KeyError with df = client.export_to_table(project=project_id)

[shorng-medknowts]

---

KeyError Traceback (most recent call last)
Cell In[8], line 1
----> 1 df = client.export_to_table(project=project_id)

File ~/opt/anaconda3/envs/research/lib/python3.10/site-packages/labelpandas/client.py:46, in Client.export_to_table(self, project, include_metadata, include_performance, include_agreement, verbose, mask_method, divider)
29 def export_to_table(
30 self, project,
31 include_metadata:bool=False, include_performance:bool=False, include_agreement:bool=False,
32 verbose:bool=False, mask_method:str="png", divider="///"):
33 """ Creates a Pandas DataFrame given a Labelbox Projet ID
34 Args:
35 project : Required (str / lablebox.Project) - Labelbox Project ID or lablebox.Project object to export labels from
(...)
44 divider : Optional (str) - String delimiter for schema name keys and suffix added to duplocate global keys
45 """
---> 46 flattened_labels_dict = export_and_flatten_labels(
47 client=self.lb_client, project=project,
48 include_metadata=include_metadata, include_performance=include_performance, include_agreement=include_agreement,
49 mask_method=mask_method, verbose=verbose, divider=divider
50 )
52 table = pd.DataFrame.from_dict(flattened_labels_dict)
54 if verbose:

File ~/opt/anaconda3/envs/research/lib/python3.10/site-packages/labelbase/downloader.py:59, in export_and_flatten_labels(client, project, include_metadata, include_performance, include_agreement, verbose, mask_method, divider)
51 if not label['Skipped']:
52 flat_label = {
53 "global_key" : label["Global Key"],
54 "row_data" : label["Labeled Data"],
(...)
57 "external_id" : label["External ID"]
58 }
---> 59 res = flatten_label(label_dict=label, ontology_index=ontology_index, schema_to_name_path=schema_to_name_path, mask_method=mask_method, divider=divider)
60 for key, val in res.items():
61 flat_label[f"annotation{divider}{str(key)}"] = val

File ~/opt/anaconda3/envs/research/lib/python3.10/site-packages/labelbase/annotate.py:117, in flatten_label(label_dict, ontology_index, schema_to_name_path, mask_method, divider)
115 annotation_value = [array, [255,255,255]]
116 else:
--> 117 png = mask_to_bytes(input=obj["instanceURI"], method="url", color=[255,255,255], output="png")
118 annotation_value = [png, "null"]
119 if "classifications" in obj.keys():

KeyError: 'instanceURI'

[shorng-medknowts]

the problem, I think is that I'm trying to export text annotations, so there is no mask per say.