Start Label Detection

rekognition_start_label_detection

R Documentation

Starts asynchronous detection of labels in a stored video¶

Description¶

Starts asynchronous detection of labels in a stored video.

Amazon Rekognition Video can detect labels in a video. Labels are instances of real-world entities. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; concepts like landscape, evening, and nature; and activities like a person getting out of a car or a person skiing.

The video must be stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. start_label_detection returns a job identifier (JobId) which you use to get the results of the operation. When label detection is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel.

To get the results of the label detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call get_label_detection and pass the job identifier (JobId) from the initial call to start_label_detection.

Optional Parameters

start_label_detection has the GENERAL_LABELS Feature applied by default. This feature allows you to provide filtering criteria to the Settings parameter. You can filter with sets of individual labels or with label categories. You can specify inclusive filters, exclusive filters, or a combination of inclusive and exclusive filters. For more information on filtering, see Detecting labels in a video.

You can specify MinConfidence to control the confidence threshold for the labels returned. The default is 50.

Usage¶

rekognition_start_label_detection(Video, ClientRequestToken,
  MinConfidence, NotificationChannel, JobTag, Features, Settings)

Arguments¶

Video

[required] The video in which you want to detect labels. The video must be stored in an Amazon S3 bucket.

ClientRequestToken

Idempotent token used to identify the start request. If you use the same token with multiple start_label_detection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.

MinConfidence

Specifies the minimum confidence that Amazon Rekognition Video must have in order to return a detected label. Confidence represents how certain Amazon Rekognition is that a label is correctly identified.0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition Video doesn't return any labels with a confidence level lower than this specified value.

If you don't specify MinConfidence, the operation returns labels and bounding boxes (if detected) with confidence values greater than or equal to 50 percent.

NotificationChannel

The Amazon SNS topic ARN you want Amazon Rekognition Video to publish the completion status of the label detection operation to. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy.

JobTag

An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.

Features

The features to return after video analysis. You can specify that GENERAL_LABELS are returned.

Settings

The settings for a StartLabelDetection request.Contains the specified parameters for the label detection request of an asynchronous label analysis operation. Settings can include filters for GENERAL_LABELS.

Value¶

A list with the following syntax:

list(
  JobId = "string"
)

Request syntax¶

svc$start_label_detection(
  Video = list(
    S3Object = list(
      Bucket = "string",
      Name = "string",
      Version = "string"
    )
  ),
  ClientRequestToken = "string",
  MinConfidence = 123.0,
  NotificationChannel = list(
    SNSTopicArn = "string",
    RoleArn = "string"
  ),
  JobTag = "string",
  Features = list(
    "GENERAL_LABELS"
  ),
  Settings = list(
    GeneralLabels = list(
      LabelInclusionFilters = list(
        "string"
      ),
      LabelExclusionFilters = list(
        "string"
      ),
      LabelCategoryInclusionFilters = list(
        "string"
      ),
      LabelCategoryExclusionFilters = list(
        "string"
      )
    )
  )
)