| # |
| # 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 |
| |
| from __future__ import absolute_import |
| |
| import unittest |
| |
| 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 equal_to |
| |
| # Protect against environments where Google Cloud Vision client is not |
| # available. |
| try: |
| from apache_beam.ml.gcp.visionml import AnnotateImage |
| from google.cloud import vision |
| except ImportError: |
| vision = None |
| |
| |
| def extract(response): |
| for r in response.responses: |
| for text_annotation in r.text_annotations: |
| yield text_annotation.description |
| |
| |
| @attr('IT') |
| @unittest.skipIf(vision is None, 'GCP dependencies are not installed') |
| class VisionMlTestIT(unittest.TestCase): |
| def test_text_detection_with_language_hint(self): |
| IMAGES_TO_ANNOTATE = [ |
| 'gs://apache-beam-samples/advanced_analytics/vision/sign.jpg' |
| ] |
| IMAGE_CONTEXT = [vision.types.ImageContext(language_hints=['en'])] |
| |
| with TestPipeline(is_integration_test=True) as p: |
| contexts = p | 'Create context' >> beam.Create( |
| dict(zip(IMAGES_TO_ANNOTATE, IMAGE_CONTEXT))) |
| |
| output = ( |
| p |
| | beam.Create(IMAGES_TO_ANNOTATE) |
| | AnnotateImage( |
| features=[vision.types.Feature(type='TEXT_DETECTION')], |
| context_side_input=beam.pvalue.AsDict(contexts)) |
| | beam.ParDo(extract)) |
| |
| assert_that( |
| output, |
| equal_to([ |
| 'WAITING?\nPLEASE\nTURN OFF\nYOUR\nENGINE', |
| 'WAITING?', |
| 'PLEASE', |
| 'TURN', |
| 'OFF', |
| 'YOUR', |
| 'ENGINE' |
| ])) |
| |
| |
| if __name__ == '__main__': |
| unittest.main() |