Successfully built and inferred with object classification model

extensions/chaeo/examples/transfer_labels_to_ilastik_object_classifier.py

@@ -41,13 +41,15 @@ class PatchStackObjectClassifier(IlastikObjectClassifierFromSegmentationModel):
         obmaps = self.shell.workflow.batchProcessingApplet.run_export(dsi, export_to_array=True) # [z x h x w x n]
 
         assert len(obmaps) == 1, 'ilastik generated more than one object map'
-        assert obmaps[0].shape == (input_img.nz, 1, input_img.hw[0], input_img.hw[1], 1) # z(1)yx(1)
 
+        # for some reason these axes get scrambled to Z(1)YX(1)
+        assert obmaps[0].shape == (input_img.nz, 1, input_img.hw[0], input_img.hw[1], 1)
         yxcz = np.moveaxis(
             obmaps[0][:, :, :, :, 0],
             [2, 3, 1, 0],
             [0, 1, 2, 3]
         )
+
         assert yxcz.shape == input_img.shape
         return InMemoryDataAccessor(data=yxcz), {'success': True}
 
@@ -75,40 +77,6 @@ def get_dataset_info(h5, lane=0):
     }
     return info
 
-def transfer_labels_to_ilastik_ilp(ilp, df_stack_meta, dump_csv=False):
-
-    with h5py.File(ilp, 'r+') as h5:
-        # TODO: force make copy if ilp file starts with template_
-        # TODO: enforce somehow that zstack and df_stack_meta are from same export run
-        where_out = Path(ilp).parent
-
-        # export complete HDF5 tree
-        if dump_csv:
-            with open(where_out / 'h5tree.txt', 'w') as hf:
-                tt = []
-                h5.visititems(lambda k, v: tt.append([k, str(v)]))
-                for line in tt:
-                    hf.write(f'{line[0]} --- {line[1]}\n')
-
-        # put certain h5 groups in scope
-        h5info = get_dataset_info(h5)
-
-        # change key of label names
-        ln = ['none'] + list(df_stack_meta.sort_values('annotation_class_id').annotation_class.unique())
-        del h5['ObjectClassification/LabelNames']
-        h5.create_dataset('ObjectClassification/LabelNames', data=np.array(ln).astype('O'))
-
-        # change object labels
-        ts = h5['ObjectClassification']['LabelInputs']['0000']
-        for ti in ts.items():
-            assert len(ti) == 2  # one for unlabeled area, one for labeled area
-            idx = int(ti[0])  # important because keys are strings and hence not sorted numerically
-            ds = ti[1]
-            la_old = ds[1]
-
-            # unit index, i.e. reserve 1 for no object
-            ds[1] = float(df_stack_meta.loc[df_stack_meta.zi == idx, 'annotation_class_id'].iat[0])
-            print(f'Changed label {ti} from {la_old} to {ds[1]}')
 
 def generate_ilastik_object_classifier(template_ilp, where: str, lane=0):
 
@@ -181,20 +149,21 @@ def generate_ilastik_object_classifier(template_ilp, where: str, lane=0):
 
     return new_ilp
 
+
 if __name__ == '__main__':
     root =  Path('c:/Users/rhodes/projects/proj0011-plankton-seg/')
     template_ilp = root / 'exp0014/template_obj.ilp'
+    # template_ilp = root / 'exp0014/test_obj_from_seg.ilp'
     where_patch_stack = root / 'exp0009/output/labeled_patches-20231016-0002'
 
-    # new_ilp = generate_ilastik_object_classifier(
-    #     template_ilp,
-    #     where_patch_stack,
-    # )
+    new_ilp = generate_ilastik_object_classifier(
+        template_ilp,
+        where_patch_stack,
+    )
 
     train_zstack_raw = generate_file_accessor(where_patch_stack / 'zstack_train_raw.tif')
     train_zstack_mask = generate_file_accessor(where_patch_stack / 'zstack_train_mask.tif')
 
-    new_ilp = root / 'exp0014/test_obj_from_seg.ilp'
     mod = PatchStackObjectClassifier({'project_file': new_ilp})
 
     result_acc, _ = mod.infer(train_zstack_raw, train_zstack_mask)