Removed external build_zmask_meta function

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,