How to import annotations on HTML data and sample import formats.
Open this Colab for an interactive tutorial on importing annotations on HTML data.
Supported annotations
To import annotations in Labelbox, you need to create the annotations payload. In this section, we provide this payload for every annotation type.
Labelbox supports two formats for the annotations payload:
- Python Annotation types (recommended)
- NDJSON
Both are described below.
Classification: Radio (Single-choice)
text_annotation = lb_types.ClassificationAnnotation(
name="text_html",
value=lb_types.Text(answer="sample text")
)
text_annotation_ndjson = {
'name': 'text_html',
'answer': 'sample text',
}
Classification: Checklist (Multi-choice)
checklist_annotation= lb_types.ClassificationAnnotation(
name="checklist_html", # must match your ontology feature's name
value=lb_types.Checklist(
answer = [
lb_types.ClassificationAnswer(
name = "first_checklist_answer"
),
lb_types.ClassificationAnswer(
name = "second_checklist_answer"
)
]
)
)
checklist_annotation_ndjson = {
'name': 'checklist_html',
'answers': [
{'name': 'first_checklist_answer'},
{'name': 'second_checklist_answer'}
]
}
Classification: Free-form text
text_annotation = lb_types.ClassificationAnnotation(
name="text_html",
value=lb_types.Text(answer="sample text")
)
text_annotation_ndjson = {
'name': 'text_html',
'answer': 'sample text',
}
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. Steps 5 and 6 (creating and 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
import labelbox.types as lb_types
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 image data row in Catalog.
global_key="2560px-Kitano_Street_Kobe01s5s41102.jpeg"
test_img_url = {
"row_data": "https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg" ,
"global_key": global_key
}
dataset = client.create_dataset(name="demo_dataset_img")
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 an ontology
Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name fields in your annotations to ensure the correct feature schemas are matched.
For example, when we create the text annotation, we provided the name
as text_html
. Now, when we set up our ontology, we must ensure that the name of the tool is also text_html
. The same alignment must hold true for the other tools and classifications we create in our ontology.
ontology_builder = lb.OntologyBuilder(
classifications=[
lb.Classification(
class_type=lb.Classification.Type.TEXT,
name="text_html"),
lb.Classification(
class_type=lb.Classification.Type.CHECKLIST,
name="checklist_html",
options=[
lb.Option(value="first_checklist_answer"),
lb.Option(value="second_checklist_answer")
]
),
lb.Classification(
class_type=lb.Classification.Type.RADIO,
name="radio_html",
options=[
lb.Option(value="first_radio_answer"),
lb.Option(value="second_radio_answer")
]
)
]
)
ontology = client.create_ontology("Ontology HTML Annotations", ontology_builder.asdict(), media_type=lb.MediaType.Html)
Step 3: Create a labeling project
Connect the ontology to the labeling project.
project = client.create_project(name="html_project",
media_type=lb.MediaType.Html)
# Setup your ontology
project.setup_editor(ontology)
Step 4: Send a batch of data rows to the project
batch = project.create_batch(
"first-batch-html-demo", # Each batch in a project must have a unique name
global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys
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. Both approaches are described below with instructions to compose annotations into Labels attached to the data rows.
The resulting label
and label_ndjson
from each approach will include every annotation (created above) supported by the respective method.
label = []
label.append(
lb_types.Label(
data=lb_types.HTMLData(
global_key=global_key
),
annotations=[
text_annotation,
checklist_annotation,
radio_annotation
]
)
)
label_ndjson = []
for annotations in [text_annotation_ndjson,
checklist_annotation_ndjson,
radio_annotation_ndjson]:
annotations.update({
'dataRow': {
'globalKey': global_key
}
})
label_ndjson.append(annotations)
Step 6: Import the annotation payload
For both options, you can pass either the label
or label_ndjson
payload as the value for the predictions or labels parameter.
Option A: Upload to a labeling project as pre-labels (Model-assisted labeling)
upload_job = lb.MALPredictionImport.create_from_objects(
client = client,
project_id = project.uid,
name=f"mal_job-{str(uuid.uuid4())}",
predictions=label)
upload_job.wait_until_done();
print("Errors:", upload_job.errors)
print("Status of uploads: ", upload_job.statuses)
Option B: Upload to a labeling project as ground truth
upload_job = lb.LabelImport.create_from_objects(
client = client,
project_id = project.uid,
name="label_import_job"+str(uuid.uuid4()),
labels=label_ndjson)
print("Errors:", upload_job.errors)