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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 face
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
from google.cloud import videointelligence
# [END imports]
def analyze_faces(path):
# [START construct_request]
""" Detects faces given a GCS path. """
video_client = videointelligence.VideoIntelligenceServiceClient()
features = [videointelligence.enums.Feature.FACE_DETECTION]
operation = video_client.annotate_video(path, features=features)
# [END construct_request]
print('\nProcessing video for face annotations:')
# [START check_operation]
result = operation.result(timeout=600)
print('\nFinished processing.')
# [END check_operation]
# [START parse_response]
# first result is retrieved because a single video was processed
faces = result.annotation_results[0].face_annotations
for face_id, face in enumerate(faces):
print('Thumbnail size: {}'.format(len(face.thumbnail)))
for segment_id, segment in enumerate(face.segments):
start_time = (segment.segment.start_time_offset.seconds +
segment.segment.start_time_offset.nanos / 1e9)
end_time = (segment.segment.end_time_offset.seconds +
segment.segment.end_time_offset.nanos / 1e9)
positions = '{}s to {}s'.format(start_time, end_time)
print('\tSegment {}: {}'.format(segment_id, positions))
# [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]