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Class: Aws::Rekognition::Client

Inherits:
Seahorse::Client::Base show all
Includes:
ClientStubs
Defined in:
gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb

Instance Attribute Summary

Attributes inherited from Seahorse::Client::Base

#config, #handlers

API Operations collapse

Instance Method Summary collapse

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

Parameters:

  • options (Hash)

Options Hash (options):

  • :credentials (required, Aws::CredentialProvider) —

    Your AWS credentials. This can be an instance of any one of the following classes:

    • Aws::Credentials - Used for configuring static, non-refreshing credentials.

    • Aws::InstanceProfileCredentials - Used for loading credentials from an EC2 IMDS on an EC2 instance.

    • Aws::SharedCredentials - Used for loading credentials from a shared file, such as ~/.aws/config.

    • Aws::AssumeRoleCredentials - Used when you need to assume a role.

    When :credentials are not configured directly, the following locations will be searched for credentials:

    • Aws.config[:credentials]
    • The :access_key_id, :secret_access_key, and :session_token options.
    • ENV['AWS_ACCESS_KEY_ID'], ENV['AWS_SECRET_ACCESS_KEY']
    • ~/.aws/credentials
    • ~/.aws/config
    • EC2 IMDS instance profile - When used by default, the timeouts are very aggressive. Construct and pass an instance of Aws::InstanceProfileCredentails to enable retries and extended timeouts.
  • :region (required, String) —

    The AWS region to connect to. The configured :region is used to determine the service :endpoint. When not passed, a default :region is search for in the following locations:

    • Aws.config[:region]
    • ENV['AWS_REGION']
    • ENV['AMAZON_REGION']
    • ENV['AWS_DEFAULT_REGION']
    • ~/.aws/credentials
    • ~/.aws/config
  • :access_key_id (String)
  • :active_endpoint_cache (Boolean) — default: false —

    When set to true, a thread polling for endpoints will be running in the background every 60 secs (default). Defaults to false.

  • :client_side_monitoring (Boolean) — default: false —

    When true, client-side metrics will be collected for all API requests from this client.

  • :client_side_monitoring_client_id (String) — default: "" —

    Allows you to provide an identifier for this client which will be attached to all generated client side metrics. Defaults to an empty string.

  • :client_side_monitoring_host (String) — default: "127.0.0.1" —

    Allows you to specify the DNS hostname or IPv4 or IPv6 address that the client side monitoring agent is running on, where client metrics will be published via UDP.

  • :client_side_monitoring_port (Integer) — default: 31000 —

    Required for publishing client metrics. The port that the client side monitoring agent is running on, where client metrics will be published via UDP.

  • :client_side_monitoring_publisher (Aws::ClientSideMonitoring::Publisher) — default: Aws::ClientSideMonitoring::Publisher —

    Allows you to provide a custom client-side monitoring publisher class. By default, will use the Client Side Monitoring Agent Publisher.

  • :convert_params (Boolean) — default: true —

    When true, an attempt is made to coerce request parameters into the required types.

  • :disable_host_prefix_injection (Boolean) — default: false —

    Set to true to disable SDK automatically adding host prefix to default service endpoint when available.

  • :endpoint (String) —

    The client endpoint is normally constructed from the :region option. You should only configure an :endpoint when connecting to test endpoints. This should be avalid HTTP(S) URI.

  • :endpoint_cache_max_entries (Integer) — default: 1000 —

    Used for the maximum size limit of the LRU cache storing endpoints data for endpoint discovery enabled operations. Defaults to 1000.

  • :endpoint_cache_max_threads (Integer) — default: 10 —

    Used for the maximum threads in use for polling endpoints to be cached, defaults to 10.

  • :endpoint_cache_poll_interval (Integer) — default: 60 —

    When :endpoint_discovery and :active_endpoint_cache is enabled, Use this option to config the time interval in seconds for making requests fetching endpoints information. Defaults to 60 sec.

  • :endpoint_discovery (Boolean) — default: false —

    When set to true, endpoint discovery will be enabled for operations when available. Defaults to false.

  • :log_formatter (Aws::Log::Formatter) — default: Aws::Log::Formatter.default —

    The log formatter.

  • :log_level (Symbol) — default: :info —

    The log level to send messages to the :logger at.

  • :logger (Logger) —

    The Logger instance to send log messages to. If this option is not set, logging will be disabled.

  • :profile (String) — default: "default" —

    Used when loading credentials from the shared credentials file at HOME/.aws/credentials. When not specified, 'default' is used.

  • :retry_base_delay (Float) — default: 0.3 —

    The base delay in seconds used by the default backoff function.

  • :retry_jitter (Symbol) — default: :none —

    A delay randomiser function used by the default backoff function. Some predefined functions can be referenced by name - :none, :equal, :full, otherwise a Proc that takes and returns a number.

    @see https://www.awsarchitectureblog.com/2015/03/backoff.html

  • :retry_limit (Integer) — default: 3 —

    The maximum number of times to retry failed requests. Only ~ 500 level server errors and certain ~ 400 level client errors are retried. Generally, these are throttling errors, data checksum errors, networking errors, timeout errors and auth errors from expired credentials.

  • :retry_max_delay (Integer) — default: 0 —

    The maximum number of seconds to delay between retries (0 for no limit) used by the default backoff function.

  • :secret_access_key (String)
  • :session_token (String)
  • :simple_json (Boolean) — default: false —

    Disables request parameter conversion, validation, and formatting. Also disable response data type conversions. This option is useful when you want to ensure the highest level of performance by avoiding overhead of walking request parameters and response data structures.

    When :simple_json is enabled, the request parameters hash must be formatted exactly as the DynamoDB API expects.

  • :stub_responses (Boolean) — default: false —

    Causes the client to return stubbed responses. By default fake responses are generated and returned. You can specify the response data to return or errors to raise by calling ClientStubs#stub_responses. See ClientStubs for more information.

    Please note When response stubbing is enabled, no HTTP requests are made, and retries are disabled.

  • :validate_params (Boolean) — default: true —

    When true, request parameters are validated before sending the request.

  • :http_proxy (URI::HTTP, String) —

    A proxy to send requests through. Formatted like 'http://proxy.com:123'.

  • :http_open_timeout (Float) — default: 15 —

    The number of seconds to wait when opening a HTTP session before rasing a Timeout::Error.

  • :http_read_timeout (Integer) — default: 60 —

    The default number of seconds to wait for response data. This value can safely be set per-request on the session yeidled by #session_for.

  • :http_idle_timeout (Float) — default: 5 —

    The number of seconds a connection is allowed to sit idble before it is considered stale. Stale connections are closed and removed from the pool before making a request.

  • :http_continue_timeout (Float) — default: 1 —

    The number of seconds to wait for a 100-continue response before sending the request body. This option has no effect unless the request has "Expect" header set to "100-continue". Defaults to nil which disables this behaviour. This value can safely be set per request on the session yeidled by #session_for.

  • :http_wire_trace (Boolean) — default: false —

    When true, HTTP debug output will be sent to the :logger.

  • :ssl_verify_peer (Boolean) — default: true —

    When true, SSL peer certificates are verified when establishing a connection.

  • :ssl_ca_bundle (String) —

    Full path to the SSL certificate authority bundle file that should be used when verifying peer certificates. If you do not pass :ssl_ca_bundle or :ssl_ca_directory the the system default will be used if available.

  • :ssl_ca_directory (String) —

    Full path of the directory that contains the unbundled SSL certificate authority files for verifying peer certificates. If you do not pass :ssl_ca_bundle or :ssl_ca_directory the the system default will be used if available.



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 261

def initialize(*args)
  super
end

Instance Method Details

#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.

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, role, 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.

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.

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.

Examples:

Example: To compare two images


# This operation compares the largest face detected in the source image with each face detected in the target image.

resp = client.compare_faces({
  similarity_threshold: 90, 
  source_image: {
    s3_object: {
      bucket: "mybucket", 
      name: "mysourceimage", 
    }, 
  }, 
  target_image: {
    s3_object: {
      bucket: "mybucket", 
      name: "mytargetimage", 
    }, 
  }, 
})

resp.to_h outputs the following:
{
  face_matches: [
    {
      face: {
        bounding_box: {
          height: 0.33481481671333313, 
          left: 0.31888890266418457, 
          top: 0.4933333396911621, 
          width: 0.25, 
        }, 
        confidence: 99.9991226196289, 
      }, 
      similarity: 100, 
    }, 
  ], 
  source_image_face: {
    bounding_box: {
      height: 0.33481481671333313, 
      left: 0.31888890266418457, 
      top: 0.4933333396911621, 
      width: 0.25, 
    }, 
    confidence: 99.9991226196289, 
  }, 
}

Request syntax with placeholder values


resp = client.compare_faces({
  source_image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  target_image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  similarity_threshold: 1.0,
  quality_filter: "NONE", # accepts NONE, AUTO, LOW, MEDIUM, HIGH
})

Response structure


resp.source_image_face.bounding_box.width #=> Float
resp.source_image_face.bounding_box.height #=> Float
resp.source_image_face.bounding_box.left #=> Float
resp.source_image_face.bounding_box.top #=> Float
resp.source_image_face.confidence #=> Float
resp.face_matches #=> Array
resp.face_matches[0].similarity #=> Float
resp.face_matches[0].face.bounding_box.width #=> Float
resp.face_matches[0].face.bounding_box.height #=> Float
resp.face_matches[0].face.bounding_box.left #=> Float
resp.face_matches[0].face.bounding_box.top #=> Float
resp.face_matches[0].face.confidence #=> Float
resp.face_matches[0].face.landmarks #=> Array
resp.face_matches[0].face.landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.face_matches[0].face.landmarks[0].x #=> Float
resp.face_matches[0].face.landmarks[0].y #=> Float
resp.face_matches[0].face.pose.roll #=> Float
resp.face_matches[0].face.pose.yaw #=> Float
resp.face_matches[0].face.pose.pitch #=> Float
resp.face_matches[0].face.quality.brightness #=> Float
resp.face_matches[0].face.quality.sharpness #=> Float
resp.unmatched_faces #=> Array
resp.unmatched_faces[0].bounding_box.width #=> Float
resp.unmatched_faces[0].bounding_box.height #=> Float
resp.unmatched_faces[0].bounding_box.left #=> Float
resp.unmatched_faces[0].bounding_box.top #=> Float
resp.unmatched_faces[0].confidence #=> Float
resp.unmatched_faces[0].landmarks #=> Array
resp.unmatched_faces[0].landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.unmatched_faces[0].landmarks[0].x #=> Float
resp.unmatched_faces[0].landmarks[0].y #=> Float
resp.unmatched_faces[0].pose.roll #=> Float
resp.unmatched_faces[0].pose.yaw #=> Float
resp.unmatched_faces[0].pose.pitch #=> Float
resp.unmatched_faces[0].quality.brightness #=> Float
resp.unmatched_faces[0].quality.sharpness #=> Float
resp.source_image_orientation_correction #=> String, one of "ROTATE_0", "ROTATE_90", "ROTATE_180", "ROTATE_270"
resp.target_image_orientation_correction #=> String, one of "ROTATE_0", "ROTATE_90", "ROTATE_180", "ROTATE_270"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :source_image (required, Types::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 (required, Types::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.

  • :similarity_threshold (Float) —

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

  • :quality_filter (String) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 493

def compare_faces(params = {}, options = {})
  req = build_request(:compare_faces, params)
  req.send_request(options)
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.

Collection names are case-sensitive.

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

Examples:

Example: To create a collection


# This operation creates a Rekognition collection for storing image data.

resp = client.create_collection({
  collection_id: "myphotos", 
})

resp.to_h outputs the following:
{
  collection_arn: "aws:rekognition:us-west-2:123456789012:collection/myphotos", 
  status_code: 200, 
}

Request syntax with placeholder values


resp = client.create_collection({
  collection_id: "CollectionId", # required
})

Response structure


resp.status_code #=> Integer
resp.collection_arn #=> String
resp.face_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :collection_id (required, String) —

    ID for the collection that you are creating.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 554

def create_collection(params = {}, options = {})
  req = build_request(:create_collection, params)
  req.send_request(options)
end

#create_project(params = {}) ⇒ Types::CreateProjectResponse

Creates a new Amazon Rekognition Custom Labels project. A project is a logical grouping of resources (images, Labels, models) and operations (training, evaluation and detection).

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

Examples:

Request syntax with placeholder values


resp = client.create_project({
  project_name: "ProjectName", # required
})

Response structure


resp.project_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :project_name (required, String) —

    The name of the project to create.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 585

def create_project(params = {}, options = {})
  req = build_request(:create_project, params)
  req.send_request(options)
end

#create_project_version(params = {}) ⇒ Types::CreateProjectVersionResponse

Creates a new version of a model and begins training. Models are managed as part of an Amazon Rekognition Custom Labels project. You can specify one training dataset and one testing dataset. The response from CreateProjectVersion is an Amazon Resource Name (ARN) for the version of the model.

Training takes a while to complete. You can get the current status by calling DescribeProjectVersions.

Once training has successfully completed, call DescribeProjectVersions to get the training results and evaluate the model.

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

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

Examples:

Request syntax with placeholder values


resp = client.create_project_version({
  project_arn: "ProjectArn", # required
  version_name: "VersionName", # required
  output_config: { # required
    s3_bucket: "S3Bucket",
    s3_key_prefix: "S3KeyPrefix",
  },
  training_data: { # required
    assets: [
      {
        ground_truth_manifest: {
          s3_object: {
            bucket: "S3Bucket",
            name: "S3ObjectName",
            version: "S3ObjectVersion",
          },
        },
      },
    ],
  },
  testing_data: { # required
    assets: [
      {
        ground_truth_manifest: {
          s3_object: {
            bucket: "S3Bucket",
            name: "S3ObjectName",
            version: "S3ObjectVersion",
          },
        },
      },
    ],
    auto_create: false,
  },
})

Response structure


resp.project_version_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :project_arn (required, String) —

    The ARN of the Amazon Rekognition Custom Labels project that manages the model that you want to train.

  • :version_name (required, String) —

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

  • :output_config (required, Types::OutputConfig) —

    The Amazon S3 location to store the results of training.

  • :training_data (required, Types::TrainingData) —

    The dataset to use for training.

  • :testing_data (required, Types::TestingData) —

    The dataset to use for testing.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 672

def create_project_version(params = {}, options = {})
  req = build_request(:create_project_version, params)
  req.send_request(options)
end

#create_stream_processor(params = {}) ⇒ Types::CreateStreamProcessorResponse

Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces in a streaming video.

Amazon Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. Amazon Rekognition Video sends analysis results to Amazon Kinesis Data Streams.

You provide as input a Kinesis video stream (Input) and a Kinesis data stream (Output) stream. You also specify the face recognition criteria in Settings. For example, the collection containing faces that you want to recognize. 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.

After you have finished analyzing a streaming video, use StopStreamProcessor to stop processing. You can delete the stream processor by calling DeleteStreamProcessor.

Examples:

Request syntax with placeholder values


resp = client.create_stream_processor({
  input: { # required
    kinesis_video_stream: {
      arn: "KinesisVideoArn",
    },
  },
  output: { # required
    kinesis_data_stream: {
      arn: "KinesisDataArn",
    },
  },
  name: "StreamProcessorName", # required
  settings: { # required
    face_search: {
      collection_id: "CollectionId",
      face_match_threshold: 1.0,
    },
  },
  role_arn: "RoleArn", # required
})

Response structure


resp.stream_processor_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :input (required, Types::StreamProcessorInput) —

    Kinesis video stream stream that provides the source streaming video. If you are using the AWS CLI, the parameter name is StreamProcessorInput.

  • :output (required, Types::StreamProcessorOutput) —

    Kinesis data stream stream to which Amazon Rekognition Video puts the analysis results. If you are using the AWS CLI, the parameter name is StreamProcessorOutput.

  • :name (required, String) —

    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.

  • :settings (required, Types::StreamProcessorSettings) —

    Face recognition input parameters to be used by the stream processor. Includes the collection to use for face recognition and the face attributes to detect.

  • :role_arn (required, String) —

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

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 753

def create_stream_processor(params = {}, options = {})
  req = build_request(:create_stream_processor, params)
  req.send_request(options)
end

#delete_collection(params = {}) ⇒ Types::DeleteCollectionResponse

Deletes the specified collection. Note that this operation removes all faces in the collection. For an example, see delete-collection-procedure.

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

Examples:

Example: To delete a collection


# This operation deletes a Rekognition collection.

resp = client.delete_collection({
  collection_id: "myphotos", 
})

resp.to_h outputs the following:
{
  status_code: 200, 
}

Request syntax with placeholder values


resp = client.delete_collection({
  collection_id: "CollectionId", # required
})

Response structure


resp.status_code #=> Integer

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :collection_id (required, String) —

    ID of the collection to delete.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 798

def delete_collection(params = {}, options = {})
  req = build_request(:delete_collection, params)
  req.send_request(options)
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.

Examples:

Example: To delete a face


# This operation deletes one or more faces from a Rekognition collection.

resp = client.delete_faces({
  collection_id: "myphotos", 
  face_ids: [
    "ff43d742-0c13-5d16-a3e8-03d3f58e980b", 
  ], 
})

resp.to_h outputs the following:
{
  deleted_faces: [
    "ff43d742-0c13-5d16-a3e8-03d3f58e980b", 
  ], 
}

Request syntax with placeholder values


resp = client.delete_faces({
  collection_id: "CollectionId", # required
  face_ids: ["FaceId"], # required
})

Response structure


resp.deleted_faces #=> Array
resp.deleted_faces[0] #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :collection_id (required, String) —

    Collection from which to remove the specific faces.

  • :face_ids (required, Array<String>) —

    An array of face IDs to delete.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 852

def delete_faces(params = {}, options = {})
  req = build_request(:delete_faces, params)
  req.send_request(options)
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.

Examples:

Request syntax with placeholder values


resp = client.delete_stream_processor({
  name: "StreamProcessorName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name (required, String) —

    The name of the stream processor you want to delete.

Returns:

  • (Struct) —

    Returns an empty response.



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 876

def delete_stream_processor(params = {}, options = {})
  req = build_request(:delete_stream_processor, params)
  req.send_request(options)
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.

Examples:

Request syntax with placeholder values


resp = client.describe_collection({
  collection_id: "CollectionId", # required
})

Response structure


resp.face_count #=> Integer
resp.face_model_version #=> String
resp.collection_arn #=> String
resp.creation_timestamp #=> Time

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :collection_id (required, String) —

    The ID of the collection to describe.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 914

def describe_collection(params = {}, options = {})
  req = build_request(:describe_collection, params)
  req.send_request(options)
end

#describe_project_versions(params = {}) ⇒ Types::DescribeProjectVersionsResponse

Lists and describes the models 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 models are returned.

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

Examples:

Request syntax with placeholder values


resp = client.describe_project_versions({
  project_arn: "ProjectArn", # required
  version_names: ["VersionName"],
  next_token: "ExtendedPaginationToken",
  max_results: 1,
})

Response structure


resp.project_version_descriptions #=> Array
resp.project_version_descriptions[0].project_version_arn #=> String
resp.project_version_descriptions[0].creation_timestamp #=> Time
resp.project_version_descriptions[0].min_inference_units #=> Integer
resp.project_version_descriptions[0].status #=> String, one of "TRAINING_IN_PROGRESS", "TRAINING_COMPLETED", "TRAINING_FAILED", "STARTING", "RUNNING", "FAILED", "STOPPING", "STOPPED", "DELETING"
resp.project_version_descriptions[0].status_message #=> String
resp.project_version_descriptions[0].billable_training_time_in_seconds #=> Integer
resp.project_version_descriptions[0].training_end_timestamp #=> Time
resp.project_version_descriptions[0].output_config.s3_bucket #=> String
resp.project_version_descriptions[0].output_config.s3_key_prefix #=> String
resp.project_version_descriptions[0].training_data_result.input.assets #=> Array
resp.project_version_descriptions[0].training_data_result.input.assets[0].ground_truth_manifest.s3_object.bucket #=> String
resp.project_version_descriptions[0].training_data_result.input.assets[0].ground_truth_manifest.s3_object.name #=> String
resp.project_version_descriptions[0].training_data_result.input.assets[0].ground_truth_manifest.s3_object.version #=> String
resp.project_version_descriptions[0].training_data_result.output.assets #=> Array
resp.project_version_descriptions[0].training_data_result.output.assets[0].ground_truth_manifest.s3_object.bucket #=> String
resp.project_version_descriptions[0].training_data_result.output.assets[0].ground_truth_manifest.s3_object.name #=> String
resp.project_version_descriptions[0].training_data_result.output.assets[0].ground_truth_manifest.s3_object.version #=> String
resp.project_version_descriptions[0].testing_data_result.input.assets #=> Array
resp.project_version_descriptions[0].testing_data_result.input.assets[0].ground_truth_manifest.s3_object.bucket #=> String
resp.project_version_descriptions[0].testing_data_result.input.assets[0].ground_truth_manifest.s3_object.name #=> String
resp.project_version_descriptions[0].testing_data_result.input.assets[0].ground_truth_manifest.s3_object.version #=> String
resp.project_version_descriptions[0].testing_data_result.input.auto_create #=> Boolean
resp.project_version_descriptions[0].testing_data_result.output.assets #=> Array
resp.project_version_descriptions[0].testing_data_result.output.assets[0].ground_truth_manifest.s3_object.bucket #=> String
resp.project_version_descriptions[0].testing_data_result.output.assets[0].ground_truth_manifest.s3_object.name #=> String
resp.project_version_descriptions[0].testing_data_result.output.assets[0].ground_truth_manifest.s3_object.version #=> String
resp.project_version_descriptions[0].testing_data_result.output.auto_create #=> Boolean
resp.project_version_descriptions[0].evaluation_result.f1_score #=> Float
resp.project_version_descriptions[0].evaluation_result.summary.s3_object.bucket #=> String
resp.project_version_descriptions[0].evaluation_result.summary.s3_object.name #=> String
resp.project_version_descriptions[0].evaluation_result.summary.s3_object.version #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :project_arn (required, String) —

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

  • :version_names (Array<String>) —

    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.

  • :next_token (String) —

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

  • :max_results (Integer) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1000

def describe_project_versions(params = {}, options = {})
  req = build_request(:describe_project_versions, params)
  req.send_request(options)
end

#describe_projects(params = {}) ⇒ Types::DescribeProjectsResponse

Lists and gets information about your Amazon Rekognition Custom Labels projects.

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

Examples:

Request syntax with placeholder values


resp = client.describe_projects({
  next_token: "ExtendedPaginationToken",
  max_results: 1,
})

Response structure


resp.project_descriptions #=> Array
resp.project_descriptions[0].project_arn #=> String
resp.project_descriptions[0].creation_timestamp #=> Time
resp.project_descriptions[0].status #=> String, one of "CREATING", "CREATED", "DELETING"
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

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

  • :max_results (Integer) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1045

def describe_projects(params = {}, options = {})
  req = build_request(:describe_projects, params)
  req.send_request(options)
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.

Examples:

Request syntax with placeholder values


resp = client.describe_stream_processor({
  name: "StreamProcessorName", # required
})

Response structure


resp.name #=> String
resp.stream_processor_arn #=> String
resp.status #=> String, one of "STOPPED", "STARTING", "RUNNING", "FAILED", "STOPPING"
resp.status_message #=> String
resp.creation_timestamp #=> Time
resp.last_update_timestamp #=> Time
resp.input.kinesis_video_stream.arn #=> String
resp.output.kinesis_data_stream.arn #=> String
resp.role_arn #=> String
resp.settings.face_search.collection_id #=> String
resp.settings.face_search.face_match_threshold #=> Float

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name (required, String) —

    Name of the stream processor for which you want information.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1093

def describe_stream_processor(params = {}, options = {})
  req = build_request(:describe_stream_processor, params)
  req.send_request(options)
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).

During training model calculates a threshold value that determines if a prediction for a label is true. By default, DetectCustomLabels doesn't return labels whose confidence value is below the model's calculated threshold value. To filter labels that are returned, specify a value for MinConfidence that is higher than the model's calculated threshold. You can get the model's calculated threshold from the model's training results shown in the Amazon Rekognition Custom Labels console. To get all labels, regardless of confidence, specify a MinConfidence value of 0.

You can also add the MaxResults parameter to limit the number of labels returned.

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.

Examples:

Request syntax with placeholder values


resp = client.detect_custom_labels({
  project_version_arn: "ProjectVersionArn", # required
  image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  max_results: 1,
  min_confidence: 1.0,
})

Response structure


resp.custom_labels #=> Array
resp.custom_labels[0].name #=> String
resp.custom_labels[0].confidence #=> Float
resp.custom_labels[0].geometry.bounding_box.width #=> Float
resp.custom_labels[0].geometry.bounding_box.height #=> Float
resp.custom_labels[0].geometry.bounding_box.left #=> Float
resp.custom_labels[0].geometry.bounding_box.top #=> Float
resp.custom_labels[0].geometry.polygon #=> Array
resp.custom_labels[0].geometry.polygon[0].x #=> Float
resp.custom_labels[0].geometry.polygon[0].y #=> Float

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :project_version_arn (required, String) —

    The ARN of the model version that you want to use.

  • :image (required, Types::Image) —

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

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

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

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

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

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

    For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see Resource Based Policies in the Amazon Rekognition Developer Guide.

  • :max_results (Integer) —

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

  • :min_confidence (Float) —

    Specifies the minimum confidence level for the labels to return. Amazon Rekognition doesn't return any labels with a confidence lower than this specified value. If you specify a value of 0, all labels are return, regardless of the default thresholds that the model version applies.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1214

def detect_custom_labels(params = {}, options = {})
  req = build_request(:detect_custom_labels, params)
  req.send_request(options)
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), presence of beard, sunglasses, and so on.

The face-detection algorithm is most effective on frontal faces. For non-frontal or obscured faces, the algorithm might not detect the faces or might detect faces with lower confidence.

You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the 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.

Examples:

Example: To detect faces in an image


# This operation detects faces in an image stored in an AWS S3 bucket.

resp = client.detect_faces({
  image: {
    s3_object: {
      bucket: "mybucket", 
      name: "myphoto", 
    }, 
  }, 
})

resp.to_h outputs the following:
{
  face_details: [
    {
      bounding_box: {
        height: 0.18000000715255737, 
        left: 0.5555555820465088, 
        top: 0.33666667342185974, 
        width: 0.23999999463558197, 
      }, 
      confidence: 100, 
      landmarks: [
        {
          type: "eyeLeft", 
          x: 0.6394737362861633, 
          y: 0.40819624066352844, 
        }, 
        {
          type: "eyeRight", 
          x: 0.7266660928726196, 
          y: 0.41039225459098816, 
        }, 
        {
          type: "eyeRight", 
          x: 0.6912462115287781, 
          y: 0.44240960478782654, 
        }, 
        {
          type: "mouthDown", 
          x: 0.6306198239326477, 
          y: 0.46700039505958557, 
        }, 
        {
          type: "mouthUp", 
          x: 0.7215608954429626, 
          y: 0.47114261984825134, 
        }, 
      ], 
      pose: {
        pitch: 4.050806522369385, 
        roll: 0.9950747489929199, 
        yaw: 13.693790435791016, 
      }, 
      quality: {
        brightness: 37.60169982910156, 
        sharpness: 80, 
      }, 
    }, 
  ], 
  orientation_correction: "ROTATE_0", 
}

Request syntax with placeholder values


resp = client.detect_faces({
  image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  attributes: ["DEFAULT"], # accepts DEFAULT, ALL
})

Response structure


resp.face_details #=> Array
resp.face_details[0].bounding_box.width #=> Float
resp.face_details[0].bounding_box.height #=> Float
resp.face_details[0].bounding_box.left #=> Float
resp.face_details[0].bounding_box.top #=> Float
resp.face_details[0].age_range.low #=> Integer
resp.face_details[0].age_range.high #=> Integer
resp.face_details[0].smile.value #=> Boolean
resp.face_details[0].smile.confidence #=> Float
resp.face_details[0].eyeglasses.value #=> Boolean
resp.face_details[0].eyeglasses.confidence #=> Float
resp.face_details[0].sunglasses.value #=> Boolean
resp.face_details[0].sunglasses.confidence #=> Float
resp.face_details[0].gender.value #=> String, one of "Male", "Female"
resp.face_details[0].gender.confidence #=> Float
resp.face_details[0].beard.value #=> Boolean
resp.face_details[0].beard.confidence #=> Float
resp.face_details[0].mustache.value #=> Boolean
resp.face_details[0].mustache.confidence #=> Float
resp.face_details[0].eyes_open.value #=> Boolean
resp.face_details[0].eyes_open.confidence #=> Float
resp.face_details[0].mouth_open.value #=> Boolean
resp.face_details[0].mouth_open.confidence #=> Float
resp.face_details[0].emotions #=> Array
resp.face_details[0].emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.face_details[0].emotions[0].confidence #=> Float
resp.face_details[0].landmarks #=> Array
resp.face_details[0].landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.face_details[0].landmarks[0].x #=> Float
resp.face_details[0].landmarks[0].y #=> Float
resp.face_details[0].pose.roll #=> Float
resp.face_details[0].pose.yaw #=> Float
resp.face_details[0].pose.pitch #=> Float
resp.face_details[0].quality.brightness #=> Float
resp.face_details[0].quality.sharpness #=> Float
resp.face_details[0].confidence #=> Float
resp.orientation_correction #=> String, one of "ROTATE_0", "ROTATE_90", "ROTATE_180", "ROTATE_270"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :image (required, Types::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.

  • :attributes (Array<String>) —

    An array of facial attributes you want to be returned. This can be the default list of attributes or all attributes. If you don't specify a value for Attributes or if you specify ["DEFAULT"], the API returns the following subset of facial attributes: BoundingBox, Confidence, Pose, Quality, and Landmarks. If you provide ["ALL"], all facial attributes are returned, but the operation takes longer to complete.

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

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1395

def detect_faces(params = {}, options = {})
  req = build_request(:detect_faces, params)
  req.send_request(options)
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.

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

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

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

\{Name: lighthouse, Confidence: 98.4629\}

\{Name: rock,Confidence: 79.2097\}

\{Name: sea,Confidence: 75.061\}

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

\{Name: flower,Confidence: 99.0562\}

\{Name: plant,Confidence: 99.0562\}

\{Name: tulip,Confidence: 99.0562\}

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

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

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

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

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

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

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

Examples:

Example: To detect labels


# This operation detects labels in the supplied image

resp = client.detect_labels({
  image: {
    s3_object: {
      bucket: "mybucket", 
      name: "myphoto", 
    }, 
  }, 
  max_labels: 123, 
  min_confidence: 70, 
})

resp.to_h outputs the following:
{
  labels: [
    {
      confidence: 99.25072479248047, 
      name: "People", 
    }, 
    {
      confidence: 99.25074005126953, 
      name: "Person", 
    }, 
  ], 
}

Request syntax with placeholder values


resp = client.detect_labels({
  image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  max_labels: 1,
  min_confidence: 1.0,
})

Response structure


resp.labels #=> Array
resp.labels[0].name #=> String
resp.labels[0].confidence #=> Float
resp.labels[0].instances #=> Array
resp.labels[0].instances[0].bounding_box.width #=> Float
resp.labels[0].instances[0].bounding_box.height #=> Float
resp.labels[0].instances[0].bounding_box.left #=> Float
resp.labels[0].instances[0].bounding_box.top #=> Float
resp.labels[0].instances[0].confidence #=> Float
resp.labels[0].parents #=> Array
resp.labels[0].parents[0].name #=> String
resp.orientation_correction #=> String, one of "ROTATE_0", "ROTATE_90", "ROTATE_180", "ROTATE_270"
resp.label_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :image (required, Types::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.

  • :max_labels (Integer) —

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

  • :min_confidence (Float) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1570

def detect_labels(params = {}, options = {})
  req = build_request(:detect_labels, params)
  req.send_request(options)
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.

Examples:

Request syntax with placeholder values


resp = client.detect_moderation_labels({
  image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  min_confidence: 1.0,
  human_loop_config: {
    human_loop_name: "HumanLoopName", # required
    flow_definition_arn: "FlowDefinitionArn", # required
    data_attributes: {
      content_classifiers: ["FreeOfPersonallyIdentifiableInformation"], # accepts FreeOfPersonallyIdentifiableInformation, FreeOfAdultContent
    },
  },
})

Response structure


resp.moderation_labels #=> Array
resp.moderation_labels[0].confidence #=> Float
resp.moderation_labels[0].name #=> String
resp.moderation_labels[0].parent_name #=> String
resp.moderation_model_version #=> String
resp.human_loop_activation_output.human_loop_arn #=> String
resp.human_loop_activation_output.human_loop_activation_reasons #=> Array
resp.human_loop_activation_output.human_loop_activation_reasons[0] #=> String
resp.human_loop_activation_output.human_loop_activation_conditions_evaluation_results #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :image (required, Types::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.

  • :min_confidence (Float) —

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

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

  • :human_loop_config (Types::HumanLoopConfig) —

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

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1654

def detect_moderation_labels(params = {}, options = {})
  req = build_request(:detect_moderation_labels, params)
  req.send_request(options)
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 ISO basic latin script characters that are not separated by spaces. DetectText can detect up to 50 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 DetectText in the Amazon Rekognition Developer Guide.

Examples:

Request syntax with placeholder values


resp = client.detect_text({
  image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
})

Response structure


resp.text_detections #=> Array
resp.text_detections[0].detected_text #=> String
resp.text_detections[0].type #=> String, one of "LINE", "WORD"
resp.text_detections[0].id #=> Integer
resp.text_detections[0].parent_id #=> Integer
resp.text_detections[0].confidence #=> Float
resp.text_detections[0].geometry.bounding_box.width #=> Float
resp.text_detections[0].geometry.bounding_box.height #=> Float
resp.text_detections[0].geometry.bounding_box.left #=> Float
resp.text_detections[0].geometry.bounding_box.top #=> Float
resp.text_detections[0].geometry.polygon #=> Array
resp.text_detections[0].geometry.polygon[0].x #=> Float
resp.text_detections[0].geometry.polygon[0].y #=> Float

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :image (required, Types::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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1742

def detect_text(params = {}, options = {})
  req = build_request(:detect_text, params)
  req.send_request(options)
end

#get_celebrity_info(params = {}) ⇒ Types::GetCelebrityInfoResponse

Gets the name and additional information about a celebrity based on his or her 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 Recognizing Celebrities in an Image in the Amazon Rekognition Developer Guide.

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

Examples:

Request syntax with placeholder values


resp = client.get_celebrity_info({
  id: "RekognitionUniqueId", # required
})

Response structure


resp.urls #=> Array
resp.urls[0] #=> String
resp.name #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :id (required, String) —

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

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1782

def get_celebrity_info(params = {}, options = {})
  req = build_request(:get_celebrity_info, params)
  req.send_request(options)
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.

GetCelebrityRecognition 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 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.

Examples:

Request syntax with placeholder values


resp = client.get_celebrity_recognition({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
  sort_by: "ID", # accepts ID, TIMESTAMP
})

Response structure


resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
resp.status_message #=> String
resp.video_metadata.codec #=> String
resp.video_metadata.duration_millis #=> Integer
resp.video_metadata.format #=> String
resp.video_metadata.frame_rate #=> Float
resp.video_metadata.frame_height #=> Integer
resp.video_metadata.frame_width #=> Integer
resp.next_token #=> String
resp.celebrities #=> Array
resp.celebrities[0].timestamp #=> Integer
resp.celebrities[0].celebrity.urls #=> Array
resp.celebrities[0].celebrity.urls[0] #=> String
resp.celebrities[0].celebrity.name #=> String
resp.celebrities[0].celebrity.id #=> String
resp.celebrities[0].celebrity.confidence #=> Float
resp.celebrities[0].celebrity.bounding_box.width #=> Float
resp.celebrities[0].celebrity.bounding_box.height #=> Float
resp.celebrities[0].celebrity.bounding_box.left #=> Float
resp.celebrities[0].celebrity.bounding_box.top #=> Float
resp.celebrities[0].celebrity.face.bounding_box.width #=> Float
resp.celebrities[0].celebrity.face.bounding_box.height #=> Float
resp.celebrities[0].celebrity.face.bounding_box.left #=> Float
resp.celebrities[0].celebrity.face.bounding_box.top #=> Float
resp.celebrities[0].celebrity.face.age_range.low #=> Integer
resp.celebrities[0].celebrity.face.age_range.high #=> Integer
resp.celebrities[0].celebrity.face.smile.value #=> Boolean
resp.celebrities[0].celebrity.face.smile.confidence #=> Float
resp.celebrities[0].celebrity.face.eyeglasses.value #=> Boolean
resp.celebrities[0].celebrity.face.eyeglasses.confidence #=> Float
resp.celebrities[0].celebrity.face.sunglasses.value #=> Boolean
resp.celebrities[0].celebrity.face.sunglasses.confidence #=> Float
resp.celebrities[0].celebrity.face.gender.value #=> String, one of "Male", "Female"
resp.celebrities[0].celebrity.face.gender.confidence #=> Float
resp.celebrities[0].celebrity.face.beard.value #=> Boolean
resp.celebrities[0].celebrity.face.beard.confidence #=> Float
resp.celebrities[0].celebrity.face.mustache.value #=> Boolean
resp.celebrities[0].celebrity.face.mustache.confidence #=> Float
resp.celebrities[0].celebrity.face.eyes_open.value #=> Boolean
resp.celebrities[0].celebrity.face.eyes_open.confidence #=> Float
resp.celebrities[0].celebrity.face.mouth_open.value #=> Boolean
resp.celebrities[0].celebrity.face.mouth_open.confidence #=> Float
resp.celebrities[0].celebrity.face.emotions #=> Array
resp.celebrities[0].celebrity.face.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.celebrities[0].celebrity.face.emotions[0].confidence #=> Float
resp.celebrities[0].celebrity.face.landmarks #=> Array
resp.celebrities[0].celebrity.face.landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.celebrities[0].celebrity.face.landmarks[0].x #=> Float
resp.celebrities[0].celebrity.face.landmarks[0].y #=> Float
resp.celebrities[0].celebrity.face.pose.roll #=> Float
resp.celebrities[0].celebrity.face.pose.yaw #=> Float
resp.celebrities[0].celebrity.face.pose.pitch #=> Float
resp.celebrities[0].celebrity.face.quality.brightness #=> Float
resp.celebrities[0].celebrity.face.quality.sharpness #=> Float
resp.celebrities[0].celebrity.face.confidence #=> Float

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_id (required, String) —

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

  • :max_results (Integer) —

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

  • :next_token (String) —

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

  • :sort_by (String) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1935

def get_celebrity_recognition(params = {}, options = {})
  req = build_request(:get_celebrity_recognition, params)
  req.send_request(options)
end

#get_content_moderation(params = {}) ⇒ Types::GetContentModerationResponse

Gets the unsafe content analysis results for a Amazon Rekognition Video analysis started by StartContentModeration.

Unsafe content analysis of a 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 unsafe 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 unsafe content 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 Detecting Unsafe Content in the Amazon Rekognition Developer Guide.

Examples:

Request syntax with placeholder values


resp = client.get_content_moderation({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
  sort_by: "NAME", # accepts NAME, TIMESTAMP
})

Response structure


resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
resp.status_message #=> String
resp.video_metadata.codec #=> String
resp.video_metadata.duration_millis #=> Integer
resp.video_metadata.format #=> String
resp.video_metadata.frame_rate #=> Float
resp.video_metadata.frame_height #=> Integer
resp.video_metadata.frame_width #=> Integer
resp.moderation_labels #=> Array
resp.moderation_labels[0].timestamp #=> Integer
resp.moderation_labels[0].moderation_label.confidence #=> Float
resp.moderation_labels[0].moderation_label.name #=> String
resp.moderation_labels[0].moderation_label.parent_name #=> String
resp.next_token #=> String
resp.moderation_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_id (required, String) —

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

  • :max_results (Integer) —

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

  • :next_token (String) —

    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 unsafe content labels.

  • :sort_by (String) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2038

def get_content_moderation(params = {}, options = {})
  req = build_request(:get_content_moderation, params)
  req.send_request(options)
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.

Examples:

Request syntax with placeholder values


resp = client.get_face_detection({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
})

Response structure


resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
resp.status_message #=> String
resp.video_metadata.codec #=> String
resp.video_metadata.duration_millis #=> Integer
resp.video_metadata.format #=> String
resp.video_metadata.frame_rate #=> Float
resp.video_metadata.frame_height #=> Integer
resp.video_metadata.frame_width #=> Integer
resp.next_token #=> String
resp.faces #=> Array
resp.faces[0].timestamp #=> Integer
resp.faces[0].face.bounding_box.width #=> Float
resp.faces[0].face.bounding_box.height #=> Float
resp.faces[0].face.bounding_box.left #=> Float
resp.faces[0].face.bounding_box.top #=> Float
resp.faces[0].face.age_range.low #=> Integer
resp.faces[0].face.age_range.high #=> Integer
resp.faces[0].face.smile.value #=> Boolean
resp.faces[0].face.smile.confidence #=> Float
resp.faces[0].face.eyeglasses.value #=> Boolean
resp.faces[0].face.eyeglasses.confidence #=> Float
resp.faces[0].face.sunglasses.value #=> Boolean
resp.faces[0].face.sunglasses.confidence #=> Float
resp.faces[0].face.gender.value #=> String, one of "Male", "Female"
resp.faces[0].face.gender.confidence #=> Float
resp.faces[0].face.beard.value #=> Boolean
resp.faces[0].face.beard.confidence #=> Float
resp.faces[0].face.mustache.value #=> Boolean
resp.faces[0].face.mustache.confidence #=> Float
resp.faces[0].face.eyes_open.value #=> Boolean
resp.faces[0].face.eyes_open.confidence #=> Float
resp.faces[0].face.mouth_open.value #=> Boolean
resp.faces[0].face.mouth_open.confidence #=> Float
resp.faces[0].face.emotions #=> Array
resp.faces[0].face.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.faces[0].face.emotions[0].confidence #=> Float
resp.faces[0].face.landmarks #=> Array
resp.faces[0].face.landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.faces[0].face.landmarks[0].x #=> Float
resp.faces[0].face.landmarks[0].y #=> Float
resp.faces[0].face.pose.roll #=> Float
resp.faces[0].face.pose.yaw #=> Float
resp.faces[0].face.pose.pitch #=> Float
resp.faces[0].face.quality.brightness #=> Float
resp.faces[0].face.quality.sharpness #=> Float
resp.faces[0].face.confidence #=> Float

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_id (required, String) —

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

  • :max_results (Integer) —

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

  • :next_token (String) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2150

def get_face_detection(params = {}, options = {})
  req = build_request(:get_face_detection, params)
  req.send_request(options)
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.

Examples:

Request syntax with placeholder values


resp = client.get_face_search({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
  sort_by: "INDEX", # accepts INDEX, TIMESTAMP
})

Response structure


resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
resp.status_message #=> String
resp.next_token #=> String
resp.video_metadata.codec #=> String
resp.video_metadata.duration_millis #=> Integer
resp.video_metadata.format #=> String
resp.video_metadata.frame_rate #=> Float
resp.video_metadata.frame_height #=> Integer
resp.video_metadata.frame_width #=> Integer
resp.persons #=> Array
resp.persons[0].timestamp #=> Integer
resp.persons[0].person.index #=> Integer
resp.persons[0].person.bounding_box.width #=> Float
resp.persons[0].person.bounding_box.height #=> Float
resp.persons[0].person.bounding_box.left #=> Float
resp.persons[0].person.bounding_box.top #=> Float
resp.persons[0].person.face.bounding_box.width #=> Float
resp.persons[0].person.face.bounding_box.height #=> Float
resp.persons[0].person.face.bounding_box.left #=> Float
resp.persons[0].person.face.bounding_box.top #=> Float
resp.persons[0].person.face.age_range.low #=> Integer
resp.persons[0].person.face.age_range.high #=> Integer
resp.persons[0].person.face.smile.value #=> Boolean
resp.persons[0].person.face.smile.confidence #=> Float
resp.persons[0].person.face.eyeglasses.value #=> Boolean
resp.persons[0].person.face.eyeglasses.confidence #=> Float
resp.persons[0].person.face.sunglasses.value #=> Boolean
resp.persons[0].person.face.sunglasses.confidence #=> Float
resp.persons[0].person.face.gender.value #=> String, one of "Male", "Female"
resp.persons[0].person.face.gender.confidence #=> Float
resp.persons[0].person.face.beard.value #=> Boolean
resp.persons[0].person.face.beard.confidence #=> Float
resp.persons[0].person.face.mustache.value #=> Boolean
resp.persons[0].person.face.mustache.confidence #=> Float
resp.persons[0].person.face.eyes_open.value #=> Boolean
resp.persons[0].person.face.eyes_open.confidence #=> Float
resp.persons[0].person.face.mouth_open.value #=> Boolean
resp.persons[0].person.face.mouth_open.confidence #=> Float
resp.persons[0].person.face.emotions #=> Array
resp.persons[0].person.face.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.persons[0].person.face.emotions[0].confidence #=> Float
resp.persons[0].person.face.landmarks #=> Array
resp.persons[0].person.face.landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.persons[0].person.face.landmarks[0].x #=> Float
resp.persons[0].person.face.landmarks[0].y #=> Float
resp.persons[0].person.face.pose.roll #=> Float
resp.persons[0].person.face.pose.yaw #=> Float
resp.persons[0].person.face.pose.pitch #=> Float
resp.persons[0].person.face.quality.brightness #=> Float
resp.persons[0].person.face.quality.sharpness #=> Float
resp.persons[0].person.face.confidence #=> Float
resp.persons[0].face_matches #=> Array
resp.persons[0].face_matches[0].similarity #=> Float
resp.persons[0].face_matches[0].face.face_id #=> String
resp.persons[0].face_matches[0].face.bounding_box.width #=> Float
resp.persons[0].face_matches[0].face.bounding_box.height #=> Float
resp.persons[0].face_matches[0].face.bounding_box.left #=> Float
resp.persons[0].face_matches[0].face.bounding_box.top #=> Float
resp.persons[0].face_matches[0].face.image_id #=> String
resp.persons[0].face_matches[0].face.external_image_id #=> String
resp.persons[0].face_matches[0].face.confidence #=> Float

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_id (required, String) —

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

  • :max_results (Integer) —

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

  • :next_token (String) —

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

  • :sort_by (String) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2295

def get_face_search(params = {}, options = {})
  req = build_request(:get_face_search, params)
  req.send_request(options)
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.

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

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

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

Examples:

Request syntax with placeholder values


resp = client.get_label_detection({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
  sort_by: "NAME", # accepts NAME, TIMESTAMP
})

Response structure


resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
resp.status_message #=> String
resp.video_metadata.codec #=> String
resp.video_metadata.duration_millis #=> Integer
resp.video_metadata.format #=> String
resp.video_metadata.frame_rate #=> Float
resp.video_metadata.frame_height #=> Integer
resp.video_metadata.frame_width #=> Integer
resp.next_token #=> String
resp.labels #=> Array
resp.labels[0].timestamp #=> Integer
resp.labels[0].label.name #=> String
resp.labels[0].label.confidence #=> Float
resp.labels[0].label.instances #=> Array
resp.labels[0].label.instances[0].bounding_box.width #=> Float
resp.labels[0].label.instances[0].bounding_box.height #=> Float
resp.labels[0].label.instances[0].bounding_box.left #=> Float
resp.labels[0].label.instances[0].bounding_box.top #=> Float
resp.labels[0].label.instances[0].confidence #=> Float
resp.labels[0].label.parents #=> Array
resp.labels[0].label.parents[0].name #=> String
resp.label_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_id (required, String) —

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

  • :max_results (Integer) —

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

  • :next_token (String) —

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

  • :sort_by (String) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2402

def get_label_detection(params = {}, options = {})
  req = build_request(:get_label_detection, params)
  req.send_request(options)
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.

Examples:

Request syntax with placeholder values


resp = client.get_person_tracking({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
  sort_by: "INDEX", # accepts INDEX, TIMESTAMP
})

Response structure


resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED"
resp.status_message #=> String
resp.video_metadata.codec #=> String
resp.video_metadata.duration_millis #=> Integer
resp.video_metadata.format #=> String
resp.video_metadata.frame_rate #=> Float
resp.video_metadata.frame_height #=> Integer
resp.video_metadata.frame_width #=> Integer
resp.next_token #=> String
resp.persons #=> Array
resp.persons[0].timestamp #=> Integer
resp.persons[0].person.index #=> Integer
resp.persons[0].person.bounding_box.width #=> Float
resp.persons[0].person.bounding_box.height #=> Float
resp.persons[0].person.bounding_box.left #=> Float
resp.persons[0].person.bounding_box.top #=> Float
resp.persons[0].person.face.bounding_box.width #=> Float
resp.persons[0].person.face.bounding_box.height #=> Float
resp.persons[0].person.face.bounding_box.left #=> Float
resp.persons[0].person.face.bounding_box.top #=> Float
resp.persons[0].person.face.age_range.low #=> Integer
resp.persons[0].person.face.age_range.high #=> Integer
resp.persons[0].person.face.smile.value #=> Boolean
resp.persons[0].person.face.smile.confidence #=> Float
resp.persons[0].person.face.eyeglasses.value #=> Boolean
resp.persons[0].person.face.eyeglasses.confidence #=> Float
resp.persons[0].person.face.sunglasses.value #=> Boolean
resp.persons[0].person.face.sunglasses.confidence #=> Float
resp.persons[0].person.face.gender.value #=> String, one of "Male", "Female"
resp.persons[0].person.face.gender.confidence #=> Float
resp.persons[0].person.face.beard.value #=> Boolean
resp.persons[0].person.face.beard.confidence #=> Float
resp.persons[0].person.face.mustache.value #=> Boolean
resp.persons[0].person.face.mustache.confidence #=> Float
resp.persons[0].person.face.eyes_open.value #=> Boolean
resp.persons[0].person.face.eyes_open.confidence #=> Float
resp.persons[0].person.face.mouth_open.value #=> Boolean
resp.persons[0].person.face.mouth_open.confidence #=> Float
resp.persons[0].person.face.emotions #=> Array
resp.persons[0].person.face.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.persons[0].person.face.emotions[0].confidence #=> Float
resp.persons[0].person.face.landmarks #=> Array
resp.persons[0].person.face.landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.persons[0].person.face.landmarks[0].x #=> Float
resp.persons[0].person.face.landmarks[0].y #=> Float
resp.persons[0].person.face.pose.roll #=> Float
resp.persons[0].person.face.pose.yaw #=> Float
resp.persons[0].person.face.pose.pitch #=> Float
resp.persons[0].person.face.quality.brightness #=> Float
resp.persons[0].person.face.quality.sharpness #=> Float
resp.persons[0].person.face.confidence #=> Float

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_id (required, String) —

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

  • :max_results (Integer) —

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

  • :next_token (String) —

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

  • :sort_by (String) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2542

def get_person_tracking(params = {}, options = {})
  req = build_request(:get_person_tracking, params)
  req.send_request(options)
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.

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

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

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

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

  • The face is too blurry.

  • The image is too dark.

  • The face has an extreme pose.

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

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

  • The bounding box, BoundingBox, of the detected face.

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

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

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

If you request all facial attributes (by using the detectionAttributes parameter), Amazon Rekognition returns detailed facial attributes, such as facial landmarks (for example, location of eye and mouth) and other facial attributes. If you provide the same image, specify the same collection, and use the same external ID in the IndexFaces operation, Amazon Rekognition doesn't save duplicate face metadata.

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

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

Examples:

Example: To add a face to a collection


# This operation detects faces in an image and adds them to the specified Rekognition collection.

resp = client.index_faces({
  collection_id: "myphotos", 
  detection_attributes: [
  ], 
  external_image_id: "myphotoid", 
  image: {
    s3_object: {
      bucket: "mybucket", 
      name: "myphoto", 
    }, 
  }, 
})

resp.to_h outputs the following:
{
  face_records: [
    {
      face: {
        bounding_box: {
          height: 0.33481481671333313, 
          left: 0.31888890266418457, 
          top: 0.4933333396911621, 
          width: 0.25, 
        }, 
        confidence: 99.9991226196289, 
        face_id: "ff43d742-0c13-5d16-a3e8-03d3f58e980b", 
        image_id: "465f4e93-763e-51d0-b030-b9667a2d94b1", 
      }, 
      face_detail: {
        bounding_box: {
          height: 0.33481481671333313, 
          left: 0.31888890266418457, 
          top: 0.4933333396911621, 
          width: 0.25, 
        }, 
        confidence: 99.9991226196289, 
        landmarks: [
          {
            type: "eyeLeft", 
            x: 0.3976764678955078, 
            y: 0.6248345971107483, 
          }, 
          {
            type: "eyeRight", 
            x: 0.4810936450958252, 
            y: 0.6317117214202881, 
          }, 
          {
            type: "noseLeft", 
            x: 0.41986238956451416, 
            y: 0.7111940383911133, 
          }, 
          {
            type: "mouthDown", 
            x: 0.40525302290916443, 
            y: 0.7497701048851013, 
          }, 
          {
            type: "mouthUp", 
            x: 0.4753248989582062, 
            y: 0.7558549642562866, 
          }, 
        ], 
        pose: {
          pitch: -9.713645935058594, 
          roll: 4.707281112670898, 
          yaw: -24.438663482666016, 
        }, 
        quality: {
          brightness: 29.23358917236328, 
          sharpness: 80, 
        }, 
      }, 
    }, 
    {
      face: {
        bounding_box: {
          height: 0.32592591643333435, 
          left: 0.5144444704055786, 
          top: 0.15111111104488373, 
          width: 0.24444444477558136, 
        }, 
        confidence: 99.99950408935547, 
        face_id: "8be04dba-4e58-520d-850e-9eae4af70eb2", 
        image_id: "465f4e93-763e-51d0-b030-b9667a2d94b1", 
      }, 
      face_detail: {
        bounding_box: {
          height: 0.32592591643333435, 
          left: 0.5144444704055786, 
          top: 0.15111111104488373, 
          width: 0.24444444477558136, 
        }, 
        confidence: 99.99950408935547, 
        landmarks: [
          {
            type: "eyeLeft", 
            x: 0.6006892323493958, 
            y: 0.290842205286026, 
          }, 
          {
            type: "eyeRight", 
            x: 0.6808141469955444, 
            y: 0.29609042406082153, 
          }, 
          {
            type: "noseLeft", 
            x: 0.6395332217216492, 
            y: 0.3522595763206482, 
          }, 
          {
            type: "mouthDown", 
            x: 0.5892083048820496, 
            y: 0.38689887523651123, 
          }, 
          {
            type: "mouthUp", 
            x: 0.674560010433197, 
            y: 0.394125759601593, 
          }, 
        ], 
        pose: {
          pitch: -4.683138370513916, 
          roll: 2.1029529571533203, 
          yaw: 6.716655254364014, 
        }, 
        quality: {
          brightness: 34.951698303222656, 
          sharpness: 160, 
        }, 
      }, 
    }, 
  ], 
  orientation_correction: "ROTATE_0", 
}

Request syntax with placeholder values


resp = client.index_faces({
  collection_id: "CollectionId", # required
  image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  external_image_id: "ExternalImageId",
  detection_attributes: ["DEFAULT"], # accepts DEFAULT, ALL
  max_faces: 1,
  quality_filter: "NONE", # accepts NONE, AUTO, LOW, MEDIUM, HIGH
})

Response structure


resp.face_records #=> Array
resp.face_records[0].face.face_id #=> String
resp.face_records[0].face.bounding_box.width #=> Float
resp.face_records[0].face.bounding_box.height #=> Float
resp.face_records[0].face.bounding_box.left #=> Float
resp.face_records[0].face.bounding_box.top #=> Float
resp.face_records[0].face.image_id #=> String
resp.face_records[0].face.external_image_id #=> String
resp.face_records[0].face.confidence #=> Float
resp.face_records[0].face_detail.bounding_box.width #=> Float
resp.face_records[0].face_detail.bounding_box.height #=> Float
resp.face_records[0].face_detail.bounding_box.left #=> Float
resp.face_records[0].face_detail.bounding_box.top #=> Float
resp.face_records[0].face_detail.age_range.low #=> Integer
resp.face_records[0].face_detail.age_range.high #=> Integer
resp.face_records[0].face_detail.smile.value #=> Boolean
resp.face_records[0].face_detail.smile.confidence #=> Float
resp.face_records[0].face_detail.eyeglasses.value #=> Boolean
resp.face_records[0].face_detail.eyeglasses.confidence #=> Float
resp.face_records[0].face_detail.sunglasses.value #=> Boolean
resp.face_records[0].face_detail.sunglasses.confidence #=> Float
resp.face_records[0].face_detail.gender.value #=> String, one of "Male", "Female"
resp.face_records[0].face_detail.gender.confidence #=> Float
resp.face_records[0].face_detail.beard.value #=> Boolean
resp.face_records[0].face_detail.beard.confidence #=> Float
resp.face_records[0].face_detail.mustache.value #=> Boolean
resp.face_records[0].face_detail.mustache.confidence #=> Float
resp.face_records[0].face_detail.eyes_open.value #=> Boolean
resp.face_records[0].face_detail.eyes_open.confidence #=> Float
resp.face_records[0].face_detail.mouth_open.value #=> Boolean
resp.face_records[0].face_detail.mouth_open.confidence #=> Float
resp.face_records[0].face_detail.emotions #=> Array
resp.face_records[0].face_detail.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.face_records[0].face_detail.emotions[0].confidence #=> Float
resp.face_records[0].face_detail.landmarks #=> Array
resp.face_records[0].face_detail.landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.face_records[0].face_detail.landmarks[0].x #=> Float
resp.face_records[0].face_detail.landmarks[0].y #=> Float
resp.face_records[0].face_detail.pose.roll #=> Float
resp.face_records[0].face_detail.pose.yaw #=> Float
resp.face_records[0].face_detail.pose.pitch #=> Float
resp.face_records[0].face_detail.quality.brightness #=> Float
resp.face_records[0].face_detail.quality.sharpness #=> Float
resp.face_records[0].face_detail.confidence #=> Float
resp.orientation_correction #=> String, one of "ROTATE_0", "ROTATE_90", "ROTATE_180", "ROTATE_270"
resp.face_model_version #=> String
resp.unindexed_faces #=> Array
resp.unindexed_faces[0].reasons #=> Array
resp.unindexed_faces[0].reasons[0] #=> String, one of "EXCEEDS_MAX_FACES", "EXTREME_POSE", "LOW_BRIGHTNESS", "LOW_SHARPNESS", "LOW_CONFIDENCE", "SMALL_BOUNDING_BOX", "LOW_FACE_QUALITY"
resp.unindexed_faces[0].face_detail.bounding_box.width #=> Float
resp.unindexed_faces[0].face_detail.bounding_box.height #=> Float
resp.unindexed_faces[0].face_detail.bounding_box.left #=> Float
resp.unindexed_faces[0].face_detail.bounding_box.top #=> Float
resp.unindexed_faces[0].face_detail.age_range.low #=> Integer
resp.unindexed_faces[0].face_detail.age_range.high #=> Integer
resp.unindexed_faces[0].face_detail.smile.value #=> Boolean
resp.unindexed_faces[0].face_detail.smile.confidence #=> Float
resp.unindexed_faces[0].face_detail.eyeglasses.value #=> Boolean
resp.unindexed_faces[0].face_detail.eyeglasses.confidence #=> Float
resp.unindexed_faces[0].face_detail.sunglasses.value #=> Boolean
resp.unindexed_faces[0].face_detail.sunglasses.confidence #=> Float
resp.unindexed_faces[0].face_detail.gender.value #=> String, one of "Male", "Female"
resp.unindexed_faces[0].face_detail.gender.confidence #=> Float
resp.unindexed_faces[0].face_detail.beard.value #=> Boolean
resp.unindexed_faces[0].face_detail.beard.confidence #=> Float
resp.unindexed_faces[0].face_detail.mustache.value #=> Boolean
resp.unindexed_faces[0].face_detail.mustache.confidence #=> Float
resp.unindexed_faces[0].face_detail.eyes_open.value #=> Boolean
resp.unindexed_faces[0].face_detail.eyes_open.confidence #=> Float
resp.unindexed_faces[0].face_detail.mouth_open.value #=> Boolean
resp.unindexed_faces[0].face_detail.mouth_open.confidence #=> Float
resp.unindexed_faces[0].face_detail.emotions #=> Array
resp.unindexed_faces[0].face_detail.emotions[0].type #=> String, one of "HAPPY", "SAD", "ANGRY", "CONFUSED", "DISGUSTED", "SURPRISED", "CALM", "UNKNOWN", "FEAR"
resp.unindexed_faces[0].face_detail.emotions[0].confidence #=> Float
resp.unindexed_faces[0].face_detail.landmarks #=> Array
resp.unindexed_faces[0].face_detail.landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.unindexed_faces[0].face_detail.landmarks[0].x #=> Float
resp.unindexed_faces[0].face_detail.landmarks[0].y #=> Float
resp.unindexed_faces[0].face_detail.pose.roll #=> Float
resp.unindexed_faces[0].face_detail.pose.yaw #=> Float
resp.unindexed_faces[0].face_detail.pose.pitch #=> Float
resp.unindexed_faces[0].face_detail.quality.brightness #=> Float
resp.unindexed_faces[0].face_detail.quality.sharpness #=> Float
resp.unindexed_faces[0].face_detail.confidence #=> Float

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :collection_id (required, String) —

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

  • :image (required, Types::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.

  • :external_image_id (String) —

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

  • :detection_attributes (Array<String>) —

    An array of facial attributes that you want to be returned. This can be the default list of attributes or all attributes. If you don't specify a value for Attributes or if you specify ["DEFAULT"], the API returns the following subset of facial attributes: BoundingBox, Confidence, Pose, Quality, and Landmarks. If you provide ["ALL"], all facial attributes are returned, but the operation takes longer to complete.

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

  • :max_faces (Integer) —

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

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

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

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

  • :quality_filter (String) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2972

def index_faces(params = {}, options = {})
  req = build_request(:index_faces, params)
  req.send_request(options)
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.

Examples:

Example: To list the collections


# This operation returns a list of Rekognition collections.

resp = client.list_collections({
})

resp.to_h outputs the following:
{
  collection_ids: [
    "myphotos", 
  ], 
}

Request syntax with placeholder values


resp = client.list_collections({
  next_token: "PaginationToken",
  max_results: 1,
})

Response structure


resp.collection_ids #=> Array
resp.collection_ids[0] #=> String
resp.next_token #=> String
resp.face_model_versions #=> Array
resp.face_model_versions[0] #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    Pagination token from the previous response.

  • :max_results (Integer) —

    Maximum number of collection IDs to return.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3031

def list_collections(params = {}, options = {})
  req = build_request(:list_collections, params)
  req.send_request(options)
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.

Examples:

Example: To list the faces in a collection


# This operation lists the faces in a Rekognition collection.

resp = client.list_faces({
  collection_id: "myphotos", 
  max_results: 20, 
})

resp.to_h outputs the following:
{
  faces: [
    {
      bounding_box: {
        height: 0.18000000715255737, 
        left: 0.5555559992790222, 
        top: 0.336667001247406, 
        width: 0.23999999463558197, 
      }, 
      confidence: 100, 
      face_id: "1c62e8b5-69a7-5b7d-b3cd-db4338a8a7e7", 
      image_id: "147fdf82-7a71-52cf-819b-e786c7b9746e", 
    }, 
    {
      bounding_box: {
        height: 0.16555599868297577, 
        left: 0.30963000655174255, 
        top: 0.7066670060157776, 
        width: 0.22074100375175476, 
      }, 
      confidence: 100, 
      face_id: "29a75abe-397b-5101-ba4f-706783b2246c", 
      image_id: "147fdf82-7a71-52cf-819b-e786c7b9746e", 
    }, 
    {
      bounding_box: {
        height: 0.3234420120716095, 
        left: 0.3233329951763153, 
        top: 0.5, 
        width: 0.24222199618816376, 
      }, 
      confidence: 99.99829864501953, 
      face_id: "38271d79-7bc2-5efb-b752-398a8d575b85", 
      image_id: "d5631190-d039-54e4-b267-abd22c8647c5", 
    }, 
    {
      bounding_box: {
        height: 0.03555560111999512, 
        left: 0.37388700246810913, 
        top: 0.2477779984474182, 
        width: 0.04747769981622696, 
      }, 
      confidence: 99.99210357666016, 
      face_id: "3b01bef0-c883-5654-ba42-d5ad28b720b3", 
      image_id: "812d9f04-86f9-54fc-9275-8d0dcbcb6784", 
    }, 
    {
      bounding_box: {
        height: 0.05333330109715462, 
        left: 0.2937690019607544, 
        top: 0.35666701197624207, 
        width: 0.07121659815311432, 
      }, 
      confidence: 99.99919891357422, 
      face_id: "4839a608-49d0-566c-8301-509d71b534d1", 
      image_id: "812d9f04-86f9-54fc-9275-8d0dcbcb6784", 
    }, 
    {
      bounding_box: {
        height: 0.3249259889125824, 
        left: 0.5155559778213501, 
        top: 0.1513350009918213, 
        width: 0.24333299696445465, 
      }, 
      confidence: 99.99949645996094, 
      face_id: "70008e50-75e4-55d0-8e80-363fb73b3a14", 
      image_id: "d5631190-d039-54e4-b267-abd22c8647c5", 
    }, 
    {
      bounding_box: {
        height: 0.03777780011296272, 
        left: 0.7002969980239868, 
        top: 0.18777799606323242, 
        width: 0.05044509842991829, 
      }, 
      confidence: 99.92639923095703, 
      face_id: "7f5f88ed-d684-5a88-b0df-01e4a521552b", 
      image_id: "812d9f04-86f9-54fc-9275-8d0dcbcb6784", 
    }, 
    {
      bounding_box: {
        height: 0.05555560067296028, 
        left: 0.13946600258350372, 
        top: 0.46333301067352295, 
        width: 0.07270029932260513, 
      }, 
      confidence: 99.99469757080078, 
      face_id: "895b4e2c-81de-5902-a4bd-d1792bda00b2", 
      image_id: "812d9f04-86f9-54fc-9275-8d0dcbcb6784", 
    }, 
    {
      bounding_box: {
        height: 0.3259260058403015, 
        left: 0.5144439935684204, 
        top: 0.15111100673675537, 
        width: 0.24444399774074554, 
      }, 
      confidence: 99.99949645996094, 
      face_id: "8be04dba-4e58-520d-850e-9eae4af70eb2", 
      image_id: "465f4e93-763e-51d0-b030-b9667a2d94b1", 
    }, 
    {
      bounding_box: {
        height: 0.18888899683952332, 
        left: 0.3783380091190338, 
        top: 0.2355560064315796, 
        width: 0.25222599506378174, 
      }, 
      confidence: 99.9999008178711, 
      face_id: "908544ad-edc3-59df-8faf-6a87cc256cf5", 
      image_id: "3c731605-d772-541a-a5e7-0375dbc68a07", 
    }, 
    {
      bounding_box: {
        height: 0.33481499552726746, 
        left: 0.31888899207115173, 
        top: 0.49333301186561584, 
        width: 0.25, 
      }, 
      confidence: 99.99909973144531, 
      face_id: "ff43d742-0c13-5d16-a3e8-03d3f58e980b", 
      image_id: "465f4e93-763e-51d0-b030-b9667a2d94b1", 
    }, 
  ], 
}

Request syntax with placeholder values


resp = client.list_faces({
  collection_id: "CollectionId", # required
  next_token: "PaginationToken",
  max_results: 1,
})

Response structure


resp.faces #=> Array
resp.faces[0].face_id #=> String
resp.faces[0].bounding_box.width #=> Float
resp.faces[0].bounding_box.height #=> Float
resp.faces[0].bounding_box.left #=> Float
resp.faces[0].bounding_box.top #=> Float
resp.faces[0].image_id #=> String
resp.faces[0].external_image_id #=> String
resp.faces[0].confidence #=> Float
resp.next_token #=> String
resp.face_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :collection_id (required, String) —

    ID of the collection from which to list the faces.

  • :next_token (String) —

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

  • :max_results (Integer) —

    Maximum number of faces to return.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3224

def list_faces(params = {}, options = {})
  req = build_request(:list_faces, params)
  req.send_request(options)
end

#list_stream_processors(params = {}) ⇒ Types::ListStreamProcessorsResponse

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

Examples:

Request syntax with placeholder values


resp = client.list_stream_processors({
  next_token: "PaginationToken",
  max_results: 1,
})

Response structure


resp.next_token #=> String
resp.stream_processors #=> Array
resp.stream_processors[0].name #=> String
resp.stream_processors[0].status #=> String, one of "STOPPED", "STARTING", "RUNNING", "FAILED", "STOPPING"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

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

  • :max_results (Integer) —

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

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3263

def list_stream_processors(params = {}, options = {})
  req = build_request(:list_stream_processors, params)
  req.send_request(options)
end

#recognize_celebrities(params = {}) ⇒ Types::RecognizeCelebritiesResponse

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

RecognizeCelebrities returns the 100 largest faces in the image. It lists recognized celebrities in the CelebrityFaces array and unrecognized faces in the UnrecognizedFaces array. RecognizeCelebrities doesn't return celebrities whose faces aren't among the largest 100 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.

Examples:

Request syntax with placeholder values


resp = client.recognize_celebrities({
  image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
})

Response structure


resp.celebrity_faces #=> Array
resp.celebrity_faces[0].urls #=> Array
resp.celebrity_faces[0].urls[0] #=> String
resp.celebrity_faces[0].name #=> String
resp.celebrity_faces[0].id #=> String
resp.celebrity_faces[0].face.bounding_box.width #=> Float
resp.celebrity_faces[0].face.bounding_box.height #=> Float
resp.celebrity_faces[0].face.bounding_box.left #=> Float
resp.celebrity_faces[0].face.bounding_box.top #=> Float
resp.celebrity_faces[0].face.confidence #=> Float
resp.celebrity_faces[0].face.landmarks #=> Array
resp.celebrity_faces[0].face.landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.celebrity_faces[0].face.landmarks[0].x #=> Float
resp.celebrity_faces[0].face.landmarks[0].y #=> Float
resp.celebrity_faces[0].face.pose.roll #=> Float
resp.celebrity_faces[0].face.pose.yaw #=> Float
resp.celebrity_faces[0].face.pose.pitch #=> Float
resp.celebrity_faces[0].face.quality.brightness #=> Float
resp.celebrity_faces[0].face.quality.sharpness #=> Float
resp.celebrity_faces[0].match_confidence #=> Float
resp.unrecognized_faces #=> Array
resp.unrecognized_faces[0].bounding_box.width #=> Float
resp.unrecognized_faces[0].bounding_box.height #=> Float
resp.unrecognized_faces[0].bounding_box.left #=> Float
resp.unrecognized_faces[0].bounding_box.top #=> Float
resp.unrecognized_faces[0].confidence #=> Float
resp.unrecognized_faces[0].landmarks #=> Array
resp.unrecognized_faces[0].landmarks[0].type #=> String, one of "eyeLeft", "eyeRight", "nose", "mouthLeft", "mouthRight", "leftEyeBrowLeft", "leftEyeBrowRight", "leftEyeBrowUp", "rightEyeBrowLeft", "rightEyeBrowRight", "rightEyeBrowUp", "leftEyeLeft", "leftEyeRight", "leftEyeUp", "leftEyeDown", "rightEyeLeft", "rightEyeRight", "rightEyeUp", "rightEyeDown", "noseLeft", "noseRight", "mouthUp", "mouthDown", "leftPupil", "rightPupil", "upperJawlineLeft", "midJawlineLeft", "chinBottom", "midJawlineRight", "upperJawlineRight"
resp.unrecognized_faces[0].landmarks[0].x #=> Float
resp.unrecognized_faces[0].landmarks[0].y #=> Float
resp.unrecognized_faces[0].pose.roll #=> Float
resp.unrecognized_faces[0].pose.yaw #=> Float
resp.unrecognized_faces[0].pose.pitch #=> Float
resp.unrecognized_faces[0].quality.brightness #=> Float
resp.unrecognized_faces[0].quality.sharpness #=> Float
resp.orientation_correction #=> String, one of "ROTATE_0", "ROTATE_90", "ROTATE_180", "ROTATE_270"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :image (required, Types::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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3373

def recognize_celebrities(params = {}, options = {})
  req = build_request(:recognize_celebrities, params)
  req.send_request(options)
end

#search_faces(params = {}) ⇒ Types::SearchFacesResponse

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

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

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

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.

Examples:

Example: To delete a face


# This operation searches for matching faces in the collection the supplied face belongs to.

resp = client.search_faces({
  collection_id: "myphotos", 
  face_id: "70008e50-75e4-55d0-8e80-363fb73b3a14", 
  face_match_threshold: 90, 
  max_faces: 10, 
})

resp.to_h outputs the following:
{
  face_matches: [
    {
      face: {
        bounding_box: {
          height: 0.3259260058403015, 
          left: 0.5144439935684204, 
          top: 0.15111100673675537, 
          width: 0.24444399774074554, 
        }, 
        confidence: 99.99949645996094, 
        face_id: "8be04dba-4e58-520d-850e-9eae4af70eb2", 
        image_id: "465f4e93-763e-51d0-b030-b9667a2d94b1", 
      }, 
      similarity: 99.97222137451172, 
    }, 
    {
      face: {
        bounding_box: {
          height: 0.16555599868297577, 
          left: 0.30963000655174255, 
          top: 0.7066670060157776, 
          width: 0.22074100375175476, 
        }, 
        confidence: 100, 
        face_id: "29a75abe-397b-5101-ba4f-706783b2246c", 
        image_id: "147fdf82-7a71-52cf-819b-e786c7b9746e", 
      }, 
      similarity: 97.04154968261719, 
    }, 
    {
      face: {
        bounding_box: {
          height: 0.18888899683952332, 
          left: 0.3783380091190338, 
          top: 0.2355560064315796, 
          width: 0.25222599506378174, 
        }, 
        confidence: 99.9999008178711, 
        face_id: "908544ad-edc3-59df-8faf-6a87cc256cf5", 
        image_id: "3c731605-d772-541a-a5e7-0375dbc68a07", 
      }, 
      similarity: 95.94520568847656, 
    }, 
  ], 
  searched_face_id: "70008e50-75e4-55d0-8e80-363fb73b3a14", 
}

Request syntax with placeholder values


resp = client.search_faces({
  collection_id: "CollectionId", # required
  face_id: "FaceId", # required
  max_faces: 1,
  face_match_threshold: 1.0,
})

Response structure


resp.searched_face_id #=> String
resp.face_matches #=> Array
resp.face_matches[0].similarity #=> Float
resp.face_matches[0].face.face_id #=> String
resp.face_matches[0].face.bounding_box.width #=> Float
resp.face_matches[0].face.bounding_box.height #=> Float
resp.face_matches[0].face.bounding_box.left #=> Float
resp.face_matches[0].face.bounding_box.top #=> Float
resp.face_matches[0].face.image_id #=> String
resp.face_matches[0].face.external_image_id #=> String
resp.face_matches[0].face.confidence #=> Float
resp.face_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :collection_id (required, String) —

    ID of the collection the face belongs to.

  • :face_id (required, String) —

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

  • :max_faces (Integer) —

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

  • :face_match_threshold (Float) —

    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%.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3510

def search_faces(params = {}, options = {})
  req = build_request(:search_faces, params)
  req.send_request(options)
end

#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. 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.

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.

Examples:

Example: To search for faces matching a supplied image


# This operation searches for faces in a Rekognition collection that match the largest face in an S3 bucket stored image.

resp = client.search_faces_by_image({
  collection_id: "myphotos", 
  face_match_threshold: 95, 
  image: {
    s3_object: {
      bucket: "mybucket", 
      name: "myphoto", 
    }, 
  }, 
  max_faces: 5, 
})

resp.to_h outputs the following:
{
  face_matches: [
    {
      face: {
        bounding_box: {
          height: 0.3234420120716095, 
          left: 0.3233329951763153, 
          top: 0.5, 
          width: 0.24222199618816376, 
        }, 
        confidence: 99.99829864501953, 
        face_id: "38271d79-7bc2-5efb-b752-398a8d575b85", 
        image_id: "d5631190-d039-54e4-b267-abd22c8647c5", 
      }, 
      similarity: 99.97036743164062, 
    }, 
  ], 
  searched_face_bounding_box: {
    height: 0.33481481671333313, 
    left: 0.31888890266418457, 
    top: 0.4933333396911621, 
    width: 0.25, 
  }, 
  searched_face_confidence: 99.9991226196289, 
}

Request syntax with placeholder values


resp = client.search_faces_by_image({
  collection_id: "CollectionId", # required
  image: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  max_faces: 1,
  face_match_threshold: 1.0,
  quality_filter: "NONE", # accepts NONE, AUTO, LOW, MEDIUM, HIGH
})

Response structure


resp.searched_face_bounding_box.width #=> Float
resp.searched_face_bounding_box.height #=> Float
resp.searched_face_bounding_box.left #=> Float
resp.searched_face_bounding_box.top #=> Float
resp.searched_face_confidence #=> Float
resp.face_matches #=> Array
resp.face_matches[0].similarity #=> Float
resp.face_matches[0].face.face_id #=> String
resp.face_matches[0].face.bounding_box.width #=> Float
resp.face_matches[0].face.bounding_box.height #=> Float
resp.face_matches[0].face.bounding_box.left #=> Float
resp.face_matches[0].face.bounding_box.top #=> Float
resp.face_matches[0].face.image_id #=> String
resp.face_matches[0].face.external_image_id #=> String
resp.face_matches[0].face.confidence #=> Float
resp.face_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :collection_id (required, String) —

    ID of the collection to search.

  • :image (required, Types::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.

  • :max_faces (Integer) —

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

  • :face_match_threshold (Float) — default: Optional —

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

  • :quality_filter (String) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3689

def search_faces_by_image(params = {}, options = {})
  req = build_request(:search_faces_by_image, params)
  req.send_request(options)
end

#start_celebrity_recognition(params = {}) ⇒ Types::StartCelebrityRecognitionResponse

Starts asynchronous recognition of celebrities in a stored video.

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

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

Examples:

Request syntax with placeholder values


resp = client.start_celebrity_recognition({
  video: { # required
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  client_request_token: "ClientRequestToken",
  notification_channel: {
    sns_topic_arn: "SNSTopicArn", # required
    role_arn: "RoleArn", # required
  },
  job_tag: "JobTag",
})

Response structure


resp.job_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :video (required, Types::Video) —

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

  • :client_request_token (String) —

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

  • :notification_channel (Types::NotificationChannel) —

    The Amazon SNS topic ARN that you want Amazon Rekognition Video to publish the completion status of the celebrity recognition analysis to.

  • :job_tag (String) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3761

def start_celebrity_recognition(params = {}, options = {})
  req = build_request(:start_celebrity_recognition, params)
  req.send_request(options)
end

#start_content_moderation(params = {}) ⇒ Types::StartContentModerationResponse

Starts asynchronous detection of unsafe content in a stored video.

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 unsafe 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 unsafe 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 Detecting Unsafe Content in the Amazon Rekognition Developer Guide.

Examples:

Request syntax with placeholder values


resp = client.start_content_moderation({
  video: { # required
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  min_confidence: 1.0,
  client_request_token: "ClientRequestToken",
  notification_channel: {
    sns_topic_arn: "SNSTopicArn", # required
    role_arn: "RoleArn", # required
  },
  job_tag: "JobTag",
})

Response structure


resp.job_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :video (required, Types::Video) —

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

  • :min_confidence (Float) —

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

  • :client_request_token (String) —

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

  • :notification_channel (Types::NotificationChannel) —

    The Amazon SNS topic ARN that you want Amazon Rekognition Video to publish the completion status of the unsafe content analysis to.

  • :job_tag (String) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3843

def start_content_moderation(params = {}, options = {})
  req = build_request(:start_content_moderation, params)
  req.send_request(options)
end

#start_face_detection(params = {}) ⇒ Types::StartFaceDetectionResponse

Starts asynchronous detection of faces in a stored video.

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

For more information, see Detecting Faces in a Stored Video in the Amazon Rekognition Developer Guide.

Examples:

Request syntax with placeholder values


resp = client.start_face_detection({
  video: { # required
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  client_request_token: "ClientRequestToken",
  notification_channel: {
    sns_topic_arn: "SNSTopicArn", # required
    role_arn: "RoleArn", # required
  },
  face_attributes: "DEFAULT", # accepts DEFAULT, ALL
  job_tag: "JobTag",
})

Response structure


resp.job_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :video (required, Types::Video) —

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

  • :client_request_token (String) —

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

  • :notification_channel (Types::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.

  • :face_attributes (String) —

    The face attributes you want returned.

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

    ALL - All facial attributes are returned.

  • :job_tag (String) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3923

def start_face_detection(params = {}, options = {})
  req = build_request(:start_face_detection, params)
  req.send_request(options)
end

#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.

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 procedure-person-search-videos.

Examples:

Request syntax with placeholder values


resp = client.start_face_search({
  video: { # required
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  client_request_token: "ClientRequestToken",
  face_match_threshold: 1.0,
  collection_id: "CollectionId", # required
  notification_channel: {
    sns_topic_arn: "SNSTopicArn", # required
    role_arn: "RoleArn", # required
  },
  job_tag: "JobTag",
})

Response structure


resp.job_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :video (required, Types::Video) —

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

  • :client_request_token (String) —

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

  • :face_match_threshold (Float) —

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

  • :collection_id (required, String) —

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

  • :notification_channel (Types::NotificationChannel) —

    The ARN of the Amazon SNS topic to which you want Amazon Rekognition Video to publish the completion status of the search.

  • :job_tag (String) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4001

def start_face_search(params = {}, options = {})
  req = build_request(:start_face_search, params)
  req.send_request(options)
end

#start_label_detection(params = {}) ⇒ Types::StartLabelDetectionResponse

Starts asynchronous detection of labels in a stored video.

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

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

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

Examples:

Request syntax with placeholder values


resp = client.start_label_detection({
  video: { # required
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  client_request_token: "ClientRequestToken",
  min_confidence: 1.0,
  notification_channel: {
    sns_topic_arn: "SNSTopicArn", # required
    role_arn: "RoleArn", # required
  },
  job_tag: "JobTag",
})

Response structure


resp.job_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :video (required, Types::Video) —

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

  • :client_request_token (String) —

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

  • :min_confidence (Float) —

    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 with confidence values greater than or equal to 50 percent.

  • :notification_channel (Types::NotificationChannel) —

    The Amazon SNS topic ARN you want Amazon Rekognition Video to publish the completion status of the label detection operation to.

  • :job_tag (String) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4086

def start_label_detection(params = {}, options = {})
  req = build_request(:start_label_detection, params)
  req.send_request(options)
end

#start_person_tracking(params = {}) ⇒ Types::StartPersonTrackingResponse

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

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

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

Examples:

Request syntax with placeholder values


resp = client.start_person_tracking({
  video: { # required
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  client_request_token: "ClientRequestToken",
  notification_channel: {
    sns_topic_arn: "SNSTopicArn", # required
    role_arn: "RoleArn", # required
  },
  job_tag: "JobTag",
})

Response structure


resp.job_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :video (required, Types::Video) —

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

  • :client_request_token (String) —

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

  • :notification_channel (Types::NotificationChannel) —

    The Amazon SNS topic ARN you want Amazon Rekognition Video to publish the completion status of the people detection operation to.

  • :job_tag (String) —

    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.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4155

def start_person_tracking(params = {}, options = {})
  req = build_request(:start_person_tracking, params)
  req.send_request(options)
end

#start_project_version(params = {}) ⇒ Types::StartProjectVersionResponse

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

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

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

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

Examples:

Request syntax with placeholder values


resp = client.start_project_version({
  project_version_arn: "ProjectVersionArn", # required
  min_inference_units: 1, # required
})

Response structure


resp.status #=> String, one of "TRAINING_IN_PROGRESS", "TRAINING_COMPLETED", "TRAINING_FAILED", "STARTING", "RUNNING", "FAILED", "STOPPING", "STOPPED", "DELETING"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :project_version_arn (required, String) —

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

  • :min_inference_units (required, Integer) —

    The minimum number of inference units to use. A single inference unit represents 1 hour of processing and can support up to 5 Transaction Pers Second (TPS). Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use.

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4203

def start_project_version(params = {}, options = {})
  req = build_request(:start_project_version, params)
  req.send_request(options)
end

#start_stream_processor(params = {}) ⇒ Struct

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.

Examples:

Request syntax with placeholder values


resp = client.start_stream_processor({
  name: "StreamProcessorName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name (required, String) —

    The name of the stream processor to start processing.

Returns:

  • (Struct) —

    Returns an empty response.



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4226

def start_stream_processor(params = {}, options = {})
  req = build_request(:start_stream_processor, params)
  req.send_request(options)
end

#stop_project_version(params = {}) ⇒ Types::StopProjectVersionResponse

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

Examples:

Request syntax with placeholder values


resp = client.stop_project_version({
  project_version_arn: "ProjectVersionArn", # required
})

Response structure


resp.status #=> String, one of "TRAINING_IN_PROGRESS", "TRAINING_COMPLETED", "TRAINING_FAILED", "STARTING", "RUNNING", "FAILED", "STOPPING", "STOPPED", "DELETING"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :project_version_arn (required, String) —

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

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

Returns:



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4257

def stop_project_version(params = {}, options = {})
  req = build_request(:stop_project_version, params)
  req.send_request(options)
end

#stop_stream_processor(params = {}) ⇒ Struct

Stops a running stream processor that was created by CreateStreamProcessor.

Examples:

Request syntax with placeholder values


resp = client.stop_stream_processor({
  name: "StreamProcessorName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name (required, String) —

    The name of a stream processor created by CreateStreamProcessor.

Returns:

  • (Struct) —

    Returns an empty response.



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4278

def stop_stream_processor(params = {}, options = {})
  req = build_request(:stop_stream_processor, params)
  req.send_request(options)
end

#wait_until(waiter_name, params = {}, options = {}) {|w.waiter| ... } ⇒ Boolean

Polls an API operation until a resource enters a desired state.

Basic Usage

A waiter will call an API operation until:

  • It is successful
  • It enters a terminal state
  • It makes the maximum number of attempts

In between attempts, the waiter will sleep.

# polls in a loop, sleeping between attempts
client.wait_until(waiter_name, params)

Configuration

You can configure the maximum number of polling attempts, and the delay (in seconds) between each polling attempt. You can pass configuration as the final arguments hash.

# poll for ~25 seconds
client.wait_until(waiter_name, params, {
  max_attempts: 5,
  delay: 5,
})

Callbacks

You can be notified before each polling attempt and before each delay. If you throw :success or :failure from these callbacks, it will terminate the waiter.

started_at = Time.now
client.wait_until(waiter_name, params, {

  # disable max attempts
  max_attempts: nil,

  # poll for 1 hour, instead of a number of attempts
  before_wait: -> (attempts, response) do
    throw :failure if Time.now - started_at > 3600
  end
})

Handling Errors

When a waiter is unsuccessful, it will raise an error. All of the failure errors extend from Waiters::Errors::WaiterFailed.

begin
  client.wait_until(...)
rescue Aws::Waiters::Errors::WaiterFailed
  # resource did not enter the desired state in time
end

Valid Waiters

The following table lists the valid waiter names, the operations they call, and the default :delay and :max_attempts values.

waiter_name params :delay :max_attempts
project_version_running #describe_project_versions 30 40
project_version_training_completed #describe_project_versions 120 360

Parameters:

  • waiter_name (Symbol)
  • params (Hash) (defaults to: {}) —

    ({})

  • options (Hash) (defaults to: {}) —

    ({})

Options Hash (options):

  • :max_attempts (Integer)
  • :delay (Integer)
  • :before_attempt (Proc)
  • :before_wait (Proc)

Yields:

  • (w.waiter)

Returns:

  • (Boolean) —

    Returns true if the waiter was successful.

Raises:

  • (Errors::FailureStateError) —

    Raised when the waiter terminates because the waiter has entered a state that it will not transition out of, preventing success.

  • (Errors::TooManyAttemptsError) —

    Raised when the configured maximum number of attempts have been made, and the waiter is not yet successful.

  • (Errors::UnexpectedError) —

    Raised when an error is encounted while polling for a resource that is not expected.

  • (Errors::NoSuchWaiterError) —

    Raised when you request to wait for an unknown state.



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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4389

def wait_until(waiter_name, params = {}, options = {})
  w = waiter(waiter_name, options)
  yield(w.waiter) if block_given? # deprecated
  w.wait(params)
end