diff --git a/extensions/chaeo/test_zstack.py b/extensions/chaeo/test_zstack.py
index e423d0c5de352d7791c1774f5cf11e79a1b81731..ebe1172203187b345d9e4b16b35a5f932fd2d3ab 100644
--- a/extensions/chaeo/test_zstack.py
+++ b/extensions/chaeo/test_zstack.py
@@ -25,12 +25,13 @@ class TestZStackDerivedDataProducts(unittest.TestCase):
self.obmap = InMemoryDataAccessor(self.pxmap.data > pipeline_params['threshold'])
# write_accessor_data_to_file(output_path / 'obmap.tif', self.obmap)
- def test_zmask_makes_correct_boxes(self, mask_type='boxes', filters=None):
+ def test_zmask_makes_correct_boxes(self, mask_type='boxes', filters=None, expand_box_by=None):
zmask, meta = build_zmask_from_object_mask(
self.obmap.get_one_channel_data(0),
self.stack.get_one_channel_data(0),
mask_type=mask_type,
filters=filters,
+ expand_box_by=expand_box_by,
)
zmask_acc = InMemoryDataAccessor(zmask)
self.assertTrue(zmask_acc.is_mask())
@@ -54,4 +55,7 @@ class TestZStackDerivedDataProducts(unittest.TestCase):
return self.test_zmask_makes_correct_boxes(mask_type='contours')
def test_zmask_makes_correct_boxes_with_filters(self):
- return self.test_zmask_makes_correct_boxes(filters={'area': (1e3, 1e4)})
\ No newline at end of file
+ return self.test_zmask_makes_correct_boxes(filters={'area': (1e3, 1e4)})
+
+ def test_zmask_makes_correct_expanded_boxes(self):
+ return self.test_zmask_makes_correct_boxes(expand_box_by=(64, 2))
\ No newline at end of file
diff --git a/extensions/chaeo/zmask.py b/extensions/chaeo/zmask.py
index 0141386b82319c8dfad81d5533b296f7ffa00da4..03188688db74cff9dc168745ac0dbcb2ca61ea42 100644
--- a/extensions/chaeo/zmask.py
+++ b/extensions/chaeo/zmask.py
@@ -8,27 +8,40 @@ from model_server.accessors import GenericImageDataAccessor
# build a single boolean 3d mask (objects v. bboxes) and return bounding boxes
def build_zmask_from_object_mask(
obmask: GenericImageDataAccessor,
- stack: GenericImageDataAccessor,
+ zstack: GenericImageDataAccessor,
filters=None,
mask_type='contour',
- expand_box_by=(0, 0)
+ expand_box_by=(0, 0),
):
"""
- Given a 2D
- filters: dict of (min, max) tuples
- expand_box_by: (xy, z) pixelsf
+ Given a 2D mask of objects, build a 3D mask, where each object's z-position is determined by the index of
+ maximum intensity in z. Return this zmask and a list of each object's meta information.
+ :param obmask: GenericImageDataAccessor monochrome 2D inary mask of objects
+ :param zstack: GenericImageDataAccessor monochrome zstack of same Y, X dimension as obmask
+ :param filters: dictionary of form {attribute: (min, max)}; valid attributes are 'area' and 'solidity'
+ :param mask_type: if 'boxes', zmask is True in each object's complete bounding box; otherwise 'contours'
+ :param expand_box_by: (xy, z) expands bounding box by (xy, z) pixels except where this hits a boundary
+ :return: tuple (zmask, meta)
+ np.ndarray zmask: boolean mask of same size as stack
+ meta: List containing one Dict per object, with keys:
+ info: object's properties from skimage.measure.regionprops_table, including bounding box (y0, y1, x0, x1)
+ slice: named slice (np.s_) of (optionally) expanded bounding box
+ relative_bounding_box: bounding box (y0, y1, x0, x1) in relative frame of (optionally) expanded bounding box
+ contour: object's contour returned by skimage.measure.find_contours
+ mask: mask of object in relative frame of (optionally) expanded bounding box
"""
# validate inputs
- assert stack.chroma == 1
- assert stack.shape_dict['Z'] > 1
+ assert zstack.chroma == 1
+ assert zstack.nz > 1
assert mask_type in ('contours', 'boxes'), mask_type
assert obmask.is_mask()
assert obmask.chroma == 1
- assert obmask.shape_dict['Z'] == 1
- lamap = label(obmask.data[:, :, 0, 0])
+ assert obmask.nz == 1
+ assert zstack.hw == obmask.hw
- # build object query
+ # assign object labels and build object query
+ lamap = label(obmask.data[:, :, 0, 0])
query_str = 'label > 0' # always true
if filters is not None:
for k in filters.keys():
@@ -38,7 +51,7 @@ def build_zmask_from_object_mask(
query_str = query_str + f' & {k} > {vmin} & {k} < {vmax}'
# build dataframe of objects, assign z index to each object
- argmax = stack.data.argmax(axis=3, keepdims=True)[:, :, 0, 0]
+ argmax = zstack.data.argmax(axis=3, keepdims=True)[:, :, 0, 0]
df = (
pd.DataFrame(
regionprops_table(
@@ -63,11 +76,11 @@ def build_zmask_from_object_mask(
lut = np.zeros(lamap.max() + 1) - 1
lut[df.label] = df.zi
- # convert bounding boxes to slices
+ # convert bounding boxes to numpy slice objects
ebxy, ebz = expand_box_by
- h, w, c, nz = stack.shape
+ h, w, c, nz = zstack.shape
- boxes = []
+ meta = []
for ob in df.itertuples(name='LabeledObject'):
y0 = max(ob.y0 - ebxy, 0)
y1 = min(ob.y1 + ebxy, h - 1)
@@ -84,7 +97,6 @@ def build_zmask_from_object_mask(
'x1': ob.x1 - x0,
}
- # sl = np.s_[z0: z1 + 1, y0: y1, x0: x1]
sl = np.s_[y0: y1, x0: x1, 0, z0: z1 + 1]
# compute contours
@@ -92,7 +104,7 @@ def build_zmask_from_object_mask(
contour = find_contours(obmask)
mask = obmask[ob.y0: ob.y1, ob.x0: ob.x1]
- boxes.append({
+ meta.append({
'info': ob,
'slice': sl,
'relative_bounding_box': rbb,
@@ -101,7 +113,7 @@ def build_zmask_from_object_mask(
})
# build mask z-stack
- zi_st = np.zeros(stack.shape, dtype='bool')
+ zi_st = np.zeros(zstack.shape, dtype='bool')
if mask_type == 'contours':
zi_map = (lut[lamap] + 1.0).astype('int')
idxs = np.array(zi_map) - 1
@@ -116,8 +128,8 @@ def build_zmask_from_object_mask(
zi_st[:, :, :, -1][lamap == 0] = 0
elif mask_type == 'boxes':
- for bb in boxes:
+ for bb in meta:
sl = bb['slice']
zi_st[sl] = 1
- return zi_st, boxes
\ No newline at end of file
+ return zi_st, meta
\ No newline at end of file
diff --git a/model_server/accessors.py b/model_server/accessors.py
index d9211c4876c21e24ac4330af4de5806193a215a5..7183036a63c653328315761e89c066a30fc64d64 100644
--- a/model_server/accessors.py
+++ b/model_server/accessors.py
@@ -39,6 +39,22 @@ class GenericImageDataAccessor(ABC):
c = int(channel)
return InMemoryDataAccessor(self.data[:, :, c:(c+1), :])
+ @property
+ def dtype(self):
+ return self.data.dtype
+
+ @property
+ def hw(self):
+ """
+ Get data height and width as a tuple
+ :return: tuple of (Y, X) dimensions
+ """
+ return self.shape_dict['Y'], self.shape_dict['X']
+
+ @property
+ def nz(self):
+ return self.shape_dict['Z']
+
@property
def data(self):
"""