def test_visualize_patch_sampling_2d(
test_output_dirs: TestOutputDirectories) -> None:
"""
Tests if patch sampling works for 2D images.
:param test_output_dirs:
"""
set_random_seed(0)
shape = (1, 20, 30)
foreground_classes = ["fg"]
class_weights = equally_weighted_classes(foreground_classes)
config = SegmentationModelBase(should_validate=False,
crop_size=(1, 5, 10),
class_weights=class_weights)
image = np.random.rand(1, *shape).astype(np.float32) * 1000
mask = np.ones(shape)
labels = np.zeros((len(class_weights), ) + shape)
labels[1, 0, 8:12, 5:25] = 1
labels[0] = 1 - labels[1]
output_folder = Path(test_output_dirs.root_dir)
image_header = None
sample = Sample(image=image,
mask=mask,
labels=labels,
metadata=PatientMetadata(patient_id='123',
image_header=image_header))
heatmap = visualize_random_crops(sample,
config,
output_folder=output_folder)
expected_folder = full_ml_test_data_path("patch_sampling")
expected_heatmap = expected_folder / "sampling_2d.npy"
# To update the stored results, uncomment this line:
# np.save(str(expected_heatmap), heatmap)
assert np.allclose(heatmap, np.load(
str(expected_heatmap))), "Patch sampling created a different heatmap."
assert len(list(output_folder.rglob("*.nii.gz"))) == 0
assert len(list(output_folder.rglob("*.png"))) == 1
actual_file = output_folder / "123_sampled_patches.png"
assert_file_exists(actual_file)
expected = expected_folder / "sampling_2d.png"
# To update the stored results, uncomment this line:
# expected.write_bytes(actual_file.read_bytes())
if not is_running_on_azure():
# When running on the Azure build agents, it appears that the bounding box of the images
# is slightly different than on local runs, even with equal dpi settings.
# It says: Image sizes don't match: actual (685, 469), expected (618, 424)
# Not able to figure out how to make the run results consistent, hence disable in cloud runs.
assert_binary_files_match(actual_file, expected)
def test_visualize_patch_sampling(test_output_dirs: TestOutputDirectories,
labels_to_boundary: bool) -> None:
"""
Tests if patch sampling and producing diagnostic images works as expected.
:param test_output_dirs:
:param labels_to_boundary: If true, the ground truth labels are placed close to the image boundary, so that
crops have to be adjusted inwards. If false, ground truth labels are all far from the image boundaries.
"""
set_random_seed(0)
shape = (10, 30, 30)
foreground_classes = ["fg"]
class_weights = equally_weighted_classes(foreground_classes)
config = SegmentationModelBase(should_validate=False,
crop_size=(2, 10, 10),
class_weights=class_weights)
image = np.random.rand(1, *shape).astype(np.float32) * 1000
mask = np.ones(shape)
labels = np.zeros((len(class_weights), ) + shape)
if labels_to_boundary:
# Generate foreground labels in such a way that a patch centered around a foreground pixel would
# reach outside of the image.
labels[1, 4:8, 3:27, 3:27] = 1
else:
labels[1, 4:8, 15:18, 15:18] = 1
labels[0] = 1 - labels[1]
output_folder = Path(test_output_dirs.root_dir)
image_header = get_unit_image_header()
sample = Sample(image=image,
mask=mask,
labels=labels,
metadata=PatientMetadata(patient_id='123',
image_header=image_header))
expected_folder = full_ml_test_data_path("patch_sampling")
heatmap = visualize_random_crops(sample,
config,
output_folder=output_folder)
expected_heatmap = expected_folder / ("sampled_to_boundary.npy"
if labels_to_boundary else
"sampled_center.npy")
# To update the stored results, uncomment this line:
# np.save(str(expected_heatmap), heatmap)
assert np.allclose(heatmap, np.load(
str(expected_heatmap))), "Patch sampling created a different heatmap."
f1 = output_folder / "123_ct.nii.gz"
assert_file_exists(f1)
f2 = output_folder / "123_sampled_patches.nii.gz"
assert_file_exists(f2)
thumbnails = [
"123_sampled_patches_dim0.png",
"123_sampled_patches_dim1.png",
"123_sampled_patches_dim2.png",
]
for f in thumbnails:
assert_file_exists(output_folder / f)
expected = expected_folder / ("sampled_to_boundary.nii.gz"
if labels_to_boundary else
"sampled_center.nii.gz")
# To update test results:
# shutil.copy(str(f2), str(expected))
expected_image = io_util.load_nifti_image(expected)
actual_image = io_util.load_nifti_image(f2)
np.allclose(expected_image.image, actual_image.image)
if labels_to_boundary:
for f in thumbnails:
# Uncomment this line to update test results
# (expected_folder / f).write_bytes((output_folder / f).read_bytes())
if not is_running_on_azure():
# When running on the Azure build agents, it appears that the bounding box of the images
# is slightly different than on local runs, even with equal dpi settings.
# Not able to figure out how to make the run results consistent, hence disable in cloud runs.
assert_binary_files_match(output_folder / f,
expected_folder / f)