prowler-sdk-poc/node_modules/aws-sdk/clients/rekognition.d.ts

# Python

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, role, 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, role, 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>;
  /**
   * 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 project. A project is a logical grouping of resources (images, Labels, models) and operations (training, evaluation and detection).  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 logical grouping of resources (images, Labels, models) and operations (training, evaluation and detection).  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. You can specify one training dataset and one testing dataset. The response from CreateProjectVersion is an Amazon Resource Name (ARN) for the version of the model.  Training takes a while to complete. You can get the current status by calling DescribeProjectVersions. Once training has successfully completed, call DescribeProjectVersions to get the training results and evaluate the model.  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. You can specify one training dataset and one testing dataset. The response from CreateProjectVersion is an Amazon Resource Name (ARN) for the version of the model.  Training takes a while to complete. You can get the current status by calling DescribeProjectVersions. Once training has successfully completed, call DescribeProjectVersions to get the training results and evaluate the model.  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 in a streaming video. Amazon Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. Amazon Rekognition Video sends analysis results to Amazon Kinesis Data Streams. You provide as input a Kinesis video stream (Input) and a Kinesis data stream (Output) stream. You also specify the face recognition criteria in Settings. For example, the collection containing faces that you want to recognize. 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.  After you have finished analyzing a streaming video, use StopStreamProcessor to stop processing. You can delete the stream processor by calling DeleteStreamProcessor. 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 in a streaming video. Amazon Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. Amazon Rekognition Video sends analysis results to Amazon Kinesis Data Streams. You provide as input a Kinesis video stream (Input) and a Kinesis data stream (Output) stream. You also specify the face recognition criteria in Settings. For example, the collection containing faces that you want to recognize. 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.  After you have finished analyzing a streaming video, use StopStreamProcessor to stop processing. You can delete the stream processor by calling DeleteStreamProcessor. 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 delete-collection-procedure. 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 delete-collection-procedure. 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 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. 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. 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 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>;
  /**
   * Lists and describes the models 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 models 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 models 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 models 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>;
  /**
   * Lists and 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>;
  /**
   * Lists and 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.   DetectLabels does not support the detection of activities. However, activity detection is supported for label detection in videos. For more information, see StartLabelDetection 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.   For each object, scene, and concept the API returns one or more labels. Each label provides the object name, and the level of confidence that the image contains the object. For example, suppose the input image has a lighthouse, the sea, and a rock. The response includes all three labels, one for each object.   {Name: lighthouse, Confidence: 98.4629}   {Name: rock,Confidence: 79.2097}    {Name: sea,Confidence: 75.061}  In the preceding example, the operation returns one label for each of the three objects. The operation can also return multiple labels for the same object in the image. 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. In response, the API returns an array of labels. In addition, the response also includes the orientation correction. Optionally, 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.   If the object detected is a person, the operation doesn't provide the same facial details that the DetectFaces operation provides.   DetectLabels returns bounding boxes for instances of common object labels in an array of Instance objects. An Instance object contains a BoundingBox object, for the location of the label on the image. It also includes the confidence by which the bounding box was detected.  DetectLabels also 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 returns the entire list of ancestors for a label. Each ancestor is a unique label in the response. In the previous example, Car, Vehicle, and Transportation are returned as unique labels in the response.  This is a stateless API operation. That is, the operation does not persist 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 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.   DetectLabels does not support the detection of activities. However, activity detection is supported for label detection in videos. For more information, see StartLabelDetection 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.   For each object, scene, and concept the API returns one or more labels. Each label provides the object name, and the level of confidence that the image contains the object. For example, suppose the input image has a lighthouse, the sea, and a rock. The response includes all three labels, one for each object.   {Name: lighthouse, Confidence: 98.4629}   {Name: rock,Confidence: 79.2097}    {Name: sea,Confidence: 75.061}  In the preceding example, the operation returns one label for each of the three objects. The operation can also return multiple labels for the same object in the image. 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. In response, the API returns an array of labels. In addition, the response also includes the orientation correction. Optionally, 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.   If the object detected is a person, the operation doesn't provide the same facial details that the DetectFaces operation provides.   DetectLabels returns bounding boxes for instances of common object labels in an array of Instance objects. An Instance object contains a BoundingBox object, for the location of the label on the image. It also includes the confidence by which the bounding box was detected.  DetectLabels also 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 returns the entire list of ancestors for a label. Each ancestor is a unique label in the response. In the previous example, Car, Vehicle, and Transportation are returned as unique labels in the response.  This is a stateless API operation. That is, the operation does not persist any data. This operation requires permissions to perform the rekognition:DetectLabels action. 
   */
  detectLabels(callback?: (err: AWSError, data: Rekognition.Types.DetectLabelsResponse) => void): Request<Rekognition.Types.DetectLabelsResponse, AWSError>;
  /**
   * Detects unsafe content in a specified JPEG or PNG format image. Use DetectModerationLabels to moderate images depending on your requirements. For example, you might want to filter images that contain nudity, but not images containing suggestive content. To filter images, use the labels returned by DetectModerationLabels to determine which types of content are appropriate. For information about moderation labels, see Detecting Unsafe Content in the Amazon Rekognition Developer Guide. You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file. 
   */
  detectModerationLabels(params: Rekognition.Types.DetectModerationLabelsRequest, callback?: (err: AWSError, data: Rekognition.Types.DetectModerationLabelsResponse) => void): Request<Rekognition.Types.DetectModerationLabelsResponse, AWSError>;
  /**
   * Detects unsafe content in a specified JPEG or PNG format image. Use DetectModerationLabels to moderate images depending on your requirements. For example, you might want to filter images that contain nudity, but not images containing suggestive content. To filter images, use the labels returned by DetectModerationLabels to determine which types of content are appropriate. For information about moderation labels, see Detecting Unsafe Content in the Amazon Rekognition Developer Guide. You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file. 
   */
  detectModerationLabels(callback?: (err: AWSError, data: Rekognition.Types.DetectModerationLabelsResponse) => void): Request<Rekognition.Types.DetectModerationLabelsResponse, AWSError>;
  /**
   * Detects Personal Protective Equipment (PPE) worn by people detected in an image. Amazon Rekognition can detect the following types of PPE.   Face cover   Hand cover   Head cover   You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. The image must be either a PNG or JPG formatted file.   DetectProtectiveEquipment detects PPE worn by up to 15 persons detected in an image. For each person detected in the image the API returns an array of body parts (face, head, left-hand, right-hand). For each body part, an array of detected items of PPE is returned, including an indicator of whether or not the PPE covers the body part. The API returns the confidence it has in each detection (person, PPE, body part and body part coverage). It also returns a bounding box (BoundingBox) for each detected person and each detected item of PPE.  You can optionally request a summary of detected PPE items with the SummarizationAttributes input parameter. The summary provides the following information.    The persons detected as wearing all of the types of PPE that you specify.   The persons detected as not wearing all of the types PPE that you specify.   The persons detected where PPE adornment could not be determined.    This is a stateless API operation. That is, the operation does not persist any data. This operation requires permissions to perform the rekognition:DetectProtectiveEquipment action. 
   */
  detectProtectiveEquipment(params: Rekognition.Types.DetectProtectiveEquipmentRequest, callback?: (err: AWSError, data: Rekognition.Types.DetectProtectiveEquipmentResponse) => void): Request<Rekognition.Types.DetectProtectiveEquipmentResponse, AWSError>;
  /**
   * Detects Personal Protective Equipment (PPE) worn by people detected in an image. Amazon Rekognition can detect the following types of PPE.   Face cover   Hand cover   Head cover   You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. The image must be either a PNG or JPG formatted file.   DetectProtectiveEquipment detects PPE worn by up to 15 persons detected in an image. For each person detected in the image the API returns an array of body parts (face, head, left-hand, right-hand). For each body part, an array of detected items of PPE is returned, including an indicator of whether or not the PPE covers the body part. The API returns the confidence it has in each detection (person, PPE, body part and body part coverage). It also returns a bounding box (BoundingBox) for each detected person and each detected item of PPE.  You can optionally request a summary of detected PPE items with the SummarizationAttributes input parameter. The summary provides the following information.    The persons detected as wearing all of the types of PPE that you specify.   The persons detected as not wearing all of the types PPE that you specify.   The persons detected where PPE adornment could not be determined.    This is a stateless API operation. That is, the operation does not persist any data. This operation requires permissions to perform the rekognition:DetectProtectiveEquipment action. 
   */
  detectProtectiveEquipment(callback?: (err: AWSError, data: Rekognition.Types.DetectProtectiveEquipmentResponse) => void): Request<Rekognition.Types.DetectProtectiveEquipmentResponse, AWSError>;
  /**
   * Detects text in the input image and converts it into machine-readable text. Pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, you must pass it as a reference to an image in an Amazon S3 bucket. For the AWS CLI, passing image bytes is not supported. The image must be either a .png or .jpeg formatted file.  The DetectText operation returns text in an array of TextDetection elements, TextDetections. Each TextDetection element provides information about a single word or line of text that was detected in the image.  A word is one or more ISO basic latin script characters that are not separated by spaces. DetectText can detect up to 100 words in an image. A line is a string of equally spaced words. A line isn't necessarily a complete sentence. For example, a driver's license number is detected as a line. A line ends when there is no aligned text after it. Also, a line ends when there is a large gap between words, relative to the length of the words. This means, depending on the gap between words, Amazon Rekognition may detect multiple lines in text aligned in the same direction. Periods don't represent the end of a line. If a sentence spans multiple lines, the DetectText operation returns multiple lines. To determine whether a TextDetection element is a line of text or a word, use the TextDetection object Type field.  To be detected, text must be within +/- 90 degrees orientation of the horizontal axis. For more information, see DetectText in the Amazon Rekognition Developer Guide.
   */
  detectText(params: Rekognition.Types.DetectTextRequest, callback?: (err: AWSError, data: Rekognition.Types.DetectTextResponse) => void): Request<Rekognition.Types.DetectTextResponse, AWSError>;
  /**
   * Detects text in the input image and converts it into machine-readable text. Pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, you must pass it as a reference to an image in an Amazon S3 bucket. For the AWS CLI, passing image bytes is not supported. The image must be either a .png or .jpeg formatted file.  The DetectText operation returns text in an array of TextDetection elements, TextDetections. Each TextDetection element provides information about a single word or line of text that was detected in the image.  A word is one or more ISO basic latin script characters that are not separated by spaces. DetectText can detect up to 100 words in an image. A line is a string of equally spaced words. A line isn't necessarily a complete sentence. For example, a driver's license number is detected as a line. A line ends when there is no aligned text after it. Also, a line ends when there is a large gap between words, relative to the length of the words. This means, depending on the gap between words, Amazon Rekognition may detect multiple lines in text aligned in the same direction. Periods don't represent the end of a line. If a sentence spans multiple lines, the DetectText operation returns multiple lines. To determine whether a TextDetection element is a line of text or a word, use the TextDetection object Type field.  To be detected, text must be within +/- 90 degrees orientation of the horizontal axis. For more information, see DetectText in the Amazon Rekognition Developer Guide.
   */
  detectText(callback?: (err: AWSError, data: Rekognition.Types.DetectTextResponse) => void): Request<Rekognition.Types.DetectTextResponse, AWSError>;
  /**
   * Gets the name and additional information about a celebrity based on their Amazon Rekognition ID. The additional information is returned as an array of URLs. If there is no additional information about the celebrity, this list is empty. For more information, see Recognizing Celebrities in an Image in the Amazon Rekognition Developer Guide. This operation requires permissions to perform the rekognition:GetCelebrityInfo action. 
   */
  getCelebrityInfo(params: Rekognition.Types.GetCelebrityInfoRequest, callback?: (err: AWSError, data: Rekognition.Types.GetCelebrityInfoResponse) => void): Request<Rekognition.Types.GetCelebrityInfoResponse, AWSError>;
  /**
   * Gets the name and additional information about a celebrity based on their Amazon Rekognition ID. The additional information is returned as an array of URLs. If there is no additional information about the celebrity, this list is empty. For more information, see Recognizing Celebrities in an Image in the Amazon Rekognition Developer Guide. This operation requires permissions to perform the rekognition:GetCelebrityInfo action. 
   */
  getCelebrityInfo(callback?: (err: AWSError, data: Rekognition.Types.GetCelebrityInfoResponse) => void): Request<Rekognition.Types.GetCelebrityInfoResponse, AWSError>;
  /**
   * Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by StartCelebrityRecognition. Celebrity recognition in a video is an asynchronous operation. Analysis is started by a call to StartCelebrityRecognition which returns a job identifier (JobId). When the celebrity recognition operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartCelebrityRecognition. To get the results of the celebrity recognition analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetCelebrityDetection and pass the job identifier (JobId) from the initial call to StartCelebrityDetection.  For more information, see Working With Stored Videos in the Amazon Rekognition Developer Guide.  GetCelebrityRecognition returns detected celebrities and the time(s) they are detected in an array (Celebrities) of CelebrityRecognition objects. Each CelebrityRecognition contains information about the celebrity in a CelebrityDetail object and the time, Timestamp, the celebrity was detected.    GetCelebrityRecognition only returns the default facial attributes (BoundingBox, Confidence, Landmarks, Pose, and Quality). The other facial attributes listed in the Face object of the following response syntax are not returned. For more information, see FaceDetail in the Amazon Rekognition Developer Guide.   By default, the Celebrities array is sorted by time (milliseconds from the start of the video). You can also sort the array by celebrity by specifying the value ID in the SortBy input parameter. The CelebrityDetail object includes the celebrity identifer and additional information urls. If you don't store the additional information urls, you can get them later by calling GetCelebrityInfo with the celebrity identifer. No information is returned for faces not recognized as celebrities. Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetCelebrityDetection and populate the NextToken request parameter with the token value returned from the previous call to GetCelebrityRecognition.
   */
  getCelebrityRecognition(params: Rekognition.Types.GetCelebrityRecognitionRequest, callback?: (err: AWSError, data: Rekognition.Types.GetCelebrityRecognitionResponse) => void): Request<Rekognition.Types.GetCelebrityRecognitionResponse, AWSError>;
  /**
   * Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by StartCelebrityRecognition. Celebrity recognition in a video is an asynchronous operation. Analysis is started by a call to StartCelebrityRecognition which returns a job identifier (JobId). When the celebrity recognition operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic reg