Class: Aws::Rekognition::Client

Inherits:
Seahorse::Client::Base show all
Includes:
ClientStubs
Defined in:
gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb

Overview

An API client for Rekognition. To construct a client, you need to configure a :region and :credentials.

client = Aws::Rekognition::Client.new(
  region: region_name,
  credentials: credentials,
  # ...
)

For details on configuring region and credentials see the developer guide.

See #initialize for a full list of supported configuration options.

Instance Attribute Summary

Attributes inherited from Seahorse::Client::Base

#config, #handlers

API Operations collapse

Instance Method Summary collapse

Methods included from ClientStubs

#api_requests, #stub_data, #stub_responses

Methods inherited from Seahorse::Client::Base

add_plugin, api, clear_plugins, define, new, #operation_names, plugins, remove_plugin, set_api, set_plugins

Methods included from Seahorse::Client::HandlerBuilder

#handle, #handle_request, #handle_response

Constructor Details

#initialize(options) ⇒ Client

Returns a new instance of Client.

Parameters:

  • options (Hash)

Options Hash (options):

  • :credentials (required, Aws::CredentialProvider)

    Your AWS credentials. This can be an instance of any one of the following classes:

    • Aws::Credentials - Used for configuring static, non-refreshing credentials.

    • Aws::SharedCredentials - Used for loading static credentials from a shared file, such as ~/.aws/config.

    • Aws::AssumeRoleCredentials - Used when you need to assume a role.

    • Aws::AssumeRoleWebIdentityCredentials - Used when you need to assume a role after providing credentials via the web.

    • Aws::SSOCredentials - Used for loading credentials from AWS SSO using an access token generated from aws login.

    • Aws::ProcessCredentials - Used for loading credentials from a process that outputs to stdout.

    • Aws::InstanceProfileCredentials - Used for loading credentials from an EC2 IMDS on an EC2 instance.

    • Aws::ECSCredentials - Used for loading credentials from instances running in ECS.

    • Aws::CognitoIdentityCredentials - Used for loading credentials from the Cognito Identity service.

    When :credentials are not configured directly, the following locations will be searched for credentials:

    • Aws.config[:credentials]
    • The :access_key_id, :secret_access_key, and :session_token options.
    • ENV['AWS_ACCESS_KEY_ID'], ENV['AWS_SECRET_ACCESS_KEY']
    • ~/.aws/credentials
    • ~/.aws/config
    • EC2/ECS IMDS instance profile - When used by default, the timeouts are very aggressive. Construct and pass an instance of Aws::InstanceProfileCredentails or Aws::ECSCredentials to enable retries and extended timeouts. Instance profile credential fetching can be disabled by setting ENV['AWS_EC2_METADATA_DISABLED'] to true.
  • :region (required, String)

    The AWS region to connect to. The configured :region is used to determine the service :endpoint. When not passed, a default :region is searched for in the following locations:

    • Aws.config[:region]
    • ENV['AWS_REGION']
    • ENV['AMAZON_REGION']
    • ENV['AWS_DEFAULT_REGION']
    • ~/.aws/credentials
    • ~/.aws/config
  • :access_key_id (String)
  • :active_endpoint_cache (Boolean) — default: false

    When set to true, a thread polling for endpoints will be running in the background every 60 secs (default). Defaults to false.

  • :adaptive_retry_wait_to_fill (Boolean) — default: true

    Used only in adaptive retry mode. When true, the request will sleep until there is sufficent client side capacity to retry the request. When false, the request will raise a RetryCapacityNotAvailableError and will not retry instead of sleeping.

  • :client_side_monitoring (Boolean) — default: false

    When true, client-side metrics will be collected for all API requests from this client.

  • :client_side_monitoring_client_id (String) — default: ""

    Allows you to provide an identifier for this client which will be attached to all generated client side metrics. Defaults to an empty string.

  • :client_side_monitoring_host (String) — default: "127.0.0.1"

    Allows you to specify the DNS hostname or IPv4 or IPv6 address that the client side monitoring agent is running on, where client metrics will be published via UDP.

  • :client_side_monitoring_port (Integer) — default: 31000

    Required for publishing client metrics. The port that the client side monitoring agent is running on, where client metrics will be published via UDP.

  • :client_side_monitoring_publisher (Aws::ClientSideMonitoring::Publisher) — default: Aws::ClientSideMonitoring::Publisher

    Allows you to provide a custom client-side monitoring publisher class. By default, will use the Client Side Monitoring Agent Publisher.

  • :convert_params (Boolean) — default: true

    When true, an attempt is made to coerce request parameters into the required types.

  • :correct_clock_skew (Boolean) — default: true

    Used only in standard and adaptive retry modes. Specifies whether to apply a clock skew correction and retry requests with skewed client clocks.

  • :defaults_mode (String) — default: "legacy"

    See DefaultsModeConfiguration for a list of the accepted modes and the configuration defaults that are included.

  • :disable_host_prefix_injection (Boolean) — default: false

    Set to true to disable SDK automatically adding host prefix to default service endpoint when available.

  • :disable_request_compression (Boolean) — default: false

    When set to 'true' the request body will not be compressed for supported operations.

  • :endpoint (String)

    The client endpoint is normally constructed from the :region option. You should only configure an :endpoint when connecting to test or custom endpoints. This should be a valid HTTP(S) URI.

  • :endpoint_cache_max_entries (Integer) — default: 1000

    Used for the maximum size limit of the LRU cache storing endpoints data for endpoint discovery enabled operations. Defaults to 1000.

  • :endpoint_cache_max_threads (Integer) — default: 10

    Used for the maximum threads in use for polling endpoints to be cached, defaults to 10.

  • :endpoint_cache_poll_interval (Integer) — default: 60

    When :endpoint_discovery and :active_endpoint_cache is enabled, Use this option to config the time interval in seconds for making requests fetching endpoints information. Defaults to 60 sec.

  • :endpoint_discovery (Boolean) — default: false

    When set to true, endpoint discovery will be enabled for operations when available.

  • :ignore_configured_endpoint_urls (Boolean)

    Setting to true disables use of endpoint URLs provided via environment variables and the shared configuration file.

  • :log_formatter (Aws::Log::Formatter) — default: Aws::Log::Formatter.default

    The log formatter.

  • :log_level (Symbol) — default: :info

    The log level to send messages to the :logger at.

  • :logger (Logger)

    The Logger instance to send log messages to. If this option is not set, logging will be disabled.

  • :max_attempts (Integer) — default: 3

    An integer representing the maximum number attempts that will be made for a single request, including the initial attempt. For example, setting this value to 5 will result in a request being retried up to 4 times. Used in standard and adaptive retry modes.

  • :profile (String) — default: "default"

    Used when loading credentials from the shared credentials file at HOME/.aws/credentials. When not specified, 'default' is used.

  • :request_min_compression_size_bytes (Integer) — default: 10240

    The minimum size in bytes that triggers compression for request bodies. The value must be non-negative integer value between 0 and 10485780 bytes inclusive.

  • :retry_backoff (Proc)

    A proc or lambda used for backoff. Defaults to 2**retries * retry_base_delay. This option is only used in the legacy retry mode.

  • :retry_base_delay (Float) — default: 0.3

    The base delay in seconds used by the default backoff function. This option is only used in the legacy retry mode.

  • :retry_jitter (Symbol) — default: :none

    A delay randomiser function used by the default backoff function. Some predefined functions can be referenced by name - :none, :equal, :full, otherwise a Proc that takes and returns a number. This option is only used in the legacy retry mode.

    @see https://www.awsarchitectureblog.com/2015/03/backoff.html

  • :retry_limit (Integer) — default: 3

    The maximum number of times to retry failed requests. Only ~ 500 level server errors and certain ~ 400 level client errors are retried. Generally, these are throttling errors, data checksum errors, networking errors, timeout errors, auth errors, endpoint discovery, and errors from expired credentials. This option is only used in the legacy retry mode.

  • :retry_max_delay (Integer) — default: 0

    The maximum number of seconds to delay between retries (0 for no limit) used by the default backoff function. This option is only used in the legacy retry mode.

  • :retry_mode (String) — default: "legacy"

    Specifies which retry algorithm to use. Values are:

    • legacy - The pre-existing retry behavior. This is default value if no retry mode is provided.

    • standard - A standardized set of retry rules across the AWS SDKs. This includes support for retry quotas, which limit the number of unsuccessful retries a client can make.

    • adaptive - An experimental retry mode that includes all the functionality of standard mode along with automatic client side throttling. This is a provisional mode that may change behavior in the future.

  • :sdk_ua_app_id (String)

    A unique and opaque application ID that is appended to the User-Agent header as app/. It should have a maximum length of 50.

  • :secret_access_key (String)
  • :session_token (String)
  • :simple_json (Boolean) — default: false

    Disables request parameter conversion, validation, and formatting. Also disable response data type conversions. This option is useful when you want to ensure the highest level of performance by avoiding overhead of walking request parameters and response data structures.

    When :simple_json is enabled, the request parameters hash must be formatted exactly as the DynamoDB API expects.

  • :stub_responses (Boolean) — default: false

    Causes the client to return stubbed responses. By default fake responses are generated and returned. You can specify the response data to return or errors to raise by calling ClientStubs#stub_responses. See ClientStubs for more information.

    Please note When response stubbing is enabled, no HTTP requests are made, and retries are disabled.

  • :token_provider (Aws::TokenProvider)

    A Bearer Token Provider. This can be an instance of any one of the following classes:

    • Aws::StaticTokenProvider - Used for configuring static, non-refreshing tokens.

    • Aws::SSOTokenProvider - Used for loading tokens from AWS SSO using an access token generated from aws login.

    When :token_provider is not configured directly, the Aws::TokenProviderChain will be used to search for tokens configured for your profile in shared configuration files.

  • :use_dualstack_endpoint (Boolean)

    When set to true, dualstack enabled endpoints (with .aws TLD) will be used if available.

  • :use_fips_endpoint (Boolean)

    When set to true, fips compatible endpoints will be used if available. When a fips region is used, the region is normalized and this config is set to true.

  • :validate_params (Boolean) — default: true

    When true, request parameters are validated before sending the request.

  • :endpoint_provider (Aws::Rekognition::EndpointProvider)

    The endpoint provider used to resolve endpoints. Any object that responds to #resolve_endpoint(parameters) where parameters is a Struct similar to Aws::Rekognition::EndpointParameters

  • :http_proxy (URI::HTTP, String)

    A proxy to send requests through. Formatted like 'http://proxy.com:123'.

  • :http_open_timeout (Float) — default: 15

    The number of seconds to wait when opening a HTTP session before raising a Timeout::Error.

  • :http_read_timeout (Float) — default: 60

    The default number of seconds to wait for response data. This value can safely be set per-request on the session.

  • :http_idle_timeout (Float) — default: 5

    The number of seconds a connection is allowed to sit idle before it is considered stale. Stale connections are closed and removed from the pool before making a request.

  • :http_continue_timeout (Float) — default: 1

    The number of seconds to wait for a 100-continue response before sending the request body. This option has no effect unless the request has "Expect" header set to "100-continue". Defaults to nil which disables this behaviour. This value can safely be set per request on the session.

  • :ssl_timeout (Float) — default: nil

    Sets the SSL timeout in seconds.

  • :http_wire_trace (Boolean) — default: false

    When true, HTTP debug output will be sent to the :logger.

  • :ssl_verify_peer (Boolean) — default: true

    When true, SSL peer certificates are verified when establishing a connection.

  • :ssl_ca_bundle (String)

    Full path to the SSL certificate authority bundle file that should be used when verifying peer certificates. If you do not pass :ssl_ca_bundle or :ssl_ca_directory the the system default will be used if available.

  • :ssl_ca_directory (String)

    Full path of the directory that contains the unbundled SSL certificate authority files for verifying peer certificates. If you do not pass :ssl_ca_bundle or :ssl_ca_directory the the system default will be used if available.



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 395

def initialize(*args)
  super
end

Instance Method Details

#associate_faces(params = {}) ⇒ Types::AssociateFacesResponse

Associates one or more faces with an existing UserID. Takes an array of FaceIds. Each FaceId that are present in the FaceIds list is associated with the provided UserID. The maximum number of total FaceIds per UserID is 100.

The UserMatchThreshold parameter specifies the minimum user match confidence required for the face to be associated with a UserID that has at least one FaceID already associated. This ensures that the FaceIds are associated with the right UserID. The value ranges from 0-100 and default value is 75.

If successful, an array of AssociatedFace objects containing the associated FaceIds is returned. If a given face is already associated with the given UserID, it will be ignored and will not be returned in the response. If a given face is already associated to a different UserID, isn't found in the collection, doesn’t meet the UserMatchThreshold, or there are already 100 faces associated with the UserID, it will be returned as part of an array of UnsuccessfulFaceAssociations.

The UserStatus reflects the status of an operation which updates a UserID representation with a list of given faces. The UserStatus can be:

  • ACTIVE - All associations or disassociations of FaceID(s) for a UserID are complete.

  • CREATED - A UserID has been created, but has no FaceID(s) associated with it.

  • UPDATING - A UserID is being updated and there are current associations or disassociations of FaceID(s) taking place.

Examples:

Example: AssociateFaces


# This operation associates one or more faces with an existing UserID.

resp = client.associate_faces({
  client_request_token: "550e8400-e29b-41d4-a716-446655440002", 
  collection_id: "MyCollection", 
  face_ids: [
    "f5817d37-94f6-4335-bfee-6cf79a3d806e", 
    "851cb847-dccc-4fea-9309-9f4805967855", 
    "35ebbb41-7f67-4263-908d-dd0ecba05ab9", 
  ], 
  user_id: "DemoUser", 
  user_match_threshold: 70, 
})

resp.to_h outputs the following:
{
  associated_faces: [
    {
      face_id: "35ebbb41-7f67-4263-908d-dd0ecba05ab9", 
    }, 
  ], 
  unsuccessful_face_associations: [
    {
      confidence: 0.9375374913215637, 
      face_id: "f5817d37-94f6-4335-bfee-6cf79a3d806e", 
      reasons: [
        "LOW_MATCH_CONFIDENCE", 
      ], 
    }, 
    {
      face_id: "851cb847-dccc-4fea-9309-9f4805967855", 
      reasons: [
        "ASSOCIATED_TO_A_DIFFERENT_USER", 
      ], 
      user_id: "demoUser2", 
    }, 
  ], 
  user_status: "UPDATING", 
}

Request syntax with placeholder values


resp = client.associate_faces({
  collection_id: "CollectionId", # required
  user_id: "UserId", # required
  face_ids: ["FaceId"], # required
  user_match_threshold: 1.0,
  client_request_token: "ClientRequestToken",
})

Response structure


resp.associated_faces #=> Array
resp.associated_faces[0].face_id #=> String
resp.unsuccessful_face_associations #=> Array
resp.unsuccessful_face_associations[0].face_id #=> String
resp.unsuccessful_face_associations[0].user_id #=> String
resp.unsuccessful_face_associations[0].confidence #=> Float
resp.unsuccessful_face_associations[0].reasons #=> Array
resp.unsuccessful_face_associations[0].reasons[0] #=> String, one of "FACE_NOT_FOUND", "ASSOCIATED_TO_A_DIFFERENT_USER", "LOW_MATCH_CONFIDENCE"
resp.user_status #=> String, one of "ACTIVE", "UPDATING", "CREATING", "CREATED"

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :collection_id (required, String)

    The ID of an existing collection containing the UserID.

  • :user_id (required, String)

    The ID for the existing UserID.

  • :face_ids (required, Array<String>)

    An array of FaceIDs to associate with the UserID.

  • :user_match_threshold (Float)

    An optional value specifying the minimum confidence in the UserID match to return. The default value is 75.

  • :client_request_token (String)

    Idempotent token used to identify the request to AssociateFaces. If you use the same token with multiple AssociateFaces requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.

    A suitable default value is auto-generated. You should normally not need to pass this option.**

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 529

def associate_faces(params = {}, options = {})
  req = build_request(:associate_faces, params)
  req.send_request(options)
end

#compare_faces(params = {}) ⇒ Types::CompareFacesResponse

Compares a face in the source input image with each of the 100 largest faces detected in the target input image.

If the source image contains multiple faces, the service detects the largest face and compares it with each face detected in the target image.

CompareFaces uses machine learning algorithms, which are probabilistic. A false negative is an incorrect prediction that a face in the target image has a low similarity confidence score when compared to the face in the source image. To reduce the probability of false negatives, we recommend that you compare the target image against multiple source images. If you plan to use CompareFaces to make a decision that impacts an individual's rights, privacy, or access to services, we recommend that you pass the result to a human for review and further validation before taking action.

You pass the input and target images either as base64-encoded image bytes or as references to images in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file.

In response, the operation returns an array of face matches ordered by similarity score in descending order. For each face match, the response provides a bounding box of the face, facial landmarks, pose details (pitch, roll, and yaw), quality (brightness and sharpness), and confidence value (indicating the level of confidence that the bounding box contains a face). The response also provides a similarity score, which indicates how closely the faces match.

By default, only faces with a similarity score of greater than or equal to 80% are returned in the response. You can change this value by specifying the SimilarityThreshold parameter.

CompareFaces also returns an array of faces that don't match the source image. For each face, it returns a bounding box, confidence value, landmarks, pose details, and quality. The response also returns information about the face in the source image, including the bounding box of the face and confidence value.

The QualityFilter input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. Use QualityFilter to set the quality bar by specifying LOW, MEDIUM, or HIGH. If you do not want to filter detected faces, specify NONE. The default value is NONE.

If the image doesn't contain Exif metadata, CompareFaces returns orientation information for the source and target images. Use these values to display the images with the correct image orientation.

If no faces are detected in the source or target images, CompareFaces returns an InvalidParameterException error.

This is a stateless API operation. That is, data returned by this operation doesn't persist.

For an example, see Comparing Faces in Images in the Amazon Rekognition Developer Guide.

This operation requires permissions to perform the rekognition:CompareFaces action.

Examples:

Example: To compare two images


# This operation compares the largest face detected in the source image with each face detected in the target image.

resp = client.compare_faces({
  similarity_threshold: 90, 
  source_image: {
    s3_object: {
      bucket: "mybucket", 
      name: "mysourceimage", 
    }, 
  }, 
  target_image: {
    s3_object: {
      bucket: "mybucket", 
      name: "mytargetimage", 
    }, 
  }, 
})

resp.to_h outputs the following:
{
  face_matches: [
    {
      face: {
        bounding_box: {
          height: 0.33481481671333313, 
          left: 0.31888890266418457, 
          top: 0.4933333396911621, 
          width: 0.25, 
        }, 
        confidence: 99.9991226196289, 
      }, 
      similarity: 100, 
    }, 
  ], 
  source_image_face: {
    bounding_box: {
      height: 0.33481481671333313, 
      left: 0.31888890266418457, 
      top: 0.4933333396911621, 
      width: 0.25, 
    }, 
    confidence: 99.9991226196289, 
  }, 
}

Request syntax with placeholder values


resp = client.compare_faces({
  source_image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  target_image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  similarity_threshold: 1.0,
  quality_filter: "NONE", # accepts NONE, AUTO, LOW, MEDIUM, HIGH
})

Response structure


resp.source_image_face.bounding_box.width #=> Float
resp.source_image_face.bounding_box.height #=> Float
resp.source_image_face.bounding_box.left #=> Float
resp.source_image_face.bounding_box.top #=> Float
resp.source_image_face.confidence #=> Float
resp.face_matches #=> Array
resp.face_matches[0].similarity #=> Float
resp.face_matches[0].face.bounding_box.width #=> Float
resp.face_matches[0].face.bounding_box.height #=> Float
resp.face_matches[0].face.bounding_box.left #=> Float
resp.face_matches[0].face.bounding_box.top #=> Float
resp.face_matches[0].face.confidence #=> Float
resp.face_matches[0].face.landmarks #=> Array
resp.face_matches[0].face.landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.face_matches[0].face.landmarks[0].x #=> Float
resp.face_matches[0].face.landmarks[0].y #=> Float
resp.face_matches[0].face.pose.roll #=> Float
resp.face_matches[0].face.pose.yaw #=> Float
resp.face_matches[0].face.pose.pitch #=> Float
resp.face_matches[0].face.quality.brightness #=> Float
resp.face_matches[0].face.quality.sharpness #=> Float
resp.face_matches[0].face.emotions #=> Array
resp.face_matches[0].face.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.face_matches[0].face.emotions[0].confidence #=> Float
resp.face_matches[0].face.smile.value #=> Boolean
resp.face_matches[0].face.smile.confidence #=> Float
resp.unmatched_faces #=> Array
resp.unmatched_faces[0].bounding_box.width #=> Float
resp.unmatched_faces[0].bounding_box.height #=> Float
resp.unmatched_faces[0].bounding_box.left #=> Float
resp.unmatched_faces[0].bounding_box.top #=> Float
resp.unmatched_faces[0].confidence #=> Float
resp.unmatched_faces[0].landmarks #=> Array
resp.unmatched_faces[0].landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.unmatched_faces[0].landmarks[0].x #=> Float
resp.unmatched_faces[0].landmarks[0].y #=> Float
resp.unmatched_faces[0].pose.roll #=> Float
resp.unmatched_faces[0].pose.yaw #=> Float
resp.unmatched_faces[0].pose.pitch #=> Float
resp.unmatched_faces[0].quality.brightness #=> Float
resp.unmatched_faces[0].quality.sharpness #=> Float
resp.unmatched_faces[0].emotions #=> Array
resp.unmatched_faces[0].emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.unmatched_faces[0].emotions[0].confidence #=> Float
resp.unmatched_faces[0].smile.value #=> Boolean
resp.unmatched_faces[0].smile.confidence #=> Float
resp.source_image_orientation_correction #=> String, one of "ROTATE_0", "ROTATE_90", "ROTATE_180", "ROTATE_270"
resp.target_image_orientation_correction #=> String, one of "ROTATE_0", "ROTATE_90", "ROTATE_180", "ROTATE_270"

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :source_image (required, Types::Image)

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

  • :target_image (required, Types::Image)

    The target image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

  • :similarity_threshold (Float)

    The minimum level of confidence in the face matches that a match must meet to be included in the FaceMatches array.

  • :quality_filter (String)

    A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren't compared. If you specify AUTO, Amazon Rekognition chooses the quality bar. If you specify LOW, MEDIUM, or HIGH, filtering removes all faces that don’t meet the chosen quality bar. The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that's misidentified as a face, a face that's too blurry, or a face with a pose that's too extreme to use. If you specify NONE, no filtering is performed. The default value is NONE.

    To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 774

def compare_faces(params = {}, options = {})
  req = build_request(:compare_faces, params)
  req.send_request(options)
end

#copy_project_version(params = {}) ⇒ Types::CopyProjectVersionResponse

This operation applies only to Amazon Rekognition Custom Labels.

Copies a version of an Amazon Rekognition Custom Labels model from a source project to a destination project. The source and destination projects can be in different AWS accounts but must be in the same AWS Region. You can't copy a model to another AWS service.

To copy a model version to a different AWS account, you need to create a resource-based policy known as a project policy. You attach the project policy to the source project by calling PutProjectPolicy. The project policy gives permission to copy the model version from a trusting AWS account to a trusted account.

For more information creating and attaching a project policy, see Attaching a project policy (SDK) in the Amazon Rekognition Custom Labels Developer Guide.

If you are copying a model version to a project in the same AWS account, you don't need to create a project policy.

Copying project versions is supported only for Custom Labels models.

To copy a model, the destination project, source project, and source model version must already exist.

Copying a model version takes a while to complete. To get the current status, call DescribeProjectVersions and check the value of Status in the ProjectVersionDescription object. The copy operation has finished when the value of Status is COPYING_COMPLETED.

This operation requires permissions to perform the rekognition:CopyProjectVersion action.

Examples:

Example: CopyProjectVersion


# Copies a version of an Amazon Rekognition Custom Labels model from a source project to a destination project.

resp = client.copy_project_version({
  destination_project_arn: "arn:aws:rekognition:us-east-1:555555555555:project/DestinationProject/1656705098765", 
  kms_key_id: "arn:1234abcd-12ab-34cd-56ef-1234567890ab", 
  output_config: {
    s3_bucket: "bucket-name", 
    s3_key_prefix: "path_to_folder", 
  }, 
  source_project_arn: "arn:aws:rekognition:us-east-1:111122223333:project/SourceProject/16565123456", 
  source_project_version_arn: "arn:aws:rekognition:us-east-1:111122223333:project/SourceProject/version/model_1/1656611123456", 
  tags: {
    "key1" => "val1", 
  }, 
  version_name: "DestinationVersionName_cross_account", 
})

resp.to_h outputs the following:
{
  project_version_arn: "arn:aws:rekognition:us-east-1:555555555555:project/DestinationProject/version/DestinationVersionName_cross_account/16567050987651", 
}

Request syntax with placeholder values


resp = client.copy_project_version({
  source_project_arn: "ProjectArn", # required
  source_project_version_arn: "ProjectVersionArn", # required
  destination_project_arn: "ProjectArn", # required
  version_name: "VersionName", # required
  output_config: { # required
    s3_bucket: "S3Bucket",
    s3_key_prefix: "S3KeyPrefix",
  },
  tags: {
    "TagKey" => "TagValue",
  },
  kms_key_id: "KmsKeyId",
})

Response structure


resp.project_version_arn #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :source_project_arn (required, String)

    The ARN of the source project in the trusting AWS account.

  • :source_project_version_arn (required, String)

    The ARN of the model version in the source project that you want to copy to a destination project.

  • :destination_project_arn (required, String)

    The ARN of the project in the trusted AWS account that you want to copy the model version to.

  • :version_name (required, String)

    A name for the version of the model that's copied to the destination project.

  • :output_config (required, Types::OutputConfig)

    The S3 bucket and folder location where the training output for the source model version is placed.

  • :tags (Hash<String,String>)

    The key-value tags to assign to the model version.

  • :kms_key_id (String)

    The identifier for your AWS Key Management Service key (AWS KMS key). You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt training results and manifest files written to the output Amazon S3 bucket (OutputConfig).

    If you choose to use your own KMS key, you need the following permissions on the KMS key.

    • kms:CreateGrant

    • kms:DescribeKey

    • kms:GenerateDataKey

    • kms:Decrypt

    If you don't specify a value for KmsKeyId, images copied into the service are encrypted using a key that AWS owns and manages.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 911

def copy_project_version(params = {}, options = {})
  req = build_request(:copy_project_version, params)
  req.send_request(options)
end

#create_collection(params = {}) ⇒ Types::CreateCollectionResponse

Creates a collection in an AWS Region. You can add faces to the collection using the IndexFaces operation.

For example, you might create collections, one for each of your application users. A user can then index faces using the IndexFaces operation and persist results in a specific collection. Then, a user can search the collection for faces in the user-specific container.

When you create a collection, it is associated with the latest version of the face model version.

Collection names are case-sensitive.

This operation requires permissions to perform the rekognition:CreateCollection action. If you want to tag your collection, you also require permission to perform the rekognition:TagResource operation.

Examples:

Example: To create a collection


# This operation creates a Rekognition collection for storing image data.

resp = client.create_collection({
  collection_id: "myphotos", 
})

resp.to_h outputs the following:
{
  collection_arn: "aws:rekognition:us-west-2:123456789012:collection/myphotos", 
  status_code: 200, 
}

Request syntax with placeholder values


resp = client.create_collection({
  collection_id: "CollectionId", # required
  tags: {
    "TagKey" => "TagValue",
  },
})

Response structure


resp.status_code #=> Integer
resp.collection_arn #=> String
resp.face_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :collection_id (required, String)

    ID for the collection that you are creating.

  • :tags (Hash<String,String>)

    A set of tags (key-value pairs) that you want to attach to the collection.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 981

def create_collection(params = {}, options = {})
  req = build_request(:create_collection, params)
  req.send_request(options)
end

#create_dataset(params = {}) ⇒ Types::CreateDatasetResponse

This operation applies only to Amazon Rekognition Custom Labels.

Creates a new Amazon Rekognition Custom Labels dataset. You can create a dataset by using an Amazon Sagemaker format manifest file or by copying an existing Amazon Rekognition Custom Labels dataset.

To create a training dataset for a project, specify TRAIN for the value of DatasetType. To create the test dataset for a project, specify TEST for the value of DatasetType.

The response from CreateDataset is the Amazon Resource Name (ARN) for the dataset. Creating a dataset takes a while to complete. Use DescribeDataset to check the current status. The dataset created successfully if the value of Status is CREATE_COMPLETE.

To check if any non-terminal errors occurred, call ListDatasetEntries and check for the presence of errors lists in the JSON Lines.

Dataset creation fails if a terminal error occurs (Status = CREATE_FAILED). Currently, you can't access the terminal error information.

For more information, see Creating dataset in the Amazon Rekognition Custom Labels Developer Guide.

This operation requires permissions to perform the rekognition:CreateDataset action. If you want to copy an existing dataset, you also require permission to perform the rekognition:ListDatasetEntries action.

Examples:

Example: To create an Amazon Rekognition Custom Labels dataset


# Creates an Amazon Rekognition Custom Labels dataset with a manifest file stored in an Amazon S3 bucket.

resp = client.create_dataset({
  dataset_source: {
    ground_truth_manifest: {
      s3_object: {
        bucket: "my-bucket", 
        name: "datasets/flowers_training/manifests/output/output.manifest", 
      }, 
    }, 
  }, 
  dataset_type: "TRAIN", 
  project_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/1690474772815", 
})

resp.to_h outputs the following:
{
  dataset_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/dataset/train/1690476084535", 
}

Request syntax with placeholder values


resp = client.create_dataset({
  dataset_source: {
    ground_truth_manifest: {
      s3_object: {
        bucket: "S3Bucket",
        name: "S3ObjectName",
        version: "S3ObjectVersion",
      },
    },
    dataset_arn: "DatasetArn",
  },
  dataset_type: "TRAIN", # required, accepts TRAIN, TEST
  project_arn: "ProjectArn", # required
})

Response structure


resp.dataset_arn #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :dataset_source (Types::DatasetSource)

    The source files for the dataset. You can specify the ARN of an existing dataset or specify the Amazon S3 bucket location of an Amazon Sagemaker format manifest file. If you don't specify datasetSource, an empty dataset is created. To add labeled images to the dataset, You can use the console or call UpdateDatasetEntries.

  • :dataset_type (required, String)

    The type of the dataset. Specify TRAIN to create a training dataset. Specify TEST to create a test dataset.

  • :project_arn (required, String)

    The ARN of the Amazon Rekognition Custom Labels project to which you want to asssign the dataset.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1083

def create_dataset(params = {}, options = {})
  req = build_request(:create_dataset, params)
  req.send_request(options)
end

#create_face_liveness_session(params = {}) ⇒ Types::CreateFaceLivenessSessionResponse

This API operation initiates a Face Liveness session. It returns a SessionId, which you can use to start streaming Face Liveness video and get the results for a Face Liveness session.

You can use the OutputConfig option in the Settings parameter to provide an Amazon S3 bucket location. The Amazon S3 bucket stores reference images and audit images. If no Amazon S3 bucket is defined, raw bytes are sent instead.

You can use AuditImagesLimit to limit the number of audit images returned when GetFaceLivenessSessionResults is called. This number is between 0 and 4. By default, it is set to 0. The limit is best effort and based on the duration of the selfie-video.

Examples:

Request syntax with placeholder values


resp = client.create_face_liveness_session({
  kms_key_id: "KmsKeyId",
  settings: {
    output_config: {
      s3_bucket: "S3Bucket", # required
      s3_key_prefix: "LivenessS3KeyPrefix",
    },
    audit_images_limit: 1,
  },
  client_request_token: "ClientRequestToken",
})

Response structure


resp.session_id #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :kms_key_id (String)

    The identifier for your AWS Key Management Service key (AWS KMS key). Used to encrypt audit images and reference images.

  • :settings (Types::CreateFaceLivenessSessionRequestSettings)

    A session settings object. It contains settings for the operation to be performed. For Face Liveness, it accepts OutputConfig and AuditImagesLimit.

  • :client_request_token (String)

    Idempotent token is used to recognize the Face Liveness request. If the same token is used with multiple CreateFaceLivenessSession requests, the same session is returned. This token is employed to avoid unintentionally creating the same session multiple times.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1141

def create_face_liveness_session(params = {}, options = {})
  req = build_request(:create_face_liveness_session, params)
  req.send_request(options)
end

#create_project(params = {}) ⇒ Types::CreateProjectResponse

Creates a new Amazon Rekognition project. A project is a group of resources (datasets, model versions) that you use to create and manage a Amazon Rekognition Custom Labels Model or custom adapter. You can specify a feature to create the project with, if no feature is specified then Custom Labels is used by default. For adapters, you can also choose whether or not to have the project auto update by using the AutoUpdate argument. This operation requires permissions to perform the rekognition:CreateProject action.

Examples:

Example: To create an Amazon Rekognition Custom Labels project


# Creates an Amazon Rekognition Custom Labels project.

resp = client.create_project({
  project_name: "my-project", 
})

resp.to_h outputs the following:
{
  project_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/1690405809285", 
}

Request syntax with placeholder values


resp = client.create_project({
  project_name: "ProjectName", # required
  feature: "CONTENT_MODERATION", # accepts CONTENT_MODERATION, CUSTOM_LABELS
  auto_update: "ENABLED", # accepts ENABLED, DISABLED
})

Response structure


resp.project_arn #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :project_name (required, String)

    The name of the project to create.

  • :feature (String)

    Specifies feature that is being customized. If no value is provided CUSTOM_LABELS is used as a default.

  • :auto_update (String)

    Specifies whether automatic retraining should be attempted for the versions of the project. Automatic retraining is done as a best effort. Required argument for Content Moderation. Applicable only to adapters.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1200

def create_project(params = {}, options = {})
  req = build_request(:create_project, params)
  req.send_request(options)
end

#create_project_version(params = {}) ⇒ Types::CreateProjectVersionResponse

Creates a new version of Amazon Rekognition project (like a Custom Labels model or a custom adapter) and begins training. Models and adapters are managed as part of a Rekognition project. The response from CreateProjectVersion is an Amazon Resource Name (ARN) for the project version.

The FeatureConfig operation argument allows you to configure specific model or adapter settings. You can provide a description to the project version by using the VersionDescription argment. Training can take a while to complete. You can get the current status by calling DescribeProjectVersions. Training completed successfully if the value of the Status field is TRAINING_COMPLETED. Once training has successfully completed, call DescribeProjectVersions to get the training results and evaluate the model.

This operation requires permissions to perform the rekognition:CreateProjectVersion action.

The following applies only to projects with Amazon Rekognition Custom Labels as the chosen feature:

You can train a model in a project that doesn't have associated datasets by specifying manifest files in the TrainingData and TestingData fields.

If you open the console after training a model with manifest files, Amazon Rekognition Custom Labels creates the datasets for you using the most recent manifest files. You can no longer train a model version for the project by specifying manifest files.

Instead of training with a project without associated datasets, we recommend that you use the manifest files to create training and test datasets for the project.

Examples:

Example: To train an Amazon Rekognition Custom Labels model


# Trains a version of an Amazon Rekognition Custom Labels model.

resp = client.create_project_version({
  output_config: {
    s3_bucket: "output_bucket", 
    s3_key_prefix: "output_folder", 
  }, 
  project_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/1690474772815", 
  version_name: "1", 
})

resp.to_h outputs the following:
{
  project_version_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958", 
}

Request syntax with placeholder values


resp = client.create_project_version({
  project_arn: "ProjectArn", # required
  version_name: "VersionName", # required
  output_config: { # required
    s3_bucket: "S3Bucket",
    s3_key_prefix: "S3KeyPrefix",
  },
  training_data: {
    assets: [
      {
        ground_truth_manifest: {
          s3_object: {
            bucket: "S3Bucket",
            name: "S3ObjectName",
            version: "S3ObjectVersion",
          },
        },
      },
    ],
  },
  testing_data: {
    assets: [
      {
        ground_truth_manifest: {
          s3_object: {
            bucket: "S3Bucket",
            name: "S3ObjectName",
            version: "S3ObjectVersion",
          },
        },
      },
    ],
    auto_create: false,
  },
  tags: {
    "TagKey" => "TagValue",
  },
  kms_key_id: "KmsKeyId",
  version_description: "VersionDescription",
  feature_config: {
    content_moderation: {
      confidence_threshold: 1.0,
    },
  },
})

Response structure


resp.project_version_arn #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :project_arn (required, String)

    The ARN of the Amazon Rekognition project that will manage the project version you want to train.

  • :version_name (required, String)

    A name for the version of the project version. This value must be unique.

  • :output_config (required, Types::OutputConfig)

    The Amazon S3 bucket location to store the results of training. The bucket can be any S3 bucket in your AWS account. You need s3:PutObject permission on the bucket.

  • :training_data (Types::TrainingData)

    Specifies an external manifest that the services uses to train the project version. If you specify TrainingData you must also specify TestingData. The project must not have any associated datasets.

  • :testing_data (Types::TestingData)

    Specifies an external manifest that the service uses to test the project version. If you specify TestingData you must also specify TrainingData. The project must not have any associated datasets.

  • :tags (Hash<String,String>)

    A set of tags (key-value pairs) that you want to attach to the project version.

  • :kms_key_id (String)

    The identifier for your AWS Key Management Service key (AWS KMS key). You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt training images, test images, and manifest files copied into the service for the project version. Your source images are unaffected. The key is also used to encrypt training results and manifest files written to the output Amazon S3 bucket (OutputConfig).

    If you choose to use your own KMS key, you need the following permissions on the KMS key.

    • kms:CreateGrant

    • kms:DescribeKey

    • kms:GenerateDataKey

    • kms:Decrypt

    If you don't specify a value for KmsKeyId, images copied into the service are encrypted using a key that AWS owns and manages.

  • :version_description (String)

    A description applied to the project version being created.

  • :feature_config (Types::CustomizationFeatureConfig)

    Feature-specific configuration of the training job. If the job configuration does not match the feature type associated with the project, an InvalidParameterException is returned.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1377

def create_project_version(params = {}, options = {})
  req = build_request(:create_project_version, params)
  req.send_request(options)
end

#create_stream_processor(params = {}) ⇒ Types::CreateStreamProcessorResponse

Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces or to detect labels in a streaming video.

Amazon Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. There are two different settings for stream processors in Amazon Rekognition: detecting faces and detecting labels.

  • If you are creating a stream processor for detecting faces, you provide as input a Kinesis video stream (Input) and a Kinesis data stream (Output) stream for receiving the output. You must use the FaceSearch option in Settings, specifying the collection that contains the faces you want to recognize. After you have finished analyzing a streaming video, use StopStreamProcessor to stop processing.

  • If you are creating a stream processor to detect labels, you provide as input a Kinesis video stream (Input), Amazon S3 bucket information (Output), and an Amazon SNS topic ARN (NotificationChannel). You can also provide a KMS key ID to encrypt the data sent to your Amazon S3 bucket. You specify what you want to detect by using the ConnectedHome option in settings, and selecting one of the following: PERSON, PET, PACKAGE, ALL You can also specify where in the frame you want Amazon Rekognition to monitor with RegionsOfInterest. When you run the StartStreamProcessor operation on a label detection stream processor, you input start and stop information to determine the length of the processing time.

Use Name to assign an identifier for the stream processor. You use Name to manage the stream processor. For example, you can start processing the source video by calling StartStreamProcessor with the Name field.

This operation requires permissions to perform the rekognition:CreateStreamProcessor action. If you want to tag your stream processor, you also require permission to perform the rekognition:TagResource operation.

Examples:

Request syntax with placeholder values


resp = client.create_stream_processor({
  input: { # required
    kinesis_video_stream: {
      arn: "KinesisVideoArn",
    },
  },
  output: { # required
    kinesis_data_stream: {
      arn: "KinesisDataArn",
    },
    s3_destination: {
      bucket: "S3Bucket",
      key_prefix: "S3KeyPrefix",
    },
  },
  name: "StreamProcessorName", # required
  settings: { # required
    face_search: {
      collection_id: "CollectionId",
      face_match_threshold: 1.0,
    },
    connected_home: {
      labels: ["ConnectedHomeLabel"], # required
      min_confidence: 1.0,
    },
  },
  role_arn: "RoleArn", # required
  tags: {
    "TagKey" => "TagValue",
  },
  notification_channel: {
    sns_topic_arn: "SNSTopicArn", # required
  },
  kms_key_id: "KmsKeyId",
  regions_of_interest: [
    {
      bounding_box: {
        width: 1.0,
        height: 1.0,
        left: 1.0,
        top: 1.0,
      },
      polygon: [
        {
          x: 1.0,
          y: 1.0,
        },
      ],
    },
  ],
  data_sharing_preference: {
    opt_in: false, # required
  },
})

Response structure


resp.stream_processor_arn #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :input (required, Types::StreamProcessorInput)

    Kinesis video stream stream that provides the source streaming video. If you are using the AWS CLI, the parameter name is StreamProcessorInput. This is required for both face search and label detection stream processors.

  • :output (required, Types::StreamProcessorOutput)

    Kinesis data stream stream or Amazon S3 bucket location to which Amazon Rekognition Video puts the analysis results. If you are using the AWS CLI, the parameter name is StreamProcessorOutput. This must be a S3Destination of an Amazon S3 bucket that you own for a label detection stream processor or a Kinesis data stream ARN for a face search stream processor.

  • :name (required, String)

    An identifier you assign to the stream processor. You can use Name to manage the stream processor. For example, you can get the current status of the stream processor by calling DescribeStreamProcessor. Name is idempotent. This is required for both face search and label detection stream processors.

  • :settings (required, Types::StreamProcessorSettings)

    Input parameters used in a streaming video analyzed by a stream processor. You can use FaceSearch to recognize faces in a streaming video, or you can use ConnectedHome to detect labels.

  • :role_arn (required, String)

    The Amazon Resource Number (ARN) of the IAM role that allows access to the stream processor. The IAM role provides Rekognition read permissions for a Kinesis stream. It also provides write permissions to an Amazon S3 bucket and Amazon Simple Notification Service topic for a label detection stream processor. This is required for both face search and label detection stream processors.

  • :tags (Hash<String,String>)

    A set of tags (key-value pairs) that you want to attach to the stream processor.

  • :notification_channel (Types::StreamProcessorNotificationChannel)

    The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.

    Amazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications, one for a person at second 2 and one for a pet at second 4.

    Amazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.

  • :kms_key_id (String)

    The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor. You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt results and data published to your Amazon S3 bucket, which includes image frames and hero images. Your source images are unaffected.

  • :regions_of_interest (Array<Types::RegionOfInterest>)

    Specifies locations in the frames where Amazon Rekognition checks for objects or people. You can specify up to 10 regions of interest, and each region has either a polygon or a bounding box. This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor.

  • :data_sharing_preference (Types::StreamProcessorDataSharingPreference)

    Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1564

def create_stream_processor(params = {}, options = {})
  req = build_request(:create_stream_processor, params)
  req.send_request(options)
end

#create_user(params = {}) ⇒ Struct

Creates a new User within a collection specified by CollectionId. Takes UserId as a parameter, which is a user provided ID which should be unique within the collection. The provided UserId will alias the system generated UUID to make the UserId more user friendly.

Uses a ClientToken, an idempotency token that ensures a call to CreateUser completes only once. If the value is not supplied, the AWS SDK generates an idempotency token for the requests. This prevents retries after a network error results from making multiple CreateUser calls.

Examples:

Example: CreateUser


# Creates a new User within a collection specified by CollectionId.

resp = client.create_user({
  collection_id: "MyCollection", 
  user_id: "DemoUser", 
})

Request syntax with placeholder values


resp = client.create_user({
  collection_id: "CollectionId", # required
  user_id: "UserId", # required
  client_request_token: "ClientRequestToken",
})

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :collection_id (required, String)

    The ID of an existing collection to which the new UserID needs to be created.

  • :user_id (required, String)

    ID for the UserID to be created. This ID needs to be unique within the collection.

  • :client_request_token (String)

    Idempotent token used to identify the request to CreateUser. If you use the same token with multiple CreateUser requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.

    A suitable default value is auto-generated. You should normally not need to pass this option.**

Returns:

  • (Struct)

    Returns an empty response.



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1620

def create_user(params = {}, options = {})
  req = build_request(:create_user, params)
  req.send_request(options)
end

#delete_collection(params = {}) ⇒ Types::DeleteCollectionResponse

Deletes the specified collection. Note that this operation removes all faces in the collection. For an example, see Deleting a collection.

This operation requires permissions to perform the rekognition:DeleteCollection action.

Examples:

Example: To delete a collection


# This operation deletes a Rekognition collection.

resp = client.delete_collection({
  collection_id: "myphotos", 
})

resp.to_h outputs the following:
{
  status_code: 200, 
}

Request syntax with placeholder values


resp = client.delete_collection({
  collection_id: "CollectionId", # required
})

Response structure


resp.status_code #=> Integer

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :collection_id (required, String)

    ID of the collection to delete.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1669

def delete_collection(params = {}, options = {})
  req = build_request(:delete_collection, params)
  req.send_request(options)
end

#delete_dataset(params = {}) ⇒ Struct

This operation applies only to Amazon Rekognition Custom Labels.

Deletes an existing Amazon Rekognition Custom Labels dataset. Deleting a dataset might take while. Use DescribeDataset to check the current status. The dataset is still deleting if the value of Status is DELETE_IN_PROGRESS. If you try to access the dataset after it is deleted, you get a ResourceNotFoundException exception.

You can't delete a dataset while it is creating (Status = CREATE_IN_PROGRESS) or if the dataset is updating (Status = UPDATE_IN_PROGRESS).

This operation requires permissions to perform the rekognition:DeleteDataset action.

Examples:

Example: To delete an Amazon Rekognition Custom Labels dataset


# Deletes an Amazon Rekognition Custom Labels dataset.

resp = client.delete_dataset({
  dataset_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/dataset/test/1690556733321", 
})

resp.to_h outputs the following:
{
}

Request syntax with placeholder values


resp = client.delete_dataset({
  dataset_arn: "DatasetArn", # required
})

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :dataset_arn (required, String)

    The ARN of the Amazon Rekognition Custom Labels dataset that you want to delete.

Returns:

  • (Struct)

    Returns an empty response.



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1718

def delete_dataset(params = {}, options = {})
  req = build_request(:delete_dataset, params)
  req.send_request(options)
end

#delete_faces(params = {}) ⇒ Types::DeleteFacesResponse

Deletes faces from a collection. You specify a collection ID and an array of face IDs to remove from the collection.

This operation requires permissions to perform the rekognition:DeleteFaces action.

Examples:

Example: To delete a face


# This operation deletes one or more faces from a Rekognition collection.

resp = client.delete_faces({
  collection_id: "myphotos", 
  face_ids: [
    "ff43d742-0c13-5d16-a3e8-03d3f58e980b", 
  ], 
})

resp.to_h outputs the following:
{
  deleted_faces: [
    "ff43d742-0c13-5d16-a3e8-03d3f58e980b", 
  ], 
}

Request syntax with placeholder values


resp = client.delete_faces({
  collection_id: "CollectionId", # required
  face_ids: ["FaceId"], # required
})

Response structure


resp.deleted_faces #=> Array
resp.deleted_faces[0] #=> String
resp.unsuccessful_face_deletions #=> Array
resp.unsuccessful_face_deletions[0].face_id #=> String
resp.unsuccessful_face_deletions[0].user_id #=> String
resp.unsuccessful_face_deletions[0].reasons #=> Array
resp.unsuccessful_face_deletions[0].reasons[0] #=> String, one of "ASSOCIATED_TO_AN_EXISTING_USER", "FACE_NOT_FOUND"

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :collection_id (required, String)

    Collection from which to remove the specific faces.

  • :face_ids (required, Array<String>)

    An array of face IDs to delete.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1778

def delete_faces(params = {}, options = {})
  req = build_request(:delete_faces, params)
  req.send_request(options)
end

#delete_project(params = {}) ⇒ Types::DeleteProjectResponse

Deletes a Amazon Rekognition project. To delete a project you must first delete all models or adapters associated with the project. To delete a model or adapter, see DeleteProjectVersion.

DeleteProject is an asynchronous operation. To check if the project is deleted, call DescribeProjects. The project is deleted when the project no longer appears in the response. Be aware that deleting a given project will also delete any ProjectPolicies associated with that project.

This operation requires permissions to perform the rekognition:DeleteProject action.

Examples:

Example: To delete an Amazon Rekognition Custom Labels project


# Deletes an Amazon Rekognition Custom Labels projects.

resp = client.delete_project({
  project_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/1690405809285", 
})

resp.to_h outputs the following:
{
  status: "DELETING", 
}

Request syntax with placeholder values


resp = client.delete_project({
  project_arn: "ProjectArn", # required
})

Response structure


resp.status #=> String, one of "CREATING", "CREATED", "DELETING"

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :project_arn (required, String)

    The Amazon Resource Name (ARN) of the project that you want to delete.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1829

def delete_project(params = {}, options = {})
  req = build_request(:delete_project, params)
  req.send_request(options)
end

#delete_project_policy(params = {}) ⇒ Struct

This operation applies only to Amazon Rekognition Custom Labels.

Deletes an existing project policy.

To get a list of project policies attached to a project, call ListProjectPolicies. To attach a project policy to a project, call PutProjectPolicy.

This operation requires permissions to perform the rekognition:DeleteProjectPolicy action.

Examples:

Example: DeleteProjectPolicy


# This operation deletes a revision of an existing project policy from an Amazon Rekognition Custom Labels project.

resp = client.delete_project_policy({
  policy_name: "testPolicy1", 
  policy_revision_id: "3b274c25e9203a56a99e00e3ff205fbc", 
  project_arn: "arn:aws:rekognition:us-east-1:111122223333:project/SourceProject/1656557123456", 
})

resp.to_h outputs the following:
{
}

Request syntax with placeholder values


resp = client.delete_project_policy({
  project_arn: "ProjectArn", # required
  policy_name: "ProjectPolicyName", # required
  policy_revision_id: "ProjectPolicyRevisionId",
})

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :project_arn (required, String)

    The Amazon Resource Name (ARN) of the project that the project policy you want to delete is attached to.

  • :policy_name (required, String)

    The name of the policy that you want to delete.

  • :policy_revision_id (String)

    The ID of the project policy revision that you want to delete.

Returns:

  • (Struct)

    Returns an empty response.



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1884

def delete_project_policy(params = {}, options = {})
  req = build_request(:delete_project_policy, params)
  req.send_request(options)
end

#delete_project_version(params = {}) ⇒ Types::DeleteProjectVersionResponse

Deletes a Rekognition project model or project version, like a Amazon Rekognition Custom Labels model or a custom adapter.

You can't delete a project version if it is running or if it is training. To check the status of a project version, use the Status field returned from DescribeProjectVersions. To stop a project version call StopProjectVersion. If the project version is training, wait until it finishes.

This operation requires permissions to perform the rekognition:DeleteProjectVersion action.

Examples:

Example: To delete an Amazon Rekognition Custom Labels model


# Deletes a version of an Amazon Rekognition Custom Labels model.

resp = client.delete_project_version({
  project_version_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958", 
})

resp.to_h outputs the following:
{
  status: "DELETING", 
}

Request syntax with placeholder values


resp = client.delete_project_version({
  project_version_arn: "ProjectVersionArn", # required
})

Response structure


resp.status #=> String, one of "TRAINING_IN_PROGRESS", "TRAINING_COMPLETED", "TRAINING_FAILED", "STARTING", "RUNNING", "FAILED", "STOPPING", "STOPPED", "DELETING", "COPYING_IN_PROGRESS", "COPYING_COMPLETED", "COPYING_FAILED", "DEPRECATED", "EXPIRED"

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :project_version_arn (required, String)

    The Amazon Resource Name (ARN) of the project version that you want to delete.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1935

def delete_project_version(params = {}, options = {})
  req = build_request(:delete_project_version, params)
  req.send_request(options)
end

#delete_stream_processor(params = {}) ⇒ Struct

Deletes the stream processor identified by Name. You assign the value for Name when you create the stream processor with CreateStreamProcessor. You might not be able to use the same name for a stream processor for a few seconds after calling DeleteStreamProcessor.

Examples:

Request syntax with placeholder values


resp = client.delete_stream_processor({
  name: "StreamProcessorName", # required
})

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :name (required, String)

    The name of the stream processor you want to delete.

Returns:

  • (Struct)

    Returns an empty response.



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1959

def delete_stream_processor(params = {}, options = {})
  req = build_request(:delete_stream_processor, params)
  req.send_request(options)
end

#delete_user(params = {}) ⇒ Struct

Deletes the specified UserID within the collection. Faces that are associated with the UserID are disassociated from the UserID before deleting the specified UserID. If the specified Collection or UserID is already deleted or not found, a ResourceNotFoundException will be thrown. If the action is successful with a 200 response, an empty HTTP body is returned.

Examples:

Example: DeleteUser


# Deletes the specified UserID within the collection.

resp = client.delete_user({
  client_request_token: "550e8400-e29b-41d4-a716-446655440001", 
  collection_id: "MyCollection", 
  user_id: "DemoUser", 
})

Request syntax with placeholder values


resp = client.delete_user({
  collection_id: "CollectionId", # required
  user_id: "UserId", # required
  client_request_token: "ClientRequestToken",
})

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :collection_id (required, String)

    The ID of an existing collection from which the UserID needs to be deleted.

  • :user_id (required, String)

    ID for the UserID to be deleted.

  • :client_request_token (String)

    Idempotent token used to identify the request to DeleteUser. If you use the same token with multiple DeleteUserrequests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.

    A suitable default value is auto-generated. You should normally not need to pass this option.**

Returns:

  • (Struct)

    Returns an empty response.



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2010

def delete_user(params = {}, options = {})
  req = build_request(:delete_user, params)
  req.send_request(options)
end

#describe_collection(params = {}) ⇒ Types::DescribeCollectionResponse

Describes the specified collection. You can use DescribeCollection to get information, such as the number of faces indexed into a collection and the version of the model used by the collection for face detection.

For more information, see Describing a Collection in the Amazon Rekognition Developer Guide.

Examples:

Request syntax with placeholder values


resp = client.describe_collection({
  collection_id: "CollectionId", # required
})

Response structure


resp.face_count #=> Integer
resp.face_model_version #=> String
resp.collection_arn #=> String
resp.creation_timestamp #=> Time
resp.user_count #=> Integer

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :collection_id (required, String)

    The ID of the collection to describe.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2050

def describe_collection(params = {}, options = {})
  req = build_request(:describe_collection, params)
  req.send_request(options)
end

#describe_dataset(params = {}) ⇒ Types::DescribeDatasetResponse

This operation applies only to Amazon Rekognition Custom Labels.

Describes an Amazon Rekognition Custom Labels dataset. You can get information such as the current status of a dataset and statistics about the images and labels in a dataset.

This operation requires permissions to perform the rekognition:DescribeDataset action.

Examples:

Example: To describe an Amazon Rekognition Custom Labels dataset


# Describes an Amazon Rekognition Custom Labels dataset.

resp = client.describe_dataset({
  dataset_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/dataset/train/1690476084535", 
})

resp.to_h outputs the following:
{
  dataset_description: {
    creation_timestamp: Time.parse("2023-07-27T09:41:24.738000-07:00"), 
    dataset_stats: {
      error_entries: 0, 
      labeled_entries: 15, 
      total_entries: 15, 
      total_labels: 9, 
    }, 
    last_updated_timestamp: Time.parse("2023-07-28T09:46:45.406000-07:00"), 
    status: "UPDATE_FAILED", 
    status_message: "The manifest file contains images from multiple S3 buckets.", 
    status_message_code: "CLIENT_ERROR", 
  }, 
}

Request syntax with placeholder values


resp = client.describe_dataset({
  dataset_arn: "DatasetArn", # required
})

Response structure


resp.dataset_description.creation_timestamp #=> Time
resp.dataset_description.last_updated_timestamp #=> Time
resp.dataset_description.status #=> String, one of "CREATE_IN_PROGRESS", "CREATE_COMPLETE", "CREATE_FAILED", "UPDATE_IN_PROGRESS", "UPDATE_COMPLETE", "UPDATE_FAILED", "DELETE_IN_PROGRESS"
resp.dataset_description.status_message #=> String
resp.dataset_description.status_message_code #=> String, one of "SUCCESS", "SERVICE_ERROR", "CLIENT_ERROR"
resp.dataset_description.dataset_stats.labeled_entries #=> Integer
resp.dataset_description.dataset_stats.total_entries #=> Integer
resp.dataset_description.dataset_stats.total_labels #=> Integer
resp.dataset_description.dataset_stats.error_entries #=> Integer

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :dataset_arn (required, String)

    The Amazon Resource Name (ARN) of the dataset that you want to describe.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2120

def describe_dataset(params = {}, options = {})
  req = build_request(:describe_dataset, params)
  req.send_request(options)
end

#describe_project_versions(params = {}) ⇒ Types::DescribeProjectVersionsResponse

Lists and describes the versions of an Amazon Rekognition project. You can specify up to 10 model or adapter versions in ProjectVersionArns. If you don't specify a value, descriptions for all model/adapter versions in the project are returned.

This operation requires permissions to perform the rekognition:DescribeProjectVersions action.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

The following waiters are defined for this operation (see #wait_until for detailed usage):

  • project_version_running
  • project_version_training_completed

Examples:

Example: To describe an Amazon Rekognition Custom Labels model


# Describes a version of an Amazon Rekognition Custom Labels model.

resp = client.describe_project_versions({
  project_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/1690474772815", 
  version_names: [
    "1", 
  ], 
})

resp.to_h outputs the following:
{
  next_token: "", 
  project_version_descriptions: [
    {
      billable_training_time_in_seconds: 1899, 
      creation_timestamp: Time.parse("2023-07-28T08:05:51.958000-07:00"), 
      evaluation_result: {
        f1_score: 1, 
        summary: {
          s3_object: {
            bucket: "custom-labels-console-us-east-1-111111111", 
            name: "my-model-output/EvaluationResultSummary-my-project-1.json", 
          }, 
        }, 
      }, 
      manifest_summary: {
        s3_object: {
          bucket: "custom-labels-console-us-east-1-111111111", 
          name: "my-model-output/ManifestSummary-my-project-1.json", 
        }, 
      }, 
      output_config: {
        s3_bucket: "custom-labels-console-us-east-1-111111111", 
        s3_key_prefix: "my-model-output", 
      }, 
      project_version_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958", 
      status: "TRAINING_COMPLETED", 
      status_message: "The model is ready to run.", 
      testing_data_result: {
        input: {
          auto_create: false, 
        }, 
        output: {
          assets: [
            {
              ground_truth_manifest: {
                s3_object: {
                  bucket: "custom-labels-console-us-east-1-111111111", 
                  name: "my-model-output/TestingGroundTruth-my-project-1.json", 
                }, 
              }, 
            }, 
          ], 
          auto_create: false, 
        }, 
        validation: {
          assets: [
            {
              ground_truth_manifest: {
                s3_object: {
                  bucket: "custom-labels-console-us-east-1-111111111", 
                  name: "my-model-output/TestingManifestWithValidation-my-project-1.json", 
                }, 
              }, 
            }, 
          ], 
        }, 
      }, 
      training_data_result: {
        input: {
        }, 
        output: {
          assets: [
            {
              ground_truth_manifest: {
                s3_object: {
                  bucket: "custom-labels-console-us-east-1-111111111", 
                  name: "my-model-output/TrainingGroundTruth-my-project-1.json", 
                }, 
              }, 
            }, 
          ], 
        }, 
        validation: {
          assets: [
            {
              ground_truth_manifest: {
                s3_object: {
                  bucket: "custom-labels-console-us-east-1-111111111", 
                  name: "my-model-output/TrainingManifestWithValidation-my-project-1.json", 
                }, 
              }, 
            }, 
          ], 
        }, 
      }, 
      training_end_timestamp: Time.parse("2023-07-28T08:33:10.827000-07:00"), 
    }, 
  ], 
}

Request syntax with placeholder values


resp = client.describe_project_versions({
  project_arn: "ProjectArn", # required
  version_names: ["VersionName"],
  next_token: "ExtendedPaginationToken",
  max_results: 1,
})

Response structure


resp.project_version_descriptions #=> Array
resp.project_version_descriptions[0].project_version_arn #=> String
resp.project_version_descriptions[0].creation_timestamp #=> Time
resp.project_version_descriptions[0].min_inference_units #=> Integer
resp.project_version_descriptions[0].status #=> String, one of "TRAINING_IN_PROGRESS", "TRAINING_COMPLETED", "TRAINING_FAILED", "STARTING", "RUNNING", "FAILED", "STOPPING", "STOPPED", "DELETING", "COPYING_IN_PROGRESS", "COPYING_COMPLETED", "COPYING_FAILED", "DEPRECATED", "EXPIRED"
resp.project_version_descriptions[0].status_message #=> String
resp.project_version_descriptions[0].billable_training_time_in_seconds #=> Integer
resp.project_version_descriptions[0].training_end_timestamp #=> Time
resp.project_version_descriptions[0].output_config.s3_bucket #=> String
resp.project_version_descriptions[0].output_config.s3_key_prefix #=> String
resp.project_version_descriptions[0].training_data_result.input.assets #=> Array
resp.project_version_descriptions[0].training_data_result.input.assets[0].ground_truth_manifest.s3_object.bucket #=> String
resp.project_version_descriptions[0].training_data_result.input.assets[0].ground_truth_manifest.s3_object.name #=> String
resp.project_version_descriptions[0].training_data_result.input.assets[0].ground_truth_manifest.s3_object.version #=> String
resp.project_version_descriptions[0].training_data_result.output.assets #=> Array
resp.project_version_descriptions[0].training_data_result.output.assets[0].ground_truth_manifest.s3_object.bucket #=> String
resp.project_version_descriptions[0].training_data_result.output.assets[0].ground_truth_manifest.s3_object.name #=> String
resp.project_version_descriptions[0].training_data_result.output.assets[0].ground_truth_manifest.s3_object.version #=> String
resp.project_version_descriptions[0].training_data_result.validation.assets #=> Array
resp.project_version_descriptions[0].training_data_result.validation.assets[0].ground_truth_manifest.s3_object.bucket #=> String
resp.project_version_descriptions[0].training_data_result.validation.assets[0].ground_truth_manifest.s3_object.name #=> String
resp.project_version_descriptions[0].training_data_result.validation.assets[0].ground_truth_manifest.s3_object.version #=> String
resp.project_version_descriptions[0].testing_data_result.input.assets #=> Array
resp.project_version_descriptions[0].testing_data_result.input.assets[0].ground_truth_manifest.s3_object.bucket #=> String
resp.project_version_descriptions[0].testing_data_result.input.assets[0].ground_truth_manifest.s3_object.name #=> String
resp.project_version_descriptions[0].testing_data_result.input.assets[0].ground_truth_manifest.s3_object.version #=> String
resp.project_version_descriptions[0].testing_data_result.input.auto_create #=> Boolean
resp.project_version_descriptions[0].testing_data_result.output.assets #=> Array
resp.project_version_descriptions[0].testing_data_result.output.assets[0].ground_truth_manifest.s3_object.bucket #=> String
resp.project_version_descriptions[0].testing_data_result.output.assets[0].ground_truth_manifest.s3_object.name #=> String
resp.project_version_descriptions[0].testing_data_result.output.assets[0].ground_truth_manifest.s3_object.version #=> String
resp.project_version_descriptions[0].testing_data_result.output.auto_create #=> Boolean
resp.project_version_descriptions[0].testing_data_result.validation.assets #=> Array
resp.project_version_descriptions[0].testing_data_result.validation.assets[0].ground_truth_manifest.s3_object.bucket #=> String
resp.project_version_descriptions[0].testing_data_result.validation.assets[0].ground_truth_manifest.s3_object.name #=> String
resp.project_version_descriptions[0].testing_data_result.validation.assets[0].ground_truth_manifest.s3_object.version #=> String
resp.project_version_descriptions[0].evaluation_result.f1_score #=> Float
resp.project_version_descriptions[0].evaluation_result.summary.s3_object.bucket #=> String
resp.project_version_descriptions[0].evaluation_result.summary.s3_object.name #=> String
resp.project_version_descriptions[0].evaluation_result.summary.s3_object.version #=> String
resp.project_version_descriptions[0].manifest_summary.s3_object.bucket #=> String
resp.project_version_descriptions[0].manifest_summary.s3_object.name #=> String
resp.project_version_descriptions[0].manifest_summary.s3_object.version #=> String
resp.project_version_descriptions[0].kms_key_id #=> String
resp.project_version_descriptions[0].max_inference_units #=> Integer
resp.project_version_descriptions[0].source_project_version_arn #=> String
resp.project_version_descriptions[0].version_description #=> String
resp.project_version_descriptions[0].feature #=> String, one of "CONTENT_MODERATION", "CUSTOM_LABELS"
resp.project_version_descriptions[0].base_model_version #=> String
resp.project_version_descriptions[0].feature_config.content_moderation.confidence_threshold #=> Float
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :project_arn (required, String)

    The Amazon Resource Name (ARN) of the project that contains the model/adapter you want to describe.

  • :version_names (Array<String>)

    A list of model or project version names that you want to describe. You can add up to 10 model or project version names to the list. If you don't specify a value, all project version descriptions are returned. A version name is part of a project version ARN. For example, my-model.2020-01-21T09.10.15 is the version name in the following ARN. arn:aws:rekognition:us-east-1:123456789012:project/getting-started/version/my-model.2020-01-21T09.10.15/1234567890123.

  • :next_token (String)

    If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

  • :max_results (Integer)

    The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2340

def describe_project_versions(params = {}, options = {})
  req = build_request(:describe_project_versions, params)
  req.send_request(options)
end

#describe_projects(params = {}) ⇒ Types::DescribeProjectsResponse

Gets information about your Rekognition projects.

This operation requires permissions to perform the rekognition:DescribeProjects action.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Example: To describe an Amazon Rekognition Custom Labels project.


# Describes an Amazon Rekognition Custom Labels projects.

resp = client.describe_projects({
  project_names: [
    "my-project", 
  ], 
})

resp.to_h outputs the following:
{
  project_descriptions: [
    {
      creation_timestamp: Time.parse("2022-06-13T15:16:00.634000-07:00"), 
      datasets: [
        {
          creation_timestamp: Time.parse("2022-06-13T15:17:34.620000-07:00"), 
          dataset_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/dataset/test/1655158654491", 
          dataset_type: "TEST", 
          status: "CREATE_COMPLETE", 
          status_message: "The dataset was successfully created from the manifest file.", 
          status_message_code: "SUCCESS", 
        }, 
        {
          creation_timestamp: Time.parse("2022-06-13T15:17:50.118000-07:00"), 
          dataset_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/dataset/train/1655158669954", 
          dataset_type: "TRAIN", 
          status: "CREATE_COMPLETE", 
          status_message: "The dataset was successfully created from the manifest file.", 
          status_message_code: "SUCCESS", 
        }, 
      ], 
      project_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/1655158560634", 
      status: "CREATED", 
    }, 
  ], 
}

Request syntax with placeholder values


resp = client.describe_projects({
  next_token: "ExtendedPaginationToken",
  max_results: 1,
  project_names: ["ProjectName"],
  features: ["CONTENT_MODERATION"], # accepts CONTENT_MODERATION, CUSTOM_LABELS
})

Response structure


resp.project_descriptions #=> Array
resp.project_descriptions[0].project_arn #=> String
resp.project_descriptions[0].creation_timestamp #=> Time
resp.project_descriptions[0].status #=> String, one of "CREATING", "CREATED", "DELETING"
resp.project_descriptions[0].datasets #=> Array
resp.project_descriptions[0].datasets[0].creation_timestamp #=> Time
resp.project_descriptions[0].datasets[0].dataset_type #=> String, one of "TRAIN", "TEST"
resp.project_descriptions[0].datasets[0].dataset_arn #=> String
resp.project_descriptions[0].datasets[0].status #=> String, one of "CREATE_IN_PROGRESS", "CREATE_COMPLETE", "CREATE_FAILED", "UPDATE_IN_PROGRESS", "UPDATE_COMPLETE", "UPDATE_FAILED", "DELETE_IN_PROGRESS"
resp.project_descriptions[0].datasets[0].status_message #=> String
resp.project_descriptions[0].datasets[0].status_message_code #=> String, one of "SUCCESS", "SERVICE_ERROR", "CLIENT_ERROR"
resp.project_descriptions[0].feature #=> String, one of "CONTENT_MODERATION", "CUSTOM_LABELS"
resp.project_descriptions[0].auto_update #=> String, one of "ENABLED", "DISABLED"
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :next_token (String)

    If the previous response was incomplete (because there is more results to retrieve), Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

  • :max_results (Integer)

    The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.

  • :project_names (Array<String>)

    A list of the projects that you want Rekognition to describe. If you don't specify a value, the response includes descriptions for all the projects in your AWS account.

  • :features (Array<String>)

    Specifies the type of customization to filter projects by. If no value is specified, CUSTOM_LABELS is used as a default.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2445

def describe_projects(params = {}, options = {})
  req = build_request(:describe_projects, params)
  req.send_request(options)
end

#describe_stream_processor(params = {}) ⇒ Types::DescribeStreamProcessorResponse

Provides information about a stream processor created by CreateStreamProcessor. You can get information about the input and output streams, the input parameters for the face recognition being performed, and the current status of the stream processor.

Examples:

Request syntax with placeholder values


resp = client.describe_stream_processor({
  name: "StreamProcessorName", # required
})

Response structure


resp.name #=> String
resp.stream_processor_arn #=> String
resp.status #=> String, one of "STOPPED", "STARTING", "RUNNING", "FAILED", "STOPPING", "UPDATING"
resp.status_message #=> String
resp.creation_timestamp #=> Time
resp.last_update_timestamp #=> Time
resp.input.kinesis_video_stream.arn #=> String
resp.output.kinesis_data_stream.arn #=> String
resp.output.s3_destination.bucket #=> String
resp.output.s3_destination.key_prefix #=> String
resp.role_arn #=> String
resp.settings.face_search.collection_id #=> String
resp.settings.face_search.face_match_threshold #=> Float
resp.settings.connected_home.labels #=> Array
resp.settings.connected_home.labels[0] #=> String
resp.settings.connected_home.min_confidence #=> Float
resp.notification_channel.sns_topic_arn #=> String
resp.kms_key_id #=> String
resp.regions_of_interest #=> Array
resp.regions_of_interest[0].bounding_box.width #=> Float
resp.regions_of_interest[0].bounding_box.height #=> Float
resp.regions_of_interest[0].bounding_box.left #=> Float
resp.regions_of_interest[0].bounding_box.top #=> Float
resp.regions_of_interest[0].polygon #=> Array
resp.regions_of_interest[0].polygon[0].x #=> Float
resp.regions_of_interest[0].polygon[0].y #=> Float
resp.data_sharing_preference.opt_in #=> Boolean

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :name (required, String)

    Name of the stream processor for which you want information.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2513

def describe_stream_processor(params = {}, options = {})
  req = build_request(:describe_stream_processor, params)
  req.send_request(options)
end

#detect_custom_labels(params = {}) ⇒ Types::DetectCustomLabelsResponse

This operation applies only to Amazon Rekognition Custom Labels.

Detects custom labels in a supplied image by using an Amazon Rekognition Custom Labels model.

You specify which version of a model version to use by using the ProjectVersionArn input parameter.

You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

For each object that the model version detects on an image, the API returns a (CustomLabel) object in an array (CustomLabels). Each CustomLabel object provides the label name (Name), the level of confidence that the image contains the object (Confidence), and object location information, if it exists, for the label on the image (Geometry). Note that for the DetectCustomLabelsLabels operation, Polygons are not returned in the Geometry section of the response.

To filter labels that are returned, specify a value for MinConfidence. DetectCustomLabelsLabels only returns labels with a confidence that's higher than the specified value. The value of MinConfidence maps to the assumed threshold values created during training. For more information, see Assumed threshold in the Amazon Rekognition Custom Labels Developer Guide. Amazon Rekognition Custom Labels metrics expresses an assumed threshold as a floating point value between 0-1. The range of MinConfidence normalizes the threshold value to a percentage value (0-100). Confidence responses from DetectCustomLabels are also returned as a percentage. You can use MinConfidence to change the precision and recall or your model. For more information, see Analyzing an image in the Amazon Rekognition Custom Labels Developer Guide.

If you don't specify a value for MinConfidence, DetectCustomLabels returns labels based on the assumed threshold of each label.

This is a stateless API operation. That is, the operation does not persist any data.

This operation requires permissions to perform the rekognition:DetectCustomLabels action.

For more information, see Analyzing an image in the Amazon Rekognition Custom Labels Developer Guide.

Examples:

Example: To detect custom labels in an image with an Amazon Rekognition Custom Labels model


# Detects custom labels in an image with an Amazon Rekognition Custom Labels model

resp = client.detect_custom_labels({
  image: {
    s3_object: {
      bucket: "custom-labels-console-us-east-1-1111111111", 
      name: "assets/flowers_1_test_dataset/camellia4.jpg", 
    }, 
  }, 
  max_results: 100, 
  min_confidence: 50, 
  project_version_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/my-project.2023-07-31T11.49.37/1690829378219", 
})

resp.to_h outputs the following:
{
  custom_labels: [
    {
      confidence: 67.56399536132812, 
      name: "with_leaves", 
    }, 
    {
      confidence: 50.65699768066406, 
      name: "without_leaves", 
    }, 
  ], 
}

Request syntax with placeholder values


resp = client.detect_custom_labels({
  project_version_arn: "ProjectVersionArn", # required
  image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  max_results: 1,
  min_confidence: 1.0,
})

Response structure


resp.custom_labels #=> Array
resp.custom_labels[0].name #=> String
resp.custom_labels[0].confidence #=> Float
resp.custom_labels[0].geometry.bounding_box.width #=> Float
resp.custom_labels[0].geometry.bounding_box.height #=> Float
resp.custom_labels[0].geometry.bounding_box.left #=> Float
resp.custom_labels[0].geometry.bounding_box.top #=> Float
resp.custom_labels[0].geometry.polygon #=> Array
resp.custom_labels[0].geometry.polygon[0].x #=> Float
resp.custom_labels[0].geometry.polygon[0].y #=> Float

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :project_version_arn (required, String)

    The ARN of the model version that you want to use. Only models associated with Custom Labels projects accepted by the operation. If a provided ARN refers to a model version associated with a project for a different feature type, then an InvalidParameterException is returned.

  • :image (required, Types::Image)

    Provides the input image either as bytes or an S3 object.

    You pass image bytes to an Amazon Rekognition API operation by using the Bytes property. For example, you would use the Bytes property to pass an image loaded from a local file system. Image bytes passed by using the Bytes property must be base64-encoded. Your code may not need to encode image bytes if you are using an AWS SDK to call Amazon Rekognition API operations.

    For more information, see Analyzing an Image Loaded from a Local File System in the Amazon Rekognition Developer Guide.

    You pass images stored in an S3 bucket to an Amazon Rekognition API operation by using the S3Object property. Images stored in an S3 bucket do not need to be base64-encoded.

    The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.

    If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes using the Bytes property is not supported. You must first upload the image to an Amazon S3 bucket and then call the operation using the S3Object property.

    For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.

  • :max_results (Integer)

    Maximum number of results you want the service to return in the response. The service returns the specified number of highest confidence labels ranked from highest confidence to lowest.

  • :min_confidence (Float)

    Specifies the minimum confidence level for the labels to return. DetectCustomLabels doesn't return any labels with a confidence value that's lower than this specified value. If you specify a value of 0, DetectCustomLabels returns all labels, regardless of the assumed threshold applied to each label. If you don't specify a value for MinConfidence, DetectCustomLabels returns labels based on the assumed threshold of each label.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2684

def detect_custom_labels(params = {}, options = {})
  req = build_request(:detect_custom_labels, params)
  req.send_request(options)
end

#detect_faces(params = {}) ⇒ Types::DetectFacesResponse

Detects faces within an image that is provided as input.

DetectFaces detects the 100 largest faces in the image. For each face detected, the operation returns face details. These details include a bounding box of the face, a confidence value (that the bounding box contains a face), and a fixed set of attributes such as facial landmarks (for example, coordinates of eye and mouth), pose, presence of facial occlusion, and so on.

The face-detection algorithm is most effective on frontal faces. For non-frontal or obscured faces, the algorithm might not detect the faces or might detect faces with lower confidence.

You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

This is a stateless API operation. That is, the operation does not persist any data.

This operation requires permissions to perform the rekognition:DetectFaces action.

Examples:

Example: To detect faces in an image


# This operation detects faces in an image stored in an AWS S3 bucket.

resp = client.detect_faces({
  image: {
    s3_object: {
      bucket: "mybucket", 
      name: "myphoto", 
    }, 
  }, 
})

resp.to_h outputs the following:
{
  face_details: [
    {
      bounding_box: {
        height: 0.18000000715255737, 
        left: 0.5555555820465088, 
        top: 0.33666667342185974, 
        width: 0.23999999463558197, 
      }, 
      confidence: 100, 
      landmarks: [
        {
          type: "eyeLeft", 
          x: 0.6394737362861633, 
          y: 0.40819624066352844, 
        }, 
        {
          type: "eyeRight", 
          x: 0.7266660928726196, 
          y: 0.41039225459098816, 
        }, 
        {
          type: "eyeRight", 
          x: 0.6912462115287781, 
          y: 0.44240960478782654, 
        }, 
        {
          type: "mouthDown", 
          x: 0.6306198239326477, 
          y: 0.46700039505958557, 
        }, 
        {
          type: "mouthUp", 
          x: 0.7215608954429626, 
          y: 0.47114261984825134, 
        }, 
      ], 
      pose: {
        pitch: 4.050806522369385, 
        roll: 0.9950747489929199, 
        yaw: 13.693790435791016, 
      }, 
      quality: {
        brightness: 37.60169982910156, 
        sharpness: 80, 
      }, 
    }, 
  ], 
  orientation_correction: "ROTATE_0", 
}

Request syntax with placeholder values


resp = client.detect_faces({
  image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  attributes: ["DEFAULT"], # accepts DEFAULT, ALL, AGE_RANGE, BEARD, EMOTIONS, EYE_DIRECTION, EYEGLASSES, EYES_OPEN, GENDER, MOUTH_OPEN, MUSTACHE, FACE_OCCLUDED, SMILE, SUNGLASSES
})

Response structure


resp.face_details #=> Array
resp.face_details[0].bounding_box.width #=> Float
resp.face_details[0].bounding_box.height #=> Float
resp.face_details[0].bounding_box.left #=> Float
resp.face_details[0].bounding_box.top #=> Float
resp.face_details[0].age_range.low #=> Integer
resp.face_details[0].age_range.high #=> Integer
resp.face_details[0].smile.value #=> Boolean
resp.face_details[0].smile.confidence #=> Float
resp.face_details[0].eyeglasses.value #=> Boolean
resp.face_details[0].eyeglasses.confidence #=> Float
resp.face_details[0].sunglasses.value #=> Boolean
resp.face_details[0].sunglasses.confidence #=> Float
resp.face_details[0].gender.value #=> String, one of "Male", "Female"
resp.face_details[0].gender.confidence #=> Float
resp.face_details[0].beard.value #=> Boolean
resp.face_details[0].beard.confidence #=> Float
resp.face_details[0].mustache.value #=> Boolean
resp.face_details[0].mustache.confidence #=> Float
resp.face_details[0].eyes_open.value #=> Boolean
resp.face_details[0].eyes_open.confidence #=> Float
resp.face_details[0].mouth_open.value #=> Boolean
resp.face_details[0].mouth_open.confidence #=> Float
resp.face_details[0].emotions #=> Array
resp.face_details[0].emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.face_details[0].emotions[0].confidence #=> Float
resp.face_details[0].landmarks #=> Array
resp.face_details[0].landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.face_details[0].landmarks[0].x #=> Float
resp.face_details[0].landmarks[0].y #=> Float
resp.face_details[0].pose.roll #=> Float
resp.face_details[0].pose.yaw #=> Float
resp.face_details[0].pose.pitch #=> Float
resp.face_details[0].quality.brightness #=> Float
resp.face_details[0].quality.sharpness #=> Float
resp.face_details[0].confidence #=> Float
resp.face_details[0].face_occluded.value #=> Boolean
resp.face_details[0].face_occluded.confidence #=> Float
resp.face_details[0].eye_direction.yaw #=> Float
resp.face_details[0].eye_direction.pitch #=> Float
resp.face_details[0].eye_direction.confidence #=> Float
resp.orientation_correction #=> String, one of "ROTATE_0", "ROTATE_90", "ROTATE_180", "ROTATE_270"

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :image (required, Types::Image)

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

  • :attributes (Array<String>)

    An array of facial attributes you want to be returned. A DEFAULT subset of facial attributes - BoundingBox, Confidence, Pose, Quality, and Landmarks - will always be returned. You can request for specific facial attributes (in addition to the default list) - by using ["DEFAULT", "FACE_OCCLUDED"] or just ["FACE_OCCLUDED"]. You can request for all facial attributes by using ["ALL"]. Requesting more attributes may increase response time.

    If you provide both, ["ALL", "DEFAULT"], the service uses a logical "AND" operator to determine which attributes to return (in this case, all attributes).

    Note that while the FaceOccluded and EyeDirection attributes are supported when using DetectFaces, they aren't supported when analyzing videos with StartFaceDetection and GetFaceDetection.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2874

def detect_faces(params = {}, options = {})
  req = build_request(:detect_faces, params)
  req.send_request(options)
end

#detect_labels(params = {}) ⇒ Types::DetectLabelsResponse

Detects instances of real-world entities within an image (JPEG or PNG) provided as input. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; and concepts like landscape, evening, and nature.

For an example, see Analyzing images stored in an Amazon S3 bucket in the Amazon Rekognition Developer Guide.

You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

Optional Parameters

You can specify one or both of the GENERAL_LABELS and IMAGE_PROPERTIES feature types when calling the DetectLabels API. Including GENERAL_LABELS will ensure the response includes the labels detected in the input image, while including IMAGE_PROPERTIES will ensure the response includes information about the image quality and color.

When using GENERAL_LABELS and/or IMAGE_PROPERTIES you can 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 an Image.

When getting labels, you can specify MinConfidence to control the confidence threshold for the labels returned. The default is 55%. You can also add the MaxLabels parameter to limit the number of labels returned. The default and upper limit is 1000 labels. These arguments are only valid when supplying GENERAL_LABELS as a feature type.

Response Elements

For each object, scene, and concept the API returns one or more labels. The API returns the following types of information about labels:

  • Name - The name of the detected label.

  • Confidence - The level of confidence in the label assigned to a detected object.

  • Parents - The ancestor labels for a detected label. DetectLabels returns a hierarchical taxonomy of detected labels. For example, a detected car might be assigned the label car. The label car has two parent labels: Vehicle (its parent) and Transportation (its grandparent). The response includes the all ancestors for a label, where every ancestor is a unique label. In the previous example, Car, Vehicle, and Transportation are returned as unique labels in the response.

  • Aliases - Possible Aliases for the label.

  • Categories - The label categories that the detected label belongs to.

  • BoundingBox — Bounding boxes are described for all instances of detected common object labels, returned in an array of Instance objects. An Instance object contains a BoundingBox object, describing the location of the label on the input image. It also includes the confidence for the accuracy of the detected bounding box.

The API returns the following information regarding the image, as part of the ImageProperties structure:

  • Quality - Information about the Sharpness, Brightness, and Contrast of the input image, scored between 0 to 100. Image quality is returned for the entire image, as well as the background and the foreground.

  • Dominant Color - An array of the dominant colors in the image.

  • Foreground - Information about the sharpness, brightness, and dominant colors of the input image’s foreground.

  • Background - Information about the sharpness, brightness, and dominant colors of the input image’s background.

The list of returned labels will include at least one label for every detected object, along with information about that label. In the following example, suppose the input image has a lighthouse, the sea, and a rock. The response includes all three labels, one for each object, as well as the confidence in the label:

\{Name: lighthouse, Confidence: 98.4629\}

\{Name: rock,Confidence: 79.2097\}

\{Name: sea,Confidence: 75.061\}

The list of labels can include multiple labels for the same object. For example, if the input image shows a flower (for example, a tulip), the operation might return the following three labels.

\{Name: flower,Confidence: 99.0562\}

\{Name: plant,Confidence: 99.0562\}

\{Name: tulip,Confidence: 99.0562\}

In this example, the detection algorithm more precisely identifies the flower as a tulip.

If the object detected is a person, the operation doesn't provide the same facial details that the DetectFaces operation provides.

This is a stateless API operation that doesn't return any data.

This operation requires permissions to perform the rekognition:DetectLabels action.

Examples:

Example: To detect labels


# This operation detects labels in the supplied image

resp = client.detect_labels({
  image: {
    s3_object: {
      bucket: "mybucket", 
      name: "myphoto", 
    }, 
  }, 
  max_labels: 123, 
  min_confidence: 70, 
})

resp.to_h outputs the following:
{
  labels: [
    {
      confidence: 99.25072479248047, 
      name: "People", 
    }, 
    {
      confidence: 99.25074005126953, 
      name: "Person", 
    }, 
  ], 
}

Request syntax with placeholder values


resp = client.detect_labels({
  image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  max_labels: 1,
  min_confidence: 1.0,
  features: ["GENERAL_LABELS"], # accepts GENERAL_LABELS, IMAGE_PROPERTIES
  settings: {
    general_labels: {
      label_inclusion_filters: ["GeneralLabelsFilterValue"],
      label_exclusion_filters: ["GeneralLabelsFilterValue"],
      label_category_inclusion_filters: ["GeneralLabelsFilterValue"],
      label_category_exclusion_filters: ["GeneralLabelsFilterValue"],
    },
    image_properties: {
      max_dominant_colors: 1,
    },
  },
})

Response structure


resp.labels #=> Array
resp.labels[0].name #=> String
resp.labels[0].confidence #=> Float
resp.labels[0].instances #=> Array
resp.labels[0].instances[0].bounding_box.width #=> Float
resp.labels[0].instances[0].bounding_box.height #=> Float
resp.labels[0].instances[0].bounding_box.left #=> Float
resp.labels[0].instances[0].bounding_box.top #=> Float
resp.labels[0].instances[0].confidence #=> Float
resp.labels[0].instances[0].dominant_colors #=> Array
resp.labels[0].instances[0].dominant_colors[0].red #=> Integer
resp.labels[0].instances[0].dominant_colors[0].blue #=> Integer
resp.labels[0].instances[0].dominant_colors[0].green #=> Integer
resp.labels[0].instances[0].dominant_colors[0].hex_code #=> String
resp.labels[0].instances[0].dominant_colors[0].css_color #=> String
resp.labels[0].instances[0].dominant_colors[0].simplified_color #=> String
resp.labels[0].instances[0].dominant_colors[0].pixel_percent #=> Float
resp.labels[0].parents #=> Array
resp.labels[0].parents[0].name #=> String
resp.labels[0].aliases #=> Array
resp.labels[0].aliases[0].name #=> String
resp.labels[0].categories #=> Array
resp.labels[0].categories[0].name #=> String
resp.orientation_correction #=> String, one of "ROTATE_0", "ROTATE_90", "ROTATE_180", "ROTATE_270"
resp.label_model_version #=> String
resp.image_properties.quality.brightness #=> Float
resp.image_properties.quality.sharpness #=> Float
resp.image_properties.quality.contrast #=> Float
resp.image_properties.dominant_colors #=> Array
resp.image_properties.dominant_colors[0].red #=> Integer
resp.image_properties.dominant_colors[0].blue #=> Integer
resp.image_properties.dominant_colors[0].green #=> Integer
resp.image_properties.dominant_colors[0].hex_code #=> String
resp.image_properties.dominant_colors[0].css_color #=> String
resp.image_properties.dominant_colors[0].simplified_color #=> String
resp.image_properties.dominant_colors[0].pixel_percent #=> Float
resp.image_properties.foreground.quality.brightness #=> Float
resp.image_properties.foreground.quality.sharpness #=> Float
resp.image_properties.foreground.quality.contrast #=> Float
resp.image_properties.foreground.dominant_colors #=> Array
resp.image_properties.foreground.dominant_colors[0].red #=> Integer
resp.image_properties.foreground.dominant_colors[0].blue #=> Integer
resp.image_properties.foreground.dominant_colors[0].green #=> Integer
resp.image_properties.foreground.dominant_colors[0].hex_code #=> String
resp.image_properties.foreground.dominant_colors[0].css_color #=> String
resp.image_properties.foreground.dominant_colors[0].simplified_color #=> String
resp.image_properties.foreground.dominant_colors[0].pixel_percent #=> Float
resp.image_properties.background.quality.brightness #=> Float
resp.image_properties.background.quality.sharpness #=> Float
resp.image_properties.background.quality.contrast #=> Float
resp.image_properties.background.dominant_colors #=> Array
resp.image_properties.background.dominant_colors[0].red #=> Integer
resp.image_properties.background.dominant_colors[0].blue #=> Integer
resp.image_properties.background.dominant_colors[0].green #=> Integer
resp.image_properties.background.dominant_colors[0].hex_code #=> String
resp.image_properties.background.dominant_colors[0].css_color #=> String
resp.image_properties.background.dominant_colors[0].simplified_color #=> String
resp.image_properties.background.dominant_colors[0].pixel_percent #=> Float

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :image (required, Types::Image)

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. Images stored in an S3 Bucket do not need to be base64-encoded.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

  • :max_labels (Integer)

    Maximum number of labels you want the service to return in the response. The service returns the specified number of highest confidence labels. Only valid when GENERAL_LABELS is specified as a feature type in the Feature input parameter.

  • :min_confidence (Float)

    Specifies the minimum confidence level for the labels to return. Amazon Rekognition doesn't return any labels with confidence lower than this specified value.

    If MinConfidence is not specified, the operation returns labels with a confidence values greater than or equal to 55 percent. Only valid when GENERAL_LABELS is specified as a feature type in the Feature input parameter.

  • :features (Array<String>)

    A list of the types of analysis to perform. Specifying GENERAL_LABELS uses the label detection feature, while specifying IMAGE_PROPERTIES returns information regarding image color and quality. If no option is specified GENERAL_LABELS is used by default.

  • :settings (Types::DetectLabelsSettings)

    A list of the filters to be applied to returned detected labels and image properties. Specified filters can be inclusive, exclusive, or a combination of both. Filters can be used for individual labels or label categories. The exact label names or label categories must be supplied. For a full list of labels and label categories, see Detecting labels.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3173

def detect_labels(params = {}, options = {})
  req = build_request(:detect_labels, params)
  req.send_request(options)
end

#detect_moderation_labels(params = {}) ⇒ Types::DetectModerationLabelsResponse

Detects unsafe content in a specified JPEG or PNG format image. Use DetectModerationLabels to moderate images depending on your requirements. For example, you might want to filter images that contain nudity, but not images containing suggestive content.

To filter images, use the labels returned by DetectModerationLabels to determine which types of content are appropriate.

For information about moderation labels, see Detecting Unsafe Content in the Amazon Rekognition Developer Guide.

You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

You can specify an adapter to use when retrieving label predictions by providing a ProjectVersionArn to the ProjectVersion argument.

Examples:

Request syntax with placeholder values


resp = client.detect_moderation_labels({
  image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  min_confidence: 1.0,
  human_loop_config: {
    human_loop_name: "HumanLoopName", # required
    flow_definition_arn: "FlowDefinitionArn", # required
    data_attributes: {
      content_classifiers: ["FreeOfPersonallyIdentifiableInformation"], # accepts FreeOfPersonallyIdentifiableInformation, FreeOfAdultContent
    },
  },
  project_version: "ProjectVersionId",
})

Response structure


resp.moderation_labels #=> Array
resp.moderation_labels[0].confidence #=> Float
resp.moderation_labels[0].name #=> String
resp.moderation_labels[0].parent_name #=> String
resp.moderation_labels[0].taxonomy_level #=> Integer
resp.moderation_model_version #=> String
resp.human_loop_activation_output.human_loop_arn #=> String
resp.human_loop_activation_output.human_loop_activation_reasons #=> Array
resp.human_loop_activation_output.human_loop_activation_reasons[0] #=> String
resp.human_loop_activation_output.human_loop_activation_conditions_evaluation_results #=> String
resp.project_version #=> String
resp.content_types #=> Array
resp.content_types[0].confidence #=> Float
resp.content_types[0].name #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :image (required, Types::Image)

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

  • :min_confidence (Float)

    Specifies the minimum confidence level for the labels to return. Amazon Rekognition doesn't return any labels with a confidence level lower than this specified value.

    If you don't specify MinConfidence, the operation returns labels with confidence values greater than or equal to 50 percent.

  • :human_loop_config (Types::HumanLoopConfig)

    Sets up the configuration for human evaluation, including the FlowDefinition the image will be sent to.

  • :project_version (String)

    Identifier for the custom adapter. Expects the ProjectVersionArn as a value. Use the CreateProject or CreateProjectVersion APIs to create a custom adapter.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3273

def detect_moderation_labels(params = {}, options = {})
  req = build_request(:detect_moderation_labels, params)
  req.send_request(options)
end

#detect_protective_equipment(params = {}) ⇒ Types::DetectProtectiveEquipmentResponse

Detects Personal Protective Equipment (PPE) worn by people detected in an image. Amazon Rekognition can detect the following types of PPE.

  • Face cover

  • Hand cover

  • Head cover

You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. The image must be either a PNG or JPG formatted file.

DetectProtectiveEquipment detects PPE worn by up to 15 persons detected in an image.

For each person detected in the image the API returns an array of body parts (face, head, left-hand, right-hand). For each body part, an array of detected items of PPE is returned, including an indicator of whether or not the PPE covers the body part. The API returns the confidence it has in each detection (person, PPE, body part and body part coverage). It also returns a bounding box (BoundingBox) for each detected person and each detected item of PPE.

You can optionally request a summary of detected PPE items with the SummarizationAttributes input parameter. The summary provides the following information.

  • The persons detected as wearing all of the types of PPE that you specify.

  • The persons detected as not wearing all of the types PPE that you specify.

  • The persons detected where PPE adornment could not be determined.

This is a stateless API operation. That is, the operation does not persist any data.

This operation requires permissions to perform the rekognition:DetectProtectiveEquipment action.

Examples:

Request syntax with placeholder values


resp = client.detect_protective_equipment({
  image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  summarization_attributes: {
    min_confidence: 1.0, # required
    required_equipment_types: ["FACE_COVER"], # required, accepts FACE_COVER, HAND_COVER, HEAD_COVER
  },
})

Response structure


resp.protective_equipment_model_version #=> String
resp.persons #=> Array
resp.persons[0].body_parts #=> Array
resp.persons[0].body_parts[0].name #=> String, one of "FACE", "HEAD", "LEFT_HAND", "RIGHT_HAND"
resp.persons[0].body_parts[0].confidence #=> Float
resp.persons[0].body_parts[0].equipment_detections #=> Array
resp.persons[0].body_parts[0].equipment_detections[0].bounding_box.width #=> Float
resp.persons[0].body_parts[0].equipment_detections[0].bounding_box.height #=> Float
resp.persons[0].body_parts[0].equipment_detections[0].bounding_box.left #=> Float
resp.persons[0].body_parts[0].equipment_detections[0].bounding_box.top #=> Float
resp.persons[0].body_parts[0].equipment_detections[0].confidence #=> Float
resp.persons[0].body_parts[0].equipment_detections[0].type #=> String, one of "FACE_COVER", "HAND_COVER", "HEAD_COVER"
resp.persons[0].body_parts[0].equipment_detections[0].covers_body_part.confidence #=> Float
resp.persons[0].body_parts[0].equipment_detections[0].covers_body_part.value #=> Boolean
resp.persons[0].bounding_box.width #=> Float
resp.persons[0].bounding_box.height #=> Float
resp.persons[0].bounding_box.left #=> Float
resp.persons[0].bounding_box.top #=> Float
resp.persons[0].confidence #=> Float
resp.persons[0].id #=> Integer
resp.summary.persons_with_required_equipment #=> Array
resp.summary.persons_with_required_equipment[0] #=> Integer
resp.summary.persons_without_required_equipment #=> Array
resp.summary.persons_without_required_equipment[0] #=> Integer
resp.summary.persons_indeterminate #=> Array
resp.summary.persons_indeterminate[0] #=> Integer

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :image (required, Types::Image)

    The image in which you want to detect PPE on detected persons. The image can be passed as image bytes or you can reference an image stored in an Amazon S3 bucket.

  • :summarization_attributes (Types::ProtectiveEquipmentSummarizationAttributes)

    An array of PPE types that you want to summarize.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3382

def detect_protective_equipment(params = {}, options = {})
  req = build_request(:detect_protective_equipment, params)
  req.send_request(options)
end

#detect_text(params = {}) ⇒ Types::DetectTextResponse

Detects text in the input image and converts it into machine-readable text.

Pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, you must pass it as a reference to an image in an Amazon S3 bucket. For the AWS CLI, passing image bytes is not supported. The image must be either a .png or .jpeg formatted file.

The DetectText operation returns text in an array of TextDetection elements, TextDetections. Each TextDetection element provides information about a single word or line of text that was detected in the image.

A word is one or more script characters that are not separated by spaces. DetectText can detect up to 100 words in an image.

A line is a string of equally spaced words. A line isn't necessarily a complete sentence. For example, a driver's license number is detected as a line. A line ends when there is no aligned text after it. Also, a line ends when there is a large gap between words, relative to the length of the words. This means, depending on the gap between words, Amazon Rekognition may detect multiple lines in text aligned in the same direction. Periods don't represent the end of a line. If a sentence spans multiple lines, the DetectText operation returns multiple lines.

To determine whether a TextDetection element is a line of text or a word, use the TextDetection object Type field.

To be detected, text must be within +/- 90 degrees orientation of the horizontal axis.

For more information, see Detecting text in the Amazon Rekognition Developer Guide.

Examples:

Request syntax with placeholder values


resp = client.detect_text({
  image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  filters: {
    word_filter: {
      min_confidence: 1.0,
      min_bounding_box_height: 1.0,
      min_bounding_box_width: 1.0,
    },
    regions_of_interest: [
      {
        bounding_box: {
          width: 1.0,
          height: 1.0,
          left: 1.0,
          top: 1.0,
        },
        polygon: [
          {
            x: 1.0,
            y: 1.0,
          },
        ],
      },
    ],
  },
})

Response structure


resp.text_detections #=> Array
resp.text_detections[0].detected_text #=> String
resp.text_detections[0].type #=> String, one of "LINE", "WORD"
resp.text_detections[0].id #=> Integer
resp.text_detections[0].parent_id #=> Integer
resp.text_detections[0].confidence #=> Float
resp.text_detections[0].geometry.bounding_box.width #=> Float
resp.text_detections[0].geometry.bounding_box.height #=> Float
resp.text_detections[0].geometry.bounding_box.left #=> Float
resp.text_detections[0].geometry.bounding_box.top #=> Float
resp.text_detections[0].geometry.polygon #=> Array
resp.text_detections[0].geometry.polygon[0].x #=> Float
resp.text_detections[0].geometry.polygon[0].y #=> Float
resp.text_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :image (required, Types::Image)

    The input image as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Rekognition operations, you can't pass image bytes.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

  • :filters (Types::DetectTextFilters)

    Optional parameters that let you set the criteria that the text must meet to be included in your response.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3498

def detect_text(params = {}, options = {})
  req = build_request(:detect_text, params)
  req.send_request(options)
end

#disassociate_faces(params = {}) ⇒ Types::DisassociateFacesResponse

Removes the association between a Face supplied in an array of FaceIds and the User. If the User is not present already, then a ResourceNotFound exception is thrown. If successful, an array of faces that are disassociated from the User is returned. If a given face is already disassociated from the given UserID, it will be ignored and not be returned in the response. If a given face is already associated with a different User or not found in the collection it will be returned as part of UnsuccessfulDisassociations. You can remove 1 - 100 face IDs from a user at one time.

Examples:

Example: DisassociateFaces


# Removes the association between a Face supplied in an array of FaceIds and the User.

resp = client.disassociate_faces({
  client_request_token: "550e8400-e29b-41d4-a716-446655440003", 
  collection_id: "MyCollection", 
  face_ids: [
    "f5817d37-94f6-4335-bfee-6cf79a3d806e", 
    "c92265d4-5f9c-43af-a58e-12be0ce02bc3", 
  ], 
  user_id: "DemoUser", 
})

resp.to_h outputs the following:
{
  disassociated_faces: [
    {
      face_id: "c92265d4-5f9c-43af-a58e-12be0ce02bc3", 
    }, 
  ], 
  unsuccessful_face_disassociations: [
    {
      face_id: "f5817d37-94f6-4335-bfee-6cf79a3d806e", 
      reasons: [
        "ASSOCIATED_TO_A_DIFFERENT_USER", 
      ], 
      user_id: "demoUser1", 
    }, 
  ], 
  user_status: "UPDATING", 
}

Request syntax with placeholder values


resp = client.disassociate_faces({
  collection_id: "CollectionId", # required
  user_id: "UserId", # required
  client_request_token: "ClientRequestToken",
  face_ids: ["FaceId"], # required
})

Response structure


resp.disassociated_faces #=> Array
resp.disassociated_faces[0].face_id #=> String
resp.unsuccessful_face_disassociations #=> Array
resp.unsuccessful_face_disassociations[0].face_id #=> String
resp.unsuccessful_face_disassociations[0].user_id #=> String
resp.unsuccessful_face_disassociations[0].reasons #=> Array
resp.unsuccessful_face_disassociations[0].reasons[0] #=> String, one of "FACE_NOT_FOUND", "ASSOCIATED_TO_A_DIFFERENT_USER"
resp.user_status #=> String, one of "ACTIVE", "UPDATING", "CREATING", "CREATED"

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :collection_id (required, String)

    The ID of an existing collection containing the UserID.

  • :user_id (required, String)

    ID for the existing UserID.

  • :client_request_token (String)

    Idempotent token used to identify the request to DisassociateFaces. If you use the same token with multiple DisassociateFaces requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.

    A suitable default value is auto-generated. You should normally not need to pass this option.**

  • :face_ids (required, Array<String>)

    An array of face IDs to disassociate from the UserID.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3594

def disassociate_faces(params = {}, options = {})
  req = build_request(:disassociate_faces, params)
  req.send_request(options)
end

#distribute_dataset_entries(params = {}) ⇒ Struct

This operation applies only to Amazon Rekognition Custom Labels.

Distributes the entries (images) in a training dataset across the training dataset and the test dataset for a project. DistributeDatasetEntries moves 20% of the training dataset images to the test dataset. An entry is a JSON Line that describes an image.

You supply the Amazon Resource Names (ARN) of a project's training dataset and test dataset. The training dataset must contain the images that you want to split. The test dataset must be empty. The datasets must belong to the same project. To create training and test datasets for a project, call CreateDataset.

Distributing a dataset takes a while to complete. To check the status call DescribeDataset. The operation is complete when the Status field for the training dataset and the test dataset is UPDATE_COMPLETE. If the dataset split fails, the value of Status is UPDATE_FAILED.

This operation requires permissions to perform the rekognition:DistributeDatasetEntries action.

Examples:

Example: To distribute an Amazon Rekognition Custom Labels dataset


# Distributes an Amazon Rekognition Custom Labels training dataset to a test dataset.

resp = client.distribute_dataset_entries({
  datasets: [
    {
      arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-proj-2/dataset/train/1690564858106", 
    }, 
    {
      arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-proj-2/dataset/test/1690564858106", 
    }, 
  ], 
})

resp.to_h outputs the following:
{
}

Request syntax with placeholder values


resp = client.distribute_dataset_entries({
  datasets: [ # required
    {
      arn: "DatasetArn", # required
    },
  ],
})

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :datasets (required, Array<Types::DistributeDataset>)

    The ARNS for the training dataset and test dataset that you want to use. The datasets must belong to the same project. The test dataset must be empty.

Returns:

  • (Struct)

    Returns an empty response.



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3662

def distribute_dataset_entries(params = {}, options = {})
  req = build_request(:distribute_dataset_entries, params)
  req.send_request(options)
end

#get_celebrity_info(params = {}) ⇒ Types::GetCelebrityInfoResponse

Gets the name and additional information about a celebrity based on their Amazon Rekognition ID. The additional information is returned as an array of URLs. If there is no additional information about the celebrity, this list is empty.

For more information, see Getting information about a celebrity in the Amazon Rekognition Developer Guide.

This operation requires permissions to perform the rekognition:GetCelebrityInfo action.

Examples:

Request syntax with placeholder values


resp = client.get_celebrity_info({
  id: "RekognitionUniqueId", # required
})

Response structure


resp.urls #=> Array
resp.urls[0] #=> String
resp.name #=> String
resp.known_gender.type #=> String, one of "Male", "Female", "Nonbinary", "Unlisted"

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :id (required, String)

    The ID for the celebrity. You get the celebrity ID from a call to the RecognizeCelebrities operation, which recognizes celebrities in an image.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3704

def get_celebrity_info(params = {}, options = {})
  req = build_request(:get_celebrity_info, params)
  req.send_request(options)
end

#get_celebrity_recognition(params = {}) ⇒ Types::GetCelebrityRecognitionResponse

Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by StartCelebrityRecognition.

Celebrity recognition in a video is an asynchronous operation. Analysis is started by a call to StartCelebrityRecognition which returns a job identifier (JobId).

When the celebrity recognition operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartCelebrityRecognition. To get the results of the celebrity recognition analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetCelebrityDetection and pass the job identifier (JobId) from the initial call to StartCelebrityDetection.

For more information, see Working With Stored Videos in the Amazon Rekognition Developer Guide.

GetCelebrityRecognition returns detected celebrities and the time(s) they are detected in an array (Celebrities) of CelebrityRecognition objects. Each CelebrityRecognition contains information about the celebrity in a CelebrityDetail object and the time, Timestamp, the celebrity was detected. This CelebrityDetail object stores information about the detected celebrity's face attributes, a face bounding box, known gender, the celebrity's name, and a confidence estimate.

GetCelebrityRecognition only returns the default facial attributes (BoundingBox, Confidence, Landmarks, Pose, and Quality). The BoundingBox field only applies to the detected face instance. The other facial attributes listed in the Face object of the following response syntax are not returned. For more information, see FaceDetail in the Amazon Rekognition Developer Guide.

By default, the Celebrities array is sorted by time (milliseconds from the start of the video). You can also sort the array by celebrity by specifying the value ID in the SortBy input parameter.

The CelebrityDetail object includes the celebrity identifer and additional information urls. If you don't store the additional information urls, you can get them later by calling GetCelebrityInfo with the celebrity identifer.

No information is returned for faces not recognized as celebrities.

Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetCelebrityDetection and populate the NextToken request parameter with the token value returned from the previous call to GetCelebrityRecognition.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.get_celebrity_recognition({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
  sort_by: "ID", # accepts ID, TIMESTAMP
})

Response structure


resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
resp.status_message #=> String
resp..codec #=> String
resp..duration_millis #=> Integer
resp..format #=> String
resp..frame_rate #=> Float
resp..frame_height #=> Integer
resp..frame_width #=> Integer
resp..color_range #=> String, one of "FULL", "LIMITED"
resp.next_token #=> String
resp.celebrities #=> Array
resp.celebrities[0].timestamp #=> Integer
resp.celebrities[0].celebrity.urls #=> Array
resp.celebrities[0].celebrity.urls[0] #=> String
resp.celebrities[0].celebrity.name #=> String
resp.celebrities[0].celebrity.id #=> String
resp.celebrities[0].celebrity.confidence #=> Float
resp.celebrities[0].celebrity.bounding_box.width #=> Float
resp.celebrities[0].celebrity.bounding_box.height #=> Float
resp.celebrities[0].celebrity.bounding_box.left #=> Float
resp.celebrities[0].celebrity.bounding_box.top #=> Float
resp.celebrities[0].celebrity.face.bounding_box.width #=> Float
resp.celebrities[0].celebrity.face.bounding_box.height #=> Float
resp.celebrities[0].celebrity.face.bounding_box.left #=> Float
resp.celebrities[0].celebrity.face.bounding_box.top #=> Float
resp.celebrities[0].celebrity.face.age_range.low #=> Integer
resp.celebrities[0].celebrity.face.age_range.high #=> Integer
resp.celebrities[0].celebrity.face.smile.value #=> Boolean
resp.celebrities[0].celebrity.face.smile.confidence #=> Float
resp.celebrities[0].celebrity.face.eyeglasses.value #=> Boolean
resp.celebrities[0].celebrity.face.eyeglasses.confidence #=> Float
resp.celebrities[0].celebrity.face.sunglasses.value #=> Boolean
resp.celebrities[0].celebrity.face.sunglasses.confidence #=> Float
resp.celebrities[0].celebrity.face.gender.value #=> String, one of "Male", "Female"
resp.celebrities[0].celebrity.face.gender.confidence #=> Float
resp.celebrities[0].celebrity.face.beard.value #=> Boolean
resp.celebrities[0].celebrity.face.beard.confidence #=> Float
resp.celebrities[0].celebrity.face.mustache.value #=> Boolean
resp.celebrities[0].celebrity.face.mustache.confidence #=> Float
resp.celebrities[0].celebrity.face.eyes_open.value #=> Boolean
resp.celebrities[0].celebrity.face.eyes_open.confidence #=> Float
resp.celebrities[0].celebrity.face.mouth_open.value #=> Boolean
resp.celebrities[0].celebrity.face.mouth_open.confidence #=> Float
resp.celebrities[0].celebrity.face.emotions #=> Array
resp.celebrities[0].celebrity.face.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.celebrities[0].celebrity.face.emotions[0].confidence #=> Float
resp.celebrities[0].celebrity.face.landmarks #=> Array
resp.celebrities[0].celebrity.face.landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.celebrities[0].celebrity.face.landmarks[0].x #=> Float
resp.celebrities[0].celebrity.face.landmarks[0].y #=> Float
resp.celebrities[0].celebrity.face.pose.roll #=> Float
resp.celebrities[0].celebrity.face.pose.yaw #=> Float
resp.celebrities[0].celebrity.face.pose.pitch #=> Float
resp.celebrities[0].celebrity.face.quality.brightness #=> Float
resp.celebrities[0].celebrity.face.quality.sharpness #=> Float
resp.celebrities[0].celebrity.face.confidence #=> Float
resp.celebrities[0].celebrity.face.face_occluded.value #=> Boolean
resp.celebrities[0].celebrity.face.face_occluded.confidence #=> Float
resp.celebrities[0].celebrity.face.eye_direction.yaw #=> Float
resp.celebrities[0].celebrity.face.eye_direction.pitch #=> Float
resp.celebrities[0].celebrity.face.eye_direction.confidence #=> Float
resp.celebrities[0].celebrity.known_gender.type #=> String, one of "Male", "Female", "Nonbinary", "Unlisted"
resp.job_id #=> String
resp.video.s3_object.bucket #=> String
resp.video.s3_object.name #=> String
resp.video.s3_object.version #=> String
resp.job_tag #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :job_id (required, String)

    Job identifier for the required celebrity recognition analysis. You can get the job identifer from a call to StartCelebrityRecognition.

  • :max_results (Integer)

    Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.

  • :next_token (String)

    If the previous response was incomplete (because there is more recognized celebrities to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of celebrities.

  • :sort_by (String)

    Sort to use for celebrities returned in Celebrities field. Specify ID to sort by the celebrity identifier, specify TIMESTAMP to sort by the time the celebrity was recognized.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3879

def get_celebrity_recognition(params = {}, options = {})
  req = build_request(:get_celebrity_recognition, params)
  req.send_request(options)
end

#get_content_moderation(params = {}) ⇒ Types::GetContentModerationResponse

Gets the inappropriate, unwanted, or offensive content analysis results for a Amazon Rekognition Video analysis started by StartContentModeration. For a list of moderation labels in Amazon Rekognition, see Using the image and video moderation APIs.

Amazon Rekognition Video inappropriate or offensive content detection in a stored video is an asynchronous operation. You start analysis by calling StartContentModeration which returns a job identifier (JobId). When analysis finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartContentModeration. To get the results of the content analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetContentModeration and pass the job identifier (JobId) from the initial call to StartContentModeration.

For more information, see Working with Stored Videos in the Amazon Rekognition Devlopers Guide.

GetContentModeration returns detected inappropriate, unwanted, or offensive content moderation labels, and the time they are detected, in an array, ModerationLabels, of ContentModerationDetection objects.

By default, the moderated labels are returned sorted by time, in milliseconds from the start of the video. You can also sort them by moderated label by specifying NAME for the SortBy input parameter.

Since video analysis can return a large number of results, use the MaxResults parameter to limit the number of labels returned in a single call to GetContentModeration. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetContentModeration and populate the NextToken request parameter with the value of NextToken returned from the previous call to GetContentModeration.

For more information, see moderating content in the Amazon Rekognition Developer Guide.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.get_content_moderation({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
  sort_by: "NAME", # accepts NAME, TIMESTAMP
  aggregate_by: "TIMESTAMPS", # accepts TIMESTAMPS, SEGMENTS
})

Response structure


resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
resp.status_message #=> String
resp..codec #=> String
resp..duration_millis #=> Integer
resp..format #=> String
resp..frame_rate #=> Float
resp..frame_height #=> Integer
resp..frame_width #=> Integer
resp..color_range #=> String, one of "FULL", "LIMITED"
resp.moderation_labels #=> Array
resp.moderation_labels[0].timestamp #=> Integer
resp.moderation_labels[0].moderation_label.confidence #=> Float
resp.moderation_labels[0].moderation_label.name #=> String
resp.moderation_labels[0].moderation_label.parent_name #=> String
resp.moderation_labels[0].moderation_label.taxonomy_level #=> Integer
resp.moderation_labels[0].start_timestamp_millis #=> Integer
resp.moderation_labels[0].end_timestamp_millis #=> Integer
resp.moderation_labels[0].duration_millis #=> Integer
resp.moderation_labels[0].content_types #=> Array
resp.moderation_labels[0].content_types[0].confidence #=> Float
resp.moderation_labels[0].content_types[0].name #=> String
resp.next_token #=> String
resp.moderation_model_version #=> String
resp.job_id #=> String
resp.video.s3_object.bucket #=> String
resp.video.s3_object.name #=> String
resp.video.s3_object.version #=> String
resp.job_tag #=> String
resp..sort_by #=> String, one of "NAME", "TIMESTAMP"
resp..aggregate_by #=> String, one of "TIMESTAMPS", "SEGMENTS"

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :job_id (required, String)

    The identifier for the inappropriate, unwanted, or offensive content moderation job. Use JobId to identify the job in a subsequent call to GetContentModeration.

  • :max_results (Integer)

    Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.

  • :next_token (String)

    If the previous response was incomplete (because there is more data to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of content moderation labels.

  • :sort_by (String)

    Sort to use for elements in the ModerationLabelDetections array. Use TIMESTAMP to sort array elements by the time labels are detected. Use NAME to alphabetically group elements for a label together. Within each label group, the array element are sorted by detection confidence. The default sort is by TIMESTAMP.

  • :aggregate_by (String)

    Defines how to aggregate results of the StartContentModeration request. Default aggregation option is TIMESTAMPS. SEGMENTS mode aggregates moderation labels over time.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4017

def get_content_moderation(params = {}, options = {})
  req = build_request(:get_content_moderation, params)
  req.send_request(options)
end

#get_face_detection(params = {}) ⇒ Types::GetFaceDetectionResponse

Gets face detection results for a Amazon Rekognition Video analysis started by StartFaceDetection.

Face detection with Amazon Rekognition Video is an asynchronous operation. You start face detection by calling StartFaceDetection which returns a job identifier (JobId). When the face detection operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartFaceDetection. To get the results of the face detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetFaceDetection and pass the job identifier (JobId) from the initial call to StartFaceDetection.

GetFaceDetection returns an array of detected faces (Faces) sorted by the time the faces were detected.

Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetFaceDetection and populate the NextToken request parameter with the token value returned from the previous call to GetFaceDetection.

Note that for the GetFaceDetection operation, the returned values for FaceOccluded and EyeDirection will always be "null".

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.get_face_detection({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
})

Response structure


resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
resp.status_message #=> String
resp..codec #=> String
resp..duration_millis #=> Integer
resp..format #=> String
resp..frame_rate #=> Float
resp..frame_height #=> Integer
resp..frame_width #=> Integer
resp..color_range #=> String, one of "FULL", "LIMITED"
resp.next_token #=> String
resp.faces #=> Array
resp.faces[0].timestamp #=> Integer
resp.faces[0].face.bounding_box.width #=> Float
resp.faces[0].face.bounding_box.height #=> Float
resp.faces[0].face.bounding_box.left #=> Float
resp.faces[0].face.bounding_box.top #=> Float
resp.faces[0].face.age_range.low #=> Integer
resp.faces[0].face.age_range.high #=> Integer
resp.faces[0].face.smile.value #=> Boolean
resp.faces[0].face.smile.confidence #=> Float
resp.faces[0].face.eyeglasses.value #=> Boolean
resp.faces[0].face.eyeglasses.confidence #=> Float
resp.faces[0].face.sunglasses.value #=> Boolean
resp.faces[0].face.sunglasses.confidence #=> Float
resp.faces[0].face.gender.value #=> String, one of "Male", "Female"
resp.faces[0].face.gender.confidence #=> Float
resp.faces[0].face.beard.value #=> Boolean
resp.faces[0].face.beard.confidence #=> Float
resp.faces[0].face.mustache.value #=> Boolean
resp.faces[0].face.mustache.confidence #=> Float
resp.faces[0].face.eyes_open.value #=> Boolean
resp.faces[0].face.eyes_open.confidence #=> Float
resp.faces[0].face.mouth_open.value #=> Boolean
resp.faces[0].face.mouth_open.confidence #=> Float
resp.faces[0].face.emotions #=> Array
resp.faces[0].face.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.faces[0].face.emotions[0].confidence #=> Float
resp.faces[0].face.landmarks #=> Array
resp.faces[0].face.landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.faces[0].face.landmarks[0].x #=> Float
resp.faces[0].face.landmarks[0].y #=> Float
resp.faces[0].face.pose.roll #=> Float
resp.faces[0].face.pose.yaw #=> Float
resp.faces[0].face.pose.pitch #=> Float
resp.faces[0].face.quality.brightness #=> Float
resp.faces[0].face.quality.sharpness #=> Float
resp.faces[0].face.confidence #=> Float
resp.faces[0].face.face_occluded.value #=> Boolean
resp.faces[0].face.face_occluded.confidence #=> Float
resp.faces[0].face.eye_direction.yaw #=> Float
resp.faces[0].face.eye_direction.pitch #=> Float
resp.faces[0].face.eye_direction.confidence #=> Float
resp.job_id #=> String
resp.video.s3_object.bucket #=> String
resp.video.s3_object.name #=> String
resp.video.s3_object.version #=> String
resp.job_tag #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :job_id (required, String)

    Unique identifier for the face detection job. The JobId is returned from StartFaceDetection.

  • :max_results (Integer)

    Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.

  • :next_token (String)

    If the previous response was incomplete (because there are more faces to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of faces.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4148

def get_face_detection(params = {}, options = {})
  req = build_request(:get_face_detection, params)
  req.send_request(options)
end

#get_face_liveness_session_results(params = {}) ⇒ Types::GetFaceLivenessSessionResultsResponse

Retrieves the results of a specific Face Liveness session. It requires the sessionId as input, which was created using CreateFaceLivenessSession. Returns the corresponding Face Liveness confidence score, a reference image that includes a face bounding box, and audit images that also contain face bounding boxes. The Face Liveness confidence score ranges from 0 to 100.

The number of audit images returned by GetFaceLivenessSessionResults is defined by the AuditImagesLimit paramater when calling CreateFaceLivenessSession. Reference images are always returned when possible.

Examples:

Request syntax with placeholder values


resp = client.get_face_liveness_session_results({
  session_id: "LivenessSessionId", # required
})

Response structure


resp.session_id #=> String
resp.status #=> String, one of "CREATED", "IN_PROGRESS", "SUCCEEDED", "FAILED", "EXPIRED"
resp.confidence #=> Float
resp.reference_image.bytes #=> String
resp.reference_image.s3_object.bucket #=> String
resp.reference_image.s3_object.name #=> String
resp.reference_image.s3_object.version #=> String
resp.reference_image.bounding_box.width #=> Float
resp.reference_image.bounding_box.height #=> Float
resp.reference_image.bounding_box.left #=> Float
resp.reference_image.bounding_box.top #=> Float
resp.audit_images #=> Array
resp.audit_images[0].bytes #=> String
resp.audit_images[0].s3_object.bucket #=> String
resp.audit_images[0].s3_object.name #=> String
resp.audit_images[0].s3_object.version #=> String
resp.audit_images[0].bounding_box.width #=> Float
resp.audit_images[0].bounding_box.height #=> Float
resp.audit_images[0].bounding_box.left #=> Float
resp.audit_images[0].bounding_box.top #=> Float

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :session_id (required, String)

    A unique 128-bit UUID. This is used to uniquely identify the session and also acts as an idempotency token for all operations associated with the session.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4209

def get_face_liveness_session_results(params = {}, options = {})
  req = build_request(:get_face_liveness_session_results, params)
  req.send_request(options)
end

#get_face_search(params = {}) ⇒ Types::GetFaceSearchResponse

Gets the face search results for Amazon Rekognition Video face search started by StartFaceSearch. The search returns faces in a collection that match the faces of persons detected in a video. It also includes the time(s) that faces are matched in the video.

Face search in a video is an asynchronous operation. You start face search by calling to StartFaceSearch which returns a job identifier (JobId). When the search operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartFaceSearch. To get the search results, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetFaceSearch and pass the job identifier (JobId) from the initial call to StartFaceSearch.

For more information, see Searching Faces in a Collection in the Amazon Rekognition Developer Guide.

The search results are retured in an array, Persons, of PersonMatch objects. EachPersonMatch element contains details about the matching faces in the input collection, person information (facial attributes, bounding boxes, and person identifer) for the matched person, and the time the person was matched in the video.

GetFaceSearch only returns the default facial attributes (BoundingBox, Confidence, Landmarks, Pose, and Quality). The other facial attributes listed in the Face object of the following response syntax are not returned. For more information, see FaceDetail in the Amazon Rekognition Developer Guide.

By default, the Persons array is sorted by the time, in milliseconds from the start of the video, persons are matched. You can also sort by persons by specifying INDEX for the SORTBY input parameter.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.get_face_search({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
  sort_by: "INDEX", # accepts INDEX, TIMESTAMP
})

Response structure


resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
resp.status_message #=> String
resp.next_token #=> String
resp..codec #=> String
resp..duration_millis #=> Integer
resp..format #=> String
resp..frame_rate #=> Float
resp..frame_height #=> Integer
resp..frame_width #=> Integer
resp..color_range #=> String, one of "FULL", "LIMITED"
resp.persons #=> Array
resp.persons[0].timestamp #=> Integer
resp.persons[0].person.index #=> Integer
resp.persons[0].person.bounding_box.width #=> Float
resp.persons[0].person.bounding_box.height #=> Float
resp.persons[0].person.bounding_box.left #=> Float
resp.persons[0].person.bounding_box.top #=> Float
resp.persons[0].person.face.bounding_box.width #=> Float
resp.persons[0].person.face.bounding_box.height #=> Float
resp.persons[0].person.face.bounding_box.left #=> Float
resp.persons[0].person.face.bounding_box.top #=> Float
resp.persons[0].person.face.age_range.low #=> Integer
resp.persons[0].person.face.age_range.high #=> Integer
resp.persons[0].person.face.smile.value #=> Boolean
resp.persons[0].person.face.smile.confidence #=> Float
resp.persons[0].person.face.eyeglasses.value #=> Boolean
resp.persons[0].person.face.eyeglasses.confidence #=> Float
resp.persons[0].person.face.sunglasses.value #=> Boolean
resp.persons[0].person.face.sunglasses.confidence #=> Float
resp.persons[0].person.face.gender.value #=> String, one of "Male", "Female"
resp.persons[0].person.face.gender.confidence #=> Float
resp.persons[0].person.face.beard.value #=> Boolean
resp.persons[0].person.face.beard.confidence #=> Float
resp.persons[0].person.face.mustache.value #=> Boolean
resp.persons[0].person.face.mustache.confidence #=> Float
resp.persons[0].person.face.eyes_open.value #=> Boolean
resp.persons[0].person.face.eyes_open.confidence #=> Float
resp.persons[0].person.face.mouth_open.value #=> Boolean
resp.persons[0].person.face.mouth_open.confidence #=> Float
resp.persons[0].person.face.emotions #=> Array
resp.persons[0].person.face.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.persons[0].person.face.emotions[0].confidence #=> Float
resp.persons[0].person.face.landmarks #=> Array
resp.persons[0].person.face.landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.persons[0].person.face.landmarks[0].x #=> Float
resp.persons[0].person.face.landmarks[0].y #=> Float
resp.persons[0].person.face.pose.roll #=> Float
resp.persons[0].person.face.pose.yaw #=> Float
resp.persons[0].person.face.pose.pitch #=> Float
resp.persons[0].person.face.quality.brightness #=> Float
resp.persons[0].person.face.quality.sharpness #=> Float
resp.persons[0].person.face.confidence #=> Float
resp.persons[0].person.face.face_occluded.value #=> Boolean
resp.persons[0].person.face.face_occluded.confidence #=> Float
resp.persons[0].person.face.eye_direction.yaw #=> Float
resp.persons[0].person.face.eye_direction.pitch #=> Float
resp.persons[0].person.face.eye_direction.confidence #=> Float
resp.persons[0].face_matches #=> Array
resp.persons[0].face_matches[0].similarity #=> Float
resp.persons[0].face_matches[0].face.face_id #=> String
resp.persons[0].face_matches[0].face.bounding_box.width #=> Float
resp.persons[0].face_matches[0].face.bounding_box.height #=> Float
resp.persons[0].face_matches[0].face.bounding_box.left #=> Float
resp.persons[0].face_matches[0].face.bounding_box.top #=> Float
resp.persons[0].face_matches[0].face.image_id #=> String
resp.persons[0].face_matches[0].face.external_image_id #=> String
resp.persons[0].face_matches[0].face.confidence #=> Float
resp.persons[0].face_matches[0].face.index_faces_model_version #=> String
resp.persons[0].face_matches[0].face.user_id #=> String
resp.job_id #=> String
resp.video.s3_object.bucket #=> String
resp.video.s3_object.name #=> String
resp.video.s3_object.version #=> String
resp.job_tag #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :job_id (required, String)

    The job identifer for the search request. You get the job identifier from an initial call to StartFaceSearch.

  • :max_results (Integer)

    Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.

  • :next_token (String)

    If the previous response was incomplete (because there is more search results to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of search results.

  • :sort_by (String)

    Sort to use for grouping faces in the response. Use TIMESTAMP to group faces by the time that they are recognized. Use INDEX to sort by recognized faces.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4372

def get_face_search(params = {}, options = {})
  req = build_request(:get_face_search, params)
  req.send_request(options)
end

#get_label_detection(params = {}) ⇒ Types::GetLabelDetectionResponse

Gets the label detection results of a Amazon Rekognition Video analysis started by StartLabelDetection.

The label detection operation is started by a call to StartLabelDetection which returns a job identifier (JobId). When the label detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartlabelDetection.

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 GetLabelDetection and pass the job identifier (JobId) from the initial call to StartLabelDetection.

GetLabelDetection returns an array of detected labels (Labels) sorted by the time the labels were detected. You can also sort by the label name by specifying NAME for the SortBy input parameter. If there is no NAME specified, the default sort is by timestamp.

You can select how results are aggregated by using the AggregateBy input parameter. The default aggregation method is TIMESTAMPS. You can also aggregate by SEGMENTS, which aggregates all instances of labels detected in a given segment.

The returned Labels array may include the following attributes:

  • Name - The name of the detected label.

  • Confidence - The level of confidence in the label assigned to a detected object.

  • Parents - The ancestor labels for a detected label. GetLabelDetection returns a hierarchical taxonomy of detected labels. For example, a detected car might be assigned the label car. The label car has two parent labels: Vehicle (its parent) and Transportation (its grandparent). The response includes the all ancestors for a label, where every ancestor is a unique label. In the previous example, Car, Vehicle, and Transportation are returned as unique labels in the response.

  • Aliases - Possible Aliases for the label.

  • Categories - The label categories that the detected label belongs to.

  • BoundingBox — Bounding boxes are described for all instances of detected common object labels, returned in an array of Instance objects. An Instance object contains a BoundingBox object, describing the location of the label on the input image. It also includes the confidence for the accuracy of the detected bounding box.

  • Timestamp - Time, in milliseconds from the start of the video, that the label was detected. For aggregation by SEGMENTS, the StartTimestampMillis, EndTimestampMillis, and DurationMillis structures are what define a segment. Although the “Timestamp” structure is still returned with each label, its value is set to be the same as StartTimestampMillis.

Timestamp and Bounding box information are returned for detected Instances, only if aggregation is done by TIMESTAMPS. If aggregating by SEGMENTS, information about detected instances isn’t returned.

The version of the label model used for the detection is also returned.

Note DominantColors isn't returned for Instances, although it is shown as part of the response in the sample seen below.

Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetlabelDetection and populate the NextToken request parameter with the token value returned from the previous call to GetLabelDetection.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.get_label_detection({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
  sort_by: "NAME", # accepts NAME, TIMESTAMP
  aggregate_by: "TIMESTAMPS", # accepts TIMESTAMPS, SEGMENTS
})

Response structure


resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
resp.status_message #=> String
resp..codec #=> String
resp..duration_millis #=> Integer
resp..format #=> String
resp..frame_rate #=> Float
resp..frame_height #=> Integer
resp..frame_width #=> Integer
resp..color_range #=> String, one of "FULL", "LIMITED"
resp.next_token #=> String
resp.labels #=> Array
resp.labels[0].timestamp #=> Integer
resp.labels[0].label.name #=> String
resp.labels[0].label.confidence #=> Float
resp.labels[0].label.instances #=> Array
resp.labels[0].label.instances[0].bounding_box.width #=> Float
resp.labels[0].label.instances[0].bounding_box.height #=> Float
resp.labels[0].label.instances[0].bounding_box.left #=> Float
resp.labels[0].label.instances[0].bounding_box.top #=> Float
resp.labels[0].label.instances[0].confidence #=> Float
resp.labels[0].label.instances[0].dominant_colors #=> Array
resp.labels[0].label.instances[0].dominant_colors[0].red #=> Integer
resp.labels[0].label.instances[0].dominant_colors[0].blue #=> Integer
resp.labels[0].label.instances[0].dominant_colors[0].green #=> Integer
resp.labels[0].label.instances[0].dominant_colors[0].hex_code #=> String
resp.labels[0].label.instances[0].dominant_colors[0].css_color #=> String
resp.labels[0].label.instances[0].dominant_colors[0].simplified_color #=> String
resp.labels[0].label.instances[0].dominant_colors[0].pixel_percent #=> Float
resp.labels[0].label.parents #=> Array
resp.labels[0].label.parents[0].name #=> String
resp.labels[0].label.aliases #=> Array
resp.labels[0].label.aliases[0].name #=> String
resp.labels[0].label.categories #=> Array
resp.labels[0].label.categories[0].name #=> String
resp.labels[0].start_timestamp_millis #=> Integer
resp.labels[0].end_timestamp_millis #=> Integer
resp.labels[0].duration_millis #=> Integer
resp.label_model_version #=> String
resp.job_id #=> String
resp.video.s3_object.bucket #=> String
resp.video.s3_object.name #=> String
resp.video.s3_object.version #=> String
resp.job_tag #=> String
resp..sort_by #=> String, one of "NAME", "TIMESTAMP"
resp..aggregate_by #=> String, one of "TIMESTAMPS", "SEGMENTS"

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :job_id (required, String)

    Job identifier for the label detection operation for which you want results returned. You get the job identifer from an initial call to StartlabelDetection.

  • :max_results (Integer)

    Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.

  • :next_token (String)

    If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of labels.

  • :sort_by (String)

    Sort to use for elements in the Labels array. Use TIMESTAMP to sort array elements by the time labels are detected. Use NAME to alphabetically group elements for a label together. Within each label group, the array element are sorted by detection confidence. The default sort is by TIMESTAMP.

  • :aggregate_by (String)

    Defines how to aggregate the returned results. Results can be aggregated by timestamps or segments.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4557

def get_label_detection(params = {}, options = {})
  req = build_request(:get_label_detection, params)
  req.send_request(options)
end

#get_media_analysis_job(params = {}) ⇒ Types::GetMediaAnalysisJobResponse

Retrieves the results for a given media analysis job. Takes a JobId returned by StartMediaAnalysisJob.

Examples:

Example: GetMediaAnalysisJob


# Retrieves the results for a given media analysis job.

resp = client.get_media_analysis_job({
  job_id: "861a0645d98ef88efb75477628c011c04942d9d5f58faf2703c393c8cf8c1537", 
})

resp.to_h outputs the following:
{
  completion_timestamp: Time.parse("2023-07-28T08:05:51.958000-07:00"), 
  creation_timestamp: Time.parse("2023-07-28T08:05:51.958000-06:00"), 
  input: {
    s3_object: {
      bucket: "input-bucket", 
      name: "input-manifest.json", 
    }, 
  }, 
  job_id: "861a0645d98ef88efb75477628c011c04942d9d5f58faf2703c393c8cf8c1537", 
  job_name: "job-name", 
  manifest_summary: {
    s3_object: {
      bucket: "output-bucket", 
      name: "output-location/861a0645d98ef88efb75477628c011c04942d9d5f58faf2703c393c8cf8c1537-manifest-summary.json", 
    }, 
  }, 
  operations_config: {
    detect_moderation_labels: {
      min_confidence: 50, 
      project_version: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958", 
    }, 
  }, 
  output_config: {
    s3_bucket: "output-bucket", 
    s3_key_prefix: "output-location", 
  }, 
  results: {
    s3_object: {
      bucket: "output-bucket", 
      name: "output-location/861a0645d98ef88efb75477628c011c04942d9d5f58faf2703c393c8cf8c1537-results.jsonl", 
    }, 
  }, 
  status: "SUCCEEDED", 
}

Request syntax with placeholder values


resp = client.get_media_analysis_job({
  job_id: "MediaAnalysisJobId", # required
})

Response structure


resp.job_id #=> String
resp.job_name #=> String
resp.operations_config.detect_moderation_labels.min_confidence #=> Float
resp.operations_config.detect_moderation_labels.project_version #=> String
resp.status #=> String, one of "CREATED", "QUEUED", "IN_PROGRESS", "SUCCEEDED", "FAILED"
resp.failure_details.code #=> String, one of "INTERNAL_ERROR", "INVALID_S3_OBJECT", "INVALID_MANIFEST", "INVALID_OUTPUT_CONFIG", "INVALID_KMS_KEY", "ACCESS_DENIED", "RESOURCE_NOT_FOUND", "RESOURCE_NOT_READY", "THROTTLED"
resp.failure_details.message #=> String
resp.creation_timestamp #=> Time
resp.completion_timestamp #=> Time
resp.input.s3_object.bucket #=> String
resp.input.s3_object.name #=> String
resp.input.s3_object.version #=> String
resp.output_config.s3_bucket #=> String
resp.output_config.s3_key_prefix #=> String
resp.kms_key_id #=> String
resp.results.s3_object.bucket #=> String
resp.results.s3_object.name #=> String
resp.results.s3_object.version #=> String
resp.results.model_versions.moderation #=> String
resp.manifest_summary.s3_object.bucket #=> String
resp.manifest_summary.s3_object.name #=> String
resp.manifest_summary.s3_object.version #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :job_id (required, String)

    Unique identifier for the media analysis job for which you want to retrieve results.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4663

def get_media_analysis_job(params = {}, options = {})
  req = build_request(:get_media_analysis_job, params)
  req.send_request(options)
end

#get_person_tracking(params = {}) ⇒ Types::GetPersonTrackingResponse

Gets the path tracking results of a Amazon Rekognition Video analysis started by StartPersonTracking.

The person path tracking operation is started by a call to StartPersonTracking which returns a job identifier (JobId). When the operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartPersonTracking.

To get the results of the person path tracking operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetPersonTracking and pass the job identifier (JobId) from the initial call to StartPersonTracking.

GetPersonTracking returns an array, Persons, of tracked persons and the time(s) their paths were tracked in the video.

GetPersonTracking only returns the default facial attributes (BoundingBox, Confidence, Landmarks, Pose, and Quality). The other facial attributes listed in the Face object of the following response syntax are not returned.

For more information, see FaceDetail in the Amazon Rekognition Developer Guide.

By default, the array is sorted by the time(s) a person's path is tracked in the video. You can sort by tracked persons by specifying INDEX for the SortBy input parameter.

Use the MaxResults parameter to limit the number of items returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetPersonTracking and populate the NextToken request parameter with the token value returned from the previous call to GetPersonTracking.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.get_person_tracking({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
  sort_by: "INDEX", # accepts INDEX, TIMESTAMP
})

Response structure


resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
resp.status_message #=> String
resp..codec #=> String
resp..duration_millis #=> Integer
resp..format #=> String
resp..frame_rate #=> Float
resp..frame_height #=> Integer
resp..frame_width #=> Integer
resp..color_range #=> String, one of "FULL", "LIMITED"
resp.next_token #=> String
resp.persons #=> Array
resp.persons[0].timestamp #=> Integer
resp.persons[0].person.index #=> Integer
resp.persons[0].person.bounding_box.width #=> Float
resp.persons[0].person.bounding_box.height #=> Float
resp.persons[0].person.bounding_box.left #=> Float
resp.persons[0].person.bounding_box.top #=> Float
resp.persons[0].person.face.bounding_box.width #=> Float
resp.persons[0].person.face.bounding_box.height #=> Float
resp.persons[0].person.face.bounding_box.left #=> Float
resp.persons[0].person.face.bounding_box.top #=> Float
resp.persons[0].person.face.age_range.low #=> Integer
resp.persons[0].person.face.age_range.high #=> Integer
resp.persons[0].person.face.smile.value #=> Boolean
resp.persons[0].person.face.smile.confidence #=> Float
resp.persons[0].person.face.eyeglasses.value #=> Boolean
resp.persons[0].person.face.eyeglasses.confidence #=> Float
resp.persons[0].person.face.sunglasses.value #=> Boolean
resp.persons[0].person.face.sunglasses.confidence #=> Float
resp.persons[0].person.face.gender.value #=> String, one of "Male", "Female"
resp.persons[0].person.face.gender.confidence #=> Float
resp.persons[0].person.face.beard.value #=> Boolean
resp.persons[0].person.face.beard.confidence #=> Float
resp.persons[0].person.face.mustache.value #=> Boolean
resp.persons[0].person.face.mustache.confidence #=> Float
resp.persons[0].person.face.eyes_open.value #=> Boolean
resp.persons[0].person.face.eyes_open.confidence #=> Float
resp.persons[0].person.face.mouth_open.value #=> Boolean
resp.persons[0].person.face.mouth_open.confidence #=> Float
resp.persons[0].person.face.emotions #=> Array
resp.persons[0].person.face.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.persons[0].person.face.emotions[0].confidence #=> Float
resp.persons[0].person.face.landmarks #=> Array
resp.persons[0].person.face.landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.persons[0].person.face.landmarks[0].x #=> Float
resp.persons[0].person.face.landmarks[0].y #=> Float
resp.persons[0].person.face.pose.roll #=> Float
resp.persons[0].person.face.pose.yaw #=> Float
resp.persons[0].person.face.pose.pitch #=> Float
resp.persons[0].person.face.quality.brightness #=> Float
resp.persons[0].person.face.quality.sharpness #=> Float
resp.persons[0].person.face.confidence #=> Float
resp.persons[0].person.face.face_occluded.value #=> Boolean
resp.persons[0].person.face.face_occluded.confidence #=> Float
resp.persons[0].person.face.eye_direction.yaw #=> Float
resp.persons[0].person.face.eye_direction.pitch #=> Float
resp.persons[0].person.face.eye_direction.confidence #=> Float
resp.job_id #=> String
resp.video.s3_object.bucket #=> String
resp.video.s3_object.name #=> String
resp.video.s3_object.version #=> String
resp.job_tag #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :job_id (required, String)

    The identifier for a job that tracks persons in a video. You get the JobId from a call to StartPersonTracking.

  • :max_results (Integer)

    Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.

  • :next_token (String)

    If the previous response was incomplete (because there are more persons to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of persons.

  • :sort_by (String)

    Sort to use for elements in the Persons array. Use TIMESTAMP to sort array elements by the time persons are detected. Use INDEX to sort by the tracked persons. If you sort by INDEX, the array elements for each person are sorted by detection confidence. The default sort is by TIMESTAMP.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4819

def get_person_tracking(params = {}, options = {})
  req = build_request(:get_person_tracking, params)
  req.send_request(options)
end

#get_segment_detection(params = {}) ⇒ Types::GetSegmentDetectionResponse

Gets the segment detection results of a Amazon Rekognition Video analysis started by StartSegmentDetection.

Segment detection with Amazon Rekognition Video is an asynchronous operation. You start segment detection by calling StartSegmentDetection which returns a job identifier (JobId). When the segment detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartSegmentDetection. To get the results of the segment detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. if so, call GetSegmentDetection and pass the job identifier (JobId) from the initial call of StartSegmentDetection.

GetSegmentDetection returns detected segments in an array (Segments) of SegmentDetection objects. Segments is sorted by the segment types specified in the SegmentTypes input parameter of StartSegmentDetection. Each element of the array includes the detected segment, the precentage confidence in the acuracy of the detected segment, the type of the segment, and the frame in which the segment was detected.

Use SelectedSegmentTypes to find out the type of segment detection requested in the call to StartSegmentDetection.

Use the MaxResults parameter to limit the number of segment detections returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetSegmentDetection and populate the NextToken request parameter with the token value returned from the previous call to GetSegmentDetection.

For more information, see Detecting video segments in stored video in the Amazon Rekognition Developer Guide.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.get_segment_detection({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
})

Response structure


resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
resp.status_message #=> String
resp. #=> Array
resp.[0].codec #=> String
resp.[0].duration_millis #=> Integer
resp.[0].format #=> String
resp.[0].frame_rate #=> Float
resp.[0].frame_height #=> Integer
resp.[0].frame_width #=> Integer
resp.[0].color_range #=> String, one of "FULL", "LIMITED"
resp. #=> Array
resp.[0].codec #=> String
resp.[0].duration_millis #=> Integer
resp.[0].sample_rate #=> Integer
resp.[0].number_of_channels #=> Integer
resp.next_token #=> String
resp.segments #=> Array
resp.segments[0].type #=> String, one of "TECHNICAL_CUE", "SHOT"
resp.segments[0].start_timestamp_millis #=> Integer
resp.segments[0].end_timestamp_millis #=> Integer
resp.segments[0].duration_millis #=> Integer
resp.segments[0].start_timecode_smpte #=> String
resp.segments[0].end_timecode_smpte #=> String
resp.segments[0].duration_smpte #=> String
resp.segments[0].technical_cue_segment.type #=> String, one of "ColorBars", "EndCredits", "BlackFrames", "OpeningCredits", "StudioLogo", "Slate", "Content"
resp.segments[0].technical_cue_segment.confidence #=> Float
resp.segments[0].shot_segment.index #=> Integer
resp.segments[0].shot_segment.confidence #=> Float
resp.segments[0].start_frame_number #=> Integer
resp.segments[0].end_frame_number #=> Integer
resp.segments[0].duration_frames #=> Integer
resp.selected_segment_types #=> Array
resp.selected_segment_types[0].type #=> String, one of "TECHNICAL_CUE", "SHOT"
resp.selected_segment_types[0].model_version #=> String
resp.job_id #=> String
resp.video.s3_object.bucket #=> String
resp.video.s3_object.name #=> String
resp.video.s3_object.version #=> String
resp.job_tag #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :job_id (required, String)

    Job identifier for the text detection operation for which you want results returned. You get the job identifer from an initial call to StartSegmentDetection.

  • :max_results (Integer)

    Maximum number of results to return per paginated call. The largest value you can specify is 1000.

  • :next_token (String)

    If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of text.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4941

def get_segment_detection(params = {}, options = {})
  req = build_request(:get_segment_detection, params)
  req.send_request(options)
end

#get_text_detection(params = {}) ⇒ Types::GetTextDetectionResponse

Gets the text detection results of a Amazon Rekognition Video analysis started by StartTextDetection.

Text detection with Amazon Rekognition Video is an asynchronous operation. You start text detection by calling StartTextDetection which returns a job identifier (JobId) When the text detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartTextDetection. To get the results of the text detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. if so, call GetTextDetection and pass the job identifier (JobId) from the initial call of StartLabelDetection.

GetTextDetection returns an array of detected text (TextDetections) sorted by the time the text was detected, up to 100 words per frame of video.

Each element of the array includes the detected text, the precentage confidence in the acuracy of the detected text, the time the text was detected, bounding box information for where the text was located, and unique identifiers for words and their lines.

Use MaxResults parameter to limit the number of text detections returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetTextDetection and populate the NextToken request parameter with the token value returned from the previous call to GetTextDetection.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.get_text_detection({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
})

Response structure


resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
resp.status_message #=> String
resp..codec #=> String
resp..duration_millis #=> Integer
resp..format #=> String
resp..frame_rate #=> Float
resp..frame_height #=> Integer
resp..frame_width #=> Integer
resp..color_range #=> String, one of "FULL", "LIMITED"
resp.text_detections #=> Array
resp.text_detections[0].timestamp #=> Integer
resp.text_detections[0].text_detection.detected_text #=> String
resp.text_detections[0].text_detection.type #=> String, one of "LINE", "WORD"
resp.text_detections[0].text_detection.id #=> Integer
resp.text_detections[0].text_detection.parent_id #=> Integer
resp.text_detections[0].text_detection.confidence #=> Float
resp.text_detections[0].text_detection.geometry.bounding_box.width #=> Float
resp.text_detections[0].text_detection.geometry.bounding_box.height #=> Float
resp.text_detections[0].text_detection.geometry.bounding_box.left #=> Float
resp.text_detections[0].text_detection.geometry.bounding_box.top #=> Float
resp.text_detections[0].text_detection.geometry.polygon #=> Array
resp.text_detections[0].text_detection.geometry.polygon[0].x #=> Float
resp.text_detections[0].text_detection.geometry.polygon[0].y #=> Float
resp.next_token #=> String
resp.text_model_version #=> String
resp.job_id #=> String
resp.video.s3_object.bucket #=> String
resp.video.s3_object.name #=> String
resp.video.s3_object.version #=> String
resp.job_tag #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :job_id (required, String)

    Job identifier for the text detection operation for which you want results returned. You get the job identifer from an initial call to StartTextDetection.

  • :max_results (Integer)

    Maximum number of results to return per paginated call. The largest value you can specify is 1000.

  • :next_token (String)

    If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of text.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5049

def get_text_detection(params = {}, options = {})
  req = build_request(:get_text_detection, params)
  req.send_request(options)
end

#index_faces(params = {}) ⇒ Types::IndexFacesResponse

Detects faces in the input image and adds them to the specified collection.

Amazon Rekognition doesn't save the actual faces that are detected. Instead, the underlying detection algorithm first detects the faces in the input image. For each face, the algorithm extracts facial features into a feature vector, and stores it in the backend database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the SearchFaces and SearchFacesByImage operations.

For more information, see Adding faces to a collection in the Amazon Rekognition Developer Guide.

To get the number of faces in a collection, call DescribeCollection.

If you're using version 1.0 of the face detection model, IndexFaces indexes the 15 largest faces in the input image. Later versions of the face detection model index the 100 largest faces in the input image.

If you're using version 4 or later of the face model, image orientation information is not returned in the OrientationCorrection field.

To determine which version of the model you're using, call DescribeCollection and supply the collection ID. You can also get the model version from the value of FaceModelVersion in the response from IndexFaces

For more information, see Model Versioning in the Amazon Rekognition Developer Guide.

If you provide the optional ExternalImageId for the input image you provided, Amazon Rekognition associates this ID with all faces that it detects. When you call the ListFaces operation, the response returns the external ID. You can use this external image ID to create a client-side index to associate the faces with each image. You can then use the index to find all faces in an image.

You can specify the maximum number of faces to index with the MaxFaces input parameter. This is useful when you want to index the largest faces in an image and don't want to index smaller faces, such as those belonging to people standing in the background.

The QualityFilter input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. By default, IndexFaces chooses the quality bar that's used to filter faces. You can also explicitly choose the quality bar. Use QualityFilter, to set the quality bar by specifying LOW, MEDIUM, or HIGH. If you do not want to filter detected faces, specify NONE.

To use quality filtering, you need a collection associated with version 3 of the face model or higher. To get the version of the face model associated with a collection, call DescribeCollection.

Information about faces detected in an image, but not indexed, is returned in an array of UnindexedFace objects, UnindexedFaces. Faces aren't indexed for reasons such as:

  • The number of faces detected exceeds the value of the MaxFaces request parameter.

  • The face is too small compared to the image dimensions.

  • The face is too blurry.

  • The image is too dark.

  • The face has an extreme pose.

  • The face doesn’t have enough detail to be suitable for face search.

In response, the IndexFaces operation returns an array of metadata for all detected faces, FaceRecords. This includes:

  • The bounding box, BoundingBox, of the detected face.

  • A confidence value, Confidence, which indicates the confidence that the bounding box contains a face.

  • A face ID, FaceId, assigned by the service for each face that's detected and stored.

  • An image ID, ImageId, assigned by the service for the input image.

If you request ALL or specific facial attributes (e.g., FACE_OCCLUDED) by using the detectionAttributes parameter, Amazon Rekognition returns detailed facial attributes, such as facial landmarks (for example, location of eye and mouth), facial occlusion, and other facial attributes.

If you provide the same image, specify the same collection, and use the same external ID in the IndexFaces operation, Amazon Rekognition doesn't save duplicate face metadata.

The input image is passed either as base64-encoded image bytes, or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file.

This operation requires permissions to perform the rekognition:IndexFaces action.

Examples:

Example: To add a face to a collection


# This operation detects faces in an image and adds them to the specified Rekognition collection.

resp = client.index_faces({
  collection_id: "myphotos", 
  detection_attributes: [
  ], 
  external_image_id: "myphotoid", 
  image: {
    s3_object: {
      bucket: "mybucket", 
      name: "myphoto", 
    }, 
  }, 
})

resp.to_h outputs the following:
{
  face_records: [
    {
      face: {
        bounding_box: {
          height: 0.33481481671333313, 
          left: 0.31888890266418457, 
          top: 0.4933333396911621, 
          width: 0.25, 
        }, 
        confidence: 99.9991226196289, 
        face_id: "ff43d742-0c13-5d16-a3e8-03d3f58e980b", 
        image_id: "465f4e93-763e-51d0-b030-b9667a2d94b1", 
      }, 
      face_detail: {
        bounding_box: {
          height: 0.33481481671333313, 
          left: 0.31888890266418457, 
          top: 0.4933333396911621, 
          width: 0.25, 
        }, 
        confidence: 99.9991226196289, 
        landmarks: [
          {
            type: "eyeLeft", 
            x: 0.3976764678955078, 
            y: 0.6248345971107483, 
          }, 
          {
            type: "eyeRight", 
            x: 0.4810936450958252, 
            y: 0.6317117214202881, 
          }, 
          {
            type: "noseLeft", 
            x: 0.41986238956451416, 
            y: 0.7111940383911133, 
          }, 
          {
            type: "mouthDown", 
            x: 0.40525302290916443, 
            y: 0.7497701048851013, 
          }, 
          {
            type: "mouthUp", 
            x: 0.4753248989582062, 
            y: 0.7558549642562866, 
          }, 
        ], 
        pose: {
          pitch: -9.713645935058594, 
          roll: 4.707281112670898, 
          yaw: -24.438663482666016, 
        }, 
        quality: {
          brightness: 29.23358917236328, 
          sharpness: 80, 
        }, 
      }, 
    }, 
    {
      face: {
        bounding_box: {
          height: 0.32592591643333435, 
          left: 0.5144444704055786, 
          top: 0.15111111104488373, 
          width: 0.24444444477558136, 
        }, 
        confidence: 99.99950408935547, 
        face_id: "8be04dba-4e58-520d-850e-9eae4af70eb2", 
        image_id: "465f4e93-763e-51d0-b030-b9667a2d94b1", 
      }, 
      face_detail: {
        bounding_box: {
          height: 0.32592591643333435, 
          left: 0.5144444704055786, 
          top: 0.15111111104488373, 
          width: 0.24444444477558136, 
        }, 
        confidence: 99.99950408935547, 
        landmarks: [
          {
            type: "eyeLeft", 
            x: 0.6006892323493958, 
            y: 0.290842205286026, 
          }, 
          {
            type: "eyeRight", 
            x: 0.6808141469955444, 
            y: 0.29609042406082153, 
          }, 
          {
            type: "noseLeft", 
            x: 0.6395332217216492, 
            y: 0.3522595763206482, 
          }, 
          {
            type: "mouthDown", 
            x: 0.5892083048820496, 
            y: 0.38689887523651123, 
          }, 
          {
            type: "mouthUp", 
            x: 0.674560010433197, 
            y: 0.394125759601593, 
          }, 
        ], 
        pose: {
          pitch: -4.683138370513916, 
          roll: 2.1029529571533203, 
          yaw: 6.716655254364014, 
        }, 
        quality: {
          brightness: 34.951698303222656, 
          sharpness: 160, 
        }, 
      }, 
    }, 
  ], 
  orientation_correction: "ROTATE_0", 
}

Request syntax with placeholder values


resp = client.index_faces({
  collection_id: "CollectionId", # required
  image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  external_image_id: "ExternalImageId",
  detection_attributes: ["DEFAULT"], # accepts DEFAULT, ALL, AGE_RANGE, BEARD, EMOTIONS, EYE_DIRECTION, EYEGLASSES, EYES_OPEN, GENDER, MOUTH_OPEN, MUSTACHE, FACE_OCCLUDED, SMILE, SUNGLASSES
  max_faces: 1,
  quality_filter: "NONE", # accepts NONE, AUTO, LOW, MEDIUM, HIGH
})

Response structure


resp.face_records #=> Array
resp.face_records[0].face.face_id #=> String
resp.face_records[0].face.bounding_box.width #=> Float
resp.face_records[0].face.bounding_box.height #=> Float
resp.face_records[0].face.bounding_box.left #=> Float
resp.face_records[0].face.bounding_box.top #=> Float
resp.face_records[0].face.image_id #=> String
resp.face_records[0].face.external_image_id #=> String
resp.face_records[0].face.confidence #=> Float
resp.face_records[0].face.index_faces_model_version #=> String
resp.face_records[0].face.user_id #=> String
resp.face_records[0].face_detail.bounding_box.width #=> Float
resp.face_records[0].face_detail.bounding_box.height #=> Float
resp.face_records[0].face_detail.bounding_box.left #=> Float
resp.face_records[0].face_detail.bounding_box.top #=> Float
resp.face_records[0].face_detail.age_range.low #=> Integer
resp.face_records[0].face_detail.age_range.high #=> Integer
resp.face_records[0].face_detail.smile.value #=> Boolean
resp.face_records[0].face_detail.smile.confidence #=> Float
resp.face_records[0].face_detail.eyeglasses.value #=> Boolean
resp.face_records[0].face_detail.eyeglasses.confidence #=> Float
resp.face_records[0].face_detail.sunglasses.value #=> Boolean
resp.face_records[0].face_detail.sunglasses.confidence #=> Float
resp.face_records[0].face_detail.gender.value #=> String, one of "Male", "Female"
resp.face_records[0].face_detail.gender.confidence #=> Float
resp.face_records[0].face_detail.beard.value #=> Boolean
resp.face_records[0].face_detail.beard.confidence #=> Float
resp.face_records[0].face_detail.mustache.value #=> Boolean
resp.face_records[0].face_detail.mustache.confidence #=> Float
resp.face_records[0].face_detail.eyes_open.value #=> Boolean
resp.face_records[0].face_detail.eyes_open.confidence #=> Float
resp.face_records[0].face_detail.mouth_open.value #=> Boolean
resp.face_records[0].face_detail.mouth_open.confidence #=> Float
resp.face_records[0].face_detail.emotions #=> Array
resp.face_records[0].face_detail.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.face_records[0].face_detail.emotions[0].confidence #=> Float
resp.face_records[0].face_detail.landmarks #=> Array
resp.face_records[0].face_detail.landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.face_records[0].face_detail.landmarks[0].x #=> Float
resp.face_records[0].face_detail.landmarks[0].y #=> Float
resp.face_records[0].face_detail.pose.roll #=> Float
resp.face_records[0].face_detail.pose.yaw #=> Float
resp.face_records[0].face_detail.pose.pitch #=> Float
resp.face_records[0].face_detail.quality.brightness #=> Float
resp.face_records[0].face_detail.quality.sharpness #=> Float
resp.face_records[0].face_detail.confidence #=> Float
resp.face_records[0].face_detail.face_occluded.value #=> Boolean
resp.face_records[0].face_detail.face_occluded.confidence #=> Float
resp.face_records[0].face_detail.eye_direction.yaw #=> Float
resp.face_records[0].face_detail.eye_direction.pitch #=> Float
resp.face_records[0].face_detail.eye_direction.confidence #=> Float
resp.orientation_correction #=> String, one of "ROTATE_0", "ROTATE_90", "ROTATE_180", "ROTATE_270"
resp.face_model_version #=> String
resp.unindexed_faces #=> Array
resp.unindexed_faces[0].reasons #=> Array
resp.unindexed_faces[0].reasons[0] #=> String, one of "EXCEEDS_MAX_FACES", "EXTREME_POSE", "LOW_BRIGHTNESS", "LOW_SHARPNESS", "LOW_CONFIDENCE", "SMALL_BOUNDING_BOX", "LOW_FACE_QUALITY"
resp.unindexed_faces[0].face_detail.bounding_box.width #=> Float
resp.unindexed_faces[0].face_detail.bounding_box.height #=> Float
resp.unindexed_faces[0].face_detail.bounding_box.left #=> Float
resp.unindexed_faces[0].face_detail.bounding_box.top #=> Float
resp.unindexed_faces[0].face_detail.age_range.low #=> Integer
resp.unindexed_faces[0].face_detail.age_range.high #=> Integer
resp.unindexed_faces[0].face_detail.smile.value #=> Boolean
resp.unindexed_faces[0].face_detail.smile.confidence #=> Float
resp.unindexed_faces[0].face_detail.eyeglasses.value #=> Boolean
resp.unindexed_faces[0].face_detail.eyeglasses.confidence #=> Float
resp.unindexed_faces[0].face_detail.sunglasses.value #=> Boolean
resp.unindexed_faces[0].face_detail.sunglasses.confidence #=> Float
resp.unindexed_faces[0].face_detail.gender.value #=> String, one of "Male", "Female"
resp.unindexed_faces[0].face_detail.gender.confidence #=> Float
resp.unindexed_faces[0].face_detail.beard.value #=> Boolean
resp.unindexed_faces[0].face_detail.beard.confidence #=> Float
resp.unindexed_faces[0].face_detail.mustache.value #=> Boolean
resp.unindexed_faces[0].face_detail.mustache.confidence #=> Float
resp.unindexed_faces[0].face_detail.eyes_open.value #=> Boolean
resp.unindexed_faces[0].face_detail.eyes_open.confidence #=> Float
resp.unindexed_faces[0].face_detail.mouth_open.value #=> Boolean
resp.unindexed_faces[0].face_detail.mouth_open.confidence #=> Float
resp.unindexed_faces[0].face_detail.emotions #=> Array
resp.unindexed_faces[0].face_detail.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.unindexed_faces[0].face_detail.emotions[0].confidence #=> Float
resp.unindexed_faces[0].face_detail.landmarks #=> Array
resp.unindexed_faces[0].face_detail.landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.unindexed_faces[0].face_detail.landmarks[0].x #=> Float
resp.unindexed_faces[0].face_detail.landmarks[0].y #=> Float
resp.unindexed_faces[0].face_detail.pose.roll #=> Float
resp.unindexed_faces[0].face_detail.pose.yaw #=> Float
resp.unindexed_faces[0].face_detail.pose.pitch #=> Float
resp.unindexed_faces[0].face_detail.quality.brightness #=> Float
resp.unindexed_faces[0].face_detail.quality.sharpness #=> Float
resp.unindexed_faces[0].face_detail.confidence #=> Float
resp.unindexed_faces[0].face_detail.face_occluded.value #=> Boolean
resp.unindexed_faces[0].face_detail.face_occluded.confidence #=> Float
resp.unindexed_faces[0].face_detail.eye_direction.yaw #=> Float
resp.unindexed_faces[0].face_detail.eye_direction.pitch #=> Float
resp.unindexed_faces[0].face_detail.eye_direction.confidence #=> Float

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :collection_id (required, String)

    The ID of an existing collection to which you want to add the faces that are detected in the input images.

  • :image (required, Types::Image)

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes isn't supported.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

  • :external_image_id (String)

    The ID you want to assign to all the faces detected in the image.

  • :detection_attributes (Array<String>)

    An array of facial attributes you want to be returned. A DEFAULT subset of facial attributes - BoundingBox, Confidence, Pose, Quality, and Landmarks - will always be returned. You can request for specific facial attributes (in addition to the default list) - by using ["DEFAULT", "FACE_OCCLUDED"] or just ["FACE_OCCLUDED"]. You can request for all facial attributes by using ["ALL"]. Requesting more attributes may increase response time.

    If you provide both, ["ALL", "DEFAULT"], the service uses a logical AND operator to determine which attributes to return (in this case, all attributes).

  • :max_faces (Integer)

    The maximum number of faces to index. The value of MaxFaces must be greater than or equal to 1. IndexFaces returns no more than 100 detected faces in an image, even if you specify a larger value for MaxFaces.

    If IndexFaces detects more faces than the value of MaxFaces, the faces with the lowest quality are filtered out first. If there are still more faces than the value of MaxFaces, the faces with the smallest bounding boxes are filtered out (up to the number that's needed to satisfy the value of MaxFaces). Information about the unindexed faces is available in the UnindexedFaces array.

    The faces that are returned by IndexFaces are sorted by the largest face bounding box size to the smallest size, in descending order.

    MaxFaces can be used with a collection associated with any version of the face model.

  • :quality_filter (String)

    A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren't indexed. If you specify AUTO, Amazon Rekognition chooses the quality bar. If you specify LOW, MEDIUM, or HIGH, filtering removes all faces that don’t meet the chosen quality bar. The default value is AUTO. The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that's misidentified as a face, a face that's too blurry, or a face with a pose that's too extreme to use. If you specify NONE, no filtering is performed.

    To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5493

def index_faces(params = {}, options = {})
  req = build_request(:index_faces, params)
  req.send_request(options)
end

#list_collections(params = {}) ⇒ Types::ListCollectionsResponse

Returns list of collection IDs in your account. If the result is truncated, the response also provides a NextToken that you can use in the subsequent request to fetch the next set of collection IDs.

For an example, see Listing collections in the Amazon Rekognition Developer Guide.

This operation requires permissions to perform the rekognition:ListCollections action.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Example: To list the collections


# This operation returns a list of Rekognition collections.

resp = client.list_collections({
})

resp.to_h outputs the following:
{
  collection_ids: [
    "myphotos", 
  ], 
}

Request syntax with placeholder values


resp = client.list_collections({
  next_token: "PaginationToken",
  max_results: 1,
})

Response structure


resp.collection_ids #=> Array
resp.collection_ids[0] #=> String
resp.next_token #=> String
resp.face_model_versions #=> Array
resp.face_model_versions[0] #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :next_token (String)

    Pagination token from the previous response.

  • :max_results (Integer)

    Maximum number of collection IDs to return.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5554

def list_collections(params = {}, options = {})
  req = build_request(:list_collections, params)
  req.send_request(options)
end

#list_dataset_entries(params = {}) ⇒ Types::ListDatasetEntriesResponse

This operation applies only to Amazon Rekognition Custom Labels.

Lists the entries (images) within a dataset. An entry is a JSON Line that contains the information for a single image, including the image location, assigned labels, and object location bounding boxes. For more information, see Creating a manifest file.

JSON Lines in the response include information about non-terminal errors found in the dataset. Non terminal errors are reported in errors lists within each JSON Line. The same information is reported in the training and testing validation result manifests that Amazon Rekognition Custom Labels creates during model training.

You can filter the response in variety of ways, such as choosing which labels to return and returning JSON Lines created after a specific date.

This operation requires permissions to perform the rekognition:ListDatasetEntries action.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Example: To list the entries in an Amazon Rekognition Custom Labels dataset


# Lists the JSON line entries in an Amazon Rekognition Custom Labels dataset.

resp = client.list_dataset_entries({
  contains_labels: [
    "camellia", 
  ], 
  dataset_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-proj-2/dataset/train/1690564858106", 
  has_errors: true, 
  labeled: true, 
  max_results: 100, 
  next_token: "", 
  source_ref_contains: "camellia4.jpg", 
})

resp.to_h outputs the following:
{
  dataset_entries: [
    "{\"source-ref\":\"s3://custom-labels-console-us-east-1-1111111111/assets/flowers_1_train_dataset/camellia4.jpg\",\"camellia\":1,\"camellia-metadata\":{\"confidence\":1,\"job-name\":\"labeling-job/camellia\",\"class-name\":\"camellia\",\"human-annotated\":\"yes\",\"creation-date\":\"2021-07-11T03:32:13.456Z\",\"type\":\"groundtruth/image-classification\"},\"with_leaves\":1,\"with_leaves-metadata\":{\"confidence\":1,\"job-name\":\"labeling-job/with_leaves\",\"class-name\":\"with_leaves\",\"human-annotated\":\"yes\",\"creation-date\":\"2021-07-11T03:32:13.456Z\",\"type\":\"groundtruth/image-classification\"},\"cl-metadata\":{\"is_labeled\":true}}", 
  ], 
  next_token: "", 
}

Request syntax with placeholder values


resp = client.list_dataset_entries({
  dataset_arn: "DatasetArn", # required
  contains_labels: ["DatasetLabel"],
  labeled: false,
  source_ref_contains: "QueryString",
  has_errors: false,
  next_token: "ExtendedPaginationToken",
  max_results: 1,
})

Response structure


resp.dataset_entries #=> Array
resp.dataset_entries[0] #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :dataset_arn (required, String)

    The Amazon Resource Name (ARN) for the dataset that you want to use.

  • :contains_labels (Array<String>)

    Specifies a label filter for the response. The response includes an entry only if one or more of the labels in ContainsLabels exist in the entry.

  • :labeled (Boolean)

    Specify true to get only the JSON Lines where the image is labeled. Specify false to get only the JSON Lines where the image isn't labeled. If you don't specify Labeled, ListDatasetEntries returns JSON Lines for labeled and unlabeled images.

  • :source_ref_contains (String)

    If specified, ListDatasetEntries only returns JSON Lines where the value of SourceRefContains is part of the source-ref field. The source-ref field contains the Amazon S3 location of the image. You can use SouceRefContains for tasks such as getting the JSON Line for a single image, or gettting JSON Lines for all images within a specific folder.

  • :has_errors (Boolean)

    Specifies an error filter for the response. Specify True to only include entries that have errors.

  • :next_token (String)

    If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

  • :max_results (Integer)

    The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5675

def list_dataset_entries(params = {}, options = {})
  req = build_request(:list_dataset_entries, params)
  req.send_request(options)
end

#list_dataset_labels(params = {}) ⇒ Types::ListDatasetLabelsResponse

This operation applies only to Amazon Rekognition Custom Labels.

Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see Labeling images.

Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see Labeling images in the Amazon Rekognition Custom Labels Developer Guide.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Example: To list the entries in an Amazon Rekognition Custom Labels dataset


# Lists the JSON line entries in an Amazon Rekognition Custom Labels dataset.

resp = client.list_dataset_labels({
  dataset_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-proj-2/dataset/train/1690564858106", 
  max_results: 100, 
  next_token: "", 
})

resp.to_h outputs the following:
{
  dataset_label_descriptions: [
    {
      label_name: "camellia", 
      label_stats: {
        entry_count: 1, 
      }, 
    }, 
    {
      label_name: "with_leaves", 
      label_stats: {
        entry_count: 2, 
      }, 
    }, 
    {
      label_name: "mediterranean_spurge", 
      label_stats: {
        entry_count: 1, 
      }, 
    }, 
  ], 
}

Request syntax with placeholder values


resp = client.list_dataset_labels({
  dataset_arn: "DatasetArn", # required
  next_token: "ExtendedPaginationToken",
  max_results: 1,
})

Response structure


resp.dataset_label_descriptions #=> Array
resp.dataset_label_descriptions[0].label_name #=> String
resp.dataset_label_descriptions[0].label_stats.entry_count #=> Integer
resp.dataset_label_descriptions[0].label_stats.bounding_box_count #=> Integer
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :dataset_arn (required, String)

    The Amazon Resource Name (ARN) of the dataset that you want to use.

  • :next_token (String)

    If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

  • :max_results (Integer)

    The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5771

def list_dataset_labels(params = {}, options = {})
  req = build_request(:list_dataset_labels, params)
  req.send_request(options)
end

#list_faces(params = {}) ⇒ Types::ListFacesResponse

Returns metadata for faces in the specified collection. This metadata includes information such as the bounding box coordinates, the confidence (that the bounding box contains a face), and face ID. For an example, see Listing Faces in a Collection in the Amazon Rekognition Developer Guide.

This operation requires permissions to perform the rekognition:ListFaces action.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Example: To list the faces in a collection


# This operation lists the faces in a Rekognition collection.

resp = client.list_faces({
  collection_id: "myphotos", 
  max_results: 20, 
})

resp.to_h outputs the following:
{
  face_model_version: "6.0", 
  faces: [
    {
      bounding_box: {
        height: 0.056759100407361984, 
        left: 0.3453829884529114, 
        top: 0.36568498611450195, 
        width: 0.03177810087800026, 
      }, 
      confidence: 99.76940155029297, 
      face_id: "c92265d4-5f9c-43af-a58e-12be0ce02bc3", 
      image_id: "56a0ca74-1c83-39dd-b363-051a64168a65", 
      index_faces_model_version: "6.0", 
      user_id: "demoUser2", 
    }, 
    {
      bounding_box: {
        height: 0.06347999721765518, 
        left: 0.5160620212554932, 
        top: 0.6080359816551208, 
        width: 0.03254450112581253, 
      }, 
      confidence: 99.94369506835938, 
      face_id: "851cb847-dccc-4fea-9309-9f4805967855", 
      image_id: "a8aed589-ceec-35f7-9c04-82e0b546b024", 
      index_faces_model_version: "6.0", 
    }, 
    {
      bounding_box: {
        height: 0.05266290158033371, 
        left: 0.6513839960098267, 
        top: 0.4218429923057556, 
        width: 0.03094629943370819, 
      }, 
      confidence: 99.82969665527344, 
      face_id: "c0eb3b65-24a0-41e1-b23a-1908b1aaeac1", 
      image_id: "56a0ca74-1c83-39dd-b363-051a64168a65", 
      index_faces_model_version: "6.0", 
    }, 
  ], 
}

Request syntax with placeholder values


resp = client.list_faces({
  collection_id: "CollectionId", # required
  next_token: "PaginationToken",
  max_results: 1,
  user_id: "UserId",
  face_ids: ["FaceId"],
})

Response structure


resp.faces #=> Array
resp.faces[0].face_id #=> String
resp.faces[0].bounding_box.width #=> Float
resp.faces[0].bounding_box.height #=> Float
resp.faces[0].bounding_box.left #=> Float
resp.faces[0].bounding_box.top #=> Float
resp.faces[0].image_id #=> String
resp.faces[0].external_image_id #=> String
resp.faces[0].confidence #=> Float
resp.faces[0].index_faces_model_version #=> String
resp.faces[0].user_id #=> String
resp.next_token #=> String
resp.face_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :collection_id (required, String)

    ID of the collection from which to list the faces.

  • :next_token (String)

    If the previous response was incomplete (because there is more data to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of faces.

  • :max_results (Integer)

    Maximum number of faces to return.

  • :user_id (String)

    An array of user IDs to filter results with when listing faces in a collection.

  • :face_ids (Array<String>)

    An array of face IDs to filter results with when listing faces in