Adds tutorials using Cloud Client [(#930)](#930)

* Adds tutorials.

* Removes unused enumerate

videointelligence/samples/analyze/README.md

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+# Google Cloud Video Intelligence Sample
+
+Demonstrates face detection, label detection, and shot change detection using
+the Google Cloud API..
+
+## Setup
+Please follow the [Set Up Your Project](https://cloud.google.com/video-intelligence/docs/getting-started#set_up_your_project)
+steps in the Quickstart doc to create a project and enable the Google Cloud
+Video Intelligence API. Following those steps, make sure that you
+[Set Up a Service Account](https://cloud.google.com/video-intelligence/docs/common/auth#set_up_a_service_account),
+and export the following environment variable:
+
+```
+export GOOGLE_APPLICATION_CREDENTIALS=/path/to/your-project-credentials.json
+```
+
+## Run the sample
+
+Install [pip](https://pip.pypa.io/en/stable/installing) if not already installed.
+
+Install the necessary libraries using pip:
+
+```sh
+$ pip install -r requirements.txt
+```
+
+Run the sample, for example:
+```
+python analyze.py faces gs://demomaker/google_gmail.mp4
+python analyze.py labels gs://demomaker/cat.mp4
+python analyze.py shots gs://demomaker/gbikes_dinosaur.mp4
+```

videointelligence/samples/analyze/analyze.py

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+#!/usr/bin/env python
+
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed 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.
+
+"""This application demonstrates how to perform basic operations with the
+Google Cloud Video Intelligence API.
+
+For more information, check out the documentation at
+https://cloud.google.com/videointelligence/docs.
+"""
+
+import argparse
+import sys
+import time
+
+from google.cloud.gapic.videointelligence.v1beta1 import enums
+from google.cloud.gapic.videointelligence.v1beta1 import (
+    video_intelligence_service_client)
+
+
+def analyze_faces(path):
+    """ Detects faces given a GCS path. """
+    video_client = (video_intelligence_service_client.
+                    VideoIntelligenceServiceClient())
+    features = [enums.Feature.FACE_DETECTION]
+    operation = video_client.annotate_video(path, features)
+    print('\nProcessing video for label annotations:')
+
+    while not operation.done():
+        sys.stdout.write('.')
+        sys.stdout.flush()
+        time.sleep(20)
+
+    print('\nFinished processing.')
+
+    # first result is retrieved because a single video was processed
+    face_annotations = (operation.result().annotation_results[0].
+                        face_annotations)
+
+    for face_id, face in enumerate(face_annotations):
+        print('Thumbnail size: {}'.format(len(face.thumbnail)))
+
+        for segment_id, segment in enumerate(face.segments):
+            print('Track {}: {} to {}'.format(
+                segment_id,
+                segment.start_time_offset,
+                segment.end_time_offset))
+
+
+def analyze_labels(path):
+    """ Detects labels given a GCS path. """
+    video_client = (video_intelligence_service_client.
+                    VideoIntelligenceServiceClient())
+    features = [enums.Feature.LABEL_DETECTION]
+    operation = video_client.annotate_video(path, features)
+    print('\nProcessing video for label annotations:')
+
+    while not operation.done():
+        sys.stdout.write('.')
+        sys.stdout.flush()
+        time.sleep(20)
+
+    print('\nFinished processing.')
+
+    # first result is retrieved because a single video was processed
+    results = operation.result().annotation_results[0]
+
+    for label in results.label_annotations:
+        print('Label description: {}'.format(label.description))
+        print('Locations:')
+
+        for l, location in enumerate(label.locations):
+            print('\t{}: {} to {}'.format(
+                l,
+                location.segment.start_time_offset,
+                location.segment.end_time_offset))
+
+
+def analyze_shots(path):
+    """ Detects camera shot changes. """
+    video_client = (video_intelligence_service_client.
+                    VideoIntelligenceServiceClient())
+    features = [enums.Feature.SHOT_CHANGE_DETECTION]
+    operation = video_client.annotate_video(path, features)
+    print('\nProcessing video for shot change annotations:')
+
+    while not operation.done():
+        sys.stdout.write('.')
+        sys.stdout.flush()
+        time.sleep(20)
+
+    print('\nFinished processing.')
+
+    # first result is retrieved because a single video was processed
+    shots = operation.result().annotation_results[0]
+
+    for note, shot in enumerate(shots.shot_annotations):
+        print('Scene {}: {} to {}'.format(
+            note,
+            shot.start_time_offset,
+            shot.end_time_offset))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description=__doc__,
+        formatter_class=argparse.RawDescriptionHelpFormatter)
+    subparsers = parser.add_subparsers(dest='command')
+    analyze_faces_parser = subparsers.add_parser(
+        'faces', help=analyze_faces.__doc__)
+    analyze_faces_parser.add_argument('path')
+    analyze_labels_parser = subparsers.add_parser(
+        'labels', help=analyze_labels.__doc__)
+    analyze_labels_parser.add_argument('path')
+    analyze_shots_parser = subparsers.add_parser(
+        'shots', help=analyze_shots.__doc__)
+    analyze_shots_parser.add_argument('path')
+
+    args = parser.parse_args()
+
+    if args.command == 'faces':
+        analyze_faces(args.path)
+    if args.command == 'labels':
+        analyze_labels(args.path)
+    if args.command == 'shots':
+        analyze_shots(args.path)

videointelligence/samples/analyze/analyze_test.py

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+#!/usr/bin/env python
+
+# Copyright 2017 Google, Inc
+#
+# Licensed 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.
+
+import os
+
+import pytest
+
+import analyze
+
+BUCKET = os.environ['CLOUD_STORAGE_BUCKET']
+LABELS_FILE_PATH = '/video/cat.mp4'
+FACES_FILE_PATH = '/video/googlework.mp4'
+SHOTS_FILE_PATH = '/video/gbikes_dinosaur.mp4'
+
+
+@pytest.mark.slow
+def test_cat_video_shots(capsys):
+    analyze.analyze_shots(
+        'gs://{}{}'.format(BUCKET, SHOTS_FILE_PATH))
+    out, _ = capsys.readouterr()
+    assert 'Scene 1:' in out
+
+
+@pytest.mark.slow
+def test_work_video_faces(capsys):
+    analyze.analyze_faces(
+        'gs://{}{}'.format(BUCKET, FACES_FILE_PATH))
+    out, _ = capsys.readouterr()
+    assert 'Thumbnail' in out
+
+
+@pytest.mark.slow
+def test_dino_video_labels(capsys):
+    analyze.analyze_labels(
+        'gs://{}{}'.format(BUCKET, LABELS_FILE_PATH))
+    out, _ = capsys.readouterr()
+    assert 'Whiskers' in out

videointelligence/samples/analyze/requirements.txt

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+https://storage.googleapis.com/videointelligence-alpha/videointelligence-python.zip