old_contents: #!/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 shot change detection with the
  Google Cloud Video Intelligence API.
  
  For more information, check out the documentation at
  https://cloud.google.com/videointelligence/docs.
  
  Usage Example:
  
      python faces.py gs://cloudmleap/video/googlework.mp4
  
  """
  
  # [START full_tutorial]
  # [START imports]
  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)
  # [END imports]
  
  
  def analyze_faces(path):
      # [START construct_request]
      """ 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)
      # [END construct_request]
      print('\nProcessing video for face annotations:')
  
      # [START check_operation]
      while not operation.done():
          sys.stdout.write('.')
          sys.stdout.flush()
          time.sleep(20)
  
      print('\nFinished processing.')
      # [END check_operation]
  
      # [START parse_response]
      # 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))
      # [END parse_response]
  
  
  if __name__ == '__main__':
      # [START running_app]
      parser = argparse.ArgumentParser(
          description=__doc__,
          formatter_class=argparse.RawDescriptionHelpFormatter)
      parser.add_argument('path', help='GCS file path for face detection.')
      args = parser.parse_args()
  
      analyze_faces(args.path)
      # [END running_app]
  # [END full_tutorial]
  
new_contents: #!/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 shot change detection with the
  Google Cloud Video Intelligence API.
  
  For more information, check out the documentation at
  https://cloud.google.com/videointelligence/docs.
  
  Usage Example:
  
      python faces.py gs://demomaker/google_gmail.mp4
  
  """
  
  # [START full_tutorial]
  # [START imports]
  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)
  # [END imports]
  
  
  def analyze_faces(path):
      # [START construct_request]
      """ 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)
      # [END construct_request]
      print('\nProcessing video for face annotations:')
  
      # [START check_operation]
      while not operation.done():
          sys.stdout.write('.')
          sys.stdout.flush()
          time.sleep(20)
  
      print('\nFinished processing.')
      # [END check_operation]
  
      # [START parse_response]
      # 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))
      # [END parse_response]
  
  
  if __name__ == '__main__':
      # [START running_app]
      parser = argparse.ArgumentParser(
          description=__doc__,
          formatter_class=argparse.RawDescriptionHelpFormatter)
      parser.add_argument('path', help='GCS file path for face detection.')
      args = parser.parse_args()
  
      analyze_faces(args.path)
      # [END running_app]
  # [END full_tutorial]
  
subject: change the usage file as it does not exist (#959)