From 3e8ca3db67c43fff1e6f69cc90bccb60a9734ee5 Mon Sep 17 00:00:00 2001
From: Christopher Rhodes <christopher.rhodes@embl.de>
Date: Sat, 3 Feb 2024 15:48:14 +0100
Subject: [PATCH] Moved dataframe logic outside of constructor; losing
alignment of patch stacks df
---
.../extensions/chaeo/tests/test_zstack.py | 10 +-
model_server/extensions/chaeo/zmask.py | 120 ++++++++++++------
2 files changed, 88 insertions(+), 42 deletions(-)
diff --git a/model_server/extensions/chaeo/tests/test_zstack.py b/model_server/extensions/chaeo/tests/test_zstack.py
index a925a503..aa4deb86 100644
--- a/model_server/extensions/chaeo/tests/test_zstack.py
+++ b/model_server/extensions/chaeo/tests/test_zstack.py
@@ -123,16 +123,16 @@ class TestZStackDerivedDataProducts(unittest.TestCase):
id_map = get_label_ids(self.seg_mask)
roiset = RoiSet(id_map, self.stack_ch_pa, params=RoiSetMetaParams(mask_type='boxes'))
- df = roiset.df
+ df = roiset.get_df(filters=None)
from model_server.extensions.chaeo.zmask import project_stack_from_focal_points
- dff = df[df['keeper']]
+ # dff = df[df['keeper']]
img = project_stack_from_focal_points(
- dff['centroid-0'].to_numpy(),
- dff['centroid-1'].to_numpy(),
- dff['zi'].to_numpy(),
+ df['centroid-0'].to_numpy(),
+ df['centroid-1'].to_numpy(),
+ df['zi'].to_numpy(),
self.stack,
degree=4,
)
diff --git a/model_server/extensions/chaeo/zmask.py b/model_server/extensions/chaeo/zmask.py
index 587c801b..9034e1ce 100644
--- a/model_server/extensions/chaeo/zmask.py
+++ b/model_server/extensions/chaeo/zmask.py
@@ -15,7 +15,7 @@ from model_server.base.process import pad, rescale, resample_to_8bit
from model_server.extensions.chaeo.annotators import draw_boxes_on_3d_image
from model_server.extensions.chaeo.products import export_patches_from_zstack, export_multichannel_patches_from_zstack
-from extensions.chaeo.params import RoiSetMetaParams, RoiSetExportParams
+from extensions.chaeo.params import RoiFilter, RoiSetMetaParams, RoiSetExportParams
from model_server.extensions.chaeo.accessors import MonoPatchStack
from model_server.extensions.chaeo.process import mask_largest_object
from model_server.extensions.chaeo.products import get_patches_from_zmask_meta, get_patch_masks, export_patch_masks_from_zstack
@@ -32,17 +32,57 @@ 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}'
+ # # 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.make_df(self.acc_raw, self.acc_obj_ids)
+ # remaining zmask_meta write ops
+ self.zmask_meta, _, self.interm = build_zmask_from_object_mask(
+ acc_obj_ids,
+ acc_raw,
+ self.get_df(filters=params.filters),
+ params=params,
+ )
+
+ self.count = len(self.zmask_meta)
+ self.object_id_labels = self.interm['label_map']
+ self.object_class_map = None
+
+ @staticmethod
+ def make_df(acc_raw, acc_obj_ids):
# build dataframe of objects, assign z index to each object
argmax = acc_raw.data.argmax(axis=3, keepdims=True)[:, :, 0, 0].astype('uint16')
df = (
@@ -54,27 +94,26 @@ class RoiSet(object):
)
)
.rename(
- columns={'bbox-0': 'y0', 'bbox-1': 'x0', 'bbox-2': 'y1', 'bbox-3': 'x1',}
+ 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
+ return df
- # remaining zmask_meta write ops
- self.zmask_meta, _, self.interm = build_zmask_from_object_mask(
- acc_obj_ids,
- acc_raw,
- df,
- params=params,
- )
- self.acc_obj_ids = acc_obj_ids
- self.acc_raw = acc_raw
- self.count = len(self.zmask_meta)
- self.object_id_labels = self.interm['label_map']
- self.object_class_map = None
+ def get_df(self, filters: RoiFilter = None) -> pd.DataFrame:
+ query_str = 'label > 0' # always true
+ if filters is not None: # parse filters
+ 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}'
+ # df.loc[df.query(query_str).index, 'keeper'] = True
+ return self._df.loc[self._df.query(query_str).index, :]
+ def add_df_col(self, name, se: pd.Series) -> None:
+ self._df[name] = se
def get_multichannel_projection(self): # TODO: document and test
dff = self.df[self.df['keeper']]
@@ -121,7 +160,7 @@ class RoiSet(object):
def get_slices(self):
return [zm.slice for zm in self.zmask_meta]
- def get_zmask(self, mask_type='boxes'):
+ def get_zmask(self, mask_type='boxes', filters: RoiFilter = None):
"""
Return a mask of same dimensionality as raw data
@@ -135,7 +174,8 @@ class RoiSet(object):
# make an object map where label is replaced by focus position in stack and background is -1
lut = np.zeros(lamap.max() + 1) - 1
- lut[self.df.label] = self.df.zi
+ df = self.get_df(filters=filters)
+ lut[df.label] = df.zi
if mask_type == 'contours':
zi_map = (lut[lamap] + 1.0).astype('int')
@@ -157,24 +197,30 @@ class RoiSet(object):
return zi_st
- def classify_by(self, channel, object_classification_model: InstanceSegmentationModel):
+ def classify_by(self, channel, object_classification_model: InstanceSegmentationModel, filters: RoiFilter = None):
+ # adds a column to self._df
# do this on a patch basis, i.e. only one object per frame
obmap_patches = object_classification_model.label_instance_class(
- self.get_raw_patches(channel),
+ self.get_raw_patches(channel), # TODO: enforce df index
self.get_patch_masks()
)
lamap = self.object_id_labels
om = np.zeros(lamap.shape, dtype=lamap.dtype)
- self.df['instance_class'] = np.nan
+ # self.df['instance_class'] = np.nan
+
+ df = self.get_df(filters=filters)
+ idx = df.index
+ se = pd.Series(data=np.nan, index=idx)
# assign labels to object map:
- for ii in range(0, self.count):
- object_id = self.zmask_meta[ii]['info'].label
- result_patch = mask_largest_object(obmap_patches.iat(ii))
+ for i in idx:
+ # object_id = self.zmask_meta[i]['info'].label
+ object_id = df.loc[i, 'label']
+ result_patch = mask_largest_object(obmap_patches.iat(i))
object_class = np.unique(result_patch)[1]
om[self.object_id_labels == object_id] = object_class
- self.df[object_id, 'instance_class'] = object_class
+ se.loc[i] = object_class
self.object_class_map = InMemoryDataAccessor(om)
@@ -262,7 +308,7 @@ def build_zmask_from_object_mask(
h, w, c, nz = zstack.shape
meta = []
- for ob in df[df['keeper']].itertuples(name='LabeledObject'):
+ 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)
--
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