diff --git a/model_server/extensions/chaeo/zmask.py b/model_server/extensions/chaeo/zmask.py
index 7e852e6d0d41a277168a1f792d7f8bd794dc63a5..557bdddb9dc763b689b65ccff34e1c6b33598124 100644
--- a/model_server/extensions/chaeo/zmask.py
+++ b/model_server/extensions/chaeo/zmask.py
@@ -32,62 +32,14 @@ class RoiSet(object):
acc_raw: GenericImageDataAccessor,
params: RoiSetMetaParams = RoiSetMetaParams(),
):
- # # parse filters
- # filters = params.filters
- # query_str = 'label > 0' # always true
- # if filters is not None:
- # for k, val in filters.dict(exclude_unset=True).items():
- # assert k in ('area', 'solidity')
- # vmin = val['min']
- # vmax = val['max']
- # assert vmin >= 0
- # query_str = query_str + f' & {k} > {vmin} & {k} < {vmax}'
- #
- # # build dataframe of objects, assign z index to each object
- # argmax = acc_raw.data.argmax(axis=3, keepdims=True)[:, :, 0, 0].astype('uint16')
- # df = (
- # pd.DataFrame(
- # regionprops_table(
- # acc_obj_ids,
- # intensity_image=argmax,
- # properties=('label', 'area', 'intensity_mean', 'solidity', 'bbox', 'centroid')
- # )
- # )
- # .rename(
- # columns={'bbox-0': 'y0', 'bbox-1': 'x0', 'bbox-2': 'y1', 'bbox-3': 'x1',}
- # )
- # )
- # df['zi'] = df['intensity_mean'].round().astype('int')
- # df['keeper'] = False
- # df.loc[df.query(query_str).index, 'keeper'] = True
- # self.df = df
-
- # remaining zmask_meta write ops
-
self.acc_obj_ids = acc_obj_ids
self.acc_raw = acc_raw
-
-
self._df = self.filter_df(
self.make_df(self.acc_raw, self.acc_obj_ids, expand_box_by=params.expand_box_by),
params.filters,
)
- # self._df = self.make_slices(
- # self._df,
- # expand_box_by=params.expand_box_by,
- # shape=acc_raw.shape
- # )
-
- # remaining zmask_meta write ops
- # self.zmask_meta, _, self.interm = build_zmask_from_object_mask(
- # acc_obj_ids,
- # acc_raw,
- # self.get_df(),
- # params=params,
- # )
-
# temporarily build zmask meta here
meta = []
for ob in self.get_df().itertuples(name='LabeledObject'):
@@ -206,19 +158,6 @@ class RoiSet(object):
def get_object_mask_by_class(self, class_id):
return self.object_id_labels == class_id
-
-
- # def loc_mask(self, i):
- # # compute contours
- # # obmask = (lamap == ob.label) # TODO: on-the-fly
- # # contour = find_contours(obmask) # TODO: on-the-fly
- # # mask = obmask[ob.y0: ob.y1, ob.x0: ob.x1]
- # ob = self.df.loc[i]
- # return
- #
- # def loc_contour(self, i):
- # pass
-
def get_patch_masks(self, **kwargs) -> MonoPatchStack:
return get_patch_masks(self, **kwargs)
@@ -339,93 +278,6 @@ class RoiSet(object):
self.df.to_csv(dfpa, index=False)
-def build_zmask_from_object_mask(
- obmask: GenericImageDataAccessor,
- zstack: GenericImageDataAccessor,
- df,
- params: RoiSetMetaParams = RoiSetMetaParams(),
-):
- # """
- # 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 2D map of objects IDs
- # :param zstack: GenericImageDataAccessor monochrome zstack of same Y, X dimension as obmask
- # :param params: RoiSetMetaParams
- # filters: dictionary of form {attribute: (min, max)}; valid attributes are 'area' and 'solidity'
- # mask_type: if 'boxes', zmask is True in each object's complete bounding box; otherwise 'contours'
- # expand_box_by: (xy, z) expands bounding box by (xy, z) pixels except where this hits a boundary
- # :return: tuple (zmask, meta)
- # np.ndarray:
- # boolean mask of same size as stack
- # 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
- # pd.DataFrame: objects, including bounding, box information after filtering
- # Dict of intermediate image products:
- # label_map: np.ndarray (h x w) where each unique object has an integer label
- # argmax: np.ndarray (h x w x 1 x 1) z-index of highest intensity in zstack
- # """
- filters = params.filters
- expand_box_by = params.expand_box_by
- # validate inputs
- assert zstack.hw == obmask.shape
-
- lamap = obmask
-
- argmax = zstack.data.argmax(axis=3, keepdims=True)[:, :, 0, 0].astype('uint16')
-
- # convert bounding boxes to numpy slice objects
- ebxy, ebz = expand_box_by
- h, w, c, nz = zstack.shape
-
- meta = []
- for ob in df.itertuples(name='LabeledObject'):
- y0 = max(ob.y0 - ebxy, 0)
- y1 = min(ob.y1 + ebxy, h)
- x0 = max(ob.x0 - ebxy, 0)
- x1 = min(ob.x1 + ebxy, w)
- z0 = max(ob.zi - ebz, 0)
- z1 = min(ob.zi + ebz, nz)
-
- # relative bounding box positions
- rbb = { # TODO: just put in the DF
- 'y0': ob.y0 - y0,
- 'y1': ob.y1 - y0,
- 'x0': ob.x0 - x0,
- 'x1': ob.x1 - x0,
- }
-
- sl = np.s_[y0: y1, x0: x1, :, z0: z1 + 1] # TODO: on-the-fly in RoiSet, given DF
-
- # compute contours
- obmask = (lamap == ob.label) # TODO: on-the-fly
- contour = find_contours(obmask) # TODO: on-the-fly
- mask = obmask[ob.y0: ob.y1, ob.x0: ob.x1]
-
- assert rbb['x1'] <= (x1 - x0)
- assert rbb['y1'] <= (y1 - y0)
-
- meta.append({
- 'df_index': ob.Index,
- 'info': ob,
- # 'slice': sl,
- 'relative_bounding_box': rbb, # TODO: put in DF
- 'contour': contour, # TODO: delegate to getter
- 'mask': mask # TODO: delegate to getter
- })
-
- # return intermediate image arrays
- interm = {
- 'label_map': lamap,
- 'argmax': argmax,
- }
-
- return meta, df, interm
-
-
def project_stack_from_focal_points(
xx: np.ndarray,
yy: np.ndarray,