def test_normalizer(min_labels):
# tf.executing_eagerly()
labels_reader = providers.LabelsReader(min_labels)
ds_img = labels_reader.make_dataset()
normalizer = normalization.Normalizer(ensure_grayscale=True)
ds = normalizer.transform_dataset(ds_img)
example = next(iter(ds))
assert example["image"].shape[-1] == 1
normalizer = normalization.Normalizer(ensure_float=True,
ensure_grayscale=True)
ds = normalizer.transform_dataset(ds_img)
example = next(iter(ds))
assert example["image"].dtype == tf.float32
assert example["image"].shape[-1] == 1
normalizer = normalization.Normalizer(ensure_float=True, ensure_rgb=True)
ds = normalizer.transform_dataset(ds_img)
example = next(iter(ds))
assert example["image"].dtype == tf.float32
assert example["image"].shape[-1] == 3
normalizer = normalization.Normalizer(ensure_grayscale=True,
ensure_rgb=True)
ds = normalizer.transform_dataset(ds_img)
example = next(iter(ds))
assert example["image"].shape[-1] == 1
def test_ensure_rgb_from_provider(centered_pair_vid):
video = providers.VideoReader(
video=centered_pair_vid,
example_indices=[0],
)
normalizer = normalization.Normalizer(image_key="image", ensure_rgb=True)
ds = video.make_dataset()
ds = normalizer.transform_dataset(ds)
example = next(iter(ds))
assert example["image"].shape[-1] == 3
def test_normalizer(min_labels):
tf.executing_eagerly()
labels_reader = providers.LabelsReader(min_labels)
normalizer = normalization.Normalizer(image_key="image",
ensure_float=True,
ensure_grayscale=True)
ds = labels_reader.make_dataset()
ds = normalizer.transform_dataset(ds)
example = next(iter(ds))
assert example["image"].dtype == tf.float32
def test_ensure_grayscale_from_provider(small_robot_mp4_vid):
video = providers.VideoReader(
video=small_robot_mp4_vid,
example_indices=[0],
)
normalizer = normalization.Normalizer(image_key="image",
ensure_grayscale=True)
ds = video.make_dataset()
ds = normalizer.transform_dataset(ds)
example = next(iter(ds))
assert example["image"].shape[-1] == 1