diff --git a/extensions/chaeo/examples/test_modify_object_classifier.py b/extensions/chaeo/examples/test_modify_object_classifier.py
index 1c5343e5fa39dddd5d72b8c77f60b2df35f78aa3..671a0f7865ef88f22c635b49fb8b6b1252c0e1f7 100644
--- a/extensions/chaeo/examples/test_modify_object_classifier.py
+++ b/extensions/chaeo/examples/test_modify_object_classifier.py
@@ -31,7 +31,7 @@ def modify_ilastik_object_classifier(template_ilp: Path, lane: int = 0):
key = f'Input Data/infos/lane{lane:04d}/{gk}'
assert h5[f'{key}/location'][()] == 'FileSystem'.encode()
pa = Path(h5[f'{key}/filePath'][()].decode())
- assert pa.is_absolute()
+ # assert pa.is_absolute()
rel_pa = pa.name
assert (proj_dir / rel_pa).exists()
del h5[f'{key}/filePath']
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 78418b8b6be12f1985e6c7d4942f73c71aac3a6a..748212ce140aff5780b17893abbd1069efd97d0e 100644
--- a/extensions/chaeo/examples/transfer_labels_to_ilastik_object_classifier.py
+++ b/extensions/chaeo/examples/transfer_labels_to_ilastik_object_classifier.py
@@ -7,110 +7,99 @@ import pandas as pd
import skimage
import uuid
-from extensions.chaeo.accessors import MonoPatchStackFromFile
+from extensions.chaeo.accessors import MonoPatchStack, MonoPatchStackFromFile
from extensions.chaeo.h5util import get_dataset_info
from extensions.chaeo.models import PatchStackObjectClassifier
from model_server.accessors import generate_file_accessor, GenericImageDataAccessor, write_accessor_data_to_file
def generate_ilastik_object_classifier(
- template_ilp: str,
- where: str,
- stack_name: str = 'train',
+ template_ilp: Path,
+ target_ilp: Path,
+ raw_tif: Path,
+ mask_tif: Path,
+ label_tif: Path,
+ label_names: list,
lane: int = 0,
- proj_name='auto_obj'
):
"""
- Starting with a template project file, transfer input data and labels to a duplicate project file.
-
- :param template_ilp: absolute path to existing ilastik object classifier to use as a template
- :param where: absolute path to folder containing input data, segmentation maps, labels, and label descriptions
- :poram stack_name: prefix of .tif and .csv files that contain classifier training data (e.g. train, test)
+ Starting with a template project file, transfer input data and labels to a new project file.
+ :param template_ilp: path to existing ilastik object classifier to use as a template
+ :param target_ilp: path to new classifier
+ :param raw_tif: path to stack of patches containing raw data
+ :param mask_tif: path to stack of patches containing object masks
+ :param label_tif: path to stack of patches containing object labels
+ :param label_names: list of label names
:param lane: ilastik lane identifier
- :return: (str) name of new ilastik classifier project file
+ :return:
"""
+ new_ilp = shutil.copy(template_ilp, target_ilp)
- # validate z-stack input data
- root = Path(where)
- rel_paths = {
- 'Raw Data': Path(f'zstack_{stack_name}_raw.tif'),
- 'Segmentation Image': Path(f'zstack_{stack_name}_mask.tif'),
+ paths = {
+ 'Raw Data': raw_tif,
+ 'Segmentation Image': mask_tif,
}
+ accessors = {k: MonoPatchStackFromFile(root / pa) for k, pa in paths.items()}
- accessors = {k: generate_file_accessor(root / pa) for k, pa in rel_paths.items()}
-
- 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
+ # get labels from label image
+ acc_labels = MonoPatchStackFromFile(label_tif)
+ labels = []
+ for ii in range(0, acc_labels.count):
+ unique = np.unique(acc_labels.iat(ii))
+ assert len(unique) >= 2, 'Label image contains more than one non-zero value'
+ assert unique[0] == 0, 'Label image does not contain unlabeled background'
+ assert unique[-1] < len(label_names) + 1, f'Label ID {unique[-1]} exceeds number of label names: {len(label_names)}'
+ labels.append(unique[-1])
- # now load CSV
- csv_path = root / f'{stack_name}_stack.csv'
- assert csv_path.exists()
- df_patches = pd.read_csv(csv_path)
- assert np.all(
- 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
+ # write to new project file
+ with h5py.File(new_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 gk in ['Raw Data', 'Segmentation Image']:
+ group = f'Input Data/infos/lane{lane:04d}/{gk}'
- for hg in ['Raw Data', 'Segmentation Image']:
- assert info[hg]['location'] == b'FileSystem'
- assert info[hg]['axes'] == ['t', 'y', 'x']
+ # set path to input image files
+ del h5[f'{group}/filePath']
+ h5[f'{group}/filePath'] = paths[gk].name
+ assert not Path(h5[f'{group}/filePath'][()].decode()).is_absolute()
+ assert h5[f'{group}/filePath'][()] == paths[gk].name.encode()
+ assert h5[f'{group}/location'][()] == 'FileSystem'.encode()
- new_ilp = shutil.copy(template_ilp, where / (proj_name + '.ilp'))
+ # set input nickname
+ del h5[f'{group}/nickname']
+ h5[f'{group}/nickname'] = paths[gk].stem
- # write to new project file
- lns = f'{lane:04d}'
- with h5py.File(where / new_ilp, 'r+') as h5:
- def set_ds(grp, ds, val):
- 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']:
- set_ds(hg, 'filePath', rel_paths[hg].__str__())
- set_ds(hg, 'nickname', rel_paths[hg].stem)
- shape_zyx = [accessors[hg].shape_dict[ax] for ax in ['Z', 'Y', 'X']]
- set_ds(hg, 'shape', np.array(shape_zyx))
+ # set input shape
+ del h5[f'{group}/shape']
+ shape_zyx = [accessors[gk].shape_dict[ax] for ax in ['Z', 'Y', 'X']]
+ h5[f'{group}/shape'] = np.array(shape_zyx)
# change key of label names
- if (k_ln := 'ObjectClassification/LabelNames') in h5.keys():
- del h5[k_ln]
+ if (k := 'ObjectClassification/LabelNames') in h5.keys():
+ del h5[k]
ln = np.array(label_names)
- h5.create_dataset(k_ln, data=ln.astype('O'))
+ h5.create_dataset(k, data=ln.astype('O'))
- if (k_mn := 'ObjectClassification/MaxNumObj') in h5.keys():
- del h5[k_mn]
- # h5.create_dataset(k_mn, data=(len(label_names) - 1))
- h5[k_mn] = len(label_names) - 1
+ if (k := 'ObjectClassification/MaxNumObj') in h5.keys():
+ del h5[k]
+ h5[k] = len(label_names) - 1
del h5['currentApplet']
h5['currentApplet'] = 1
# change object labels
- if (k_li := f'ObjectClassification/LabelInputs/{lns}') in h5.keys():
- del h5[k_li]
- lag = h5.create_group(k_li)
- for zi in range(0, nz):
- lag[f'{zi}'] = np.array([0., float(get_label(zi))])
+ if (k := f'ObjectClassification/LabelInputs/{lane:04d}') in h5.keys():
+ del h5[k]
+ lag = h5.create_group(k)
+ # for zi in range(0, nz):
+ # lag[f'{zi}'] = np.array([0., float(get_label(zi))])
+ for zi, la in enumerate(labels):
+ lag[f'{zi}'] = np.array([0., float(la)])
# delete existing classification weights
- if (k_rf := f'ObjectExtraction/RegionFeatures/{lane:04d}') in h5.keys():
- del h5[k_rf]
- if (k_cf := 'ObjectClassification/ClassificationForests') in h5.keys():
- del h5[k_cf]
-
+ if (k := f'ObjectExtraction/RegionFeatures/{lane:04d}') in h5.keys():
+ del h5[k]
+ if (k := 'ObjectClassification/ClassificationForests') in h5.keys():
+ del h5[k]
return new_ilp
@@ -149,65 +138,46 @@ def compare_object_maps(truth: GenericImageDataAccessor, inferred: GenericImageD
return pd.DataFrame(labels)
if __name__ == '__main__':
- root = Path('c:/Users/rhodes/projects/proj0011-plankton-seg/')
- template_ilp = root / 'exp0014/template_obj.ilp'
- where_patch_stack = root / 'exp0009/output/labeled_patches-20231030-0002'
+ root = Path('c:/Users/rhodes/projects/proj0011-plankton-seg/exp0009/output/labeled_patches-20231030-0002')
+ template_ilp = Path('c:/Users/rhodes/projects/proj0011-plankton-seg/exp0014/template_obj.ilp')
+
+ 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
# auto-populate an object classifier
- auto_ilp = generate_ilastik_object_classifier(
+ new_ilp = generate_ilastik_object_classifier(
template_ilp,
- where_patch_stack,
- stack_name='train',
- proj_name='auto_obj_before'
+ root / 'new_auto_obj.ilp',
+ root / 'zstack_train_raw.tif',
+ root / 'zstack_train_mask.tif',
+ root / 'zstack_train_label.tif',
+ label_names,
)
- # auto_ilp = 'auto_obj_before.ilp'
-
- def infer_and_compare_training_set(ilp, suffix):
- # infer object labels from the same data used to train the classifier
- train_zstack_raw = MonoPatchStackFromFile(where_patch_stack / 'zstack_train_raw.tif')
- train_zstack_mask = MonoPatchStackFromFile(where_patch_stack / 'zstack_train_mask.tif')
- train_truth_labels = MonoPatchStackFromFile(where_patch_stack / f'zstack_train_label.tif')
- infer_and_compare(ilp, 'train', suffix, train_zstack_raw, train_zstack_mask, train_truth_labels)
-
- def infer_and_compare_test_set(ilp, suffix):
- # infer object labels from test dataset
- test_zstack_raw = MonoPatchStackFromFile(where_patch_stack / 'zstack_test_raw.tif')
- test_zstack_mask = MonoPatchStackFromFile(where_patch_stack / 'zstack_test_mask.tif')
- test_truth_labels = MonoPatchStackFromFile(where_patch_stack / f'zstack_test_label.tif')
- infer_and_compare(ilp, 'test', suffix, test_zstack_raw, test_zstack_mask, test_truth_labels)
-
- def infer_and_compare(ilp, prefix, suffix, raw, mask, labels):
- mod = PatchStackObjectClassifier({'project_file': where_patch_stack / ilp})
+
+ from extensions.chaeo.examples.test_modify_object_classifier import modify_ilastik_object_classifier
+ auto_ilp = modify_ilastik_object_classifier(new_ilp)
+ # auto_ilp = new_ilp
+
+ def infer_and_compare(ilp, prefix, raw, mask, labels):
+ mod = PatchStackObjectClassifier({'project_file': root / ilp})
result_acc, _ = mod.infer(raw, mask)
- write_accessor_data_to_file(where_patch_stack / f'zstack_train_result_{suffix}.tif', result_acc)
+ write_accessor_data_to_file(root / f'zstack_train_result.tif', result_acc)
# write comparison tables
df_comp = compare_object_maps(labels, result_acc)
- df_comp.to_csv(where_patch_stack / f'compare_{prefix}_result_{suffix}.csv', index=False)
+ df_comp.to_csv(root / f'compare_{prefix}_result.csv', index=False)
print(f'Generated ilastik project {ilp}')
print('Truth and inferred labels match?')
print(pd.value_counts(df_comp['truth_label'] == df_comp['inferred_label']))
- # report out some things for debugging
- rks = [
- 'ObjectClassification/MaxNumObj',
- 'ObjectClassification/LabelInputs/0000/0',
- 'currentApplet'
- ]
- with h5py.File(ilp, 'r') as h5:
- for r in rks:
- print(f'{r}: {h5[r][()]}')
-
- # infer object labels from the same data used to train the classifier
- infer_and_compare_training_set(auto_ilp, 'before')
-
- # copy project and prompt user input once ilastik file has been modified in-app
- mod_ilp = shutil.copy(auto_ilp, where_patch_stack / 'auto_obj_after.ilp')
- print(f'Press enter when project file {mod_ilp} has been updated in ilastik')
- input()
-
- # repeat inference with the modified project file
- infer_and_compare_training_set(mod_ilp, 'after')
- infer_and_compare_test_set(mod_ilp, 'after')
-
+ # infer_and_compare_training_set(auto_ilp, 'before')
+ infer_and_compare(
+ auto_ilp,
+ 'train',
+ MonoPatchStackFromFile(root / 'zstack_train_raw.tif'),
+ MonoPatchStackFromFile(root / 'zstack_train_mask.tif'),
+ MonoPatchStackFromFile(root / 'zstack_train_label.tif')
+ )