cdk-amazon-chime-resources/node_modules/aws-sdk/clients/rekognition.d.ts


import {Request} from '../lib/request';
import {Response} from '../lib/response';
import {AWSError} from '../lib/error';
import {Service} from '../lib/service';
import {WaiterConfiguration} from '../lib/service';
import {ServiceConfigurationOptions} from '../lib/service';
import {ConfigBase as Config} from '../lib/config-base';
interface Blob {}
declare class Rekognition extends Service {
  /**
   * Constructs a service object. This object has one method for each API operation.
   */
  constructor(options?: Rekognition.Types.ClientConfiguration)
  config: Config & Rekognition.Types.ClientConfiguration;
  /**
   * Compares a face in the source input image with each of the 100 largest faces detected in the target input image.   If the source image contains multiple faces, the service detects the largest face and compares it with each face detected in the target image.   CompareFaces uses machine learning algorithms, which are probabilistic. A false negative is an incorrect prediction that a face in the target image has a low similarity confidence score when compared to the face in the source image. To reduce the probability of false negatives, we recommend that you compare the target image against multiple source images. If you plan to use CompareFaces to make a decision that impacts an individual's rights, privacy, or access to services, we recommend that you pass the result to a human for review and further validation before taking action.  You pass the input and target images either as base64-encoded image bytes or as references to images in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file.  In response, the operation returns an array of face matches ordered by similarity score in descending order. For each face match, the response provides a bounding box of the face, facial landmarks, pose details (pitch, roll, and yaw), quality (brightness and sharpness), and confidence value (indicating the level of confidence that the bounding box contains a face). The response also provides a similarity score, which indicates how closely the faces match.   By default, only faces with a similarity score of greater than or equal to 80% are returned in the response. You can change this value by specifying the SimilarityThreshold parameter.   CompareFaces also returns an array of faces that don't match the source image. For each face, it returns a bounding box, confidence value, landmarks, pose details, and quality. The response also returns information about the face in the source image, including the bounding box of the face and confidence value. The QualityFilter input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. Use QualityFilter to set the quality bar by specifying LOW, MEDIUM, or HIGH. If you do not want to filter detected faces, specify NONE. The default value is NONE.  If the image doesn't contain Exif metadata, CompareFaces returns orientation information for the source and target images. Use these values to display the images with the correct image orientation. If no faces are detected in the source or target images, CompareFaces returns an InvalidParameterException error.    This is a stateless API operation. That is, data returned by this operation doesn't persist.  For an example, see Comparing Faces in Images in the Amazon Rekognition Developer Guide. This operation requires permissions to perform the rekognition:CompareFaces action.
   */
  compareFaces(params: Rekognition.Types.CompareFacesRequest, callback?: (err: AWSError, data: Rekognition.Types.CompareFacesResponse) => void): Request<Rekognition.Types.CompareFacesResponse, AWSError>;
  /**
   * Compares a face in the source input image with each of the 100 largest faces detected in the target input image.   If the source image contains multiple faces, the service detects the largest face and compares it with each face detected in the target image.   CompareFaces uses machine learning algorithms, which are probabilistic. A false negative is an incorrect prediction that a face in the target image has a low similarity confidence score when compared to the face in the source image. To reduce the probability of false negatives, we recommend that you compare the target image against multiple source images. If you plan to use CompareFaces to make a decision that impacts an individual's rights, privacy, or access to services, we recommend that you pass the result to a human for review and further validation before taking action.  You pass the input and target images either as base64-encoded image bytes or as references to images in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file.  In response, the operation returns an array of face matches ordered by similarity score in descending order. For each face match, the response provides a bounding box of the face, facial landmarks, pose details (pitch, roll, and yaw), quality (brightness and sharpness), and confidence value (indicating the level of confidence that the bounding box contains a face). The response also provides a similarity score, which indicates how closely the faces match.   By default, only faces with a similarity score of greater than or equal to 80% are returned in the response. You can change this value by specifying the SimilarityThreshold parameter.   CompareFaces also returns an array of faces that don't match the source image. For each face, it returns a bounding box, confidence value, landmarks, pose details, and quality. The response also returns information about the face in the source image, including the bounding box of the face and confidence value. The QualityFilter input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. Use QualityFilter to set the quality bar by specifying LOW, MEDIUM, or HIGH. If you do not want to filter detected faces, specify NONE. The default value is NONE.  If the image doesn't contain Exif metadata, CompareFaces returns orientation information for the source and target images. Use these values to display the images with the correct image orientation. If no faces are detected in the source or target images, CompareFaces returns an InvalidParameterException error.    This is a stateless API operation. That is, data returned by this operation doesn't persist.  For an example, see Comparing Faces in Images in the Amazon Rekognition Developer Guide. This operation requires permissions to perform the rekognition:CompareFaces action.
   */
  compareFaces(callback?: (err: AWSError, data: Rekognition.Types.CompareFacesResponse) => void): Request<Rekognition.Types.CompareFacesResponse, AWSError>;
  /**
   * Copies a version of an Amazon Rekognition Custom Labels model from a source project to a destination project. The source and destination projects can be in different AWS accounts but must be in the same AWS Region. You can't copy a model to another AWS service.  To copy a model version to a different AWS account, you need to create a resource-based policy known as a project policy. You attach the project policy to the source project by calling PutProjectPolicy. The project policy gives permission to copy the model version from a trusting AWS account to a trusted account. For more information creating and attaching a project policy, see Attaching a project policy (SDK) in the Amazon Rekognition Custom Labels Developer Guide.  If you are copying a model version to a project in the same AWS account, you don't need to create a project policy.  To copy a model, the destination project, source project, and source model version must already exist.  Copying a model version takes a while to complete. To get the current status, call DescribeProjectVersions and check the value of Status in the ProjectVersionDescription object. The copy operation has finished when the value of Status is COPYING_COMPLETED. This operation requires permissions to perform the rekognition:CopyProjectVersion action.
   */
  copyProjectVersion(params: Rekognition.Types.CopyProjectVersionRequest, callback?: (err: AWSError, data: Rekognition.Types.CopyProjectVersionResponse) => void): Request<Rekognition.Types.CopyProjectVersionResponse, AWSError>;
  /**
   * Copies a version of an Amazon Rekognition Custom Labels model from a source project to a destination project. The source and destination projects can be in different AWS accounts but must be in the same AWS Region. You can't copy a model to another AWS service.  To copy a model version to a different AWS account, you need to create a resource-based policy known as a project policy. You attach the project policy to the source project by calling PutProjectPolicy. The project policy gives permission to copy the model version from a trusting AWS account to a trusted account. For more information creating and attaching a project policy, see Attaching a project policy (SDK) in the Amazon Rekognition Custom Labels Developer Guide.  If you are copying a model version to a project in the same AWS account, you don't need to create a project policy.  To copy a model, the destination project, source project, and source model version must already exist.  Copying a model version takes a while to complete. To get the current status, call DescribeProjectVersions and check the value of Status in the ProjectVersionDescription object. The copy operation has finished when the value of Status is COPYING_COMPLETED. This operation requires permissions to perform the rekognition:CopyProjectVersion action.
   */
  copyProjectVersion(callback?: (err: AWSError, data: Rekognition.Types.CopyProjectVersionResponse) => void): Request<Rekognition.Types.CopyProjectVersionResponse, AWSError>;
  /**
   * Creates a collection in an AWS Region. You can add faces to the collection using the IndexFaces operation.  For example, you might create collections, one for each of your application users. A user can then index faces using the IndexFaces operation and persist results in a specific collection. Then, a user can search the collection for faces in the user-specific container.  When you create a collection, it is associated with the latest version of the face model version.  Collection names are case-sensitive.  This operation requires permissions to perform the rekognition:CreateCollection action. If you want to tag your collection, you also require permission to perform the rekognition:TagResource operation.
   */
  createCollection(params: Rekognition.Types.CreateCollectionRequest, callback?: (err: AWSError, data: Rekognition.Types.CreateCollectionResponse) => void): Request<Rekognition.Types.CreateCollectionResponse, AWSError>;
  /**
   * Creates a collection in an AWS Region. You can add faces to the collection using the IndexFaces operation.  For example, you might create collections, one for each of your application users. A user can then index faces using the IndexFaces operation and persist results in a specific collection. Then, a user can search the collection for faces in the user-specific container.  When you create a collection, it is associated with the latest version of the face model version.  Collection names are case-sensitive.  This operation requires permissions to perform the rekognition:CreateCollection action. If you want to tag your collection, you also require permission to perform the rekognition:TagResource operation.
   */
  createCollection(callback?: (err: AWSError, data: Rekognition.Types.CreateCollectionResponse) => void): Request<Rekognition.Types.CreateCollectionResponse, AWSError>;
  /**
   * Creates a new Amazon Rekognition Custom Labels dataset. You can create a dataset by using an Amazon Sagemaker format manifest file or by copying an existing Amazon Rekognition Custom Labels dataset. To create a training dataset for a project, specify train for the value of DatasetType. To create the test dataset for a project, specify test for the value of DatasetType.  The response from CreateDataset is the Amazon Resource Name (ARN) for the dataset. Creating a dataset takes a while to complete. Use DescribeDataset to check the current status. The dataset created successfully if the value of Status is CREATE_COMPLETE.  To check if any non-terminal errors occurred, call ListDatasetEntries and check for the presence of errors lists in the JSON Lines. Dataset creation fails if a terminal error occurs (Status = CREATE_FAILED). Currently, you can't access the terminal error information.  For more information, see Creating dataset in the Amazon Rekognition Custom Labels Developer Guide. This operation requires permissions to perform the rekognition:CreateDataset action. If you want to copy an existing dataset, you also require permission to perform the rekognition:ListDatasetEntries action.
   */
  createDataset(params: Rekognition.Types.CreateDatasetRequest, callback?: (err: AWSError, data: Rekognition.Types.CreateDatasetResponse) => void): Request<Rekognition.Types.CreateDatasetResponse, AWSError>;
  /**
   * Creates a new Amazon Rekognition Custom Labels dataset. You can create a dataset by using an Amazon Sagemaker format manifest file or by copying an existing Amazon Rekognition Custom Labels dataset. To create a training dataset for a project, specify train for the value of DatasetType. To create the test dataset for a project, specify test for the value of DatasetType.  The response from CreateDataset is the Amazon Resource Name (ARN) for the dataset. Creating a dataset takes a while to complete. Use DescribeDataset to check the current status. The dataset created successfully if the value of Status is CREATE_COMPLETE.  To check if any non-terminal errors occurred, call ListDatasetEntries and check for the presence of errors lists in the JSON Lines. Dataset creation fails if a terminal error occurs (Status = CREATE_FAILED). Currently, you can't access the terminal error information.  For more information, see Creating dataset in the Amazon Rekognition Custom Labels Developer Guide. This operation requires permissions to perform the rekognition:CreateDataset action. If you want to copy an existing dataset, you also require permission to perform the rekognition:ListDatasetEntries action.
   */
  createDataset(callback?: (err: AWSError, data: Rekognition.Types.CreateDatasetResponse) => void): Request<Rekognition.Types.CreateDatasetResponse, AWSError>;
  /**
   * This API operation initiates a Face Liveness session. It returns a SessionId, which you can use to start streaming Face Liveness video and get the results for a Face Liveness session. You can use the OutputConfig option in the Settings parameter to provide an Amazon S3 bucket location. The Amazon S3 bucket stores reference images and audit images. You can use AuditImagesLimit to limit of audit images returned. 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. 
   */
  createFaceLivenessSession(params: Rekognition.Types.CreateFaceLivenessSessionRequest, callback?: (err: AWSError, data: Rekognition.Types.CreateFaceLivenessSessionResponse) => void): Request<Rekognition.Types.CreateFaceLivenessSessionResponse, AWSError>;
  /**
   * This API operation initiates a Face Liveness session. It returns a SessionId, which you can use to start streaming Face Liveness video and get the results for a Face Liveness session. You can use the OutputConfig option in the Settings parameter to provide an Amazon S3 bucket location. The Amazon S3 bucket stores reference images and audit images. You can use AuditImagesLimit to limit of audit images returned. 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. 
   */
  createFaceLivenessSession(callback?: (err: AWSError, data: Rekognition.Types.CreateFaceLivenessSessionResponse) => void): Request<Rekognition.Types.CreateFaceLivenessSessionResponse, AWSError>;
  /**
   * Creates a new Amazon Rekognition Custom Labels project. A project is a group of resources (datasets, model versions) that you use to create and manage Amazon Rekognition Custom Labels models.  This operation requires permissions to perform the rekognition:CreateProject action.
   */
  createProject(params: Rekognition.Types.CreateProjectRequest, callback?: (err: AWSError, data: Rekognition.Types.CreateProjectResponse) => void): Request<Rekognition.Types.CreateProjectResponse, AWSError>;
  /**
   * Creates a new Amazon Rekognition Custom Labels project. A project is a group of resources (datasets, model versions) that you use to create and manage Amazon Rekognition Custom Labels models.  This operation requires permissions to perform the rekognition:CreateProject action.
   */
  createProject(callback?: (err: AWSError, data: Rekognition.Types.CreateProjectResponse) => void): Request<Rekognition.Types.CreateProjectResponse, AWSError>;
  /**
   * Creates a new version of a model and begins training. Models are managed as part of an Amazon Rekognition Custom Labels project. The response from CreateProjectVersion is an Amazon Resource Name (ARN) for the version of the model.  Training uses the training and test datasets associated with the project. For more information, see Creating training and test dataset in the Amazon Rekognition Custom Labels Developer Guide.   You can train a model in a project that doesn't have associated datasets by specifying manifest files in the TrainingData and TestingData fields.  If you open the console after training a model with manifest files, Amazon Rekognition Custom Labels creates the datasets for you using the most recent manifest files. You can no longer train a model version for the project by specifying manifest files.  Instead of training with a project without associated datasets, we recommend that you use the manifest files to create training and test datasets for the project.  Training takes a while to complete. You can get the current status by calling DescribeProjectVersions. Training completed successfully if the value of the Status field is TRAINING_COMPLETED. If training fails, see Debugging a failed model training in the Amazon Rekognition Custom Labels developer guide.  Once training has successfully completed, call DescribeProjectVersions to get the training results and evaluate the model. For more information, see Improving a trained Amazon Rekognition Custom Labels model in the Amazon Rekognition Custom Labels developers guide.  After evaluating the model, you start the model by calling StartProjectVersion. This operation requires permissions to perform the rekognition:CreateProjectVersion action.
   */
  createProjectVersion(params: Rekognition.Types.CreateProjectVersionRequest, callback?: (err: AWSError, data: Rekognition.Types.CreateProjectVersionResponse) => void): Request<Rekognition.Types.CreateProjectVersionResponse, AWSError>;
  /**
   * Creates a new version of a model and begins training. Models are managed as part of an Amazon Rekognition Custom Labels project. The response from CreateProjectVersion is an Amazon Resource Name (ARN) for the version of the model.  Training uses the training and test datasets associated with the project. For more information, see Creating training and test dataset in the Amazon Rekognition Custom Labels Developer Guide.   You can train a model in a project that doesn't have associated datasets by specifying manifest files in the TrainingData and TestingData fields.  If you open the console after training a model with manifest files, Amazon Rekognition Custom Labels creates the datasets for you using the most recent manifest files. You can no longer train a model version for the project by specifying manifest files.  Instead of training with a project without associated datasets, we recommend that you use the manifest files to create training and test datasets for the project.  Training takes a while to complete. You can get the current status by calling DescribeProjectVersions. Training completed successfully if the value of the Status field is TRAINING_COMPLETED. If training fails, see Debugging a failed model training in the Amazon Rekognition Custom Labels developer guide.  Once training has successfully completed, call DescribeProjectVersions to get the training results and evaluate the model. For more information, see Improving a trained Amazon Rekognition Custom Labels model in the Amazon Rekognition Custom Labels developers guide.  After evaluating the model, you start the model by calling StartProjectVersion. This operation requires permissions to perform the rekognition:CreateProjectVersion action.
   */
  createProjectVersion(callback?: (err: AWSError, data: Rekognition.Types.CreateProjectVersionResponse) => void): Request<Rekognition.Types.CreateProjectVersionResponse, AWSError>;
  /**
   * Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces or to detect labels in a streaming video. Amazon Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. There are two different settings for stream processors in Amazon Rekognition: detecting faces and detecting labels.   If you are creating a stream processor for detecting faces, you provide as input a Kinesis video stream (Input) and a Kinesis data stream (Output) stream for receiving the output. You must use the FaceSearch option in Settings, specifying the collection that contains the faces you want to recognize. After you have finished analyzing a streaming video, use StopStreamProcessor to stop processing.   If you are creating a stream processor to detect labels, you provide as input a Kinesis video stream (Input), Amazon S3 bucket information (Output), and an Amazon SNS topic ARN (NotificationChannel). You can also provide a KMS key ID to encrypt the data sent to your Amazon S3 bucket. You specify what you want to detect by using the ConnectedHome option in settings, and selecting one of the following: PERSON, PET, PACKAGE, ALL You can also specify where in the frame you want Amazon Rekognition to monitor with RegionsOfInterest. When you run the StartStreamProcessor operation on a label detection stream processor, you input start and stop information to determine the length of the processing time.    Use Name to assign an identifier for the stream processor. You use Name to manage the stream processor. For example, you can start processing the source video by calling StartStreamProcessor with the Name field.  This operation requires permissions to perform the rekognition:CreateStreamProcessor action. If you want to tag your stream processor, you also require permission to perform the rekognition:TagResource operation.
   */
  createStreamProcessor(params: Rekognition.Types.CreateStreamProcessorRequest, callback?: (err: AWSError, data: Rekognition.Types.CreateStreamProcessorResponse) => void): Request<Rekognition.Types.CreateStreamProcessorResponse, AWSError>;
  /**
   * Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces or to detect labels in a streaming video. Amazon Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. There are two different settings for stream processors in Amazon Rekognition: detecting faces and detecting labels.   If you are creating a stream processor for detecting faces, you provide as input a Kinesis video stream (Input) and a Kinesis data stream (Output) stream for receiving the output. You must use the FaceSearch option in Settings, specifying the collection that contains the faces you want to recognize. After you have finished analyzing a streaming video, use StopStreamProcessor to stop processing.   If you are creating a stream processor to detect labels, you provide as input a Kinesis video stream (Input), Amazon S3 bucket information (Output), and an Amazon SNS topic ARN (NotificationChannel). You can also provide a KMS key ID to encrypt the data sent to your Amazon S3 bucket. You specify what you want to detect by using the ConnectedHome option in settings, and selecting one of the following: PERSON, PET, PACKAGE, ALL You can also specify where in the frame you want Amazon Rekognition to monitor with RegionsOfInterest. When you run the StartStreamProcessor operation on a label detection stream processor, you input start and stop information to determine the length of the processing time.    Use Name to assign an identifier for the stream processor. You use Name to manage the stream processor. For example, you can start processing the source video by calling StartStreamProcessor with the Name field.  This operation requires permissions to perform the rekognition:CreateStreamProcessor action. If you want to tag your stream processor, you also require permission to perform the rekognition:TagResource operation.
   */
  createStreamProcessor(callback?: (err: AWSError, data: Rekognition.Types.CreateStreamProcessorResponse) => void): Request<Rekognition.Types.CreateStreamProcessorResponse, AWSError>;
  /**
   * Deletes the specified collection. Note that this operation removes all faces in the collection. For an example, see Deleting a collection. This operation requires permissions to perform the rekognition:DeleteCollection action.
   */
  deleteCollection(params: Rekognition.Types.DeleteCollectionRequest, callback?: (err: AWSError, data: Rekognition.Types.DeleteCollectionResponse) => void): Request<Rekognition.Types.DeleteCollectionResponse, AWSError>;
  /**
   * Deletes the specified collection. Note that this operation removes all faces in the collection. For an example, see Deleting a collection. This operation requires permissions to perform the rekognition:DeleteCollection action.
   */
  deleteCollection(callback?: (err: AWSError, data: Rekognition.Types.DeleteCollectionResponse) => void): Request<Rekognition.Types.DeleteCollectionResponse, AWSError>;
  /**
   * Deletes an existing Amazon Rekognition Custom Labels dataset. Deleting a dataset might take while. Use DescribeDataset to check the current status. The dataset is still deleting if the value of Status is DELETE_IN_PROGRESS. If you try to access the dataset after it is deleted, you get a ResourceNotFoundException exception.  You can't delete a dataset while it is creating (Status = CREATE_IN_PROGRESS) or if the dataset is updating (Status = UPDATE_IN_PROGRESS). This operation requires permissions to perform the rekognition:DeleteDataset action.
   */
  deleteDataset(params: Rekognition.Types.DeleteDatasetRequest, callback?: (err: AWSError, data: Rekognition.Types.DeleteDatasetResponse) => void): Request<Rekognition.Types.DeleteDatasetResponse, AWSError>;
  /**
   * Deletes an existing Amazon Rekognition Custom Labels dataset. Deleting a dataset might take while. Use DescribeDataset to check the current status. The dataset is still deleting if the value of Status is DELETE_IN_PROGRESS. If you try to access the dataset after it is deleted, you get a ResourceNotFoundException exception.  You can't delete a dataset while it is creating (Status = CREATE_IN_PROGRESS) or if the dataset is updating (Status = UPDATE_IN_PROGRESS). This operation requires permissions to perform the rekognition:DeleteDataset action.
   */
  deleteDataset(callback?: (err: AWSError, data: Rekognition.Types.DeleteDatasetResponse) => void): Request<Rekognition.Types.DeleteDatasetResponse, AWSError>;
  /**
   * Deletes faces from a collection. You specify a collection ID and an array of face IDs to remove from the collection. This operation requires permissions to perform the rekognition:DeleteFaces action.
   */
  deleteFaces(params: Rekognition.Types.DeleteFacesRequest, callback?: (err: AWSError, data: Rekognition.Types.DeleteFacesResponse) => void): Request<Rekognition.Types.DeleteFacesResponse, AWSError>;
  /**
   * Deletes faces from a collection. You specify a collection ID and an array of face IDs to remove from the collection. This operation requires permissions to perform the rekognition:DeleteFaces action.
   */
  deleteFaces(callback?: (err: AWSError, data: Rekognition.Types.DeleteFacesResponse) => void): Request<Rekognition.Types.DeleteFacesResponse, AWSError>;
  /**
   * Deletes an Amazon Rekognition Custom Labels project. To delete a project you must first delete all models associated with the project. To delete a model, see DeleteProjectVersion.  DeleteProject is an asynchronous operation. To check if the project is deleted, call DescribeProjects. The project is deleted when the project no longer appears in the response. Be aware that deleting a given project will also delete any ProjectPolicies associated with that project. This operation requires permissions to perform the rekognition:DeleteProject action. 
   */
  deleteProject(params: Rekognition.Types.DeleteProjectRequest, callback?: (err: AWSError, data: Rekognition.Types.DeleteProjectResponse) => void): Request<Rekognition.Types.DeleteProjectResponse, AWSError>;
  /**
   * Deletes an Amazon Rekognition Custom Labels project. To delete a project you must first delete all models associated with the project. To delete a model, see DeleteProjectVersion.  DeleteProject is an asynchronous operation. To check if the project is deleted, call DescribeProjects. The project is deleted when the project no longer appears in the response. Be aware that deleting a given project will also delete any ProjectPolicies associated with that project. This operation requires permissions to perform the rekognition:DeleteProject action. 
   */
  deleteProject(callback?: (err: AWSError, data: Rekognition.Types.DeleteProjectResponse) => void): Request<Rekognition.Types.DeleteProjectResponse, AWSError>;
  /**
   * Deletes an existing project policy. To get a list of project policies attached to a project, call ListProjectPolicies. To attach a project policy to a project, call PutProjectPolicy. This operation requires permissions to perform the rekognition:DeleteProjectPolicy action.
   */
  deleteProjectPolicy(params: Rekognition.Types.DeleteProjectPolicyRequest, callback?: (err: AWSError, data: Rekognition.Types.DeleteProjectPolicyResponse) => void): Request<Rekognition.Types.DeleteProjectPolicyResponse, AWSError>;
  /**
   * Deletes an existing project policy. To get a list of project policies attached to a project, call ListProjectPolicies. To attach a project policy to a project, call PutProjectPolicy. This operation requires permissions to perform the rekognition:DeleteProjectPolicy action.
   */
  deleteProjectPolicy(callback?: (err: AWSError, data: Rekognition.Types.DeleteProjectPolicyResponse) => void): Request<Rekognition.Types.DeleteProjectPolicyResponse, AWSError>;
  /**
   * Deletes an Amazon Rekognition Custom Labels model.  You can't delete a model if it is running or if it is training. To check the status of a model, use the Status field returned from DescribeProjectVersions. To stop a running model call StopProjectVersion. If the model is training, wait until it finishes. This operation requires permissions to perform the rekognition:DeleteProjectVersion action. 
   */
  deleteProjectVersion(params: Rekognition.Types.DeleteProjectVersionRequest, callback?: (err: AWSError, data: Rekognition.Types.DeleteProjectVersionResponse) => void): Request<Rekognition.Types.DeleteProjectVersionResponse, AWSError>;
  /**
   * Deletes an Amazon Rekognition Custom Labels model.  You can't delete a model if it is running or if it is training. To check the status of a model, use the Status field returned from DescribeProjectVersions. To stop a running model call StopProjectVersion. If the model is training, wait until it finishes. This operation requires permissions to perform the rekognition:DeleteProjectVersion action. 
   */
  deleteProjectVersion(callback?: (err: AWSError, data: Rekognition.Types.DeleteProjectVersionResponse) => void): Request<Rekognition.Types.DeleteProjectVersionResponse, AWSError>;
  /**
   * Deletes the stream processor identified by Name. You assign the value for Name when you create the stream processor with CreateStreamProcessor. You might not be able to use the same name for a stream processor for a few seconds after calling DeleteStreamProcessor.
   */
  deleteStreamProcessor(params: Rekognition.Types.DeleteStreamProcessorRequest, callback?: (err: AWSError, data: Rekognition.Types.DeleteStreamProcessorResponse) => void): Request<Rekognition.Types.DeleteStreamProcessorResponse, AWSError>;
  /**
   * Deletes the stream processor identified by Name. You assign the value for Name when you create the stream processor with CreateStreamProcessor. You might not be able to use the same name for a stream processor for a few seconds after calling DeleteStreamProcessor.
   */
  deleteStreamProcessor(callback?: (err: AWSError, data: Rekognition.Types.DeleteStreamProcessorResponse) => void): Request<Rekognition.Types.DeleteStreamProcessorResponse, AWSError>;
  /**
   * Describes the specified collection. You can use DescribeCollection to get information, such as the number of faces indexed into a collection and the version of the model used by the collection for face detection. For more information, see Describing a Collection in the Amazon Rekognition Developer Guide.
   */
  describeCollection(params: Rekognition.Types.DescribeCollectionRequest, callback?: (err: AWSError, data: Rekognition.Types.DescribeCollectionResponse) => void): Request<Rekognition.Types.DescribeCollectionResponse, AWSError>;
  /**
   * Describes the specified collection. You can use DescribeCollection to get information, such as the number of faces indexed into a collection and the version of the model used by the collection for face detection. For more information, see Describing a Collection in the Amazon Rekognition Developer Guide.
   */
  describeCollection(callback?: (err: AWSError, data: Rekognition.Types.DescribeCollectionResponse) => void): Request<Rekognition.Types.DescribeCollectionResponse, AWSError>;
  /**
   *  Describes an Amazon Rekognition Custom Labels dataset. You can get information such as the current status of a dataset and statistics about the images and labels in a dataset.  This operation requires permissions to perform the rekognition:DescribeDataset action.
   */
  describeDataset(params: Rekognition.Types.DescribeDatasetRequest, callback?: (err: AWSError, data: Rekognition.Types.DescribeDatasetResponse) => void): Request<Rekognition.Types.DescribeDatasetResponse, AWSError>;
  /**
   *  Describes an Amazon Rekognition Custom Labels dataset. You can get information such as the current status of a dataset and statistics about the images and labels in a dataset.  This operation requires permissions to perform the rekognition:DescribeDataset action.
   */
  describeDataset(callback?: (err: AWSError, data: Rekognition.Types.DescribeDatasetResponse) => void): Request<Rekognition.Types.DescribeDatasetResponse, AWSError>;
  /**
   * Lists and describes the versions of a model in an Amazon Rekognition Custom Labels project. You can specify up to 10 model versions in ProjectVersionArns. If you don't specify a value, descriptions for all model versions in the project are returned. This operation requires permissions to perform the rekognition:DescribeProjectVersions action.
   */
  describeProjectVersions(params: Rekognition.Types.DescribeProjectVersionsRequest, callback?: (err: AWSError, data: Rekognition.Types.DescribeProjectVersionsResponse) => void): Request<Rekognition.Types.DescribeProjectVersionsResponse, AWSError>;
  /**
   * Lists and describes the versions of a model in an Amazon Rekognition Custom Labels project. You can specify up to 10 model versions in ProjectVersionArns. If you don't specify a value, descriptions for all model versions in the project are returned. This operation requires permissions to perform the rekognition:DescribeProjectVersions action.
   */
  describeProjectVersions(callback?: (err: AWSError, data: Rekognition.Types.DescribeProjectVersionsResponse) => void): Request<Rekognition.Types.DescribeProjectVersionsResponse, AWSError>;
  /**
   * Gets information about your Amazon Rekognition Custom Labels projects.  This operation requires permissions to perform the rekognition:DescribeProjects action.
   */
  describeProjects(params: Rekognition.Types.DescribeProjectsRequest, callback?: (err: AWSError, data: Rekognition.Types.DescribeProjectsResponse) => void): Request<Rekognition.Types.DescribeProjectsResponse, AWSError>;
  /**
   * Gets information about your Amazon Rekognition Custom Labels projects.  This operation requires permissions to perform the rekognition:DescribeProjects action.
   */
  describeProjects(callback?: (err: AWSError, data: Rekognition.Types.DescribeProjectsResponse) => void): Request<Rekognition.Types.DescribeProjectsResponse, AWSError>;
  /**
   * Provides information about a stream processor created by CreateStreamProcessor. You can get information about the input and output streams, the input parameters for the face recognition being performed, and the current status of the stream processor.
   */
  describeStreamProcessor(params: Rekognition.Types.DescribeStreamProcessorRequest, callback?: (err: AWSError, data: Rekognition.Types.DescribeStreamProcessorResponse) => void): Request<Rekognition.Types.DescribeStreamProcessorResponse, AWSError>;
  /**
   * Provides information about a stream processor created by CreateStreamProcessor. You can get information about the input and output streams, the input parameters for the face recognition being performed, and the current status of the stream processor.
   */
  describeStreamProcessor(callback?: (err: AWSError, data: Rekognition.Types.DescribeStreamProcessorResponse) => void): Request<Rekognition.Types.DescribeStreamProcessorResponse, AWSError>;
  /**
   * Detects custom labels in a supplied image by using an Amazon Rekognition Custom Labels model.  You specify which version of a model version to use by using the ProjectVersionArn input parameter.  You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.   For each object that the model version detects on an image, the API returns a (CustomLabel) object in an array (CustomLabels). Each CustomLabel object provides the label name (Name), the level of confidence that the image contains the object (Confidence), and object location information, if it exists, for the label on the image (Geometry).  To filter labels that are returned, specify a value for MinConfidence. DetectCustomLabelsLabels only returns labels with a confidence that's higher than the specified value. The value of MinConfidence maps to the assumed threshold values created during training. For more information, see Assumed threshold in the Amazon Rekognition Custom Labels Developer Guide. Amazon Rekognition Custom Labels metrics expresses an assumed threshold as a floating point value between 0-1. The range of MinConfidence normalizes the threshold value to a percentage value (0-100). Confidence responses from DetectCustomLabels are also returned as a percentage. You can use MinConfidence to change the precision and recall or your model. For more information, see Analyzing an image in the Amazon Rekognition Custom Labels Developer Guide.  If you don't specify a value for MinConfidence, DetectCustomLabels returns labels based on the assumed threshold of each label. This is a stateless API operation. That is, the operation does not persist any data. This operation requires permissions to perform the rekognition:DetectCustomLabels action.  For more information, see Analyzing an image in the Amazon Rekognition Custom Labels Developer Guide. 
   */
  detectCustomLabels(params: Rekognition.Types.DetectCustomLabelsRequest, callback?: (err: AWSError, data: Rekognition.Types.DetectCustomLabelsResponse) => void): Request<Rekognition.Types.DetectCustomLabelsResponse, AWSError>;
  /**
   * Detects custom labels in a supplied image by using an Amazon Rekognition Custom Labels model.  You specify which version of a model version to use by using the ProjectVersionArn input parameter.  You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.   For each object that the model version detects on an image, the API returns a (CustomLabel) object in an array (CustomLabels). Each CustomLabel object provides the label name (Name), the level of confidence that the image contains the object (Confidence), and object location information, if it exists, for the label on the image (Geometry).  To filter labels that are returned, specify a value for MinConfidence. DetectCustomLabelsLabels only returns labels with a confidence that's higher than the specified value. The value of MinConfidence maps to the assumed threshold values created during training. For more information, see Assumed threshold in the Amazon Rekognition Custom Labels Developer Guide. Amazon Rekognition Custom Labels metrics expresses an assumed threshold as a floating point value between 0-1. The range of MinConfidence normalizes the threshold value to a percentage value (0-100). Confidence responses from DetectCustomLabels are also returned as a percentage. You can use MinConfidence to change the precision and recall or your model. For more information, see Analyzing an image in the Amazon Rekognition Custom Labels Developer Guide.  If you don't specify a value for MinConfidence, DetectCustomLabels returns labels based on the assumed threshold of each label. This is a stateless API operation. That is, the operation does not persist any data. This operation requires permissions to perform the rekognition:DetectCustomLabels action.  For more information, see Analyzing an image in the Amazon Rekognition Custom Labels Developer Guide. 
   */
  detectCustomLabels(callback?: (err: AWSError, data: Rekognition.Types.DetectCustomLabelsResponse) => void): Request<Rekognition.Types.DetectCustomLabelsResponse, AWSError>;
  /**
   * Detects faces within an image that is provided as input.  DetectFaces detects the 100 largest faces in the image. For each face detected, the operation returns face details. These details include a bounding box of the face, a confidence value (that the bounding box contains a face), and a fixed set of attributes such as facial landmarks (for example, coordinates of eye and mouth), presence of beard, sunglasses, and so on.  The face-detection algorithm is most effective on frontal faces. For non-frontal or obscured faces, the algorithm might not detect the faces or might detect faces with lower confidence.  You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.   This is a stateless API operation. That is, the operation does not persist any data.  This operation requires permissions to perform the rekognition:DetectFaces action. 
   */
  detectFaces(params: Rekognition.Types.DetectFacesRequest, callback?: (err: AWSError, data: Rekognition.Types.DetectFacesResponse) => void): Request<Rekognition.Types.DetectFacesResponse, AWSError>;
  /**
   * Detects faces within an image that is provided as input.  DetectFaces detects the 100 largest faces in the image. For each face detected, the operation returns face details. These details include a bounding box of the face, a confidence value (that the bounding box contains a face), and a fixed set of attributes such as facial landmarks (for example, coordinates of eye and mouth), presence of beard, sunglasses, and so on.  The face-detection algorithm is most effective on frontal faces. For non-frontal or obscured faces, the algorithm might not detect the faces or might detect faces with lower confidence.  You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.   This is a stateless API operation. That is, the operation does not persist any data.  This operation requires permissions to perform the rekognition:DetectFaces action. 
   */
  detectFaces(callback?: (err: AWSError, data: Rekognition.Types.DetectFacesResponse) => void): Request<Rekognition.Types.DetectFacesResponse, AWSError>;
  /**
   * Detects instances of real-world entities within an image (JPEG or PNG) provided as input. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; and concepts like landscape, evening, and nature.  For an example, see Analyzing images stored in an Amazon S3 bucket in the Amazon Rekognition Developer Guide. You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.   Optional Parameters  You can specify one or both of the GENERAL_LABELS and IMAGE_PROPERTIES feature types when calling the DetectLabels API. Including GENERAL_LABELS will ensure the response includes the labels detected in the input image, while including IMAGE_PROPERTIES will ensure the response includes information about the image quality and color. When using GENERAL_LABELS and/or IMAGE_PROPERTIES you can provide filtering criteria to the Settings parameter. You can filter with sets of individual labels or with label categories. You can specify inclusive filters, exclusive filters, or a combination of inclusive and exclusive filters. For more information on filtering see Detecting Labels in an Image. You can specify MinConfidence to control the confidence threshold for the labels returned. The default is 55%. You can also add the MaxLabels parameter to limit the number of labels returned. The default and upper limit is 1000 labels.  Response Elements   For each object, scene, and concept the API returns one or more labels. The API returns the following types of information about labels:    Name - The name of the detected label.     Confidence - The level of confidence in the label assigned to a detected object.     Parents - The ancestor labels for a detected label. DetectLabels returns a hierarchical taxonomy of detected labels. For example, a detected car might be assigned the label car. The label car has two parent labels: Vehicle (its parent) and Transportation (its grandparent). The response includes the all ancestors for a label, where every ancestor is a unique label. In the previous example, Car, Vehicle, and Transportation are returned as unique labels in the response.     Aliases - Possible Aliases for the label.     Categories - The label categories that the detected label belongs to.     BoundingBox — Bounding boxes are described for all instances of detected common object labels, returned in an array of Instance objects. An Instance object contains a BoundingBox object, describing the location of the label on the input image. It also includes the confidence for the accuracy of the detected bounding box.     The API returns the following information regarding the image, as part of the ImageProperties structure:   Quality - Information about the Sharpness, Brightness, and Contrast of the input image, scored between 0 to 100. Image quality is returned for the entire image, as well as the background and the foreground.    Dominant Color - An array of the dominant colors in the image.    Foreground - Information about the sharpness, brightness, and dominant colors of the input image’s foreground.    Background - Information about the sharpness, brightness, and dominant colors of the input image’s background.   The list of returned labels will include at least one label for every detected object, along with information about that label. In the following example, suppose the input image has a lighthouse, the sea, and a rock. The response includes all three labels, one for each object, as well as the confidence in the label:  {Name: lighthouse, Confidence: 98.4629}   {Name: rock,Confidence: 79.2097}    {Name: sea,Confidence: 75.061}  The list of labels can include multiple labels for the same object. For example, if the input image shows a flower (for example, a tulip), the operation might return the following three labels.   {Name: flower,Confidence: 99.0562}   {Name: plant,Confidence: 99.0562}   {Name: tulip,Confidence: 99.0562}  In this example, the detection algorithm more precisely identifies the flower as a tulip.  If the object detected is a person, the operation doesn't provide the same facial details that the DetectFaces operation provides.  This is a stateless API operation that doesn't return any data. This operation requires permissions to perform the rekognition:DetectLabels action. 
   */
  detectLabels(params: Rekognition.Types.DetectLabelsRequest, callback?: (err: AWSError, data: Rekognition.Types.DetectLabelsResponse) => void): Request<Rekognition.Types.DetectLabelsResponse, AWSError>;
  /**
   * Detects instances of real-world entities within an image (JPEG or PNG) provided as input. This in