github.com/apache/beam/sdks/v2@v2.48.2/python/apache_beam/ml/gcp/videointelligenceml_test_it.py (about)

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     9  #    http://www.apache.org/licenses/LICENSE-2.0
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    14  # See the License for the specific language governing permissions and
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    16  #
    17  # pytype: skip-file
    18  
    19  """An integration test that labels entities appearing in a video and checks
    20  if some expected entities were properly recognized."""
    21  
    22  import unittest
    23  
    24  import hamcrest as hc
    25  import pytest
    26  
    27  import apache_beam as beam
    28  from apache_beam.testing.test_pipeline import TestPipeline
    29  from apache_beam.testing.util import assert_that
    30  from apache_beam.testing.util import matches_all
    31  
    32  # Protect against environments where Google Cloud VideoIntelligence client is
    33  # not available.
    34  try:
    35    from apache_beam.ml.gcp.videointelligenceml import AnnotateVideoWithContext
    36    from google.cloud.videointelligence import enums
    37    from google.cloud.videointelligence import types
    38  except ImportError:
    39    AnnotateVideoWithContext = None
    40  
    41  
    42  def extract_entities_descriptions(response):
    43    for result in response.annotation_results:
    44      for segment in result.segment_presence_label_annotations:
    45        yield segment.entity.description
    46  
    47  
    48  @pytest.mark.it_postcommit
    49  @unittest.skipIf(
    50      AnnotateVideoWithContext is None, 'GCP dependencies are not installed')
    51  class VideoIntelligenceMlTestIT(unittest.TestCase):
    52    VIDEO_PATH = 'gs://apache-beam-samples/advanced_analytics/video/' \
    53                 'gbikes_dinosaur.mp4'
    54  
    55    def test_label_detection_with_video_context(self):
    56      with TestPipeline(is_integration_test=True) as p:
    57        output = (
    58            p
    59            | beam.Create([(
    60                self.VIDEO_PATH,
    61                types.VideoContext(
    62                    label_detection_config=types.LabelDetectionConfig(
    63                        label_detection_mode=enums.LabelDetectionMode.SHOT_MODE,
    64                        model='builtin/latest')))])
    65            | AnnotateVideoWithContext(features=[enums.Feature.LABEL_DETECTION])
    66            | beam.ParDo(extract_entities_descriptions)
    67            | beam.combiners.ToList())
    68  
    69        # Search for at least one entity that contains 'bicycle'.
    70        assert_that(
    71            output, matches_all([hc.has_item(hc.contains_string('bicycle'))]))
    72  
    73  
    74  if __name__ == '__main__':
    75    unittest.main()