Pass accessors, not paths, to model generator

extensions/chaeo/examples/transfer_labels_to_ilastik_object_classifier.py

@@ -54,6 +54,7 @@ def infer_and_compare(classifier: PatchStackObjectClassifier, prefix, raw, mask,
     print('Truth and inferred labels match?')
     print(pd.value_counts(df_comp['truth_label'] == df_comp['inferred_label']))
 
+
 if __name__ == '__main__':
     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')
@@ -67,9 +68,9 @@ if __name__ == '__main__':
     classifier_file = generate_ilastik_object_classifier(
         template_ilp,
         root / 'new_auto_obj.ilp',
-        root / 'zstack_train_raw.tif',
-        root / 'zstack_train_mask.tif',
-        root / 'zstack_train_label.tif',
+        MonoPatchStackFromFile(root / 'zstack_train_raw.tif'),
+        MonoPatchStackFromFile(root / 'zstack_train_mask.tif'),
+        MonoPatchStackFromFile(root / 'zstack_train_label.tif'),
         label_names,
     )
     classifier = PatchStackObjectClassifier({'project_file': classifier_file})

extensions/chaeo/models.py

@@ -7,7 +7,6 @@ import vigra
 
 from extensions.chaeo.accessors import MonoPatchStack, MonoPatchStackFromFile
 from extensions.ilastik.models import IlastikObjectClassifierFromSegmentationModel
-from model_server.accessors import InMemoryDataAccessor
 
 
 class PatchStackObjectClassifier(IlastikObjectClassifierFromSegmentationModel):
@@ -50,9 +49,9 @@ class PatchStackObjectClassifier(IlastikObjectClassifierFromSegmentationModel):
 def generate_ilastik_object_classifier(
         template_ilp: Path,
         target_ilp: Path,
-        raw_tif: Path,
-        mask_tif: Path,
-        label_tif: Path,
+        raw_stack: MonoPatchStackFromFile,
+        mask_stack: MonoPatchStackFromFile,
+        label_stack: MonoPatchStackFromFile,
         label_names: list,
         lane: int = 0,
 ) -> Path:
@@ -60,27 +59,27 @@ def generate_ilastik_object_classifier(
     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 raw_stack: stack of patches containing raw data
+    :param mask_stack: stack of patches containing object masks
+    :param label_stack: stack of patches containing object labels
     :param label_names: list of label names
     :param lane: ilastik lane identifier
     :return: path to generated object classifier
     """
+    assert mask_stack.shape == raw_stack.shape
+    assert label_stack.shape == raw_stack.shape
+
     new_ilp = shutil.copy(template_ilp, target_ilp)
 
-    paths = {
-        'Raw Data': raw_tif,
-        'Segmentation Image': mask_tif,
+    accessors = {
+        'Raw Data': raw_stack,
+        'Segmentation Image': mask_stack,
     }
-    root = raw_tif.parent
-    accessors = {k: MonoPatchStackFromFile(root / pa) for k, pa in paths.items()}
 
     # 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))
+    for ii in range(0, label_stack.count):
+        unique = np.unique(label_stack.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)}'
@@ -94,14 +93,14 @@ def generate_ilastik_object_classifier(
 
             # set path to input image files
             del h5[f'{group}/filePath']
-            h5[f'{group}/filePath'] = paths[gk].name
+            h5[f'{group}/filePath'] = accessors[gk].fpath.name
             assert not Path(h5[f'{group}/filePath'][()].decode()).is_absolute()
-            assert h5[f'{group}/filePath'][()] == paths[gk].name.encode()
+            assert h5[f'{group}/filePath'][()] == accessors[gk].fpath.name.encode()
             assert h5[f'{group}/location'][()] == 'FileSystem'.encode()
 
             # set input nickname
             del h5[f'{group}/nickname']
-            h5[f'{group}/nickname'] = paths[gk].stem
+            h5[f'{group}/nickname'] = accessors[gk].fpath.stem
 
             # set input shape
             del h5[f'{group}/shape']