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25 | 25 |
|
26 | 26 | def run_quickstart():
|
27 | 27 | # [START videointelligence_quickstart]
|
28 |
| - import sys |
29 |
| - import time |
| 28 | + from google.cloud import videointelligence |
30 | 29 |
|
31 |
| - from google.cloud import videointelligence_v1beta2 |
32 |
| - from google.cloud.videointelligence_v1beta2 import enums |
33 |
| - |
34 |
| - video_client = videointelligence_v1beta2.VideoIntelligenceServiceClient() |
35 |
| - features = [enums.Feature.LABEL_DETECTION] |
36 |
| - operation = video_client.annotate_video('gs://demomaker/cat.mp4', features) |
| 30 | + video_client = videointelligence.VideoIntelligenceServiceClient() |
| 31 | + features = [videointelligence.enums.Feature.LABEL_DETECTION] |
| 32 | + operation = video_client.annotate_video( |
| 33 | + 'gs://demomaker/cat.mp4', features=features) |
37 | 34 | print('\nProcessing video for label annotations:')
|
38 | 35 |
|
39 |
| - while not operation.done(): |
40 |
| - sys.stdout.write('.') |
41 |
| - sys.stdout.flush() |
42 |
| - time.sleep(15) |
43 |
| - |
| 36 | + result = operation.result(timeout=90) |
44 | 37 | print('\nFinished processing.')
|
45 | 38 |
|
46 | 39 | # first result is retrieved because a single video was processed
|
47 |
| - results = operation.result().annotation_results[0] |
48 |
| - |
49 |
| - for i, segment_label in enumerate(results.segment_label_annotations): |
| 40 | + segment_labels = result.annotation_results[0].segment_label_annotations |
| 41 | + for i, segment_label in enumerate(segment_labels): |
50 | 42 | print('Video label description: {}'.format(
|
51 | 43 | segment_label.entity.description))
|
52 | 44 | for category_entity in segment_label.category_entities:
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|
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