Source code for apache_beam.ml.gcp.videointelligenceml_test_it

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# pytype: skip-file

"""An integration test that labels entities appearing in a video and checks
if some expected entities were properly recognized."""

from __future__ import absolute_import
from __future__ import unicode_literals

import unittest

import hamcrest as hc
from nose.plugins.attrib import attr

import apache_beam as beam
from apache_beam.testing.test_pipeline import TestPipeline
from apache_beam.testing.util import assert_that
from apache_beam.testing.util import matches_all

# Protect against environments where Google Cloud VideoIntelligence client is
# not available.
try:
  from apache_beam.ml.gcp.videointelligenceml import AnnotateVideoWithContext
  from google.cloud.videointelligence import enums
  from google.cloud.videointelligence import types
except ImportError:
  AnnotateVideoWithContext = None


[docs]def extract_entities_descriptions(response): for result in response.annotation_results: for segment in result.segment_presence_label_annotations: yield segment.entity.description
[docs]@attr('IT') @unittest.skipIf( AnnotateVideoWithContext is None, 'GCP dependencies are not installed') class VideoIntelligenceMlTestIT(unittest.TestCase): VIDEO_PATH = 'gs://apache-beam-samples/advanced_analytics/video/' \ 'gbikes_dinosaur.mp4'
[docs] def test_label_detection_with_video_context(self): with TestPipeline(is_integration_test=True) as p: output = ( p | beam.Create([( self.VIDEO_PATH, types.VideoContext( label_detection_config=types.LabelDetectionConfig( label_detection_mode=enums.LabelDetectionMode.SHOT_MODE, model='builtin/latest')))]) | AnnotateVideoWithContext(features=[enums.Feature.LABEL_DETECTION]) | beam.ParDo(extract_entities_descriptions) | beam.combiners.ToList()) # Search for at least one entity that contains 'bicycle'. assert_that( output, matches_all([hc.has_item(hc.contains_string('bicycle'))]))
if __name__ == '__main__': unittest.main()