def test_output_schema_element():
"""test that attempts to keep the TypedDict and subschema element in sync
"""
s = DenseROI(id=1,
x=23,
y=34,
width=128,
height=128,
valid_roi=True,
mask_matrix=[[True, True], [False, True]],
max_correction_up=12,
max_correction_down=12,
max_correction_left=12,
max_correction_right=12,
mask_image_plane=0,
exclusion_labels=['small_size', 'motion_border'])
# does this example have exactly the keys specified in DenseROI?
assert set(list(s.keys())) == set(list(DenseROI.__annotations__.keys()))
# can't really validate the above, but we can check against our
# output schema
# validate the object with a marshmallow load()
subschema = DenseROISchema()
subschema.load(s)
assert subschema.dump(s) == s
def test_morphological_transform(mask_matrix, expect_None):
d = DenseROI(id=1,
x=23,
y=34,
width=128,
height=128,
valid_roi=True,
mask_matrix=mask_matrix,
max_correction_up=12,
max_correction_down=12,
max_correction_left=12,
max_correction_right=12,
mask_image_plane=0,
exclusion_labels=['small_size', 'motion_border'])
if expect_None:
assert rois_utils.morphological_transform(d, (50, 50)) is None
return
morphed = rois_utils.morphological_transform(d, (50, 50))
DenseROISchema().validate(morphed)
for k in ['id', 'valid_roi', 'mask_image_plane', 'exclusion_labels']:
assert morphed[k] == d[k]
for k in d.keys():
if 'max_correction' in k:
assert morphed[k] == d[k]
def test_dense_to_extract():
d = DenseROI(id=1,
x=23,
y=34,
width=128,
height=128,
valid_roi=True,
mask_matrix=[[True, True], [False, True]],
max_correction_up=12,
max_correction_down=12,
max_correction_left=12,
max_correction_right=12,
mask_image_plane=0,
exclusion_labels=['small_size', 'motion_border'])
e = rois_utils.dense_to_extract(d)
for k in ['id', 'x', 'y', 'width', 'height']:
assert e[k] == d[k]
assert e['mask'] == d['mask_matrix']
assert e['valid'] == d['valid_roi']
def _coo_mask_to_LIMS_compatible_format(
coo_mask: coo_matrix) -> Union[DenseROI, None]:
"""
This functions transforms ROI mask data from COO format
to the LIMS expected format.
Parameters
----------
coo_mask: coo_matrix
The coo roi matrix to be converted
Returns
-------
DenseROI
or None if the coo_mask is empty
"""
bounds = roi_bounds(coo_mask)
if bounds is None:
return None
height = bounds[1] - bounds[0]
width = bounds[3] - bounds[2]
mask_matrix = crop_roi_mask(coo_mask).toarray()
mask_matrix = np.array(mask_matrix, dtype=bool)
compatible_roi = DenseROI(
x=int(bounds[2]),
y=int(bounds[0]),
width=int(width),
height=int(height),
mask_matrix=mask_matrix.tolist(),
# following are placeholders
valid_roi=True,
mask_image_plane=0,
exclusion_labels=[],
id=-1,
max_correction_up=-1,
max_correction_down=-1,
max_correction_left=-1,
max_correction_right=-1)
return compatible_roi
def mock_compatible_path(tmp_path):
cpath = tmp_path / "myrois.json"
rois = []
for i in range(10):
rois.append(
DenseROI(id=i + 1,
x=23,
y=34,
width=128,
height=128,
valid_roi=True,
mask_matrix=[[True, True], [False, True]],
max_correction_up=12,
max_correction_down=12,
max_correction_left=12,
max_correction_right=12,
mask_image_plane=0,
exclusion_labels=['small_size', 'motion_border']))
with open(cpath, "w") as f:
json.dump(rois, f)
yield cpath