google_cloud_video_intelligence_label_detection_from_gcs.py

1import argparse
2
3from google.cloud import videointelligence
4
5def analyze_labels(path):
6    """Detects labels given a GCS path."""
7    video_client = videointelligence.VideoIntelligenceServiceClient()
8    features = [videointelligence.Feature.LABEL_DETECTION]
9    operation = video_client.annotate_video(
10        request={"features": features, "input_uri": path}
11    )
12
13    print("\nProcessing video for label annotations:")
14    result = operation.result(timeout=180)
15    print("\nFinished processing.")
16
17    # Process video/segment level label annotations
18    segment_labels = result.annotation_results[0].segment_label_annotations
19    for i, segment_label in enumerate(segment_labels):
20        print(f"Video label description: {segment_label.entity.description}")
21        for j, category_entity in enumerate(segment_label.category_entities):
22            print(
23                f"\tLabel category description: {category_entity.description}"
24            )
25
26        for j, segment in enumerate(segment_label.segments):
27            start_time = (
28                segment.segment.start_time_offset.seconds
29                + segment.segment.start_time_offset.microseconds / 1e6
30            )
31            end_time = (
32                segment.segment.end_time_offset.seconds
33                + segment.segment.end_time_offset.microseconds / 1e6
34            )
35            positions = f"{start_time}s to {end_time}s"
36            confidence = segment.confidence
37            print(f"\tSegment {j}: {positions}")
38            print(f"\tConfidence: {confidence}")
39        print("\n")
40
41
42if __name__ == "__main__":
43    parser = argparse.ArgumentParser(
44        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
45    )
46    parser.add_argument("path", help="GCS file path for label detection.")
47    args = parser.parse_args()
48
49    analyze_labels(args.path)