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Add auto-generated Logo Recognition Samples
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‎video/cloud-client/analyze/requirements.txt

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google-cloud-videointelligence==1.8.0
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google-cloud-videointelligence==1.10.0
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google-cloud-storage==1.14.0

‎video/cloud-client/analyze/video_detect_logo_beta.py

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# -*- coding: utf-8 -*-
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#
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# Copyright 2019 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# DO NOT EDIT! This is a generated sample ("LongRunningPromise", "video_detect_logo_beta")
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# To install the latest published package dependency, execute the following:
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# pip install google-cloud-video-intelligence
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# sample-metadata
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# title:
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# description: Performs asynchronous video annotation for logo recognition on a local file.
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# usage: python3 samples/v1p3beta1/video_detect_logo_beta.py [--local_file_path "resources/googlework_short.mp4"]
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import sys
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# [START video_detect_logo_beta]
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from google.cloud import videointelligence_v1p3beta1
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from google.cloud.videointelligence_v1p3beta1 import enums
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import io
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def sample_annotate_video(local_file_path):
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"""
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Performs asynchronous video annotation for logo recognition on a local file.
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Args:
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local_file_path Path to local video file, e.g. /path/video.mp4
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"""
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client = videointelligence_v1p3beta1.VideoIntelligenceServiceClient()
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# local_file_path = 'resources/googlework_short.mp4'
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with io.open(local_file_path, 'rb') as f:
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input_content = f.read()
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features_element = enums.Feature.LOGO_RECOGNITION
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features = [features_element]
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operation = client.annotate_video(input_content=input_content, features=features)
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print(u'Waiting for operation to complete...')
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response = operation.result()
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# Get the first response, since we sent only one video.
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annotation_result = response.annotation_results[0]
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# Annotations for list of logos detected, tracked and recognized in video.
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for logo_recognition_annotation in annotation_result.logo_recognition_annotations:
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entity = logo_recognition_annotation.entity
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# Opaque entity ID. Some IDs may be available in [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/).
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print(u'Entity Id : {}'.format(entity.entity_id))
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# Textual description, e.g. `Google`.
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print(u'Description : {}'.format(entity.description))
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# All logo tracks where the recognized logo appears. Each track corresponds to one logo instance appearing in consecutive frames.
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for track in logo_recognition_annotation.tracks:
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# Video segment of a track.
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segment = track.segment
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segment_start_time_offset = segment.start_time_offset
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print(u'\n\tStart Time Offset : {}.{}'.format(segment_start_time_offset.seconds, segment_start_time_offset.nanos))
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segment_end_time_offset = segment.end_time_offset
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print(u'\tEnd Time Offset : {}.{}'.format(segment_end_time_offset.seconds, segment_end_time_offset.nanos))
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print(u'\tConfidence : {}'.format(track.confidence))
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# The object with timestamp and attributes per frame in the track.
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for timestamped_object in track.timestamped_objects:
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# Normalized Bounding box in a frame, where the object is located.
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normalized_bounding_box = timestamped_object.normalized_bounding_box
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print(u'\n\t\tLeft : {}'.format(normalized_bounding_box.left))
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print(u'\t\tTop : {}'.format(normalized_bounding_box.top))
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print(u'\t\tRight : {}'.format(normalized_bounding_box.right))
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print(u'\t\tBottom : {}'.format(normalized_bounding_box.bottom))
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# Optional. The attributes of the object in the bounding box.
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for attribute in timestamped_object.attributes:
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print(u'\n\t\t\tName : {}'.format(attribute.name))
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print(u'\t\t\tConfidence : {}'.format(attribute.confidence))
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print(u'\t\t\tValue : {}'.format(attribute.value))
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# Optional. Attributes in the track level.
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for track_attribute in track.attributes:
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print(u'\n\t\tName : {}'.format(track_attribute.name))
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print(u'\t\tConfidence : {}'.format(track_attribute.confidence))
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print(u'\t\tValue : {}'.format(track_attribute.value))
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# All video segments where the recognized logo appears. There might be multiple instances of the same logo class appearing in one VideoSegment.
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for logo_recognition_annotation_segment in logo_recognition_annotation.segments:
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logo_recognition_annotation_segment_start_time_offset = logo_recognition_annotation_segment.start_time_offset
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print(u'\n\tStart Time Offset : {}.{}'.format(logo_recognition_annotation_segment_start_time_offset.seconds, logo_recognition_annotation_segment_start_time_offset.nanos))
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logo_recognition_annotation_segment_end_time_offset = logo_recognition_annotation_segment.end_time_offset
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print(u'\tEnd Time Offset : {}.{}'.format(logo_recognition_annotation_segment_end_time_offset.seconds, logo_recognition_annotation_segment_end_time_offset.nanos))
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# [END video_detect_logo_beta]
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def main():
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('--local_file_path', type=str, default='resources/googlework_short.mp4')
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args = parser.parse_args()
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sample_annotate_video(args.local_file_path)
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if __name__ == '__main__':
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main()

‎video/cloud-client/analyze/video_detect_logo_gcs_beta.py

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# -*- coding: utf-8 -*-
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#
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# Copyright 2019 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# DO NOT EDIT! This is a generated sample ("LongRunningPromise", "video_detect_logo_gcs_beta")
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# To install the latest published package dependency, execute the following:
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# pip install google-cloud-video-intelligence
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# sample-metadata
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# title:
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# description: Performs asynchronous video annotation for logo recognition on a file hosted in
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# GCS.
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# usage: python3 samples/v1p3beta1/video_detect_logo_gcs_beta.py
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import sys
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# [START video_detect_logo_gcs_beta]
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from google.cloud import videointelligence_v1p3beta1
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from google.cloud.videointelligence_v1p3beta1 import enums
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def sample_annotate_video():
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client = videointelligence_v1p3beta1.VideoIntelligenceServiceClient()
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input_uri = 'gs://cloud-samples-data/video/googlework_short.mp4'
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features_element = enums.Feature.LOGO_RECOGNITION
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features = [features_element]
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operation = client.annotate_video(input_uri=input_uri, features=features)
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print(u'Waiting for operation to complete...')
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response = operation.result()
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# Get the first response, since we sent only one video.
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annotation_result = response.annotation_results[0]
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# Annotations for list of logos detected, tracked and recognized in video.
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for logo_recognition_annotation in annotation_result.logo_recognition_annotations:
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entity = logo_recognition_annotation.entity
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# Opaque entity ID. Some IDs may be available in [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/).
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print(u'Entity Id : {}'.format(entity.entity_id))
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# Textual description, e.g. `Google`.
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print(u'Description : {}'.format(entity.description))
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# All logo tracks where the recognized logo appears. Each track corresponds to one logo instance appearing in consecutive frames.
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for track in logo_recognition_annotation.tracks:
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# Video segment of a track.
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segment = track.segment
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segment_start_time_offset = segment.start_time_offset
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print(u'\n\tStart Time Offset : {}.{}'.format(segment_start_time_offset.seconds, segment_start_time_offset.nanos))
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segment_end_time_offset = segment.end_time_offset
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print(u'\tEnd Time Offset : {}.{}'.format(segment_end_time_offset.seconds, segment_end_time_offset.nanos))
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print(u'\tConfidence : {}'.format(track.confidence))
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# The object with timestamp and attributes per frame in the track.
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for timestamped_object in track.timestamped_objects:
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# Normalized Bounding box in a frame, where the object is located.
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normalized_bounding_box = timestamped_object.normalized_bounding_box
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print(u'\n\t\tLeft : {}'.format(normalized_bounding_box.left))
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print(u'\t\tTop : {}'.format(normalized_bounding_box.top))
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print(u'\t\tRight : {}'.format(normalized_bounding_box.right))
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print(u'\t\tBottom : {}'.format(normalized_bounding_box.bottom))
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# Optional. The attributes of the object in the bounding box.
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for attribute in timestamped_object.attributes:
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print(u'\n\t\t\tName : {}'.format(attribute.name))
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print(u'\t\t\tConfidence : {}'.format(attribute.confidence))
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print(u'\t\t\tValue : {}'.format(attribute.value))
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# Optional. Attributes in the track level.
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for track_attribute in track.attributes:
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print(u'\n\t\tName : {}'.format(track_attribute.name))
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print(u'\t\tConfidence : {}'.format(track_attribute.confidence))
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print(u'\t\tValue : {}'.format(track_attribute.value))
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# All video segments where the recognized logo appears. There might be multiple instances of the same logo class appearing in one VideoSegment.
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for logo_recognition_annotation_segment in logo_recognition_annotation.segments:
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logo_recognition_annotation_segment_start_time_offset = logo_recognition_annotation_segment.start_time_offset
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print(u'\n\tStart Time Offset : {}.{}'.format(logo_recognition_annotation_segment_start_time_offset.seconds, logo_recognition_annotation_segment_start_time_offset.nanos))
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logo_recognition_annotation_segment_end_time_offset = logo_recognition_annotation_segment.end_time_offset
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print(u'\tEnd Time Offset : {}.{}'.format(logo_recognition_annotation_segment_end_time_offset.seconds, logo_recognition_annotation_segment_end_time_offset.nanos))
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# [END video_detect_logo_gcs_beta]
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def main():
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sample_annotate_video()
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if __name__ == '__main__':
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main()

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