Adds new examples, replaces markdown with restructured text

video/cloud-client/analyze/README.rst

@@ -0,0 +1,126 @@
+.. This file is automatically generated. Do not edit this file directly.
+
+Google Cloud Video Intelligence API Python Samples
+===============================================================================
+
+This directory contains samples for Google Cloud Video Intelligence API. `Google Cloud Video Intelligence API`_ allows developers to easily integrate feature detection in video.
+
+
+
+
+.. _Google Cloud Video Intelligence API: https://cloud.google.com/video-intelligence/docs
+
+Setup
+-------------------------------------------------------------------------------
+
+
+Authentication
+++++++++++++++
+
+Authentication is typically done through `Application Default Credentials`_,
+which means you do not have to change the code to authenticate as long as
+your environment has credentials. You have a few options for setting up
+authentication:
+
+#. When running locally, use the `Google Cloud SDK`_
+
+    .. code-block:: bash
+
+        gcloud auth application-default login
+
+
+#. When running on App Engine or Compute Engine, credentials are already
+   set-up. However, you may need to configure your Compute Engine instance
+   with `additional scopes`_.
+
+#. You can create a `Service Account key file`_. This file can be used to
+   authenticate to Google Cloud Platform services from any environment. To use
+   the file, set the ``GOOGLE_APPLICATION_CREDENTIALS`` environment variable to
+   the path to the key file, for example:
+
+    .. code-block:: bash
+
+        export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service_account.json
+
+.. _Application Default Credentials: https://cloud.google.com/docs/authentication#getting_credentials_for_server-centric_flow
+.. _additional scopes: https://cloud.google.com/compute/docs/authentication#using
+.. _Service Account key file: https://developers.google.com/identity/protocols/OAuth2ServiceAccount#creatinganaccount
+
+Install Dependencies
+++++++++++++++++++++
+
+#. Install `pip`_ and `virtualenv`_ if you do not already have them.
+
+#. Create a virtualenv. Samples are compatible with Python 2.7 and 3.4+.
+
+    .. code-block:: bash
+
+        $ virtualenv env
+        $ source env/bin/activate
+
+#. Install the dependencies needed to run the samples.
+
+    .. code-block:: bash
+
+        $ pip install -r requirements.txt
+
+.. _pip: https://pip.pypa.io/
+.. _virtualenv: https://virtualenv.pypa.io/
+
+Samples
+-------------------------------------------------------------------------------
+
+analyze
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+
+
+To run this sample:
+
+.. code-block:: bash
+
+    $ python analyze.py
+
+    usage: analyze.py [-h] {faces,labels,labels_file,safe_search,shots} ...
+
+    This application demonstrates face detection, label detection, safe search,
+    and shot change detection using the Google Cloud API.
+
+    Usage Examples:
+
+        python analyze.py faces gs://demomaker/volleyball_court.mp4
+        python analyze.py labels gs://demomaker/cat.mp4
+        python analyze.py labels_file resources/cat.mp4
+        python analyze.py shots gs://demomaker/gbikes_dinosaur.mp4
+        python analyze.py safe_search gs://demomaker/cat.mp4
+
+    positional arguments:
+      {faces,labels,labels_file,safe_search,shots}
+        faces               Detects faces given a GCS path.
+        labels              Detects labels given a GCS path.
+        labels_file         Detects labels given a file path.
+        safe_search         Detects safe search features the GCS path to a video.
+        shots               Detects camera shot changes.
+
+    optional arguments:
+      -h, --help            show this help message and exit
+
+
+
+
+The client library
+-------------------------------------------------------------------------------
+
+This sample uses the `Google Cloud Client Library for Python`_.
+You can read the documentation for more details on API usage and use GitHub
+to `browse the source`_ and  `report issues`_.
+
+.. Google Cloud Client Library for Python:
+    https://googlecloudplatform.github.io/google-cloud-python/
+.. browse the source:
+    https://github.com/GoogleCloudPlatform/google-cloud-python
+.. report issues:
+    https://github.com/GoogleCloudPlatform/google-cloud-python/issues
+
+
+.. _Google Cloud SDK: https://cloud.google.com/sdk/

video/cloud-client/analyze/README.rst.in

@@ -0,0 +1,20 @@
+# This file is used to generate README.rst
+
+product:
+  name: Google Cloud Video Intelligence API
+  short_name: Cloud Video Intelligence API
+  url: https://cloud.google.com/video-intelligence/docs
+  description: >
+    `Google Cloud Video Intelligence API`_ allows developers to easily
+    integrate feature detection in video.
+
+setup:
+- auth
+- install_deps
+
+samples:
+- name: analyze
+  file: analyze.py
+  show_help: True
+
+cloud_client_library: true

video/cloud-client/analyze/analyze.py

@@ -14,14 +14,21 @@
 # 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.
+"""This application demonstrates face detection, label detection, safe search,
+and shot change detection using the Google Cloud API.
+
+Usage Examples:
+
+    python analyze.py faces gs://demomaker/volleyball_court.mp4
+    python analyze.py labels gs://demomaker/cat.mp4
+    python analyze.py labels_file resources/cat.mp4
+    python analyze.py shots gs://demomaker/gbikes_dinosaur.mp4
+    python analyze.py safe_search gs://demomaker/cat.mp4
 
-For more information, check out the documentation at
-https://cloud.google.com/videointelligence/docs.
 """
 
 import argparse
+import base64
 import sys
 import time
 
@@ -30,18 +37,49 @@
     video_intelligence_service_client)
 
 
+def analyze_safe_search(path):
+    """ Detects safe search features the GCS path to a video. """
+    video_client = (video_intelligence_service_client.
+                    VideoIntelligenceServiceClient())
+    features = [enums.Feature.SAFE_SEARCH_DETECTION]
+    operation = video_client.annotate_video(path, features)
+    print('\nProcessing video for safe search annotations:')
+
+    while not operation.done():
+        sys.stdout.write('.')
+        sys.stdout.flush()
+        time.sleep(1)
+
+    print('\nFinished processing.')
+
+    # first result is retrieved because a single video was processed
+    safe_annotations = (operation.result().annotation_results[0].
+                        safe_search_annotations)
+
+    likely_string = ("Unknown", "Very unlikely", "Unlikely", "Possible",
+                     "Likely", "Very likely")
+
+    for note in safe_annotations:
+        print('Time: {}s').format(note.time_offset / 1000000.0)
+        print('\tadult: {}').format(likely_string[note.adult])
+        print('\tspoof: {}').format(likely_string[note.spoof])
+        print('\tmedical: {}').format(likely_string[note.medical])
+        print('\tracy: {}').format(likely_string[note.racy])
+        print('\tviolent: {}\n').format(likely_string[note.violent])
+
+
 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:')
+    print('\nProcessing video for face annotations:')
 
     while not operation.done():
         sys.stdout.write('.')
         sys.stdout.flush()
-        time.sleep(20)
+        time.sleep(1)
 
     print('\nFinished processing.')
 
@@ -53,10 +91,16 @@ def analyze_faces(path):
         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))
+            positions = 'Entire video'
+            if (segment.start_time_offset != -1 or
+                    segment.end_time_offset != -1):
+                positions = '{}s to {}s'.format(
+                    segment.start_time_offset / 1000000.0,
+                    segment.end_time_offset / 1000000.0)
+
+            print('\tTrack {}: {}'.format(segment_id, positions))
+
+        print('\n')
 
 
 def analyze_labels(path):
@@ -70,22 +114,66 @@ def analyze_labels(path):
     while not operation.done():
         sys.stdout.write('.')
         sys.stdout.flush()
-        time.sleep(20)
+        time.sleep(1)
+
+    print('\nFinished processing.')
+
+    # first result is retrieved because a single video was processed
+    results = operation.result().annotation_results[0]
+
+    for i, label in enumerate(results.label_annotations):
+        print('Label description: {}'.format(label.description))
+        print('Locations:')
+
+        for l, location in enumerate(label.locations):
+            positions = 'Entire video'
+            if (location.segment.start_time_offset != -1 or
+                    location.segment.end_time_offset != -1):
+                positions = '{}s to {}s'.format(
+                    location.segment.start_time_offset / 1000000.0,
+                    location.segment.end_time_offset / 1000000.0)
+            print('\t{}: {}'.format(l, positions))
+
+        print('\n')
+
+
+def analyze_labels_file(path):
+    """ Detects labels given a file path. """
+    video_client = (video_intelligence_service_client.
+                    VideoIntelligenceServiceClient())
+    features = [enums.Feature.LABEL_DETECTION]
+
+    with open(path, "rb") as movie:
+        content_base64 = base64.b64encode(movie.read())
+
+    operation = video_client.annotate_video('', features,
+                                            input_content=content_base64)
+    print('\nProcessing video for label annotations:')
+
+    while not operation.done():
+        sys.stdout.write('.')
+        sys.stdout.flush()
+        time.sleep(1)
 
     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:
+    for i, label in enumerate(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))
+            positions = 'Entire video'
+            if (location.segment.start_time_offset != -1 or
+                    location.segment.end_time_offset != -1):
+                positions = '{} to {}'.format(
+                    location.segment.start_time_offset / 1000000.0,
+                    location.segment.end_time_offset / 1000000.0)
+            print('\t{}: {}'.format(l, positions))
+
+        print('\n')
 
 
 def analyze_shots(path):
@@ -99,18 +187,18 @@ def analyze_shots(path):
     while not operation.done():
         sys.stdout.write('.')
         sys.stdout.flush()
-        time.sleep(20)
+        time.sleep(1)
 
     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(
+        print('\tScene {}: {} to {}'.format(
             note,
-            shot.start_time_offset,
-            shot.end_time_offset))
+            shot.start_time_offset / 1000000.0,
+            shot.end_time_offset / 1000000.0))
 
 
 if __name__ == '__main__':
@@ -124,6 +212,12 @@ def analyze_shots(path):
     analyze_labels_parser = subparsers.add_parser(
         'labels', help=analyze_labels.__doc__)
     analyze_labels_parser.add_argument('path')
+    analyze_labels_file_parser = subparsers.add_parser(
+        'labels_file', help=analyze_labels_file.__doc__)
+    analyze_labels_file_parser.add_argument('path')
+    analyze_safe_search_parser = subparsers.add_parser(
+        'safe_search', help=analyze_safe_search.__doc__)
+    analyze_safe_search_parser.add_argument('path')
     analyze_shots_parser = subparsers.add_parser(
         'shots', help=analyze_shots.__doc__)
     analyze_shots_parser.add_argument('path')
@@ -134,5 +228,9 @@ def analyze_shots(path):
         analyze_faces(args.path)
     if args.command == 'labels':
         analyze_labels(args.path)
+    if args.command == 'labels_file':
+        analyze_labels_file(args.path)
     if args.command == 'shots':
         analyze_shots(args.path)
+    if args.command == 'safe_search':
+        analyze_safe_search(args.path)