Struct Client

pub struct Client { /* private fields */ }

Client for Amazon Rekognition

Client for invoking operations on Amazon Rekognition. Each operation on Amazon Rekognition is a method on this this struct. .send() MUST be invoked on the generated operations to dispatch the request to the service.

Constructing a Client

A Config is required to construct a client. For most use cases, the aws-config crate should be used to automatically resolve this config using aws_config::load_from_env(), since this will resolve an SdkConfig which can be shared across multiple different AWS SDK clients. This config resolution process can be customized by calling aws_config::from_env() instead, which returns a ConfigLoader that uses the builder pattern to customize the default config.

In the simplest case, creating a client looks as follows:

let config = aws_config::load_from_env().await;
let client = aws_sdk_rekognition::Client::new(&config);

Occasionally, SDKs may have additional service-specific values that can be set on the Config that is absent from SdkConfig, or slightly different settings for a specific client may be desired. The Builder struct implements From<&SdkConfig>, so setting these specific settings can be done as follows:

let sdk_config = ::aws_config::load_from_env().await;
let config = aws_sdk_rekognition::config::Builder::from(&sdk_config)
    .some_service_specific_setting("value")
    .build();

See the aws-config docs and Config for more information on customizing configuration.

Note: Client construction is expensive due to connection thread pool initialization, and should be done once at application start-up.

Using the Client

A client has a function for every operation that can be performed by the service. For example, the AssociateFaces operation has a Client::associate_faces, function which returns a builder for that operation. The fluent builder ultimately has a send() function that returns an async future that returns a result, as illustrated below:

let result = client.associate_faces()
    .collection_id("example")
    .send()
    .await;

The underlying HTTP requests that get made by this can be modified with the customize_operation function on the fluent builder. See the customize module for more information.

Waiters

This client provides wait_until methods behind the Waiters trait. To use them, simply import the trait, and then call one of the wait_until methods. This will return a waiter fluent builder that takes various parameters, which are documented on the builder type. Once parameters have been provided, the wait method can be called to initiate waiting.

For example, if there was a wait_until_thing method, it could look like:

let result = client.wait_until_thing()
    .thing_id("someId")
    .wait(Duration::from_secs(120))
    .await;

Implementations

impl Client

pub fn associate_faces(&self) -> AssociateFacesFluentBuilder

Constructs a fluent builder for the AssociateFaces operation.

• The fluent builder is configurable:
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    The ID of an existing collection containing the UserID.

    
    
  • user_id(impl Into<String>) / set_user_id(Option<String>):
    required: true
    

    The ID for the existing UserID.

    
    
  • face_ids(impl Into<String>) / set_face_ids(Option<Vec::<String>>):
    required: true
    

    An array of FaceIDs to associate with the UserID.

    
    
  • user_match_threshold(f32) / set_user_match_threshold(Option<f32>):
    required: false
    

    An optional value specifying the minimum confidence in the UserID match to return. The default value is 75.

    
    
  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):
    required: false
    

    Idempotent token used to identify the request to AssociateFaces. If you use the same token with multiple AssociateFaces requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.

    
    
• On success, responds with AssociateFacesOutput with field(s):
  • associated_faces(Option<Vec::<AssociatedFace>>):

    An array of AssociatedFace objects containing FaceIDs that have been successfully associated with the UserID. Returned if the AssociateFaces action is successful.

  • unsuccessful_face_associations(Option<Vec::<UnsuccessfulFaceAssociation>>):

    An array of UnsuccessfulAssociation objects containing FaceIDs that are not successfully associated along with the reasons. Returned if the AssociateFaces action is successful.

  • user_status(Option<UserStatus>):

    The status of an update made to a UserID. Reflects if the UserID has been updated for every requested change.

• On failure, responds with SdkError<AssociateFacesError>

impl Client

pub fn compare_faces(&self) -> CompareFacesFluentBuilder

Constructs a fluent builder for the CompareFaces operation.

• The fluent builder is configurable:
  • source_image(Image) / set_source_image(Option<Image>):
    required: true
    

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

    
    
  • target_image(Image) / set_target_image(Option<Image>):
    required: true
    

    The target image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

    
    
  • similarity_threshold(f32) / set_similarity_threshold(Option<f32>):
    required: false
    

    The minimum level of confidence in the face matches that a match must meet to be included in the FaceMatches array.

    
    
  • quality_filter(QualityFilter) / set_quality_filter(Option<QualityFilter>):
    required: false
    

    A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren’t compared. If you specify AUTO, Amazon Rekognition chooses the quality bar. If you specify LOW, MEDIUM, or HIGH, filtering removes all faces that don’t meet the chosen quality bar. The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that’s misidentified as a face, a face that’s too blurry, or a face with a pose that’s too extreme to use. If you specify NONE, no filtering is performed. The default value is NONE.

    To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.

    
    
• On success, responds with CompareFacesOutput with field(s):
  • source_image_face(Option<ComparedSourceImageFace>):

    The face in the source image that was used for comparison.

  • face_matches(Option<Vec::<CompareFacesMatch>>):

    An array of faces in the target image that match the source image face. Each CompareFacesMatch object provides the bounding box, the confidence level that the bounding box contains a face, and the similarity score for the face in the bounding box and the face in the source image.

  • unmatched_faces(Option<Vec::<ComparedFace>>):

    An array of faces in the target image that did not match the source image face.

  • source_image_orientation_correction(Option<OrientationCorrection>):

    The value of SourceImageOrientationCorrection is always null.

    If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image’s orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don’t contain Exif metadata.

    Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren’t translated and represent the object locations before the image is rotated.

  • target_image_orientation_correction(Option<OrientationCorrection>):

    The value of TargetImageOrientationCorrection is always null.

    If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image’s orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don’t contain Exif metadata.

    Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren’t translated and represent the object locations before the image is rotated.

• On failure, responds with SdkError<CompareFacesError>

impl Client

pub fn copy_project_version(&self) -> CopyProjectVersionFluentBuilder

Constructs a fluent builder for the CopyProjectVersion operation.

• The fluent builder is configurable:
  • source_project_arn(impl Into<String>) / set_source_project_arn(Option<String>):
    required: true
    

    The ARN of the source project in the trusting AWS account.

    
    
  • source_project_version_arn(impl Into<String>) / set_source_project_version_arn(Option<String>):
    required: true
    

    The ARN of the model version in the source project that you want to copy to a destination project.

    
    
  • destination_project_arn(impl Into<String>) / set_destination_project_arn(Option<String>):
    required: true
    

    The ARN of the project in the trusted AWS account that you want to copy the model version to.

    
    
  • version_name(impl Into<String>) / set_version_name(Option<String>):
    required: true
    

    A name for the version of the model that’s copied to the destination project.

    
    
  • output_config(OutputConfig) / set_output_config(Option<OutputConfig>):
    required: true
    

    The S3 bucket and folder location where the training output for the source model version is placed.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: false
    

    The key-value tags to assign to the model version.

    
    
  • kms_key_id(impl Into<String>) / set_kms_key_id(Option<String>):
    required: false
    

    The identifier for your AWS Key Management Service key (AWS KMS key). You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt training results and manifest files written to the output Amazon S3 bucket (OutputConfig).

    If you choose to use your own KMS key, you need the following permissions on the KMS key.

    • kms:CreateGrant

    • kms:DescribeKey

    • kms:GenerateDataKey

    • kms:Decrypt

    If you don’t specify a value for KmsKeyId, images copied into the service are encrypted using a key that AWS owns and manages.

    
    
• On success, responds with CopyProjectVersionOutput with field(s):
  • project_version_arn(Option<String>):

    The ARN of the copied model version in the destination project.

• On failure, responds with SdkError<CopyProjectVersionError>

impl Client

pub fn create_collection(&self) -> CreateCollectionFluentBuilder

Constructs a fluent builder for the CreateCollection operation.

• The fluent builder is configurable:
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    ID for the collection that you are creating.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: false
    

    A set of tags (key-value pairs) that you want to attach to the collection.

    
    
• On success, responds with CreateCollectionOutput with field(s):
  • status_code(Option<i32>):

    HTTP status code indicating the result of the operation.

  • collection_arn(Option<String>):

    Amazon Resource Name (ARN) of the collection. You can use this to manage permissions on your resources.

  • face_model_version(Option<String>):

    Version number of the face detection model associated with the collection you are creating.

• On failure, responds with SdkError<CreateCollectionError>

impl Client

pub fn create_dataset(&self) -> CreateDatasetFluentBuilder

Constructs a fluent builder for the CreateDataset operation.

• The fluent builder is configurable:
  • dataset_source(DatasetSource) / set_dataset_source(Option<DatasetSource>):
    required: false
    

    The source files for the dataset. You can specify the ARN of an existing dataset or specify the Amazon S3 bucket location of an Amazon Sagemaker format manifest file. If you don’t specify datasetSource, an empty dataset is created. To add labeled images to the dataset, You can use the console or call UpdateDatasetEntries.

    
    
  • dataset_type(DatasetType) / set_dataset_type(Option<DatasetType>):
    required: true
    

    The type of the dataset. Specify TRAIN to create a training dataset. Specify TEST to create a test dataset.

    
    
  • project_arn(impl Into<String>) / set_project_arn(Option<String>):
    required: true
    

    The ARN of the Amazon Rekognition Custom Labels project to which you want to asssign the dataset.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: false
    

    A set of tags (key-value pairs) that you want to attach to the dataset.

    
    
• On success, responds with CreateDatasetOutput with field(s):
  • dataset_arn(Option<String>):

    The ARN of the created Amazon Rekognition Custom Labels dataset.

• On failure, responds with SdkError<CreateDatasetError>

impl Client

pub fn create_face_liveness_session( &self, ) -> CreateFaceLivenessSessionFluentBuilder

Constructs a fluent builder for the CreateFaceLivenessSession operation.

• The fluent builder is configurable:
  • kms_key_id(impl Into<String>) / set_kms_key_id(Option<String>):
    required: false
    

    The identifier for your AWS Key Management Service key (AWS KMS key). Used to encrypt audit images and reference images.

    
    
  • settings(CreateFaceLivenessSessionRequestSettings) / set_settings(Option<CreateFaceLivenessSessionRequestSettings>):
    required: false
    

    A session settings object. It contains settings for the operation to be performed. For Face Liveness, it accepts OutputConfig and AuditImagesLimit.

    
    
  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):
    required: false
    

    Idempotent token is used to recognize the Face Liveness request. If the same token is used with multiple CreateFaceLivenessSession requests, the same session is returned. This token is employed to avoid unintentionally creating the same session multiple times.

    
    
• On success, responds with CreateFaceLivenessSessionOutput with field(s):
  • session_id(String):

    A unique 128-bit UUID identifying a Face Liveness session. A new sessionID must be used for every Face Liveness check. If a given sessionID is used for subsequent Face Liveness checks, the checks will fail. Additionally, a SessionId expires 3 minutes after it’s sent, making all Liveness data associated with the session (e.g., sessionID, reference image, audit images, etc.) unavailable.

• On failure, responds with SdkError<CreateFaceLivenessSessionError>

impl Client

pub fn create_project(&self) -> CreateProjectFluentBuilder

Constructs a fluent builder for the CreateProject operation.

• The fluent builder is configurable:
  • project_name(impl Into<String>) / set_project_name(Option<String>):
    required: true
    

    The name of the project to create.

    
    
  • feature(CustomizationFeature) / set_feature(Option<CustomizationFeature>):
    required: false
    

    Specifies feature that is being customized. If no value is provided CUSTOM_LABELS is used as a default.

    
    
  • auto_update(ProjectAutoUpdate) / set_auto_update(Option<ProjectAutoUpdate>):
    required: false
    

    Specifies whether automatic retraining should be attempted for the versions of the project. Automatic retraining is done as a best effort. Required argument for Content Moderation. Applicable only to adapters.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: false
    

    A set of tags (key-value pairs) that you want to attach to the project.

    
    
• On success, responds with CreateProjectOutput with field(s):
  • project_arn(Option<String>):

    The Amazon Resource Name (ARN) of the new project. You can use the ARN to configure IAM access to the project.

• On failure, responds with SdkError<CreateProjectError>

impl Client

pub fn create_project_version(&self) -> CreateProjectVersionFluentBuilder

Constructs a fluent builder for the CreateProjectVersion operation.

• The fluent builder is configurable:
  • project_arn(impl Into<String>) / set_project_arn(Option<String>):
    required: true
    

    The ARN of the Amazon Rekognition project that will manage the project version you want to train.

    
    
  • version_name(impl Into<String>) / set_version_name(Option<String>):
    required: true
    

    A name for the version of the project version. This value must be unique.

    
    
  • output_config(OutputConfig) / set_output_config(Option<OutputConfig>):
    required: true
    

    The Amazon S3 bucket location to store the results of training. The bucket can be any S3 bucket in your AWS account. You need s3:PutObject permission on the bucket.

    
    
  • training_data(TrainingData) / set_training_data(Option<TrainingData>):
    required: false
    

    Specifies an external manifest that the services uses to train the project version. If you specify TrainingData you must also specify TestingData. The project must not have any associated datasets.

    
    
  • testing_data(TestingData) / set_testing_data(Option<TestingData>):
    required: false
    

    Specifies an external manifest that the service uses to test the project version. If you specify TestingData you must also specify TrainingData. The project must not have any associated datasets.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: false
    

    A set of tags (key-value pairs) that you want to attach to the project version.

    
    
  • kms_key_id(impl Into<String>) / set_kms_key_id(Option<String>):
    required: false
    

    The identifier for your AWS Key Management Service key (AWS KMS key). You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt training images, test images, and manifest files copied into the service for the project version. Your source images are unaffected. The key is also used to encrypt training results and manifest files written to the output Amazon S3 bucket (OutputConfig).

    If you choose to use your own KMS key, you need the following permissions on the KMS key.

    • kms:CreateGrant

    • kms:DescribeKey

    • kms:GenerateDataKey

    • kms:Decrypt

    If you don’t specify a value for KmsKeyId, images copied into the service are encrypted using a key that AWS owns and manages.

    
    
  • version_description(impl Into<String>) / set_version_description(Option<String>):
    required: false
    

    A description applied to the project version being created.

    
    
  • feature_config(CustomizationFeatureConfig) / set_feature_config(Option<CustomizationFeatureConfig>):
    required: false
    

    Feature-specific configuration of the training job. If the job configuration does not match the feature type associated with the project, an InvalidParameterException is returned.

    
    
• On success, responds with CreateProjectVersionOutput with field(s):
  • project_version_arn(Option<String>):

    The ARN of the model or the project version that was created. Use DescribeProjectVersion to get the current status of the training operation.

• On failure, responds with SdkError<CreateProjectVersionError>

impl Client

pub fn create_stream_processor(&self) -> CreateStreamProcessorFluentBuilder

Constructs a fluent builder for the CreateStreamProcessor operation.

• The fluent builder is configurable:
  • input(StreamProcessorInput) / set_input(Option<StreamProcessorInput>):
    required: true
    

    Kinesis video stream stream that provides the source streaming video. If you are using the AWS CLI, the parameter name is StreamProcessorInput. This is required for both face search and label detection stream processors.

    
    
  • output(StreamProcessorOutput) / set_output(Option<StreamProcessorOutput>):
    required: true
    

    Kinesis data stream stream or Amazon S3 bucket location to which Amazon Rekognition Video puts the analysis results. If you are using the AWS CLI, the parameter name is StreamProcessorOutput. This must be a S3Destination of an Amazon S3 bucket that you own for a label detection stream processor or a Kinesis data stream ARN for a face search stream processor.

    
    
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    An identifier you assign to the stream processor. You can use Name to manage the stream processor. For example, you can get the current status of the stream processor by calling DescribeStreamProcessor. Name is idempotent. This is required for both face search and label detection stream processors.

    
    
  • settings(StreamProcessorSettings) / set_settings(Option<StreamProcessorSettings>):
    required: true
    

    Input parameters used in a streaming video analyzed by a stream processor. You can use FaceSearch to recognize faces in a streaming video, or you can use ConnectedHome to detect labels.

    
    
  • role_arn(impl Into<String>) / set_role_arn(Option<String>):
    required: true
    

    The Amazon Resource Number (ARN) of the IAM role that allows access to the stream processor. The IAM role provides Rekognition read permissions for a Kinesis stream. It also provides write permissions to an Amazon S3 bucket and Amazon Simple Notification Service topic for a label detection stream processor. This is required for both face search and label detection stream processors.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: false
    

    A set of tags (key-value pairs) that you want to attach to the stream processor.

    
    
  • notification_channel(StreamProcessorNotificationChannel) / set_notification_channel(Option<StreamProcessorNotificationChannel>):
    required: false
    

    The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.

    Amazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications, one for a person at second 2 and one for a pet at second 4.

    Amazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.

    
    
  • kms_key_id(impl Into<String>) / set_kms_key_id(Option<String>):
    required: false
    

    The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor. You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt results and data published to your Amazon S3 bucket, which includes image frames and hero images. Your source images are unaffected.

    
    
  • regions_of_interest(RegionOfInterest) / set_regions_of_interest(Option<Vec::<RegionOfInterest>>):
    required: false
    

    Specifies locations in the frames where Amazon Rekognition checks for objects or people. You can specify up to 10 regions of interest, and each region has either a polygon or a bounding box. This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor.

    
    
  • data_sharing_preference(StreamProcessorDataSharingPreference) / set_data_sharing_preference(Option<StreamProcessorDataSharingPreference>):
    required: false
    

    Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams.

    
    
• On success, responds with CreateStreamProcessorOutput with field(s):
  • stream_processor_arn(Option<String>):

    Amazon Resource Number for the newly created stream processor.

• On failure, responds with SdkError<CreateStreamProcessorError>

impl Client

pub fn create_user(&self) -> CreateUserFluentBuilder

Constructs a fluent builder for the CreateUser operation.

• The fluent builder is configurable:
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    The ID of an existing collection to which the new UserID needs to be created.

    
    
  • user_id(impl Into<String>) / set_user_id(Option<String>):
    required: true
    

    ID for the UserID to be created. This ID needs to be unique within the collection.

    
    
  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):
    required: false
    

    Idempotent token used to identify the request to CreateUser. If you use the same token with multiple CreateUser requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.

    
    
• On success, responds with CreateUserOutput
• On failure, responds with SdkError<CreateUserError>

impl Client

pub fn delete_collection(&self) -> DeleteCollectionFluentBuilder

Constructs a fluent builder for the DeleteCollection operation.

• The fluent builder is configurable:
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    ID of the collection to delete.

    
    
• On success, responds with DeleteCollectionOutput with field(s):
  • status_code(Option<i32>):

    HTTP status code that indicates the result of the operation.

• On failure, responds with SdkError<DeleteCollectionError>

impl Client

pub fn delete_dataset(&self) -> DeleteDatasetFluentBuilder

Constructs a fluent builder for the DeleteDataset operation.

• The fluent builder is configurable:
  • dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
    required: true
    

    The ARN of the Amazon Rekognition Custom Labels dataset that you want to delete.

    
    
• On success, responds with DeleteDatasetOutput
• On failure, responds with SdkError<DeleteDatasetError>

impl Client

pub fn delete_faces(&self) -> DeleteFacesFluentBuilder

Constructs a fluent builder for the DeleteFaces operation.

• The fluent builder is configurable:
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    Collection from which to remove the specific faces.

    
    
  • face_ids(impl Into<String>) / set_face_ids(Option<Vec::<String>>):
    required: true
    

    An array of face IDs to delete.

    
    
• On success, responds with DeleteFacesOutput with field(s):
  • deleted_faces(Option<Vec::<String>>):

    An array of strings (face IDs) of the faces that were deleted.

  • unsuccessful_face_deletions(Option<Vec::<UnsuccessfulFaceDeletion>>):

    An array of any faces that weren’t deleted.

• On failure, responds with SdkError<DeleteFacesError>

impl Client

pub fn delete_project(&self) -> DeleteProjectFluentBuilder

Constructs a fluent builder for the DeleteProject operation.

• The fluent builder is configurable:
  • project_arn(impl Into<String>) / set_project_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the project that you want to delete.

    
    
• On success, responds with DeleteProjectOutput with field(s):
  • status(Option<ProjectStatus>):

    The current status of the delete project operation.

• On failure, responds with SdkError<DeleteProjectError>

impl Client

pub fn delete_project_policy(&self) -> DeleteProjectPolicyFluentBuilder

Constructs a fluent builder for the DeleteProjectPolicy operation.

• The fluent builder is configurable:
  • project_arn(impl Into<String>) / set_project_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the project that the project policy you want to delete is attached to.

    
    
  • policy_name(impl Into<String>) / set_policy_name(Option<String>):
    required: true
    

    The name of the policy that you want to delete.

    
    
  • policy_revision_id(impl Into<String>) / set_policy_revision_id(Option<String>):
    required: false
    

    The ID of the project policy revision that you want to delete.

    
    
• On success, responds with DeleteProjectPolicyOutput
• On failure, responds with SdkError<DeleteProjectPolicyError>

impl Client

pub fn delete_project_version(&self) -> DeleteProjectVersionFluentBuilder

Constructs a fluent builder for the DeleteProjectVersion operation.

• The fluent builder is configurable:
  • project_version_arn(impl Into<String>) / set_project_version_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the project version that you want to delete.

    
    
• On success, responds with DeleteProjectVersionOutput with field(s):
  • status(Option<ProjectVersionStatus>):

    The status of the deletion operation.

• On failure, responds with SdkError<DeleteProjectVersionError>

impl Client

pub fn delete_stream_processor(&self) -> DeleteStreamProcessorFluentBuilder

Constructs a fluent builder for the DeleteStreamProcessor operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of the stream processor you want to delete.

    
    
• On success, responds with DeleteStreamProcessorOutput
• On failure, responds with SdkError<DeleteStreamProcessorError>

impl Client

pub fn delete_user(&self) -> DeleteUserFluentBuilder

Constructs a fluent builder for the DeleteUser operation.

• The fluent builder is configurable:
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    The ID of an existing collection from which the UserID needs to be deleted.

    
    
  • user_id(impl Into<String>) / set_user_id(Option<String>):
    required: true
    

    ID for the UserID to be deleted.

    
    
  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):
    required: false
    

    Idempotent token used to identify the request to DeleteUser. If you use the same token with multiple DeleteUser requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.

    
    
• On success, responds with DeleteUserOutput
• On failure, responds with SdkError<DeleteUserError>

impl Client

pub fn describe_collection(&self) -> DescribeCollectionFluentBuilder

Constructs a fluent builder for the DescribeCollection operation.

• The fluent builder is configurable:
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    The ID of the collection to describe.

    
    
• On success, responds with DescribeCollectionOutput with field(s):
  • face_count(Option<i64>):

    The number of faces that are indexed into the collection. To index faces into a collection, use IndexFaces.

  • face_model_version(Option<String>):

    The version of the face model that’s used by the collection for face detection.

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

  • collection_arn(Option<String>):

    The Amazon Resource Name (ARN) of the collection.

  • creation_timestamp(Option<DateTime>):

    The number of milliseconds since the Unix epoch time until the creation of the collection. The Unix epoch time is 00:00:00 Coordinated Universal Time (UTC), Thursday, 1 January 1970.

  • user_count(Option<i64>):

    The number of UserIDs assigned to the specified colleciton.

• On failure, responds with SdkError<DescribeCollectionError>

impl Client

pub fn describe_dataset(&self) -> DescribeDatasetFluentBuilder

Constructs a fluent builder for the DescribeDataset operation.

• The fluent builder is configurable:
  • dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the dataset that you want to describe.

    
    
• On success, responds with DescribeDatasetOutput with field(s):
  • dataset_description(Option<DatasetDescription>):

    The description for the dataset.

• On failure, responds with SdkError<DescribeDatasetError>

impl Client

pub fn describe_project_versions(&self) -> DescribeProjectVersionsFluentBuilder

Constructs a fluent builder for the DescribeProjectVersions operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • project_arn(impl Into<String>) / set_project_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the project that contains the model/adapter you want to describe.

    
    
  • version_names(impl Into<String>) / set_version_names(Option<Vec::<String>>):
    required: false
    

    A list of model or project version names that you want to describe. You can add up to 10 model or project version names to the list. If you don’t specify a value, all project version descriptions are returned. A version name is part of a project version ARN. For example, my-model.2020-01-21T09.10.15 is the version name in the following ARN. arn:aws:rekognition:us-east-1:123456789012:project/getting-started/version/my-model.2020-01-21T09.10.15/1234567890123.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.

    
    
• On success, responds with DescribeProjectVersionsOutput with field(s):
  • project_version_descriptions(Option<Vec::<ProjectVersionDescription>>):

    A list of project version descriptions. The list is sorted by the creation date and time of the project versions, latest to earliest.

  • next_token(Option<String>):

    If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

• On failure, responds with SdkError<DescribeProjectVersionsError>

impl Client

pub fn describe_projects(&self) -> DescribeProjectsFluentBuilder

Constructs a fluent builder for the DescribeProjects operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the previous response was incomplete (because there is more results to retrieve), Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.

    
    
  • project_names(impl Into<String>) / set_project_names(Option<Vec::<String>>):
    required: false
    

    A list of the projects that you want Rekognition to describe. If you don’t specify a value, the response includes descriptions for all the projects in your AWS account.

    
    
  • features(CustomizationFeature) / set_features(Option<Vec::<CustomizationFeature>>):
    required: false
    

    Specifies the type of customization to filter projects by. If no value is specified, CUSTOM_LABELS is used as a default.

    
    
• On success, responds with DescribeProjectsOutput with field(s):
  • project_descriptions(Option<Vec::<ProjectDescription>>):

    A list of project descriptions. The list is sorted by the date and time the projects are created.

  • next_token(Option<String>):

    If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

• On failure, responds with SdkError<DescribeProjectsError>

impl Client

pub fn describe_stream_processor(&self) -> DescribeStreamProcessorFluentBuilder

Constructs a fluent builder for the DescribeStreamProcessor operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    Name of the stream processor for which you want information.

    
    
• On success, responds with DescribeStreamProcessorOutput with field(s):
  • name(Option<String>):

    Name of the stream processor.

  • stream_processor_arn(Option<String>):

    ARN of the stream processor.

  • status(Option<StreamProcessorStatus>):

    Current status of the stream processor.

  • status_message(Option<String>):

    Detailed status message about the stream processor.

  • creation_timestamp(Option<DateTime>):

    Date and time the stream processor was created

  • last_update_timestamp(Option<DateTime>):

    The time, in Unix format, the stream processor was last updated. For example, when the stream processor moves from a running state to a failed state, or when the user starts or stops the stream processor.

  • input(Option<StreamProcessorInput>):

    Kinesis video stream that provides the source streaming video.

  • output(Option<StreamProcessorOutput>):

    Kinesis data stream to which Amazon Rekognition Video puts the analysis results.

  • role_arn(Option<String>):

    ARN of the IAM role that allows access to the stream processor.

  • settings(Option<StreamProcessorSettings>):

    Input parameters used in a streaming video analyzed by a stream processor. You can use FaceSearch to recognize faces in a streaming video, or you can use ConnectedHome to detect labels.

  • notification_channel(Option<StreamProcessorNotificationChannel>):

    The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.

    Amazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications, one for a person at second 2 and one for a pet at second 4.

    Amazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.

  • kms_key_id(Option<String>):

    The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors.

  • regions_of_interest(Option<Vec::<RegionOfInterest>>):

    Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors.

  • data_sharing_preference(Option<StreamProcessorDataSharingPreference>):

    Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams.

• On failure, responds with SdkError<DescribeStreamProcessorError>

impl Client

pub fn detect_custom_labels(&self) -> DetectCustomLabelsFluentBuilder

Constructs a fluent builder for the DetectCustomLabels operation.

• The fluent builder is configurable:
  • project_version_arn(impl Into<String>) / set_project_version_arn(Option<String>):
    required: true
    

    The ARN of the model version that you want to use. Only models associated with Custom Labels projects accepted by the operation. If a provided ARN refers to a model version associated with a project for a different feature type, then an InvalidParameterException is returned.

    
    
  • image(Image) / set_image(Option<Image>):
    required: true
    

    Provides the input image either as bytes or an S3 object.

    You pass image bytes to an Amazon Rekognition API operation by using the Bytes property. For example, you would use the Bytes property to pass an image loaded from a local file system. Image bytes passed by using the Bytes property must be base64-encoded. Your code may not need to encode image bytes if you are using an AWS SDK to call Amazon Rekognition API operations.

    For more information, see Analyzing an Image Loaded from a Local File System in the Amazon Rekognition Developer Guide.

    You pass images stored in an S3 bucket to an Amazon Rekognition API operation by using the S3Object property. Images stored in an S3 bucket do not need to be base64-encoded.

    The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.

    If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes using the Bytes property is not supported. You must first upload the image to an Amazon S3 bucket and then call the operation using the S3Object property.

    For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    Maximum number of results you want the service to return in the response. The service returns the specified number of highest confidence labels ranked from highest confidence to lowest.

    
    
  • min_confidence(f32) / set_min_confidence(Option<f32>):
    required: false
    

    Specifies the minimum confidence level for the labels to return. DetectCustomLabels doesn’t return any labels with a confidence value that’s lower than this specified value. If you specify a value of 0, DetectCustomLabels returns all labels, regardless of the assumed threshold applied to each label. If you don’t specify a value for MinConfidence, DetectCustomLabels returns labels based on the assumed threshold of each label.

    
    
• On success, responds with DetectCustomLabelsOutput with field(s):
  • custom_labels(Option<Vec::<CustomLabel>>):

    An array of custom labels detected in the input image.

• On failure, responds with SdkError<DetectCustomLabelsError>

impl Client

pub fn detect_faces(&self) -> DetectFacesFluentBuilder

Constructs a fluent builder for the DetectFaces operation.

• The fluent builder is configurable:
  • image(Image) / set_image(Option<Image>):
    required: true
    

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

    
    
  • attributes(Attribute) / set_attributes(Option<Vec::<Attribute>>):
    required: false
    

    An array of facial attributes you want to be returned. A DEFAULT subset of facial attributes - BoundingBox, Confidence, Pose, Quality, and Landmarks - will always be returned. You can request for specific facial attributes (in addition to the default list) - by using [“DEFAULT”, “FACE_OCCLUDED”] or just [“FACE_OCCLUDED”]. You can request for all facial attributes by using [“ALL”]. Requesting more attributes may increase response time.

    If you provide both, [“ALL”, “DEFAULT”], the service uses a logical “AND” operator to determine which attributes to return (in this case, all attributes).

    Note that while the FaceOccluded and EyeDirection attributes are supported when using DetectFaces, they aren’t supported when analyzing videos with StartFaceDetection and GetFaceDetection.

    
    
• On success, responds with DetectFacesOutput with field(s):
  • face_details(Option<Vec::<FaceDetail>>):

    Details of each face found in the image.

  • orientation_correction(Option<OrientationCorrection>):

    The value of OrientationCorrection is always null.

    If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image’s orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don’t contain Exif metadata.

    Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren’t translated and represent the object locations before the image is rotated.

• On failure, responds with SdkError<DetectFacesError>

impl Client

pub fn detect_labels(&self) -> DetectLabelsFluentBuilder

Constructs a fluent builder for the DetectLabels operation.

• The fluent builder is configurable:
  • image(Image) / set_image(Option<Image>):
    required: true
    

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. Images stored in an S3 Bucket do not need to be base64-encoded.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

    
    
  • max_labels(i32) / set_max_labels(Option<i32>):
    required: false
    

    Maximum number of labels you want the service to return in the response. The service returns the specified number of highest confidence labels. Only valid when GENERAL_LABELS is specified as a feature type in the Feature input parameter.

    
    
  • min_confidence(f32) / set_min_confidence(Option<f32>):
    required: false
    

    Specifies the minimum confidence level for the labels to return. Amazon Rekognition doesn’t return any labels with confidence lower than this specified value.

    If MinConfidence is not specified, the operation returns labels with a confidence values greater than or equal to 55 percent. Only valid when GENERAL_LABELS is specified as a feature type in the Feature input parameter.

    
    
  • features(DetectLabelsFeatureName) / set_features(Option<Vec::<DetectLabelsFeatureName>>):
    required: false
    

    A list of the types of analysis to perform. Specifying GENERAL_LABELS uses the label detection feature, while specifying IMAGE_PROPERTIES returns information regarding image color and quality. If no option is specified GENERAL_LABELS is used by default.

    
    
  • settings(DetectLabelsSettings) / set_settings(Option<DetectLabelsSettings>):
    required: false
    

    A list of the filters to be applied to returned detected labels and image properties. Specified filters can be inclusive, exclusive, or a combination of both. Filters can be used for individual labels or label categories. The exact label names or label categories must be supplied. For a full list of labels and label categories, see Detecting labels.

    
    
• On success, responds with DetectLabelsOutput with field(s):
  • labels(Option<Vec::<Label>>):

    An array of labels for the real-world objects detected.

  • orientation_correction(Option<OrientationCorrection>):

    The value of OrientationCorrection is always null.

    If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image’s orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don’t contain Exif metadata.

    Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren’t translated and represent the object locations before the image is rotated.

  • label_model_version(Option<String>):

    Version number of the label detection model that was used to detect labels.

  • image_properties(Option<DetectLabelsImageProperties>):

    Information about the properties of the input image, such as brightness, sharpness, contrast, and dominant colors.

• On failure, responds with SdkError<DetectLabelsError>

impl Client

pub fn detect_moderation_labels(&self) -> DetectModerationLabelsFluentBuilder

Constructs a fluent builder for the DetectModerationLabels operation.

• The fluent builder is configurable:
  • image(Image) / set_image(Option<Image>):
    required: true
    

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

    
    
  • min_confidence(f32) / set_min_confidence(Option<f32>):
    required: false
    

    Specifies the minimum confidence level for the labels to return. Amazon Rekognition doesn’t return any labels with a confidence level lower than this specified value.

    If you don’t specify MinConfidence, the operation returns labels with confidence values greater than or equal to 50 percent.

    
    
  • human_loop_config(HumanLoopConfig) / set_human_loop_config(Option<HumanLoopConfig>):
    required: false
    

    Sets up the configuration for human evaluation, including the FlowDefinition the image will be sent to.

    
    
  • project_version(impl Into<String>) / set_project_version(Option<String>):
    required: false
    

    Identifier for the custom adapter. Expects the ProjectVersionArn as a value. Use the CreateProject or CreateProjectVersion APIs to create a custom adapter.

    
    
• On success, responds with DetectModerationLabelsOutput with field(s):
  • moderation_labels(Option<Vec::<ModerationLabel>>):

    Array of detected Moderation labels. For video operations, this includes the time, in milliseconds from the start of the video, they were detected.

  • moderation_model_version(Option<String>):

    Version number of the base moderation detection model that was used to detect unsafe content.

  • human_loop_activation_output(Option<HumanLoopActivationOutput>):

    Shows the results of the human in the loop evaluation.

  • project_version(Option<String>):

    Identifier of the custom adapter that was used during inference. If during inference the adapter was EXPIRED, then the parameter will not be returned, indicating that a base moderation detection project version was used.

  • content_types(Option<Vec::<ContentType>>):

    A list of predicted results for the type of content an image contains. For example, the image content might be from animation, sports, or a video game.

• On failure, responds with SdkError<DetectModerationLabelsError>

impl Client

pub fn detect_protective_equipment( &self, ) -> DetectProtectiveEquipmentFluentBuilder

Constructs a fluent builder for the DetectProtectiveEquipment operation.

• The fluent builder is configurable:
  • image(Image) / set_image(Option<Image>):
    required: true
    

    The image in which you want to detect PPE on detected persons. The image can be passed as image bytes or you can reference an image stored in an Amazon S3 bucket.

    
    
  • summarization_attributes(ProtectiveEquipmentSummarizationAttributes) / set_summarization_attributes(Option<ProtectiveEquipmentSummarizationAttributes>):
    required: false
    

    An array of PPE types that you want to summarize.

    
    
• On success, responds with DetectProtectiveEquipmentOutput with field(s):
  • protective_equipment_model_version(Option<String>):

    The version number of the PPE detection model used to detect PPE in the image.

  • persons(Option<Vec::<ProtectiveEquipmentPerson>>):

    An array of persons detected in the image (including persons not wearing PPE).

  • summary(Option<ProtectiveEquipmentSummary>):

    Summary information for the types of PPE specified in the SummarizationAttributes input parameter.

• On failure, responds with SdkError<DetectProtectiveEquipmentError>

impl Client

pub fn detect_text(&self) -> DetectTextFluentBuilder

Constructs a fluent builder for the DetectText operation.

• The fluent builder is configurable:
  • image(Image) / set_image(Option<Image>):
    required: true
    

    The input image as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Rekognition operations, you can’t pass image bytes.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

    
    
  • filters(DetectTextFilters) / set_filters(Option<DetectTextFilters>):
    required: false
    

    Optional parameters that let you set the criteria that the text must meet to be included in your response.

    
    
• On success, responds with DetectTextOutput with field(s):
  • text_detections(Option<Vec::<TextDetection>>):

    An array of text that was detected in the input image.

  • text_model_version(Option<String>):

    The model version used to detect text.

• On failure, responds with SdkError<DetectTextError>

impl Client

pub fn disassociate_faces(&self) -> DisassociateFacesFluentBuilder

Constructs a fluent builder for the DisassociateFaces operation.

• The fluent builder is configurable:
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    The ID of an existing collection containing the UserID.

    
    
  • user_id(impl Into<String>) / set_user_id(Option<String>):
    required: true
    

    ID for the existing UserID.

    
    
  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):
    required: false
    

    Idempotent token used to identify the request to DisassociateFaces. If you use the same token with multiple DisassociateFaces requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.

    
    
  • face_ids(impl Into<String>) / set_face_ids(Option<Vec::<String>>):
    required: true
    

    An array of face IDs to disassociate from the UserID.

    
    
• On success, responds with DisassociateFacesOutput with field(s):
  • disassociated_faces(Option<Vec::<DisassociatedFace>>):

    An array of DissociatedFace objects containing FaceIds that are successfully disassociated with the UserID is returned. Returned if the DisassociatedFaces action is successful.

  • unsuccessful_face_disassociations(Option<Vec::<UnsuccessfulFaceDisassociation>>):

    An array of UnsuccessfulDisassociation objects containing FaceIds that are not successfully associated, along with the reasons for the failure to associate. Returned if the DisassociateFaces action is successful.

  • user_status(Option<UserStatus>):

    The status of an update made to a User. Reflects if the User has been updated for every requested change.

• On failure, responds with SdkError<DisassociateFacesError>

impl Client

pub fn distribute_dataset_entries( &self, ) -> DistributeDatasetEntriesFluentBuilder

Constructs a fluent builder for the DistributeDatasetEntries operation.

• The fluent builder is configurable:
  • datasets(DistributeDataset) / set_datasets(Option<Vec::<DistributeDataset>>):
    required: true
    

    The ARNS for the training dataset and test dataset that you want to use. The datasets must belong to the same project. The test dataset must be empty.

    
    
• On success, responds with DistributeDatasetEntriesOutput
• On failure, responds with SdkError<DistributeDatasetEntriesError>

impl Client

pub fn get_celebrity_info(&self) -> GetCelebrityInfoFluentBuilder

Constructs a fluent builder for the GetCelebrityInfo operation.

• The fluent builder is configurable:
  • id(impl Into<String>) / set_id(Option<String>):
    required: true
    

    The ID for the celebrity. You get the celebrity ID from a call to the RecognizeCelebrities operation, which recognizes celebrities in an image.

    
    
• On success, responds with GetCelebrityInfoOutput with field(s):
  • urls(Option<Vec::<String>>):

    An array of URLs pointing to additional celebrity information.

  • name(Option<String>):

    The name of the celebrity.

  • known_gender(Option<KnownGender>):

    Retrieves the known gender for the celebrity.

• On failure, responds with SdkError<GetCelebrityInfoError>

impl Client

pub fn get_celebrity_recognition(&self) -> GetCelebrityRecognitionFluentBuilder

Constructs a fluent builder for the GetCelebrityRecognition operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • job_id(impl Into<String>) / set_job_id(Option<String>):
    required: true
    

    Job identifier for the required celebrity recognition analysis. You can get the job identifer from a call to StartCelebrityRecognition.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the previous response was incomplete (because there is more recognized celebrities to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of celebrities.

    
    
  • sort_by(CelebrityRecognitionSortBy) / set_sort_by(Option<CelebrityRecognitionSortBy>):
    required: false
    

    Sort to use for celebrities returned in Celebrities field. Specify ID to sort by the celebrity identifier, specify TIMESTAMP to sort by the time the celebrity was recognized.

    
    
• On success, responds with GetCelebrityRecognitionOutput with field(s):
  • job_status(Option<VideoJobStatus>):

    The current status of the celebrity recognition job.

  • status_message(Option<String>):

    If the job fails, StatusMessage provides a descriptive error message.

  • video_metadata(Option<VideoMetadata>):

    Information about a video that Amazon Rekognition Video analyzed. Videometadata is returned in every page of paginated responses from a Amazon Rekognition Video operation.

  • next_token(Option<String>):

    If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of celebrities.

  • celebrities(Option<Vec::<CelebrityRecognition>>):

    Array of celebrities recognized in the video.

  • job_id(Option<String>):

    Job identifier for the celebrity recognition operation for which you want to obtain results. The job identifer is returned by an initial call to StartCelebrityRecognition.

  • video(Option<Video>):

    Video file stored in an Amazon S3 bucket. Amazon Rekognition video start operations such as StartLabelDetection use Video to specify a video for analysis. The supported file formats are .mp4, .mov and .avi.

  • job_tag(Option<String>):

    A job identifier specified in the call to StartCelebrityRecognition and returned in the job completion notification sent to your Amazon Simple Notification Service topic.

• On failure, responds with SdkError<GetCelebrityRecognitionError>

impl Client

pub fn get_content_moderation(&self) -> GetContentModerationFluentBuilder

Constructs a fluent builder for the GetContentModeration operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • job_id(impl Into<String>) / set_job_id(Option<String>):
    required: true
    

    The identifier for the inappropriate, unwanted, or offensive content moderation job. Use JobId to identify the job in a subsequent call to GetContentModeration.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the previous response was incomplete (because there is more data to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of content moderation labels.

    
    
  • sort_by(ContentModerationSortBy) / set_sort_by(Option<ContentModerationSortBy>):
    required: false
    

    Sort to use for elements in the ModerationLabelDetections array. Use TIMESTAMP to sort array elements by the time labels are detected. Use NAME to alphabetically group elements for a label together. Within each label group, the array element are sorted by detection confidence. The default sort is by TIMESTAMP.

    
    
  • aggregate_by(ContentModerationAggregateBy) / set_aggregate_by(Option<ContentModerationAggregateBy>):
    required: false
    

    Defines how to aggregate results of the StartContentModeration request. Default aggregation option is TIMESTAMPS. SEGMENTS mode aggregates moderation labels over time.

    
    
• On success, responds with GetContentModerationOutput with field(s):
  • job_status(Option<VideoJobStatus>):

    The current status of the content moderation analysis job.

  • status_message(Option<String>):

    If the job fails, StatusMessage provides a descriptive error message.

  • video_metadata(Option<VideoMetadata>):

    Information about a video that Amazon Rekognition analyzed. Videometadata is returned in every page of paginated responses from GetContentModeration.

  • moderation_labels(Option<Vec::<ContentModerationDetection>>):

    The detected inappropriate, unwanted, or offensive content moderation labels and the time(s) they were detected.

  • next_token(Option<String>):

    If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of content moderation labels.

  • moderation_model_version(Option<String>):

    Version number of the moderation detection model that was used to detect inappropriate, unwanted, or offensive content.

  • job_id(Option<String>):

    Job identifier for the content moderation operation for which you want to obtain results. The job identifer is returned by an initial call to StartContentModeration.

  • video(Option<Video>):

    Video file stored in an Amazon S3 bucket. Amazon Rekognition video start operations such as StartLabelDetection use Video to specify a video for analysis. The supported file formats are .mp4, .mov and .avi.

  • job_tag(Option<String>):

    A job identifier specified in the call to StartContentModeration and returned in the job completion notification sent to your Amazon Simple Notification Service topic.

  • get_request_metadata(Option<GetContentModerationRequestMetadata>):

    Information about the paramters used when getting a response. Includes information on aggregation and sorting methods.

• On failure, responds with SdkError<GetContentModerationError>

impl Client

pub fn get_face_detection(&self) -> GetFaceDetectionFluentBuilder

Constructs a fluent builder for the GetFaceDetection operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • job_id(impl Into<String>) / set_job_id(Option<String>):
    required: true
    

    Unique identifier for the face detection job. The JobId is returned from StartFaceDetection.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the previous response was incomplete (because there are more faces to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of faces.

    
    
• On success, responds with GetFaceDetectionOutput with field(s):
  • job_status(Option<VideoJobStatus>):

    The current status of the face detection job.

  • status_message(Option<String>):

    If the job fails, StatusMessage provides a descriptive error message.

  • video_metadata(Option<VideoMetadata>):

    Information about a video that Amazon Rekognition Video analyzed. Videometadata is returned in every page of paginated responses from a Amazon Rekognition video operation.

  • next_token(Option<String>):

    If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces.

  • faces(Option<Vec::<FaceDetection>>):

    An array of faces detected in the video. Each element contains a detected face’s details and the time, in milliseconds from the start of the video, the face was detected.

  • job_id(Option<String>):

    Job identifier for the face detection operation for which you want to obtain results. The job identifer is returned by an initial call to StartFaceDetection.

  • video(Option<Video>):

    Video file stored in an Amazon S3 bucket. Amazon Rekognition video start operations such as StartLabelDetection use Video to specify a video for analysis. The supported file formats are .mp4, .mov and .avi.

  • job_tag(Option<String>):

    A job identifier specified in the call to StartFaceDetection and returned in the job completion notification sent to your Amazon Simple Notification Service topic.

• On failure, responds with SdkError<GetFaceDetectionError>

impl Client

pub fn get_face_liveness_session_results( &self, ) -> GetFaceLivenessSessionResultsFluentBuilder

Constructs a fluent builder for the GetFaceLivenessSessionResults operation.

• The fluent builder is configurable:
  • session_id(impl Into<String>) / set_session_id(Option<String>):
    required: true
    

    A unique 128-bit UUID. This is used to uniquely identify the session and also acts as an idempotency token for all operations associated with the session.

    
    
• On success, responds with GetFaceLivenessSessionResultsOutput with field(s):
  • session_id(String):

    The sessionId for which this request was called.

  • status(LivenessSessionStatus):

    Represents a status corresponding to the state of the session. Possible statuses are: CREATED, IN_PROGRESS, SUCCEEDED, FAILED, EXPIRED.

  • confidence(Option<f32>):

    Probabalistic confidence score for if the person in the given video was live, represented as a float value between 0 to 100.

  • reference_image(Option<AuditImage>):

    A high-quality image from the Face Liveness video that can be used for face comparison or search. It includes a bounding box of the face and the Base64-encoded bytes that return an image. If the CreateFaceLivenessSession request included an OutputConfig argument, the image will be uploaded to an S3Object specified in the output configuration. In case the reference image is not returned, it’s recommended to retry the Liveness check.

  • audit_images(Option<Vec::<AuditImage>>):

    A set of images from the Face Liveness video that can be used for audit purposes. It includes a bounding box of the face and the Base64-encoded bytes that return an image. If the CreateFaceLivenessSession request included an OutputConfig argument, the image will be uploaded to an S3Object specified in the output configuration. If no Amazon S3 bucket is defined, raw bytes are sent instead.

• On failure, responds with SdkError<GetFaceLivenessSessionResultsError>

impl Client

pub fn get_face_search(&self) -> GetFaceSearchFluentBuilder

Constructs a fluent builder for the GetFaceSearch operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • job_id(impl Into<String>) / set_job_id(Option<String>):
    required: true
    

    The job identifer for the search request. You get the job identifier from an initial call to StartFaceSearch.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the previous response was incomplete (because there is more search results to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of search results.

    
    
  • sort_by(FaceSearchSortBy) / set_sort_by(Option<FaceSearchSortBy>):
    required: false
    

    Sort to use for grouping faces in the response. Use TIMESTAMP to group faces by the time that they are recognized. Use INDEX to sort by recognized faces.

    
    
• On success, responds with GetFaceSearchOutput with field(s):
  • job_status(Option<VideoJobStatus>):

    The current status of the face search job.

  • status_message(Option<String>):

    If the job fails, StatusMessage provides a descriptive error message.

  • next_token(Option<String>):

    If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of search results.

  • video_metadata(Option<VideoMetadata>):

    Information about a video that Amazon Rekognition analyzed. Videometadata is returned in every page of paginated responses from a Amazon Rekognition Video operation.

  • persons(Option<Vec::<PersonMatch>>):

    An array of persons, PersonMatch, in the video whose face(s) match the face(s) in an Amazon Rekognition collection. It also includes time information for when persons are matched in the video. You specify the input collection in an initial call to StartFaceSearch. Each Persons element includes a time the person was matched, face match details (FaceMatches) for matching faces in the collection, and person information (Person) for the matched person.

  • job_id(Option<String>):

    Job identifier for the face search operation for which you want to obtain results. The job identifer is returned by an initial call to StartFaceSearch.

  • video(Option<Video>):

    Video file stored in an Amazon S3 bucket. Amazon Rekognition video start operations such as StartLabelDetection use Video to specify a video for analysis. The supported file formats are .mp4, .mov and .avi.

  • job_tag(Option<String>):

    A job identifier specified in the call to StartFaceSearch and returned in the job completion notification sent to your Amazon Simple Notification Service topic.

• On failure, responds with SdkError<GetFaceSearchError>

impl Client

pub fn get_label_detection(&self) -> GetLabelDetectionFluentBuilder

Constructs a fluent builder for the GetLabelDetection operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • job_id(impl Into<String>) / set_job_id(Option<String>):
    required: true
    

    Job identifier for the label detection operation for which you want results returned. You get the job identifer from an initial call to StartlabelDetection.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of labels.

    
    
  • sort_by(LabelDetectionSortBy) / set_sort_by(Option<LabelDetectionSortBy>):
    required: false
    

    Sort to use for elements in the Labels array. Use TIMESTAMP to sort array elements by the time labels are detected. Use NAME to alphabetically group elements for a label together. Within each label group, the array element are sorted by detection confidence. The default sort is by TIMESTAMP.

    
    
  • aggregate_by(LabelDetectionAggregateBy) / set_aggregate_by(Option<LabelDetectionAggregateBy>):
    required: false
    

    Defines how to aggregate the returned results. Results can be aggregated by timestamps or segments.

    
    
• On success, responds with GetLabelDetectionOutput with field(s):
  • job_status(Option<VideoJobStatus>):

    The current status of the label detection job.

  • status_message(Option<String>):

    If the job fails, StatusMessage provides a descriptive error message.

  • video_metadata(Option<VideoMetadata>):

    Information about a video that Amazon Rekognition Video analyzed. Videometadata is returned in every page of paginated responses from a Amazon Rekognition video operation.

  • next_token(Option<String>):

    If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of labels.

  • labels(Option<Vec::<LabelDetection>>):

    An array of labels detected in the video. Each element contains the detected label and the time, in milliseconds from the start of the video, that the label was detected.

  • label_model_version(Option<String>):

    Version number of the label detection model that was used to detect labels.

  • job_id(Option<String>):

    Job identifier for the label detection operation for which you want to obtain results. The job identifer is returned by an initial call to StartLabelDetection.

  • video(Option<Video>):

    Video file stored in an Amazon S3 bucket. Amazon Rekognition video start operations such as StartLabelDetection use Video to specify a video for analysis. The supported file formats are .mp4, .mov and .avi.

  • job_tag(Option<String>):

    A job identifier specified in the call to StartLabelDetection and returned in the job completion notification sent to your Amazon Simple Notification Service topic.

  • get_request_metadata(Option<GetLabelDetectionRequestMetadata>):

    Information about the paramters used when getting a response. Includes information on aggregation and sorting methods.

• On failure, responds with SdkError<GetLabelDetectionError>

impl Client

pub fn get_media_analysis_job(&self) -> GetMediaAnalysisJobFluentBuilder

Constructs a fluent builder for the GetMediaAnalysisJob operation.

• The fluent builder is configurable:
  • job_id(impl Into<String>) / set_job_id(Option<String>):
    required: true
    

    Unique identifier for the media analysis job for which you want to retrieve results.

    
    
• On success, responds with GetMediaAnalysisJobOutput with field(s):
  • job_id(String):

    The identifier for the media analysis job.

  • job_name(Option<String>):

    The name of the media analysis job.

  • operations_config(Option<MediaAnalysisOperationsConfig>):

    Operation configurations that were provided during job creation.

  • status(MediaAnalysisJobStatus):

    The current status of the media analysis job.

  • failure_details(Option<MediaAnalysisJobFailureDetails>):

    Details about the error that resulted in failure of the job.

  • creation_timestamp(DateTime):

    The Unix date and time when the job was started.

  • completion_timestamp(Option<DateTime>):

    The Unix date and time when the job finished.

  • input(Option<MediaAnalysisInput>):

    Reference to the input manifest that was provided in the job creation request.

  • output_config(Option<MediaAnalysisOutputConfig>):

    Output configuration that was provided in the creation request.

  • kms_key_id(Option<String>):

    KMS Key that was provided in the creation request.

  • results(Option<MediaAnalysisResults>):

    Output manifest that contains prediction results.

  • manifest_summary(Option<MediaAnalysisManifestSummary>):

    The summary manifest provides statistics on input manifest and errors identified in the input manifest.

• On failure, responds with SdkError<GetMediaAnalysisJobError>

impl Client

pub fn get_person_tracking(&self) -> GetPersonTrackingFluentBuilder

Constructs a fluent builder for the GetPersonTracking operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • job_id(impl Into<String>) / set_job_id(Option<String>):
    required: true
    

    The identifier for a job that tracks persons in a video. You get the JobId from a call to StartPersonTracking.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    Maximum number of results to return per paginated call. The largest value you can specify is 1000. If you specify a value greater than 1000, a maximum of 1000 results is returned. The default value is 1000.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the previous response was incomplete (because there are more persons to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of persons.

    
    
  • sort_by(PersonTrackingSortBy) / set_sort_by(Option<PersonTrackingSortBy>):
    required: false
    

    Sort to use for elements in the Persons array. Use TIMESTAMP to sort array elements by the time persons are detected. Use INDEX to sort by the tracked persons. If you sort by INDEX, the array elements for each person are sorted by detection confidence. The default sort is by TIMESTAMP.

    
    
• On success, responds with GetPersonTrackingOutput with field(s):
  • job_status(Option<VideoJobStatus>):

    The current status of the person tracking job.

  • status_message(Option<String>):

    If the job fails, StatusMessage provides a descriptive error message.

  • video_metadata(Option<VideoMetadata>):

    Information about a video that Amazon Rekognition Video analyzed. Videometadata is returned in every page of paginated responses from a Amazon Rekognition Video operation.

  • next_token(Option<String>):

    If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of persons.

  • persons(Option<Vec::<PersonDetection>>):

    An array of the persons detected in the video and the time(s) their path was tracked throughout the video. An array element will exist for each time a person’s path is tracked.

  • job_id(Option<String>):

    Job identifier for the person tracking operation for which you want to obtain results. The job identifer is returned by an initial call to StartPersonTracking.

  • video(Option<Video>):

    Video file stored in an Amazon S3 bucket. Amazon Rekognition video start operations such as StartLabelDetection use Video to specify a video for analysis. The supported file formats are .mp4, .mov and .avi.

  • job_tag(Option<String>):

    A job identifier specified in the call to StartCelebrityRecognition and returned in the job completion notification sent to your Amazon Simple Notification Service topic.

• On failure, responds with SdkError<GetPersonTrackingError>

impl Client

pub fn get_segment_detection(&self) -> GetSegmentDetectionFluentBuilder

Constructs a fluent builder for the GetSegmentDetection operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • job_id(impl Into<String>) / set_job_id(Option<String>):
    required: true
    

    Job identifier for the text detection operation for which you want results returned. You get the job identifer from an initial call to StartSegmentDetection.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    Maximum number of results to return per paginated call. The largest value you can specify is 1000.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of text.

    
    
• On success, responds with GetSegmentDetectionOutput with field(s):
  • job_status(Option<VideoJobStatus>):

    Current status of the segment detection job.

  • status_message(Option<String>):

    If the job fails, StatusMessage provides a descriptive error message.

  • video_metadata(Option<Vec::<VideoMetadata>>):

    Currently, Amazon Rekognition Video returns a single object in the VideoMetadata array. The object contains information about the video stream in the input file that Amazon Rekognition Video chose to analyze. The VideoMetadata object includes the video codec, video format and other information. Video metadata is returned in each page of information returned by GetSegmentDetection.

  • audio_metadata(Option<Vec::<AudioMetadata>>):

    An array of objects. There can be multiple audio streams. Each AudioMetadata object contains metadata for a single audio stream. Audio information in an AudioMetadata objects includes the audio codec, the number of audio channels, the duration of the audio stream, and the sample rate. Audio metadata is returned in each page of information returned by GetSegmentDetection.

  • next_token(Option<String>):

    If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of text.

  • segments(Option<Vec::<SegmentDetection>>):

    An array of segments detected in a video. The array is sorted by the segment types (TECHNICAL_CUE or SHOT) specified in the SegmentTypes input parameter of StartSegmentDetection. Within each segment type the array is sorted by timestamp values.

  • selected_segment_types(Option<Vec::<SegmentTypeInfo>>):

    An array containing the segment types requested in the call to StartSegmentDetection.

  • job_id(Option<String>):

    Job identifier for the segment detection operation for which you want to obtain results. The job identifer is returned by an initial call to StartSegmentDetection.

  • video(Option<Video>):

    Video file stored in an Amazon S3 bucket. Amazon Rekognition video start operations such as StartLabelDetection use Video to specify a video for analysis. The supported file formats are .mp4, .mov and .avi.

  • job_tag(Option<String>):

    A job identifier specified in the call to StartSegmentDetection and returned in the job completion notification sent to your Amazon Simple Notification Service topic.

• On failure, responds with SdkError<GetSegmentDetectionError>

impl Client

pub fn get_text_detection(&self) -> GetTextDetectionFluentBuilder

Constructs a fluent builder for the GetTextDetection operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • job_id(impl Into<String>) / set_job_id(Option<String>):
    required: true
    

    Job identifier for the text detection operation for which you want results returned. You get the job identifer from an initial call to StartTextDetection.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    Maximum number of results to return per paginated call. The largest value you can specify is 1000.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of text.

    
    
• On success, responds with GetTextDetectionOutput with field(s):
  • job_status(Option<VideoJobStatus>):

    Current status of the text detection job.

  • status_message(Option<String>):

    If the job fails, StatusMessage provides a descriptive error message.

  • video_metadata(Option<VideoMetadata>):

    Information about a video that Amazon Rekognition analyzed. Videometadata is returned in every page of paginated responses from a Amazon Rekognition video operation.

  • text_detections(Option<Vec::<TextDetectionResult>>):

    An array of text detected in the video. Each element contains the detected text, the time in milliseconds from the start of the video that the text was detected, and where it was detected on the screen.

  • next_token(Option<String>):

    If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of text.

  • text_model_version(Option<String>):

    Version number of the text detection model that was used to detect text.

  • job_id(Option<String>):

    Job identifier for the text detection operation for which you want to obtain results. The job identifer is returned by an initial call to StartTextDetection.

  • video(Option<Video>):

    Video file stored in an Amazon S3 bucket. Amazon Rekognition video start operations such as StartLabelDetection use Video to specify a video for analysis. The supported file formats are .mp4, .mov and .avi.

  • job_tag(Option<String>):

    A job identifier specified in the call to StartTextDetection and returned in the job completion notification sent to your Amazon Simple Notification Service topic.

• On failure, responds with SdkError<GetTextDetectionError>

impl Client

pub fn index_faces(&self) -> IndexFacesFluentBuilder

Constructs a fluent builder for the IndexFaces operation.

• The fluent builder is configurable:
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    The ID of an existing collection to which you want to add the faces that are detected in the input images.

    
    
  • image(Image) / set_image(Option<Image>):
    required: true
    

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes isn’t supported.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

    
    
  • external_image_id(impl Into<String>) / set_external_image_id(Option<String>):
    required: false
    

    The ID you want to assign to all the faces detected in the image.

    
    
  • detection_attributes(Attribute) / set_detection_attributes(Option<Vec::<Attribute>>):
    required: false
    

    An array of facial attributes you want to be returned. A DEFAULT subset of facial attributes - BoundingBox, Confidence, Pose, Quality, and Landmarks - will always be returned. You can request for specific facial attributes (in addition to the default list) - by using [“DEFAULT”, “FACE_OCCLUDED”] or just [“FACE_OCCLUDED”]. You can request for all facial attributes by using [“ALL”]. Requesting more attributes may increase response time.

    If you provide both, [“ALL”, “DEFAULT”], the service uses a logical AND operator to determine which attributes to return (in this case, all attributes).

    
    
  • max_faces(i32) / set_max_faces(Option<i32>):
    required: false
    

    The maximum number of faces to index. The value of MaxFaces must be greater than or equal to 1. IndexFaces returns no more than 100 detected faces in an image, even if you specify a larger value for MaxFaces.

    If IndexFaces detects more faces than the value of MaxFaces, the faces with the lowest quality are filtered out first. If there are still more faces than the value of MaxFaces, the faces with the smallest bounding boxes are filtered out (up to the number that’s needed to satisfy the value of MaxFaces). Information about the unindexed faces is available in the UnindexedFaces array.

    The faces that are returned by IndexFaces are sorted by the largest face bounding box size to the smallest size, in descending order.

    MaxFaces can be used with a collection associated with any version of the face model.

    
    
  • quality_filter(QualityFilter) / set_quality_filter(Option<QualityFilter>):
    required: false
    

    A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren’t indexed. If you specify AUTO, Amazon Rekognition chooses the quality bar. If you specify LOW, MEDIUM, or HIGH, filtering removes all faces that don’t meet the chosen quality bar. The default value is AUTO. The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that’s misidentified as a face, a face that’s too blurry, or a face with a pose that’s too extreme to use. If you specify NONE, no filtering is performed.

    To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.

    
    
• On success, responds with IndexFacesOutput with field(s):
  • face_records(Option<Vec::<FaceRecord>>):

    An array of faces detected and added to the collection. For more information, see Searching Faces in a Collection in the Amazon Rekognition Developer Guide.

  • orientation_correction(Option<OrientationCorrection>):

    If your collection is associated with a face detection model that’s later than version 3.0, the value of OrientationCorrection is always null and no orientation information is returned.

    If your collection is associated with a face detection model that’s version 3.0 or earlier, the following applies:

    • If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image’s orientation. Amazon Rekognition uses this orientation information to perform image correction - the bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don’t contain Exif metadata. The value of OrientationCorrection is null.

    • If the image doesn’t contain orientation information in its Exif metadata, Amazon Rekognition returns an estimated orientation (ROTATE_0, ROTATE_90, ROTATE_180, ROTATE_270). Amazon Rekognition doesn’t perform image correction for images. The bounding box coordinates aren’t translated and represent the object locations before the image is rotated.

    Bounding box information is returned in the FaceRecords array. You can get the version of the face detection model by calling DescribeCollection.

  • face_model_version(Option<String>):

    The version number of the face detection model that’s associated with the input collection (CollectionId).

  • unindexed_faces(Option<Vec::<UnindexedFace>>):

    An array of faces that were detected in the image but weren’t indexed. They weren’t indexed because the quality filter identified them as low quality, or the MaxFaces request parameter filtered them out. To use the quality filter, you specify the QualityFilter request parameter.

• On failure, responds with SdkError<IndexFacesError>

impl Client

pub fn list_collections(&self) -> ListCollectionsFluentBuilder

Constructs a fluent builder for the ListCollections operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    Pagination token from the previous response.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    Maximum number of collection IDs to return.

    
    
• On success, responds with ListCollectionsOutput with field(s):
  • collection_ids(Option<Vec::<String>>):

    An array of collection IDs.

  • next_token(Option<String>):

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

  • face_model_versions(Option<Vec::<String>>):

    Version numbers of the face detection models associated with the collections in the array CollectionIds. For example, the value of FaceModelVersions[2] is the version number for the face detection model used by the collection in CollectionId[2].

• On failure, responds with SdkError<ListCollectionsError>

impl Client

pub fn list_dataset_entries(&self) -> ListDatasetEntriesFluentBuilder

Constructs a fluent builder for the ListDatasetEntries operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) for the dataset that you want to use.

    
    
  • contains_labels(impl Into<String>) / set_contains_labels(Option<Vec::<String>>):
    required: false
    

    Specifies a label filter for the response. The response includes an entry only if one or more of the labels in ContainsLabels exist in the entry.

    
    
  • labeled(bool) / set_labeled(Option<bool>):
    required: false
    

    Specify true to get only the JSON Lines where the image is labeled. Specify false to get only the JSON Lines where the image isn’t labeled. If you don’t specify Labeled, ListDatasetEntries returns JSON Lines for labeled and unlabeled images.

    
    
  • source_ref_contains(impl Into<String>) / set_source_ref_contains(Option<String>):
    required: false
    

    If specified, ListDatasetEntries only returns JSON Lines where the value of SourceRefContains is part of the source-ref field. The source-ref field contains the Amazon S3 location of the image. You can use SouceRefContains for tasks such as getting the JSON Line for a single image, or gettting JSON Lines for all images within a specific folder.

    
    
  • has_errors(bool) / set_has_errors(Option<bool>):
    required: false
    

    Specifies an error filter for the response. Specify True to only include entries that have errors.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.

    
    
• On success, responds with ListDatasetEntriesOutput with field(s):
  • dataset_entries(Option<Vec::<String>>):

    A list of entries (images) in the dataset.

  • next_token(Option<String>):

    If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

• On failure, responds with SdkError<ListDatasetEntriesError>

impl Client

pub fn list_dataset_labels(&self) -> ListDatasetLabelsFluentBuilder

Constructs a fluent builder for the ListDatasetLabels operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the dataset that you want to use.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.

    
    
• On success, responds with ListDatasetLabelsOutput with field(s):
  • dataset_label_descriptions(Option<Vec::<DatasetLabelDescription>>):

    A list of the labels in the dataset.

  • next_token(Option<String>):

    If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

• On failure, responds with SdkError<ListDatasetLabelsError>

impl Client

pub fn list_faces(&self) -> ListFacesFluentBuilder

Constructs a fluent builder for the ListFaces operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    ID of the collection from which to list the faces.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the previous response was incomplete (because there is more data to retrieve), Amazon Rekognition returns a pagination token in the response. You can use this pagination token to retrieve the next set of faces.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    Maximum number of faces to return.

    
    
  • user_id(impl Into<String>) / set_user_id(Option<String>):
    required: false
    

    An array of user IDs to filter results with when listing faces in a collection.

    
    
  • face_ids(impl Into<String>) / set_face_ids(Option<Vec::<String>>):
    required: false
    

    An array of face IDs to filter results with when listing faces in a collection.

    
    
• On success, responds with ListFacesOutput with field(s):
  • faces(Option<Vec::<Face>>):

    An array of Face objects.

  • next_token(Option<String>):

    If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces.

  • face_model_version(Option<String>):

    Version number of the face detection model associated with the input collection (CollectionId).

• On failure, responds with SdkError<ListFacesError>

impl Client

pub fn list_media_analysis_jobs(&self) -> ListMediaAnalysisJobsFluentBuilder

Constructs a fluent builder for the ListMediaAnalysisJobs operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    Pagination token, if the previous response was incomplete.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum number of results to return per paginated call. The largest value user can specify is 100. If user specifies a value greater than 100, an InvalidParameterException error occurs. The default value is 100.

    
    
• On success, responds with ListMediaAnalysisJobsOutput with field(s):
  • next_token(Option<String>):

    Pagination token, if the previous response was incomplete.

  • media_analysis_jobs(Vec::<MediaAnalysisJobDescription>):

    Contains a list of all media analysis jobs.

• On failure, responds with SdkError<ListMediaAnalysisJobsError>

impl Client

pub fn list_project_policies(&self) -> ListProjectPoliciesFluentBuilder

Constructs a fluent builder for the ListProjectPolicies operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • project_arn(impl Into<String>) / set_project_arn(Option<String>):
    required: true
    

    The ARN of the project for which you want to list the project policies.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum number of results to return per paginated call. The largest value you can specify is 5. If you specify a value greater than 5, a ValidationException error occurs. The default value is 5.

    
    
• On success, responds with ListProjectPoliciesOutput with field(s):
  • project_policies(Option<Vec::<ProjectPolicy>>):

    A list of project policies attached to the project.

  • next_token(Option<String>):

    If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of project policies.

• On failure, responds with SdkError<ListProjectPoliciesError>

impl Client

pub fn list_stream_processors(&self) -> ListStreamProcessorsFluentBuilder

Constructs a fluent builder for the ListStreamProcessors operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the previous response was incomplete (because there are more stream processors to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of stream processors.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    Maximum number of stream processors you want Amazon Rekognition Video to return in the response. The default is 1000.

    
    
• On success, responds with ListStreamProcessorsOutput with field(s):
  • next_token(Option<String>):

    If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of stream processors.

  • stream_processors(Option<Vec::<StreamProcessor>>):

    List of stream processors that you have created.

• On failure, responds with SdkError<ListStreamProcessorsError>

impl Client

pub fn list_tags_for_resource(&self) -> ListTagsForResourceFluentBuilder

Constructs a fluent builder for the ListTagsForResource operation.

• The fluent builder is configurable:
  • resource_arn(impl Into<String>) / set_resource_arn(Option<String>):
    required: true
    

    Amazon Resource Name (ARN) of the model, collection, or stream processor that contains the tags that you want a list of.

    
    
• On success, responds with ListTagsForResourceOutput with field(s):
  • tags(Option<HashMap::<String, String>>):

    A list of key-value tags assigned to the resource.

• On failure, responds with SdkError<ListTagsForResourceError>

impl Client

pub fn list_users(&self) -> ListUsersFluentBuilder

Constructs a fluent builder for the ListUsers operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    The ID of an existing collection.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    Maximum number of UsersID to return.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    Pagingation token to receive the next set of UsersID.

    
    
• On success, responds with ListUsersOutput with field(s):
  • users(Option<Vec::<User>>):

    List of UsersID associated with the specified collection.

  • next_token(Option<String>):

    A pagination token to be used with the subsequent request if the response is truncated.

• On failure, responds with SdkError<ListUsersError>

impl Client

pub fn put_project_policy(&self) -> PutProjectPolicyFluentBuilder

Constructs a fluent builder for the PutProjectPolicy operation.

• The fluent builder is configurable:
  • project_arn(impl Into<String>) / set_project_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the project that the project policy is attached to.

    
    
  • policy_name(impl Into<String>) / set_policy_name(Option<String>):
    required: true
    

    A name for the policy.

    
    
  • policy_revision_id(impl Into<String>) / set_policy_revision_id(Option<String>):
    required: false
    

    The revision ID for the Project Policy. Each time you modify a policy, Amazon Rekognition Custom Labels generates and assigns a new PolicyRevisionId and then deletes the previous version of the policy.

    
    
  • policy_document(impl Into<String>) / set_policy_document(Option<String>):
    required: true
    

    A resource policy to add to the model. The policy is a JSON structure that contains one or more statements that define the policy. The policy must follow the IAM syntax. For more information about the contents of a JSON policy document, see IAM JSON policy reference.

    
    
• On success, responds with PutProjectPolicyOutput with field(s):
  • policy_revision_id(Option<String>):

    The ID of the project policy.

• On failure, responds with SdkError<PutProjectPolicyError>

impl Client

pub fn recognize_celebrities(&self) -> RecognizeCelebritiesFluentBuilder

Constructs a fluent builder for the RecognizeCelebrities operation.

• The fluent builder is configurable:
  • image(Image) / set_image(Option<Image>):
    required: true
    

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

    
    
• On success, responds with RecognizeCelebritiesOutput with field(s):
  • celebrity_faces(Option<Vec::<Celebrity>>):

    Details about each celebrity found in the image. Amazon Rekognition can detect a maximum of 64 celebrities in an image. Each celebrity object includes the following attributes: Face, Confidence, Emotions, Landmarks, Pose, Quality, Smile, Id, KnownGender, MatchConfidence, Name, Urls.

  • unrecognized_faces(Option<Vec::<ComparedFace>>):

    Details about each unrecognized face in the image.

  • orientation_correction(Option<OrientationCorrection>):

    Support for estimating image orientation using the the OrientationCorrection field has ceased as of August 2021. Any returned values for this field included in an API response will always be NULL.

    The orientation of the input image (counterclockwise direction). If your application displays the image, you can use this value to correct the orientation. The bounding box coordinates returned in CelebrityFaces and UnrecognizedFaces represent face locations before the image orientation is corrected.

    If the input image is in .jpeg format, it might contain exchangeable image (Exif) metadata that includes the image’s orientation. If so, and the Exif metadata for the input image populates the orientation field, the value of OrientationCorrection is null. The CelebrityFaces and UnrecognizedFaces bounding box coordinates represent face locations after Exif metadata is used to correct the image orientation. Images in .png format don’t contain Exif metadata.

• On failure, responds with SdkError<RecognizeCelebritiesError>

impl Client

pub fn search_faces(&self) -> SearchFacesFluentBuilder

Constructs a fluent builder for the SearchFaces operation.

• The fluent builder is configurable:
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    ID of the collection the face belongs to.

    
    
  • face_id(impl Into<String>) / set_face_id(Option<String>):
    required: true
    

    ID of a face to find matches for in the collection.

    
    
  • max_faces(i32) / set_max_faces(Option<i32>):
    required: false
    

    Maximum number of faces to return. The operation returns the maximum number of faces with the highest confidence in the match.

    
    
  • face_match_threshold(f32) / set_face_match_threshold(Option<f32>):
    required: false
    

    Optional value specifying the minimum confidence in the face match to return. For example, don’t return any matches where confidence in matches is less than 70%. The default value is 80%.

    
    
• On success, responds with SearchFacesOutput with field(s):
  • searched_face_id(Option<String>):

    ID of the face that was searched for matches in a collection.

  • face_matches(Option<Vec::<FaceMatch>>):

    An array of faces that matched the input face, along with the confidence in the match.

  • face_model_version(Option<String>):

    Version number of the face detection model associated with the input collection (CollectionId).

• On failure, responds with SdkError<SearchFacesError>

impl Client

pub fn search_faces_by_image(&self) -> SearchFacesByImageFluentBuilder

Constructs a fluent builder for the SearchFacesByImage operation.

• The fluent builder is configurable:
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    ID of the collection to search.

    
    
  • image(Image) / set_image(Option<Image>):
    required: true
    

    The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported.

    If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.

    
    
  • max_faces(i32) / set_max_faces(Option<i32>):
    required: false
    

    Maximum number of faces to return. The operation returns the maximum number of faces with the highest confidence in the match.

    
    
  • face_match_threshold(f32) / set_face_match_threshold(Option<f32>):
    required: false
    

    (Optional) Specifies the minimum confidence in the face match to return. For example, don’t return any matches where confidence in matches is less than 70%. The default value is 80%.

    
    
  • quality_filter(QualityFilter) / set_quality_filter(Option<QualityFilter>):
    required: false
    

    A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren’t searched for in the collection. If you specify AUTO, Amazon Rekognition chooses the quality bar. If you specify LOW, MEDIUM, or HIGH, filtering removes all faces that don’t meet the chosen quality bar. The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that’s misidentified as a face, a face that’s too blurry, or a face with a pose that’s too extreme to use. If you specify NONE, no filtering is performed. The default value is NONE.

    To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.

    
    
• On success, responds with SearchFacesByImageOutput with field(s):
  • searched_face_bounding_box(Option<BoundingBox>):

    The bounding box around the face in the input image that Amazon Rekognition used for the search.

  • searched_face_confidence(Option<f32>):

    The level of confidence that the searchedFaceBoundingBox, contains a face.

  • face_matches(Option<Vec::<FaceMatch>>):

    An array of faces that match the input face, along with the confidence in the match.

  • face_model_version(Option<String>):

    Version number of the face detection model associated with the input collection (CollectionId).

• On failure, responds with SdkError<SearchFacesByImageError>

impl Client

pub fn search_users(&self) -> SearchUsersFluentBuilder

Constructs a fluent builder for the SearchUsers operation.

• The fluent builder is configurable:
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    The ID of an existing collection containing the UserID, used with a UserId or FaceId. If a FaceId is provided, UserId isn’t required to be present in the Collection.

    
    
  • user_id(impl Into<String>) / set_user_id(Option<String>):
    required: false
    

    ID for the existing User.

    
    
  • face_id(impl Into<String>) / set_face_id(Option<String>):
    required: false
    

    ID for the existing face.

    
    
  • user_match_threshold(f32) / set_user_match_threshold(Option<f32>):
    required: false
    

    Optional value that specifies the minimum confidence in the matched UserID to return. Default value of 80.

    
    
  • max_users(i32) / set_max_users(Option<i32>):
    required: false
    

    Maximum number of identities to return.

    
    
• On success, responds with SearchUsersOutput with field(s):
  • user_matches(Option<Vec::<UserMatch>>):

    An array of UserMatch objects that matched the input face along with the confidence in the match. Array will be empty if there are no matches.

  • face_model_version(Option<String>):

    Version number of the face detection model associated with the input CollectionId.

  • searched_face(Option<SearchedFace>):

    Contains the ID of a face that was used to search for matches in a collection.

  • searched_user(Option<SearchedUser>):

    Contains the ID of the UserID that was used to search for matches in a collection.

• On failure, responds with SdkError<SearchUsersError>

impl Client

pub fn search_users_by_image(&self) -> SearchUsersByImageFluentBuilder

Constructs a fluent builder for the SearchUsersByImage operation.

• The fluent builder is configurable:
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    The ID of an existing collection containing the UserID.

    
    
  • image(Image) / set_image(Option<Image>):
    required: true
    

    Provides the input image either as bytes or an S3 object.

    You pass image bytes to an Amazon Rekognition API operation by using the Bytes property. For example, you would use the Bytes property to pass an image loaded from a local file system. Image bytes passed by using the Bytes property must be base64-encoded. Your code may not need to encode image bytes if you are using an AWS SDK to call Amazon Rekognition API operations.

    For more information, see Analyzing an Image Loaded from a Local File System in the Amazon Rekognition Developer Guide.

    You pass images stored in an S3 bucket to an Amazon Rekognition API operation by using the S3Object property. Images stored in an S3 bucket do not need to be base64-encoded.

    The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.

    If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes using the Bytes property is not supported. You must first upload the image to an Amazon S3 bucket and then call the operation using the S3Object property.

    For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.

    
    
  • user_match_threshold(f32) / set_user_match_threshold(Option<f32>):
    required: false
    

    Specifies the minimum confidence in the UserID match to return. Default value is 80.

    
    
  • max_users(i32) / set_max_users(Option<i32>):
    required: false
    

    Maximum number of UserIDs to return.

    
    
  • quality_filter(QualityFilter) / set_quality_filter(Option<QualityFilter>):
    required: false
    

    A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren’t searched for in the collection. The default value is NONE.

    
    
• On success, responds with SearchUsersByImageOutput with field(s):
  • user_matches(Option<Vec::<UserMatch>>):

    An array of UserID objects that matched the input face, along with the confidence in the match. The returned structure will be empty if there are no matches. Returned if the SearchUsersByImageResponse action is successful.

  • face_model_version(Option<String>):

    Version number of the face detection model associated with the input collection CollectionId.

  • searched_face(Option<SearchedFaceDetails>):

    A list of FaceDetail objects containing the BoundingBox for the largest face in image, as well as the confidence in the bounding box, that was searched for matches. If no valid face is detected in the image the response will contain no SearchedFace object.

  • unsearched_faces(Option<Vec::<UnsearchedFace>>):

    List of UnsearchedFace objects. Contains the face details infered from the specified image but not used for search. Contains reasons that describe why a face wasn’t used for Search.

• On failure, responds with SdkError<SearchUsersByImageError>

impl Client

pub fn start_celebrity_recognition( &self, ) -> StartCelebrityRecognitionFluentBuilder

Constructs a fluent builder for the StartCelebrityRecognition operation.

• The fluent builder is configurable:
  • video(Video) / set_video(Option<Video>):
    required: true
    

    The video in which you want to recognize celebrities. The video must be stored in an Amazon S3 bucket.

    
    
  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):
    required: false
    

    Idempotent token used to identify the start request. If you use the same token with multiple StartCelebrityRecognition requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.

    
    
  • notification_channel(NotificationChannel) / set_notification_channel(Option<NotificationChannel>):
    required: false
    

    The Amazon SNS topic ARN that you want Amazon Rekognition Video to publish the completion status of the celebrity recognition analysis to. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy.

    
    
  • job_tag(impl Into<String>) / set_job_tag(Option<String>):
    required: false
    

    An identifier you specify that’s returned in the completion notification that’s published to your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.

    
    
• On success, responds with StartCelebrityRecognitionOutput with field(s):
  • job_id(Option<String>):

    The identifier for the celebrity recognition analysis job. Use JobId to identify the job in a subsequent call to GetCelebrityRecognition.

• On failure, responds with SdkError<StartCelebrityRecognitionError>

impl Client

pub fn start_content_moderation(&self) -> StartContentModerationFluentBuilder

Constructs a fluent builder for the StartContentModeration operation.

• The fluent builder is configurable:
  • video(Video) / set_video(Option<Video>):
    required: true
    

    The video in which you want to detect inappropriate, unwanted, or offensive content. The video must be stored in an Amazon S3 bucket.

    
    
  • min_confidence(f32) / set_min_confidence(Option<f32>):
    required: false
    

    Specifies the minimum confidence that Amazon Rekognition must have in order to return a moderated content label. Confidence represents how certain Amazon Rekognition is that the moderated content is correctly identified. 0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition doesn’t return any moderated content labels with a confidence level lower than this specified value. If you don’t specify MinConfidence, GetContentModeration returns labels with confidence values greater than or equal to 50 percent.

    
    
  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):
    required: false
    

    Idempotent token used to identify the start request. If you use the same token with multiple StartContentModeration requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.

    
    
  • notification_channel(NotificationChannel) / set_notification_channel(Option<NotificationChannel>):
    required: false
    

    The Amazon SNS topic ARN that you want Amazon Rekognition Video to publish the completion status of the content analysis to. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy to access the topic.

    
    
  • job_tag(impl Into<String>) / set_job_tag(Option<String>):
    required: false
    

    An identifier you specify that’s returned in the completion notification that’s published to your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.

    
    
• On success, responds with StartContentModerationOutput with field(s):
  • job_id(Option<String>):

    The identifier for the content analysis job. Use JobId to identify the job in a subsequent call to GetContentModeration.

• On failure, responds with SdkError<StartContentModerationError>

impl Client

pub fn start_face_detection(&self) -> StartFaceDetectionFluentBuilder

Constructs a fluent builder for the StartFaceDetection operation.

• The fluent builder is configurable:
  • video(Video) / set_video(Option<Video>):
    required: true
    

    The video in which you want to detect faces. The video must be stored in an Amazon S3 bucket.

    
    
  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):
    required: false
    

    Idempotent token used to identify the start request. If you use the same token with multiple StartFaceDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.

    
    
  • notification_channel(NotificationChannel) / set_notification_channel(Option<NotificationChannel>):
    required: false
    

    The ARN of the Amazon SNS topic to which you want Amazon Rekognition Video to publish the completion status of the face detection operation. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy.

    
    
  • face_attributes(FaceAttributes) / set_face_attributes(Option<FaceAttributes>):
    required: false
    

    The face attributes you want returned.

    DEFAULT - The following subset of facial attributes are returned: BoundingBox, Confidence, Pose, Quality and Landmarks.

    ALL - All facial attributes are returned.

    
    
  • job_tag(impl Into<String>) / set_job_tag(Option<String>):
    required: false
    

    An identifier you specify that’s returned in the completion notification that’s published to your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.

    
    
• On success, responds with StartFaceDetectionOutput with field(s):
  • job_id(Option<String>):

    The identifier for the face detection job. Use JobId to identify the job in a subsequent call to GetFaceDetection.

• On failure, responds with SdkError<StartFaceDetectionError>

impl Client

pub fn start_face_search(&self) -> StartFaceSearchFluentBuilder

Constructs a fluent builder for the StartFaceSearch operation.

• The fluent builder is configurable:
  • video(Video) / set_video(Option<Video>):
    required: true
    

    The video you want to search. The video must be stored in an Amazon S3 bucket.

    
    
  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):
    required: false
    

    Idempotent token used to identify the start request. If you use the same token with multiple StartFaceSearch requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.

    
    
  • face_match_threshold(f32) / set_face_match_threshold(Option<f32>):
    required: false
    

    The minimum confidence in the person match to return. For example, don’t return any matches where confidence in matches is less than 70%. The default value is 80%.

    
    
  • collection_id(impl Into<String>) / set_collection_id(Option<String>):
    required: true
    

    ID of the collection that contains the faces you want to search for.

    
    
  • notification_channel(NotificationChannel) / set_notification_channel(Option<NotificationChannel>):
    required: false
    

    The ARN of the Amazon SNS topic to which you want Amazon Rekognition Video to publish the completion status of the search. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy to access the topic.

    
    
  • job_tag(impl Into<String>) / set_job_tag(Option<String>):
    required: false
    

    An identifier you specify that’s returned in the completion notification that’s published to your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.

    
    
• On success, responds with StartFaceSearchOutput with field(s):
  • job_id(Option<String>):

    The identifier for the search job. Use JobId to identify the job in a subsequent call to GetFaceSearch.

• On failure, responds with SdkError<StartFaceSearchError>

impl Client

pub fn start_label_detection(&self) -> StartLabelDetectionFluentBuilder

Constructs a fluent builder for the StartLabelDetection operation.

• The fluent builder is configurable:
  • video(Video) / set_video(Option<Video>):
    required: true
    

    The video in which you want to detect labels. The video must be stored in an Amazon S3 bucket.

    
    
  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):
    required: false
    

    Idempotent token used to identify the start request. If you use the same token with multiple StartLabelDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.

    
    
  • min_confidence(f32) / set_min_confidence(Option<f32>):
    required: false
    

    Specifies the minimum confidence that Amazon Rekognition Video must have in order to return a detected label. Confidence represents how certain Amazon Rekognition is that a label is correctly identified.0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition Video doesn’t return any labels with a confidence level lower than this specified value.

    If you don’t specify MinConfidence, the operation returns labels and bounding boxes (if detected) with confidence values greater than or equal to 50 percent.

    
    
  • notification_channel(NotificationChannel) / set_notification_channel(Option<NotificationChannel>):
    required: false
    

    The Amazon SNS topic ARN you want Amazon Rekognition Video to publish the completion status of the label detection operation to. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy.

    
    
  • job_tag(impl Into<String>) / set_job_tag(Option<String>):
    required: false
    

    An identifier you specify that’s returned in the completion notification that’s published to your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.

    
    
  • features(LabelDetectionFeatureName) / set_features(Option<Vec::<LabelDetectionFeatureName>>):
    required: false
    

    The features to return after video analysis. You can specify that GENERAL_LABELS are returned.

    
    
  • settings(LabelDetectionSettings) / set_settings(Option<LabelDetectionSettings>):
    required: false
    

    The settings for a StartLabelDetection request.Contains the specified parameters for the label detection request of an asynchronous label analysis operation. Settings can include filters for GENERAL_LABELS.

    
    
• On success, responds with StartLabelDetectionOutput with field(s):
  • job_id(Option<String>):

    The identifier for the label detection job. Use JobId to identify the job in a subsequent call to GetLabelDetection.

• On failure, responds with SdkError<StartLabelDetectionError>

impl Client

pub fn start_media_analysis_job(&self) -> StartMediaAnalysisJobFluentBuilder

Constructs a fluent builder for the StartMediaAnalysisJob operation.

• The fluent builder is configurable:
  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):
    required: false
    

    Idempotency token used to prevent the accidental creation of duplicate versions. If you use the same token with multiple StartMediaAnalysisJobRequest requests, the same response is returned. Use ClientRequestToken to prevent the same request from being processed more than once.

    
    
  • job_name(impl Into<String>) / set_job_name(Option<String>):
    required: false
    

    The name of the job. Does not have to be unique.

    
    
  • operations_config(MediaAnalysisOperationsConfig) / set_operations_config(Option<MediaAnalysisOperationsConfig>):
    required: true
    

    Configuration options for the media analysis job to be created.

    
    
  • input(MediaAnalysisInput) / set_input(Option<MediaAnalysisInput>):
    required: true
    

    Input data to be analyzed by the job.

    
    
  • output_config(MediaAnalysisOutputConfig) / set_output_config(Option<MediaAnalysisOutputConfig>):
    required: true
    

    The Amazon S3 bucket location to store the results.

    
    
  • kms_key_id(impl Into<String>) / set_kms_key_id(Option<String>):
    required: false
    

    The identifier of customer managed AWS KMS key (name or ARN). The key is used to encrypt images copied into the service. The key is also used to encrypt results and manifest files written to the output Amazon S3 bucket.

    
    
• On success, responds with StartMediaAnalysisJobOutput with field(s):
  • job_id(String):

    Identifier for the created job.

• On failure, responds with SdkError<StartMediaAnalysisJobError>

impl Client

pub fn start_person_tracking(&self) -> StartPersonTrackingFluentBuilder

Constructs a fluent builder for the StartPersonTracking operation.

• The fluent builder is configurable:
  • video(Video) / set_video(Option<Video>):
    required: true
    

    The video in which you want to detect people. The video must be stored in an Amazon S3 bucket.

    
    
  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):
    required: false
    

    Idempotent token used to identify the start request. If you use the same token with multiple StartPersonTracking requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.

    
    
  • notification_channel(NotificationChannel) / set_notification_channel(Option<NotificationChannel>):
    required: false
    

    The Amazon SNS topic ARN you want Amazon Rekognition Video to publish the completion status of the people detection operation to. The Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy.

    
    
  • job_tag(impl Into<String>) / set_job_tag(Option<String>):
    required: false
    

    An identifier you specify that’s returned in the completion notification that’s published to your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.

    
    
• On success, responds with StartPersonTrackingOutput with field(s):
  • job_id(Option<String>):

    The identifier for the person detection job. Use JobId to identify the job in a subsequent call to GetPersonTracking.

• On failure, responds with SdkError<StartPersonTrackingError>

impl Client

pub fn start_project_version(&self) -> StartProjectVersionFluentBuilder

Constructs a fluent builder for the StartProjectVersion operation.

• The fluent builder is configurable:
  • project_version_arn(impl Into<String>) / set_project_version_arn(Option<String>):
    required: true
    

    The Amazon Resource Name(ARN) of the model version that you want to start.

    
    
  • min_inference_units(i32) / set_min_inference_units(Option<i32>):
    required: true
    

    The minimum number of inference units to use. A single inference unit represents 1 hour of processing.

    Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use.

    
    
  • max_inference_units(i32) / set_max_inference_units(Option<i32>):
    required: false
    

    The maximum number of inference units to use for auto-scaling the model. If you don’t specify a value, Amazon Rekognition Custom Labels doesn’t auto-scale the model.

    
    
• On success, responds with StartProjectVersionOutput with field(s):
  • status(Option<ProjectVersionStatus>):

    The current running status of the model.

• On failure, responds with SdkError<StartProjectVersionError>

impl Client

pub fn start_segment_detection(&self) -> StartSegmentDetectionFluentBuilder

Constructs a fluent builder for the StartSegmentDetection operation.

• The fluent builder is configurable:
  • video(Video) / set_video(Option<Video>):
    required: true
    

    Video file stored in an Amazon S3 bucket. Amazon Rekognition video start operations such as StartLabelDetection use Video to specify a video for analysis. The supported file formats are .mp4, .mov and .avi.

    
    
  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):
    required: false
    

    Idempotent token used to identify the start request. If you use the same token with multiple StartSegmentDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidently started more than once.

    
    
  • notification_channel(NotificationChannel) / set_notification_channel(Option<NotificationChannel>):
    required: false
    

    The ARN of the Amazon SNS topic to which you want Amazon Rekognition Video to publish the completion status of the segment detection operation. Note that the Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy to access the topic.

    
    
  • job_tag(impl Into<String>) / set_job_tag(Option<String>):
    required: false
    

    An identifier you specify that’s returned in the completion notification that’s published to your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.

    
    
  • filters(StartSegmentDetectionFilters) / set_filters(Option<StartSegmentDetectionFilters>):
    required: false
    

    Filters for technical cue or shot detection.

    
    
  • segment_types(SegmentType) / set_segment_types(Option<Vec::<SegmentType>>):
    required: true
    

    An array of segment types to detect in the video. Valid values are TECHNICAL_CUE and SHOT.

    
    
• On success, responds with StartSegmentDetectionOutput with field(s):
  • job_id(Option<String>):

    Unique identifier for the segment detection job. The JobId is returned from StartSegmentDetection.

• On failure, responds with SdkError<StartSegmentDetectionError>

impl Client

pub fn start_stream_processor(&self) -> StartStreamProcessorFluentBuilder

Constructs a fluent builder for the StartStreamProcessor operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of the stream processor to start processing.

    
    
  • start_selector(StreamProcessingStartSelector) / set_start_selector(Option<StreamProcessingStartSelector>):
    required: false
    

    Specifies the starting point in the Kinesis stream to start processing. You can use the producer timestamp or the fragment number. If you use the producer timestamp, you must put the time in milliseconds. For more information about fragment numbers, see Fragment.

    This is a required parameter for label detection stream processors and should not be used to start a face search stream processor.

    
    
  • stop_selector(StreamProcessingStopSelector) / set_stop_selector(Option<StreamProcessingStopSelector>):
    required: false
    

    Specifies when to stop processing the stream. You can specify a maximum amount of time to process the video.

    This is a required parameter for label detection stream processors and should not be used to start a face search stream processor.

    
    
• On success, responds with StartStreamProcessorOutput with field(s):
  • session_id(Option<String>):

    A unique identifier for the stream processing session.

• On failure, responds with SdkError<StartStreamProcessorError>

impl Client

pub fn start_text_detection(&self) -> StartTextDetectionFluentBuilder

Constructs a fluent builder for the StartTextDetection operation.

• The fluent builder is configurable:
  • video(Video) / set_video(Option<Video>):
    required: true
    

    Video file stored in an Amazon S3 bucket. Amazon Rekognition video start operations such as StartLabelDetection use Video to specify a video for analysis. The supported file formats are .mp4, .mov and .avi.

    
    
  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):
    required: false
    

    Idempotent token used to identify the start request. If you use the same token with multiple StartTextDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidentaly started more than once.

    
    
  • notification_channel(NotificationChannel) / set_notification_channel(Option<NotificationChannel>):
    required: false
    

    The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the completion status of a video analysis operation. For more information, see Calling Amazon Rekognition Video operations. Note that the Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy to access the topic. For more information, see Giving access to multiple Amazon SNS topics.

    
    
  • job_tag(impl Into<String>) / set_job_tag(Option<String>):
    required: false
    

    An identifier returned in the completion status published by your Amazon Simple Notification Service topic. For example, you can use JobTag to group related jobs and identify them in the completion notification.

    
    
  • filters(StartTextDetectionFilters) / set_filters(Option<StartTextDetectionFilters>):
    required: false
    

    Optional parameters that let you set criteria the text must meet to be included in your response.

    
    
• On success, responds with StartTextDetectionOutput with field(s):
  • job_id(Option<String>):

    Identifier for the text detection job. Use JobId to identify the job in a subsequent call to GetTextDetection.

• On failure, responds with SdkError<StartTextDetectionError>

impl Client

pub fn stop_project_version(&self) -> StopProjectVersionFluentBuilder

Constructs a fluent builder for the StopProjectVersion operation.

• The fluent builder is configurable:
  • project_version_arn(impl Into<String>) / set_project_version_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the model version that you want to stop.

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

    
    
• On success, responds with StopProjectVersionOutput with field(s):
  • status(Option<ProjectVersionStatus>):

    The current status of the stop operation.

• On failure, responds with SdkError<StopProjectVersionError>

impl Client

pub fn stop_stream_processor(&self) -> StopStreamProcessorFluentBuilder

Constructs a fluent builder for the StopStreamProcessor operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of a stream processor created by CreateStreamProcessor.

    
    
• On success, responds with StopStreamProcessorOutput
• On failure, responds with SdkError<StopStreamProcessorError>

impl Client

pub fn tag_resource(&self) -> TagResourceFluentBuilder

Constructs a fluent builder for the TagResource operation.

• The fluent builder is configurable:
  • resource_arn(impl Into<String>) / set_resource_arn(Option<String>):
    required: true
    

    Amazon Resource Name (ARN) of the model, collection, or stream processor that you want to assign the tags to.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: true
    

    The key-value tags to assign to the resource.

    
    
• On success, responds with TagResourceOutput
• On failure, responds with SdkError<TagResourceError>

impl Client

pub fn untag_resource(&self) -> UntagResourceFluentBuilder

Constructs a fluent builder for the UntagResource operation.

• The fluent builder is configurable:
  • resource_arn(impl Into<String>) / set_resource_arn(Option<String>):
    required: true
    

    Amazon Resource Name (ARN) of the model, collection, or stream processor that you want to remove the tags from.

    
    
  • tag_keys(impl Into<String>) / set_tag_keys(Option<Vec::<String>>):
    required: true
    

    A list of the tags that you want to remove.

    
    
• On success, responds with UntagResourceOutput
• On failure, responds with SdkError<UntagResourceError>

impl Client

pub fn update_dataset_entries(&self) -> UpdateDatasetEntriesFluentBuilder

Constructs a fluent builder for the UpdateDatasetEntries operation.

• The fluent builder is configurable:
  • dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the dataset that you want to update.

    
    
  • changes(DatasetChanges) / set_changes(Option<DatasetChanges>):
    required: true
    

    The changes that you want to make to the dataset.

    
    
• On success, responds with UpdateDatasetEntriesOutput
• On failure, responds with SdkError<UpdateDatasetEntriesError>

impl Client

pub fn update_stream_processor(&self) -> UpdateStreamProcessorFluentBuilder

Constructs a fluent builder for the UpdateStreamProcessor operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    Name of the stream processor that you want to update.

    
    
  • settings_for_update(StreamProcessorSettingsForUpdate) / set_settings_for_update(Option<StreamProcessorSettingsForUpdate>):
    required: false
    

    The stream processor settings that you want to update. Label detection settings can be updated to detect different labels with a different minimum confidence.

    
    
  • regions_of_interest_for_update(RegionOfInterest) / set_regions_of_interest_for_update(Option<Vec::<RegionOfInterest>>):
    required: false
    

    Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors.

    
    
  • data_sharing_preference_for_update(StreamProcessorDataSharingPreference) / set_data_sharing_preference_for_update(Option<StreamProcessorDataSharingPreference>):
    required: false
    

    Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams.

    
    
  • parameters_to_delete(StreamProcessorParameterToDelete) / set_parameters_to_delete(Option<Vec::<StreamProcessorParameterToDelete>>):
    required: false
    

    A list of parameters you want to delete from the stream processor.

    
    
• On success, responds with UpdateStreamProcessorOutput
• On failure, responds with SdkError<UpdateStreamProcessorError>

impl Client

pub fn from_conf(conf: Config) -> Self

Creates a new client from the service Config.

Panics

This method will panic in the following cases:

• Retries or timeouts are enabled without a sleep_impl configured.
• Identity caching is enabled without a sleep_impl and time_source configured.
• No behavior_version is provided.

The panic message for each of these will have instructions on how to resolve them.

pub fn config(&self) -> &Config

Returns the client’s configuration.

impl Client

pub fn new(sdk_config: &SdkConfig) -> Self

Creates a new client from an SDK Config.

Panics

• This method will panic if the sdk_config is missing an async sleep implementation. If you experience this panic, set the sleep_impl on the Config passed into this function to fix it.
• This method will panic if the sdk_config is missing an HTTP connector. If you experience this panic, set the http_connector on the Config passed into this function to fix it.
• This method will panic if no BehaviorVersion is provided. If you experience this panic, set behavior_version on the Config or enable the behavior-version-latest Cargo feature.

Trait Implementations

impl Clone for Client

fn clone(&self) -> Client

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Client

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Waiters for Client

fn wait_until_project_version_running( &self, ) -> ProjectVersionRunningFluentBuilder

Wait until the ProjectVersion is running.

fn wait_until_project_version_training_completed( &self, ) -> ProjectVersionTrainingCompletedFluentBuilder

Wait until the ProjectVersion training completes.