attitude))
def test_rotate_quaternion_to_camera_frame(self):
pose_deg = numpy.array([1, 1.5, 1.2])
# Degrees to radians
pose_quat = Quaternion(
Rotation.from_euler("ZYX", pose_deg).as_quat()[[3, 0, 1, 2]])
segment = Segment(root="",
trajectory="",
camera_view="",
start_frame_index=0,
segment_length=1,
hdf5=None)
remapped_quat = segment._rotate_quaternion_to_camera_frame(pose_quat)
pose_deg_unmapped_ZYX = Rotation.from_quat(
pose_quat.elements[[1, 2, 3, 0]]).as_euler("ZYX")
pose_deg_unmapped_YXZ = Rotation.from_quat(
pose_quat.elements[[1, 2, 3, 0]]).as_euler("YXZ")
pose_deg_remapped_YXZ = Rotation.from_quat(
remapped_quat.elements[[1, 2, 3, 0]]).as_euler("YXZ")
self.assertNotEqual(pose_deg_unmapped_ZYX[0], pose_deg_unmapped_YXZ[0])
self.assertNotEqual(pose_deg_unmapped_ZYX[1], pose_deg_unmapped_YXZ[1])
self.assertNotEqual(pose_deg_unmapped_ZYX[2], pose_deg_unmapped_YXZ[2])
numpy.testing.assert_almost_equal(pose_deg_unmapped_ZYX, pose_deg)
# Remaped quaternion should have yaw-pitch-roll axis as YXZ respectively
numpy.testing.assert_array_equal(pose_deg_unmapped_ZYX,
pose_deg_remapped_YXZ)
def test_remap_position_axis_to_camera_frame(self):
pose = numpy.array([[1, 1.5, 1.2], [1, 1.5, 1.2]])
segment = Segment(root="",
trajectory="",
camera_view="",
start_frame_index=0,
segment_length=1,
hdf5=None)
remapped_pose = segment._rotate_world_frame_to_camera_frame(
torch.Tensor(pose)).numpy()
numpy.testing.assert_almost_equal(remapped_pose,
[[1.5, 1.2, 1], [1.5, 1.2, 1]])
def _rotate_quaternions_to_world_frame(self, quatertions):
# Rotate the i,j,k (x,y,z) component of the quaternion to the camera frame using the rotation matrix
remapped_quaternions = []
for quaternion in quatertions:
world_frame_quat = numpy.append(
quaternion.elements[:1], quaternion.elements[1:].dot(
self._get_to_world_frame_rotation_matrix()))
remapped_quaternions.append(Quaternion(world_frame_quat))
return remapped_quaternions
def _rotate_camera_frame_to_world_frame(
self, position: torch.Tensor) -> torch.Tensor:
return torch.mm(
position, torch.Tensor(self._get_to_world_frame_rotation_matrix()))
def _get_to_world_frame_rotation_matrix(self) -> numpy.ndarray:
"""
Rotation matrix describing the tranformation to the world frame of midair (x: forward, y:right, z:down)
from the camera frame (x: right, y:down, z:forward)
@return:
"""
to_camera_frame_rotation_matrix = numpy.asarray([[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0],
[1.0, 0.0, 0.0]])
return to_camera_frame_rotation_matrix
class TestMidAirImageSequenceDataset(TestCase):
def setUp(self):
with zipfile.ZipFile("./Ressources/MidAir.zip", "r") as zip_ref:
zip_ref.extractall("./Ressources")
self.processor = MidAirDataPreprocessor("./Ressources/MidAir")
self.processor.clean()
data_segmenter = MidAirDataSegmenter("./Ressources/MidAir/Kite_test")
data_segmenter.segment((4, ), 0)
def tearDown(self):
shutil.rmtree("./Ressources/MidAir")
def test_givenDataset_whenCallingLen_ShouldReturnTotalNumberOfSegmentsInTheDatasetPath(
self):
dataset = MidAirImageSequenceDataset("./Ressources/MidAir/Kite_test",
new_size=(512, 512))
total_number_of_segments = 0
with dataset.HDF5["./Ressources/MidAir/Kite_test/cloudy"] as hdf5:
for trajectory in hdf5:
total_number_of_segments += len(
hdf5[trajectory]["trajectory_segments"])
self.assertEqual(total_number_of_segments, dataset.__len__())
def test_givenSpecificTrajectories_whenCallingLen_ShouldReturnTotalNumberOfSegmentsInTheTrajectoryPath(
self):
dataset = MidAirImageSequenceDataset("./Ressources/MidAir/Kite_test",
new_size=(512, 512),
trajectories=["trajectory_3001"])
with dataset.HDF5["./Ressources/MidAir/Kite_test/cloudy"] as hdf5:
total_number_of_segments = len(
hdf5["trajectory_3001"]["trajectory_segments"])
self.assertEqual(total_number_of_segments, dataset.__len__())
def test_givenDataset_whenCallingItem_ShouldReturnTheTupleOfTheCorrespondingSegment(
self):
dataset = MidAirImageSequenceDataset("./Ressources/MidAir/Kite_test",
new_size=(512, 512),
img_mean=[1, 1, 1],
img_std=[1, 1, 1])
first_segment, img_sequence = dataset.__getitem__(0)
# First item should be the segment
self.assertTrue(isinstance(first_segment, Segment))
# Second item should be the segment's images as image tensors
self.assertEqual(len(img_sequence), 4)
# verify the image size and its type
for image in img_sequence:
self.assertTrue(isinstance(image, torch.FloatTensor))
self.assertEqual(image.shape, (3, 512, 512))
def test_givenDataset_when_no_trajectories_specified_should_map_all_segments(
self):
# re-segment so that 1 trajectory equals 1 segment
data_segmenter = MidAirDataSegmenter("./Ressources/MidAir/Kite_test")
data_segmenter.segment()
dataset = MidAirImageSequenceDataset("./Ressources/MidAir/Kite_test",
new_size=(512, 512))
self.assertEqual(dataset.__len__(), 5)
def test_given_sensor_record_when_trajectories_specified_should_map_segments_of_specified_trajectories_only(
self):
# re-segment so that 1 trajectory equals 1 segment
data_segmenter = MidAirDataSegmenter("./Ressources/MidAir/Kite_test")
data_segmenter.segment()
dataset = MidAirImageSequenceDataset(
"./Ressources/MidAir/Kite_test",
new_size=(512, 512),
trajectories=["trajectory_3000", "trajectory_3001"])
self.assertEqual(dataset.__len__(), 2)
for i in range(0, dataset.__len__()):
self.assertTrue(
dataset._get_segment(i).trajectory in
["trajectory_3000", "trajectory_3001"])
class TestMidAirDataSegmenter(TestCase):
def setUp(self):
with zipfile.ZipFile("./Ressources/MidAir.zip", "r") as zip_ref:
zip_ref.extractall("./Ressources")
self.processor = MidAirDataPreprocessor("./Ressources/MidAir")
self.processor.clean()
def tearDown(self):
shutil.rmtree("./Ressources/MidAir")
def test_segment_givenSingleSegmentLength_shouldProduceFixedLengthSegments(
self):
data_segmenter = MidAirDataSegmenter("./Ressources/MidAir/Kite_test")
data_segmenter.segment((4, ), 0)
sensor_records = h5py.File(
"./Ressources/MidAir/Kite_test/cloudy/sensor_records.hdf5", "r+")
for trajectory in sensor_records:
dataframe = pd.DataFrame(
sensor_records[trajectory]["trajectory_segments"],
columns=["sequence_length", "start_frame_index"])
expected_length = len(
list(
range(
0,
data_segmenter._get_trajectory_length(
sensor_records[trajectory]) - 4, 4)))
self.assertEqual(len(dataframe), expected_length)
expected_start_Frame_index = 0
for i, row in dataframe.iterrows():
self.assertEqual(row["sequence_length"], 4)
self.assertEqual(row["start_frame_index"],
expected_start_Frame_index)
expected_start_Frame_index += 4
def test_segment_when_resegmenting_should_overwrite_segment(self):
data_segmenter = MidAirDataSegmenter("./Ressources/MidAir/Kite_test")
# first segmentation
data_segmenter.segment((4, ), 0)
# second segmentation
data_segmenter.segment((6, ), 0)
sensor_records = h5py.File(
"./Ressources/MidAir/Kite_test/cloudy/sensor_records.hdf5", "r+")
for trajectory in sensor_records:
dataframe = pd.DataFrame(
sensor_records[trajectory]["trajectory_segments"],
columns=["sequence_length", "start_frame_index"])
expected_length = len(
list(
range(
0,
data_segmenter._get_trajectory_length(
sensor_records[trajectory]) - 6, 6)))
self.assertEqual(len(dataframe), expected_length)
expected_start_Frame_index = 0
for i, row in dataframe.iterrows():
self.assertEqual(row["sequence_length"], 6)
self.assertEqual(row["start_frame_index"],
expected_start_Frame_index)
expected_start_Frame_index += 6
def test_segment_givenNoneSegmentLength_shouldSegmentWillBeTrajectory(
self):
data_segmenter = MidAirDataSegmenter("./Ressources/MidAir/Kite_test")
data_segmenter.segment(None)
sensor_records = h5py.File(
"./Ressources/MidAir/Kite_test/cloudy/sensor_records.hdf5", "r+")
for trajectory in sensor_records:
dataframe = pd.DataFrame(
sensor_records[trajectory]["trajectory_segments"],
columns=["sequence_length", "start_frame_index"])
expected_length = 1
self.assertEqual(len(dataframe), expected_length)
for i, row in dataframe.iterrows():
self.assertEqual(
row["sequence_length"],
data_segmenter._get_trajectory_length(
sensor_records[trajectory]))
self.assertEqual(row["start_frame_index"], 0)
def test_segment_givenSingleSegmentLengthAndOverlap_shouldProduceFixedLengthOverlappingSegments(
self):
data_segmenter = MidAirDataSegmenter("./Ressources/MidAir/Kite_test")
data_segmenter.segment((4, ), 1)
sensor_records = h5py.File(
"./Ressources/MidAir/Kite_test/cloudy/sensor_records.hdf5", "r+")
for trajectory in sensor_records:
dataframe = pd.DataFrame(
sensor_records[trajectory]["trajectory_segments"],
columns=["sequence_length", "start_frame_index"])
expected_length = len(
list(
range(
0,
data_segmenter._get_trajectory_length(
sensor_records[trajectory]) - 4, 3)))
self.assertEqual(len(dataframe), expected_length)
expected_start_Frame_index = 0
for i, row in dataframe.iterrows():
self.assertEqual(row["sequence_length"], 4)
self.assertEqual(row["start_frame_index"],
expected_start_Frame_index)
expected_start_Frame_index += 3
def test_segment_givenRangeSegmentLength_shouldProduceVariableLengthOverlappingSegments(
self):
data_segmenter = MidAirDataSegmenter("./Ressources/MidAir/Kite_test")
data_segmenter.segment((2, 4), 1)
sensor_records = h5py.File(
"./Ressources/MidAir/Kite_test/cloudy/sensor_records.hdf5", "r+")
for trajectory in sensor_records:
dataframe = pd.DataFrame(
sensor_records[trajectory]["trajectory_segments"],
columns=["sequence_length", "start_frame_index"])
self.assertTrue(3 in dataframe["sequence_length"])
self.assertTrue(4 in dataframe["sequence_length"])
self.assertTrue(2 in dataframe["sequence_length"])
expected_start_Frame_index = 0
for i, row in dataframe.iterrows():
self.assertTrue(row["sequence_length"] <= 4
or row["sequence_length"] >= 2)
self.assertEqual(row["start_frame_index"],
expected_start_Frame_index)
expected_start_Frame_index += row["sequence_length"] - 1
def test_segment_givenRangeSegmentLength_shouldProduceVariableLengthSegments(
self):
data_segmenter = MidAirDataSegmenter("./Ressources/MidAir/Kite_test")
data_segmenter.segment((2, 4), 0)
sensor_records = h5py.File(
"./Ressources/MidAir/Kite_test/cloudy/sensor_records.hdf5", "r+")
for trajectory in sensor_records:
dataframe = pd.DataFrame(
sensor_records[trajectory]["trajectory_segments"],
columns=["sequence_length", "start_frame_index"])
self.assertTrue(3 in dataframe["sequence_length"])
self.assertTrue(4 in dataframe["sequence_length"])
self.assertTrue(2 in dataframe["sequence_length"])
expected_start_Frame_index = 0
for i, row in dataframe.iterrows():
self.assertTrue(row["sequence_length"] <= 4
or row["sequence_length"] >= 2)
self.assertEqual(row["start_frame_index"],
expected_start_Frame_index)
expected_start_Frame_index += row["sequence_length"]
class MidAirDataPreprocessorTest(unittest.TestCase):
def setUp(self):
with zipfile.ZipFile("./Ressources/MidAir.zip", "r") as zip_ref:
zip_ref.extractall("./Ressources")
self.processor = MidAirDataPreprocessor("./Ressources/MidAir")
def tearDown(self):
shutil.rmtree("./Ressources/MidAir")
def test_given_midAirDataset_whenCallingClean_ShouldExtractFramesAndDeleteArchive(
self):
self.processor.clean()
self.assertTrue(
os.path.exists(
"./Ressources/MidAir/Kite_test/cloudy/color_left/trajectory_3000/000000.JPEG"
))
self.assertFalse(
os.path.exists(
"./Ressources/MidAir/Kite_test/cloudy/color_left/trajectory_3000/frames.zip"
))
def test_given_midAirDataset_whenCallingClean_ShouldExtractSensorRecordAndDeleteArchive(
self):
self.processor.clean()
self.assertTrue(
os.path.exists(
"./Ressources/MidAir/Kite_test/cloudy/sensor_records.hdf5"))
self.assertTrue(
os.path.exists(
"./Ressources/MidAir/Kite_test/cloudy/sensor_records.zip"))
def test_givenMidAirDataset_whenCallingClean_shouldOnlyKeepPositionAndAttitude(
self):
self.processor.clean()
sensor_records = h5py.File(
"./Ressources/MidAir/Kite_test/cloudy/sensor_records.hdf5", "r+")
self.assertFalse(
"velocity" in sensor_records["trajectory_3000"]["groundtruth"])
self.assertFalse(
"acceleration" in sensor_records["trajectory_3000"]["groundtruth"])
self.assertFalse("angular_velocity" in
sensor_records["trajectory_3000"]["groundtruth"])
self.assertTrue(
"attitude" in sensor_records["trajectory_3000"]["groundtruth"])
self.assertTrue(
"position" in sensor_records["trajectory_3000"]["groundtruth"])
def test_givenMidAirDataset_whenCallingClean_shouldReduceGroundTruthFrom100HzTo25Hz(
self):
sensor_records_untouched = h5py.File(
"./Ressources/MidAir/Kite_test/cloudy/sensor_records_untouched.hdf5",
"r+")
self.processor.clean()
sensor_records = h5py.File(
"./Ressources/MidAir/Kite_test/cloudy/sensor_records.hdf5", "r+")
self.assertEqual(
sensor_records["trajectory_3000"]["groundtruth"]["position"].len(),
sensor_records["trajectory_3000"]["camera_data"]
["color_left"].len())
self.assertEqual(
sensor_records["trajectory_3000"]["groundtruth"]["attitude"].len(),
sensor_records["trajectory_3000"]["camera_data"]
["color_left"].len())
# select every frame at every start of a second
hundredHz1Sec_pos = sensor_records_untouched["trajectory_3000"][
"groundtruth"]["position"][0::100]
twentyfiveHz1Sec_pos = sensor_records["trajectory_3000"][
"groundtruth"]["position"][0::25]
hundredHz1Sec_att = sensor_records_untouched["trajectory_3000"][
"groundtruth"]["attitude"][0::100]
twentyfiveHz1Sec_att = sensor_records["trajectory_3000"][
"groundtruth"]["attitude"][0::25]
self.assertTrue(
numpy.array_equal(hundredHz1Sec_pos, twentyfiveHz1Sec_pos))
self.assertTrue(
numpy.array_equal(hundredHz1Sec_att, twentyfiveHz1Sec_att))
def test_givenMidAirDataset_whenCallingComputingMeanStd_shouldCreateFileWithValidValues(
self):
processor = MidAirDataPreprocessor("./Ressources/MidAir_mean_std_test")
processor.compute_dataset_image_mean_std()
mean = pickle.load(
open("./Ressources/MidAir_mean_std_test/Means.pkl", "rb"))
std = pickle.load(
open("./Ressources/MidAir_mean_std_test/StandardDevs.pkl", "rb"))
self.assertSequenceEqual(mean["mean_np"], [1.0, 1.0, 1.0])
self.assertSequenceEqual(
mean["mean_tensor"],
[0.003921568859368563, 0.003921568859368563, 0.003921568859368563])
self.assertSequenceEqual(std["std_np"], [0.0, 0.0, 0.0])
self.assertSequenceEqual(std["std_tensor"], [0.0, 0.0, 0.0])
def test_givenMidAirDataset_whenCallingComputingMeanStdMinus0point5_shouldCreateFileWithValidValues(
self):
processor = MidAirDataPreprocessor("./Ressources/MidAir_mean_std_test")
processor.compute_dataset_image_mean_std(True)
mean = pickle.load(
open("./Ressources/MidAir_mean_std_test/Means.pkl", "rb"))
std = pickle.load(
open("./Ressources/MidAir_mean_std_test/StandardDevs.pkl", "rb"))
self.assertSequenceEqual(mean["mean_np"], [1.0, 1.0, 1.0])
self.assertSequenceEqual(
mean["mean_tensor"],
[-0.4960784316062927, -0.4960784316062927, -0.4960784316062927])
self.assertSequenceEqual(std["std_np"], [0.0, 0.0, 0.0])
self.assertSequenceEqual(std["std_tensor"], [0.0, 0.0, 0.0])
from Datasets.KITTI import KITTIDataPreprocessor
from Datasets.MidAir import MidAirDataPreprocessor
from Parameters import Parameters
if __name__ == '__main__':
param = Parameters()
if param.dataset is "MidAir" or param.dataset is "all":
processor = MidAirDataPreprocessor(param.midair_path)
print("Cleaning Midair dataset")
processor.clean()
print("Computing mean and std dev for the images of the MidAir dataset, this will take a while...")
processor.compute_dataset_image_mean_std()
if param.dataset is "KITTI" or param.dataset is "all":
processor = KITTIDataPreprocessor(param.kitti_image_dir, param.kitti_pose_dir, param.kitti_path)
print("Cleaning KITTI dataset")
processor.clean()
print("Computing mean and std dev for the images of the KITTI dataset, this will take a while...")
processor.compute_dataset_image_mean_std()