#
# 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
[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)))
])
| 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()