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Rekognition

Service object for interacting with AWS Rekognition service.

public struct Rekognition: AWSService 

This is the API Reference for Amazon Rekognition Image, Amazon Rekognition Custom Labels, Amazon Rekognition Stored Video, Amazon Rekognition Streaming Video. It provides descriptions of actions, data types, common parameters, and common errors.

Amazon Rekognition Image

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Inheritance

AWSService

Initializers

init(client:region:partition:endpoint:timeout:byteBufferAllocator:options:)

Initialize the Rekognition client

public init(
        client: AWSClient,
        region: SotoCore.Region? = nil,
        partition: AWSPartition = .aws,
        endpoint: String? = nil,
        timeout: TimeAmount? = nil,
        byteBufferAllocator: ByteBufferAllocator = ByteBufferAllocator(),
        options: AWSServiceConfig.Options = []
    ) 

Parameters

  • client: AWSClient used to process requests
  • region: Region of server you want to communicate with. This will override the partition parameter.
  • partition: AWS partition where service resides, standard (.aws), china (.awscn), government (.awsusgov).
  • endpoint: Custom endpoint URL to use instead of standard AWS servers
  • timeout: Timeout value for HTTP requests

init(from:patch:)

Initializer required by AWSService.with(middlewares:​timeout:​byteBufferAllocator:​options). You are not able to use this initializer directly as there are no public initializers for AWSServiceConfig.Patch. Please use AWSService.with(middlewares:​timeout:​byteBufferAllocator:​options) instead.

public init(from: Rekognition, patch: AWSServiceConfig.Patch) 

Properties

client

Client used for communication with AWS

public let client: AWSClient

config

Service configuration

public let config: AWSServiceConfig

Methods

compareFaces(_:logger:on:)

public func compareFaces(_ input: CompareFacesRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<CompareFacesResponse> 

Compares a face in the source input image with each of the 100 largest faces detected in the target input image.

     <p> If the source image contains multiple faces, the service detects the largest face
    and compares it with each face detected in the target image. </p>


     <note>
        <p>CompareFaces uses machine learning algorithms, which are probabilistic. 
  A false negative is an incorrect prediction that
  a face in the target image has a low similarity confidence score when compared to the face
  in the source image. To reduce the probability of false negatives, 
  we recommend that you compare the target image against multiple source images.
  If you plan to use <code>CompareFaces</code> to make a decision that impacts an individual's rights,
  privacy, or access to services, we recommend that you pass the result to a human for review and further
  validation before taking action.</p>
     </note>
     <p>You pass the input and target images either as base64-encoded image bytes or as
  references to images in an Amazon S3 bucket. If you use the
  AWS
  CLI to call Amazon Rekognition operations, passing image bytes isn't
  supported. The image must be formatted as a PNG or JPEG file. </p>
     <p>In response, the operation returns an array of face matches ordered by similarity score
  in descending order. For each face match, the response provides a bounding box of the face,
  facial landmarks, pose details (pitch, roll, and yaw), quality (brightness and sharpness), and
  confidence value (indicating the level of confidence that the bounding box contains a face).
  The response also provides a similarity score, which indicates how closely the faces match. </p>
     <note>
        <p>By default, only faces with a similarity score of greater than or equal to 80% are
    returned in the response. You can change this value by specifying the
      <code>SimilarityThreshold</code> parameter.</p>
     </note>
     <p>
        <code>CompareFaces</code> also returns an array of faces that don't match the source image. 
  For each face, it returns a bounding box, confidence value, landmarks, pose details, and quality.
The response also returns information about the face in the source image, including the bounding box
  of the face and confidence value.</p>

     <p>The <code>QualityFilter</code> input parameter allows you to filter out detected faces
  that don’t meet a required quality bar. The quality bar is based on a
  variety of common use cases.  Use <code>QualityFilter</code> to set the quality bar
  by specifying <code>LOW</code>, <code>MEDIUM</code>, or <code>HIGH</code>.
  If you do not want to filter detected faces, specify <code>NONE</code>. The default value is <code>NONE</code>. </p>
     <p>If the image doesn't contain Exif metadata, <code>CompareFaces</code> returns orientation information for the
    source and target images. Use these values to display the images with the correct image orientation.</p>
     <p>If no faces are detected in the source or target images, <code>CompareFaces</code> returns an 
<code>InvalidParameterException</code> error. </p>
     <note>
        <p> This is a stateless API operation. That is, data returned by this operation doesn't persist.</p>
     </note>

     <p>For an example, see Comparing Faces in Images in the Amazon Rekognition Developer Guide.</p>
     <p>This operation requires permissions to perform the <code>rekognition:CompareFaces</code>
  action.</p>

createCollection(_:logger:on:)

public func createCollection(_ input: CreateCollectionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<CreateCollectionResponse> 

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.

createDataset(_:logger:on:)

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.

public func createDataset(_ input: CreateDatasetRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<CreateDatasetResponse> 

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.

     <p>The response from <code>CreateDataset</code> is the Amazon Resource Name (ARN) for the dataset.

     <p>For more information, see Creating dataset in the <i>Amazon Rekognition Custom Labels Developer Guide</i>.</p>

     <p>This operation requires permissions to perform the <code>rekognition:CreateDataset</code> action.
If you want to copy an existing dataset, you also require permission to perform the rekognition:ListDatasetEntries action.

createProject(_:logger:on:)

public func createProject(_ input: CreateProjectRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<CreateProjectResponse> 

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.

createProjectVersion(_:logger:on:)

public func createProjectVersion(_ input: CreateProjectVersionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<CreateProjectVersionResponse> 

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.

     <p>Training uses the training and test datasets associated with the project. 
     For more information, see Creating training and test dataset in the <i>Amazon Rekognition Custom Labels Developer Guide</i>.
  </p>
  
 
     <note>
        <p>You can train a modelin a project that doesn't have associated datasets by specifying manifest files in the
     <code>TrainingData</code> and <code>TestingData</code> fields.
     </p>
        <p>If you open the console after training a model with manifest files, Amazon Rekognition Custom Labels creates
        the datasets for you using the most recent manifest files. You can no longer train
        a model version for the project by specifying manifest files. </p>
        <p>Instead of training with a project without associated datasets,
        we recommend that you use the manifest
        files to create training and test datasets for the project.</p>
     </note>
     
  
     <p>Training takes a while to complete. You can get the current status by calling
     <a>DescribeProjectVersions</a>. Training completed successfully if
     the value of the <code>Status</code> field is <code>TRAINING_COMPLETED</code>.</p>
  
     <p>If training 
     fails, see Debugging a failed model training in the <i>Amazon Rekognition Custom Labels</i> developer guide. </p>
  
  
     <p>Once training has successfully completed, call <a>DescribeProjectVersions</a> to
     get the training results and evaluate the model.  For more information, see Improving a trained Amazon Rekognition Custom Labels model
     in the <i>Amazon Rekognition Custom Labels</i> developers guide.
  </p>
  
     <p>After evaluating the model, you start the model
   by calling <a>StartProjectVersion</a>.</p>
     <p>This operation requires permissions to perform the <code>rekognition:CreateProjectVersion</code> action.</p>

createStreamProcessor(_:logger:on:)

public func createStreamProcessor(_ input: CreateStreamProcessorRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<CreateStreamProcessorResponse> 

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. You also specify the face recognition criteria in Settings. For example, the collection containing faces that 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 in ConnectedHomeSettings, such as people, packages and people, or pets, people, and packages. 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.

    <p>
        Use <code>Name</code> to assign an identifier for the stream processor. You use <code>Name</code>
        to manage the stream processor. For example, you can start processing the source video by calling <a>StartStreamProcessor</a> with
        the <code>Name</code> field. </p> 
    <p>This operation requires permissions to perform the
        <code>rekognition:CreateStreamProcessor</code> action. If you want to tag your stream processor, you also require permission to perform the <code>rekognition:TagResource</code> operation.</p>

deleteCollection(_:logger:on:)

public func deleteCollection(_ input: DeleteCollectionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DeleteCollectionResponse> 

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.

deleteDataset(_:logger:on:)

public func deleteDataset(_ input: DeleteDatasetRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DeleteDatasetResponse> 

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.

deleteFaces(_:logger:on:)

public func deleteFaces(_ input: DeleteFacesRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DeleteFacesResponse> 

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.

deleteProject(_:logger:on:)

public func deleteProject(_ input: DeleteProjectRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DeleteProjectResponse> 

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.

This operation requires permissions to perform the rekognition:DeleteProject action.

deleteProjectVersion(_:logger:on:)

public func deleteProjectVersion(_ input: DeleteProjectVersionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DeleteProjectVersionResponse> 

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.

deleteStreamProcessor(_:logger:on:)

public func deleteStreamProcessor(_ input: DeleteStreamProcessorRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DeleteStreamProcessorResponse> 

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.

describeCollection(_:logger:on:)

public func describeCollection(_ input: DescribeCollectionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DescribeCollectionResponse> 

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.

     <p>For more information, see Describing a Collection in the 
 Amazon Rekognition Developer Guide.</p>

describeDataset(_:logger:on:)

public func describeDataset(_ input: DescribeDatasetRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DescribeDatasetResponse> 

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.

describeProjectVersions(_:logger:on:)

public func describeProjectVersions(_ input: DescribeProjectVersionsRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DescribeProjectVersionsResponse> 

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.

describeProjects(_:logger:on:)

public func describeProjects(_ input: DescribeProjectsRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DescribeProjectsResponse> 

Gets information about your Amazon Rekognition Custom Labels projects.

This operation requires permissions to perform the rekognition:DescribeProjects action.

describeStreamProcessor(_:logger:on:)

public func describeStreamProcessor(_ input: DescribeStreamProcessorRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DescribeStreamProcessorResponse> 

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.

detectCustomLabels(_:logger:on:)

public func detectCustomLabels(_ input: DetectCustomLabelsRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DetectCustomLabelsResponse> 

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).

     <p>To filter labels that are returned, specify a value for <code>MinConfidence</code>.
     <code>DetectCustomLabelsLabels</code> only returns labels with a confidence that's higher than
     the specified value.
     The value of <code>MinConfidence</code> maps to the assumed threshold values
     created during training. For more information, see <i>Assumed threshold</i>
     in the Amazon Rekognition Custom Labels Developer Guide. 
     Amazon Rekognition Custom Labels metrics expresses an assumed threshold as a floating point value between 0-1. The range of
     <code>MinConfidence</code> normalizes the threshold value to a percentage value (0-100). Confidence
     responses from <code>DetectCustomLabels</code> are also returned as a percentage. 
     You can use <code>MinConfidence</code> to change the precision and recall or your model. 
     For more information, see 
     <i>Analyzing an image</i> in the Amazon Rekognition Custom Labels Developer Guide. </p>
     
     <p>If you don't specify a value for <code>MinConfidence</code>,  <code>DetectCustomLabels</code>
  returns labels based on the assumed threshold of each label.</p>
  

  
   
     <p>This is a stateless API operation. That is, the operation does not persist any
     data.</p>
     <p>This operation requires permissions to perform the
     <code>rekognition:DetectCustomLabels</code> action. </p>



     <p>For more information, see 
  <i>Analyzing an image</i> in the Amazon Rekognition Custom Labels Developer Guide. </p>

detectFaces(_:logger:on:)

public func detectFaces(_ input: DetectFacesRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DetectFacesResponse> 

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.

detectLabels(_:logger:on:)

public func detectLabels(_ input: DetectLabelsRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DetectLabelsResponse> 

Detects instances of real-world entities within an image (JPEG or PNG) provided as input. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; and concepts like landscape, evening, and nature.

     <p>For an example, see Analyzing images stored in an Amazon S3 bucket in the Amazon Rekognition Developer Guide.</p> 
     <note>
        <p>
           <code>DetectLabels</code> 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.</p> 
     </note>

     <p>You pass the input image as base64-encoded image bytes or as a reference to an image in
  an Amazon S3 bucket. If you use the
  AWS
  CLI to call Amazon Rekognition operations, passing image bytes is not
  supported. The image must be either a PNG or JPEG formatted file. </p>
     <p> For each object, 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. </p>

     <p>
        <code>{Name: lighthouse, Confidence: 98.4629}</code>
     </p>
     <p>
        <code>{Name: rock,Confidence: 79.2097}</code>
     </p>
     <p>
        <code> {Name: sea,Confidence: 75.061}</code>
     </p>
     <p>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. </p>
     <p>
        <code>{Name: flower,Confidence: 99.0562}</code>
     </p>
     <p>
        <code>{Name: plant,Confidence: 99.0562}</code>
     </p>
     <p>
        <code>{Name: tulip,Confidence: 99.0562}</code>
     </p>
     <p>In this example, the detection algorithm more precisely identifies the flower as a
  tulip.</p>
     <p>In response, the API returns an array of labels. In addition, the response also
  includes the orientation correction. Optionally, you can specify <code>MinConfidence</code> to
  control the confidence threshold for the labels returned. The default is 55%. You can also add
  the <code>MaxLabels</code> parameter to limit the number of labels returned. </p>
     <note>
        <p>If the object detected is a person, the operation doesn't provide the same facial
    details that the <a>DetectFaces</a> operation provides.</p>
     </note>
     <p>
        <code>DetectLabels</code> returns bounding boxes for instances of common object labels in an array of
  <a>Instance</a> objects. An <code>Instance</code> object contains a 
  <a>BoundingBox</a> object, for the location of the label on the image. It also includes 
  the confidence by which the bounding box was detected.</p>
     <p>
        <code>DetectLabels</code> also returns a hierarchical taxonomy of detected labels. For example,
  a detected car might be assigned the label <i>car</i>. The label <i>car</i>
  has two parent labels: <i>Vehicle</i> (its parent) and <i>Transportation</i> (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, <i>Car</i>, <i>Vehicle</i>, and <i>Transportation</i>
  are returned as unique labels in the response.
  </p>
     <p>This is a stateless API operation. That is, the operation does not persist any
  data.</p>
     <p>This operation requires permissions to perform the
    <code>rekognition:DetectLabels</code> action. </p>

detectModerationLabels(_:logger:on:)

public func detectModerationLabels(_ input: DetectModerationLabelsRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DetectModerationLabelsResponse> 

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.

     <p>For information about moderation labels,
  see Detecting Unsafe Content in the Amazon Rekognition Developer Guide.</p>
     <p>You pass the input image either as base64-encoded image bytes or as a reference to an
  image in an Amazon S3 bucket. If you use the
  AWS
  CLI to call Amazon Rekognition operations, passing image bytes is not
  supported. The image must be either a PNG or JPEG formatted file. </p>

detectProtectiveEquipment(_:logger:on:)

public func detectProtectiveEquipment(_ input: DetectProtectiveEquipmentRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DetectProtectiveEquipmentResponse> 

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

     <p>You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket.
     The image must be either a PNG or JPG formatted file. </p>
  
     <p>
        <code>DetectProtectiveEquipment</code> detects PPE worn by up to 15 persons detected in an image.</p>
     <p>For each person detected in the image the API returns an array of body parts (face, head, left-hand, right-hand).
     For each body part, an array of detected items of PPE is returned, including an indicator of whether or not the PPE
     covers the body part. The API returns the confidence it has in each detection
     (person, PPE, body part and body part coverage). It also returns a bounding box (<a>BoundingBox</a>) for each detected 
     person and each detected item of PPE. </p>
     <p>You can optionally request a summary of detected PPE items with the <code>SummarizationAttributes</code> input parameter.
     The summary provides the following information. </p>
     <ul>
        <li>
           <p>The persons detected as wearing all of the types of PPE that you specify.</p>
        </li>
        <li>
           <p>The persons detected as not wearing all of the types PPE that you specify.</p>
        </li>
        <li>
           <p>The persons detected where PPE adornment could not be determined. </p>
        </li>
     </ul>
     <p>This is a stateless API operation. That is, the operation does not persist any data.</p>
  
     <p>This operation requires permissions to perform the <code>rekognition:DetectProtectiveEquipment</code> action. </p>

detectText(_:logger:on:)

public func detectText(_ input: DetectTextRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DetectTextResponse> 

Detects text in the input image and converts it into machine-readable text.

Pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, you must pass it as a reference to an image in an Amazon S3 bucket. For the AWS CLI, passing image bytes is not supported. The image must be either a .png or .jpeg formatted file.

The DetectText operation returns text in an array of TextDetection elements, TextDetections. Each TextDetection element provides information about a single word or line of text that was detected in the image.

A word is one or more script characters that are not separated by spaces. DetectText can detect up to 100 words in an image.

A line is a string of equally spaced words. A line isn't necessarily a complete sentence. For example, a driver's license number is detected as a line. A line ends when there is no aligned text after it. Also, a line ends when there is a large gap between words, relative to the length of the words. This means, depending on the gap between words, Amazon Rekognition may detect multiple lines in text aligned in the same direction. Periods don't represent the end of a line. If a sentence spans multiple lines, the DetectText operation returns multiple lines.

To determine whether a TextDetection element is a line of text or a word, use the TextDetection object Type field.

To be detected, text must be within +/- 90 degrees orientation of the horizontal axis.

     <p>For more information, see Detecting text in the Amazon Rekognition Developer Guide.</p>

distributeDatasetEntries(_:logger:on:)

public func distributeDatasetEntries(_ input: DistributeDatasetEntriesRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DistributeDatasetEntriesResponse> 

Distributes the entries (images) in a training dataset across the training dataset and the test dataset for a project. DistributeDatasetEntries moves 20% of the training dataset images to the test dataset. An entry is a JSON Line that describes an image.

You supply the Amazon Resource Names (ARN) of a project's training dataset and test dataset. The training dataset must contain the images that you want to split. The test dataset must be empty. The datasets must belong to the same project. To create training and test datasets for a project, call CreateDataset.

Distributing a dataset takes a while to complete. To check the status call DescribeDataset. The operation is complete when the Status field for the training dataset and the test dataset is UPDATE_COMPLETE. If the dataset split fails, the value of Status is UPDATE_FAILED.

This operation requires permissions to perform the rekognition:DistributeDatasetEntries action.

getCelebrityInfo(_:logger:on:)

public func getCelebrityInfo(_ input: GetCelebrityInfoRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<GetCelebrityInfoResponse> 

Gets the name and additional information about a celebrity based on their Amazon Rekognition ID. The additional information is returned as an array of URLs. If there is no additional information about the celebrity, this list is empty.

     <p>For more information, see Getting information about a celebrity in
  the Amazon Rekognition Developer Guide.</p>
     <p>This operation requires permissions to perform the
    <code>rekognition:GetCelebrityInfo</code> action. </p>

getCelebrityRecognition(_:logger:on:)

public func getCelebrityRecognition(_ input: GetCelebrityRecognitionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<GetCelebrityRecognitionResponse> 

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.

     <p>For more information, see Working With Stored Videos in the Amazon Rekognition Developer Guide.</p>
     <p>
        <code>GetCelebrityRecognition</code> returns detected celebrities and the time(s) they
  are detected in an array (<code>Celebrities</code>) of <a>CelebrityRecognition</a>
   objects. Each <code>CelebrityRecognition</code>
  contains information about the celebrity in a <a>CelebrityDetail</a> object and the
  time, <code>Timestamp</code>, the celebrity was detected.  This <a>CelebrityDetail</a> object stores information about the detected celebrity's face
  attributes, a face bounding box, known gender, the celebrity's name, and a confidence
  estimate.</p>
     <note>
        <p>
           <code>GetCelebrityRecognition</code> only returns the default facial
    attributes (<code>BoundingBox</code>, <code>Confidence</code>, <code>Landmarks</code>,
      <code>Pose</code>, and <code>Quality</code>). The <code>BoundingBox</code> field only
    applies to the detected face instance. The other facial attributes listed in the
      <code>Face</code> object of the following response syntax are not returned. For more
    information, see FaceDetail in the Amazon Rekognition Developer Guide. </p>
     </note>
     <p>By default, the <code>Celebrities</code> array is sorted by time (milliseconds from the start of the video).
  You can also sort the array by celebrity by specifying the value <code>ID</code> in the <code>SortBy</code> input parameter.</p>
     <p>The <code>CelebrityDetail</code> object includes the celebrity identifer and additional information urls. If you don't store
  the additional information urls, you can get them later by calling <a>GetCelebrityInfo</a> with the celebrity identifer.</p>
     <p>No information is returned for faces not recognized as celebrities.</p>
     <p>Use MaxResults parameter to limit the number of labels returned. If there are more results than
  specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a
  pagination token for getting the next set of results. To get the next page of results, call <code>GetCelebrityDetection</code>
  and populate the <code>NextToken</code> request parameter with the token
  value returned from the previous call to <code>GetCelebrityRecognition</code>.</p>

getContentModeration(_:logger:on:)

public func getContentModeration(_ input: GetContentModerationRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<GetContentModerationResponse> 

Gets the inappropriate, unwanted, or offensive content analysis results for a Amazon Rekognition Video analysis started by StartContentModeration. For a list of moderation labels in Amazon Rekognition, see Using the image and video moderation APIs.

Amazon Rekognition Video inappropriate or offensive content detection in a stored video is an asynchronous operation. You start analysis by calling StartContentModeration which returns a job identifier (JobId). When analysis finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartContentModeration. To get the results of the content analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetContentModeration and pass the job identifier (JobId) from the initial call to StartContentModeration.

     <p>For more information, see Working with Stored Videos in the
 Amazon Rekognition Devlopers Guide.</p>
     <p>
        <code>GetContentModeration</code> returns detected inappropriate, unwanted, or offensive content moderation labels,
  and the time they are detected, in an array, <code>ModerationLabels</code>, of
  <a>ContentModerationDetection</a> objects.
 </p>
     <p>By default, the moderated labels are returned sorted by time, in milliseconds from the start of the
   video. You can also sort them by moderated label by specifying <code>NAME</code> for the <code>SortBy</code>
   input parameter. </p>
     <p>Since video analysis can return a large number of results, use the <code>MaxResults</code> parameter to limit
  the number of labels returned in a single call to <code>GetContentModeration</code>. If there are more results than
   specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a
   pagination token for getting the next set of results. To get the next page of results, call <code>GetContentModeration</code>
   and populate the <code>NextToken</code> request parameter with the value of <code>NextToken</code>
   returned from the previous call to <code>GetContentModeration</code>.</p>
 
     <p>For more information, see moderating content in the Amazon Rekognition Developer Guide.</p>

getFaceDetection(_:logger:on:)

public func getFaceDetection(_ input: GetFaceDetectionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<GetFaceDetectionResponse> 

Gets face detection results for a Amazon Rekognition Video analysis started by StartFaceDetection.

Face detection with Amazon Rekognition Video is an asynchronous operation. You start face detection by calling StartFaceDetection which returns a job identifier (JobId). When the face detection operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartFaceDetection. To get the results of the face detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetFaceDetection and pass the job identifier (JobId) from the initial call to StartFaceDetection.

GetFaceDetection returns an array of detected faces (Faces) sorted by the time the faces were detected.

Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetFaceDetection and populate the NextToken request parameter with the token value returned from the previous call to GetFaceDetection.

getFaceSearch(_:logger:on:)

public func getFaceSearch(_ input: GetFaceSearchRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<GetFaceSearchResponse> 

Gets the face search results for Amazon Rekognition Video face search started by StartFaceSearch. The search returns faces in a collection that match the faces of persons detected in a video. It also includes the time(s) that faces are matched in the video.

Face search in a video is an asynchronous operation. You start face search by calling to StartFaceSearch which returns a job identifier (JobId). When the search operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartFaceSearch. To get the search results, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetFaceSearch and pass the job identifier (JobId) from the initial call to StartFaceSearch.

     <p>For more information, see Searching Faces in a Collection in the
  Amazon Rekognition Developer Guide.</p>
     <p>The search results are retured in an array, <code>Persons</code>, of
<a>PersonMatch</a> objects. Each<code>PersonMatch</code> element contains
details about the matching faces in the input collection, person information (facial attributes,
bounding boxes, and person identifer)
for the matched person, and the time the person was matched in the video.</p>
     <note>
        <p>
           <code>GetFaceSearch</code> only returns the default  
    facial attributes (<code>BoundingBox</code>, <code>Confidence</code>, 
    <code>Landmarks</code>, <code>Pose</code>, and <code>Quality</code>). The other facial attributes listed
    in the <code>Face</code> object of the following response syntax are not returned. For more information,
    see FaceDetail in the Amazon Rekognition Developer Guide. </p>
     </note>
     <p>By default, the <code>Persons</code> array is sorted by the time, in milliseconds from the
start of the video, persons are matched.
You can also sort by persons by specifying <code>INDEX</code> for the <code>SORTBY</code> input
parameter.</p>

getLabelDetection(_:logger:on:)

public func getLabelDetection(_ input: GetLabelDetectionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<GetLabelDetectionResponse> 

Gets the label detection results of a Amazon Rekognition Video analysis started by StartLabelDetection.

The label detection operation is started by a call to StartLabelDetection which returns a job identifier (JobId). When the label detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartlabelDetection. To get the results of the label detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetLabelDetection and pass the job identifier (JobId) from the initial call to StartLabelDetection.

GetLabelDetection returns an array of detected labels (Labels) sorted by the time the labels were detected. You can also sort by the label name by specifying NAME for the SortBy input parameter.

The labels returned include the label name, the percentage confidence in the accuracy of the detected label, and the time the label was detected in the video.

The returned labels also include bounding box information for common objects, a hierarchical taxonomy of detected labels, and the version of the label model used for detection.

     <p>Use MaxResults parameter to limit the number of labels returned. If there are more results than
specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a pagination token for getting the next set
of results. To get the next page of results, call <code>GetlabelDetection</code> and populate the <code>NextToken</code> request parameter with the token
 value returned from the previous call to <code>GetLabelDetection</code>.</p>

getPersonTracking(_:logger:on:)

specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetPersonTracking and populate the NextToken request parameter with the token value returned from the previous call to GetPersonTracking.

public func getPersonTracking(_ input: GetPersonTrackingRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<GetPersonTrackingResponse> 

Gets the path tracking results of a Amazon Rekognition Video analysis started by StartPersonTracking.

The person path tracking operation is started by a call to StartPersonTracking which returns a job identifier (JobId). When the operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartPersonTracking.

To get the results of the person path tracking operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetPersonTracking and pass the job identifier (JobId) from the initial call to StartPersonTracking.

GetPersonTracking returns an array, Persons, of tracked persons and the time(s) their paths were tracked in the video.

GetPersonTracking only returns the default facial attributes (BoundingBox, Confidence, Landmarks, Pose, and Quality). The other facial attributes listed in the Face object of the following response syntax are not returned.

        <p>For more information, see FaceDetail in the Amazon Rekognition Developer Guide.</p>
     </note>

     <p>By default, the array is sorted by the time(s) a person's path is tracked in the video.
  You can sort by tracked persons by specifying <code>INDEX</code> for the <code>SortBy</code> input parameter.</p>
     <p>Use the <code>MaxResults</code> parameter to limit the number of items returned. If there are more results than

getSegmentDetection(_:logger:on:)

public func getSegmentDetection(_ input: GetSegmentDetectionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<GetSegmentDetectionResponse> 

Gets the segment detection results of a Amazon Rekognition Video analysis started by StartSegmentDetection.

Segment detection with Amazon Rekognition Video is an asynchronous operation. You start segment detection by calling StartSegmentDetection which returns a job identifier (JobId). When the segment detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartSegmentDetection. To get the results of the segment detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. if so, call GetSegmentDetection and pass the job identifier (JobId) from the initial call of StartSegmentDetection.

GetSegmentDetection returns detected segments in an array (Segments) of SegmentDetection objects. Segments is sorted by the segment types specified in the SegmentTypes input parameter of StartSegmentDetection. Each element of the array includes the detected segment, the precentage confidence in the acuracy of the detected segment, the type of the segment, and the frame in which the segment was detected.

Use SelectedSegmentTypes to find out the type of segment detection requested in the call to StartSegmentDetection.

Use the MaxResults parameter to limit the number of segment detections returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetSegmentDetection and populate the NextToken request parameter with the token value returned from the previous call to GetSegmentDetection.

     <p>For more information, see Detecting video segments in stored video in the Amazon Rekognition Developer Guide.</p>

getTextDetection(_:logger:on:)

public func getTextDetection(_ input: GetTextDetectionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<GetTextDetectionResponse> 

Gets the text detection results of a Amazon Rekognition Video analysis started by StartTextDetection.

Text detection with Amazon Rekognition Video is an asynchronous operation. You start text detection by calling StartTextDetection which returns a job identifier (JobId) When the text detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartTextDetection. To get the results of the text detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. if so, call GetTextDetection and pass the job identifier (JobId) from the initial call of StartLabelDetection.

GetTextDetection returns an array of detected text (TextDetections) sorted by the time the text was detected, up to 50 words per frame of video.

Each element of the array includes the detected text, the precentage confidence in the acuracy of the detected text, the time the text was detected, bounding box information for where the text was located, and unique identifiers for words and their lines.

Use MaxResults parameter to limit the number of text detections returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetTextDetection and populate the NextToken request parameter with the token value returned from the previous call to GetTextDetection.

indexFaces(_:logger:on:)

public func indexFaces(_ input: IndexFacesRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<IndexFacesResponse> 

Detects faces in the input image and adds them to the specified collection.

Amazon Rekognition doesn't save the actual faces that are detected. Instead, the underlying detection algorithm first detects the faces in the input image. For each face, the algorithm extracts facial features into a feature vector, and stores it in the backend database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the SearchFaces and SearchFacesByImage operations.

     <p>For more information, see Adding faces to a collection in the Amazon Rekognition
  Developer Guide.</p>
     <p>To get the number of faces in a collection, call <a>DescribeCollection</a>. </p>
     <p>If you're using version 1.0 of the face detection model, <code>IndexFaces</code>
  indexes the 15 largest faces in the input image. Later versions of the face detection model
  index the 100 largest faces in the input image. </p>
     <p>If you're using version 4 or later of the face model, image orientation information
 is not returned in the <code>OrientationCorrection</code> field. </p>
     <p>To determine which version of the model you're using, call <a>DescribeCollection</a>
  and supply the collection ID. You can also get the model version from the value of <code>FaceModelVersion</code> in the response
  from <code>IndexFaces</code>
     </p>

     <p>For more information, see Model Versioning in the Amazon Rekognition Developer
  Guide.</p>
     <p>If you provide the optional <code>ExternalImageId</code> for the input image you
  provided, Amazon Rekognition associates this ID with all faces that it detects. When you call the <a>ListFaces</a> operation, the response returns the external ID. You can use this
  external image ID to create a client-side index to associate the faces with each image. You
  can then use the index to find all faces in an image.</p>
     <p>You can specify the maximum number of faces to index with the <code>MaxFaces</code> input
  parameter. This is useful when you want to index the largest faces in an image and don't want to index
  smaller faces, such as those belonging to people standing in the background.</p>
     <p>The <code>QualityFilter</code> input parameter allows you to filter out detected faces
  that don’t meet a required quality bar. The quality bar is based on a
  variety of common use cases. By default, <code>IndexFaces</code> chooses the quality bar that's 
  used to filter faces.  You can also explicitly choose
  the quality bar. Use <code>QualityFilter</code>, to set the quality bar
  by specifying <code>LOW</code>, <code>MEDIUM</code>, or <code>HIGH</code>.
  If you do not want to filter detected faces, specify <code>NONE</code>. </p>
     <note>
        <p>To use quality filtering, you need a collection associated with version 3 of the 
face model or higher. To get the version of the face model associated with a collection, call 
  <a>DescribeCollection</a>. </p>
     </note>
     <p>Information about faces detected in an image, but not indexed, is returned in an array of
  <a>UnindexedFace</a> objects, <code>UnindexedFaces</code>. Faces aren't
  indexed for reasons such as:</p>
     <ul>
        <li>
           <p>The number of faces detected exceeds the value of the <code>MaxFaces</code> request
      parameter.</p>
        </li>
        <li>
           <p>The face is too small compared to the image dimensions.</p>
        </li>
        <li>
           <p>The face is too blurry.</p>
        </li>
        <li>
           <p>The image is too dark.</p>
        </li>
        <li>
           <p>The face has an extreme pose.</p>
        </li>
        <li>
           <p>The face doesn’t have enough detail to be suitable for face search.</p>
        </li>
     </ul>
     <p>In response, the <code>IndexFaces</code> operation returns an array of metadata for 
  all detected faces, <code>FaceRecords</code>. This includes: </p>
     <ul>
        <li>
           <p>The bounding box, <code>BoundingBox</code>, of the detected face. </p>
        </li>
        <li>
           <p>A confidence value, <code>Confidence</code>, which indicates the confidence that the
      bounding box contains a face.</p>
        </li>
        <li>
           <p>A face ID, <code>FaceId</code>, assigned by the service for each face that's detected
      and stored.</p>
        </li>
        <li>
           <p>An image ID, <code>ImageId</code>, assigned by the service for the input image.</p>
        </li>
     </ul>
     <p>If you request all facial attributes (by using the <code>detectionAttributes</code>
  parameter), Amazon Rekognition returns detailed facial attributes, such as facial landmarks (for
  example, location of eye and mouth) and other facial attributes. If you provide
  the same image, specify the same collection, and use the same external ID in the
    <code>IndexFaces</code> operation, Amazon Rekognition doesn't save duplicate face metadata.</p>

     <p></p>

     <p>The input image is passed either as base64-encoded image bytes, or as a reference to an
  image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations,
  passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file. </p>
     <p>This operation requires permissions to perform the <code>rekognition:IndexFaces</code>
  action.</p>

listCollections(_:logger:on:)

public func listCollections(_ input: ListCollectionsRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<ListCollectionsResponse> 

Returns list of collection IDs in your account. If the result is truncated, the response also provides a NextToken that you can use in the subsequent request to fetch the next set of collection IDs.

     <p>For an example, see Listing collections in the Amazon Rekognition Developer Guide.</p>
     <p>This operation requires permissions to perform the <code>rekognition:ListCollections</code> action.</p>

listDatasetEntries(_:logger:on:)

public func listDatasetEntries(_ input: ListDatasetEntriesRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<ListDatasetEntriesResponse> 

Lists the entries (images) within a dataset. An entry is a JSON Line that contains the information for a single image, including the image location, assigned labels, and object location bounding boxes. For more information, see Creating a manifest file.

JSON Lines in the response include information about non-terminal errors found in the dataset. Non terminal errors are reported in errors lists within each JSON Line. The same information is reported in the training and testing validation result manifests that Amazon Rekognition Custom Labels creates during model training.

     <p>You can filter the response in variety of ways, such as choosing which labels to return and returning JSON Lines created after a specific date.

This operation requires permissions to perform the rekognition:ListDatasetEntries action.

listDatasetLabels(_:logger:on:)

public func listDatasetLabels(_ input: ListDatasetLabelsRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<ListDatasetLabelsResponse> 

Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see Labeling images.

Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see Labeling images in the Amazon Rekognition Custom Labels Developer Guide.

listFaces(_:logger:on:)

public func listFaces(_ input: ListFacesRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<ListFacesResponse> 

Returns metadata for faces in the specified collection. This metadata includes information such as the bounding box coordinates, the confidence (that the bounding box contains a face), and face ID. For an example, see Listing Faces in a Collection in the Amazon Rekognition Developer Guide.

     <p>This operation requires permissions to perform the
  <code>rekognition:ListFaces</code> action.</p>

listStreamProcessors(_:logger:on:)

public func listStreamProcessors(_ input: ListStreamProcessorsRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<ListStreamProcessorsResponse> 

Gets a list of stream processors that you have created with CreateStreamProcessor.

listTagsForResource(_:logger:on:)

public func listTagsForResource(_ input: ListTagsForResourceRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<ListTagsForResourceResponse> 

Returns a list of tags in an Amazon Rekognition collection, stream processor, or Custom Labels model.

This operation requires permissions to perform the rekognition:ListTagsForResource action.

recognizeCelebrities(_:logger:on:)

public func recognizeCelebrities(_ input: RecognizeCelebritiesRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<RecognizeCelebritiesResponse> 

Returns an array of celebrities recognized in the input image. For more information, see Recognizing celebrities in the Amazon Rekognition Developer Guide.

RecognizeCelebrities returns the 64 largest faces in the image. It lists the recognized celebrities in the CelebrityFaces array and any unrecognized faces in the UnrecognizedFaces array. RecognizeCelebrities doesn't return celebrities whose faces aren't among the largest 64 faces in the image.

For each celebrity recognized, RecognizeCelebrities returns a Celebrity object. The Celebrity object contains the celebrity name, ID, URL links to additional information, match confidence, and a ComparedFace object that you can use to locate the celebrity's face on the image.

Amazon Rekognition doesn't retain information about which images a celebrity has been recognized in. Your application must store this information and use the Celebrity ID property as a unique identifier for the celebrity. If you don't store the celebrity name or additional information URLs returned by RecognizeCelebrities, you will need the ID to identify the celebrity in a call to the GetCelebrityInfo operation.

You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

     <p>For an example, see Recognizing celebrities in an image in the Amazon Rekognition Developer Guide.</p>
     <p>This operation requires permissions to perform the
    <code>rekognition:RecognizeCelebrities</code> operation.</p>

searchFaces(_:logger:on:)

public func searchFaces(_ input: SearchFacesRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<SearchFacesResponse> 

For a given input face ID, searches for matching faces in the collection the face belongs to. You get a face ID when you add a face to the collection using the IndexFaces operation. The operation compares the features of the input face with faces in the specified collection.

You can also search faces without indexing faces by using the SearchFacesByImage operation.

The operation response returns an array of faces that match, ordered by similarity score with the highest similarity first. More specifically, it is an array of metadata for each face match that is found. Along with the metadata, the response also includes a confidence value for each face match, indicating the confidence that the specific face matches the input face.

     <p>For an example, see Searching for a face using its face ID in the Amazon Rekognition Developer Guide.</p>
     <p>This operation requires permissions to perform the <code>rekognition:SearchFaces</code>
  action.</p>

searchFacesByImage(_:logger:on:)

public func searchFacesByImage(_ input: SearchFacesByImageRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<SearchFacesByImageResponse> 

For a given input image, first detects the largest face in the image, and then searches the specified collection for matching faces. The operation compares the features of the input face with faces in the specified collection.

To search for all faces in an input image, you might first call the IndexFaces operation, and then use the face IDs returned in subsequent calls to the SearchFaces operation.

You can also call the DetectFaces operation and use the bounding boxes in the response to make face crops, which then you can pass in to the SearchFacesByImage operation.

You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

The response returns an array of faces that match, ordered by similarity score with the highest similarity first. More specifically, it is an array of metadata for each face match found. Along with the metadata, the response also includes a similarity indicating how similar the face is to the input face. In the response, the operation also returns the bounding box (and a confidence level that the bounding box contains a face) of the face that Amazon Rekognition used for the input image.

If no faces are detected in the input image, SearchFacesByImage returns an InvalidParameterException error.

     <p>For an example, Searching for a Face Using an Image in the Amazon Rekognition Developer Guide.</p>

     <p>The <code>QualityFilter</code> input parameter allows you to filter out detected faces
  that don’t meet a required quality bar. The quality bar is based on a
  variety of common use cases. 
  Use <code>QualityFilter</code> to set the quality bar for 
  filtering by specifying <code>LOW</code>, <code>MEDIUM</code>, or <code>HIGH</code>.
  If you do not want to filter detected faces, specify <code>NONE</code>. The default
  value is <code>NONE</code>.</p>
     <note>
        <p>To use quality filtering, you need a collection associated with version 3 of the 
  face model or higher. To get the version of the face model associated with a collection, call 
  <a>DescribeCollection</a>. </p>
     </note>

     <p>This operation requires permissions to perform the <code>rekognition:SearchFacesByImage</code>
  action.</p>

startCelebrityRecognition(_:logger:on:)

public func startCelebrityRecognition(_ input: StartCelebrityRecognitionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<StartCelebrityRecognitionResponse> 

Starts asynchronous recognition of celebrities in a stored video.

Amazon Rekognition Video can detect celebrities in a video must be stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartCelebrityRecognition returns a job identifier (JobId) which you use to get the results of the analysis. When celebrity recognition analysis is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel. To get the results of the celebrity recognition analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetCelebrityRecognition and pass the job identifier (JobId) from the initial call to StartCelebrityRecognition.

     <p>For more information, see Recognizing celebrities in the Amazon Rekognition Developer Guide.</p>

startContentModeration(_:logger:on:)

public func startContentModeration(_ input: StartContentModerationRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<StartContentModerationResponse> 

Starts asynchronous detection of inappropriate, unwanted, or offensive content in a stored video. For a list of moderation labels in Amazon Rekognition, see Using the image and video moderation APIs.

Amazon Rekognition Video can moderate content in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartContentModeration returns a job identifier (JobId) which you use to get the results of the analysis. When content analysis is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel.

To get the results of the content analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetContentModeration and pass the job identifier (JobId) from the initial call to StartContentModeration.

     <p>For more information, see Moderating content in the Amazon Rekognition Developer Guide.</p>

startFaceDetection(_:logger:on:)

public func startFaceDetection(_ input: StartFaceDetectionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<StartFaceDetectionResponse> 

Starts asynchronous detection of faces in a stored video.

Amazon Rekognition Video can detect faces in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartFaceDetection returns a job identifier (JobId) that you use to get the results of the operation. When face detection is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel. To get the results of the face detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetFaceDetection and pass the job identifier (JobId) from the initial call to StartFaceDetection.

     <p>For more information, see Detecting faces in a stored video in the 
 Amazon Rekognition Developer Guide.</p>

startFaceSearch(_:logger:on:)

public func startFaceSearch(_ input: StartFaceSearchRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<StartFaceSearchResponse> 

Starts the asynchronous search for faces in a collection that match the faces of persons detected in a stored video.

The video must be stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartFaceSearch returns a job identifier (JobId) which you use to get the search results once the search has completed. When searching is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel. To get the search results, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetFaceSearch and pass the job identifier (JobId) from the initial call to StartFaceSearch. For more information, see Searching stored videos for faces.

startLabelDetection(_:logger:on:)

public func startLabelDetection(_ input: StartLabelDetectionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<StartLabelDetectionResponse> 

Starts asynchronous detection of labels in a stored video.

Amazon Rekognition Video can detect labels in a video. Labels are instances of real-world entities. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; concepts like landscape, evening, and nature; and activities like a person getting out of a car or a person skiing.

The video must be stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartLabelDetection returns a job identifier (JobId) which you use to get the results of the operation. When label detection is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel.

To get the results of the label detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetLabelDetection and pass the job identifier (JobId) from the initial call to StartLabelDetection.

startPersonTracking(_:logger:on:)

public func startPersonTracking(_ input: StartPersonTrackingRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<StartPersonTrackingResponse> 

Starts the asynchronous tracking of a person's path in a stored video.

Amazon Rekognition Video can track the path of people in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartPersonTracking returns a job identifier (JobId) which you use to get the results of the operation. When label detection is finished, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel.

To get the results of the person detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetPersonTracking and pass the job identifier (JobId) from the initial call to StartPersonTracking.

startProjectVersion(_:logger:on:)

public func startProjectVersion(_ input: StartProjectVersionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<StartProjectVersionResponse> 

Starts the running of the version of a model. Starting a model takes a while to complete. To check the current state of the model, use DescribeProjectVersions.

Once the model is running, you can detect custom labels in new images by calling DetectCustomLabels.

You are charged for the amount of time that the model is running. To stop a running model, call StopProjectVersion.

This operation requires permissions to perform the rekognition:StartProjectVersion action.

startSegmentDetection(_:logger:on:)

public func startSegmentDetection(_ input: StartSegmentDetectionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<StartSegmentDetectionResponse> 

Starts asynchronous detection of segment detection in a stored video.

Amazon Rekognition Video can detect segments in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartSegmentDetection returns a job identifier (JobId) which you use to get the results of the operation. When segment detection is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel.

You can use the Filters (StartSegmentDetectionFilters) input parameter to specify the minimum detection confidence returned in the response. Within Filters, use ShotFilter (StartShotDetectionFilter) to filter detected shots. Use TechnicalCueFilter (StartTechnicalCueDetectionFilter) to filter technical cues.

To get the results of the segment detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. if so, call GetSegmentDetection and pass the job identifier (JobId) from the initial call to StartSegmentDetection.

     <p>For more information, see Detecting video segments in stored video in the Amazon Rekognition Developer Guide.</p>

startStreamProcessor(_:logger:on:)

public func startStreamProcessor(_ input: StartStreamProcessorRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<StartStreamProcessorResponse> 

Starts processing a stream processor. You create a stream processor by calling CreateStreamProcessor. To tell StartStreamProcessor which stream processor to start, use the value of the Name field specified in the call to CreateStreamProcessor.

If you are using a label detection stream processor to detect labels, you need to provide a Start selector and a Stop selector to determine the length of the stream processing time.

startTextDetection(_:logger:on:)

public func startTextDetection(_ input: StartTextDetectionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<StartTextDetectionResponse> 

Starts asynchronous detection of text in a stored video.

Amazon Rekognition Video can detect text in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartTextDetection returns a job identifier (JobId) which you use to get the results of the operation. When text detection is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel.

To get the results of the text detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. if so, call GetTextDetection and pass the job identifier (JobId) from the initial call to StartTextDetection.

stopProjectVersion(_:logger:on:)

public func stopProjectVersion(_ input: StopProjectVersionRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<StopProjectVersionResponse> 

Stops a running model. The operation might take a while to complete. To check the current status, call DescribeProjectVersions.

stopStreamProcessor(_:logger:on:)

public func stopStreamProcessor(_ input: StopStreamProcessorRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<StopStreamProcessorResponse> 

Stops a running stream processor that was created by CreateStreamProcessor.

tagResource(_:logger:on:)

public func tagResource(_ input: TagResourceRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<TagResourceResponse> 

Adds one or more key-value tags to an Amazon Rekognition collection, stream processor, or Custom Labels model. For more information, see Tagging AWS Resources.

This operation requires permissions to perform the rekognition:TagResource action.

untagResource(_:logger:on:)

public func untagResource(_ input: UntagResourceRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<UntagResourceResponse> 

Removes one or more tags from an Amazon Rekognition collection, stream processor, or Custom Labels model.

This operation requires permissions to perform the rekognition:UntagResource action.

updateDatasetEntries(_:logger:on:)

than 5MB.

public func updateDatasetEntries(_ input: UpdateDatasetEntriesRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<UpdateDatasetEntriesResponse> 

Adds or updates one or more entries (images) in a dataset. An entry is a JSON Line which contains the information for a single image, including the image location, assigned labels, and object location bounding boxes. For more information, see Image-Level labels in manifest files and Object localization in manifest files in the Amazon Rekognition Custom Labels Developer Guide.

     <p>If the <code>source-ref</code> field in the JSON line references an existing image, the existing image in the dataset
  is updated. 
  If <code>source-ref</code> field doesn't reference an existing image, the image is added as a new image to the dataset. </p>

     <p>You specify the changes that you want to make in the <code>Changes</code> input parameter. 
There isn't a limit to the number JSON Lines that you can change, but the size of <code>Changes</code> must be less
     <p>
        <code>UpdateDatasetEntries</code> returns immediatly, but the dataset update might take a while to complete.
  Use <a>DescribeDataset</a> to check the 
  current status. The dataset updated successfully if the value of <code>Status</code> is
  <code>UPDATE_COMPLETE</code>. </p>
     <p>To check if any non-terminal errors occured, call <a>ListDatasetEntries</a>
  and check for the presence of <code>errors</code> lists in the JSON Lines.</p>
     <p>Dataset update fails if a terminal error occurs (<code>Status</code> = <code>UPDATE_FAILED</code>). 
  Currently, you can't access the terminal error information from the Amazon Rekognition Custom Labels SDK.

This operation requires permissions to perform the rekognition:UpdateDatasetEntries action.

updateStreamProcessor(_:logger:on:)

public func updateStreamProcessor(_ input: UpdateStreamProcessorRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<UpdateStreamProcessorResponse> 

Allows you to update a stream processor. You can change some settings and regions of interest and delete certain parameters.

describeProjectVersionsPaginator(_:logger:on:)

compiler(>=5.5.2) && canImport(_Concurrency)
public func describeProjectVersionsPaginator(
        _ input: DescribeProjectVersionsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<DescribeProjectVersionsRequest, DescribeProjectVersionsResponse> 

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.

Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

describeProjectsPaginator(_:logger:on:)

compiler(>=5.5.2) && canImport(_Concurrency)
public func describeProjectsPaginator(
        _ input: DescribeProjectsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<DescribeProjectsRequest, DescribeProjectsResponse> 

Gets information about your Amazon Rekognition Custom Labels projects.

This operation requires permissions to perform the rekognition:DescribeProjects action.

Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

getCelebrityRecognitionPaginator(_:logger:on:)

compiler(>=5.5.2) && canImport(_Concurrency)
Return PaginatorSequence for operation.
public func getCelebrityRecognitionPaginator(
        _ input: GetCelebrityRecognitionRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<GetCelebrityRecognitionRequest, GetCelebrityRecognitionResponse> 

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.

      <p>For more information, see Working With Stored Videos in the Amazon Rekognition Developer Guide.</p>
      <p>
         <code>GetCelebrityRecognition</code> returns detected celebrities and the time(s) they
   are detected in an array (<code>Celebrities</code>) of <a>CelebrityRecognition</a>
    objects. Each <code>CelebrityRecognition</code>
   contains information about the celebrity in a <a>CelebrityDetail</a> object and the
   time, <code>Timestamp</code>, the celebrity was detected.  This <a>CelebrityDetail</a> object stores information about the detected celebrity's face
   attributes, a face bounding box, known gender, the celebrity's name, and a confidence
   estimate.</p>
      <note>
         <p>
            <code>GetCelebrityRecognition</code> only returns the default facial
     attributes (<code>BoundingBox</code>, <code>Confidence</code>, <code>Landmarks</code>,
       <code>Pose</code>, and <code>Quality</code>). The <code>BoundingBox</code> field only
     applies to the detected face instance. The other facial attributes listed in the
       <code>Face</code> object of the following response syntax are not returned. For more
     information, see FaceDetail in the Amazon Rekognition Developer Guide. </p>
      </note>
      <p>By default, the <code>Celebrities</code> array is sorted by time (milliseconds from the start of the video).
   You can also sort the array by celebrity by specifying the value <code>ID</code> in the <code>SortBy</code> input parameter.</p>
      <p>The <code>CelebrityDetail</code> object includes the celebrity identifer and additional information urls. If you don't store
   the additional information urls, you can get them later by calling <a>GetCelebrityInfo</a> with the celebrity identifer.</p>
      <p>No information is returned for faces not recognized as celebrities.</p>
      <p>Use MaxResults parameter to limit the number of labels returned. If there are more results than
   specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a
   pagination token for getting the next set of results. To get the next page of results, call <code>GetCelebrityDetection</code>
   and populate the <code>NextToken</code> request parameter with the token
   value returned from the previous call to <code>GetCelebrityRecognition</code>.</p>

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

getContentModerationPaginator(_:logger:on:)

compiler(>=5.5.2) && canImport(_Concurrency)
Return PaginatorSequence for operation.
public func getContentModerationPaginator(
        _ input: GetContentModerationRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<GetContentModerationRequest, GetContentModerationResponse> 

Gets the inappropriate, unwanted, or offensive content analysis results for a Amazon Rekognition Video analysis started by StartContentModeration. For a list of moderation labels in Amazon Rekognition, see Using the image and video moderation APIs.

Amazon Rekognition Video inappropriate or offensive content detection in a stored video is an asynchronous operation. You start analysis by calling StartContentModeration which returns a job identifier (JobId). When analysis finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartContentModeration. To get the results of the content analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetContentModeration and pass the job identifier (JobId) from the initial call to StartContentModeration.

      <p>For more information, see Working with Stored Videos in the
  Amazon Rekognition Devlopers Guide.</p>
      <p>
         <code>GetContentModeration</code> returns detected inappropriate, unwanted, or offensive content moderation labels,
   and the time they are detected, in an array, <code>ModerationLabels</code>, of
   <a>ContentModerationDetection</a> objects.
  </p>
      <p>By default, the moderated labels are returned sorted by time, in milliseconds from the start of the
    video. You can also sort them by moderated label by specifying <code>NAME</code> for the <code>SortBy</code>
    input parameter. </p>
      <p>Since video analysis can return a large number of results, use the <code>MaxResults</code> parameter to limit
   the number of labels returned in a single call to <code>GetContentModeration</code>. If there are more results than
    specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a
    pagination token for getting the next set of results. To get the next page of results, call <code>GetContentModeration</code>
    and populate the <code>NextToken</code> request parameter with the value of <code>NextToken</code>
    returned from the previous call to <code>GetContentModeration</code>.</p>
  
      <p>For more information, see moderating content in the Amazon Rekognition Developer Guide.</p>

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

getFaceDetectionPaginator(_:logger:on:)

compiler(>=5.5.2) && canImport(_Concurrency)
public func getFaceDetectionPaginator(
        _ input: GetFaceDetectionRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<GetFaceDetectionRequest, GetFaceDetectionResponse> 

Gets face detection results for a Amazon Rekognition Video analysis started by StartFaceDetection.

Face detection with Amazon Rekognition Video is an asynchronous operation. You start face detection by calling StartFaceDetection which returns a job identifier (JobId). When the face detection operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartFaceDetection. To get the results of the face detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetFaceDetection and pass the job identifier (JobId) from the initial call to StartFaceDetection.

GetFaceDetection returns an array of detected faces (Faces) sorted by the time the faces were detected.

Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetFaceDetection and populate the NextToken request parameter with the token value returned from the previous call to GetFaceDetection.

Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

getFaceSearchPaginator(_:logger:on:)

compiler(>=5.5.2) && canImport(_Concurrency)
Return PaginatorSequence for operation.
public func getFaceSearchPaginator(
        _ input: GetFaceSearchRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<GetFaceSearchRequest, GetFaceSearchResponse> 

Gets the face search results for Amazon Rekognition Video face search started by StartFaceSearch. The search returns faces in a collection that match the faces of persons detected in a video. It also includes the time(s) that faces are matched in the video.

Face search in a video is an asynchronous operation. You start face search by calling to StartFaceSearch which returns a job identifier (JobId). When the search operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartFaceSearch. To get the search results, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetFaceSearch and pass the job identifier (JobId) from the initial call to StartFaceSearch.

      <p>For more information, see Searching Faces in a Collection in the
   Amazon Rekognition Developer Guide.</p>
      <p>The search results are retured in an array, <code>Persons</code>, of
 <a>PersonMatch</a> objects. Each<code>PersonMatch</code> element contains
 details about the matching faces in the input collection, person information (facial attributes,
 bounding boxes, and person identifer)
 for the matched person, and the time the person was matched in the video.</p>
      <note>
         <p>
            <code>GetFaceSearch</code> only returns the default  
     facial attributes (<code>BoundingBox</code>, <code>Confidence</code>, 
     <code>Landmarks</code>, <code>Pose</code>, and <code>Quality</code>). The other facial attributes listed
     in the <code>Face</code> object of the following response syntax are not returned. For more information,
     see FaceDetail in the Amazon Rekognition Developer Guide. </p>
      </note>
      <p>By default, the <code>Persons</code> array is sorted by the time, in milliseconds from the
 start of the video, persons are matched.
 You can also sort by persons by specifying <code>INDEX</code> for the <code>SORTBY</code> input
 parameter.</p>

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

getLabelDetectionPaginator(_:logger:on:)

compiler(>=5.5.2) && canImport(_Concurrency)
Return PaginatorSequence for operation.
public func getLabelDetectionPaginator(
        _ input: GetLabelDetectionRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<GetLabelDetectionRequest, GetLabelDetectionResponse> 

Gets the label detection results of a Amazon Rekognition Video analysis started by StartLabelDetection.

The label detection operation is started by a call to StartLabelDetection which returns a job identifier (JobId). When the label detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartlabelDetection. To get the results of the label detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetLabelDetection and pass the job identifier (JobId) from the initial call to StartLabelDetection.

GetLabelDetection returns an array of detected labels (Labels) sorted by the time the labels were detected. You can also sort by the label name by specifying NAME for the SortBy input parameter.

The labels returned include the label name, the percentage confidence in the accuracy of the detected label, and the time the label was detected in the video.

The returned labels also include bounding box information for common objects, a hierarchical taxonomy of detected labels, and the version of the label model used for detection.

      <p>Use MaxResults parameter to limit the number of labels returned. If there are more results than
 specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a pagination token for getting the next set
 of results. To get the next page of results, call <code>GetlabelDetection</code> and populate the <code>NextToken</code> request parameter with the token
  value returned from the previous call to <code>GetLabelDetection</code>.</p>

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

getPersonTrackingPaginator(_:logger:on:)

compiler(>=5.5.2) && canImport(_Concurrency)
Return PaginatorSequence for operation.
public func getPersonTrackingPaginator(
        _ input: GetPersonTrackingRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<GetPersonTrackingRequest, GetPersonTrackingResponse> 

Gets the path tracking results of a Amazon Rekognition Video analysis started by StartPersonTracking.

The person path tracking operation is started by a call to StartPersonTracking which returns a job identifier (JobId). When the operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartPersonTracking.

To get the results of the person path tracking operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetPersonTracking and pass the job identifier (JobId) from the initial call to StartPersonTracking.

GetPersonTracking returns an array, Persons, of tracked persons and the time(s) their paths were tracked in the video.

GetPersonTracking only returns the default facial attributes (BoundingBox, Confidence, Landmarks, Pose, and Quality). The other facial attributes listed in the Face object of the following response syntax are not returned.

         <p>For more information, see FaceDetail in the Amazon Rekognition Developer Guide.</p>
      </note>
 
      <p>By default, the array is sorted by the time(s) a person's path is tracked in the video.
   You can sort by tracked persons by specifying <code>INDEX</code> for the <code>SortBy</code> input parameter.</p>
      <p>Use the <code>MaxResults</code> parameter to limit the number of items returned. If there are more results than
specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a pagination token for getting the next set
of results. To get the next page of results, call <code>GetPersonTracking</code> and populate the <code>NextToken</code> request parameter with the token
 value returned from the previous call to <code>GetPersonTracking</code>.</p>

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

getSegmentDetectionPaginator(_:logger:on:)

compiler(>=5.5.2) && canImport(_Concurrency)
Return PaginatorSequence for operation.
public func getSegmentDetectionPaginator(
        _ input: GetSegmentDetectionRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<GetSegmentDetectionRequest, GetSegmentDetectionResponse> 

Gets the segment detection results of a Amazon Rekognition Video analysis started by StartSegmentDetection.

Segment detection with Amazon Rekognition Video is an asynchronous operation. You start segment detection by calling StartSegmentDetection which returns a job identifier (JobId). When the segment detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartSegmentDetection. To get the results of the segment detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. if so, call GetSegmentDetection and pass the job identifier (JobId) from the initial call of StartSegmentDetection.

GetSegmentDetection returns detected segments in an array (Segments) of SegmentDetection objects. Segments is sorted by the segment types specified in the SegmentTypes input parameter of StartSegmentDetection. Each element of the array includes the detected segment, the precentage confidence in the acuracy of the detected segment, the type of the segment, and the frame in which the segment was detected.

Use SelectedSegmentTypes to find out the type of segment detection requested in the call to StartSegmentDetection.

Use the MaxResults parameter to limit the number of segment detections returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetSegmentDetection and populate the NextToken request parameter with the token value returned from the previous call to GetSegmentDetection.

      <p>For more information, see Detecting video segments in stored video in the Amazon Rekognition Developer Guide.</p>

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

getTextDetectionPaginator(_:logger:on:)

compiler(>=5.5.2) && canImport(_Concurrency)
public func getTextDetectionPaginator(
        _ input: GetTextDetectionRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<GetTextDetectionRequest, GetTextDetectionResponse> 

Gets the text detection results of a Amazon Rekognition Video analysis started by StartTextDetection.

Text detection with Amazon Rekognition Video is an asynchronous operation. You start text detection by calling StartTextDetection which returns a job identifier (JobId) When the text detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartTextDetection. To get the results of the text detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. if so, call GetTextDetection and pass the job identifier (JobId) from the initial call of StartLabelDetection.

GetTextDetection returns an array of detected text (TextDetections) sorted by the time the text was detected, up to 50 words per frame of video.

Each element of the array includes the detected text, the precentage confidence in the acuracy of the detected text, the time the text was detected, bounding box information for where the text was located, and unique identifiers for words and their lines.

Use MaxResults parameter to limit the number of text detections returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetTextDetection and populate the NextToken request parameter with the token value returned from the previous call to GetTextDetection.

Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

listCollectionsPaginator(_:logger:on:)

compiler(>=5.5.2) && canImport(_Concurrency)
Return PaginatorSequence for operation.
public func listCollectionsPaginator(
        _ input: ListCollectionsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<ListCollectionsRequest, ListCollectionsResponse> 

Returns list of collection IDs in your account. If the result is truncated, the response also provides a NextToken that you can use in the subsequent request to fetch the next set of collection IDs.

      <p>For an example, see Listing collections in the Amazon Rekognition Developer Guide.</p>
      <p>This operation requires permissions to perform the <code>rekognition:ListCollections</code> action.</p>

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

listDatasetEntriesPaginator(_:logger:on:)

compiler(>=5.5.2) && canImport(_Concurrency)
public func listDatasetEntriesPaginator(
        _ input: ListDatasetEntriesRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<ListDatasetEntriesRequest, ListDatasetEntriesResponse> 

Lists the entries (images) within a dataset. An entry is a JSON Line that contains the information for a single image, including the image location, assigned labels, and object location bounding boxes. For more information, see Creating a manifest file.

JSON Lines in the response include information about non-terminal errors found in the dataset. Non terminal errors are reported in errors lists within each JSON Line. The same information is reported in the training and testing validation result manifests that Amazon Rekognition Custom Labels creates during model training.

      <p>You can filter the response in variety of ways, such as choosing which labels to return and returning JSON Lines created after a specific date.

This operation requires permissions to perform the rekognition:ListDatasetEntries action.

Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

listDatasetLabelsPaginator(_:logger:on:)

compiler(>=5.5.2) && canImport(_Concurrency)
public func listDatasetLabelsPaginator(
        _ input: ListDatasetLabelsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<ListDatasetLabelsRequest, ListDatasetLabelsResponse> 

Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see Labeling images.

Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see Labeling images in the Amazon Rekognition Custom Labels Developer Guide.

Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

listFacesPaginator(_:logger:on:)

compiler(>=5.5.2) && canImport(_Concurrency)
Return PaginatorSequence for operation.
public func listFacesPaginator(
        _ input: ListFacesRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<ListFacesRequest, ListFacesResponse> 

Returns metadata for faces in the specified collection. This metadata includes information such as the bounding box coordinates, the confidence (that the bounding box contains a face), and face ID. For an example, see Listing Faces in a Collection in the Amazon Rekognition Developer Guide.

      <p>This operation requires permissions to perform the
   <code>rekognition:ListFaces</code> action.</p>

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

listStreamProcessorsPaginator(_:logger:on:)

compiler(>=5.5.2) && canImport(_Concurrency)
public func listStreamProcessorsPaginator(
        _ input: ListStreamProcessorsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<ListStreamProcessorsRequest, ListStreamProcessorsResponse> 

Gets a list of stream processors that you have created with CreateStreamProcessor.

Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

describeProjectVersionsPaginator(_:_:logger:on:onPage:)

Provide paginated results to closure `onPage` for it to combine them into one result. This works in a similar manner to `Array.reduce(_:​_:​) -> Result`.
public func describeProjectVersionsPaginator<Result>(
        _ input: DescribeProjectVersionsRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, DescribeProjectVersionsResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result> 

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.

Parameters: - input: Input for request - initialValue: The value to use as the initial accumulating value. `initialValue` is passed to `onPage` the first time it is called. - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

describeProjectVersionsPaginator(_:logger:on:onPage:)

Provide paginated results to closure `onPage`.
public func describeProjectVersionsPaginator(
        _ input: DescribeProjectVersionsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (DescribeProjectVersionsResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void> 

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

describeProjectsPaginator(_:_:logger:on:onPage:)

Provide paginated results to closure `onPage` for it to combine them into one result. This works in a similar manner to `Array.reduce(_:​_:​) -> Result`.
public func describeProjectsPaginator<Result>(
        _ input: DescribeProjectsRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, DescribeProjectsResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result> 

Gets information about your Amazon Rekognition Custom Labels projects.

This operation requires permissions to perform the rekognition:DescribeProjects action.

Parameters: - input: Input for request - initialValue: The value to use as the initial accumulating value. `initialValue` is passed to `onPage` the first time it is called. - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

describeProjectsPaginator(_:logger:on:onPage:)

Provide paginated results to closure `onPage`.
public func describeProjectsPaginator(
        _ input: DescribeProjectsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (DescribeProjectsResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void> 

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

getCelebrityRecognitionPaginator(_:_:logger:on:onPage:)

Provide paginated results to closure `onPage` for it to combine them into one result. This works in a similar manner to `Array.reduce(_:​_:​) -> Result`.
public func getCelebrityRecognitionPaginator<Result>(
        _ input: GetCelebrityRecognitionRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, GetCelebrityRecognitionResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result> 

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.

      <p>For more information, see Working With Stored Videos in the Amazon Rekognition Developer Guide.</p>
      <p>
         <code>GetCelebrityRecognition</code> returns detected celebrities and the time(s) they
   are detected in an array (<code>Celebrities</code>) of <a>CelebrityRecognition</a>
    objects. Each <code>CelebrityRecognition</code>
   contains information about the celebrity in a <a>CelebrityDetail</a> object and the
   time, <code>Timestamp</code>, the celebrity was detected.  This <a>CelebrityDetail</a> object stores information about the detected celebrity's face
   attributes, a face bounding box, known gender, the celebrity's name, and a confidence
   estimate.</p>
      <note>
         <p>
            <code>GetCelebrityRecognition</code> only returns the default facial
     attributes (<code>BoundingBox</code>, <code>Confidence</code>, <code>Landmarks</code>,
       <code>Pose</code>, and <code>Quality</code>). The <code>BoundingBox</code> field only
     applies to the detected face instance. The other facial attributes listed in the
       <code>Face</code> object of the following response syntax are not returned. For more
     information, see FaceDetail in the Amazon Rekognition Developer Guide. </p>
      </note>
      <p>By default, the <code>Celebrities</code> array is sorted by time (milliseconds from the start of the video).
   You can also sort the array by celebrity by specifying the value <code>ID</code> in the <code>SortBy</code> input parameter.</p>
      <p>The <code>CelebrityDetail</code> object includes the celebrity identifer and additional information urls. If you don't store
   the additional information urls, you can get them later by calling <a>GetCelebrityInfo</a> with the celebrity identifer.</p>
      <p>No information is returned for faces not recognized as celebrities.</p>
      <p>Use MaxResults parameter to limit the number of labels returned. If there are more results than
   specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a
   pagination token for getting the next set of results. To get the next page of results, call <code>GetCelebrityDetection</code>
   and populate the <code>NextToken</code> request parameter with the token
   value returned from the previous call to <code>GetCelebrityRecognition</code>.</p>
Parameters: - input: Input for request - initialValue: The value to use as the initial accumulating value. `initialValue` is passed to `onPage` the first time it is called. - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

getCelebrityRecognitionPaginator(_:logger:on:onPage:)

Provide paginated results to closure `onPage`.
public func getCelebrityRecognitionPaginator(
        _ input: GetCelebrityRecognitionRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (GetCelebrityRecognitionResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void> 

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

getContentModerationPaginator(_:_:logger:on:onPage:)

Provide paginated results to closure `onPage` for it to combine them into one result. This works in a similar manner to `Array.reduce(_:​_:​) -> Result`.
public func getContentModerationPaginator<Result>(
        _ input: GetContentModerationRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, GetContentModerationResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result> 

Gets the inappropriate, unwanted, or offensive content analysis results for a Amazon Rekognition Video analysis started by StartContentModeration. For a list of moderation labels in Amazon Rekognition, see Using the image and video moderation APIs.

Amazon Rekognition Video inappropriate or offensive content detection in a stored video is an asynchronous operation. You start analysis by calling StartContentModeration which returns a job identifier (JobId). When analysis finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartContentModeration. To get the results of the content analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetContentModeration and pass the job identifier (JobId) from the initial call to StartContentModeration.

      <p>For more information, see Working with Stored Videos in the
  Amazon Rekognition Devlopers Guide.</p>
      <p>
         <code>GetContentModeration</code> returns detected inappropriate, unwanted, or offensive content moderation labels,
   and the time they are detected, in an array, <code>ModerationLabels</code>, of
   <a>ContentModerationDetection</a> objects.
  </p>
      <p>By default, the moderated labels are returned sorted by time, in milliseconds from the start of the
    video. You can also sort them by moderated label by specifying <code>NAME</code> for the <code>SortBy</code>
    input parameter. </p>
      <p>Since video analysis can return a large number of results, use the <code>MaxResults</code> parameter to limit
   the number of labels returned in a single call to <code>GetContentModeration</code>. If there are more results than
    specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a
    pagination token for getting the next set of results. To get the next page of results, call <code>GetContentModeration</code>
    and populate the <code>NextToken</code> request parameter with the value of <code>NextToken</code>
    returned from the previous call to <code>GetContentModeration</code>.</p>
  
      <p>For more information, see moderating content in the Amazon Rekognition Developer Guide.</p>
Parameters: - input: Input for request - initialValue: The value to use as the initial accumulating value. `initialValue` is passed to `onPage` the first time it is called. - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

getContentModerationPaginator(_:logger:on:onPage:)

Provide paginated results to closure `onPage`.
public func getContentModerationPaginator(
        _ input: GetContentModerationRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (GetContentModerationResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void> 

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

getFaceDetectionPaginator(_:_:logger:on:onPage:)

Provide paginated results to closure `onPage` for it to combine them into one result. This works in a similar manner to `Array.reduce(_:​_:​) -> Result`.
public func getFaceDetectionPaginator<Result>(
        _ input: GetFaceDetectionRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, GetFaceDetectionResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result> 

Gets face detection results for a Amazon Rekognition Video analysis started by StartFaceDetection.

Face detection with Amazon Rekognition Video is an asynchronous operation. You start face detection by calling StartFaceDetection which returns a job identifier (JobId). When the face detection operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartFaceDetection. To get the results of the face detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetFaceDetection and pass the job identifier (JobId) from the initial call to StartFaceDetection.

GetFaceDetection returns an array of detected faces (Faces) sorted by the time the faces were detected.

Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetFaceDetection and populate the NextToken request parameter with the token value returned from the previous call to GetFaceDetection.

Parameters: - input: Input for request - initialValue: The value to use as the initial accumulating value. `initialValue` is passed to `onPage` the first time it is called. - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

getFaceDetectionPaginator(_:logger:on:onPage:)

Provide paginated results to closure `onPage`.
public func getFaceDetectionPaginator(
        _ input: GetFaceDetectionRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (GetFaceDetectionResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void> 

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

getFaceSearchPaginator(_:_:logger:on:onPage:)

Provide paginated results to closure `onPage` for it to combine them into one result. This works in a similar manner to `Array.reduce(_:​_:​) -> Result`.
public func getFaceSearchPaginator<Result>(
        _ input: GetFaceSearchRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, GetFaceSearchResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result> 

Gets the face search results for Amazon Rekognition Video face search started by StartFaceSearch. The search returns faces in a collection that match the faces of persons detected in a video. It also includes the time(s) that faces are matched in the video.

Face search in a video is an asynchronous operation. You start face search by calling to StartFaceSearch which returns a job identifier (JobId). When the search operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartFaceSearch. To get the search results, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetFaceSearch and pass the job identifier (JobId) from the initial call to StartFaceSearch.

      <p>For more information, see Searching Faces in a Collection in the
   Amazon Rekognition Developer Guide.</p>
      <p>The search results are retured in an array, <code>Persons</code>, of
 <a>PersonMatch</a> objects. Each<code>PersonMatch</code> element contains
 details about the matching faces in the input collection, person information (facial attributes,
 bounding boxes, and person identifer)
 for the matched person, and the time the person was matched in the video.</p>
      <note>
         <p>
            <code>GetFaceSearch</code> only returns the default  
     facial attributes (<code>BoundingBox</code>, <code>Confidence</code>, 
     <code>Landmarks</code>, <code>Pose</code>, and <code>Quality</code>). The other facial attributes listed
     in the <code>Face</code> object of the following response syntax are not returned. For more information,
     see FaceDetail in the Amazon Rekognition Developer Guide. </p>
      </note>
      <p>By default, the <code>Persons</code> array is sorted by the time, in milliseconds from the
 start of the video, persons are matched.
 You can also sort by persons by specifying <code>INDEX</code> for the <code>SORTBY</code> input
 parameter.</p>
Parameters: - input: Input for request - initialValue: The value to use as the initial accumulating value. `initialValue` is passed to `onPage` the first time it is called. - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

getFaceSearchPaginator(_:logger:on:onPage:)

Provide paginated results to closure `onPage`.
public func getFaceSearchPaginator(
        _ input: GetFaceSearchRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (GetFaceSearchResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void> 

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

getLabelDetectionPaginator(_:_:logger:on:onPage:)

Provide paginated results to closure `onPage` for it to combine them into one result. This works in a similar manner to `Array.reduce(_:​_:​) -> Result`.
public func getLabelDetectionPaginator<Result>(
        _ input: GetLabelDetectionRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, GetLabelDetectionResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result> 

Gets the label detection results of a Amazon Rekognition Video analysis started by StartLabelDetection.

The label detection operation is started by a call to StartLabelDetection which returns a job identifier (JobId). When the label detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartlabelDetection. To get the results of the label detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetLabelDetection and pass the job identifier (JobId) from the initial call to StartLabelDetection.

GetLabelDetection returns an array of detected labels (Labels) sorted by the time the labels were detected. You can also sort by the label name by specifying NAME for the SortBy input parameter.

The labels returned include the label name, the percentage confidence in the accuracy of the detected label, and the time the label was detected in the video.

The returned labels also include bounding box information for common objects, a hierarchical taxonomy of detected labels, and the version of the label model used for detection.

      <p>Use MaxResults parameter to limit the number of labels returned. If there are more results than
 specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a pagination token for getting the next set
 of results. To get the next page of results, call <code>GetlabelDetection</code> and populate the <code>NextToken</code> request parameter with the token
  value returned from the previous call to <code>GetLabelDetection</code>.</p>
Parameters: - input: Input for request - initialValue: The value to use as the initial accumulating value. `initialValue` is passed to `onPage` the first time it is called. - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

getLabelDetectionPaginator(_:logger:on:onPage:)

Provide paginated results to closure `onPage`.
public func getLabelDetectionPaginator(
        _ input: GetLabelDetectionRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (GetLabelDetectionResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void> 

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

getPersonTrackingPaginator(_:_:logger:on:onPage:)

Provide paginated results to closure `onPage` for it to combine them into one result. This works in a similar manner to `Array.reduce(_:​_:​) -> Result`.
public func getPersonTrackingPaginator<Result>(
        _ input: GetPersonTrackingRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, GetPersonTrackingResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result> 

Gets the path tracking results of a Amazon Rekognition Video analysis started by StartPersonTracking.

The person path tracking operation is started by a call to StartPersonTracking which returns a job identifier (JobId). When the operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartPersonTracking.

To get the results of the person path tracking operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetPersonTracking and pass the job identifier (JobId) from the initial call to StartPersonTracking.

GetPersonTracking returns an array, Persons, of tracked persons and the time(s) their paths were tracked in the video.

GetPersonTracking only returns the default facial attributes (BoundingBox, Confidence, Landmarks, Pose, and Quality). The other facial attributes listed in the Face object of the following response syntax are not returned.

         <p>For more information, see FaceDetail in the Amazon Rekognition Developer Guide.</p>
      </note>
 
      <p>By default, the array is sorted by the time(s) a person's path is tracked in the video.
   You can sort by tracked persons by specifying <code>INDEX</code> for the <code>SortBy</code> input parameter.</p>
      <p>Use the <code>MaxResults</code> parameter to limit the number of items returned. If there are more results than
specified in <code>MaxResults</code>, the value of <code>NextToken</code> in the operation response contains a pagination token for getting the next set
of results. To get the next page of results, call <code>GetPersonTracking</code> and populate the <code>NextToken</code> request parameter with the token
 value returned from the previous call to <code>GetPersonTracking</code>.</p>
Parameters: - input: Input for request - initialValue: The value to use as the initial accumulating value. `initialValue` is passed to `onPage` the first time it is called. - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

getPersonTrackingPaginator(_:logger:on:onPage:)

Provide paginated results to closure `onPage`.
public func getPersonTrackingPaginator(
        _ input: GetPersonTrackingRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (GetPersonTrackingResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void> 

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

getSegmentDetectionPaginator(_:_:logger:on:onPage:)

Provide paginated results to closure `onPage` for it to combine them into one result. This works in a similar manner to `Array.reduce(_:​_:​) -> Result`.
public func getSegmentDetectionPaginator<Result>(
        _ input: GetSegmentDetectionRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, GetSegmentDetectionResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result> 

Gets the segment detection results of a Amazon Rekognition Video analysis started by StartSegmentDetection.

Segment detection with Amazon Rekognition Video is an asynchronous operation. You start segment detection by calling StartSegmentDetection which returns a job identifier (JobId). When the segment detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartSegmentDetection. To get the results of the segment detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. if so, call GetSegmentDetection and pass the job identifier (JobId) from the initial call of StartSegmentDetection.

GetSegmentDetection returns detected segments in an array (Segments) of SegmentDetection objects. Segments is sorted by the segment types specified in the SegmentTypes input parameter of StartSegmentDetection. Each element of the array includes the detected segment, the precentage confidence in the acuracy of the detected segment, the type of the segment, and the frame in which the segment was detected.

Use SelectedSegmentTypes to find out the type of segment detection requested in the call to StartSegmentDetection.

Use the MaxResults parameter to limit the number of segment detections returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetSegmentDetection and populate the NextToken request parameter with the token value returned from the previous call to GetSegmentDetection.

      <p>For more information, see Detecting video segments in stored video in the Amazon Rekognition Developer Guide.</p>
Parameters: - input: Input for request - initialValue: The value to use as the initial accumulating value. `initialValue` is passed to `onPage` the first time it is called. - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

getSegmentDetectionPaginator(_:logger:on:onPage:)

Provide paginated results to closure `onPage`.
public func getSegmentDetectionPaginator(
        _ input: GetSegmentDetectionRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (GetSegmentDetectionResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void> 

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

getTextDetectionPaginator(_:_:logger:on:onPage:)

Provide paginated results to closure `onPage` for it to combine them into one result. This works in a similar manner to `Array.reduce(_:​_:​) -> Result`.
public func getTextDetectionPaginator<Result>(
        _ input: GetTextDetectionRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, GetTextDetectionResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result> 

Gets the text detection results of a Amazon Rekognition Video analysis started by StartTextDetection.

Text detection with Amazon Rekognition Video is an asynchronous operation. You start text detection by calling StartTextDetection which returns a job identifier (JobId) When the text detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartTextDetection. To get the results of the text detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. if so, call GetTextDetection and pass the job identifier (JobId) from the initial call of StartLabelDetection.

GetTextDetection returns an array of detected text (TextDetections) sorted by the time the text was detected, up to 50 words per frame of video.

Each element of the array includes the detected text, the precentage confidence in the acuracy of the detected text, the time the text was detected, bounding box information for where the text was located, and unique identifiers for words and their lines.

Use MaxResults parameter to limit the number of text detections returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetTextDetection and populate the NextToken request parameter with the token value returned from the previous call to GetTextDetection.

Parameters: - input: Input for request - initialValue: The value to use as the initial accumulating value. `initialValue` is passed to `onPage` the first time it is called. - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

getTextDetectionPaginator(_:logger:on:onPage:)

Provide paginated results to closure `onPage`.
public func getTextDetectionPaginator(
        _ input: GetTextDetectionRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (GetTextDetectionResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void> 

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

listCollectionsPaginator(_:_:logger:on:onPage:)

Provide paginated results to closure `onPage` for it to combine them into one result. This works in a similar manner to `Array.reduce(_:​_:​) -> Result`.
public func listCollectionsPaginator<Result>(
        _ input: ListCollectionsRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, ListCollectionsResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result> 

Returns list of collection IDs in your account. If the result is truncated, the response also provides a NextToken that you can use in the subsequent request to fetch the next set of collection IDs.

      <p>For an example, see Listing collections in the Amazon Rekognition Developer Guide.</p>
      <p>This operation requires permissions to perform the <code>rekognition:ListCollections</code> action.</p>
Parameters: - input: Input for request - initialValue: The value to use as the initial accumulating value. `initialValue` is passed to `onPage` the first time it is called. - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

listCollectionsPaginator(_:logger:on:onPage:)

Provide paginated results to closure `onPage`.
public func listCollectionsPaginator(
        _ input: ListCollectionsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (ListCollectionsResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void> 

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

listDatasetEntriesPaginator(_:_:logger:on:onPage:)

Provide paginated results to closure `onPage` for it to combine them into one result. This works in a similar manner to `Array.reduce(_:​_:​) -> Result`.
public func listDatasetEntriesPaginator<Result>(
        _ input: ListDatasetEntriesRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, ListDatasetEntriesResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result> 

Lists the entries (images) within a dataset. An entry is a JSON Line that contains the information for a single image, including the image location, assigned labels, and object location bounding boxes. For more information, see Creating a manifest file.

JSON Lines in the response include information about non-terminal errors found in the dataset. Non terminal errors are reported in errors lists within each JSON Line. The same information is reported in the training and testing validation result manifests that Amazon Rekognition Custom Labels creates during model training.

      <p>You can filter the response in variety of ways, such as choosing which labels to return and returning JSON Lines created after a specific date.

This operation requires permissions to perform the rekognition:ListDatasetEntries action.

Parameters: - input: Input for request - initialValue: The value to use as the initial accumulating value. `initialValue` is passed to `onPage` the first time it is called. - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

listDatasetEntriesPaginator(_:logger:on:onPage:)

Provide paginated results to closure `onPage`.
public func listDatasetEntriesPaginator(
        _ input: ListDatasetEntriesRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (ListDatasetEntriesResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void> 

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

listDatasetLabelsPaginator(_:_:logger:on:onPage:)

Provide paginated results to closure `onPage` for it to combine them into one result. This works in a similar manner to `Array.reduce(_:​_:​) -> Result`.
public func listDatasetLabelsPaginator<Result>(
        _ input: ListDatasetLabelsRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, ListDatasetLabelsResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result> 

Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see Labeling images.

Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see Labeling images in the Amazon Rekognition Custom Labels Developer Guide.

Parameters: - input: Input for request - initialValue: The value to use as the initial accumulating value. `initialValue` is passed to `onPage` the first time it is called. - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

listDatasetLabelsPaginator(_:logger:on:onPage:)

Provide paginated results to closure `onPage`.
public func listDatasetLabelsPaginator(
        _ input: ListDatasetLabelsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (ListDatasetLabelsResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void> 

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

listFacesPaginator(_:_:logger:on:onPage:)

Provide paginated results to closure `onPage` for it to combine them into one result. This works in a similar manner to `Array.reduce(_:​_:​) -> Result`.
public func listFacesPaginator<Result>(
        _ input: ListFacesRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, ListFacesResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result> 

Returns metadata for faces in the specified collection. This metadata includes information such as the bounding box coordinates, the confidence (that the bounding box contains a face), and face ID. For an example, see Listing Faces in a Collection in the Amazon Rekognition Developer Guide.

      <p>This operation requires permissions to perform the
   <code>rekognition:ListFaces</code> action.</p>
Parameters: - input: Input for request - initialValue: The value to use as the initial accumulating value. `initialValue` is passed to `onPage` the first time it is called. - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

listFacesPaginator(_:logger:on:onPage:)

Provide paginated results to closure `onPage`.
public func listFacesPaginator(
        _ input: ListFacesRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (ListFacesResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void> 

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

listStreamProcessorsPaginator(_:_:logger:on:onPage:)

Provide paginated results to closure `onPage` for it to combine them into one result. This works in a similar manner to `Array.reduce(_:​_:​) -> Result`.
public func listStreamProcessorsPaginator<Result>(
        _ input: ListStreamProcessorsRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, ListStreamProcessorsResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result> 

Gets a list of stream processors that you have created with CreateStreamProcessor.

Parameters: - input: Input for request - initialValue: The value to use as the initial accumulating value. `initialValue` is passed to `onPage` the first time it is called. - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

listStreamProcessorsPaginator(_:logger:on:onPage:)

Provide paginated results to closure `onPage`.
public func listStreamProcessorsPaginator(
        _ input: ListStreamProcessorsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (ListStreamProcessorsResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void> 

Parameters

- input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on - onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

waitUntilProjectVersionRunning(_:maxWaitTime:logger:on:)

Wait until the ProjectVersion is running.
public func waitUntilProjectVersionRunning(
        _ input: DescribeProjectVersionsRequest,
        maxWaitTime: TimeAmount? = nil,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> EventLoopFuture<Void> 

waitUntilProjectVersionTrainingCompleted(_:maxWaitTime:logger:on:)

Wait until the ProjectVersion training completes.
public func waitUntilProjectVersionTrainingCompleted(
        _ input: DescribeProjectVersionsRequest,
        maxWaitTime: TimeAmount? = nil,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> EventLoopFuture<Void>