Revert "[SDK-503] Prevent users from uploading video annotations over… (#1384)

Author: sfendell-labelbox

labelbox/schema/annotation_import.py

@@ -23,7 +23,6 @@
     from labelbox.types import Label
 
 NDJSON_MIME_TYPE = "application/x-ndjson"
-LABEL_LIMIT = 5000
 logger = logging.getLogger(__name__)
 
 
@@ -154,9 +153,6 @@ def _get_ndjson_from_objects(cls, objects: Union[List[Dict[str, Any]],
             )
 
         objects = serialize_labels(objects)
-        if len(objects) > LABEL_LIMIT:
-            raise ValueError(
-                f"Label count {len(objects)} exceeds limit of {(LABEL_LIMIT)}")
         cls._validate_data_rows(objects)
 
         data_str = parser.dumps(objects)
@@ -506,8 +502,8 @@ def create_from_objects(
         Returns:
             MALPredictionImport
         """
-
         data = cls._get_ndjson_from_objects(predictions, 'annotations')
+
         if len(predictions) > 0 and isinstance(predictions[0], Dict):
             predictions_dicts = cast(List[Dict[str, Any]], predictions)
             has_confidence = LabelsConfidencePresenceChecker.check(

tests/integration/annotation_import/test_bulk_import_request.py

@@ -370,69 +370,3 @@ def test_nested_video_object_annotations(client,
     import_annotations.wait_until_done()
 
     assert import_annotations.errors == []
-
-
-def test_below_annotation_limit_on_single_data_row(
-        client, configured_project_with_one_data_row, video_data, rand_gen):
-    _, data_row_uids = video_data
-
-    def create_label():
-        return Label(data=VideoData(uid=data_row_uids[0]),
-                     annotations=[
-                         VideoObjectAnnotation(name="bbox",
-                                               keyframe=True,
-                                               frame=4,
-                                               segment_index=0,
-                                               value=Rectangle(
-                                                   start=Point(x=100, y=100),
-                                                   end=Point(x=105, y=105),
-                                               ))
-                     ])
-
-    configured_project_with_one_data_row.update(media_type=MediaType.Video)
-    configured_project_with_one_data_row.create_batch(
-        rand_gen(str),
-        data_row_uids,  # sample of data row objects
-        5  # priority between 1(Highest) - 5(lowest)
-    )
-    labels = [create_label() for _ in range(5000)]
-    import_annotations = MALPredictionImport.create_from_objects(
-        client=client,
-        project_id=configured_project_with_one_data_row.uid,
-        name=f"import {str(uuid.uuid4())}",
-        predictions=labels)
-    import_annotations.wait_until_done()
-
-    assert import_annotations.errors == []
-
-
-def test_above_annotation_limit_on_single_data_row(
-        client, configured_project_with_one_data_row, video_data, rand_gen):
-    _, data_row_uids = video_data
-
-    def create_label():
-        return Label(data=VideoData(uid=data_row_uids[0]),
-                     annotations=[
-                         VideoObjectAnnotation(name="bbox",
-                                               keyframe=True,
-                                               frame=4,
-                                               segment_index=0,
-                                               value=Rectangle(
-                                                   start=Point(x=100, y=100),
-                                                   end=Point(x=105, y=105),
-                                               ))
-                     ])
-
-    configured_project_with_one_data_row.update(media_type=MediaType.Video)
-    configured_project_with_one_data_row.create_batch(
-        rand_gen(str),
-        data_row_uids,  # sample of data row objects
-        5  # priority between 1(Highest) - 5(lowest)
-    )
-    labels = [create_label() for _ in range(5001)]
-    with pytest.raises(ValueError):
-        MALPredictionImport.create_from_objects(
-            client=client,
-            project_id=configured_project_with_one_data_row.uid,
-            name=f"import {str(uuid.uuid4())}",
-            predictions=labels)