| # |
| # Licensed to the Apache Software Foundation (ASF) under one or more |
| # contributor license agreements. See the NOTICE file distributed with |
| # this work for additional information regarding copyright ownership. |
| # The ASF licenses this file to You under the Apache License, Version 2.0 |
| # (the "License"); you may not use this file except in compliance with |
| # the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # |
| # 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 |
| |
| |
| def extract_entities_descriptions(response): |
| for result in response.annotation_results: |
| for segment in result.segment_presence_label_annotations: |
| yield segment.entity.description |
| |
| |
| @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' |
| |
| 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() |