Project

from labelbox import MediaType, Client

client = Client(api_key="<YOUR_API_KEY>")

# Create a new project
project = client.create_project(name="my-test-project",
                                description="a description",
                                media_type=MediaType.Image)

from labelbox import Client
from labelbox.schema.queue_mode import QueueMode

client = Client(api_key="<YOUR_API_KEY>")

# Create a batch project with benchmark quality control
benchmark_quality = {
  "auto_audit_percentage": 1,
  "auto_audit_number_of_labels": 1
}
project = client.create_project(name="benchmark-batch-project",
                                description="a description",
                                media_type=MediaType.Image,
                                queue_mode=QueueMode.Batch,
                                **benchmark_quality)

# Create a dataset project with consensus quality control.
# In this case, 10% of all data rows need to be annotated by three labelers.
consensus_quality = {
  "auto_audit_percentage": 0.1,
  "auto_audit_number_of_labels": 3
}
project = client.create_project(name="consensus-dataset-project",
                                description="a description",
                                media_type=MediaType.Image,
                                queue_mode=QueueMode.Dataset,
                                **consensus_quality)

# And if your organization has access to new paradigm, you can create
# a batch-based project with consensus quality control.
# https://labelbox-group.readme.io/docs/migrating-to-workflows#new-projects
project = client.create_project(name="consensus-batch-project",
                                description="a description",
                                media_type=MediaType.Image,
                                queue_mode=QueueMode.Batch,
                                **consensus_quality)

# Get exsiting ontology (you can share an ontology across projects) or create a new one following Ontology section
ontology = client.get_ontology("ONTOLOGY_ID")

# Setup project editor with ontology
project.setup_editor(ontology)

project.upsert_instructions("LOCAL_FILE_PATH (PDF or HTML)")

# Get dataset
dataset = client.get_dataset("DATASET_ID")

#Attach the dataset to the project
project.datasets.connect(dataset)

#Detach the dataset from the project
project.datasets.disconnect(dataset)

 # Create a batch with data row objects from an existent dataset

  # Select a sample of data row objects (optional)
sample = random.sample(list(dataset.export_data_rows()), 3)

batch = project.create_batch(
  "Initial batch",
   sample, # sample of data row objects
  5 # priority between 1(Highest) - 5(lowest)
)


# list batches in a project
for batch in project.batches():
    print(batch.name)
    
   

tags = project.update_project_resource_tags(["tag1", "tag2"])

# Get project by id
project = client.get_project(PROJECT_ID)

# Alternatively you can query by name
projects = client.get_projects(
    where=((labelbox.Project.name == PROJECT_NAME) &
           (labelbox.Project.description == "new description field")))
project = projects.get_one()

ontology = project.ontology()
batches = list(project.batches())

data_rows = project.export_queued_data_rows(timeout_seconds=120)

labels = project.label_generator()

## Alternatively you can specify date range to export desired labels from a project
labels = project.label_generator(start="2020-01-01", end="2020-01-02")

for label in labels:
  print(label.annotations)

project.delete()