Renamed a few output files

extensions/chaeo/examples/transfer_labels_to_ilastik_object_classifier.py

@@ -8,7 +8,6 @@ import skimage
 import uuid
 import vigra
 
-from extensions.chaeo.util import autonumber_new_file
 from extensions.ilastik.models import IlastikObjectClassifierFromSegmentationModel
 from model_server.accessors import generate_file_accessor, GenericImageDataAccessor, InMemoryDataAccessor, write_accessor_data_to_file
 
@@ -88,7 +87,13 @@ def get_dataset_info(h5: h5py.File, lane : int = 0):
     return info
 
 
-def generate_ilastik_object_classifier(template_ilp: str, where: str, stack_name: str = 'train', lane: int = 0):
+def generate_ilastik_object_classifier(
+        template_ilp: str,
+        where: str,
+        stack_name: str = 'train',
+        lane: int = 0,
+        proj_name='auto_obj'
+):
     """
     Starting with a template project file, transfer input data and labels to a duplicate project file.
 
@@ -134,11 +139,11 @@ def generate_ilastik_object_classifier(template_ilp: str, where: str, stack_name
             assert info[hg]['location'] == b'FileSystem'
             assert info[hg]['axes'] == ['t', 'y', 'x']
 
-    new_ilp = shutil.copy(template_ilp, root / autonumber_new_file(root, 'auto-obj', 'ilp'))
+    new_ilp = shutil.copy(template_ilp, where / (proj_name + '.ilp'))
 
     # write to new project file
     lns = f'{lane:04d}'
-    with h5py.File(new_ilp, 'r+') as h5:
+    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
@@ -203,13 +208,14 @@ def compare_object_maps(truth: GenericImageDataAccessor, inferred: GenericImageD
 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-20231018-0001'
+    where_patch_stack = root / 'exp0009/output/labeled_patches-20231018-0002'
 
     # auto-populate an object classifier
-    new_ilp = generate_ilastik_object_classifier(
+    auto_ilp = generate_ilastik_object_classifier(
         template_ilp,
         where_patch_stack,
-        stack_name='train'
+        stack_name='train',
+        proj_name='auto_obj_before'
     )
 
     def infer_and_compare(ilp, suffix):
@@ -223,24 +229,16 @@ if __name__ == '__main__':
         # write comparison tables
         train_truth_labels = generate_file_accessor(where_patch_stack / f'zstack_train_label.tif')
         df_comp = compare_object_maps(train_truth_labels, result_acc)
-        df_comp.to_csv(
-            where_patch_stack / autonumber_new_file(
-                where_patch_stack, f'compare_train_result_{suffix}', 'csv'
-            ),
-            index=False
-        )
+        df_comp.to_csv(where_patch_stack / f'compare_train_result_{suffix}.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']))
 
     # infer object labels from the same data used to train the classifier
-    infer_and_compare(new_ilp, 'before')
+    infer_and_compare(auto_ilp, 'before')
 
     # copy project and prompt user input once ilastik file has been modified in-app
-    mod_ilp = shutil.copy(
-        new_ilp,
-        where_patch_stack / autonumber_new_file(where_patch_stack, 'auto-obj', 'ilp')
-    )
+    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()