# frozen_string_literal: true

# WARNING ABOUT GENERATED CODE
#
# This file is generated. See the contributing guide for more information:
# https://github.com/aws/aws-sdk-ruby/blob/version-3/CONTRIBUTING.md
#
# WARNING ABOUT GENERATED CODE

require 'seahorse/client/plugins/content_length'
require 'aws-sdk-core/plugins/credentials_configuration'
require 'aws-sdk-core/plugins/logging'
require 'aws-sdk-core/plugins/param_converter'
require 'aws-sdk-core/plugins/param_validator'
require 'aws-sdk-core/plugins/user_agent'
require 'aws-sdk-core/plugins/helpful_socket_errors'
require 'aws-sdk-core/plugins/retry_errors'
require 'aws-sdk-core/plugins/global_configuration'
require 'aws-sdk-core/plugins/regional_endpoint'
require 'aws-sdk-core/plugins/endpoint_discovery'
require 'aws-sdk-core/plugins/endpoint_pattern'
require 'aws-sdk-core/plugins/response_paging'
require 'aws-sdk-core/plugins/stub_responses'
require 'aws-sdk-core/plugins/idempotency_token'
require 'aws-sdk-core/plugins/invocation_id'
require 'aws-sdk-core/plugins/jsonvalue_converter'
require 'aws-sdk-core/plugins/client_metrics_plugin'
require 'aws-sdk-core/plugins/client_metrics_send_plugin'
require 'aws-sdk-core/plugins/transfer_encoding'
require 'aws-sdk-core/plugins/http_checksum'
require 'aws-sdk-core/plugins/checksum_algorithm'
require 'aws-sdk-core/plugins/request_compression'
require 'aws-sdk-core/plugins/defaults_mode'
require 'aws-sdk-core/plugins/recursion_detection'
require 'aws-sdk-core/plugins/telemetry'
require 'aws-sdk-core/plugins/sign'
require 'aws-sdk-core/plugins/protocols/json_rpc'

module Aws::Rekognition
  # 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](/sdk-for-ruby/v3/developer-guide/setup-config.html).
  #
  # See {#initialize} for a full list of supported configuration options.
  class Client < Seahorse::Client::Base

    include Aws::ClientStubs

    @identifier = :rekognition

    set_api(ClientApi::API)

    add_plugin(Seahorse::Client::Plugins::ContentLength)
    add_plugin(Aws::Plugins::CredentialsConfiguration)
    add_plugin(Aws::Plugins::Logging)
    add_plugin(Aws::Plugins::ParamConverter)
    add_plugin(Aws::Plugins::ParamValidator)
    add_plugin(Aws::Plugins::UserAgent)
    add_plugin(Aws::Plugins::HelpfulSocketErrors)
    add_plugin(Aws::Plugins::RetryErrors)
    add_plugin(Aws::Plugins::GlobalConfiguration)
    add_plugin(Aws::Plugins::RegionalEndpoint)
    add_plugin(Aws::Plugins::EndpointDiscovery)
    add_plugin(Aws::Plugins::EndpointPattern)
    add_plugin(Aws::Plugins::ResponsePaging)
    add_plugin(Aws::Plugins::StubResponses)
    add_plugin(Aws::Plugins::IdempotencyToken)
    add_plugin(Aws::Plugins::InvocationId)
    add_plugin(Aws::Plugins::JsonvalueConverter)
    add_plugin(Aws::Plugins::ClientMetricsPlugin)
    add_plugin(Aws::Plugins::ClientMetricsSendPlugin)
    add_plugin(Aws::Plugins::TransferEncoding)
    add_plugin(Aws::Plugins::HttpChecksum)
    add_plugin(Aws::Plugins::ChecksumAlgorithm)
    add_plugin(Aws::Plugins::RequestCompression)
    add_plugin(Aws::Plugins::DefaultsMode)
    add_plugin(Aws::Plugins::RecursionDetection)
    add_plugin(Aws::Plugins::Telemetry)
    add_plugin(Aws::Plugins::Sign)
    add_plugin(Aws::Plugins::Protocols::JsonRpc)
    add_plugin(Aws::Rekognition::Plugins::Endpoints)

    # @overload initialize(options)
    #   @param [Hash] options
    #
    #   @option options [Array<Seahorse::Client::Plugin>] :plugins ([]])
    #     A list of plugins to apply to the client. Each plugin is either a
    #     class name or an instance of a plugin class.
    #
    #   @option options [required, Aws::CredentialProvider] :credentials
    #     Your AWS credentials used for authentication. This can be any class that includes and implements
    #     `Aws::CredentialProvider`, or 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`, `:session_token`, and
    #       `:account_id` options.
    #
    #     * `ENV['AWS_ACCESS_KEY_ID']`, `ENV['AWS_SECRET_ACCESS_KEY']`,
    #       `ENV['AWS_SESSION_TOKEN']`, and `ENV['AWS_ACCOUNT_ID']`.
    #
    #     * `~/.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::InstanceProfileCredentials` 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`.
    #
    #   @option options [required, String] :region
    #     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`
    #
    #   @option options [String] :access_key_id
    #
    #   @option options [String] :account_id
    #
    #   @option options [Boolean] :active_endpoint_cache (false)
    #     When set to `true`, a thread polling for endpoints will be running in
    #     the background every 60 secs (default). Defaults to `false`.
    #
    #   @option options [Boolean] :adaptive_retry_wait_to_fill (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.
    #
    #   @option options [Array<String>] :auth_scheme_preference
    #     A list of preferred authentication schemes to use when making a request. Supported values are:
    #     `sigv4`, `sigv4a`, `httpBearerAuth`, and `noAuth`. When set using `ENV['AWS_AUTH_SCHEME_PREFERENCE']` or in
    #     shared config as `auth_scheme_preference`, the value should be a comma-separated list.
    #
    #   @option options [Boolean] :client_side_monitoring (false)
    #     When `true`, client-side metrics will be collected for all API requests from
    #     this client.
    #
    #   @option options [String] :client_side_monitoring_client_id ("")
    #     Allows you to provide an identifier for this client which will be attached to
    #     all generated client side metrics. Defaults to an empty string.
    #
    #   @option options [String] :client_side_monitoring_host ("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.
    #
    #   @option options [Integer] :client_side_monitoring_port (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.
    #
    #   @option options [Aws::ClientSideMonitoring::Publisher] :client_side_monitoring_publisher (Aws::ClientSideMonitoring::Publisher)
    #     Allows you to provide a custom client-side monitoring publisher class. By default,
    #     will use the Client Side Monitoring Agent Publisher.
    #
    #   @option options [Boolean] :convert_params (true)
    #     When `true`, an attempt is made to coerce request parameters into
    #     the required types.
    #
    #   @option options [Boolean] :correct_clock_skew (true)
    #     Used only in `standard` and adaptive retry modes. Specifies whether to apply
    #     a clock skew correction and retry requests with skewed client clocks.
    #
    #   @option options [String] :defaults_mode ("legacy")
    #     See {Aws::DefaultsModeConfiguration} for a list of the
    #     accepted modes and the configuration defaults that are included.
    #
    #   @option options [Boolean] :disable_host_prefix_injection (false)
    #     When `true`, the SDK will not prepend the modeled host prefix to the endpoint.
    #
    #   @option options [Boolean] :disable_request_compression (false)
    #     When set to 'true' the request body will not be compressed
    #     for supported operations.
    #
    #   @option options [String, URI::HTTPS, URI::HTTP] :endpoint
    #     Normally you should not configure the `:endpoint` option
    #     directly. This is normally constructed from the `:region`
    #     option. Configuring `:endpoint` is normally reserved for
    #     connecting to test or custom endpoints. The endpoint should
    #     be a URI formatted like:
    #
    #         'http://example.com'
    #         'https://example.com'
    #         'http://example.com:123'
    #
    #   @option options [Integer] :endpoint_cache_max_entries (1000)
    #     Used for the maximum size limit of the LRU cache storing endpoints data
    #     for endpoint discovery enabled operations. Defaults to 1000.
    #
    #   @option options [Integer] :endpoint_cache_max_threads (10)
    #     Used for the maximum threads in use for polling endpoints to be cached, defaults to 10.
    #
    #   @option options [Integer] :endpoint_cache_poll_interval (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.
    #
    #   @option options [Boolean] :endpoint_discovery (false)
    #     When set to `true`, endpoint discovery will be enabled for operations when available.
    #
    #   @option options [Boolean] :ignore_configured_endpoint_urls
    #     Setting to true disables use of endpoint URLs provided via environment
    #     variables and the shared configuration file.
    #
    #   @option options [Aws::Log::Formatter] :log_formatter (Aws::Log::Formatter.default)
    #     The log formatter.
    #
    #   @option options [Symbol] :log_level (:info)
    #     The log level to send messages to the `:logger` at.
    #
    #   @option options [Logger] :logger
    #     The Logger instance to send log messages to.  If this option
    #     is not set, logging will be disabled.
    #
    #   @option options [Integer] :max_attempts (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.
    #
    #   @option options [String] :profile ("default")
    #     Used when loading credentials from the shared credentials file at `HOME/.aws/credentials`.
    #     When not specified, 'default' is used.
    #
    #   @option options [String] :request_checksum_calculation ("when_supported")
    #     Determines when a checksum will be calculated for request payloads. Values are:
    #
    #     * `when_supported` - (default) When set, a checksum will be
    #       calculated for all request payloads of operations modeled with the
    #       `httpChecksum` trait where `requestChecksumRequired` is `true` and/or a
    #       `requestAlgorithmMember` is modeled.
    #     * `when_required` - When set, a checksum will only be calculated for
    #       request payloads of operations modeled with the  `httpChecksum` trait where
    #       `requestChecksumRequired` is `true` or where a `requestAlgorithmMember`
    #       is modeled and supplied.
    #
    #   @option options [Integer] :request_min_compression_size_bytes (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.
    #
    #   @option options [String] :response_checksum_validation ("when_supported")
    #     Determines when checksum validation will be performed on response payloads. Values are:
    #
    #     * `when_supported` - (default) When set, checksum validation is performed on all
    #       response payloads of operations modeled with the `httpChecksum` trait where
    #       `responseAlgorithms` is modeled, except when no modeled checksum algorithms
    #       are supported.
    #     * `when_required` - When set, checksum validation is not performed on
    #       response payloads of operations unless the checksum algorithm is supported and
    #       the `requestValidationModeMember` member is set to `ENABLED`.
    #
    #   @option options [Proc] :retry_backoff
    #     A proc or lambda used for backoff. Defaults to 2**retries * retry_base_delay.
    #     This option is only used in the `legacy` retry mode.
    #
    #   @option options [Float] :retry_base_delay (0.3)
    #     The base delay in seconds used by the default backoff function. This option
    #     is only used in the `legacy` retry mode.
    #
    #   @option options [Symbol] :retry_jitter (: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
    #
    #   @option options [Integer] :retry_limit (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.
    #
    #   @option options [Integer] :retry_max_delay (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.
    #
    #   @option options [String] :retry_mode ("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.
    #
    #   @option options [String] :sdk_ua_app_id
    #     A unique and opaque application ID that is appended to the
    #     User-Agent header as app/sdk_ua_app_id. It should have a
    #     maximum length of 50. This variable is sourced from environment
    #     variable AWS_SDK_UA_APP_ID or the shared config profile attribute sdk_ua_app_id.
    #
    #   @option options [String] :secret_access_key
    #
    #   @option options [String] :session_token
    #
    #   @option options [Array] :sigv4a_signing_region_set
    #     A list of regions that should be signed with SigV4a signing. When
    #     not passed, a default `:sigv4a_signing_region_set` is searched for
    #     in the following locations:
    #
    #     * `Aws.config[:sigv4a_signing_region_set]`
    #     * `ENV['AWS_SIGV4A_SIGNING_REGION_SET']`
    #     * `~/.aws/config`
    #
    #   @option options [Boolean] :simple_json (false)
    #     Disables request parameter conversion, validation, and formatting.
    #     Also disables response data type conversions. The request parameters
    #     hash must be formatted exactly as the API expects.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.
    #
    #   @option options [Boolean] :stub_responses (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.
    #
    #   @option options [Aws::Telemetry::TelemetryProviderBase] :telemetry_provider (Aws::Telemetry::NoOpTelemetryProvider)
    #     Allows you to provide a telemetry provider, which is used to
    #     emit telemetry data. By default, uses `NoOpTelemetryProvider` which
    #     will not record or emit any telemetry data. The SDK supports the
    #     following telemetry providers:
    #
    #     * OpenTelemetry (OTel) - To use the OTel provider, install and require the
    #     `opentelemetry-sdk` gem and then, pass in an instance of a
    #     `Aws::Telemetry::OTelProvider` for telemetry provider.
    #
    #   @option options [Aws::TokenProvider] :token_provider
    #     Your Bearer token used for authentication. This can be any class that includes and implements
    #     `Aws::TokenProvider`, or 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.
    #
    #   @option options [Boolean] :use_dualstack_endpoint
    #     When set to `true`, dualstack enabled endpoints (with `.aws` TLD)
    #     will be used if available.
    #
    #   @option options [Boolean] :use_fips_endpoint
    #     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`.
    #
    #   @option options [Boolean] :validate_params (true)
    #     When `true`, request parameters are validated before
    #     sending the request.
    #
    #   @option options [Aws::Rekognition::EndpointProvider] :endpoint_provider
    #     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`.
    #
    #   @option options [Float] :http_continue_timeout (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.
    #
    #   @option options [Float] :http_idle_timeout (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.
    #
    #   @option options [Float] :http_open_timeout (15)
    #     The default number of seconds to wait for response data.
    #     This value can safely be set per-request on the session.
    #
    #   @option options [URI::HTTP,String] :http_proxy
    #     A proxy to send requests through.  Formatted like 'http://proxy.com:123'.
    #
    #   @option options [Float] :http_read_timeout (60)
    #     The default number of seconds to wait for response data.
    #     This value can safely be set per-request on the session.
    #
    #   @option options [Boolean] :http_wire_trace (false)
    #     When `true`,  HTTP debug output will be sent to the `:logger`.
    #
    #   @option options [Proc] :on_chunk_received
    #     When a Proc object is provided, it will be used as callback when each chunk
    #     of the response body is received. It provides three arguments: the chunk,
    #     the number of bytes received, and the total number of
    #     bytes in the response (or nil if the server did not send a `content-length`).
    #
    #   @option options [Proc] :on_chunk_sent
    #     When a Proc object is provided, it will be used as callback when each chunk
    #     of the request body is sent. It provides three arguments: the chunk,
    #     the number of bytes read from the body, and the total number of
    #     bytes in the body.
    #
    #   @option options [Boolean] :raise_response_errors (true)
    #     When `true`, response errors are raised.
    #
    #   @option options [String] :ssl_ca_bundle
    #     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.
    #
    #   @option options [String] :ssl_ca_directory
    #     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.
    #
    #   @option options [String] :ssl_ca_store
    #     Sets the X509::Store to verify peer certificate.
    #
    #   @option options [OpenSSL::X509::Certificate] :ssl_cert
    #     Sets a client certificate when creating http connections.
    #
    #   @option options [OpenSSL::PKey] :ssl_key
    #     Sets a client key when creating http connections.
    #
    #   @option options [Float] :ssl_timeout
    #     Sets the SSL timeout in seconds
    #
    #   @option options [Boolean] :ssl_verify_peer (true)
    #     When `true`, SSL peer certificates are verified when establishing a connection.
    #
    def initialize(*args)
      super
    end

    # @!group API Operations

    # 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 number of FaceIds that can be
    # used as input in a single request is limited to 100.
    #
    # Note that the total number of faces that can be associated with a
    # single `UserID` is also limited to 100. Once a `UserID` has 100 faces
    # associated with it, no additional faces can be added. If more API
    # calls are made after the limit is reached, a
    # `ServiceQuotaExceededException` will result.
    #
    # 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.
    #
    # @option params [required, String] :collection_id
    #   The ID of an existing collection containing the UserID.
    #
    # @option params [required, String] :user_id
    #   The ID for the existing UserID.
    #
    # @option params [required, Array<String>] :face_ids
    #   An array of FaceIDs to associate with the UserID.
    #
    # @option params [Float] :user_match_threshold
    #   An optional value specifying the minimum confidence in the UserID
    #   match to return. The default value is 75.
    #
    # @option params [String] :client_request_token
    #   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.**
    #
    # @return [Types::AssociateFacesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::AssociateFacesResponse#associated_faces #associated_faces} => Array&lt;Types::AssociatedFace&gt;
    #   * {Types::AssociateFacesResponse#unsuccessful_face_associations #unsuccessful_face_associations} => Array&lt;Types::UnsuccessfulFaceAssociation&gt;
    #   * {Types::AssociateFacesResponse#user_status #user_status} => String
    #
    #
    # @example 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", 
    #   }
    #
    # @example 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",
    #   })
    #
    # @example 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"
    #
    # @overload associate_faces(params = {})
    # @param [Hash] params ({})
    def associate_faces(params = {}, options = {})
      req = build_request(:associate_faces, params)
      req.send_request(options)
    end

    # 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.
    #
    # <note markdown="1"> 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.
    #
    #  </note>
    #
    # 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.
    #
    # <note markdown="1"> 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.
    #
    #  </note>
    #
    # `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.
    #
    # <note markdown="1"> This is a stateless API operation. That is, data returned by this
    # operation doesn't persist.
    #
    #  </note>
    #
    # For an example, see Comparing Faces in Images in the Amazon
    # Rekognition Developer Guide.
    #
    # This operation requires permissions to perform the
    # `rekognition:CompareFaces` action.
    #
    # @option params [required, Types::Image] :source_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.
    #
    # @option params [required, Types::Image] :target_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.
    #
    # @option params [Float] :similarity_threshold
    #   The minimum level of confidence in the face matches that a match must
    #   meet to be included in the `FaceMatches` array.
    #
    # @option params [String] :quality_filter
    #   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.
    #
    # @return [Types::CompareFacesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::CompareFacesResponse#source_image_face #source_image_face} => Types::ComparedSourceImageFace
    #   * {Types::CompareFacesResponse#face_matches #face_matches} => Array&lt;Types::CompareFacesMatch&gt;
    #   * {Types::CompareFacesResponse#unmatched_faces #unmatched_faces} => Array&lt;Types::ComparedFace&gt;
    #   * {Types::CompareFacesResponse#source_image_orientation_correction #source_image_orientation_correction} => String
    #   * {Types::CompareFacesResponse#target_image_orientation_correction #target_image_orientation_correction} => String
    #
    #
    # @example 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, 
    #     }, 
    #   }
    #
    # @example 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
    #   })
    #
    # @example 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"
    #
    # @overload compare_faces(params = {})
    # @param [Hash] params ({})
    def compare_faces(params = {}, options = {})
      req = build_request(:compare_faces, params)
      req.send_request(options)
    end

    # <note markdown="1"> This operation applies only to Amazon Rekognition Custom Labels.
    #
    #  </note>
    #
    # 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.
    #
    # <note markdown="1"> 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.
    #
    #  </note>
    #
    # 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.
    #
    # @option params [required, String] :source_project_arn
    #   The ARN of the source project in the trusting AWS account.
    #
    # @option params [required, String] :source_project_version_arn
    #   The ARN of the model version in the source project that you want to
    #   copy to a destination project.
    #
    # @option params [required, String] :destination_project_arn
    #   The ARN of the project in the trusted AWS account that you want to
    #   copy the model version to.
    #
    # @option params [required, String] :version_name
    #   A name for the version of the model that's copied to the destination
    #   project.
    #
    # @option params [required, Types::OutputConfig] :output_config
    #   The S3 bucket and folder location where the training output for the
    #   source model version is placed.
    #
    # @option params [Hash<String,String>] :tags
    #   The key-value tags to assign to the model version.
    #
    # @option params [String] :kms_key_id
    #   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.
    #
    # @return [Types::CopyProjectVersionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::CopyProjectVersionResponse#project_version_arn #project_version_arn} => String
    #
    #
    # @example 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", 
    #   }
    #
    # @example 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",
    #   })
    #
    # @example Response structure
    #
    #   resp.project_version_arn #=> String
    #
    # @overload copy_project_version(params = {})
    # @param [Hash] params ({})
    def copy_project_version(params = {}, options = {})
      req = build_request(:copy_project_version, params)
      req.send_request(options)
    end

    # 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.
    #
    # <note markdown="1"> Collection names are case-sensitive.
    #
    #  </note>
    #
    # 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.
    #
    # @option params [required, String] :collection_id
    #   ID for the collection that you are creating.
    #
    # @option params [Hash<String,String>] :tags
    #   A set of tags (key-value pairs) that you want to attach to the
    #   collection.
    #
    # @return [Types::CreateCollectionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::CreateCollectionResponse#status_code #status_code} => Integer
    #   * {Types::CreateCollectionResponse#collection_arn #collection_arn} => String
    #   * {Types::CreateCollectionResponse#face_model_version #face_model_version} => String
    #
    #
    # @example 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, 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.create_collection({
    #     collection_id: "CollectionId", # required
    #     tags: {
    #       "TagKey" => "TagValue",
    #     },
    #   })
    #
    # @example Response structure
    #
    #   resp.status_code #=> Integer
    #   resp.collection_arn #=> String
    #   resp.face_model_version #=> String
    #
    # @overload create_collection(params = {})
    # @param [Hash] params ({})
    def create_collection(params = {}, options = {})
      req = build_request(:create_collection, params)
      req.send_request(options)
    end

    # <note markdown="1"> This operation applies only to Amazon Rekognition Custom Labels.
    #
    #  </note>
    #
    # 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.
    #
    # @option params [Types::DatasetSource] :dataset_source
    #   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.
    #
    # @option params [required, String] :dataset_type
    #   The type of the dataset. Specify `TRAIN` to create a training dataset.
    #   Specify `TEST` to create a test dataset.
    #
    # @option params [required, String] :project_arn
    #   The ARN of the Amazon Rekognition Custom Labels project to which you
    #   want to asssign the dataset.
    #
    # @option params [Hash<String,String>] :tags
    #   A set of tags (key-value pairs) that you want to attach to the
    #   dataset.
    #
    # @return [Types::CreateDatasetResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::CreateDatasetResponse#dataset_arn #dataset_arn} => String
    #
    #
    # @example 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", 
    #   }
    #
    # @example 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
    #     tags: {
    #       "TagKey" => "TagValue",
    #     },
    #   })
    #
    # @example Response structure
    #
    #   resp.dataset_arn #=> String
    #
    # @overload create_dataset(params = {})
    # @param [Hash] params ({})
    def create_dataset(params = {}, options = {})
      req = build_request(:create_dataset, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [String] :kms_key_id
    #   The identifier for your AWS Key Management Service key (AWS KMS key).
    #   Used to encrypt audit images and reference images.
    #
    # @option params [Types::CreateFaceLivenessSessionRequestSettings] :settings
    #   A session settings object. It contains settings for the operation to
    #   be performed. For Face Liveness, it accepts `OutputConfig` and
    #   `AuditImagesLimit`.
    #
    # @option params [String] :client_request_token
    #   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.
    #
    # @return [Types::CreateFaceLivenessSessionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::CreateFaceLivenessSessionResponse#session_id #session_id} => String
    #
    # @example 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,
    #       challenge_preferences: [
    #         {
    #           type: "FaceMovementAndLightChallenge", # required, accepts FaceMovementAndLightChallenge, FaceMovementChallenge
    #           versions: {
    #             minimum: "Version",
    #             maximum: "Version",
    #           },
    #         },
    #       ],
    #     },
    #     client_request_token: "ClientRequestToken",
    #   })
    #
    # @example Response structure
    #
    #   resp.session_id #=> String
    #
    # @overload create_face_liveness_session(params = {})
    # @param [Hash] params ({})
    def create_face_liveness_session(params = {}, options = {})
      req = build_request(:create_face_liveness_session, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, String] :project_name
    #   The name of the project to create.
    #
    # @option params [String] :feature
    #   Specifies feature that is being customized. If no value is provided
    #   CUSTOM\_LABELS is used as a default.
    #
    # @option params [String] :auto_update
    #   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.
    #
    # @option params [Hash<String,String>] :tags
    #   A set of tags (key-value pairs) that you want to attach to the
    #   project.
    #
    # @return [Types::CreateProjectResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::CreateProjectResponse#project_arn #project_arn} => String
    #
    #
    # @example 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", 
    #   }
    #
    # @example 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
    #     tags: {
    #       "TagKey" => "TagValue",
    #     },
    #   })
    #
    # @example Response structure
    #
    #   resp.project_arn #=> String
    #
    # @overload create_project(params = {})
    # @param [Hash] params ({})
    def create_project(params = {}, options = {})
      req = build_request(:create_project, params)
      req.send_request(options)
    end

    # 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.
    #
    # <note markdown="1"> *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.
    #
    #  </note>
    #
    # @option params [required, String] :project_arn
    #   The ARN of the Amazon Rekognition project that will manage the project
    #   version you want to train.
    #
    # @option params [required, String] :version_name
    #   A name for the version of the project version. This value must be
    #   unique.
    #
    # @option params [required, Types::OutputConfig] :output_config
    #   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.
    #
    # @option params [Types::TrainingData] :training_data
    #   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.
    #
    # @option params [Types::TestingData] :testing_data
    #   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.
    #
    # @option params [Hash<String,String>] :tags
    #   A set of tags (key-value pairs) that you want to attach to the project
    #   version.
    #
    # @option params [String] :kms_key_id
    #   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.
    #
    # @option params [String] :version_description
    #   A description applied to the project version being created.
    #
    # @option params [Types::CustomizationFeatureConfig] :feature_config
    #   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.
    #
    # @return [Types::CreateProjectVersionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::CreateProjectVersionResponse#project_version_arn #project_version_arn} => String
    #
    #
    # @example 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", 
    #   }
    #
    # @example 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,
    #       },
    #     },
    #   })
    #
    # @example Response structure
    #
    #   resp.project_version_arn #=> String
    #
    # @overload create_project_version(params = {})
    # @param [Hash] params ({})
    def create_project_version(params = {}, options = {})
      req = build_request(:create_project_version, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, Types::StreamProcessorInput] :input
    #   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.
    #
    # @option params [required, Types::StreamProcessorOutput] :output
    #   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.
    #
    # @option params [required, String] :name
    #   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.
    #
    # @option params [required, Types::StreamProcessorSettings] :settings
    #   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.
    #
    # @option params [required, String] :role_arn
    #   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.
    #
    # @option params [Hash<String,String>] :tags
    #   A set of tags (key-value pairs) that you want to attach to the stream
    #   processor.
    #
    # @option params [Types::StreamProcessorNotificationChannel] :notification_channel
    #   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.
    #
    # @option params [String] :kms_key_id
    #   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.
    #
    # @option params [Array<Types::RegionOfInterest>] :regions_of_interest
    #   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.
    #
    # @option params [Types::StreamProcessorDataSharingPreference] :data_sharing_preference
    #   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.
    #
    # @return [Types::CreateStreamProcessorResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::CreateStreamProcessorResponse#stream_processor_arn #stream_processor_arn} => String
    #
    # @example 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
    #     },
    #   })
    #
    # @example Response structure
    #
    #   resp.stream_processor_arn #=> String
    #
    # @overload create_stream_processor(params = {})
    # @param [Hash] params ({})
    def create_stream_processor(params = {}, options = {})
      req = build_request(:create_stream_processor, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, String] :collection_id
    #   The ID of an existing collection to which the new UserID needs to be
    #   created.
    #
    # @option params [required, String] :user_id
    #   ID for the UserID to be created. This ID needs to be unique within the
    #   collection.
    #
    # @option params [String] :client_request_token
    #   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.**
    #
    # @return [Struct] Returns an empty {Seahorse::Client::Response response}.
    #
    #
    # @example Example: CreateUser
    #
    #   # Creates a new User within a collection specified by CollectionId.
    #
    #   resp = client.create_user({
    #     collection_id: "MyCollection", 
    #     user_id: "DemoUser", 
    #   })
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.create_user({
    #     collection_id: "CollectionId", # required
    #     user_id: "UserId", # required
    #     client_request_token: "ClientRequestToken",
    #   })
    #
    # @overload create_user(params = {})
    # @param [Hash] params ({})
    def create_user(params = {}, options = {})
      req = build_request(:create_user, params)
      req.send_request(options)
    end

    # Deletes the specified collection. Note that this operation removes all
    # faces in the collection. For an example, see [Deleting a
    # collection][1].
    #
    # This operation requires permissions to perform the
    # `rekognition:DeleteCollection` action.
    #
    #
    #
    # [1]: https://docs.aws.amazon.com/rekognition/latest/dg/delete-collection-procedure.html
    #
    # @option params [required, String] :collection_id
    #   ID of the collection to delete.
    #
    # @return [Types::DeleteCollectionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DeleteCollectionResponse#status_code #status_code} => Integer
    #
    #
    # @example 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, 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.delete_collection({
    #     collection_id: "CollectionId", # required
    #   })
    #
    # @example Response structure
    #
    #   resp.status_code #=> Integer
    #
    # @overload delete_collection(params = {})
    # @param [Hash] params ({})
    def delete_collection(params = {}, options = {})
      req = build_request(:delete_collection, params)
      req.send_request(options)
    end

    # <note markdown="1"> This operation applies only to Amazon Rekognition Custom Labels.
    #
    #  </note>
    #
    # 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.
    #
    # @option params [required, String] :dataset_arn
    #   The ARN of the Amazon Rekognition Custom Labels dataset that you want
    #   to delete.
    #
    # @return [Struct] Returns an empty {Seahorse::Client::Response response}.
    #
    #
    # @example 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:
    #   {
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.delete_dataset({
    #     dataset_arn: "DatasetArn", # required
    #   })
    #
    # @overload delete_dataset(params = {})
    # @param [Hash] params ({})
    def delete_dataset(params = {}, options = {})
      req = build_request(:delete_dataset, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, String] :collection_id
    #   Collection from which to remove the specific faces.
    #
    # @option params [required, Array<String>] :face_ids
    #   An array of face IDs to delete.
    #
    # @return [Types::DeleteFacesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DeleteFacesResponse#deleted_faces #deleted_faces} => Array&lt;String&gt;
    #   * {Types::DeleteFacesResponse#unsuccessful_face_deletions #unsuccessful_face_deletions} => Array&lt;Types::UnsuccessfulFaceDeletion&gt;
    #
    #
    # @example 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", 
    #     ], 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.delete_faces({
    #     collection_id: "CollectionId", # required
    #     face_ids: ["FaceId"], # required
    #   })
    #
    # @example 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"
    #
    # @overload delete_faces(params = {})
    # @param [Hash] params ({})
    def delete_faces(params = {}, options = {})
      req = build_request(:delete_faces, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, String] :project_arn
    #   The Amazon Resource Name (ARN) of the project that you want to delete.
    #
    # @return [Types::DeleteProjectResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DeleteProjectResponse#status #status} => String
    #
    #
    # @example 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", 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.delete_project({
    #     project_arn: "ProjectArn", # required
    #   })
    #
    # @example Response structure
    #
    #   resp.status #=> String, one of "CREATING", "CREATED", "DELETING"
    #
    # @overload delete_project(params = {})
    # @param [Hash] params ({})
    def delete_project(params = {}, options = {})
      req = build_request(:delete_project, params)
      req.send_request(options)
    end

    # <note markdown="1"> This operation applies only to Amazon Rekognition Custom Labels.
    #
    #  </note>
    #
    # 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.
    #
    # @option params [required, String] :project_arn
    #   The Amazon Resource Name (ARN) of the project that the project policy
    #   you want to delete is attached to.
    #
    # @option params [required, String] :policy_name
    #   The name of the policy that you want to delete.
    #
    # @option params [String] :policy_revision_id
    #   The ID of the project policy revision that you want to delete.
    #
    # @return [Struct] Returns an empty {Seahorse::Client::Response response}.
    #
    #
    # @example 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:
    #   {
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.delete_project_policy({
    #     project_arn: "ProjectArn", # required
    #     policy_name: "ProjectPolicyName", # required
    #     policy_revision_id: "ProjectPolicyRevisionId",
    #   })
    #
    # @overload delete_project_policy(params = {})
    # @param [Hash] params ({})
    def delete_project_policy(params = {}, options = {})
      req = build_request(:delete_project_policy, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, String] :project_version_arn
    #   The Amazon Resource Name (ARN) of the project version that you want to
    #   delete.
    #
    # @return [Types::DeleteProjectVersionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DeleteProjectVersionResponse#status #status} => String
    #
    #
    # @example 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", 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.delete_project_version({
    #     project_version_arn: "ProjectVersionArn", # required
    #   })
    #
    # @example 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"
    #
    # @overload delete_project_version(params = {})
    # @param [Hash] params ({})
    def delete_project_version(params = {}, options = {})
      req = build_request(:delete_project_version, params)
      req.send_request(options)
    end

    # 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`.
    #
    # @option params [required, String] :name
    #   The name of the stream processor you want to delete.
    #
    # @return [Struct] Returns an empty {Seahorse::Client::Response response}.
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.delete_stream_processor({
    #     name: "StreamProcessorName", # required
    #   })
    #
    # @overload delete_stream_processor(params = {})
    # @param [Hash] params ({})
    def delete_stream_processor(params = {}, options = {})
      req = build_request(:delete_stream_processor, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, String] :collection_id
    #   The ID of an existing collection from which the UserID needs to be
    #   deleted.
    #
    # @option params [required, String] :user_id
    #   ID for the UserID to be deleted.
    #
    # @option params [String] :client_request_token
    #   Idempotent token used to identify the request to `DeleteUser`. If you
    #   use the same token with multiple `DeleteUser `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.**
    #
    # @return [Struct] Returns an empty {Seahorse::Client::Response response}.
    #
    #
    # @example 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", 
    #   })
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.delete_user({
    #     collection_id: "CollectionId", # required
    #     user_id: "UserId", # required
    #     client_request_token: "ClientRequestToken",
    #   })
    #
    # @overload delete_user(params = {})
    # @param [Hash] params ({})
    def delete_user(params = {}, options = {})
      req = build_request(:delete_user, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, String] :collection_id
    #   The ID of the collection to describe.
    #
    # @return [Types::DescribeCollectionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DescribeCollectionResponse#face_count #face_count} => Integer
    #   * {Types::DescribeCollectionResponse#face_model_version #face_model_version} => String
    #   * {Types::DescribeCollectionResponse#collection_arn #collection_arn} => String
    #   * {Types::DescribeCollectionResponse#creation_timestamp #creation_timestamp} => Time
    #   * {Types::DescribeCollectionResponse#user_count #user_count} => Integer
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.describe_collection({
    #     collection_id: "CollectionId", # required
    #   })
    #
    # @example Response structure
    #
    #   resp.face_count #=> Integer
    #   resp.face_model_version #=> String
    #   resp.collection_arn #=> String
    #   resp.creation_timestamp #=> Time
    #   resp.user_count #=> Integer
    #
    # @overload describe_collection(params = {})
    # @param [Hash] params ({})
    def describe_collection(params = {}, options = {})
      req = build_request(:describe_collection, params)
      req.send_request(options)
    end

    # <note markdown="1"> This operation applies only to Amazon Rekognition Custom Labels.
    #
    #  </note>
    #
    # 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.
    #
    # @option params [required, String] :dataset_arn
    #   The Amazon Resource Name (ARN) of the dataset that you want to
    #   describe.
    #
    # @return [Types::DescribeDatasetResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DescribeDatasetResponse#dataset_description #dataset_description} => Types::DatasetDescription
    #
    #
    # @example 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", 
    #     }, 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.describe_dataset({
    #     dataset_arn: "DatasetArn", # required
    #   })
    #
    # @example 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
    #
    # @overload describe_dataset(params = {})
    # @param [Hash] params ({})
    def describe_dataset(params = {}, options = {})
      req = build_request(:describe_dataset, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, String] :project_arn
    #   The Amazon Resource Name (ARN) of the project that contains the
    #   model/adapter you want to describe.
    #
    # @option params [Array<String>] :version_names
    #   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`.
    #
    # @option params [String] :next_token
    #   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.
    #
    # @option params [Integer] :max_results
    #   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.
    #
    # @return [Types::DescribeProjectVersionsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DescribeProjectVersionsResponse#project_version_descriptions #project_version_descriptions} => Array&lt;Types::ProjectVersionDescription&gt;
    #   * {Types::DescribeProjectVersionsResponse#next_token #next_token} => String
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    #
    # @example 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"), 
    #       }, 
    #     ], 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.describe_project_versions({
    #     project_arn: "ProjectArn", # required
    #     version_names: ["VersionName"],
    #     next_token: "ExtendedPaginationToken",
    #     max_results: 1,
    #   })
    #
    # @example 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
    #
    #
    # The following waiters are defined for this operation (see {Client#wait_until} for detailed usage):
    #
    #   * project_version_running
    #   * project_version_training_completed
    #
    # @overload describe_project_versions(params = {})
    # @param [Hash] params ({})
    def describe_project_versions(params = {}, options = {})
      req = build_request(:describe_project_versions, params)
      req.send_request(options)
    end

    # Gets information about your Rekognition projects.
    #
    # This operation requires permissions to perform the
    # `rekognition:DescribeProjects` action.
    #
    # @option params [String] :next_token
    #   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.
    #
    # @option params [Integer] :max_results
    #   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.
    #
    # @option params [Array<String>] :project_names
    #   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.
    #
    # @option params [Array<String>] :features
    #   Specifies the type of customization to filter projects by. If no value
    #   is specified, CUSTOM\_LABELS is used as a default.
    #
    # @return [Types::DescribeProjectsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DescribeProjectsResponse#project_descriptions #project_descriptions} => Array&lt;Types::ProjectDescription&gt;
    #   * {Types::DescribeProjectsResponse#next_token #next_token} => String
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    #
    # @example 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", 
    #       }, 
    #     ], 
    #   }
    #
    # @example 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
    #   })
    #
    # @example 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
    #
    # @overload describe_projects(params = {})
    # @param [Hash] params ({})
    def describe_projects(params = {}, options = {})
      req = build_request(:describe_projects, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, String] :name
    #   Name of the stream processor for which you want information.
    #
    # @return [Types::DescribeStreamProcessorResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DescribeStreamProcessorResponse#name #name} => String
    #   * {Types::DescribeStreamProcessorResponse#stream_processor_arn #stream_processor_arn} => String
    #   * {Types::DescribeStreamProcessorResponse#status #status} => String
    #   * {Types::DescribeStreamProcessorResponse#status_message #status_message} => String
    #   * {Types::DescribeStreamProcessorResponse#creation_timestamp #creation_timestamp} => Time
    #   * {Types::DescribeStreamProcessorResponse#last_update_timestamp #last_update_timestamp} => Time
    #   * {Types::DescribeStreamProcessorResponse#input #input} => Types::StreamProcessorInput
    #   * {Types::DescribeStreamProcessorResponse#output #output} => Types::StreamProcessorOutput
    #   * {Types::DescribeStreamProcessorResponse#role_arn #role_arn} => String
    #   * {Types::DescribeStreamProcessorResponse#settings #settings} => Types::StreamProcessorSettings
    #   * {Types::DescribeStreamProcessorResponse#notification_channel #notification_channel} => Types::StreamProcessorNotificationChannel
    #   * {Types::DescribeStreamProcessorResponse#kms_key_id #kms_key_id} => String
    #   * {Types::DescribeStreamProcessorResponse#regions_of_interest #regions_of_interest} => Array&lt;Types::RegionOfInterest&gt;
    #   * {Types::DescribeStreamProcessorResponse#data_sharing_preference #data_sharing_preference} => Types::StreamProcessorDataSharingPreference
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.describe_stream_processor({
    #     name: "StreamProcessorName", # required
    #   })
    #
    # @example 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
    #
    # @overload describe_stream_processor(params = {})
    # @param [Hash] params ({})
    def describe_stream_processor(params = {}, options = {})
      req = build_request(:describe_stream_processor, params)
      req.send_request(options)
    end

    # <note markdown="1"> This operation applies only to Amazon Rekognition Custom Labels.
    #
    #  </note>
    #
    # 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`).
    #
    # 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.
    #
    # @option params [required, String] :project_version_arn
    #   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.
    #
    # @option params [required, Types::Image] :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.
    #
    # @option params [Integer] :max_results
    #   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.
    #
    # @option params [Float] :min_confidence
    #   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.
    #
    # @return [Types::DetectCustomLabelsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DetectCustomLabelsResponse#custom_labels #custom_labels} => Array&lt;Types::CustomLabel&gt;
    #
    #
    # @example 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", 
    #       }, 
    #     ], 
    #   }
    #
    # @example 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,
    #   })
    #
    # @example 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
    #
    # @overload detect_custom_labels(params = {})
    # @param [Hash] params ({})
    def detect_custom_labels(params = {}, options = {})
      req = build_request(:detect_custom_labels, params)
      req.send_request(options)
    end

    # 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.
    #
    # <note markdown="1"> This is a stateless API operation. That is, the operation does not
    # persist any data.
    #
    #  </note>
    #
    # This operation requires permissions to perform the
    # `rekognition:DetectFaces` action.
    #
    # @option params [required, Types::Image] :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.
    #
    # @option params [Array<String>] :attributes
    #   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`.
    #
    # @return [Types::DetectFacesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DetectFacesResponse#face_details #face_details} => Array&lt;Types::FaceDetail&gt;
    #   * {Types::DetectFacesResponse#orientation_correction #orientation_correction} => String
    #
    #
    # @example 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", 
    #   }
    #
    # @example 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
    #   })
    #
    # @example 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"
    #
    # @overload detect_faces(params = {})
    # @param [Hash] params ({})
    def detect_faces(params = {}, options = {})
      req = build_request(:detect_faces, params)
      req.send_request(options)
    end

    # 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][1].
    #
    # 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.
    #
    # <note markdown="1"> If the object detected is a person, the operation doesn't provide the
    # same facial details that the DetectFaces operation provides.
    #
    #  </note>
    #
    # This is a stateless API operation that doesn't return any data.
    #
    # This operation requires permissions to perform the
    # `rekognition:DetectLabels` action.
    #
    #
    #
    # [1]: https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html
    #
    # @option params [required, Types::Image] :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.
    #
    # @option params [Integer] :max_labels
    #   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.
    #
    # @option params [Float] :min_confidence
    #   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.
    #
    # @option params [Array<String>] :features
    #   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.
    #
    # @option params [Types::DetectLabelsSettings] :settings
    #   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][1].
    #
    #
    #
    #   [1]: https://docs.aws.amazon.com/rekognition/latest/dg/labels.html
    #
    # @return [Types::DetectLabelsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DetectLabelsResponse#labels #labels} => Array&lt;Types::Label&gt;
    #   * {Types::DetectLabelsResponse#orientation_correction #orientation_correction} => String
    #   * {Types::DetectLabelsResponse#label_model_version #label_model_version} => String
    #   * {Types::DetectLabelsResponse#image_properties #image_properties} => Types::DetectLabelsImageProperties
    #
    #
    # @example 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", 
    #       }, 
    #     ], 
    #   }
    #
    # @example 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,
    #       },
    #     },
    #   })
    #
    # @example 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
    #
    # @overload detect_labels(params = {})
    # @param [Hash] params ({})
    def detect_labels(params = {}, options = {})
      req = build_request(:detect_labels, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, Types::Image] :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.
    #
    # @option params [Float] :min_confidence
    #   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.
    #
    # @option params [Types::HumanLoopConfig] :human_loop_config
    #   Sets up the configuration for human evaluation, including the
    #   FlowDefinition the image will be sent to.
    #
    # @option params [String] :project_version
    #   Identifier for the custom adapter. Expects the ProjectVersionArn as a
    #   value. Use the CreateProject or CreateProjectVersion APIs to create a
    #   custom adapter.
    #
    # @return [Types::DetectModerationLabelsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DetectModerationLabelsResponse#moderation_labels #moderation_labels} => Array&lt;Types::ModerationLabel&gt;
    #   * {Types::DetectModerationLabelsResponse#moderation_model_version #moderation_model_version} => String
    #   * {Types::DetectModerationLabelsResponse#human_loop_activation_output #human_loop_activation_output} => Types::HumanLoopActivationOutput
    #   * {Types::DetectModerationLabelsResponse#project_version #project_version} => String
    #   * {Types::DetectModerationLabelsResponse#content_types #content_types} => Array&lt;Types::ContentType&gt;
    #
    # @example 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",
    #   })
    #
    # @example 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
    #
    # @overload detect_moderation_labels(params = {})
    # @param [Hash] params ({})
    def detect_moderation_labels(params = {}, options = {})
      req = build_request(:detect_moderation_labels, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, Types::Image] :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.
    #
    # @option params [Types::ProtectiveEquipmentSummarizationAttributes] :summarization_attributes
    #   An array of PPE types that you want to summarize.
    #
    # @return [Types::DetectProtectiveEquipmentResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DetectProtectiveEquipmentResponse#protective_equipment_model_version #protective_equipment_model_version} => String
    #   * {Types::DetectProtectiveEquipmentResponse#persons #persons} => Array&lt;Types::ProtectiveEquipmentPerson&gt;
    #   * {Types::DetectProtectiveEquipmentResponse#summary #summary} => Types::ProtectiveEquipmentSummary
    #
    # @example 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
    #     },
    #   })
    #
    # @example 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
    #
    # @overload detect_protective_equipment(params = {})
    # @param [Hash] params ({})
    def detect_protective_equipment(params = {}, options = {})
      req = build_request(:detect_protective_equipment, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, Types::Image] :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.
    #
    # @option params [Types::DetectTextFilters] :filters
    #   Optional parameters that let you set the criteria that the text must
    #   meet to be included in your response.
    #
    # @return [Types::DetectTextResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DetectTextResponse#text_detections #text_detections} => Array&lt;Types::TextDetection&gt;
    #   * {Types::DetectTextResponse#text_model_version #text_model_version} => String
    #
    # @example 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,
    #             },
    #           ],
    #         },
    #       ],
    #     },
    #   })
    #
    # @example 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
    #
    # @overload detect_text(params = {})
    # @param [Hash] params ({})
    def detect_text(params = {}, options = {})
      req = build_request(:detect_text, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, String] :collection_id
    #   The ID of an existing collection containing the UserID.
    #
    # @option params [required, String] :user_id
    #   ID for the existing UserID.
    #
    # @option params [String] :client_request_token
    #   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.**
    #
    # @option params [required, Array<String>] :face_ids
    #   An array of face IDs to disassociate from the UserID.
    #
    # @return [Types::DisassociateFacesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::DisassociateFacesResponse#disassociated_faces #disassociated_faces} => Array&lt;Types::DisassociatedFace&gt;
    #   * {Types::DisassociateFacesResponse#unsuccessful_face_disassociations #unsuccessful_face_disassociations} => Array&lt;Types::UnsuccessfulFaceDisassociation&gt;
    #   * {Types::DisassociateFacesResponse#user_status #user_status} => String
    #
    #
    # @example 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", 
    #   }
    #
    # @example 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
    #   })
    #
    # @example 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"
    #
    # @overload disassociate_faces(params = {})
    # @param [Hash] params ({})
    def disassociate_faces(params = {}, options = {})
      req = build_request(:disassociate_faces, params)
      req.send_request(options)
    end

    # <note markdown="1"> This operation applies only to Amazon Rekognition Custom Labels.
    #
    #  </note>
    #
    # 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.
    #
    # @option params [required, Array<Types::DistributeDataset>] :datasets
    #   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.
    #
    # @return [Struct] Returns an empty {Seahorse::Client::Response response}.
    #
    #
    # @example 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:
    #   {
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.distribute_dataset_entries({
    #     datasets: [ # required
    #       {
    #         arn: "DatasetArn", # required
    #       },
    #     ],
    #   })
    #
    # @overload distribute_dataset_entries(params = {})
    # @param [Hash] params ({})
    def distribute_dataset_entries(params = {}, options = {})
      req = build_request(:distribute_dataset_entries, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, String] :id
    #   The ID for the celebrity. You get the celebrity ID from a call to the
    #   RecognizeCelebrities operation, which recognizes celebrities in an
    #   image.
    #
    # @return [Types::GetCelebrityInfoResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::GetCelebrityInfoResponse#urls #urls} => Array&lt;String&gt;
    #   * {Types::GetCelebrityInfoResponse#name #name} => String
    #   * {Types::GetCelebrityInfoResponse#known_gender #known_gender} => Types::KnownGender
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.get_celebrity_info({
    #     id: "RekognitionUniqueId", # required
    #   })
    #
    # @example Response structure
    #
    #   resp.urls #=> Array
    #   resp.urls[0] #=> String
    #   resp.name #=> String
    #   resp.known_gender.type #=> String, one of "Male", "Female", "Nonbinary", "Unlisted"
    #
    # @overload get_celebrity_info(params = {})
    # @param [Hash] params ({})
    def get_celebrity_info(params = {}, options = {})
      req = build_request(:get_celebrity_info, params)
      req.send_request(options)
    end

    # 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.
    #
    # <note markdown="1"> `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.
    #
    #  </note>
    #
    # 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`.
    #
    # @option params [required, String] :job_id
    #   Job identifier for the required celebrity recognition analysis. You
    #   can get the job identifer from a call to `StartCelebrityRecognition`.
    #
    # @option params [Integer] :max_results
    #   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.
    #
    # @option params [String] :next_token
    #   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.
    #
    # @option params [String] :sort_by
    #   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.
    #
    # @return [Types::GetCelebrityRecognitionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::GetCelebrityRecognitionResponse#job_status #job_status} => String
    #   * {Types::GetCelebrityRecognitionResponse#status_message #status_message} => String
    #   * {Types::GetCelebrityRecognitionResponse#video_metadata #video_metadata} => Types::VideoMetadata
    #   * {Types::GetCelebrityRecognitionResponse#next_token #next_token} => String
    #   * {Types::GetCelebrityRecognitionResponse#celebrities #celebrities} => Array&lt;Types::CelebrityRecognition&gt;
    #   * {Types::GetCelebrityRecognitionResponse#job_id #job_id} => String
    #   * {Types::GetCelebrityRecognitionResponse#video #video} => Types::Video
    #   * {Types::GetCelebrityRecognitionResponse#job_tag #job_tag} => String
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    # @example 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
    #   })
    #
    # @example Response structure
    #
    #   resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
    #   resp.status_message #=> String
    #   resp.video_metadata.codec #=> String
    #   resp.video_metadata.duration_millis #=> Integer
    #   resp.video_metadata.format #=> String
    #   resp.video_metadata.frame_rate #=> Float
    #   resp.video_metadata.frame_height #=> Integer
    #   resp.video_metadata.frame_width #=> Integer
    #   resp.video_metadata.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
    #
    # @overload get_celebrity_recognition(params = {})
    # @param [Hash] params ({})
    def get_celebrity_recognition(params = {}, options = {})
      req = build_request(:get_celebrity_recognition, params)
      req.send_request(options)
    end

    # 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][1].
    #
    # 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.
    #
    #
    #
    # [1]: https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api
    #
    # @option params [required, String] :job_id
    #   The identifier for the inappropriate, unwanted, or offensive content
    #   moderation job. Use `JobId` to identify the job in a subsequent call
    #   to `GetContentModeration`.
    #
    # @option params [Integer] :max_results
    #   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.
    #
    # @option params [String] :next_token
    #   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.
    #
    # @option params [String] :sort_by
    #   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`.
    #
    # @option params [String] :aggregate_by
    #   Defines how to aggregate results of the StartContentModeration
    #   request. Default aggregation option is TIMESTAMPS. SEGMENTS mode
    #   aggregates moderation labels over time.
    #
    # @return [Types::GetContentModerationResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::GetContentModerationResponse#job_status #job_status} => String
    #   * {Types::GetContentModerationResponse#status_message #status_message} => String
    #   * {Types::GetContentModerationResponse#video_metadata #video_metadata} => Types::VideoMetadata
    #   * {Types::GetContentModerationResponse#moderation_labels #moderation_labels} => Array&lt;Types::ContentModerationDetection&gt;
    #   * {Types::GetContentModerationResponse#next_token #next_token} => String
    #   * {Types::GetContentModerationResponse#moderation_model_version #moderation_model_version} => String
    #   * {Types::GetContentModerationResponse#job_id #job_id} => String
    #   * {Types::GetContentModerationResponse#video #video} => Types::Video
    #   * {Types::GetContentModerationResponse#job_tag #job_tag} => String
    #   * {Types::GetContentModerationResponse#get_request_metadata #get_request_metadata} => Types::GetContentModerationRequestMetadata
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    # @example 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
    #   })
    #
    # @example Response structure
    #
    #   resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
    #   resp.status_message #=> String
    #   resp.video_metadata.codec #=> String
    #   resp.video_metadata.duration_millis #=> Integer
    #   resp.video_metadata.format #=> String
    #   resp.video_metadata.frame_rate #=> Float
    #   resp.video_metadata.frame_height #=> Integer
    #   resp.video_metadata.frame_width #=> Integer
    #   resp.video_metadata.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.get_request_metadata.sort_by #=> String, one of "NAME", "TIMESTAMP"
    #   resp.get_request_metadata.aggregate_by #=> String, one of "TIMESTAMPS", "SEGMENTS"
    #
    # @overload get_content_moderation(params = {})
    # @param [Hash] params ({})
    def get_content_moderation(params = {}, options = {})
      req = build_request(:get_content_moderation, params)
      req.send_request(options)
    end

    # 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".
    #
    # @option params [required, String] :job_id
    #   Unique identifier for the face detection job. The `JobId` is returned
    #   from `StartFaceDetection`.
    #
    # @option params [Integer] :max_results
    #   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.
    #
    # @option params [String] :next_token
    #   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.
    #
    # @return [Types::GetFaceDetectionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::GetFaceDetectionResponse#job_status #job_status} => String
    #   * {Types::GetFaceDetectionResponse#status_message #status_message} => String
    #   * {Types::GetFaceDetectionResponse#video_metadata #video_metadata} => Types::VideoMetadata
    #   * {Types::GetFaceDetectionResponse#next_token #next_token} => String
    #   * {Types::GetFaceDetectionResponse#faces #faces} => Array&lt;Types::FaceDetection&gt;
    #   * {Types::GetFaceDetectionResponse#job_id #job_id} => String
    #   * {Types::GetFaceDetectionResponse#video #video} => Types::Video
    #   * {Types::GetFaceDetectionResponse#job_tag #job_tag} => String
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.get_face_detection({
    #     job_id: "JobId", # required
    #     max_results: 1,
    #     next_token: "PaginationToken",
    #   })
    #
    # @example Response structure
    #
    #   resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
    #   resp.status_message #=> String
    #   resp.video_metadata.codec #=> String
    #   resp.video_metadata.duration_millis #=> Integer
    #   resp.video_metadata.format #=> String
    #   resp.video_metadata.frame_rate #=> Float
    #   resp.video_metadata.frame_height #=> Integer
    #   resp.video_metadata.frame_width #=> Integer
    #   resp.video_metadata.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
    #
    # @overload get_face_detection(params = {})
    # @param [Hash] params ({})
    def get_face_detection(params = {}, options = {})
      req = build_request(:get_face_detection, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, String] :session_id
    #   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.
    #
    # @return [Types::GetFaceLivenessSessionResultsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::GetFaceLivenessSessionResultsResponse#session_id #session_id} => String
    #   * {Types::GetFaceLivenessSessionResultsResponse#status #status} => String
    #   * {Types::GetFaceLivenessSessionResultsResponse#confidence #confidence} => Float
    #   * {Types::GetFaceLivenessSessionResultsResponse#reference_image #reference_image} => Types::AuditImage
    #   * {Types::GetFaceLivenessSessionResultsResponse#audit_images #audit_images} => Array&lt;Types::AuditImage&gt;
    #   * {Types::GetFaceLivenessSessionResultsResponse#challenge #challenge} => Types::Challenge
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.get_face_liveness_session_results({
    #     session_id: "LivenessSessionId", # required
    #   })
    #
    # @example 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
    #   resp.challenge.type #=> String, one of "FaceMovementAndLightChallenge", "FaceMovementChallenge"
    #   resp.challenge.version #=> String
    #
    # @overload get_face_liveness_session_results(params = {})
    # @param [Hash] params ({})
    def get_face_liveness_session_results(params = {}, options = {})
      req = build_request(:get_face_liveness_session_results, params)
      req.send_request(options)
    end

    # 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. Each`PersonMatch` 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.
    #
    # <note markdown="1"> `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.
    #
    #  </note>
    #
    # 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.
    #
    # @option params [required, String] :job_id
    #   The job identifer for the search request. You get the job identifier
    #   from an initial call to `StartFaceSearch`.
    #
    # @option params [Integer] :max_results
    #   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.
    #
    # @option params [String] :next_token
    #   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.
    #
    # @option params [String] :sort_by
    #   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.
    #
    # @return [Types::GetFaceSearchResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::GetFaceSearchResponse#job_status #job_status} => String
    #   * {Types::GetFaceSearchResponse#status_message #status_message} => String
    #   * {Types::GetFaceSearchResponse#next_token #next_token} => String
    #   * {Types::GetFaceSearchResponse#video_metadata #video_metadata} => Types::VideoMetadata
    #   * {Types::GetFaceSearchResponse#persons #persons} => Array&lt;Types::PersonMatch&gt;
    #   * {Types::GetFaceSearchResponse#job_id #job_id} => String
    #   * {Types::GetFaceSearchResponse#video #video} => Types::Video
    #   * {Types::GetFaceSearchResponse#job_tag #job_tag} => String
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    # @example 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
    #   })
    #
    # @example Response structure
    #
    #   resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
    #   resp.status_message #=> String
    #   resp.next_token #=> String
    #   resp.video_metadata.codec #=> String
    #   resp.video_metadata.duration_millis #=> Integer
    #   resp.video_metadata.format #=> String
    #   resp.video_metadata.frame_rate #=> Float
    #   resp.video_metadata.frame_height #=> Integer
    #   resp.video_metadata.frame_width #=> Integer
    #   resp.video_metadata.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
    #
    # @overload get_face_search(params = {})
    # @param [Hash] params ({})
    def get_face_search(params = {}, options = {})
      req = build_request(:get_face_search, params)
      req.send_request(options)
    end

    # 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`.
    #
    # If you are retrieving results while using the Amazon Simple
    # Notification Service, note that you will receive an "ERROR"
    # notification if the job encounters an issue.
    #
    # @option params [required, String] :job_id
    #   Job identifier for the label detection operation for which you want
    #   results returned. You get the job identifer from an initial call to
    #   `StartlabelDetection`.
    #
    # @option params [Integer] :max_results
    #   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.
    #
    # @option params [String] :next_token
    #   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.
    #
    # @option params [String] :sort_by
    #   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`.
    #
    # @option params [String] :aggregate_by
    #   Defines how to aggregate the returned results. Results can be
    #   aggregated by timestamps or segments.
    #
    # @return [Types::GetLabelDetectionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::GetLabelDetectionResponse#job_status #job_status} => String
    #   * {Types::GetLabelDetectionResponse#status_message #status_message} => String
    #   * {Types::GetLabelDetectionResponse#video_metadata #video_metadata} => Types::VideoMetadata
    #   * {Types::GetLabelDetectionResponse#next_token #next_token} => String
    #   * {Types::GetLabelDetectionResponse#labels #labels} => Array&lt;Types::LabelDetection&gt;
    #   * {Types::GetLabelDetectionResponse#label_model_version #label_model_version} => String
    #   * {Types::GetLabelDetectionResponse#job_id #job_id} => String
    #   * {Types::GetLabelDetectionResponse#video #video} => Types::Video
    #   * {Types::GetLabelDetectionResponse#job_tag #job_tag} => String
    #   * {Types::GetLabelDetectionResponse#get_request_metadata #get_request_metadata} => Types::GetLabelDetectionRequestMetadata
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    # @example 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
    #   })
    #
    # @example Response structure
    #
    #   resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
    #   resp.status_message #=> String
    #   resp.video_metadata.codec #=> String
    #   resp.video_metadata.duration_millis #=> Integer
    #   resp.video_metadata.format #=> String
    #   resp.video_metadata.frame_rate #=> Float
    #   resp.video_metadata.frame_height #=> Integer
    #   resp.video_metadata.frame_width #=> Integer
    #   resp.video_metadata.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.get_request_metadata.sort_by #=> String, one of "NAME", "TIMESTAMP"
    #   resp.get_request_metadata.aggregate_by #=> String, one of "TIMESTAMPS", "SEGMENTS"
    #
    # @overload get_label_detection(params = {})
    # @param [Hash] params ({})
    def get_label_detection(params = {}, options = {})
      req = build_request(:get_label_detection, params)
      req.send_request(options)
    end

    # Retrieves the results for a given media analysis job. Takes a `JobId`
    # returned by StartMediaAnalysisJob.
    #
    # @option params [required, String] :job_id
    #   Unique identifier for the media analysis job for which you want to
    #   retrieve results.
    #
    # @return [Types::GetMediaAnalysisJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::GetMediaAnalysisJobResponse#job_id #job_id} => String
    #   * {Types::GetMediaAnalysisJobResponse#job_name #job_name} => String
    #   * {Types::GetMediaAnalysisJobResponse#operations_config #operations_config} => Types::MediaAnalysisOperationsConfig
    #   * {Types::GetMediaAnalysisJobResponse#status #status} => String
    #   * {Types::GetMediaAnalysisJobResponse#failure_details #failure_details} => Types::MediaAnalysisJobFailureDetails
    #   * {Types::GetMediaAnalysisJobResponse#creation_timestamp #creation_timestamp} => Time
    #   * {Types::GetMediaAnalysisJobResponse#completion_timestamp #completion_timestamp} => Time
    #   * {Types::GetMediaAnalysisJobResponse#input #input} => Types::MediaAnalysisInput
    #   * {Types::GetMediaAnalysisJobResponse#output_config #output_config} => Types::MediaAnalysisOutputConfig
    #   * {Types::GetMediaAnalysisJobResponse#kms_key_id #kms_key_id} => String
    #   * {Types::GetMediaAnalysisJobResponse#results #results} => Types::MediaAnalysisResults
    #   * {Types::GetMediaAnalysisJobResponse#manifest_summary #manifest_summary} => Types::MediaAnalysisManifestSummary
    #
    #
    # @example 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", 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.get_media_analysis_job({
    #     job_id: "MediaAnalysisJobId", # required
    #   })
    #
    # @example 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
    #
    # @overload get_media_analysis_job(params = {})
    # @param [Hash] params ({})
    def get_media_analysis_job(params = {}, options = {})
      req = build_request(:get_media_analysis_job, params)
      req.send_request(options)
    end

    # <note markdown="1"> *End of support notice:* On October 31, 2025, AWS will discontinue
    # support for Amazon Rekognition People Pathing. After October 31, 2025,
    # you will no longer be able to use the Rekognition People Pathing
    # capability. For more information, visit this [blog post][1].
    #
    #  </note>
    #
    # 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.
    #
    # <note markdown="1"> `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.
    #
    #  </note>
    #
    # 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`.
    #
    #
    #
    # [1]: https://aws.amazon.com/blogs/machine-learning/transitioning-from-amazon-rekognition-people-pathing-exploring-other-alternatives/
    #
    # @option params [required, String] :job_id
    #   The identifier for a job that tracks persons in a video. You get the
    #   `JobId` from a call to `StartPersonTracking`.
    #
    # @option params [Integer] :max_results
    #   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.
    #
    # @option params [String] :next_token
    #   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.
    #
    # @option params [String] :sort_by
    #   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`.
    #
    # @return [Types::GetPersonTrackingResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::GetPersonTrackingResponse#job_status #job_status} => String
    #   * {Types::GetPersonTrackingResponse#status_message #status_message} => String
    #   * {Types::GetPersonTrackingResponse#video_metadata #video_metadata} => Types::VideoMetadata
    #   * {Types::GetPersonTrackingResponse#next_token #next_token} => String
    #   * {Types::GetPersonTrackingResponse#persons #persons} => Array&lt;Types::PersonDetection&gt;
    #   * {Types::GetPersonTrackingResponse#job_id #job_id} => String
    #   * {Types::GetPersonTrackingResponse#video #video} => Types::Video
    #   * {Types::GetPersonTrackingResponse#job_tag #job_tag} => String
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    # @example 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
    #   })
    #
    # @example Response structure
    #
    #   resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
    #   resp.status_message #=> String
    #   resp.video_metadata.codec #=> String
    #   resp.video_metadata.duration_millis #=> Integer
    #   resp.video_metadata.format #=> String
    #   resp.video_metadata.frame_rate #=> Float
    #   resp.video_metadata.frame_height #=> Integer
    #   resp.video_metadata.frame_width #=> Integer
    #   resp.video_metadata.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
    #
    # @overload get_person_tracking(params = {})
    # @param [Hash] params ({})
    def get_person_tracking(params = {}, options = {})
      req = build_request(:get_person_tracking, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, String] :job_id
    #   Job identifier for the text detection operation for which you want
    #   results returned. You get the job identifer from an initial call to
    #   `StartSegmentDetection`.
    #
    # @option params [Integer] :max_results
    #   Maximum number of results to return per paginated call. The largest
    #   value you can specify is 1000.
    #
    # @option params [String] :next_token
    #   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.
    #
    # @return [Types::GetSegmentDetectionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::GetSegmentDetectionResponse#job_status #job_status} => String
    #   * {Types::GetSegmentDetectionResponse#status_message #status_message} => String
    #   * {Types::GetSegmentDetectionResponse#video_metadata #video_metadata} => Array&lt;Types::VideoMetadata&gt;
    #   * {Types::GetSegmentDetectionResponse#audio_metadata #audio_metadata} => Array&lt;Types::AudioMetadata&gt;
    #   * {Types::GetSegmentDetectionResponse#next_token #next_token} => String
    #   * {Types::GetSegmentDetectionResponse#segments #segments} => Array&lt;Types::SegmentDetection&gt;
    #   * {Types::GetSegmentDetectionResponse#selected_segment_types #selected_segment_types} => Array&lt;Types::SegmentTypeInfo&gt;
    #   * {Types::GetSegmentDetectionResponse#job_id #job_id} => String
    #   * {Types::GetSegmentDetectionResponse#video #video} => Types::Video
    #   * {Types::GetSegmentDetectionResponse#job_tag #job_tag} => String
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.get_segment_detection({
    #     job_id: "JobId", # required
    #     max_results: 1,
    #     next_token: "PaginationToken",
    #   })
    #
    # @example Response structure
    #
    #   resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
    #   resp.status_message #=> String
    #   resp.video_metadata #=> Array
    #   resp.video_metadata[0].codec #=> String
    #   resp.video_metadata[0].duration_millis #=> Integer
    #   resp.video_metadata[0].format #=> String
    #   resp.video_metadata[0].frame_rate #=> Float
    #   resp.video_metadata[0].frame_height #=> Integer
    #   resp.video_metadata[0].frame_width #=> Integer
    #   resp.video_metadata[0].color_range #=> String, one of "FULL", "LIMITED"
    #   resp.audio_metadata #=> Array
    #   resp.audio_metadata[0].codec #=> String
    #   resp.audio_metadata[0].duration_millis #=> Integer
    #   resp.audio_metadata[0].sample_rate #=> Integer
    #   resp.audio_metadata[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
    #
    # @overload get_segment_detection(params = {})
    # @param [Hash] params ({})
    def get_segment_detection(params = {}, options = {})
      req = build_request(:get_segment_detection, params)
      req.send_request(options)
    end

    # 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`.
    #
    # @option params [required, String] :job_id
    #   Job identifier for the text detection operation for which you want
    #   results returned. You get the job identifer from an initial call to
    #   `StartTextDetection`.
    #
    # @option params [Integer] :max_results
    #   Maximum number of results to return per paginated call. The largest
    #   value you can specify is 1000.
    #
    # @option params [String] :next_token
    #   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.
    #
    # @return [Types::GetTextDetectionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::GetTextDetectionResponse#job_status #job_status} => String
    #   * {Types::GetTextDetectionResponse#status_message #status_message} => String
    #   * {Types::GetTextDetectionResponse#video_metadata #video_metadata} => Types::VideoMetadata
    #   * {Types::GetTextDetectionResponse#text_detections #text_detections} => Array&lt;Types::TextDetectionResult&gt;
    #   * {Types::GetTextDetectionResponse#next_token #next_token} => String
    #   * {Types::GetTextDetectionResponse#text_model_version #text_model_version} => String
    #   * {Types::GetTextDetectionResponse#job_id #job_id} => String
    #   * {Types::GetTextDetectionResponse#video #video} => Types::Video
    #   * {Types::GetTextDetectionResponse#job_tag #job_tag} => String
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.get_text_detection({
    #     job_id: "JobId", # required
    #     max_results: 1,
    #     next_token: "PaginationToken",
    #   })
    #
    # @example Response structure
    #
    #   resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
    #   resp.status_message #=> String
    #   resp.video_metadata.codec #=> String
    #   resp.video_metadata.duration_millis #=> Integer
    #   resp.video_metadata.format #=> String
    #   resp.video_metadata.frame_rate #=> Float
    #   resp.video_metadata.frame_height #=> Integer
    #   resp.video_metadata.frame_width #=> Integer
    #   resp.video_metadata.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
    #
    # @overload get_text_detection(params = {})
    # @param [Hash] params ({})
    def get_text_detection(params = {}, options = {})
      req = build_request(:get_text_detection, params)
      req.send_request(options)
    end

    # 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`.
    #
    # <note markdown="1"> 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.
    #
    #  </note>
    #
    # 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.
    #
    # @option params [required, String] :collection_id
    #   The ID of an existing collection to which you want to add the faces
    #   that are detected in the input images.
    #
    # @option params [required, Types::Image] :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.
    #
    # @option params [String] :external_image_id
    #   The ID you want to assign to all the faces detected in the image.
    #
    # @option params [Array<String>] :detection_attributes
    #   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).
    #
    # @option params [Integer] :max_faces
    #   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.
    #
    # @option params [String] :quality_filter
    #   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.
    #
    # @return [Types::IndexFacesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::IndexFacesResponse#face_records #face_records} => Array&lt;Types::FaceRecord&gt;
    #   * {Types::IndexFacesResponse#orientation_correction #orientation_correction} => String
    #   * {Types::IndexFacesResponse#face_model_version #face_model_version} => String
    #   * {Types::IndexFacesResponse#unindexed_faces #unindexed_faces} => Array&lt;Types::UnindexedFace&gt;
    #
    #
    # @example 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", 
    #   }
    #
    # @example 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
    #   })
    #
    # @example 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
    #
    # @overload index_faces(params = {})
    # @param [Hash] params ({})
    def index_faces(params = {}, options = {})
      req = build_request(:index_faces, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [String] :next_token
    #   Pagination token from the previous response.
    #
    # @option params [Integer] :max_results
    #   Maximum number of collection IDs to return.
    #
    # @return [Types::ListCollectionsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::ListCollectionsResponse#collection_ids #collection_ids} => Array&lt;String&gt;
    #   * {Types::ListCollectionsResponse#next_token #next_token} => String
    #   * {Types::ListCollectionsResponse#face_model_versions #face_model_versions} => Array&lt;String&gt;
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    #
    # @example 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", 
    #     ], 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.list_collections({
    #     next_token: "PaginationToken",
    #     max_results: 1,
    #   })
    #
    # @example 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
    #
    # @overload list_collections(params = {})
    # @param [Hash] params ({})
    def list_collections(params = {}, options = {})
      req = build_request(:list_collections, params)
      req.send_request(options)
    end

    # <note markdown="1"> This operation applies only to Amazon Rekognition Custom Labels.
    #
    #  </note>
    #
    # 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][1].
    #
    # 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.
    #
    #
    #
    # [1]: https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-manifest-files.html
    #
    # @option params [required, String] :dataset_arn
    #   The Amazon Resource Name (ARN) for the dataset that you want to use.
    #
    # @option params [Array<String>] :contains_labels
    #   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.
    #
    # @option params [Boolean] :labeled
    #   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.
    #
    # @option params [String] :source_ref_contains
    #   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.
    #
    # @option params [Boolean] :has_errors
    #   Specifies an error filter for the response. Specify `True` to only
    #   include entries that have errors.
    #
    # @option params [String] :next_token
    #   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.
    #
    # @option params [Integer] :max_results
    #   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.
    #
    # @return [Types::ListDatasetEntriesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::ListDatasetEntriesResponse#dataset_entries #dataset_entries} => Array&lt;String&gt;
    #   * {Types::ListDatasetEntriesResponse#next_token #next_token} => String
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    #
    # @example 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: "", 
    #   }
    #
    # @example 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,
    #   })
    #
    # @example Response structure
    #
    #   resp.dataset_entries #=> Array
    #   resp.dataset_entries[0] #=> String
    #   resp.next_token #=> String
    #
    # @overload list_dataset_entries(params = {})
    # @param [Hash] params ({})
    def list_dataset_entries(params = {}, options = {})
      req = build_request(:list_dataset_entries, params)
      req.send_request(options)
    end

    # <note markdown="1"> This operation applies only to Amazon Rekognition Custom Labels.
    #
    #  </note>
    #
    # Lists the labels in a dataset. Amazon Rekognition Custom Labels uses
    # labels to describe images. For more information, see [Labeling
    # images][1].
    #
    # 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*.
    #
    #
    #
    # [1]: https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-labeling-images.html
    #
    # @option params [required, String] :dataset_arn
    #   The Amazon Resource Name (ARN) of the dataset that you want to use.
    #
    # @option params [String] :next_token
    #   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.
    #
    # @option params [Integer] :max_results
    #   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.
    #
    # @return [Types::ListDatasetLabelsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::ListDatasetLabelsResponse#dataset_label_descriptions #dataset_label_descriptions} => Array&lt;Types::DatasetLabelDescription&gt;
    #   * {Types::ListDatasetLabelsResponse#next_token #next_token} => String
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    #
    # @example 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, 
    #         }, 
    #       }, 
    #     ], 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.list_dataset_labels({
    #     dataset_arn: "DatasetArn", # required
    #     next_token: "ExtendedPaginationToken",
    #     max_results: 1,
    #   })
    #
    # @example 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
    #
    # @overload list_dataset_labels(params = {})
    # @param [Hash] params ({})
    def list_dataset_labels(params = {}, options = {})
      req = build_request(:list_dataset_labels, params)
      req.send_request(options)
    end

    # 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.
    #
    # @option params [required, String] :collection_id
    #   ID of the collection from which to list the faces.
    #
    # @option params [String] :next_token
    #   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.
    #
    # @option params [Integer] :max_results
    #   Maximum number of faces to return.
    #
    # @option params [String] :user_id
    #   An array of user IDs to filter results with when listing faces in a
    #   collection.
    #
    # @option params [Array<String>] :face_ids
    #   An array of face IDs to filter results with when listing faces in a
    #   collection.
    #
    # @return [Types::ListFacesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::ListFacesResponse#faces #faces} => Array&lt;Types::Face&gt;
    #   * {Types::ListFacesResponse#next_token #next_token} => String
    #   * {Types::ListFacesResponse#face_model_version #face_model_version} => String
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    #
    # @example 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", 
    #       }, 
    #     ], 
    #   }
    #
    # @example 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"],
    #   })
    #
    # @example 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
    #
    # @overload list_faces(params = {})
    # @param [Hash] params ({})
    def list_faces(params = {}, options = {})
      req = build_request(:list_faces, params)
      req.send_request(options)
    end

    # Returns a list of media analysis jobs. Results are sorted by
    # `CreationTimestamp` in descending order.
    #
    # @option params [String] :next_token
    #   Pagination token, if the previous response was incomplete.
    #
    # @option params [Integer] :max_results
    #   The maximum number of results to return per paginated call. The
    #   largest value user can specify is 100. If user specifies a value
    #   greater than 100, an `InvalidParameterException` error occurs. The
    #   default value is 100.
    #
    # @return [Types::ListMediaAnalysisJobsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::ListMediaAnalysisJobsResponse#next_token #next_token} => String
    #   * {Types::ListMediaAnalysisJobsResponse#media_analysis_jobs #media_analysis_jobs} => Array&lt;Types::MediaAnalysisJobDescription&gt;
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    #
    # @example Example: ListMediaAnalysisJobs
    #
    #   # Returns a list of media analysis jobs.
    #
    #   resp = client.list_media_analysis_jobs({
    #     max_results: 10, 
    #   })
    #
    #   resp.to_h outputs the following:
    #   {
    #     media_analysis_jobs: [
    #       {
    #         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", 
    #       }, 
    #     ], 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.list_media_analysis_jobs({
    #     next_token: "ExtendedPaginationToken",
    #     max_results: 1,
    #   })
    #
    # @example Response structure
    #
    #   resp.next_token #=> String
    #   resp.media_analysis_jobs #=> Array
    #   resp.media_analysis_jobs[0].job_id #=> String
    #   resp.media_analysis_jobs[0].job_name #=> String
    #   resp.media_analysis_jobs[0].operations_config.detect_moderation_labels.min_confidence #=> Float
    #   resp.media_analysis_jobs[0].operations_config.detect_moderation_labels.project_version #=> String
    #   resp.media_analysis_jobs[0].status #=> String, one of "CREATED", "QUEUED", "IN_PROGRESS", "SUCCEEDED", "FAILED"
    #   resp.media_analysis_jobs[0].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.media_analysis_jobs[0].failure_details.message #=> String
    #   resp.media_analysis_jobs[0].creation_timestamp #=> Time
    #   resp.media_analysis_jobs[0].completion_timestamp #=> Time
    #   resp.media_analysis_jobs[0].input.s3_object.bucket #=> String
    #   resp.media_analysis_jobs[0].input.s3_object.name #=> String
    #   resp.media_analysis_jobs[0].input.s3_object.version #=> String
    #   resp.media_analysis_jobs[0].output_config.s3_bucket #=> String
    #   resp.media_analysis_jobs[0].output_config.s3_key_prefix #=> String
    #   resp.media_analysis_jobs[0].kms_key_id #=> String
    #   resp.media_analysis_jobs[0].results.s3_object.bucket #=> String
    #   resp.media_analysis_jobs[0].results.s3_object.name #=> String
    #   resp.media_analysis_jobs[0].results.s3_object.version #=> String
    #   resp.media_analysis_jobs[0].results.model_versions.moderation #=> String
    #   resp.media_analysis_jobs[0].manifest_summary.s3_object.bucket #=> String
    #   resp.media_analysis_jobs[0].manifest_summary.s3_object.name #=> String
    #   resp.media_analysis_jobs[0].manifest_summary.s3_object.version #=> String
    #
    # @overload list_media_analysis_jobs(params = {})
    # @param [Hash] params ({})
    def list_media_analysis_jobs(params = {}, options = {})
      req = build_request(:list_media_analysis_jobs, params)
      req.send_request(options)
    end

    # <note markdown="1"> This operation applies only to Amazon Rekognition Custom Labels.
    #
    #  </note>
    #
    # Gets a list of the project policies attached to a project.
    #
    # To attach a project policy to a project, call PutProjectPolicy. To
    # remove a project policy from a project, call DeleteProjectPolicy.
    #
    # This operation requires permissions to perform the
    # `rekognition:ListProjectPolicies` action.
    #
    # @option params [required, String] :project_arn
    #   The ARN of the project for which you want to list the project
    #   policies.
    #
    # @option params [String] :next_token
    #   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.
    #
    # @option params [Integer] :max_results
    #   The maximum number of results to return per paginated call. The
    #   largest value you can specify is 5. If you specify a value greater
    #   than 5, a ValidationException error occurs. The default value is 5.
    #
    # @return [Types::ListProjectPoliciesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::ListProjectPoliciesResponse#project_policies #project_policies} => Array&lt;Types::ProjectPolicy&gt;
    #   * {Types::ListProjectPoliciesResponse#next_token #next_token} => String
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    #
    # @example Example: ListProjectPolicies
    #
    #   # This operation lists the project policies that are attached to an Amazon Rekognition Custom Labels project.
    #
    #   resp = client.list_project_policies({
    #     max_results: 5, 
    #     next_token: "", 
    #     project_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-sdk-project/1656557051929", 
    #   })
    #
    #   resp.to_h outputs the following:
    #   {
    #     next_token: "", 
    #     project_policies: [
    #       {
    #         creation_timestamp: Time.parse("2022-07-01T11:51:27.086000-07:00"), 
    #         last_updated_timestamp: Time.parse("2022-07-01T11:51:27.086000-07:00"), 
    #         policy_document: "{\"Version\":\"2012-10-17\",\"Statement\":[{\"Sid\":\"Statemented1\",\"Effect\":\"Allow\",\"Principal\":{\"AWS\":\"arn:aws:iam::111122223333:root\"},\"Action\":\"rekognition:CopyProjectVersion\",\"Resource\":\"*\"}]}", 
    #         policy_name: "testPolicy", 
    #         policy_revision_id: "3b274c25e9203a56a99e00e3ff205fbc", 
    #         project_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-sdk-project/1656557051929", 
    #       }, 
    #     ], 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.list_project_policies({
    #     project_arn: "ProjectArn", # required
    #     next_token: "ExtendedPaginationToken",
    #     max_results: 1,
    #   })
    #
    # @example Response structure
    #
    #   resp.project_policies #=> Array
    #   resp.project_policies[0].project_arn #=> String
    #   resp.project_policies[0].policy_name #=> String
    #   resp.project_policies[0].policy_revision_id #=> String
    #   resp.project_policies[0].policy_document #=> String
    #   resp.project_policies[0].creation_timestamp #=> Time
    #   resp.project_policies[0].last_updated_timestamp #=> Time
    #   resp.next_token #=> String
    #
    # @overload list_project_policies(params = {})
    # @param [Hash] params ({})
    def list_project_policies(params = {}, options = {})
      req = build_request(:list_project_policies, params)
      req.send_request(options)
    end

    # Gets a list of stream processors that you have created with
    # CreateStreamProcessor.
    #
    # @option params [String] :next_token
    #   If the previous response was incomplete (because there are more stream
    #   processors to retrieve), Amazon Rekognition Video returns a pagination
    #   token in the response. You can use this pagination token to retrieve
    #   the next set of stream processors.
    #
    # @option params [Integer] :max_results
    #   Maximum number of stream processors you want Amazon Rekognition Video
    #   to return in the response. The default is 1000.
    #
    # @return [Types::ListStreamProcessorsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::ListStreamProcessorsResponse#next_token #next_token} => String
    #   * {Types::ListStreamProcessorsResponse#stream_processors #stream_processors} => Array&lt;Types::StreamProcessor&gt;
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.list_stream_processors({
    #     next_token: "PaginationToken",
    #     max_results: 1,
    #   })
    #
    # @example Response structure
    #
    #   resp.next_token #=> String
    #   resp.stream_processors #=> Array
    #   resp.stream_processors[0].name #=> String
    #   resp.stream_processors[0].status #=> String, one of "STOPPED", "STARTING", "RUNNING", "FAILED", "STOPPING", "UPDATING"
    #
    # @overload list_stream_processors(params = {})
    # @param [Hash] params ({})
    def list_stream_processors(params = {}, options = {})
      req = build_request(:list_stream_processors, params)
      req.send_request(options)
    end

    # Returns a list of tags in an Amazon Rekognition collection, stream
    # processor, or Custom Labels model.
    #
    # This operation requires permissions to perform the
    # `rekognition:ListTagsForResource` action.
    #
    # @option params [required, String] :resource_arn
    #   Amazon Resource Name (ARN) of the model, collection, or stream
    #   processor that contains the tags that you want a list of.
    #
    # @return [Types::ListTagsForResourceResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::ListTagsForResourceResponse#tags #tags} => Hash&lt;String,String&gt;
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.list_tags_for_resource({
    #     resource_arn: "ResourceArn", # required
    #   })
    #
    # @example Response structure
    #
    #   resp.tags #=> Hash
    #   resp.tags["TagKey"] #=> String
    #
    # @overload list_tags_for_resource(params = {})
    # @param [Hash] params ({})
    def list_tags_for_resource(params = {}, options = {})
      req = build_request(:list_tags_for_resource, params)
      req.send_request(options)
    end

    # Returns metadata of the User such as `UserID` in the specified
    # collection. Anonymous User (to reserve faces without any identity) is
    # not returned as part of this request. The results are sorted by system
    # generated primary key ID. If the response is truncated, `NextToken` is
    # returned in the response that can be used in the subsequent request to
    # retrieve the next set of identities.
    #
    # @option params [required, String] :collection_id
    #   The ID of an existing collection.
    #
    # @option params [Integer] :max_results
    #   Maximum number of UsersID to return.
    #
    # @option params [String] :next_token
    #   Pagingation token to receive the next set of UsersID.
    #
    # @return [Types::ListUsersResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::ListUsersResponse#users #users} => Array&lt;Types::User&gt;
    #   * {Types::ListUsersResponse#next_token #next_token} => String
    #
    # The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
    #
    #
    # @example Example: ListUsers
    #
    #   # Returns metadata of the User such as UserID in the specified collection.
    #
    #   resp = client.list_users({
    #     collection_id: "MyCollection", 
    #   })
    #
    #   resp.to_h outputs the following:
    #   {
    #     next_token: "MGYZLAHX1T5a....", 
    #     users: [
    #       {
    #         user_id: "demoUser4", 
    #         user_status: "CREATED", 
    #       }, 
    #       {
    #         user_id: "demoUser2", 
    #         user_status: "CREATED", 
    #       }, 
    #     ], 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.list_users({
    #     collection_id: "CollectionId", # required
    #     max_results: 1,
    #     next_token: "PaginationToken",
    #   })
    #
    # @example Response structure
    #
    #   resp.users #=> Array
    #   resp.users[0].user_id #=> String
    #   resp.users[0].user_status #=> String, one of "ACTIVE", "UPDATING", "CREATING", "CREATED"
    #   resp.next_token #=> String
    #
    # @overload list_users(params = {})
    # @param [Hash] params ({})
    def list_users(params = {}, options = {})
      req = build_request(:list_users, params)
      req.send_request(options)
    end

    # <note markdown="1"> This operation applies only to Amazon Rekognition Custom Labels.
    #
    #  </note>
    #
    # Attaches a project policy to a Amazon Rekognition Custom Labels
    # project in a trusting AWS account. A project policy specifies that a
    # trusted AWS account can copy a model version from a trusting AWS
    # account to a project in the trusted AWS account. To copy a model
    # version you use the CopyProjectVersion operation. Only applies to
    # Custom Labels projects.
    #
    # For more information about the format of a project policy document,
    # see Attaching a project policy (SDK) in the *Amazon Rekognition Custom
    # Labels Developer Guide*.
    #
    # The response from `PutProjectPolicy` is a revision ID for the project
    # policy. You can attach multiple project policies to a project. You can
    # also update an existing project policy by specifying the policy
    # revision ID of the existing policy.
    #
    # To remove a project policy from a project, call DeleteProjectPolicy.
    # To get a list of project policies attached to a project, call
    # ListProjectPolicies.
    #
    # You copy a model version by calling CopyProjectVersion.
    #
    # This operation requires permissions to perform the
    # `rekognition:PutProjectPolicy` action.
    #
    # @option params [required, String] :project_arn
    #   The Amazon Resource Name (ARN) of the project that the project policy
    #   is attached to.
    #
    # @option params [required, String] :policy_name
    #   A name for the policy.
    #
    # @option params [String] :policy_revision_id
    #   The revision ID for the Project Policy. Each time you modify a policy,
    #   Amazon Rekognition Custom Labels generates and assigns a new
    #   `PolicyRevisionId` and then deletes the previous version of the
    #   policy.
    #
    # @option params [required, String] :policy_document
    #   A resource policy to add to the model. The policy is a JSON structure
    #   that contains one or more statements that define the policy. The
    #   policy must follow the IAM syntax. For more information about the
    #   contents of a JSON policy document, see [IAM JSON policy
    #   reference][1].
    #
    #
    #
    #   [1]: https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies.html
    #
    # @return [Types::PutProjectPolicyResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::PutProjectPolicyResponse#policy_revision_id #policy_revision_id} => String
    #
    #
    # @example Example: PutProjectPolicy
    #
    #   # This operation attaches a project policy to a Amazon Rekognition Custom Labels project in a trusting AWS account.
    #
    #   resp = client.put_project_policy({
    #     policy_document: "'{\"Version\":\"2012-10-17\",\"Statement\":[{\"Effect\":\"ALLOW\",\"Principal\":{\"AWS\":\"principal\"},\"Action\":\"rekognition:CopyProjectVersion\",\"Resource\":\"arn:aws:rekognition:us-east-1:123456789012:project/my-sdk-project/version/DestinationVersionName/1627045542080\"}]}'", 
    #     policy_name: "SamplePolicy", 
    #     policy_revision_id: "0123456789abcdef", 
    #     project_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-sdk-project/1656557051929", 
    #   })
    #
    #   resp.to_h outputs the following:
    #   {
    #     policy_revision_id: "0123456789abcdef", 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.put_project_policy({
    #     project_arn: "ProjectArn", # required
    #     policy_name: "ProjectPolicyName", # required
    #     policy_revision_id: "ProjectPolicyRevisionId",
    #     policy_document: "ProjectPolicyDocument", # required
    #   })
    #
    # @example Response structure
    #
    #   resp.policy_revision_id #=> String
    #
    # @overload put_project_policy(params = {})
    # @param [Hash] params ({})
    def put_project_policy(params = {}, options = {})
      req = build_request(:put_project_policy, params)
      req.send_request(options)
    end

    # Returns an array of celebrities recognized in the input image. For
    # more information, see Recognizing celebrities in the Amazon
    # Rekognition Developer Guide.
    #
    # `RecognizeCelebrities` returns the 64 largest faces in the image. It
    # lists the recognized celebrities in the `CelebrityFaces` array and any
    # unrecognized faces in the `UnrecognizedFaces` array.
    # `RecognizeCelebrities` doesn't return celebrities whose faces aren't
    # among the largest 64 faces in the image.
    #
    # For each celebrity recognized, `RecognizeCelebrities` returns a
    # `Celebrity` object. The `Celebrity` object contains the celebrity
    # name, ID, URL links to additional information, match confidence, and a
    # `ComparedFace` object that you can use to locate the celebrity's face
    # on the image.
    #
    # Amazon Rekognition doesn't retain information about which images a
    # celebrity has been recognized in. Your application must store this
    # information and use the `Celebrity` ID property as a unique identifier
    # for the celebrity. If you don't store the celebrity name or
    # additional information URLs returned by `RecognizeCelebrities`, you
    # will need the ID to identify the celebrity in a call to the
    # GetCelebrityInfo operation.
    #
    # 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.
    #
    # For an example, see Recognizing celebrities in an image in the Amazon
    # Rekognition Developer Guide.
    #
    # This operation requires permissions to perform the
    # `rekognition:RecognizeCelebrities` operation.
    #
    # @option params [required, Types::Image] :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.
    #
    # @return [Types::RecognizeCelebritiesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::RecognizeCelebritiesResponse#celebrity_faces #celebrity_faces} => Array&lt;Types::Celebrity&gt;
    #   * {Types::RecognizeCelebritiesResponse#unrecognized_faces #unrecognized_faces} => Array&lt;Types::ComparedFace&gt;
    #   * {Types::RecognizeCelebritiesResponse#orientation_correction #orientation_correction} => String
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.recognize_celebrities({
    #     image: { # required
    #       bytes: "data",
    #       s3_object: {
    #         bucket: "S3Bucket",
    #         name: "S3ObjectName",
    #         version: "S3ObjectVersion",
    #       },
    #     },
    #   })
    #
    # @example Response structure
    #
    #   resp.celebrity_faces #=> Array
    #   resp.celebrity_faces[0].urls #=> Array
    #   resp.celebrity_faces[0].urls[0] #=> String
    #   resp.celebrity_faces[0].name #=> String
    #   resp.celebrity_faces[0].id #=> String
    #   resp.celebrity_faces[0].face.bounding_box.width #=> Float
    #   resp.celebrity_faces[0].face.bounding_box.height #=> Float
    #   resp.celebrity_faces[0].face.bounding_box.left #=> Float
    #   resp.celebrity_faces[0].face.bounding_box.top #=> Float
    #   resp.celebrity_faces[0].face.confidence #=> Float
    #   resp.celebrity_faces[0].face.landmarks #=> Array
    #   resp.celebrity_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.celebrity_faces[0].face.landmarks[0].x #=> Float
    #   resp.celebrity_faces[0].face.landmarks[0].y #=> Float
    #   resp.celebrity_faces[0].face.pose.roll #=> Float
    #   resp.celebrity_faces[0].face.pose.yaw #=> Float
    #   resp.celebrity_faces[0].face.pose.pitch #=> Float
    #   resp.celebrity_faces[0].face.quality.brightness #=> Float
    #   resp.celebrity_faces[0].face.quality.sharpness #=> Float
    #   resp.celebrity_faces[0].face.emotions #=> Array
    #   resp.celebrity_faces[0].face.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
    #   resp.celebrity_faces[0].face.emotions[0].confidence #=> Float
    #   resp.celebrity_faces[0].face.smile.value #=> Boolean
    #   resp.celebrity_faces[0].face.smile.confidence #=> Float
    #   resp.celebrity_faces[0].match_confidence #=> Float
    #   resp.celebrity_faces[0].known_gender.type #=> String, one of "Male", "Female", "Nonbinary", "Unlisted"
    #   resp.unrecognized_faces #=> Array
    #   resp.unrecognized_faces[0].bounding_box.width #=> Float
    #   resp.unrecognized_faces[0].bounding_box.height #=> Float
    #   resp.unrecognized_faces[0].bounding_box.left #=> Float
    #   resp.unrecognized_faces[0].bounding_box.top #=> Float
    #   resp.unrecognized_faces[0].confidence #=> Float
    #   resp.unrecognized_faces[0].landmarks #=> Array
    #   resp.unrecognized_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.unrecognized_faces[0].landmarks[0].x #=> Float
    #   resp.unrecognized_faces[0].landmarks[0].y #=> Float
    #   resp.unrecognized_faces[0].pose.roll #=> Float
    #   resp.unrecognized_faces[0].pose.yaw #=> Float
    #   resp.unrecognized_faces[0].pose.pitch #=> Float
    #   resp.unrecognized_faces[0].quality.brightness #=> Float
    #   resp.unrecognized_faces[0].quality.sharpness #=> Float
    #   resp.unrecognized_faces[0].emotions #=> Array
    #   resp.unrecognized_faces[0].emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
    #   resp.unrecognized_faces[0].emotions[0].confidence #=> Float
    #   resp.unrecognized_faces[0].smile.value #=> Boolean
    #   resp.unrecognized_faces[0].smile.confidence #=> Float
    #   resp.orientation_correction #=> String, one of "ROTATE_0", "ROTATE_90", "ROTATE_180", "ROTATE_270"
    #
    # @overload recognize_celebrities(params = {})
    # @param [Hash] params ({})
    def recognize_celebrities(params = {}, options = {})
      req = build_request(:recognize_celebrities, params)
      req.send_request(options)
    end

    # For a given input face ID, searches for matching faces in the
    # collection the face belongs to. You get a face ID when you add a face
    # to the collection using the IndexFaces operation. The operation
    # compares the features of the input face with faces in the specified
    # collection.
    #
    # <note markdown="1"> You can also search faces without indexing faces by using the
    # `SearchFacesByImage` operation.
    #
    #  </note>
    #
    # The operation response returns an array of faces that match, ordered
    # by similarity score with the highest similarity first. More
    # specifically, it is an array of metadata for each face match that is
    # found. Along with the metadata, the response also includes a
    # `confidence` value for each face match, indicating the confidence that
    # the specific face matches the input face.
    #
    # For an example, see Searching for a face using its face ID in the
    # Amazon Rekognition Developer Guide.
    #
    # This operation requires permissions to perform the
    # `rekognition:SearchFaces` action.
    #
    # @option params [required, String] :collection_id
    #   ID of the collection the face belongs to.
    #
    # @option params [required, String] :face_id
    #   ID of a face to find matches for in the collection.
    #
    # @option params [Integer] :max_faces
    #   Maximum number of faces to return. The operation returns the maximum
    #   number of faces with the highest confidence in the match.
    #
    # @option params [Float] :face_match_threshold
    #   Optional value specifying the minimum confidence in the face match to
    #   return. For example, don't return any matches where confidence in
    #   matches is less than 70%. The default value is 80%.
    #
    # @return [Types::SearchFacesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::SearchFacesResponse#searched_face_id #searched_face_id} => String
    #   * {Types::SearchFacesResponse#face_matches #face_matches} => Array&lt;Types::FaceMatch&gt;
    #   * {Types::SearchFacesResponse#face_model_version #face_model_version} => String
    #
    #
    # @example Example: To delete a face
    #
    #   # This operation searches for matching faces in the collection the supplied face belongs to.
    #
    #   resp = client.search_faces({
    #     collection_id: "myphotos", 
    #     face_id: "70008e50-75e4-55d0-8e80-363fb73b3a14", 
    #     face_match_threshold: 90, 
    #     max_faces: 10, 
    #   })
    #
    #   resp.to_h outputs the following:
    #   {
    #     face_matches: [
    #       {
    #         face: {
    #           bounding_box: {
    #             height: 0.3259260058403015, 
    #             left: 0.5144439935684204, 
    #             top: 0.15111100673675537, 
    #             width: 0.24444399774074554, 
    #           }, 
    #           confidence: 99.99949645996094, 
    #           face_id: "8be04dba-4e58-520d-850e-9eae4af70eb2", 
    #           image_id: "465f4e93-763e-51d0-b030-b9667a2d94b1", 
    #         }, 
    #         similarity: 99.97222137451172, 
    #       }, 
    #       {
    #         face: {
    #           bounding_box: {
    #             height: 0.16555599868297577, 
    #             left: 0.30963000655174255, 
    #             top: 0.7066670060157776, 
    #             width: 0.22074100375175476, 
    #           }, 
    #           confidence: 100, 
    #           face_id: "29a75abe-397b-5101-ba4f-706783b2246c", 
    #           image_id: "147fdf82-7a71-52cf-819b-e786c7b9746e", 
    #         }, 
    #         similarity: 97.04154968261719, 
    #       }, 
    #       {
    #         face: {
    #           bounding_box: {
    #             height: 0.18888899683952332, 
    #             left: 0.3783380091190338, 
    #             top: 0.2355560064315796, 
    #             width: 0.25222599506378174, 
    #           }, 
    #           confidence: 99.9999008178711, 
    #           face_id: "908544ad-edc3-59df-8faf-6a87cc256cf5", 
    #           image_id: "3c731605-d772-541a-a5e7-0375dbc68a07", 
    #         }, 
    #         similarity: 95.94520568847656, 
    #       }, 
    #     ], 
    #     searched_face_id: "70008e50-75e4-55d0-8e80-363fb73b3a14", 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.search_faces({
    #     collection_id: "CollectionId", # required
    #     face_id: "FaceId", # required
    #     max_faces: 1,
    #     face_match_threshold: 1.0,
    #   })
    #
    # @example Response structure
    #
    #   resp.searched_face_id #=> String
    #   resp.face_matches #=> Array
    #   resp.face_matches[0].similarity #=> Float
    #   resp.face_matches[0].face.face_id #=> String
    #   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.image_id #=> String
    #   resp.face_matches[0].face.external_image_id #=> String
    #   resp.face_matches[0].face.confidence #=> Float
    #   resp.face_matches[0].face.index_faces_model_version #=> String
    #   resp.face_matches[0].face.user_id #=> String
    #   resp.face_model_version #=> String
    #
    # @overload search_faces(params = {})
    # @param [Hash] params ({})
    def search_faces(params = {}, options = {})
      req = build_request(:search_faces, params)
      req.send_request(options)
    end

    # For a given input image, first detects the largest face in the image,
    # and then searches the specified collection for matching faces. The
    # operation compares the features of the input face with faces in the
    # specified collection.
    #
    # <note markdown="1"> To search for all faces in an input image, you might first call the
    # IndexFaces operation, and then use the face IDs returned in subsequent
    # calls to the SearchFaces operation.
    #
    #  You can also call the `DetectFaces` operation and use the bounding
    # boxes in the response to make face crops, which then you can pass in
    # to the `SearchFacesByImage` operation.
    #
    #  </note>
    #
    # 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.
    #
    # The response returns an array of faces that match, ordered by
    # similarity score with the highest similarity first. More specifically,
    # it is an array of metadata for each face match found. Along with the
    # metadata, the response also includes a `similarity` indicating how
    # similar the face is to the input face. In the response, the operation
    # also returns the bounding box (and a confidence level that the
    # bounding box contains a face) of the face that Amazon Rekognition used
    # for the input image.
    #
    # If no faces are detected in the input image, `SearchFacesByImage`
    # returns an `InvalidParameterException` error.
    #
    # For an example, Searching for a Face Using an Image in the Amazon
    # Rekognition Developer Guide.
    #
    # 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 for filtering by specifying `LOW`, `MEDIUM`, or `HIGH`. If
    # you do not want to filter detected faces, specify `NONE`. The default
    # value is `NONE`.
    #
    # <note markdown="1"> 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.
    #
    #  </note>
    #
    # This operation requires permissions to perform the
    # `rekognition:SearchFacesByImage` action.
    #
    # @option params [required, String] :collection_id
    #   ID of the collection to search.
    #
    # @option params [required, Types::Image] :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.
    #
    # @option params [Integer] :max_faces
    #   Maximum number of faces to return. The operation returns the maximum
    #   number of faces with the highest confidence in the match.
    #
    # @option params [Float] :face_match_threshold
    #   (Optional) Specifies the minimum confidence in the face match to
    #   return. For example, don't return any matches where confidence in
    #   matches is less than 70%. The default value is 80%.
    #
    # @option params [String] :quality_filter
    #   A filter that specifies a quality bar for how much filtering is done
    #   to identify faces. Filtered faces aren't searched for in the
    #   collection. 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.
    #
    # @return [Types::SearchFacesByImageResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::SearchFacesByImageResponse#searched_face_bounding_box #searched_face_bounding_box} => Types::BoundingBox
    #   * {Types::SearchFacesByImageResponse#searched_face_confidence #searched_face_confidence} => Float
    #   * {Types::SearchFacesByImageResponse#face_matches #face_matches} => Array&lt;Types::FaceMatch&gt;
    #   * {Types::SearchFacesByImageResponse#face_model_version #face_model_version} => String
    #
    #
    # @example Example: To search for faces matching a supplied image
    #
    #   # This operation searches for faces in a Rekognition collection that match the largest face in an S3 bucket stored image.
    #
    #   resp = client.search_faces_by_image({
    #     collection_id: "myphotos", 
    #     face_match_threshold: 95, 
    #     image: {
    #       s3_object: {
    #         bucket: "mybucket", 
    #         name: "myphoto", 
    #       }, 
    #     }, 
    #     max_faces: 5, 
    #   })
    #
    #   resp.to_h outputs the following:
    #   {
    #     face_matches: [
    #       {
    #         face: {
    #           bounding_box: {
    #             height: 0.3234420120716095, 
    #             left: 0.3233329951763153, 
    #             top: 0.5, 
    #             width: 0.24222199618816376, 
    #           }, 
    #           confidence: 99.99829864501953, 
    #           face_id: "38271d79-7bc2-5efb-b752-398a8d575b85", 
    #           image_id: "d5631190-d039-54e4-b267-abd22c8647c5", 
    #         }, 
    #         similarity: 99.97036743164062, 
    #       }, 
    #     ], 
    #     searched_face_bounding_box: {
    #       height: 0.33481481671333313, 
    #       left: 0.31888890266418457, 
    #       top: 0.4933333396911621, 
    #       width: 0.25, 
    #     }, 
    #     searched_face_confidence: 99.9991226196289, 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.search_faces_by_image({
    #     collection_id: "CollectionId", # required
    #     image: { # required
    #       bytes: "data",
    #       s3_object: {
    #         bucket: "S3Bucket",
    #         name: "S3ObjectName",
    #         version: "S3ObjectVersion",
    #       },
    #     },
    #     max_faces: 1,
    #     face_match_threshold: 1.0,
    #     quality_filter: "NONE", # accepts NONE, AUTO, LOW, MEDIUM, HIGH
    #   })
    #
    # @example Response structure
    #
    #   resp.searched_face_bounding_box.width #=> Float
    #   resp.searched_face_bounding_box.height #=> Float
    #   resp.searched_face_bounding_box.left #=> Float
    #   resp.searched_face_bounding_box.top #=> Float
    #   resp.searched_face_confidence #=> Float
    #   resp.face_matches #=> Array
    #   resp.face_matches[0].similarity #=> Float
    #   resp.face_matches[0].face.face_id #=> String
    #   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.image_id #=> String
    #   resp.face_matches[0].face.external_image_id #=> String
    #   resp.face_matches[0].face.confidence #=> Float
    #   resp.face_matches[0].face.index_faces_model_version #=> String
    #   resp.face_matches[0].face.user_id #=> String
    #   resp.face_model_version #=> String
    #
    # @overload search_faces_by_image(params = {})
    # @param [Hash] params ({})
    def search_faces_by_image(params = {}, options = {})
      req = build_request(:search_faces_by_image, params)
      req.send_request(options)
    end

    # Searches for UserIDs within a collection based on a `FaceId` or
    # `UserId`. This API can be used to find the closest UserID (with a
    # highest similarity) to associate a face. The request must be provided
    # with either `FaceId` or `UserId`. The operation returns an array of
    # UserID that match the `FaceId` or `UserId`, ordered by similarity
    # score with the highest similarity first.
    #
    # @option params [required, String] :collection_id
    #   The ID of an existing collection containing the UserID, used with a
    #   UserId or FaceId. If a FaceId is provided, UserId isn’t required to be
    #   present in the Collection.
    #
    # @option params [String] :user_id
    #   ID for the existing User.
    #
    # @option params [String] :face_id
    #   ID for the existing face.
    #
    # @option params [Float] :user_match_threshold
    #   Optional value that specifies the minimum confidence in the matched
    #   UserID to return. Default value of 80.
    #
    # @option params [Integer] :max_users
    #   Maximum number of identities to return.
    #
    # @return [Types::SearchUsersResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::SearchUsersResponse#user_matches #user_matches} => Array&lt;Types::UserMatch&gt;
    #   * {Types::SearchUsersResponse#face_model_version #face_model_version} => String
    #   * {Types::SearchUsersResponse#searched_face #searched_face} => Types::SearchedFace
    #   * {Types::SearchUsersResponse#searched_user #searched_user} => Types::SearchedUser
    #
    #
    # @example Example: SearchUsers
    #
    #   # Searches for UserIDs within a collection based on a FaceId or UserId.
    #
    #   resp = client.search_users({
    #     collection_id: "MyCollection", 
    #     max_users: 2, 
    #     user_id: "DemoUser", 
    #     user_match_threshold: 70, 
    #   })
    #
    #   resp.to_h outputs the following:
    #   {
    #     face_model_version: "6", 
    #     searched_user: {
    #       user_id: "DemoUser", 
    #     }, 
    #     user_matches: [
    #       {
    #         similarity: 99.88186645507812, 
    #         user: {
    #           user_id: "demoUser1", 
    #           user_status: "ACTIVE", 
    #         }, 
    #       }, 
    #     ], 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.search_users({
    #     collection_id: "CollectionId", # required
    #     user_id: "UserId",
    #     face_id: "FaceId",
    #     user_match_threshold: 1.0,
    #     max_users: 1,
    #   })
    #
    # @example Response structure
    #
    #   resp.user_matches #=> Array
    #   resp.user_matches[0].similarity #=> Float
    #   resp.user_matches[0].user.user_id #=> String
    #   resp.user_matches[0].user.user_status #=> String, one of "ACTIVE", "UPDATING", "CREATING", "CREATED"
    #   resp.face_model_version #=> String
    #   resp.searched_face.face_id #=> String
    #   resp.searched_user.user_id #=> String
    #
    # @overload search_users(params = {})
    # @param [Hash] params ({})
    def search_users(params = {}, options = {})
      req = build_request(:search_users, params)
      req.send_request(options)
    end

    # Searches for UserIDs using a supplied image. It first detects the
    # largest face in the image, and then searches a specified collection
    # for matching UserIDs.
    #
    # The operation returns an array of UserIDs that match the face in the
    # supplied image, ordered by similarity score with the highest
    # similarity first. It also returns a bounding box for the face found in
    # the input image.
    #
    # Information about faces detected in the supplied image, but not used
    # for the search, is returned in an array of `UnsearchedFace` objects.
    # If no valid face is detected in the image, the response will contain
    # an empty `UserMatches` list and no `SearchedFace` object.
    #
    # @option params [required, String] :collection_id
    #   The ID of an existing collection containing the UserID.
    #
    # @option params [required, Types::Image] :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.
    #
    # @option params [Float] :user_match_threshold
    #   Specifies the minimum confidence in the UserID match to return.
    #   Default value is 80.
    #
    # @option params [Integer] :max_users
    #   Maximum number of UserIDs to return.
    #
    # @option params [String] :quality_filter
    #   A filter that specifies a quality bar for how much filtering is done
    #   to identify faces. Filtered faces aren't searched for in the
    #   collection. The default value is NONE.
    #
    # @return [Types::SearchUsersByImageResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::SearchUsersByImageResponse#user_matches #user_matches} => Array&lt;Types::UserMatch&gt;
    #   * {Types::SearchUsersByImageResponse#face_model_version #face_model_version} => String
    #   * {Types::SearchUsersByImageResponse#searched_face #searched_face} => Types::SearchedFaceDetails
    #   * {Types::SearchUsersByImageResponse#unsearched_faces #unsearched_faces} => Array&lt;Types::UnsearchedFace&gt;
    #
    #
    # @example Example: SearchUsersByImage
    #
    #   # Searches for UserIDs using a supplied image.
    #
    #   resp = client.search_users_by_image({
    #     collection_id: "MyCollection", 
    #     image: {
    #       s3_object: {
    #         bucket: "bucket", 
    #         name: "input.jpg", 
    #       }, 
    #     }, 
    #     max_users: 2, 
    #     quality_filter: "MEDIUM", 
    #     user_match_threshold: 70, 
    #   })
    #
    #   resp.to_h outputs the following:
    #   {
    #     face_model_version: "6", 
    #     searched_face: {
    #       face_detail: {
    #         bounding_box: {
    #           height: 0.07510016113519669, 
    #           left: 0.3598678708076477, 
    #           top: 0.5391526818275452, 
    #           width: 0.03692837432026863, 
    #         }, 
    #       }, 
    #     }, 
    #     unsearched_faces: [
    #       {
    #         face_details: {
    #           bounding_box: {
    #             height: 0.0682177022099495, 
    #             left: 0.6102562546730042, 
    #             top: 0.5593535900115967, 
    #             width: 0.031677018851041794, 
    #           }, 
    #         }, 
    #         reasons: [
    #           "FACE_NOT_LARGEST", 
    #         ], 
    #       }, 
    #       {
    #         face_details: {
    #           bounding_box: {
    #             height: 0.06347997486591339, 
    #             left: 0.516062319278717, 
    #             top: 0.6080358028411865, 
    #             width: 0.03254449740052223, 
    #           }, 
    #         }, 
    #         reasons: [
    #           "FACE_NOT_LARGEST", 
    #         ], 
    #       }, 
    #     ], 
    #     user_matches: [
    #       {
    #         similarity: 99.88186645507812, 
    #         user: {
    #           user_id: "demoUser1", 
    #           user_status: "ACTIVE", 
    #         }, 
    #       }, 
    #     ], 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.search_users_by_image({
    #     collection_id: "CollectionId", # required
    #     image: { # required
    #       bytes: "data",
    #       s3_object: {
    #         bucket: "S3Bucket",
    #         name: "S3ObjectName",
    #         version: "S3ObjectVersion",
    #       },
    #     },
    #     user_match_threshold: 1.0,
    #     max_users: 1,
    #     quality_filter: "NONE", # accepts NONE, AUTO, LOW, MEDIUM, HIGH
    #   })
    #
    # @example Response structure
    #
    #   resp.user_matches #=> Array
    #   resp.user_matches[0].similarity #=> Float
    #   resp.user_matches[0].user.user_id #=> String
    #   resp.user_matches[0].user.user_status #=> String, one of "ACTIVE", "UPDATING", "CREATING", "CREATED"
    #   resp.face_model_version #=> String
    #   resp.searched_face.face_detail.bounding_box.width #=> Float
    #   resp.searched_face.face_detail.bounding_box.height #=> Float
    #   resp.searched_face.face_detail.bounding_box.left #=> Float
    #   resp.searched_face.face_detail.bounding_box.top #=> Float
    #   resp.searched_face.face_detail.age_range.low #=> Integer
    #   resp.searched_face.face_detail.age_range.high #=> Integer
    #   resp.searched_face.face_detail.smile.value #=> Boolean
    #   resp.searched_face.face_detail.smile.confidence #=> Float
    #   resp.searched_face.face_detail.eyeglasses.value #=> Boolean
    #   resp.searched_face.face_detail.eyeglasses.confidence #=> Float
    #   resp.searched_face.face_detail.sunglasses.value #=> Boolean
    #   resp.searched_face.face_detail.sunglasses.confidence #=> Float
    #   resp.searched_face.face_detail.gender.value #=> String, one of "Male", "Female"
    #   resp.searched_face.face_detail.gender.confidence #=> Float
    #   resp.searched_face.face_detail.beard.value #=> Boolean
    #   resp.searched_face.face_detail.beard.confidence #=> Float
    #   resp.searched_face.face_detail.mustache.value #=> Boolean
    #   resp.searched_face.face_detail.mustache.confidence #=> Float
    #   resp.searched_face.face_detail.eyes_open.value #=> Boolean
    #   resp.searched_face.face_detail.eyes_open.confidence #=> Float
    #   resp.searched_face.face_detail.mouth_open.value #=> Boolean
    #   resp.searched_face.face_detail.mouth_open.confidence #=> Float
    #   resp.searched_face.face_detail.emotions #=> Array
    #   resp.searched_face.face_detail.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
    #   resp.searched_face.face_detail.emotions[0].confidence #=> Float
    #   resp.searched_face.face_detail.landmarks #=> Array
    #   resp.searched_face.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.searched_face.face_detail.landmarks[0].x #=> Float
    #   resp.searched_face.face_detail.landmarks[0].y #=> Float
    #   resp.searched_face.face_detail.pose.roll #=> Float
    #   resp.searched_face.face_detail.pose.yaw #=> Float
    #   resp.searched_face.face_detail.pose.pitch #=> Float
    #   resp.searched_face.face_detail.quality.brightness #=> Float
    #   resp.searched_face.face_detail.quality.sharpness #=> Float
    #   resp.searched_face.face_detail.confidence #=> Float
    #   resp.searched_face.face_detail.face_occluded.value #=> Boolean
    #   resp.searched_face.face_detail.face_occluded.confidence #=> Float
    #   resp.searched_face.face_detail.eye_direction.yaw #=> Float
    #   resp.searched_face.face_detail.eye_direction.pitch #=> Float
    #   resp.searched_face.face_detail.eye_direction.confidence #=> Float
    #   resp.unsearched_faces #=> Array
    #   resp.unsearched_faces[0].face_details.bounding_box.width #=> Float
    #   resp.unsearched_faces[0].face_details.bounding_box.height #=> Float
    #   resp.unsearched_faces[0].face_details.bounding_box.left #=> Float
    #   resp.unsearched_faces[0].face_details.bounding_box.top #=> Float
    #   resp.unsearched_faces[0].face_details.age_range.low #=> Integer
    #   resp.unsearched_faces[0].face_details.age_range.high #=> Integer
    #   resp.unsearched_faces[0].face_details.smile.value #=> Boolean
    #   resp.unsearched_faces[0].face_details.smile.confidence #=> Float
    #   resp.unsearched_faces[0].face_details.eyeglasses.value #=> Boolean
    #   resp.unsearched_faces[0].face_details.eyeglasses.confidence #=> Float
    #   resp.unsearched_faces[0].face_details.sunglasses.value #=> Boolean
    #   resp.unsearched_faces[0].face_details.sunglasses.confidence #=> Float
    #   resp.unsearched_faces[0].face_details.gender.value #=> String, one of "Male", "Female"
    #   resp.unsearched_faces[0].face_details.gender.confidence #=> Float
    #   resp.unsearched_faces[0].face_details.beard.value #=> Boolean
    #   resp.unsearched_faces[0].face_details.beard.confidence #=> Float
    #   resp.unsearched_faces[0].face_details.mustache.value #=> Boolean
    #   resp.unsearched_faces[0].face_details.mustache.confidence #=> Float
    #   resp.unsearched_faces[0].face_details.eyes_open.value #=> Boolean
    #   resp.unsearched_faces[0].face_details.eyes_open.confidence #=> Float
    #   resp.unsearched_faces[0].face_details.mouth_open.value #=> Boolean
    #   resp.unsearched_faces[0].face_details.mouth_open.confidence #=> Float
    #   resp.unsearched_faces[0].face_details.emotions #=> Array
    #   resp.unsearched_faces[0].face_details.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
    #   resp.unsearched_faces[0].face_details.emotions[0].confidence #=> Float
    #   resp.unsearched_faces[0].face_details.landmarks #=> Array
    #   resp.unsearched_faces[0].face_details.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.unsearched_faces[0].face_details.landmarks[0].x #=> Float
    #   resp.unsearched_faces[0].face_details.landmarks[0].y #=> Float
    #   resp.unsearched_faces[0].face_details.pose.roll #=> Float
    #   resp.unsearched_faces[0].face_details.pose.yaw #=> Float
    #   resp.unsearched_faces[0].face_details.pose.pitch #=> Float
    #   resp.unsearched_faces[0].face_details.quality.brightness #=> Float
    #   resp.unsearched_faces[0].face_details.quality.sharpness #=> Float
    #   resp.unsearched_faces[0].face_details.confidence #=> Float
    #   resp.unsearched_faces[0].face_details.face_occluded.value #=> Boolean
    #   resp.unsearched_faces[0].face_details.face_occluded.confidence #=> Float
    #   resp.unsearched_faces[0].face_details.eye_direction.yaw #=> Float
    #   resp.unsearched_faces[0].face_details.eye_direction.pitch #=> Float
    #   resp.unsearched_faces[0].face_details.eye_direction.confidence #=> Float
    #   resp.unsearched_faces[0].reasons #=> Array
    #   resp.unsearched_faces[0].reasons[0] #=> String, one of "FACE_NOT_LARGEST", "EXCEEDS_MAX_FACES", "EXTREME_POSE", "LOW_BRIGHTNESS", "LOW_SHARPNESS", "LOW_CONFIDENCE", "SMALL_BOUNDING_BOX", "LOW_FACE_QUALITY"
    #
    # @overload search_users_by_image(params = {})
    # @param [Hash] params ({})
    def search_users_by_image(params = {}, options = {})
      req = build_request(:search_users_by_image, params)
      req.send_request(options)
    end

    # Starts asynchronous recognition of celebrities in a stored video.
    #
    # Amazon Rekognition Video can detect celebrities in a video must be
    # stored in an Amazon S3 bucket. Use Video to specify the bucket name
    # and the filename of the video. `StartCelebrityRecognition` returns a
    # job identifier (`JobId`) which you use to get the results of the
    # analysis. When celebrity recognition analysis is finished, Amazon
    # Rekognition Video publishes a completion status to the Amazon Simple
    # Notification Service topic that you specify in `NotificationChannel`.
    # To get the results of the celebrity recognition analysis, first check
    # that the status value published to the Amazon SNS topic is
    # `SUCCEEDED`. If so, call GetCelebrityRecognition and pass the job
    # identifier (`JobId`) from the initial call to
    # `StartCelebrityRecognition`.
    #
    # For more information, see Recognizing celebrities in the Amazon
    # Rekognition Developer Guide.
    #
    # @option params [required, Types::Video] :video
    #   The video in which you want to recognize celebrities. The video must
    #   be stored in an Amazon S3 bucket.
    #
    # @option params [String] :client_request_token
    #   Idempotent token used to identify the start request. If you use the
    #   same token with multiple `StartCelebrityRecognition` requests, the
    #   same `JobId` is returned. Use `ClientRequestToken` to prevent the same
    #   job from being accidently started more than once.
    #
    # @option params [Types::NotificationChannel] :notification_channel
    #   The Amazon SNS topic ARN that you want Amazon Rekognition Video to
    #   publish the completion status of the celebrity recognition analysis
    #   to. The Amazon SNS topic must have a topic name that begins with
    #   *AmazonRekognition* if you are using the AmazonRekognitionServiceRole
    #   permissions policy.
    #
    # @option params [String] :job_tag
    #   An identifier you specify that's returned in the completion
    #   notification that's published to your Amazon Simple Notification
    #   Service topic. For example, you can use `JobTag` to group related jobs
    #   and identify them in the completion notification.
    #
    # @return [Types::StartCelebrityRecognitionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::StartCelebrityRecognitionResponse#job_id #job_id} => String
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.start_celebrity_recognition({
    #     video: { # required
    #       s3_object: {
    #         bucket: "S3Bucket",
    #         name: "S3ObjectName",
    #         version: "S3ObjectVersion",
    #       },
    #     },
    #     client_request_token: "ClientRequestToken",
    #     notification_channel: {
    #       sns_topic_arn: "SNSTopicArn", # required
    #       role_arn: "RoleArn", # required
    #     },
    #     job_tag: "JobTag",
    #   })
    #
    # @example Response structure
    #
    #   resp.job_id #=> String
    #
    # @overload start_celebrity_recognition(params = {})
    # @param [Hash] params ({})
    def start_celebrity_recognition(params = {}, options = {})
      req = build_request(:start_celebrity_recognition, params)
      req.send_request(options)
    end

    # Starts asynchronous detection of inappropriate, unwanted, or offensive
    # content in a stored video. For a list of moderation labels in Amazon
    # Rekognition, see [Using the image and video moderation APIs][1].
    #
    # Amazon Rekognition Video can moderate content in a video stored in an
    # Amazon S3 bucket. Use Video to specify the bucket name and the
    # filename of the video. `StartContentModeration` returns a job
    # identifier (`JobId`) which you use to get the results of the analysis.
    # When content analysis is finished, Amazon Rekognition Video publishes
    # a completion status to the Amazon Simple Notification Service topic
    # that you specify in `NotificationChannel`.
    #
    # To get the results of the 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 Moderating content in the Amazon Rekognition
    # Developer Guide.
    #
    #
    #
    # [1]: https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api
    #
    # @option params [required, Types::Video] :video
    #   The video in which you want to detect inappropriate, unwanted, or
    #   offensive content. The video must be stored in an Amazon S3 bucket.
    #
    # @option params [Float] :min_confidence
    #   Specifies the minimum confidence that Amazon Rekognition must have in
    #   order to return a moderated content label. Confidence represents how
    #   certain Amazon Rekognition is that the moderated content is correctly
    #   identified. 0 is the lowest confidence. 100 is the highest confidence.
    #   Amazon Rekognition doesn't return any moderated content labels with a
    #   confidence level lower than this specified value. If you don't
    #   specify `MinConfidence`, `GetContentModeration` returns labels with
    #   confidence values greater than or equal to 50 percent.
    #
    # @option params [String] :client_request_token
    #   Idempotent token used to identify the start request. If you use the
    #   same token with multiple `StartContentModeration` requests, the same
    #   `JobId` is returned. Use `ClientRequestToken` to prevent the same job
    #   from being accidently started more than once.
    #
    # @option params [Types::NotificationChannel] :notification_channel
    #   The Amazon SNS topic ARN that you want Amazon Rekognition Video to
    #   publish the completion status of the content analysis to. The Amazon
    #   SNS topic must have a topic name that begins with *AmazonRekognition*
    #   if you are using the AmazonRekognitionServiceRole permissions policy
    #   to access the topic.
    #
    # @option params [String] :job_tag
    #   An identifier you specify that's returned in the completion
    #   notification that's published to your Amazon Simple Notification
    #   Service topic. For example, you can use `JobTag` to group related jobs
    #   and identify them in the completion notification.
    #
    # @return [Types::StartContentModerationResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::StartContentModerationResponse#job_id #job_id} => String
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.start_content_moderation({
    #     video: { # required
    #       s3_object: {
    #         bucket: "S3Bucket",
    #         name: "S3ObjectName",
    #         version: "S3ObjectVersion",
    #       },
    #     },
    #     min_confidence: 1.0,
    #     client_request_token: "ClientRequestToken",
    #     notification_channel: {
    #       sns_topic_arn: "SNSTopicArn", # required
    #       role_arn: "RoleArn", # required
    #     },
    #     job_tag: "JobTag",
    #   })
    #
    # @example Response structure
    #
    #   resp.job_id #=> String
    #
    # @overload start_content_moderation(params = {})
    # @param [Hash] params ({})
    def start_content_moderation(params = {}, options = {})
      req = build_request(:start_content_moderation, params)
      req.send_request(options)
    end

    # Starts asynchronous detection of faces in a stored video.
    #
    # Amazon Rekognition Video can detect faces in a video stored in an
    # Amazon S3 bucket. Use Video to specify the bucket name and the
    # filename of the video. `StartFaceDetection` returns a job identifier
    # (`JobId`) that you use to get the results of the operation. When face
    # detection is finished, Amazon Rekognition Video publishes a completion
    # status to the Amazon Simple Notification Service topic that you
    # specify in `NotificationChannel`. To get the results of the 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`.
    #
    # For more information, see Detecting faces in a stored video in the
    # Amazon Rekognition Developer Guide.
    #
    # @option params [required, Types::Video] :video
    #   The video in which you want to detect faces. The video must be stored
    #   in an Amazon S3 bucket.
    #
    # @option params [String] :client_request_token
    #   Idempotent token used to identify the start request. If you use the
    #   same token with multiple `StartFaceDetection` requests, the same
    #   `JobId` is returned. Use `ClientRequestToken` to prevent the same job
    #   from being accidently started more than once.
    #
    # @option params [Types::NotificationChannel] :notification_channel
    #   The ARN of the Amazon SNS topic to which you want Amazon Rekognition
    #   Video to publish the completion status of the face detection
    #   operation. The Amazon SNS topic must have a topic name that begins
    #   with *AmazonRekognition* if you are using the
    #   AmazonRekognitionServiceRole permissions policy.
    #
    # @option params [String] :face_attributes
    #   The face attributes you want returned.
    #
    #   `DEFAULT` - The following subset of facial attributes are returned:
    #   BoundingBox, Confidence, Pose, Quality and Landmarks.
    #
    #   `ALL` - All facial attributes are returned.
    #
    # @option params [String] :job_tag
    #   An identifier you specify that's returned in the completion
    #   notification that's published to your Amazon Simple Notification
    #   Service topic. For example, you can use `JobTag` to group related jobs
    #   and identify them in the completion notification.
    #
    # @return [Types::StartFaceDetectionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::StartFaceDetectionResponse#job_id #job_id} => String
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.start_face_detection({
    #     video: { # required
    #       s3_object: {
    #         bucket: "S3Bucket",
    #         name: "S3ObjectName",
    #         version: "S3ObjectVersion",
    #       },
    #     },
    #     client_request_token: "ClientRequestToken",
    #     notification_channel: {
    #       sns_topic_arn: "SNSTopicArn", # required
    #       role_arn: "RoleArn", # required
    #     },
    #     face_attributes: "DEFAULT", # accepts DEFAULT, ALL
    #     job_tag: "JobTag",
    #   })
    #
    # @example Response structure
    #
    #   resp.job_id #=> String
    #
    # @overload start_face_detection(params = {})
    # @param [Hash] params ({})
    def start_face_detection(params = {}, options = {})
      req = build_request(:start_face_detection, params)
      req.send_request(options)
    end

    # Starts the asynchronous search for faces in a collection that match
    # the faces of persons detected in a stored video.
    #
    # The video must be stored in an Amazon S3 bucket. Use Video to specify
    # the bucket name and the filename of the video. `StartFaceSearch`
    # returns a job identifier (`JobId`) which you use to get the search
    # results once the search has completed. When searching is finished,
    # Amazon Rekognition Video publishes a completion status to the Amazon
    # Simple Notification Service topic that you specify in
    # `NotificationChannel`. 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 stored videos for faces][1].
    #
    #
    #
    # [1]: https://docs.aws.amazon.com/rekognition/latest/dg/procedure-person-search-videos.html
    #
    # @option params [required, Types::Video] :video
    #   The video you want to search. The video must be stored in an Amazon S3
    #   bucket.
    #
    # @option params [String] :client_request_token
    #   Idempotent token used to identify the start request. If you use the
    #   same token with multiple `StartFaceSearch` requests, the same `JobId`
    #   is returned. Use `ClientRequestToken` to prevent the same job from
    #   being accidently started more than once.
    #
    # @option params [Float] :face_match_threshold
    #   The minimum confidence in the person match to return. For example,
    #   don't return any matches where confidence in matches is less than
    #   70%. The default value is 80%.
    #
    # @option params [required, String] :collection_id
    #   ID of the collection that contains the faces you want to search for.
    #
    # @option params [Types::NotificationChannel] :notification_channel
    #   The ARN of the Amazon SNS topic to which you want Amazon Rekognition
    #   Video to publish the completion status of the search. The Amazon SNS
    #   topic must have a topic name that begins with *AmazonRekognition* if
    #   you are using the AmazonRekognitionServiceRole permissions policy to
    #   access the topic.
    #
    # @option params [String] :job_tag
    #   An identifier you specify that's returned in the completion
    #   notification that's published to your Amazon Simple Notification
    #   Service topic. For example, you can use `JobTag` to group related jobs
    #   and identify them in the completion notification.
    #
    # @return [Types::StartFaceSearchResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::StartFaceSearchResponse#job_id #job_id} => String
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.start_face_search({
    #     video: { # required
    #       s3_object: {
    #         bucket: "S3Bucket",
    #         name: "S3ObjectName",
    #         version: "S3ObjectVersion",
    #       },
    #     },
    #     client_request_token: "ClientRequestToken",
    #     face_match_threshold: 1.0,
    #     collection_id: "CollectionId", # required
    #     notification_channel: {
    #       sns_topic_arn: "SNSTopicArn", # required
    #       role_arn: "RoleArn", # required
    #     },
    #     job_tag: "JobTag",
    #   })
    #
    # @example Response structure
    #
    #   resp.job_id #=> String
    #
    # @overload start_face_search(params = {})
    # @param [Hash] params ({})
    def start_face_search(params = {}, options = {})
      req = build_request(:start_face_search, params)
      req.send_request(options)
    end

    # Starts asynchronous detection of labels in a stored video.
    #
    # Amazon Rekognition Video can detect labels in a video. Labels are
    # instances of real-world entities. This includes objects like flower,
    # tree, and table; events like wedding, graduation, and birthday party;
    # concepts like landscape, evening, and nature; and activities like a
    # person getting out of a car or a person skiing.
    #
    # The video must be stored in an Amazon S3 bucket. Use Video to specify
    # the bucket name and the filename of the video. `StartLabelDetection`
    # returns a job identifier (`JobId`) which you use to get the results of
    # the operation. When label detection is finished, Amazon Rekognition
    # Video publishes a completion status to the Amazon Simple Notification
    # Service topic that you specify in `NotificationChannel`.
    #
    # To get the results of the label detection operation, first check that
    # the status value published to the Amazon SNS topic is `SUCCEEDED`. If
    # so, call GetLabelDetection and pass the job identifier (`JobId`) from
    # the initial call to `StartLabelDetection`.
    #
    # *Optional Parameters*
    #
    # `StartLabelDetection` has the `GENERAL_LABELS` Feature applied by
    # default. This feature allows you to provide filtering criteria to the
    # `Settings` parameter. You can filter with sets of individual labels or
    # with label categories. You can specify inclusive filters, exclusive
    # filters, or a combination of inclusive and exclusive filters. For more
    # information on filtering, see [Detecting labels in a video][1].
    #
    # You can specify `MinConfidence` to control the confidence threshold
    # for the labels returned. The default is 50.
    #
    #
    #
    # [1]: https://docs.aws.amazon.com/rekognition/latest/dg/labels-detecting-labels-video.html
    #
    # @option params [required, Types::Video] :video
    #   The video in which you want to detect labels. The video must be stored
    #   in an Amazon S3 bucket.
    #
    # @option params [String] :client_request_token
    #   Idempotent token used to identify the start request. If you use the
    #   same token with multiple `StartLabelDetection` requests, the same
    #   `JobId` is returned. Use `ClientRequestToken` to prevent the same job
    #   from being accidently started more than once.
    #
    # @option params [Float] :min_confidence
    #   Specifies the minimum confidence that Amazon Rekognition Video must
    #   have in order to return a detected label. Confidence represents how
    #   certain Amazon Rekognition is that a label is correctly identified.0
    #   is the lowest confidence. 100 is the highest confidence. Amazon
    #   Rekognition Video doesn't return any labels with a confidence level
    #   lower than this specified value.
    #
    #   If you don't specify `MinConfidence`, the operation returns labels
    #   and bounding boxes (if detected) with confidence values greater than
    #   or equal to 50 percent.
    #
    # @option params [Types::NotificationChannel] :notification_channel
    #   The Amazon SNS topic ARN you want Amazon Rekognition Video to publish
    #   the completion status of the label detection operation to. The Amazon
    #   SNS topic must have a topic name that begins with *AmazonRekognition*
    #   if you are using the AmazonRekognitionServiceRole permissions policy.
    #
    # @option params [String] :job_tag
    #   An identifier you specify that's returned in the completion
    #   notification that's published to your Amazon Simple Notification
    #   Service topic. For example, you can use `JobTag` to group related jobs
    #   and identify them in the completion notification.
    #
    # @option params [Array<String>] :features
    #   The features to return after video analysis. You can specify that
    #   GENERAL\_LABELS are returned.
    #
    # @option params [Types::LabelDetectionSettings] :settings
    #   The settings for a StartLabelDetection request.Contains the specified
    #   parameters for the label detection request of an asynchronous label
    #   analysis operation. Settings can include filters for GENERAL\_LABELS.
    #
    # @return [Types::StartLabelDetectionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::StartLabelDetectionResponse#job_id #job_id} => String
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.start_label_detection({
    #     video: { # required
    #       s3_object: {
    #         bucket: "S3Bucket",
    #         name: "S3ObjectName",
    #         version: "S3ObjectVersion",
    #       },
    #     },
    #     client_request_token: "ClientRequestToken",
    #     min_confidence: 1.0,
    #     notification_channel: {
    #       sns_topic_arn: "SNSTopicArn", # required
    #       role_arn: "RoleArn", # required
    #     },
    #     job_tag: "JobTag",
    #     features: ["GENERAL_LABELS"], # accepts GENERAL_LABELS
    #     settings: {
    #       general_labels: {
    #         label_inclusion_filters: ["GeneralLabelsFilterValue"],
    #         label_exclusion_filters: ["GeneralLabelsFilterValue"],
    #         label_category_inclusion_filters: ["GeneralLabelsFilterValue"],
    #         label_category_exclusion_filters: ["GeneralLabelsFilterValue"],
    #       },
    #     },
    #   })
    #
    # @example Response structure
    #
    #   resp.job_id #=> String
    #
    # @overload start_label_detection(params = {})
    # @param [Hash] params ({})
    def start_label_detection(params = {}, options = {})
      req = build_request(:start_label_detection, params)
      req.send_request(options)
    end

    # Initiates a new media analysis job. Accepts a manifest file in an
    # Amazon S3 bucket. The output is a manifest file and a summary of the
    # manifest stored in the Amazon S3 bucket.
    #
    # @option params [String] :client_request_token
    #   Idempotency token used to prevent the accidental creation of duplicate
    #   versions. If you use the same token with multiple
    #   `StartMediaAnalysisJobRequest` 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.**
    #
    # @option params [String] :job_name
    #   The name of the job. Does not have to be unique.
    #
    # @option params [required, Types::MediaAnalysisOperationsConfig] :operations_config
    #   Configuration options for the media analysis job to be created.
    #
    # @option params [required, Types::MediaAnalysisInput] :input
    #   Input data to be analyzed by the job.
    #
    # @option params [required, Types::MediaAnalysisOutputConfig] :output_config
    #   The Amazon S3 bucket location to store the results.
    #
    # @option params [String] :kms_key_id
    #   The identifier of customer managed AWS KMS key (name or ARN). The key
    #   is used to encrypt images copied into the service. The key is also
    #   used to encrypt results and manifest files written to the output
    #   Amazon S3 bucket.
    #
    # @return [Types::StartMediaAnalysisJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::StartMediaAnalysisJobResponse#job_id #job_id} => String
    #
    #
    # @example Example: StartMediaAnalysisJob
    #
    #   # Initiates a new media analysis job.
    #
    #   resp = client.start_media_analysis_job({
    #     input: {
    #       s3_object: {
    #         bucket: "input-bucket", 
    #         name: "input-manifest.json", 
    #       }, 
    #     }, 
    #     job_name: "job-name", 
    #     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", 
    #     }, 
    #   })
    #
    #   resp.to_h outputs the following:
    #   {
    #     job_id: "861a0645d98ef88efb75477628c011c04942d9d5f58faf2703c393c8cf8c1537", 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.start_media_analysis_job({
    #     client_request_token: "ClientRequestToken",
    #     job_name: "MediaAnalysisJobName",
    #     operations_config: { # required
    #       detect_moderation_labels: {
    #         min_confidence: 1.0,
    #         project_version: "ProjectVersionId",
    #       },
    #     },
    #     input: { # required
    #       s3_object: { # required
    #         bucket: "S3Bucket",
    #         name: "S3ObjectName",
    #         version: "S3ObjectVersion",
    #       },
    #     },
    #     output_config: { # required
    #       s3_bucket: "S3Bucket", # required
    #       s3_key_prefix: "MediaAnalysisS3KeyPrefix",
    #     },
    #     kms_key_id: "KmsKeyId",
    #   })
    #
    # @example Response structure
    #
    #   resp.job_id #=> String
    #
    # @overload start_media_analysis_job(params = {})
    # @param [Hash] params ({})
    def start_media_analysis_job(params = {}, options = {})
      req = build_request(:start_media_analysis_job, params)
      req.send_request(options)
    end

    # <note markdown="1"> *End of support notice:* On October 31, 2025, AWS will discontinue
    # support for Amazon Rekognition People Pathing. After October 31, 2025,
    # you will no longer be able to use the Rekognition People Pathing
    # capability. For more information, visit this [blog post][1].
    #
    #  </note>
    #
    # Starts the asynchronous tracking of a person's path in a stored
    # video.
    #
    # Amazon Rekognition Video can track the path of people in a video
    # stored in an Amazon S3 bucket. Use Video to specify the bucket name
    # and the filename of the video. `StartPersonTracking` returns a job
    # identifier (`JobId`) which you use to get the results of the
    # operation. When label detection is finished, Amazon Rekognition
    # publishes a completion status to the Amazon Simple Notification
    # Service topic that you specify in `NotificationChannel`.
    #
    # To get the results of the person detection 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`.
    #
    #
    #
    # [1]: https://aws.amazon.com/blogs/machine-learning/transitioning-from-amazon-rekognition-people-pathing-exploring-other-alternatives/
    #
    # @option params [required, Types::Video] :video
    #   The video in which you want to detect people. The video must be stored
    #   in an Amazon S3 bucket.
    #
    # @option params [String] :client_request_token
    #   Idempotent token used to identify the start request. If you use the
    #   same token with multiple `StartPersonTracking` requests, the same
    #   `JobId` is returned. Use `ClientRequestToken` to prevent the same job
    #   from being accidently started more than once.
    #
    # @option params [Types::NotificationChannel] :notification_channel
    #   The Amazon SNS topic ARN you want Amazon Rekognition Video to publish
    #   the completion status of the people detection operation to. The Amazon
    #   SNS topic must have a topic name that begins with *AmazonRekognition*
    #   if you are using the AmazonRekognitionServiceRole permissions policy.
    #
    # @option params [String] :job_tag
    #   An identifier you specify that's returned in the completion
    #   notification that's published to your Amazon Simple Notification
    #   Service topic. For example, you can use `JobTag` to group related jobs
    #   and identify them in the completion notification.
    #
    # @return [Types::StartPersonTrackingResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::StartPersonTrackingResponse#job_id #job_id} => String
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.start_person_tracking({
    #     video: { # required
    #       s3_object: {
    #         bucket: "S3Bucket",
    #         name: "S3ObjectName",
    #         version: "S3ObjectVersion",
    #       },
    #     },
    #     client_request_token: "ClientRequestToken",
    #     notification_channel: {
    #       sns_topic_arn: "SNSTopicArn", # required
    #       role_arn: "RoleArn", # required
    #     },
    #     job_tag: "JobTag",
    #   })
    #
    # @example Response structure
    #
    #   resp.job_id #=> String
    #
    # @overload start_person_tracking(params = {})
    # @param [Hash] params ({})
    def start_person_tracking(params = {}, options = {})
      req = build_request(:start_person_tracking, params)
      req.send_request(options)
    end

    # <note markdown="1"> This operation applies only to Amazon Rekognition Custom Labels.
    #
    #  </note>
    #
    # Starts the running of the version of a model. Starting a model takes a
    # while to complete. To check the current state of the model, use
    # DescribeProjectVersions.
    #
    # Once the model is running, you can detect custom labels in new images
    # by calling DetectCustomLabels.
    #
    # <note markdown="1"> You are charged for the amount of time that the model is running. To
    # stop a running model, call StopProjectVersion.
    #
    #  </note>
    #
    # This operation requires permissions to perform the
    # `rekognition:StartProjectVersion` action.
    #
    # @option params [required, String] :project_version_arn
    #   The Amazon Resource Name(ARN) of the model version that you want to
    #   start.
    #
    # @option params [required, Integer] :min_inference_units
    #   The minimum number of inference units to use. A single inference unit
    #   represents 1 hour of processing.
    #
    #   Use a higher number to increase the TPS throughput of your model. You
    #   are charged for the number of inference units that you use.
    #
    # @option params [Integer] :max_inference_units
    #   The maximum number of inference units to use for auto-scaling the
    #   model. If you don't specify a value, Amazon Rekognition Custom Labels
    #   doesn't auto-scale the model.
    #
    # @return [Types::StartProjectVersionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::StartProjectVersionResponse#status #status} => String
    #
    #
    # @example Example: To start an Amazon Rekognition Custom Labels model
    #
    #   # Starts a version of an Amazon Rekognition Custom Labels model.
    #
    #   resp = client.start_project_version({
    #     max_inference_units: 1, 
    #     min_inference_units: 1, 
    #     project_version_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958", 
    #   })
    #
    #   resp.to_h outputs the following:
    #   {
    #     status: "STARTING", 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.start_project_version({
    #     project_version_arn: "ProjectVersionArn", # required
    #     min_inference_units: 1, # required
    #     max_inference_units: 1,
    #   })
    #
    # @example 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"
    #
    # @overload start_project_version(params = {})
    # @param [Hash] params ({})
    def start_project_version(params = {}, options = {})
      req = build_request(:start_project_version, params)
      req.send_request(options)
    end

    # Starts asynchronous detection of segment detection in a stored video.
    #
    # Amazon Rekognition Video can detect segments in a video stored in an
    # Amazon S3 bucket. Use Video to specify the bucket name and the
    # filename of the video. `StartSegmentDetection` returns a job
    # identifier (`JobId`) which you use to get the results of the
    # operation. When segment detection is finished, Amazon Rekognition
    # Video publishes a completion status to the Amazon Simple Notification
    # Service topic that you specify in `NotificationChannel`.
    #
    # You can use the `Filters` (StartSegmentDetectionFilters) input
    # parameter to specify the minimum detection confidence returned in the
    # response. Within `Filters`, use `ShotFilter`
    # (StartShotDetectionFilter) to filter detected shots. Use
    # `TechnicalCueFilter` (StartTechnicalCueDetectionFilter) to filter
    # technical cues.
    #
    # 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 to `StartSegmentDetection`.
    #
    # For more information, see Detecting video segments in stored video in
    # the Amazon Rekognition Developer Guide.
    #
    # @option params [required, Types::Video] :video
    #   Video file stored in an Amazon S3 bucket. Amazon Rekognition video
    #   start operations such as StartLabelDetection use `Video` to specify a
    #   video for analysis. The supported file formats are .mp4, .mov and
    #   .avi.
    #
    # @option params [String] :client_request_token
    #   Idempotent token used to identify the start request. If you use the
    #   same token with multiple `StartSegmentDetection` requests, the same
    #   `JobId` is returned. Use `ClientRequestToken` to prevent the same job
    #   from being accidently started more than once.
    #
    # @option params [Types::NotificationChannel] :notification_channel
    #   The ARN of the Amazon SNS topic to which you want Amazon Rekognition
    #   Video to publish the completion status of the segment detection
    #   operation. Note that the Amazon SNS topic must have a topic name that
    #   begins with *AmazonRekognition* if you are using the
    #   AmazonRekognitionServiceRole permissions policy to access the topic.
    #
    # @option params [String] :job_tag
    #   An identifier you specify that's returned in the completion
    #   notification that's published to your Amazon Simple Notification
    #   Service topic. For example, you can use `JobTag` to group related jobs
    #   and identify them in the completion notification.
    #
    # @option params [Types::StartSegmentDetectionFilters] :filters
    #   Filters for technical cue or shot detection.
    #
    # @option params [required, Array<String>] :segment_types
    #   An array of segment types to detect in the video. Valid values are
    #   TECHNICAL\_CUE and SHOT.
    #
    # @return [Types::StartSegmentDetectionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::StartSegmentDetectionResponse#job_id #job_id} => String
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.start_segment_detection({
    #     video: { # required
    #       s3_object: {
    #         bucket: "S3Bucket",
    #         name: "S3ObjectName",
    #         version: "S3ObjectVersion",
    #       },
    #     },
    #     client_request_token: "ClientRequestToken",
    #     notification_channel: {
    #       sns_topic_arn: "SNSTopicArn", # required
    #       role_arn: "RoleArn", # required
    #     },
    #     job_tag: "JobTag",
    #     filters: {
    #       technical_cue_filter: {
    #         min_segment_confidence: 1.0,
    #         black_frame: {
    #           max_pixel_threshold: 1.0,
    #           min_coverage_percentage: 1.0,
    #         },
    #       },
    #       shot_filter: {
    #         min_segment_confidence: 1.0,
    #       },
    #     },
    #     segment_types: ["TECHNICAL_CUE"], # required, accepts TECHNICAL_CUE, SHOT
    #   })
    #
    # @example Response structure
    #
    #   resp.job_id #=> String
    #
    # @overload start_segment_detection(params = {})
    # @param [Hash] params ({})
    def start_segment_detection(params = {}, options = {})
      req = build_request(:start_segment_detection, params)
      req.send_request(options)
    end

    # Starts processing a stream processor. You create a stream processor by
    # calling CreateStreamProcessor. To tell `StartStreamProcessor` which
    # stream processor to start, use the value of the `Name` field specified
    # in the call to `CreateStreamProcessor`.
    #
    # If you are using a label detection stream processor to detect labels,
    # you need to provide a `Start selector` and a `Stop selector` to
    # determine the length of the stream processing time.
    #
    # @option params [required, String] :name
    #   The name of the stream processor to start processing.
    #
    # @option params [Types::StreamProcessingStartSelector] :start_selector
    #   Specifies the starting point in the Kinesis stream to start
    #   processing. You can use the producer timestamp or the fragment number.
    #   If you use the producer timestamp, you must put the time in
    #   milliseconds. For more information about fragment numbers, see
    #   [Fragment][1].
    #
    #   This is a required parameter for label detection stream processors and
    #   should not be used to start a face search stream processor.
    #
    #
    #
    #   [1]: https://docs.aws.amazon.com/kinesisvideostreams/latest/dg/API_reader_Fragment.html
    #
    # @option params [Types::StreamProcessingStopSelector] :stop_selector
    #   Specifies when to stop processing the stream. You can specify a
    #   maximum amount of time to process the video.
    #
    #   This is a required parameter for label detection stream processors and
    #   should not be used to start a face search stream processor.
    #
    # @return [Types::StartStreamProcessorResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::StartStreamProcessorResponse#session_id #session_id} => String
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.start_stream_processor({
    #     name: "StreamProcessorName", # required
    #     start_selector: {
    #       kvs_stream_start_selector: {
    #         producer_timestamp: 1,
    #         fragment_number: "KinesisVideoStreamFragmentNumber",
    #       },
    #     },
    #     stop_selector: {
    #       max_duration_in_seconds: 1,
    #     },
    #   })
    #
    # @example Response structure
    #
    #   resp.session_id #=> String
    #
    # @overload start_stream_processor(params = {})
    # @param [Hash] params ({})
    def start_stream_processor(params = {}, options = {})
      req = build_request(:start_stream_processor, params)
      req.send_request(options)
    end

    # Starts asynchronous detection of text in a stored video.
    #
    # Amazon Rekognition Video can detect text in a video stored in an
    # Amazon S3 bucket. Use Video to specify the bucket name and the
    # filename of the video. `StartTextDetection` returns a job identifier
    # (`JobId`) which you use to get the results of the operation. When text
    # detection is finished, Amazon Rekognition Video publishes a completion
    # status to the Amazon Simple Notification Service topic that you
    # specify in `NotificationChannel`.
    #
    # To get the results of the 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 to `StartTextDetection`.
    #
    # @option params [required, Types::Video] :video
    #   Video file stored in an Amazon S3 bucket. Amazon Rekognition video
    #   start operations such as StartLabelDetection use `Video` to specify a
    #   video for analysis. The supported file formats are .mp4, .mov and
    #   .avi.
    #
    # @option params [String] :client_request_token
    #   Idempotent token used to identify the start request. If you use the
    #   same token with multiple `StartTextDetection` requests, the same
    #   `JobId` is returned. Use `ClientRequestToken` to prevent the same job
    #   from being accidentaly started more than once.
    #
    # @option params [Types::NotificationChannel] :notification_channel
    #   The Amazon Simple Notification Service topic to which Amazon
    #   Rekognition publishes the completion status of a video analysis
    #   operation. For more information, see [Calling Amazon Rekognition Video
    #   operations][1]. Note that the Amazon SNS topic must have a topic name
    #   that begins with *AmazonRekognition* if you are using the
    #   AmazonRekognitionServiceRole permissions policy to access the topic.
    #   For more information, see [Giving access to multiple Amazon SNS
    #   topics][2].
    #
    #
    #
    #   [1]: https://docs.aws.amazon.com/rekognition/latest/dg/api-video.html
    #   [2]: https://docs.aws.amazon.com/rekognition/latest/dg/api-video-roles.html#api-video-roles-all-topics
    #
    # @option params [String] :job_tag
    #   An identifier returned in the completion status published by your
    #   Amazon Simple Notification Service topic. For example, you can use
    #   `JobTag` to group related jobs and identify them in the completion
    #   notification.
    #
    # @option params [Types::StartTextDetectionFilters] :filters
    #   Optional parameters that let you set criteria the text must meet to be
    #   included in your response.
    #
    # @return [Types::StartTextDetectionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::StartTextDetectionResponse#job_id #job_id} => String
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.start_text_detection({
    #     video: { # required
    #       s3_object: {
    #         bucket: "S3Bucket",
    #         name: "S3ObjectName",
    #         version: "S3ObjectVersion",
    #       },
    #     },
    #     client_request_token: "ClientRequestToken",
    #     notification_channel: {
    #       sns_topic_arn: "SNSTopicArn", # required
    #       role_arn: "RoleArn", # required
    #     },
    #     job_tag: "JobTag",
    #     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,
    #             },
    #           ],
    #         },
    #       ],
    #     },
    #   })
    #
    # @example Response structure
    #
    #   resp.job_id #=> String
    #
    # @overload start_text_detection(params = {})
    # @param [Hash] params ({})
    def start_text_detection(params = {}, options = {})
      req = build_request(:start_text_detection, params)
      req.send_request(options)
    end

    # <note markdown="1"> This operation applies only to Amazon Rekognition Custom Labels.
    #
    #  </note>
    #
    # Stops a running model. The operation might take a while to complete.
    # To check the current status, call DescribeProjectVersions. Only
    # applies to Custom Labels projects.
    #
    # This operation requires permissions to perform the
    # `rekognition:StopProjectVersion` action.
    #
    # @option params [required, String] :project_version_arn
    #   The Amazon Resource Name (ARN) of the model version that you want to
    #   stop.
    #
    #   This operation requires permissions to perform the
    #   `rekognition:StopProjectVersion` action.
    #
    # @return [Types::StopProjectVersionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
    #
    #   * {Types::StopProjectVersionResponse#status #status} => String
    #
    #
    # @example Example: To stop an Amazon Rekognition Custom Labels model.
    #
    #   # Stops a version of an Amazon Rekognition Custom Labels model.
    #
    #   resp = client.stop_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: "STOPPING", 
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.stop_project_version({
    #     project_version_arn: "ProjectVersionArn", # required
    #   })
    #
    # @example 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"
    #
    # @overload stop_project_version(params = {})
    # @param [Hash] params ({})
    def stop_project_version(params = {}, options = {})
      req = build_request(:stop_project_version, params)
      req.send_request(options)
    end

    # Stops a running stream processor that was created by
    # CreateStreamProcessor.
    #
    # @option params [required, String] :name
    #   The name of a stream processor created by CreateStreamProcessor.
    #
    # @return [Struct] Returns an empty {Seahorse::Client::Response response}.
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.stop_stream_processor({
    #     name: "StreamProcessorName", # required
    #   })
    #
    # @overload stop_stream_processor(params = {})
    # @param [Hash] params ({})
    def stop_stream_processor(params = {}, options = {})
      req = build_request(:stop_stream_processor, params)
      req.send_request(options)
    end

    # Adds one or more key-value tags to an Amazon Rekognition collection,
    # stream processor, or Custom Labels model. For more information, see
    # [Tagging AWS Resources][1].
    #
    # This operation requires permissions to perform the
    # `rekognition:TagResource` action.
    #
    #
    #
    # [1]: https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html
    #
    # @option params [required, String] :resource_arn
    #   Amazon Resource Name (ARN) of the model, collection, or stream
    #   processor that you want to assign the tags to.
    #
    # @option params [required, Hash<String,String>] :tags
    #   The key-value tags to assign to the resource.
    #
    # @return [Struct] Returns an empty {Seahorse::Client::Response response}.
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.tag_resource({
    #     resource_arn: "ResourceArn", # required
    #     tags: { # required
    #       "TagKey" => "TagValue",
    #     },
    #   })
    #
    # @overload tag_resource(params = {})
    # @param [Hash] params ({})
    def tag_resource(params = {}, options = {})
      req = build_request(:tag_resource, params)
      req.send_request(options)
    end

    # Removes one or more tags from an Amazon Rekognition collection, stream
    # processor, or Custom Labels model.
    #
    # This operation requires permissions to perform the
    # `rekognition:UntagResource` action.
    #
    # @option params [required, String] :resource_arn
    #   Amazon Resource Name (ARN) of the model, collection, or stream
    #   processor that you want to remove the tags from.
    #
    # @option params [required, Array<String>] :tag_keys
    #   A list of the tags that you want to remove.
    #
    # @return [Struct] Returns an empty {Seahorse::Client::Response response}.
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.untag_resource({
    #     resource_arn: "ResourceArn", # required
    #     tag_keys: ["TagKey"], # required
    #   })
    #
    # @overload untag_resource(params = {})
    # @param [Hash] params ({})
    def untag_resource(params = {}, options = {})
      req = build_request(:untag_resource, params)
      req.send_request(options)
    end

    # <note markdown="1"> This operation applies only to Amazon Rekognition Custom Labels.
    #
    #  </note>
    #
    # Adds or updates one or more entries (images) in a dataset. An entry is
    # a JSON Line which contains the information for a single image,
    # including the image location, assigned labels, and object location
    # bounding boxes. For more information, see Image-Level labels in
    # manifest files and Object localization in manifest files in the
    # *Amazon Rekognition Custom Labels Developer Guide*.
    #
    # If the `source-ref` field in the JSON line references an existing
    # image, the existing image in the dataset is updated. If `source-ref`
    # field doesn't reference an existing image, the image is added as a
    # new image to the dataset.
    #
    # You specify the changes that you want to make in the `Changes` input
    # parameter. There isn't a limit to the number JSON Lines that you can
    # change, but the size of `Changes` must be less than 5MB.
    #
    # `UpdateDatasetEntries` returns immediatly, but the dataset update
    # might take a while to complete. Use DescribeDataset to check the
    # current status. The dataset updated successfully if the value of
    # `Status` is `UPDATE_COMPLETE`.
    #
    # To check if any non-terminal errors occured, call ListDatasetEntries
    # and check for the presence of `errors` lists in the JSON Lines.
    #
    # Dataset update fails if a terminal error occurs (`Status` =
    # `UPDATE_FAILED`). Currently, you can't access the terminal error
    # information from the Amazon Rekognition Custom Labels SDK.
    #
    # This operation requires permissions to perform the
    # `rekognition:UpdateDatasetEntries` action.
    #
    # @option params [required, String] :dataset_arn
    #   The Amazon Resource Name (ARN) of the dataset that you want to update.
    #
    # @option params [required, Types::DatasetChanges] :changes
    #   The changes that you want to make to the dataset.
    #
    # @return [Struct] Returns an empty {Seahorse::Client::Response response}.
    #
    #
    # @example Example: To-add dataset entries to an Amazon Rekognition Custom Labels dataset
    #
    #   # Adds dataset entries to an Amazon Rekognition Custom Labels dataset.
    #
    #   resp = client.update_dataset_entries({
    #     changes: {
    #       ground_truth: "{\"source-ref\":\"s3://custom-labels-console-us-east-1-111111111/assets/flowers_1_test_dataset/mediterranean_spurge4.jpg\",\"mediterranean_spurge\":1,\"mediterranean_spurge-metadata\":{\"confidence\":1,\"job-name\":\"labeling-job/mediterranean_spurge\",\"class-name\":\"mediterranean_spurge\",\"human-annotated\":\"yes\",\"creation-date\":\"2021-07-11T03:33:42.025Z\",\"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\":\"2021-07-11T03:33:42.025Z\",\"type\":\"groundtruth/image-classification\"}}", 
    #     }, 
    #     dataset_arn: "arn:aws:rekognition:us-east-1:111122223333:project/my-proj-2/dataset/train/1690564858106", 
    #   })
    #
    #   resp.to_h outputs the following:
    #   {
    #   }
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.update_dataset_entries({
    #     dataset_arn: "DatasetArn", # required
    #     changes: { # required
    #       ground_truth: "data", # required
    #     },
    #   })
    #
    # @overload update_dataset_entries(params = {})
    # @param [Hash] params ({})
    def update_dataset_entries(params = {}, options = {})
      req = build_request(:update_dataset_entries, params)
      req.send_request(options)
    end

    # Allows you to update a stream processor. You can change some settings
    # and regions of interest and delete certain parameters.
    #
    # @option params [required, String] :name
    #   Name of the stream processor that you want to update.
    #
    # @option params [Types::StreamProcessorSettingsForUpdate] :settings_for_update
    #   The stream processor settings that you want to update. Label detection
    #   settings can be updated to detect different labels with a different
    #   minimum confidence.
    #
    # @option params [Array<Types::RegionOfInterest>] :regions_of_interest_for_update
    #   Specifies locations in the frames where Amazon Rekognition checks for
    #   objects or people. This is an optional parameter for label detection
    #   stream processors.
    #
    # @option params [Types::StreamProcessorDataSharingPreference] :data_sharing_preference_for_update
    #   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.
    #
    # @option params [Array<String>] :parameters_to_delete
    #   A list of parameters you want to delete from the stream processor.
    #
    # @return [Struct] Returns an empty {Seahorse::Client::Response response}.
    #
    # @example Request syntax with placeholder values
    #
    #   resp = client.update_stream_processor({
    #     name: "StreamProcessorName", # required
    #     settings_for_update: {
    #       connected_home_for_update: {
    #         labels: ["ConnectedHomeLabel"],
    #         min_confidence: 1.0,
    #       },
    #     },
    #     regions_of_interest_for_update: [
    #       {
    #         bounding_box: {
    #           width: 1.0,
    #           height: 1.0,
    #           left: 1.0,
    #           top: 1.0,
    #         },
    #         polygon: [
    #           {
    #             x: 1.0,
    #             y: 1.0,
    #           },
    #         ],
    #       },
    #     ],
    #     data_sharing_preference_for_update: {
    #       opt_in: false, # required
    #     },
    #     parameters_to_delete: ["ConnectedHomeMinConfidence"], # accepts ConnectedHomeMinConfidence, RegionsOfInterest
    #   })
    #
    # @overload update_stream_processor(params = {})
    # @param [Hash] params ({})
    def update_stream_processor(params = {}, options = {})
      req = build_request(:update_stream_processor, params)
      req.send_request(options)
    end

    # @!endgroup

    # @param params ({})
    # @api private
    def build_request(operation_name, params = {})
      handlers = @handlers.for(operation_name)
      tracer = config.telemetry_provider.tracer_provider.tracer(
        Aws::Telemetry.module_to_tracer_name('Aws::Rekognition')
      )
      context = Seahorse::Client::RequestContext.new(
        operation_name: operation_name,
        operation: config.api.operation(operation_name),
        client: self,
        params: params,
        config: config,
        tracer: tracer
      )
      context[:gem_name] = 'aws-sdk-rekognition'
      context[:gem_version] = '1.122.0'
      Seahorse::Client::Request.new(handlers, context)
    end

    # Polls an API operation until a resource enters a desired state.
    #
    # ## Basic Usage
    #
    # A waiter will call an API operation until:
    #
    # * It is successful
    # * It enters a terminal state
    # * It makes the maximum number of attempts
    #
    # In between attempts, the waiter will sleep.
    #
    #     # polls in a loop, sleeping between attempts
    #     client.wait_until(waiter_name, params)
    #
    # ## Configuration
    #
    # You can configure the maximum number of polling attempts, and the
    # delay (in seconds) between each polling attempt. You can pass
    # configuration as the final arguments hash.
    #
    #     # poll for ~25 seconds
    #     client.wait_until(waiter_name, params, {
    #       max_attempts: 5,
    #       delay: 5,
    #     })
    #
    # ## Callbacks
    #
    # You can be notified before each polling attempt and before each
    # delay. If you throw `:success` or `:failure` from these callbacks,
    # it will terminate the waiter.
    #
    #     started_at = Time.now
    #     client.wait_until(waiter_name, params, {
    #
    #       # disable max attempts
    #       max_attempts: nil,
    #
    #       # poll for 1 hour, instead of a number of attempts
    #       before_wait: -> (attempts, response) do
    #         throw :failure if Time.now - started_at > 3600
    #       end
    #     })
    #
    # ## Handling Errors
    #
    # When a waiter is unsuccessful, it will raise an error.
    # All of the failure errors extend from
    # {Aws::Waiters::Errors::WaiterFailed}.
    #
    #     begin
    #       client.wait_until(...)
    #     rescue Aws::Waiters::Errors::WaiterFailed
    #       # resource did not enter the desired state in time
    #     end
    #
    # ## Valid Waiters
    #
    # The following table lists the valid waiter names, the operations they call,
    # and the default `:delay` and `:max_attempts` values.
    #
    # | waiter_name                        | params                             | :delay   | :max_attempts |
    # | ---------------------------------- | ---------------------------------- | -------- | ------------- |
    # | project_version_running            | {Client#describe_project_versions} | 30       | 40            |
    # | project_version_training_completed | {Client#describe_project_versions} | 120      | 360           |
    #
    # @raise [Errors::FailureStateError] Raised when the waiter terminates
    #   because the waiter has entered a state that it will not transition
    #   out of, preventing success.
    #
    # @raise [Errors::TooManyAttemptsError] Raised when the configured
    #   maximum number of attempts have been made, and the waiter is not
    #   yet successful.
    #
    # @raise [Errors::UnexpectedError] Raised when an error is encounted
    #   while polling for a resource that is not expected.
    #
    # @raise [Errors::NoSuchWaiterError] Raised when you request to wait
    #   for an unknown state.
    #
    # @return [Boolean] Returns `true` if the waiter was successful.
    # @param [Symbol] waiter_name
    # @param [Hash] params ({})
    # @param [Hash] options ({})
    # @option options [Integer] :max_attempts
    # @option options [Integer] :delay
    # @option options [Proc] :before_attempt
    # @option options [Proc] :before_wait
    def wait_until(waiter_name, params = {}, options = {})
      w = waiter(waiter_name, options)
      yield(w.waiter) if block_given? # deprecated
      w.wait(params)
    end

    # @api private
    # @deprecated
    def waiter_names
      waiters.keys
    end

    private

    # @param [Symbol] waiter_name
    # @param [Hash] options ({})
    def waiter(waiter_name, options = {})
      waiter_class = waiters[waiter_name]
      if waiter_class
        waiter_class.new(options.merge(client: self))
      else
        raise Aws::Waiters::Errors::NoSuchWaiterError.new(waiter_name, waiters.keys)
      end
    end

    def waiters
      {
        project_version_running: Waiters::ProjectVersionRunning,
        project_version_training_completed: Waiters::ProjectVersionTrainingCompleted
      }
    end

    class << self

      # @api private
      attr_reader :identifier

      # @api private
      def errors_module
        Errors
      end

    end
  end
end
