diff --git a/model_server/base/rois/__init__.py b/model_server/base/rois/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/model_server/base/rois/df.py b/model_server/base/rois/df.py
new file mode 100644
index 0000000000000000000000000000000000000000..69ca70f89bd07aa8a2d0f14c21f49be1537df031
--- /dev/null
+++ b/model_server/base/rois/df.py
@@ -0,0 +1,124 @@
+import itertools
+
+import numpy as np
+import pandas as pd
+
+def filter_df(df: pd.DataFrame, filters: dict = {}) -> pd.DataFrame:
+ query_str = 'label > 0' # always true
+ if filters is not None: # parse filters
+ for k, val in filters.items():
+ assert k in ('area', 'diag', 'min_hw')
+ if val is None:
+ continue
+ vmin = val['min']
+ vmax = val['max']
+ assert vmin >= 0
+ query_str = query_str + f' & {k} > {vmin} & {k} < {vmax}'
+ return df.loc[df.bounding_box.query(query_str).index, :]
+
+
+def filter_df_overlap_bbox(df1: pd.DataFrame, df2: pd.DataFrame = None) -> pd.DataFrame:
+ """
+ If passed a single DataFrame, return the subset whose bounding boxes overlap in 3D space. If passed two DataFrames,
+ return the subset where a ROI in the first overlaps a ROI in the second. May return duplicates entries where a ROI
+ overlaps with multiple neighbors.
+ :param df1: DataFrame with potentially overlapping bounding boxes
+ :param df2: (optional) second DataFrame
+ :return DataFrame describing subset of overlapping ROIs
+ bbox_overlaps_with: index of ROI that overlaps
+ bbox_intersec: pixel area of intersecting region
+ """
+
+ def _compare(r0, r1):
+ olx = (r0.x0 < r1.x1) and (r0.x1 > r1.x0)
+ oly = (r0.y0 < r1.y1) and (r0.y1 > r1.y0)
+ olz = (r0.zi == r1.zi)
+ return olx and oly and olz
+
+ def _intersec(r0, r1):
+ return (r0.x1 - r1.x0) * (r0.y1 - r1.y0)
+
+ first = []
+ second = []
+ intersec = []
+
+ if df2 is not None:
+ for pair in itertools.product(df1.index, df2.index):
+ if _compare(
+ df1.bounding_box.loc[pair[0]],
+ df2.bounding_box.loc[pair[1]]
+ ):
+ first.append(pair[0])
+ second.append(pair[1])
+ intersec.append(
+ _intersec(
+ df1.bounding_box.loc[pair[0]],
+ df2.bounding_box.loc[pair[1]]
+ )
+ )
+ else:
+ for pair in itertools.combinations(df1.index, 2):
+ if _compare(
+ df1.bounding_box.loc[pair[0]],
+ df1.bounding_box.loc[pair[1]]
+ ):
+ first.append(pair[0])
+ second.append(pair[1])
+ first.append(pair[1])
+ second.append(pair[0])
+ isc = _intersec(
+ df1.bounding_box.loc[pair[0]],
+ df1.bounding_box.loc[pair[1]]
+ )
+ intersec.append(isc)
+ intersec.append(isc)
+
+ sdf = df1.bounding_box.loc[first]
+ sdf.loc[:, 'overlaps_with'] = second
+ sdf.loc[:, 'bbox_intersec'] = intersec
+ return sdf
+
+
+def filter_df_overlap_seg(df1: pd.DataFrame, df2: pd.DataFrame = None) -> pd.DataFrame:
+ """
+ If passed a single DataFrame, return the subset whose segmentations overlap in 3D space. If passed two DataFrames,
+ return the subset where a ROI in the first overlaps a ROI in the second. May return duplicates entries where a ROI
+ overlaps with multiple neighbors.
+ :param df1: DataFrame with potentially overlapping bounding boxes
+ :param df2: (optional) second DataFrame
+ :return DataFrame describing subset of overlapping ROIs
+ seg_overlaps_with: index of ROI that overlaps
+ seg_intersec: pixel area of intersecting region
+ seg_iou: intersection over union
+ """
+
+ dfbb = filter_df_overlap_bbox(df1, df2)
+
+ def _overlap_seg(r):
+ roi1 = df1.loc[r.name]
+ if df2 is not None:
+ roi2 = df2.loc[r.overlaps_with]
+ else:
+ roi2 = df1.loc[r.overlaps_with]
+ bb1 = roi1.bounding_box
+ bb2 = roi2.bounding_box
+ ex0 = min(bb1.x0, bb2.x0, bb1.x1, bb2.x1)
+ ew = max(bb1.x0, bb2.x0, bb1.x1, bb2.x1) - ex0
+ ey0 = min(bb1.y0, bb2.y0, bb1.y1, bb2.y1)
+ eh = max(bb1.y0, bb2.y0, bb1.y1, bb2.y1) - ey0
+ emask = np.zeros((eh, ew), dtype='uint8')
+ sl1 = np.s_[(bb1.y0 - ey0): (bb1.y1 - ey0), (bb1.x0 - ex0): (bb1.x1 - ex0)]
+ sl2 = np.s_[(bb2.y0 - ey0): (bb2.y1 - ey0), (bb2.x0 - ex0): (bb2.x1 - ex0)]
+ emask[sl1] = roi1.masks.binary_mask
+ emask[sl2] = emask[sl2] + roi2.masks.binary_mask
+ return emask
+
+ emasks = dfbb.apply(_overlap_seg, axis=1)
+ dfbb['seg_overlaps'] = emasks.apply(lambda x: np.any(x > 1))
+ dfbb['seg_intersec'] = emasks.apply(lambda x: (x == 2).sum())
+ dfbb['seg_iou'] = emasks.apply(lambda x: (x == 2).sum() / (x > 0).sum())
+ return dfbb
+
+
+def is_df_3d(df: pd.DataFrame) -> bool:
+ return 'z0' in df.bounding_box.columns and 'z1' in df.bounding_box.columns
diff --git a/model_server/base/roiset.py b/model_server/base/roiset.py
index 244acc651d6125d86d64c87f10e7e9af99da8850..bf729a6a419d4b1fd9bec2a39799e63dca4327e7 100644
--- a/model_server/base/roiset.py
+++ b/model_server/base/roiset.py
@@ -1,5 +1,4 @@
from collections import OrderedDict
-import itertools
from math import sqrt, floor
from pathlib import Path
from typing import Dict, List, Union
@@ -23,6 +22,7 @@ from .process import get_safe_contours, pad, rescale, make_rgb
from .annotators import draw_box_on_patch, draw_contours_on_patch, draw_boxes_on_3d_image
from .accessors import generate_file_accessor, PatchStack
from .process import mask_largest_object
+from .rois.df import filter_df, filter_df_overlap_seg, is_df_3d
class PatchParams(BaseModel):
@@ -140,127 +140,6 @@ def focus_metrics():
}
-def filter_df(df: pd.DataFrame, 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', 'diag', 'min_hw')
- if val is None:
- continue
- vmin = val['min']
- vmax = val['max']
- assert vmin >= 0
- query_str = query_str + f' & {k} > {vmin} & {k} < {vmax}'
- return df.loc[df.bounding_box.query(query_str).index, :]
-
-
-def filter_df_overlap_bbox(df1: pd.DataFrame, df2: pd.DataFrame = None) -> pd.DataFrame:
- """
- If passed a single DataFrame, return the subset whose bounding boxes overlap in 3D space. If passed two DataFrames,
- return the subset where a ROI in the first overlaps a ROI in the second. May return duplicates entries where a ROI
- overlaps with multiple neighbors.
- :param df1: DataFrame with potentially overlapping bounding boxes
- :param df2: (optional) second DataFrame
- :return DataFrame describing subset of overlapping ROIs
- bbox_overlaps_with: index of ROI that overlaps
- bbox_intersec: pixel area of intersecting region
- """
-
- def _compare(r0, r1):
- olx = (r0.x0 < r1.x1) and (r0.x1 > r1.x0)
- oly = (r0.y0 < r1.y1) and (r0.y1 > r1.y0)
- olz = (r0.zi == r1.zi)
- return olx and oly and olz
-
- def _intersec(r0, r1):
- return (r0.x1 - r1.x0) * (r0.y1 - r1.y0)
-
- first = []
- second = []
- intersec = []
-
- if df2 is not None:
- for pair in itertools.product(df1.index, df2.index):
- if _compare(
- df1.bounding_box.loc[pair[0]],
- df2.bounding_box.loc[pair[1]]
- ):
- first.append(pair[0])
- second.append(pair[1])
- intersec.append(
- _intersec(
- df1.bounding_box.loc[pair[0]],
- df2.bounding_box.loc[pair[1]]
- )
- )
- else:
- for pair in itertools.combinations(df1.index, 2):
- if _compare(
- df1.bounding_box.loc[pair[0]],
- df1.bounding_box.loc[pair[1]]
- ):
- first.append(pair[0])
- second.append(pair[1])
- first.append(pair[1])
- second.append(pair[0])
- isc = _intersec(
- df1.bounding_box.loc[pair[0]],
- df1.bounding_box.loc[pair[1]]
- )
- intersec.append(isc)
- intersec.append(isc)
-
- sdf = df1.bounding_box.loc[first]
- sdf.loc[:, 'overlaps_with'] = second
- sdf.loc[:, 'bbox_intersec'] = intersec
- return sdf
-
-
-def filter_df_overlap_seg(df1: pd.DataFrame, df2: pd.DataFrame = None) -> pd.DataFrame:
- """
- If passed a single DataFrame, return the subset whose segmentations overlap in 3D space. If passed two DataFrames,
- return the subset where a ROI in the first overlaps a ROI in the second. May return duplicates entries where a ROI
- overlaps with multiple neighbors.
- :param df1: DataFrame with potentially overlapping bounding boxes
- :param df2: (optional) second DataFrame
- :return DataFrame describing subset of overlapping ROIs
- seg_overlaps_with: index of ROI that overlaps
- seg_intersec: pixel area of intersecting region
- seg_iou: intersection over union
- """
-
- dfbb = filter_df_overlap_bbox(df1, df2)
-
- def _overlap_seg(r):
- roi1 = df1.loc[r.name]
- if df2 is not None:
- roi2 = df2.loc[r.overlaps_with]
- else:
- roi2 = df1.loc[r.overlaps_with]
- bb1 = roi1.bounding_box
- bb2 = roi2.bounding_box
- ex0 = min(bb1.x0, bb2.x0, bb1.x1, bb2.x1)
- ew = max(bb1.x0, bb2.x0, bb1.x1, bb2.x1) - ex0
- ey0 = min(bb1.y0, bb2.y0, bb1.y1, bb2.y1)
- eh = max(bb1.y0, bb2.y0, bb1.y1, bb2.y1) - ey0
- emask = np.zeros((eh, ew), dtype='uint8')
- sl1 = np.s_[(bb1.y0 - ey0): (bb1.y1 - ey0), (bb1.x0 - ex0): (bb1.x1 - ex0)]
- sl2 = np.s_[(bb2.y0 - ey0): (bb2.y1 - ey0), (bb2.x0 - ex0): (bb2.x1 - ex0)]
- emask[sl1] = roi1.masks.binary_mask
- emask[sl2] = emask[sl2] + roi2.masks.binary_mask
- return emask
-
- emasks = dfbb.apply(_overlap_seg, axis=1)
- dfbb['seg_overlaps'] = emasks.apply(lambda x: np.any(x > 1))
- dfbb['seg_intersec'] = emasks.apply(lambda x: (x == 2).sum())
- dfbb['seg_iou'] = emasks.apply(lambda x: (x == 2).sum() / (x > 0).sum())
- return dfbb
-
-
-def is_df_3d(df: pd.DataFrame) -> bool:
- return 'z0' in df.bounding_box.columns and 'z1' in df.bounding_box.columns
-
-
def make_df_from_object_ids(
acc_raw,
acc_obj_ids,
@@ -360,7 +239,8 @@ def make_df_from_object_ids(
df = df.set_index('label')
insert_level(df, 'bounding_box')
df = df_insert_slices(df, acc_raw.shape_dict, expand_box_by)
- df_fil = filter_df(df, filters)
+ filters_dict = {} if filters is None else filters.dict(exclude_unset=True)
+ df_fil = filter_df(df, filters_dict)
df_fil['masks', 'binary_mask'] = df_fil.bounding_box.apply(
_make_binary_mask,
axis=1,
diff --git a/tests/base/test_roiset.py b/tests/base/test_roiset.py
index fc017505e8a6b30e38def265b60e409de3aa22ca..ade3a5efe5c713ee5dc4b3980b27e34132e55416 100644
--- a/tests/base/test_roiset.py
+++ b/tests/base/test_roiset.py
@@ -7,7 +7,8 @@ from pathlib import Path
import pandas as pd
from model_server.base.process import smooth
-from model_server.base.roiset import filter_df_overlap_bbox, filter_df_overlap_seg, IntensityThresholdInstanceMaskSegmentationModel, read_roiset_df, RoiSet, RoiSetExportParams, RoiSetMetaParams
+from model_server.base.roiset import IntensityThresholdInstanceMaskSegmentationModel, read_roiset_df, RoiSet, RoiSetExportParams, RoiSetMetaParams
+from model_server.base.rois.df import filter_df_overlap_bbox, filter_df_overlap_seg
from model_server.base.accessors import generate_file_accessor, InMemoryDataAccessor, PatchStack, write_accessor_data_to_file
import model_server.conf.testing as conf
from model_server.conf.testing import DummyInstanceMaskSegmentationModel