Source code for tests.system.providers.google.cloud.video_intelligence.example_video_intelligence

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"""
Example Airflow DAG that demonstrates operators for the Google Cloud Video Intelligence service in the Google
Cloud Platform.

This DAG relies on the following OS environment variables:

* BUCKET_NAME - Google Cloud Storage bucket where the file exists.
"""

from __future__ import annotations

import os
from datetime import datetime

from google.api_core.retry import Retry

from airflow.models.baseoperator import chain
from airflow.models.dag import DAG
from airflow.operators.bash import BashOperator
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator
from airflow.providers.google.cloud.operators.video_intelligence import (
    CloudVideoIntelligenceDetectVideoExplicitContentOperator,
    CloudVideoIntelligenceDetectVideoLabelsOperator,
    CloudVideoIntelligenceDetectVideoShotsOperator,
)
from airflow.providers.google.cloud.transfers.gcs_to_gcs import GCSToGCSOperator
from airflow.utils.trigger_rule import TriggerRule

[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")

[docs]DAG_ID = "example_gcp_video_intelligence"

# Public bucket holding the sample data
[docs]BUCKET_NAME_SRC = "cloud-samples-data"
# Path to the data inside the public bucket
[docs]PATH_SRC = "video/cat.mp4"

# [START howto_operator_video_intelligence_os_args]
[docs]BUCKET_NAME_DST = f"bucket-src-{DAG_ID}-{ENV_ID}"
# [END howto_operator_video_intelligence_os_args]

[docs]FILE_NAME = "video.mp4"

# [START howto_operator_video_intelligence_other_args]
[docs]INPUT_URI = f"gs://{BUCKET_NAME_DST}/{FILE_NAME}"
# [END howto_operator_video_intelligence_other_args]

with DAG(
    DAG_ID,
    schedule="@once",
    start_date=datetime(2021, 1, 1),
    catchup=False,
    tags=["example"],
) as dag:
[docs]    create_bucket = GCSCreateBucketOperator(task_id="create_bucket", bucket_name=BUCKET_NAME_DST)

    copy_single_file = GCSToGCSOperator(
        task_id="copy_single_gcs_file",
        source_bucket=BUCKET_NAME_SRC,
        source_object=PATH_SRC,
        destination_bucket=BUCKET_NAME_DST,
        destination_object=FILE_NAME,
    )

    # [START howto_operator_video_intelligence_detect_labels]
    detect_video_label = CloudVideoIntelligenceDetectVideoLabelsOperator(
        input_uri=INPUT_URI,
        output_uri=None,
        video_context=None,
        timeout=5,
        task_id="detect_video_label",
    )
    # [END howto_operator_video_intelligence_detect_labels]

    # [START howto_operator_video_intelligence_detect_labels_result]
    detect_video_label_result = BashOperator(
        bash_command="echo {{ task_instance.xcom_pull('detect_video_label')"
        "['annotationResults'][0]['shotLabelAnnotations'][0]['entity']}}",
        task_id="detect_video_label_result",
    )
    # [END howto_operator_video_intelligence_detect_labels_result]

    # [START howto_operator_video_intelligence_detect_explicit_content]
    detect_video_explicit_content = CloudVideoIntelligenceDetectVideoExplicitContentOperator(
        input_uri=INPUT_URI,
        output_uri=None,
        video_context=None,
        retry=Retry(maximum=10.0),
        timeout=5,
        task_id="detect_video_explicit_content",
    )
    # [END howto_operator_video_intelligence_detect_explicit_content]

    # [START howto_operator_video_intelligence_detect_explicit_content_result]
    detect_video_explicit_content_result = BashOperator(
        bash_command="echo {{ task_instance.xcom_pull('detect_video_explicit_content')"
        "['annotationResults'][0]['explicitAnnotation']['frames'][0]}}",
        task_id="detect_video_explicit_content_result",
    )
    # [END howto_operator_video_intelligence_detect_explicit_content_result]

    # [START howto_operator_video_intelligence_detect_video_shots]
    detect_video_shots = CloudVideoIntelligenceDetectVideoShotsOperator(
        input_uri=INPUT_URI,
        output_uri=None,
        video_context=None,
        retry=Retry(maximum=10.0),
        timeout=5,
        task_id="detect_video_shots",
    )
    # [END howto_operator_video_intelligence_detect_video_shots]

    # [START howto_operator_video_intelligence_detect_video_shots_result]
    detect_video_shots_result = BashOperator(
        bash_command="echo {{ task_instance.xcom_pull('detect_video_shots')"
        "['annotationResults'][0]['shotAnnotations'][0]}}",
        task_id="detect_video_shots_result",
    )
    # [END howto_operator_video_intelligence_detect_video_shots_result]

    delete_bucket = GCSDeleteBucketOperator(
        task_id="delete_bucket", bucket_name=BUCKET_NAME_DST, trigger_rule=TriggerRule.ALL_DONE
    )

    chain(
        # TEST SETUP
        create_bucket,
        copy_single_file,
        # TEST BODY
        detect_video_label,
        detect_video_label_result,
        detect_video_explicit_content,
        detect_video_explicit_content_result,
        detect_video_shots,
        detect_video_shots_result,
        # TEST TEARDOWN
        delete_bucket,
    )

    from tests.system.utils.watcher import watcher

    # This test needs watcher in order to properly mark success/failure
    # when "tearDown" task with trigger rule is part of the DAG
    list(dag.tasks) >> watcher()


from tests.system.utils import get_test_run  # noqa: E402

# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
[docs]test_run = get_test_run(dag)