# WARNING: DO NOT EDIT, AUTO-GENERATED CODE!
# See https://github.com/aws-beam/aws-codegen for more details.

defmodule AWS.Rekognition do
  @moduledoc """
  This is the API Reference for [Amazon Rekognition Image](https://docs.aws.amazon.com/rekognition/latest/dg/images.html), [Amazon Rekognition Custom
  Labels](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/what-is.html),
  [Amazon Rekognition Stored Video](https://docs.aws.amazon.com/rekognition/latest/dg/video.html), [Amazon Rekognition Streaming
  Video](https://docs.aws.amazon.com/rekognition/latest/dg/streaming-video.html).

  It provides descriptions of actions, data types, common parameters, and common
  errors.

  ## Amazon Rekognition Image

    *
  [CompareFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CompareFaces.html)     *
  [CreateCollection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateCollection.html)

    *
  [DeleteCollection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteCollection.html)     *
  [DeleteFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteFaces.html)

    *
  [DescribeCollection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeCollection.html)     *
  [DetectFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectFaces.html)

    *
  [DetectLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectLabels.html)     *
  [DetectModerationLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectModerationLabels.html)

    *
  [DetectProtectiveEquipment](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectProtectiveEquipment.html)     *
  [DetectText](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectText.html)

    *
  [GetCelebrityInfo](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetCelebrityInfo.html)     *
  [IndexFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_IndexFaces.html)

    *
  [ListCollections](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListCollections.html)     *
  [ListFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListFaces.html)

    *
  [RecognizeCelebrities](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_RecognizeCelebrities.html)     *
  [SearchFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchFaces.html)

    *
  [SearchFacesByImage](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchFacesByImage.html) 

  ## Amazon Rekognition Custom Labels

    *
  [CopyProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CopyProjectVersion.html)

    *
  [CreateDataset](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateDataset.html)     *
  [CreateProject](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateProject.html)

    *
  [CreateProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateProjectVersion.html)     *
  [DeleteDataset](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteDataset.html)

    *
  [DeleteProject](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteProject.html)     *
  [DeleteProjectPolicy](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteProjectPolicy.html)

    *
  [DeleteProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteProjectVersion.html)     *
  [DescribeDataset](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeDataset.html)

    *
  [DescribeProjects](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeProjects.html)     *
  [DescribeProjectVersions](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeProjectVersions.html)

    *
  [DetectCustomLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectCustomLabels.html)     *
  [DistributeDatasetEntries](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DistributeDatasetEntries.html)

    *
  [ListDatasetEntries](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListDatasetEntries.html)     *
  [ListDatasetLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListDatasetLabels.html)

    *
  [ListProjectPolicies](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListProjectPolicies.html)     *
  [PutProjectPolicy](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_PutProjectPolicy.html)

    *
  [StartProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartProjectVersion.html)     *
  [StopProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StopProjectVersion.html)

    *
  [UpdateDatasetEntries](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_UpdateDatasetEntries.html) 

  ## Amazon Rekognition Video Stored Video

    *
  [GetCelebrityRecognition](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetCelebrityRecognition.html)

    *
  [GetContentModeration](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetContentModeration.html)     *
  [GetFaceDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetFaceDetection.html)

    *
  [GetFaceSearch](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetFaceSearch.html)     *
  [GetLabelDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetLabelDetection.html)

    *
  [GetPersonTracking](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetPersonTracking.html)     *
  [GetSegmentDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetSegmentDetection.html)

    *
  [GetTextDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetTextDetection.html)     *
  [StartCelebrityRecognition](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartCelebrityRecognition.html)

    *
  [StartContentModeration](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartContentModeration.html)     *
  [StartFaceDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartFaceDetection.html)

    *
  [StartFaceSearch](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartFaceSearch.html)     *
  [StartLabelDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartLabelDetection.html)

    *
  [StartPersonTracking](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartPersonTracking.html)     *
  [StartSegmentDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartSegmentDetection.html)

    *
  [StartTextDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartTextDetection.html) 

  ## Amazon Rekognition Video Streaming Video

    *
  [CreateStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateStreamProcessor.html)

    *
  [DeleteStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteStreamProcessor.html)     *
  [DescribeStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeStreamProcessor.html)

    *
  [ListStreamProcessors](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListStreamProcessors.html)     *
  [StartStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartStreamProcessor.html)

    *
  [StopStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StopStreamProcessor.html)     *
  [UpdateStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_UpdateStreamProcessor.html)
  """

  alias AWS.Client
  alias AWS.Request

  def metadata do
    %{
      abbreviation: nil,
      api_version: "2016-06-27",
      content_type: "application/x-amz-json-1.1",
      credential_scope: nil,
      endpoint_prefix: "rekognition",
      global?: false,
      protocol: "json",
      service_id: "Rekognition",
      signature_version: "v4",
      signing_name: "rekognition",
      target_prefix: "RekognitionService"
    }
  end

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

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

  CompareFaces uses machine learning algorithms, which are probabilistic. A false
  negative is an incorrect prediction that a face in the target image has a low
  similarity confidence score when compared to the face in the source image. To
  reduce the probability of false negatives, we recommend that you compare the
  target image against multiple source images. If you plan to use `CompareFaces`
  to make a decision that impacts an individual's rights, privacy, or access to
  services, we recommend that you pass the result to a human for review and
  further validation before taking action.

  You pass the input and target images either as base64-encoded image bytes or as
  references to images in an Amazon S3 bucket. If you use the AWS CLI to call
  Amazon Rekognition operations, passing image bytes isn't supported. The image
  must be formatted as a PNG or JPEG file.

  In response, the operation returns an array of face matches ordered by
  similarity score in descending order. For each face match, the response provides
  a bounding box of the face, facial landmarks, pose details (pitch, roll, and
  yaw), quality (brightness and sharpness), and confidence value (indicating the
  level of confidence that the bounding box contains a face). The response also
  provides a similarity score, which indicates how closely the faces match.

  By default, only faces with a similarity score of greater than or equal to 80%
  are returned in the response. You can change this value by specifying the
  `SimilarityThreshold` parameter.

  `CompareFaces` also returns an array of faces that don't match the source image.
  For each face, it returns a bounding box, confidence value, landmarks, pose
  details, and quality. The response also returns information about the face in
  the source image, including the bounding box of the face and confidence value.

  The `QualityFilter` input parameter allows you to filter out detected faces that
  don’t meet a required quality bar. The quality bar is based on a variety of
  common use cases. Use `QualityFilter` to set the quality bar by specifying
  `LOW`, `MEDIUM`, or `HIGH`. If you do not want to filter detected faces, specify
  `NONE`. The default value is `NONE`.

  If the image doesn't contain Exif metadata, `CompareFaces` returns orientation
  information for the source and target images. Use these values to display the
  images with the correct image orientation.

  If no faces are detected in the source or target images, `CompareFaces` returns
  an `InvalidParameterException` error.

  This is a stateless API operation. That is, data returned by this operation
  doesn't persist.

  For an example, see Comparing Faces in Images in the Amazon Rekognition
  Developer Guide.

  This operation requires permissions to perform the `rekognition:CompareFaces`
  action.
  """
  def compare_faces(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CompareFaces", input, options)
  end

  @doc """
  Copies a version of an Amazon Rekognition Custom Labels model from a source
  project to a destination project.

  The source and destination projects can be in different AWS accounts but must be
  in the same AWS Region. You can't copy a model to another AWS service.

  To copy a model version to a different AWS account, you need to create a
  resource-based policy known as a *project policy*. You attach the project policy
  to the source project by calling `PutProjectPolicy`. The project policy gives
  permission to copy the model version from a trusting AWS account to a trusted
  account.

  For more information creating and attaching a project policy, see Attaching a
  project policy (SDK) in the *Amazon Rekognition Custom Labels Developer Guide*.

  If you are copying a model version to a project in the same AWS account, you
  don't need to create a project policy.

  To copy a model, the destination project, source project, and source model
  version must already exist.

  Copying a model version takes a while to complete. To get the current status,
  call `DescribeProjectVersions` and check the value of `Status` in the
  `ProjectVersionDescription` object. The copy operation has finished when the
  value of `Status` is `COPYING_COMPLETED`.
  """
  def copy_project_version(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CopyProjectVersion", input, options)
  end

  @doc """
  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.
  """
  def create_collection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CreateCollection", input, options)
  end

  @doc """
  Creates a new Amazon Rekognition Custom Labels dataset.

  You can create a dataset by using an Amazon Sagemaker format manifest file or by
  copying an existing Amazon Rekognition Custom Labels dataset.

  To create a training dataset for a project, specify `train` for the value of
  `DatasetType`. To create the test dataset for a project, specify `test` for the
  value of `DatasetType`.

  The response from `CreateDataset` is the Amazon Resource Name (ARN) for the
  dataset. Creating a dataset takes a while to complete. Use `DescribeDataset` to
  check the current status. The dataset created successfully if the value of
  `Status` is `CREATE_COMPLETE`.

  To check if any non-terminal errors occurred, call `ListDatasetEntries` and
  check for the presence of `errors` lists in the JSON Lines.

  Dataset creation fails if a terminal error occurs (`Status` = `CREATE_FAILED`).
  Currently, you can't access the terminal error information.

  For more information, see Creating dataset in the *Amazon Rekognition Custom
  Labels Developer Guide*.

  This operation requires permissions to perform the `rekognition:CreateDataset`
  action. If you want to copy an existing dataset, you also require permission to
  perform the `rekognition:ListDatasetEntries` action.
  """
  def create_dataset(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CreateDataset", input, options)
  end

  @doc """
  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.
  """
  def create_project(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CreateProject", input, options)
  end

  @doc """
  Creates a new version of a model and begins training.

  Models are managed as part of an Amazon Rekognition Custom Labels project. The
  response from `CreateProjectVersion` is an Amazon Resource Name (ARN) for the
  version of the model.

  Training uses the training and test datasets associated with the project. For
  more information, see Creating training and test dataset in the *Amazon
  Rekognition Custom Labels Developer Guide*.

  You can train a model in a project that doesn't have associated datasets by
  specifying manifest files in the `TrainingData` and `TestingData` fields.

  If you open the console after training a model with manifest files, Amazon
  Rekognition Custom Labels creates the datasets for you using the most recent
  manifest files. You can no longer train a model version for the project by
  specifying manifest files.

  Instead of training with a project without associated datasets, we recommend
  that you use the manifest files to create training and test datasets for the
  project.

  Training takes a while to complete. You can get the current status by calling
  `DescribeProjectVersions`. Training completed successfully if the value of the
  `Status` field is `TRAINING_COMPLETED`.

  If training fails, see Debugging a failed model training in the *Amazon
  Rekognition Custom Labels* developer guide.

  Once training has successfully completed, call `DescribeProjectVersions` to get
  the training results and evaluate the model. For more information, see Improving
  a trained Amazon Rekognition Custom Labels model in the *Amazon Rekognition
  Custom Labels* developers guide.

  After evaluating the model, you start the model by calling
  `StartProjectVersion`.

  This operation requires permissions to perform the
  `rekognition:CreateProjectVersion` action.
  """
  def create_project_version(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CreateProjectVersion", input, options)
  end

  @doc """
  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.

  Use `Name` to assign an identifier for the stream processor. You use `Name` to
  manage the stream processor. For example, you can start processing the source
  video by calling `StartStreamProcessor` with the `Name` field.

  This operation requires permissions to perform the
  `rekognition:CreateStreamProcessor` action. If you want to tag your stream
  processor, you also require permission to perform the `rekognition:TagResource`
  operation.
  """
  def create_stream_processor(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CreateStreamProcessor", input, options)
  end

  @doc """
  Deletes the specified collection.

  Note that this operation removes all faces in the collection. For an example,
  see [Deleting a collection](https://docs.aws.amazon.com/rekognition/latest/dg/delete-collection-procedure.html).

  This operation requires permissions to perform the
  `rekognition:DeleteCollection` action.
  """
  def delete_collection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DeleteCollection", input, options)
  end

  @doc """
  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.
  """
  def delete_dataset(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DeleteDataset", input, options)
  end

  @doc """
  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.
  """
  def delete_faces(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DeleteFaces", input, options)
  end

  @doc """
  Deletes an Amazon Rekognition Custom Labels project.

  To delete a project you must first delete all models associated with the
  project. To delete a model, see `DeleteProjectVersion`.

  `DeleteProject` is an asynchronous operation. To check if the project is
  deleted, call `DescribeProjects`. The project is deleted when the project no
  longer appears in the response. Be aware that deleting a given project will also
  delete any `ProjectPolicies` associated with that project.

  This operation requires permissions to perform the `rekognition:DeleteProject`
  action.
  """
  def delete_project(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DeleteProject", input, options)
  end

  @doc """
  Deletes an existing project policy.

  To get a list of project policies attached to a project, call
  `ListProjectPolicies`. To attach a project policy to a project, call
  `PutProjectPolicy`.
  """
  def delete_project_policy(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DeleteProjectPolicy", input, options)
  end

  @doc """
  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.
  """
  def delete_project_version(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DeleteProjectVersion", input, options)
  end

  @doc """
  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`.
  """
  def delete_stream_processor(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DeleteStreamProcessor", input, options)
  end

  @doc """
  Describes the specified collection.

  You can use `DescribeCollection` to get information, such as the number of faces
  indexed into a collection and the version of the model used by the collection
  for face detection.

  For more information, see Describing a Collection in the Amazon Rekognition
  Developer Guide.
  """
  def describe_collection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DescribeCollection", input, options)
  end

  @doc """
  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.
  """
  def describe_dataset(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DescribeDataset", input, options)
  end

  @doc """
  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.
  """
  def describe_project_versions(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DescribeProjectVersions", input, options)
  end

  @doc """
  Gets information about your Amazon Rekognition Custom Labels projects.

  This operation requires permissions to perform the
  `rekognition:DescribeProjects` action.
  """
  def describe_projects(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DescribeProjects", input, options)
  end

  @doc """
  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.
  """
  def describe_stream_processor(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DescribeStreamProcessor", input, options)
  end

  @doc """
  Detects custom labels in a supplied image by using an Amazon Rekognition Custom
  Labels model.

  You specify which version of a model version to use by using the
  `ProjectVersionArn` input parameter.

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

  For each object that the model version detects on an image, the API returns a
  (`CustomLabel`) object in an array (`CustomLabels`). Each `CustomLabel` object
  provides the label name (`Name`), the level of confidence that the image
  contains the object (`Confidence`), and object location information, if it
  exists, for the label on the image (`Geometry`).

  To filter labels that are returned, specify a value for `MinConfidence`.
  `DetectCustomLabelsLabels` only returns labels with a confidence that's higher
  than the specified value. The value of `MinConfidence` maps to the assumed
  threshold values created during training. For more information, see *Assumed
  threshold* in the Amazon Rekognition Custom Labels Developer Guide. Amazon
  Rekognition Custom Labels metrics expresses an assumed threshold as a floating
  point value between 0-1. The range of `MinConfidence` normalizes the threshold
  value to a percentage value (0-100). Confidence responses from
  `DetectCustomLabels` are also returned as a percentage. You can use
  `MinConfidence` to change the precision and recall or your model. For more
  information, see *Analyzing an image* in the Amazon Rekognition Custom Labels
  Developer Guide.

  If you don't specify a value for `MinConfidence`, `DetectCustomLabels` returns
  labels based on the assumed threshold of each label.

  This is a stateless API operation. That is, the operation does not persist any
  data.

  This operation requires permissions to perform the
  `rekognition:DetectCustomLabels` action.

  For more information, see *Analyzing an image* in the Amazon Rekognition Custom
  Labels Developer Guide.
  """
  def detect_custom_labels(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DetectCustomLabels", input, options)
  end

  @doc """
  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.
  """
  def detect_faces(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DetectFaces", input, options)
  end

  @doc """
  Detects instances of real-world entities within an image (JPEG or PNG) provided
  as input.

  This includes objects like flower, tree, and table; events like wedding,
  graduation, and birthday party; and concepts like landscape, evening, and
  nature.

  For an example, see Analyzing images stored in an Amazon S3 bucket in the Amazon
  Rekognition Developer Guide.

  `DetectLabels` does not support the detection of activities. However, activity
  detection is supported for label detection in videos. For more information, see
  StartLabelDetection in the Amazon Rekognition Developer Guide.

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

  For each object, scene, and concept the API returns one or more labels. Each
  label provides the object name, and the level of confidence that the image
  contains the object. For example, suppose the input image has a lighthouse, the
  sea, and a rock. The response includes all three labels, one for each object.

  `{Name: lighthouse, Confidence: 98.4629}`

  `{Name: rock,Confidence: 79.2097}`

  ` {Name: sea,Confidence: 75.061}`

  In the preceding example, the operation returns one label for each of the three
  objects. The operation can also return multiple labels for the same object in
  the image. For example, if the input image shows a flower (for example, a
  tulip), the operation might return the following three labels.

  `{Name: flower,Confidence: 99.0562}`

  `{Name: plant,Confidence: 99.0562}`

  `{Name: tulip,Confidence: 99.0562}`

  In this example, the detection algorithm more precisely identifies the flower as
  a tulip.

  In response, the API returns an array of labels. In addition, the response also
  includes the orientation correction. Optionally, you can specify `MinConfidence`
  to control the confidence threshold for the labels returned. The default is 55%.
  You can also add the `MaxLabels` parameter to limit the number of labels
  returned.

  If the object detected is a person, the operation doesn't provide the same
  facial details that the `DetectFaces` operation provides.

  `DetectLabels` returns bounding boxes for instances of common object labels in
  an array of `Instance` objects. An `Instance` object contains a `BoundingBox`
  object, for the location of the label on the image. It also includes the
  confidence by which the bounding box was detected.

  `DetectLabels` also returns a hierarchical taxonomy of detected labels. For
  example, a detected car might be assigned the label *car*. The label *car* has
  two parent labels: *Vehicle* (its parent) and *Transportation* (its
  grandparent). The response returns the entire list of ancestors for a label.
  Each ancestor is a unique label in the response. In the previous example, *Car*,
  *Vehicle*, and *Transportation* are returned as unique labels in the response.

  This is a stateless API operation. That is, the operation does not persist any
  data.

  This operation requires permissions to perform the `rekognition:DetectLabels`
  action.
  """
  def detect_labels(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DetectLabels", input, options)
  end

  @doc """
  Detects unsafe content in a specified JPEG or PNG format image.

  Use `DetectModerationLabels` to moderate images depending on your requirements.
  For example, you might want to filter images that contain nudity, but not images
  containing suggestive content.

  To filter images, use the labels returned by `DetectModerationLabels` to
  determine which types of content are appropriate.

  For information about moderation labels, see Detecting Unsafe Content in the
  Amazon Rekognition Developer Guide.

  You pass the input image either as base64-encoded image bytes or as a reference
  to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon
  Rekognition operations, passing image bytes is not supported. The image must be
  either a PNG or JPEG formatted file.
  """
  def detect_moderation_labels(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DetectModerationLabels", input, options)
  end

  @doc """
  Detects Personal Protective Equipment (PPE) worn by people detected in an image.

  Amazon Rekognition can detect the following types of PPE.

    * Face cover

    * Hand cover

    * Head cover

  You pass the input image as base64-encoded image bytes or as a reference to an
  image in an Amazon S3 bucket. The image must be either a PNG or JPG formatted
  file.

  `DetectProtectiveEquipment` detects PPE worn by up to 15 persons detected in an
  image.

  For each person detected in the image the API returns an array of body parts
  (face, head, left-hand, right-hand). For each body part, an array of detected
  items of PPE is returned, including an indicator of whether or not the PPE
  covers the body part. The API returns the confidence it has in each detection
  (person, PPE, body part and body part coverage). It also returns a bounding box
  (`BoundingBox`) for each detected person and each detected item of PPE.

  You can optionally request a summary of detected PPE items with the
  `SummarizationAttributes` input parameter. The summary provides the following
  information.

    * The persons detected as wearing all of the types of PPE that you
  specify.

    * The persons detected as not wearing all of the types PPE that you
  specify.

    * The persons detected where PPE adornment could not be determined.

  This is a stateless API operation. That is, the operation does not persist any
  data.

  This operation requires permissions to perform the
  `rekognition:DetectProtectiveEquipment` action.
  """
  def detect_protective_equipment(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DetectProtectiveEquipment", input, options)
  end

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

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

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

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

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

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

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

  For more information, see Detecting text in the Amazon Rekognition Developer
  Guide.
  """
  def detect_text(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DetectText", input, options)
  end

  @doc """
  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.
  """
  def distribute_dataset_entries(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DistributeDatasetEntries", input, options)
  end

  @doc """
  Gets the name and additional information about a celebrity based on their Amazon
  Rekognition ID.

  The additional information is returned as an array of URLs. If there is no
  additional information about the celebrity, this list is empty.

  For more information, see Getting information about a celebrity in the Amazon
  Rekognition Developer Guide.

  This operation requires permissions to perform the
  `rekognition:GetCelebrityInfo` action.
  """
  def get_celebrity_info(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetCelebrityInfo", input, options)
  end

  @doc """
  Gets the celebrity recognition results for a Amazon Rekognition Video analysis
  started by `StartCelebrityRecognition`.

  Celebrity recognition in a video is an asynchronous operation. Analysis is
  started by a call to `StartCelebrityRecognition` which returns a job identifier
  (`JobId`).

  When the celebrity recognition operation finishes, Amazon Rekognition Video
  publishes a completion status to the Amazon Simple Notification Service topic
  registered in the initial call to `StartCelebrityRecognition`. To get the
  results of the celebrity recognition analysis, first check that the status value
  published to the Amazon SNS topic is `SUCCEEDED`. If so, call
  `GetCelebrityDetection` and pass the job identifier (`JobId`) from the initial
  call to `StartCelebrityDetection`.

  For more information, see Working With Stored Videos in the Amazon Rekognition
  Developer Guide.

  `GetCelebrityRecognition` returns detected celebrities and the time(s) they are
  detected in an array (`Celebrities`) of `CelebrityRecognition` objects. Each
  `CelebrityRecognition` contains information about the celebrity in a
  `CelebrityDetail` object and the time, `Timestamp`, the celebrity was detected.
  This `CelebrityDetail` object stores information about the detected celebrity's
  face attributes, a face bounding box, known gender, the celebrity's name, and a
  confidence estimate.

  `GetCelebrityRecognition` only returns the default facial attributes
  (`BoundingBox`, `Confidence`, `Landmarks`, `Pose`, and `Quality`). The
  `BoundingBox` field only applies to the detected face instance. The other facial
  attributes listed in the `Face` object of the following response syntax are not
  returned. For more information, see FaceDetail in the Amazon Rekognition
  Developer Guide.

  By default, the `Celebrities` array is sorted by time (milliseconds from the
  start of the video). You can also sort the array by celebrity by specifying the
  value `ID` in the `SortBy` input parameter.

  The `CelebrityDetail` object includes the celebrity identifer and additional
  information urls. If you don't store the additional information urls, you can
  get them later by calling `GetCelebrityInfo` with the celebrity identifer.

  No information is returned for faces not recognized as celebrities.

  Use MaxResults parameter to limit the number of labels returned. If there are
  more results than specified in `MaxResults`, the value of `NextToken` in the
  operation response contains a pagination token for getting the next set of
  results. To get the next page of results, call `GetCelebrityDetection` and
  populate the `NextToken` request parameter with the token value returned from
  the previous call to `GetCelebrityRecognition`.
  """
  def get_celebrity_recognition(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetCelebrityRecognition", input, options)
  end

  @doc """
  Gets the inappropriate, unwanted, or offensive content analysis results for a
  Amazon Rekognition Video analysis started by `StartContentModeration`.

  For a list of moderation labels in Amazon Rekognition, see [Using the image and video moderation
  APIs](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api).

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

  For more information, see Working with Stored Videos in the Amazon Rekognition
  Devlopers Guide.

  `GetContentModeration` returns detected inappropriate, unwanted, or offensive
  content moderation labels, and the time they are detected, in an array,
  `ModerationLabels`, of `ContentModerationDetection` objects.

  By default, the moderated labels are returned sorted by time, in milliseconds
  from the start of the video. You can also sort them by moderated label by
  specifying `NAME` for the `SortBy` input parameter.

  Since video analysis can return a large number of results, use the `MaxResults`
  parameter to limit the number of labels returned in a single call to
  `GetContentModeration`. If there are more results than specified in
  `MaxResults`, the value of `NextToken` in the operation response contains a
  pagination token for getting the next set of results. To get the next page of
  results, call `GetContentModeration` and populate the `NextToken` request
  parameter with the value of `NextToken` returned from the previous call to
  `GetContentModeration`.

  For more information, see moderating content in the Amazon Rekognition Developer
  Guide.
  """
  def get_content_moderation(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetContentModeration", input, options)
  end

  @doc """
  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`.
  """
  def get_face_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetFaceDetection", input, options)
  end

  @doc """
  Gets the face search results for Amazon Rekognition Video face search started by
  `StartFaceSearch`.

  The search returns faces in a collection that match the faces of persons
  detected in a video. It also includes the time(s) that faces are matched in the
  video.

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

  For more information, see Searching Faces in a Collection in the Amazon
  Rekognition Developer Guide.

  The search results are retured in an array, `Persons`, of `PersonMatch` objects.
  Each`PersonMatch` element contains details about the matching faces in the input
  collection, person information (facial attributes, bounding boxes, and person
  identifer) for the matched person, and the time the person was matched in the
  video.

  `GetFaceSearch` only returns the default facial attributes (`BoundingBox`,
  `Confidence`, `Landmarks`, `Pose`, and `Quality`). The other facial attributes
  listed in the `Face` object of the following response syntax are not returned.
  For more information, see FaceDetail in the Amazon Rekognition Developer Guide.

  By default, the `Persons` array is sorted by the time, in milliseconds from the
  start of the video, persons are matched. You can also sort by persons by
  specifying `INDEX` for the `SORTBY` input parameter.
  """
  def get_face_search(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetFaceSearch", input, options)
  end

  @doc """
  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.

  Use MaxResults parameter to limit the number of labels returned. If there are
  more results than specified in `MaxResults`, the value of `NextToken` in the
  operation response contains a pagination token for getting the next set of
  results. To get the next page of results, call `GetlabelDetection` and populate
  the `NextToken` request parameter with the token value returned from the
  previous call to `GetLabelDetection`.
  """
  def get_label_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetLabelDetection", input, options)
  end

  @doc """
  Gets the path tracking results of a Amazon Rekognition Video analysis started by
  `StartPersonTracking`.

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

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

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

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

  For more information, see FaceDetail in the Amazon Rekognition Developer Guide.

  By default, the array is sorted by the time(s) a person's path is tracked in the
  video. You can sort by tracked persons by specifying `INDEX` for the `SortBy`
  input parameter.

  Use the `MaxResults` parameter to limit the number of items returned. If there
  are more results than specified in `MaxResults`, the value of `NextToken` in the
  operation response contains a pagination token for getting the next set of
  results. To get the next page of results, call `GetPersonTracking` and populate
  the `NextToken` request parameter with the token value returned from the
  previous call to `GetPersonTracking`.
  """
  def get_person_tracking(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetPersonTracking", input, options)
  end

  @doc """
  Gets the segment detection results of a Amazon Rekognition Video analysis
  started by `StartSegmentDetection`.

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

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

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

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

  For more information, see Detecting video segments in stored video in the Amazon
  Rekognition Developer Guide.
  """
  def get_segment_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetSegmentDetection", input, options)
  end

  @doc """
  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`.
  """
  def get_text_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetTextDetection", input, options)
  end

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

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

  For more information, see Adding faces to a collection in the Amazon Rekognition
  Developer Guide.

  To get the number of faces in a collection, call `DescribeCollection`.

  If you're using version 1.0 of the face detection model, `IndexFaces` indexes
  the 15 largest faces in the input image. Later versions of the face detection
  model index the 100 largest faces in the input image.

  If you're using version 4 or later of the face model, image orientation
  information is not returned in the `OrientationCorrection` field.

  To determine which version of the model you're using, call `DescribeCollection`
  and supply the collection ID. You can also get the model version from the value
  of `FaceModelVersion` in the response from `IndexFaces`

  For more information, see Model Versioning in the Amazon Rekognition Developer
  Guide.

  If you provide the optional `ExternalImageId` for the input image you provided,
  Amazon Rekognition associates this ID with all faces that it detects. When you
  call the `ListFaces` operation, the response returns the external ID. You can
  use this external image ID to create a client-side index to associate the faces
  with each image. You can then use the index to find all faces in an image.

  You can specify the maximum number of faces to index with the `MaxFaces` input
  parameter. This is useful when you want to index the largest faces in an image
  and don't want to index smaller faces, such as those belonging to people
  standing in the background.

  The `QualityFilter` input parameter allows you to filter out detected faces that
  don’t meet a required quality bar. The quality bar is based on a variety of
  common use cases. By default, `IndexFaces` chooses the quality bar that's used
  to filter faces. You can also explicitly choose the quality bar. Use
  `QualityFilter`, to set the quality bar by specifying `LOW`, `MEDIUM`, or
  `HIGH`. If you do not want to filter detected faces, specify `NONE`.

  To use quality filtering, you need a collection associated with version 3 of the
  face model or higher. To get the version of the face model associated with a
  collection, call `DescribeCollection`.

  Information about faces detected in an image, but not indexed, is returned in an
  array of `UnindexedFace` objects, `UnindexedFaces`. Faces aren't indexed for
  reasons such as:

    * The number of faces detected exceeds the value of the `MaxFaces`
  request parameter.

    * The face is too small compared to the image dimensions.

    * The face is too blurry.

    * The image is too dark.

    * The face has an extreme pose.

    * The face doesn’t have enough detail to be suitable for face
  search.

  In response, the `IndexFaces` operation returns an array of metadata for all
  detected faces, `FaceRecords`. This includes:

    * The bounding box, `BoundingBox`, of the detected face.

    * A confidence value, `Confidence`, which indicates the confidence
  that the bounding box contains a face.

    * A face ID, `FaceId`, assigned by the service for each face that's
  detected and stored.

    * An image ID, `ImageId`, assigned by the service for the input
  image.

  If you request all facial attributes (by using the `detectionAttributes`
  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 `IndexFaces` operation, Amazon Rekognition doesn't
  save duplicate face metadata.

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

  This operation requires permissions to perform the `rekognition:IndexFaces`
  action.
  """
  def index_faces(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "IndexFaces", input, options)
  end

  @doc """
  Returns list of collection IDs in your account.

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

  For an example, see Listing collections in the Amazon Rekognition Developer
  Guide.

  This operation requires permissions to perform the `rekognition:ListCollections`
  action.
  """
  def list_collections(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListCollections", input, options)
  end

  @doc """
  Lists the entries (images) within a dataset.

  An entry is a JSON Line that contains the information for a single image,
  including the image location, assigned labels, and object location bounding
  boxes. For more information, see [Creating a manifest file](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-manifest-files.html).

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

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

  This operation requires permissions to perform the
  `rekognition:ListDatasetEntries` action.
  """
  def list_dataset_entries(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListDatasetEntries", input, options)
  end

  @doc """
  Lists the labels in a dataset.

  Amazon Rekognition Custom Labels uses labels to describe images. For more
  information, see [Labeling images](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-labeling-images.html).

  Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to
  describe images. For more information, see Labeling images in the *Amazon
  Rekognition Custom Labels Developer Guide*.
  """
  def list_dataset_labels(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListDatasetLabels", input, options)
  end

  @doc """
  Returns metadata for faces in the specified collection.

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

  This operation requires permissions to perform the `rekognition:ListFaces`
  action.
  """
  def list_faces(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListFaces", input, options)
  end

  @doc """
  Gets a list of the project policies attached to a project.

  To attach a project policy to a project, call `PutProjectPolicy`. To remove a
  project policy from a project, call `DeleteProjectPolicy`.
  """
  def list_project_policies(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListProjectPolicies", input, options)
  end

  @doc """
  Gets a list of stream processors that you have created with
  `CreateStreamProcessor`.
  """
  def list_stream_processors(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListStreamProcessors", input, options)
  end

  @doc """
  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.
  """
  def list_tags_for_resource(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListTagsForResource", input, options)
  end

  @doc """
  Attaches a project policy to a Amazon Rekognition Custom Labels project in a
  trusting AWS account.

  A project policy specifies that a trusted AWS account can copy a model version
  from a trusting AWS account to a project in the trusted AWS account. To copy a
  model version you use the `CopyProjectVersion` operation.

  For more information about the format of a project policy document, see
  Attaching a project policy (SDK) in the *Amazon Rekognition Custom Labels
  Developer Guide*.

  The response from `PutProjectPolicy` is a revision ID for the project policy.
  You can attach multiple project policies to a project. You can also update an
  existing project policy by specifying the policy revision ID of the existing
  policy.

  To remove a project policy from a project, call `DeleteProjectPolicy`. To get a
  list of project policies attached to a project, call `ListProjectPolicies`.

  You copy a model version by calling `CopyProjectVersion`.
  """
  def put_project_policy(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "PutProjectPolicy", input, options)
  end

  @doc """
  Returns an array of celebrities recognized in the input image.

  For more information, see Recognizing celebrities in the Amazon Rekognition
  Developer Guide.

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

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

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

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

  For an example, see Recognizing celebrities in an image in the Amazon
  Rekognition Developer Guide.

  This operation requires permissions to perform the
  `rekognition:RecognizeCelebrities` operation.
  """
  def recognize_celebrities(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "RecognizeCelebrities", input, options)
  end

  @doc """
  For a given input face ID, searches for matching faces in the collection the
  face belongs to.

  You get a face ID when you add a face to the collection using the `IndexFaces`
  operation. The operation compares the features of the input face with faces in
  the specified collection.

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

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

  For an example, see Searching for a face using its face ID in the Amazon
  Rekognition Developer Guide.

  This operation requires permissions to perform the `rekognition:SearchFaces`
  action.
  """
  def search_faces(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "SearchFaces", input, options)
  end

  @doc """
  For a given input image, first detects the largest face in the image, and then
  searches the specified collection for matching faces.

  The operation compares the features of the input face with faces in the
  specified collection.

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

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

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

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

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

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

  The `QualityFilter` input parameter allows you to filter out detected faces that
  don’t meet a required quality bar. The quality bar is based on a variety of
  common use cases. Use `QualityFilter` to set the quality bar for filtering by
  specifying `LOW`, `MEDIUM`, or `HIGH`. If you do not want to filter detected
  faces, specify `NONE`. The default value is `NONE`.

  To use quality filtering, you need a collection associated with version 3 of the
  face model or higher. To get the version of the face model associated with a
  collection, call `DescribeCollection`.

  This operation requires permissions to perform the
  `rekognition:SearchFacesByImage` action.
  """
  def search_faces_by_image(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "SearchFacesByImage", input, options)
  end

  @doc """
  Starts asynchronous recognition of celebrities in a stored video.

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

  For more information, see Recognizing celebrities in the Amazon Rekognition
  Developer Guide.
  """
  def start_celebrity_recognition(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartCelebrityRecognition", input, options)
  end

  @doc """
  Starts asynchronous detection of inappropriate, unwanted, or offensive content
  in a stored video.

  For a list of moderation labels in Amazon Rekognition, see [Using the image and video moderation
  APIs](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api).

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

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

  For more information, see Moderating content in the Amazon Rekognition Developer
  Guide.
  """
  def start_content_moderation(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartContentModeration", input, options)
  end

  @doc """
  Starts asynchronous detection of faces in a stored video.

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

  For more information, see Detecting faces in a stored video in the Amazon
  Rekognition Developer Guide.
  """
  def start_face_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartFaceDetection", input, options)
  end

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

  The video must be stored in an Amazon S3 bucket. Use `Video` to specify the
  bucket name and the filename of the video. `StartFaceSearch` returns a job
  identifier (`JobId`) which you use to get the search results once the search has
  completed. When searching is finished, Amazon Rekognition Video publishes a
  completion status to the Amazon Simple Notification Service topic that you
  specify in `NotificationChannel`. To get the search results, first check that
  the status value published to the Amazon SNS topic is `SUCCEEDED`. If so, call
  `GetFaceSearch` and pass the job identifier (`JobId`) from the initial call to
  `StartFaceSearch`. For more information, see [Searching stored videos for faces](https://docs.aws.amazon.com/rekognition/latest/dg/procedure-person-search-videos.html).
  """
  def start_face_search(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartFaceSearch", input, options)
  end

  @doc """
  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`.
  """
  def start_label_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartLabelDetection", input, options)
  end

  @doc """
  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`.
  """
  def start_person_tracking(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartPersonTracking", input, options)
  end

  @doc """
  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`.

  For more information, see *Running a trained Amazon Rekognition Custom Labels
  model* in the Amazon Rekognition Custom Labels Guide.

  This operation requires permissions to perform the
  `rekognition:StartProjectVersion` action.
  """
  def start_project_version(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartProjectVersion", input, options)
  end

  @doc """
  Starts asynchronous detection of segment detection in a stored video.

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

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

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

  For more information, see Detecting video segments in stored video in the Amazon
  Rekognition Developer Guide.
  """
  def start_segment_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartSegmentDetection", input, options)
  end

  @doc """
  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.
  """
  def start_stream_processor(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartStreamProcessor", input, options)
  end

  @doc """
  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`.
  """
  def start_text_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartTextDetection", input, options)
  end

  @doc """
  Stops a running model.

  The operation might take a while to complete. To check the current status, call
  `DescribeProjectVersions`.
  """
  def stop_project_version(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StopProjectVersion", input, options)
  end

  @doc """
  Stops a running stream processor that was created by `CreateStreamProcessor`.
  """
  def stop_stream_processor(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StopStreamProcessor", input, options)
  end

  @doc """
  Adds one or more key-value tags to an Amazon Rekognition collection, stream
  processor, or Custom Labels model.

  For more information, see [Tagging AWS Resources](https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html).

  This operation requires permissions to perform the `rekognition:TagResource`
  action.
  """
  def tag_resource(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "TagResource", input, options)
  end

  @doc """
  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.
  """
  def untag_resource(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "UntagResource", input, options)
  end

  @doc """
  Adds or updates one or more entries (images) in a dataset.

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

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

  You specify the changes that you want to make in the `Changes` input parameter.
  There isn't a limit to the number JSON Lines that you can change, but the size
  of `Changes` must be less than 5MB.

  `UpdateDatasetEntries` returns immediatly, but the dataset update might take a
  while to complete. Use `DescribeDataset` to check the current status. The
  dataset updated successfully if the value of `Status` is `UPDATE_COMPLETE`.

  To check if any non-terminal errors occured, call `ListDatasetEntries` and check
  for the presence of `errors` lists in the JSON Lines.

  Dataset update fails if a terminal error occurs (`Status` = `UPDATE_FAILED`).
  Currently, you can't access the terminal error information from the Amazon
  Rekognition Custom Labels SDK.

  This operation requires permissions to perform the
  `rekognition:UpdateDatasetEntries` action.
  """
  def update_dataset_entries(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "UpdateDatasetEntries", input, options)
  end

  @doc """
  Allows you to update a stream processor.

  You can change some settings and regions of interest and delete certain
  parameters.
  """
  def update_stream_processor(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "UpdateStreamProcessor", input, options)
  end
end