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📁 ..	-	2025-05-25 12:27
📄 api-2.json { "version":"2.0", "metadata":{ "apiVersion":"2016-06-27", "endpointPrefix":"rekognition", "jsonVersion":"1.1", "protocol":"json", "protocols":["json"], "serviceFullName":"Amazon Rekognition", "serviceId":"Rekognition", "signatureVersion":"v4", "targetPrefix":"RekognitionService", "uid":"rekognition-2016-06-27", "auth":["aws.auth#sigv4"] }, "operations":{ "AssociateFaces":{ "name":"AssociateFaces", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"AssociateFacesRequest"}, "output":{"shape":"AssociateFacesResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"IdempotentParameterMismatchException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ConflictException"}, {"shape":"ServiceQuotaExceededException"} ] }, "CompareFaces":{ "name":"CompareFaces", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"CompareFacesRequest"}, "output":{"shape":"CompareFacesResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"InvalidS3ObjectException"}, {"shape":"ImageTooLargeException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidImageFormatException"} ] }, "CopyProjectVersion":{ "name":"CopyProjectVersion", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"CopyProjectVersionRequest"}, "output":{"shape":"CopyProjectVersionResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"LimitExceededException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ThrottlingException"}, {"shape":"ServiceQuotaExceededException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceInUseException"} ] }, "CreateCollection":{ "name":"CreateCollection", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"CreateCollectionRequest"}, "output":{"shape":"CreateCollectionResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceAlreadyExistsException"}, {"shape":"ServiceQuotaExceededException"} ] }, "CreateDataset":{ "name":"CreateDataset", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"CreateDatasetRequest"}, "output":{"shape":"CreateDatasetResponse"}, "errors":[ {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"LimitExceededException"}, {"shape":"InvalidS3ObjectException"}, {"shape":"ResourceAlreadyExistsException"}, {"shape":"ResourceNotFoundException"} ] }, "CreateFaceLivenessSession":{ "name":"CreateFaceLivenessSession", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"CreateFaceLivenessSessionRequest"}, "output":{"shape":"CreateFaceLivenessSessionResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"} ], "idempotent":true }, "CreateProject":{ "name":"CreateProject", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"CreateProjectRequest"}, "output":{"shape":"CreateProjectResponse"}, "errors":[ {"shape":"ResourceInUseException"}, {"shape":"LimitExceededException"}, {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"} ] }, "CreateProjectVersion":{ "name":"CreateProjectVersion", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"CreateProjectVersionRequest"}, "output":{"shape":"CreateProjectVersionResponse"}, "errors":[ {"shape":"ResourceInUseException"}, {"shape":"ResourceNotFoundException"}, {"shape":"LimitExceededException"}, {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ServiceQuotaExceededException"} ] }, "CreateStreamProcessor":{ "name":"CreateStreamProcessor", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"CreateStreamProcessorRequest"}, "output":{"shape":"CreateStreamProcessorResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"InvalidParameterException"}, {"shape":"LimitExceededException"}, {"shape":"ResourceInUseException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ServiceQuotaExceededException"} ] }, "CreateUser":{ "name":"CreateUser", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"CreateUserRequest"}, "output":{"shape":"CreateUserResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"ConflictException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ServiceQuotaExceededException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"IdempotentParameterMismatchException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"} ] }, "DeleteCollection":{ "name":"DeleteCollection", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DeleteCollectionRequest"}, "output":{"shape":"DeleteCollectionResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotFoundException"} ] }, "DeleteDataset":{ "name":"DeleteDataset", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DeleteDatasetRequest"}, "output":{"shape":"DeleteDatasetResponse"}, "errors":[ {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"LimitExceededException"}, {"shape":"ResourceInUseException"}, {"shape":"ResourceNotFoundException"} ] }, "DeleteFaces":{ "name":"DeleteFaces", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DeleteFacesRequest"}, "output":{"shape":"DeleteFacesResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotFoundException"} ] }, "DeleteProject":{ "name":"DeleteProject", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DeleteProjectRequest"}, "output":{"shape":"DeleteProjectResponse"}, "errors":[ {"shape":"ResourceInUseException"}, {"shape":"ResourceNotFoundException"}, {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"} ] }, "DeleteProjectPolicy":{ "name":"DeleteProjectPolicy", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DeleteProjectPolicyRequest"}, "output":{"shape":"DeleteProjectPolicyResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidPolicyRevisionIdException"} ] }, "DeleteProjectVersion":{ "name":"DeleteProjectVersion", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DeleteProjectVersionRequest"}, "output":{"shape":"DeleteProjectVersionResponse"}, "errors":[ {"shape":"ResourceNotFoundException"}, {"shape":"ResourceInUseException"}, {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"} ] }, "DeleteStreamProcessor":{ "name":"DeleteStreamProcessor", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DeleteStreamProcessorRequest"}, "output":{"shape":"DeleteStreamProcessorResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"InvalidParameterException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ResourceInUseException"}, {"shape":"ProvisionedThroughputExceededException"} ] }, "DeleteUser":{ "name":"DeleteUser", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DeleteUserRequest"}, "output":{"shape":"DeleteUserResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"ConflictException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"IdempotentParameterMismatchException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"} ] }, "DescribeCollection":{ "name":"DescribeCollection", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DescribeCollectionRequest"}, "output":{"shape":"DescribeCollectionResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotFoundException"} ] }, "DescribeDataset":{ "name":"DescribeDataset", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DescribeDatasetRequest"}, "output":{"shape":"DescribeDatasetResponse"}, "errors":[ {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"ResourceNotFoundException"} ] }, "DescribeProjectVersions":{ "name":"DescribeProjectVersions", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DescribeProjectVersionsRequest"}, "output":{"shape":"DescribeProjectVersionsResponse"}, "errors":[ {"shape":"ResourceNotFoundException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"} ] }, "DescribeProjects":{ "name":"DescribeProjects", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DescribeProjectsRequest"}, "output":{"shape":"DescribeProjectsResponse"}, "errors":[ {"shape":"InvalidPaginationTokenException"}, {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"} ] }, "DescribeStreamProcessor":{ "name":"DescribeStreamProcessor", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DescribeStreamProcessorRequest"}, "output":{"shape":"DescribeStreamProcessorResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"InvalidParameterException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ProvisionedThroughputExceededException"} ] }, "DetectCustomLabels":{ "name":"DetectCustomLabels", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DetectCustomLabelsRequest"}, "output":{"shape":"DetectCustomLabelsResponse"}, "errors":[ {"shape":"ResourceNotFoundException"}, {"shape":"ResourceNotReadyException"}, {"shape":"InvalidS3ObjectException"}, {"shape":"InvalidParameterException"}, {"shape":"ImageTooLargeException"}, {"shape":"LimitExceededException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidImageFormatException"} ] }, "DetectFaces":{ "name":"DetectFaces", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DetectFacesRequest"}, "output":{"shape":"DetectFacesResponse"}, "errors":[ {"shape":"InvalidS3ObjectException"}, {"shape":"InvalidParameterException"}, {"shape":"ImageTooLargeException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidImageFormatException"} ] }, "DetectLabels":{ "name":"DetectLabels", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DetectLabelsRequest"}, "output":{"shape":"DetectLabelsResponse"}, "errors":[ {"shape":"InvalidS3ObjectException"}, {"shape":"InvalidParameterException"}, {"shape":"ImageTooLargeException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidImageFormatException"} ] }, "DetectModerationLabels":{ "name":"DetectModerationLabels", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DetectModerationLabelsRequest"}, "output":{"shape":"DetectModerationLabelsResponse"}, "errors":[ {"shape":"InvalidS3ObjectException"}, {"shape":"InvalidParameterException"}, {"shape":"ImageTooLargeException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidImageFormatException"}, {"shape":"HumanLoopQuotaExceededException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ResourceNotReadyException"} ] }, "DetectProtectiveEquipment":{ "name":"DetectProtectiveEquipment", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DetectProtectiveEquipmentRequest"}, "output":{"shape":"DetectProtectiveEquipmentResponse"}, "errors":[ {"shape":"InvalidS3ObjectException"}, {"shape":"InvalidParameterException"}, {"shape":"ImageTooLargeException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidImageFormatException"} ] }, "DetectText":{ "name":"DetectText", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DetectTextRequest"}, "output":{"shape":"DetectTextResponse"}, "errors":[ {"shape":"InvalidS3ObjectException"}, {"shape":"InvalidParameterException"}, {"shape":"ImageTooLargeException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidImageFormatException"} ] }, "DisassociateFaces":{ "name":"DisassociateFaces", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DisassociateFacesRequest"}, "output":{"shape":"DisassociateFacesResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"IdempotentParameterMismatchException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ConflictException"} ] }, "DistributeDatasetEntries":{ "name":"DistributeDatasetEntries", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"DistributeDatasetEntriesRequest"}, "output":{"shape":"DistributeDatasetEntriesResponse"}, "errors":[ {"shape":"ResourceNotFoundException"}, {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotReadyException"} ] }, "GetCelebrityInfo":{ "name":"GetCelebrityInfo", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"GetCelebrityInfoRequest"}, "output":{"shape":"GetCelebrityInfoResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotFoundException"} ] }, "GetCelebrityRecognition":{ "name":"GetCelebrityRecognition", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"GetCelebrityRecognitionRequest"}, "output":{"shape":"GetCelebrityRecognitionResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ThrottlingException"} ] }, "GetContentModeration":{ "name":"GetContentModeration", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"GetContentModerationRequest"}, "output":{"shape":"GetContentModerationResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ThrottlingException"} ] }, "GetFaceDetection":{ "name":"GetFaceDetection", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"GetFaceDetectionRequest"}, "output":{"shape":"GetFaceDetectionResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ThrottlingException"} ] }, "GetFaceLivenessSessionResults":{ "name":"GetFaceLivenessSessionResults", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"GetFaceLivenessSessionResultsRequest"}, "output":{"shape":"GetFaceLivenessSessionResultsResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"SessionNotFoundException"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"} ] }, "GetFaceSearch":{ "name":"GetFaceSearch", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"GetFaceSearchRequest"}, "output":{"shape":"GetFaceSearchResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ThrottlingException"} ] }, "GetLabelDetection":{ "name":"GetLabelDetection", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"GetLabelDetectionRequest"}, "output":{"shape":"GetLabelDetectionResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ThrottlingException"} ] }, "GetMediaAnalysisJob":{ "name":"GetMediaAnalysisJob", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"GetMediaAnalysisJobRequest"}, "output":{"shape":"GetMediaAnalysisJobResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"ResourceNotFoundException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ThrottlingException"} ] }, "GetPersonTracking":{ "name":"GetPersonTracking", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"GetPersonTrackingRequest"}, "output":{"shape":"GetPersonTrackingResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ThrottlingException"} ] }, "GetSegmentDetection":{ "name":"GetSegmentDetection", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"GetSegmentDetectionRequest"}, "output":{"shape":"GetSegmentDetectionResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ThrottlingException"} ] }, "GetTextDetection":{ "name":"GetTextDetection", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"GetTextDetectionRequest"}, "output":{"shape":"GetTextDetectionResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ThrottlingException"} ] }, "IndexFaces":{ "name":"IndexFaces", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"IndexFacesRequest"}, "output":{"shape":"IndexFacesResponse"}, "errors":[ {"shape":"InvalidS3ObjectException"}, {"shape":"InvalidParameterException"}, {"shape":"ImageTooLargeException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotFoundException"}, {"shape":"InvalidImageFormatException"}, {"shape":"ServiceQuotaExceededException"} ] }, "ListCollections":{ "name":"ListCollections", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"ListCollectionsRequest"}, "output":{"shape":"ListCollectionsResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"ResourceNotFoundException"} ] }, "ListDatasetEntries":{ "name":"ListDatasetEntries", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"ListDatasetEntriesRequest"}, "output":{"shape":"ListDatasetEntriesResponse"}, "errors":[ {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"ResourceInUseException"}, {"shape":"ResourceNotFoundException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"ResourceNotReadyException"} ] }, "ListDatasetLabels":{ "name":"ListDatasetLabels", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"ListDatasetLabelsRequest"}, "output":{"shape":"ListDatasetLabelsResponse"}, "errors":[ {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"ResourceInUseException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ResourceNotReadyException"} ] }, "ListFaces":{ "name":"ListFaces", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"ListFacesRequest"}, "output":{"shape":"ListFacesResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"ResourceNotFoundException"} ] }, "ListMediaAnalysisJobs":{ "name":"ListMediaAnalysisJobs", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"ListMediaAnalysisJobsRequest"}, "output":{"shape":"ListMediaAnalysisJobsResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ThrottlingException"} ] }, "ListProjectPolicies":{ "name":"ListProjectPolicies", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"ListProjectPoliciesRequest"}, "output":{"shape":"ListProjectPoliciesResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidPaginationTokenException"} ] }, "ListStreamProcessors":{ "name":"ListStreamProcessors", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"ListStreamProcessorsRequest"}, "output":{"shape":"ListStreamProcessorsResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"ProvisionedThroughputExceededException"} ] }, "ListTagsForResource":{ "name":"ListTagsForResource", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"ListTagsForResourceRequest"}, "output":{"shape":"ListTagsForResourceResponse"}, "errors":[ {"shape":"ResourceNotFoundException"}, {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"} ] }, "ListUsers":{ "name":"ListUsers", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"ListUsersRequest"}, "output":{"shape":"ListUsersResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"ResourceNotFoundException"}, {"shape":"InvalidPaginationTokenException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"} ] }, "PutProjectPolicy":{ "name":"PutProjectPolicy", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"PutProjectPolicyRequest"}, "output":{"shape":"PutProjectPolicyResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidPolicyRevisionIdException"}, {"shape":"MalformedPolicyDocumentException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ResourceAlreadyExistsException"}, {"shape":"ThrottlingException"}, {"shape":"ServiceQuotaExceededException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"LimitExceededException"} ] }, "RecognizeCelebrities":{ "name":"RecognizeCelebrities", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"RecognizeCelebritiesRequest"}, "output":{"shape":"RecognizeCelebritiesResponse"}, "errors":[ {"shape":"InvalidS3ObjectException"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidImageFormatException"}, {"shape":"ImageTooLargeException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"InvalidImageFormatException"} ] }, "SearchFaces":{ "name":"SearchFaces", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"SearchFacesRequest"}, "output":{"shape":"SearchFacesResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotFoundException"} ] }, "SearchFacesByImage":{ "name":"SearchFacesByImage", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"SearchFacesByImageRequest"}, "output":{"shape":"SearchFacesByImageResponse"}, "errors":[ {"shape":"InvalidS3ObjectException"}, {"shape":"InvalidParameterException"}, {"shape":"ImageTooLargeException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"ResourceNotFoundException"}, {"shape":"InvalidImageFormatException"} ] }, "SearchUsers":{ "name":"SearchUsers", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"SearchUsersRequest"}, "output":{"shape":"SearchUsersResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"} ] }, "SearchUsersByImage":{ "name":"SearchUsersByImage", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"SearchUsersByImageRequest"}, "output":{"shape":"SearchUsersByImageResponse"}, "errors":[ {"shape":"InvalidParameterException"}, {"shape":"ResourceNotFoundException"}, {"shape":"InvalidImageFormatException"}, {"shape":"InvalidS3ObjectException"}, {"shape":"ImageTooLargeException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"AccessDeniedException"}, {"shape":"InternalServerError"}, {"shape":"ThrottlingException"} ] }, "StartCelebrityRecognition":{ "name":"StartCelebrityRecognition", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"StartCelebrityRecognitionRequest"}, "output":{"shape":"StartCelebrityRecognitionResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"IdempotentParameterMismatchException"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidS3ObjectException"}, {"shape":"InternalServerError"}, {"shape":"VideoTooLargeException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"LimitExceededException"}, {"shape":"ThrottlingException"} ], "idempotent":true }, "StartContentModeration":{ "name":"StartContentModeration", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"StartContentModerationRequest"}, "output":{"shape":"StartContentModerationResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"IdempotentParameterMismatchException"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidS3ObjectException"}, {"shape":"InternalServerError"}, {"shape":"VideoTooLargeException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"LimitExceededException"}, {"shape":"ThrottlingException"} ], "idempotent":true }, "StartFaceDetection":{ "name":"StartFaceDetection", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"StartFaceDetectionRequest"}, "output":{"shape":"StartFaceDetectionResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"IdempotentParameterMismatchException"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidS3ObjectException"}, {"shape":"InternalServerError"}, {"shape":"VideoTooLargeException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"LimitExceededException"}, {"shape":"ThrottlingException"} ], "idempotent":true }, "StartFaceSearch":{ "name":"StartFaceSearch", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"StartFaceSearchRequest"}, "output":{"shape":"StartFaceSearchResponse"}, "errors":[ {"shape":"AccessDeniedException"}, {"shape":"IdempotentParameterMismatchException"}, {"shape":"InvalidParameterException"}, {"shape":"InvalidS3ObjectException"}, {"shape":"InternalServerError"}, {"shape":"VideoTooLargeException"}, {"shape":"ProvisionedThroughputExceededException"}, {"shape":"LimitExceededException"}, {"shape":"ResourceNotFoundException"}, {"shape":"ThrottlingException"} ], "idempotent":true }, "StartLabelDetection":{ "name":"StartLabelDetection", 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()':,;?]+" }, "VersionName":{ "type":"string", "max":255, "min":1, "pattern":"[a-zA-Z0-9_.\\-]+" }, "VersionNames":{ "type":"list", "member":{"shape":"VersionName"}, "max":10, "min":1 }, "Video":{ "type":"structure", "members":{ "S3Object":{"shape":"S3Object"} } }, "VideoColorRange":{ "type":"string", "enum":[ "FULL", "LIMITED" ] }, "VideoJobStatus":{ "type":"string", "enum":[ "IN_PROGRESS", "SUCCEEDED", "FAILED" ] }, "VideoMetadata":{ "type":"structure", "members":{ "Codec":{"shape":"String"}, "DurationMillis":{"shape":"ULong"}, "Format":{"shape":"String"}, "FrameRate":{"shape":"Float"}, "FrameHeight":{"shape":"ULong"}, "FrameWidth":{"shape":"ULong"}, "ColorRange":{"shape":"VideoColorRange"} } }, "VideoMetadataList":{ "type":"list", "member":{"shape":"VideoMetadata"} }, "VideoTooLargeException":{ "type":"structure", "members":{ }, "exception":true } } }	146.55 KB	2025-05-25 12:27
📄 docs-2.json { "version": "2.0", "service": "<p>This is the API Reference for <a href=\"https://docs.aws.amazon.com/rekognition/latest/dg/images.html\">Amazon Rekognition Image</a>, <a href=\"https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/what-is.html\">Amazon Rekognition Custom Labels</a>, <a href=\"https://docs.aws.amazon.com/rekognition/latest/dg/video.html\">Amazon Rekognition Stored Video</a>, <a href=\"https://docs.aws.amazon.com/rekognition/latest/dg/streaming-video.html\">Amazon Rekognition Streaming Video</a>. It provides descriptions of actions, data types, common parameters, and common errors.</p> <p> <b>Amazon Rekognition Image</b> </p> <ul> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_AssociateFaces.html\">AssociateFaces</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CompareFaces.html\">CompareFaces</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateCollection.html\">CreateCollection</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateUser.html\">CreateUser</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteCollection.html\">DeleteCollection</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteFaces.html\">DeleteFaces</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteUser.html\">DeleteUser</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeCollection.html\">DescribeCollection</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectFaces.html\">DetectFaces</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectLabels.html\">DetectLabels</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectModerationLabels.html\">DetectModerationLabels</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectProtectiveEquipment.html\">DetectProtectiveEquipment</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectText.html\">DetectText</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DisassociateFaces.html\">DisassociateFaces</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetCelebrityInfo.html\">GetCelebrityInfo</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetMediaAnalysisJob.html\">GetMediaAnalysisJob</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_IndexFaces.html\">IndexFaces</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListCollections.html\">ListCollections</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListMediaAnalysisJob.html\">ListMediaAnalysisJob</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListFaces.html\">ListFaces</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListFaces.html\">ListUsers</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_RecognizeCelebrities.html\">RecognizeCelebrities</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchFaces.html\">SearchFaces</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchFacesByImage.html\">SearchFacesByImage</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchUsers.html\">SearchUsers</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchUsersByImage.html\">SearchUsersByImage</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartMediaAnalysisJob.html\">StartMediaAnalysisJob</a> </p> </li> </ul> <p> <b>Amazon Rekognition Custom Labels</b> </p> <ul> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CopyProjectVersion.html\">CopyProjectVersion</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateDataset.html\">CreateDataset</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateProject.html\">CreateProject</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateProjectVersion.html\">CreateProjectVersion</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteDataset.html\">DeleteDataset</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteProject.html\">DeleteProject</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteProjectPolicy.html\">DeleteProjectPolicy</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteProjectVersion.html\">DeleteProjectVersion</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeDataset.html\">DescribeDataset</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeProjects.html\">DescribeProjects</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeProjectVersions.html\">DescribeProjectVersions</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectCustomLabels.html\">DetectCustomLabels</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DistributeDatasetEntries.html\">DistributeDatasetEntries</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListDatasetEntries.html\">ListDatasetEntries</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListDatasetLabels.html\">ListDatasetLabels</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListProjectPolicies.html\">ListProjectPolicies</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_PutProjectPolicy.html\">PutProjectPolicy</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartProjectVersion.html\">StartProjectVersion</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StopProjectVersion.html\">StopProjectVersion</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_UpdateDatasetEntries.html\">UpdateDatasetEntries</a> </p> </li> </ul> <p> <b>Amazon Rekognition Video Stored Video</b> </p> <ul> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetCelebrityRecognition.html\">GetCelebrityRecognition</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetContentModeration.html\">GetContentModeration</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetFaceDetection.html\">GetFaceDetection</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetFaceSearch.html\">GetFaceSearch</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetLabelDetection.html\">GetLabelDetection</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetPersonTracking.html\">GetPersonTracking</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetSegmentDetection.html\">GetSegmentDetection</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetTextDetection.html\">GetTextDetection</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartCelebrityRecognition.html\">StartCelebrityRecognition</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartContentModeration.html\">StartContentModeration</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartFaceDetection.html\">StartFaceDetection</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartFaceSearch.html\">StartFaceSearch</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartLabelDetection.html\">StartLabelDetection</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartPersonTracking.html\">StartPersonTracking</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartSegmentDetection.html\">StartSegmentDetection</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartTextDetection.html\">StartTextDetection</a> </p> </li> </ul> <p> <b>Amazon Rekognition Video Streaming Video</b> </p> <ul> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateStreamProcessor.html\">CreateStreamProcessor</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteStreamProcessor.html\">DeleteStreamProcessor</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeStreamProcessor.html\">DescribeStreamProcessor</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListStreamProcessors.html\">ListStreamProcessors</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartStreamProcessor.html\">StartStreamProcessor</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StopStreamProcessor.html\">StopStreamProcessor</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/rekognition/latest/APIReference/API_UpdateStreamProcessor.html\">UpdateStreamProcessor</a> </p> </li> </ul>", "operations": { "AssociateFaces": "<p>Associates one or more faces with an existing UserID. Takes an array of <code>FaceIds</code>. Each <code>FaceId</code> that are present in the <code>FaceIds</code> list is associated with the provided UserID. The maximum number of total <code>FaceIds</code> per UserID is 100. </p> <p>The <code>UserMatchThreshold</code> parameter specifies the minimum user match confidence required for the face to be associated with a UserID that has at least one <code>FaceID</code> already associated. This ensures that the <code>FaceIds</code> are associated with the right UserID. The value ranges from 0-100 and default value is 75. </p> <p>If successful, an array of <code>AssociatedFace</code> objects containing the associated <code>FaceIds</code> is returned. If a given face is already associated with the given <code>UserID</code>, it will be ignored and will not be returned in the response. If a given face is already associated to a different <code>UserID</code>, isn't found in the collection, doesn’t meet the <code>UserMatchThreshold</code>, or there are already 100 faces associated with the <code>UserID</code>, it will be returned as part of an array of <code>UnsuccessfulFaceAssociations.</code> </p> <p>The <code>UserStatus</code> reflects the status of an operation which updates a UserID representation with a list of given faces. The <code>UserStatus</code> can be: </p> <ul> <li> <p>ACTIVE - All associations or disassociations of FaceID(s) for a UserID are complete.</p> </li> <li> <p>CREATED - A UserID has been created, but has no FaceID(s) associated with it.</p> </li> <li> <p>UPDATING - A UserID is being updated and there are current associations or disassociations of FaceID(s) taking place.</p> </li> </ul>", "CompareFaces": "<p>Compares a face in the <i>source</i> input image with each of the 100 largest faces detected in the <i>target</i> input image. </p> <p> If the source image contains multiple faces, the service detects the largest face and compares it with each face detected in the target image. </p> <note> <p>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 <code>CompareFaces</code> 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.</p> </note> <p>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. </p> <p>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. </p> <note> <p>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 <code>SimilarityThreshold</code> parameter.</p> </note> <p> <code>CompareFaces</code> 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.</p> <p>The <code>QualityFilter</code> 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 <code>QualityFilter</code> to set the quality bar by specifying <code>LOW</code>, <code>MEDIUM</code>, or <code>HIGH</code>. If you do not want to filter detected faces, specify <code>NONE</code>. The default value is <code>NONE</code>. </p> <p>If the image doesn't contain Exif metadata, <code>CompareFaces</code> returns orientation information for the source and target images. Use these values to display the images with the correct image orientation.</p> <p>If no faces are detected in the source or target images, <code>CompareFaces</code> returns an <code>InvalidParameterException</code> error. </p> <note> <p> This is a stateless API operation. That is, data returned by this operation doesn't persist.</p> </note> <p>For an example, see Comparing Faces in Images in the Amazon Rekognition Developer Guide.</p> <p>This operation requires permissions to perform the <code>rekognition:CompareFaces</code> action.</p>", "CopyProjectVersion": "<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p>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. </p> <p>To copy a model version to a different AWS account, you need to create a resource-based policy known as a <i>project policy</i>. You attach the project policy to the source project by calling <a>PutProjectPolicy</a>. The project policy gives permission to copy the model version from a trusting AWS account to a trusted account.</p> <p>For more information creating and attaching a project policy, see Attaching a project policy (SDK) in the <i>Amazon Rekognition Custom Labels Developer Guide</i>. </p> <p>If you are copying a model version to a project in the same AWS account, you don't need to create a project policy.</p> <note> <p>Copying project versions is supported only for Custom Labels models. </p> <p>To copy a model, the destination project, source project, and source model version must already exist.</p> </note> <p>Copying a model version takes a while to complete. To get the current status, call <a>DescribeProjectVersions</a> and check the value of <code>Status</code> in the <a>ProjectVersionDescription</a> object. The copy operation has finished when the value of <code>Status</code> is <code>COPYING_COMPLETED</code>.</p> <p>This operation requires permissions to perform the <code>rekognition:CopyProjectVersion</code> action.</p>", "CreateCollection": "<p>Creates a collection in an AWS Region. You can add faces to the collection using the <a>IndexFaces</a> operation. </p> <p>For example, you might create collections, one for each of your application users. A user can then index faces using the <code>IndexFaces</code> operation and persist results in a specific collection. Then, a user can search the collection for faces in the user-specific container. </p> <p>When you create a collection, it is associated with the latest version of the face model version.</p> <note> <p>Collection names are case-sensitive.</p> </note> <p>This operation requires permissions to perform the <code>rekognition:CreateCollection</code> action. If you want to tag your collection, you also require permission to perform the <code>rekognition:TagResource</code> operation.</p>", "CreateDataset": "<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p>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.</p> <p>To create a training dataset for a project, specify <code>TRAIN</code> for the value of <code>DatasetType</code>. To create the test dataset for a project, specify <code>TEST</code> for the value of <code>DatasetType</code>. </p> <p>The response from <code>CreateDataset</code> is the Amazon Resource Name (ARN) for the dataset. Creating a dataset takes a while to complete. Use <a>DescribeDataset</a> to check the current status. The dataset created successfully if the value of <code>Status</code> is <code>CREATE_COMPLETE</code>. </p> <p>To check if any non-terminal errors occurred, call <a>ListDatasetEntries</a> and check for the presence of <code>errors</code> lists in the JSON Lines.</p> <p>Dataset creation fails if a terminal error occurs (<code>Status</code> = <code>CREATE_FAILED</code>). Currently, you can't access the terminal error information. </p> <p>For more information, see Creating dataset in the <i>Amazon Rekognition Custom Labels Developer Guide</i>.</p> <p>This operation requires permissions to perform the <code>rekognition:CreateDataset</code> action. If you want to copy an existing dataset, you also require permission to perform the <code>rekognition:ListDatasetEntries</code> action.</p>", "CreateFaceLivenessSession": "<p>This API operation initiates a Face Liveness session. It returns a <code>SessionId</code>, which you can use to start streaming Face Liveness video and get the results for a Face Liveness session. </p> <p>You can use the <code>OutputConfig</code> 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. </p> <p>You can use <code>AuditImagesLimit</code> to limit the number of audit images returned when <code>GetFaceLivenessSessionResults</code> 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. </p>", "CreateProject": "<p>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 <code>rekognition:CreateProject</code> action.</p>", "CreateProjectVersion": "<p>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 <code>CreateProjectVersion</code> is an Amazon Resource Name (ARN) for the project version. </p> <p>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 <a>DescribeProjectVersions</a>. Training completed successfully if the value of the <code>Status</code> field is <code>TRAINING_COMPLETED</code>. Once training has successfully completed, call <a>DescribeProjectVersions</a> to get the training results and evaluate the model.</p> <p>This operation requires permissions to perform the <code>rekognition:CreateProjectVersion</code> action.</p> <note> <p> <i>The following applies only to projects with Amazon Rekognition Custom Labels as the chosen feature:</i> </p> <p>You can train a model in a project that doesn't have associated datasets by specifying manifest files in the <code>TrainingData</code> and <code>TestingData</code> fields. </p> <p>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. </p> <p>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.</p> </note> <p/>", "CreateStreamProcessor": "<p>Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces or to detect labels in a streaming video.</p> <p>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.</p> <ul> <li> <p>If you are creating a stream processor for detecting faces, you provide as input a Kinesis video stream (<code>Input</code>) and a Kinesis data stream (<code>Output</code>) stream for receiving the output. You must use the <code>FaceSearch</code> option in <code>Settings</code>, specifying the collection that contains the faces you want to recognize. After you have finished analyzing a streaming video, use <a>StopStreamProcessor</a> to stop processing.</p> </li> <li> <p>If you are creating a stream processor to detect labels, you provide as input a Kinesis video stream (<code>Input</code>), Amazon S3 bucket information (<code>Output</code>), and an Amazon SNS topic ARN (<code>NotificationChannel</code>). 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 <code>ConnectedHome</code> option in settings, and selecting one of the following: <code>PERSON</code>, <code>PET</code>, <code>PACKAGE</code>, <code>ALL</code> You can also specify where in the frame you want Amazon Rekognition to monitor with <code>RegionsOfInterest</code>. When you run the <a>StartStreamProcessor</a> operation on a label detection stream processor, you input start and stop information to determine the length of the processing time.</p> </li> </ul> <p> Use <code>Name</code> to assign an identifier for the stream processor. You use <code>Name</code> to manage the stream processor. For example, you can start processing the source video by calling <a>StartStreamProcessor</a> with the <code>Name</code> field. </p> <p>This operation requires permissions to perform the <code>rekognition:CreateStreamProcessor</code> action. If you want to tag your stream processor, you also require permission to perform the <code>rekognition:TagResource</code> operation.</p>", "CreateUser": "<p>Creates a new User within a collection specified by <code>CollectionId</code>. Takes <code>UserId</code> as a parameter, which is a user provided ID which should be unique within the collection. The provided <code>UserId</code> will alias the system generated UUID to make the <code>UserId</code> more user friendly. </p> <p>Uses a <code>ClientToken</code>, an idempotency token that ensures a call to <code>CreateUser</code> 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 <code>CreateUser</code> calls. </p>", "DeleteCollection": "<p>Deletes the specified collection. Note that this operation removes all faces in the collection. For an example, see <a href=\"https://docs.aws.amazon.com/rekognition/latest/dg/delete-collection-procedure.html\">Deleting a collection</a>.</p> <p>This operation requires permissions to perform the <code>rekognition:DeleteCollection</code> action.</p>", "DeleteDataset": "<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p>Deletes an existing Amazon Rekognition Custom Labels dataset. Deleting a dataset might take while. Use <a>DescribeDataset</a> to check the current status. The dataset is still deleting if the value of <code>Status</code> is <code>DELETE_IN_PROGRESS</code>. If you try to access the dataset after it is deleted, you get a <code>ResourceNotFoundException</code> exception. </p> <p>You can't delete a dataset while it is creating (<code>Status</code> = <code>CREATE_IN_PROGRESS</code>) or if the dataset is updating (<code>Status</code> = <code>UPDATE_IN_PROGRESS</code>).</p> <p>This operation requires permissions to perform the <code>rekognition:DeleteDataset</code> action.</p>", "DeleteFaces": "<p>Deletes faces from a collection. You specify a collection ID and an array of face IDs to remove from the collection.</p> <p>This operation requires permissions to perform the <code>rekognition:DeleteFaces</code> action.</p>", "DeleteProject": "<p>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 <a>DeleteProjectVersion</a>.</p> <p> <code>DeleteProject</code> is an asynchronous operation. To check if the project is deleted, call <a>DescribeProjects</a>. The project is deleted when the project no longer appears in the response. Be aware that deleting a given project will also delete any <code>ProjectPolicies</code> associated with that project.</p> <p>This operation requires permissions to perform the <code>rekognition:DeleteProject</code> action. </p>", "DeleteProjectPolicy": "<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p>Deletes an existing project policy.</p> <p>To get a list of project policies attached to a project, call <a>ListProjectPolicies</a>. To attach a project policy to a project, call <a>PutProjectPolicy</a>.</p> <p>This operation requires permissions to perform the <code>rekognition:DeleteProjectPolicy</code> action.</p>", "DeleteProjectVersion": "<p>Deletes a Rekognition project model or project version, like a Amazon Rekognition Custom Labels model or a custom adapter.</p> <p>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 <a>DescribeProjectVersions</a>. To stop a project version call <a>StopProjectVersion</a>. If the project version is training, wait until it finishes.</p> <p>This operation requires permissions to perform the <code>rekognition:DeleteProjectVersion</code> action. </p>", "DeleteStreamProcessor": "<p>Deletes the stream processor identified by <code>Name</code>. You assign the value for <code>Name</code> when you create the stream processor with <a>CreateStreamProcessor</a>. You might not be able to use the same name for a stream processor for a few seconds after calling <code>DeleteStreamProcessor</code>.</p>", "DeleteUser": "<p>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 <code>Collection</code> or <code>UserID</code> is already deleted or not found, a <code>ResourceNotFoundException</code> will be thrown. If the action is successful with a 200 response, an empty HTTP body is returned. </p>", "DescribeCollection": "<p>Describes the specified collection. You can use <code>DescribeCollection</code> 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.</p> <p>For more information, see Describing a Collection in the Amazon Rekognition Developer Guide.</p>", "DescribeDataset": "<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p> 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. </p> <p>This operation requires permissions to perform the <code>rekognition:DescribeDataset</code> action.</p>", "DescribeProjectVersions": "<p>Lists and describes the versions of an Amazon Rekognition project. You can specify up to 10 model or adapter versions in <code>ProjectVersionArns</code>. If you don't specify a value, descriptions for all model/adapter versions in the project are returned.</p> <p>This operation requires permissions to perform the <code>rekognition:DescribeProjectVersions</code> action.</p>", "DescribeProjects": "<p>Gets information about your Rekognition projects.</p> <p>This operation requires permissions to perform the <code>rekognition:DescribeProjects</code> action.</p>", "DescribeStreamProcessor": "<p>Provides information about a stream processor created by <a>CreateStreamProcessor</a>. 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.</p>", "DetectCustomLabels": "<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p>Detects custom labels in a supplied image by using an Amazon Rekognition Custom Labels model. </p> <p>You specify which version of a model version to use by using the <code>ProjectVersionArn</code> input parameter. </p> <p>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. </p> <p> For each object that the model version detects on an image, the API returns a (<code>CustomLabel</code>) object in an array (<code>CustomLabels</code>). Each <code>CustomLabel</code> object provides the label name (<code>Name</code>), the level of confidence that the image contains the object (<code>Confidence</code>), and object location information, if it exists, for the label on the image (<code>Geometry</code>). Note that for the <code>DetectCustomLabelsLabels</code> operation, <code>Polygons</code> are not returned in the <code>Geometry</code> section of the response.</p> <p>To filter labels that are returned, specify a value for <code>MinConfidence</code>. <code>DetectCustomLabelsLabels</code> only returns labels with a confidence that's higher than the specified value. The value of <code>MinConfidence</code> maps to the assumed threshold values created during training. For more information, see <i>Assumed threshold</i> 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 <code>MinConfidence</code> normalizes the threshold value to a percentage value (0-100). Confidence responses from <code>DetectCustomLabels</code> are also returned as a percentage. You can use <code>MinConfidence</code> to change the precision and recall or your model. For more information, see <i>Analyzing an image</i> in the Amazon Rekognition Custom Labels Developer Guide. </p> <p>If you don't specify a value for <code>MinConfidence</code>, <code>DetectCustomLabels</code> returns labels based on the assumed threshold of each label.</p> <p>This is a stateless API operation. That is, the operation does not persist any data.</p> <p>This operation requires permissions to perform the <code>rekognition:DetectCustomLabels</code> action. </p> <p>For more information, see <i>Analyzing an image</i> in the Amazon Rekognition Custom Labels Developer Guide. </p>", "DetectFaces": "<p>Detects faces within an image that is provided as input.</p> <p> <code>DetectFaces</code> 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.</p> <p>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. </p> <p>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. </p> <note> <p>This is a stateless API operation. That is, the operation does not persist any data.</p> </note> <p>This operation requires permissions to perform the <code>rekognition:DetectFaces</code> action. </p>", "DetectLabels": "<p>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. </p> <p>For an example, see Analyzing images stored in an Amazon S3 bucket in the Amazon Rekognition Developer Guide.</p> <p>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. </p> <p> <b>Optional Parameters</b> </p> <p>You can specify one or both of the <code>GENERAL_LABELS</code> and <code>IMAGE_PROPERTIES</code> feature types when calling the DetectLabels API. Including <code>GENERAL_LABELS</code> will ensure the response includes the labels detected in the input image, while including <code>IMAGE_PROPERTIES </code>will ensure the response includes information about the image quality and color.</p> <p>When using <code>GENERAL_LABELS</code> and/or <code>IMAGE_PROPERTIES</code> 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 <a href=\"https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html\">Detecting Labels in an Image</a>.</p> <p>When getting labels, you can specify <code>MinConfidence</code> to control the confidence threshold for the labels returned. The default is 55%. You can also add the <code>MaxLabels</code> 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.</p> <p> <b>Response Elements</b> </p> <p> For each object, scene, and concept the API returns one or more labels. The API returns the following types of information about labels:</p> <ul> <li> <p> Name - The name of the detected label. </p> </li> <li> <p> Confidence - The level of confidence in the label assigned to a detected object. </p> </li> <li> <p> 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. </p> </li> <li> <p> Aliases - Possible Aliases for the label. </p> </li> <li> <p> Categories - The label categories that the detected label belongs to. </p> </li> <li> <p> 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. </p> </li> </ul> <p> The API returns the following information regarding the image, as part of the ImageProperties structure:</p> <ul> <li> <p>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. </p> </li> <li> <p>Dominant Color - An array of the dominant colors in the image. </p> </li> <li> <p>Foreground - Information about the sharpness, brightness, and dominant colors of the input image’s foreground. </p> </li> <li> <p>Background - Information about the sharpness, brightness, and dominant colors of the input image’s background.</p> </li> </ul> <p>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:</p> <p> <code>{Name: lighthouse, Confidence: 98.4629}</code> </p> <p> <code>{Name: rock,Confidence: 79.2097}</code> </p> <p> <code> {Name: sea,Confidence: 75.061}</code> </p> <p>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. </p> <p> <code>{Name: flower,Confidence: 99.0562}</code> </p> <p> <code>{Name: plant,Confidence: 99.0562}</code> </p> <p> <code>{Name: tulip,Confidence: 99.0562}</code> </p> <p>In this example, the detection algorithm more precisely identifies the flower as a tulip.</p> <note> <p>If the object detected is a person, the operation doesn't provide the same facial details that the <a>DetectFaces</a> operation provides.</p> </note> <p>This is a stateless API operation that doesn't return any data.</p> <p>This operation requires permissions to perform the <code>rekognition:DetectLabels</code> action. </p>", "DetectModerationLabels": "<p>Detects unsafe content in a specified JPEG or PNG format image. Use <code>DetectModerationLabels</code> to moderate images depending on your requirements. For example, you might want to filter images that contain nudity, but not images containing suggestive content.</p> <p>To filter images, use the labels returned by <code>DetectModerationLabels</code> to determine which types of content are appropriate.</p> <p>For information about moderation labels, see Detecting Unsafe Content in the Amazon Rekognition Developer Guide.</p> <p>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. </p> <p>You can specify an adapter to use when retrieving label predictions by providing a <code>ProjectVersionArn</code> to the <code>ProjectVersion</code> argument.</p>", "DetectProtectiveEquipment": "<p>Detects Personal Protective Equipment (PPE) worn by people detected in an image. Amazon Rekognition can detect the following types of PPE.</p> <ul> <li> <p>Face cover</p> </li> <li> <p>Hand cover</p> </li> <li> <p>Head cover</p> </li> </ul> <p>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. </p> <p> <code>DetectProtectiveEquipment</code> detects PPE worn by up to 15 persons detected in an image.</p> <p>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 (<a>BoundingBox</a>) for each detected person and each detected item of PPE. </p> <p>You can optionally request a summary of detected PPE items with the <code>SummarizationAttributes</code> input parameter. The summary provides the following information. </p> <ul> <li> <p>The persons detected as wearing all of the types of PPE that you specify.</p> </li> <li> <p>The persons detected as not wearing all of the types PPE that you specify.</p> </li> <li> <p>The persons detected where PPE adornment could not be determined. </p> </li> </ul> <p>This is a stateless API operation. That is, the operation does not persist any data.</p> <p>This operation requires permissions to perform the <code>rekognition:DetectProtectiveEquipment</code> action. </p>", "DetectText": "<p>Detects text in the input image and converts it into machine-readable text.</p> <p>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. </p> <p>The <code>DetectText</code> operation returns text in an array of <a>TextDetection</a> elements, <code>TextDetections</code>. Each <code>TextDetection</code> element provides information about a single word or line of text that was detected in the image. </p> <p>A word is one or more script characters that are not separated by spaces. <code>DetectText</code> can detect up to 100 words in an image.</p> <p>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 <code>DetectText</code> operation returns multiple lines.</p> <p>To determine whether a <code>TextDetection</code> element is a line of text or a word, use the <code>TextDetection</code> object <code>Type</code> field. </p> <p>To be detected, text must be within +/- 90 degrees orientation of the horizontal axis.</p> <p>For more information, see Detecting text in the Amazon Rekognition Developer Guide.</p>", "DisassociateFaces": "<p>Removes the association between a <code>Face</code> supplied in an array of <code>FaceIds</code> and the User. If the User is not present already, then a <code>ResourceNotFound</code> 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 <code>UnsuccessfulDisassociations</code>. You can remove 1 - 100 face IDs from a user at one time.</p>", "DistributeDatasetEntries": "<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p>Distributes the entries (images) in a training dataset across the training dataset and the test dataset for a project. <code>DistributeDatasetEntries</code> moves 20% of the training dataset images to the test dataset. An entry is a JSON Line that describes an image. </p> <p>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 <a>CreateDataset</a>.</p> <p>Distributing a dataset takes a while to complete. To check the status call <code>DescribeDataset</code>. The operation is complete when the <code>Status</code> field for the training dataset and the test dataset is <code>UPDATE_COMPLETE</code>. If the dataset split fails, the value of <code>Status</code> is <code>UPDATE_FAILED</code>.</p> <p>This operation requires permissions to perform the <code>rekognition:DistributeDatasetEntries</code> action.</p>", "GetCelebrityInfo": "<p>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.</p> <p>For more information, see Getting information about a celebrity in the Amazon Rekognition Developer Guide.</p> <p>This operation requires permissions to perform the <code>rekognition:GetCelebrityInfo</code> action. </p>", "GetCelebrityRecognition": "<p>Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by <a>StartCelebrityRecognition</a>.</p> <p>Celebrity recognition in a video is an asynchronous operation. Analysis is started by a call to <a>StartCelebrityRecognition</a> which returns a job identifier (<code>JobId</code>). </p> <p>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 <code>StartCelebrityRecognition</code>. To get the results of the celebrity recognition analysis, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. If so, call <code>GetCelebrityDetection</code> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartCelebrityDetection</code>. </p> <p>For more information, see Working With Stored Videos in the Amazon Rekognition Developer Guide.</p> <p> <code>GetCelebrityRecognition</code> returns detected celebrities and the time(s) they are detected in an array (<code>Celebrities</code>) of <a>CelebrityRecognition</a> objects. Each <code>CelebrityRecognition</code> contains information about the celebrity in a <a>CelebrityDetail</a> object and the time, <code>Timestamp</code>, the celebrity was detected. This <a>CelebrityDetail</a> object stores information about the detected celebrity's face attributes, a face bounding box, known gender, the celebrity's name, and a confidence estimate.</p> <note> <p> <code>GetCelebrityRecognition</code> only returns the default facial attributes (<code>BoundingBox</code>, <code>Confidence</code>, <code>Landmarks</code>, <code>Pose</code>, and <code>Quality</code>). The <code>BoundingBox</code> field only applies to the detected face instance. The other facial attributes listed in the <code>Face</code> object of the following response syntax are not returned. For more information, see FaceDetail in the Amazon Rekognition Developer Guide. </p> </note> <p>By default, the <code>Celebrities</code> array is sorted by time (milliseconds from the start of the video). You can also sort the array by celebrity by specifying the value <code>ID</code> in the <code>SortBy</code> input parameter.</p> <p>The <code>CelebrityDetail</code> 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 <a>GetCelebrityInfo</a> with the celebrity identifer.</p> <p>No information is returned for faces not recognized as celebrities.</p> <p>Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call <code>GetCelebrityDetection</code> and populate the <code>NextToken</code> request parameter with the token value returned from the previous call to <code>GetCelebrityRecognition</code>.</p>", "GetContentModeration": "<p>Gets the inappropriate, unwanted, or offensive content analysis results for a Amazon Rekognition Video analysis started by <a>StartContentModeration</a>. For a list of moderation labels in Amazon Rekognition, see <a href=\"https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api\">Using the image and video moderation APIs</a>.</p> <p>Amazon Rekognition Video inappropriate or offensive content detection in a stored video is an asynchronous operation. You start analysis by calling <a>StartContentModeration</a> which returns a job identifier (<code>JobId</code>). When analysis finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to <code>StartContentModeration</code>. To get the results of the content analysis, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. If so, call <code>GetContentModeration</code> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartContentModeration</code>. </p> <p>For more information, see Working with Stored Videos in the Amazon Rekognition Devlopers Guide.</p> <p> <code>GetContentModeration</code> returns detected inappropriate, unwanted, or offensive content moderation labels, and the time they are detected, in an array, <code>ModerationLabels</code>, of <a>ContentModerationDetection</a> objects. </p> <p>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 <code>NAME</code> for the <code>SortBy</code> input parameter. </p> <p>Since video analysis can return a large number of results, use the <code>MaxResults</code> parameter to limit the number of labels returned in a single call to <code>GetContentModeration</code>. If there are more results than specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call <code>GetContentModeration</code> and populate the <code>NextToken</code> request parameter with the value of <code>NextToken</code> returned from the previous call to <code>GetContentModeration</code>.</p> <p>For more information, see moderating content in the Amazon Rekognition Developer Guide.</p>", "GetFaceDetection": "<p>Gets face detection results for a Amazon Rekognition Video analysis started by <a>StartFaceDetection</a>.</p> <p>Face detection with Amazon Rekognition Video is an asynchronous operation. You start face detection by calling <a>StartFaceDetection</a> which returns a job identifier (<code>JobId</code>). 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 <code>StartFaceDetection</code>. To get the results of the face detection operation, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. If so, call <a>GetFaceDetection</a> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartFaceDetection</code>.</p> <p> <code>GetFaceDetection</code> returns an array of detected faces (<code>Faces</code>) sorted by the time the faces were detected. </p> <p>Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call <code>GetFaceDetection</code> and populate the <code>NextToken</code> request parameter with the token value returned from the previous call to <code>GetFaceDetection</code>.</p> <p>Note that for the <code>GetFaceDetection</code> operation, the returned values for <code>FaceOccluded</code> and <code>EyeDirection</code> will always be \"null\".</p>", "GetFaceLivenessSessionResults": "<p>Retrieves the results of a specific Face Liveness session. It requires the <code>sessionId</code> as input, which was created using <code>CreateFaceLivenessSession</code>. 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. </p> <p>The number of audit images returned by <code>GetFaceLivenessSessionResults</code> is defined by the <code>AuditImagesLimit</code> paramater when calling <code>CreateFaceLivenessSession</code>. Reference images are always returned when possible.</p>", "GetFaceSearch": "<p>Gets the face search results for Amazon Rekognition Video face search started by <a>StartFaceSearch</a>. 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.</p> <p>Face search in a video is an asynchronous operation. You start face search by calling to <a>StartFaceSearch</a> which returns a job identifier (<code>JobId</code>). 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 <code>StartFaceSearch</code>. To get the search results, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. If so, call <code>GetFaceSearch</code> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartFaceSearch</code>.</p> <p>For more information, see Searching Faces in a Collection in the Amazon Rekognition Developer Guide.</p> <p>The search results are retured in an array, <code>Persons</code>, of <a>PersonMatch</a> objects. Each<code>PersonMatch</code> 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.</p> <note> <p> <code>GetFaceSearch</code> only returns the default facial attributes (<code>BoundingBox</code>, <code>Confidence</code>, <code>Landmarks</code>, <code>Pose</code>, and <code>Quality</code>). The other facial attributes listed in the <code>Face</code> object of the following response syntax are not returned. For more information, see FaceDetail in the Amazon Rekognition Developer Guide. </p> </note> <p>By default, the <code>Persons</code> 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 <code>INDEX</code> for the <code>SORTBY</code> input parameter.</p>", "GetLabelDetection": "<p>Gets the label detection results of a Amazon Rekognition Video analysis started by <a>StartLabelDetection</a>. </p> <p>The label detection operation is started by a call to <a>StartLabelDetection</a> which returns a job identifier (<code>JobId</code>). 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 <code>StartlabelDetection</code>. </p> <p>To get the results of the label detection operation, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. If so, call <a>GetLabelDetection</a> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartLabelDetection</code>.</p> <p> <code>GetLabelDetection</code> returns an array of detected labels (<code>Labels</code>) sorted by the time the labels were detected. You can also sort by the label name by specifying <code>NAME</code> for the <code>SortBy</code> input parameter. If there is no <code>NAME</code> specified, the default sort is by timestamp.</p> <p>You can select how results are aggregated by using the <code>AggregateBy</code> input parameter. The default aggregation method is <code>TIMESTAMPS</code>. You can also aggregate by <code>SEGMENTS</code>, which aggregates all instances of labels detected in a given segment. </p> <p>The returned Labels array may include the following attributes:</p> <ul> <li> <p>Name - The name of the detected label.</p> </li> <li> <p>Confidence - The level of confidence in the label assigned to a detected object. </p> </li> <li> <p>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. </p> </li> <li> <p> Aliases - Possible Aliases for the label. </p> </li> <li> <p>Categories - The label categories that the detected label belongs to.</p> </li> <li> <p>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.</p> </li> <li> <p>Timestamp - Time, in milliseconds from the start of the video, that the label was detected. For aggregation by <code>SEGMENTS</code>, the <code>StartTimestampMillis</code>, <code>EndTimestampMillis</code>, and <code>DurationMillis</code> 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 <code>StartTimestampMillis</code>.</p> </li> </ul> <p>Timestamp and Bounding box information are returned for detected Instances, only if aggregation is done by <code>TIMESTAMPS</code>. If aggregating by <code>SEGMENTS</code>, information about detected instances isn’t returned. </p> <p>The version of the label model used for the detection is also returned.</p> <p> <b>Note <code>DominantColors</code> isn't returned for <code>Instances</code>, although it is shown as part of the response in the sample seen below.</b> </p> <p>Use <code>MaxResults</code> parameter to limit the number of labels returned. If there are more results than specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call <code>GetlabelDetection</code> and populate the <code>NextToken</code> request parameter with the token value returned from the previous call to <code>GetLabelDetection</code>.</p> <p>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.</p>", "GetMediaAnalysisJob": "<p>Retrieves the results for a given media analysis job. Takes a <code>JobId</code> returned by StartMediaAnalysisJob.</p>", "GetPersonTracking": "<p>Gets the path tracking results of a Amazon Rekognition Video analysis started by <a>StartPersonTracking</a>.</p> <p>The person path tracking operation is started by a call to <code>StartPersonTracking</code> which returns a job identifier (<code>JobId</code>). When the operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to <code>StartPersonTracking</code>.</p> <p>To get the results of the person path tracking operation, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. If so, call <a>GetPersonTracking</a> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartPersonTracking</code>.</p> <p> <code>GetPersonTracking</code> returns an array, <code>Persons</code>, of tracked persons and the time(s) their paths were tracked in the video. </p> <note> <p> <code>GetPersonTracking</code> only returns the default facial attributes (<code>BoundingBox</code>, <code>Confidence</code>, <code>Landmarks</code>, <code>Pose</code>, and <code>Quality</code>). The other facial attributes listed in the <code>Face</code> object of the following response syntax are not returned. </p> <p>For more information, see FaceDetail in the Amazon Rekognition Developer Guide.</p> </note> <p>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 <code>INDEX</code> for the <code>SortBy</code> input parameter.</p> <p>Use the <code>MaxResults</code> parameter to limit the number of items returned. If there are more results than specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call <code>GetPersonTracking</code> and populate the <code>NextToken</code> request parameter with the token value returned from the previous call to <code>GetPersonTracking</code>.</p>", "GetSegmentDetection": "<p>Gets the segment detection results of a Amazon Rekognition Video analysis started by <a>StartSegmentDetection</a>.</p> <p>Segment detection with Amazon Rekognition Video is an asynchronous operation. You start segment detection by calling <a>StartSegmentDetection</a> which returns a job identifier (<code>JobId</code>). 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 <code>StartSegmentDetection</code>. To get the results of the segment detection operation, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. if so, call <code>GetSegmentDetection</code> and pass the job identifier (<code>JobId</code>) from the initial call of <code>StartSegmentDetection</code>.</p> <p> <code>GetSegmentDetection</code> returns detected segments in an array (<code>Segments</code>) of <a>SegmentDetection</a> objects. <code>Segments</code> is sorted by the segment types specified in the <code>SegmentTypes</code> input parameter of <code>StartSegmentDetection</code>. 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.</p> <p>Use <code>SelectedSegmentTypes</code> to find out the type of segment detection requested in the call to <code>StartSegmentDetection</code>.</p> <p>Use the <code>MaxResults</code> parameter to limit the number of segment detections returned. If there are more results than specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call <code>GetSegmentDetection</code> and populate the <code>NextToken</code> request parameter with the token value returned from the previous call to <code>GetSegmentDetection</code>.</p> <p>For more information, see Detecting video segments in stored video in the Amazon Rekognition Developer Guide.</p>", "GetTextDetection": "<p>Gets the text detection results of a Amazon Rekognition Video analysis started by <a>StartTextDetection</a>.</p> <p>Text detection with Amazon Rekognition Video is an asynchronous operation. You start text detection by calling <a>StartTextDetection</a> which returns a job identifier (<code>JobId</code>) 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 <code>StartTextDetection</code>. To get the results of the text detection operation, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. if so, call <code>GetTextDetection</code> and pass the job identifier (<code>JobId</code>) from the initial call of <code>StartLabelDetection</code>.</p> <p> <code>GetTextDetection</code> returns an array of detected text (<code>TextDetections</code>) sorted by the time the text was detected, up to 100 words per frame of video.</p> <p>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.</p> <p>Use MaxResults parameter to limit the number of text detections returned. If there are more results than specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call <code>GetTextDetection</code> and populate the <code>NextToken</code> request parameter with the token value returned from the previous call to <code>GetTextDetection</code>.</p>", "IndexFaces": "<p>Detects faces in the input image and adds them to the specified collection. </p> <p>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 <a>SearchFaces</a> and <a>SearchFacesByImage</a> operations.</p> <p>For more information, see Adding faces to a collection in the Amazon Rekognition Developer Guide.</p> <p>To get the number of faces in a collection, call <a>DescribeCollection</a>. </p> <p>If you're using version 1.0 of the face detection model, <code>IndexFaces</code> 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. </p> <p>If you're using version 4 or later of the face model, image orientation information is not returned in the <code>OrientationCorrection</code> field. </p> <p>To determine which version of the model you're using, call <a>DescribeCollection</a> and supply the collection ID. You can also get the model version from the value of <code>FaceModelVersion</code> in the response from <code>IndexFaces</code> </p> <p>For more information, see Model Versioning in the Amazon Rekognition Developer Guide.</p> <p>If you provide the optional <code>ExternalImageId</code> for the input image you provided, Amazon Rekognition associates this ID with all faces that it detects. When you call the <a>ListFaces</a> 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.</p> <p>You can specify the maximum number of faces to index with the <code>MaxFaces</code> 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.</p> <p>The <code>QualityFilter</code> 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, <code>IndexFaces</code> chooses the quality bar that's used to filter faces. You can also explicitly choose the quality bar. Use <code>QualityFilter</code>, to set the quality bar by specifying <code>LOW</code>, <code>MEDIUM</code>, or <code>HIGH</code>. If you do not want to filter detected faces, specify <code>NONE</code>. </p> <note> <p>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 <a>DescribeCollection</a>. </p> </note> <p>Information about faces detected in an image, but not indexed, is returned in an array of <a>UnindexedFace</a> objects, <code>UnindexedFaces</code>. Faces aren't indexed for reasons such as:</p> <ul> <li> <p>The number of faces detected exceeds the value of the <code>MaxFaces</code> request parameter.</p> </li> <li> <p>The face is too small compared to the image dimensions.</p> </li> <li> <p>The face is too blurry.</p> </li> <li> <p>The image is too dark.</p> </li> <li> <p>The face has an extreme pose.</p> </li> <li> <p>The face doesn’t have enough detail to be suitable for face search.</p> </li> </ul> <p>In response, the <code>IndexFaces</code> operation returns an array of metadata for all detected faces, <code>FaceRecords</code>. This includes: </p> <ul> <li> <p>The bounding box, <code>BoundingBox</code>, of the detected face. </p> </li> <li> <p>A confidence value, <code>Confidence</code>, which indicates the confidence that the bounding box contains a face.</p> </li> <li> <p>A face ID, <code>FaceId</code>, assigned by the service for each face that's detected and stored.</p> </li> <li> <p>An image ID, <code>ImageId</code>, assigned by the service for the input image.</p> </li> </ul> <p>If you request <code>ALL</code> or specific facial attributes (e.g., <code>FACE_OCCLUDED</code>) 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.</p> <p>If you provide the same image, specify the same collection, and use the same external ID in the <code>IndexFaces</code> operation, Amazon Rekognition doesn't save duplicate face metadata.</p> <p/> <p>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. </p> <p>This operation requires permissions to perform the <code>rekognition:IndexFaces</code> action.</p>", "ListCollections": "<p>Returns list of collection IDs in your account. If the result is truncated, the response also provides a <code>NextToken</code> that you can use in the subsequent request to fetch the next set of collection IDs.</p> <p>For an example, see Listing collections in the Amazon Rekognition Developer Guide.</p> <p>This operation requires permissions to perform the <code>rekognition:ListCollections</code> action.</p>", "ListDatasetEntries": "<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p> 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 <a href=\"https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-manifest-files.html\">Creating a manifest file</a>.</p> <p>JSON Lines in the response include information about non-terminal errors found in the dataset. Non terminal errors are reported in <code>errors</code> 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. </p> <p>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. </p> <p>This operation requires permissions to perform the <code>rekognition:ListDatasetEntries</code> action.</p>", "ListDatasetLabels": "<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p>Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see <a href=\"https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-labeling-images.html\">Labeling images</a>. </p> <p> Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see Labeling images in the <i>Amazon Rekognition Custom Labels Developer Guide</i>. </p>", "ListFaces": "<p>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.</p> <p>This operation requires permissions to perform the <code>rekognition:ListFaces</code> action.</p>", "ListMediaAnalysisJobs": "<p>Returns a list of media analysis jobs. Results are sorted by <code>CreationTimestamp</code> in descending order.</p>", "ListProjectPolicies": "<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p>Gets a list of the project policies attached to a project.</p> <p>To attach a project policy to a project, call <a>PutProjectPolicy</a>. To remove a project policy from a project, call <a>DeleteProjectPolicy</a>.</p> <p>This operation requires permissions to perform the <code>rekognition:ListProjectPolicies</code> action.</p>", "ListStreamProcessors": "<p>Gets a list of stream processors that you have created with <a>CreateStreamProcessor</a>. </p>", "ListTagsForResource": "<p> Returns a list of tags in an Amazon Rekognition collection, stream processor, or Custom Labels model. </p> <p>This operation requires permissions to perform the <code>rekognition:ListTagsForResource</code> action. </p>", "ListUsers": "<p>Returns metadata of the User such as <code>UserID</code> 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, <code>NextToken</code> is returned in the response that can be used in the subsequent request to retrieve the next set of identities.</p>", "PutProjectPolicy": "<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p>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 <a>CopyProjectVersion</a> operation. Only applies to Custom Labels projects.</p> <p>For more information about the format of a project policy document, see Attaching a project policy (SDK) in the <i>Amazon Rekognition Custom Labels Developer Guide</i>. </p> <p>The response from <code>PutProjectPolicy</code> 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.</p> <p>To remove a project policy from a project, call <a>DeleteProjectPolicy</a>. To get a list of project policies attached to a project, call <a>ListProjectPolicies</a>. </p> <p>You copy a model version by calling <a>CopyProjectVersion</a>.</p> <p>This operation requires permissions to perform the <code>rekognition:PutProjectPolicy</code> action.</p>", "RecognizeCelebrities": "<p>Returns an array of celebrities recognized in the input image. For more information, see Recognizing celebrities in the Amazon Rekognition Developer Guide. </p> <p> <code>RecognizeCelebrities</code> returns the 64 largest faces in the image. It lists the recognized celebrities in the <code>CelebrityFaces</code> array and any unrecognized faces in the <code>UnrecognizedFaces</code> array. <code>RecognizeCelebrities</code> doesn't return celebrities whose faces aren't among the largest 64 faces in the image.</p> <p>For each celebrity recognized, <code>RecognizeCelebrities</code> returns a <code>Celebrity</code> object. The <code>Celebrity</code> object contains the celebrity name, ID, URL links to additional information, match confidence, and a <code>ComparedFace</code> object that you can use to locate the celebrity's face on the image.</p> <p>Amazon Rekognition doesn't retain information about which images a celebrity has been recognized in. Your application must store this information and use the <code>Celebrity</code> ID property as a unique identifier for the celebrity. If you don't store the celebrity name or additional information URLs returned by <code>RecognizeCelebrities</code>, you will need the ID to identify the celebrity in a call to the <a>GetCelebrityInfo</a> operation.</p> <p>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. </p> <p>For an example, see Recognizing celebrities in an image in the Amazon Rekognition Developer Guide.</p> <p>This operation requires permissions to perform the <code>rekognition:RecognizeCelebrities</code> operation.</p>", "SearchFaces": "<p>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 <a>IndexFaces</a> operation. The operation compares the features of the input face with faces in the specified collection. </p> <note> <p>You can also search faces without indexing faces by using the <code>SearchFacesByImage</code> operation.</p> </note> <p> 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 <code>confidence</code> value for each face match, indicating the confidence that the specific face matches the input face. </p> <p>For an example, see Searching for a face using its face ID in the Amazon Rekognition Developer Guide.</p> <p>This operation requires permissions to perform the <code>rekognition:SearchFaces</code> action.</p>", "SearchFacesByImage": "<p>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. </p> <note> <p>To search for all faces in an input image, you might first call the <a>IndexFaces</a> operation, and then use the face IDs returned in subsequent calls to the <a>SearchFaces</a> operation. </p> <p> You can also call the <code>DetectFaces</code> operation and use the bounding boxes in the response to make face crops, which then you can pass in to the <code>SearchFacesByImage</code> operation. </p> </note> <p>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. </p> <p> 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 <code>similarity</code> 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. </p> <p>If no faces are detected in the input image, <code>SearchFacesByImage</code> returns an <code>InvalidParameterException</code> error. </p> <p>For an example, Searching for a Face Using an Image in the Amazon Rekognition Developer Guide.</p> <p>The <code>QualityFilter</code> 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 <code>QualityFilter</code> to set the quality bar for filtering by specifying <code>LOW</code>, <code>MEDIUM</code>, or <code>HIGH</code>. If you do not want to filter detected faces, specify <code>NONE</code>. The default value is <code>NONE</code>.</p> <note> <p>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 <a>DescribeCollection</a>. </p> </note> <p>This operation requires permissions to perform the <code>rekognition:SearchFacesByImage</code> action.</p>", "SearchUsers": "<p>Searches for UserIDs within a collection based on a <code>FaceId</code> or <code>UserId</code>. 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 <code>FaceId</code> or <code>UserId</code>. The operation returns an array of UserID that match the <code>FaceId</code> or <code>UserId</code>, ordered by similarity score with the highest similarity first. </p>", "SearchUsersByImage": "<p>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. </p> <p>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. </p> <p>Information about faces detected in the supplied image, but not used for the search, is returned in an array of <code>UnsearchedFace</code> objects. If no valid face is detected in the image, the response will contain an empty <code>UserMatches</code> list and no <code>SearchedFace</code> object. </p>", "StartCelebrityRecognition": "<p>Starts asynchronous recognition of celebrities in a stored video.</p> <p>Amazon Rekognition Video can detect celebrities in a video must be stored in an Amazon S3 bucket. Use <a>Video</a> to specify the bucket name and the filename of the video. <code>StartCelebrityRecognition</code> returns a job identifier (<code>JobId</code>) 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 <code>NotificationChannel</code>. To get the results of the celebrity recognition analysis, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. If so, call <a>GetCelebrityRecognition</a> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartCelebrityRecognition</code>. </p> <p>For more information, see Recognizing celebrities in the Amazon Rekognition Developer Guide.</p>", "StartContentModeration": "<p> Starts asynchronous detection of inappropriate, unwanted, or offensive content in a stored video. For a list of moderation labels in Amazon Rekognition, see <a href=\"https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api\">Using the image and video moderation APIs</a>.</p> <p>Amazon Rekognition Video can moderate content in a video stored in an Amazon S3 bucket. Use <a>Video</a> to specify the bucket name and the filename of the video. <code>StartContentModeration</code> returns a job identifier (<code>JobId</code>) 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 <code>NotificationChannel</code>.</p> <p>To get the results of the content analysis, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. If so, call <a>GetContentModeration</a> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartContentModeration</code>. </p> <p>For more information, see Moderating content in the Amazon Rekognition Developer Guide.</p>", "StartFaceDetection": "<p>Starts asynchronous detection of faces in a stored video.</p> <p>Amazon Rekognition Video can detect faces in a video stored in an Amazon S3 bucket. Use <a>Video</a> to specify the bucket name and the filename of the video. <code>StartFaceDetection</code> returns a job identifier (<code>JobId</code>) 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 <code>NotificationChannel</code>. To get the results of the face detection operation, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. If so, call <a>GetFaceDetection</a> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartFaceDetection</code>.</p> <p>For more information, see Detecting faces in a stored video in the Amazon Rekognition Developer Guide.</p>", "StartFaceSearch": "<p>Starts the asynchronous search for faces in a collection that match the faces of persons detected in a stored video.</p> <p>The video must be stored in an Amazon S3 bucket. Use <a>Video</a> to specify the bucket name and the filename of the video. <code>StartFaceSearch</code> returns a job identifier (<code>JobId</code>) 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 <code>NotificationChannel</code>. To get the search results, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. If so, call <a>GetFaceSearch</a> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartFaceSearch</code>. For more information, see <a href=\"https://docs.aws.amazon.com/rekognition/latest/dg/procedure-person-search-videos.html\">Searching stored videos for faces</a>. </p>", "StartLabelDetection": "<p>Starts asynchronous detection of labels in a stored video.</p> <p>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.</p> <p>The video must be stored in an Amazon S3 bucket. Use <a>Video</a> to specify the bucket name and the filename of the video. <code>StartLabelDetection</code> returns a job identifier (<code>JobId</code>) 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 <code>NotificationChannel</code>.</p> <p>To get the results of the label detection operation, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. If so, call <a>GetLabelDetection</a> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartLabelDetection</code>.</p> <p> <i>Optional Parameters</i> </p> <p> <code>StartLabelDetection</code> has the <code>GENERAL_LABELS</code> Feature applied by default. This feature allows you to provide filtering criteria to the <code>Settings</code> 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 <a href=\"https://docs.aws.amazon.com/rekognition/latest/dg/labels-detecting-labels-video.html\">Detecting labels in a video</a>.</p> <p>You can specify <code>MinConfidence</code> to control the confidence threshold for the labels returned. The default is 50.</p>", "StartMediaAnalysisJob": "<p>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.</p>", "StartPersonTracking": "<p>Starts the asynchronous tracking of a person's path in a stored video.</p> <p>Amazon Rekognition Video can track the path of people in a video stored in an Amazon S3 bucket. Use <a>Video</a> to specify the bucket name and the filename of the video. <code>StartPersonTracking</code> returns a job identifier (<code>JobId</code>) 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 <code>NotificationChannel</code>. </p> <p>To get the results of the person detection operation, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. If so, call <a>GetPersonTracking</a> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartPersonTracking</code>.</p>", "StartProjectVersion": "<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p>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 <a>DescribeProjectVersions</a>. </p> <p>Once the model is running, you can detect custom labels in new images by calling <a>DetectCustomLabels</a>.</p> <note> <p>You are charged for the amount of time that the model is running. To stop a running model, call <a>StopProjectVersion</a>.</p> </note> <p>This operation requires permissions to perform the <code>rekognition:StartProjectVersion</code> action.</p>", "StartSegmentDetection": "<p>Starts asynchronous detection of segment detection in a stored video.</p> <p>Amazon Rekognition Video can detect segments in a video stored in an Amazon S3 bucket. Use <a>Video</a> to specify the bucket name and the filename of the video. <code>StartSegmentDetection</code> returns a job identifier (<code>JobId</code>) 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 <code>NotificationChannel</code>.</p> <p>You can use the <code>Filters</code> (<a>StartSegmentDetectionFilters</a>) input parameter to specify the minimum detection confidence returned in the response. Within <code>Filters</code>, use <code>ShotFilter</code> (<a>StartShotDetectionFilter</a>) to filter detected shots. Use <code>TechnicalCueFilter</code> (<a>StartTechnicalCueDetectionFilter</a>) to filter technical cues. </p> <p>To get the results of the segment detection operation, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. if so, call <a>GetSegmentDetection</a> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartSegmentDetection</code>. </p> <p>For more information, see Detecting video segments in stored video in the Amazon Rekognition Developer Guide.</p>", "StartStreamProcessor": "<p>Starts processing a stream processor. You create a stream processor by calling <a>CreateStreamProcessor</a>. To tell <code>StartStreamProcessor</code> which stream processor to start, use the value of the <code>Name</code> field specified in the call to <code>CreateStreamProcessor</code>.</p> <p>If you are using a label detection stream processor to detect labels, you need to provide a <code>Start selector</code> and a <code>Stop selector</code> to determine the length of the stream processing time.</p>", "StartTextDetection": "<p>Starts asynchronous detection of text in a stored video.</p> <p>Amazon Rekognition Video can detect text in a video stored in an Amazon S3 bucket. Use <a>Video</a> to specify the bucket name and the filename of the video. <code>StartTextDetection</code> returns a job identifier (<code>JobId</code>) 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 <code>NotificationChannel</code>.</p> <p>To get the results of the text detection operation, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. if so, call <a>GetTextDetection</a> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartTextDetection</code>. </p>", "StopProjectVersion": "<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p>Stops a running model. The operation might take a while to complete. To check the current status, call <a>DescribeProjectVersions</a>. Only applies to Custom Labels projects.</p> <p>This operation requires permissions to perform the <code>rekognition:StopProjectVersion</code> action.</p>", "StopStreamProcessor": "<p>Stops a running stream processor that was created by <a>CreateStreamProcessor</a>.</p>", "TagResource": "<p> Adds one or more key-value tags to an Amazon Rekognition collection, stream processor, or Custom Labels model. For more information, see <a href=\"https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html\">Tagging AWS Resources</a>. </p> <p>This operation requires permissions to perform the <code>rekognition:TagResource</code> action. </p>", "UntagResource": "<p> Removes one or more tags from an Amazon Rekognition collection, stream processor, or Custom Labels model. </p> <p>This operation requires permissions to perform the <code>rekognition:UntagResource</code> action. </p>", "UpdateDatasetEntries": "<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p>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 <i>Amazon Rekognition Custom Labels Developer Guide</i>. </p> <p>If the <code>source-ref</code> field in the JSON line references an existing image, the existing image in the dataset is updated. If <code>source-ref</code> field doesn't reference an existing image, the image is added as a new image to the dataset. </p> <p>You specify the changes that you want to make in the <code>Changes</code> input parameter. There isn't a limit to the number JSON Lines that you can change, but the size of <code>Changes</code> must be less than 5MB.</p> <p> <code>UpdateDatasetEntries</code> returns immediatly, but the dataset update might take a while to complete. Use <a>DescribeDataset</a> to check the current status. The dataset updated successfully if the value of <code>Status</code> is <code>UPDATE_COMPLETE</code>. </p> <p>To check if any non-terminal errors occured, call <a>ListDatasetEntries</a> and check for the presence of <code>errors</code> lists in the JSON Lines.</p> <p>Dataset update fails if a terminal error occurs (<code>Status</code> = <code>UPDATE_FAILED</code>). Currently, you can't access the terminal error information from the Amazon Rekognition Custom Labels SDK. </p> <p>This operation requires permissions to perform the <code>rekognition:UpdateDatasetEntries</code> action.</p>", "UpdateStreamProcessor": "<p> Allows you to update a stream processor. You can change some settings and regions of interest and delete certain parameters. </p>" }, "shapes": { "AccessDeniedException": { "base": "<p>You are not authorized to perform the action.</p>", "refs": { } }, "AgeRange": { "base": "<p>Structure containing the estimated age range, in years, for a face.</p> <p>Amazon Rekognition estimates an age range for faces detected in the input image. Estimated age ranges can overlap. A face of a 5-year-old might have an estimated range of 4-6, while the face of a 6-year-old might have an estimated range of 4-8.</p>", "refs": { "FaceDetail$AgeRange": "<p>The estimated age range, in years, for the face. Low represents the lowest estimated age and High represents the highest estimated age.</p>" } }, "Asset": { "base": "<p>Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training. </p>", "refs": { "Assets$member": null } }, "Assets": { "base": null, "refs": { "TestingData$Assets": "<p>The assets used for testing.</p>", "TrainingData$Assets": "<p>A manifest file that contains references to the training images and ground-truth annotations.</p>", "ValidationData$Assets": "<p>The assets that comprise the validation data. </p>" } }, "AssociateFacesRequest": { "base": null, "refs": { } }, "AssociateFacesResponse": { "base": null, "refs": { } }, "AssociatedFace": { "base": "<p>Provides face metadata for the faces that are associated to a specific UserID.</p>", "refs": { "AssociatedFacesList$member": null } }, "AssociatedFacesList": { "base": null, "refs": { "AssociateFacesResponse$AssociatedFaces": "<p>An array of AssociatedFace objects containing FaceIDs that have been successfully associated with the UserID. Returned if the AssociateFaces action is successful.</p>" } }, "Attribute": { "base": null, "refs": { "Attributes$member": null } }, "Attributes": { "base": null, "refs": { "DetectFacesRequest$Attributes": "<p>An array of facial attributes you want to be returned. A <code>DEFAULT</code> subset of facial attributes - <code>BoundingBox</code>, <code>Confidence</code>, <code>Pose</code>, <code>Quality</code>, and <code>Landmarks</code> - will always be returned. You can request for specific facial attributes (in addition to the default list) - by using [<code>\"DEFAULT\", \"FACE_OCCLUDED\"</code>] or just [<code>\"FACE_OCCLUDED\"</code>]. You can request for all facial attributes by using [<code>\"ALL\"]</code>. Requesting more attributes may increase response time.</p> <p>If you provide both, <code>[\"ALL\", \"DEFAULT\"]</code>, the service uses a logical \"AND\" operator to determine which attributes to return (in this case, all attributes). </p> <p>Note that while the FaceOccluded and EyeDirection attributes are supported when using <code>DetectFaces</code>, they aren't supported when analyzing videos with <code>StartFaceDetection</code> and <code>GetFaceDetection</code>.</p>", "IndexFacesRequest$DetectionAttributes": "<p>An array of facial attributes you want to be returned. A <code>DEFAULT</code> subset of facial attributes - <code>BoundingBox</code>, <code>Confidence</code>, <code>Pose</code>, <code>Quality</code>, and <code>Landmarks</code> - will always be returned. You can request for specific facial attributes (in addition to the default list) - by using <code>[\"DEFAULT\", \"FACE_OCCLUDED\"]</code> or just <code>[\"FACE_OCCLUDED\"]</code>. You can request for all facial attributes by using <code>[\"ALL\"]</code>. Requesting more attributes may increase response time.</p> <p>If you provide both, <code>[\"ALL\", \"DEFAULT\"]</code>, the service uses a logical AND operator to determine which attributes to return (in this case, all attributes). </p>" } }, "AudioMetadata": { "base": "<p>Metadata information about an audio stream. An array of <code>AudioMetadata</code> objects for the audio streams found in a stored video is returned by <a>GetSegmentDetection</a>. </p>", "refs": { "AudioMetadataList$member": null } }, "AudioMetadataList": { "base": null, "refs": { "GetSegmentDetectionResponse$AudioMetadata": "<p>An array of objects. There can be multiple audio streams. Each <code>AudioMetadata</code> object contains metadata for a single audio stream. Audio information in an <code>AudioMetadata</code> objects includes the audio codec, the number of audio channels, the duration of the audio stream, and the sample rate. Audio metadata is returned in each page of information returned by <code>GetSegmentDetection</code>.</p>" } }, "AuditImage": { "base": "<p>An image that is picked from the Face Liveness video and returned for audit trail purposes, returned as Base64-encoded bytes.</p>", "refs": { "AuditImages$member": null, "GetFaceLivenessSessionResultsResponse$ReferenceImage": "<p>A high-quality image from the Face Liveness video that can be used for face comparison or search. It includes a bounding box of the face and the Base64-encoded bytes that return an image. If the CreateFaceLivenessSession request included an OutputConfig argument, the image will be uploaded to an S3Object specified in the output configuration. In case the reference image is not returned, it's recommended to retry the Liveness check.</p>" } }, "AuditImages": { "base": null, "refs": { "GetFaceLivenessSessionResultsResponse$AuditImages": "<p>A set of images from the Face Liveness video that can be used for audit purposes. It includes a bounding box of the face and the Base64-encoded bytes that return an image. If the CreateFaceLivenessSession request included an OutputConfig argument, the image will be uploaded to an S3Object specified in the output configuration. If no Amazon S3 bucket is defined, raw bytes are sent instead.</p>" } }, "AuditImagesLimit": { "base": null, "refs": { "CreateFaceLivenessSessionRequestSettings$AuditImagesLimit": "<p>Number of audit images to be returned back. Takes an integer between 0-4. Any integer less than 0 will return 0, any integer above 4 will return 4 images in the response. By default, it is set to 0. The limit is best effort and is based on the actual duration of the selfie-video.</p>" } }, "Beard": { "base": "<p>Indicates whether or not the face has a beard, and the confidence level in the determination.</p>", "refs": { "FaceDetail$Beard": "<p>Indicates whether or not the face has a beard, and the confidence level in the determination.</p>" } }, "BlackFrame": { "base": "<p> A filter that allows you to control the black frame detection by specifying the black levels and pixel coverage of black pixels in a frame. As videos can come from multiple sources, formats, and time periods, they may contain different standards and varying noise levels for black frames that need to be accounted for. For more information, see <a>StartSegmentDetection</a>. </p>", "refs": { "StartTechnicalCueDetectionFilter$BlackFrame": "<p> A filter that allows you to control the black frame detection by specifying the black levels and pixel coverage of black pixels in a frame. Videos can come from multiple sources, formats, and time periods, with different standards and varying noise levels for black frames that need to be accounted for. </p>" } }, "BodyPart": { "base": null, "refs": { "ProtectiveEquipmentBodyPart$Name": "<p>The detected body part.</p>" } }, "BodyParts": { "base": null, "refs": { "ProtectiveEquipmentPerson$BodyParts": "<p>An array of body parts detected on a person's body (including body parts without PPE). </p>" } }, "Boolean": { "base": null, "refs": { "Beard$Value": "<p>Boolean value that indicates whether the face has beard or not.</p>", "CoversBodyPart$Value": "<p>True if the PPE covers the corresponding body part, otherwise false.</p>", "EyeOpen$Value": "<p>Boolean value that indicates whether the eyes on the face are open.</p>", "Eyeglasses$Value": "<p>Boolean value that indicates whether the face is wearing eye glasses or not.</p>", "FaceOccluded$Value": "<p>True if a detected face’s eyes, nose, and mouth are partially captured or if they are covered by masks, dark sunglasses, cell phones, hands, or other objects. False if common occurrences that do not impact face verification are detected, such as eye glasses, lightly tinted sunglasses, strands of hair, and others.</p>", "MouthOpen$Value": "<p>Boolean value that indicates whether the mouth on the face is open or not.</p>", "Mustache$Value": "<p>Boolean value that indicates whether the face has mustache or not.</p>", "Smile$Value": "<p>Boolean value that indicates whether the face is smiling or not.</p>", "StreamProcessorDataSharingPreference$OptIn": "<p> If this option is set to true, you choose to share data with Rekognition to improve model performance. </p>", "Sunglasses$Value": "<p>Boolean value that indicates whether the face is wearing sunglasses or not.</p>", "TestingData$AutoCreate": "<p>If specified, Rekognition splits training dataset to create a test dataset for the training job.</p>" } }, "BoundingBox": { "base": "<p>Identifies the bounding box around the label, face, text, object of interest, or personal protective equipment. The <code>left</code> (x-coordinate) and <code>top</code> (y-coordinate) are coordinates representing the top and left sides of the bounding box. Note that the upper-left corner of the image is the origin (0,0). </p> <p>The <code>top</code> and <code>left</code> values returned are ratios of the overall image size. For example, if the input image is 700x200 pixels, and the top-left coordinate of the bounding box is 350x50 pixels, the API returns a <code>left</code> value of 0.5 (350/700) and a <code>top</code> value of 0.25 (50/200).</p> <p>The <code>width</code> and <code>height</code> values represent the dimensions of the bounding box as a ratio of the overall image dimension. For example, if the input image is 700x200 pixels, and the bounding box width is 70 pixels, the width returned is 0.1. </p> <note> <p> The bounding box coordinates can have negative values. For example, if Amazon Rekognition is able to detect a face that is at the image edge and is only partially visible, the service can return coordinates that are outside the image bounds and, depending on the image edge, you might get negative values or values greater than 1 for the <code>left</code> or <code>top</code> values. </p> </note>", "refs": { "AuditImage$BoundingBox": null, "CelebrityDetail$BoundingBox": "<p>Bounding box around the body of a celebrity.</p>", "ComparedFace$BoundingBox": "<p>Bounding box of the face.</p>", "ComparedSourceImageFace$BoundingBox": "<p>Bounding box of the face.</p>", "EquipmentDetection$BoundingBox": "<p>A bounding box surrounding the item of detected PPE.</p>", "Face$BoundingBox": "<p>Bounding box of the face.</p>", "FaceDetail$BoundingBox": "<p>Bounding box of the face. Default attribute.</p>", "Geometry$BoundingBox": "<p>An axis-aligned coarse representation of the detected item's location on the image.</p>", "Instance$BoundingBox": "<p>The position of the label instance on the image.</p>", "PersonDetail$BoundingBox": "<p>Bounding box around the detected person.</p>", "ProtectiveEquipmentPerson$BoundingBox": "<p>A bounding box around the detected person.</p>", "RegionOfInterest$BoundingBox": "<p>The box representing a region of interest on screen.</p>", "SearchFacesByImageResponse$SearchedFaceBoundingBox": "<p>The bounding box around the face in the input image that Amazon Rekognition used for the search.</p>" } }, "BoundingBoxHeight": { "base": null, "refs": { "DetectionFilter$MinBoundingBoxHeight": "<p>Sets the minimum height of the word bounding box. Words with bounding box heights lesser than this value will be excluded from the result. Value is relative to the video frame height.</p>" } }, "BoundingBoxWidth": { "base": null, "refs": { "DetectionFilter$MinBoundingBoxWidth": "<p>Sets the minimum width of the word bounding box. Words with bounding boxes widths lesser than this value will be excluded from the result. Value is relative to the video frame width.</p>" } }, "Celebrity": { "base": "<p>Provides information about a celebrity recognized by the <a>RecognizeCelebrities</a> operation.</p>", "refs": { "CelebrityList$member": null } }, "CelebrityDetail": { "base": "<p>Information about a recognized celebrity.</p>", "refs": { "CelebrityRecognition$Celebrity": "<p>Information about a recognized celebrity.</p>" } }, "CelebrityList": { "base": null, "refs": { "RecognizeCelebritiesResponse$CelebrityFaces": "<p>Details about each celebrity found in the image. Amazon Rekognition can detect a maximum of 64 celebrities in an image. Each celebrity object includes the following attributes: <code>Face</code>, <code>Confidence</code>, <code>Emotions</code>, <code>Landmarks</code>, <code>Pose</code>, <code>Quality</code>, <code>Smile</code>, <code>Id</code>, <code>KnownGender</code>, <code>MatchConfidence</code>, <code>Name</code>, <code>Urls</code>.</p>" } }, "CelebrityRecognition": { "base": "<p>Information about a detected celebrity and the time the celebrity was detected in a stored video. For more information, see GetCelebrityRecognition in the Amazon Rekognition Developer Guide.</p>", "refs": { "CelebrityRecognitions$member": null } }, "CelebrityRecognitionSortBy": { "base": null, "refs": { "GetCelebrityRecognitionRequest$SortBy": "<p>Sort to use for celebrities returned in <code>Celebrities</code> field. Specify <code>ID</code> to sort by the celebrity identifier, specify <code>TIMESTAMP</code> to sort by the time the celebrity was recognized.</p>" } }, "CelebrityRecognitions": { "base": null, "refs": { "GetCelebrityRecognitionResponse$Celebrities": "<p>Array of celebrities recognized in the video.</p>" } }, "ClientRequestToken": { "base": null, "refs": { "AssociateFacesRequest$ClientRequestToken": "<p>Idempotent token used to identify the request to <code>AssociateFaces</code>. If you use the same token with multiple <code>AssociateFaces</code> requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.</p>", "CreateFaceLivenessSessionRequest$ClientRequestToken": "<p>Idempotent token is used to recognize the Face Liveness request. If the same token is used with multiple <code>CreateFaceLivenessSession</code> requests, the same session is returned. This token is employed to avoid unintentionally creating the same session multiple times.</p>", "CreateUserRequest$ClientRequestToken": "<p>Idempotent token used to identify the request to <code>CreateUser</code>. If you use the same token with multiple <code>CreateUser</code> requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.</p>", "DeleteUserRequest$ClientRequestToken": "<p>Idempotent token used to identify the request to <code>DeleteUser</code>. If you use the same token with multiple <code>DeleteUser </code>requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.</p>", "DisassociateFacesRequest$ClientRequestToken": "<p>Idempotent token used to identify the request to <code>DisassociateFaces</code>. If you use the same token with multiple <code>DisassociateFaces</code> requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.</p>", "StartCelebrityRecognitionRequest$ClientRequestToken": "<p>Idempotent token used to identify the start request. If you use the same token with multiple <code>StartCelebrityRecognition</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidently started more than once. </p>", "StartContentModerationRequest$ClientRequestToken": "<p>Idempotent token used to identify the start request. If you use the same token with multiple <code>StartContentModeration</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidently started more than once. </p>", "StartFaceDetectionRequest$ClientRequestToken": "<p>Idempotent token used to identify the start request. If you use the same token with multiple <code>StartFaceDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidently started more than once. </p>", "StartFaceSearchRequest$ClientRequestToken": "<p>Idempotent token used to identify the start request. If you use the same token with multiple <code>StartFaceSearch</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidently started more than once. </p>", "StartLabelDetectionRequest$ClientRequestToken": "<p>Idempotent token used to identify the start request. If you use the same token with multiple <code>StartLabelDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidently started more than once. </p>", "StartMediaAnalysisJobRequest$ClientRequestToken": "<p>Idempotency token used to prevent the accidental creation of duplicate versions. If you use the same token with multiple <code>StartMediaAnalysisJobRequest</code> requests, the same response is returned. Use <code>ClientRequestToken</code> to prevent the same request from being processed more than once.</p>", "StartPersonTrackingRequest$ClientRequestToken": "<p>Idempotent token used to identify the start request. If you use the same token with multiple <code>StartPersonTracking</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidently started more than once. </p>", "StartSegmentDetectionRequest$ClientRequestToken": "<p>Idempotent token used to identify the start request. If you use the same token with multiple <code>StartSegmentDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidently started more than once. </p>", "StartTextDetectionRequest$ClientRequestToken": "<p>Idempotent token used to identify the start request. If you use the same token with multiple <code>StartTextDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentaly started more than once.</p>" } }, "CollectionId": { "base": null, "refs": { "AssociateFacesRequest$CollectionId": "<p>The ID of an existing collection containing the UserID.</p>", "CollectionIdList$member": null, "CreateCollectionRequest$CollectionId": "<p>ID for the collection that you are creating.</p>", "CreateUserRequest$CollectionId": "<p>The ID of an existing collection to which the new UserID needs to be created.</p>", "DeleteCollectionRequest$CollectionId": "<p>ID of the collection to delete.</p>", "DeleteFacesRequest$CollectionId": "<p>Collection from which to remove the specific faces.</p>", "DeleteUserRequest$CollectionId": "<p>The ID of an existing collection from which the UserID needs to be deleted. </p>", "DescribeCollectionRequest$CollectionId": "<p>The ID of the collection to describe.</p>", "DisassociateFacesRequest$CollectionId": "<p>The ID of an existing collection containing the UserID.</p>", "FaceSearchSettings$CollectionId": "<p>The ID of a collection that contains faces that you want to search for.</p>", "IndexFacesRequest$CollectionId": "<p>The ID of an existing collection to which you want to add the faces that are detected in the input images.</p>", "ListFacesRequest$CollectionId": "<p>ID of the collection from which to list the faces.</p>", "ListUsersRequest$CollectionId": "<p>The ID of an existing collection.</p>", "SearchFacesByImageRequest$CollectionId": "<p>ID of the collection to search.</p>", "SearchFacesRequest$CollectionId": "<p>ID of the collection the face belongs to.</p>", "SearchUsersByImageRequest$CollectionId": "<p>The ID of an existing collection containing the UserID.</p>", "SearchUsersRequest$CollectionId": "<p>The ID of an existing collection containing the UserID, used with a UserId or FaceId. If a FaceId is provided, UserId isn’t required to be present in the Collection.</p>", "StartFaceSearchRequest$CollectionId": "<p>ID of the collection that contains the faces you want to search for.</p>" } }, "CollectionIdList": { "base": null, "refs": { "ListCollectionsResponse$CollectionIds": "<p>An array of collection IDs.</p>" } }, "CompareFacesMatch": { "base": "<p>Provides information about a face in a target image that matches the source image face analyzed by <code>CompareFaces</code>. The <code>Face</code> property contains the bounding box of the face in the target image. The <code>Similarity</code> property is the confidence that the source image face matches the face in the bounding box.</p>", "refs": { "CompareFacesMatchList$member": null } }, "CompareFacesMatchList": { "base": null, "refs": { "CompareFacesResponse$FaceMatches": "<p>An array of faces in the target image that match the source image face. Each <code>CompareFacesMatch</code> object provides the bounding box, the confidence level that the bounding box contains a face, and the similarity score for the face in the bounding box and the face in the source image.</p>" } }, "CompareFacesRequest": { "base": null, "refs": { } }, "CompareFacesResponse": { "base": null, "refs": { } }, "CompareFacesUnmatchList": { "base": null, "refs": { "CompareFacesResponse$UnmatchedFaces": "<p>An array of faces in the target image that did not match the source image face.</p>" } }, "ComparedFace": { "base": "<p>Provides face metadata for target image faces that are analyzed by <code>CompareFaces</code> and <code>RecognizeCelebrities</code>.</p>", "refs": { "Celebrity$Face": "<p>Provides information about the celebrity's face, such as its location on the image.</p>", "CompareFacesMatch$Face": "<p>Provides face metadata (bounding box and confidence that the bounding box actually contains a face).</p>", "CompareFacesUnmatchList$member": null, "ComparedFaceList$member": null } }, "ComparedFaceList": { "base": null, "refs": { "RecognizeCelebritiesResponse$UnrecognizedFaces": "<p>Details about each unrecognized face in the image.</p>" } }, "ComparedSourceImageFace": { "base": "<p>Type that describes the face Amazon Rekognition chose to compare with the faces in the target. This contains a bounding box for the selected face and confidence level that the bounding box contains a face. Note that Amazon Rekognition selects the largest face in the source image for this comparison. </p>", "refs": { "CompareFacesResponse$SourceImageFace": "<p>The face in the source image that was used for comparison.</p>" } }, "ConflictException": { "base": "<p> A User with the same Id already exists within the collection, or the update or deletion of the User caused an inconsistent state. ** </p>", "refs": { } }, "ConnectedHomeLabel": { "base": null, "refs": { "ConnectedHomeLabels$member": null } }, "ConnectedHomeLabels": { "base": null, "refs": { "ConnectedHomeSettings$Labels": "<p> Specifies what you want to detect in the video, such as people, packages, or pets. The current valid labels you can include in this list are: \"PERSON\", \"PET\", \"PACKAGE\", and \"ALL\". </p>", "ConnectedHomeSettingsForUpdate$Labels": "<p> Specifies what you want to detect in the video, such as people, packages, or pets. The current valid labels you can include in this list are: \"PERSON\", \"PET\", \"PACKAGE\", and \"ALL\". </p>" } }, "ConnectedHomeSettings": { "base": "<p> Label detection settings to use on a streaming video. Defining the settings is required in the request parameter for <a>CreateStreamProcessor</a>. Including this setting in the <code>CreateStreamProcessor</code> request enables you to use the stream processor for label detection. You can then select what you want the stream processor to detect, such as people or pets. When the stream processor has started, one notification is sent for each object class specified. For example, if packages and pets are selected, one SNS notification is published the first time a package is detected and one SNS notification is published the first time a pet is detected, as well as an end-of-session summary. </p>", "refs": { "StreamProcessorSettings$ConnectedHome": null } }, "ConnectedHomeSettingsForUpdate": { "base": "<p> The label detection settings you want to use in your stream processor. This includes the labels you want the stream processor to detect and the minimum confidence level allowed to label objects. </p>", "refs": { "StreamProcessorSettingsForUpdate$ConnectedHomeForUpdate": "<p> The label detection settings you want to use for your stream processor. </p>" } }, "ContentClassifier": { "base": null, "refs": { "ContentClassifiers$member": null } }, "ContentClassifiers": { "base": null, "refs": { "HumanLoopDataAttributes$ContentClassifiers": "<p>Sets whether the input image is free of personally identifiable information.</p>" } }, "ContentModerationAggregateBy": { "base": null, "refs": { "GetContentModerationRequest$AggregateBy": "<p>Defines how to aggregate results of the StartContentModeration request. Default aggregation option is TIMESTAMPS. SEGMENTS mode aggregates moderation labels over time.</p>", "GetContentModerationRequestMetadata$AggregateBy": "<p>The aggregation method chosen for a GetContentModeration request.</p>" } }, "ContentModerationDetection": { "base": "<p>Information about an inappropriate, unwanted, or offensive content label detection in a stored video.</p>", "refs": { "ContentModerationDetections$member": null } }, "ContentModerationDetections": { "base": null, "refs": { "GetContentModerationResponse$ModerationLabels": "<p>The detected inappropriate, unwanted, or offensive content moderation labels and the time(s) they were detected.</p>" } }, "ContentModerationSortBy": { "base": null, "refs": { "GetContentModerationRequest$SortBy": "<p>Sort to use for elements in the <code>ModerationLabelDetections</code> array. Use <code>TIMESTAMP</code> to sort array elements by the time labels are detected. Use <code>NAME</code> to alphabetically group elements for a label together. Within each label group, the array element are sorted by detection confidence. The default sort is by <code>TIMESTAMP</code>.</p>", "GetContentModerationRequestMetadata$SortBy": "<p>The sorting method chosen for a GetContentModeration request.</p>" } }, "ContentType": { "base": "<p>Contains information regarding the confidence and name of a detected content type.</p>", "refs": { "ContentTypes$member": null } }, "ContentTypes": { "base": null, "refs": { "ContentModerationDetection$ContentTypes": "<p>A list of predicted results for the type of content an image contains. For example, the image content might be from animation, sports, or a video game.</p>", "DetectModerationLabelsResponse$ContentTypes": "<p>A list of predicted results for the type of content an image contains. For example, the image content might be from animation, sports, or a video game.</p>" } }, "CopyProjectVersionRequest": { "base": null, "refs": { } }, "CopyProjectVersionResponse": { "base": null, "refs": { } }, "CoversBodyPart": { "base": "<p>Information about an item of Personal Protective Equipment covering a corresponding body part. For more information, see <a>DetectProtectiveEquipment</a>.</p>", "refs": { "EquipmentDetection$CoversBodyPart": "<p>Information about the body part covered by the detected PPE.</p>" } }, "CreateCollectionRequest": { "base": null, "refs": { } }, "CreateCollectionResponse": { "base": null, "refs": { } }, "CreateDatasetRequest": { "base": null, "refs": { } }, "CreateDatasetResponse": { "base": null, "refs": { } }, "CreateFaceLivenessSessionRequest": { "base": null, "refs": { } }, "CreateFaceLivenessSessionRequestSettings": { "base": "<p>A session settings object. It contains settings for the operation to be performed. It accepts arguments for OutputConfig and AuditImagesLimit.</p>", "refs": { "CreateFaceLivenessSessionRequest$Settings": "<p>A session settings object. It contains settings for the operation to be performed. For Face Liveness, it accepts <code>OutputConfig</code> and <code>AuditImagesLimit</code>.</p>" } }, "CreateFaceLivenessSessionResponse": { "base": null, "refs": { } }, "CreateProjectRequest": { "base": null, "refs": { } }, "CreateProjectResponse": { "base": null, "refs": { } }, "CreateProjectVersionRequest": { "base": null, "refs": { } }, "CreateProjectVersionResponse": { "base": null, "refs": { } }, "CreateStreamProcessorRequest": { "base": null, "refs": { } }, "CreateStreamProcessorResponse": { "base": null, "refs": { } }, "CreateUserRequest": { "base": null, "refs": { } }, "CreateUserResponse": { "base": null, "refs": { } }, "CustomLabel": { "base": "<p>A custom label detected in an image by a call to <a>DetectCustomLabels</a>.</p>", "refs": { "CustomLabels$member": null } }, "CustomLabels": { "base": null, "refs": { "DetectCustomLabelsResponse$CustomLabels": "<p>An array of custom labels detected in the input image.</p>" } }, "CustomizationFeature": { "base": null, "refs": { "CreateProjectRequest$Feature": "<p>Specifies feature that is being customized. If no value is provided CUSTOM_LABELS is used as a default.</p>", "CustomizationFeatures$member": null, "ProjectDescription$Feature": "<p>Specifies the project that is being customized.</p>", "ProjectVersionDescription$Feature": "<p>The feature that was customized.</p>" } }, "CustomizationFeatureConfig": { "base": "<p>Feature specific configuration for the training job. Configuration provided for the job must match the feature type parameter associated with project. If configuration and feature type do not match an InvalidParameterException is returned.</p>", "refs": { "CreateProjectVersionRequest$FeatureConfig": "<p>Feature-specific configuration of the training job. If the job configuration does not match the feature type associated with the project, an InvalidParameterException is returned.</p>", "ProjectVersionDescription$FeatureConfig": "<p>Feature specific configuration that was applied during training.</p>" } }, "CustomizationFeatureContentModerationConfig": { "base": "<p>Configuration options for Content Moderation training.</p>", "refs": { "CustomizationFeatureConfig$ContentModeration": "<p>Configuration options for Custom Moderation training.</p>" } }, "CustomizationFeatures": { "base": null, "refs": { "DescribeProjectsRequest$Features": "<p>Specifies the type of customization to filter projects by. If no value is specified, CUSTOM_LABELS is used as a default.</p>" } }, "DatasetArn": { "base": null, "refs": { "CreateDatasetResponse$DatasetArn": "<p> The ARN of the created Amazon Rekognition Custom Labels dataset. </p>", "DatasetMetadata$DatasetArn": "<p> The Amazon Resource Name (ARN) for the dataset. </p>", "DatasetSource$DatasetArn": "<p> The ARN of an Amazon Rekognition Custom Labels dataset that you want to copy. </p>", "DeleteDatasetRequest$DatasetArn": "<p> The ARN of the Amazon Rekognition Custom Labels dataset that you want to delete. </p>", "DescribeDatasetRequest$DatasetArn": "<p> The Amazon Resource Name (ARN) of the dataset that you want to describe. </p>", "DistributeDataset$Arn": "<p>The Amazon Resource Name (ARN) of the dataset that you want to use. </p>", "ListDatasetEntriesRequest$DatasetArn": "<p> The Amazon Resource Name (ARN) for the dataset that you want to use. </p>", "ListDatasetLabelsRequest$DatasetArn": "<p> The Amazon Resource Name (ARN) of the dataset that you want to use. </p>", "UpdateDatasetEntriesRequest$DatasetArn": "<p> The Amazon Resource Name (ARN) of the dataset that you want to update. </p>" } }, "DatasetChanges": { "base": "<p> Describes updates or additions to a dataset. A Single update or addition is an entry (JSON Line) that provides information about a single image. To update an existing entry, you match the <code>source-ref</code> field of the update entry with the <code>source-ref</code> filed of the entry that you want to update. If the <code>source-ref</code> field doesn't match an existing entry, the entry is added to dataset as a new entry. </p>", "refs": { "UpdateDatasetEntriesRequest$Changes": "<p> The changes that you want to make to the dataset. </p>" } }, "DatasetDescription": { "base": "<p> A description for a dataset. For more information, see <a>DescribeDataset</a>.</p> <p>The status fields <code>Status</code>, <code>StatusMessage</code>, and <code>StatusMessageCode</code> reflect the last operation on the dataset. </p>", "refs": { "DescribeDatasetResponse$DatasetDescription": "<p> The description for the dataset. </p>" } }, "DatasetEntries": { "base": null, "refs": { "ListDatasetEntriesResponse$DatasetEntries": "<p> A list of entries (images) in the dataset. </p>" } }, "DatasetEntry": { "base": null, "refs": { "DatasetEntries$member": null } }, "DatasetLabel": { "base": null, "refs": { "DatasetLabelDescription$LabelName": "<p> The name of the label. </p>", "DatasetLabels$member": null } }, "DatasetLabelDescription": { "base": "<p> Describes a dataset label. For more information, see <a>ListDatasetLabels</a>. </p>", "refs": { "DatasetLabelDescriptions$member": null } }, "DatasetLabelDescriptions": { "base": null, "refs": { "ListDatasetLabelsResponse$DatasetLabelDescriptions": "<p> A list of the labels in the dataset. </p>" } }, "DatasetLabelStats": { "base": "<p> Statistics about a label used in a dataset. For more information, see <a>DatasetLabelDescription</a>. </p>", "refs": { "DatasetLabelDescription$LabelStats": "<p> Statistics about the label. </p>" } }, "DatasetLabels": { "base": null, "refs": { "ListDatasetEntriesRequest$ContainsLabels": "<p>Specifies a label filter for the response. The response includes an entry only if one or more of the labels in <code>ContainsLabels</code> exist in the entry. </p>" } }, "DatasetMetadata": { "base": "<p> Summary information for an Amazon Rekognition Custom Labels dataset. For more information, see <a>ProjectDescription</a>. </p>", "refs": { "DatasetMetadataList$member": null } }, "DatasetMetadataList": { "base": null, "refs": { "ProjectDescription$Datasets": "<p> Information about the training and test datasets in the project. </p>" } }, "DatasetSource": { "base": "<p> The source that Amazon Rekognition Custom Labels uses to create a dataset. To use an Amazon Sagemaker format manifest file, specify the S3 bucket location in the <code>GroundTruthManifest</code> field. The S3 bucket must be in your AWS account. To create a copy of an existing dataset, specify the Amazon Resource Name (ARN) of an existing dataset in <code>DatasetArn</code>.</p> <p>You need to specify a value for <code>DatasetArn</code> or <code>GroundTruthManifest</code>, but not both. if you supply both values, or if you don't specify any values, an InvalidParameterException exception occurs. </p> <p>For more information, see <a>CreateDataset</a>.</p>", "refs": { "CreateDatasetRequest$DatasetSource": "<p> The source files for the dataset. You can specify the ARN of an existing dataset or specify the Amazon S3 bucket location of an Amazon Sagemaker format manifest file. If you don't specify <code>datasetSource</code>, an empty dataset is created. To add labeled images to the dataset, You can use the console or call <a>UpdateDatasetEntries</a>. </p>" } }, "DatasetStats": { "base": "<p> Provides statistics about a dataset. For more information, see <a>DescribeDataset</a>. </p>", "refs": { "DatasetDescription$DatasetStats": "<p> The status message code for the dataset. </p>" } }, "DatasetStatus": { "base": null, "refs": { "DatasetDescription$Status": "<p> The status of the dataset. </p>", "DatasetMetadata$Status": "<p> The status for the dataset. </p>" } }, "DatasetStatusMessageCode": { "base": null, "refs": { "DatasetDescription$StatusMessageCode": "<p> The status message code for the dataset operation. If a service error occurs, try the API call again later. If a client error occurs, check the input parameters to the dataset API call that failed. </p>", "DatasetMetadata$StatusMessageCode": "<p> The status message code for the dataset operation. If a service error occurs, try the API call again later. If a client error occurs, check the input parameters to the dataset API call that failed. </p>" } }, "DatasetType": { "base": null, "refs": { "CreateDatasetRequest$DatasetType": "<p> The type of the dataset. Specify <code>TRAIN</code> to create a training dataset. Specify <code>TEST</code> to create a test dataset. </p>", "DatasetMetadata$DatasetType": "<p> The type of the dataset. </p>" } }, "DateTime": { "base": null, "refs": { "DatasetDescription$CreationTimestamp": "<p> The Unix timestamp for the time and date that the dataset was created. </p>", "DatasetDescription$LastUpdatedTimestamp": "<p> The Unix timestamp for the date and time that the dataset was last updated. </p>", "DatasetMetadata$CreationTimestamp": "<p> The Unix timestamp for the date and time that the dataset was created. </p>", "DescribeCollectionResponse$CreationTimestamp": "<p>The number of milliseconds since the Unix epoch time until the creation of the collection. The Unix epoch time is 00:00:00 Coordinated Universal Time (UTC), Thursday, 1 January 1970.</p>", "DescribeStreamProcessorResponse$CreationTimestamp": "<p>Date and time the stream processor was created</p>", "DescribeStreamProcessorResponse$LastUpdateTimestamp": "<p>The time, in Unix format, the stream processor was last updated. For example, when the stream processor moves from a running state to a failed state, or when the user starts or stops the stream processor.</p>", "GetMediaAnalysisJobResponse$CreationTimestamp": "<p>The Unix date and time when the job was started.</p>", "GetMediaAnalysisJobResponse$CompletionTimestamp": "<p>The Unix date and time when the job finished.</p>", "MediaAnalysisJobDescription$CreationTimestamp": "<p>The Unix date and time when the job was started.</p>", "MediaAnalysisJobDescription$CompletionTimestamp": "<p>The Unix date and time when the job finished.</p>", "ProjectDescription$CreationTimestamp": "<p>The Unix timestamp for the date and time that the project was created.</p>", "ProjectPolicy$CreationTimestamp": "<p>The Unix datetime for the creation of the project policy.</p>", "ProjectPolicy$LastUpdatedTimestamp": "<p>The Unix datetime for when the project policy was last updated. </p>", "ProjectVersionDescription$CreationTimestamp": "<p>The Unix datetime for the date and time that training started.</p>", "ProjectVersionDescription$TrainingEndTimestamp": "<p>The Unix date and time that training of the model ended.</p>" } }, "Degree": { "base": null, "refs": { "EyeDirection$Yaw": "<p>Value representing eye direction on the yaw axis.</p>", "EyeDirection$Pitch": "<p>Value representing eye direction on the pitch axis.</p>", "Pose$Roll": "<p>Value representing the face rotation on the roll axis.</p>", "Pose$Yaw": "<p>Value representing the face rotation on the yaw axis.</p>", "Pose$Pitch": "<p>Value representing the face rotation on the pitch axis.</p>" } }, "DeleteCollectionRequest": { "base": null, "refs": { } }, "DeleteCollectionResponse": { "base": null, "refs": { } }, "DeleteDatasetRequest": { "base": null, "refs": { } }, "DeleteDatasetResponse": { "base": null, "refs": { } }, "DeleteFacesRequest": { "base": null, "refs": { } }, "DeleteFacesResponse": { "base": null, "refs": { } }, "DeleteProjectPolicyRequest": { "base": null, "refs": { } }, "DeleteProjectPolicyResponse": { "base": null, "refs": { } }, "DeleteProjectRequest": { "base": null, "refs": { } }, "DeleteProjectResponse": { "base": null, "refs": { } }, "DeleteProjectVersionRequest": { "base": null, "refs": { } }, "DeleteProjectVersionResponse": { "base": null, "refs": { } }, "DeleteStreamProcessorRequest": { "base": null, "refs": { } }, "DeleteStreamProcessorResponse": { "base": null, "refs": { } }, "DeleteUserRequest": { "base": null, "refs": { } }, "DeleteUserResponse": { "base": null, "refs": { } }, "DescribeCollectionRequest": { "base": null, "refs": { } }, "DescribeCollectionResponse": { "base": null, "refs": { } }, "DescribeDatasetRequest": { "base": null, "refs": { } }, "DescribeDatasetResponse": { "base": null, "refs": { } }, "DescribeProjectVersionsRequest": { "base": null, "refs": { } }, "DescribeProjectVersionsResponse": { "base": null, "refs": { } }, "DescribeProjectsRequest": { "base": null, "refs": { } }, "DescribeProjectsResponse": { "base": null, "refs": { } }, "DescribeStreamProcessorRequest": { "base": null, "refs": { } }, "DescribeStreamProcessorResponse": { "base": null, "refs": { } }, "DetectCustomLabelsRequest": { "base": null, "refs": { } }, "DetectCustomLabelsResponse": { "base": null, "refs": { } }, "DetectFacesRequest": { "base": null, "refs": { } }, "DetectFacesResponse": { "base": null, "refs": { } }, "DetectLabelsFeatureList": { "base": null, "refs": { "DetectLabelsRequest$Features": "<p>A list of the types of analysis to perform. Specifying GENERAL_LABELS uses the label detection feature, while specifying IMAGE_PROPERTIES returns information regarding image color and quality. If no option is specified GENERAL_LABELS is used by default.</p>" } }, "DetectLabelsFeatureName": { "base": null, "refs": { "DetectLabelsFeatureList$member": null } }, "DetectLabelsImageBackground": { "base": "<p>The background of the image with regard to image quality and dominant colors.</p>", "refs": { "DetectLabelsImageProperties$Background": "<p>Information about the properties of an image’s background, including the background’s quality and dominant colors, including the quality and dominant colors of the image.</p>" } }, "DetectLabelsImageForeground": { "base": "<p>The foreground of the image with regard to image quality and dominant colors.</p>", "refs": { "DetectLabelsImageProperties$Foreground": "<p>Information about the properties of an image’s foreground, including the foreground’s quality and dominant colors, including the quality and dominant colors of the image.</p>" } }, "DetectLabelsImageProperties": { "base": "<p>Information about the quality and dominant colors of an input image. Quality and color information is returned for the entire image, foreground, and background.</p>", "refs": { "DetectLabelsResponse$ImageProperties": "<p>Information about the properties of the input image, such as brightness, sharpness, contrast, and dominant colors.</p>" } }, "DetectLabelsImagePropertiesSettings": { "base": "<p>Settings for the IMAGE_PROPERTIES feature type.</p>", "refs": { "DetectLabelsSettings$ImageProperties": "<p>Contains the chosen number of maximum dominant colors in an image.</p>" } }, "DetectLabelsImageQuality": { "base": "<p>The quality of an image provided for label detection, with regard to brightness, sharpness, and contrast.</p>", "refs": { "DetectLabelsImageBackground$Quality": "<p>The quality of the image background as defined by brightness and sharpness.</p>", "DetectLabelsImageForeground$Quality": "<p>The quality of the image foreground as defined by brightness and sharpness.</p>", "DetectLabelsImageProperties$Quality": "<p>Information about the quality of the image foreground as defined by brightness, sharpness, and contrast. The higher the value the greater the brightness, sharpness, and contrast respectively.</p>" } }, "DetectLabelsMaxDominantColors": { "base": null, "refs": { "DetectLabelsImagePropertiesSettings$MaxDominantColors": "<p>The maximum number of dominant colors to return when detecting labels in an image. The default value is 10.</p>" } }, "DetectLabelsRequest": { "base": null, "refs": { } }, "DetectLabelsResponse": { "base": null, "refs": { } }, "DetectLabelsSettings": { "base": "<p>Settings for the DetectLabels request. Settings can include filters for both GENERAL_LABELS and IMAGE_PROPERTIES. GENERAL_LABELS filters can be inclusive or exclusive and applied to individual labels or label categories. IMAGE_PROPERTIES filters allow specification of a maximum number of dominant colors.</p>", "refs": { "DetectLabelsRequest$Settings": "<p>A list of the filters to be applied to returned detected labels and image properties. Specified filters can be inclusive, exclusive, or a combination of both. Filters can be used for individual labels or label categories. The exact label names or label categories must be supplied. For a full list of labels and label categories, see <a href=\"https://docs.aws.amazon.com/rekognition/latest/dg/labels.html\">Detecting labels</a>.</p>" } }, "DetectModerationLabelsRequest": { "base": null, "refs": { } }, "DetectModerationLabelsResponse": { "base": null, "refs": { } }, "DetectProtectiveEquipmentRequest": { "base": null, "refs": { } }, "DetectProtectiveEquipmentResponse": { "base": null, "refs": { } }, "DetectTextFilters": { "base": "<p>A set of optional parameters that you can use to set the criteria that the text must meet to be included in your response. <code>WordFilter</code> looks at a word’s height, width, and minimum confidence. <code>RegionOfInterest</code> lets you set a specific region of the image to look for text in. </p>", "refs": { "DetectTextRequest$Filters": "<p>Optional parameters that let you set the criteria that the text must meet to be included in your response.</p>" } }, "DetectTextRequest": { "base": null, "refs": { } }, "DetectTextResponse": { "base": null, "refs": { } }, "DetectionFilter": { "base": "<p>A set of parameters that allow you to filter out certain results from your returned results.</p>", "refs": { "DetectTextFilters$WordFilter": null, "StartTextDetectionFilters$WordFilter": "<p>Filters focusing on qualities of the text, such as confidence or size.</p>" } }, "DisassociateFacesRequest": { "base": null, "refs": { } }, "DisassociateFacesResponse": { "base": null, "refs": { } }, "DisassociatedFace": { "base": "<p>Provides face metadata for the faces that are disassociated from a specific UserID.</p>", "refs": { "DisassociatedFacesList$member": null } }, "DisassociatedFacesList": { "base": null, "refs": { "DisassociateFacesResponse$DisassociatedFaces": "<p>An array of DissociatedFace objects containing FaceIds that are successfully disassociated with the UserID is returned. Returned if the DisassociatedFaces action is successful.</p>" } }, "DistributeDataset": { "base": "<p> A training dataset or a test dataset used in a dataset distribution operation. For more information, see <a>DistributeDatasetEntries</a>. </p>", "refs": { "DistributeDatasetMetadataList$member": null } }, "DistributeDatasetEntriesRequest": { "base": null, "refs": { } }, "DistributeDatasetEntriesResponse": { "base": null, "refs": { } }, "DistributeDatasetMetadataList": { "base": null, "refs": { "DistributeDatasetEntriesRequest$Datasets": "<p>The ARNS for the training dataset and test dataset that you want to use. The datasets must belong to the same project. The test dataset must be empty. </p>" } }, "DominantColor": { "base": "<p>A description of the dominant colors in an image.</p>", "refs": { "DominantColors$member": null } }, "DominantColors": { "base": null, "refs": { "DetectLabelsImageBackground$DominantColors": "<p>The dominant colors found in the background of an image, defined with RGB values, CSS color name, simplified color name, and PixelPercentage (the percentage of image pixels that have a particular color).</p>", "DetectLabelsImageForeground$DominantColors": "<p>The dominant colors found in the foreground of an image, defined with RGB values, CSS color name, simplified color name, and PixelPercentage (the percentage of image pixels that have a particular color).</p>", "DetectLabelsImageProperties$DominantColors": "<p>Information about the dominant colors found in an image, described with RGB values, CSS color name, simplified color name, and PixelPercentage (the percentage of image pixels that have a particular color).</p>", "Instance$DominantColors": "<p>The dominant colors found in an individual instance of a label.</p>" } }, "Emotion": { "base": "<p>The emotions that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.</p>", "refs": { "Emotions$member": null } }, "EmotionName": { "base": null, "refs": { "Emotion$Type": "<p>Type of emotion detected.</p>" } }, "Emotions": { "base": null, "refs": { "ComparedFace$Emotions": "<p> The emotions that appear to be expressed on the face, and the confidence level in the determination. Valid values include \"Happy\", \"Sad\", \"Angry\", \"Confused\", \"Disgusted\", \"Surprised\", \"Calm\", \"Unknown\", and \"Fear\". </p>", "FaceDetail$Emotions": "<p>The emotions that appear to be expressed on the face, and the confidence level in the determination. The API is only making a determination of the physical appearance of a person's face. It is not a determination of the person’s internal emotional state and should not be used in such a way. For example, a person pretending to have a sad face might not be sad emotionally.</p>" } }, "EquipmentDetection": { "base": "<p>Information about an item of Personal Protective Equipment (PPE) detected by <a>DetectProtectiveEquipment</a>. For more information, see <a>DetectProtectiveEquipment</a>.</p>", "refs": { "EquipmentDetections$member": null } }, "EquipmentDetections": { "base": null, "refs": { "ProtectiveEquipmentBodyPart$EquipmentDetections": "<p>An array of Personal Protective Equipment items detected around a body part.</p>" } }, "EvaluationResult": { "base": "<p>The evaluation results for the training of a model.</p>", "refs": { "ProjectVersionDescription$EvaluationResult": "<p>The training results. <code>EvaluationResult</code> is only returned if training is successful.</p>" } }, "ExtendedPaginationToken": { "base": null, "refs": { "DescribeProjectVersionsRequest$NextToken": "<p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>", "DescribeProjectVersionsResponse$NextToken": "<p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>", "DescribeProjectsRequest$NextToken": "<p>If the previous response was incomplete (because there is more results to retrieve), Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>", "DescribeProjectsResponse$NextToken": "<p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>", "ListDatasetEntriesRequest$NextToken": "<p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>", "ListDatasetEntriesResponse$NextToken": "<p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>", "ListDatasetLabelsRequest$NextToken": "<p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>", "ListDatasetLabelsResponse$NextToken": "<p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>", "ListMediaAnalysisJobsRequest$NextToken": "<p>Pagination token, if the previous response was incomplete.</p>", "ListMediaAnalysisJobsResponse$NextToken": "<p>Pagination token, if the previous response was incomplete.</p>", "ListProjectPoliciesRequest$NextToken": "<p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>", "ListProjectPoliciesResponse$NextToken": "<p>If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of project policies.</p>" } }, "ExternalImageId": { "base": null, "refs": { "Face$ExternalImageId": "<p>Identifier that you assign to all the faces in the input image.</p>", "IndexFacesRequest$ExternalImageId": "<p>The ID you want to assign to all the faces detected in the image.</p>" } }, "EyeDirection": { "base": "<p>Indicates the direction the eyes are gazing in (independent of the head pose) as determined by its pitch and yaw. </p>", "refs": { "FaceDetail$EyeDirection": "<p>Indicates the direction the eyes are gazing in, as defined by pitch and yaw.</p>" } }, "EyeOpen": { "base": "<p>Indicates whether or not the eyes on the face are open, and the confidence level in the determination.</p>", "refs": { "FaceDetail$EyesOpen": "<p>Indicates whether or not the eyes on the face are open, and the confidence level in the determination.</p>" } }, "Eyeglasses": { "base": "<p>Indicates whether or not the face is wearing eye glasses, and the confidence level in the determination.</p>", "refs": { "FaceDetail$Eyeglasses": "<p>Indicates whether or not the face is wearing eye glasses, and the confidence level in the determination.</p>" } }, "Face": { "base": "<p>Describes the face properties such as the bounding box, face ID, image ID of the input image, and external image ID that you assigned. </p>", "refs": { "FaceList$member": null, "FaceMatch$Face": "<p>Describes the face properties such as the bounding box, face ID, image ID of the source image, and external image ID that you assigned.</p>", "FaceRecord$Face": "<p>Describes the face properties such as the bounding box, face ID, image ID of the input image, and external image ID that you assigned. </p>" } }, "FaceAttributes": { "base": null, "refs": { "StartFaceDetectionRequest$FaceAttributes": "<p>The face attributes you want returned.</p> <p> <code>DEFAULT</code> - The following subset of facial attributes are returned: BoundingBox, Confidence, Pose, Quality and Landmarks. </p> <p> <code>ALL</code> - All facial attributes are returned.</p>" } }, "FaceDetail": { "base": "<p>Structure containing attributes of the face that the algorithm detected.</p> <p>A <code>FaceDetail</code> object contains either the default facial attributes or all facial attributes. The default attributes are <code>BoundingBox</code>, <code>Confidence</code>, <code>Landmarks</code>, <code>Pose</code>, and <code>Quality</code>.</p> <p> <a>GetFaceDetection</a> is the only Amazon Rekognition Video stored video operation that can return a <code>FaceDetail</code> object with all attributes. To specify which attributes to return, use the <code>FaceAttributes</code> input parameter for <a>StartFaceDetection</a>. The following Amazon Rekognition Video operations return only the default attributes. The corresponding Start operations don't have a <code>FaceAttributes</code> input parameter:</p> <ul> <li> <p>GetCelebrityRecognition</p> </li> <li> <p>GetPersonTracking</p> </li> <li> <p>GetFaceSearch</p> </li> </ul> <p>The Amazon Rekognition Image <a>DetectFaces</a> and <a>IndexFaces</a> operations can return all facial attributes. To specify which attributes to return, use the <code>Attributes</code> input parameter for <code>DetectFaces</code>. For <code>IndexFaces</code>, use the <code>DetectAttributes</code> input parameter.</p>", "refs": { "CelebrityDetail$Face": "<p>Face details for the recognized celebrity.</p>", "FaceDetailList$member": null, "FaceDetection$Face": "<p>The face properties for the detected face.</p>", "FaceRecord$FaceDetail": "<p>Structure containing attributes of the face that the algorithm detected.</p>", "PersonDetail$Face": "<p>Face details for the detected person.</p>", "SearchedFaceDetails$FaceDetail": null, "UnindexedFace$FaceDetail": "<p>The structure that contains attributes of a face that <code>IndexFaces</code>detected, but didn't index. </p>", "UnsearchedFace$FaceDetails": null } }, "FaceDetailList": { "base": null, "refs": { "DetectFacesResponse$FaceDetails": "<p>Details of each face found in the image. </p>" } }, "FaceDetection": { "base": "<p>Information about a face detected in a video analysis request and the time the face was detected in the video. </p>", "refs": { "FaceDetections$member": null } }, "FaceDetections": { "base": null, "refs": { "GetFaceDetectionResponse$Faces": "<p>An array of faces detected in the video. Each element contains a detected face's details and the time, in milliseconds from the start of the video, the face was detected. </p>" } }, "FaceId": { "base": null, "refs": { "AssociatedFace$FaceId": "<p>Unique identifier assigned to the face.</p>", "DisassociatedFace$FaceId": "<p>Unique identifier assigned to the face.</p>", "Face$FaceId": "<p>Unique identifier that Amazon Rekognition assigns to the face.</p>", "FaceIdList$member": null, "SearchFacesRequest$FaceId": "<p>ID of a face to find matches for in the collection.</p>", "SearchFacesResponse$SearchedFaceId": "<p>ID of the face that was searched for matches in a collection.</p>", "SearchUsersRequest$FaceId": "<p>ID for the existing face.</p>", "SearchedFace$FaceId": "<p> Unique identifier assigned to the face.</p>", "UnsuccessfulFaceAssociation$FaceId": "<p>A unique identifier assigned to the face. </p>", "UnsuccessfulFaceDeletion$FaceId": "<p> A unique identifier assigned to the face.</p>", "UnsuccessfulFaceDisassociation$FaceId": "<p>A unique identifier assigned to the face. </p>", "UserFaceIdList$member": null } }, "FaceIdList": { "base": null, "refs": { "DeleteFacesRequest$FaceIds": "<p>An array of face IDs to delete.</p>", "DeleteFacesResponse$DeletedFaces": "<p>An array of strings (face IDs) of the faces that were deleted.</p>", "ListFacesRequest$FaceIds": "<p>An array of face IDs to filter results with when listing faces in a collection.</p>" } }, "FaceList": { "base": null, "refs": { "ListFacesResponse$Faces": "<p>An array of <code>Face</code> objects. </p>" } }, "FaceMatch": { "base": "<p>Provides face metadata. In addition, it also provides the confidence in the match of this face with the input face.</p>", "refs": { "FaceMatchList$member": null } }, "FaceMatchList": { "base": null, "refs": { "PersonMatch$FaceMatches": "<p>Information about the faces in the input collection that match the face of a person in the video.</p>", "SearchFacesByImageResponse$FaceMatches": "<p>An array of faces that match the input face, along with the confidence in the match.</p>", "SearchFacesResponse$FaceMatches": "<p>An array of faces that matched the input face, along with the confidence in the match.</p>" } }, "FaceModelVersionList": { "base": null, "refs": { "ListCollectionsResponse$FaceModelVersions": "<p>Version numbers of the face detection models associated with the collections in the array <code>CollectionIds</code>. For example, the value of <code>FaceModelVersions[2]</code> is the version number for the face detection model used by the collection in <code>CollectionId[2]</code>.</p>" } }, "FaceOccluded": { "base": "<p> <code>FaceOccluded</code> should return \"true\" with a high confidence score if a detected face’s eyes, nose, and mouth are partially captured or if they are covered by masks, dark sunglasses, cell phones, hands, or other objects. <code>FaceOccluded</code> should return \"false\" with a high confidence score if common occurrences that do not impact face verification are detected, such as eye glasses, lightly tinted sunglasses, strands of hair, and others. </p> <p>You can use <code>FaceOccluded</code> to determine if an obstruction on a face negatively impacts using the image for face matching.</p>", "refs": { "FaceDetail$FaceOccluded": "<p> <code>FaceOccluded</code> should return \"true\" with a high confidence score if a detected face’s eyes, nose, and mouth are partially captured or if they are covered by masks, dark sunglasses, cell phones, hands, or other objects. <code>FaceOccluded</code> should return \"false\" with a high confidence score if common occurrences that do not impact face verification are detected, such as eye glasses, lightly tinted sunglasses, strands of hair, and others. </p>" } }, "FaceRecord": { "base": "<p>Object containing both the face metadata (stored in the backend database), and facial attributes that are detected but aren't stored in the database.</p>", "refs": { "FaceRecordList$member": null } }, "FaceRecordList": { "base": null, "refs": { "IndexFacesResponse$FaceRecords": "<p>An array of faces detected and added to the collection. For more information, see Searching Faces in a Collection in the Amazon Rekognition Developer Guide. </p>" } }, "FaceSearchSettings": { "base": "<p>Input face recognition parameters for an Amazon Rekognition stream processor. Includes the collection to use for face recognition and the face attributes to detect. Defining the settings is required in the request parameter for <a>CreateStreamProcessor</a>.</p>", "refs": { "StreamProcessorSettings$FaceSearch": "<p>Face search settings to use on a streaming video. </p>" } }, "FaceSearchSortBy": { "base": null, "refs": { "GetFaceSearchRequest$SortBy": "<p>Sort to use for grouping faces in the response. Use <code>TIMESTAMP</code> to group faces by the time that they are recognized. Use <code>INDEX</code> to sort by recognized faces. </p>" } }, "Float": { "base": null, "refs": { "BoundingBox$Width": "<p>Width of the bounding box as a ratio of the overall image width.</p>", "BoundingBox$Height": "<p>Height of the bounding box as a ratio of the overall image height.</p>", "BoundingBox$Left": "<p>Left coordinate of the bounding box as a ratio of overall image width.</p>", "BoundingBox$Top": "<p>Top coordinate of the bounding box as a ratio of overall image height.</p>", "DetectLabelsImageQuality$Brightness": "<p>The brightness of an image provided for label detection.</p>", "DetectLabelsImageQuality$Sharpness": "<p>The sharpness of an image provided for label detection.</p>", "DetectLabelsImageQuality$Contrast": "<p>The contrast of an image provided for label detection.</p>", "EvaluationResult$F1Score": "<p>The F1 score for the evaluation of all labels. The F1 score metric evaluates the overall precision and recall performance of the model as a single value. A higher value indicates better precision and recall performance. A lower score indicates that precision, recall, or both are performing poorly. </p>", "ImageQuality$Brightness": "<p>Value representing brightness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a brighter face image.</p>", "ImageQuality$Sharpness": "<p>Value representing sharpness of the face. The service returns a value between 0 and 100 (inclusive). A higher value indicates a sharper face image.</p>", "Landmark$X": "<p>The x-coordinate of the landmark expressed as a ratio of the width of the image. The x-coordinate is measured from the left-side of the image. For example, if the image is 700 pixels wide and the x-coordinate of the landmark is at 350 pixels, this value is 0.5. </p>", "Landmark$Y": "<p>The y-coordinate of the landmark expressed as a ratio of the height of the image. The y-coordinate is measured from the top of the image. For example, if the image height is 200 pixels and the y-coordinate of the landmark is at 50 pixels, this value is 0.25.</p>", "Point$X": "<p>The value of the X coordinate for a point on a <code>Polygon</code>.</p>", "Point$Y": "<p>The value of the Y coordinate for a point on a <code>Polygon</code>.</p>", "VideoMetadata$FrameRate": "<p>Number of frames per second in the video.</p>" } }, "FlowDefinitionArn": { "base": null, "refs": { "HumanLoopConfig$FlowDefinitionArn": "<p>The Amazon Resource Name (ARN) of the flow definition. You can create a flow definition by using the Amazon Sagemaker <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateFlowDefinition.html\">CreateFlowDefinition</a> Operation. </p>" } }, "Gender": { "base": "<p>The predicted gender of a detected face. </p> <p>Amazon Rekognition makes gender binary (male/female) predictions based on the physical appearance of a face in a particular image. This kind of prediction is not designed to categorize a person’s gender identity, and you shouldn't use Amazon Rekognition to make such a determination. For example, a male actor wearing a long-haired wig and earrings for a role might be predicted as female.</p> <p>Using Amazon Rekognition to make gender binary predictions is best suited for use cases where aggregate gender distribution statistics need to be analyzed without identifying specific users. For example, the percentage of female users compared to male users on a social media platform. </p> <p>We don't recommend using gender binary predictions to make decisions that impact an individual's rights, privacy, or access to services.</p>", "refs": { "FaceDetail$Gender": "<p>The predicted gender of a detected face. </p>" } }, "GenderType": { "base": null, "refs": { "Gender$Value": "<p>The predicted gender of the face.</p>" } }, "GeneralLabelsFilterList": { "base": null, "refs": { "GeneralLabelsSettings$LabelInclusionFilters": "<p>The labels that should be included in the return from DetectLabels.</p>", "GeneralLabelsSettings$LabelExclusionFilters": "<p>The labels that should be excluded from the return from DetectLabels.</p>", "GeneralLabelsSettings$LabelCategoryInclusionFilters": "<p>The label categories that should be included in the return from DetectLabels.</p>", "GeneralLabelsSettings$LabelCategoryExclusionFilters": "<p>The label categories that should be excluded from the return from DetectLabels.</p>" } }, "GeneralLabelsFilterValue": { "base": null, "refs": { "GeneralLabelsFilterList$member": null } }, "GeneralLabelsSettings": { "base": "<p>Contains filters for the object labels returned by DetectLabels. Filters can be inclusive, exclusive, or a combination of both and can be applied to individual labels or entire label categories. To see a list of label categories, see <a href=\"https://docs.aws.amazon.com/rekognition/latest/dg/labels.html\">Detecting Labels</a>.</p>", "refs": { "DetectLabelsSettings$GeneralLabels": "<p>Contains the specified filters for GENERAL_LABELS.</p>", "LabelDetectionSettings$GeneralLabels": null } }, "Geometry": { "base": "<p>Information about where an object (<a>DetectCustomLabels</a>) or text (<a>DetectText</a>) is located on an image.</p>", "refs": { "CustomLabel$Geometry": "<p>The location of the detected object on the image that corresponds to the custom label. Includes an axis aligned coarse bounding box surrounding the object and a finer grain polygon for more accurate spatial information.</p>", "TextDetection$Geometry": "<p>The location of the detected text on the image. Includes an axis aligned coarse bounding box surrounding the text and a finer grain polygon for more accurate spatial information.</p>" } }, "GetCelebrityInfoRequest": { "base": null, "refs": { } }, "GetCelebrityInfoResponse": { "base": null, "refs": { } }, "GetCelebrityRecognitionRequest": { "base": null, "refs": { } }, "GetCelebrityRecognitionResponse": { "base": null, "refs": { } }, "GetContentModerationRequest": { "base": null, "refs": { } }, "GetContentModerationRequestMetadata": { "base": "<p>Contains metadata about a content moderation request, including the SortBy and AggregateBy options.</p>", "refs": { "GetContentModerationResponse$GetRequestMetadata": "<p>Information about the paramters used when getting a response. Includes information on aggregation and sorting methods.</p>" } }, "GetContentModerationResponse": { "base": null, "refs": { } }, "GetFaceDetectionRequest": { "base": null, "refs": { } }, "GetFaceDetectionResponse": { "base": null, "refs": { } }, "GetFaceLivenessSessionResultsRequest": { "base": null, "refs": { } }, "GetFaceLivenessSessionResultsResponse": { "base": null, "refs": { } }, "GetFaceSearchRequest": { "base": null, "refs": { } }, "GetFaceSearchResponse": { "base": null, "refs": { } }, "GetLabelDetectionRequest": { "base": null, "refs": { } }, "GetLabelDetectionRequestMetadata": { "base": "<p>Contains metadata about a label detection request, including the SortBy and AggregateBy options.</p>", "refs": { "GetLabelDetectionResponse$GetRequestMetadata": "<p>Information about the paramters used when getting a response. Includes information on aggregation and sorting methods.</p>" } }, "GetLabelDetectionResponse": { "base": null, "refs": { } }, "GetMediaAnalysisJobRequest": { "base": null, "refs": { } }, "GetMediaAnalysisJobResponse": { "base": null, "refs": { } }, "GetPersonTrackingRequest": { "base": null, "refs": { } }, "GetPersonTrackingResponse": { "base": null, "refs": { } }, "GetSegmentDetectionRequest": { "base": null, "refs": { } }, "GetSegmentDetectionResponse": { "base": null, "refs": { } }, "GetTextDetectionRequest": { "base": null, "refs": { } }, "GetTextDetectionResponse": { "base": null, "refs": { } }, "GroundTruthBlob": { "base": null, "refs": { "DatasetChanges$GroundTruth": "<p>A Base64-encoded binary data object containing one or JSON lines that either update the dataset or are additions to the dataset. You change a dataset by calling <a>UpdateDatasetEntries</a>. If you are using an AWS SDK to call <code>UpdateDatasetEntries</code>, you don't need to encode <code>Changes</code> as the SDK encodes the data for you. </p> <p>For example JSON lines, see Image-Level labels in manifest files and and Object localization in manifest files in the <i>Amazon Rekognition Custom Labels Developer Guide</i>. </p>" } }, "GroundTruthManifest": { "base": "<p>The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file. </p>", "refs": { "Asset$GroundTruthManifest": null, "DatasetSource$GroundTruthManifest": null, "ProjectVersionDescription$ManifestSummary": "<p>The location of the summary manifest. The summary manifest provides aggregate data validation results for the training and test datasets.</p>" } }, "HasErrors": { "base": null, "refs": { "ListDatasetEntriesRequest$HasErrors": "<p>Specifies an error filter for the response. Specify <code>True</code> to only include entries that have errors. </p>" } }, "HumanLoopActivationConditionsEvaluationResults": { "base": null, "refs": { "HumanLoopActivationOutput$HumanLoopActivationConditionsEvaluationResults": "<p>Shows the result of condition evaluations, including those conditions which activated a human review.</p>" } }, "HumanLoopActivationOutput": { "base": "<p>Shows the results of the human in the loop evaluation. If there is no HumanLoopArn, the input did not trigger human review.</p>", "refs": { "DetectModerationLabelsResponse$HumanLoopActivationOutput": "<p>Shows the results of the human in the loop evaluation.</p>" } }, "HumanLoopActivationReason": { "base": null, "refs": { "HumanLoopActivationReasons$member": null } }, "HumanLoopActivationReasons": { "base": null, "refs": { "HumanLoopActivationOutput$HumanLoopActivationReasons": "<p>Shows if and why human review was needed.</p>" } }, "HumanLoopArn": { "base": null, "refs": { "HumanLoopActivationOutput$HumanLoopArn": "<p>The Amazon Resource Name (ARN) of the HumanLoop created.</p>" } }, "HumanLoopConfig": { "base": "<p>Sets up the flow definition the image will be sent to if one of the conditions is met. You can also set certain attributes of the image before review.</p>", "refs": { "DetectModerationLabelsRequest$HumanLoopConfig": "<p>Sets up the configuration for human evaluation, including the FlowDefinition the image will be sent to.</p>" } }, "HumanLoopDataAttributes": { "base": "<p>Allows you to set attributes of the image. Currently, you can declare an image as free of personally identifiable information.</p>", "refs": { "HumanLoopConfig$DataAttributes": "<p>Sets attributes of the input data.</p>" } }, "HumanLoopName": { "base": null, "refs": { "HumanLoopConfig$HumanLoopName": "<p>The name of the human review used for this image. This should be kept unique within a region.</p>" } }, "HumanLoopQuotaExceededException": { "base": "<p>The number of in-progress human reviews you have has exceeded the number allowed.</p>", "refs": { } }, "IdempotentParameterMismatchException": { "base": "<p>A <code>ClientRequestToken</code> input parameter was reused with an operation, but at least one of the other input parameters is different from the previous call to the operation.</p>", "refs": { } }, "Image": { "base": "<p>Provides the input image either as bytes or an S3 object.</p> <p>You pass image bytes to an Amazon Rekognition API operation by using the <code>Bytes</code> property. For example, you would use the <code>Bytes</code> property to pass an image loaded from a local file system. Image bytes passed by using the <code>Bytes</code> property must be base64-encoded. Your code may not need to encode image bytes if you are using an AWS SDK to call Amazon Rekognition API operations. </p> <p>For more information, see Analyzing an Image Loaded from a Local File System in the Amazon Rekognition Developer Guide.</p> <p> You pass images stored in an S3 bucket to an Amazon Rekognition API operation by using the <code>S3Object</code> property. Images stored in an S3 bucket do not need to be base64-encoded.</p> <p>The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.</p> <p>If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes using the Bytes property is not supported. You must first upload the image to an Amazon S3 bucket and then call the operation using the S3Object property.</p> <p>For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide. </p>", "refs": { "CompareFacesRequest$SourceImage": "<p>The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported. </p> <p>If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the <code>Bytes</code> field. For more information, see Images in the Amazon Rekognition developer guide.</p>", "CompareFacesRequest$TargetImage": "<p>The target image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported. </p> <p>If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the <code>Bytes</code> field. For more information, see Images in the Amazon Rekognition developer guide.</p>", "DetectCustomLabelsRequest$Image": null, "DetectFacesRequest$Image": "<p>The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported. </p> <p>If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the <code>Bytes</code> field. For more information, see Images in the Amazon Rekognition developer guide.</p>", "DetectLabelsRequest$Image": "<p>The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. Images stored in an S3 Bucket do not need to be base64-encoded.</p> <p>If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the <code>Bytes</code> field. For more information, see Images in the Amazon Rekognition developer guide.</p>", "DetectModerationLabelsRequest$Image": "<p>The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported. </p> <p>If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the <code>Bytes</code> field. For more information, see Images in the Amazon Rekognition developer guide.</p>", "DetectProtectiveEquipmentRequest$Image": "<p>The image in which you want to detect PPE on detected persons. The image can be passed as image bytes or you can reference an image stored in an Amazon S3 bucket. </p>", "DetectTextRequest$Image": "<p>The input image as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Rekognition operations, you can't pass image bytes. </p> <p>If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the <code>Bytes</code> field. For more information, see Images in the Amazon Rekognition developer guide.</p>", "IndexFacesRequest$Image": "<p>The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes isn't supported. </p> <p>If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the <code>Bytes</code> field. For more information, see Images in the Amazon Rekognition developer guide.</p>", "RecognizeCelebritiesRequest$Image": "<p>The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported. </p> <p>If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the <code>Bytes</code> field. For more information, see Images in the Amazon Rekognition developer guide.</p>", "SearchFacesByImageRequest$Image": "<p>The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported. </p> <p>If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the <code>Bytes</code> field. For more information, see Images in the Amazon Rekognition developer guide.</p>", "SearchUsersByImageRequest$Image": null } }, "ImageBlob": { "base": null, "refs": { "Image$Bytes": "<p>Blob of image bytes up to 5 MBs. Note that the maximum image size you can pass to <code>DetectCustomLabels</code> is 4MB. </p>" } }, "ImageId": { "base": null, "refs": { "Face$ImageId": "<p>Unique identifier that Amazon Rekognition assigns to the input image.</p>" } }, "ImageQuality": { "base": "<p>Identifies face image brightness and sharpness. </p>", "refs": { "ComparedFace$Quality": "<p>Identifies face image brightness and sharpness. </p>", "FaceDetail$Quality": "<p>Identifies image brightness and sharpness. Default attribute.</p>" } }, "ImageTooLargeException": { "base": "<p>The input image size exceeds the allowed limit. If you are calling DetectProtectiveEquipment, the image size or resolution exceeds the allowed limit. For more information, see Guidelines and quotas in Amazon Rekognition in the Amazon Rekognition Developer Guide. </p>", "refs": { } }, "IndexFacesModelVersion": { "base": null, "refs": { "Face$IndexFacesModelVersion": "<p> The version of the face detect and storage model that was used when indexing the face vector. </p>" } }, "IndexFacesRequest": { "base": null, "refs": { } }, "IndexFacesResponse": { "base": null, "refs": { } }, "InferenceUnits": { "base": null, "refs": { "ProjectVersionDescription$MinInferenceUnits": "<p>The minimum number of inference units used by the model. Applies only to Custom Labels projects. For more information, see <a>StartProjectVersion</a>.</p>", "ProjectVersionDescription$MaxInferenceUnits": "<p>The maximum number of inference units Amazon Rekognition uses to auto-scale the model. Applies only to Custom Labels projects. For more information, see <a>StartProjectVersion</a>.</p>", "StartProjectVersionRequest$MinInferenceUnits": "<p>The minimum number of inference units to use. A single inference unit represents 1 hour of processing. </p> <p>Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use. </p>", "StartProjectVersionRequest$MaxInferenceUnits": "<p>The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Rekognition Custom Labels doesn't auto-scale the model.</p>" } }, "Instance": { "base": "<p>An instance of a label returned by Amazon Rekognition Image (<a>DetectLabels</a>) or by Amazon Rekognition Video (<a>GetLabelDetection</a>).</p>", "refs": { "Instances$member": null } }, "Instances": { "base": null, "refs": { "Label$Instances": "<p>If <code>Label</code> represents an object, <code>Instances</code> contains the bounding boxes for each instance of the detected object. Bounding boxes are returned for common object labels such as people, cars, furniture, apparel or pets.</p>" } }, "InternalServerError": { "base": "<p>Amazon Rekognition experienced a service issue. Try your call again.</p>", "refs": { } }, "InvalidImageFormatException": { "base": "<p>The provided image format is not supported. </p>", "refs": { } }, "InvalidManifestException": { "base": "<p>Indicates that a provided manifest file is empty or larger than the allowed limit.</p>", "refs": { } }, "InvalidPaginationTokenException": { "base": "<p>Pagination token in the request is not valid.</p>", "refs": { } }, "InvalidParameterException": { "base": "<p>Input parameter violated a constraint. Validate your parameter before calling the API operation again.</p>", "refs": { } }, "InvalidPolicyRevisionIdException": { "base": "<p>The supplied revision id for the project policy is invalid.</p>", "refs": { } }, "InvalidS3ObjectException": { "base": "<p>Amazon Rekognition is unable to access the S3 object specified in the request.</p>", "refs": { } }, "IsLabeled": { "base": null, "refs": { "ListDatasetEntriesRequest$Labeled": "<p> Specify <code>true</code> to get only the JSON Lines where the image is labeled. Specify <code>false</code> to get only the JSON Lines where the image isn't labeled. If you don't specify <code>Labeled</code>, <code>ListDatasetEntries</code> returns JSON Lines for labeled and unlabeled images. </p>" } }, "JobId": { "base": null, "refs": { "GetCelebrityRecognitionRequest$JobId": "<p>Job identifier for the required celebrity recognition analysis. You can get the job identifer from a call to <code>StartCelebrityRecognition</code>.</p>", "GetCelebrityRecognitionResponse$JobId": "<p>Job identifier for the celebrity recognition operation for which you want to obtain results. The job identifer is returned by an initial call to StartCelebrityRecognition.</p>", "GetContentModerationRequest$JobId": "<p>The identifier for the inappropriate, unwanted, or offensive content moderation job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetContentModeration</code>.</p>", "GetContentModerationResponse$JobId": "<p>Job identifier for the content moderation operation for which you want to obtain results. The job identifer is returned by an initial call to StartContentModeration.</p>", "GetFaceDetectionRequest$JobId": "<p>Unique identifier for the face detection job. The <code>JobId</code> is returned from <code>StartFaceDetection</code>.</p>", "GetFaceDetectionResponse$JobId": "<p>Job identifier for the face detection operation for which you want to obtain results. The job identifer is returned by an initial call to StartFaceDetection.</p>", "GetFaceSearchRequest$JobId": "<p>The job identifer for the search request. You get the job identifier from an initial call to <code>StartFaceSearch</code>.</p>", "GetFaceSearchResponse$JobId": "<p>Job identifier for the face search operation for which you want to obtain results. The job identifer is returned by an initial call to StartFaceSearch.</p>", "GetLabelDetectionRequest$JobId": "<p>Job identifier for the label detection operation for which you want results returned. You get the job identifer from an initial call to <code>StartlabelDetection</code>.</p>", "GetLabelDetectionResponse$JobId": "<p>Job identifier for the label detection operation for which you want to obtain results. The job identifer is returned by an initial call to StartLabelDetection.</p>", "GetPersonTrackingRequest$JobId": "<p>The identifier for a job that tracks persons in a video. You get the <code>JobId</code> from a call to <code>StartPersonTracking</code>. </p>", "GetPersonTrackingResponse$JobId": "<p>Job identifier for the person tracking operation for which you want to obtain results. The job identifer is returned by an initial call to StartPersonTracking.</p>", "GetSegmentDetectionRequest$JobId": "<p>Job identifier for the text detection operation for which you want results returned. You get the job identifer from an initial call to <code>StartSegmentDetection</code>.</p>", "GetSegmentDetectionResponse$JobId": "<p>Job identifier for the segment detection operation for which you want to obtain results. The job identifer is returned by an initial call to StartSegmentDetection.</p>", "GetTextDetectionRequest$JobId": "<p>Job identifier for the text detection operation for which you want results returned. You get the job identifer from an initial call to <code>StartTextDetection</code>.</p>", "GetTextDetectionResponse$JobId": "<p>Job identifier for the text detection operation for which you want to obtain results. The job identifer is returned by an initial call to StartTextDetection.</p>", "StartCelebrityRecognitionResponse$JobId": "<p>The identifier for the celebrity recognition analysis job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetCelebrityRecognition</code>.</p>", "StartContentModerationResponse$JobId": "<p>The identifier for the content analysis job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetContentModeration</code>.</p>", "StartFaceDetectionResponse$JobId": "<p>The identifier for the face detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetFaceDetection</code>.</p>", "StartFaceSearchResponse$JobId": "<p>The identifier for the search job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetFaceSearch</code>. </p>", "StartLabelDetectionResponse$JobId": "<p>The identifier for the label detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetLabelDetection</code>. </p>", "StartPersonTrackingResponse$JobId": "<p>The identifier for the person detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetPersonTracking</code>.</p>", "StartSegmentDetectionResponse$JobId": "<p>Unique identifier for the segment detection job. The <code>JobId</code> is returned from <code>StartSegmentDetection</code>. </p>", "StartTextDetectionResponse$JobId": "<p>Identifier for the text detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetTextDetection</code>.</p>" } }, "JobTag": { "base": null, "refs": { "GetCelebrityRecognitionResponse$JobTag": "<p>A job identifier specified in the call to StartCelebrityRecognition and returned in the job completion notification sent to your Amazon Simple Notification Service topic.</p>", "GetContentModerationResponse$JobTag": "<p>A job identifier specified in the call to StartContentModeration and returned in the job completion notification sent to your Amazon Simple Notification Service topic.</p>", "GetFaceDetectionResponse$JobTag": "<p>A job identifier specified in the call to StartFaceDetection and returned in the job completion notification sent to your Amazon Simple Notification Service topic.</p>", "GetFaceSearchResponse$JobTag": "<p>A job identifier specified in the call to StartFaceSearch and returned in the job completion notification sent to your Amazon Simple Notification Service topic.</p>", "GetLabelDetectionResponse$JobTag": "<p>A job identifier specified in the call to StartLabelDetection and returned in the job completion notification sent to your Amazon Simple Notification Service topic.</p>", "GetPersonTrackingResponse$JobTag": "<p>A job identifier specified in the call to StartCelebrityRecognition and returned in the job completion notification sent to your Amazon Simple Notification Service topic.</p>", "GetSegmentDetectionResponse$JobTag": "<p>A job identifier specified in the call to StartSegmentDetection and returned in the job completion notification sent to your Amazon Simple Notification Service topic.</p>", "GetTextDetectionResponse$JobTag": "<p>A job identifier specified in the call to StartTextDetection and returned in the job completion notification sent to your Amazon Simple Notification Service topic.</p>", "StartCelebrityRecognitionRequest$JobTag": "<p>An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use <code>JobTag</code> to group related jobs and identify them in the completion notification.</p>", "StartContentModerationRequest$JobTag": "<p>An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use <code>JobTag</code> to group related jobs and identify them in the completion notification.</p>", "StartFaceDetectionRequest$JobTag": "<p>An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use <code>JobTag</code> to group related jobs and identify them in the completion notification.</p>", "StartFaceSearchRequest$JobTag": "<p>An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use <code>JobTag</code> to group related jobs and identify them in the completion notification.</p>", "StartLabelDetectionRequest$JobTag": "<p>An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use <code>JobTag</code> to group related jobs and identify them in the completion notification.</p>", "StartPersonTrackingRequest$JobTag": "<p>An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use <code>JobTag</code> to group related jobs and identify them in the completion notification.</p>", "StartSegmentDetectionRequest$JobTag": "<p>An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use <code>JobTag</code> to group related jobs and identify them in the completion notification.</p>", "StartTextDetectionRequest$JobTag": "<p>An identifier returned in the completion status published by your Amazon Simple Notification Service topic. For example, you can use <code>JobTag</code> to group related jobs and identify them in the completion notification.</p>" } }, "KinesisDataArn": { "base": null, "refs": { "KinesisDataStream$Arn": "<p>ARN of the output Amazon Kinesis Data Streams stream.</p>" } }, "KinesisDataStream": { "base": "<p>The Kinesis data stream Amazon Rekognition to which the analysis results of a Amazon Rekognition stream processor are streamed. For more information, see CreateStreamProcessor in the Amazon Rekognition Developer Guide.</p>", "refs": { "StreamProcessorOutput$KinesisDataStream": "<p>The Amazon Kinesis Data Streams stream to which the Amazon Rekognition stream processor streams the analysis results.</p>" } }, "KinesisVideoArn": { "base": null, "refs": { "KinesisVideoStream$Arn": "<p>ARN of the Kinesis video stream stream that streams the source video.</p>" } }, "KinesisVideoStream": { "base": "<p>Kinesis video stream stream that provides the source streaming video for a Amazon Rekognition Video stream processor. For more information, see CreateStreamProcessor in the Amazon Rekognition Developer Guide.</p>", "refs": { "StreamProcessorInput$KinesisVideoStream": "<p>The Kinesis video stream input stream for the source streaming video.</p>" } }, "KinesisVideoStreamFragmentNumber": { "base": null, "refs": { "KinesisVideoStreamStartSelector$FragmentNumber": "<p> The unique identifier of the fragment. This value monotonically increases based on the ingestion order. </p>" } }, "KinesisVideoStreamStartSelector": { "base": "<p>Specifies the starting point in a Kinesis stream to start processing. You can use the producer timestamp or the fragment number. One of either producer timestamp or fragment number is required. If you use the producer timestamp, you must put the time in milliseconds. For more information about fragment numbers, see <a href=\"https://docs.aws.amazon.com/kinesisvideostreams/latest/dg/API_reader_Fragment.html\">Fragment</a>. </p>", "refs": { "StreamProcessingStartSelector$KVSStreamStartSelector": "<p> Specifies the starting point in the stream to start processing. This can be done with a producer timestamp or a fragment number in a Kinesis stream. </p>" } }, "KmsKeyId": { "base": null, "refs": { "CopyProjectVersionRequest$KmsKeyId": "<p>The identifier for your AWS Key Management Service key (AWS KMS key). You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt training results and manifest files written to the output Amazon S3 bucket (<code>OutputConfig</code>).</p> <p>If you choose to use your own KMS key, you need the following permissions on the KMS key.</p> <ul> <li> <p>kms:CreateGrant</p> </li> <li> <p>kms:DescribeKey</p> </li> <li> <p>kms:GenerateDataKey</p> </li> <li> <p>kms:Decrypt</p> </li> </ul> <p>If you don't specify a value for <code>KmsKeyId</code>, images copied into the service are encrypted using a key that AWS owns and manages.</p>", "CreateFaceLivenessSessionRequest$KmsKeyId": "<p> The identifier for your AWS Key Management Service key (AWS KMS key). Used to encrypt audit images and reference images.</p>", "CreateProjectVersionRequest$KmsKeyId": "<p>The identifier for your AWS Key Management Service key (AWS KMS key). You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt training images, test images, and manifest files copied into the service for the project version. Your source images are unaffected. The key is also used to encrypt training results and manifest files written to the output Amazon S3 bucket (<code>OutputConfig</code>).</p> <p>If you choose to use your own KMS key, you need the following permissions on the KMS key.</p> <ul> <li> <p>kms:CreateGrant</p> </li> <li> <p>kms:DescribeKey</p> </li> <li> <p>kms:GenerateDataKey</p> </li> <li> <p>kms:Decrypt</p> </li> </ul> <p>If you don't specify a value for <code>KmsKeyId</code>, images copied into the service are encrypted using a key that AWS owns and manages.</p>", "CreateStreamProcessorRequest$KmsKeyId": "<p> The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor. You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt results and data published to your Amazon S3 bucket, which includes image frames and hero images. Your source images are unaffected. </p> <p> </p>", "DescribeStreamProcessorResponse$KmsKeyId": "<p> The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors. </p>", "GetMediaAnalysisJobResponse$KmsKeyId": "<p>KMS Key that was provided in the creation request.</p>", "MediaAnalysisJobDescription$KmsKeyId": "<p>KMS Key that was provided in the creation request.</p>", "ProjectVersionDescription$KmsKeyId": "<p>The identifer for the AWS Key Management Service key (AWS KMS key) that was used to encrypt the model during training. </p>", "StartMediaAnalysisJobRequest$KmsKeyId": "<p>The identifier of customer managed AWS KMS key (name or ARN). The key is used to encrypt images copied into the service. The key is also used to encrypt results and manifest files written to the output Amazon S3 bucket.</p>" } }, "KnownGender": { "base": "<p>The known gender identity for the celebrity that matches the provided ID. The known gender identity can be Male, Female, Nonbinary, or Unlisted.</p>", "refs": { "Celebrity$KnownGender": null, "CelebrityDetail$KnownGender": "<p>Retrieves the known gender for the celebrity.</p>", "GetCelebrityInfoResponse$KnownGender": "<p>Retrieves the known gender for the celebrity.</p>" } }, "KnownGenderType": { "base": "<p>A list of enum string of possible gender values that Celebrity returns.</p>", "refs": { "KnownGender$Type": "<p>A string value of the KnownGender info about the Celebrity.</p>" } }, "Label": { "base": "<p>Structure containing details about the detected label, including the name, detected instances, parent labels, and level of confidence.</p> <p> </p>", "refs": { "LabelDetection$Label": "<p>Details about the detected label.</p>", "Labels$member": null } }, "LabelAlias": { "base": "<p>A potential alias of for a given label.</p>", "refs": { "LabelAliases$member": null } }, "LabelAliases": { "base": null, "refs": { "Label$Aliases": "<p>A list of potential aliases for a given label.</p>" } }, "LabelCategories": { "base": null, "refs": { "Label$Categories": "<p>A list of the categories associated with a given label.</p>" } }, "LabelCategory": { "base": "<p>The category that applies to a given label.</p>", "refs": { "LabelCategories$member": null } }, "LabelDetection": { "base": "<p>Information about a label detected in a video analysis request and the time the label was detected in the video. </p>", "refs": { "LabelDetections$member": null } }, "LabelDetectionAggregateBy": { "base": null, "refs": { "GetLabelDetectionRequest$AggregateBy": "<p>Defines how to aggregate the returned results. Results can be aggregated by timestamps or segments.</p>", "GetLabelDetectionRequestMetadata$AggregateBy": "<p>The aggregation method chosen for a GetLabelDetection request.</p>" } }, "LabelDetectionFeatureList": { "base": null, "refs": { "StartLabelDetectionRequest$Features": "<p>The features to return after video analysis. You can specify that GENERAL_LABELS are returned.</p>" } }, "LabelDetectionFeatureName": { "base": null, "refs": { "LabelDetectionFeatureList$member": null } }, "LabelDetectionSettings": { "base": "<p>Contains the specified filters that should be applied to a list of returned GENERAL_LABELS.</p>", "refs": { "StartLabelDetectionRequest$Settings": "<p>The settings for a StartLabelDetection request.Contains the specified parameters for the label detection request of an asynchronous label analysis operation. Settings can include filters for GENERAL_LABELS.</p>" } }, "LabelDetectionSortBy": { "base": null, "refs": { "GetLabelDetectionRequest$SortBy": "<p>Sort to use for elements in the <code>Labels</code> array. Use <code>TIMESTAMP</code> to sort array elements by the time labels are detected. Use <code>NAME</code> to alphabetically group elements for a label together. Within each label group, the array element are sorted by detection confidence. The default sort is by <code>TIMESTAMP</code>.</p>", "GetLabelDetectionRequestMetadata$SortBy": "<p>The sorting method chosen for a GetLabelDetection request.</p>" } }, "LabelDetections": { "base": null, "refs": { "GetLabelDetectionResponse$Labels": "<p>An array of labels detected in the video. Each element contains the detected label and the time, in milliseconds from the start of the video, that the label was detected. </p>" } }, "Labels": { "base": null, "refs": { "DetectLabelsResponse$Labels": "<p>An array of labels for the real-world objects detected. </p>" } }, "Landmark": { "base": "<p>Indicates the location of the landmark on the face.</p>", "refs": { "Landmarks$member": null } }, "LandmarkType": { "base": null, "refs": { "Landmark$Type": "<p>Type of landmark.</p>" } }, "Landmarks": { "base": null, "refs": { "ComparedFace$Landmarks": "<p>An array of facial landmarks.</p>", "FaceDetail$Landmarks": "<p>Indicates the location of landmarks on the face. Default attribute.</p>" } }, "LimitExceededException": { "base": "<p>An Amazon Rekognition service limit was exceeded. For example, if you start too many jobs concurrently, subsequent calls to start operations (ex: <code>StartLabelDetection</code>) will raise a <code>LimitExceededException</code> exception (HTTP status code: 400) until the number of concurrently running jobs is below the Amazon Rekognition service limit. </p>", "refs": { } }, "ListCollectionsRequest": { "base": null, "refs": { } }, "ListCollectionsResponse": { "base": null, "refs": { } }, "ListDatasetEntriesPageSize": { "base": null, "refs": { "ListDatasetEntriesRequest$MaxResults": "<p>The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100. </p>" } }, "ListDatasetEntriesRequest": { "base": null, "refs": { } }, "ListDatasetEntriesResponse": { "base": null, "refs": { } }, "ListDatasetLabelsPageSize": { "base": null, "refs": { "ListDatasetLabelsRequest$MaxResults": "<p>The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100. </p>" } }, "ListDatasetLabelsRequest": { "base": null, "refs": { } }, "ListDatasetLabelsResponse": { "base": null, "refs": { } }, "ListFacesRequest": { "base": null, "refs": { } }, "ListFacesResponse": { "base": null, "refs": { } }, "ListMediaAnalysisJobsPageSize": { "base": null, "refs": { "ListMediaAnalysisJobsRequest$MaxResults": "<p>The maximum number of results to return per paginated call. The largest value user can specify is 100. If user specifies a value greater than 100, an <code>InvalidParameterException</code> error occurs. The default value is 100.</p>" } }, "ListMediaAnalysisJobsRequest": { "base": null, "refs": { } }, "ListMediaAnalysisJobsResponse": { "base": null, "refs": { } }, "ListProjectPoliciesPageSize": { "base": null, "refs": { "ListProjectPoliciesRequest$MaxResults": "<p>The maximum number of results to return per paginated call. The largest value you can specify is 5. If you specify a value greater than 5, a ValidationException error occurs. The default value is 5. </p>" } }, "ListProjectPoliciesRequest": { "base": null, "refs": { } }, "ListProjectPoliciesResponse": { "base": null, "refs": { } }, "ListStreamProcessorsRequest": { "base": null, "refs": { } }, "ListStreamProcessorsResponse": { "base": null, "refs": { } }, "ListTagsForResourceRequest": { "base": null, "refs": { } }, "ListTagsForResourceResponse": { "base": null, "refs": { } }, "ListUsersRequest": { "base": null, "refs": { } }, "ListUsersResponse": { "base": null, "refs": { } }, "LivenessImageBlob": { "base": null, "refs": { "AuditImage$Bytes": "<p>The Base64-encoded bytes representing an image selected from the Face Liveness video and returned for audit purposes.</p>" } }, "LivenessOutputConfig": { "base": "<p>Contains settings that specify the location of an Amazon S3 bucket used to store the output of a Face Liveness session. Note that the S3 bucket must be located in the caller's AWS account and in the same region as the Face Liveness end-point. Additionally, the Amazon S3 object keys are auto-generated by the Face Liveness system. </p>", "refs": { "CreateFaceLivenessSessionRequestSettings$OutputConfig": "<p>Can specify the location of an Amazon S3 bucket, where reference and audit images will be stored. Note that the Amazon S3 bucket must be located in the caller's AWS account and in the same region as the Face Liveness end-point. Additionally, the Amazon S3 object keys are auto-generated by the Face Liveness system. Requires that the caller has the <code>s3:PutObject</code> permission on the Amazon S3 bucket.</p>" } }, "LivenessS3KeyPrefix": { "base": null, "refs": { "LivenessOutputConfig$S3KeyPrefix": "<p>The prefix prepended to the output files for the Face Liveness session results.</p>" } }, "LivenessSessionId": { "base": null, "refs": { "CreateFaceLivenessSessionResponse$SessionId": "<p>A unique 128-bit UUID identifying a Face Liveness session. A new sessionID must be used for every Face Liveness check. If a given sessionID is used for subsequent Face Liveness checks, the checks will fail. Additionally, a SessionId expires 3 minutes after it's sent, making all Liveness data associated with the session (e.g., sessionID, reference image, audit images, etc.) unavailable. </p>", "GetFaceLivenessSessionResultsRequest$SessionId": "<p>A unique 128-bit UUID. This is used to uniquely identify the session and also acts as an idempotency token for all operations associated with the session.</p>", "GetFaceLivenessSessionResultsResponse$SessionId": "<p>The sessionId for which this request was called.</p>" } }, "LivenessSessionStatus": { "base": null, "refs": { "GetFaceLivenessSessionResultsResponse$Status": "<p>Represents a status corresponding to the state of the session. Possible statuses are: CREATED, IN_PROGRESS, SUCCEEDED, FAILED, EXPIRED.</p>" } }, "MalformedPolicyDocumentException": { "base": "<p>The format of the project policy document that you supplied to <code>PutProjectPolicy</code> is incorrect. </p>", "refs": { } }, "MatchedUser": { "base": "<p>Contains metadata for a UserID matched with a given face.</p>", "refs": { "UserMatch$User": "<p> Confidence in the match of this UserID with the input face. </p>" } }, "MaxDurationInSecondsULong": { "base": null, "refs": { "StreamProcessingStopSelector$MaxDurationInSeconds": "<p> Specifies the maximum amount of time in seconds that you want the stream to be processed. The largest amount of time is 2 minutes. The default is 10 seconds. </p>" } }, "MaxFaces": { "base": null, "refs": { "SearchFacesByImageRequest$MaxFaces": "<p>Maximum number of faces to return. The operation returns the maximum number of faces with the highest confidence in the match.</p>", "SearchFacesRequest$MaxFaces": "<p>Maximum number of faces to return. The operation returns the maximum number of faces with the highest confidence in the match.</p>" } }, "MaxFacesToIndex": { "base": null, "refs": { "IndexFacesRequest$MaxFaces": "<p>The maximum number of faces to index. The value of <code>MaxFaces</code> must be greater than or equal to 1. <code>IndexFaces</code> returns no more than 100 detected faces in an image, even if you specify a larger value for <code>MaxFaces</code>.</p> <p>If <code>IndexFaces</code> detects more faces than the value of <code>MaxFaces</code>, the faces with the lowest quality are filtered out first. If there are still more faces than the value of <code>MaxFaces</code>, the faces with the smallest bounding boxes are filtered out (up to the number that's needed to satisfy the value of <code>MaxFaces</code>). Information about the unindexed faces is available in the <code>UnindexedFaces</code> array. </p> <p>The faces that are returned by <code>IndexFaces</code> are sorted by the largest face bounding box size to the smallest size, in descending order.</p> <p> <code>MaxFaces</code> can be used with a collection associated with any version of the face model.</p>" } }, "MaxPixelThreshold": { "base": null, "refs": { "BlackFrame$MaxPixelThreshold": "<p> A threshold used to determine the maximum luminance value for a pixel to be considered black. In a full color range video, luminance values range from 0-255. A pixel value of 0 is pure black, and the most strict filter. The maximum black pixel value is computed as follows: max_black_pixel_value = minimum_luminance + MaxPixelThreshold luminance_range. </p> <p>For example, for a full range video with BlackPixelThreshold = 0.1, max_black_pixel_value is 0 + 0.1 (255-0) = 25.5.</p> <p>The default value of MaxPixelThreshold is 0.2, which maps to a max_black_pixel_value of 51 for a full range video. You can lower this threshold to be more strict on black levels.</p>" } }, "MaxResults": { "base": null, "refs": { "GetCelebrityRecognitionRequest$MaxResults": "<p>Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.</p>", "GetContentModerationRequest$MaxResults": "<p>Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.</p>", "GetFaceDetectionRequest$MaxResults": "<p>Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.</p>", "GetFaceSearchRequest$MaxResults": "<p>Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.</p>", "GetLabelDetectionRequest$MaxResults": "<p>Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.</p>", "GetPersonTrackingRequest$MaxResults": "<p>Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.</p>", "GetSegmentDetectionRequest$MaxResults": "<p>Maximum number of results to return per paginated call. The largest value you can specify is 1000.</p>", "GetTextDetectionRequest$MaxResults": "<p>Maximum number of results to return per paginated call. The largest value you can specify is 1000.</p>", "ListStreamProcessorsRequest$MaxResults": "<p>Maximum number of stream processors you want Amazon Rekognition Video to return in the response. The default is 1000. </p>" } }, "MaxUserResults": { "base": null, "refs": { "ListUsersRequest$MaxResults": "<p>Maximum number of UsersID to return. </p>", "SearchUsersByImageRequest$MaxUsers": "<p>Maximum number of UserIDs to return.</p>", "SearchUsersRequest$MaxUsers": "<p>Maximum number of identities to return.</p>" } }, "MediaAnalysisDetectModerationLabelsConfig": { "base": "<p>Configuration for Moderation Labels Detection.</p>", "refs": { "MediaAnalysisOperationsConfig$DetectModerationLabels": "<p>Contains configuration options for a DetectModerationLabels job.</p>" } }, "MediaAnalysisInput": { "base": "<p>Contains input information for a media analysis job.</p>", "refs": { "GetMediaAnalysisJobResponse$Input": "<p>Reference to the input manifest that was provided in the job creation request.</p>", "MediaAnalysisJobDescription$Input": "<p>Reference to the input manifest that was provided in the job creation request.</p>", "StartMediaAnalysisJobRequest$Input": "<p>Input data to be analyzed by the job.</p>" } }, "MediaAnalysisJobDescription": { "base": "<p>Description for a media analysis job.</p>", "refs": { "MediaAnalysisJobDescriptions$member": null } }, "MediaAnalysisJobDescriptions": { "base": null, "refs": { "ListMediaAnalysisJobsResponse$MediaAnalysisJobs": "<p>Contains a list of all media analysis jobs.</p>" } }, "MediaAnalysisJobFailureCode": { "base": null, "refs": { "MediaAnalysisJobFailureDetails$Code": "<p>Error code for the failed job.</p>" } }, "MediaAnalysisJobFailureDetails": { "base": "<p>Details about the error that resulted in failure of the job.</p>", "refs": { "GetMediaAnalysisJobResponse$FailureDetails": "<p>Details about the error that resulted in failure of the job.</p>", "MediaAnalysisJobDescription$FailureDetails": "<p>Details about the error that resulted in failure of the job.</p>" } }, "MediaAnalysisJobId": { "base": null, "refs": { "GetMediaAnalysisJobRequest$JobId": "<p>Unique identifier for the media analysis job for which you want to retrieve results.</p>", "GetMediaAnalysisJobResponse$JobId": "<p>The identifier for the media analysis job.</p>", "MediaAnalysisJobDescription$JobId": "<p>The identifier for a media analysis job.</p>", "StartMediaAnalysisJobResponse$JobId": "<p>Identifier for the created job.</p>" } }, "MediaAnalysisJobName": { "base": null, "refs": { "GetMediaAnalysisJobResponse$JobName": "<p>The name of the media analysis job.</p>", "MediaAnalysisJobDescription$JobName": "<p>The name of a media analysis job.</p>", "StartMediaAnalysisJobRequest$JobName": "<p>The name of the job. Does not have to be unique.</p>" } }, "MediaAnalysisJobStatus": { "base": null, "refs": { "GetMediaAnalysisJobResponse$Status": "<p>The current status of the media analysis job.</p>", "MediaAnalysisJobDescription$Status": "<p>The status of the media analysis job being retrieved.</p>" } }, "MediaAnalysisManifestSummary": { "base": "<p>Summary that provides statistics on input manifest and errors identified in the input manifest.</p>", "refs": { "GetMediaAnalysisJobResponse$ManifestSummary": "<p>The summary manifest provides statistics on input manifest and errors identified in the input manifest.</p>", "MediaAnalysisJobDescription$ManifestSummary": "<p>Provides statistics on input manifest and errors identified in the input manifest.</p>" } }, "MediaAnalysisModelVersions": { "base": "<p>Object containing information about the model versions of selected features in a given job.</p>", "refs": { "MediaAnalysisResults$ModelVersions": "<p>Information about the model versions for the features selected in a given job.</p>" } }, "MediaAnalysisOperationsConfig": { "base": "<p>Configuration options for a media analysis job. Configuration is operation-specific.</p>", "refs": { "GetMediaAnalysisJobResponse$OperationsConfig": "<p>Operation configurations that were provided during job creation.</p>", "MediaAnalysisJobDescription$OperationsConfig": "<p>Operation configurations that were provided during job creation.</p>", "StartMediaAnalysisJobRequest$OperationsConfig": "<p>Configuration options for the media analysis job to be created.</p>" } }, "MediaAnalysisOutputConfig": { "base": "<p>Output configuration provided in the job creation request.</p>", "refs": { "GetMediaAnalysisJobResponse$OutputConfig": "<p>Output configuration that was provided in the creation request.</p>", "MediaAnalysisJobDescription$OutputConfig": "<p>Output configuration that was provided in the creation request.</p>", "StartMediaAnalysisJobRequest$OutputConfig": "<p>The Amazon S3 bucket location to store the results.</p>" } }, "MediaAnalysisResults": { "base": "<p>Contains the results for a media analysis job created with StartMediaAnalysisJob.</p>", "refs": { "GetMediaAnalysisJobResponse$Results": "<p>Output manifest that contains prediction results.</p>", "MediaAnalysisJobDescription$Results": "<p>Output manifest that contains prediction results.</p>" } }, "MediaAnalysisS3KeyPrefix": { "base": null, "refs": { "MediaAnalysisOutputConfig$S3KeyPrefix": "<p>Specifies the Amazon S3 key prefix that comes after the name of the bucket you have designated for storage.</p>" } }, "MinCoveragePercentage": { "base": null, "refs": { "BlackFrame$MinCoveragePercentage": "<p> The minimum percentage of pixels in a frame that need to have a luminance below the max_black_pixel_value for a frame to be considered a black frame. Luminance is calculated using the BT.709 matrix. </p> <p>The default value is 99, which means at least 99% of all pixels in the frame are black pixels as per the <code>MaxPixelThreshold</code> set. You can reduce this value to allow more noise on the black frame.</p>" } }, "ModerationLabel": { "base": "<p>Provides information about a single type of inappropriate, unwanted, or offensive content found in an image or video. Each type of moderated content has a label within a hierarchical taxonomy. For more information, see Content moderation in the Amazon Rekognition Developer Guide.</p>", "refs": { "ContentModerationDetection$ModerationLabel": "<p>The content moderation label detected by in the stored video.</p>", "ModerationLabels$member": null } }, "ModerationLabels": { "base": null, "refs": { "DetectModerationLabelsResponse$ModerationLabels": "<p>Array of detected Moderation labels. For video operations, this includes the time, in milliseconds from the start of the video, they were detected.</p>" } }, "MouthOpen": { "base": "<p>Indicates whether or not the mouth on the face is open, and the confidence level in the determination.</p>", "refs": { "FaceDetail$MouthOpen": "<p>Indicates whether or not the mouth on the face is open, and the confidence level in the determination.</p>" } }, "Mustache": { "base": "<p>Indicates whether or not the face has a mustache, and the confidence level in the determination.</p>", "refs": { "FaceDetail$Mustache": "<p>Indicates whether or not the face has a mustache, and the confidence level in the determination.</p>" } }, "NotificationChannel": { "base": "<p>The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the completion status of a video analysis operation. For more information, see <a href=\"https://docs.aws.amazon.com/rekognition/latest/dg/api-video.html\">Calling Amazon Rekognition Video operations</a>. Note that the Amazon SNS topic must have a topic name that begins with <i>AmazonRekognition</i> if you are using the AmazonRekognitionServiceRole permissions policy to access the topic. For more information, see <a href=\"https://docs.aws.amazon.com/rekognition/latest/dg/api-video-roles.html#api-video-roles-all-topics\">Giving access to multiple Amazon SNS topics</a>.</p>", "refs": { "StartCelebrityRecognitionRequest$NotificationChannel": "<p>The Amazon SNS topic ARN that you want Amazon Rekognition Video to publish the completion status of the celebrity recognition analysis to. The Amazon SNS topic must have a topic name that begins with <i>AmazonRekognition</i> if you are using the AmazonRekognitionServiceRole permissions policy.</p>", "StartContentModerationRequest$NotificationChannel": "<p>The Amazon SNS topic ARN that you want Amazon Rekognition Video to publish the completion status of the content analysis to. The Amazon SNS topic must have a topic name that begins with <i>AmazonRekognition</i> if you are using the AmazonRekognitionServiceRole permissions policy to access the topic.</p>", "StartFaceDetectionRequest$NotificationChannel": "<p>The ARN of the Amazon SNS topic to which you want Amazon Rekognition Video to publish the completion status of the face detection operation. The Amazon SNS topic must have a topic name that begins with <i>AmazonRekognition</i> if you are using the AmazonRekognitionServiceRole permissions policy.</p>", "StartFaceSearchRequest$NotificationChannel": "<p>The ARN of the Amazon SNS topic to which you want Amazon Rekognition Video to publish the completion status of the search. The Amazon SNS topic must have a topic name that begins with <i>AmazonRekognition</i> if you are using the AmazonRekognitionServiceRole permissions policy to access the topic.</p>", "StartLabelDetectionRequest$NotificationChannel": "<p>The Amazon SNS topic ARN you want Amazon Rekognition Video to publish the completion status of the label detection operation to. The Amazon SNS topic must have a topic name that begins with <i>AmazonRekognition</i> if you are using the AmazonRekognitionServiceRole permissions policy.</p>", "StartPersonTrackingRequest$NotificationChannel": "<p>The Amazon SNS topic ARN you want Amazon Rekognition Video to publish the completion status of the people detection operation to. The Amazon SNS topic must have a topic name that begins with <i>AmazonRekognition</i> if you are using the AmazonRekognitionServiceRole permissions policy.</p>", "StartSegmentDetectionRequest$NotificationChannel": "<p>The ARN of the Amazon SNS topic to which you want Amazon Rekognition Video to publish the completion status of the segment detection operation. Note that the Amazon SNS topic must have a topic name that begins with <i>AmazonRekognition</i> if you are using the AmazonRekognitionServiceRole permissions policy to access the topic.</p>", "StartTextDetectionRequest$NotificationChannel": null } }, "OrientationCorrection": { "base": null, "refs": { "CompareFacesResponse$SourceImageOrientationCorrection": "<p>The value of <code>SourceImageOrientationCorrection</code> is always null.</p> <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p> <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>", "CompareFacesResponse$TargetImageOrientationCorrection": "<p>The value of <code>TargetImageOrientationCorrection</code> is always null.</p> <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p> <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>", "DetectFacesResponse$OrientationCorrection": "<p>The value of <code>OrientationCorrection</code> is always null.</p> <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p> <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>", "DetectLabelsResponse$OrientationCorrection": "<p>The value of <code>OrientationCorrection</code> is always null.</p> <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p> <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>", "IndexFacesResponse$OrientationCorrection": "<p>If your collection is associated with a face detection model that's later than version 3.0, the value of <code>OrientationCorrection</code> is always null and no orientation information is returned.</p> <p>If your collection is associated with a face detection model that's version 3.0 or earlier, the following applies:</p> <ul> <li> <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction - the bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata. The value of <code>OrientationCorrection</code> is null.</p> </li> <li> <p>If the image doesn't contain orientation information in its Exif metadata, Amazon Rekognition returns an estimated orientation (ROTATE_0, ROTATE_90, ROTATE_180, ROTATE_270). Amazon Rekognition doesn’t perform image correction for images. The bounding box coordinates aren't translated and represent the object locations before the image is rotated.</p> </li> </ul> <p>Bounding box information is returned in the <code>FaceRecords</code> array. You can get the version of the face detection model by calling <a>DescribeCollection</a>. </p>", "RecognizeCelebritiesResponse$OrientationCorrection": "<note> <p>Support for estimating image orientation using the the OrientationCorrection field has ceased as of August 2021. Any returned values for this field included in an API response will always be NULL.</p> </note> <p>The orientation of the input image (counterclockwise direction). If your application displays the image, you can use this value to correct the orientation. The bounding box coordinates returned in <code>CelebrityFaces</code> and <code>UnrecognizedFaces</code> represent face locations before the image orientation is corrected. </p> <note> <p>If the input image is in .jpeg format, it might contain exchangeable image (Exif) metadata that includes the image's orientation. If so, and the Exif metadata for the input image populates the orientation field, the value of <code>OrientationCorrection</code> is null. The <code>CelebrityFaces</code> and <code>UnrecognizedFaces</code> bounding box coordinates represent face locations after Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata. </p> </note>" } }, "OutputConfig": { "base": "<p>The S3 bucket and folder location where training output is placed.</p>", "refs": { "CopyProjectVersionRequest$OutputConfig": "<p>The S3 bucket and folder location where the training output for the source model version is placed.</p>", "CreateProjectVersionRequest$OutputConfig": "<p>The Amazon S3 bucket location to store the results of training. The bucket can be any S3 bucket in your AWS account. You need <code>s3:PutObject</code> permission on the bucket. </p>", "ProjectVersionDescription$OutputConfig": "<p>The location where training results are saved.</p>" } }, "PageSize": { "base": null, "refs": { "ListCollectionsRequest$MaxResults": "<p>Maximum number of collection IDs to return. </p>", "ListFacesRequest$MaxResults": "<p>Maximum number of faces to return.</p>" } }, "PaginationToken": { "base": null, "refs": { "GetCelebrityRecognitionRequest$NextToken": "<p>If the previous response was incomplete (because there is more recognized celebrities to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of celebrities. </p>", "GetCelebrityRecognitionResponse$NextToken": "<p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of celebrities.</p>", "GetContentModerationRequest$NextToken": "<p>If the previous response was incomplete (because there is more data to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of content moderation labels.</p>", "GetContentModerationResponse$NextToken": "<p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of content moderation labels. </p>", "GetFaceDetectionRequest$NextToken": "<p>If the previous response was incomplete (because there are more faces to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of faces.</p>", "GetFaceDetectionResponse$NextToken": "<p>If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces. </p>", "GetFaceSearchRequest$NextToken": "<p>If the previous response was incomplete (because there is more search results to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of search results. </p>", "GetFaceSearchResponse$NextToken": "<p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of search results. </p>", "GetLabelDetectionRequest$NextToken": "<p>If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of labels. </p>", "GetLabelDetectionResponse$NextToken": "<p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of labels.</p>", "GetPersonTrackingRequest$NextToken": "<p>If the previous response was incomplete (because there are more persons to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of persons. </p>", "GetPersonTrackingResponse$NextToken": "<p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of persons. </p>", "GetSegmentDetectionRequest$NextToken": "<p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of text.</p>", "GetSegmentDetectionResponse$NextToken": "<p>If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of text.</p>", "GetTextDetectionRequest$NextToken": "<p>If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of text.</p>", "GetTextDetectionResponse$NextToken": "<p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of text.</p>", "ListCollectionsRequest$NextToken": "<p>Pagination token from the previous response.</p>", "ListCollectionsResponse$NextToken": "<p>If the result is truncated, the response provides a <code>NextToken</code> that you can use in the subsequent request to fetch the next set of collection IDs.</p>", "ListFacesRequest$NextToken": "<p>If the previous response was incomplete (because there is more data to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of faces.</p>", "ListStreamProcessorsRequest$NextToken": "<p>If the previous response was incomplete (because there are more stream processors to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of stream processors. </p>", "ListStreamProcessorsResponse$NextToken": "<p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of stream processors. </p>", "ListUsersRequest$NextToken": "<p>Pagingation token to receive the next set of UsersID.</p>", "ListUsersResponse$NextToken": "<p>A pagination token to be used with the subsequent request if the response is truncated.</p>" } }, "Parent": { "base": "<p>A parent label for a label. A label can have 0, 1, or more parents. </p>", "refs": { "Parents$member": null } }, "Parents": { "base": null, "refs": { "Label$Parents": "<p>The parent labels for a label. The response includes all ancestor labels.</p>" } }, "Percent": { "base": null, "refs": { "AssociateFacesRequest$UserMatchThreshold": "<p>An optional value specifying the minimum confidence in the UserID match to return. The default value is 75.</p>", "Beard$Confidence": "<p>Level of confidence in the determination.</p>", "Celebrity$MatchConfidence": "<p>The confidence, in percentage, that Amazon Rekognition has that the recognized face is the celebrity.</p>", "CelebrityDetail$Confidence": "<p>The confidence, in percentage, that Amazon Rekognition has that the recognized face is the celebrity. </p>", "CompareFacesMatch$Similarity": "<p>Level of confidence that the faces match.</p>", "CompareFacesRequest$SimilarityThreshold": "<p>The minimum level of confidence in the face matches that a match must meet to be included in the <code>FaceMatches</code> array.</p>", "ComparedFace$Confidence": "<p>Level of confidence that what the bounding box contains is a face.</p>", "ComparedSourceImageFace$Confidence": "<p>Confidence level that the selected bounding box contains a face.</p>", "ConnectedHomeSettings$MinConfidence": "<p> The minimum confidence required to label an object in the video. </p>", "ConnectedHomeSettingsForUpdate$MinConfidence": "<p> The minimum confidence required to label an object in the video. </p>", "ContentType$Confidence": "<p>The confidence level of the label given</p>", "CoversBodyPart$Confidence": "<p>The confidence that Amazon Rekognition has in the value of <code>Value</code>.</p>", "CustomLabel$Confidence": "<p>The confidence that the model has in the detection of the custom label. The range is 0-100. A higher value indicates a higher confidence.</p>", "CustomizationFeatureContentModerationConfig$ConfidenceThreshold": "<p>The confidence level you plan to use to identify if unsafe content is present during inference.</p>", "DetectCustomLabelsRequest$MinConfidence": "<p>Specifies the minimum confidence level for the labels to return. <code>DetectCustomLabels</code> doesn't return any labels with a confidence value that's lower than this specified value. If you specify a value of 0, <code>DetectCustomLabels</code> returns all labels, regardless of the assumed threshold applied to each label. If you don't specify a value for <code>MinConfidence</code>, <code>DetectCustomLabels</code> returns labels based on the assumed threshold of each label.</p>", "DetectLabelsRequest$MinConfidence": "<p>Specifies the minimum confidence level for the labels to return. Amazon Rekognition doesn't return any labels with confidence lower than this specified value.</p> <p>If <code>MinConfidence</code> is not specified, the operation returns labels with a confidence values greater than or equal to 55 percent. Only valid when GENERAL_LABELS is specified as a feature type in the Feature input parameter.</p>", "DetectModerationLabelsRequest$MinConfidence": "<p>Specifies the minimum confidence level for the labels to return. Amazon Rekognition doesn't return any labels with a confidence level lower than this specified value.</p> <p>If you don't specify <code>MinConfidence</code>, the operation returns labels with confidence values greater than or equal to 50 percent.</p>", "DetectionFilter$MinConfidence": "<p>Sets the confidence of word detection. Words with detection confidence below this will be excluded from the result. Values should be between 0 and 100. The default MinConfidence is 80.</p>", "DominantColor$PixelPercent": "<p>The percentage of image pixels that have a given dominant color.</p>", "Emotion$Confidence": "<p>Level of confidence in the determination.</p>", "EquipmentDetection$Confidence": "<p>The confidence that Amazon Rekognition has that the bounding box (<code>BoundingBox</code>) contains an item of PPE.</p>", "EyeDirection$Confidence": "<p>The confidence that the service has in its predicted eye direction.</p>", "EyeOpen$Confidence": "<p>Level of confidence in the determination.</p>", "Eyeglasses$Confidence": "<p>Level of confidence in the determination.</p>", "Face$Confidence": "<p>Confidence level that the bounding box contains a face (and not a different object such as a tree).</p>", "FaceDetail$Confidence": "<p>Confidence level that the bounding box contains a face (and not a different object such as a tree). Default attribute.</p>", "FaceMatch$Similarity": "<p>Confidence in the match of this face with the input face.</p>", "FaceOccluded$Confidence": "<p>The confidence that the service has detected the presence of a face occlusion.</p>", "FaceSearchSettings$FaceMatchThreshold": "<p>Minimum face match confidence score that must be met to return a result for a recognized face. The default is 80. 0 is the lowest confidence. 100 is the highest confidence. Values between 0 and 100 are accepted, and values lower than 80 are set to 80.</p>", "Gender$Confidence": "<p>Level of confidence in the prediction.</p>", "GetFaceLivenessSessionResultsResponse$Confidence": "<p>Probabalistic confidence score for if the person in the given video was live, represented as a float value between 0 to 100.</p>", "Instance$Confidence": "<p>The confidence that Amazon Rekognition has in the accuracy of the bounding box.</p>", "Label$Confidence": "<p>Level of confidence.</p>", "MediaAnalysisDetectModerationLabelsConfig$MinConfidence": "<p>Specifies the minimum confidence level for the moderation labels to return. Amazon Rekognition doesn't return any labels with a confidence level lower than this specified value. </p>", "ModerationLabel$Confidence": "<p>Specifies the confidence that Amazon Rekognition has that the label has been correctly identified.</p> <p>If you don't specify the <code>MinConfidence</code> parameter in the call to <code>DetectModerationLabels</code>, the operation returns labels with a confidence value greater than or equal to 50 percent.</p>", "MouthOpen$Confidence": "<p>Level of confidence in the determination.</p>", "Mustache$Confidence": "<p>Level of confidence in the determination.</p>", "ProtectiveEquipmentBodyPart$Confidence": "<p>The confidence that Amazon Rekognition has in the detection accuracy of the detected body part. </p>", "ProtectiveEquipmentPerson$Confidence": "<p>The confidence that Amazon Rekognition has that the bounding box contains a person.</p>", "ProtectiveEquipmentSummarizationAttributes$MinConfidence": "<p>The minimum confidence level for which you want summary information. The confidence level applies to person detection, body part detection, equipment detection, and body part coverage. Amazon Rekognition doesn't return summary information with a confidence than this specified value. There isn't a default value.</p> <p>Specify a <code>MinConfidence</code> value that is between 50-100% as <code>DetectProtectiveEquipment</code> returns predictions only where the detection confidence is between 50% - 100%. If you specify a value that is less than 50%, the results are the same specifying a value of 50%.</p> <p> </p>", "SearchFacesByImageRequest$FaceMatchThreshold": "<p>(Optional) Specifies the minimum confidence in the face match to return. For example, don't return any matches where confidence in matches is less than 70%. The default value is 80%.</p>", "SearchFacesByImageResponse$SearchedFaceConfidence": "<p>The level of confidence that the <code>searchedFaceBoundingBox</code>, contains a face.</p>", "SearchFacesRequest$FaceMatchThreshold": "<p>Optional value specifying the minimum confidence in the face match to return. For example, don't return any matches where confidence in matches is less than 70%. The default value is 80%. </p>", "SearchUsersByImageRequest$UserMatchThreshold": "<p>Specifies the minimum confidence in the UserID match to return. Default value is 80.</p>", "SearchUsersRequest$UserMatchThreshold": "<p>Optional value that specifies the minimum confidence in the matched UserID to return. Default value of 80.</p>", "Smile$Confidence": "<p>Level of confidence in the determination.</p>", "StartContentModerationRequest$MinConfidence": "<p>Specifies the minimum confidence that Amazon Rekognition must have in order to return a moderated content label. Confidence represents how certain Amazon Rekognition is that the moderated content is correctly identified. 0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition doesn't return any moderated content labels with a confidence level lower than this specified value. If you don't specify <code>MinConfidence</code>, <code>GetContentModeration</code> returns labels with confidence values greater than or equal to 50 percent.</p>", "StartFaceSearchRequest$FaceMatchThreshold": "<p>The minimum confidence in the person match to return. For example, don't return any matches where confidence in matches is less than 70%. The default value is 80%.</p>", "StartLabelDetectionRequest$MinConfidence": "<p>Specifies the minimum confidence that Amazon Rekognition Video must have in order to return a detected label. Confidence represents how certain Amazon Rekognition is that a label is correctly identified.0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition Video doesn't return any labels with a confidence level lower than this specified value.</p> <p>If you don't specify <code>MinConfidence</code>, the operation returns labels and bounding boxes (if detected) with confidence values greater than or equal to 50 percent.</p>", "Sunglasses$Confidence": "<p>Level of confidence in the determination.</p>", "TextDetection$Confidence": "<p>The confidence that Amazon Rekognition has in the accuracy of the detected text and the accuracy of the geometry points around the detected text.</p>", "UnsuccessfulFaceAssociation$Confidence": "<p>Match confidence with the UserID, provides information regarding if a face association was unsuccessful because it didn't meet UserMatchThreshold.</p>", "UserMatch$Similarity": "<p> Describes the UserID metadata.</p>" } }, "PersonDetail": { "base": "<p>Details about a person detected in a video analysis request.</p>", "refs": { "PersonDetection$Person": "<p>Details about a person whose path was tracked in a video.</p>", "PersonMatch$Person": "<p>Information about the matched person.</p>" } }, "PersonDetection": { "base": "<p>Details and path tracking information for a single time a person's path is tracked in a video. Amazon Rekognition operations that track people's paths return an array of <code>PersonDetection</code> objects with elements for each time a person's path is tracked in a video. </p> <p>For more information, see GetPersonTracking in the Amazon Rekognition Developer Guide. </p>", "refs": { "PersonDetections$member": null } }, "PersonDetections": { "base": null, "refs": { "GetPersonTrackingResponse$Persons": "<p>An array of the persons detected in the video and the time(s) their path was tracked throughout the video. An array element will exist for each time a person's path is tracked. </p>" } }, "PersonIndex": { "base": null, "refs": { "PersonDetail$Index": "<p>Identifier for the person detected person within a video. Use to keep track of the person throughout the video. The identifier is not stored by Amazon Rekognition.</p>" } }, "PersonMatch": { "base": "<p>Information about a person whose face matches a face(s) in an Amazon Rekognition collection. Includes information about the faces in the Amazon Rekognition collection (<a>FaceMatch</a>), information about the person (<a>PersonDetail</a>), and the time stamp for when the person was detected in a video. An array of <code>PersonMatch</code> objects is returned by <a>GetFaceSearch</a>. </p>", "refs": { "PersonMatches$member": null } }, "PersonMatches": { "base": null, "refs": { "GetFaceSearchResponse$Persons": "<p>An array of persons, <a>PersonMatch</a>, in the video whose face(s) match the face(s) in an Amazon Rekognition collection. It also includes time information for when persons are matched in the video. You specify the input collection in an initial call to <code>StartFaceSearch</code>. Each <code>Persons</code> element includes a time the person was matched, face match details (<code>FaceMatches</code>) for matching faces in the collection, and person information (<code>Person</code>) for the matched person. </p>" } }, "PersonTrackingSortBy": { "base": null, "refs": { "GetPersonTrackingRequest$SortBy": "<p>Sort to use for elements in the <code>Persons</code> array. Use <code>TIMESTAMP</code> to sort array elements by the time persons are detected. Use <code>INDEX</code> to sort by the tracked persons. If you sort by <code>INDEX</code>, the array elements for each person are sorted by detection confidence. The default sort is by <code>TIMESTAMP</code>.</p>" } }, "Point": { "base": "<p>The X and Y coordinates of a point on an image or video frame. The X and Y values are ratios of the overall image size or video resolution. For example, if an input image is 700x200 and the values are X=0.5 and Y=0.25, then the point is at the (350,50) pixel coordinate on the image.</p> <p>An array of <code>Point</code> objects makes up a <code>Polygon</code>. A <code>Polygon</code> is returned by <a>DetectText</a> and by <a>DetectCustomLabels</a> <code>Polygon</code> represents a fine-grained polygon around a detected item. For more information, see Geometry in the Amazon Rekognition Developer Guide. </p>", "refs": { "Polygon$member": null } }, "Polygon": { "base": null, "refs": { "Geometry$Polygon": "<p>Within the bounding box, a fine-grained polygon around the detected item.</p>", "RegionOfInterest$Polygon": "<p> Specifies a shape made up of up to 10 <code>Point</code> objects to define a region of interest. </p>" } }, "Pose": { "base": "<p>Indicates the pose of the face as determined by its pitch, roll, and yaw.</p>", "refs": { "ComparedFace$Pose": "<p>Indicates the pose of the face as determined by its pitch, roll, and yaw.</p>", "FaceDetail$Pose": "<p>Indicates the pose of the face as determined by its pitch, roll, and yaw. Default attribute.</p>" } }, "ProjectArn": { "base": null, "refs": { "CopyProjectVersionRequest$SourceProjectArn": "<p>The ARN of the source project in the trusting AWS account.</p>", "CopyProjectVersionRequest$DestinationProjectArn": "<p>The ARN of the project in the trusted AWS account that you want to copy the model version to. </p>", "CreateDatasetRequest$ProjectArn": "<p> The ARN of the Amazon Rekognition Custom Labels project to which you want to asssign the dataset. </p>", "CreateProjectResponse$ProjectArn": "<p>The Amazon Resource Name (ARN) of the new project. You can use the ARN to configure IAM access to the project. </p>", "CreateProjectVersionRequest$ProjectArn": "<p>The ARN of the Amazon Rekognition project that will manage the project version you want to train.</p>", "DeleteProjectPolicyRequest$ProjectArn": "<p>The Amazon Resource Name (ARN) of the project that the project policy you want to delete is attached to.</p>", "DeleteProjectRequest$ProjectArn": "<p>The Amazon Resource Name (ARN) of the project that you want to delete.</p>", "DescribeProjectVersionsRequest$ProjectArn": "<p>The Amazon Resource Name (ARN) of the project that contains the model/adapter you want to describe.</p>", "ListProjectPoliciesRequest$ProjectArn": "<p>The ARN of the project for which you want to list the project policies.</p>", "ProjectDescription$ProjectArn": "<p>The Amazon Resource Name (ARN) of the project.</p>", "ProjectPolicy$ProjectArn": "<p>The Amazon Resource Name (ARN) of the project to which the project policy is attached.</p>", "PutProjectPolicyRequest$ProjectArn": "<p>The Amazon Resource Name (ARN) of the project that the project policy is attached to.</p>" } }, "ProjectAutoUpdate": { "base": null, "refs": { "CreateProjectRequest$AutoUpdate": "<p>Specifies whether automatic retraining should be attempted for the versions of the project. Automatic retraining is done as a best effort. Required argument for Content Moderation. Applicable only to adapters.</p>", "ProjectDescription$AutoUpdate": "<p>Indicates whether automatic retraining will be attempted for the versions of the project. Applies only to adapters. </p>" } }, "ProjectDescription": { "base": "<p>A description of an Amazon Rekognition Custom Labels project. For more information, see <a>DescribeProjects</a>.</p>", "refs": { "ProjectDescriptions$member": null } }, "ProjectDescriptions": { "base": null, "refs": { "DescribeProjectsResponse$ProjectDescriptions": "<p>A list of project descriptions. The list is sorted by the date and time the projects are created.</p>" } }, "ProjectName": { "base": null, "refs": { "CreateProjectRequest$ProjectName": "<p>The name of the project to create.</p>", "ProjectNames$member": null } }, "ProjectNames": { "base": null, "refs": { "DescribeProjectsRequest$ProjectNames": "<p>A list of the projects that you want Rekognition to describe. If you don't specify a value, the response includes descriptions for all the projects in your AWS account.</p>" } }, "ProjectPolicies": { "base": null, "refs": { "ListProjectPoliciesResponse$ProjectPolicies": "<p>A list of project policies attached to the project.</p>" } }, "ProjectPolicy": { "base": "<p>Describes a project policy in the response from <a>ListProjectPolicies</a>. </p> <p> </p>", "refs": { "ProjectPolicies$member": null } }, "ProjectPolicyDocument": { "base": null, "refs": { "ProjectPolicy$PolicyDocument": "<p>The JSON document for the project policy.</p>", "PutProjectPolicyRequest$PolicyDocument": "<p>A resource policy to add to the model. The policy is a JSON structure that contains one or more statements that define the policy. The policy must follow the IAM syntax. For more information about the contents of a JSON policy document, see <a href=\"https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies.html\">IAM JSON policy reference</a>. </p>" } }, "ProjectPolicyName": { "base": null, "refs": { "DeleteProjectPolicyRequest$PolicyName": "<p>The name of the policy that you want to delete.</p>", "ProjectPolicy$PolicyName": "<p>The name of the project policy.</p>", "PutProjectPolicyRequest$PolicyName": "<p>A name for the policy.</p>" } }, "ProjectPolicyRevisionId": { "base": null, "refs": { "DeleteProjectPolicyRequest$PolicyRevisionId": "<p>The ID of the project policy revision that you want to delete.</p>", "ProjectPolicy$PolicyRevisionId": "<p>The revision ID of the project policy.</p>", "PutProjectPolicyRequest$PolicyRevisionId": "<p>The revision ID for the Project Policy. Each time you modify a policy, Amazon Rekognition Custom Labels generates and assigns a new <code>PolicyRevisionId</code> and then deletes the previous version of the policy.</p>", "PutProjectPolicyResponse$PolicyRevisionId": "<p>The ID of the project policy.</p>" } }, "ProjectStatus": { "base": null, "refs": { "DeleteProjectResponse$Status": "<p>The current status of the delete project operation.</p>", "ProjectDescription$Status": "<p>The current status of the project.</p>" } }, "ProjectVersionArn": { "base": null, "refs": { "CopyProjectVersionRequest$SourceProjectVersionArn": "<p>The ARN of the model version in the source project that you want to copy to a destination project.</p>", "CopyProjectVersionResponse$ProjectVersionArn": "<p>The ARN of the copied model version in the destination project. </p>", "CreateProjectVersionResponse$ProjectVersionArn": "<p>The ARN of the model or the project version that was created. Use <code>DescribeProjectVersion</code> to get the current status of the training operation.</p>", "DeleteProjectVersionRequest$ProjectVersionArn": "<p>The Amazon Resource Name (ARN) of the project version that you want to delete.</p>", "DetectCustomLabelsRequest$ProjectVersionArn": "<p>The ARN of the model version that you want to use. Only models associated with Custom Labels projects accepted by the operation. If a provided ARN refers to a model version associated with a project for a different feature type, then an InvalidParameterException is returned.</p>", "ProjectVersionDescription$ProjectVersionArn": "<p>The Amazon Resource Name (ARN) of the project version. </p>", "ProjectVersionDescription$SourceProjectVersionArn": "<p>If the model version was copied from a different project, <code>SourceProjectVersionArn</code> contains the ARN of the source model version. </p>", "StartProjectVersionRequest$ProjectVersionArn": "<p>The Amazon Resource Name(ARN) of the model version that you want to start.</p>", "StopProjectVersionRequest$ProjectVersionArn": "<p>The Amazon Resource Name (ARN) of the model version that you want to stop.</p> <p>This operation requires permissions to perform the <code>rekognition:StopProjectVersion</code> action.</p>" } }, "ProjectVersionDescription": { "base": "<p>A description of a version of a Amazon Rekognition project version.</p>", "refs": { "ProjectVersionDescriptions$member": null } }, "ProjectVersionDescriptions": { "base": null, "refs": { "DescribeProjectVersionsResponse$ProjectVersionDescriptions": "<p>A list of project version descriptions. The list is sorted by the creation date and time of the project versions, latest to earliest.</p>" } }, "ProjectVersionId": { "base": null, "refs": { "DetectModerationLabelsRequest$ProjectVersion": "<p>Identifier for the custom adapter. Expects the ProjectVersionArn as a value. Use the CreateProject or CreateProjectVersion APIs to create a custom adapter.</p>", "DetectModerationLabelsResponse$ProjectVersion": "<p>Identifier of the custom adapter that was used during inference. If during inference the adapter was EXPIRED, then the parameter will not be returned, indicating that a base moderation detection project version was used.</p>", "MediaAnalysisDetectModerationLabelsConfig$ProjectVersion": "<p>Specifies the custom moderation model to be used during the label detection job. If not provided the pre-trained model is used.</p>" } }, "ProjectVersionStatus": { "base": null, "refs": { "DeleteProjectVersionResponse$Status": "<p>The status of the deletion operation.</p>", "ProjectVersionDescription$Status": "<p>The current status of the model version.</p>", "StartProjectVersionResponse$Status": "<p>The current running status of the model. </p>", "StopProjectVersionResponse$Status": "<p>The current status of the stop operation. </p>" } }, "ProjectVersionsPageSize": { "base": null, "refs": { "DescribeProjectVersionsRequest$MaxResults": "<p>The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100. </p>" } }, "ProjectsPageSize": { "base": null, "refs": { "DescribeProjectsRequest$MaxResults": "<p>The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100. </p>" } }, "ProtectiveEquipmentBodyPart": { "base": "<p>Information about a body part detected by <a>DetectProtectiveEquipment</a> that contains PPE. An array of <code>ProtectiveEquipmentBodyPart</code> objects is returned for each person detected by <code>DetectProtectiveEquipment</code>. </p>", "refs": { "BodyParts$member": null } }, "ProtectiveEquipmentPerson": { "base": "<p>A person detected by a call to <a>DetectProtectiveEquipment</a>. The API returns all persons detected in the input image in an array of <code>ProtectiveEquipmentPerson</code> objects.</p>", "refs": { "ProtectiveEquipmentPersons$member": null } }, "ProtectiveEquipmentPersonIds": { "base": null, "refs": { "ProtectiveEquipmentSummary$PersonsWithRequiredEquipment": "<p>An array of IDs for persons who are wearing detected personal protective equipment. </p>", "ProtectiveEquipmentSummary$PersonsWithoutRequiredEquipment": "<p>An array of IDs for persons who are not wearing all of the types of PPE specified in the <code>RequiredEquipmentTypes</code> field of the detected personal protective equipment. </p>", "ProtectiveEquipmentSummary$PersonsIndeterminate": "<p>An array of IDs for persons where it was not possible to determine if they are wearing personal protective equipment. </p>" } }, "ProtectiveEquipmentPersons": { "base": null, "refs": { "DetectProtectiveEquipmentResponse$Persons": "<p>An array of persons detected in the image (including persons not wearing PPE).</p>" } }, "ProtectiveEquipmentSummarizationAttributes": { "base": "<p>Specifies summary attributes to return from a call to <a>DetectProtectiveEquipment</a>. You can specify which types of PPE to summarize. You can also specify a minimum confidence value for detections. Summary information is returned in the <code>Summary</code> (<a>ProtectiveEquipmentSummary</a>) field of the response from <code>DetectProtectiveEquipment</code>. The summary includes which persons in an image were detected wearing the requested types of person protective equipment (PPE), which persons were detected as not wearing PPE, and the persons in which a determination could not be made. For more information, see <a>ProtectiveEquipmentSummary</a>.</p>", "refs": { "DetectProtectiveEquipmentRequest$SummarizationAttributes": "<p>An array of PPE types that you want to summarize.</p>" } }, "ProtectiveEquipmentSummary": { "base": "<p>Summary information for required items of personal protective equipment (PPE) detected on persons by a call to <a>DetectProtectiveEquipment</a>. You specify the required type of PPE in the <code>SummarizationAttributes</code> (<a>ProtectiveEquipmentSummarizationAttributes</a>) input parameter. The summary includes which persons were detected wearing the required personal protective equipment (<code>PersonsWithRequiredEquipment</code>), which persons were detected as not wearing the required PPE (<code>PersonsWithoutRequiredEquipment</code>), and the persons in which a determination could not be made (<code>PersonsIndeterminate</code>).</p> <p>To get a total for each category, use the size of the field array. For example, to find out how many people were detected as wearing the specified PPE, use the size of the <code>PersonsWithRequiredEquipment</code> array. If you want to find out more about a person, such as the location (<a>BoundingBox</a>) of the person on the image, use the person ID in each array element. Each person ID matches the ID field of a <a>ProtectiveEquipmentPerson</a> object returned in the <code>Persons</code> array by <code>DetectProtectiveEquipment</code>.</p>", "refs": { "DetectProtectiveEquipmentResponse$Summary": "<p>Summary information for the types of PPE specified in the <code>SummarizationAttributes</code> input parameter.</p>" } }, "ProtectiveEquipmentType": { "base": null, "refs": { "EquipmentDetection$Type": "<p>The type of detected PPE.</p>", "ProtectiveEquipmentTypes$member": null } }, "ProtectiveEquipmentTypes": { "base": null, "refs": { "ProtectiveEquipmentSummarizationAttributes$RequiredEquipmentTypes": "<p>An array of personal protective equipment types for which you want summary information. If a person is detected wearing a required requipment type, the person's ID is added to the <code>PersonsWithRequiredEquipment</code> array field returned in <a>ProtectiveEquipmentSummary</a> by <code>DetectProtectiveEquipment</code>. </p>" } }, "ProvisionedThroughputExceededException": { "base": "<p>The number of requests exceeded your throughput limit. If you want to increase this limit, contact Amazon Rekognition.</p>", "refs": { } }, "PutProjectPolicyRequest": { "base": null, "refs": { } }, "PutProjectPolicyResponse": { "base": null, "refs": { } }, "QualityFilter": { "base": null, "refs": { "CompareFacesRequest$QualityFilter": "<p>A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren't compared. If you specify <code>AUTO</code>, Amazon Rekognition chooses the quality bar. If you specify <code>LOW</code>, <code>MEDIUM</code>, or <code>HIGH</code>, filtering removes all faces that don’t meet the chosen quality bar. The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that's misidentified as a face, a face that's too blurry, or a face with a pose that's too extreme to use. If you specify <code>NONE</code>, no filtering is performed. The default value is <code>NONE</code>. </p> <p>To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.</p>", "IndexFacesRequest$QualityFilter": "<p>A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren't indexed. If you specify <code>AUTO</code>, Amazon Rekognition chooses the quality bar. If you specify <code>LOW</code>, <code>MEDIUM</code>, or <code>HIGH</code>, filtering removes all faces that don’t meet the chosen quality bar. The default value is <code>AUTO</code>. The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that's misidentified as a face, a face that's too blurry, or a face with a pose that's too extreme to use. If you specify <code>NONE</code>, no filtering is performed. </p> <p>To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.</p>", "SearchFacesByImageRequest$QualityFilter": "<p>A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren't searched for in the collection. If you specify <code>AUTO</code>, Amazon Rekognition chooses the quality bar. If you specify <code>LOW</code>, <code>MEDIUM</code>, or <code>HIGH</code>, filtering removes all faces that don’t meet the chosen quality bar. The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that's misidentified as a face, a face that's too blurry, or a face with a pose that's too extreme to use. If you specify <code>NONE</code>, no filtering is performed. The default value is <code>NONE</code>. </p> <p>To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.</p>", "SearchUsersByImageRequest$QualityFilter": "<p>A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren't searched for in the collection. The default value is NONE.</p>" } }, "QueryString": { "base": null, "refs": { "ListDatasetEntriesRequest$SourceRefContains": "<p>If specified, <code>ListDatasetEntries</code> only returns JSON Lines where the value of <code>SourceRefContains</code> is part of the <code>source-ref</code> field. The <code>source-ref</code> field contains the Amazon S3 location of the image. You can use <code>SouceRefContains</code> for tasks such as getting the JSON Line for a single image, or gettting JSON Lines for all images within a specific folder.</p>" } }, "Reason": { "base": null, "refs": { "Reasons$member": null } }, "Reasons": { "base": null, "refs": { "UnindexedFace$Reasons": "<p>An array of reasons that specify why a face wasn't indexed. </p> <ul> <li> <p>EXTREME_POSE - The face is at a pose that can't be detected. For example, the head is turned too far away from the camera.</p> </li> <li> <p>EXCEEDS_MAX_FACES - The number of faces detected is already higher than that specified by the <code>MaxFaces</code> input parameter for <code>IndexFaces</code>.</p> </li> <li> <p>LOW_BRIGHTNESS - The image is too dark.</p> </li> <li> <p>LOW_SHARPNESS - The image is too blurry.</p> </li> <li> <p>LOW_CONFIDENCE - The face was detected with a low confidence.</p> </li> <li> <p>SMALL_BOUNDING_BOX - The bounding box around the face is too small.</p> </li> </ul>" } }, "RecognizeCelebritiesRequest": { "base": null, "refs": { } }, "RecognizeCelebritiesResponse": { "base": null, "refs": { } }, "RegionOfInterest": { "base": "<p>Specifies a location within the frame that Rekognition checks for objects of interest such as text, labels, or faces. It uses a <code>BoundingBox</code> or <code>Polygon</code> to set a region of the screen.</p> <p>A word, face, or label is included in the region if it is more than half in that region. If there is more than one region, the word, face, or label is compared with all regions of the screen. Any object of interest that is more than half in a region is kept in the results.</p>", "refs": { "RegionsOfInterest$member": null } }, "RegionsOfInterest": { "base": null, "refs": { "CreateStreamProcessorRequest$RegionsOfInterest": "<p> Specifies locations in the frames where Amazon Rekognition checks for objects or people. You can specify up to 10 regions of interest, and each region has either a polygon or a bounding box. This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor. </p>", "DescribeStreamProcessorResponse$RegionsOfInterest": "<p> Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors. </p>", "DetectTextFilters$RegionsOfInterest": "<p> A Filter focusing on a certain area of the image. Uses a <code>BoundingBox</code> object to set the region of the image.</p>", "StartTextDetectionFilters$RegionsOfInterest": "<p>Filter focusing on a certain area of the frame. Uses a <code>BoundingBox</code> object to set the region of the screen.</p>", "UpdateStreamProcessorRequest$RegionsOfInterestForUpdate": "<p> Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors. </p>" } }, "RekognitionUniqueId": { "base": null, "refs": { "Celebrity$Id": "<p>A unique identifier for the celebrity. </p>", "CelebrityDetail$Id": "<p>The unique identifier for the celebrity. </p>", "GetCelebrityInfoRequest$Id": "<p>The ID for the celebrity. You get the celebrity ID from a call to the <a>RecognizeCelebrities</a> operation, which recognizes celebrities in an image. </p>" } }, "ResourceAlreadyExistsException": { "base": "<p>A resource with the specified ID already exists.</p>", "refs": { } }, "ResourceArn": { "base": null, "refs": { "ListTagsForResourceRequest$ResourceArn": "<p> Amazon Resource Name (ARN) of the model, collection, or stream processor that contains the tags that you want a list of. </p>", "TagResourceRequest$ResourceArn": "<p> Amazon Resource Name (ARN) of the model, collection, or stream processor that you want to assign the tags to. </p>", "UntagResourceRequest$ResourceArn": "<p> Amazon Resource Name (ARN) of the model, collection, or stream processor that you want to remove the tags from. </p>" } }, "ResourceInUseException": { "base": "<p>The specified resource is already being used.</p>", "refs": { } }, "ResourceNotFoundException": { "base": "<p>The resource specified in the request cannot be found.</p>", "refs": { } }, "ResourceNotReadyException": { "base": "<p>The requested resource isn't ready. For example, this exception occurs when you call <code>DetectCustomLabels</code> with a model version that isn't deployed. </p>", "refs": { } }, "RoleArn": { "base": null, "refs": { "CreateStreamProcessorRequest$RoleArn": "<p>The Amazon Resource Number (ARN) of the IAM role that allows access to the stream processor. The IAM role provides Rekognition read permissions for a Kinesis stream. It also provides write permissions to an Amazon S3 bucket and Amazon Simple Notification Service topic for a label detection stream processor. This is required for both face search and label detection stream processors.</p>", "DescribeStreamProcessorResponse$RoleArn": "<p>ARN of the IAM role that allows access to the stream processor.</p>", "NotificationChannel$RoleArn": "<p>The ARN of an IAM role that gives Amazon Rekognition publishing permissions to the Amazon SNS topic. </p>" } }, "S3Bucket": { "base": null, "refs": { "LivenessOutputConfig$S3Bucket": "<p>The path to an AWS Amazon S3 bucket used to store Face Liveness session results.</p>", "MediaAnalysisOutputConfig$S3Bucket": "<p>Specifies the Amazon S3 bucket to contain the output of the media analysis job.</p>", "OutputConfig$S3Bucket": "<p>The S3 bucket where training output is placed.</p>", "S3Destination$Bucket": "<p> The name of the Amazon S3 bucket you want to associate with the streaming video project. You must be the owner of the Amazon S3 bucket. </p>", "S3Object$Bucket": "<p>Name of the S3 bucket.</p>" } }, "S3Destination": { "base": "<p> The Amazon S3 bucket location to which Amazon Rekognition publishes the detailed inference results of a video analysis operation. These results include the name of the stream processor resource, the session ID of the stream processing session, and labeled timestamps and bounding boxes for detected labels. </p>", "refs": { "StreamProcessorOutput$S3Destination": "<p> The Amazon S3 bucket location to which Amazon Rekognition publishes the detailed inference results of a video analysis operation. </p>" } }, "S3KeyPrefix": { "base": null, "refs": { "OutputConfig$S3KeyPrefix": "<p>The prefix applied to the training output files. </p>", "S3Destination$KeyPrefix": "<p> The prefix value of the location within the bucket that you want the information to be published to. For more information, see <a href=\"https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-prefixes.html\">Using prefixes</a>. </p>" } }, "S3Object": { "base": "<p>Provides the S3 bucket name and object name.</p> <p>The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.</p> <p>For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide. </p>", "refs": { "AuditImage$S3Object": null, "GroundTruthManifest$S3Object": null, "Image$S3Object": "<p>Identifies an S3 object as the image source.</p>", "MediaAnalysisInput$S3Object": null, "MediaAnalysisManifestSummary$S3Object": null, "MediaAnalysisResults$S3Object": null, "Summary$S3Object": null, "Video$S3Object": "<p>The Amazon S3 bucket name and file name for the video.</p>" } }, "S3ObjectName": { "base": null, "refs": { "S3Object$Name": "<p>S3 object key name.</p>" } }, "S3ObjectVersion": { "base": null, "refs": { "S3Object$Version": "<p>If the bucket is versioning enabled, you can specify the object version. </p>" } }, "SNSTopicArn": { "base": null, "refs": { "NotificationChannel$SNSTopicArn": "<p>The Amazon SNS topic to which Amazon Rekognition posts the completion status.</p>", "StreamProcessorNotificationChannel$SNSTopicArn": "<p> The Amazon Resource Number (ARN) of the Amazon Amazon Simple Notification Service topic to which Amazon Rekognition posts the completion status. </p>" } }, "SearchFacesByImageRequest": { "base": null, "refs": { } }, "SearchFacesByImageResponse": { "base": null, "refs": { } }, "SearchFacesRequest": { "base": null, "refs": { } }, "SearchFacesResponse": { "base": null, "refs": { } }, "SearchUsersByImageRequest": { "base": null, "refs": { } }, "SearchUsersByImageResponse": { "base": null, "refs": { } }, "SearchUsersRequest": { "base": null, "refs": { } }, "SearchUsersResponse": { "base": null, "refs": { } }, "SearchedFace": { "base": "<p>Provides face metadata such as FaceId, BoundingBox, Confidence of the input face used for search.</p>", "refs": { "SearchUsersResponse$SearchedFace": "<p>Contains the ID of a face that was used to search for matches in a collection.</p>" } }, "SearchedFaceDetails": { "base": "<p>Contains data regarding the input face used for a search.</p>", "refs": { "SearchUsersByImageResponse$SearchedFace": "<p>A list of FaceDetail objects containing the BoundingBox for the largest face in image, as well as the confidence in the bounding box, that was searched for matches. If no valid face is detected in the image the response will contain no SearchedFace object.</p>" } }, "SearchedUser": { "base": "<p>Contains metadata about a User searched for within a collection.</p>", "refs": { "SearchUsersResponse$SearchedUser": "<p>Contains the ID of the UserID that was used to search for matches in a collection.</p>" } }, "SegmentConfidence": { "base": null, "refs": { "ShotSegment$Confidence": "<p>The confidence that Amazon Rekognition Video has in the accuracy of the detected segment.</p>", "StartShotDetectionFilter$MinSegmentConfidence": "<p>Specifies the minimum confidence that Amazon Rekognition Video must have in order to return a detected segment. Confidence represents how certain Amazon Rekognition is that a segment is correctly identified. 0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition Video doesn't return any segments with a confidence level lower than this specified value.</p> <p>If you don't specify <code>MinSegmentConfidence</code>, the <code>GetSegmentDetection</code> returns segments with confidence values greater than or equal to 50 percent.</p>", "StartTechnicalCueDetectionFilter$MinSegmentConfidence": "<p>Specifies the minimum confidence that Amazon Rekognition Video must have in order to return a detected segment. Confidence represents how certain Amazon Rekognition is that a segment is correctly identified. 0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition Video doesn't return any segments with a confidence level lower than this specified value.</p> <p>If you don't specify <code>MinSegmentConfidence</code>, <code>GetSegmentDetection</code> returns segments with confidence values greater than or equal to 50 percent.</p>", "TechnicalCueSegment$Confidence": "<p>The confidence that Amazon Rekognition Video has in the accuracy of the detected segment.</p>" } }, "SegmentDetection": { "base": "<p>A technical cue or shot detection segment detected in a video. An array of <code>SegmentDetection</code> objects containing all segments detected in a stored video is returned by <a>GetSegmentDetection</a>. </p>", "refs": { "SegmentDetections$member": null } }, "SegmentDetections": { "base": null, "refs": { "GetSegmentDetectionResponse$Segments": "<p>An array of segments detected in a video. The array is sorted by the segment types (TECHNICAL_CUE or SHOT) specified in the <code>SegmentTypes</code> input parameter of <code>StartSegmentDetection</code>. Within each segment type the array is sorted by timestamp values.</p>" } }, "SegmentType": { "base": null, "refs": { "SegmentDetection$Type": "<p>The type of the segment. Valid values are <code>TECHNICAL_CUE</code> and <code>SHOT</code>.</p>", "SegmentTypeInfo$Type": "<p>The type of a segment (technical cue or shot detection).</p>", "SegmentTypes$member": null } }, "SegmentTypeInfo": { "base": "<p>Information about the type of a segment requested in a call to <a>StartSegmentDetection</a>. An array of <code>SegmentTypeInfo</code> objects is returned by the response from <a>GetSegmentDetection</a>.</p>", "refs": { "SegmentTypesInfo$member": null } }, "SegmentTypes": { "base": null, "refs": { "StartSegmentDetectionRequest$SegmentTypes": "<p>An array of segment types to detect in the video. Valid values are TECHNICAL_CUE and SHOT.</p>" } }, "SegmentTypesInfo": { "base": null, "refs": { "GetSegmentDetectionResponse$SelectedSegmentTypes": "<p>An array containing the segment types requested in the call to <code>StartSegmentDetection</code>. </p>" } }, "ServiceQuotaExceededException": { "base": "<p/> <p>The size of the collection exceeds the allowed limit. For more information, see Guidelines and quotas in Amazon Rekognition in the Amazon Rekognition Developer Guide. </p>", "refs": { } }, "SessionNotFoundException": { "base": "<p>Occurs when a given sessionId is not found.</p>", "refs": { } }, "ShotSegment": { "base": "<p>Information about a shot detection segment detected in a video. For more information, see <a>SegmentDetection</a>.</p>", "refs": { "SegmentDetection$ShotSegment": "<p>If the segment is a shot detection, contains information about the shot detection.</p>" } }, "Smile": { "base": "<p>Indicates whether or not the face is smiling, and the confidence level in the determination.</p>", "refs": { "ComparedFace$Smile": "<p> Indicates whether or not the face is smiling, and the confidence level in the determination. </p>", "FaceDetail$Smile": "<p>Indicates whether or not the face is smiling, and the confidence level in the determination.</p>" } }, "StartCelebrityRecognitionRequest": { "base": null, "refs": { } }, "StartCelebrityRecognitionResponse": { "base": null, "refs": { } }, "StartContentModerationRequest": { "base": null, "refs": { } }, "StartContentModerationResponse": { "base": null, "refs": { } }, "StartFaceDetectionRequest": { "base": null, "refs": { } }, "StartFaceDetectionResponse": { "base": null, "refs": { } }, "StartFaceSearchRequest": { "base": null, "refs": { } }, "StartFaceSearchResponse": { "base": null, "refs": { } }, "StartLabelDetectionRequest": { "base": null, "refs": { } }, "StartLabelDetectionResponse": { "base": null, "refs": { } }, "StartMediaAnalysisJobRequest": { "base": null, "refs": { } }, "StartMediaAnalysisJobResponse": { "base": null, "refs": { } }, "StartPersonTrackingRequest": { "base": null, "refs": { } }, "StartPersonTrackingResponse": { "base": null, "refs": { } }, "StartProjectVersionRequest": { "base": null, "refs": { } }, "StartProjectVersionResponse": { "base": null, "refs": { } }, "StartSegmentDetectionFilters": { "base": "<p>Filters applied to the technical cue or shot detection segments. For more information, see <a>StartSegmentDetection</a>. </p>", "refs": { "StartSegmentDetectionRequest$Filters": "<p>Filters for technical cue or shot detection.</p>" } }, "StartSegmentDetectionRequest": { "base": null, "refs": { } }, "StartSegmentDetectionResponse": { "base": null, "refs": { } }, "StartShotDetectionFilter": { "base": "<p>Filters for the shot detection segments returned by <code>GetSegmentDetection</code>. For more information, see <a>StartSegmentDetectionFilters</a>.</p>", "refs": { "StartSegmentDetectionFilters$ShotFilter": "<p>Filters that are specific to shot detections.</p>" } }, "StartStreamProcessorRequest": { "base": null, "refs": { } }, "StartStreamProcessorResponse": { "base": null, "refs": { } }, "StartStreamProcessorSessionId": { "base": null, "refs": { "StartStreamProcessorResponse$SessionId": "<p> A unique identifier for the stream processing session. </p>" } }, "StartTechnicalCueDetectionFilter": { "base": "<p>Filters for the technical segments returned by <a>GetSegmentDetection</a>. For more information, see <a>StartSegmentDetectionFilters</a>.</p>", "refs": { "StartSegmentDetectionFilters$TechnicalCueFilter": "<p>Filters that are specific to technical cues.</p>" } }, "StartTextDetectionFilters": { "base": "<p>Set of optional parameters that let you set the criteria text must meet to be included in your response. <code>WordFilter</code> looks at a word's height, width and minimum confidence. <code>RegionOfInterest</code> lets you set a specific region of the screen to look for text in.</p>", "refs": { "StartTextDetectionRequest$Filters": "<p>Optional parameters that let you set criteria the text must meet to be included in your response.</p>" } }, "StartTextDetectionRequest": { "base": null, "refs": { } }, "StartTextDetectionResponse": { "base": null, "refs": { } }, "StatusMessage": { "base": null, "refs": { "DatasetDescription$StatusMessage": "<p> The status message for the dataset. </p>", "DatasetMetadata$StatusMessage": "<p> The status message for the dataset. </p>", "GetCelebrityRecognitionResponse$StatusMessage": "<p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>", "GetContentModerationResponse$StatusMessage": "<p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>", "GetFaceDetectionResponse$StatusMessage": "<p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>", "GetFaceSearchResponse$StatusMessage": "<p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>", "GetLabelDetectionResponse$StatusMessage": "<p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>", "GetPersonTrackingResponse$StatusMessage": "<p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>", "GetSegmentDetectionResponse$StatusMessage": "<p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>", "GetTextDetectionResponse$StatusMessage": "<p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>", "ProjectVersionDescription$StatusMessage": "<p>A descriptive message for an error or warning that occurred.</p>" } }, "StopProjectVersionRequest": { "base": null, "refs": { } }, "StopProjectVersionResponse": { "base": null, "refs": { } }, "StopStreamProcessorRequest": { "base": null, "refs": { } }, "StopStreamProcessorResponse": { "base": null, "refs": { } }, "StreamProcessingStartSelector": { "base": "<p>This is a required parameter for label detection stream processors and should not be used to start a face search stream processor.</p>", "refs": { "StartStreamProcessorRequest$StartSelector": "<p> Specifies the starting point in the Kinesis stream to start processing. You can use the producer timestamp or the fragment number. If you use the producer timestamp, you must put the time in milliseconds. For more information about fragment numbers, see <a href=\"https://docs.aws.amazon.com/kinesisvideostreams/latest/dg/API_reader_Fragment.html\">Fragment</a>. </p> <p>This is a required parameter for label detection stream processors and should not be used to start a face search stream processor.</p>" } }, "StreamProcessingStopSelector": { "base": "<p> Specifies when to stop processing the stream. You can specify a maximum amount of time to process the video. </p>", "refs": { "StartStreamProcessorRequest$StopSelector": "<p> Specifies when to stop processing the stream. You can specify a maximum amount of time to process the video. </p> <p>This is a required parameter for label detection stream processors and should not be used to start a face search stream processor.</p>" } }, "StreamProcessor": { "base": "<p>An object that recognizes faces or labels in a streaming video. An Amazon Rekognition stream processor is created by a call to <a>CreateStreamProcessor</a>. The request parameters for <code>CreateStreamProcessor</code> describe the Kinesis video stream source for the streaming video, face recognition parameters, and where to stream the analysis resullts. </p>", "refs": { "StreamProcessorList$member": null } }, "StreamProcessorArn": { "base": null, "refs": { "CreateStreamProcessorResponse$StreamProcessorArn": "<p>Amazon Resource Number for the newly created stream processor.</p>", "DescribeStreamProcessorResponse$StreamProcessorArn": "<p>ARN of the stream processor.</p>" } }, "StreamProcessorDataSharingPreference": { "base": "<p> Allows you to opt in or opt out to share data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams. </p>", "refs": { "CreateStreamProcessorRequest$DataSharingPreference": "<p> Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams. </p>", "DescribeStreamProcessorResponse$DataSharingPreference": "<p> Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams. </p>", "UpdateStreamProcessorRequest$DataSharingPreferenceForUpdate": "<p> Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams. </p>" } }, "StreamProcessorInput": { "base": "<p>Information about the source streaming video. </p>", "refs": { "CreateStreamProcessorRequest$Input": "<p>Kinesis video stream stream that provides the source streaming video. If you are using the AWS CLI, the parameter name is <code>StreamProcessorInput</code>. This is required for both face search and label detection stream processors.</p>", "DescribeStreamProcessorResponse$Input": "<p>Kinesis video stream that provides the source streaming video.</p>" } }, "StreamProcessorList": { "base": null, "refs": { "ListStreamProcessorsResponse$StreamProcessors": "<p>List of stream processors that you have created.</p>" } }, "StreamProcessorName": { "base": null, "refs": { "CreateStreamProcessorRequest$Name": "<p>An identifier you assign to the stream processor. You can use <code>Name</code> to manage the stream processor. For example, you can get the current status of the stream processor by calling <a>DescribeStreamProcessor</a>. <code>Name</code> is idempotent. This is required for both face search and label detection stream processors. </p>", "DeleteStreamProcessorRequest$Name": "<p>The name of the stream processor you want to delete.</p>", "DescribeStreamProcessorRequest$Name": "<p>Name of the stream processor for which you want information.</p>", "DescribeStreamProcessorResponse$Name": "<p>Name of the stream processor. </p>", "StartStreamProcessorRequest$Name": "<p>The name of the stream processor to start processing.</p>", "StopStreamProcessorRequest$Name": "<p>The name of a stream processor created by <a>CreateStreamProcessor</a>.</p>", "StreamProcessor$Name": "<p>Name of the Amazon Rekognition stream processor. </p>", "UpdateStreamProcessorRequest$Name": "<p> Name of the stream processor that you want to update. </p>" } }, "StreamProcessorNotificationChannel": { "base": "<p>The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.</p> <p>Amazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications, one for a person at second 2 and one for a pet at second 4.</p> <p>Amazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.</p>", "refs": { "CreateStreamProcessorRequest$NotificationChannel": null, "DescribeStreamProcessorResponse$NotificationChannel": null } }, "StreamProcessorOutput": { "base": "<p>Information about the Amazon Kinesis Data Streams stream to which a Amazon Rekognition Video stream processor streams the results of a video analysis. For more information, see CreateStreamProcessor in the Amazon Rekognition Developer Guide.</p>", "refs": { "CreateStreamProcessorRequest$Output": "<p>Kinesis data stream stream or Amazon S3 bucket location to which Amazon Rekognition Video puts the analysis results. If you are using the AWS CLI, the parameter name is <code>StreamProcessorOutput</code>. This must be a <a>S3Destination</a> of an Amazon S3 bucket that you own for a label detection stream processor or a Kinesis data stream ARN for a face search stream processor.</p>", "DescribeStreamProcessorResponse$Output": "<p>Kinesis data stream to which Amazon Rekognition Video puts the analysis results.</p>" } }, "StreamProcessorParameterToDelete": { "base": null, "refs": { "StreamProcessorParametersToDelete$member": null } }, "StreamProcessorParametersToDelete": { "base": null, "refs": { "UpdateStreamProcessorRequest$ParametersToDelete": "<p> A list of parameters you want to delete from the stream processor. </p>" } }, "StreamProcessorSettings": { "base": "<p>Input parameters used in a streaming video analyzed by a Amazon Rekognition stream processor. You can use <code>FaceSearch</code> to recognize faces in a streaming video, or you can use <code>ConnectedHome</code> to detect labels. </p>", "refs": { "CreateStreamProcessorRequest$Settings": "<p>Input parameters used in a streaming video analyzed by a stream processor. You can use <code>FaceSearch</code> to recognize faces in a streaming video, or you can use <code>ConnectedHome</code> to detect labels.</p>", "DescribeStreamProcessorResponse$Settings": "<p>Input parameters used in a streaming video analyzed by a stream processor. You can use <code>FaceSearch</code> to recognize faces in a streaming video, or you can use <code>ConnectedHome</code> to detect labels.</p>" } }, "StreamProcessorSettingsForUpdate": { "base": "<p> The stream processor settings that you want to update. <code>ConnectedHome</code> settings can be updated to detect different labels with a different minimum confidence. </p>", "refs": { "UpdateStreamProcessorRequest$SettingsForUpdate": "<p> The stream processor settings that you want to update. Label detection settings can be updated to detect different labels with a different minimum confidence. </p>" } }, "StreamProcessorStatus": { "base": null, "refs": { "DescribeStreamProcessorResponse$Status": "<p>Current status of the stream processor.</p>", "StreamProcessor$Status": "<p>Current status of the Amazon Rekognition stream processor.</p>" } }, "String": { "base": null, "refs": { "AudioMetadata$Codec": "<p>The audio codec used to encode or decode the audio stream. </p>", "Celebrity$Name": "<p>The name of the celebrity.</p>", "CelebrityDetail$Name": "<p>The name of the celebrity.</p>", "ContentType$Name": "<p>The name of the label</p>", "CreateCollectionResponse$CollectionArn": "<p>Amazon Resource Name (ARN) of the collection. You can use this to manage permissions on your resources. </p>", "CreateCollectionResponse$FaceModelVersion": "<p>Version number of the face detection model associated with the collection you are creating.</p>", "CustomLabel$Name": "<p>The name of the custom label.</p>", "DescribeCollectionResponse$FaceModelVersion": "<p>The version of the face model that's used by the collection for face detection.</p> <p>For more information, see Model versioning in the Amazon Rekognition Developer Guide.</p>", "DescribeCollectionResponse$CollectionARN": "<p>The Amazon Resource Name (ARN) of the collection.</p>", "DescribeStreamProcessorResponse$StatusMessage": "<p>Detailed status message about the stream processor.</p>", "DetectLabelsResponse$LabelModelVersion": "<p>Version number of the label detection model that was used to detect labels.</p>", "DetectModerationLabelsResponse$ModerationModelVersion": "<p>Version number of the base moderation detection model that was used to detect unsafe content.</p>", "DetectProtectiveEquipmentResponse$ProtectiveEquipmentModelVersion": "<p>The version number of the PPE detection model used to detect PPE in the image.</p>", "DetectTextResponse$TextModelVersion": "<p>The model version used to detect text.</p>", "DominantColor$HexCode": "<p>The Hex code equivalent of the RGB values for a dominant color.</p>", "DominantColor$CSSColor": "<p>The CSS color name of a dominant color.</p>", "DominantColor$SimplifiedColor": "<p>One of 12 simplified color names applied to a dominant color.</p>", "FaceModelVersionList$member": null, "GetCelebrityInfoResponse$Name": "<p>The name of the celebrity.</p>", "GetContentModerationResponse$ModerationModelVersion": "<p>Version number of the moderation detection model that was used to detect inappropriate, unwanted, or offensive content.</p>", "GetLabelDetectionResponse$LabelModelVersion": "<p>Version number of the label detection model that was used to detect labels.</p>", "GetTextDetectionResponse$TextModelVersion": "<p>Version number of the text detection model that was used to detect text.</p>", "HumanLoopQuotaExceededException$ResourceType": "<p>The resource type.</p>", "HumanLoopQuotaExceededException$QuotaCode": "<p>The quota code.</p>", "HumanLoopQuotaExceededException$ServiceCode": "<p>The service code.</p>", "IndexFacesResponse$FaceModelVersion": "<p>The version number of the face detection model that's associated with the input collection (<code>CollectionId</code>).</p>", "Label$Name": "<p>The name (label) of the object or scene.</p>", "LabelAlias$Name": "<p>The name of an alias for a given label.</p>", "LabelCategory$Name": "<p>The name of a category that applies to a given label.</p>", "ListFacesResponse$NextToken": "<p>If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces.</p>", "ListFacesResponse$FaceModelVersion": "<p>Version number of the face detection model associated with the input collection (<code>CollectionId</code>).</p>", "MediaAnalysisJobFailureDetails$Message": "<p>Human readable error message.</p>", "MediaAnalysisModelVersions$Moderation": "<p>The Moderation base model version.</p>", "ModerationLabel$Name": "<p>The label name for the type of unsafe content detected in the image.</p>", "ModerationLabel$ParentName": "<p>The name for the parent label. Labels at the top level of the hierarchy have the parent label <code>\"\"</code>.</p>", "Parent$Name": "<p>The name of the parent label.</p>", "ProjectVersionDescription$BaseModelVersion": "<p>The base detection model version used to create the project version.</p>", "SearchFacesByImageResponse$FaceModelVersion": "<p>Version number of the face detection model associated with the input collection (<code>CollectionId</code>).</p>", "SearchFacesResponse$FaceModelVersion": "<p>Version number of the face detection model associated with the input collection (<code>CollectionId</code>).</p>", "SearchUsersByImageResponse$FaceModelVersion": "<p>Version number of the face detection model associated with the input collection CollectionId.</p>", "SearchUsersResponse$FaceModelVersion": "<p>Version number of the face detection model associated with the input CollectionId.</p>", "SegmentTypeInfo$ModelVersion": "<p>The version of the model used to detect segments.</p>", "TextDetection$DetectedText": "<p>The word or line of text recognized by Amazon Rekognition. </p>", "VideoMetadata$Codec": "<p>Type of compression used in the analyzed video. </p>", "VideoMetadata$Format": "<p>Format of the analyzed video. Possible values are MP4, MOV and AVI. </p>" } }, "Summary": { "base": "<p>The S3 bucket that contains the training summary. The training summary includes aggregated evaluation metrics for the entire testing dataset and metrics for each individual label. </p> <p>You get the training summary S3 bucket location by calling <a>DescribeProjectVersions</a>. </p>", "refs": { "EvaluationResult$Summary": "<p>The S3 bucket that contains the training summary.</p>" } }, "Sunglasses": { "base": "<p>Indicates whether or not the face is wearing sunglasses, and the confidence level in the determination.</p>", "refs": { "FaceDetail$Sunglasses": "<p>Indicates whether or not the face is wearing sunglasses, and the confidence level in the determination.</p>" } }, "TagKey": { "base": null, "refs": { "TagKeyList$member": null, "TagMap$key": null } }, "TagKeyList": { "base": null, "refs": { "UntagResourceRequest$TagKeys": "<p> A list of the tags that you want to remove. </p>" } }, "TagMap": { "base": null, "refs": { "CopyProjectVersionRequest$Tags": "<p>The key-value tags to assign to the model version. </p>", "CreateCollectionRequest$Tags": "<p> A set of tags (key-value pairs) that you want to attach to the collection. </p>", "CreateDatasetRequest$Tags": "<p>A set of tags (key-value pairs) that you want to attach to the dataset.</p>", "CreateProjectRequest$Tags": "<p>A set of tags (key-value pairs) that you want to attach to the project.</p>", "CreateProjectVersionRequest$Tags": "<p> A set of tags (key-value pairs) that you want to attach to the project version. </p>", "CreateStreamProcessorRequest$Tags": "<p> A set of tags (key-value pairs) that you want to attach to the stream processor. </p>", "ListTagsForResourceResponse$Tags": "<p> A list of key-value tags assigned to the resource. </p>", "TagResourceRequest$Tags": "<p> The key-value tags to assign to the resource. </p>" } }, "TagResourceRequest": { "base": null, "refs": { } }, "TagResourceResponse": { "base": null, "refs": { } }, "TagValue": { "base": null, "refs": { "TagMap$value": null } }, "TechnicalCueSegment": { "base": "<p>Information about a technical cue segment. For more information, see <a>SegmentDetection</a>.</p>", "refs": { "SegmentDetection$TechnicalCueSegment": "<p>If the segment is a technical cue, contains information about the technical cue.</p>" } }, "TechnicalCueType": { "base": null, "refs": { "TechnicalCueSegment$Type": "<p>The type of the technical cue.</p>" } }, "TestingData": { "base": "<p>The dataset used for testing. Optionally, if <code>AutoCreate</code> is set, Amazon Rekognition uses the training dataset to create a test dataset with a temporary split of the training dataset. </p>", "refs": { "CreateProjectVersionRequest$TestingData": "<p>Specifies an external manifest that the service uses to test the project version. If you specify <code>TestingData</code> you must also specify <code>TrainingData</code>. The project must not have any associated datasets.</p>", "TestingDataResult$Input": "<p>The testing dataset that was supplied for training.</p>", "TestingDataResult$Output": "<p>The subset of the dataset that was actually tested. Some images (assets) might not be tested due to file formatting and other issues. </p>" } }, "TestingDataResult": { "base": "<p>Sagemaker Groundtruth format manifest files for the input, output and validation datasets that are used and created during testing.</p>", "refs": { "ProjectVersionDescription$TestingDataResult": "<p>Contains information about the testing results.</p>" } }, "TextDetection": { "base": "<p>Information about a word or line of text detected by <a>DetectText</a>.</p> <p>The <code>DetectedText</code> field contains the text that Amazon Rekognition detected in the image. </p> <p>Every word and line has an identifier (<code>Id</code>). Each word belongs to a line and has a parent identifier (<code>ParentId</code>) that identifies the line of text in which the word appears. The word <code>Id</code> is also an index for the word within a line of words. </p> <p>For more information, see Detecting text in the Amazon Rekognition Developer Guide.</p>", "refs": { "TextDetectionList$member": null, "TextDetectionResult$TextDetection": "<p>Details about text detected in a video.</p>" } }, "TextDetectionList": { "base": null, "refs": { "DetectTextResponse$TextDetections": "<p>An array of text that was detected in the input image.</p>" } }, "TextDetectionResult": { "base": "<p>Information about text detected in a video. Incudes the detected text, the time in milliseconds from the start of the video that the text was detected, and where it was detected on the screen.</p>", "refs": { "TextDetectionResults$member": null } }, "TextDetectionResults": { "base": null, "refs": { "GetTextDetectionResponse$TextDetections": "<p>An array of text detected in the video. Each element contains the detected text, the time in milliseconds from the start of the video that the text was detected, and where it was detected on the screen.</p>" } }, "TextTypes": { "base": null, "refs": { "TextDetection$Type": "<p>The type of text that was detected.</p>" } }, "ThrottlingException": { "base": "<p>Amazon Rekognition is temporarily unable to process the request. Try your call again.</p>", "refs": { } }, "Timecode": { "base": null, "refs": { "SegmentDetection$StartTimecodeSMPTE": "<p>The frame-accurate SMPTE timecode, from the start of a video, for the start of a detected segment. <code>StartTimecode</code> is in <i>HH:MM:SS:fr</i> format (and <i>;fr</i> for drop frame-rates). </p>", "SegmentDetection$EndTimecodeSMPTE": "<p>The frame-accurate SMPTE timecode, from the start of a video, for the end of a detected segment. <code>EndTimecode</code> is in <i>HH:MM:SS:fr</i> format (and <i>;fr</i> for drop frame-rates).</p>", "SegmentDetection$DurationSMPTE": "<p>The duration of the timecode for the detected segment in SMPTE format.</p>" } }, "Timestamp": { "base": null, "refs": { "CelebrityRecognition$Timestamp": "<p>The time, in milliseconds from the start of the video, that the celebrity was recognized. Note that <code>Timestamp</code> is not guaranteed to be accurate to the individual frame where the celebrity first appears.</p>", "ContentModerationDetection$Timestamp": "<p>Time, in milliseconds from the beginning of the video, that the content moderation label was detected. Note that <code>Timestamp</code> is not guaranteed to be accurate to the individual frame where the moderated content first appears.</p>", "FaceDetection$Timestamp": "<p>Time, in milliseconds from the start of the video, that the face was detected. Note that <code>Timestamp</code> is not guaranteed to be accurate to the individual frame where the face first appears.</p>", "LabelDetection$Timestamp": "<p>Time, in milliseconds from the start of the video, that the label was detected. Note that <code>Timestamp</code> is not guaranteed to be accurate to the individual frame where the label first appears.</p>", "PersonDetection$Timestamp": "<p>The time, in milliseconds from the start of the video, that the person's path was tracked. Note that <code>Timestamp</code> is not guaranteed to be accurate to the individual frame where the person's path first appears.</p>", "PersonMatch$Timestamp": "<p>The time, in milliseconds from the beginning of the video, that the person was matched in the video.</p>", "SegmentDetection$StartTimestampMillis": "<p>The start time of the detected segment in milliseconds from the start of the video. This value is rounded down. For example, if the actual timestamp is 100.6667 milliseconds, Amazon Rekognition Video returns a value of 100 millis.</p>", "SegmentDetection$EndTimestampMillis": "<p>The end time of the detected segment, in milliseconds, from the start of the video. This value is rounded down.</p>", "TextDetectionResult$Timestamp": "<p>The time, in milliseconds from the start of the video, that the text was detected. Note that <code>Timestamp</code> is not guaranteed to be accurate to the individual frame where the text first appears.</p>" } }, "TrainingData": { "base": "<p>The dataset used for training.</p>", "refs": { "CreateProjectVersionRequest$TrainingData": "<p>Specifies an external manifest that the services uses to train the project version. If you specify <code>TrainingData</code> you must also specify <code>TestingData</code>. The project must not have any associated datasets. </p>", "TrainingDataResult$Input": "<p>The training data that you supplied.</p>", "TrainingDataResult$Output": "<p>Reference to images (assets) that were actually used during training with trained model predictions.</p>" } }, "TrainingDataResult": { "base": "<p>The data validation manifest created for the training dataset during model training.</p>", "refs": { "ProjectVersionDescription$TrainingDataResult": "<p>Contains information about the training results.</p>" } }, "UInteger": { "base": null, "refs": { "AgeRange$Low": "<p>The lowest estimated age.</p>", "AgeRange$High": "<p>The highest estimated age.</p>", "CreateCollectionResponse$StatusCode": "<p>HTTP status code indicating the result of the operation.</p>", "DatasetLabelStats$EntryCount": "<p> The total number of images that use the label. </p>", "DatasetLabelStats$BoundingBoxCount": "<p> The total number of images that have the label assigned to a bounding box. </p>", "DatasetStats$LabeledEntries": "<p> The total number of images in the dataset that have labels. </p>", "DatasetStats$TotalEntries": "<p> The total number of images in the dataset. </p>", "DatasetStats$TotalLabels": "<p> The total number of labels declared in the dataset. </p>", "DatasetStats$ErrorEntries": "<p> The total number of entries that contain at least one error. </p>", "DeleteCollectionResponse$StatusCode": "<p>HTTP status code that indicates the result of the operation.</p>", "DetectCustomLabelsRequest$MaxResults": "<p>Maximum number of results you want the service to return in the response. The service returns the specified number of highest confidence labels ranked from highest confidence to lowest.</p>", "DetectLabelsRequest$MaxLabels": "<p>Maximum number of labels you want the service to return in the response. The service returns the specified number of highest confidence labels. Only valid when GENERAL_LABELS is specified as a feature type in the Feature input parameter.</p>", "DominantColor$Red": "<p>The Red RGB value for a dominant color.</p>", "DominantColor$Blue": "<p>The Blue RGB value for a dominant color.</p>", "DominantColor$Green": "<p>The Green RGB value for a dominant color.</p>", "ModerationLabel$TaxonomyLevel": "<p>The level of the moderation label with regard to its taxonomy, from 1 to 3.</p>", "ProtectiveEquipmentPerson$Id": "<p>The identifier for the detected person. The identifier is only unique for a single call to <code>DetectProtectiveEquipment</code>.</p>", "ProtectiveEquipmentPersonIds$member": null, "TextDetection$Id": "<p>The identifier for the detected text. The identifier is only unique for a single call to <code>DetectText</code>. </p>", "TextDetection$ParentId": "<p>The Parent identifier for the detected text identified by the value of <code>ID</code>. If the type of detected text is <code>LINE</code>, the value of <code>ParentId</code> is <code>Null</code>. </p>" } }, "ULong": { "base": null, "refs": { "AudioMetadata$DurationMillis": "<p>The duration of the audio stream in milliseconds.</p>", "AudioMetadata$SampleRate": "<p>The sample rate for the audio stream.</p>", "AudioMetadata$NumberOfChannels": "<p>The number of audio channels in the segment.</p>", "ContentModerationDetection$StartTimestampMillis": "<p>The time in milliseconds defining the start of the timeline segment containing a continuously detected moderation label.</p>", "ContentModerationDetection$EndTimestampMillis": "<p> The time in milliseconds defining the end of the timeline segment containing a continuously detected moderation label. </p>", "ContentModerationDetection$DurationMillis": "<p> The time duration of a segment in milliseconds, I.e. time elapsed from StartTimestampMillis to EndTimestampMillis. </p>", "DescribeCollectionResponse$FaceCount": "<p>The number of faces that are indexed into the collection. To index faces into a collection, use <a>IndexFaces</a>.</p>", "DescribeCollectionResponse$UserCount": "<p>The number of UserIDs assigned to the specified colleciton.</p>", "KinesisVideoStreamStartSelector$ProducerTimestamp": "<p> The timestamp from the producer corresponding to the fragment, in milliseconds, expressed in unix time format. </p>", "LabelDetection$StartTimestampMillis": "<p>The time in milliseconds defining the start of the timeline segment containing a continuously detected label.</p>", "LabelDetection$EndTimestampMillis": "<p>The time in milliseconds defining the end of the timeline segment containing a continuously detected label.</p>", "LabelDetection$DurationMillis": "<p>The time duration of a segment in milliseconds, I.e. time elapsed from StartTimestampMillis to EndTimestampMillis.</p>", "ProjectVersionDescription$BillableTrainingTimeInSeconds": "<p>The duration, in seconds, that you were billed for a successful training of the model version. This value is only returned if the model version has been successfully trained.</p>", "SegmentDetection$DurationMillis": "<p>The duration of the detected segment in milliseconds. </p>", "SegmentDetection$StartFrameNumber": "<p> The frame number of the start of a video segment, using a frame index that starts with 0. </p>", "SegmentDetection$EndFrameNumber": "<p> The frame number at the end of a video segment, using a frame index that starts with 0. </p>", "SegmentDetection$DurationFrames": "<p> The duration of a video segment, expressed in frames. </p>", "ShotSegment$Index": "<p>An Identifier for a shot detection segment detected in a video. </p>", "VideoMetadata$DurationMillis": "<p>Length of the video in milliseconds.</p>", "VideoMetadata$FrameHeight": "<p>Vertical pixel dimension of the video.</p>", "VideoMetadata$FrameWidth": "<p>Horizontal pixel dimension of the video.</p>" } }, "UnindexedFace": { "base": "<p>A face that <a>IndexFaces</a> detected, but didn't index. Use the <code>Reasons</code> response attribute to determine why a face wasn't indexed.</p>", "refs": { "UnindexedFaces$member": null } }, "UnindexedFaces": { "base": null, "refs": { "IndexFacesResponse$UnindexedFaces": "<p>An array of faces that were detected in the image but weren't indexed. They weren't indexed because the quality filter identified them as low quality, or the <code>MaxFaces</code> request parameter filtered them out. To use the quality filter, you specify the <code>QualityFilter</code> request parameter.</p>" } }, "UnsearchedFace": { "base": "<p>Face details inferred from the image but not used for search. The response attribute contains reasons for why a face wasn't used for Search. </p>", "refs": { "UnsearchedFacesList$member": null } }, "UnsearchedFaceReason": { "base": null, "refs": { "UnsearchedFaceReasons$member": null } }, "UnsearchedFaceReasons": { "base": null, "refs": { "UnsearchedFace$Reasons": "<p> Reasons why a face wasn't used for Search. </p>" } }, "UnsearchedFacesList": { "base": null, "refs": { "SearchUsersByImageResponse$UnsearchedFaces": "<p>List of UnsearchedFace objects. Contains the face details infered from the specified image but not used for search. Contains reasons that describe why a face wasn't used for Search. </p>" } }, "UnsuccessfulFaceAssociation": { "base": "<p>Contains metadata like FaceId, UserID, and Reasons, for a face that was unsuccessfully associated.</p>", "refs": { "UnsuccessfulFaceAssociationList$member": null } }, "UnsuccessfulFaceAssociationList": { "base": null, "refs": { "AssociateFacesResponse$UnsuccessfulFaceAssociations": "<p>An array of UnsuccessfulAssociation objects containing FaceIDs that are not successfully associated along with the reasons. Returned if the AssociateFaces action is successful.</p>" } }, "UnsuccessfulFaceAssociationReason": { "base": null, "refs": { "UnsuccessfulFaceAssociationReasons$member": null } }, "UnsuccessfulFaceAssociationReasons": { "base": null, "refs": { "UnsuccessfulFaceAssociation$Reasons": "<p> The reason why the association was unsuccessful. </p>" } }, "UnsuccessfulFaceDeletion": { "base": "<p>Contains metadata like FaceId, UserID, and Reasons, for a face that was unsuccessfully deleted.</p>", "refs": { "UnsuccessfulFaceDeletionsList$member": null } }, "UnsuccessfulFaceDeletionReason": { "base": null, "refs": { "UnsuccessfulFaceDeletionReasons$member": null } }, "UnsuccessfulFaceDeletionReasons": { "base": null, "refs": { "UnsuccessfulFaceDeletion$Reasons": "<p>The reason why the deletion was unsuccessful. </p>" } }, "UnsuccessfulFaceDeletionsList": { "base": null, "refs": { "DeleteFacesResponse$UnsuccessfulFaceDeletions": "<p>An array of any faces that weren't deleted.</p>" } }, "UnsuccessfulFaceDisassociation": { "base": "<p>Contains metadata like FaceId, UserID, and Reasons, for a face that was unsuccessfully disassociated.</p>", "refs": { "UnsuccessfulFaceDisassociationList$member": null } }, "UnsuccessfulFaceDisassociationList": { "base": null, "refs": { "DisassociateFacesResponse$UnsuccessfulFaceDisassociations": "<p>An array of UnsuccessfulDisassociation objects containing FaceIds that are not successfully associated, along with the reasons for the failure to associate. Returned if the DisassociateFaces action is successful.</p>" } }, "UnsuccessfulFaceDisassociationReason": { "base": null, "refs": { "UnsuccessfulFaceDisassociationReasons$member": null } }, "UnsuccessfulFaceDisassociationReasons": { "base": null, "refs": { "UnsuccessfulFaceDisassociation$Reasons": "<p>The reason why the deletion was unsuccessful. </p>" } }, "UntagResourceRequest": { "base": null, "refs": { } }, "UntagResourceResponse": { "base": null, "refs": { } }, "UpdateDatasetEntriesRequest": { "base": null, "refs": { } }, "UpdateDatasetEntriesResponse": { "base": null, "refs": { } }, "UpdateStreamProcessorRequest": { "base": null, "refs": { } }, "UpdateStreamProcessorResponse": { "base": null, "refs": { } }, "Url": { "base": null, "refs": { "Urls$member": null } }, "Urls": { "base": null, "refs": { "Celebrity$Urls": "<p>An array of URLs pointing to additional information about the celebrity. If there is no additional information about the celebrity, this list is empty.</p>", "CelebrityDetail$Urls": "<p>An array of URLs pointing to additional celebrity information. </p>", "GetCelebrityInfoResponse$Urls": "<p>An array of URLs pointing to additional celebrity information. </p>" } }, "User": { "base": "<p>Metadata of the user stored in a collection.</p>", "refs": { "UserList$member": null } }, "UserFaceIdList": { "base": null, "refs": { "AssociateFacesRequest$FaceIds": "<p>An array of FaceIDs to associate with the UserID.</p>", "DisassociateFacesRequest$FaceIds": "<p>An array of face IDs to disassociate from the UserID. </p>" } }, "UserId": { "base": null, "refs": { "AssociateFacesRequest$UserId": "<p>The ID for the existing UserID.</p>", "CreateUserRequest$UserId": "<p>ID for the UserID to be created. This ID needs to be unique within the collection.</p>", "DeleteUserRequest$UserId": "<p>ID for the UserID to be deleted. </p>", "DisassociateFacesRequest$UserId": "<p>ID for the existing UserID.</p>", "Face$UserId": "<p>Unique identifier assigned to the user.</p>", "ListFacesRequest$UserId": "<p>An array of user IDs to filter results with when listing faces in a collection.</p>", "MatchedUser$UserId": "<p>A provided ID for the UserID. Unique within the collection.</p>", "SearchUsersRequest$UserId": "<p>ID for the existing User.</p>", "SearchedUser$UserId": "<p> A provided ID for the UserID. Unique within the collection. </p>", "UnsuccessfulFaceAssociation$UserId": "<p>A provided ID for the UserID. Unique within the collection. </p>", "UnsuccessfulFaceDeletion$UserId": "<p> A provided ID for the UserID. Unique within the collection. </p>", "UnsuccessfulFaceDisassociation$UserId": "<p>A provided ID for the UserID. Unique within the collection. </p>", "User$UserId": "<p> A provided ID for the User. Unique within the collection.</p>" } }, "UserList": { "base": null, "refs": { "ListUsersResponse$Users": "<p>List of UsersID associated with the specified collection.</p>" } }, "UserMatch": { "base": "<p>Provides UserID metadata along with the confidence in the match of this UserID with the input face.</p>", "refs": { "UserMatchList$member": null } }, "UserMatchList": { "base": null, "refs": { "SearchUsersByImageResponse$UserMatches": "<p>An array of UserID objects that matched the input face, along with the confidence in the match. The returned structure will be empty if there are no matches. Returned if the SearchUsersByImageResponse action is successful.</p>", "SearchUsersResponse$UserMatches": "<p>An array of UserMatch objects that matched the input face along with the confidence in the match. Array will be empty if there are no matches.</p>" } }, "UserStatus": { "base": null, "refs": { "AssociateFacesResponse$UserStatus": "<p>The status of an update made to a UserID. Reflects if the UserID has been updated for every requested change.</p>", "DisassociateFacesResponse$UserStatus": "<p>The status of an update made to a User. Reflects if the User has been updated for every requested change.</p>", "MatchedUser$UserStatus": "<p>The status of the user matched to a provided FaceID.</p>", "User$UserStatus": "<p> Communicates if the UserID has been updated with latest set of faces to be associated with the UserID. </p>" } }, "ValidationData": { "base": "<p>Contains the Amazon S3 bucket location of the validation data for a model training job. </p> <p>The validation data includes error information for individual JSON Lines in the dataset. For more information, see <i>Debugging a Failed Model Training</i> in the Amazon Rekognition Custom Labels Developer Guide. </p> <p>You get the <code>ValidationData</code> object for the training dataset (<a>TrainingDataResult</a>) and the test dataset (<a>TestingDataResult</a>) by calling <a>DescribeProjectVersions</a>. </p> <p>The assets array contains a single <a>Asset</a> object. The <a>GroundTruthManifest</a> field of the Asset object contains the S3 bucket location of the validation data. </p>", "refs": { "TestingDataResult$Validation": "<p>The location of the data validation manifest. The data validation manifest is created for the test dataset during model training.</p>", "TrainingDataResult$Validation": "<p>A manifest that you supplied for training, with validation results for each line.</p>" } }, "VersionDescription": { "base": null, "refs": { "CreateProjectVersionRequest$VersionDescription": "<p>A description applied to the project version being created.</p>", "ProjectVersionDescription$VersionDescription": "<p>A user-provided description of the project version.</p>" } }, "VersionName": { "base": null, "refs": { "CopyProjectVersionRequest$VersionName": "<p>A name for the version of the model that's copied to the destination project.</p>", "CreateProjectVersionRequest$VersionName": "<p>A name for the version of the project version. This value must be unique.</p>", "VersionNames$member": null } }, "VersionNames": { "base": null, "refs": { "DescribeProjectVersionsRequest$VersionNames": "<p>A list of model or project version names that you want to describe. You can add up to 10 model or project version names to the list. If you don't specify a value, all project version descriptions are returned. A version name is part of a project version ARN. For example, <code>my-model.2020-01-21T09.10.15</code> is the version name in the following ARN. <code>arn:aws:rekognition:us-east-1:123456789012:project/getting-started/version/<i>my-model.2020-01-21T09.10.15</i>/1234567890123</code>.</p>" } }, "Video": { "base": "<p>Video file stored in an Amazon S3 bucket. Amazon Rekognition video start operations such as <a>StartLabelDetection</a> use <code>Video</code> to specify a video for analysis. The supported file formats are .mp4, .mov and .avi.</p>", "refs": { "GetCelebrityRecognitionResponse$Video": null, "GetContentModerationResponse$Video": null, "GetFaceDetectionResponse$Video": null, "GetFaceSearchResponse$Video": null, "GetLabelDetectionResponse$Video": null, "GetPersonTrackingResponse$Video": null, "GetSegmentDetectionResponse$Video": null, "GetTextDetectionResponse$Video": null, "StartCelebrityRecognitionRequest$Video": "<p>The video in which you want to recognize celebrities. The video must be stored in an Amazon S3 bucket.</p>", "StartContentModerationRequest$Video": "<p>The video in which you want to detect inappropriate, unwanted, or offensive content. The video must be stored in an Amazon S3 bucket.</p>", "StartFaceDetectionRequest$Video": "<p>The video in which you want to detect faces. The video must be stored in an Amazon S3 bucket.</p>", "StartFaceSearchRequest$Video": "<p>The video you want to search. The video must be stored in an Amazon S3 bucket. </p>", "StartLabelDetectionRequest$Video": "<p>The video in which you want to detect labels. The video must be stored in an Amazon S3 bucket.</p>", "StartPersonTrackingRequest$Video": "<p>The video in which you want to detect people. The video must be stored in an Amazon S3 bucket.</p>", "StartSegmentDetectionRequest$Video": null, "StartTextDetectionRequest$Video": null } }, "VideoColorRange": { "base": null, "refs": { "VideoMetadata$ColorRange": "<p> A description of the range of luminance values in a video, either LIMITED (16 to 235) or FULL (0 to 255). </p>" } }, "VideoJobStatus": { "base": null, "refs": { "GetCelebrityRecognitionResponse$JobStatus": "<p>The current status of the celebrity recognition job.</p>", "GetContentModerationResponse$JobStatus": "<p>The current status of the content moderation analysis job.</p>", "GetFaceDetectionResponse$JobStatus": "<p>The current status of the face detection job.</p>", "GetFaceSearchResponse$JobStatus": "<p>The current status of the face search job.</p>", "GetLabelDetectionResponse$JobStatus": "<p>The current status of the label detection job.</p>", "GetPersonTrackingResponse$JobStatus": "<p>The current status of the person tracking job.</p>", "GetSegmentDetectionResponse$JobStatus": "<p>Current status of the segment detection job.</p>", "GetTextDetectionResponse$JobStatus": "<p>Current status of the text detection job.</p>" } }, "VideoMetadata": { "base": "<p>Information about a video that Amazon Rekognition analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition video operation.</p>", "refs": { "GetCelebrityRecognitionResponse$VideoMetadata": "<p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition Video operation.</p>", "GetContentModerationResponse$VideoMetadata": "<p>Information about a video that Amazon Rekognition analyzed. <code>Videometadata</code> is returned in every page of paginated responses from <code>GetContentModeration</code>. </p>", "GetFaceDetectionResponse$VideoMetadata": "<p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition video operation.</p>", "GetFaceSearchResponse$VideoMetadata": "<p>Information about a video that Amazon Rekognition analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition Video operation. </p>", "GetLabelDetectionResponse$VideoMetadata": "<p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition video operation.</p>", "GetPersonTrackingResponse$VideoMetadata": "<p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition Video operation.</p>", "GetTextDetectionResponse$VideoMetadata": null, "VideoMetadataList$member": null } }, "VideoMetadataList": { "base": null, "refs": { "GetSegmentDetectionResponse$VideoMetadata": "<p>Currently, Amazon Rekognition Video returns a single object in the <code>VideoMetadata</code> array. The object contains information about the video stream in the input file that Amazon Rekognition Video chose to analyze. The <code>VideoMetadata</code> object includes the video codec, video format and other information. Video metadata is returned in each page of information returned by <code>GetSegmentDetection</code>.</p>" } }, "VideoTooLargeException": { "base": "<p>The file size or duration of the supplied media is too large. The maximum file size is 10GB. The maximum duration is 6 hours. </p>", "refs": { } } } }	337.69 KB	2025-05-25 12:27
📄 endpoint-rule-set-1.json { "version": "1.0", "parameters": { "Region": { "builtIn": "AWS::Region", "required": false, "documentation": "The AWS region used to dispatch the request.", "type": "String" }, "UseDualStack": { "builtIn": "AWS::UseDualStack", "required": true, "default": false, "documentation": "When true, use the dual-stack endpoint. If the configured endpoint does not support dual-stack, dispatching the request MAY return an error.", "type": "Boolean" }, "UseFIPS": { "builtIn": "AWS::UseFIPS", "required": true, "default": false, "documentation": "When true, send this request to the FIPS-compliant regional endpoint. If the configured endpoint does not have a FIPS compliant endpoint, dispatching the request will return an error.", "type": "Boolean" }, "Endpoint": { "builtIn": "SDK::Endpoint", "required": false, "documentation": "Override the endpoint used to send this request", "type": "String" } }, "rules": [ { "conditions": [ { "fn": "isSet", "argv": [ { "ref": "Endpoint" } ] } ], "rules": [ { "conditions": [ { "fn": "booleanEquals", "argv": [ { "ref": "UseFIPS" }, true ] } ], "error": "Invalid Configuration: FIPS and custom endpoint are not supported", "type": "error" }, { "conditions": [ { "fn": "booleanEquals", "argv": [ { "ref": "UseDualStack" }, true ] } ], "error": "Invalid Configuration: Dualstack and custom endpoint are not supported", "type": "error" }, { "conditions": [], "endpoint": { "url": { "ref": "Endpoint" }, "properties": {}, "headers": {} }, "type": "endpoint" } ], "type": "tree" }, { "conditions": [ { "fn": "isSet", "argv": [ { "ref": "Region" } ] } ], "rules": [ { "conditions": [ { "fn": "aws.partition", "argv": [ { "ref": "Region" } ], "assign": "PartitionResult" } ], "rules": [ { "conditions": [ { "fn": "booleanEquals", "argv": [ { "ref": "UseFIPS" }, true ] }, { "fn": "booleanEquals", "argv": [ { "ref": "UseDualStack" }, true ] } ], "rules": [ { "conditions": [ { "fn": "booleanEquals", "argv": [ true, { "fn": "getAttr", "argv": [ { "ref": "PartitionResult" }, "supportsFIPS" ] } ] }, { "fn": "booleanEquals", "argv": [ true, { "fn": "getAttr", "argv": [ { "ref": "PartitionResult" }, "supportsDualStack" ] } ] } ], "rules": [ { "conditions": [], "endpoint": { "url": "https://rekognition-fips.{Region}.{PartitionResult#dualStackDnsSuffix}", "properties": {}, "headers": {} }, "type": "endpoint" } ], "type": "tree" }, { "conditions": [], "error": "FIPS and DualStack are enabled, but this partition does not support one or both", "type": "error" } ], "type": "tree" }, { "conditions": [ { "fn": "booleanEquals", "argv": [ { "ref": "UseFIPS" }, true ] } ], "rules": [ { "conditions": [ { "fn": "booleanEquals", "argv": [ { "fn": "getAttr", "argv": [ { "ref": "PartitionResult" }, "supportsFIPS" ] }, true ] } ], "rules": [ { "conditions": [], "endpoint": { "url": "https://rekognition-fips.{Region}.{PartitionResult#dnsSuffix}", "properties": {}, "headers": {} }, "type": "endpoint" } ], "type": "tree" }, { "conditions": [], "error": "FIPS is enabled but this partition does not support FIPS", "type": "error" } ], "type": "tree" }, { "conditions": [ { "fn": "booleanEquals", "argv": [ { "ref": "UseDualStack" }, true ] } ], "rules": [ { "conditions": [ { "fn": "booleanEquals", "argv": [ true, { "fn": "getAttr", "argv": [ { "ref": "PartitionResult" }, "supportsDualStack" ] } ] } ], "rules": [ { "conditions": [], "endpoint": { "url": "https://rekognition.{Region}.{PartitionResult#dualStackDnsSuffix}", "properties": {}, "headers": {} }, "type": "endpoint" } ], "type": "tree" }, { "conditions": [], "error": "DualStack is enabled but this partition does not support DualStack", "type": "error" } ], "type": "tree" }, { "conditions": [], "endpoint": { "url": "https://rekognition.{Region}.{PartitionResult#dnsSuffix}", "properties": {}, "headers": {} }, "type": "endpoint" } ], "type": "tree" } ], "type": "tree" }, { "conditions": [], "error": "Invalid Configuration: Missing Region", "type": "error" } ] }	13.42 KB	2025-05-25 12:27
📄 endpoint-tests-1.json { "testCases": [ { "documentation": "For region ap-northeast-1 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.ap-northeast-1.amazonaws.com" } }, "params": { "Region": "ap-northeast-1", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region ap-northeast-2 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.ap-northeast-2.amazonaws.com" } }, "params": { "Region": "ap-northeast-2", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region ap-south-1 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.ap-south-1.amazonaws.com" } }, "params": { "Region": "ap-south-1", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region ap-southeast-1 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.ap-southeast-1.amazonaws.com" } }, "params": { "Region": "ap-southeast-1", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region ap-southeast-2 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.ap-southeast-2.amazonaws.com" } }, "params": { "Region": "ap-southeast-2", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region ca-central-1 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.ca-central-1.amazonaws.com" } }, "params": { "Region": "ca-central-1", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region ca-central-1 with FIPS enabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition-fips.ca-central-1.amazonaws.com" } }, "params": { "Region": "ca-central-1", "UseFIPS": true, "UseDualStack": false } }, { "documentation": "For region eu-central-1 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.eu-central-1.amazonaws.com" } }, "params": { "Region": "eu-central-1", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region eu-west-1 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.eu-west-1.amazonaws.com" } }, "params": { "Region": "eu-west-1", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region eu-west-2 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.eu-west-2.amazonaws.com" } }, "params": { "Region": "eu-west-2", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region us-east-1 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.us-east-1.amazonaws.com" } }, "params": { "Region": "us-east-1", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region us-east-1 with FIPS enabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition-fips.us-east-1.amazonaws.com" } }, "params": { "Region": "us-east-1", "UseFIPS": true, "UseDualStack": false } }, { "documentation": "For region us-east-2 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.us-east-2.amazonaws.com" } }, "params": { "Region": "us-east-2", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region us-east-2 with FIPS enabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition-fips.us-east-2.amazonaws.com" } }, "params": { "Region": "us-east-2", "UseFIPS": true, "UseDualStack": false } }, { "documentation": "For region us-west-1 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.us-west-1.amazonaws.com" } }, "params": { "Region": "us-west-1", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region us-west-1 with FIPS enabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition-fips.us-west-1.amazonaws.com" } }, "params": { "Region": "us-west-1", "UseFIPS": true, "UseDualStack": false } }, { "documentation": "For region us-west-2 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.us-west-2.amazonaws.com" } }, "params": { "Region": "us-west-2", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region us-west-2 with FIPS enabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition-fips.us-west-2.amazonaws.com" } }, "params": { "Region": "us-west-2", "UseFIPS": true, "UseDualStack": false } }, { "documentation": "For region us-east-1 with FIPS enabled and DualStack enabled", "expect": { "endpoint": { "url": "https://rekognition-fips.us-east-1.api.aws" } }, "params": { "Region": "us-east-1", "UseFIPS": true, "UseDualStack": true } }, { "documentation": "For region us-east-1 with FIPS disabled and DualStack enabled", "expect": { "endpoint": { "url": "https://rekognition.us-east-1.api.aws" } }, "params": { "Region": "us-east-1", "UseFIPS": false, "UseDualStack": true } }, { "documentation": "For region cn-north-1 with FIPS enabled and DualStack enabled", "expect": { "endpoint": { "url": "https://rekognition-fips.cn-north-1.api.amazonwebservices.com.cn" } }, "params": { "Region": "cn-north-1", "UseFIPS": true, "UseDualStack": true } }, { "documentation": "For region cn-north-1 with FIPS enabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition-fips.cn-north-1.amazonaws.com.cn" } }, "params": { "Region": "cn-north-1", "UseFIPS": true, "UseDualStack": false } }, { "documentation": "For region cn-north-1 with FIPS disabled and DualStack enabled", "expect": { "endpoint": { "url": "https://rekognition.cn-north-1.api.amazonwebservices.com.cn" } }, "params": { "Region": "cn-north-1", "UseFIPS": false, "UseDualStack": true } }, { "documentation": "For region cn-north-1 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.cn-north-1.amazonaws.com.cn" } }, "params": { "Region": "cn-north-1", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region us-gov-west-1 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.us-gov-west-1.amazonaws.com" } }, "params": { "Region": "us-gov-west-1", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region us-gov-west-1 with FIPS enabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition-fips.us-gov-west-1.amazonaws.com" } }, "params": { "Region": "us-gov-west-1", "UseFIPS": true, "UseDualStack": false } }, { "documentation": "For region us-gov-east-1 with FIPS enabled and DualStack enabled", "expect": { "endpoint": { "url": "https://rekognition-fips.us-gov-east-1.api.aws" } }, "params": { "Region": "us-gov-east-1", "UseFIPS": true, "UseDualStack": true } }, { "documentation": "For region us-gov-east-1 with FIPS enabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition-fips.us-gov-east-1.amazonaws.com" } }, "params": { "Region": "us-gov-east-1", "UseFIPS": true, "UseDualStack": false } }, { "documentation": "For region us-gov-east-1 with FIPS disabled and DualStack enabled", "expect": { "endpoint": { "url": "https://rekognition.us-gov-east-1.api.aws" } }, "params": { "Region": "us-gov-east-1", "UseFIPS": false, "UseDualStack": true } }, { "documentation": "For region us-gov-east-1 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.us-gov-east-1.amazonaws.com" } }, "params": { "Region": "us-gov-east-1", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region us-iso-east-1 with FIPS enabled and DualStack enabled", "expect": { "error": "FIPS and DualStack are enabled, but this partition does not support one or both" }, "params": { "Region": "us-iso-east-1", "UseFIPS": true, "UseDualStack": true } }, { "documentation": "For region us-iso-east-1 with FIPS enabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition-fips.us-iso-east-1.c2s.ic.gov" } }, "params": { "Region": "us-iso-east-1", "UseFIPS": true, "UseDualStack": false } }, { "documentation": "For region us-iso-east-1 with FIPS disabled and DualStack enabled", "expect": { "error": "DualStack is enabled but this partition does not support DualStack" }, "params": { "Region": "us-iso-east-1", "UseFIPS": false, "UseDualStack": true } }, { "documentation": "For region us-iso-east-1 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.us-iso-east-1.c2s.ic.gov" } }, "params": { "Region": "us-iso-east-1", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For region us-isob-east-1 with FIPS enabled and DualStack enabled", "expect": { "error": "FIPS and DualStack are enabled, but this partition does not support one or both" }, "params": { "Region": "us-isob-east-1", "UseFIPS": true, "UseDualStack": true } }, { "documentation": "For region us-isob-east-1 with FIPS enabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition-fips.us-isob-east-1.sc2s.sgov.gov" } }, "params": { "Region": "us-isob-east-1", "UseFIPS": true, "UseDualStack": false } }, { "documentation": "For region us-isob-east-1 with FIPS disabled and DualStack enabled", "expect": { "error": "DualStack is enabled but this partition does not support DualStack" }, "params": { "Region": "us-isob-east-1", "UseFIPS": false, "UseDualStack": true } }, { "documentation": "For region us-isob-east-1 with FIPS disabled and DualStack disabled", "expect": { "endpoint": { "url": "https://rekognition.us-isob-east-1.sc2s.sgov.gov" } }, "params": { "Region": "us-isob-east-1", "UseFIPS": false, "UseDualStack": false } }, { "documentation": "For custom endpoint with region set and fips disabled and dualstack disabled", "expect": { "endpoint": { "url": "https://example.com" } }, "params": { "Region": "us-east-1", "UseFIPS": false, "UseDualStack": false, "Endpoint": "https://example.com" } }, { "documentation": "For custom endpoint with region not set and fips disabled and dualstack disabled", "expect": { "endpoint": { "url": "https://example.com" } }, "params": { "UseFIPS": false, "UseDualStack": false, "Endpoint": "https://example.com" } }, { "documentation": "For custom endpoint with fips enabled and dualstack disabled", "expect": { "error": "Invalid Configuration: FIPS and custom endpoint are not supported" }, "params": { "Region": "us-east-1", "UseFIPS": true, "UseDualStack": false, "Endpoint": "https://example.com" } }, { "documentation": "For custom endpoint with fips disabled and dualstack enabled", "expect": { "error": "Invalid Configuration: Dualstack and custom endpoint are not supported" }, "params": { "Region": "us-east-1", "UseFIPS": false, "UseDualStack": true, "Endpoint": "https://example.com" } }, { "documentation": "Missing region", "expect": { "error": "Invalid Configuration: Missing Region" } } ], "version": "1.0" }	17.93 KB	2025-05-25 12:27
📄 examples-1.json { "version": "1.0", "examples": { "AssociateFaces": [ { "input": { "ClientRequestToken": "550e8400-e29b-41d4-a716-446655440002", "CollectionId": "MyCollection", "FaceIds": [ "f5817d37-94f6-4335-bfee-6cf79a3d806e", "851cb847-dccc-4fea-9309-9f4805967855", "35ebbb41-7f67-4263-908d-dd0ecba05ab9" ], "UserId": "DemoUser", "UserMatchThreshold": 70 }, "output": { "AssociatedFaces": [ { "FaceId": "35ebbb41-7f67-4263-908d-dd0ecba05ab9" } ], "UnsuccessfulFaceAssociations": [ { "Confidence": 0.9375374913215637, "FaceId": "f5817d37-94f6-4335-bfee-6cf79a3d806e", "Reasons": [ "LOW_MATCH_CONFIDENCE" ] }, { "FaceId": "851cb847-dccc-4fea-9309-9f4805967855", "Reasons": [ "ASSOCIATED_TO_A_DIFFERENT_USER" ], "UserId": "demoUser2" } ], "UserStatus": "UPDATING" }, "comments": { "input": { }, "output": { } }, "description": "This operation associates one or more faces with an existing UserID.", "id": "associatefaces-1686181269281", "title": "AssociateFaces" } ], "CompareFaces": [ { "input": { "SimilarityThreshold": 90, "SourceImage": { "S3Object": { "Bucket": "mybucket", "Name": "mysourceimage" } }, "TargetImage": { "S3Object": { "Bucket": "mybucket", "Name": "mytargetimage" } } }, "output": { "FaceMatches": [ { "Face": { "BoundingBox": { "Height": 0.33481481671333313, "Left": 0.31888890266418457, "Top": 0.4933333396911621, "Width": 0.25 }, "Confidence": 99.9991226196289 }, "Similarity": 100 } ], "SourceImageFace": { "BoundingBox": { "Height": 0.33481481671333313, "Left": 0.31888890266418457, "Top": 0.4933333396911621, "Width": 0.25 }, "Confidence": 99.9991226196289 } }, "comments": { "input": { }, "output": { } }, "description": "This operation compares the largest face detected in the source image with each face detected in the target image.", "id": "to-compare-two-images-1482181985581", "title": "To compare two images" } ], "CopyProjectVersion": [ { "input": { "DestinationProjectArn": "arn:aws:rekognition:us-east-1:555555555555:project/DestinationProject/1656705098765", "KmsKeyId": "arn:1234abcd-12ab-34cd-56ef-1234567890ab", "OutputConfig": { "S3Bucket": "bucket-name", "S3KeyPrefix": "path_to_folder" }, "SourceProjectArn": "arn:aws:rekognition:us-east-1:111122223333:project/SourceProject/16565123456", "SourceProjectVersionArn": "arn:aws:rekognition:us-east-1:111122223333:project/SourceProject/version/model_1/1656611123456", "Tags": { "key1": "val1" }, "VersionName": "DestinationVersionName_cross_account" }, "output": { "ProjectVersionArn": "arn:aws:rekognition:us-east-1:555555555555:project/DestinationProject/version/DestinationVersionName_cross_account/16567050987651" }, "comments": { "input": { }, "output": { } }, "description": "Copies a version of an Amazon Rekognition Custom Labels model from a source project to a destination project.", "id": "copyprojectversion-1658203943815", "title": "CopyProjectVersion" } ], "CreateCollection": [ { "input": { "CollectionId": "myphotos" }, "output": { "CollectionArn": "aws:rekognition:us-west-2:123456789012:collection/myphotos", "StatusCode": 200 }, "comments": { "input": { }, "output": { } }, "description": "This operation creates a Rekognition collection for storing image data.", "id": "to-create-a-collection-1481833313674", "title": "To create a collection" } ], "CreateDataset": [ { "input": { "DatasetSource": { "GroundTruthManifest": { "S3Object": { "Bucket": "my-bucket", "Name": "datasets/flowers_training/manifests/output/output.manifest" } } }, "DatasetType": "TRAIN", "ProjectArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/1690474772815" }, "output": { "DatasetArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/dataset/train/1690476084535" }, "comments": { "input": { }, "output": { } }, "description": "Creates an Amazon Rekognition Custom Labels dataset with a manifest file stored in an Amazon S3 bucket.", "id": "to-create-an-amazon-rekognition-custom-labels-dataset-1690476237145", "title": "To create an Amazon Rekognition Custom Labels dataset" } ], "CreateProject": [ { "input": { "ProjectName": "my-project" }, "output": { "ProjectArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/1690405809285" }, "comments": { "input": { }, "output": { } }, "description": "Creates an Amazon Rekognition Custom Labels project.", "id": "to-create-an-amazon-rekognition-custom-labels-project-1690405898597", "title": "To create an Amazon Rekognition Custom Labels project" } ], "CreateProjectVersion": [ { "input": { "OutputConfig": { "S3Bucket": "output_bucket", "S3KeyPrefix": "output_folder" }, "ProjectArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/1690474772815", "VersionName": "1" }, "output": { "ProjectVersionArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958" }, "comments": { "input": { } }, "description": "Trains a version of an Amazon Rekognition Custom Labels model.", "id": "to-train-an-amazon-rekognition-custom-labels-model-1690555832843", "title": "To train an Amazon Rekognition Custom Labels model" } ], "CreateUser": [ { "input": { "CollectionId": "MyCollection", "UserId": "DemoUser" }, "comments": { "input": { }, "output": { } }, "description": "Creates a new User within a collection specified by CollectionId.", "id": "createuser-1686181562299", "title": "CreateUser" } ], "DeleteCollection": [ { "input": { "CollectionId": "myphotos" }, "output": { "StatusCode": 200 }, "comments": { "input": { }, "output": { } }, "description": "This operation deletes a Rekognition collection.", "id": "to-delete-a-collection-1481838179973", "title": "To delete a collection" } ], "DeleteDataset": [ { "input": { "DatasetArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/dataset/test/1690556733321" }, "output": { }, "comments": { "input": { }, "output": { } }, "description": "Deletes an Amazon Rekognition Custom Labels dataset.", "id": "to-delete-an-amazon-rekognition-custom-labels-dataset-1690826556349", "title": "To delete an Amazon Rekognition Custom Labels dataset" } ], "DeleteFaces": [ { "input": { "CollectionId": "myphotos", "FaceIds": [ "ff43d742-0c13-5d16-a3e8-03d3f58e980b" ] }, "output": { "DeletedFaces": [ "ff43d742-0c13-5d16-a3e8-03d3f58e980b" ] }, "comments": { "input": { }, "output": { } }, "description": "This operation deletes one or more faces from a Rekognition collection.", "id": "to-delete-a-face-1482182799377", "title": "To delete a face" } ], "DeleteProject": [ { "input": { "ProjectArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/1690405809285" }, "output": { "Status": "DELETING" }, "comments": { "input": { }, "output": { } }, "description": "Deletes an Amazon Rekognition Custom Labels projects.", "id": "to-delete-an-amazon-rekognition-custom-labels-project-1690409093478", "title": "To delete an Amazon Rekognition Custom Labels project" } ], "DeleteProjectPolicy": [ { "input": { "PolicyName": "testPolicy1", "PolicyRevisionId": "3b274c25e9203a56a99e00e3ff205fbc", "ProjectArn": "arn:aws:rekognition:us-east-1:111122223333:project/SourceProject/1656557123456" }, "output": { }, "comments": { "input": { }, "output": { } }, "description": "This operation deletes a revision of an existing project policy from an Amazon Rekognition Custom Labels project.", "id": "deleteprojectpolicy-1658204413810", "title": "DeleteProjectPolicy" } ], "DeleteProjectVersion": [ { "input": { "ProjectVersionArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958" }, "output": { "Status": "DELETING" }, "comments": { "input": { }, "output": { } }, "description": "Deletes a version of an Amazon Rekognition Custom Labels model.", "id": "to-delete-an-amazon-rekognition-custom-labels-model-1690827562055", "title": "To delete an Amazon Rekognition Custom Labels model" } ], "DeleteUser": [ { "input": { "ClientRequestToken": "550e8400-e29b-41d4-a716-446655440001", "CollectionId": "MyCollection", "UserId": "DemoUser" }, "comments": { "input": { }, "output": { } }, "description": "Deletes the specified UserID within the collection.", "id": "deleteuser-1686181913475", "title": "DeleteUser" } ], "DescribeDataset": [ { "input": { "DatasetArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/dataset/train/1690476084535" }, "output": { "DatasetDescription": { "CreationTimestamp": "2023-07-27T09:41:24.738000-07:00", "DatasetStats": { "ErrorEntries": 0, "LabeledEntries": 15, "TotalEntries": 15, "TotalLabels": 9 }, "LastUpdatedTimestamp": "2023-07-28T09:46:45.406000-07:00", "Status": "UPDATE_FAILED", "StatusMessage": "The manifest file contains images from multiple S3 buckets.", "StatusMessageCode": "CLIENT_ERROR" } }, "comments": { "input": { }, "output": { } }, "description": "Describes an Amazon Rekognition Custom Labels dataset.", "id": "to-describe-an-amazon-rekognition-custom-labels-dataset-1690827075604", "title": "To describe an Amazon Rekognition Custom Labels dataset" } ], "DescribeProjectVersions": [ { "input": { "ProjectArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/1690474772815", "VersionNames": [ "1" ] }, "output": { "NextToken": "", "ProjectVersionDescriptions": [ { "BillableTrainingTimeInSeconds": 1899, "CreationTimestamp": "2023-07-28T08:05:51.958000-07:00", "EvaluationResult": { "F1Score": 1, "Summary": { "S3Object": { "Bucket": "custom-labels-console-us-east-1-111111111", "Name": "my-model-output/EvaluationResultSummary-my-project-1.json" } } }, "ManifestSummary": { "S3Object": { "Bucket": "custom-labels-console-us-east-1-111111111", "Name": "my-model-output/ManifestSummary-my-project-1.json" } }, "OutputConfig": { "S3Bucket": "custom-labels-console-us-east-1-111111111", "S3KeyPrefix": "my-model-output" }, "ProjectVersionArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958", "Status": "TRAINING_COMPLETED", "StatusMessage": "The model is ready to run.", "TestingDataResult": { "Input": { "AutoCreate": false }, "Output": { "Assets": [ { "GroundTruthManifest": { "S3Object": { "Bucket": "custom-labels-console-us-east-1-111111111", "Name": "my-model-output/TestingGroundTruth-my-project-1.json" } } } ], "AutoCreate": false }, "Validation": { "Assets": [ { "GroundTruthManifest": { "S3Object": { "Bucket": "custom-labels-console-us-east-1-111111111", "Name": "my-model-output/TestingManifestWithValidation-my-project-1.json" } } } ] } }, "TrainingDataResult": { "Input": { }, "Output": { "Assets": [ { "GroundTruthManifest": { "S3Object": { "Bucket": "custom-labels-console-us-east-1-111111111", "Name": "my-model-output/TrainingGroundTruth-my-project-1.json" } } } ] }, "Validation": { "Assets": [ { "GroundTruthManifest": { "S3Object": { "Bucket": "custom-labels-console-us-east-1-111111111", "Name": "my-model-output/TrainingManifestWithValidation-my-project-1.json" } } } ] } }, "TrainingEndTimestamp": "2023-07-28T08:33:10.827000-07:00" } ] }, "comments": { "input": { } }, "description": "Describes a version of an Amazon Rekognition Custom Labels model.", "id": "to-describes-an-amazon-rekognition-custom-labels-model-1690558265950", "title": "To describe an Amazon Rekognition Custom Labels model" } ], "DescribeProjects": [ { "input": { "ProjectNames": [ "my-project" ] }, "output": { "ProjectDescriptions": [ { "CreationTimestamp": "2022-06-13T15:16:00.634000-07:00", "Datasets": [ { "CreationTimestamp": "2022-06-13T15:17:34.620000-07:00", "DatasetArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/dataset/test/1655158654491", "DatasetType": "TEST", "Status": "CREATE_COMPLETE", "StatusMessage": "The dataset was successfully created from the manifest file.", "StatusMessageCode": "SUCCESS" }, { "CreationTimestamp": "2022-06-13T15:17:50.118000-07:00", "DatasetArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/dataset/train/1655158669954", "DatasetType": "TRAIN", "Status": "CREATE_COMPLETE", "StatusMessage": "The dataset was successfully created from the manifest file.", "StatusMessageCode": "SUCCESS" } ], "ProjectArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/1655158560634", "Status": "CREATED" } ] }, "comments": { "input": { }, "output": { } }, "description": "Describes an Amazon Rekognition Custom Labels projects.", "id": "to-describe-an-amazon-rekognition-custom-labels-project-1690400952376", "title": "To describe an Amazon Rekognition Custom Labels project." } ], "DetectCustomLabels": [ { "input": { "Image": { "S3Object": { "Bucket": "custom-labels-console-us-east-1-1111111111", "Name": "assets/flowers_1_test_dataset/camellia4.jpg" } }, "MaxResults": 100, "MinConfidence": 50, "ProjectVersionArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/my-project.2023-07-31T11.49.37/1690829378219" }, "output": { "CustomLabels": [ { "Confidence": 67.56399536132812, "Name": "with_leaves" }, { "Confidence": 50.65699768066406, "Name": "without_leaves" } ] }, "comments": { "input": { }, "output": { } }, "description": "Detects custom labels in an image with an Amazon Rekognition Custom Labels model", "id": "to-detect-custom-labels-in-images-with-an-amazon-rekognition-custom-labels-model-1690832037913", "title": "To detect custom labels in an image with an Amazon Rekognition Custom Labels model" } ], "DetectFaces": [ { "input": { "Image": { "S3Object": { "Bucket": "mybucket", "Name": "myphoto" } } }, "output": { "FaceDetails": [ { "BoundingBox": { "Height": 0.18000000715255737, "Left": 0.5555555820465088, "Top": 0.33666667342185974, "Width": 0.23999999463558197 }, "Confidence": 100, "Landmarks": [ { "Type": "eyeLeft", "X": 0.6394737362861633, "Y": 0.40819624066352844 }, { "Type": "eyeRight", "X": 0.7266660928726196, "Y": 0.41039225459098816 }, { "Type": "eyeRight", "X": 0.6912462115287781, "Y": 0.44240960478782654 }, { "Type": "mouthDown", "X": 0.6306198239326477, "Y": 0.46700039505958557 }, { "Type": "mouthUp", "X": 0.7215608954429626, "Y": 0.47114261984825134 } ], "Pose": { "Pitch": 4.050806522369385, "Roll": 0.9950747489929199, "Yaw": 13.693790435791016 }, "Quality": { "Brightness": 37.60169982910156, "Sharpness": 80 } } ], "OrientationCorrection": "ROTATE_0" }, "comments": { "input": { }, "output": { } }, "description": "This operation detects faces in an image stored in an AWS S3 bucket.", "id": "to-detect-faces-in-an-image-1481841782793", "title": "To detect faces in an image" } ], "DetectLabels": [ { "input": { "Image": { "S3Object": { "Bucket": "mybucket", "Name": "myphoto" } }, "MaxLabels": 123, "MinConfidence": 70 }, "output": { "Labels": [ { "Confidence": 99.25072479248047, "Name": "People" }, { "Confidence": 99.25074005126953, "Name": "Person" } ] }, "comments": { "input": { }, "output": { } }, "description": "This operation detects labels in the supplied image", "id": "to-detect-labels-1481834255770", "title": "To detect labels" } ], "DisassociateFaces": [ { "input": { "ClientRequestToken": "550e8400-e29b-41d4-a716-446655440003", "CollectionId": "MyCollection", "FaceIds": [ "f5817d37-94f6-4335-bfee-6cf79a3d806e", "c92265d4-5f9c-43af-a58e-12be0ce02bc3" ], "UserId": "DemoUser" }, "output": { "DisassociatedFaces": [ { "FaceId": "c92265d4-5f9c-43af-a58e-12be0ce02bc3" } ], "UnsuccessfulFaceDisassociations": [ { "FaceId": "f5817d37-94f6-4335-bfee-6cf79a3d806e", "Reasons": [ "ASSOCIATED_TO_A_DIFFERENT_USER" ], "UserId": "demoUser1" } ], "UserStatus": "UPDATING" }, "comments": { "input": { }, "output": { } }, "description": "Removes the association between a Face supplied in an array of FaceIds and the User.", "id": "disassociatefaces-1686182627295", "title": "DisassociateFaces" } ], "DistributeDatasetEntries": [ { "input": { "Datasets": [ { "Arn": "arn:aws:rekognition:us-east-1:111122223333:project/my-proj-2/dataset/train/1690564858106" }, { "Arn": "arn:aws:rekognition:us-east-1:111122223333:project/my-proj-2/dataset/test/1690564858106" } ] }, "output": { }, "comments": { "input": { }, "output": { } }, "description": "Distributes an Amazon Rekognition Custom Labels training dataset to a test dataset.", "id": "to-distribute-to-an-amazon-rekognition-custom-labels-dataset-1690816977073", "title": "To distribute an Amazon Rekognition Custom Labels dataset" } ], "GetMediaAnalysisJob": [ { "input": { "JobId": "861a0645d98ef88efb75477628c011c04942d9d5f58faf2703c393c8cf8c1537" }, "output": { "CompletionTimestamp": "2023-07-28T08:05:51.958000-07:00", "CreationTimestamp": "2023-07-28T08:05:51.958000-06:00", "Input": { "S3Object": { "Bucket": "input-bucket", "Name": "input-manifest.json" } }, "JobId": "861a0645d98ef88efb75477628c011c04942d9d5f58faf2703c393c8cf8c1537", "JobName": "job-name", "ManifestSummary": { "S3Object": { "Bucket": "output-bucket", "Name": "output-location/861a0645d98ef88efb75477628c011c04942d9d5f58faf2703c393c8cf8c1537-manifest-summary.json" } }, "OperationsConfig": { "DetectModerationLabels": { "MinConfidence": 50, "ProjectVersion": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958" } }, "OutputConfig": { "S3Bucket": "output-bucket", "S3KeyPrefix": "output-location" }, "Results": { "S3Object": { "Bucket": "output-bucket", "Name": "output-location/861a0645d98ef88efb75477628c011c04942d9d5f58faf2703c393c8cf8c1537-results.jsonl" } }, "Status": "SUCCEEDED" }, "comments": { "input": { }, "output": { } }, "description": "Retrieves the results for a given media analysis job.", "id": "getmediaanalysisjob-1697650068124", "title": "GetMediaAnalysisJob" } ], "IndexFaces": [ { "input": { "CollectionId": "myphotos", "DetectionAttributes": [ ], "ExternalImageId": "myphotoid", "Image": { "S3Object": { "Bucket": "mybucket", "Name": "myphoto" } } }, "output": { "FaceRecords": [ { "Face": { "BoundingBox": { "Height": 0.33481481671333313, "Left": 0.31888890266418457, "Top": 0.4933333396911621, "Width": 0.25 }, "Confidence": 99.9991226196289, "FaceId": "ff43d742-0c13-5d16-a3e8-03d3f58e980b", "ImageId": "465f4e93-763e-51d0-b030-b9667a2d94b1" }, "FaceDetail": { "BoundingBox": { "Height": 0.33481481671333313, "Left": 0.31888890266418457, "Top": 0.4933333396911621, "Width": 0.25 }, "Confidence": 99.9991226196289, "Landmarks": [ { "Type": "eyeLeft", "X": 0.3976764678955078, "Y": 0.6248345971107483 }, { "Type": "eyeRight", "X": 0.4810936450958252, "Y": 0.6317117214202881 }, { "Type": "noseLeft", "X": 0.41986238956451416, "Y": 0.7111940383911133 }, { "Type": "mouthDown", "X": 0.40525302290916443, "Y": 0.7497701048851013 }, { "Type": "mouthUp", "X": 0.4753248989582062, "Y": 0.7558549642562866 } ], "Pose": { "Pitch": -9.713645935058594, "Roll": 4.707281112670898, "Yaw": -24.438663482666016 }, "Quality": { "Brightness": 29.23358917236328, "Sharpness": 80 } } }, { "Face": { "BoundingBox": { "Height": 0.32592591643333435, "Left": 0.5144444704055786, "Top": 0.15111111104488373, "Width": 0.24444444477558136 }, "Confidence": 99.99950408935547, "FaceId": "8be04dba-4e58-520d-850e-9eae4af70eb2", "ImageId": "465f4e93-763e-51d0-b030-b9667a2d94b1" }, "FaceDetail": { "BoundingBox": { "Height": 0.32592591643333435, "Left": 0.5144444704055786, "Top": 0.15111111104488373, "Width": 0.24444444477558136 }, "Confidence": 99.99950408935547, "Landmarks": [ { "Type": "eyeLeft", "X": 0.6006892323493958, "Y": 0.290842205286026 }, { "Type": "eyeRight", "X": 0.6808141469955444, "Y": 0.29609042406082153 }, { "Type": "noseLeft", "X": 0.6395332217216492, "Y": 0.3522595763206482 }, { "Type": "mouthDown", "X": 0.5892083048820496, "Y": 0.38689887523651123 }, { "Type": "mouthUp", "X": 0.674560010433197, "Y": 0.394125759601593 } ], "Pose": { "Pitch": -4.683138370513916, "Roll": 2.1029529571533203, "Yaw": 6.716655254364014 }, "Quality": { "Brightness": 34.951698303222656, "Sharpness": 160 } } } ], "OrientationCorrection": "ROTATE_0" }, "comments": { "input": { }, "output": { } }, "description": "This operation detects faces in an image and adds them to the specified Rekognition collection.", "id": "to-add-a-face-to-a-collection-1482179542923", "title": "To add a face to a collection" } ], "ListCollections": [ { "input": { }, "output": { "CollectionIds": [ "myphotos" ] }, "comments": { "input": { }, "output": { } }, "description": "This operation returns a list of Rekognition collections.", "id": "to-list-the-collections-1482179199088", "title": "To list the collections" } ], "ListDatasetEntries": [ { "input": { "ContainsLabels": [ "camellia" ], "DatasetArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-proj-2/dataset/train/1690564858106", "HasErrors": true, "Labeled": true, "MaxResults": 100, "NextToken": "", "SourceRefContains": "camellia4.jpg" }, "output": { "DatasetEntries": [ "{\"source-ref\":\"s3://custom-labels-console-us-east-1-1111111111/assets/flowers_1_train_dataset/camellia4.jpg\",\"camellia\":1,\"camellia-metadata\":{\"confidence\":1,\"job-name\":\"labeling-job/camellia\",\"class-name\":\"camellia\",\"human-annotated\":\"yes\",\"creation-date\":\"2021-07-11T03:32:13.456Z\",\"type\":\"groundtruth/image-classification\"},\"with_leaves\":1,\"with_leaves-metadata\":{\"confidence\":1,\"job-name\":\"labeling-job/with_leaves\",\"class-name\":\"with_leaves\",\"human-annotated\":\"yes\",\"creation-date\":\"2021-07-11T03:32:13.456Z\",\"type\":\"groundtruth/image-classification\"},\"cl-metadata\":{\"is_labeled\":true}}" ], "NextToken": "" }, "comments": { "input": { }, "output": { } }, "description": "Lists the JSON line entries in an Amazon Rekognition Custom Labels dataset.", "id": "to-list-the-entries-in-an-amazon-rekognition-custom-labels-dataset-1690823292345", "title": "To list the entries in an Amazon Rekognition Custom Labels dataset" } ], "ListDatasetLabels": [ { "input": { "DatasetArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-proj-2/dataset/train/1690564858106", "MaxResults": 100, "NextToken": "" }, "output": { "DatasetLabelDescriptions": [ { "LabelName": "camellia", "LabelStats": { "EntryCount": 1 } }, { "LabelName": "with_leaves", "LabelStats": { "EntryCount": 2 } }, { "LabelName": "mediterranean_spurge", "LabelStats": { "EntryCount": 1 } } ] }, "comments": { "input": { }, "output": { } }, "description": "Lists the JSON line entries in an Amazon Rekognition Custom Labels dataset.", "id": "to-list-the-entries-in-an-amazon-rekognition-custom-labels-dataset-1690823292345", "title": "To list the entries in an Amazon Rekognition Custom Labels dataset" } ], "ListFaces": [ { "input": { "CollectionId": "myphotos", "MaxResults": 20 }, "output": { "FaceModelVersion": "6.0", "Faces": [ { "BoundingBox": { "Height": 0.056759100407361984, "Left": 0.3453829884529114, "Top": 0.36568498611450195, "Width": 0.03177810087800026 }, "Confidence": 99.76940155029297, "FaceId": "c92265d4-5f9c-43af-a58e-12be0ce02bc3", "ImageId": "56a0ca74-1c83-39dd-b363-051a64168a65", "IndexFacesModelVersion": "6.0", "UserId": "demoUser2" }, { "BoundingBox": { "Height": 0.06347999721765518, "Left": 0.5160620212554932, "Top": 0.6080359816551208, "Width": 0.03254450112581253 }, "Confidence": 99.94369506835938, "FaceId": "851cb847-dccc-4fea-9309-9f4805967855", "ImageId": "a8aed589-ceec-35f7-9c04-82e0b546b024", "IndexFacesModelVersion": "6.0" }, { "BoundingBox": { "Height": 0.05266290158033371, "Left": 0.6513839960098267, "Top": 0.4218429923057556, "Width": 0.03094629943370819 }, "Confidence": 99.82969665527344, "FaceId": "c0eb3b65-24a0-41e1-b23a-1908b1aaeac1", "ImageId": "56a0ca74-1c83-39dd-b363-051a64168a65", "IndexFacesModelVersion": "6.0" } ] }, "comments": { "input": { }, "output": { } }, "description": "This operation lists the faces in a Rekognition collection.", "id": "to-list-the-faces-in-a-collection-1482181416530", "title": "To list the faces in a collection" } ], "ListMediaAnalysisJobs": [ { "input": { "MaxResults": 10 }, "output": { "MediaAnalysisJobs": [ { "CompletionTimestamp": "2023-07-28T08:05:51.958000-07:00", "CreationTimestamp": "2023-07-28T08:05:51.958000-06:00", "Input": { "S3Object": { "Bucket": "input-bucket", "Name": "input-manifest.json" } }, "JobId": "861a0645d98ef88efb75477628c011c04942d9d5f58faf2703c393c8cf8c1537", "JobName": "job-name", "ManifestSummary": { "S3Object": { "Bucket": "output-bucket", "Name": "output-location/861a0645d98ef88efb75477628c011c04942d9d5f58faf2703c393c8cf8c1537-manifest-summary.json" } }, "OperationsConfig": { "DetectModerationLabels": { "MinConfidence": 50, "ProjectVersion": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958" } }, "OutputConfig": { "S3Bucket": "output-bucket", "S3KeyPrefix": "output-location" }, "Results": { "S3Object": { "Bucket": "output-bucket", "Name": "output-location/861a0645d98ef88efb75477628c011c04942d9d5f58faf2703c393c8cf8c1537-results.jsonl" } }, "Status": "SUCCEEDED" } ] }, "comments": { "input": { }, "output": { } }, "description": "Returns a list of media analysis jobs.", "id": "listmediaanalysisjobs-1697650653077", "title": "ListMediaAnalysisJobs" } ], "ListProjectPolicies": [ { "input": { "MaxResults": 5, "NextToken": "", "ProjectArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-sdk-project/1656557051929" }, "output": { "NextToken": "", "ProjectPolicies": [ { "CreationTimestamp": "2022-07-01T11:51:27.086000-07:00", "LastUpdatedTimestamp": "2022-07-01T11:51:27.086000-07:00", "PolicyDocument": "{\"Version\":\"2012-10-17\",\"Statement\":[{\"Sid\":\"Statemented1\",\"Effect\":\"Allow\",\"Principal\":{\"AWS\":\"arn:aws:iam::111122223333:root\"},\"Action\":\"rekognition:CopyProjectVersion\",\"Resource\":\"*\"}]}", "PolicyName": "testPolicy", "PolicyRevisionId": "3b274c25e9203a56a99e00e3ff205fbc", "ProjectArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-sdk-project/1656557051929" } ] }, "comments": { "input": { }, "output": { } }, "description": "This operation lists the project policies that are attached to an Amazon Rekognition Custom Labels project.", "id": "listprojectpolicies-1658202290173", "title": "ListProjectPolicies" } ], "ListUsers": [ { "input": { "CollectionId": "MyCollection" }, "output": { "NextToken": "MGYZLAHX1T5a....", "Users": [ { "UserId": "demoUser4", "UserStatus": "CREATED" }, { "UserId": "demoUser2", "UserStatus": "CREATED" } ] }, "comments": { "input": { }, "output": { } }, "description": "Returns metadata of the User such as UserID in the specified collection.", "id": "listusers-1686182360075", "title": "ListUsers" } ], "PutProjectPolicy": [ { "input": { "PolicyDocument": "'{\"Version\":\"2012-10-17\",\"Statement\":[{\"Effect\":\"ALLOW\",\"Principal\":{\"AWS\":\"principal\"},\"Action\":\"rekognition:CopyProjectVersion\",\"Resource\":\"arn:aws:rekognition:us-east-1:123456789012:project/my-sdk-project/version/DestinationVersionName/1627045542080\"}]}'", "PolicyName": "SamplePolicy", "PolicyRevisionId": "0123456789abcdef", "ProjectArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-sdk-project/1656557051929" }, "output": { "PolicyRevisionId": "0123456789abcdef" }, "comments": { "input": { }, "output": { } }, "description": "This operation attaches a project policy to a Amazon Rekognition Custom Labels project in a trusting AWS account.", "id": "putprojectpolicy-1658201727623", "title": "PutProjectPolicy" } ], "SearchFaces": [ { "input": { "CollectionId": "myphotos", "FaceId": "70008e50-75e4-55d0-8e80-363fb73b3a14", "FaceMatchThreshold": 90, "MaxFaces": 10 }, "output": { "FaceMatches": [ { "Face": { "BoundingBox": { "Height": 0.3259260058403015, "Left": 0.5144439935684204, "Top": 0.15111100673675537, "Width": 0.24444399774074554 }, "Confidence": 99.99949645996094, "FaceId": "8be04dba-4e58-520d-850e-9eae4af70eb2", "ImageId": "465f4e93-763e-51d0-b030-b9667a2d94b1" }, "Similarity": 99.97222137451172 }, { "Face": { "BoundingBox": { "Height": 0.16555599868297577, "Left": 0.30963000655174255, "Top": 0.7066670060157776, "Width": 0.22074100375175476 }, "Confidence": 100, "FaceId": "29a75abe-397b-5101-ba4f-706783b2246c", "ImageId": "147fdf82-7a71-52cf-819b-e786c7b9746e" }, "Similarity": 97.04154968261719 }, { "Face": { "BoundingBox": { "Height": 0.18888899683952332, "Left": 0.3783380091190338, "Top": 0.2355560064315796, "Width": 0.25222599506378174 }, "Confidence": 99.9999008178711, "FaceId": "908544ad-edc3-59df-8faf-6a87cc256cf5", "ImageId": "3c731605-d772-541a-a5e7-0375dbc68a07" }, "Similarity": 95.94520568847656 } ], "SearchedFaceId": "70008e50-75e4-55d0-8e80-363fb73b3a14" }, "comments": { "input": { }, "output": { } }, "description": "This operation searches for matching faces in the collection the supplied face belongs to.", "id": "to-delete-a-face-1482182799377", "title": "To delete a face" } ], "SearchFacesByImage": [ { "input": { "CollectionId": "myphotos", "FaceMatchThreshold": 95, "Image": { "S3Object": { "Bucket": "mybucket", "Name": "myphoto" } }, "MaxFaces": 5 }, "output": { "FaceMatches": [ { "Face": { "BoundingBox": { "Height": 0.3234420120716095, "Left": 0.3233329951763153, "Top": 0.5, "Width": 0.24222199618816376 }, "Confidence": 99.99829864501953, "FaceId": "38271d79-7bc2-5efb-b752-398a8d575b85", "ImageId": "d5631190-d039-54e4-b267-abd22c8647c5" }, "Similarity": 99.97036743164062 } ], "SearchedFaceBoundingBox": { "Height": 0.33481481671333313, "Left": 0.31888890266418457, "Top": 0.4933333396911621, "Width": 0.25 }, "SearchedFaceConfidence": 99.9991226196289 }, "comments": { "input": { }, "output": { } }, "description": "This operation searches for faces in a Rekognition collection that match the largest face in an S3 bucket stored image.", "id": "to-search-for-faces-matching-a-supplied-image-1482175994491", "title": "To search for faces matching a supplied image" } ], "SearchUsers": [ { "input": { "CollectionId": "MyCollection", "MaxUsers": 2, "UserId": "DemoUser", "UserMatchThreshold": 70 }, "output": { "FaceModelVersion": "6", "SearchedUser": { "UserId": "DemoUser" }, "UserMatches": [ { "Similarity": 99.88186645507812, "User": { "UserId": "demoUser1", "UserStatus": "ACTIVE" } } ] }, "comments": { "input": { }, "output": { } }, "description": "Searches for UserIDs within a collection based on a FaceId or UserId.", "id": "searchusers-1686182912030", "title": "SearchUsers" } ], "SearchUsersByImage": [ { "input": { "CollectionId": "MyCollection", "Image": { "S3Object": { "Bucket": "bucket", "Name": "input.jpg" } }, "MaxUsers": 2, "QualityFilter": "MEDIUM", "UserMatchThreshold": 70 }, "output": { "FaceModelVersion": "6", "SearchedFace": { "FaceDetail": { "BoundingBox": { "Height": 0.07510016113519669, "Left": 0.3598678708076477, "Top": 0.5391526818275452, "Width": 0.03692837432026863 } } }, "UnsearchedFaces": [ { "FaceDetails": { "BoundingBox": { "Height": 0.0682177022099495, "Left": 0.6102562546730042, "Top": 0.5593535900115967, "Width": 0.031677018851041794 } }, "Reasons": [ "FACE_NOT_LARGEST" ] }, { "FaceDetails": { "BoundingBox": { "Height": 0.06347997486591339, "Left": 0.516062319278717, "Top": 0.6080358028411865, "Width": 0.03254449740052223 } }, "Reasons": [ "FACE_NOT_LARGEST" ] } ], "UserMatches": [ { "Similarity": 99.88186645507812, "User": { "UserId": "demoUser1", "UserStatus": "ACTIVE" } } ] }, "comments": { "input": { }, "output": { } }, "description": "Searches for UserIDs using a supplied image.", "id": "searchusersbyimage-1686183178610", "title": "SearchUsersByImage" } ], "StartMediaAnalysisJob": [ { "input": { "Input": { "S3Object": { "Bucket": "input-bucket", "Name": "input-manifest.json" } }, "JobName": "job-name", "OperationsConfig": { "DetectModerationLabels": { "MinConfidence": 50, "ProjectVersion": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958" } }, "OutputConfig": { "S3Bucket": "output-bucket", "S3KeyPrefix": "output-location" } }, "output": { "JobId": "861a0645d98ef88efb75477628c011c04942d9d5f58faf2703c393c8cf8c1537" }, "comments": { "input": { }, "output": { } }, "description": "Initiates a new media analysis job.", "id": "startmediaanalysisjob-1697651090922", "title": "StartMediaAnalysisJob" } ], "StartProjectVersion": [ { "input": { "MaxInferenceUnits": 1, "MinInferenceUnits": 1, "ProjectVersionArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958" }, "output": { "Status": "STARTING" }, "comments": { "input": { }, "output": { } }, "description": "Starts a version of an Amazon Rekognition Custom Labels model.", "id": "to-start-an-amazon-rekognition-custom-labels-model-1690559168398", "title": "To start an Amazon Rekognition Custom Labels model" } ], "StopProjectVersion": [ { "input": { "ProjectVersionArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958" }, "output": { "Status": "STOPPING" }, "comments": { "input": { }, "output": { } }, "description": "Stops a version of an Amazon Rekognition Custom Labels model.", "id": "to-stop-an-amazon-rekognition-custom-labels-model-1690561110698", "title": "To stop an Amazon Rekognition Custom Labels model." } ], "UpdateDatasetEntries": [ { "input": { "Changes": { "GroundTruth": "{\"source-ref\":\"s3://custom-labels-console-us-east-1-111111111/assets/flowers_1_test_dataset/mediterranean_spurge4.jpg\",\"mediterranean_spurge\":1,\"mediterranean_spurge-metadata\":{\"confidence\":1,\"job-name\":\"labeling-job/mediterranean_spurge\",\"class-name\":\"mediterranean_spurge\",\"human-annotated\":\"yes\",\"creation-date\":\"2021-07-11T03:33:42.025Z\",\"type\":\"groundtruth/image-classification\"},\"with_leaves\":1,\"with_leaves-metadata\":{\"confidence\":1,\"job-name\":\"labeling-job/with_leaves\",\"class-name\":\"with_leaves\",\"human-annotated\":\"yes\",\"creation-date\":\"2021-07-11T03:33:42.025Z\",\"type\":\"groundtruth/image-classification\"}}" }, "DatasetArn": "arn:aws:rekognition:us-east-1:111122223333:project/my-proj-2/dataset/train/1690564858106" }, "output": { }, "comments": { "input": { }, "output": { } }, "description": "Adds dataset entries to an Amazon Rekognition Custom Labels dataset.", "id": "to-add-dataset-entries-to-an-amazon-rekognition-custom-labels-dataset-1690816977073", "title": "To-add dataset entries to an Amazon Rekognition Custom Labels dataset" } ] } }	50.43 KB	2025-05-25 12:27
📄 paginators-1.json { "pagination": { "DescribeProjectVersions": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken", "result_key": "ProjectVersionDescriptions" }, "DescribeProjects": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken", "result_key": "ProjectDescriptions" }, "GetCelebrityRecognition": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken" }, "GetContentModeration": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken" }, "GetFaceDetection": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken" }, "GetFaceSearch": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken" }, "GetLabelDetection": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken" }, "GetPersonTracking": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken" }, "GetSegmentDetection": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken" }, "GetTextDetection": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken" }, "ListCollections": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken", "result_key": "CollectionIds" }, "ListDatasetEntries": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken", "result_key": "DatasetEntries" }, "ListDatasetLabels": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken", "result_key": "DatasetLabelDescriptions" }, "ListFaces": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken", "result_key": "Faces" }, "ListMediaAnalysisJobs": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken" }, "ListProjectPolicies": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken", "result_key": "ProjectPolicies" }, "ListStreamProcessors": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken" }, "ListUsers": { "input_token": "NextToken", "limit_key": "MaxResults", "output_token": "NextToken", "result_key": "Users" } } }	2.7 KB	2025-05-25 12:27
📄 smoke-2.json { "version": 2, "testCases": [ { "id": "ListCollectionsSuccess", "operationName": "ListCollections", "input": {}, "expectation": { "success": {} }, "config": { "region": "us-west-2" } } ] }	334 B	2025-05-25 12:27
📄 smoke.json { "version": 1, "defaultRegion": "us-west-2", "testCases": [ { "operationName": "ListCollections", "input": {}, "errorExpectedFromService": false } ] }	220 B	2025-05-25 12:27
📄 waiters-2.json { "version": 2, "waiters": { "ProjectVersionTrainingCompleted": { "description": "Wait until the ProjectVersion training completes.", "operation": "DescribeProjectVersions", "delay": 120, "maxAttempts": 360, "acceptors": [ { "state": "success", "matcher": "pathAll", "argument": "ProjectVersionDescriptions[].Status", "expected": "TRAINING_COMPLETED" }, { "state": "failure", "matcher": "pathAny", "argument": "ProjectVersionDescriptions[].Status", "expected": "TRAINING_FAILED" } ] }, "ProjectVersionRunning": { "description": "Wait until the ProjectVersion is running.", "delay": 30, "maxAttempts": 40, "operation": "DescribeProjectVersions", "acceptors": [ { "state": "success", "matcher": "pathAll", "argument": "ProjectVersionDescriptions[].Status", "expected": "RUNNING" }, { "state": "failure", "matcher": "pathAny", "argument": "ProjectVersionDescriptions[].Status", "expected": "FAILED" } ] } } }	1.51 KB	2025-05-25 12:27

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