From ecf4bdfed8789aa2571b8b360414fdc1683bf29c Mon Sep 17 00:00:00 2001
From: Christopher Rhodes <christopher.rhodes@embl.de>
Date: Mon, 16 Oct 2023 16:04:16 +0200
Subject: [PATCH] Populate input data in object classification model
---
...fer_labels_to_ilastik_object_classifier.py | 107 +++++++++++-------
extensions/chaeo/util.py | 8 ++
extensions/chaeo/workflows.py | 4 +
3 files changed, 80 insertions(+), 39 deletions(-)
diff --git a/extensions/chaeo/examples/transfer_labels_to_ilastik_object_classifier.py b/extensions/chaeo/examples/transfer_labels_to_ilastik_object_classifier.py
index d528f86b..14c4a985 100644
--- a/extensions/chaeo/examples/transfer_labels_to_ilastik_object_classifier.py
+++ b/extensions/chaeo/examples/transfer_labels_to_ilastik_object_classifier.py
@@ -1,3 +1,4 @@
+import shutil
from pathlib import Path
import h5py
import json
@@ -5,10 +6,12 @@ import numpy as np
import pandas as pd
import uuid
+from extensions.chaeo.util import autonumber_new_file
from model_server.accessors import generate_file_accessor
-def get_dataset_info(h5):
- lane = 'Input Data/infos/lane0000'
+def get_dataset_info(h5, lane=0):
+ lns = f'{lane:04d}'
+ lane = f'Input Data/infos/lane{lns}'
info = {}
for gk in ['Raw Data', 'Segmentation Image']:
info[gk] = {}
@@ -23,9 +26,14 @@ def get_dataset_info(h5):
info[gk]['id'] = '<invalid UUID>'
info[gk]['axistags'] = json.loads(h5[f'{lane}/{gk}/axistags'][()].decode())
info[gk]['axes'] = [ax['key'] for ax in info[gk]['axistags']['axes']]
+
+ obj_cl_group = h5[f'ObjectClassification/LabelInputs/{lns}']
+ info['misc'] = {
+ 'number_of_label_inputs': len(obj_cl_group.items())
+ }
return info
-def transfer_labels_to_ilastik_ilp(ilp, df_stack_meta):
+def transfer_labels_to_ilastik_ilp(ilp, df_stack_meta, dump_csv=False):
with h5py.File(ilp, 'r+') as h5:
# TODO: force make copy if ilp file starts with template_
@@ -33,12 +41,12 @@ def transfer_labels_to_ilastik_ilp(ilp, df_stack_meta):
where_out = Path(ilp).parent
# export complete HDF5 tree
- with open(where_out / 'h5tree.txt', 'w') as hf:
- tt = []
- h5.visititems(lambda k, v: tt.append([k, str(v)]))
- for line in tt:
- hf.write(f'{line[0]} --- {line[1]}\n')
- h5.visititems(lambda k, v: print(k + ' : ' + str(v)))
+ if dump_csv:
+ with open(where_out / 'h5tree.txt', 'w') as hf:
+ tt = []
+ h5.visititems(lambda k, v: tt.append([k, str(v)]))
+ for line in tt:
+ hf.write(f'{line[0]} --- {line[1]}\n')
# put certain h5 groups in scope
h5info = get_dataset_info(h5)
@@ -60,57 +68,78 @@ def transfer_labels_to_ilastik_ilp(ilp, df_stack_meta):
ds[1] = float(df_stack_meta.loc[df_stack_meta.zi == idx, 'annotation_class_id'].iat[0])
print(f'Changed label {ti} from {la_old} to {ds[1]}')
-def generate_ilastik_object_classifier(template_ilp, where_training: str):
+def generate_ilastik_object_classifier(template_ilp, where: str, lane=0):
# validate z-stack input data
- where = Path(where_training)
- zstacks = {
- 'Raw Data': {
- 'path': where / 'zstack_train_raw.tif',
- },
- 'Segmentation Image': {
- 'path': where / 'zstack_train_mask.tif',
- }
+ root = Path(where)
+ paths = {
+ 'Raw Data': root / 'zstack_train_raw.tif',
+ 'Segmentation Image': root / 'zstack_train_mask.tif',
}
- for k, v in zstacks.items():
- v['acc'] = generate_file_accessor(v['path'])
-
- assert zstacks['Segmentation Image']['acc'].is_mask()
+ accessors = {k: generate_file_accessor(pa) for k, pa in paths.items()}
- assert len(set([v['acc'].hw for k, v in zstacks.items()])) == 1 # same height and width
- assert len(set([v['acc'].nz for k, v in zstacks.items()])) == 1 # same z-depth
+ assert accessors['Raw Data'].chroma == 1
+ assert accessors['Segmentation Image'].is_mask()
+ assert len(set([a.hw for a in accessors.values()])) == 1 # same height and width
+ assert len(set([a.nz for a in accessors.values()])) == 1 # same z-depth
+ nz = accessors['Raw Data'].nz
# now load CSV
- csv_path = where / 'train_stack.csv'
+ csv_path = root / 'train_stack.csv'
assert csv_path.exists()
- df_meta = pd.read_csv(csv_path)
+ df_patches = pd.read_csv(root / 'train_stack.csv')
assert np.all(
- df_meta['zi'].sort_values().to_numpy() == np.arange(0, zstacks['Raw Data']['acc'].nz)
+ df_patches['zi'].sort_values().to_numpy() == np.arange(0, nz)
)
+ df_labels = pd.read_csv(root / 'labels_key.csv')
+ label_names = list(df_labels.sort_values('annotation_class_id').annotation_class.unique())
+ label_names[0] = 'none'
+ assert len(label_names) >= 2
- with h5py.File(template_ilp, 'r+') as h5:
+ # open, validate, and copy template project file
+ with h5py.File(template_ilp, 'r') as h5:
info = get_dataset_info(h5)
+ for hg in ['Raw Data', 'Segmentation Image']:
+ assert info[hg]['location'] == b'FileSystem'
+ assert info[hg]['axes'] == ['t', 'y', 'x']
+
+ new_ilp = shutil.copy(template_ilp, root / autonumber_new_file(root, 'auto-obj', 'ilp'))
+
+ # write to new project file
+ lns = f'{lane:04d}'
+ with h5py.File(new_ilp, 'r+') as h5:
def set_ds(grp, ds, val):
- ds = h5[f'Input Data/infos/lane0000/{grp}/{ds}']
+ ds = h5[f'Input Data/infos/lane{lns}/{grp}/{ds}']
ds[()] = val
return ds[()]
+ def get_label(idx):
+ return df_patches.loc[df_patches.zi == idx, 'annotation_class_id'].iat[0]
+
for hg in ['Raw Data', 'Segmentation Image']:
- assert info[hg]['location'] == b'FileSystem'
- assert info[hg]['axes'] == ['t', 'y', 'x']
- set_ds(hg, 'filePath', zstacks[hg]['path'].__str__())
- set_ds(hg, 'nickname', zstacks[hg]['path'].stem)
- shape_zyx = [zstacks[hg]['acc'].shape_dict[ax] for ax in ['Z', 'Y', 'X']]
+ set_ds(hg, 'filePath', paths[hg].__str__())
+ set_ds(hg, 'nickname', paths[hg].stem)
+ shape_zyx = [accessors[hg].shape_dict[ax] for ax in ['Z', 'Y', 'X']]
set_ds(hg, 'shape', np.array(shape_zyx))
- new_info = get_dataset_info(h5)
+ # change key of label names
+ del h5['ObjectClassification/LabelNames']
+ ln = np.array(label_names)
+ h5.create_dataset('ObjectClassification/LabelNames', data=ln.astype('O'))
+
+ # change object labels
+ la_groupname = f'ObjectClassification/LabelInputs/{lns}'
+ # la_group = h5[la_groupname]
+
+ del h5[la_groupname]
+ lag = h5.create_group(la_groupname)
+ for zi in range(0, nz):
+ lag[f'{zi}'] = np.array([0., float(get_label(zi))])
if __name__ == '__main__':
- ilp = 'c:/Users/rhodes/model-server/ilastik/test_autolabel_obj - Copy.ilp'
-
generate_ilastik_object_classifier(
- 'c:/Users/rhodes/model-server/ilastik/test_template_obj.ilp',
- 'c:/Users/rhodes/projects/proj0011-plankton-seg/exp0009/output/labeled_patches-20231014-0004'
+ 'c:/Users/rhodes/projects/proj0011-plankton-seg/exp0014/template_obj.ilp',
+ 'c:/Users/rhodes/projects/proj0011-plankton-seg/exp0009/output/labeled_patches-20231016-0002'
)
\ No newline at end of file
diff --git a/extensions/chaeo/util.py b/extensions/chaeo/util.py
index 5432a95c..9bed0644 100644
--- a/extensions/chaeo/util.py
+++ b/extensions/chaeo/util.py
@@ -18,6 +18,14 @@ def autonumber_new_directory(where: str, prefix: str) -> str:
new_path.mkdir(parents=True, exist_ok=False)
return new_path.__str__()
+def autonumber_new_file(where: str, prefix: str, ext: str) -> str:
+ idx = 0
+ for ff in Path(where).iterdir():
+ ma = re.match(f'{prefix}-([\d]+).{ext}', ff.name)
+ if ma:
+ idx = max(idx, int(ma.groups()[0]) + 1)
+ return f'{prefix}-{idx:04d}.{ext}'
+
def get_matching_files(where: str, ext: str, coord_filter: dict={}) -> str:
files = []
diff --git a/extensions/chaeo/workflows.py b/extensions/chaeo/workflows.py
index d949323d..5ef2ec16 100644
--- a/extensions/chaeo/workflows.py
+++ b/extensions/chaeo/workflows.py
@@ -4,6 +4,7 @@ from uuid import uuid4
import numpy as np
import pandas as pd
+from skimage.morphology import dilation
from sklearn.model_selection import train_test_split
from extensions.ilastik.models import IlastikPixelClassifierModel
@@ -176,6 +177,7 @@ def transfer_ecotaxa_labels_to_patch_stacks(
where_output: str,
patch_size: tuple = (256, 256),
tr_split=0.6,
+ dilate_label_mask: bool = True, # to mitigate connected components error in ilastik
) -> Dict:
assert tr_split > 0.5 # reduce chance that low-probability objects are omitted from training
@@ -255,6 +257,8 @@ def transfer_ecotaxa_labels_to_patch_stacks(
assert acc_bm.chroma == 1
assert acc_bm.nz == 1
mask = acc_bm.data[:, :, 0, 0]
+ if dilate_label_mask:
+ mask = dilation(mask)
zstacks[dfk + '_mask'][:, :, 0, fi] = mask
zstacks[dfk + '_label'][:, :, 0, fi] = (mask == 255) * aci
--
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