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 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_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 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 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 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"])
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_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"]
def segment_midair(param: Parameters):
print("Segmenting the MidAir dataset")
train_data_segmenter = MidAirDataSegmenter(param.midair_training_path)
train_data_segmenter.segment(param.seq_len, overlap=param.overlap)
valid_data_segmenter = MidAirDataSegmenter(param.midair_validation_path)
valid_data_segmenter.segment(param.seq_len, overlap=param.overlap)
test_data_segmenter = MidAirDataSegmenter(param.midair_test_path)
test_data_segmenter.segment((param.sliding_window_size, ),
overlap=param.sliding_window_overlap)