chore: cleanup

Author: rishisurana-labelbox

libs/labelbox/src/labelbox/data/serialization/ndjson/label.py

@@ -48,7 +48,7 @@
     NDVideoMasks,
 )
 from .relationship import NDRelationship
-from .utils.temporal_processor import VideoTemporalProcessor, AudioTemporalProcessor
+from .utils.temporal_processor import AudioTemporalProcessor
 
 AnnotationType = Union[
     NDObjectType,
@@ -130,14 +130,41 @@ def _get_segment_frame_ranges(
     def _create_video_annotations(
         cls, label: Label
     ) -> Generator[Union[NDChecklistSubclass, NDRadioSubclass], None, None]:
-        # Handle video mask annotations separately (special case)
+        video_annotations = defaultdict(list)
         for annot in label.annotations:
-            if isinstance(annot, VideoMaskAnnotation):
+            if isinstance(
+                annot, (VideoClassificationAnnotation, VideoObjectAnnotation)
+            ):
+                video_annotations[annot.feature_schema_id or annot.name].append(
+                    annot
+                )
+            elif isinstance(annot, VideoMaskAnnotation):
                 yield NDObject.from_common(annotation=annot, data=label.data)
-        
-        # Use temporal processor for video classifications and objects
-        processor = VideoTemporalProcessor()
-        yield from processor.process_annotations(label)
+
+        for annotation_group in video_annotations.values():
+            segment_frame_ranges = cls._get_segment_frame_ranges(
+                annotation_group
+            )
+            if isinstance(annotation_group[0], VideoClassificationAnnotation):
+                annotation = annotation_group[0]
+                frames_data = []
+                for frames in segment_frame_ranges:
+                    frames_data.append({"start": frames[0], "end": frames[-1]})
+                annotation.extra.update({"frames": frames_data})
+                yield NDClassification.from_common(annotation, label.data)
+
+            elif isinstance(annotation_group[0], VideoObjectAnnotation):
+                segments = []
+                for start_frame, end_frame in segment_frame_ranges:
+                    segment = []
+                    for annotation in annotation_group:
+                        if (
+                            annotation.keyframe
+                            and start_frame <= annotation.frame <= end_frame
+                        ):
+                            segment.append(annotation)
+                    segments.append(segment)
+                yield NDObject.from_common(segments, label.data)
 
     @classmethod
     def _create_audio_annotations(

libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py

@@ -138,40 +138,3 @@ def prepare_grouped_content(self, annotation_group: List) -> None:
         # Update the template annotation
         annotation_group[0].value = Text(answer=content_structure)
 
-
-class VideoTemporalProcessor(TemporalAnnotationProcessor):
-    """Processor for video temporal annotations - matches existing behavior"""
-    
-    def get_annotation_types(self) -> tuple:
-        from ....annotation_types.video import VideoClassificationAnnotation, VideoObjectAnnotation
-        return (VideoClassificationAnnotation,), (VideoObjectAnnotation,)
-    
-    def should_group_annotations(self, annotation_group: List) -> bool:
-        """Video always groups by segment ranges"""
-        return True
-    
-    def build_frame_data(self, annotation_group: List) -> List[Dict[str, Any]]:
-        """Build frame data using existing video segment logic"""
-        from ..label import NDLabel  # Import here to avoid circular import
-        
-        segment_frame_ranges = NDLabel._get_segment_frame_ranges(annotation_group)
-        return [{"start": frames[0], "end": frames[-1]} for frames in segment_frame_ranges]
-    
-    def prepare_grouped_content(self, annotation_group: List) -> None:
-        """Video doesn't modify content - uses existing value"""
-        pass
-    
-    def _process_object_group(self, annotation_group, data):
-        """Video objects use segment-based processing"""
-        from ..label import NDLabel
-        
-        segment_frame_ranges = NDLabel._get_segment_frame_ranges(annotation_group)
-        segments = []
-        for start_frame, end_frame in segment_frame_ranges:
-            segment = []
-            for annotation in annotation_group:
-                if (annotation.keyframe and 
-                    start_frame <= annotation.frame <= end_frame):
-                    segment.append(annotation)
-            segments.append(segment)
-        yield NDObject.from_common(segments, data)