Merge branch 'master' into develop

Author: kkim-labelbox

tests/integration/annotation_import/conftest.py

@@ -468,6 +468,16 @@ def dataset_pdf_entity(client, rand_gen, document_data_row):
     dataset.delete()
 
 
+@pytest.fixture
+def video_data(client, rand_gen, video_data_row):
+    dataset = client.create_dataset(name=rand_gen(str))
+    data_row_ids = []
+    data_row = dataset.create_data_row(video_data_row)
+    data_row_ids.append(data_row.uid)
+    yield dataset, data_row_ids
+    dataset.delete()
+
+
 @pytest.fixture
 def dataset_conversation_entity(client, rand_gen, conversation_entity_data_row):
     dataset = client.create_dataset(name=rand_gen(str))

tests/integration/annotation_import/test_data_types.py

@@ -68,6 +68,106 @@ def get_annotation_comparison_dicts_from_labels(labels):
     return labels_ndjson
 
 
+def get_annotation_comparison_dicts_from_export(export_result, data_row_id,
+                                                project_id):
+    exported_data_row = [
+        dr for dr in export_result if dr['data_row']['id'] == data_row_id
+    ][0]
+    exported_label = exported_data_row['projects'][project_id]['labels'][0]
+    exported_annotations = exported_label['annotations']
+    converted_annotations = []
+    if exported_label['label_kind'] == 'Video':
+        frames = []
+        instances = []
+        for frame_id, frame in exported_annotations['frames'].items():
+            frames.append({'index': int(frame_id)})
+            for object in frame['objects'].values():
+                instances.append({'name': object['name']})
+        converted_annotations.append(
+            {'masks': {
+                'frames': frames,
+                'instances': instances,
+            }})
+    else:
+        exported_annotations = list(
+            itertools.chain(*exported_annotations.values()))
+        for annotation in exported_annotations:
+            if annotation['name'] == 'radio':
+                converted_annotations.append({
+                    'name': annotation['name'],
+                    'answer': {
+                        'name': annotation['radio_answer']['name']
+                    }
+                })
+            elif annotation['name'] == 'checklist':
+                converted_annotations.append({
+                    'name':
+                        annotation['name'],
+                    'answer': [{
+                        'name': answer['name']
+                    } for answer in annotation['checklist_answers']]
+                })
+            elif annotation['name'] == 'text':
+                converted_annotations.append({
+                    'name': annotation['name'],
+                    'answer': annotation['text_answer']['content']
+                })
+    return converted_annotations
+
+radio_annotation = lb_types.ClassificationAnnotation(
+    name="radio",
+    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(
+        name="second_radio_answer")))
+checklist_annotation = lb_types.ClassificationAnnotation(
+    name="checklist",
+    value=lb_types.Checklist(answer=[
+        lb_types.ClassificationAnswer(name="option1"),
+        lb_types.ClassificationAnswer(name="option2")
+    ]))
+text_annotation = lb_types.ClassificationAnnotation(
+    name="text", value=lb_types.Text(answer="sample text"))
+
+video_mask_annotation = lb_types.VideoMaskAnnotation(frames=[
+    lb_types.MaskFrame(
+        index=10,
+        instance_uri=
+        "https://storage.googleapis.com/labelbox-datasets/video-sample-data/mask_example.png"
+    )
+],
+                                                     instances=[
+                                                         lb_types.MaskInstance(
+                                                             color_rgb=(255,
+                                                                        255,
+                                                                        255),
+                                                             name=
+                                                             "segmentation_mask"
+                                                         )
+                                                     ])
+
+test_params = [[
+    'html', lb_types.HTMLData,
+    [radio_annotation, checklist_annotation, text_annotation]
+],
+               [
+                   'audio', lb_types.AudioData,
+                   [radio_annotation, checklist_annotation, text_annotation]
+               ], ['video', lb_types.VideoData, [video_mask_annotation]]]
+
+
+def get_annotation_comparison_dicts_from_labels(labels):
+    labels_ndjson = list(NDJsonConverter.serialize(labels))
+    for annotation in labels_ndjson:
+        annotation.pop('uuid')
+        annotation.pop('dataRow')
+
+        if 'masks' in annotation:
+            for frame in annotation['masks']['frames']:
+                frame.pop('instanceURI')
+            for instance in annotation['masks']['instances']:
+                instance.pop('colorRGB')
+    return labels_ndjson
+
+
 def get_annotation_comparison_dicts_from_export(export_result, data_row_id,
                                                 project_id):
     exported_data_row = [