def test_is_depth_available_is_true_iff_all_images_have_depth_data(self):
subject = ic.ImageCollection(
images=self.images,
type_=core.sequence_type.ImageSequenceType.SEQUENTIAL,
db_client_=self.create_mock_db_client())
self.assertTrue(subject.is_depth_available)
subject = ic.ImageCollection(
type_=core.sequence_type.ImageSequenceType.SEQUENTIAL,
images=du.defaults({1.7: make_image(depth_data=None)},
self.images),
db_client_=self.create_mock_db_client())
self.assertFalse(subject.is_depth_available)
def test_is_stereo_available_is_true_iff_all_images_are_stereo_images(
self):
stereo_images_list = {
i * 1.3: make_stereo_image(index=i)
for i in range(10)
}
subject = ic.ImageCollection(
type_=core.sequence_type.ImageSequenceType.SEQUENTIAL,
images=stereo_images_list,
db_client_=self.create_mock_db_client())
self.assertTrue(subject.is_stereo_available)
subject = ic.ImageCollection(
type_=core.sequence_type.ImageSequenceType.SEQUENTIAL,
images=du.defaults(stereo_images_list, self.images),
db_client_=self.create_mock_db_client())
self.assertFalse(subject.is_stereo_available)
def make_mock_image_collection(num_images):
return ic.ImageCollection(
id_=bson.objectid.ObjectId(),
type_=core.sequence_type.ImageSequenceType.NON_SEQUENTIAL,
images={1.2 * idx: make_image()
for idx in range(num_images)},
db_client_=None)
def test_timestamps_returns_all_timestamps_in_order(self):
subject = ic.ImageCollection(
images=self.images,
type_=core.sequence_type.ImageSequenceType.SEQUENTIAL,
db_client_=self.create_mock_db_client())
self.assertEqual([1.2 * t for t in range(10)], subject.timestamps)
for stamp in subject.timestamps:
self.assertIsNotNone(subject.get(stamp))
def make_instance(self, *args, **kwargs):
kwargs = du.defaults(
kwargs, {
'images': self.images,
'type_': core.sequence_type.ImageSequenceType.SEQUENTIAL,
'db_client_': self.create_mock_db_client()
})
return ic.ImageCollection(*args, **kwargs)
def test_is_labels_available_is_true_iff_all_images_have_bounding_boxes(
self):
subject = ic.ImageCollection(
images=self.images,
type_=core.sequence_type.ImageSequenceType.SEQUENTIAL,
db_client_=self.create_mock_db_client())
self.assertTrue(subject.is_labels_available)
subject = ic.ImageCollection(
type_=core.sequence_type.ImageSequenceType.SEQUENTIAL,
images=du.defaults(
{
1.7:
make_image(metadata=imeta.ImageMetadata(
hash_=b'\xf1\x9a\xe2|' +
np.random.randint(0, 0xFFFFFFFF).to_bytes(4, 'big'),
source_type=imeta.ImageSourceType.SYNTHETIC,
camera_pose=tf.Transform(
location=(800, 2 + np.random.uniform(-1, 1), 3),
rotation=(4, 5, 6, 7 + np.random.uniform(-4, 4))),
intrinsics=cam_intr.CameraIntrinsics(
800, 600, 550.2, 750.2, 400, 300),
environment_type=imeta.EnvironmentType.INDOOR_CLOSE,
light_level=imeta.LightingLevel.WELL_LIT,
time_of_day=imeta.TimeOfDay.DAY,
lens_focal_distance=5,
aperture=22,
simulation_world='TestSimulationWorld',
lighting_model=imeta.LightingModel.LIT,
texture_mipmap_bias=1,
normal_maps_enabled=2,
roughness_enabled=True,
geometry_decimation=0.8,
procedural_generation_seed=16234,
labelled_objects=[],
average_scene_depth=90.12))
}, self.images),
db_client_=self.create_mock_db_client())
self.assertFalse(subject.is_labels_available)