Wrote comparison table, caught case where ilastik can label multiple fragments of an object

extensions/chaeo/examples/transfer_labels_to_ilastik_object_classifier.py

@@ -4,6 +4,7 @@ import h5py
 import json
 import numpy as np
 import pandas as pd
+import skimage
 import uuid
 import vigra
 
@@ -149,6 +150,32 @@ def generate_ilastik_object_classifier(template_ilp, where: str, lane=0):
 
     return new_ilp
 
+def compare_object_maps(truth: GenericImageDataAccessor, inferred: GenericImageDataAccessor) -> pd.DataFrame:
+    assert truth.shape == inferred.shape
+    assert np.all((truth.data == 0) == (inferred.data == 0))
+    assert inferred.chroma == 1
+
+    labels = []
+    for zi in range(0, inferred.nz):
+        inf_img = inferred.data[:, :, :, zi]
+
+        unique = np.unique(inf_img)
+        assert unique[0] == 0
+
+        dd = {'zi': zi, 'truth_label': np.unique(truth.data[:, :, :, zi])[1], 'multiples': False}
+
+        if len(unique) == 1:  # no object in frame
+            dd['inferred_label'] = unique[0]
+        elif len(unique) > 2:  # multiple objects in frame, so mask out all but largest
+            ob_id = skimage.measure.label(inf_img)
+            pr = skimage.measure.regionprops_table(ob_id, properties=['label', 'area'])
+            mask = inf_img == pr['label'][pr['area'].argmax()]
+            dd['inferred_label'] = np.unique(mask * inf_img)[1]
+            dd['multiples'] = True
+        else:  # exactly one unique object class in frame
+            dd['inferred_label'] = unique[1]
+        labels.append(dd)
+    return pd.DataFrame(labels)
 
 if __name__ == '__main__':
     root =  Path('c:/Users/rhodes/projects/proj0011-plankton-seg/')
@@ -172,7 +199,6 @@ if __name__ == '__main__':
 
     # write comparison
     train_labels = generate_file_accessor(where_patch_stack / 'zstack_train_label.tif')
-    write_accessor_data_to_file(
-        where_patch_stack / 'comp.tif',
-        InMemoryDataAccessor(result_acc.data == train_labels.data)
-    )
\ No newline at end of file
+    df_comp = compare_object_maps(train_labels, result_acc)
+    df_comp.to_csv(where_patch_stack / autonumber_new_file(where_patch_stack, 'comp', 'csv'), index=False)
+