Classifier modification test is now obsolete, since generation works

extensions/chaeo/examples/test_modify_object_classifier.py

-from pathlib import Path
-import shutil
-
-import h5py
-import numpy as np
-
-from extensions.chaeo.accessors import MonoPatchStack, MonoPatchStackFromFile
-from extensions.chaeo.models import PatchStackObjectClassifier
-
-
-def modify_ilastik_object_classifier(template_ilp: Path, lane: int = 0):
-    proj_dir = template_ilp.parent
-    proj_name = template_ilp.stem
-    proj_new_ilp = shutil.copy(template_ilp, proj_dir / ('mod_' + proj_name + '.ilp'))
-
-    with h5py.File(proj_dir / proj_new_ilp, 'r+') as h5:
-
-        # delete ObjectExtraction / RegionFeatures / 0000
-        # delete ObjectClassification/ClassifierForests
-        keys_to_delete = [
-            f'ObjectExtraction/RegionFeatures/{lane:04d}',
-            'ObjectClassification/ClassifierForests'
-        ]
-        for key in keys_to_delete:
-            if key in h5.keys():
-                del h5[key]
-            assert key not in h5.keys()
-
-        # make links to input data relative
-        for gk in ['Raw Data', 'Segmentation Image']:
-            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()
-            rel_pa = pa.name
-            assert (proj_dir / rel_pa).exists()
-            del h5[f'{key}/filePath']
-            h5[f'{key}/filePath'] = rel_pa
-            assert h5[f'{key}/filePath'][()] == rel_pa.encode()
-
-    return proj_new_ilp
-
-
-if __name__ == '__main__':
-    where = Path('c:/Users/rhodes/projects/proj0011-plankton-seg/exp0009/output/labeled_patches-20231030-0001')
-    where_ilp = where / 'auto_obj_before.ilp'
-    new_ilp = modify_ilastik_object_classifier(where_ilp)
-    # new_ilp = 'mod_auto_obj_before.ilp'
-    model = PatchStackObjectClassifier({'project_file': where / new_ilp})
-
-    raw = MonoPatchStackFromFile(where / 'zstack_train_raw.tif')
-    mask = MonoPatchStackFromFile(where / 'zstack_train_mask.tif')
-    labels = MonoPatchStackFromFile(where / f'zstack_train_label.tif')
-
-    result, _ = model.infer(raw, mask)
-    assert isinstance(result, MonoPatchStack)
-
-    print(f'Unique labels in truth set: {np.unique(labels.data)}')
-    print(f'Unique labels in inferred set: {np.unique(result.data)}')
\ No newline at end of file