def test_invalid_crop_size(crop_size: Any) -> None:
with pytest.raises(Exception):
random_crop(
Sample(metadata=DummyPatientMetadata,
image=valid_image_4d,
labels=valid_labels,
mask=valid_mask), crop_size, valid_class_weights)
def test_valid_class_weights(class_weights: List[float]) -> None:
"""
Produce a large number of crops and make sure the crop center class proportions respect class weights
"""
valid_image_4d, valid_labels, valid_mask = create_valid_image()
num_classes = len(valid_labels)
image = np.zeros_like(valid_image_4d)
labels = np.zeros_like(valid_labels)
class0, class1, class2 = non_empty_classes = [0, 2, 4]
labels[class0] = 1
labels[class0][3, 3, 3] = 0
labels[class0][3, 2, 3] = 0
labels[class1][3, 3, 3] = 1
labels[class2][3, 2, 3] = 1
mask = np.ones_like(valid_mask)
sample = Sample(image=image,
labels=labels,
mask=mask,
metadata=DummyPatientMetadata)
crop_size = (1, 1, 1)
total_crops = 200
sampled_label_center_distribution = np.zeros(num_classes)
# If there is no class that has a non-zero weight and is present in the sample, there is no valid
# way to select a class, so we expect an exception to be thrown.
if class_weights is not None and sum(class_weights[c]
for c in non_empty_classes) == 0:
with pytest.raises(ValueError):
random_crop(sample, crop_size, class_weights)
return
for _ in range(0, total_crops):
crop_sample, center = random_crop(sample, crop_size, class_weights)
sampled_class = list(labels[:, center[0], center[1],
center[2]]).index(1)
sampled_label_center_distribution[sampled_class] += 1
sampled_label_center_distribution /= total_crops
if class_weights is None:
weight = 1.0 / len(non_empty_classes)
expected_label_center_distribution = [
weight if c in non_empty_classes else 0.0
for c in range(number_of_classes)
]
else:
total = sum(class_weights[c] for c in non_empty_classes)
expected_label_center_distribution = [
class_weights[c] / total if c in non_empty_classes else 0.0
for c in range(number_of_classes)
]
assert np.allclose(sampled_label_center_distribution,
expected_label_center_distribution,
atol=0.1)
def test_invalid_arrays(image: Any, labels: Any, mask: Any,
class_weights: Any) -> None:
"""
Tests failure cases of the random_crop function for invalid image, labels, mask or class
weights arguments.
"""
with pytest.raises(Exception):
random_crop(
Sample(metadata=DummyPatientMetadata,
image=image,
labels=labels,
mask=mask), valid_crop_size, class_weights)
def test_random_crop_no_fg() -> None:
with pytest.raises(Exception):
random_crop(
Sample(metadata=DummyPatientMetadata,
image=valid_image_4d,
labels=valid_labels,
mask=np.zeros_like(valid_mask)), valid_crop_size,
valid_class_weights)
with pytest.raises(Exception):
random_crop(
Sample(metadata=DummyPatientMetadata,
image=valid_image_4d,
labels=np.zeros_like(valid_labels),
mask=valid_mask), valid_crop_size, valid_class_weights)
def test_invalid_arrays(image: Any, labels: Any, mask: Any,
class_weights: Any) -> None:
"""
Tests failure cases of the random_crop function for invalid image, labels, mask or class
weights arguments.
"""
# Skip the final combination, because it is valid
if not (np.array_equal(image, valid_image_4d) and np.array_equal(
labels, valid_labels) and np.array_equal(mask, valid_mask)
and class_weights == valid_class_weights):
with pytest.raises(Exception):
random_crop(
Sample(metadata=DummyPatientMetadata,
image=image,
labels=labels,
mask=mask), valid_crop_size, class_weights)
def test_valid_full_crop() -> None:
metadata = DummyPatientMetadata
sample, _ = random_crop(sample=Sample(image=valid_image_4d,
labels=valid_labels,
mask=valid_mask,
metadata=metadata),
crop_size=valid_full_crop_size,
class_weights=valid_class_weights)
assert np.array_equal(sample.image, valid_image_4d)
assert np.array_equal(sample.labels, valid_labels)
assert np.array_equal(sample.mask, valid_mask)
assert sample.metadata == metadata
def test_random_crop(crop_size: Any) -> None:
labels = valid_labels
# create labels such that there are no foreground voxels in a particular class
# this should ne handled gracefully (class being ignored from sampling)
labels[0] = 1
labels[1] = 0
sample, _ = random_crop(
Sample(image=valid_image_4d,
labels=valid_labels,
mask=valid_mask,
metadata=DummyPatientMetadata), crop_size, valid_class_weights)
expected_img_crop_size = (valid_image_4d.shape[0], *crop_size)
expected_labels_crop_size = (valid_labels.shape[0], *crop_size)
assert sample.image.shape == expected_img_crop_size
assert sample.labels.shape == expected_labels_crop_size
assert sample.mask.shape == tuple(crop_size)
def create_random_cropped_sample(
sample: Sample,
crop_size: TupleInt3,
center_size: TupleInt3,
class_weights: Optional[List[float]] = None) -> CroppedSample:
"""
Creates an instance of a cropped sample extracted from full 3D images.
:param sample: the full size 3D sample to use for extracting a cropped sample.
:param crop_size: the size of the crop to extract.
:param center_size: the size of the center of the crop (this should be the same as the spatial dimensions
of the posteriors that the model produces)
:param class_weights: the distribution to use for the crop center class.
:return: CroppedSample
"""
# crop the original raw sample
sample, center_point = random_crop(sample=sample,
crop_size=crop_size,
class_weights=class_weights)
# crop the mask and label centers if required
if center_size == crop_size:
mask_center_crop = sample.mask
labels_center_crop = sample.labels
else:
mask_center_crop = image_util.get_center_crop(
image=sample.mask, crop_shape=center_size)
labels_center_crop = np.zeros(
shape=[len(sample.labels)] + list(center_size), # type: ignore
dtype=ImageDataType.SEGMENTATION.value)
for c in range(len(sample.labels)): # type: ignore
labels_center_crop[c] = image_util.get_center_crop(
image=sample.labels[c], crop_shape=center_size)
return CroppedSample(image=sample.image,
mask=sample.mask,
labels=sample.labels,
mask_center_crop=mask_center_crop,
labels_center_crop=labels_center_crop,
center_indices=center_point,
metadata=sample.metadata)