Class: Aws::Rekognition::Client
- Inherits:
-
Seahorse::Client::Base
- Object
- Seahorse::Client::Base
- Aws::Rekognition::Client
- Includes:
- ClientStubs
- Defined in:
- gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb
Overview
An API client for Rekognition. To construct a client, you need to configure a :region
and :credentials
.
client = Aws::Rekognition::Client.new(
region: region_name,
credentials: credentials,
# ...
)
For details on configuring region and credentials see the developer guide.
See #initialize for a full list of supported configuration options.
Instance Attribute Summary
Attributes inherited from Seahorse::Client::Base
API Operations collapse
-
#associate_faces(params = {}) ⇒ Types::AssociateFacesResponse
Associates one or more faces with an existing UserID.
-
#compare_faces(params = {}) ⇒ Types::CompareFacesResponse
Compares a face in the source input image with each of the 100 largest faces detected in the target input image.
-
#copy_project_version(params = {}) ⇒ Types::CopyProjectVersionResponse
This operation applies only to Amazon Rekognition Custom Labels. -
#create_collection(params = {}) ⇒ Types::CreateCollectionResponse
Creates a collection in an AWS Region.
-
#create_dataset(params = {}) ⇒ Types::CreateDatasetResponse
This operation applies only to Amazon Rekognition Custom Labels. -
#create_face_liveness_session(params = {}) ⇒ Types::CreateFaceLivenessSessionResponse
This API operation initiates a Face Liveness session.
-
#create_project(params = {}) ⇒ Types::CreateProjectResponse
Creates a new Amazon Rekognition project.
-
#create_project_version(params = {}) ⇒ Types::CreateProjectVersionResponse
Creates a new version of Amazon Rekognition project (like a Custom Labels model or a custom adapter) and begins training.
-
#create_stream_processor(params = {}) ⇒ Types::CreateStreamProcessorResponse
Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces or to detect labels in a streaming video.
-
#create_user(params = {}) ⇒ Struct
Creates a new User within a collection specified by
CollectionId
. -
#delete_collection(params = {}) ⇒ Types::DeleteCollectionResponse
Deletes the specified collection.
-
#delete_dataset(params = {}) ⇒ Struct
This operation applies only to Amazon Rekognition Custom Labels. -
#delete_faces(params = {}) ⇒ Types::DeleteFacesResponse
Deletes faces from a collection.
-
#delete_project(params = {}) ⇒ Types::DeleteProjectResponse
Deletes a Amazon Rekognition project.
-
#delete_project_policy(params = {}) ⇒ Struct
This operation applies only to Amazon Rekognition Custom Labels. -
#delete_project_version(params = {}) ⇒ Types::DeleteProjectVersionResponse
Deletes a Rekognition project model or project version, like a Amazon Rekognition Custom Labels model or a custom adapter.
-
#delete_stream_processor(params = {}) ⇒ Struct
Deletes the stream processor identified by
Name
. -
#delete_user(params = {}) ⇒ Struct
Deletes the specified UserID within the collection.
-
#describe_collection(params = {}) ⇒ Types::DescribeCollectionResponse
Describes the specified collection.
-
#describe_dataset(params = {}) ⇒ Types::DescribeDatasetResponse
This operation applies only to Amazon Rekognition Custom Labels. -
#describe_project_versions(params = {}) ⇒ Types::DescribeProjectVersionsResponse
Lists and describes the versions of an Amazon Rekognition project.
-
#describe_projects(params = {}) ⇒ Types::DescribeProjectsResponse
Gets information about your Rekognition projects.
-
#describe_stream_processor(params = {}) ⇒ Types::DescribeStreamProcessorResponse
Provides information about a stream processor created by CreateStreamProcessor.
-
#detect_custom_labels(params = {}) ⇒ Types::DetectCustomLabelsResponse
This operation applies only to Amazon Rekognition Custom Labels. -
#detect_faces(params = {}) ⇒ Types::DetectFacesResponse
Detects faces within an image that is provided as input.
-
#detect_labels(params = {}) ⇒ Types::DetectLabelsResponse
Detects instances of real-world entities within an image (JPEG or PNG) provided as input.
-
#detect_moderation_labels(params = {}) ⇒ Types::DetectModerationLabelsResponse
Detects unsafe content in a specified JPEG or PNG format image.
-
#detect_protective_equipment(params = {}) ⇒ Types::DetectProtectiveEquipmentResponse
Detects Personal Protective Equipment (PPE) worn by people detected in an image.
-
#detect_text(params = {}) ⇒ Types::DetectTextResponse
Detects text in the input image and converts it into machine-readable text.
-
#disassociate_faces(params = {}) ⇒ Types::DisassociateFacesResponse
Removes the association between a
Face
supplied in an array ofFaceIds
and the User. -
#distribute_dataset_entries(params = {}) ⇒ Struct
This operation applies only to Amazon Rekognition Custom Labels. -
#get_celebrity_info(params = {}) ⇒ Types::GetCelebrityInfoResponse
Gets the name and additional information about a celebrity based on their Amazon Rekognition ID.
-
#get_celebrity_recognition(params = {}) ⇒ Types::GetCelebrityRecognitionResponse
Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by StartCelebrityRecognition.
-
#get_content_moderation(params = {}) ⇒ Types::GetContentModerationResponse
Gets the inappropriate, unwanted, or offensive content analysis results for a Amazon Rekognition Video analysis started by StartContentModeration.
-
#get_face_detection(params = {}) ⇒ Types::GetFaceDetectionResponse
Gets face detection results for a Amazon Rekognition Video analysis started by StartFaceDetection.
-
#get_face_liveness_session_results(params = {}) ⇒ Types::GetFaceLivenessSessionResultsResponse
Retrieves the results of a specific Face Liveness session.
-
#get_face_search(params = {}) ⇒ Types::GetFaceSearchResponse
Gets the face search results for Amazon Rekognition Video face search started by StartFaceSearch.
-
#get_label_detection(params = {}) ⇒ Types::GetLabelDetectionResponse
Gets the label detection results of a Amazon Rekognition Video analysis started by StartLabelDetection.
-
#get_media_analysis_job(params = {}) ⇒ Types::GetMediaAnalysisJobResponse
Retrieves the results for a given media analysis job.
-
#get_person_tracking(params = {}) ⇒ Types::GetPersonTrackingResponse
Gets the path tracking results of a Amazon Rekognition Video analysis started by StartPersonTracking.
-
#get_segment_detection(params = {}) ⇒ Types::GetSegmentDetectionResponse
Gets the segment detection results of a Amazon Rekognition Video analysis started by StartSegmentDetection.
-
#get_text_detection(params = {}) ⇒ Types::GetTextDetectionResponse
Gets the text detection results of a Amazon Rekognition Video analysis started by StartTextDetection.
-
#index_faces(params = {}) ⇒ Types::IndexFacesResponse
Detects faces in the input image and adds them to the specified collection.
-
#list_collections(params = {}) ⇒ Types::ListCollectionsResponse
Returns list of collection IDs in your account.
-
#list_dataset_entries(params = {}) ⇒ Types::ListDatasetEntriesResponse
This operation applies only to Amazon Rekognition Custom Labels. -
#list_dataset_labels(params = {}) ⇒ Types::ListDatasetLabelsResponse
This operation applies only to Amazon Rekognition Custom Labels. -
#list_faces(params = {}) ⇒ Types::ListFacesResponse
Returns metadata for faces in the specified collection.
-
#list_media_analysis_jobs(params = {}) ⇒ Types::ListMediaAnalysisJobsResponse
Returns a list of media analysis jobs.
-
#list_project_policies(params = {}) ⇒ Types::ListProjectPoliciesResponse
This operation applies only to Amazon Rekognition Custom Labels. -
#list_stream_processors(params = {}) ⇒ Types::ListStreamProcessorsResponse
Gets a list of stream processors that you have created with CreateStreamProcessor.
-
#list_tags_for_resource(params = {}) ⇒ Types::ListTagsForResourceResponse
Returns a list of tags in an Amazon Rekognition collection, stream processor, or Custom Labels model.
-
#list_users(params = {}) ⇒ Types::ListUsersResponse
Returns metadata of the User such as
UserID
in the specified collection. -
#put_project_policy(params = {}) ⇒ Types::PutProjectPolicyResponse
This operation applies only to Amazon Rekognition Custom Labels. -
#recognize_celebrities(params = {}) ⇒ Types::RecognizeCelebritiesResponse
Returns an array of celebrities recognized in the input image.
-
#search_faces(params = {}) ⇒ Types::SearchFacesResponse
For a given input face ID, searches for matching faces in the collection the face belongs to.
-
#search_faces_by_image(params = {}) ⇒ Types::SearchFacesByImageResponse
For a given input image, first detects the largest face in the image, and then searches the specified collection for matching faces.
-
#search_users(params = {}) ⇒ Types::SearchUsersResponse
Searches for UserIDs within a collection based on a
FaceId
orUserId
. -
#search_users_by_image(params = {}) ⇒ Types::SearchUsersByImageResponse
Searches for UserIDs using a supplied image.
-
#start_celebrity_recognition(params = {}) ⇒ Types::StartCelebrityRecognitionResponse
Starts asynchronous recognition of celebrities in a stored video.
-
#start_content_moderation(params = {}) ⇒ Types::StartContentModerationResponse
Starts asynchronous detection of inappropriate, unwanted, or offensive content in a stored video.
-
#start_face_detection(params = {}) ⇒ Types::StartFaceDetectionResponse
Starts asynchronous detection of faces in a stored video.
-
#start_face_search(params = {}) ⇒ Types::StartFaceSearchResponse
Starts the asynchronous search for faces in a collection that match the faces of persons detected in a stored video.
-
#start_label_detection(params = {}) ⇒ Types::StartLabelDetectionResponse
Starts asynchronous detection of labels in a stored video.
-
#start_media_analysis_job(params = {}) ⇒ Types::StartMediaAnalysisJobResponse
Initiates a new media analysis job.
-
#start_person_tracking(params = {}) ⇒ Types::StartPersonTrackingResponse
Starts the asynchronous tracking of a person's path in a stored video.
-
#start_project_version(params = {}) ⇒ Types::StartProjectVersionResponse
This operation applies only to Amazon Rekognition Custom Labels. -
#start_segment_detection(params = {}) ⇒ Types::StartSegmentDetectionResponse
Starts asynchronous detection of segment detection in a stored video.
-
#start_stream_processor(params = {}) ⇒ Types::StartStreamProcessorResponse
Starts processing a stream processor.
-
#start_text_detection(params = {}) ⇒ Types::StartTextDetectionResponse
Starts asynchronous detection of text in a stored video.
-
#stop_project_version(params = {}) ⇒ Types::StopProjectVersionResponse
This operation applies only to Amazon Rekognition Custom Labels. -
#stop_stream_processor(params = {}) ⇒ Struct
Stops a running stream processor that was created by CreateStreamProcessor.
-
#tag_resource(params = {}) ⇒ Struct
Adds one or more key-value tags to an Amazon Rekognition collection, stream processor, or Custom Labels model.
-
#untag_resource(params = {}) ⇒ Struct
Removes one or more tags from an Amazon Rekognition collection, stream processor, or Custom Labels model.
-
#update_dataset_entries(params = {}) ⇒ Struct
This operation applies only to Amazon Rekognition Custom Labels. -
#update_stream_processor(params = {}) ⇒ Struct
Allows you to update a stream processor.
Instance Method Summary collapse
-
#initialize(options) ⇒ Client
constructor
A new instance of Client.
-
#wait_until(waiter_name, params = {}, options = {}) {|w.waiter| ... } ⇒ Boolean
Polls an API operation until a resource enters a desired state.
Methods included from ClientStubs
#api_requests, #stub_data, #stub_responses
Methods inherited from Seahorse::Client::Base
add_plugin, api, clear_plugins, define, new, #operation_names, plugins, remove_plugin, set_api, set_plugins
Methods included from Seahorse::Client::HandlerBuilder
#handle, #handle_request, #handle_response
Constructor Details
#initialize(options) ⇒ Client
Returns a new instance of Client.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 451 def initialize(*args) super end |
Instance Method Details
#associate_faces(params = {}) ⇒ Types::AssociateFacesResponse
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.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 585 def associate_faces(params = {}, = {}) req = build_request(:associate_faces, params) req.send_request() end |
#compare_faces(params = {}) ⇒ Types::CompareFacesResponse
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
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.
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.
For an example, see Comparing Faces in Images in the Amazon Rekognition Developer Guide.
This operation requires permissions to perform the
rekognition:CompareFaces
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 830 def compare_faces(params = {}, = {}) req = build_request(:compare_faces, params) req.send_request() end |
#copy_project_version(params = {}) ⇒ Types::CopyProjectVersionResponse
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.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 967 def copy_project_version(params = {}, = {}) req = build_request(:copy_project_version, params) req.send_request() end |
#create_collection(params = {}) ⇒ Types::CreateCollectionResponse
Creates a collection in an AWS Region. You can add faces to the collection using the IndexFaces operation.
For example, you might create collections, one for each of your
application users. A user can then index faces using the IndexFaces
operation and persist results in a specific collection. Then, a user
can search the collection for faces in the user-specific container.
When you create a collection, it is associated with the latest version of the face model version.
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.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1037 def create_collection(params = {}, = {}) req = build_request(:create_collection, params) req.send_request() end |
#create_dataset(params = {}) ⇒ Types::CreateDatasetResponse
Creates a new Amazon Rekognition Custom Labels dataset. You can create a dataset by using an Amazon Sagemaker format manifest file or by copying an existing Amazon Rekognition Custom Labels dataset.
To create a training dataset for a project, specify TRAIN
for the
value of DatasetType
. To create the test dataset for a project,
specify TEST
for the value of DatasetType
.
The response from CreateDataset
is the Amazon Resource Name (ARN)
for the dataset. Creating a dataset takes a while to complete. Use
DescribeDataset to check the current status. The dataset created
successfully if the value of Status
is CREATE_COMPLETE
.
To check if any non-terminal errors occurred, call ListDatasetEntries
and check for the presence of errors
lists in the JSON Lines.
Dataset creation fails if a terminal error occurs (Status
=
CREATE_FAILED
). Currently, you can't access the terminal error
information.
For more information, see Creating dataset in the Amazon Rekognition Custom Labels Developer Guide.
This operation requires permissions to perform the
rekognition:CreateDataset
action. If you want to copy an existing
dataset, you also require permission to perform the
rekognition:ListDatasetEntries
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1146 def create_dataset(params = {}, = {}) req = build_request(:create_dataset, params) req.send_request() end |
#create_face_liveness_session(params = {}) ⇒ Types::CreateFaceLivenessSessionResponse
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. If no Amazon S3 bucket is defined,
raw bytes are sent instead.
You can use AuditImagesLimit
to limit the number of audit images
returned when GetFaceLivenessSessionResults
is called. This number
is between 0 and 4. By default, it is set to 0. The limit is best
effort and based on the duration of the selfie-video.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1204 def create_face_liveness_session(params = {}, = {}) req = build_request(:create_face_liveness_session, params) req.send_request() end |
#create_project(params = {}) ⇒ Types::CreateProjectResponse
Creates a new Amazon Rekognition project. A project is a group of
resources (datasets, model versions) that you use to create and manage
a Amazon Rekognition Custom Labels Model or custom adapter. You can
specify a feature to create the project with, if no feature is
specified then Custom Labels is used by default. For adapters, you can
also choose whether or not to have the project auto update by using
the AutoUpdate argument. This operation requires permissions to
perform the rekognition:CreateProject
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1270 def create_project(params = {}, = {}) req = build_request(:create_project, params) req.send_request() end |
#create_project_version(params = {}) ⇒ Types::CreateProjectVersionResponse
Creates a new version of Amazon Rekognition project (like a Custom
Labels model or a custom adapter) and begins training. Models and
adapters are managed as part of a Rekognition project. The response
from CreateProjectVersion
is an Amazon Resource Name (ARN) for the
project version.
The FeatureConfig operation argument allows you to configure specific
model or adapter settings. You can provide a description to the
project version by using the VersionDescription argment. Training can
take 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
. Once training has
successfully completed, call DescribeProjectVersions to get the
training results and evaluate the model.
This operation requires permissions to perform the
rekognition:CreateProjectVersion
action.
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.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1447 def create_project_version(params = {}, = {}) req = build_request(:create_project_version, params) req.send_request() end |
#create_stream_processor(params = {}) ⇒ Types::CreateStreamProcessorResponse
Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces or to detect labels in a streaming video.
Amazon Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. There are two different settings for stream processors in Amazon Rekognition: detecting faces and detecting labels.
If you are creating a stream processor for detecting faces, you provide as input a Kinesis video stream (
Input
) and a Kinesis data stream (Output
) stream for receiving the output. You must use theFaceSearch
option inSettings
, 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 theConnectedHome
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 withRegionsOfInterest
. 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.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1634 def create_stream_processor(params = {}, = {}) req = build_request(:create_stream_processor, params) req.send_request() end |
#create_user(params = {}) ⇒ Struct
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.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1690 def create_user(params = {}, = {}) req = build_request(:create_user, params) req.send_request() end |
#delete_collection(params = {}) ⇒ Types::DeleteCollectionResponse
Deletes the specified collection. Note that this operation removes all faces in the collection. For an example, see Deleting a collection.
This operation requires permissions to perform the
rekognition:DeleteCollection
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1739 def delete_collection(params = {}, = {}) req = build_request(:delete_collection, params) req.send_request() end |
#delete_dataset(params = {}) ⇒ Struct
Deletes an existing Amazon Rekognition Custom Labels dataset. Deleting
a dataset might take while. Use DescribeDataset to check the current
status. The dataset is still deleting if the value of Status
is
DELETE_IN_PROGRESS
. If you try to access the dataset after it is
deleted, you get a ResourceNotFoundException
exception.
You can't delete a dataset while it is creating (Status
=
CREATE_IN_PROGRESS
) or if the dataset is updating (Status
=
UPDATE_IN_PROGRESS
).
This operation requires permissions to perform the
rekognition:DeleteDataset
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1788 def delete_dataset(params = {}, = {}) req = build_request(:delete_dataset, params) req.send_request() end |
#delete_faces(params = {}) ⇒ Types::DeleteFacesResponse
Deletes faces from a collection. You specify a collection ID and an array of face IDs to remove from the collection.
This operation requires permissions to perform the
rekognition:DeleteFaces
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1848 def delete_faces(params = {}, = {}) req = build_request(:delete_faces, params) req.send_request() end |
#delete_project(params = {}) ⇒ Types::DeleteProjectResponse
Deletes a Amazon Rekognition project. To delete a project you must first delete all models or adapters associated with the project. To delete a model or adapter, 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.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1899 def delete_project(params = {}, = {}) req = build_request(:delete_project, params) req.send_request() end |
#delete_project_policy(params = {}) ⇒ Struct
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.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1954 def delete_project_policy(params = {}, = {}) req = build_request(:delete_project_policy, params) req.send_request() end |
#delete_project_version(params = {}) ⇒ Types::DeleteProjectVersionResponse
Deletes a Rekognition project model or project version, like a Amazon Rekognition Custom Labels model or a custom adapter.
You can't delete a project version if it is running or if it is training. To check the status of a project version, use the Status field returned from DescribeProjectVersions. To stop a project version call StopProjectVersion. If the project version is training, wait until it finishes.
This operation requires permissions to perform the
rekognition:DeleteProjectVersion
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2005 def delete_project_version(params = {}, = {}) req = build_request(:delete_project_version, params) req.send_request() end |
#delete_stream_processor(params = {}) ⇒ Struct
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
.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2029 def delete_stream_processor(params = {}, = {}) req = build_request(:delete_stream_processor, params) req.send_request() end |
#delete_user(params = {}) ⇒ Struct
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.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2080 def delete_user(params = {}, = {}) req = build_request(:delete_user, params) req.send_request() end |
#describe_collection(params = {}) ⇒ Types::DescribeCollectionResponse
Describes the specified collection. You can use DescribeCollection
to get information, such as the number of faces indexed into a
collection and the version of the model used by the collection for
face detection.
For more information, see Describing a Collection in the Amazon Rekognition Developer Guide.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2120 def describe_collection(params = {}, = {}) req = build_request(:describe_collection, params) req.send_request() end |
#describe_dataset(params = {}) ⇒ Types::DescribeDatasetResponse
Describes an Amazon Rekognition Custom Labels dataset. You can get information such as the current status of a dataset and statistics about the images and labels in a dataset.
This operation requires permissions to perform the
rekognition:DescribeDataset
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2190 def describe_dataset(params = {}, = {}) req = build_request(:describe_dataset, params) req.send_request() end |
#describe_project_versions(params = {}) ⇒ Types::DescribeProjectVersionsResponse
Lists and describes the versions of an Amazon Rekognition project. You
can specify up to 10 model or adapter versions in
ProjectVersionArns
. If you don't specify a value, descriptions for
all model/adapter versions in the project are returned.
This operation requires permissions to perform the
rekognition:DescribeProjectVersions
action.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
The following waiters are defined for this operation (see #wait_until for detailed usage):
- project_version_running
- project_version_training_completed
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2410 def describe_project_versions(params = {}, = {}) req = build_request(:describe_project_versions, params) req.send_request() end |
#describe_projects(params = {}) ⇒ Types::DescribeProjectsResponse
Gets information about your Rekognition projects.
This operation requires permissions to perform the
rekognition:DescribeProjects
action.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2515 def describe_projects(params = {}, = {}) req = build_request(:describe_projects, params) req.send_request() end |
#describe_stream_processor(params = {}) ⇒ Types::DescribeStreamProcessorResponse
Provides information about a stream processor created by CreateStreamProcessor. You can get information about the input and output streams, the input parameters for the face recognition being performed, and the current status of the stream processor.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2583 def describe_stream_processor(params = {}, = {}) req = build_request(:describe_stream_processor, params) req.send_request() end |
#detect_custom_labels(params = {}) ⇒ Types::DetectCustomLabelsResponse
Detects custom labels in a supplied image by using an Amazon Rekognition Custom Labels model.
You specify which version of a model version to use by using the
ProjectVersionArn
input parameter.
You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.
For each object that the model version detects on an image, the API
returns a (CustomLabel
) object in an array (CustomLabels
). Each
CustomLabel
object provides the label name (Name
), the level of
confidence that the image contains the object (Confidence
), and
object location information, if it exists, for the label on the image
(Geometry
). Note that for the DetectCustomLabelsLabels
operation,
Polygons
are not returned in the Geometry
section of the response.
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.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2754 def detect_custom_labels(params = {}, = {}) req = build_request(:detect_custom_labels, params) req.send_request() end |
#detect_faces(params = {}) ⇒ Types::DetectFacesResponse
Detects faces within an image that is provided as input.
DetectFaces
detects the 100 largest faces in the image. For each
face detected, the operation returns face details. These details
include a bounding box of the face, a confidence value (that the
bounding box contains a face), and a fixed set of attributes such as
facial landmarks (for example, coordinates of eye and mouth), 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 operation requires permissions to perform the
rekognition:DetectFaces
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2944 def detect_faces(params = {}, = {}) req = build_request(:detect_faces, params) req.send_request() end |
#detect_labels(params = {}) ⇒ Types::DetectLabelsResponse
Detects instances of real-world entities within an image (JPEG or PNG) provided as input. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; and concepts like landscape, evening, and nature.
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 GENERAL_LABELS
and
IMAGE_PROPERTIES
feature types when calling the DetectLabels API.
Including GENERAL_LABELS
will ensure the response includes the
labels detected in the input image, while including IMAGE_PROPERTIES
will ensure the response includes information about the image quality
and color.
When using GENERAL_LABELS
and/or IMAGE_PROPERTIES
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.
When getting labels, 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. These arguments
are only valid when supplying GENERAL_LABELS as a feature type.
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.
This is a stateless API operation that doesn't return any data.
This operation requires permissions to perform the
rekognition:DetectLabels
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3243 def detect_labels(params = {}, = {}) req = build_request(:detect_labels, params) req.send_request() end |
#detect_moderation_labels(params = {}) ⇒ Types::DetectModerationLabelsResponse
Detects unsafe content in a specified JPEG or PNG format image. Use
DetectModerationLabels
to moderate images depending on your
requirements. For example, you might want to filter images that
contain nudity, but not images containing suggestive content.
To filter images, use the labels returned by DetectModerationLabels
to determine which types of content are appropriate.
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.
You can specify an adapter to use when retrieving label predictions by
providing a ProjectVersionArn
to the ProjectVersion
argument.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3343 def detect_moderation_labels(params = {}, = {}) req = build_request(:detect_moderation_labels, params) req.send_request() end |
#detect_protective_equipment(params = {}) ⇒ Types::DetectProtectiveEquipmentResponse
Detects Personal Protective Equipment (PPE) worn by people detected in an image. Amazon Rekognition can detect the following types of PPE.
Face cover
Hand cover
Head cover
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.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3452 def detect_protective_equipment(params = {}, = {}) req = build_request(:detect_protective_equipment, params) req.send_request() end |
#detect_text(params = {}) ⇒ Types::DetectTextResponse
Detects text in the input image and converts it into machine-readable text.
Pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, you must pass it as a reference to an image in an Amazon S3 bucket. For the AWS CLI, passing image bytes is not supported. The image must be either a .png or .jpeg formatted file.
The DetectText
operation returns text in an array of TextDetection
elements, TextDetections
. Each TextDetection
element provides
information about a single word or line of text that was detected in
the image.
A word is one or more script characters that are not separated by
spaces. DetectText
can detect up to 100 words in an image.
A line is a string of equally spaced words. A line isn't necessarily
a complete sentence. For example, a driver's license number is
detected as a line. A line ends when there is no aligned text after
it. Also, a line ends when there is a large gap between words,
relative to the length of the words. This means, depending on the gap
between words, Amazon Rekognition may detect multiple lines in text
aligned in the same direction. Periods don't represent the end of a
line. If a sentence spans multiple lines, the DetectText
operation
returns multiple lines.
To determine whether a TextDetection
element is a line of text or a
word, use the TextDetection
object Type
field.
To be detected, text must be within +/- 90 degrees orientation of the horizontal axis.
For more information, see Detecting text in the Amazon Rekognition Developer Guide.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3568 def detect_text(params = {}, = {}) req = build_request(:detect_text, params) req.send_request() end |
#disassociate_faces(params = {}) ⇒ Types::DisassociateFacesResponse
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.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3664 def disassociate_faces(params = {}, = {}) req = build_request(:disassociate_faces, params) req.send_request() end |
#distribute_dataset_entries(params = {}) ⇒ Struct
Distributes the entries (images) in a training dataset across the
training dataset and the test dataset for a project.
DistributeDatasetEntries
moves 20% of the training dataset images to
the test dataset. An entry is a JSON Line that describes an image.
You supply the Amazon Resource Names (ARN) of a project's training dataset and test dataset. The training dataset must contain the images that you want to split. The test dataset must be empty. The datasets must belong to the same project. To create training and test datasets for a project, call CreateDataset.
Distributing a dataset takes a while to complete. To check the status
call DescribeDataset
. The operation is complete when the Status
field for the training dataset and the test dataset is
UPDATE_COMPLETE
. If the dataset split fails, the value of Status
is UPDATE_FAILED
.
This operation requires permissions to perform the
rekognition:DistributeDatasetEntries
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3732 def distribute_dataset_entries(params = {}, = {}) req = build_request(:distribute_dataset_entries, params) req.send_request() end |
#get_celebrity_info(params = {}) ⇒ Types::GetCelebrityInfoResponse
Gets the name and additional information about a celebrity based on their Amazon Rekognition ID. The additional information is returned as an array of URLs. If there is no additional information about the celebrity, this list is empty.
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.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3774 def get_celebrity_info(params = {}, = {}) req = build_request(:get_celebrity_info, params) req.send_request() end |
#get_celebrity_recognition(params = {}) ⇒ Types::GetCelebrityRecognitionResponse
Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by StartCelebrityRecognition.
Celebrity recognition in a video is an asynchronous operation.
Analysis is started by a call to StartCelebrityRecognition which
returns a job identifier (JobId
).
When the celebrity recognition operation finishes, Amazon Rekognition
Video publishes a completion status to the Amazon Simple Notification
Service topic registered in the initial call to
StartCelebrityRecognition
. To get the results of the celebrity
recognition analysis, first check that the status value published to
the Amazon SNS topic is SUCCEEDED
. If so, call
GetCelebrityDetection
and pass the job identifier (JobId
) from the
initial call to StartCelebrityDetection
.
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
.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3949 def get_celebrity_recognition(params = {}, = {}) req = build_request(:get_celebrity_recognition, params) req.send_request() end |
#get_content_moderation(params = {}) ⇒ Types::GetContentModerationResponse
Gets the inappropriate, unwanted, or offensive content analysis results for a Amazon Rekognition Video analysis started by StartContentModeration. For a list of moderation labels in Amazon Rekognition, see Using the image and video moderation APIs.
Amazon Rekognition Video inappropriate or offensive content detection
in a stored video is an asynchronous operation. You start analysis by
calling StartContentModeration which returns a job identifier
(JobId
). When analysis finishes, Amazon Rekognition Video publishes
a completion status to the Amazon Simple Notification Service topic
registered in the initial call to StartContentModeration
. To get the
results of the content analysis, first check that the status value
published to the Amazon SNS topic is SUCCEEDED
. If so, call
GetContentModeration
and pass the job identifier (JobId
) from the
initial call to StartContentModeration
.
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.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4087 def get_content_moderation(params = {}, = {}) req = build_request(:get_content_moderation, params) req.send_request() end |
#get_face_detection(params = {}) ⇒ Types::GetFaceDetectionResponse
Gets face detection results for a Amazon Rekognition Video analysis started by StartFaceDetection.
Face detection with Amazon Rekognition Video is an asynchronous
operation. You start face detection by calling StartFaceDetection
which returns a job identifier (JobId
). When the face detection
operation finishes, Amazon Rekognition Video publishes a completion
status to the Amazon Simple Notification Service topic registered in
the initial call to StartFaceDetection
. To get the results of the
face detection operation, first check that the status value published
to the Amazon SNS topic is SUCCEEDED
. If so, call GetFaceDetection
and pass the job identifier (JobId
) from the initial call to
StartFaceDetection
.
GetFaceDetection
returns an array of detected faces (Faces
) sorted
by the time the faces were detected.
Use MaxResults parameter to limit the number of labels returned. If
there are more results than specified in MaxResults
, the value of
NextToken
in the operation response contains a pagination token for
getting the next set of results. To get the next page of results, call
GetFaceDetection
and populate the NextToken
request parameter with
the token value returned from the previous call to GetFaceDetection
.
Note that for the GetFaceDetection
operation, the returned values
for FaceOccluded
and EyeDirection
will always be "null".
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4218 def get_face_detection(params = {}, = {}) req = build_request(:get_face_detection, params) req.send_request() end |
#get_face_liveness_session_results(params = {}) ⇒ Types::GetFaceLivenessSessionResultsResponse
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 number of audit images returned by GetFaceLivenessSessionResults
is defined by the AuditImagesLimit
paramater when calling
CreateFaceLivenessSession
. Reference images are always returned when
possible.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4279 def get_face_liveness_session_results(params = {}, = {}) req = build_request(:get_face_liveness_session_results, params) req.send_request() end |
#get_face_search(params = {}) ⇒ Types::GetFaceSearchResponse
Gets the face search results for Amazon Rekognition Video face search started by StartFaceSearch. The search returns faces in a collection that match the faces of persons detected in a video. It also includes the time(s) that faces are matched in the video.
Face search in a video is an asynchronous operation. You start face
search by calling to StartFaceSearch which returns a job identifier
(JobId
). When the search operation finishes, Amazon Rekognition
Video publishes a completion status to the Amazon Simple Notification
Service topic registered in the initial call to StartFaceSearch
. To
get the search results, first check that the status value published to
the Amazon SNS topic is SUCCEEDED
. If so, call GetFaceSearch
and
pass the job identifier (JobId
) from the initial call to
StartFaceSearch
.
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.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4442 def get_face_search(params = {}, = {}) req = build_request(:get_face_search, params) req.send_request() end |
#get_label_detection(params = {}) ⇒ Types::GetLabelDetectionResponse
Gets the label detection results of a Amazon Rekognition Video analysis started by StartLabelDetection.
The label detection operation is started by a call to
StartLabelDetection which returns a job identifier (JobId
). When the
label detection operation finishes, Amazon Rekognition publishes a
completion status to the Amazon Simple Notification Service topic
registered in the initial call to StartlabelDetection
.
To get the results of the label detection operation, first check that
the status value published to the Amazon SNS topic is SUCCEEDED
. If
so, call GetLabelDetection and pass the job identifier (JobId
) from
the initial call to StartLabelDetection
.
GetLabelDetection
returns an array of detected labels (Labels
)
sorted by the time the labels were detected. You can also sort by the
label name by specifying NAME
for the SortBy
input parameter. 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
, theStartTimestampMillis
,EndTimestampMillis
, andDurationMillis
structures are what define a segment. Although the “Timestamp” structure is still returned with each label, its value is set to be the same asStartTimestampMillis
.
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
.
If you are retrieving results while using the Amazon Simple Notification Service, note that you will receive an "ERROR" notification if the job encounters an issue.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4631 def get_label_detection(params = {}, = {}) req = build_request(:get_label_detection, params) req.send_request() end |
#get_media_analysis_job(params = {}) ⇒ Types::GetMediaAnalysisJobResponse
Retrieves the results for a given media analysis job. Takes a JobId
returned by StartMediaAnalysisJob.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4737 def get_media_analysis_job(params = {}, = {}) req = build_request(:get_media_analysis_job, params) req.send_request() end |
#get_person_tracking(params = {}) ⇒ Types::GetPersonTrackingResponse
Gets the path tracking results of a Amazon Rekognition Video analysis started by StartPersonTracking.
The person path tracking operation is started by a call to
StartPersonTracking
which returns a job identifier (JobId
). When
the operation finishes, Amazon Rekognition Video publishes a
completion status to the Amazon Simple Notification Service topic
registered in the initial call to StartPersonTracking
.
To get the results of the person path tracking operation, first check
that the status value published to the Amazon SNS topic is
SUCCEEDED
. If so, call GetPersonTracking and pass the job identifier
(JobId
) from the initial call to StartPersonTracking
.
GetPersonTracking
returns an array, Persons
, of tracked persons
and the time(s) their paths were tracked in the video.
GetPersonTracking
only returns the default facial attributes
(BoundingBox
, Confidence
, Landmarks
, Pose
, and Quality
). The
other facial attributes listed in the Face
object of the following
response syntax are not returned.
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
.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4893 def get_person_tracking(params = {}, = {}) req = build_request(:get_person_tracking, params) req.send_request() end |
#get_segment_detection(params = {}) ⇒ Types::GetSegmentDetectionResponse
Gets the segment detection results of a Amazon Rekognition Video analysis started by StartSegmentDetection.
Segment detection with Amazon Rekognition Video is an asynchronous
operation. You start segment detection by calling
StartSegmentDetection which returns a job identifier (JobId
). When
the segment detection operation finishes, Amazon Rekognition publishes
a completion status to the Amazon Simple Notification Service topic
registered in the initial call to StartSegmentDetection
. To get the
results of the segment detection operation, first check that the
status value published to the Amazon SNS topic is SUCCEEDED
. if so,
call GetSegmentDetection
and pass the job identifier (JobId
) from
the initial call of StartSegmentDetection
.
GetSegmentDetection
returns detected segments in an array
(Segments
) of SegmentDetection objects. Segments
is sorted by the
segment types specified in the SegmentTypes
input parameter of
StartSegmentDetection
. Each element of the array includes the
detected segment, the precentage confidence in the acuracy of the
detected segment, the type of the segment, and the frame in which the
segment was detected.
Use SelectedSegmentTypes
to find out the type of segment detection
requested in the call to StartSegmentDetection
.
Use the MaxResults
parameter to limit the number of segment
detections returned. If there are more results than specified in
MaxResults
, the value of NextToken
in the operation response
contains a pagination token for getting the next set of results. To
get the next page of results, call GetSegmentDetection
and populate
the NextToken
request parameter with the token value returned from
the previous call to GetSegmentDetection
.
For more information, see Detecting video segments in stored video in the Amazon Rekognition Developer Guide.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5015 def get_segment_detection(params = {}, = {}) req = build_request(:get_segment_detection, params) req.send_request() end |
#get_text_detection(params = {}) ⇒ Types::GetTextDetectionResponse
Gets the text detection results of a Amazon Rekognition Video analysis started by StartTextDetection.
Text detection with Amazon Rekognition Video is an asynchronous
operation. You start text detection by calling StartTextDetection
which returns a job identifier (JobId
) When the text detection
operation finishes, Amazon Rekognition publishes a completion status
to the Amazon Simple Notification Service topic registered in the
initial call to StartTextDetection
. To get the results of the text
detection operation, first check that the status value published to
the Amazon SNS topic is SUCCEEDED
. if so, call GetTextDetection
and pass the job identifier (JobId
) from the initial call of
StartLabelDetection
.
GetTextDetection
returns an array of detected text
(TextDetections
) sorted by the time the text was detected, up to 100
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
.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5123 def get_text_detection(params = {}, = {}) req = build_request(:get_text_detection, params) req.send_request() end |
#index_faces(params = {}) ⇒ Types::IndexFacesResponse
Detects faces in the input image and adds them to the specified collection.
Amazon Rekognition doesn't save the actual faces that are detected. Instead, the underlying detection algorithm first detects the faces in the input image. For each face, the algorithm extracts facial features into a feature vector, and stores it in the backend database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the SearchFaces and SearchFacesByImage operations.
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
.
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.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5567 def index_faces(params = {}, = {}) req = build_request(:index_faces, params) req.send_request() end |
#list_collections(params = {}) ⇒ Types::ListCollectionsResponse
Returns list of collection IDs in your account. If the result is
truncated, the response also provides a NextToken
that you can use
in the subsequent request to fetch the next set of collection IDs.
For an example, see Listing collections in the Amazon Rekognition Developer Guide.
This operation requires permissions to perform the
rekognition:ListCollections
action.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5628 def list_collections(params = {}, = {}) req = build_request(:list_collections, params) req.send_request() end |
#list_dataset_entries(params = {}) ⇒ Types::ListDatasetEntriesResponse
Lists the entries (images) within a dataset. An entry is a JSON Line that contains the information for a single image, including the image location, assigned labels, and object location bounding boxes. For more information, see Creating a manifest file.
JSON Lines in the response include information about non-terminal
errors found in the dataset. Non terminal errors are reported in
errors
lists within each JSON Line. The same information is reported
in the training and testing validation result manifests that Amazon
Rekognition Custom Labels creates during model training.
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.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5749 def list_dataset_entries(params = {}, = {}) req = build_request(:list_dataset_entries, params) req.send_request() end |
#list_dataset_labels(params = {}) ⇒ Types::ListDatasetLabelsResponse
Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see Labeling images.
Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to describe images. For more information, see Labeling images in the Amazon Rekognition Custom Labels Developer Guide.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5845 def list_dataset_labels(params = {}, = {}) req = build_request(:list_dataset_labels, params) req.send_request() end |
#list_faces(params = {}) ⇒ Types::ListFacesResponse
Returns metadata for faces in the specified collection. This metadata includes information such as the bounding box coordinates, the confidence (that the bounding box contains a face), and face ID. For an example, see Listing Faces in a Collection in the Amazon Rekognition Developer Guide.
This operation requires permissions to perform the
rekognition:ListFaces
action.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.