Import video annotations

Open In Colab

GitHub

Overview

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

Annotation payload types

Labelbox supports two formats for the annotations payload:

Python annotation types (recommended)
- Provides a seamless transition between third-party platforms, machine learning pipelines, and Labelbox.
- Allows you to build annotations locally with local file paths, numpy arrays, or URLs
- Easily convert Python Annotation Type format to NDJSON format to quickly import annotations to Labelbox
- Supports one-level nested classification (radio, checklist, or free-form text) under a tool or classification annotation.
JSON
- Skips formatting annotation payload in the Labelbox Python annotation type
- Supports any levels of nested classification (radio, checklist, or free-form text) under a tool or classification annotation.

Label import types

Labelbox additionally supports two types of label imports:

Model-assisted labeling (MAL)
- This workflow allows you to import computer-generated predictions (or simply annotations created outside of Labelbox) as pre-labels on an asset.
Ground truth
- This workflow functionality allows you to bulk import your ground truth annotations from an external or third-party labeling system into Labelbox Annotate. Using the label import API to import external data is a useful way to consolidate and migrate all annotations into Labelbox as a single source of truth.

Supported annotations

The following annotations are supported for an video data row:

Radio
Checklist
Free-form text
Bounding box
Point
Polyline
Segmentation masks

Tool features are frame-based

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

Classifications

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"))
    )
]

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.ClassificationAnswer(
                    name = "second_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.ClassificationAnswer(
                    name = "second_checklist_answer"
                )
            ]
            )
        )
]

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"
        )
      ]
    )
 )]

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"))
)]

Free-form text (global)

text_annotation = lb_types.ClassificationAnnotation(
  name="free_text",
  value=lb_types.Text(answer="sample text")
)

Nested classifications (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_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"
)
)
)
]
)
)
)]

Tools

Bounding box (frame-based)

# Confidence scores are not supported for frame specific bounding box annotations and VideoObjectAnnotation class
bbox_dm = {
  "top":617,
  "left":1371,
  "height":419,
  "width":505
}

# Python Annotation

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

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),
        )
]

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=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)]
      )
  )

]

Masks (frame-based)

# Uploading single mask instances
##  For this example we are going to to pass all the annotations payload in a single VideoMaskAnnotation (line 56)

# Single mask

url = "https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_24_composite_mask.png"
response = requests.get(url)
img_bytes = base64.b64encode(response.content).decode('utf-8')

# We are generating our frames and instances in this step, and will later add them to the VideoMaskAnnotation that will contain

# all frames and instances

frames_mask_single=[
lb_types.MaskFrame(
index=20,
im_bytes=response.content # Instead of bytes you could also pass an instance URI : instance_uri=url
)
]
instances_mask_single=[
lb_types.MaskInstance(color_rgb=(76, 104, 177), name= "video_mask")
]

####################################################################################################################################

# Uploading annotations with composite masks

## Add multiple masks using multiple tools in different frames - Note that only once composite mask can exist per frame

frames_cp_mask_url = [
{"1": "https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_1_composite_mask.png"},
{"24": "https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_24_composite_mask.png"},
{"26": "https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_26_composite_mask.png" }
]

rgb_mask_tool = [(227, 135, 126) ,(169, 248, 152),(83, 152, 103)]
cp_masks = []
unique_colors = set()

lb_frames = []
lb_instances = []
counter = 0

for d in frames_cp_mask_url:
for frame_no, v in d.items():
response = requests.get(v)
colors = extract_rgb_colors_from_url(v)
for color in colors:
if not color in unique_colors:
unique_colors.add(color)
name = "video_mask" if color in rgb_mask_tool else "mask_with_text_subclass"
lb_instances.append(lb_types.MaskInstance(color_rgb=color, name=name))
counter += 1
lb_frames.append(
lb_types.MaskFrame(
index=frame_no,
im_bytes=response.content
)
)
cp_masks.append(lb_types.VideoMaskAnnotation(
frames=lb_frames + frames_mask_single,
instances=lb_instances + instances_mask_single
))

pp.pprint(lb_frames)
pp.pprint(cp_masks)

Bounding box with sub-classifications (frame-based)

bbox_dm2 = {
  "top": 146.0,
  "left": 98.0,
  "height": 382.0,
  "width": 341.0
}

# Python Annotation

frame_bbox_with_checklist_subclass_annotation = [
lb_types.VideoObjectAnnotation(
name = "bbox_class",
keyframe=True,
frame=10,
segment_index=0,
value = lb_types.Rectangle(
start=lb_types.Point(x=bbox_dm2["left"], y=bbox_dm2["top"]), # x = left, y = top
end=lb_types.Point(x=bbox_dm2["left"] + bbox_dm2["width"], y=bbox_dm2["top"] + bbox_dm2["height"]), # 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=bbox_dm2["left"], y=bbox_dm2["top"]),
end=lb_types.Point(x=bbox_dm2["left"] + bbox_dm2["width"], y=bbox_dm2["top"] + bbox_dm2["height"]),
),
classifications=[
lb_types.ClassificationAnnotation(
name="checklist_class",
value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(
name="first_checklist_answer")])
)
]
),
lb_types.VideoObjectAnnotation(
name = "bbox_class",
keyframe=True,
frame=13,
segment_index=0,
value = lb_types.Rectangle(
start=lb_types.Point(x=bbox_dm2["left"], y=bbox_dm2["top"]),
end=lb_types.Point(x=bbox_dm2["left"] + bbox_dm2["width"], y=bbox_dm2["top"] + bbox_dm2["height"]),
),
classifications=[
lb_types.ClassificationAnnotation(
name="checklist_class",
value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(
name="second_checklist_answer")])
)
]
)
]

Multiple instances of bounding box annotations in the same frame


bbox_dm = {
"top":617,
"left":1371,
"height":419,
"width":505
}

# Fist instance of bounding box ranging from frame 22 to 27

bbox_annotation_1 = [
lb_types.VideoObjectAnnotation(
name = "bbox_video",
keyframe=True,
frame=22,
segment_index=0,
value = lb_types.Rectangle(
start=lb_types.Point(x=bbox_dm["left"], y=bbox_dm["top"]), # x = left, y = top
end=lb_types.Point(x=bbox_dm["left"] + bbox_dm["width"], y=bbox_dm["top"] + bbox_dm["height"]), # x= left + width , y = top + height
)
),
lb_types.VideoObjectAnnotation(
name = "bbox_video",
keyframe=True,
frame=27,
segment_index=0,
value = lb_types.Rectangle(
start=lb_types.Point(x=bbox_dm["left"], y=bbox_dm["top"]),
end=lb_types.Point(x=bbox_dm["left"] + bbox_dm["width"], y=bbox_dm["top"] + bbox_dm["height"]),
)
)
]

# Second instance of bounding box ranging from frame 22 to 27

bbox_annotation_2 = [
lb_types.VideoObjectAnnotation(
name = "bbox_video",
keyframe=True,
frame=22,
segment_index=0,
value = lb_types.Rectangle(
start=lb_types.Point(x=bbox_dm2["left"], y=bbox_dm2["top"]),
end=lb_types.Point(x=bbox_dm2["left"] + bbox_dm2["width"], y=bbox_dm2["top"] + bbox_dm2["height"]),
)
),
lb_types.VideoObjectAnnotation(
name = "bbox_video",
keyframe=True,
frame=27,
segment_index=0,
value = lb_types.Rectangle(
start=lb_types.Point(x=bbox_dm2["left"], y=bbox_dm2["top"]),
end=lb_types.Point(x=bbox_dm2["left"] + bbox_dm2["width"], y=bbox_dm2["top"] + bbox_dm2["height"]),
)
)
]

Example: Import pre-labels or ground truths

The steps to import annotations as pre-labels (machine-assisted learning) are similar to those to import annotations as ground truth labels. However, they vary slightly, and we will describe the differences for each scenario.

Before you start

The below imports are needed to use the code examples in this section.

import labelbox as lb
import labelbox.types as lb_types
import uuid
from PIL import Image
import requests
import base64
from io import BytesIO

Replace the value of API_KEY with a valid API key to connect to the Labelbox client.

API_KEY = None
client = lb.Client(API_KEY)

Step 1: Import data rows

Data rows must first be uploaded to Catalog to attach annotations. This example shows how to create a data row in Catalog by attaching it to a dataset .

global_key = "sample-video-jellyfish.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",
iam_integration=None # If this argument is removed, labelbox will use the default integration for your organization.
)
task = dataset.create_data_rows([asset])
task.wait_till_done()
print(f"Failed data rows: {task.failed_data_rows}")
print(f"Errors: {task.errors}")

if task.errors:
for error in task.errors:
if 'Duplicate global key' in error['message'] and dataset.row_count == 0: # If the global key already exists in the workspace the dataset will be created empty, so we can delete it.
print(f"Deleting empty dataset: {dataset}")
dataset.delete()

Step 2: Set up ontology

Your project ontology should support the tools and classifications required by your annotations. To ensure accurate schema feature mapping, the value used as the name parameter should match the value of the name field in your annotation. For example, when we created an annotation above, we provided a nameannotation_name. Now, when we set up our ontology, we must ensure that the name of our bounding box tool is also anotations_name. The same alignment must hold true for the other tools and classifications we create in our ontology. This example shows how to create an ontology containing all supported annotation types .

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="checklist_class",
              scope = lb.Classification.Scope.INDEX,
              options=[
                lb.Option(value="first_checklist_answer"),
                lb.Option(value="second_checklist_answer")
              ]
            )
          ]
        ),
      	lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION,
                name="mask_with_text_subclass",
                classifications=[
                    lb.Classification(
                        class_type=lb.Classification.Type.TEXT,
                        name="sub_free_text")
                    ]
                )
    ],
    classifications=[
        lb.Classification(
            class_type=lb.Classification.Type.CHECKLIST,
            name="checklist_class",
            scope = lb.Classification.Scope.INDEX, ## Need to defined 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")
          ]
        ),
        lb.Classification(
          class_type=lb.Classification.Type.TEXT,
          name="free_text"
        )
    ]
)

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

Step 3: Set Up a Labeling Project

project = client.create_project(name="Video Annotation Import Demo",
                                    media_type=lb.MediaType.Video)

## connect ontology to your project

project.connect_ontology(ontology)

Step 4: Send Data Rows to Project

batch = project.create_batch(
  "first-batch-video-demo2", # Each batch in a project must have a unique name
  global_keys=[global_key], # A paginated collection of data row objects, a list of data rows or global keys
  priority=5 # priority between 1(Highest) - 5(lowest)
)

print("Batch: ", batch)

Step 5: Create annotation payloads

For help understanding annotation payloads, see overview. To declare payloads, you can use Python annotation types (preferred) or NDJSON objects. For annotations that you want to import as ground truth labels, you can also specify benchmarks using the is_benchmark_reference flag. These examples demonstrate each format and how to compose annotations into labels attached to data rows.

label = []
annotations_list = [
          checklist_annotation,
          radio_annotation,
          bbox_annotation,
          frame_bbox_with_checklist_subclass_annotation,
          bbox_annotation_1,
          bbox_annotation_2,
          point_annotation,
          polyline_annotation,
          global_checklist_annotation,
          global_radio_annotation,
          nested_checklist_annotation,
          nested_radio_annotation,
          text_annotation,
          cp_masks
      ]

for annotation in annotations_list:
label.append(
lb_types.Label(
data= {"global_key": global_key},
annotations = annotation, # Optional: set the label as a benchmark # Only supported for groud truth imports
is_benchmark_reference = True
)
)

Step 6: Import annotation payload

For prelabeled (model-assisted labeling) scenarios, pass your payload as the value of the predictions parameter. For ground truths, pass the payload to the labels parameter.

Option A: Upload as prelabels (model assisted labeling)

This option is helpful for speeding up the initial labeling process and reducing the manual labeling workload for high-volume datasets.

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

print(f"Errors: {upload_job.errors}", )
print(f"Status of uploads: {upload_job.statuses}"

Option B: Upload to a labeling project as ground truth

This option is helpful for loading high-confidence labels from another platform or previous projects that just need review rather than manual labeling effort.

# Upload label for this data row in project
upload_job = lb.LabelImport.create_from_objects(
    client = client,
    project_id = project.uid,
    name="label_import_job"+str(uuid.uuid4()),
    labels=label
)

print(f"Errors: {upload_job.errors}", )
print(f"Status of uploads: {upload_job.statuses}")

Getting Started

Annotate

Model

Catalog

Schema

Import/Export

Integrations

Manage Team

Examples

Open In Colab

GitHub

​Overview

​Annotation payload types

​Label import types

​Supported annotations

​Tool features are frame-based

​Classifications

​Radio (frame-based)

​Checklist (frame-based)

​Checklist (global)

​Radio (global)

​Free-form text (global)

​Nested classifications (global)

​Tools

​Bounding box (frame-based)

​Point (frame-based)

​Polyline (frame-based)

​Masks (frame-based)

​Bounding box with sub-classifications (frame-based)

​Multiple instances of bounding box annotations in the same frame

​Example: Import pre-labels or ground truths

​Before you start

​Step 1: Import data rows

​Step 2: Set up ontology

​Step 3: Set Up a Labeling Project

​Step 4: Send Data Rows to Project

​Step 5: Create annotation payloads

​Step 6: Import annotation payload

​Option A: Upload as prelabels (model assisted labeling)

​Option B: Upload to a labeling project as ground truth

Overview

Annotation payload types

Label import types

Supported annotations

Tool features are frame-based

Classifications

Radio (frame-based)

Checklist (frame-based)

Checklist (global)

Radio (global)

Free-form text (global)

Nested classifications (global)

Tools

Bounding box (frame-based)

Point (frame-based)

Polyline (frame-based)

Masks (frame-based)

Bounding box with sub-classifications (frame-based)

Multiple instances of bounding box annotations in the same frame

Example: Import pre-labels or ground truths

Before you start

Step 1: Import data rows

Step 2: Set up ontology

Step 3: Set Up a Labeling Project

Step 4: Send Data Rows to Project

Step 5: Create annotation payloads

Step 6: Import annotation payload

Option A: Upload as prelabels (model assisted labeling)

Option B: Upload to a labeling project as ground truth