diff --git a/analysis/validation/check_validation.py b/analysis/validation/check_validation.py
index 7e59d724fd1c3c231301abd4cc52a50642de158d..3007aa6ee9578a4114fd6c57fb66c9562c4950a4 100644
--- a/analysis/validation/check_validation.py
+++ b/analysis/validation/check_validation.py
@@ -1,27 +1,27 @@
import h5py
from heimdall import view, to_source
+from scripts.segmentation.validation import eval_cells, eval_nuclei, get_ignore_seg_ids
def check_cell_evaluation():
from scripts.segmentation.validation.eval_cells import (eval_slice,
- get_ignore_seg_ids,
get_bounding_box)
- praw = '../../data'
- pseg = '../../data'
- pann = '../../data'
+ praw = '../../data/rawdata/sbem-6dpf-1-whole-raw.h5'
+ pseg = '../../data/0.5.5/segmentations/sbem-6dpf-1-whole-segmented-cells-labels.h5'
+ pann = '../../data/rawdata/evaluation/validation_annotations.h5'
- table_path = '../../data'
+ table_path = '../../data/0.5.5/tables/sbem-6dpf-1-whole-segmented-cells-labels/regions.csv'
ignore_seg_ids = get_ignore_seg_ids(table_path)
with h5py.File(pseg, 'r') as fseg, h5py.File(pann, 'r') as fann:
ds_seg = fseg['t00000/s00/0/cells']
- ds_ann = fann['xy/']
+ ds_ann = fann['xy/1000']
print("Run evaluation ...")
res, masks = eval_slice(ds_seg, ds_ann, ignore_seg_ids, min_radius=16,
- return_maksks=True)
- fm, fs = masks['false_merges'], masks['false_splits']
+ return_masks=True)
+ fm, fs = masks['merges'], masks['splits']
print()
print("Eval result")
print(res)
@@ -30,18 +30,38 @@ def check_cell_evaluation():
print("Load raw data ...")
bb = get_bounding_box(ds_ann)
with h5py.File(praw, 'r') as f:
- raw = f['t00000/s00/1/cells'][bb]
+ raw = f['t00000/s00/1/cells'][bb].squeeze()
print("Load seg data ...")
- seg = ds_seg[bb].squeeze()
+ seg = ds_seg[bb].squeeze().astype('uint32')
view(to_source(raw, name='raw'), to_source(seg, name='seg'),
to_source(fm, name='merges'), to_source(fs, name='splits'))
-# def check_nucleus_evaluation():
-# eval_nuclei()
+def eval_all_cells():
+ pseg = '../../data/0.5.5/segmentations/sbem-6dpf-1-whole-segmented-cells-labels.h5'
+ pann = '../../data/rawdata/evaluation/validation_annotations.h5'
+
+ table_path = '../../data/0.5.5/tables/sbem-6dpf-1-whole-segmented-cells-labels/regions.csv'
+ ignore_seg_ids = get_ignore_seg_ids(table_path)
+
+ res = eval_cells(pseg, 't00000/s00/0/cells', pann,
+ ignore_seg_ids=ignore_seg_ids)
+ print("Eval result:")
+ print(res)
+
+
+# TODO
+def check_nucleus_evaluation():
+ pass
+
+
+# TODO
+def eval_all_nulcei():
+ eval_nuclei()
if __name__ == '__main__':
- check_cell_evaluation()
+ # check_cell_evaluation()
+ eval_all_cells()
diff --git a/scripts/segmentation/validation/__init__.py b/scripts/segmentation/validation/__init__.py
index 5d26b4c9b59d49a28a6cead0bfa3a46b40960ab6..5907a9b21db135ea2e45a270e1e770213542bccb 100644
--- a/scripts/segmentation/validation/__init__.py
+++ b/scripts/segmentation/validation/__init__.py
@@ -1,2 +1,2 @@
-from .eval_cells import eval_cells
+from .eval_cells import eval_cells, get_ignore_seg_ids
from .eval_nuclei import eval_nuclei
diff --git a/scripts/segmentation/validation/eval_cells.py b/scripts/segmentation/validation/eval_cells.py
index 7560650dc9e8a2dce93615536466726392c3ae6f..908d4074920531a548c6c8cedac716f2c2d0e6a3 100644
--- a/scripts/segmentation/validation/eval_cells.py
+++ b/scripts/segmentation/validation/eval_cells.py
@@ -19,7 +19,7 @@ def eval_slice(ds_seg, ds_ann, ignore_seg_ids, min_radius,
bb = get_bounding_box(ds_ann)
annotations = ds_ann[:]
- seg = ds_seg[bb].squeeze()
+ seg = ds_seg[bb].squeeze().astype('uint32')
assert annotations.shape == seg.shape
seg_eval = vigra.analysis.labelImageWithBackground(seg)
@@ -39,7 +39,7 @@ def eval_slice(ds_seg, ds_ann, ignore_seg_ids, min_radius,
def get_ignore_seg_ids(table_path, ignore_names=['cuticle', 'neuropil', 'yolk']):
- table = pd.read_csv(table_path)
+ table = pd.read_csv(table_path, sep='\t')
ignore_seg_ids = []
for name in ignore_names:
col = table[name].values.astype('uint8')
@@ -48,16 +48,25 @@ def get_ignore_seg_ids(table_path, ignore_names=['cuticle', 'neuropil', 'yolk'])
return ignore_seg_ids
+def to_scores(eval_res):
+ n = float(eval_res['n_annotations'] - eval_res['n_unmatched'])
+ n_splits = eval_res['n_splits']
+ n_merges = eval_res['n_merged_annotations']
+ return n_merges / n, n_splits / n
+
+
def eval_cells(seg_path, seg_key,
- annotation_path, annotation_key,
+ annotation_path, annotation_key=None,
ignore_seg_ids=None, min_radius=16):
- """ Evaluate the cell segmentation.
+ """ Evaluate the cell segmentation by computing
+ the percentage of falsely merged and split cell annotations
+ in manually annotated validation slices.
"""
eval_res = {}
- with open_file(seg_path, 'r') as f_seg, open_file(annotation_path) as f_ann:
+ with open_file(seg_path, 'r') as f_seg, open_file(annotation_path, 'r') as f_ann:
ds_seg = f_seg[seg_key]
- g = f_ann[annotation_key]
+ g = f_ann if annotation_key is None else f_ann[annotation_key]
def visit_annotation(name, node):
nonlocal eval_res
@@ -65,9 +74,11 @@ def eval_cells(seg_path, seg_key,
print("Evaluating:", name)
res = eval_slice(ds_seg, node, ignore_seg_ids, min_radius)
eval_res = merge_evaluations(res, eval_res)
+ # for debugging
+ # print("current eval:", eval_res)
else:
print("Group:", name)
g.visititems(visit_annotation)
- return eval_res
+ return to_scores(eval_res)
diff --git a/scripts/segmentation/validation/evaluate_annotations.py b/scripts/segmentation/validation/evaluate_annotations.py
index 73aaf8b3daa30cafebcb47c6552068490e7944e5..4fda8351ef506a65e8748ad5562421d7fbe7e75c 100644
--- a/scripts/segmentation/validation/evaluate_annotations.py
+++ b/scripts/segmentation/validation/evaluate_annotations.py
@@ -65,7 +65,7 @@ def evaluate_annotations(seg, fg_annotations, bg_annotations,
# unless we have overlap with a background annotation
# or are in the filter ids
if this_labels.size == 0:
- if not has_bg_label and radii > min_radius:
+ if not has_bg_label and radii[seg_id] > min_radius:
unmatched_ids.append(seg_id)
# one label -> this seg-id seems to be well matched
@@ -80,17 +80,26 @@ def evaluate_annotations(seg, fg_annotations, bg_annotations,
# increase the segment count
n_segments += 1
- # false splits = unmatched seg-ids and seg-ids corresponding to annotations that were matched
- # more than once
+ # false splits = unmatched seg-ids and seg-ids corresponding to annotations
+ # that were matched more than once
+
+ # first, turn matched ids and labels into numpy arrays
matched_labels = list(matched_ids.values())
matched_ids = np.array(list(matched_ids.keys()), dtype='uint32')
- matched_labels, matched_counts = np.unique(matched_labels, return_counts=True)
+
+ # find the unique matched labels, their counts and the mapping to the original array
+ matched_labels, inv_mapping, matched_counts = np.unique(matched_labels, return_inverse=True, return_counts=True)
+ matched_counts = matched_counts[inv_mapping]
+ assert len(matched_counts) == len(matched_ids)
+
+ # combine unmatched ids and ids matched more than once
+ unmatched_ids = np.array(unmatched_ids, dtype='uint32')
false_split_ids = np.concatenate([unmatched_ids, matched_ids[matched_counts > 1]])
# false merge annotations = overmatched ids
false_merge_ids = list(overmatched_ids.keys())
- false_merge_labels = np.array([lab for overmatched in overmatched_ids
- for lab in overmatched.values()], dtype='uint32')
+ false_merge_labels = np.array([lab for overmatched in overmatched_ids.values()
+ for lab in overmatched], dtype='uint32')
# find label ids that were not matched
all_matched = np.concatenate([matched_labels, false_merge_labels])
@@ -106,8 +115,10 @@ def evaluate_annotations(seg, fg_annotations, bg_annotations,
'n_merged_ids': len(false_merge_ids),
'n_unmatched': len(unmatched_labels)}
- ret = (metrics,)
+ if not return_masks and not return_ids:
+ return metrics
+ ret = (metrics,)
if return_masks:
fs_mask = np.isin(seg, false_split_ids).astype('uint32')
fm_mask = np.isin(seg, false_merge_ids).astype('uint32')