Rekognition

associate_faces(collection_id, face_ids, user_id)
associate_faces(collection_id, face_ids, user_id, params::Dict{String,<:Any})

Associates one or more faces with an existing UserID. Takes an array of FaceIds. Each FaceId that are present in the FaceIds list is associated with the provided UserID. The maximum number of total FaceIds per UserID is 100. The UserMatchThreshold parameter specifies the minimum user match confidence required for the face to be associated with a UserID that has at least one FaceID already associated. This ensures that the FaceIds are associated with the right UserID. The value ranges from 0-100 and default value is 75. If successful, an array of AssociatedFace objects containing the associated FaceIds is returned. If a given face is already associated with the given UserID, it will be ignored and will not be returned in the response. If a given face is already associated to a different UserID, isn't found in the collection, doesn’t meet the UserMatchThreshold, or there are already 100 faces associated with the UserID, it will be returned as part of an array of UnsuccessfulFaceAssociations. The UserStatus reflects the status of an operation which updates a UserID representation with a list of given faces. The UserStatus can be: ACTIVE - All associations or disassociations of FaceID(s) for a UserID are complete. CREATED - A UserID has been created, but has no FaceID(s) associated with it. UPDATING - A UserID is being updated and there are current associations or disassociations of FaceID(s) taking place.

Arguments

• collection_id: The ID of an existing collection containing the UserID.
• face_ids: An array of FaceIDs to associate with the UserID.
• user_id: The ID for the existing UserID.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "ClientRequestToken": 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.
• "UserMatchThreshold": An optional value specifying the minimum confidence in the UserID match to return. The default value is 75.

compare_faces(source_image, target_image)
compare_faces(source_image, target_image, params::Dict{String,<:Any})

Compares a face in the source input image with each of the 100 largest faces detected in the target input image. If the source image contains multiple faces, the service detects the largest face and compares it with each face detected in the target image. CompareFaces uses machine learning algorithms, which are probabilistic. A false negative is an incorrect prediction that a face in the target image has a low similarity confidence score when compared to the face in the source image. To reduce the probability of false negatives, we recommend that you compare the target image against multiple source images. If you plan to use CompareFaces to make a decision that impacts an individual's rights, privacy, or access to services, we recommend that you pass the result to a human for review and further validation before taking action. You pass the input and target images either as base64-encoded image bytes or as references to images in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file. In response, the operation returns an array of face matches ordered by similarity score in descending order. For each face match, the response provides a bounding box of the face, facial landmarks, pose details (pitch, roll, and yaw), quality (brightness and sharpness), and confidence value (indicating the level of confidence that the bounding box contains a face). The response also provides a similarity score, which indicates how closely the faces match. By default, only faces with a similarity score of greater than or equal to 80% are returned in the response. You can change this value by specifying the SimilarityThreshold parameter. CompareFaces also returns an array of faces that don't match the source image. For each face, it returns a bounding box, confidence value, landmarks, pose details, and quality. The response also returns information about the face in the source image, including the bounding box of the face and confidence value. The QualityFilter input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. Use QualityFilter to set the quality bar by specifying LOW, MEDIUM, or HIGH. If you do not want to filter detected faces, specify NONE. The default value is NONE. If the image doesn't contain Exif metadata, CompareFaces returns orientation information for the source and target images. Use these values to display the images with the correct image orientation. If no faces are detected in the source or target images, CompareFaces returns an InvalidParameterException error. This is a stateless API operation. That is, data returned by this operation doesn't persist. For an example, see Comparing Faces in Images in the Amazon Rekognition Developer Guide. This operation requires permissions to perform the rekognition:CompareFaces action.

Arguments

• source_image: 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: 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.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

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

copy_project_version(destination_project_arn, output_config, source_project_arn, source_project_version_arn, version_name)
copy_project_version(destination_project_arn, output_config, source_project_arn, source_project_version_arn, version_name, params::Dict{String,<:Any})

Copies a version of an Amazon Rekognition Custom Labels model from a source project to a destination project. The source and destination projects can be in different AWS accounts but must be in the same AWS Region. You can't copy a model to another AWS service. To copy a model version to a different AWS account, you need to create a resource-based policy known as a project policy. You attach the project policy to the source project by calling PutProjectPolicy. The project policy gives permission to copy the model version from a trusting AWS account to a trusted account. For more information creating and attaching a project policy, see Attaching a project policy (SDK) in the Amazon Rekognition Custom Labels Developer Guide. If you are copying a model version to a project in the same AWS account, you don't need to create a project policy. To copy a model, the destination project, source project, and source model version must already exist. Copying a model version takes a while to complete. To get the current status, call DescribeProjectVersions and check the value of Status in the ProjectVersionDescription object. The copy operation has finished when the value of Status is COPYING_COMPLETED. This operation requires permissions to perform the rekognition:CopyProjectVersion action.

Arguments

• destination_project_arn: The ARN of the project in the trusted AWS account that you want to copy the model version to.
• output_config: The S3 bucket and folder location where the training output for the source model version is placed.
• source_project_arn: The ARN of the source project in the trusting AWS account.
• source_project_version_arn: The ARN of the model version in the source project that you want to copy to a destination project.
• version_name: A name for the version of the model that's copied to the destination project.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "KmsKeyId": 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.
• "Tags": The key-value tags to assign to the model version.

create_collection(collection_id)
create_collection(collection_id, params::Dict{String,<:Any})

Creates a collection in an AWS Region. You can add faces to the collection using the IndexFaces operation. For example, you might create collections, one for each of your application users. A user can then index faces using the IndexFaces operation and persist results in a specific collection. Then, a user can search the collection for faces in the user-specific container. When you create a collection, it is associated with the latest version of the face model version. Collection names are case-sensitive. This operation requires permissions to perform the rekognition:CreateCollection action. If you want to tag your collection, you also require permission to perform the rekognition:TagResource operation.

Arguments

• collection_id: ID for the collection that you are creating.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

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

create_dataset(dataset_type, project_arn)
create_dataset(dataset_type, project_arn, params::Dict{String,<:Any})

Creates a new Amazon Rekognition Custom Labels dataset. You can create a dataset by using an Amazon Sagemaker format manifest file or by copying an existing Amazon Rekognition Custom Labels dataset. To create a training dataset for a project, specify train for the value of DatasetType. To create the test dataset for a project, specify test for the value of DatasetType. The response from CreateDataset is the Amazon Resource Name (ARN) for the dataset. Creating a dataset takes a while to complete. Use DescribeDataset to check the current status. The dataset created successfully if the value of Status is CREATECOMPLETE. To check if any non-terminal errors occurred, call ListDatasetEntries and check for the presence of errors lists in the JSON Lines. Dataset creation fails if a terminal error occurs (Status = CREATEFAILED). Currently, you can't access the terminal error information. For more information, see Creating dataset in the Amazon Rekognition Custom Labels Developer Guide. This operation requires permissions to perform the rekognition:CreateDataset action. If you want to copy an existing dataset, you also require permission to perform the rekognition:ListDatasetEntries action.

Arguments

• dataset_type: The type of the dataset. Specify train to create a training dataset. Specify test to create a test dataset.
• project_arn: The ARN of the Amazon Rekognition Custom Labels project to which you want to asssign the dataset.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "DatasetSource": 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.

create_face_liveness_session()
create_face_liveness_session(params::Dict{String,<:Any})

This API operation initiates a Face Liveness session. It returns a SessionId, which you can use to start streaming Face Liveness video and get the results for a Face Liveness session. You can use the OutputConfig option in the Settings parameter to provide an Amazon S3 bucket location. The Amazon S3 bucket stores reference images and audit images. You can use AuditImagesLimit to limit the number of audit images returned. This number is between 0 and

1. By default, it is set to 0. The limit is best effort and based on the duration of the

selfie-video.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "ClientRequestToken": 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.
• "KmsKeyId": The identifier for your AWS Key Management Service key (AWS KMS key). Used to encrypt audit images and reference images.
• "Settings": A session settings object. It contains settings for the operation to be performed. For Face Liveness, it accepts OutputConfig and AuditImagesLimit.

create_project(project_name)
create_project(project_name, params::Dict{String,<:Any})

Creates a new Amazon Rekognition Custom Labels project. A project is a group of resources (datasets, model versions) that you use to create and manage Amazon Rekognition Custom Labels models. This operation requires permissions to perform the rekognition:CreateProject action.

Arguments

• project_name: The name of the project to create.

create_project_version(output_config, project_arn, version_name)
create_project_version(output_config, project_arn, version_name, params::Dict{String,<:Any})

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

Arguments

• output_config: The Amazon S3 bucket location to store the results of training. The S3 bucket can be in any AWS account as long as the caller has s3:PutObject permissions on the S3 bucket.
• project_arn: The ARN of the Amazon Rekognition Custom Labels project that manages the model that you want to train.
• version_name: A name for the version of the model. This value must be unique.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "KmsKeyId": 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 and test images copied into the service for model training. 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.
• "Tags": A set of tags (key-value pairs) that you want to attach to the model.
• "TestingData": Specifies an external manifest that the service uses to test the model. If you specify TestingData you must also specify TrainingData. The project must not have any associated datasets.
• "TrainingData": Specifies an external manifest that the services uses to train the model. If you specify TrainingData you must also specify TestingData. The project must not have any associated datasets.

create_stream_processor(input, name, output, role_arn, settings)
create_stream_processor(input, name, output, role_arn, settings, params::Dict{String,<:Any})

Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces or to detect labels in a streaming video. Amazon Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. There are two different settings for stream processors in Amazon Rekognition: detecting faces and detecting labels. If you are creating a stream processor for detecting faces, you provide as input a Kinesis video stream (Input) and a Kinesis data stream (Output) stream for receiving the output. You must use the FaceSearch option in Settings, specifying the collection that contains the faces you want to recognize. After you have finished analyzing a streaming video, use StopStreamProcessor to stop processing. If you are creating a stream processor to detect labels, you provide as input a Kinesis video stream (Input), Amazon S3 bucket information (Output), and an Amazon SNS topic ARN (NotificationChannel). You can also provide a KMS key ID to encrypt the data sent to your Amazon S3 bucket. You specify what you want to detect by using the ConnectedHome option in settings, and selecting one of the following: PERSON, PET, PACKAGE, ALL You can also specify where in the frame you want Amazon Rekognition to monitor with RegionsOfInterest. When you run the StartStreamProcessor operation on a label detection stream processor, you input start and stop information to determine the length of the processing time. Use Name to assign an identifier for the stream processor. You use Name to manage the stream processor. For example, you can start processing the source video by calling StartStreamProcessor with the Name field. This operation requires permissions to perform the rekognition:CreateStreamProcessor action. If you want to tag your stream processor, you also require permission to perform the rekognition:TagResource operation.

Arguments

• input: 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.
• name: 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.
• output: 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.
• role_arn: 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.
• settings: 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.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "DataSharingPreference": 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.
• "KmsKeyId": 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.
• "NotificationChannel":
• "RegionsOfInterest": 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.
• "Tags": A set of tags (key-value pairs) that you want to attach to the stream processor.

create_user(collection_id, user_id)
create_user(collection_id, user_id, params::Dict{String,<:Any})

Creates a new User within a collection specified by CollectionId. Takes UserId as a parameter, which is a user provided ID which should be unique within the collection. The provided UserId will alias the system generated UUID to make the UserId more user friendly. Uses a ClientToken, an idempotency token that ensures a call to CreateUser completes only once. If the value is not supplied, the AWS SDK generates an idempotency token for the requests. This prevents retries after a network error results from making multiple CreateUser calls.

Arguments

• collection_id: The ID of an existing collection to which the new UserID needs to be created.
• user_id: ID for the UserID to be created. This ID needs to be unique within the collection.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "ClientRequestToken": 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.

delete_collection(collection_id)
delete_collection(collection_id, params::Dict{String,<:Any})

Deletes the specified collection. Note that this operation removes all faces in the collection. For an example, see Deleting a collection. This operation requires permissions to perform the rekognition:DeleteCollection action.

Arguments

• collection_id: ID of the collection to delete.

delete_dataset(dataset_arn)
delete_dataset(dataset_arn, params::Dict{String,<:Any})

Deletes an existing Amazon Rekognition Custom Labels dataset. Deleting a dataset might take while. Use DescribeDataset to check the current status. The dataset is still deleting if the value of Status is DELETEINPROGRESS. If you try to access the dataset after it is deleted, you get a ResourceNotFoundException exception. You can't delete a dataset while it is creating (Status = CREATEINPROGRESS) or if the dataset is updating (Status = UPDATEINPROGRESS). This operation requires permissions to perform the rekognition:DeleteDataset action.

Arguments

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

delete_faces(collection_id, face_ids)
delete_faces(collection_id, face_ids, params::Dict{String,<:Any})

Deletes faces from a collection. You specify a collection ID and an array of face IDs to remove from the collection. This operation requires permissions to perform the rekognition:DeleteFaces action.

Arguments

• collection_id: Collection from which to remove the specific faces.
• face_ids: An array of face IDs to delete.

delete_project(project_arn)
delete_project(project_arn, params::Dict{String,<:Any})

Deletes an Amazon Rekognition Custom Labels project. To delete a project you must first delete all models associated with the project. To delete a model, see DeleteProjectVersion. DeleteProject is an asynchronous operation. To check if the project is deleted, call DescribeProjects. The project is deleted when the project no longer appears in the response. Be aware that deleting a given project will also delete any ProjectPolicies associated with that project. This operation requires permissions to perform the rekognition:DeleteProject action.

Arguments

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

delete_project_policy(policy_name, project_arn)
delete_project_policy(policy_name, project_arn, params::Dict{String,<:Any})

Deletes an existing project policy. To get a list of project policies attached to a project, call ListProjectPolicies. To attach a project policy to a project, call PutProjectPolicy. This operation requires permissions to perform the rekognition:DeleteProjectPolicy action.

Arguments

• policy_name: The name of the policy that you want to delete.
• project_arn: The Amazon Resource Name (ARN) of the project that the project policy you want to delete is attached to.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "PolicyRevisionId": The ID of the project policy revision that you want to delete.

delete_project_version(project_version_arn)
delete_project_version(project_version_arn, params::Dict{String,<:Any})

Deletes an Amazon Rekognition Custom Labels model. You can't delete a model if it is running or if it is training. To check the status of a model, use the Status field returned from DescribeProjectVersions. To stop a running model call StopProjectVersion. If the model is training, wait until it finishes. This operation requires permissions to perform the rekognition:DeleteProjectVersion action.

Arguments

• project_version_arn: The Amazon Resource Name (ARN) of the model version that you want to delete.

delete_stream_processor(name)
delete_stream_processor(name, params::Dict{String,<:Any})

Deletes the stream processor identified by Name. You assign the value for Name when you create the stream processor with CreateStreamProcessor. You might not be able to use the same name for a stream processor for a few seconds after calling DeleteStreamProcessor.

Arguments

• name: The name of the stream processor you want to delete.

delete_user(collection_id, user_id)
delete_user(collection_id, user_id, params::Dict{String,<:Any})

Deletes the specified UserID within the collection. Faces that are associated with the UserID are disassociated from the UserID before deleting the specified UserID. If the specified Collection or UserID is already deleted or not found, a ResourceNotFoundException will be thrown. If the action is successful with a 200 response, an empty HTTP body is returned.

Arguments

• collection_id: The ID of an existing collection from which the UserID needs to be deleted.
• user_id: ID for the UserID to be deleted.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "ClientRequestToken": 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.

describe_collection(collection_id)
describe_collection(collection_id, params::Dict{String,<:Any})

Describes the specified collection. You can use DescribeCollection to get information, such as the number of faces indexed into a collection and the version of the model used by the collection for face detection. For more information, see Describing a Collection in the Amazon Rekognition Developer Guide.

Arguments

• collection_id: The ID of the collection to describe.

describe_dataset(dataset_arn)
describe_dataset(dataset_arn, params::Dict{String,<:Any})

Describes an Amazon Rekognition Custom Labels dataset. You can get information such as the current status of a dataset and statistics about the images and labels in a dataset. This operation requires permissions to perform the rekognition:DescribeDataset action.

Arguments

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

describe_project_versions(project_arn)
describe_project_versions(project_arn, params::Dict{String,<:Any})

Lists and describes the versions of a model in an Amazon Rekognition Custom Labels project. You can specify up to 10 model versions in ProjectVersionArns. If you don't specify a value, descriptions for all model versions in the project are returned. This operation requires permissions to perform the rekognition:DescribeProjectVersions action.

Arguments

• project_arn: The Amazon Resource Name (ARN) of the project that contains the models you want to describe.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "MaxResults": 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.
• "NextToken": 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.
• "VersionNames": A list of model version names that you want to describe. You can add up to 10 model version names to the list. If you don't specify a value, all model descriptions are returned. A version name is part of a model (ProjectVersion) 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.

describe_projects()
describe_projects(params::Dict{String,<:Any})

Gets information about your Amazon Rekognition Custom Labels projects. This operation requires permissions to perform the rekognition:DescribeProjects action.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "MaxResults": 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.
• "NextToken": 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.
• "ProjectNames": A list of the projects that you want Amazon Rekognition Custom Labels to describe. If you don't specify a value, the response includes descriptions for all the projects in your AWS account.

describe_stream_processor(name)
describe_stream_processor(name, params::Dict{String,<:Any})

Provides information about a stream processor created by CreateStreamProcessor. You can get information about the input and output streams, the input parameters for the face recognition being performed, and the current status of the stream processor.

Arguments

• name: Name of the stream processor for which you want information.

detect_custom_labels(image, project_version_arn)
detect_custom_labels(image, project_version_arn, params::Dict{String,<:Any})

Detects custom labels in a supplied image by using an Amazon Rekognition Custom Labels model. You specify which version of a model version to use by using the ProjectVersionArn input parameter. You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file. For each object that the model version detects on an image, the API returns a (CustomLabel) object in an array (CustomLabels). Each CustomLabel object provides the label name (Name), the level of confidence that the image contains the object (Confidence), and object location information, if it exists, for the label on the image (Geometry). To filter labels that are returned, specify a value for MinConfidence. DetectCustomLabelsLabels only returns labels with a confidence that's higher than the specified value. The value of MinConfidence maps to the assumed threshold values created during training. For more information, see Assumed threshold in the Amazon Rekognition Custom Labels Developer Guide. Amazon Rekognition Custom Labels metrics expresses an assumed threshold as a floating point value between 0-1. The range of MinConfidence normalizes the threshold value to a percentage value (0-100). Confidence responses from DetectCustomLabels are also returned as a percentage. You can use MinConfidence to change the precision and recall or your model. For more information, see Analyzing an image in the Amazon Rekognition Custom Labels Developer Guide. If you don't specify a value for MinConfidence, DetectCustomLabels returns labels based on the assumed threshold of each label. This is a stateless API operation. That is, the operation does not persist any data. This operation requires permissions to perform the rekognition:DetectCustomLabels action. For more information, see Analyzing an image in the Amazon Rekognition Custom Labels Developer Guide.

Arguments

• image:
• project_version_arn: The ARN of the model version that you want to use.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "MaxResults": 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.
• "MinConfidence": 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.

detect_faces(image)
detect_faces(image, params::Dict{String,<:Any})

Detects faces within an image that is provided as input. DetectFaces detects the 100 largest faces in the image. For each face detected, the operation returns face details. These details include a bounding box of the face, a confidence value (that the bounding box contains a face), and a fixed set of attributes such as facial landmarks (for example, coordinates of eye and mouth), pose, presence of facial occlusion, and so on. The face-detection algorithm is most effective on frontal faces. For non-frontal or obscured faces, the algorithm might not detect the faces or might detect faces with lower confidence. You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file. This is a stateless API operation. That is, the operation does not persist any data. This operation requires permissions to perform the rekognition:DetectFaces action.

Arguments

• image: 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.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "Attributes": 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", "FACEOCCLUDED"] or just ["FACEOCCLUDED"]. 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).

detect_labels(image)
detect_labels(image, params::Dict{String,<:Any})

Detects instances of real-world entities within an image (JPEG or PNG) provided as input. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; and concepts like landscape, evening, and nature. For an example, see Analyzing images stored in an Amazon S3 bucket in the Amazon Rekognition Developer Guide. You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file. Optional Parameters You can specify one or both of the GENERALLABELS and IMAGEPROPERTIES feature types when calling the DetectLabels API. Including GENERALLABELS will ensure the response includes the labels detected in the input image, while including IMAGEPROPERTIES will ensure the response includes information about the image quality and color. When using GENERALLABELS and/or IMAGEPROPERTIES you can provide filtering criteria to the Settings parameter. You can filter with sets of individual labels or with label categories. You can specify inclusive filters, exclusive filters, or a combination of inclusive and exclusive filters. For more information on filtering see Detecting Labels in an Image. You can specify MinConfidence to control the confidence threshold for the labels returned. The default is 55%. You can also add the MaxLabels parameter to limit the number of labels returned. The default and upper limit is 1000 labels. Response Elements For each object, scene, and concept the API returns one or more labels. The API returns the following types of information about labels: Name - The name of the detected label. Confidence - The level of confidence in the label assigned to a detected object. Parents - The ancestor labels for a detected label. DetectLabels returns a hierarchical taxonomy of detected labels. For example, a detected car might be assigned the label car. The label car has two parent labels: Vehicle (its parent) and Transportation (its grandparent). The response includes the all ancestors for a label, where every ancestor is a unique label. In the previous example, Car, Vehicle, and Transportation are returned as unique labels in the response. Aliases - Possible Aliases for the label. Categories - The label categories that the detected label belongs to. BoundingBox — Bounding boxes are described for all instances of detected common object labels, returned in an array of Instance objects. An Instance object contains a BoundingBox object, describing the location of the label on the input image. It also includes the confidence for the accuracy of the detected bounding box. The API returns the following information regarding the image, as part of the ImageProperties structure: Quality - Information about the Sharpness, Brightness, and Contrast of the input image, scored between 0 to 100. Image quality is returned for the entire image, as well as the background and the foreground. Dominant Color - An array of the dominant colors in the image. Foreground - Information about the sharpness, brightness, and dominant colors of the input image’s foreground. Background

• Information about the sharpness, brightness, and dominant colors of the input image’s

background. The list of returned labels will include at least one label for every detected object, along with information about that label. In the following example, suppose the input image has a lighthouse, the sea, and a rock. The response includes all three labels, one for each object, as well as the confidence in the label: {Name: lighthouse, Confidence: 98.4629} {Name: rock,Confidence: 79.2097} {Name: sea,Confidence: 75.061} The list of labels can include multiple labels for the same object. For example, if the input image shows a flower (for example, a tulip), the operation might return the following three labels. {Name: flower,Confidence: 99.0562} {Name: plant,Confidence: 99.0562} {Name: tulip,Confidence: 99.0562} In this example, the detection algorithm more precisely identifies the flower as a tulip. If the object detected is a person, the operation doesn't provide the same facial details that the DetectFaces operation provides. This is a stateless API operation that doesn't return any data. This operation requires permissions to perform the rekognition:DetectLabels action.

Arguments

• image: 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.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "Features": A list of the types of analysis to perform. Specifying GENERALLABELS uses the label detection feature, while specifying IMAGEPROPERTIES returns information regarding image color and quality. If no option is specified GENERAL_LABELS is used by default.
• "MaxLabels": Maximum number of labels you want the service to return in the response. The service returns the specified number of highest confidence labels.
• "MinConfidence": 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.
• "Settings": 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.

detect_moderation_labels(image)
detect_moderation_labels(image, params::Dict{String,<:Any})

Detects unsafe content in a specified JPEG or PNG format image. Use DetectModerationLabels to moderate images depending on your requirements. For example, you might want to filter images that contain nudity, but not images containing suggestive content. To filter images, use the labels returned by DetectModerationLabels to determine which types of content are appropriate. For information about moderation labels, see Detecting Unsafe Content in the Amazon Rekognition Developer Guide. You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

Arguments

• image: 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.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "HumanLoopConfig": Sets up the configuration for human evaluation, including the FlowDefinition the image will be sent to.
• "MinConfidence": 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.

detect_protective_equipment(image)
detect_protective_equipment(image, params::Dict{String,<:Any})

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

Arguments

• image: 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.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "SummarizationAttributes": An array of PPE types that you want to summarize.

detect_text(image)
detect_text(image, params::Dict{String,<:Any})

Detects text in the input image and converts it into machine-readable text. Pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, you must pass it as a reference to an image in an Amazon S3 bucket. For the AWS CLI, passing image bytes is not supported. The image must be either a .png or .jpeg formatted file. The DetectText operation returns text in an array of TextDetection elements, TextDetections. Each TextDetection element provides information about a single word or line of text that was detected in the image. A word is one or more script characters that are not separated by spaces. DetectText can detect up to 100 words in an image. A line is a string of equally spaced words. A line isn't necessarily a complete sentence. For example, a driver's license number is detected as a line. A line ends when there is no aligned text after it. Also, a line ends when there is a large gap between words, relative to the length of the words. This means, depending on the gap between words, Amazon Rekognition may detect multiple lines in text aligned in the same direction. Periods don't represent the end of a line. If a sentence spans multiple lines, the DetectText operation returns multiple lines. To determine whether a TextDetection element is a line of text or a word, use the TextDetection object Type field. To be detected, text must be within +/- 90 degrees orientation of the horizontal axis. For more information, see Detecting text in the Amazon Rekognition Developer Guide.

Arguments

• image: 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.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

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

disassociate_faces(collection_id, face_ids, user_id)
disassociate_faces(collection_id, face_ids, user_id, params::Dict{String,<:Any})

Removes the association between a Face supplied in an array of FaceIds and the User. If the User is not present already, then a ResourceNotFound exception is thrown. If successful, an array of faces that are disassociated from the User is returned. If a given face is already disassociated from the given UserID, it will be ignored and not be returned in the response. If a given face is already associated with a different User or not found in the collection it will be returned as part of UnsuccessfulDisassociations. You can remove 1 - 100 face IDs from a user at one time.

Arguments

• collection_id: The ID of an existing collection containing the UserID.
• face_ids: An array of face IDs to disassociate from the UserID.
• user_id: ID for the existing UserID.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "ClientRequestToken": 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.

distribute_dataset_entries(datasets)
distribute_dataset_entries(datasets, params::Dict{String,<:Any})

Distributes the entries (images) in a training dataset across the training dataset and the test dataset for a project. DistributeDatasetEntries moves 20% of the training dataset images to the test dataset. An entry is a JSON Line that describes an image. You supply the Amazon Resource Names (ARN) of a project's training dataset and test dataset. The training dataset must contain the images that you want to split. The test dataset must be empty. The datasets must belong to the same project. To create training and test datasets for a project, call CreateDataset. Distributing a dataset takes a while to complete. To check the status call DescribeDataset. The operation is complete when the Status field for the training dataset and the test dataset is UPDATECOMPLETE. If the dataset split fails, the value of Status is UPDATEFAILED. This operation requires permissions to perform the rekognition:DistributeDatasetEntries action.

Arguments

• datasets: 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.

get_celebrity_info(id)
get_celebrity_info(id, params::Dict{String,<:Any})

Gets the name and additional information about a celebrity based on their Amazon Rekognition ID. The additional information is returned as an array of URLs. If there is no additional information about the celebrity, this list is empty. For more information, see Getting information about a celebrity in the Amazon Rekognition Developer Guide. This operation requires permissions to perform the rekognition:GetCelebrityInfo action.

Arguments

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

get_celebrity_recognition(job_id)
get_celebrity_recognition(job_id, params::Dict{String,<:Any})

Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by StartCelebrityRecognition. Celebrity recognition in a video is an asynchronous operation. Analysis is started by a call to StartCelebrityRecognition which returns a job identifier (JobId). When the celebrity recognition operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartCelebrityRecognition. To get the results of the celebrity recognition analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetCelebrityDetection and pass the job identifier (JobId) from the initial call to StartCelebrityDetection. For more information, see Working With Stored Videos in the Amazon Rekognition Developer Guide. GetCelebrityRecognition returns detected celebrities and the time(s) they are detected in an array (Celebrities) of CelebrityRecognition objects. Each CelebrityRecognition contains information about the celebrity in a CelebrityDetail object and the time, Timestamp, the celebrity was detected. This CelebrityDetail object stores information about the detected celebrity's face attributes, a face bounding box, known gender, the celebrity's name, and a confidence estimate. GetCelebrityRecognition only returns the default facial attributes (BoundingBox, Confidence, Landmarks, Pose, and Quality). The BoundingBox field only applies to the detected face instance. The other facial attributes listed in the Face object of the following response syntax are not returned. For more information, see FaceDetail in the Amazon Rekognition Developer Guide. By default, the Celebrities array is sorted by time (milliseconds from the start of the video). You can also sort the array by celebrity by specifying the value ID in the SortBy input parameter. The CelebrityDetail object includes the celebrity identifer and additional information urls. If you don't store the additional information urls, you can get them later by calling GetCelebrityInfo with the celebrity identifer. No information is returned for faces not recognized as celebrities. Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetCelebrityDetection and populate the NextToken request parameter with the token value returned from the previous call to GetCelebrityRecognition.

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "MaxResults": 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.
• "NextToken": 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.
• "SortBy": 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.

get_content_moderation(job_id)
get_content_moderation(job_id, params::Dict{String,<:Any})

Gets the inappropriate, unwanted, or offensive content analysis results for a Amazon Rekognition Video analysis started by StartContentModeration. For a list of moderation labels in Amazon Rekognition, see Using the image and video moderation APIs. Amazon Rekognition Video inappropriate or offensive content detection in a stored video is an asynchronous operation. You start analysis by calling StartContentModeration which returns a job identifier (JobId). When analysis finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartContentModeration. To get the results of the content analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetContentModeration and pass the job identifier (JobId) from the initial call to StartContentModeration. For more information, see Working with Stored Videos in the Amazon Rekognition Devlopers Guide. GetContentModeration returns detected inappropriate, unwanted, or offensive content moderation labels, and the time they are detected, in an array, ModerationLabels, of ContentModerationDetection objects. By default, the moderated labels are returned sorted by time, in milliseconds from the start of the video. You can also sort them by moderated label by specifying NAME for the SortBy input parameter. Since video analysis can return a large number of results, use the MaxResults parameter to limit the number of labels returned in a single call to GetContentModeration. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetContentModeration and populate the NextToken request parameter with the value of NextToken returned from the previous call to GetContentModeration. For more information, see moderating content in the Amazon Rekognition Developer Guide.

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "AggregateBy": Defines how to aggregate results of the StartContentModeration request. Default aggregation option is TIMESTAMPS. SEGMENTS mode aggregates moderation labels over time.
• "MaxResults": 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.
• "NextToken": 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.
• "SortBy": 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.

get_face_detection(job_id)
get_face_detection(job_id, params::Dict{String,<:Any})

Gets face detection results for a Amazon Rekognition Video analysis started by StartFaceDetection. Face detection with Amazon Rekognition Video is an asynchronous operation. You start face detection by calling StartFaceDetection which returns a job identifier (JobId). When the face detection operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartFaceDetection. To get the results of the face detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetFaceDetection and pass the job identifier (JobId) from the initial call to StartFaceDetection. GetFaceDetection returns an array of detected faces (Faces) sorted by the time the faces were detected. Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetFaceDetection and populate the NextToken request parameter with the token value returned from the previous call to GetFaceDetection.

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "MaxResults": 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.
• "NextToken": 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.

get_face_liveness_session_results(session_id)
get_face_liveness_session_results(session_id, params::Dict{String,<:Any})

Retrieves the results of a specific Face Liveness session. It requires the sessionId as input, which was created using CreateFaceLivenessSession. Returns the corresponding Face Liveness confidence score, a reference image that includes a face bounding box, and audit images that also contain face bounding boxes. The Face Liveness confidence score ranges from 0 to 100. The reference image can optionally be returned.

Arguments

• session_id: 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.

get_face_search(job_id)
get_face_search(job_id, params::Dict{String,<:Any})

Gets the face search results for Amazon Rekognition Video face search started by StartFaceSearch. The search returns faces in a collection that match the faces of persons detected in a video. It also includes the time(s) that faces are matched in the video. Face search in a video is an asynchronous operation. You start face search by calling to StartFaceSearch which returns a job identifier (JobId). When the search operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartFaceSearch. To get the search results, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetFaceSearch and pass the job identifier (JobId) from the initial call to StartFaceSearch. For more information, see Searching Faces in a Collection in the Amazon Rekognition Developer Guide. The search results are retured in an array, Persons, of PersonMatch objects. EachPersonMatch element contains details about the matching faces in the input collection, person information (facial attributes, bounding boxes, and person identifer) for the matched person, and the time the person was matched in the video. GetFaceSearch only returns the default facial attributes (BoundingBox, Confidence, Landmarks, Pose, and Quality). The other facial attributes listed in the Face object of the following response syntax are not returned. For more information, see FaceDetail in the Amazon Rekognition Developer Guide. By default, the Persons array is sorted by the time, in milliseconds from the start of the video, persons are matched. You can also sort by persons by specifying INDEX for the SORTBY input parameter.

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "MaxResults": 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.
• "NextToken": 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.
• "SortBy": 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.

get_label_detection(job_id)
get_label_detection(job_id, params::Dict{String,<:Any})

Gets the label detection results of a Amazon Rekognition Video analysis started by StartLabelDetection. The label detection operation is started by a call to StartLabelDetection which returns a job identifier (JobId). When the label detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartlabelDetection. To get the results of the label detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetLabelDetection and pass the job identifier (JobId) from the initial call to StartLabelDetection. GetLabelDetection returns an array of detected labels (Labels) sorted by the time the labels were detected. You can also sort by the label name by specifying NAME for the SortBy input parameter. If there is no NAME specified, the default sort is by timestamp. You can select how results are aggregated by using the AggregateBy input parameter. The default aggregation method is TIMESTAMPS. You can also aggregate by SEGMENTS, which aggregates all instances of labels detected in a given segment. The returned Labels array may include the following attributes: Name - The name of the detected label. Confidence - The level of confidence in the label assigned to a detected object. Parents - The ancestor labels for a detected label. GetLabelDetection returns a hierarchical taxonomy of detected labels. For example, a detected car might be assigned the label car. The label car has two parent labels: Vehicle (its parent) and Transportation (its grandparent). The response includes the all ancestors for a label, where every ancestor is a unique label. In the previous example, Car, Vehicle, and Transportation are returned as unique labels in the response. Aliases - Possible Aliases for the label. Categories - The label categories that the detected label belongs to. BoundingBox — Bounding boxes are described for all instances of detected common object labels, returned in an array of Instance objects. An Instance object contains a BoundingBox object, describing the location of the label on the input image. It also includes the confidence for the accuracy of the detected bounding box. Timestamp - Time, in milliseconds from the start of the video, that the label was detected. For aggregation by SEGMENTS, the StartTimestampMillis, EndTimestampMillis, and DurationMillis structures are what define a segment. Although the “Timestamp” structure is still returned with each label, its value is set to be the same as StartTimestampMillis. Timestamp and Bounding box information are returned for detected Instances, only if aggregation is done by TIMESTAMPS. If aggregating by SEGMENTS, information about detected instances isn’t returned. The version of the label model used for the detection is also returned. Note DominantColors isn't returned for Instances, although it is shown as part of the response in the sample seen below. Use MaxResults parameter to limit the number of labels returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetlabelDetection and populate the NextToken request parameter with the token value returned from the previous call to GetLabelDetection.

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "AggregateBy": Defines how to aggregate the returned results. Results can be aggregated by timestamps or segments.
• "MaxResults": 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.
• "NextToken": 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.
• "SortBy": 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.

get_person_tracking(job_id)
get_person_tracking(job_id, params::Dict{String,<:Any})

Gets the path tracking results of a Amazon Rekognition Video analysis started by StartPersonTracking. The person path tracking operation is started by a call to StartPersonTracking which returns a job identifier (JobId). When the operation finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartPersonTracking. To get the results of the person path tracking operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetPersonTracking and pass the job identifier (JobId) from the initial call to StartPersonTracking. GetPersonTracking returns an array, Persons, of tracked persons and the time(s) their paths were tracked in the video. GetPersonTracking only returns the default facial attributes (BoundingBox, Confidence, Landmarks, Pose, and Quality). The other facial attributes listed in the Face object of the following response syntax are not returned. For more information, see FaceDetail in the Amazon Rekognition Developer Guide. By default, the array is sorted by the time(s) a person's path is tracked in the video. You can sort by tracked persons by specifying INDEX for the SortBy input parameter. Use the MaxResults parameter to limit the number of items returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetPersonTracking and populate the NextToken request parameter with the token value returned from the previous call to GetPersonTracking.

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "MaxResults": 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.
• "NextToken": 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.
• "SortBy": 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.

get_segment_detection(job_id)
get_segment_detection(job_id, params::Dict{String,<:Any})

Gets the segment detection results of a Amazon Rekognition Video analysis started by StartSegmentDetection. Segment detection with Amazon Rekognition Video is an asynchronous operation. You start segment detection by calling StartSegmentDetection which returns a job identifier (JobId). When the segment detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartSegmentDetection. To get the results of the segment detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. if so, call GetSegmentDetection and pass the job identifier (JobId) from the initial call of StartSegmentDetection. GetSegmentDetection returns detected segments in an array (Segments) of SegmentDetection objects. Segments is sorted by the segment types specified in the SegmentTypes input parameter of StartSegmentDetection. Each element of the array includes the detected segment, the precentage confidence in the acuracy of the detected segment, the type of the segment, and the frame in which the segment was detected. Use SelectedSegmentTypes to find out the type of segment detection requested in the call to StartSegmentDetection. Use the MaxResults parameter to limit the number of segment detections returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetSegmentDetection and populate the NextToken request parameter with the token value returned from the previous call to GetSegmentDetection. For more information, see Detecting video segments in stored video in the Amazon Rekognition Developer Guide.

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "MaxResults": Maximum number of results to return per paginated call. The largest value you can specify is 1000.
• "NextToken": 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.

get_text_detection(job_id)
get_text_detection(job_id, params::Dict{String,<:Any})

Gets the text detection results of a Amazon Rekognition Video analysis started by StartTextDetection. Text detection with Amazon Rekognition Video is an asynchronous operation. You start text detection by calling StartTextDetection which returns a job identifier (JobId) When the text detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartTextDetection. To get the results of the text detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. if so, call GetTextDetection and pass the job identifier (JobId) from the initial call of StartLabelDetection. GetTextDetection returns an array of detected text (TextDetections) sorted by the time the text was detected, up to 50 words per frame of video. Each element of the array includes the detected text, the precentage confidence in the acuracy of the detected text, the time the text was detected, bounding box information for where the text was located, and unique identifiers for words and their lines. Use MaxResults parameter to limit the number of text detections returned. If there are more results than specified in MaxResults, the value of NextToken in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call GetTextDetection and populate the NextToken request parameter with the token value returned from the previous call to GetTextDetection.

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "MaxResults": Maximum number of results to return per paginated call. The largest value you can specify is 1000.
• "NextToken": 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.

index_faces(collection_id, image)
index_faces(collection_id, image, params::Dict{String,<:Any})

Detects faces in the input image and adds them to the specified collection. Amazon Rekognition doesn't save the actual faces that are detected. Instead, the underlying detection algorithm first detects the faces in the input image. For each face, the algorithm extracts facial features into a feature vector, and stores it in the backend database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the SearchFaces and SearchFacesByImage operations. For more information, see Adding faces to a collection in the Amazon Rekognition Developer Guide. To get the number of faces in a collection, call DescribeCollection. If you're using version 1.0 of the face detection model, IndexFaces indexes the 15 largest faces in the input image. Later versions of the face detection model index the 100 largest faces in the input image. If you're using version 4 or later of the face model, image orientation information is not returned in the OrientationCorrection field. To determine which version of the model you're using, call DescribeCollection and supply the collection ID. You can also get the model version from the value of FaceModelVersion in the response from IndexFaces For more information, see Model Versioning in the Amazon Rekognition Developer Guide. If you provide the optional ExternalImageId for the input image you provided, Amazon Rekognition associates this ID with all faces that it detects. When you call the ListFaces operation, the response returns the external ID. You can use this external image ID to create a client-side index to associate the faces with each image. You can then use the index to find all faces in an image. You can specify the maximum number of faces to index with the MaxFaces input parameter. This is useful when you want to index the largest faces in an image and don't want to index smaller faces, such as those belonging to people standing in the background. The QualityFilter input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. By default, IndexFaces chooses the quality bar that's used to filter faces. You can also explicitly choose the quality bar. Use QualityFilter, to set the quality bar by specifying LOW, MEDIUM, or HIGH. If you do not want to filter detected faces, specify NONE. To use quality filtering, you need a collection associated with version 3 of the face model or higher. To get the version of the face model associated with a collection, call DescribeCollection. Information about faces detected in an image, but not indexed, is returned in an array of UnindexedFace objects, UnindexedFaces. Faces aren't indexed for reasons such as: The number of faces detected exceeds the value of the MaxFaces request parameter. The face is too small compared to the image dimensions. The face is too blurry. The image is too dark. The face has an extreme pose. The face doesn’t have enough detail to be suitable for face search. In response, the IndexFaces operation returns an array of metadata for all detected faces, FaceRecords. This includes: The bounding box, BoundingBox, of the detected face. A confidence value, Confidence, which indicates the confidence that the bounding box contains a face. A face ID, FaceId, assigned by the service for each face that's detected and stored. An image ID, ImageId, assigned by the service for the input image. If you request ALL or specific facial attributes (e.g., FACE_OCCLUDED) by using the detectionAttributes parameter, Amazon Rekognition returns detailed facial attributes, such as facial landmarks (for example, location of eye and mouth), facial occlusion, and other facial attributes. If you provide the same image, specify the same collection, and use the same external ID in the IndexFaces operation, Amazon Rekognition doesn't save duplicate face metadata. The input image is passed either as base64-encoded image bytes, or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file. This operation requires permissions to perform the rekognition:IndexFaces action.

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "DetectionAttributes": 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", "FACEOCCLUDED"] or just ["FACEOCCLUDED"]. 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).
• "ExternalImageId": The ID you want to assign to all the faces detected in the image.
• "MaxFaces": 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.
• "QualityFilter": 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.

list_collections()
list_collections(params::Dict{String,<:Any})

Returns list of collection IDs in your account. If the result is truncated, the response also provides a NextToken that you can use in the subsequent request to fetch the next set of collection IDs. For an example, see Listing collections in the Amazon Rekognition Developer Guide. This operation requires permissions to perform the rekognition:ListCollections action.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "MaxResults": Maximum number of collection IDs to return.
• "NextToken": Pagination token from the previous response.

list_dataset_entries(dataset_arn)
list_dataset_entries(dataset_arn, params::Dict{String,<:Any})

Lists the entries (images) within a dataset. An entry is a JSON Line that contains the information for a single image, including the image location, assigned labels, and object location bounding boxes. For more information, see Creating a manifest file. JSON Lines in the response include information about non-terminal errors found in the dataset. Non terminal errors are reported in errors lists within each JSON Line. The same information is reported in the training and testing validation result manifests that Amazon Rekognition Custom Labels creates during model training. You can filter the response in variety of ways, such as choosing which labels to return and returning JSON Lines created after a specific date. This operation requires permissions to perform the rekognition:ListDatasetEntries action.

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "ContainsLabels": 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.
• "HasErrors": Specifies an error filter for the response. Specify True to only include entries that have errors.
• "Labeled": 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.
• "MaxResults": 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.
• "NextToken": 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.
• "SourceRefContains": 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.

list_dataset_labels(dataset_arn)
list_dataset_labels(dataset_arn, params::Dict{String,<:Any})

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

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "MaxResults": 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.
• "NextToken": 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.

list_faces(collection_id)
list_faces(collection_id, params::Dict{String,<:Any})

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

Arguments

• collection_id: ID of the collection from which to list the faces.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "FaceIds": An array of face IDs to match when listing faces in a collection.
• "MaxResults": Maximum number of faces to return.
• "NextToken": 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.
• "UserId": An array of user IDs to match when listing faces in a collection.

list_project_policies(project_arn)
list_project_policies(project_arn, params::Dict{String,<:Any})

Gets a list of the project policies attached to a project. To attach a project policy to a project, call PutProjectPolicy. To remove a project policy from a project, call DeleteProjectPolicy. This operation requires permissions to perform the rekognition:ListProjectPolicies action.

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "MaxResults": 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.
• "NextToken": 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.

list_stream_processors()
list_stream_processors(params::Dict{String,<:Any})

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

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

list_tags_for_resource(resource_arn)
list_tags_for_resource(resource_arn, params::Dict{String,<:Any})

Returns a list of tags in an Amazon Rekognition collection, stream processor, or Custom Labels model. This operation requires permissions to perform the rekognition:ListTagsForResource action.

Arguments

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

list_users(collection_id)
list_users(collection_id, params::Dict{String,<:Any})

Returns metadata of the User such as UserID in the specified collection. Anonymous User (to reserve faces without any identity) is not returned as part of this request. The results are sorted by system generated primary key ID. If the response is truncated, NextToken is returned in the response that can be used in the subsequent request to retrieve the next set of identities.

Arguments

• collection_id: The ID of an existing collection.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "MaxResults": Maximum number of UsersID to return.
• "NextToken": Pagingation token to receive the next set of UsersID.

put_project_policy(policy_document, policy_name, project_arn)
put_project_policy(policy_document, policy_name, project_arn, params::Dict{String,<:Any})

Attaches a project policy to a Amazon Rekognition Custom Labels project in a trusting AWS account. A project policy specifies that a trusted AWS account can copy a model version from a trusting AWS account to a project in the trusted AWS account. To copy a model version you use the CopyProjectVersion operation. For more information about the format of a project policy document, see Attaching a project policy (SDK) in the Amazon Rekognition Custom Labels Developer Guide. The response from PutProjectPolicy is a revision ID for the project policy. You can attach multiple project policies to a project. You can also update an existing project policy by specifying the policy revision ID of the existing policy. To remove a project policy from a project, call DeleteProjectPolicy. To get a list of project policies attached to a project, call ListProjectPolicies. You copy a model version by calling CopyProjectVersion. This operation requires permissions to perform the rekognition:PutProjectPolicy action.

Arguments

• policy_document: 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.
• policy_name: A name for the policy.
• project_arn: The Amazon Resource Name (ARN) of the project that the project policy is attached to.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "PolicyRevisionId": 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.

recognize_celebrities(image)
recognize_celebrities(image, params::Dict{String,<:Any})

Returns an array of celebrities recognized in the input image. For more information, see Recognizing celebrities in the Amazon Rekognition Developer Guide. RecognizeCelebrities returns the 64 largest faces in the image. It lists the recognized celebrities in the CelebrityFaces array and any unrecognized faces in the UnrecognizedFaces array. RecognizeCelebrities doesn't return celebrities whose faces aren't among the largest 64 faces in the image. For each celebrity recognized, RecognizeCelebrities returns a Celebrity object. The Celebrity object contains the celebrity name, ID, URL links to additional information, match confidence, and a ComparedFace object that you can use to locate the celebrity's face on the image. Amazon Rekognition doesn't retain information about which images a celebrity has been recognized in. Your application must store this information and use the Celebrity ID property as a unique identifier for the celebrity. If you don't store the celebrity name or additional information URLs returned by RecognizeCelebrities, you will need the ID to identify the celebrity in a call to the GetCelebrityInfo operation. You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file. For an example, see Recognizing celebrities in an image in the Amazon Rekognition Developer Guide. This operation requires permissions to perform the rekognition:RecognizeCelebrities operation.

Arguments

• image: 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.

search_faces(collection_id, face_id)
search_faces(collection_id, face_id, params::Dict{String,<:Any})

For a given input face ID, searches for matching faces in the collection the face belongs to. You get a face ID when you add a face to the collection using the IndexFaces operation. The operation compares the features of the input face with faces in the specified collection. You can also search faces without indexing faces by using the SearchFacesByImage operation. The operation response returns an array of faces that match, ordered by similarity score with the highest similarity first. More specifically, it is an array of metadata for each face match that is found. Along with the metadata, the response also includes a confidence value for each face match, indicating the confidence that the specific face matches the input face. For an example, see Searching for a face using its face ID in the Amazon Rekognition Developer Guide. This operation requires permissions to perform the rekognition:SearchFaces action.

Arguments

• collection_id: ID of the collection the face belongs to.
• face_id: ID of a face to find matches for in the collection.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "FaceMatchThreshold": 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%.
• "MaxFaces": Maximum number of faces to return. The operation returns the maximum number of faces with the highest confidence in the match.

search_faces_by_image(collection_id, image)
search_faces_by_image(collection_id, image, params::Dict{String,<:Any})

For a given input image, first detects the largest face in the image, and then searches the specified collection for matching faces. The operation compares the features of the input face with faces in the specified collection. To search for all faces in an input image, you might first call the IndexFaces operation, and then use the face IDs returned in subsequent calls to the SearchFaces operation. You can also call the DetectFaces operation and use the bounding boxes in the response to make face crops, which then you can pass in to the SearchFacesByImage operation. You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file. The response returns an array of faces that match, ordered by similarity score with the highest similarity first. More specifically, it is an array of metadata for each face match found. Along with the metadata, the response also includes a similarity indicating how similar the face is to the input face. In the response, the operation also returns the bounding box (and a confidence level that the bounding box contains a face) of the face that Amazon Rekognition used for the input image. If no faces are detected in the input image, SearchFacesByImage returns an InvalidParameterException error. For an example, Searching for a Face Using an Image in the Amazon Rekognition Developer Guide. The QualityFilter input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. Use QualityFilter to set the quality bar for filtering by specifying LOW, MEDIUM, or HIGH. If you do not want to filter detected faces, specify NONE. The default value is NONE. To use quality filtering, you need a collection associated with version 3 of the face model or higher. To get the version of the face model associated with a collection, call DescribeCollection. This operation requires permissions to perform the rekognition:SearchFacesByImage action.

Arguments

• collection_id: ID of the collection to search.
• image: 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.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "FaceMatchThreshold": (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%.
• "MaxFaces": Maximum number of faces to return. The operation returns the maximum number of faces with the highest confidence in the match.
• "QualityFilter": 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.

search_users(collection_id)
search_users(collection_id, params::Dict{String,<:Any})

Searches for UserIDs within a collection based on a FaceId or UserId. This API can be used to find the closest UserID (with a highest similarity) to associate a face. The request must be provided with either FaceId or UserId. The operation returns an array of UserID that match the FaceId or UserId, ordered by similarity score with the highest similarity first.

Arguments

• collection_id: 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.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "FaceId": ID for the existing face.
• "MaxUsers": Maximum number of identities to return.
• "UserId": ID for the existing User.
• "UserMatchThreshold": Optional value that specifies the minimum confidence in the matched UserID to return. Default value of 80.

search_users_by_image(collection_id, image)
search_users_by_image(collection_id, image, params::Dict{String,<:Any})

Searches for UserIDs using a supplied image. It first detects the largest face in the image, and then searches a specified collection for matching UserIDs. The operation returns an array of UserIDs that match the face in the supplied image, ordered by similarity score with the highest similarity first. It also returns a bounding box for the face found in the input image. Information about faces detected in the supplied image, but not used for the search, is returned in an array of UnsearchedFace objects. If no valid face is detected in the image, the response will contain an empty UserMatches list and no SearchedFace object.

Arguments

• collection_id: The ID of an existing collection containing the UserID.
• image:

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "MaxUsers": Maximum number of UserIDs to return.
• "QualityFilter": 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.
• "UserMatchThreshold": Specifies the minimum confidence in the UserID match to return. Default value is 80.

start_celebrity_recognition(video)
start_celebrity_recognition(video, params::Dict{String,<:Any})

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

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "ClientRequestToken": 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.
• "JobTag": 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.
• "NotificationChannel": 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.

start_content_moderation(video)
start_content_moderation(video, params::Dict{String,<:Any})

Starts asynchronous detection of inappropriate, unwanted, or offensive content in a stored video. For a list of moderation labels in Amazon Rekognition, see Using the image and video moderation APIs. Amazon Rekognition Video can moderate content in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartContentModeration returns a job identifier (JobId) which you use to get the results of the analysis. When content analysis is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel. To get the results of the content analysis, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetContentModeration and pass the job identifier (JobId) from the initial call to StartContentModeration. For more information, see Moderating content in the Amazon Rekognition Developer Guide.

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "ClientRequestToken": 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.
• "JobTag": 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.
• "MinConfidence": 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.
• "NotificationChannel": 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.

start_face_detection(video)
start_face_detection(video, params::Dict{String,<:Any})

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

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "ClientRequestToken": 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.
• "FaceAttributes": 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.
• "JobTag": 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.
• "NotificationChannel": 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.

start_face_search(collection_id, video)
start_face_search(collection_id, video, params::Dict{String,<:Any})

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

Arguments

• collection_id: ID of the collection that contains the faces you want to search for.
• video: The video you want to search. The video must be stored in an Amazon S3 bucket.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "ClientRequestToken": 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.
• "FaceMatchThreshold": 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%.
• "JobTag": 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.
• "NotificationChannel": 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.

start_label_detection(video)
start_label_detection(video, params::Dict{String,<:Any})

Starts asynchronous detection of labels in a stored video. Amazon Rekognition Video can detect labels in a video. Labels are instances of real-world entities. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; concepts like landscape, evening, and nature; and activities like a person getting out of a car or a person skiing. The video must be stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartLabelDetection returns a job identifier (JobId) which you use to get the results of the operation. When label detection is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel. To get the results of the label detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetLabelDetection and pass the job identifier (JobId) from the initial call to StartLabelDetection. Optional Parameters StartLabelDetection has the GENERAL_LABELS Feature applied by default. This feature allows you to provide filtering criteria to the Settings parameter. You can filter with sets of individual labels or with label categories. You can specify inclusive filters, exclusive filters, or a combination of inclusive and exclusive filters. For more information on filtering, see Detecting labels in a video. You can specify MinConfidence to control the confidence threshold for the labels returned. The default is 50.

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "ClientRequestToken": 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.
• "Features": The features to return after video analysis. You can specify that GENERAL_LABELS are returned.
• "JobTag": 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.
• "MinConfidence": 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.
• "NotificationChannel": 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.
• "Settings": 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.

start_person_tracking(video)
start_person_tracking(video, params::Dict{String,<:Any})

Starts the asynchronous tracking of a person's path in a stored video. Amazon Rekognition Video can track the path of people in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartPersonTracking returns a job identifier (JobId) which you use to get the results of the operation. When label detection is finished, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel. To get the results of the person detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call GetPersonTracking and pass the job identifier (JobId) from the initial call to StartPersonTracking.

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "ClientRequestToken": 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.
• "JobTag": 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.
• "NotificationChannel": 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.

start_project_version(min_inference_units, project_version_arn)
start_project_version(min_inference_units, project_version_arn, params::Dict{String,<:Any})

Starts the running of the version of a model. Starting a model takes a while to complete. To check the current state of the model, use DescribeProjectVersions. Once the model is running, you can detect custom labels in new images by calling DetectCustomLabels. You are charged for the amount of time that the model is running. To stop a running model, call StopProjectVersion. For more information, see Running a trained Amazon Rekognition Custom Labels model in the Amazon Rekognition Custom Labels Guide. This operation requires permissions to perform the rekognition:StartProjectVersion action.

Arguments

• min_inference_units: The minimum number of inference units to use. A single inference unit represents 1 hour of processing. For information about the number of transactions per second (TPS) that an inference unit can support, see Running a trained Amazon Rekognition Custom Labels model in the Amazon Rekognition Custom Labels Guide. Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use.
• project_version_arn: The Amazon Resource Name(ARN) of the model version that you want to start.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "MaxInferenceUnits": 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.

start_segment_detection(segment_types, video)
start_segment_detection(segment_types, video, params::Dict{String,<:Any})

Starts asynchronous detection of segment detection in a stored video. Amazon Rekognition Video can detect segments in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name and the filename of the video. StartSegmentDetection returns a job identifier (JobId) which you use to get the results of the operation. When segment detection is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in NotificationChannel. You can use the Filters (StartSegmentDetectionFilters) input parameter to specify the minimum detection confidence returned in the response. Within Filters, use ShotFilter (StartShotDetectionFilter) to filter detected shots. Use TechnicalCueFilter (StartTechnicalCueDetectionFilter) to filter technical cues. To get the results of the segment detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. if so, call GetSegmentDetection and pass the job identifier (JobId) from the initial call to StartSegmentDetection. For more information, see Detecting video segments in stored video in the Amazon Rekognition Developer Guide.

Arguments

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

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "ClientRequestToken": 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.
• "Filters": Filters for technical cue or shot detection.
• "JobTag": 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.
• "NotificationChannel": 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.

start_stream_processor(name)
start_stream_processor(name, params::Dict{String,<:Any})

Starts processing a stream processor. You create a stream processor by calling CreateStreamProcessor. To tell StartStreamProcessor which stream processor to start, use the value of the Name field specified in the call to CreateStreamProcessor. If you are using a label detection stream processor to detect labels, you need to provide a Start selector and a Stop selector to determine the length of the stream processing time.

Arguments

• name: The name of the stream processor to start processing.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "StartSelector": 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.
• "StopSelector": 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.

start_text_detection(video)
start_text_detection(video, params::Dict{String,<:Any})

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

Arguments

• video:

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "ClientRequestToken": 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.
• "Filters": Optional parameters that let you set criteria the text must meet to be included in your response.
• "JobTag": 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.
• "NotificationChannel":

stop_project_version(project_version_arn)
stop_project_version(project_version_arn, params::Dict{String,<:Any})

Stops a running model. The operation might take a while to complete. To check the current status, call DescribeProjectVersions. This operation requires permissions to perform the rekognition:StopProjectVersion action.

Arguments

• project_version_arn: The Amazon Resource Name (ARN) of the model version that you want to delete. This operation requires permissions to perform the rekognition:StopProjectVersion action.

stop_stream_processor(name)
stop_stream_processor(name, params::Dict{String,<:Any})

Stops a running stream processor that was created by CreateStreamProcessor.

Arguments

• name: The name of a stream processor created by CreateStreamProcessor.

tag_resource(resource_arn, tags)
tag_resource(resource_arn, tags, params::Dict{String,<:Any})

Adds one or more key-value tags to an Amazon Rekognition collection, stream processor, or Custom Labels model. For more information, see Tagging AWS Resources. This operation requires permissions to perform the rekognition:TagResource action.

Arguments

• resource_arn: Amazon Resource Name (ARN) of the model, collection, or stream processor that you want to assign the tags to.
• tags: The key-value tags to assign to the resource.

untag_resource(resource_arn, tag_keys)
untag_resource(resource_arn, tag_keys, params::Dict{String,<:Any})

Removes one or more tags from an Amazon Rekognition collection, stream processor, or Custom Labels model. This operation requires permissions to perform the rekognition:UntagResource action.

Arguments

• resource_arn: Amazon Resource Name (ARN) of the model, collection, or stream processor that you want to remove the tags from.
• tag_keys: A list of the tags that you want to remove.

update_dataset_entries(changes, dataset_arn)
update_dataset_entries(changes, dataset_arn, params::Dict{String,<:Any})

Adds or updates one or more entries (images) in a dataset. An entry is a JSON Line which contains the information for a single image, including the image location, assigned labels, and object location bounding boxes. For more information, see Image-Level labels in manifest files and Object localization in manifest files in the Amazon Rekognition Custom Labels Developer Guide. If the source-ref field in the JSON line references an existing image, the existing image in the dataset is updated. If source-ref field doesn't reference an existing image, the image is added as a new image to the dataset. You specify the changes that you want to make in the Changes input parameter. There isn't a limit to the number JSON Lines that you can change, but the size of Changes must be less than 5MB. UpdateDatasetEntries returns immediatly, but the dataset update might take a while to complete. Use DescribeDataset to check the current status. The dataset updated successfully if the value of Status is UPDATECOMPLETE. To check if any non-terminal errors occured, call ListDatasetEntries and check for the presence of errors lists in the JSON Lines. Dataset update fails if a terminal error occurs (Status = UPDATEFAILED). Currently, you can't access the terminal error information from the Amazon Rekognition Custom Labels SDK. This operation requires permissions to perform the rekognition:UpdateDatasetEntries action.

Arguments

• changes: The changes that you want to make to the dataset.
• dataset_arn: The Amazon Resource Name (ARN) of the dataset that you want to update.

update_stream_processor(name)
update_stream_processor(name, params::Dict{String,<:Any})

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

Arguments

• name: Name of the stream processor that you want to update.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "DataSharingPreferenceForUpdate": 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.
• "ParametersToDelete": A list of parameters you want to delete from the stream processor.
• "RegionsOfInterestForUpdate": Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors.
• "SettingsForUpdate": The stream processor settings that you want to update. Label detection settings can be updated to detect different labels with a different minimum confidence.