Task Agents (SDK)

# Import dependencies
from encord.user_client import EncordUserClient
from encord.workflow import AgentStage

# Authenticate using the path to your private key
user_client = EncordUserClient.create_with_ssh_private_key(
ssh_private_key_path="<private_key_path>"
)

# Specify the Project that contains the Task agent. Replace <project_hash> with the hash of your Project
project = user_client.get_project(<project_hash>)

# Specify the Task Agent
agent_stage = project.workflow.get_stage(name="Agent 1", type_=AgentStage)
for task in agent_stage.get_tasks():

    # Now you have the agent task containing the data hash
    # Insert you custom logic here

# When the custom logic is completed, the task can be moved forward to the selected pathway
task.proceed(pathway_name="continue to Review")

#Import Dependencies
import random
from dataclasses import dataclass
from typing import Iterable

import numpy as np
from encord.objects.coordinates import BoundingBoxCoordinates
from encord.objects.ontology_labels_impl import LabelRowV2
from encord.project import Project
from encord_agents.core.data_model import Frame
from encord_agents.tasks import Depends, Runner
from encord_agents.tasks.dependencies import dep_video_iterator
from numpy.typing import NDArray
from typing_extensions import Annotated


# Set the Environment variable to authenticate with Encord
ENCORD_SSH_KEY_FILE="/path/to/file/" prelabel_video.py

runner = Runner(project_hash="<project_hash>")

# === BEGIN MOCK MODEL === #
@dataclass
class ModelPrediction:
    label: int
    coords: BoundingBoxCoordinates
    conf: float


def fake_predict(image: NDArray[np.uint8]) -> list[ModelPrediction]:
    return [
        ModelPrediction(
            label=random.choice(range(3)),
            coords=BoundingBoxCoordinates(
                top_left_x=random.random() * 0.5,
                top_left_y=random.random() * 0.5,
                width=random.random() * 0.5,
                height=random.random() * 0.5,
            ),
            conf=random.random() + 0.5,
        )
        for _ in range(10)
    ]


model = fake_predict
# === END MOCK MODEL === #

@runner.stage(stage="Pre-Labeling Agent")
def run_something(
    lr: LabelRowV2,
    project: Project,
    frames: Annotated[Iterable[Frame], Depends(dep_video_iterator)],
) -> str:
    ontology = project.ontology_structure

    # Create an object instance for each frame in the video and save the labels. 
    for frame in frames:
        outputs = model(frame.content)
        for output in outputs:
            ins = ontology.objects[output.label].create_instance()
            ins.set_for_frames(
                frames=frame.frame, coordinates=output.coords, confidence=output.conf
            )

            lr.add_object_instance(ins)

    lr.save()

    # Return the name of the stage the task should progress to
    return "Annotate 1"  


if __name__ == "__main__":
    runner.run()