Add check for pandas NaN metadata values

labelbase/annotate.py

@@ -365,7 +365,6 @@ def flatten_label(client:labelboxClient, label_dict:dict, ontology_index:dict, d
             if column_name not in flat_label.keys():
                 flat_label[column_name] = []
             if "bounding_box" in obj.keys():
-                print(obj)
                 annotation_value = [obj["bounding_box"]["top"], obj["bounding_box"]["left"], obj["bounding_box"]["height"], obj["bounding_box"]["width"]]
                 if "page_number" in obj.keys():
                     annotation_value.append(obj["page_number"])

labelbase/connector.py

@@ -187,6 +187,8 @@ def determine_actions(
     attachments_action = True if attachment_index and not create_action else False    
     # Determine if we're batching data rows
     batch_action = False if (project_id == project_id_col == "") else True
+    print(project_id)
+    print(project_id_col)
     # Determine the upload_method if we're batching to projects
     annotate_action = upload_method if (upload_method in ["mal", "import", "ground-truth"]) and annotation_index and batch_action else ""      
     # "ground-truth" defaults to "import" if no model informtion is given

labelbase/downloader.py

@@ -5,7 +5,7 @@
 from labelbase.annotate import flatten_label
 
 def export_and_flatten_labels(client:labelboxClient, project, include_metadata:bool=True, include_performance:bool=True, 
-    include_agreement:bool=False, verbose:bool=False, mask_method:str="png", divider="///", export_filters:dict=None):
+    include_agreement:bool=False, include_label_details:bool=False, verbose:bool=False, mask_method:str="png", divider="///", export_filters:dict=None):
     """ Exports and flattens labels from a Labelbox Project
     Args:
         client:                 :   Required (labelbox.Client) - Labelbox Client object
@@ -82,6 +82,7 @@ def export_and_flatten_labels(client:labelboxClient, project, include_metadata:b
                     flat_label["seconds_to_create"] = nested_label['performance_details']['seconds_to_create']
                     flat_label["seconds_to_review"] = nested_label['performance_details']['seconds_to_review']
                     flat_label["seconds_to_label"] = nested_label['performance_details']['seconds_to_create'] - nested_label['performance_details']['seconds_to_review']
+                if include_metadata:
                     for metadata in label['metadata_fields']:
                         try:
                             if metadata['value'] in metadata_schema_to_name_key.keys():
@@ -115,6 +116,10 @@ def export_and_flatten_labels(client:labelboxClient, project, include_metadata:b
                                 metadata_value = metadata['value']
                         if field_name != "lb_integration_source":
                             flat_label[f'metadata{divider}{metadata_type}{divider}{field_name}'] = metadata_value
+                if include_label_details:
+                    flat_label["created_by"] = nested_label['label_details']["created_by"]
+                    flat_label["updated_at"] = nested_label['label_details']["updated_at"]
+                    flat_label["created_at"] = nested_label['label_details']["created_at"]
                 flattened_labels.append(flat_label)
     if verbose:
         print(f"Labels flattened")            

labelbase/metadata.py

@@ -3,6 +3,7 @@
 from datetime import datetime
 from dateutil import parser
 import pytz
+import pandas
 
 def get_metadata_schema_to_type(client:labelboxClient, lb_mdo=False, invert:bool=False):
     """ Creates a dictionary where {key=metadata_schema_id: value=metadata_type} 
@@ -121,6 +122,8 @@ def process_metadata_value(metadata_value, metadata_type:str, parent_name:str, m
         return_value = None
     if str(metadata_value) == "nan": # Catch NaN values
         return_value = None
+    if pandas.isna(metadata_value): #Catch pandas df NaN values
+        return_value = None
     # By metadata type
     if metadata_type == "enum": # For enums, it must be a schema ID - if we can't match it, we have to skip it
         name_key = f"{parent_name}{divider}{str(metadata_value)}"

labelbase/uploader.py

@@ -2,6 +2,7 @@
 from labelbox import Dataset as labelboxDataset
 from labelbox import Project as labelboxProject
 import uuid
+from concurrent.futures import ThreadPoolExecutor, as_completed
 
 def create_global_key_to_label_id_dict(client:labelboxClient, project_id:str, global_keys:list):
     """ Creates a dictionary where { key=global_key : value=label_id } by exporting labels from a project
@@ -37,7 +38,7 @@ def create_global_key_to_data_row_id_dict(client:labelboxClient, global_keys:lis
                 global_key_to_data_row_dict[gks[i]] = res['results'][i]
     return global_key_to_data_row_dict
 
-def check_global_keys(client:labelboxClient, global_keys:list, batch_size=1000):
+def check_global_keys(client:labelboxClient, global_keys:list):
     """ Checks if data rows exist for a set of global keys - if data rows exist, returns as dictionary { key=data_row_id : value=global_key }
     Args:
         client                  :   Required (labelbox.client.Client) - Labelbox Client object    
@@ -52,20 +53,18 @@ def check_global_keys(client:labelboxClient, global_keys:list, batch_size=1000):
     # Enforce global keys as strings
     global_keys_list = [str(x) for x in global_keys]      
     # Batch global key checks
-    for i in range(0, len(global_keys_list), batch_size):
-        batch_gks = global_keys_list[i:] if i + batch_size >= len(global_keys_list) else global_keys_list[i:i+batch_size]  
-        # Get the datarow ids
-        res = client.get_data_row_ids_for_global_keys(batch_gks)     
-        # Check query job results for fetched data rows
-        for i in range(0, len(res["results"])):
-            data_row_id = res["results"][i]
-            if data_row_id:
-                existing_drid_to_gk[data_row_id] = batch_gks[i]
+    # Get the datarow ids
+    res = client.get_data_row_ids_for_global_keys(global_keys_list)     
+    # Check query job results for fetched data rows
+    for i in range(0, len(res["results"])):
+        data_row_id = res["results"][i]
+        if data_row_id:
+            existing_drid_to_gk[data_row_id] = global_keys_list[i]
     return existing_drid_to_gk
 
 def batch_create_data_rows(
     client:labelboxClient, upload_dict:dict, skip_duplicates:bool=True, 
-    divider:str="___", batch_size:int=20000, verbose:bool=False):
+    divider:str="___", batch_size:int=100000, verbose:bool=False):
     """ Uploads data rows, skipping duplicate global keys or auto-generating new unique ones. 
 
     upload_dict must be in the following format:
@@ -94,7 +93,7 @@ def batch_create_data_rows(
 
     """
     # Default error message    
-    e = "Success"
+    e = {}
     # Vet all global keys
     global_keys = list(upload_dict.keys()) # Get all global keys
     if verbose:
@@ -103,11 +102,14 @@ def batch_create_data_rows(
         gks = global_keys[i:] if i + batch_size >= len(global_keys) else global_keys[i:i+batch_size] # Batch of global keys to vet 
         existing_data_row_to_global_key = check_global_keys(client, gks) # Returns empty list if there are no duplicates
         loop_counter = 0
+        if skip_duplicates:
+            e['skipped_global_keys'] = []
         while existing_data_row_to_global_key:
             if skip_duplicates: # Drop in-use global keys if we're skipping duplicates
                 if verbose:
                     print(f"Warning: Global keys in this upload are in use by active data rows, skipping the upload of data rows affected") 
                 for gk in existing_data_row_to_global_key.values():
+                    e['skipped_global_keys'].append(gk)
                     del upload_dict[gk]
                 break
             else: # Create new suffix for taken global keys if we're not skipping duplicates
@@ -135,7 +137,9 @@ def batch_create_data_rows(
             dataset_id_to_upload_list[dataset_id] = []
         dataset_id_to_upload_list[dataset_id].append(data_row)
     # Perform uploads grouped by dataset ID
+    e['errors'] = []
     for dataset_id in dataset_id_to_upload_list:
+        task_list = []
         dataset = client.get_dataset(dataset_id)       
         upload_list = dataset_id_to_upload_list[dataset_id]
         if verbose:
@@ -147,16 +151,24 @@ def batch_create_data_rows(
             if verbose:
                 print(f'Batch #{batch_number}: {len(batch)} data rows')
             task = dataset.create_data_rows(batch)
-            task.wait_till_done()
-            errors = task.errors
-            if errors:
-                if verbose: 
-                    print(f'Error: Upload batch number {batch_number} unsuccessful')
-                e = errors
-                break
-            else:
-                if verbose: 
-                    print(f'Success: Upload batch number {batch_number} successful')  
+            task_list.append(task)
+            # task.wait_till_done()
+            # errors = task.errors
+            # e['upload_results'].append(task.uid)
+            # if errors:
+            #     if verbose: 
+            #         print(f'Error: Upload batch number {batch_number} unsuccessful')
+            #     e['errors'] = errors
+            #     break
+            # else:
+            #     if verbose: 
+            #         print(f'Success: Upload batch number {batch_number} successful')  
+        with ThreadPoolExecutor() as exc:
+            futures = [exc.submit(get_results_from_task, x) for x in task_list]
+            for future in as_completed(futures):
+                errors = future.result()
+                if errors:
+                    e['errors'] += errors
     if verbose:
         print(f'Upload complete - all data rows uploaded')
     return e, upload_dict
@@ -487,3 +499,7 @@ def batch_upload_predictions(
     except Exception as error:
         e = error
     return e
+
+def get_results_from_task(task):
+    task.wait_till_done()
+    return task.errors

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
       name='labelbase',
-      version='0.1.05',
+      version='0.1.06',
       author='Labelbox',
       author_email='[email protected]',
       description='Labelbox Helper Library',