type: com.google.api.codegen.ConfigProto
config_schema_version: 2.0.0
language_settings:
  python:
    package_name: google.cloud.videointelligence_v1p3beta1.gapic
  go:
    package_name: cloud.google.com/go/videointelligence/apiv1p3beta1
  csharp:
    package_name: Google.Cloud.VideoIntelligence.V1P3Beta1
  ruby:
    package_name: Google::Cloud::VideoIntelligence::V1p3beta1
    release_level: BETA
  php:
    package_name: Google\Cloud\VideoIntelligence\V1p3beta1
  nodejs:
    package_name: video-intelligence.v1p3beta1
    domain_layer_location: google-cloud
interfaces:
- name: google.cloud.videointelligence.v1p3beta1.VideoIntelligenceService
  smoke_test:
    method: AnnotateVideo
    init_fields:
    - input_uri=gs://cloud-samples-data/video/cat.mp4
    - features[0]=LABEL_DETECTION
  methods:
  - name: AnnotateVideo
    long_running:
      initial_poll_delay_millis: 20000
      poll_delay_multiplier: 1.5
      max_poll_delay_millis: 45000
      total_poll_timeout_millis: 86400000
    sample_code_init_fields:
    - input_uri=gs://cloud-samples-data/video/cat.mp4
    - features[0]=LABEL_DETECTION
    samples:
      standalone:
      - region_tag: video_detect_logo_beta
        value_sets: [video_detect_logo_beta]
      - region_tag: video_detect_logo_gcs_beta
        value_sets: [video_detect_logo_gcs_beta]
    sample_value_sets:
    - id: video_detect_logo_beta
      description: "Performs asynchronous video annotation for logo recognition from inline video content."
      parameters:
        defaults:
        - input_content="resources/googlework_short.mp4"
        - features[0]=LOGO_RECOGNITION
        attributes:
        - parameter: input_content
          sample_argument_name: local_file_path
          read_file: true
          description: Path to local video file, e.g. /path/video.mp4
      on_success:
      - comment: ["Get the first response, since we sent only one video."]
      - define: annotation_result=$resp.annotation_results[0]
      - comment: ["Annotations for list of logos detected, tracked and recognized in video."]
      - loop:
          collection: annotation_result.logo_recognition_annotations
          variable: logo_recognition_annotation
          body:
          - define: entity=logo_recognition_annotation.entity
          - comment: ["Opaque entity ID. Some IDs may be available in [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/)."]
          - print: ["Entity Id : %s", entity.entity_id]
          - comment: ["Textual description, e.g. `Google`."]
          - print: ["Description : %s", entity.description]
          - comment: ["All logo tracks where the recognized logo appears. Each track corresponds to one logo instance appearing in consecutive frames."]
          - loop:
              collection: logo_recognition_annotation.tracks
              variable: track
              body:
              - comment: ["Video segment of a track."]
              - define: segment=track.segment
              - define: segment_start_time_offset=segment.start_time_offset
              - print: ["	Start Time Offset : %s.%s", segment_start_time_offset.seconds, segment_start_time_offset.nanos]
              - define: segment_end_time_offset=segment.end_time_offset
              - print: ["	End Time Offset : %s.%s", segment_end_time_offset.seconds, segment_end_time_offset.nanos]
              - print: ["	Confidence : %s", track.confidence]
              - comment: ["The object with timestamp and attributes per frame in the track."]
              - loop:
                  collection: track.timestamped_objects
                  variable: timestamped_object
                  body:
                  - comment: ["Normalized Bounding box in a frame, where the object is located."]
                  - define: normalized_bounding_box=timestamped_object.normalized_bounding_box
                  - print: ["		Left : %s", normalized_bounding_box.left]
                  - print: ["		Top : %s", normalized_bounding_box.top]
                  - print: ["		Right : %s", normalized_bounding_box.right]
                  - print: ["		Bottom : %s", normalized_bounding_box.bottom]
                  - comment: ["Optional. The attributes of the object in the bounding box."]
                  - loop:
                      collection: timestamped_object.attributes
                      variable: attribute
                      body:
                      - print: ["			Name : %s", attribute.name]
                      - print: ["			Confidence : %s", attribute.confidence]
                      - print: ["			Value : %s", attribute.value]
              - comment: ["Optional. Attributes in the track level."]
              - loop:
                  collection: track.attributes
                  variable: track_attribute
                  body:
                  - print: ["		Name : %s", track_attribute.name]
                  - print: ["		Confidence : %s", track_attribute.confidence]
                  - print: ["		Value : %s", track_attribute.value]
          - comment: ["All video segments where the recognized logo appears. There might be multiple instances of the same logo class appearing in one VideoSegment."]
          - loop:
              collection: logo_recognition_annotation.segments
              variable: logo_recognition_annotation_segment
              body:
              - define: logo_recognition_annotation_segment_start_time_offset=logo_recognition_annotation_segment.start_time_offset
              - print: ["	Start Time Offset : %s.%s", logo_recognition_annotation_segment_start_time_offset.seconds, logo_recognition_annotation_segment_start_time_offset.nanos]
              - define: logo_recognition_annotation_segment_end_time_offset=logo_recognition_annotation_segment.end_time_offset
              - print: ["	End Time Offset : %s.%s", logo_recognition_annotation_segment_end_time_offset.seconds, logo_recognition_annotation_segment_end_time_offset.nanos]
    - id: video_detect_logo_gcs_beta
      description: "Performs asynchronous video annotation for logo recognition on a file hosted in GCS."
      parameters:
        defaults:
        - input_uri=gs://cloud-samples-data/video/googlework_short.mp4
        - features[0]=LOGO_RECOGNITION
      on_success:
      - comment: ["Get the first response, since we sent only one video."]
      - define: annotation_result=$resp.annotation_results[0]
      - comment: ["Annotations for list of logos detected, tracked and recognized in video."]
      - loop:
          collection: annotation_result.logo_recognition_annotations
          variable: logo_recognition_annotation
          body:
          - define: entity=logo_recognition_annotation.entity
          - comment: ["Opaque entity ID. Some IDs may be available in [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/)."]
          - print: ["Entity Id : %s", entity.entity_id]
          - comment: ["Textual description, e.g. `Google`."]
          - print: ["Description : %s", entity.description]
          - comment: ["All logo tracks where the recognized logo appears. Each track corresponds to one logo instance appearing in consecutive frames."]
          - loop:
              collection: logo_recognition_annotation.tracks
              variable: track
              body:
              - comment: ["Video segment of a track."]
              - define: segment=track.segment
              - define: segment_start_time_offset=segment.start_time_offset
              - print: ["	Start Time Offset : %s.%s", segment_start_time_offset.seconds, segment_start_time_offset.nanos]
              - define: segment_end_time_offset=segment.end_time_offset
              - print: ["	End Time Offset : %s.%s", segment_end_time_offset.seconds, segment_end_time_offset.nanos]
              - print: ["	Confidence : %s", track.confidence]
              - comment: ["The object with timestamp and attributes per frame in the track."]
              - loop:
                  collection: track.timestamped_objects
                  variable: timestamped_object
                  body:
                  - comment: ["Normalized Bounding box in a frame, where the object is located."]
                  - define: normalized_bounding_box=timestamped_object.normalized_bounding_box
                  - print: ["		Left : %s", normalized_bounding_box.left]
                  - print: ["		Top : %s", normalized_bounding_box.top]
                  - print: ["		Right : %s", normalized_bounding_box.right]
                  - print: ["		Bottom : %s", normalized_bounding_box.bottom]
                  - comment: ["Optional. The attributes of the object in the bounding box."]
                  - loop:
                      collection: timestamped_object.attributes
                      variable: attribute
                      body:
                      - print: ["			Name : %s", attribute.name]
                      - print: ["			Confidence : %s", attribute.confidence]
                      - print: ["			Value : %s", attribute.value]
              - comment: ["Optional. Attributes in the track level."]
              - loop:
                  collection: track.attributes
                  variable: track_attribute
                  body:
                  - print: ["		Name : %s", track_attribute.name]
                  - print: ["		Confidence : %s", track_attribute.confidence]
                  - print: ["		Value : %s", track_attribute.value]
          - comment: ["All video segments where the recognized logo appears. There might be multiple instances of the same logo class appearing in one VideoSegment."]
          - loop:
              collection: logo_recognition_annotation.segments
              variable: logo_recognition_annotation_segment
              body:
              - define: logo_recognition_annotation_segment_start_time_offset=logo_recognition_annotation_segment.start_time_offset
              - print: ["	Start Time Offset : %s.%s", logo_recognition_annotation_segment_start_time_offset.seconds, logo_recognition_annotation_segment_start_time_offset.nanos]
              - define: logo_recognition_annotation_segment_end_time_offset=logo_recognition_annotation_segment.end_time_offset
              - print: ["	End Time Offset : %s.%s", logo_recognition_annotation_segment_end_time_offset.seconds, logo_recognition_annotation_segment_end_time_offset.nanos]