google-cloud-videointelligence

import os from google.cloud import videointelligence_v1 as videointelligence # Set GOOGLE_APPLICATION_CREDENTIALS environment variable or ensure gcloud is authenticated. # For local development, run `gcloud auth application-default login`. # os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'path/to/your/key.json' def analyze_video_labels(gcs_uri): """Detects labels in the video specified by the GCS URI.""" client = videointelligence.VideoIntelligenceServiceClient() features = [videointelligence.Feature.LABEL_DETECTION] # Optional: Configure label detection mode for more granular control config = videointelligence.LabelDetectionConfig( label_detection_mode=videointelligence.LabelDetectionMode.SHOT_AND_FRAME_MODE, stationary_camera=False # Set to True if analyzing footage from a stationary camera ) video_context = videointelligence.VideoContext(label_detection_config=config) print(f'Processing video for label annotations: {gcs_uri}') operation = client.annotate_video( request={ "input_uri": gcs_uri, "features": features, "video_context": video_context } ) # Long-running operations must be waited for. print('\nWaiting for operation to complete...') result = operation.result(timeout=600) # Adjust timeout as needed (in seconds) print('\nFinished processing.') # First result is retrieved because a single video is processed annotation_result = result.annotation_results[0] for i, shot_label in enumerate(annotation_result.shot_label_annotations): print(f'Video shot label: {shot_label.entity.description} ({shot_label.entity.entity_id})') for segment in shot_label.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) print(f'\tSegment: {start_time:.1f}s to {end_time:.1f}s (confidence: {segment.confidence:.2f})') for i, frame_label in enumerate(annotation_result.frame_label_annotations): print(f'Video frame label: {frame_label.entity.description} ({frame_label.entity.entity_id})') for frame in frame_label.frames: time_offset = (frame.time_offset.seconds + frame.time_offset.nanos / 1e9) print(f'\tFrame: {time_offset:.1f}s (confidence: {frame.confidence:.2f})') if __name__ == '__main__': # Replace with your GCS video URI # Public sample video from Google Cloud documentation video_uri = "gs://cloud-samples-data/video/chicago.mp4" analyze_video_labels(video_uri)