Clean test showing that modified HDF5 isn't providing correct results

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=0):
+    where = template_ilp.parent
+    proj_name = template_ilp.stem
+    new_ilp = shutil.copy(template_ilp, where / ('mod_' + proj_name + '.ilp'))
+
+    with h5py.File(where / new_ilp, 'r+') as h5:
+
+        # delete ObjectExtraction / RegionFeatures / 0000
+        # delete ObjectClassification/ClassifierForests
+        for key in [f'ObjectExtraction/RegionFeatures/{lane:04d}', 'ObjectExtraction/ClassifierForests']:
+            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 (where / rel_pa).exists()
+            del h5[f'{key}/filePath']
+            h5[f'{key}/filePath'] = rel_pa
+            assert h5[f'{key}/filePath'][()] == rel_pa.encode()
+
+    return 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)
+    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)}')
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