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Supported annotations
To upload predictions in Labelbox, you need to create a predictions payload. In this section, we provide this payload for every supported prediction type.Uploading confidence scores is optional
If you do not specify a confidence score, the prediction will be treated as if it had a confidence score of 1. Note that confidence scores are not supported forVideoObjectAnnotation objects.Nested ClassificationAnnotation objects inside of a VideoObjectAnnotation can still have a confidence score.Bounding box
# bbox dimensions
bbox_dm = {
"top":617,
"left":1371,
"height":419,
"width":505
}
bbox_prediction = [
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=15,
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"]),
)
),
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"]),
)
)
]
# bbox dimensions
bbox_dm = {
"top":617,
"left":1371,
"height":419,
"width":505
}
bbox_prediction_ndjson = {
"name" : "bbox_video",
"segments" : [{
"keyframes" : [
{
"frame": 13,
"bbox" : bbox_dm
},
{
"frame": 15,
"bbox" : bbox_dm
},
{
"frame": 19,
"bbox" : bbox_dm
}
]
}
]
}
Point
point_prediction = [
lb_types.VideoObjectAnnotation(
name = "point_video",
keyframe=True,
frame=17,
value = lb_types.Point(x=660.134, y=407.926),
)
]
point_prediction_ndjson = {
"name": "point_video",
"confidence": 0.5,
"segments": [{
"keyframes": [{
"frame": 17,
"point" : {
"x": 660.134 ,
"y": 407.926
}
}]
}]
}
Polyline
polyline_prediction = [
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_prediction_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 (global)
global_radio_prediction = [lb_types.ClassificationAnnotation(
name="radio_class_global",
value=lb_types.Radio(answer = lb_types.ClassificationAnswer(
name = "first_radio_answer",
confidence=0.5
))
)]
global_radio_classification_ndjson = {
"name": "radio_class_global",
"answer": { "name": "first_radio_answer", "confidence": 0.5}
}
Classification: Radio (frame-based)
radio_prediction = [
lb_types.VideoClassificationAnnotation(
name="radio_class",
frame=9,
segment_index=0,
value=lb_types.Radio(answer = lb_types.ClassificationAnswer(
name = "first_radio_answer",
confidence=0.5
))
),
lb_types.VideoClassificationAnnotation(
name="radio_class",
frame=15,
segment_index=0,
value=lb_types.Radio(answer = lb_types.ClassificationAnswer(
name = "first_radio_answer",
confidence=0.5
))
)
]
frame_radio_classification_prediction_ndjson = {
"name": "radio_class",
"answer": { "name": "first_radio_answer", "frames": [{"start": 9, "end": 15}]}
}
Classification: Checklist (global)
global_checklist_prediction=[lb_types.ClassificationAnnotation(
name="checklist_class_global",
value=lb_types.Checklist(
answer = [
lb_types.ClassificationAnswer(
name = "first_checklist_answer",
confidence=0.5
),
lb_types.ClassificationAnswer(
name = "second_checklist_answer",
confidence=0.5
)
]
)
)]
global_checklist_classification_ndjson = {
"name": "checklist_class_global",
"answer": [
{ "name": "first_checklist_answer" , "confidence": 0.5},
{ "name": "second_checklist_answer", "confidence": 0.5}
]
}
Classification: Checklist (frame-based)
checklist_prediction= [
lb_types.VideoClassificationAnnotation(
name="checklist_class",
frame=29,
segment_index=0,
value=lb_types.Checklist(
answer = [
lb_types.ClassificationAnswer(
name = "first_checklist_answer",
confidence=0.5
)
]
)
),
lb_types.VideoClassificationAnnotation(
name="checklist_class",
frame=35,
segment_index=0,
value=lb_types.Checklist(
answer = [
lb_types.ClassificationAnswer(
name = "first_checklist_answer",
confidence=0.5
)
]
)
),
lb_types.VideoClassificationAnnotation(
name="checklist_class",
frame=39,
segment_index=1,
value=lb_types.Checklist(
answer = [
lb_types.ClassificationAnswer(
name = "second_checklist_answer",
confidence=0.5
)
]
)
),
lb_types.VideoClassificationAnnotation(
name="checklist_class",
frame=45,
segment_index=1,
value=lb_types.Checklist(
answer = [
lb_types.ClassificationAnswer(
name = "second_checklist_answer",
confidence=0.5
)
]
)
)
]
frame_checklist_classification_prediction_ndjson = {
"name": "checklist_class",
"answer": [
{ "name": "first_checklist_answer" , "frames": [{"start": 29, "end": 35 }]},
{ "name": "second_checklist_answer", "frames": [{"start": 39, "end": 45 }]}
]
}
Classification: Nested radio (global)
nested_radio_prediction =[lb_types.ClassificationAnnotation(
name="nested_radio_question",
value=lb_types.Radio(
answer=lb_types.ClassificationAnswer(
name="first_radio_answer",
confidence=0.5 ,
classifications=[
lb_types.ClassificationAnnotation(
name="sub_radio_question",
value=lb_types.Radio(
answer=lb_types.ClassificationAnswer(
name="first_sub_radio_answer",
confidence=0.5
)
)
)
]
)
)
)]
nested_radio_prediction_ndjson = {
"name": "nested_radio_question",
"answer": {"name": "first_radio_answer", "confidence": 0.5,
"classifications" : [
{"name": "sub_radio_question", "answer": {"name": "first_sub_radio_answer", "confidence": 0.5}}
]
}
}
Classification: Nested checklist (global)
nested_checklist_prediction = [lb_types.ClassificationAnnotation(
name="nested_checklist_question",
value=lb_types.Checklist(
answer=[lb_types.ClassificationAnswer(
name="first_checklist_answer",
confidence=0.5 ,
classifications=[
lb_types.ClassificationAnnotation(
name="sub_checklist_question",
value=lb_types.Checklist(
answer=[lb_types.ClassificationAnswer(
name="first_sub_checklist_answer",
confidence=0.5
)]
))
]
)]
)
)]
nested_checklist_prediction_ndjson = {
"name": "nested_checklist_question",
"answer": [{
"name": "first_checklist_answer",
"confidence": 0.5,
"classifications" : [
{
"name": "sub_checklist_question",
"answer": {"name": "first_sub_checklist_answer", "confidence": 0.5}
}
]
}]
}
Bounding box with sub-classifications (frame-based)
bbox_dm2 = {
"top": 146.0,
"left": 98.0,
"height": 382.0,
"width": 341.0
}
frame_bbox_with_checklist_subclass_prediction = [
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",
confidence=0.5
)])
)
]
),
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",
confidence=0.5
)])
)
]
)
]
frame_bbox_with_checklist_subclass_prediction_ndjson = {
"name": "bbox_class",
"segments": [{
"keyframes": [
{
"frame": 10,
"bbox": bbox_dm2
},
{
"frame": 11,
"bbox": bbox_dm2,
"classifications": [
{
"name": "bbox_radio",
"answer": [{"name": "first_checklist_answer", "confidence": 0.5}]
}
]
},
{
"frame": 13,
"bbox": bbox_dm2,
"classifications": [
{
"name": "bbox_radio",
"answer": [{"name": "second_checklist_answer", "confidence": 0.5}]
}
]
}
]
}
]
}
Text
text_prediction = [lb_types.ClassificationAnnotation(
name="free_text", # must match your ontology feature's name
value=lb_types.Text(answer="sample text", confidence=0.5)
)]
text_prediction_ndjson = {
'name': 'free_text',
'confidence': 0.5,
'answer': 'sample text',
}
Masks
Raster segmentation masks are not yet supported in the Model product.End-to-end example: Upload predictions to a Model Run
Follow the steps below to upload predictions to a model run.Before you start
You will need to import these libraries to use the code examples in this section:import labelbox as lb
import uuid
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 into Catalog
global_key = "sample-video-2.mp4"
test_img_url = {
"row_data": "https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4",
"global_key": global_key
}
dataset = client.create_dataset(
name="Video prediction demo",
iam_integration=None # Removing this argument will default to the organziation's default iam integration
)
task = dataset.create_data_rows([test_img_url])
task.wait_till_done()
print("Errors: ",task.errors)
print("Failed data rows: ",task.failed_data_rows)
Step 2: Create/select an ontology for your model predictions
Your project should have the correct ontology setup with all the tools and classifications supported for your predictions and the tool and classificationsname should match the name fields in your predictions to ensure the correct feature schemas are matched.
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.BBOX, name="bbox_class",
classifications=[
lb.Classification(
class_type=lb.Classification.Type.CHECKLIST,
name="checklist_class",
scope = lb.Classification.Scope.INDEX, ## defined scope for frame classifications
options=[
lb.Option(value="first_checklist_answer"),
lb.Option(value="second_checklist_answer")
]
)
]
)
],
classifications=[
lb.Classification(
class_type=lb.Classification.Type.CHECKLIST,
name="checklist_class",
scope = lb.Classification.Scope.INDEX, ## 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("Ontology Video Annotations",
ontology_builder.asdict(),
media_type=lb.MediaType.Video
)
Step 3: Create a Model and model run
# create Model
model = client.create_model(name="video_model_run_" + str(uuid.uuid4()),
ontology_id=ontology.uid)
# create model run
model_run = model.create_model_run("iteration 1")
Step 4: Send data rows to the model run
model_run.upsert_data_rows(global_keys=[global_key])
Step 5: Create the predictions payload
Create the annotations payload using the snippets of code here.label_predictions = []
annotations_list = [
point_prediction,
bbox_prediction,
polyline_prediction,
checklist_prediction,
radio_prediction,
nested_radio_prediction,
nested_checklist_prediction,
frame_bbox_with_checklist_subclass_prediction,
global_radio_prediction,
global_checklist_prediction,
text_prediction
]
flatten_list_annotations = [ann for ann_sublist in annotations_list for ann in ann_sublist]
label_predictions.append(
lb_types.Label(
data={"global_key" : global_key },
annotations = flatten_list_annotations
)
)
# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations
label_prediction_ndjson = []
for annotation in [
point_prediction_ndjson,
bbox_prediction_ndjson,
polyline_prediction_ndjson,
frame_checklist_classification_prediction_ndjson,
frame_radio_classification_prediction_ndjson,
nested_radio_prediction_ndjson,
nested_checklist_prediction_ndjson,
frame_bbox_with_checklist_subclass_prediction_ndjson,
global_radio_classification_ndjson,
global_checklist_classification_ndjson,
video_mask_prediction_ndjson,
text_prediction_ndjson
]:
annotation.update({
"dataRow": {
"globalKey": global_key
}
})
label_prediction_ndjson.append(annotation)
Step 6: Upload the predictions payload to the model run
# Upload the prediction label to the Model Run
upload_job_prediction = model_run.add_predictions(
name="prediction_upload_job"+str(uuid.uuid4()),
predictions=label_predictions)
# Errors will appear for annotation uploads that failed.
print("Errors:", upload_job_prediction.errors)
print("Status of uploads: ", upload_job_prediction.statuses)
Step 7: Send annotations to the model run (optional)
# 7.1 Create a labelbox project
# Create a Labelbox project
project = client.create_project(name="video_prediction_demo",
media_type=lb.MediaType.Video)
project.connect_ontology(ontology)
# 7.2 Create a batch to send to the project
project.create_batch(
"batch_video_prediction_demo", # Each batch in a project must have a unique name
global_keys=[global_key], # A list of data rows, data row ids or global keys
priority=5 # priority between 1(Highest) - 5(lowest)
)
# 7.3 create the annotations payload
# See here for more details:
# /reference/import-video-annotations#supported-annotations
checklist_annotation ...
radio_annotation ...
bbox_annotation ...
frame_bbox_with_checklist_subclass ...
point_annotation ...
polyline_annotation ...
global_checklist_annotation ...
global_radio_annotation ...
nested_checklist_annotation ...
nested_radio_annotation ...
text_annotation ...
# 7.4 Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations
labels = []
annotations_list = [
checklist_annotation,
radio_annotation,
bbox_annotation,
frame_bbox_with_checklist_subclass,
point_annotation,
polyline_annotation,
global_checklist_annotation,
global_radio_annotation,
nested_checklist_annotation,
nested_radio_annotation,
text_annotation
]
flatten_list_annotations = [ann for ann_sublist in annotations_list for ann in ann_sublist]
labels.append(
lb_types.Label(
data={"global_key" : global_key },
annotations = flatten_list_annotations
)
)
# 7.5 Upload annotations to the project using Label Import
upload_job_annotation = lb.LabelImport.create_from_objects(
client = client,
project_id = project.uid,
name="video_annotations_import_" + str(uuid.uuid4()),
labels=labels)
upload_job_annotation.wait_until_done()
# Errors will appear for annotation uploads that failed.
print("Errors:", upload_job_annotation.errors)
print("Status of uploads: ", upload_job_annotation.statuses)
# Errors will appear for annotation uploads that failed.
print("Errors:", upload_job_annotation.errors)
# 7.6 Send the annotations to the Model Run
# get the labels id from the project
model_run.upsert_labels(project_id=project.uid)