def test_plot_normalization_result(
test_output_dirs: TestOutputDirectories) -> None:
"""
Tests plotting of before/after histograms in photometric normalization.
:return:
"""
size = (3, 3, 3)
image = np.zeros((1, ) + size)
for i, (z, y, x) in enumerate(
itertools.product(range(size[0]), range(size[1]), range(size[2]))):
image[0, z, y, x] = i
labels = np.zeros((2, ) + size)
labels[1, 1, 1, 1] = 1
sample = Sample(image=image,
labels=labels,
mask=np.ones(size),
metadata=DummyPatientMetadata)
config = SegmentationModelBase(
norm_method=PhotometricNormalizationMethod.CtWindow,
window=4,
level=13,
should_validate=False)
normalizer = PhotometricNormalization(config)
folder = Path(test_output_dirs.root_dir)
files = plotting.plot_normalization_result(sample, normalizer, folder)
expected = ["042_slice_001.png", "042_slice_001_contour.png"]
compare_files(files, expected)
def test_plot_contours_for_all_classes(
test_output_dirs: TestOutputDirectories) -> None:
size = (3, 3, 3)
image = np.zeros((1, ) + size)
for i, (z, y, x) in enumerate(
itertools.product(range(size[0]), range(size[1]), range(size[2]))):
image[0, z, y, x] = i
# Create a fake label array: For each class, there is exactly 1 pixel foreground, at the z slice that is
# equal to the class index
labels = np.zeros((3, ) + size)
labels[0, 0, 1, 1] = 1
labels[1, 1, 1, 1] = 1
labels[2, 2, 1, 1] = 1
# Fake segmentation: Classifies all foreground pixels correctly...
segmentation = np.zeros(size)
segmentation[1, 1, 1] = 1
segmentation[2, 1, 1] = 2
# ...but has an extra foreground pixel in the largest z slice in either top left or bottom right corner:
segmentation[1, 0, 0] = 1
segmentation[2, 2, 2] = 2
sample = Sample(image=image,
labels=labels,
mask=np.ones(size),
metadata=DummyPatientMetadata)
plots = plotting.plot_contours_for_all_classes(
sample,
segmentation,
foreground_class_names=["class1", "class2"],
result_folder=Path(test_output_dirs.root_dir),
result_prefix="prefix")
expected = [
"prefix042_class1_slice_001.png", "prefix042_class2_slice_002.png"
]
compare_files(plots, expected)
with pytest.raises(ValueError) as err:
plotting.plot_contours_for_all_classes(
sample,
segmentation,
foreground_class_names=["background", "class1", "class2"],
result_folder=Path(test_output_dirs.root_dir),
result_prefix="prefix")
assert "3 classes" in str(err)
assert "background" in str(err)