commonMain.aws.sdk.kotlin.services.rekognition.RekognitionClient.kt Maven / Gradle / Ivy
// Code generated by smithy-kotlin-codegen. DO NOT EDIT!
package aws.sdk.kotlin.services.rekognition
import aws.sdk.kotlin.runtime.auth.credentials.DefaultChainCredentialsProvider
import aws.sdk.kotlin.runtime.auth.credentials.internal.manage
import aws.sdk.kotlin.runtime.client.AwsSdkClientConfig
import aws.sdk.kotlin.runtime.config.AbstractAwsSdkClientFactory
import aws.sdk.kotlin.runtime.config.endpoints.resolveEndpointUrl
import aws.sdk.kotlin.runtime.config.profile.AwsProfile
import aws.sdk.kotlin.runtime.config.profile.AwsSharedConfig
import aws.sdk.kotlin.runtime.http.retries.AwsRetryPolicy
import aws.sdk.kotlin.services.rekognition.auth.DefaultRekognitionAuthSchemeProvider
import aws.sdk.kotlin.services.rekognition.auth.RekognitionAuthSchemeProvider
import aws.sdk.kotlin.services.rekognition.endpoints.DefaultRekognitionEndpointProvider
import aws.sdk.kotlin.services.rekognition.endpoints.RekognitionEndpointParameters
import aws.sdk.kotlin.services.rekognition.endpoints.RekognitionEndpointProvider
import aws.sdk.kotlin.services.rekognition.model.AssociateFacesRequest
import aws.sdk.kotlin.services.rekognition.model.AssociateFacesResponse
import aws.sdk.kotlin.services.rekognition.model.CompareFacesRequest
import aws.sdk.kotlin.services.rekognition.model.CompareFacesResponse
import aws.sdk.kotlin.services.rekognition.model.CopyProjectVersionRequest
import aws.sdk.kotlin.services.rekognition.model.CopyProjectVersionResponse
import aws.sdk.kotlin.services.rekognition.model.CreateCollectionRequest
import aws.sdk.kotlin.services.rekognition.model.CreateCollectionResponse
import aws.sdk.kotlin.services.rekognition.model.CreateDatasetRequest
import aws.sdk.kotlin.services.rekognition.model.CreateDatasetResponse
import aws.sdk.kotlin.services.rekognition.model.CreateFaceLivenessSessionRequest
import aws.sdk.kotlin.services.rekognition.model.CreateFaceLivenessSessionResponse
import aws.sdk.kotlin.services.rekognition.model.CreateProjectRequest
import aws.sdk.kotlin.services.rekognition.model.CreateProjectResponse
import aws.sdk.kotlin.services.rekognition.model.CreateProjectVersionRequest
import aws.sdk.kotlin.services.rekognition.model.CreateProjectVersionResponse
import aws.sdk.kotlin.services.rekognition.model.CreateStreamProcessorRequest
import aws.sdk.kotlin.services.rekognition.model.CreateStreamProcessorResponse
import aws.sdk.kotlin.services.rekognition.model.CreateUserRequest
import aws.sdk.kotlin.services.rekognition.model.CreateUserResponse
import aws.sdk.kotlin.services.rekognition.model.DeleteCollectionRequest
import aws.sdk.kotlin.services.rekognition.model.DeleteCollectionResponse
import aws.sdk.kotlin.services.rekognition.model.DeleteDatasetRequest
import aws.sdk.kotlin.services.rekognition.model.DeleteDatasetResponse
import aws.sdk.kotlin.services.rekognition.model.DeleteFacesRequest
import aws.sdk.kotlin.services.rekognition.model.DeleteFacesResponse
import aws.sdk.kotlin.services.rekognition.model.DeleteProjectPolicyRequest
import aws.sdk.kotlin.services.rekognition.model.DeleteProjectPolicyResponse
import aws.sdk.kotlin.services.rekognition.model.DeleteProjectRequest
import aws.sdk.kotlin.services.rekognition.model.DeleteProjectResponse
import aws.sdk.kotlin.services.rekognition.model.DeleteProjectVersionRequest
import aws.sdk.kotlin.services.rekognition.model.DeleteProjectVersionResponse
import aws.sdk.kotlin.services.rekognition.model.DeleteStreamProcessorRequest
import aws.sdk.kotlin.services.rekognition.model.DeleteStreamProcessorResponse
import aws.sdk.kotlin.services.rekognition.model.DeleteUserRequest
import aws.sdk.kotlin.services.rekognition.model.DeleteUserResponse
import aws.sdk.kotlin.services.rekognition.model.DescribeCollectionRequest
import aws.sdk.kotlin.services.rekognition.model.DescribeCollectionResponse
import aws.sdk.kotlin.services.rekognition.model.DescribeDatasetRequest
import aws.sdk.kotlin.services.rekognition.model.DescribeDatasetResponse
import aws.sdk.kotlin.services.rekognition.model.DescribeProjectVersionsRequest
import aws.sdk.kotlin.services.rekognition.model.DescribeProjectVersionsResponse
import aws.sdk.kotlin.services.rekognition.model.DescribeProjectsRequest
import aws.sdk.kotlin.services.rekognition.model.DescribeProjectsResponse
import aws.sdk.kotlin.services.rekognition.model.DescribeStreamProcessorRequest
import aws.sdk.kotlin.services.rekognition.model.DescribeStreamProcessorResponse
import aws.sdk.kotlin.services.rekognition.model.DetectCustomLabelsRequest
import aws.sdk.kotlin.services.rekognition.model.DetectCustomLabelsResponse
import aws.sdk.kotlin.services.rekognition.model.DetectFacesRequest
import aws.sdk.kotlin.services.rekognition.model.DetectFacesResponse
import aws.sdk.kotlin.services.rekognition.model.DetectLabelsRequest
import aws.sdk.kotlin.services.rekognition.model.DetectLabelsResponse
import aws.sdk.kotlin.services.rekognition.model.DetectModerationLabelsRequest
import aws.sdk.kotlin.services.rekognition.model.DetectModerationLabelsResponse
import aws.sdk.kotlin.services.rekognition.model.DetectProtectiveEquipmentRequest
import aws.sdk.kotlin.services.rekognition.model.DetectProtectiveEquipmentResponse
import aws.sdk.kotlin.services.rekognition.model.DetectTextRequest
import aws.sdk.kotlin.services.rekognition.model.DetectTextResponse
import aws.sdk.kotlin.services.rekognition.model.DisassociateFacesRequest
import aws.sdk.kotlin.services.rekognition.model.DisassociateFacesResponse
import aws.sdk.kotlin.services.rekognition.model.DistributeDatasetEntriesRequest
import aws.sdk.kotlin.services.rekognition.model.DistributeDatasetEntriesResponse
import aws.sdk.kotlin.services.rekognition.model.GetCelebrityInfoRequest
import aws.sdk.kotlin.services.rekognition.model.GetCelebrityInfoResponse
import aws.sdk.kotlin.services.rekognition.model.GetCelebrityRecognitionRequest
import aws.sdk.kotlin.services.rekognition.model.GetCelebrityRecognitionResponse
import aws.sdk.kotlin.services.rekognition.model.GetContentModerationRequest
import aws.sdk.kotlin.services.rekognition.model.GetContentModerationResponse
import aws.sdk.kotlin.services.rekognition.model.GetFaceDetectionRequest
import aws.sdk.kotlin.services.rekognition.model.GetFaceDetectionResponse
import aws.sdk.kotlin.services.rekognition.model.GetFaceLivenessSessionResultsRequest
import aws.sdk.kotlin.services.rekognition.model.GetFaceLivenessSessionResultsResponse
import aws.sdk.kotlin.services.rekognition.model.GetFaceSearchRequest
import aws.sdk.kotlin.services.rekognition.model.GetFaceSearchResponse
import aws.sdk.kotlin.services.rekognition.model.GetLabelDetectionRequest
import aws.sdk.kotlin.services.rekognition.model.GetLabelDetectionResponse
import aws.sdk.kotlin.services.rekognition.model.GetMediaAnalysisJobRequest
import aws.sdk.kotlin.services.rekognition.model.GetMediaAnalysisJobResponse
import aws.sdk.kotlin.services.rekognition.model.GetPersonTrackingRequest
import aws.sdk.kotlin.services.rekognition.model.GetPersonTrackingResponse
import aws.sdk.kotlin.services.rekognition.model.GetSegmentDetectionRequest
import aws.sdk.kotlin.services.rekognition.model.GetSegmentDetectionResponse
import aws.sdk.kotlin.services.rekognition.model.GetTextDetectionRequest
import aws.sdk.kotlin.services.rekognition.model.GetTextDetectionResponse
import aws.sdk.kotlin.services.rekognition.model.IndexFacesRequest
import aws.sdk.kotlin.services.rekognition.model.IndexFacesResponse
import aws.sdk.kotlin.services.rekognition.model.ListCollectionsRequest
import aws.sdk.kotlin.services.rekognition.model.ListCollectionsResponse
import aws.sdk.kotlin.services.rekognition.model.ListDatasetEntriesRequest
import aws.sdk.kotlin.services.rekognition.model.ListDatasetEntriesResponse
import aws.sdk.kotlin.services.rekognition.model.ListDatasetLabelsRequest
import aws.sdk.kotlin.services.rekognition.model.ListDatasetLabelsResponse
import aws.sdk.kotlin.services.rekognition.model.ListFacesRequest
import aws.sdk.kotlin.services.rekognition.model.ListFacesResponse
import aws.sdk.kotlin.services.rekognition.model.ListMediaAnalysisJobsRequest
import aws.sdk.kotlin.services.rekognition.model.ListMediaAnalysisJobsResponse
import aws.sdk.kotlin.services.rekognition.model.ListProjectPoliciesRequest
import aws.sdk.kotlin.services.rekognition.model.ListProjectPoliciesResponse
import aws.sdk.kotlin.services.rekognition.model.ListStreamProcessorsRequest
import aws.sdk.kotlin.services.rekognition.model.ListStreamProcessorsResponse
import aws.sdk.kotlin.services.rekognition.model.ListTagsForResourceRequest
import aws.sdk.kotlin.services.rekognition.model.ListTagsForResourceResponse
import aws.sdk.kotlin.services.rekognition.model.ListUsersRequest
import aws.sdk.kotlin.services.rekognition.model.ListUsersResponse
import aws.sdk.kotlin.services.rekognition.model.PutProjectPolicyRequest
import aws.sdk.kotlin.services.rekognition.model.PutProjectPolicyResponse
import aws.sdk.kotlin.services.rekognition.model.RecognizeCelebritiesRequest
import aws.sdk.kotlin.services.rekognition.model.RecognizeCelebritiesResponse
import aws.sdk.kotlin.services.rekognition.model.SearchFacesByImageRequest
import aws.sdk.kotlin.services.rekognition.model.SearchFacesByImageResponse
import aws.sdk.kotlin.services.rekognition.model.SearchFacesRequest
import aws.sdk.kotlin.services.rekognition.model.SearchFacesResponse
import aws.sdk.kotlin.services.rekognition.model.SearchUsersByImageRequest
import aws.sdk.kotlin.services.rekognition.model.SearchUsersByImageResponse
import aws.sdk.kotlin.services.rekognition.model.SearchUsersRequest
import aws.sdk.kotlin.services.rekognition.model.SearchUsersResponse
import aws.sdk.kotlin.services.rekognition.model.StartCelebrityRecognitionRequest
import aws.sdk.kotlin.services.rekognition.model.StartCelebrityRecognitionResponse
import aws.sdk.kotlin.services.rekognition.model.StartContentModerationRequest
import aws.sdk.kotlin.services.rekognition.model.StartContentModerationResponse
import aws.sdk.kotlin.services.rekognition.model.StartFaceDetectionRequest
import aws.sdk.kotlin.services.rekognition.model.StartFaceDetectionResponse
import aws.sdk.kotlin.services.rekognition.model.StartFaceSearchRequest
import aws.sdk.kotlin.services.rekognition.model.StartFaceSearchResponse
import aws.sdk.kotlin.services.rekognition.model.StartLabelDetectionRequest
import aws.sdk.kotlin.services.rekognition.model.StartLabelDetectionResponse
import aws.sdk.kotlin.services.rekognition.model.StartMediaAnalysisJobRequest
import aws.sdk.kotlin.services.rekognition.model.StartMediaAnalysisJobResponse
import aws.sdk.kotlin.services.rekognition.model.StartPersonTrackingRequest
import aws.sdk.kotlin.services.rekognition.model.StartPersonTrackingResponse
import aws.sdk.kotlin.services.rekognition.model.StartProjectVersionRequest
import aws.sdk.kotlin.services.rekognition.model.StartProjectVersionResponse
import aws.sdk.kotlin.services.rekognition.model.StartSegmentDetectionRequest
import aws.sdk.kotlin.services.rekognition.model.StartSegmentDetectionResponse
import aws.sdk.kotlin.services.rekognition.model.StartStreamProcessorRequest
import aws.sdk.kotlin.services.rekognition.model.StartStreamProcessorResponse
import aws.sdk.kotlin.services.rekognition.model.StartTextDetectionRequest
import aws.sdk.kotlin.services.rekognition.model.StartTextDetectionResponse
import aws.sdk.kotlin.services.rekognition.model.StopProjectVersionRequest
import aws.sdk.kotlin.services.rekognition.model.StopProjectVersionResponse
import aws.sdk.kotlin.services.rekognition.model.StopStreamProcessorRequest
import aws.sdk.kotlin.services.rekognition.model.StopStreamProcessorResponse
import aws.sdk.kotlin.services.rekognition.model.TagResourceRequest
import aws.sdk.kotlin.services.rekognition.model.TagResourceResponse
import aws.sdk.kotlin.services.rekognition.model.UntagResourceRequest
import aws.sdk.kotlin.services.rekognition.model.UntagResourceResponse
import aws.sdk.kotlin.services.rekognition.model.UpdateDatasetEntriesRequest
import aws.sdk.kotlin.services.rekognition.model.UpdateDatasetEntriesResponse
import aws.sdk.kotlin.services.rekognition.model.UpdateStreamProcessorRequest
import aws.sdk.kotlin.services.rekognition.model.UpdateStreamProcessorResponse
import aws.smithy.kotlin.runtime.auth.awscredentials.CredentialsProvider
import aws.smithy.kotlin.runtime.auth.awscredentials.CredentialsProviderConfig
import aws.smithy.kotlin.runtime.awsprotocol.ClockSkewInterceptor
import aws.smithy.kotlin.runtime.client.AbstractSdkClientBuilder
import aws.smithy.kotlin.runtime.client.AbstractSdkClientFactory
import aws.smithy.kotlin.runtime.client.IdempotencyTokenConfig
import aws.smithy.kotlin.runtime.client.IdempotencyTokenProvider
import aws.smithy.kotlin.runtime.client.LogMode
import aws.smithy.kotlin.runtime.client.RetryClientConfig
import aws.smithy.kotlin.runtime.client.RetryStrategyClientConfig
import aws.smithy.kotlin.runtime.client.RetryStrategyClientConfigImpl
import aws.smithy.kotlin.runtime.client.SdkClient
import aws.smithy.kotlin.runtime.client.SdkClientConfig
import aws.smithy.kotlin.runtime.http.auth.AuthScheme
import aws.smithy.kotlin.runtime.http.auth.HttpAuthConfig
import aws.smithy.kotlin.runtime.http.config.HttpClientConfig
import aws.smithy.kotlin.runtime.http.config.HttpEngineConfig
import aws.smithy.kotlin.runtime.http.engine.HttpClientEngine
import aws.smithy.kotlin.runtime.http.engine.HttpEngineConfigImpl
import aws.smithy.kotlin.runtime.http.interceptors.HttpInterceptor
import aws.smithy.kotlin.runtime.net.url.Url
import aws.smithy.kotlin.runtime.retries.RetryStrategy
import aws.smithy.kotlin.runtime.retries.policy.RetryPolicy
import aws.smithy.kotlin.runtime.telemetry.Global
import aws.smithy.kotlin.runtime.telemetry.TelemetryConfig
import aws.smithy.kotlin.runtime.telemetry.TelemetryProvider
import aws.smithy.kotlin.runtime.util.LazyAsyncValue
import kotlin.collections.List
import kotlin.jvm.JvmStatic
public const val ServiceId: String = "Rekognition"
public const val SdkVersion: String = "1.3.14"
public const val ServiceApiVersion: String = "2016-06-27"
/**
* This is the API Reference for [Amazon Rekognition Image](https://docs.aws.amazon.com/rekognition/latest/dg/images.html), [Amazon Rekognition Custom Labels](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/what-is.html), [Amazon Rekognition Stored Video](https://docs.aws.amazon.com/rekognition/latest/dg/video.html), [Amazon Rekognition Streaming Video](https://docs.aws.amazon.com/rekognition/latest/dg/streaming-video.html). It provides descriptions of actions, data types, common parameters, and common errors.
*
* **Amazon Rekognition Image**
* + [AssociateFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_AssociateFaces.html)
* + [CompareFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CompareFaces.html)
* + [CreateCollection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateCollection.html)
* + [CreateUser](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateUser.html)
* + [DeleteCollection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteCollection.html)
* + [DeleteFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteFaces.html)
* + [DeleteUser](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteUser.html)
* + [DescribeCollection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeCollection.html)
* + [DetectFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectFaces.html)
* + [DetectLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectLabels.html)
* + [DetectModerationLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectModerationLabels.html)
* + [DetectProtectiveEquipment](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectProtectiveEquipment.html)
* + [DetectText](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectText.html)
* + [DisassociateFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DisassociateFaces.html)
* + [GetCelebrityInfo](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetCelebrityInfo.html)
* + [GetMediaAnalysisJob](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetMediaAnalysisJob.html)
* + [IndexFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_IndexFaces.html)
* + [ListCollections](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListCollections.html)
* + [ListMediaAnalysisJob](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListMediaAnalysisJob.html)
* + [ListFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListFaces.html)
* + [ListUsers](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListFaces.html)
* + [RecognizeCelebrities](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_RecognizeCelebrities.html)
* + [SearchFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchFaces.html)
* + [SearchFacesByImage](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchFacesByImage.html)
* + [SearchUsers](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchUsers.html)
* + [SearchUsersByImage](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchUsersByImage.html)
* + [StartMediaAnalysisJob](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartMediaAnalysisJob.html)
*
* **Amazon Rekognition Custom Labels**
* + [CopyProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CopyProjectVersion.html)
* + [CreateDataset](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateDataset.html)
* + [CreateProject](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateProject.html)
* + [CreateProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateProjectVersion.html)
* + [DeleteDataset](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteDataset.html)
* + [DeleteProject](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteProject.html)
* + [DeleteProjectPolicy](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteProjectPolicy.html)
* + [DeleteProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteProjectVersion.html)
* + [DescribeDataset](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeDataset.html)
* + [DescribeProjects](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeProjects.html)
* + [DescribeProjectVersions](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeProjectVersions.html)
* + [DetectCustomLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectCustomLabels.html)
* + [DistributeDatasetEntries](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DistributeDatasetEntries.html)
* + [ListDatasetEntries](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListDatasetEntries.html)
* + [ListDatasetLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListDatasetLabels.html)
* + [ListProjectPolicies](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListProjectPolicies.html)
* + [PutProjectPolicy](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_PutProjectPolicy.html)
* + [StartProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartProjectVersion.html)
* + [StopProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StopProjectVersion.html)
* + [UpdateDatasetEntries](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_UpdateDatasetEntries.html)
*
* **Amazon Rekognition Video Stored Video**
* + [GetCelebrityRecognition](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetCelebrityRecognition.html)
* + [GetContentModeration](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetContentModeration.html)
* + [GetFaceDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetFaceDetection.html)
* + [GetFaceSearch](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetFaceSearch.html)
* + [GetLabelDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetLabelDetection.html)
* + [GetPersonTracking](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetPersonTracking.html)
* + [GetSegmentDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetSegmentDetection.html)
* + [GetTextDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetTextDetection.html)
* + [StartCelebrityRecognition](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartCelebrityRecognition.html)
* + [StartContentModeration](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartContentModeration.html)
* + [StartFaceDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartFaceDetection.html)
* + [StartFaceSearch](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartFaceSearch.html)
* + [StartLabelDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartLabelDetection.html)
* + [StartPersonTracking](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartPersonTracking.html)
* + [StartSegmentDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartSegmentDetection.html)
* + [StartTextDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartTextDetection.html)
*
* **Amazon Rekognition Video Streaming Video**
* + [CreateStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateStreamProcessor.html)
* + [DeleteStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteStreamProcessor.html)
* + [DescribeStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeStreamProcessor.html)
* + [ListStreamProcessors](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListStreamProcessors.html)
* + [StartStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartStreamProcessor.html)
* + [StopStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StopStreamProcessor.html)
* + [UpdateStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_UpdateStreamProcessor.html)
*/
public interface RekognitionClient : SdkClient {
/**
* RekognitionClient's configuration
*/
public override val config: Config
public companion object : AbstractAwsSdkClientFactory()
{
@JvmStatic
override fun builder(): Builder = Builder()
override fun finalizeConfig(builder: Builder) {
super.finalizeConfig(builder)
builder.config.interceptors.add(0, ClockSkewInterceptor())
}
override suspend fun finalizeEnvironmentalConfig(builder: Builder, sharedConfig: LazyAsyncValue, activeProfile: LazyAsyncValue) {
super.finalizeEnvironmentalConfig(builder, sharedConfig, activeProfile)
builder.config.endpointUrl = builder.config.endpointUrl ?: resolveEndpointUrl(
sharedConfig,
"Rekognition",
"REKOGNITION",
"rekognition",
)
}
}
public class Builder internal constructor(): AbstractSdkClientBuilder() {
override val config: Config.Builder = Config.Builder()
override fun newClient(config: Config): RekognitionClient = DefaultRekognitionClient(config)
}
public class Config private constructor(builder: Builder) : AwsSdkClientConfig, CredentialsProviderConfig, HttpAuthConfig, HttpClientConfig, HttpEngineConfig by builder.buildHttpEngineConfig(), IdempotencyTokenConfig, RetryClientConfig, RetryStrategyClientConfig by builder.buildRetryStrategyClientConfig(), SdkClientConfig, TelemetryConfig {
override val clientName: String = builder.clientName
override val region: String? = builder.region
override val authSchemes: kotlin.collections.List = builder.authSchemes
override val credentialsProvider: CredentialsProvider = builder.credentialsProvider ?: DefaultChainCredentialsProvider(httpClient = httpClient, region = region).manage()
public val endpointProvider: RekognitionEndpointProvider = builder.endpointProvider ?: DefaultRekognitionEndpointProvider()
public val endpointUrl: Url? = builder.endpointUrl
override val idempotencyTokenProvider: IdempotencyTokenProvider = builder.idempotencyTokenProvider ?: IdempotencyTokenProvider.Default
override val interceptors: kotlin.collections.List = builder.interceptors
override val logMode: LogMode = builder.logMode ?: LogMode.Default
override val retryPolicy: RetryPolicy = builder.retryPolicy ?: AwsRetryPolicy.Default
override val telemetryProvider: TelemetryProvider = builder.telemetryProvider ?: TelemetryProvider.Global
override val useDualStack: Boolean = builder.useDualStack ?: false
override val useFips: Boolean = builder.useFips ?: false
override val applicationId: String? = builder.applicationId
public val authSchemeProvider: RekognitionAuthSchemeProvider = builder.authSchemeProvider ?: DefaultRekognitionAuthSchemeProvider()
public companion object {
public inline operator fun invoke(block: Builder.() -> kotlin.Unit): Config = Builder().apply(block).build()
}
public fun toBuilder(): Builder = Builder().apply {
clientName = [email protected]
region = [email protected]
authSchemes = [email protected]
credentialsProvider = [email protected]
endpointProvider = [email protected]
endpointUrl = [email protected]
httpClient = [email protected]
idempotencyTokenProvider = [email protected]
interceptors = [email protected]()
logMode = [email protected]
retryPolicy = [email protected]
retryStrategy = [email protected]
telemetryProvider = [email protected]
useDualStack = [email protected]
useFips = [email protected]
applicationId = [email protected]
authSchemeProvider = [email protected]
}
public class Builder : AwsSdkClientConfig.Builder, CredentialsProviderConfig.Builder, HttpAuthConfig.Builder, HttpClientConfig.Builder, HttpEngineConfig.Builder by HttpEngineConfigImpl.BuilderImpl(), IdempotencyTokenConfig.Builder, RetryClientConfig.Builder, RetryStrategyClientConfig.Builder by RetryStrategyClientConfigImpl.BuilderImpl(), SdkClientConfig.Builder, TelemetryConfig.Builder {
/**
* A reader-friendly name for the client.
*/
override var clientName: String = "Rekognition"
/**
* The AWS region (e.g. `us-west-2`) to make requests to. See about AWS
* [global infrastructure](https://aws.amazon.com/about-aws/global-infrastructure/regions_az/) for more
* information
*/
override var region: String? = null
/**
* Register new or override default [AuthScheme]s configured for this client. By default, the set
* of auth schemes configured comes from the service model. An auth scheme configured explicitly takes
* precedence over the defaults and can be used to customize identity resolution and signing for specific
* authentication schemes.
*/
override var authSchemes: kotlin.collections.List = emptyList()
/**
* The AWS credentials provider to use for authenticating requests. If not provided a
* [aws.sdk.kotlin.runtime.auth.credentials.DefaultChainCredentialsProvider] instance will be used.
* NOTE: The caller is responsible for managing the lifetime of the provider when set. The SDK
* client will not close it when the client is closed.
*/
override var credentialsProvider: CredentialsProvider? = null
/**
* The endpoint provider used to determine where to make service requests. **This is an advanced config
* option.**
*
* Endpoint resolution occurs as part of the workflow for every request made via the service client.
*
* The inputs to endpoint resolution are defined on a per-service basis (see [EndpointParameters]).
*/
public var endpointProvider: RekognitionEndpointProvider? = null
/**
* A custom endpoint to route requests to. The endpoint set here is passed to the configured
* [endpointProvider], which may inspect and modify it as needed.
*
* Setting a custom endpointUrl should generally be preferred to overriding the [endpointProvider] and is
* the recommended way to route requests to development or preview instances of a service.
*
* **This is an advanced config option.**
*/
public var endpointUrl: Url? = null
/**
* Override the default idempotency token generator. SDK clients will generate tokens for members
* that represent idempotent tokens when not explicitly set by the caller using this generator.
*/
override var idempotencyTokenProvider: IdempotencyTokenProvider? = null
/**
* Add an [aws.smithy.kotlin.runtime.client.Interceptor] that will have access to read and modify
* the request and response objects as they are processed by the SDK.
* Interceptors added using this method are executed in the order they are configured and are always
* later than any added automatically by the SDK.
*/
override var interceptors: kotlin.collections.MutableList = kotlin.collections.mutableListOf()
/**
* Configure events that will be logged. By default clients will not output
* raw requests or responses. Use this setting to opt-in to additional debug logging.
*
* This can be used to configure logging of requests, responses, retries, etc of SDK clients.
*
* **NOTE**: Logging of raw requests or responses may leak sensitive information! It may also have
* performance considerations when dumping the request/response body. This is primarily a tool for
* debug purposes.
*/
override var logMode: LogMode? = null
/**
* The policy to use for evaluating operation results and determining whether/how to retry.
*/
override var retryPolicy: RetryPolicy? = null
/**
* The telemetry provider used to instrument the SDK operations with. By default, the global telemetry
* provider will be used.
*/
override var telemetryProvider: TelemetryProvider? = null
/**
* Flag to toggle whether to use dual-stack endpoints when making requests.
* See [https://docs.aws.amazon.com/sdkref/latest/guide/feature-endpoints.html] for more information.
* ` Disabled by default.
*/
override var useDualStack: Boolean? = null
/**
* Flag to toggle whether to use [FIPS](https://aws.amazon.com/compliance/fips/) endpoints when making requests.
* ` Disabled by default.
*/
override var useFips: Boolean? = null
/**
* An optional application specific identifier.
* When set it will be appended to the User-Agent header of every request in the form of: `app/{applicationId}`.
* When not explicitly set, the value will be loaded from the following locations:
*
* - JVM System Property: `aws.userAgentAppId`
* - Environment variable: `AWS_SDK_UA_APP_ID`
* - Shared configuration profile attribute: `sdk_ua_app_id`
*
* See [shared configuration settings](https://docs.aws.amazon.com/sdkref/latest/guide/settings-reference.html)
* reference for more information on environment variables and shared config settings.
*/
override var applicationId: String? = null
/**
* Configure the provider used to resolve the authentication scheme to use for a particular operation.
*/
public var authSchemeProvider: RekognitionAuthSchemeProvider? = null
override fun build(): Config = Config(this)
}
}
/**
* Associates one or more faces with an existing UserID. Takes an array of `FaceIds`. Each `FaceId` that are present in the `FaceIds` list is associated with the provided UserID. The maximum number of total `FaceIds` per UserID is 100.
*
* The `UserMatchThreshold` parameter specifies the minimum user match confidence required for the face to be associated with a UserID that has at least one `FaceID` already associated. This ensures that the `FaceIds` are associated with the right UserID. The value ranges from 0-100 and default value is 75.
*
* If successful, an array of `AssociatedFace` objects containing the associated `FaceIds` is returned. If a given face is already associated with the given `UserID`, it will be ignored and will not be returned in the response. If a given face is already associated to a different `UserID`, isn't found in the collection, doesn’t meet the `UserMatchThreshold`, or there are already 100 faces associated with the `UserID`, it will be returned as part of an array of `UnsuccessfulFaceAssociations.`
*
* The `UserStatus` reflects the status of an operation which updates a UserID representation with a list of given faces. The `UserStatus` can be:
* + ACTIVE - All associations or disassociations of FaceID(s) for a UserID are complete.
* + CREATED - A UserID has been created, but has no FaceID(s) associated with it.
* + UPDATING - A UserID is being updated and there are current associations or disassociations of FaceID(s) taking place.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.AssociateFaces.sample
*/
public suspend fun associateFaces(input: AssociateFacesRequest): AssociateFacesResponse
/**
* Compares a face in the *source* input image with each of the 100 largest faces detected in the *target* input image.
*
* If the source image contains multiple faces, the service detects the largest face and compares it with each face detected in the target image.
*
* CompareFaces uses machine learning algorithms, which are probabilistic. A false negative is an incorrect prediction that a face in the target image has a low similarity confidence score when compared to the face in the source image. To reduce the probability of false negatives, we recommend that you compare the target image against multiple source images. If you plan to use `CompareFaces` to make a decision that impacts an individual's rights, privacy, or access to services, we recommend that you pass the result to a human for review and further validation before taking action.
*
* You pass the input and target images either as base64-encoded image bytes or as references to images in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file.
*
* In response, the operation returns an array of face matches ordered by similarity score in descending order. For each face match, the response provides a bounding box of the face, facial landmarks, pose details (pitch, roll, and yaw), quality (brightness and sharpness), and confidence value (indicating the level of confidence that the bounding box contains a face). The response also provides a similarity score, which indicates how closely the faces match.
*
* By default, only faces with a similarity score of greater than or equal to 80% are returned in the response. You can change this value by specifying the `SimilarityThreshold` parameter.
*
* `CompareFaces` also returns an array of faces that don't match the source image. For each face, it returns a bounding box, confidence value, landmarks, pose details, and quality. The response also returns information about the face in the source image, including the bounding box of the face and confidence value.
*
* The `QualityFilter` input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. Use `QualityFilter` to set the quality bar by specifying `LOW`, `MEDIUM`, or `HIGH`. If you do not want to filter detected faces, specify `NONE`. The default value is `NONE`.
*
* If the image doesn't contain Exif metadata, `CompareFaces` returns orientation information for the source and target images. Use these values to display the images with the correct image orientation.
*
* If no faces are detected in the source or target images, `CompareFaces` returns an `InvalidParameterException` error.
*
* This is a stateless API operation. That is, data returned by this operation doesn't persist.
*
* For an example, see Comparing Faces in Images in the Amazon Rekognition Developer Guide.
*
* This operation requires permissions to perform the `rekognition:CompareFaces` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.CompareFaces.sample
*/
public suspend fun compareFaces(input: CompareFacesRequest): CompareFacesResponse
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Copies a version of an Amazon Rekognition Custom Labels model from a source project to a destination project. The source and destination projects can be in different AWS accounts but must be in the same AWS Region. You can't copy a model to another AWS service.
*
* To copy a model version to a different AWS account, you need to create a resource-based policy known as a *project policy*. You attach the project policy to the source project by calling PutProjectPolicy. The project policy gives permission to copy the model version from a trusting AWS account to a trusted account.
*
* For more information creating and attaching a project policy, see Attaching a project policy (SDK) in the *Amazon Rekognition Custom Labels Developer Guide*.
*
* If you are copying a model version to a project in the same AWS account, you don't need to create a project policy.
*
* Copying project versions is supported only for Custom Labels models.
*
* To copy a model, the destination project, source project, and source model version must already exist.
*
* Copying a model version takes a while to complete. To get the current status, call DescribeProjectVersions and check the value of `Status` in the ProjectVersionDescription object. The copy operation has finished when the value of `Status` is `COPYING_COMPLETED`.
*
* This operation requires permissions to perform the `rekognition:CopyProjectVersion` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.CopyProjectVersion.sample
*/
public suspend fun copyProjectVersion(input: CopyProjectVersionRequest): CopyProjectVersionResponse
/**
* Creates a collection in an AWS Region. You can add faces to the collection using the IndexFaces operation.
*
* For example, you might create collections, one for each of your application users. A user can then index faces using the `IndexFaces` operation and persist results in a specific collection. Then, a user can search the collection for faces in the user-specific container.
*
* When you create a collection, it is associated with the latest version of the face model version.
*
* Collection names are case-sensitive.
*
* This operation requires permissions to perform the `rekognition:CreateCollection` action. If you want to tag your collection, you also require permission to perform the `rekognition:TagResource` operation.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.CreateCollection.sample
*/
public suspend fun createCollection(input: CreateCollectionRequest): CreateCollectionResponse
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Creates a new Amazon Rekognition Custom Labels dataset. You can create a dataset by using an Amazon Sagemaker format manifest file or by copying an existing Amazon Rekognition Custom Labels dataset.
*
* To create a training dataset for a project, specify `TRAIN` for the value of `DatasetType`. To create the test dataset for a project, specify `TEST` for the value of `DatasetType`.
*
* The response from `CreateDataset` is the Amazon Resource Name (ARN) for the dataset. Creating a dataset takes a while to complete. Use DescribeDataset to check the current status. The dataset created successfully if the value of `Status` is `CREATE_COMPLETE`.
*
* To check if any non-terminal errors occurred, call ListDatasetEntries and check for the presence of `errors` lists in the JSON Lines.
*
* Dataset creation fails if a terminal error occurs (`Status` = `CREATE_FAILED`). Currently, you can't access the terminal error information.
*
* For more information, see Creating dataset in the *Amazon Rekognition Custom Labels Developer Guide*.
*
* This operation requires permissions to perform the `rekognition:CreateDataset` action. If you want to copy an existing dataset, you also require permission to perform the `rekognition:ListDatasetEntries` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.CreateDataset.sample
*/
public suspend fun createDataset(input: CreateDatasetRequest): CreateDatasetResponse
/**
* 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.
*/
public suspend fun createFaceLivenessSession(input: CreateFaceLivenessSessionRequest = CreateFaceLivenessSessionRequest { }): CreateFaceLivenessSessionResponse
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.CreateProject.sample
*/
public suspend fun createProject(input: CreateProjectRequest): CreateProjectResponse
/**
* Creates a new version of Amazon Rekognition project (like a Custom Labels model or a custom adapter) and begins training. Models and adapters are managed as part of a Rekognition project. The response from `CreateProjectVersion` is an Amazon Resource Name (ARN) for the project version.
*
* The FeatureConfig operation argument allows you to configure specific model or adapter settings. You can provide a description to the project version by using the VersionDescription argment. Training can take a while to complete. You can get the current status by calling DescribeProjectVersions. Training completed successfully if the value of the `Status` field is `TRAINING_COMPLETED`. Once training has successfully completed, call DescribeProjectVersions to get the training results and evaluate the model.
*
* This operation requires permissions to perform the `rekognition:CreateProjectVersion` action.
*
* *The following applies only to projects with Amazon Rekognition Custom Labels as the chosen feature:*
*
* You can train a model in a project that doesn't have associated datasets by specifying manifest files in the `TrainingData` and `TestingData` fields.
*
* If you open the console after training a model with manifest files, Amazon Rekognition Custom Labels creates the datasets for you using the most recent manifest files. You can no longer train a model version for the project by specifying manifest files.
*
* Instead of training with a project without associated datasets, we recommend that you use the manifest files to create training and test datasets for the project.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.CreateProjectVersion.sample
*/
public suspend fun createProjectVersion(input: CreateProjectVersionRequest): CreateProjectVersionResponse
/**
* 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.
*/
public suspend fun createStreamProcessor(input: CreateStreamProcessorRequest): CreateStreamProcessorResponse
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.CreateUser.sample
*/
public suspend fun createUser(input: CreateUserRequest): CreateUserResponse
/**
* Deletes the specified collection. Note that this operation removes all faces in the collection. For an example, see [Deleting a collection](https://docs.aws.amazon.com/rekognition/latest/dg/delete-collection-procedure.html).
*
* This operation requires permissions to perform the `rekognition:DeleteCollection` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DeleteCollection.sample
*/
public suspend fun deleteCollection(input: DeleteCollectionRequest): DeleteCollectionResponse
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Deletes an existing Amazon Rekognition Custom Labels dataset. Deleting a dataset might take while. Use DescribeDataset to check the current status. The dataset is still deleting if the value of `Status` is `DELETE_IN_PROGRESS`. If you try to access the dataset after it is deleted, you get a `ResourceNotFoundException` exception.
*
* You can't delete a dataset while it is creating (`Status` = `CREATE_IN_PROGRESS`) or if the dataset is updating (`Status` = `UPDATE_IN_PROGRESS`).
*
* This operation requires permissions to perform the `rekognition:DeleteDataset` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DeleteDataset.sample
*/
public suspend fun deleteDataset(input: DeleteDatasetRequest): DeleteDatasetResponse
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DeleteFaces.sample
*/
public suspend fun deleteFaces(input: DeleteFacesRequest): DeleteFacesResponse
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DeleteProject.sample
*/
public suspend fun deleteProject(input: DeleteProjectRequest): DeleteProjectResponse
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Deletes an existing project policy.
*
* To get a list of project policies attached to a project, call ListProjectPolicies. To attach a project policy to a project, call PutProjectPolicy.
*
* This operation requires permissions to perform the `rekognition:DeleteProjectPolicy` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DeleteProjectPolicy.sample
*/
public suspend fun deleteProjectPolicy(input: DeleteProjectPolicyRequest): DeleteProjectPolicyResponse
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DeleteProjectVersion.sample
*/
public suspend fun deleteProjectVersion(input: DeleteProjectVersionRequest): DeleteProjectVersionResponse
/**
* 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`.
*/
public suspend fun deleteStreamProcessor(input: DeleteStreamProcessorRequest): DeleteStreamProcessorResponse
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DeleteUser.sample
*/
public suspend fun deleteUser(input: DeleteUserRequest): DeleteUserResponse
/**
* 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.
*/
public suspend fun describeCollection(input: DescribeCollectionRequest): DescribeCollectionResponse
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Describes an Amazon Rekognition Custom Labels dataset. You can get information such as the current status of a dataset and statistics about the images and labels in a dataset.
*
* This operation requires permissions to perform the `rekognition:DescribeDataset` action.
*/
public suspend fun describeDataset(input: DescribeDatasetRequest): DescribeDatasetResponse
/**
* 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.
*/
public suspend fun describeProjectVersions(input: DescribeProjectVersionsRequest): DescribeProjectVersionsResponse
/**
* Gets information about your Rekognition projects.
*
* This operation requires permissions to perform the `rekognition:DescribeProjects` action.
*/
public suspend fun describeProjects(input: DescribeProjectsRequest = DescribeProjectsRequest { }): DescribeProjectsResponse
/**
* 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.
*/
public suspend fun describeStreamProcessor(input: DescribeStreamProcessorRequest): DescribeStreamProcessorResponse
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Detects custom labels in a supplied image by using an Amazon Rekognition Custom Labels model.
*
* You specify which version of a model version to use by using the `ProjectVersionArn` input parameter.
*
* You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.
*
* For each object that the model version detects on an image, the API returns a (`CustomLabel`) object in an array (`CustomLabels`). Each `CustomLabel` object provides the label name (`Name`), the level of confidence that the image contains the object (`Confidence`), and object location information, if it exists, for the label on the image (`Geometry`). Note that for the `DetectCustomLabelsLabels` operation, `Polygons` are not returned in the `Geometry` section of the response.
*
* To filter labels that are returned, specify a value for `MinConfidence`. `DetectCustomLabelsLabels` only returns labels with a confidence that's higher than the specified value. The value of `MinConfidence` maps to the assumed threshold values created during training. For more information, see *Assumed threshold* in the Amazon Rekognition Custom Labels Developer Guide. Amazon Rekognition Custom Labels metrics expresses an assumed threshold as a floating point value between 0-1. The range of `MinConfidence` normalizes the threshold value to a percentage value (0-100). Confidence responses from `DetectCustomLabels` are also returned as a percentage. You can use `MinConfidence` to change the precision and recall or your model. For more information, see *Analyzing an image* in the Amazon Rekognition Custom Labels Developer Guide.
*
* If you don't specify a value for `MinConfidence`, `DetectCustomLabels` returns labels based on the assumed threshold of each label.
*
* This is a stateless API operation. That is, the operation does not persist any data.
*
* This operation requires permissions to perform the `rekognition:DetectCustomLabels` action.
*
* For more information, see *Analyzing an image* in the Amazon Rekognition Custom Labels Developer Guide.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DetectCustomLabels.sample
*/
public suspend fun detectCustomLabels(input: DetectCustomLabelsRequest): DetectCustomLabelsResponse
/**
* Detects faces within an image that is provided as input.
*
* `DetectFaces` detects the 100 largest faces in the image. For each face detected, the operation returns face details. These details include a bounding box of the face, a confidence value (that the bounding box contains a face), and a fixed set of attributes such as facial landmarks (for example, coordinates of eye and mouth), pose, presence of facial occlusion, and so on.
*
* The face-detection algorithm is most effective on frontal faces. For non-frontal or obscured faces, the algorithm might not detect the faces or might detect faces with lower confidence.
*
* You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.
*
* This is a stateless API operation. That is, the operation does not persist any data.
*
* This operation requires permissions to perform the `rekognition:DetectFaces` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DetectFaces.sample
*/
public suspend fun detectFaces(input: DetectFacesRequest): DetectFacesResponse
/**
* 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](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html).
*
* When getting labels, you can specify `MinConfidence` to control the confidence threshold for the labels returned. The default is 55%. You can also add the `MaxLabels` parameter to limit the number of labels returned. The default and upper limit is 1000 labels. These arguments are only valid when supplying GENERAL_LABELS as a feature type.
*
* **Response Elements**
*
* For each object, scene, and concept the API returns one or more labels. The API returns the following types of information about labels:
* + Name - The name of the detected label.
* + Confidence - The level of confidence in the label assigned to a detected object.
* + Parents - The ancestor labels for a detected label. DetectLabels returns a hierarchical taxonomy of detected labels. For example, a detected car might be assigned the label car. The label car has two parent labels: Vehicle (its parent) and Transportation (its grandparent). The response includes the all ancestors for a label, where every ancestor is a unique label. In the previous example, Car, Vehicle, and Transportation are returned as unique labels in the response.
* + Aliases - Possible Aliases for the label.
* + Categories - The label categories that the detected label belongs to.
* + BoundingBox — Bounding boxes are described for all instances of detected common object labels, returned in an array of Instance objects. An Instance object contains a BoundingBox object, describing the location of the label on the input image. It also includes the confidence for the accuracy of the detected bounding box.
*
* The API returns the following information regarding the image, as part of the ImageProperties structure:
* + Quality - Information about the Sharpness, Brightness, and Contrast of the input image, scored between 0 to 100. Image quality is returned for the entire image, as well as the background and the foreground.
* + Dominant Color - An array of the dominant colors in the image.
* + Foreground - Information about the sharpness, brightness, and dominant colors of the input image’s foreground.
* + Background - Information about the sharpness, brightness, and dominant colors of the input image’s background.
*
* The list of returned labels will include at least one label for every detected object, along with information about that label. In the following example, suppose the input image has a lighthouse, the sea, and a rock. The response includes all three labels, one for each object, as well as the confidence in the label:
*
* `{Name: lighthouse, Confidence: 98.4629}`
*
* `{Name: rock,Confidence: 79.2097}`
*
* ` {Name: sea,Confidence: 75.061}`
*
* The list of labels can include multiple labels for the same object. For example, if the input image shows a flower (for example, a tulip), the operation might return the following three labels.
*
* `{Name: flower,Confidence: 99.0562}`
*
* `{Name: plant,Confidence: 99.0562}`
*
* `{Name: tulip,Confidence: 99.0562}`
*
* In this example, the detection algorithm more precisely identifies the flower as a tulip.
*
* If the object detected is a person, the operation doesn't provide the same facial details that the DetectFaces operation provides.
*
* This is a stateless API operation that doesn't return any data.
*
* This operation requires permissions to perform the `rekognition:DetectLabels` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DetectLabels.sample
*/
public suspend fun detectLabels(input: DetectLabelsRequest): DetectLabelsResponse
/**
* 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.
*/
public suspend fun detectModerationLabels(input: DetectModerationLabelsRequest): DetectModerationLabelsResponse
/**
* 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.
*/
public suspend fun detectProtectiveEquipment(input: DetectProtectiveEquipmentRequest): DetectProtectiveEquipmentResponse
/**
* 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.
*/
public suspend fun detectText(input: DetectTextRequest): DetectTextResponse
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DisassociateFaces.sample
*/
public suspend fun disassociateFaces(input: DisassociateFacesRequest): DisassociateFacesResponse
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Distributes the entries (images) in a training dataset across the training dataset and the test dataset for a project. `DistributeDatasetEntries` moves 20% of the training dataset images to the test dataset. An entry is a JSON Line that describes an image.
*
* You supply the Amazon Resource Names (ARN) of a project's training dataset and test dataset. The training dataset must contain the images that you want to split. The test dataset must be empty. The datasets must belong to the same project. To create training and test datasets for a project, call CreateDataset.
*
* Distributing a dataset takes a while to complete. To check the status call `DescribeDataset`. The operation is complete when the `Status` field for the training dataset and the test dataset is `UPDATE_COMPLETE`. If the dataset split fails, the value of `Status` is `UPDATE_FAILED`.
*
* This operation requires permissions to perform the `rekognition:DistributeDatasetEntries` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DistributeDatasetEntries.sample
*/
public suspend fun distributeDatasetEntries(input: DistributeDatasetEntriesRequest): DistributeDatasetEntriesResponse
/**
* 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.
*/
public suspend fun getCelebrityInfo(input: GetCelebrityInfoRequest): GetCelebrityInfoResponse
/**
* Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by StartCelebrityRecognition.
*
* Celebrity recognition in a video is an asynchronous operation. Analysis is started by a call to StartCelebrityRecognition which returns a job identifier (`JobId`).
*
* When the celebrity recognition operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to `StartCelebrityRecognition`. To get the results of the celebrity recognition analysis, first check that the status value published to the Amazon SNS topic is `SUCCEEDED`. If so, call `GetCelebrityDetection` and pass the job identifier (`JobId`) from the initial call to `StartCelebrityDetection`.
*
* For more information, see Working With Stored Videos in the Amazon Rekognition Developer Guide.
*
* `GetCelebrityRecognition` returns detected celebrities and the time(s) they are detected in an array (`Celebrities`) of CelebrityRecognition objects. Each `CelebrityRecognition` contains information about the celebrity in a CelebrityDetail object and the time, `Timestamp`, the celebrity was detected. This CelebrityDetail object stores information about the detected celebrity's face attributes, a face bounding box, known gender, the celebrity's name, and a confidence estimate.
*
* `GetCelebrityRecognition` only returns the default facial attributes (`BoundingBox`, `Confidence`, `Landmarks`, `Pose`, and `Quality`). The `BoundingBox` field only applies to the detected face instance. The other facial attributes listed in the `Face` object of the following response syntax are not returned. For more information, see FaceDetail in the Amazon Rekognition Developer Guide.
*
* By default, the `Celebrities` array is sorted by time (milliseconds from the start of the video). You can also sort the array by celebrity by specifying the value `ID` in the `SortBy` input parameter.
*
* The `CelebrityDetail` object includes the celebrity identifer and additional information urls. If you don't store the additional information urls, you can get them later by calling GetCelebrityInfo with the celebrity identifer.
*
* No information is returned for faces not recognized as celebrities.
*
* Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in `MaxResults`, the value of `NextToken` in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call `GetCelebrityDetection` and populate the `NextToken` request parameter with the token value returned from the previous call to `GetCelebrityRecognition`.
*/
public suspend fun getCelebrityRecognition(input: GetCelebrityRecognitionRequest): GetCelebrityRecognitionResponse
/**
* 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](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api).
*
* 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.
*/
public suspend fun getContentModeration(input: GetContentModerationRequest): GetContentModerationResponse
/**
* 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".
*/
public suspend fun getFaceDetection(input: GetFaceDetectionRequest): GetFaceDetectionResponse
/**
* 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.
*/
public suspend fun getFaceLivenessSessionResults(input: GetFaceLivenessSessionResultsRequest): GetFaceLivenessSessionResultsResponse
/**
* 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.
*
* `GetFaceSearch` only returns the default facial attributes (`BoundingBox`, `Confidence`, `Landmarks`, `Pose`, and `Quality`). The other facial attributes listed in the `Face` object of the following response syntax are not returned. For more information, see FaceDetail in the Amazon Rekognition Developer Guide.
*
* By default, the `Persons` array is sorted by the time, in milliseconds from the start of the video, persons are matched. You can also sort by persons by specifying `INDEX` for the `SORTBY` input parameter.
*/
public suspend fun getFaceSearch(input: GetFaceSearchRequest): GetFaceSearchResponse
/**
* 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.
*/
public suspend fun getLabelDetection(input: GetLabelDetectionRequest): GetLabelDetectionResponse
/**
* Retrieves the results for a given media analysis job. Takes a `JobId` returned by StartMediaAnalysisJob.
*/
public suspend fun getMediaAnalysisJob(input: GetMediaAnalysisJobRequest): GetMediaAnalysisJobResponse
/**
* Gets the path tracking results of a Amazon Rekognition Video analysis started by StartPersonTracking.
*
* The person path tracking operation is started by a call to `StartPersonTracking` which returns a job identifier (`JobId`). When the operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to `StartPersonTracking`.
*
* To get the results of the person path tracking operation, first check that the status value published to the Amazon SNS topic is `SUCCEEDED`. If so, call GetPersonTracking and pass the job identifier (`JobId`) from the initial call to `StartPersonTracking`.
*
* `GetPersonTracking` returns an array, `Persons`, of tracked persons and the time(s) their paths were tracked in the video.
*
* `GetPersonTracking` only returns the default facial attributes (`BoundingBox`, `Confidence`, `Landmarks`, `Pose`, and `Quality`). The other facial attributes listed in the `Face` object of the following response syntax are not returned.
*
* For more information, see FaceDetail in the Amazon Rekognition Developer Guide.
*
* By default, the array is sorted by the time(s) a person's path is tracked in the video. You can sort by tracked persons by specifying `INDEX` for the `SortBy` input parameter.
*
* Use the `MaxResults` parameter to limit the number of items returned. If there are more results than specified in `MaxResults`, the value of `NextToken` in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call `GetPersonTracking` and populate the `NextToken` request parameter with the token value returned from the previous call to `GetPersonTracking`.
*/
public suspend fun getPersonTracking(input: GetPersonTrackingRequest): GetPersonTrackingResponse
/**
* 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.
*/
public suspend fun getSegmentDetection(input: GetSegmentDetectionRequest): GetSegmentDetectionResponse
/**
* 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`.
*/
public suspend fun getTextDetection(input: GetTextDetectionRequest): GetTextDetectionResponse
/**
* Detects faces in the input image and adds them to the specified collection.
*
* Amazon Rekognition doesn't save the actual faces that are detected. Instead, the underlying detection algorithm first detects the faces in the input image. For each face, the algorithm extracts facial features into a feature vector, and stores it in the backend database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the SearchFaces and SearchFacesByImage operations.
*
* For more information, see Adding faces to a collection in the Amazon Rekognition Developer Guide.
*
* To get the number of faces in a collection, call DescribeCollection.
*
* If you're using version 1.0 of the face detection model, `IndexFaces` indexes the 15 largest faces in the input image. Later versions of the face detection model index the 100 largest faces in the input image.
*
* If you're using version 4 or later of the face model, image orientation information is not returned in the `OrientationCorrection` field.
*
* To determine which version of the model you're using, call DescribeCollection and supply the collection ID. You can also get the model version from the value of `FaceModelVersion` in the response from `IndexFaces`
*
* For more information, see Model Versioning in the Amazon Rekognition Developer Guide.
*
* If you provide the optional `ExternalImageId` for the input image you provided, Amazon Rekognition associates this ID with all faces that it detects. When you call the ListFaces operation, the response returns the external ID. You can use this external image ID to create a client-side index to associate the faces with each image. You can then use the index to find all faces in an image.
*
* You can specify the maximum number of faces to index with the `MaxFaces` input parameter. This is useful when you want to index the largest faces in an image and don't want to index smaller faces, such as those belonging to people standing in the background.
*
* The `QualityFilter` input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. By default, `IndexFaces` chooses the quality bar that's used to filter faces. You can also explicitly choose the quality bar. Use `QualityFilter`, to set the quality bar by specifying `LOW`, `MEDIUM`, or `HIGH`. If you do not want to filter detected faces, specify `NONE`.
*
* To use quality filtering, you need a collection associated with version 3 of the face model or higher. To get the version of the face model associated with a collection, call DescribeCollection.
*
* Information about faces detected in an image, but not indexed, is returned in an array of UnindexedFace objects, `UnindexedFaces`. Faces aren't indexed for reasons such as:
* + The number of faces detected exceeds the value of the `MaxFaces` request parameter.
* + The face is too small compared to the image dimensions.
* + The face is too blurry.
* + The image is too dark.
* + The face has an extreme pose.
* + The face doesn’t have enough detail to be suitable for face search.
*
* In response, the `IndexFaces` operation returns an array of metadata for all detected faces, `FaceRecords`. This includes:
* + The bounding box, `BoundingBox`, of the detected face.
* + A confidence value, `Confidence`, which indicates the confidence that the bounding box contains a face.
* + A face ID, `FaceId`, assigned by the service for each face that's detected and stored.
* + An image ID, `ImageId`, assigned by the service for the input image.
*
* If you request `ALL` or specific facial attributes (e.g., `FACE_OCCLUDED`) by using the detectionAttributes parameter, Amazon Rekognition returns detailed facial attributes, such as facial landmarks (for example, location of eye and mouth), facial occlusion, and other facial attributes.
*
* If you provide the same image, specify the same collection, and use the same external ID in the `IndexFaces` operation, Amazon Rekognition doesn't save duplicate face metadata.
*
* The input image is passed either as base64-encoded image bytes, or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file.
*
* This operation requires permissions to perform the `rekognition:IndexFaces` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.IndexFaces.sample
*/
public suspend fun indexFaces(input: IndexFacesRequest): IndexFacesResponse
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.ListCollections.sample
*/
public suspend fun listCollections(input: ListCollectionsRequest = ListCollectionsRequest { }): ListCollectionsResponse
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Lists the entries (images) within a dataset. An entry is a JSON Line that contains the information for a single image, including the image location, assigned labels, and object location bounding boxes. For more information, see [Creating a manifest file](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-manifest-files.html).
*
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.ListDatasetEntries.sample
*/
public suspend fun listDatasetEntries(input: ListDatasetEntriesRequest): ListDatasetEntriesResponse
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see [Labeling images](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-labeling-images.html).
*
* 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*.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.ListDatasetLabels.sample
*/
public suspend fun listDatasetLabels(input: ListDatasetLabelsRequest): ListDatasetLabelsResponse
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.ListFaces.sample
*/
public suspend fun listFaces(input: ListFacesRequest): ListFacesResponse
/**
* Returns a list of media analysis jobs. Results are sorted by `CreationTimestamp` in descending order.
*/
public suspend fun listMediaAnalysisJobs(input: ListMediaAnalysisJobsRequest = ListMediaAnalysisJobsRequest { }): ListMediaAnalysisJobsResponse
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* 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.
*/
public suspend fun listProjectPolicies(input: ListProjectPoliciesRequest): ListProjectPoliciesResponse
/**
* Gets a list of stream processors that you have created with CreateStreamProcessor.
*/
public suspend fun listStreamProcessors(input: ListStreamProcessorsRequest = ListStreamProcessorsRequest { }): ListStreamProcessorsResponse
/**
* 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.
*/
public suspend fun listTagsForResource(input: ListTagsForResourceRequest): ListTagsForResourceResponse
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.ListUsers.sample
*/
public suspend fun listUsers(input: ListUsersRequest): ListUsersResponse
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.PutProjectPolicy.sample
*/
public suspend fun putProjectPolicy(input: PutProjectPolicyRequest): PutProjectPolicyResponse
/**
* 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.
*/
public suspend fun recognizeCelebrities(input: RecognizeCelebritiesRequest): RecognizeCelebritiesResponse
/**
* 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.
*
* You can also search faces without indexing faces by using the `SearchFacesByImage` operation.
*
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.SearchFaces.sample
*/
public suspend fun searchFaces(input: SearchFacesRequest): SearchFacesResponse
/**
* 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.
*
* 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.
*
* 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`.
*
* 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.
*
* This operation requires permissions to perform the `rekognition:SearchFacesByImage` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.SearchFacesByImage.sample
*/
public suspend fun searchFacesByImage(input: SearchFacesByImageRequest): SearchFacesByImageResponse
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.SearchUsers.sample
*/
public suspend fun searchUsers(input: SearchUsersRequest): SearchUsersResponse
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.SearchUsersByImage.sample
*/
public suspend fun searchUsersByImage(input: SearchUsersByImageRequest): SearchUsersByImageResponse
/**
* 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.
*/
public suspend fun startCelebrityRecognition(input: StartCelebrityRecognitionRequest): StartCelebrityRecognitionResponse
/**
* 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](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api).
*
* 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.
*/
public suspend fun startContentModeration(input: StartContentModerationRequest): StartContentModerationResponse
/**
* 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.
*/
public suspend fun startFaceDetection(input: StartFaceDetectionRequest): StartFaceDetectionResponse
/**
* 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](https://docs.aws.amazon.com/rekognition/latest/dg/procedure-person-search-videos.html).
*/
public suspend fun startFaceSearch(input: StartFaceSearchRequest): StartFaceSearchResponse
/**
* 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](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detecting-labels-video.html).
*
* You can specify `MinConfidence` to control the confidence threshold for the labels returned. The default is 50.
*/
public suspend fun startLabelDetection(input: StartLabelDetectionRequest): StartLabelDetectionResponse
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.StartMediaAnalysisJob.sample
*/
public suspend fun startMediaAnalysisJob(input: StartMediaAnalysisJobRequest): StartMediaAnalysisJobResponse
/**
* 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`.
*/
public suspend fun startPersonTracking(input: StartPersonTrackingRequest): StartPersonTrackingResponse
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* 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.
*
* You are charged for the amount of time that the model is running. To stop a running model, call StopProjectVersion.
*
* This operation requires permissions to perform the `rekognition:StartProjectVersion` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.StartProjectVersion.sample
*/
public suspend fun startProjectVersion(input: StartProjectVersionRequest): StartProjectVersionResponse
/**
* 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.
*/
public suspend fun startSegmentDetection(input: StartSegmentDetectionRequest): StartSegmentDetectionResponse
/**
* 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.
*/
public suspend fun startStreamProcessor(input: StartStreamProcessorRequest): StartStreamProcessorResponse
/**
* 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`.
*/
public suspend fun startTextDetection(input: StartTextDetectionRequest): StartTextDetectionResponse
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.StopProjectVersion.sample
*/
public suspend fun stopProjectVersion(input: StopProjectVersionRequest): StopProjectVersionResponse
/**
* Stops a running stream processor that was created by CreateStreamProcessor.
*/
public suspend fun stopStreamProcessor(input: StopStreamProcessorRequest): StopStreamProcessorResponse
/**
* 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](https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html).
*
* This operation requires permissions to perform the `rekognition:TagResource` action.
*/
public suspend fun tagResource(input: TagResourceRequest): TagResourceResponse
/**
* 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.
*/
public suspend fun untagResource(input: UntagResourceRequest): UntagResourceResponse
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.UpdateDatasetEntries.sample
*/
public suspend fun updateDatasetEntries(input: UpdateDatasetEntriesRequest): UpdateDatasetEntriesResponse
/**
* Allows you to update a stream processor. You can change some settings and regions of interest and delete certain parameters.
*/
public suspend fun updateStreamProcessor(input: UpdateStreamProcessorRequest): UpdateStreamProcessorResponse
}
/**
* Create a copy of the client with one or more configuration values overridden.
* This method allows the caller to perform scoped config overrides for one or more client operations.
*
* Any resources created on your behalf will be shared between clients, and will only be closed when ALL clients using them are closed.
* If you provide a resource (e.g. [HttpClientEngine]) to the SDK, you are responsible for managing the lifetime of that resource.
*/
public fun RekognitionClient.withConfig(block: RekognitionClient.Config.Builder.() -> Unit): RekognitionClient {
val newConfig = config.toBuilder().apply(block).build()
return DefaultRekognitionClient(newConfig)
}
/**
* Associates one or more faces with an existing UserID. Takes an array of `FaceIds`. Each `FaceId` that are present in the `FaceIds` list is associated with the provided UserID. The maximum number of total `FaceIds` per UserID is 100.
*
* The `UserMatchThreshold` parameter specifies the minimum user match confidence required for the face to be associated with a UserID that has at least one `FaceID` already associated. This ensures that the `FaceIds` are associated with the right UserID. The value ranges from 0-100 and default value is 75.
*
* If successful, an array of `AssociatedFace` objects containing the associated `FaceIds` is returned. If a given face is already associated with the given `UserID`, it will be ignored and will not be returned in the response. If a given face is already associated to a different `UserID`, isn't found in the collection, doesn’t meet the `UserMatchThreshold`, or there are already 100 faces associated with the `UserID`, it will be returned as part of an array of `UnsuccessfulFaceAssociations.`
*
* The `UserStatus` reflects the status of an operation which updates a UserID representation with a list of given faces. The `UserStatus` can be:
* + ACTIVE - All associations or disassociations of FaceID(s) for a UserID are complete.
* + CREATED - A UserID has been created, but has no FaceID(s) associated with it.
* + UPDATING - A UserID is being updated and there are current associations or disassociations of FaceID(s) taking place.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.AssociateFaces.sample
*/
public suspend inline fun RekognitionClient.associateFaces(crossinline block: AssociateFacesRequest.Builder.() -> Unit): AssociateFacesResponse = associateFaces(AssociateFacesRequest.Builder().apply(block).build())
/**
* Compares a face in the *source* input image with each of the 100 largest faces detected in the *target* input image.
*
* If the source image contains multiple faces, the service detects the largest face and compares it with each face detected in the target image.
*
* CompareFaces uses machine learning algorithms, which are probabilistic. A false negative is an incorrect prediction that a face in the target image has a low similarity confidence score when compared to the face in the source image. To reduce the probability of false negatives, we recommend that you compare the target image against multiple source images. If you plan to use `CompareFaces` to make a decision that impacts an individual's rights, privacy, or access to services, we recommend that you pass the result to a human for review and further validation before taking action.
*
* You pass the input and target images either as base64-encoded image bytes or as references to images in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file.
*
* In response, the operation returns an array of face matches ordered by similarity score in descending order. For each face match, the response provides a bounding box of the face, facial landmarks, pose details (pitch, roll, and yaw), quality (brightness and sharpness), and confidence value (indicating the level of confidence that the bounding box contains a face). The response also provides a similarity score, which indicates how closely the faces match.
*
* By default, only faces with a similarity score of greater than or equal to 80% are returned in the response. You can change this value by specifying the `SimilarityThreshold` parameter.
*
* `CompareFaces` also returns an array of faces that don't match the source image. For each face, it returns a bounding box, confidence value, landmarks, pose details, and quality. The response also returns information about the face in the source image, including the bounding box of the face and confidence value.
*
* The `QualityFilter` input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. Use `QualityFilter` to set the quality bar by specifying `LOW`, `MEDIUM`, or `HIGH`. If you do not want to filter detected faces, specify `NONE`. The default value is `NONE`.
*
* If the image doesn't contain Exif metadata, `CompareFaces` returns orientation information for the source and target images. Use these values to display the images with the correct image orientation.
*
* If no faces are detected in the source or target images, `CompareFaces` returns an `InvalidParameterException` error.
*
* This is a stateless API operation. That is, data returned by this operation doesn't persist.
*
* For an example, see Comparing Faces in Images in the Amazon Rekognition Developer Guide.
*
* This operation requires permissions to perform the `rekognition:CompareFaces` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.CompareFaces.sample
*/
public suspend inline fun RekognitionClient.compareFaces(crossinline block: CompareFacesRequest.Builder.() -> Unit): CompareFacesResponse = compareFaces(CompareFacesRequest.Builder().apply(block).build())
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Copies a version of an Amazon Rekognition Custom Labels model from a source project to a destination project. The source and destination projects can be in different AWS accounts but must be in the same AWS Region. You can't copy a model to another AWS service.
*
* To copy a model version to a different AWS account, you need to create a resource-based policy known as a *project policy*. You attach the project policy to the source project by calling PutProjectPolicy. The project policy gives permission to copy the model version from a trusting AWS account to a trusted account.
*
* For more information creating and attaching a project policy, see Attaching a project policy (SDK) in the *Amazon Rekognition Custom Labels Developer Guide*.
*
* If you are copying a model version to a project in the same AWS account, you don't need to create a project policy.
*
* Copying project versions is supported only for Custom Labels models.
*
* To copy a model, the destination project, source project, and source model version must already exist.
*
* Copying a model version takes a while to complete. To get the current status, call DescribeProjectVersions and check the value of `Status` in the ProjectVersionDescription object. The copy operation has finished when the value of `Status` is `COPYING_COMPLETED`.
*
* This operation requires permissions to perform the `rekognition:CopyProjectVersion` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.CopyProjectVersion.sample
*/
public suspend inline fun RekognitionClient.copyProjectVersion(crossinline block: CopyProjectVersionRequest.Builder.() -> Unit): CopyProjectVersionResponse = copyProjectVersion(CopyProjectVersionRequest.Builder().apply(block).build())
/**
* Creates a collection in an AWS Region. You can add faces to the collection using the IndexFaces operation.
*
* For example, you might create collections, one for each of your application users. A user can then index faces using the `IndexFaces` operation and persist results in a specific collection. Then, a user can search the collection for faces in the user-specific container.
*
* When you create a collection, it is associated with the latest version of the face model version.
*
* Collection names are case-sensitive.
*
* This operation requires permissions to perform the `rekognition:CreateCollection` action. If you want to tag your collection, you also require permission to perform the `rekognition:TagResource` operation.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.CreateCollection.sample
*/
public suspend inline fun RekognitionClient.createCollection(crossinline block: CreateCollectionRequest.Builder.() -> Unit): CreateCollectionResponse = createCollection(CreateCollectionRequest.Builder().apply(block).build())
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Creates a new Amazon Rekognition Custom Labels dataset. You can create a dataset by using an Amazon Sagemaker format manifest file or by copying an existing Amazon Rekognition Custom Labels dataset.
*
* To create a training dataset for a project, specify `TRAIN` for the value of `DatasetType`. To create the test dataset for a project, specify `TEST` for the value of `DatasetType`.
*
* The response from `CreateDataset` is the Amazon Resource Name (ARN) for the dataset. Creating a dataset takes a while to complete. Use DescribeDataset to check the current status. The dataset created successfully if the value of `Status` is `CREATE_COMPLETE`.
*
* To check if any non-terminal errors occurred, call ListDatasetEntries and check for the presence of `errors` lists in the JSON Lines.
*
* Dataset creation fails if a terminal error occurs (`Status` = `CREATE_FAILED`). Currently, you can't access the terminal error information.
*
* For more information, see Creating dataset in the *Amazon Rekognition Custom Labels Developer Guide*.
*
* This operation requires permissions to perform the `rekognition:CreateDataset` action. If you want to copy an existing dataset, you also require permission to perform the `rekognition:ListDatasetEntries` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.CreateDataset.sample
*/
public suspend inline fun RekognitionClient.createDataset(crossinline block: CreateDatasetRequest.Builder.() -> Unit): CreateDatasetResponse = createDataset(CreateDatasetRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.createFaceLivenessSession(crossinline block: CreateFaceLivenessSessionRequest.Builder.() -> Unit): CreateFaceLivenessSessionResponse = createFaceLivenessSession(CreateFaceLivenessSessionRequest.Builder().apply(block).build())
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.CreateProject.sample
*/
public suspend inline fun RekognitionClient.createProject(crossinline block: CreateProjectRequest.Builder.() -> Unit): CreateProjectResponse = createProject(CreateProjectRequest.Builder().apply(block).build())
/**
* Creates a new version of Amazon Rekognition project (like a Custom Labels model or a custom adapter) and begins training. Models and adapters are managed as part of a Rekognition project. The response from `CreateProjectVersion` is an Amazon Resource Name (ARN) for the project version.
*
* The FeatureConfig operation argument allows you to configure specific model or adapter settings. You can provide a description to the project version by using the VersionDescription argment. Training can take a while to complete. You can get the current status by calling DescribeProjectVersions. Training completed successfully if the value of the `Status` field is `TRAINING_COMPLETED`. Once training has successfully completed, call DescribeProjectVersions to get the training results and evaluate the model.
*
* This operation requires permissions to perform the `rekognition:CreateProjectVersion` action.
*
* *The following applies only to projects with Amazon Rekognition Custom Labels as the chosen feature:*
*
* You can train a model in a project that doesn't have associated datasets by specifying manifest files in the `TrainingData` and `TestingData` fields.
*
* If you open the console after training a model with manifest files, Amazon Rekognition Custom Labels creates the datasets for you using the most recent manifest files. You can no longer train a model version for the project by specifying manifest files.
*
* Instead of training with a project without associated datasets, we recommend that you use the manifest files to create training and test datasets for the project.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.CreateProjectVersion.sample
*/
public suspend inline fun RekognitionClient.createProjectVersion(crossinline block: CreateProjectVersionRequest.Builder.() -> Unit): CreateProjectVersionResponse = createProjectVersion(CreateProjectVersionRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.createStreamProcessor(crossinline block: CreateStreamProcessorRequest.Builder.() -> Unit): CreateStreamProcessorResponse = createStreamProcessor(CreateStreamProcessorRequest.Builder().apply(block).build())
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.CreateUser.sample
*/
public suspend inline fun RekognitionClient.createUser(crossinline block: CreateUserRequest.Builder.() -> Unit): CreateUserResponse = createUser(CreateUserRequest.Builder().apply(block).build())
/**
* Deletes the specified collection. Note that this operation removes all faces in the collection. For an example, see [Deleting a collection](https://docs.aws.amazon.com/rekognition/latest/dg/delete-collection-procedure.html).
*
* This operation requires permissions to perform the `rekognition:DeleteCollection` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DeleteCollection.sample
*/
public suspend inline fun RekognitionClient.deleteCollection(crossinline block: DeleteCollectionRequest.Builder.() -> Unit): DeleteCollectionResponse = deleteCollection(DeleteCollectionRequest.Builder().apply(block).build())
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Deletes an existing Amazon Rekognition Custom Labels dataset. Deleting a dataset might take while. Use DescribeDataset to check the current status. The dataset is still deleting if the value of `Status` is `DELETE_IN_PROGRESS`. If you try to access the dataset after it is deleted, you get a `ResourceNotFoundException` exception.
*
* You can't delete a dataset while it is creating (`Status` = `CREATE_IN_PROGRESS`) or if the dataset is updating (`Status` = `UPDATE_IN_PROGRESS`).
*
* This operation requires permissions to perform the `rekognition:DeleteDataset` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DeleteDataset.sample
*/
public suspend inline fun RekognitionClient.deleteDataset(crossinline block: DeleteDatasetRequest.Builder.() -> Unit): DeleteDatasetResponse = deleteDataset(DeleteDatasetRequest.Builder().apply(block).build())
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DeleteFaces.sample
*/
public suspend inline fun RekognitionClient.deleteFaces(crossinline block: DeleteFacesRequest.Builder.() -> Unit): DeleteFacesResponse = deleteFaces(DeleteFacesRequest.Builder().apply(block).build())
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DeleteProject.sample
*/
public suspend inline fun RekognitionClient.deleteProject(crossinline block: DeleteProjectRequest.Builder.() -> Unit): DeleteProjectResponse = deleteProject(DeleteProjectRequest.Builder().apply(block).build())
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Deletes an existing project policy.
*
* To get a list of project policies attached to a project, call ListProjectPolicies. To attach a project policy to a project, call PutProjectPolicy.
*
* This operation requires permissions to perform the `rekognition:DeleteProjectPolicy` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DeleteProjectPolicy.sample
*/
public suspend inline fun RekognitionClient.deleteProjectPolicy(crossinline block: DeleteProjectPolicyRequest.Builder.() -> Unit): DeleteProjectPolicyResponse = deleteProjectPolicy(DeleteProjectPolicyRequest.Builder().apply(block).build())
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DeleteProjectVersion.sample
*/
public suspend inline fun RekognitionClient.deleteProjectVersion(crossinline block: DeleteProjectVersionRequest.Builder.() -> Unit): DeleteProjectVersionResponse = deleteProjectVersion(DeleteProjectVersionRequest.Builder().apply(block).build())
/**
* 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`.
*/
public suspend inline fun RekognitionClient.deleteStreamProcessor(crossinline block: DeleteStreamProcessorRequest.Builder.() -> Unit): DeleteStreamProcessorResponse = deleteStreamProcessor(DeleteStreamProcessorRequest.Builder().apply(block).build())
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DeleteUser.sample
*/
public suspend inline fun RekognitionClient.deleteUser(crossinline block: DeleteUserRequest.Builder.() -> Unit): DeleteUserResponse = deleteUser(DeleteUserRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.describeCollection(crossinline block: DescribeCollectionRequest.Builder.() -> Unit): DescribeCollectionResponse = describeCollection(DescribeCollectionRequest.Builder().apply(block).build())
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Describes an Amazon Rekognition Custom Labels dataset. You can get information such as the current status of a dataset and statistics about the images and labels in a dataset.
*
* This operation requires permissions to perform the `rekognition:DescribeDataset` action.
*/
public suspend inline fun RekognitionClient.describeDataset(crossinline block: DescribeDatasetRequest.Builder.() -> Unit): DescribeDatasetResponse = describeDataset(DescribeDatasetRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.describeProjectVersions(crossinline block: DescribeProjectVersionsRequest.Builder.() -> Unit): DescribeProjectVersionsResponse = describeProjectVersions(DescribeProjectVersionsRequest.Builder().apply(block).build())
/**
* Gets information about your Rekognition projects.
*
* This operation requires permissions to perform the `rekognition:DescribeProjects` action.
*/
public suspend inline fun RekognitionClient.describeProjects(crossinline block: DescribeProjectsRequest.Builder.() -> Unit): DescribeProjectsResponse = describeProjects(DescribeProjectsRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.describeStreamProcessor(crossinline block: DescribeStreamProcessorRequest.Builder.() -> Unit): DescribeStreamProcessorResponse = describeStreamProcessor(DescribeStreamProcessorRequest.Builder().apply(block).build())
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Detects custom labels in a supplied image by using an Amazon Rekognition Custom Labels model.
*
* You specify which version of a model version to use by using the `ProjectVersionArn` input parameter.
*
* You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.
*
* For each object that the model version detects on an image, the API returns a (`CustomLabel`) object in an array (`CustomLabels`). Each `CustomLabel` object provides the label name (`Name`), the level of confidence that the image contains the object (`Confidence`), and object location information, if it exists, for the label on the image (`Geometry`). Note that for the `DetectCustomLabelsLabels` operation, `Polygons` are not returned in the `Geometry` section of the response.
*
* To filter labels that are returned, specify a value for `MinConfidence`. `DetectCustomLabelsLabels` only returns labels with a confidence that's higher than the specified value. The value of `MinConfidence` maps to the assumed threshold values created during training. For more information, see *Assumed threshold* in the Amazon Rekognition Custom Labels Developer Guide. Amazon Rekognition Custom Labels metrics expresses an assumed threshold as a floating point value between 0-1. The range of `MinConfidence` normalizes the threshold value to a percentage value (0-100). Confidence responses from `DetectCustomLabels` are also returned as a percentage. You can use `MinConfidence` to change the precision and recall or your model. For more information, see *Analyzing an image* in the Amazon Rekognition Custom Labels Developer Guide.
*
* If you don't specify a value for `MinConfidence`, `DetectCustomLabels` returns labels based on the assumed threshold of each label.
*
* This is a stateless API operation. That is, the operation does not persist any data.
*
* This operation requires permissions to perform the `rekognition:DetectCustomLabels` action.
*
* For more information, see *Analyzing an image* in the Amazon Rekognition Custom Labels Developer Guide.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DetectCustomLabels.sample
*/
public suspend inline fun RekognitionClient.detectCustomLabels(crossinline block: DetectCustomLabelsRequest.Builder.() -> Unit): DetectCustomLabelsResponse = detectCustomLabels(DetectCustomLabelsRequest.Builder().apply(block).build())
/**
* Detects faces within an image that is provided as input.
*
* `DetectFaces` detects the 100 largest faces in the image. For each face detected, the operation returns face details. These details include a bounding box of the face, a confidence value (that the bounding box contains a face), and a fixed set of attributes such as facial landmarks (for example, coordinates of eye and mouth), pose, presence of facial occlusion, and so on.
*
* The face-detection algorithm is most effective on frontal faces. For non-frontal or obscured faces, the algorithm might not detect the faces or might detect faces with lower confidence.
*
* You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.
*
* This is a stateless API operation. That is, the operation does not persist any data.
*
* This operation requires permissions to perform the `rekognition:DetectFaces` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DetectFaces.sample
*/
public suspend inline fun RekognitionClient.detectFaces(crossinline block: DetectFacesRequest.Builder.() -> Unit): DetectFacesResponse = detectFaces(DetectFacesRequest.Builder().apply(block).build())
/**
* 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](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html).
*
* When getting labels, you can specify `MinConfidence` to control the confidence threshold for the labels returned. The default is 55%. You can also add the `MaxLabels` parameter to limit the number of labels returned. The default and upper limit is 1000 labels. These arguments are only valid when supplying GENERAL_LABELS as a feature type.
*
* **Response Elements**
*
* For each object, scene, and concept the API returns one or more labels. The API returns the following types of information about labels:
* + Name - The name of the detected label.
* + Confidence - The level of confidence in the label assigned to a detected object.
* + Parents - The ancestor labels for a detected label. DetectLabels returns a hierarchical taxonomy of detected labels. For example, a detected car might be assigned the label car. The label car has two parent labels: Vehicle (its parent) and Transportation (its grandparent). The response includes the all ancestors for a label, where every ancestor is a unique label. In the previous example, Car, Vehicle, and Transportation are returned as unique labels in the response.
* + Aliases - Possible Aliases for the label.
* + Categories - The label categories that the detected label belongs to.
* + BoundingBox — Bounding boxes are described for all instances of detected common object labels, returned in an array of Instance objects. An Instance object contains a BoundingBox object, describing the location of the label on the input image. It also includes the confidence for the accuracy of the detected bounding box.
*
* The API returns the following information regarding the image, as part of the ImageProperties structure:
* + Quality - Information about the Sharpness, Brightness, and Contrast of the input image, scored between 0 to 100. Image quality is returned for the entire image, as well as the background and the foreground.
* + Dominant Color - An array of the dominant colors in the image.
* + Foreground - Information about the sharpness, brightness, and dominant colors of the input image’s foreground.
* + Background - Information about the sharpness, brightness, and dominant colors of the input image’s background.
*
* The list of returned labels will include at least one label for every detected object, along with information about that label. In the following example, suppose the input image has a lighthouse, the sea, and a rock. The response includes all three labels, one for each object, as well as the confidence in the label:
*
* `{Name: lighthouse, Confidence: 98.4629}`
*
* `{Name: rock,Confidence: 79.2097}`
*
* ` {Name: sea,Confidence: 75.061}`
*
* The list of labels can include multiple labels for the same object. For example, if the input image shows a flower (for example, a tulip), the operation might return the following three labels.
*
* `{Name: flower,Confidence: 99.0562}`
*
* `{Name: plant,Confidence: 99.0562}`
*
* `{Name: tulip,Confidence: 99.0562}`
*
* In this example, the detection algorithm more precisely identifies the flower as a tulip.
*
* If the object detected is a person, the operation doesn't provide the same facial details that the DetectFaces operation provides.
*
* This is a stateless API operation that doesn't return any data.
*
* This operation requires permissions to perform the `rekognition:DetectLabels` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DetectLabels.sample
*/
public suspend inline fun RekognitionClient.detectLabels(crossinline block: DetectLabelsRequest.Builder.() -> Unit): DetectLabelsResponse = detectLabels(DetectLabelsRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.detectModerationLabels(crossinline block: DetectModerationLabelsRequest.Builder.() -> Unit): DetectModerationLabelsResponse = detectModerationLabels(DetectModerationLabelsRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.detectProtectiveEquipment(crossinline block: DetectProtectiveEquipmentRequest.Builder.() -> Unit): DetectProtectiveEquipmentResponse = detectProtectiveEquipment(DetectProtectiveEquipmentRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.detectText(crossinline block: DetectTextRequest.Builder.() -> Unit): DetectTextResponse = detectText(DetectTextRequest.Builder().apply(block).build())
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DisassociateFaces.sample
*/
public suspend inline fun RekognitionClient.disassociateFaces(crossinline block: DisassociateFacesRequest.Builder.() -> Unit): DisassociateFacesResponse = disassociateFaces(DisassociateFacesRequest.Builder().apply(block).build())
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Distributes the entries (images) in a training dataset across the training dataset and the test dataset for a project. `DistributeDatasetEntries` moves 20% of the training dataset images to the test dataset. An entry is a JSON Line that describes an image.
*
* You supply the Amazon Resource Names (ARN) of a project's training dataset and test dataset. The training dataset must contain the images that you want to split. The test dataset must be empty. The datasets must belong to the same project. To create training and test datasets for a project, call CreateDataset.
*
* Distributing a dataset takes a while to complete. To check the status call `DescribeDataset`. The operation is complete when the `Status` field for the training dataset and the test dataset is `UPDATE_COMPLETE`. If the dataset split fails, the value of `Status` is `UPDATE_FAILED`.
*
* This operation requires permissions to perform the `rekognition:DistributeDatasetEntries` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.DistributeDatasetEntries.sample
*/
public suspend inline fun RekognitionClient.distributeDatasetEntries(crossinline block: DistributeDatasetEntriesRequest.Builder.() -> Unit): DistributeDatasetEntriesResponse = distributeDatasetEntries(DistributeDatasetEntriesRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.getCelebrityInfo(crossinline block: GetCelebrityInfoRequest.Builder.() -> Unit): GetCelebrityInfoResponse = getCelebrityInfo(GetCelebrityInfoRequest.Builder().apply(block).build())
/**
* Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by StartCelebrityRecognition.
*
* Celebrity recognition in a video is an asynchronous operation. Analysis is started by a call to StartCelebrityRecognition which returns a job identifier (`JobId`).
*
* When the celebrity recognition operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to `StartCelebrityRecognition`. To get the results of the celebrity recognition analysis, first check that the status value published to the Amazon SNS topic is `SUCCEEDED`. If so, call `GetCelebrityDetection` and pass the job identifier (`JobId`) from the initial call to `StartCelebrityDetection`.
*
* For more information, see Working With Stored Videos in the Amazon Rekognition Developer Guide.
*
* `GetCelebrityRecognition` returns detected celebrities and the time(s) they are detected in an array (`Celebrities`) of CelebrityRecognition objects. Each `CelebrityRecognition` contains information about the celebrity in a CelebrityDetail object and the time, `Timestamp`, the celebrity was detected. This CelebrityDetail object stores information about the detected celebrity's face attributes, a face bounding box, known gender, the celebrity's name, and a confidence estimate.
*
* `GetCelebrityRecognition` only returns the default facial attributes (`BoundingBox`, `Confidence`, `Landmarks`, `Pose`, and `Quality`). The `BoundingBox` field only applies to the detected face instance. The other facial attributes listed in the `Face` object of the following response syntax are not returned. For more information, see FaceDetail in the Amazon Rekognition Developer Guide.
*
* By default, the `Celebrities` array is sorted by time (milliseconds from the start of the video). You can also sort the array by celebrity by specifying the value `ID` in the `SortBy` input parameter.
*
* The `CelebrityDetail` object includes the celebrity identifer and additional information urls. If you don't store the additional information urls, you can get them later by calling GetCelebrityInfo with the celebrity identifer.
*
* No information is returned for faces not recognized as celebrities.
*
* Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in `MaxResults`, the value of `NextToken` in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call `GetCelebrityDetection` and populate the `NextToken` request parameter with the token value returned from the previous call to `GetCelebrityRecognition`.
*/
public suspend inline fun RekognitionClient.getCelebrityRecognition(crossinline block: GetCelebrityRecognitionRequest.Builder.() -> Unit): GetCelebrityRecognitionResponse = getCelebrityRecognition(GetCelebrityRecognitionRequest.Builder().apply(block).build())
/**
* 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](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api).
*
* 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.
*/
public suspend inline fun RekognitionClient.getContentModeration(crossinline block: GetContentModerationRequest.Builder.() -> Unit): GetContentModerationResponse = getContentModeration(GetContentModerationRequest.Builder().apply(block).build())
/**
* 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".
*/
public suspend inline fun RekognitionClient.getFaceDetection(crossinline block: GetFaceDetectionRequest.Builder.() -> Unit): GetFaceDetectionResponse = getFaceDetection(GetFaceDetectionRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.getFaceLivenessSessionResults(crossinline block: GetFaceLivenessSessionResultsRequest.Builder.() -> Unit): GetFaceLivenessSessionResultsResponse = getFaceLivenessSessionResults(GetFaceLivenessSessionResultsRequest.Builder().apply(block).build())
/**
* 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.
*
* `GetFaceSearch` only returns the default facial attributes (`BoundingBox`, `Confidence`, `Landmarks`, `Pose`, and `Quality`). The other facial attributes listed in the `Face` object of the following response syntax are not returned. For more information, see FaceDetail in the Amazon Rekognition Developer Guide.
*
* By default, the `Persons` array is sorted by the time, in milliseconds from the start of the video, persons are matched. You can also sort by persons by specifying `INDEX` for the `SORTBY` input parameter.
*/
public suspend inline fun RekognitionClient.getFaceSearch(crossinline block: GetFaceSearchRequest.Builder.() -> Unit): GetFaceSearchResponse = getFaceSearch(GetFaceSearchRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.getLabelDetection(crossinline block: GetLabelDetectionRequest.Builder.() -> Unit): GetLabelDetectionResponse = getLabelDetection(GetLabelDetectionRequest.Builder().apply(block).build())
/**
* Retrieves the results for a given media analysis job. Takes a `JobId` returned by StartMediaAnalysisJob.
*/
public suspend inline fun RekognitionClient.getMediaAnalysisJob(crossinline block: GetMediaAnalysisJobRequest.Builder.() -> Unit): GetMediaAnalysisJobResponse = getMediaAnalysisJob(GetMediaAnalysisJobRequest.Builder().apply(block).build())
/**
* Gets the path tracking results of a Amazon Rekognition Video analysis started by StartPersonTracking.
*
* The person path tracking operation is started by a call to `StartPersonTracking` which returns a job identifier (`JobId`). When the operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to `StartPersonTracking`.
*
* To get the results of the person path tracking operation, first check that the status value published to the Amazon SNS topic is `SUCCEEDED`. If so, call GetPersonTracking and pass the job identifier (`JobId`) from the initial call to `StartPersonTracking`.
*
* `GetPersonTracking` returns an array, `Persons`, of tracked persons and the time(s) their paths were tracked in the video.
*
* `GetPersonTracking` only returns the default facial attributes (`BoundingBox`, `Confidence`, `Landmarks`, `Pose`, and `Quality`). The other facial attributes listed in the `Face` object of the following response syntax are not returned.
*
* For more information, see FaceDetail in the Amazon Rekognition Developer Guide.
*
* By default, the array is sorted by the time(s) a person's path is tracked in the video. You can sort by tracked persons by specifying `INDEX` for the `SortBy` input parameter.
*
* Use the `MaxResults` parameter to limit the number of items returned. If there are more results than specified in `MaxResults`, the value of `NextToken` in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call `GetPersonTracking` and populate the `NextToken` request parameter with the token value returned from the previous call to `GetPersonTracking`.
*/
public suspend inline fun RekognitionClient.getPersonTracking(crossinline block: GetPersonTrackingRequest.Builder.() -> Unit): GetPersonTrackingResponse = getPersonTracking(GetPersonTrackingRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.getSegmentDetection(crossinline block: GetSegmentDetectionRequest.Builder.() -> Unit): GetSegmentDetectionResponse = getSegmentDetection(GetSegmentDetectionRequest.Builder().apply(block).build())
/**
* 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`.
*/
public suspend inline fun RekognitionClient.getTextDetection(crossinline block: GetTextDetectionRequest.Builder.() -> Unit): GetTextDetectionResponse = getTextDetection(GetTextDetectionRequest.Builder().apply(block).build())
/**
* Detects faces in the input image and adds them to the specified collection.
*
* Amazon Rekognition doesn't save the actual faces that are detected. Instead, the underlying detection algorithm first detects the faces in the input image. For each face, the algorithm extracts facial features into a feature vector, and stores it in the backend database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the SearchFaces and SearchFacesByImage operations.
*
* For more information, see Adding faces to a collection in the Amazon Rekognition Developer Guide.
*
* To get the number of faces in a collection, call DescribeCollection.
*
* If you're using version 1.0 of the face detection model, `IndexFaces` indexes the 15 largest faces in the input image. Later versions of the face detection model index the 100 largest faces in the input image.
*
* If you're using version 4 or later of the face model, image orientation information is not returned in the `OrientationCorrection` field.
*
* To determine which version of the model you're using, call DescribeCollection and supply the collection ID. You can also get the model version from the value of `FaceModelVersion` in the response from `IndexFaces`
*
* For more information, see Model Versioning in the Amazon Rekognition Developer Guide.
*
* If you provide the optional `ExternalImageId` for the input image you provided, Amazon Rekognition associates this ID with all faces that it detects. When you call the ListFaces operation, the response returns the external ID. You can use this external image ID to create a client-side index to associate the faces with each image. You can then use the index to find all faces in an image.
*
* You can specify the maximum number of faces to index with the `MaxFaces` input parameter. This is useful when you want to index the largest faces in an image and don't want to index smaller faces, such as those belonging to people standing in the background.
*
* The `QualityFilter` input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. By default, `IndexFaces` chooses the quality bar that's used to filter faces. You can also explicitly choose the quality bar. Use `QualityFilter`, to set the quality bar by specifying `LOW`, `MEDIUM`, or `HIGH`. If you do not want to filter detected faces, specify `NONE`.
*
* To use quality filtering, you need a collection associated with version 3 of the face model or higher. To get the version of the face model associated with a collection, call DescribeCollection.
*
* Information about faces detected in an image, but not indexed, is returned in an array of UnindexedFace objects, `UnindexedFaces`. Faces aren't indexed for reasons such as:
* + The number of faces detected exceeds the value of the `MaxFaces` request parameter.
* + The face is too small compared to the image dimensions.
* + The face is too blurry.
* + The image is too dark.
* + The face has an extreme pose.
* + The face doesn’t have enough detail to be suitable for face search.
*
* In response, the `IndexFaces` operation returns an array of metadata for all detected faces, `FaceRecords`. This includes:
* + The bounding box, `BoundingBox`, of the detected face.
* + A confidence value, `Confidence`, which indicates the confidence that the bounding box contains a face.
* + A face ID, `FaceId`, assigned by the service for each face that's detected and stored.
* + An image ID, `ImageId`, assigned by the service for the input image.
*
* If you request `ALL` or specific facial attributes (e.g., `FACE_OCCLUDED`) by using the detectionAttributes parameter, Amazon Rekognition returns detailed facial attributes, such as facial landmarks (for example, location of eye and mouth), facial occlusion, and other facial attributes.
*
* If you provide the same image, specify the same collection, and use the same external ID in the `IndexFaces` operation, Amazon Rekognition doesn't save duplicate face metadata.
*
* The input image is passed either as base64-encoded image bytes, or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file.
*
* This operation requires permissions to perform the `rekognition:IndexFaces` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.IndexFaces.sample
*/
public suspend inline fun RekognitionClient.indexFaces(crossinline block: IndexFacesRequest.Builder.() -> Unit): IndexFacesResponse = indexFaces(IndexFacesRequest.Builder().apply(block).build())
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.ListCollections.sample
*/
public suspend inline fun RekognitionClient.listCollections(crossinline block: ListCollectionsRequest.Builder.() -> Unit): ListCollectionsResponse = listCollections(ListCollectionsRequest.Builder().apply(block).build())
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Lists the entries (images) within a dataset. An entry is a JSON Line that contains the information for a single image, including the image location, assigned labels, and object location bounding boxes. For more information, see [Creating a manifest file](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-manifest-files.html).
*
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.ListDatasetEntries.sample
*/
public suspend inline fun RekognitionClient.listDatasetEntries(crossinline block: ListDatasetEntriesRequest.Builder.() -> Unit): ListDatasetEntriesResponse = listDatasetEntries(ListDatasetEntriesRequest.Builder().apply(block).build())
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see [Labeling images](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-labeling-images.html).
*
* 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*.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.ListDatasetLabels.sample
*/
public suspend inline fun RekognitionClient.listDatasetLabels(crossinline block: ListDatasetLabelsRequest.Builder.() -> Unit): ListDatasetLabelsResponse = listDatasetLabels(ListDatasetLabelsRequest.Builder().apply(block).build())
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.ListFaces.sample
*/
public suspend inline fun RekognitionClient.listFaces(crossinline block: ListFacesRequest.Builder.() -> Unit): ListFacesResponse = listFaces(ListFacesRequest.Builder().apply(block).build())
/**
* Returns a list of media analysis jobs. Results are sorted by `CreationTimestamp` in descending order.
*/
public suspend inline fun RekognitionClient.listMediaAnalysisJobs(crossinline block: ListMediaAnalysisJobsRequest.Builder.() -> Unit): ListMediaAnalysisJobsResponse = listMediaAnalysisJobs(ListMediaAnalysisJobsRequest.Builder().apply(block).build())
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* 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.
*/
public suspend inline fun RekognitionClient.listProjectPolicies(crossinline block: ListProjectPoliciesRequest.Builder.() -> Unit): ListProjectPoliciesResponse = listProjectPolicies(ListProjectPoliciesRequest.Builder().apply(block).build())
/**
* Gets a list of stream processors that you have created with CreateStreamProcessor.
*/
public suspend inline fun RekognitionClient.listStreamProcessors(crossinline block: ListStreamProcessorsRequest.Builder.() -> Unit): ListStreamProcessorsResponse = listStreamProcessors(ListStreamProcessorsRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.listTagsForResource(crossinline block: ListTagsForResourceRequest.Builder.() -> Unit): ListTagsForResourceResponse = listTagsForResource(ListTagsForResourceRequest.Builder().apply(block).build())
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.ListUsers.sample
*/
public suspend inline fun RekognitionClient.listUsers(crossinline block: ListUsersRequest.Builder.() -> Unit): ListUsersResponse = listUsers(ListUsersRequest.Builder().apply(block).build())
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.PutProjectPolicy.sample
*/
public suspend inline fun RekognitionClient.putProjectPolicy(crossinline block: PutProjectPolicyRequest.Builder.() -> Unit): PutProjectPolicyResponse = putProjectPolicy(PutProjectPolicyRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.recognizeCelebrities(crossinline block: RecognizeCelebritiesRequest.Builder.() -> Unit): RecognizeCelebritiesResponse = recognizeCelebrities(RecognizeCelebritiesRequest.Builder().apply(block).build())
/**
* 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.
*
* You can also search faces without indexing faces by using the `SearchFacesByImage` operation.
*
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.SearchFaces.sample
*/
public suspend inline fun RekognitionClient.searchFaces(crossinline block: SearchFacesRequest.Builder.() -> Unit): SearchFacesResponse = searchFaces(SearchFacesRequest.Builder().apply(block).build())
/**
* 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.
*
* 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.
*
* 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`.
*
* 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.
*
* This operation requires permissions to perform the `rekognition:SearchFacesByImage` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.SearchFacesByImage.sample
*/
public suspend inline fun RekognitionClient.searchFacesByImage(crossinline block: SearchFacesByImageRequest.Builder.() -> Unit): SearchFacesByImageResponse = searchFacesByImage(SearchFacesByImageRequest.Builder().apply(block).build())
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.SearchUsers.sample
*/
public suspend inline fun RekognitionClient.searchUsers(crossinline block: SearchUsersRequest.Builder.() -> Unit): SearchUsersResponse = searchUsers(SearchUsersRequest.Builder().apply(block).build())
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.SearchUsersByImage.sample
*/
public suspend inline fun RekognitionClient.searchUsersByImage(crossinline block: SearchUsersByImageRequest.Builder.() -> Unit): SearchUsersByImageResponse = searchUsersByImage(SearchUsersByImageRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.startCelebrityRecognition(crossinline block: StartCelebrityRecognitionRequest.Builder.() -> Unit): StartCelebrityRecognitionResponse = startCelebrityRecognition(StartCelebrityRecognitionRequest.Builder().apply(block).build())
/**
* 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](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api).
*
* 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.
*/
public suspend inline fun RekognitionClient.startContentModeration(crossinline block: StartContentModerationRequest.Builder.() -> Unit): StartContentModerationResponse = startContentModeration(StartContentModerationRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.startFaceDetection(crossinline block: StartFaceDetectionRequest.Builder.() -> Unit): StartFaceDetectionResponse = startFaceDetection(StartFaceDetectionRequest.Builder().apply(block).build())
/**
* 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](https://docs.aws.amazon.com/rekognition/latest/dg/procedure-person-search-videos.html).
*/
public suspend inline fun RekognitionClient.startFaceSearch(crossinline block: StartFaceSearchRequest.Builder.() -> Unit): StartFaceSearchResponse = startFaceSearch(StartFaceSearchRequest.Builder().apply(block).build())
/**
* 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](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detecting-labels-video.html).
*
* You can specify `MinConfidence` to control the confidence threshold for the labels returned. The default is 50.
*/
public suspend inline fun RekognitionClient.startLabelDetection(crossinline block: StartLabelDetectionRequest.Builder.() -> Unit): StartLabelDetectionResponse = startLabelDetection(StartLabelDetectionRequest.Builder().apply(block).build())
/**
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.StartMediaAnalysisJob.sample
*/
public suspend inline fun RekognitionClient.startMediaAnalysisJob(crossinline block: StartMediaAnalysisJobRequest.Builder.() -> Unit): StartMediaAnalysisJobResponse = startMediaAnalysisJob(StartMediaAnalysisJobRequest.Builder().apply(block).build())
/**
* 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`.
*/
public suspend inline fun RekognitionClient.startPersonTracking(crossinline block: StartPersonTrackingRequest.Builder.() -> Unit): StartPersonTrackingResponse = startPersonTracking(StartPersonTrackingRequest.Builder().apply(block).build())
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* 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.
*
* You are charged for the amount of time that the model is running. To stop a running model, call StopProjectVersion.
*
* This operation requires permissions to perform the `rekognition:StartProjectVersion` action.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.StartProjectVersion.sample
*/
public suspend inline fun RekognitionClient.startProjectVersion(crossinline block: StartProjectVersionRequest.Builder.() -> Unit): StartProjectVersionResponse = startProjectVersion(StartProjectVersionRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.startSegmentDetection(crossinline block: StartSegmentDetectionRequest.Builder.() -> Unit): StartSegmentDetectionResponse = startSegmentDetection(StartSegmentDetectionRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.startStreamProcessor(crossinline block: StartStreamProcessorRequest.Builder.() -> Unit): StartStreamProcessorResponse = startStreamProcessor(StartStreamProcessorRequest.Builder().apply(block).build())
/**
* 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`.
*/
public suspend inline fun RekognitionClient.startTextDetection(crossinline block: StartTextDetectionRequest.Builder.() -> Unit): StartTextDetectionResponse = startTextDetection(StartTextDetectionRequest.Builder().apply(block).build())
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.StopProjectVersion.sample
*/
public suspend inline fun RekognitionClient.stopProjectVersion(crossinline block: StopProjectVersionRequest.Builder.() -> Unit): StopProjectVersionResponse = stopProjectVersion(StopProjectVersionRequest.Builder().apply(block).build())
/**
* Stops a running stream processor that was created by CreateStreamProcessor.
*/
public suspend inline fun RekognitionClient.stopStreamProcessor(crossinline block: StopStreamProcessorRequest.Builder.() -> Unit): StopStreamProcessorResponse = stopStreamProcessor(StopStreamProcessorRequest.Builder().apply(block).build())
/**
* 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](https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html).
*
* This operation requires permissions to perform the `rekognition:TagResource` action.
*/
public suspend inline fun RekognitionClient.tagResource(crossinline block: TagResourceRequest.Builder.() -> Unit): TagResourceResponse = tagResource(TagResourceRequest.Builder().apply(block).build())
/**
* 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.
*/
public suspend inline fun RekognitionClient.untagResource(crossinline block: UntagResourceRequest.Builder.() -> Unit): UntagResourceResponse = untagResource(UntagResourceRequest.Builder().apply(block).build())
/**
* This operation applies only to Amazon Rekognition Custom Labels.
*
* 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.
*
* @sample aws.sdk.kotlin.services.rekognition.samples.UpdateDatasetEntries.sample
*/
public suspend inline fun RekognitionClient.updateDatasetEntries(crossinline block: UpdateDatasetEntriesRequest.Builder.() -> Unit): UpdateDatasetEntriesResponse = updateDatasetEntries(UpdateDatasetEntriesRequest.Builder().apply(block).build())
/**
* Allows you to update a stream processor. You can change some settings and regions of interest and delete certain parameters.
*/
public suspend inline fun RekognitionClient.updateStreamProcessor(crossinline block: UpdateStreamProcessorRequest.Builder.() -> Unit): UpdateStreamProcessorResponse = updateStreamProcessor(UpdateStreamProcessorRequest.Builder().apply(block).build())
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