Import video annotations

Open this Colab for an interactive tutorial on importing annotations on video assets.

Supported annotations

To import annotations in Labelbox, you need to create the annotations payload. In this section, we provide this payload for every supported annotation type.

📘
Tool features are frame-based
Global and frame-based classifications are supported on video assets. However, tool annotations are required to be frame-based.

Bounding box (frame-based)

bbox_annotation = [
  lb_types.VideoObjectAnnotation(
    name = "bbox_video",  
    keyframe=True,
    frame=13,
    segment_index=0,
    value = lb_types.Rectangle(
          start=lb_types.Point(x=146.0, y=98.0), # x = left, y = top 
          end=lb_types.Point(x=382.0, y=341.0),# x = left + width , y = top + height
      )
  ),
  lb_types.VideoObjectAnnotation(
    name = "bbox_video",  
    keyframe=True,
    frame=15,
    segment_index=0,
    value = lb_types.Rectangle(
          start=lb_types.Point(x=146.0, y=98.0), # x = left, y = top 
          end=lb_types.Point(x=382.0, y=341.0), # x = left + width , y = top + height
      )
  ),
  lb_types.VideoObjectAnnotation(
    name = "bbox_video",  
    keyframe=True,
    frame=19,
    segment_index=0,
    value = lb_types.Rectangle(
          start=lb_types.Point(x=146.0, y=98.0), # x = left, y = top 
          end=lb_types.Point(x=382.0, y=341.0), # x = left + width , y = top + height
      )
  )
]

bbox_annotation_ndjson = {
    "name" : "bbox_video",
    "segments" : [{
        "keyframes" : [
            {
              "frame": 13,
              "bbox" : {
                "top": 146.0,
                "left": 98.0,
                "height": 382.0,
                "width": 341.0
              }  
           },
           {
              "frame": 15,
              "bbox" : {
                "top": 146.0,
                "left": 98.0,
                "height": 382.0,
                "width": 341.0
              }  
           },
           {
              "frame": 19,
              "bbox" : {
                "top": 146.0,
                "left": 98.0,
                "height": 382.0,
                "width": 341.0
              }  
           }
        ]
      }
    ]
}

Point (frame-based)

point_annotation = [
    lb_types.VideoObjectAnnotation(
        name = "point_video",
        keyframe=True,
        frame=17,
        value = lb_types.Point(x=660.134, y=407.926),
        )
]

point_annotation_ndjson = {
    "name": "point_video", 
    "segments": [{
        "keyframes": [{
            "frame": 17,
            "point" : {
                "x": 660.134 ,
                "y": 407.926
            }
        }]
    }] 
}

Polyline (frame-based)

polyline_annotation = [
  lb_types.VideoObjectAnnotation(
    name = "line_video_frame",
    keyframe=True, 
    frame=5,
    segment_index=0,
    value=lb_types.Line( 
          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]
      )
  ),
  lb_types.VideoObjectAnnotation(
    name = "line_video_frame",
    keyframe=True, 
    frame=12,
    segment_index=0,
    value=lb_types.Line( 
          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]
      )
  ),
  lb_types.VideoObjectAnnotation(
    name = "line_video_frame",
    keyframe=True, 
    frame=20,
    segment_index=0,
    value=lb_types.Line( 
          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]
      )
  ),
  lb_types.VideoObjectAnnotation(
    name = "line_video_frame",
    keyframe=True, 
    frame=24,
    segment_index=1,
    value=lb_types.Line( 
          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]
      )
  ),
  lb_types.VideoObjectAnnotation(
    name = "line_video_frame",
    keyframe=True, 
    frame=45,
    segment_index=1,
    value=lb_types.Line( 
          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]
      )
  )
  
]

polyline_frame_annotation_ndjson = {
  "name": "line_video_frame", 
  "segments": [
      {
        "keyframes": [
          {
            "frame": 5,
            "line": [{
              "x": 680,
              "y": 100
            },{
              "x": 100,
              "y": 190
            },{
              "x": 190,
              "y": 220
            }]
          },
          {
            "frame": 12,
            "line": [{
              "x": 680,
              "y": 280
            },{
              "x": 300,
              "y": 380
            },{
              "x": 400,
              "y": 460
            }]
          },
          {
            "frame": 20,
            "line": [{
              "x": 680,
              "y": 180
            },{
              "x": 100,
              "y": 200
            },{
              "x": 200,
              "y": 260
            }]
          }
        ]
      },
      {
        "keyframes": [
          {
            "frame": 24,
            "line": [{
              "x": 300,
              "y": 310
            },{
              "x": 330,
              "y": 430
            }]
          },
          {
            "frame": 45,
            "line": [{
              "x": 600,
              "y": 810
            },{
              "x": 900,
              "y": 930
            }]
          }
        ]
      }
    ]
}

Classification: Radio (frame-based)

radio_annotation = [
    lb_types.VideoClassificationAnnotation(
        name="radio_class", 
        frame=9,
        segment_index=0,
        value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = "first_radio_answer"))
    ),
    lb_types.VideoClassificationAnnotation(
        name="radio_class", 
        frame=15,
        segment_index=0,
        value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = "first_radio_answer"))
    )
]

frame_radio_classification_ndjson = {
    "name": "radio_class", 
    "answer": { "name": "first_radio_answer", "frames": [{"start": 9, "end": 15}]}
}

Classification: Checklist (frame-based)

checklist_annotation= [
    lb_types.VideoClassificationAnnotation(
        name="checklist_class",
        frame=29,
        segment_index=0,
        value=lb_types.Checklist(
            answer = [
                lb_types.ClassificationAnswer(
                    name = "first_checklist_answer"
                )
            ]
            )
        ),
    lb_types.VideoClassificationAnnotation(
        name="checklist_class", 
        frame=35,
        segment_index=0,
        value=lb_types.Checklist(
            answer = [
                lb_types.ClassificationAnswer(
                    name = "first_checklist_answer"
                )
            ]
            )
        ),
    lb_types.VideoClassificationAnnotation(
        name="checklist_class", 
        frame=39, 
        segment_index=1,
        value=lb_types.Checklist(
            answer = [
                lb_types.ClassificationAnswer(
                    name = "second_checklist_answer"
                )
            ]
            )
        ),
    lb_types.VideoClassificationAnnotation(
        name="checklist_class", 
        frame=45, 
        segment_index=1,
        value=lb_types.Checklist(
            answer = [
                
                lb_types.ClassificationAnswer(
                    name = "second_checklist_answer"
                )
            ]
            )
        )
]

frame_checklist_classification_ndjson = {
    "name": "checklist_class", 
    "answer": [
        { "name": "first_checklist_answer" , "frames": [{"start": 29, "end": 35 }]},
        { "name": "second_checklist_answer", "frames": [{"start": 39, "end": 45 }]} 
  ]      
}

Classification: Checklist (global)

# Use ClassificationAnnotation instead of VideoClassification for global annotations
global_checklist_annotation=[lb_types.ClassificationAnnotation(
  name="checklist_class_global", 
  value=lb_types.Checklist(
      answer = [
        lb_types.ClassificationAnswer(
            name = "first_checklist_answer"
        ), 
        lb_types.ClassificationAnswer(
            name = "second_checklist_answer"
        )
      ]
    )
 )]

global_checklist_classification_ndjson = {
    "name": "checklist_class_global", 
    "answer": [
        { "name": "first_checklist_answer" },
        { "name": "second_checklist_answer"} 
  ]      
}

Classification: Radio (global)

# Use ClassificationAnnotation instead of VideoClassification for global annotations
global_radio_annotation = [lb_types.ClassificationAnnotation(
    name="radio_class_global",
    value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = "first_radio_answer"))
)]

global_checklist_classification_ndjson = {
    "name": "checklist_class_global", 
    "answer": [
        { "name": "first_checklist_answer" },
        { "name": "second_checklist_answer"} 
  ]      
}

Classification: Nested Checklist (global)

nested_checklist_annotation = [lb_types.ClassificationAnnotation(
  name="nested_checklist_question",
  value=lb_types.Checklist(
    answer=[lb_types.ClassificationAnswer(
      name="first_checklist_answer",
      classifications=[
        lb_types.ClassificationAnnotation(
          name="sub_checklist_question",
          value=lb_types.Checklist(
            answer=[lb_types.ClassificationAnswer(
            name="first_sub_checklist_answer"
          )]
        ))
      ]
    )]
  )
)]

nested_checklist_annotation_ndjson = {
  "name": "nested_checklist_question",
  "answer": [{
      "name": "first_checklist_answer",
      "classifications" : [
        {
          "name": "sub_checklist_question",
          "answer": {"name": "first_sub_checklist_answer"}
        }
      ]
  }]
}

Classification: Nested Radio (global)

nested_radio_annotation =[lb_types.ClassificationAnnotation(
  name="nested_radio_question",
  value=lb_types.Radio(
    answer=lb_types.ClassificationAnswer(
      name="first_radio_answer",
      classifications=[
        lb_types.ClassificationAnnotation(
          name="sub_radio_question",
          value=lb_types.Radio(
            answer=lb_types.ClassificationAnswer(
              name="first_sub_radio_answer"
            )
          )
        )
      ]
    )
  )
)]

nested_radio_annotation_ndjson = {
  "name": "nested_radio_question",
  "answer": {"name": "first_radio_answer"},
  "classifications" : [
    {"name": "sub_radio_question", "answer": {"name": "first_sub_radio_answer"}}
   ]
}

Bounding box with sub-classifications (frame-based)

frame_bbox_with_checklist_subclass = [
  lb_types.VideoObjectAnnotation(
    name = "bbox_class",  
    keyframe=True,
    frame=10,
    segment_index=0,
    value = lb_types.Rectangle(
          start=lb_types.Point(x=146.0, y=98.0), # x = left, y = top 
          end=lb_types.Point(x=382.0, y=341.0),# x = left + width , y = top + height
      )
  ),
  lb_types.VideoObjectAnnotation(
    name = "bbox_class",  
    keyframe=True,
    frame=11,
    segment_index=0,
    value = lb_types.Rectangle(
          start=lb_types.Point(x=146.0, y=98.0), # x = left, y = top 
          end=lb_types.Point(x=382.0, y=341.0), # x= left + width , y = top + height
      ),
    classifications=[
                lb_types.ClassificationAnnotation(
                    name='bbox_radio',
                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(
                        name='bbox_radio_answer_1',
                    ))
                )
            ]
  ),
  lb_types.VideoObjectAnnotation(
    name = "bbox_class",  
    keyframe=True,
    frame=13,
    segment_index=0,
    value = lb_types.Rectangle(
          start=lb_types.Point(x=146.0, y=98.0), # x = left, y = top 
          end=lb_types.Point(x=382.0, y=341.0), # x= left + width , y = top + height
      ),
    classifications=[
              lb_types.ClassificationAnnotation(
                  name='bbox_radio',
                  value=lb_types.Radio(answer=lb_types.ClassificationAnswer(
                      name='bbox_radio_answer_2',
                  ))
              )
          ]
  )
]

frame_bbox_with_checklist_subclass_ndjson = {
    "name": "bbox_class",
    "segments": [{
        "keyframes": [
            {
            "frame": 10,
            "bbox": {
                "top": 146.0,
                "left": 98.0,
                "height": 382.0,
                "width": 341.0
              },
            "classifications": [
              {
                "name": "bbox_radio",
                "answer": {"name": "bbox_radio_answer_1"}
              }
            ]
          },
          {  
          "frame": 11,
            "bbox": {
                "top": 146.0,
                "left": 98.0,
                "height": 382.0,
                "width": 341.0
              },
          },
          {  
          "frame": 13,
            "bbox": {
                "top": 146.0,
                "left": 98.0,
                "height": 382.0,
                "width": 341.0
              },
          }
        ]
      }
    ]
}

Masks (frame-based)

instance_uri = "https://storage.googleapis.com/labelbox-datasets/video-sample-data/mask_example.png"

video_mask_annotation=[
    lb_types.VideoMaskAnnotation(
        frames=[
            lb_types.MaskFrame(index=10, instance_uri=instance_uri)
        ],
        instances=[
            lb_types.MaskInstance(color_rgb=(255,255,255), name="video_mask")
        ]     
    )
]

instance_uri = "https://storage.googleapis.com/labelbox-datasets/video-sample-data/mask_example.png"

video_mask_annotation_ndjson = {
    "masks": {
        "frames": [{
            "index": 10,
            "instanceURI": instance_uri
        }],
        "instances": [
            {
                "colorRGB": (255, 255, 255),
                "name": "video_mask",
            }
        ]
    }
}

End-to-end example: Import pre-labels or ground truth

Whether you are importing annotations as pre-labels or as ground truth, the steps are very similar. Step 6 (importing the annotation payload) is where the process becomes slightly different and is explained below in detail.

Before you start

You will need to import these libraries to use the code examples in this section.

import labelbox as lb
import uuid

Replace with your API key

API_KEY = ""
client = lb.Client(API_KEY)

Step 1: Import data rows

To attach annotations to a data row, it must first be uploaded to Catalog. Here we create an example video data row in Catalog.

global_key = "sample-video-2.mp4"
asset = {
    "row_data": "https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4", 
    "global_key": global_key,
    "media_type": "VIDEO"
 }

dataset = client.create_dataset(name="video_demo_dataset")
task = dataset.create_data_rows([asset])
task.wait_till_done()
print("Errors :",task.errors)
print("Failed data rows:" ,task.failed_data_rows)

Step 2: Create/select an ontology

Your project should have the correct ontology set up with all the tools and classifications supported for your annotations, and the tool and classification name should match the name fields in your annotations to ensure the correct feature schemas are matched.

For example, when we create the bounding box annotation above, we provided the namebbox_video. Now, when we set up our ontology, we must ensure that the name of our bounding box tool is also bbox_video. The same alignment must hold true for the other tools and classifications we create in our ontology.

Here is an example of creating an ontology programmatically for all the sample annotations above.

ontology_builder = lb.OntologyBuilder(
    tools=[
        lb.Tool(tool=lb.Tool.Type.BBOX, name="bbox_video"),
        lb.Tool(tool=lb.Tool.Type.POINT, name="point_video"),
        lb.Tool(tool=lb.Tool.Type.LINE, name="line_video_frame"),
        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name="video_mask"),
        lb.Tool(
          tool=lb.Tool.Type.BBOX, name="bbox_class",
          classifications=[
            lb.Classification(
              class_type=lb.Classification.Type.RADIO,
              name="bbox_radio",
              scope = lb.Classification.Scope.INDEX,
              options=[
                lb.Option(value="bbox_radio_answer_1"),
                lb.Option(value="bbox_radio_answer_2"),
                lb.Option(value="bbox_radio_answer_3")
              ]
            )
          ]
        )
    ],
    classifications=[ 
        lb.Classification(
            class_type=lb.Classification.Type.CHECKLIST,
            name="checklist_class",
            scope = lb.Classification.Scope.INDEX, ## Need to define scope for frame classifications
            options=[ 
                lb.Option(value="first_checklist_answer"),
                lb.Option(value="second_checklist_answer")
            ]
        ),
        lb.Classification(
            class_type=lb.Classification.Type.RADIO,
            name="radio_class",
            scope = lb.Classification.Scope.INDEX,
            options=[ 
                lb.Option(value="first_radio_answer"),
                lb.Option(value="second_radio_answer")
            ]
        ),
         lb.Classification(
              class_type=lb.Classification.Type.RADIO,
              name="nested_radio_question",
              options=[
                  lb.Option("first_radio_answer",
                        options=[
                            lb.Classification(
                                class_type=lb.Classification.Type.RADIO,
                                name="sub_radio_question",
                                options=[lb.Option("first_sub_radio_answer")]
                            )
                        ]
                  )
              ] 
        ),
        lb.Classification(
          class_type=lb.Classification.Type.CHECKLIST,
          name="nested_checklist_question",
          options=[
              lb.Option("first_checklist_answer",
                options=[
                  lb.Classification(
                      class_type=lb.Classification.Type.CHECKLIST,
                      name="sub_checklist_question",
                      options=[lb.Option("first_sub_checklist_answer")]
                  )
              ]
            )
          ]
        ),
        lb.Classification(
          class_type=lb.Classification.Type.RADIO, 
          name="radio_class_global",
          options=[ 
                lb.Option(value="first_radio_answer"),
                lb.Option(value="second_radio_answer")
            ]
        ),
        lb.Classification(
          class_type=lb.Classification.Type.CHECKLIST,
          name="checklist_class_global",
          options=[
                lb.Option(value="first_checklist_answer"),
                lb.Option(value="second_checklist_answer")
          ]
        )
    ]  
)

ontology = client.create_ontology("Ontology Video Annotations", 
                                  ontology_builder.asdict(), 
                                  media_type=lb.MediaType.Video)

Step 3: Create a labeling project

Connect the ontology to the labeling project.

# Project defaults to batch mode with benchmark quality settings if this argument is not provided
# Queue mode will be deprecated once dataset mode is deprecated

project = client.create_project(name="video_project_demo",
                                    queue_mode=lb.QueueMode.Batch,
                                    media_type=lb.MediaType.Video)

## connect ontology to your project
project.setup_editor(ontology)

Step 4: Send a batch of data rows to the project

# Create batches

# Create a batch to send to your MAL project
batch = project.create_batch(
  "first-batch-video-demo2", # Each batch in a project must have a unique name
  global_keys=[global_key] , # a list of global keys, data rows, or data row ids
  priority=5 # priority between 1(highest) - 5(lowest)
)

print("Batch: ", batch)

Step 5: Create the annotations payload

Create the annotations payload using the snippets of code shown above.

Labelbox supports two formats for the annotations payload: NDJSON and Python annotation types.

labels = []
annotations_list = [
          checklist_annotation, 
          radio_annotation,
          bbox_annotation, 
          frame_bbox_with_checklist_subclass,
          point_annotation, 
          polyline_annotation,
          global_checklist_annotation,
          global_radio_annotation,
          video_mask_annotation,
          nested_checklist_annotation,
          nested_radio_annotation,
      ]

flatten_list_annotations = [ann for ann_sublist in annotations_list for ann in ann_sublist] 

labels.append(
    lb_types.Label(
        data=lb_types.VideoData(global_key=global_key),
        annotations = flatten_list_annotations
    )
)

## For nested frame base classifications we need to pass a featureSchemaId instead of the name. 

label_ndjson = []

for annotation in [
    point_annotation_ndjson,
    bbox_annotation_ndjson,
    polyline_frame_annotation_ndjson, 
    frame_checklist_classification_ndjson, 
    frame_radio_classification_ndjson,
    nested_radio_annotation_ndjson,
    nested_checklist_annotation_ndjson,
    frame_bbox_with_checklist_subclass_ndjson,
    global_radio_classification_ndjson,
    global_checklist_classification_ndjson,
    video_mask_annotation_ndjson
]:      
  annotation.update({
      "dataRow": {
          "globalKey": global_key
      }
  })
  label_ndjson.append(annotation)

Step 6: Import the annotation payload

Option A: Upload to a labeling project as pre-labels (Model-assisted labeling)

# Upload MAL label for this data row in project
upload_job_mal = lb.MALPredictionImport.create_from_objects(
    client = client, 
    project_id = project.uid, 
    name="mal_import_job-" + str(uuid.uuid4()), 
    predictions=labels)

upload_job_mal.wait_until_done()
print("Errors:", upload_job_mal.errors)
print("Status of uploads: ", upload_job_mal.statuses)

Option B: Upload to a labeling project as ground truth

upload_job_label_import = lb.LabelImport.create_from_objects(
    client = client,
    project_id = project.uid, 
    name = "label_import_job-" + str(uuid.uuid4()),
    labels=labels
)

upload_job_label_import.wait_until_done()
print("Errors:", upload_job_label_import.errors)
print("Status of uploads: ", upload_job_label_import.statuses)

Supported annotations

📘Tool features are frame-based

Bounding box (frame-based)

Point (frame-based)

Polyline (frame-based)

Classification: Radio (frame-based)

Classification: Checklist (frame-based)

Classification: Checklist (global)

Classification: Radio (global)

Classification: Nested Checklist (global)

Classification: Nested Radio (global)

Bounding box with sub-classifications (frame-based)

Masks (frame-based)

End-to-end example: Import pre-labels or ground truth

Before you start

Replace with your API key

Step 1: Import data rows

Step 2: Create/select an ontology

Step 3: Create a labeling project

Step 4: Send a batch of data rows to the project

Step 5: Create the annotations payload

Step 6: Import the annotation payload

Option A: Upload to a labeling project as pre-labels (Model-assisted labeling)

Option B: Upload to a labeling project as ground truth

📘
Tool features are frame-based