diff --git a/conf/testing.py b/conf/testing.py
index 5f95b5caa314ac6ff315677136094135c983ff5b..f2df5040d83ede67bd06bb1c114e2e02bc0a0772 100644
--- a/conf/testing.py
+++ b/conf/testing.py
@@ -42,6 +42,16 @@ tifffile = {
'z': 7,
}
+filename = 'mono_zstack_mask.tif'
+monozstackmask = {
+ 'filename': filename,
+ 'path': root / filename,
+ 'w': 256,
+ 'h': 256,
+ 'c': 1,
+ 'z': 85
+}
+
ilastik = {
'pixel_classifier': 'demo_px.ilp',
'object_classifier': 'demo_obj.ilp',
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 7b61d580dd9aecc7c7feaff3e48de8359d29a3c8..d528f86bc7a8709b2f33e8703e905ffa04fb761e 100644
--- a/extensions/chaeo/examples/transfer_labels_to_ilastik_object_classifier.py
+++ b/extensions/chaeo/examples/transfer_labels_to_ilastik_object_classifier.py
@@ -1,12 +1,10 @@
-import csv
from pathlib import Path
import h5py
-import ilastik.applets.objectClassification
+import json
import numpy as np
import pandas as pd
-import tifffile
+import uuid
-from extensions.ilastik.models import IlastikObjectClassifierModel
from model_server.accessors import generate_file_accessor
def get_dataset_info(h5):
@@ -14,11 +12,17 @@ def get_dataset_info(h5):
info = {}
for gk in ['Raw Data', 'Segmentation Image']:
info[gk] = {}
- for dk in ['location', 'filePath', 'shape']:
+ for dk in ['location', 'filePath', 'shape', 'nickname']:
try:
info[gk][dk] = h5[f'{lane}/{gk}/{dk}'][()]
except Exception as e:
print(e)
+ try:
+ info[gk]['id'] = uuid.UUID(h5[f'{lane}/{gk}/datasetId'][()].decode())
+ except ValueError as e:
+ 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']]
return info
def transfer_labels_to_ilastik_ilp(ilp, df_stack_meta):
@@ -58,42 +62,55 @@ def transfer_labels_to_ilastik_ilp(ilp, df_stack_meta):
def generate_ilastik_object_classifier(template_ilp, where_training: str):
- # validate input data
+ # validate z-stack input data
where = Path(where_training)
zstacks = {
- 'raw': {
+ 'Raw Data': {
'path': where / 'zstack_train_raw.tif',
},
- 'seg': {
+ 'Segmentation Image': {
'path': where / 'zstack_train_mask.tif',
}
}
for k, v in zstacks.items():
- # assert v['path'].exists(), 'Could not find input z-stack: ' + v['path']
- # ff = tifffile.imread(v['path'])
- # v['nz'] = ff.shape[0]
- # v['hw'] = ff.shape[1:2]
- # v['dtype'] = ff.dtype
v['acc'] = generate_file_accessor(v['path'])
- assert zstacks['raw']['acc'].is_binary()
- assert zstacks['raw']
+ assert zstacks['Segmentation Image']['acc'].is_mask()
+
+ 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
+
+ # now load CSV
+ csv_path = where / 'train_stack.csv'
+ assert csv_path.exists()
+ df_meta = pd.read_csv(csv_path)
+ assert np.all(
+ df_meta['zi'].sort_values().to_numpy() == np.arange(0, zstacks['Raw Data']['acc'].nz)
+ )
with h5py.File(template_ilp, 'r+') as h5:
- pass
+ info = get_dataset_info(h5)
+
+ def set_ds(grp, ds, val):
+ ds = h5[f'Input Data/infos/lane0000/{grp}/{ds}']
+ ds[()] = val
+ return ds[()]
+
+ 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, 'shape', np.array(shape_zyx))
+ new_info = get_dataset_info(h5)
if __name__ == '__main__':
ilp = 'c:/Users/rhodes/model-server/ilastik/test_autolabel_obj - Copy.ilp'
- # ilp = 'c:/Users/rhodes/model-server/ilastik/test_template_obj.ilp'
-
- df = pd.read_csv(
- 'c:/Users/rhodes/projects/proj0011-plankton-seg/exp0009/output/labeled_patches-20231014-0002/train_stack.csv'
- )
- # transfer_labels_to_ilastik_ilp(ilp, df)
generate_ilastik_object_classifier(
- ilp,
- 'c:/Users/rhodes/projects/proj0011-plankton-seg/exp0009/output/labeled_patches-20231014-0002'
+ 'c:/Users/rhodes/model-server/ilastik/test_template_obj.ilp',
+ 'c:/Users/rhodes/projects/proj0011-plankton-seg/exp0009/output/labeled_patches-20231014-0004'
)
\ No newline at end of file
diff --git a/extensions/chaeo/workflows.py b/extensions/chaeo/workflows.py
index 4d7e0ccd176a04d7d79083c8041d937410914172..d949323dd880d6afddc3c1d8dafc95047c0a6a5f 100644
--- a/extensions/chaeo/workflows.py
+++ b/extensions/chaeo/workflows.py
@@ -250,6 +250,7 @@ def transfer_ecotaxa_labels_to_patch_stacks(
stack_meta.append({'zi': fi, 'patch_filename': fn, 'annotation_class': ac, 'annotation_class_id': aci})
acc_bm = generate_file_accessor(Path(where_masks) / fn)
+ assert acc_bm.is_mask()
assert acc_bm.hw == patch_size, f'Unexpected patch size {patch_size}'
assert acc_bm.chroma == 1
assert acc_bm.nz == 1
diff --git a/model_server/accessors.py b/model_server/accessors.py
index bd6cc11e6557ae95f5104cd0567881036c8ef1d8..8ef4059eb4d3d6a7a8373035e39bb32e5ffbb6b9 100644
--- a/model_server/accessors.py
+++ b/model_server/accessors.py
@@ -34,7 +34,11 @@ class GenericImageDataAccessor(ABC):
return True if self.shape_dict['Z'] > 1 else False
def is_mask(self):
- return self._data.dtype == 'bool'
+ if self._data.dtype == 'bool':
+ return True
+ elif self._data.dtype == 'uint8':
+ return np.all(np.unique(self._data) == [0, 255])
+ return False
def get_one_channel_data (self, channel: int):
c = int(channel)
@@ -101,12 +105,18 @@ class TifSingleSeriesFileAccessor(GenericImageFileAccessor):
raise DataShapeError(f'Expect only one series in {fpath}')
se = tf.series[0]
- sd = {ch: se.shape[se.axes.index(ch)] for ch in se.axes}
- idx = {k: sd[k] for k in ['Y', 'X', 'C', 'Z']}
+ order = ['Y', 'X', 'C', 'Z']
+ axs = [a for a in se.axes if a in order]
+ da = se.asarray()
+
+ if 'C' not in axs:
+ axs.append('C')
+ da = np.expand_dims(da, len(da.shape))
+
yxcz = np.moveaxis(
- se.asarray(),
- [se.axes.index(ch) for ch in idx],
+ da,
+ [axs.index(k) for k in order],
[0, 1, 2, 3]
)
diff --git a/tests/test_accessors.py b/tests/test_accessors.py
index 4e1e7817d724dfd78216541c9960278cc632ecd6..f7a4341dc04d42aff05ed6f01f0113578590684a 100644
--- a/tests/test_accessors.py
+++ b/tests/test_accessors.py
@@ -2,7 +2,7 @@ import unittest
import numpy as np
-from conf.testing import czifile, output_path, monopngfile, rgbpngfile, tifffile
+from conf.testing import czifile, output_path, monopngfile, rgbpngfile, tifffile, monozstackmask
from model_server.accessors import CziImageFileAccessor, DataShapeError, generate_file_accessor, InMemoryDataAccessor, PngFileAccessor, write_accessor_data_to_file, TifSingleSeriesFileAccessor
class TestCziImageFileAccess(unittest.TestCase):
@@ -106,4 +106,8 @@ class TestCziImageFileAccess(unittest.TestCase):
self.assertEqual(acc.nz, 1)
def test_read_mono_png(self):
- return self.test_read_png(pngfile=monopngfile)
\ No newline at end of file
+ return self.test_read_png(pngfile=monopngfile)
+
+ def test_read_zstack_mono_mask(self):
+ acc = generate_file_accessor(monozstackmask['path'])
+ self.assertTrue(acc.is_mask())
\ No newline at end of file