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 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_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])
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()
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()