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Class: Aws::Rekognition::Client

Inherits:
Seahorse::Client::Base show all
Defined in:
(unknown)

Overview

An API client for Amazon Rekognition. To construct a client, you need to configure a :region and :credentials.

rekognition = Aws::Rekognition::Client.new(
  region: region_name,
  credentials: credentials,
  # ...
)

See #initialize for a full list of supported configuration options.

Region

You can configure a default region in the following locations:

  • ENV['AWS_REGION']
  • Aws.config[:region]

Go here for a list of supported regions.

Credentials

Default credentials are loaded automatically from the following locations:

  • ENV['AWS_ACCESS_KEY_ID'] and ENV['AWS_SECRET_ACCESS_KEY']
  • Aws.config[:credentials]
  • The shared credentials ini file at ~/.aws/credentials (more information)
  • From an instance profile when running on EC2

You can also construct a credentials object from one of the following classes:

Alternatively, you configure credentials with :access_key_id and :secret_access_key:

# load credentials from disk
creds = YAML.load(File.read('/path/to/secrets'))

Aws::Rekognition::Client.new(
  access_key_id: creds['access_key_id'],
  secret_access_key: creds['secret_access_key']
)

Always load your credentials from outside your application. Avoid configuring credentials statically and never commit them to source control.

Instance Attribute Summary

Attributes inherited from Seahorse::Client::Base

#config, #handlers

Constructor collapse

API Operations collapse

Instance Method Summary collapse

Methods inherited from Seahorse::Client::Base

add_plugin, api, #build_request, clear_plugins, define, new, #operation, #operation_names, plugins, remove_plugin, set_api, set_plugins

Methods included from Seahorse::Client::HandlerBuilder

#handle, #handle_request, #handle_response

Constructor Details

#initialize(options = {}) ⇒ Aws::Rekognition::Client

Constructs an API client.

Options Hash (options):

  • :access_key_id (String)

    Used to set credentials statically. See Plugins::RequestSigner for more details.

  • :convert_params (Boolean) — default: true

    When true, an attempt is made to coerce request parameters into the required types. See Plugins::ParamConverter for more details.

  • :credentials (required, Credentials)

    Your AWS credentials. The following locations will be searched in order for credentials:

    • :access_key_id, :secret_access_key, and :session_token options
    • ENV['AWS_ACCESS_KEY_ID'], ENV['AWS_SECRET_ACCESS_KEY']
    • HOME/.aws/credentials shared credentials file
    • EC2 instance profile credentials See Plugins::RequestSigner for more details.
  • :endpoint (String)

    A default endpoint is constructed from the :region. See Plugins::RegionalEndpoint for more details.

  • :http_continue_timeout (Float) — default: 1

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :http_idle_timeout (Integer) — default: 5

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :http_open_timeout (Integer) — default: 15

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :http_proxy (String)

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :http_read_timeout (Integer) — default: 60

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :http_wire_trace (Boolean) — default: false

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :log_level (Symbol) — default: :info

    The log level to send messages to the logger at. See Plugins::Logging for more details.

  • :log_formatter (Logging::LogFormatter)

    The log formatter. Defaults to Seahorse::Client::Logging::Formatter.default. See Plugins::Logging for more details.

  • :logger (Logger) — default: nil

    The Logger instance to send log messages to. If this option is not set, logging will be disabled. See Plugins::Logging for more details.

  • :profile (String)

    Used when loading credentials from the shared credentials file at HOME/.aws/credentials. When not specified, 'default' is used. See Plugins::RequestSigner for more details.

  • :raise_response_errors (Boolean) — default: true

    When true, response errors are raised. See Seahorse::Client::Plugins::RaiseResponseErrors for more details.

  • :region (required, String)

    The AWS region to connect to. The region is used to construct the client endpoint. Defaults to ENV['AWS_REGION']. Also checks AMAZON_REGION and AWS_DEFAULT_REGION. See Plugins::RegionalEndpoint for more details.

  • :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. See Plugins::RetryErrors for more details.

  • :secret_access_key (String)

    Used to set credentials statically. See Plugins::RequestSigner for more details.

  • :session_token (String)

    Used to set credentials statically. See Plugins::RequestSigner for more details.

  • :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. See Plugins::Protocols::JsonRpc for more details.

  • :ssl_ca_bundle (String)

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :ssl_ca_directory (String)

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :ssl_ca_store (String)

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :ssl_verify_peer (Boolean) — default: true

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :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. See Plugins::StubResponses for more details.

  • :validate_params (Boolean) — default: true

    When true, request parameters are validated before sending the request. See Plugins::ParamValidator for more details.

Instance Method Details

#compare_faces(options = {}) ⇒ 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 a references to images in an Amazon S3 bucket. If you use the Amazon CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted 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.

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 faces-compare-images.

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,
})

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"
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"
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"

Options Hash (options):

  • :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.

  • :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.

  • :similarity_threshold (Float)

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

Returns:

#create_collection(options = {}) ⇒ Types::CreateCollectionResponse

Creates a collection in an AWS Region. You can add faces to the collection using the 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.

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

Options Hash (options):

  • :collection_id (required, String)

    ID for the collection that you are creating.

Returns:

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

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

Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. 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 with the Name field.

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

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

Options Hash (options):

  • :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 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 . 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:

#delete_collection(options = {}) ⇒ 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

Options Hash (options):

  • :collection_id (required, String)

    ID of the collection to delete.

Returns:

#delete_faces(options = {}) ⇒ 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

Options Hash (options):

  • :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:

#delete_stream_processor(options = {}) ⇒ Struct

Deletes the stream processor identified by Name. You assign the value for Name when you create the stream processor with . 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
})

Options Hash (options):

  • :name (required, String)

    The name of the stream processor you want to delete.

Returns:

  • (Struct)

    Returns an empty response.

#describe_stream_processor(options = {}) ⇒ Types::DescribeStreamProcessorResponse

Provides information about a stream processor created by . 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

Options Hash (options):

  • :name (required, String)

    Name of the stream processor for which you want information.

Returns:

#detect_faces(options = {}) ⇒ 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 including 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), gender, presence of beard, sunglasses, etc.

The face-detection algorithm is most effective on frontal faces. For non-frontal or obscured faces, the algorithm may 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 Amazon CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

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

For an example, see procedure-detecting-faces-in-images.

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 #=> true/false
resp.face_details[0].smile.confidence #=> Float
resp.face_details[0].eyeglasses.value #=> true/false
resp.face_details[0].eyeglasses.confidence #=> Float
resp.face_details[0].sunglasses.value #=> true/false
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 #=> true/false
resp.face_details[0].beard.confidence #=> Float
resp.face_details[0].mustache.value #=> true/false
resp.face_details[0].mustache.confidence #=> Float
resp.face_details[0].eyes_open.value #=> true/false
resp.face_details[0].eyes_open.confidence #=> Float
resp.face_details[0].mouth_open.value #=> true/false
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"
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"
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"

Options Hash (options):

  • :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.

  • :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 will take 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:

#detect_labels(options = {}) ⇒ 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 images-s3.

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

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 Amazon 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 will include 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 50%. 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.

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.orientation_correction #=> String, one of "ROTATE_0", "ROTATE_90", "ROTATE_180", "ROTATE_270"

Options Hash (options):

  • :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.

  • :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 50 percent.

Returns:

#detect_moderation_labels(options = {}) ⇒ Types::DetectModerationLabelsResponse

Detects explicit or suggestive adult 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 moderation.

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 Amazon 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,
})

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

Options Hash (options):

  • :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.

  • :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.

Returns:

#detect_text(options = {}) ⇒ 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 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 +/- 30 degrees orientation of the horizontal axis.

For more information, see text-detection.

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

Options Hash (options):

  • :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.

Returns:

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

Gets the name and additional information about a celebrity based on his or her 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 get-celebrity-info-procedure.

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

Options Hash (options):

  • :id (required, String)

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

Returns:

#get_celebrity_recognition(options = {}) ⇒ Types::GetCelebrityRecognitionResponse

Gets the celebrity recognition results for a Rekognition Video analysis started by .

Celebrity recognition in a video is an asynchronous operation. Analysis is started by a call to which returns a job identifier (JobId). When the celebrity recognition operation finishes, 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 video.

GetCelebrityRecognition returns detected celebrities and the time(s) they are detected in an array (Celebrities) of objects. Each CelebrityRecognition contains information about the celebrity in a object and the time, Timestamp, the celebrity was detected.

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 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..codec #=> String
resp..duration_millis #=> Integer
resp..format #=> String
resp..frame_rate #=> Float
resp..frame_height #=> Integer
resp..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 #=> true/false
resp.celebrities[0].celebrity.face.smile.confidence #=> Float
resp.celebrities[0].celebrity.face.eyeglasses.value #=> true/false
resp.celebrities[0].celebrity.face.eyeglasses.confidence #=> Float
resp.celebrities[0].celebrity.face.sunglasses.value #=> true/false
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 #=> true/false
resp.celebrities[0].celebrity.face.beard.confidence #=> Float
resp.celebrities[0].celebrity.face.mustache.value #=> true/false
resp.celebrities[0].celebrity.face.mustache.confidence #=> Float
resp.celebrities[0].celebrity.face.eyes_open.value #=> true/false
resp.celebrities[0].celebrity.face.eyes_open.confidence #=> Float
resp.celebrities[0].celebrity.face.mouth_open.value #=> true/false
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"
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"
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

Options Hash (options):

  • :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 celebrities you want Rekognition Video to return in the response. The default is 1000.

  • :next_token (String)

    If the previous response was incomplete (because there is more recognized celebrities to retrieve), 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:

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

Gets the content moderation analysis results for a Rekognition Video analysis started by .

Content moderation analysis of a video is an asynchronous operation. You start analysis by calling . which returns a job identifier (JobId). When analysis finishes, Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic registered in the initial call to StartContentModeration. To get the results of the content moderation 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 video.

GetContentModeration returns detected content moderation labels, and the time they are detected, in an array, ModerationLabels, of 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 moderation.

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..codec #=> String
resp..duration_millis #=> Integer
resp..format #=> String
resp..frame_rate #=> Float
resp..frame_height #=> Integer
resp..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

Options Hash (options):

  • :job_id (required, String)

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

  • :max_results (Integer)

    Maximum number of content moderation labels to return. The default 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 content moderation 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:

#get_face_detection(options = {}) ⇒ Types::GetFaceDetectionResponse

Gets face detection results for a Rekognition Video analysis started by .

Face detection with Rekognition Video is an asynchronous operation. You start face detection by calling which returns a job identifier (JobId). When the face detection operation finishes, 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 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..codec #=> String
resp..duration_millis #=> Integer
resp..format #=> String
resp..frame_rate #=> Float
resp..frame_height #=> Integer
resp..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 #=> true/false
resp.faces[0].face.smile.confidence #=> Float
resp.faces[0].face.eyeglasses.value #=> true/false
resp.faces[0].face.eyeglasses.confidence #=> Float
resp.faces[0].face.sunglasses.value #=> true/false
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 #=> true/false
resp.faces[0].face.beard.confidence #=> Float
resp.faces[0].face.mustache.value #=> true/false
resp.faces[0].face.mustache.confidence #=> Float
resp.faces[0].face.eyes_open.value #=> true/false
resp.faces[0].face.eyes_open.confidence #=> Float
resp.faces[0].face.mouth_open.value #=> true/false
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"
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"
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

Options Hash (options):

  • :job_id (required, String)

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

  • :max_results (Integer)

    Maximum number of detected faces to return. The default is 1000.

  • :next_token (String)

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

Returns:

#get_face_search(options = {}) ⇒ Types::GetFaceSearchResponse

Gets the face search results for Rekognition Video face search started by . 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 which returns a job identifier (JobId). When the search operation finishes, 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 collections.

The search results are retured in an array, Persons, of objects. EachPersonMatch element contains details about the matching faces in the input collection, person information for the matched person, and the time the person was matched in the video.

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..codec #=> String
resp..duration_millis #=> Integer
resp..format #=> String
resp..frame_rate #=> Float
resp..frame_height #=> Integer
resp..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 #=> true/false
resp.persons[0].person.face.smile.confidence #=> Float
resp.persons[0].person.face.eyeglasses.value #=> true/false
resp.persons[0].person.face.eyeglasses.confidence #=> Float
resp.persons[0].person.face.sunglasses.value #=> true/false
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 #=> true/false
resp.persons[0].person.face.beard.confidence #=> Float
resp.persons[0].person.face.mustache.value #=> true/false
resp.persons[0].person.face.mustache.confidence #=> Float
resp.persons[0].person.face.eyes_open.value #=> true/false
resp.persons[0].person.face.eyes_open.confidence #=> Float
resp.persons[0].person.face.mouth_open.value #=> true/false
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"
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"
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

Options Hash (options):

  • :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 search results you want Rekognition Video to return in the response. The default is 1000.

  • :next_token (String)

    If the previous response was incomplete (because there is more search results to retrieve), 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:

#get_label_detection(options = {}) ⇒ Types::GetLabelDetectionResponse

Gets the label detection results of a Rekognition Video analysis started by .

The label detection operation is started by a call to 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 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.

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..codec #=> String
resp..duration_millis #=> Integer
resp..format #=> String
resp..frame_rate #=> Float
resp..frame_height #=> Integer
resp..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

Options Hash (options):

  • :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 labels you want Amazon Rekognition to return in the response. The default is 1000.

  • :next_token (String)

    If the previous response was incomplete (because there are more labels to retrieve), 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:

#get_person_tracking(options = {}) ⇒ Types::GetPersonTrackingResponse

Gets the person tracking results of a Rekognition Video analysis started by .

The person detection operation is started by a call to StartPersonTracking which returns a job identifier (JobId). When the person detection operation finishes, 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 tracking operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. If so, call and pass the job identifier (JobId) from the initial call to StartPersonTracking.

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

By default, the array is sorted by the time(s) a person 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..codec #=> String
resp..duration_millis #=> Integer
resp..format #=> String
resp..frame_rate #=> Float
resp..frame_height #=> Integer
resp..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 #=> true/false
resp.persons[0].person.face.smile.confidence #=> Float
resp.persons[0].person.face.eyeglasses.value #=> true/false
resp.persons[0].person.face.eyeglasses.confidence #=> Float
resp.persons[0].person.face.sunglasses.value #=> true/false
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 #=> true/false
resp.persons[0].person.face.beard.confidence #=> Float
resp.persons[0].person.face.mustache.value #=> true/false
resp.persons[0].person.face.mustache.confidence #=> Float
resp.persons[0].person.face.eyes_open.value #=> true/false
resp.persons[0].person.face.eyes_open.confidence #=> Float
resp.persons[0].person.face.mouth_open.value #=> true/false
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"
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"
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

Options Hash (options):

  • :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 tracked persons to return. The default is 1000.

  • :next_token (String)

    If the previous response was incomplete (because there are more persons to retrieve), 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:

#index_faces(options = {}) ⇒ Types::IndexFacesResponse

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

Amazon Rekognition does not save the actual faces detected. Instead, the underlying detection algorithm first detects the faces in the input image, and for each face extracts facial features into a feature vector, and stores it in the back-end database. Amazon Rekognition uses feature vectors when performing face match and search operations using the and operations.

If you are 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. To determine which version of the model you are using, check the the value of FaceModelVersion in the response from IndexFaces. For more information, see face-detection-model.

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 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.

In response, the operation returns an array of metadata for all detected faces. This includes, the bounding box of the detected face, confidence value (indicating the bounding box contains a face), a face ID assigned by the service for each face that is detected and stored, and an image ID assigned by the service for the input image. If you request all facial attributes (using the detectionAttributes parameter, Amazon Rekognition returns detailed facial attributes such as facial landmarks (for example, location of eye and mount) and other facial attributes such gender. 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 Amazon CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.

This operation requires permissions to perform the rekognition: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
})

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 #=> true/false
resp.face_records[0].face_detail.smile.confidence #=> Float
resp.face_records[0].face_detail.eyeglasses.value #=> true/false
resp.face_records[0].face_detail.eyeglasses.confidence #=> Float
resp.face_records[0].face_detail.sunglasses.value #=> true/false
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 #=> true/false
resp.face_records[0].face_detail.beard.confidence #=> Float
resp.face_records[0].face_detail.mustache.value #=> true/false
resp.face_records[0].face_detail.mustache.confidence #=> Float
resp.face_records[0].face_detail.eyes_open.value #=> true/false
resp.face_records[0].face_detail.eyes_open.confidence #=> Float
resp.face_records[0].face_detail.mouth_open.value #=> true/false
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"
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"
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

Options Hash (options):

  • :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 is not supported.

  • :external_image_id (String)

    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 will take 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:

#list_collections(options = {}) ⇒ 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 list-collection-procedure.

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

Options Hash (options):

  • :next_token (String)

    Pagination token from the previous response.

  • :max_results (Integer)

    Maximum number of collection IDs to return.

Returns:

#list_faces(options = {}) ⇒ 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 list-faces-in-collection-procedure.

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

Options Hash (options):

  • :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:

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

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

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"

Options Hash (options):

  • :next_token (String)

    If the previous response was incomplete (because there are more stream processors to retrieve), 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 Rekognition Video to return in the response. The default is 1000.

Returns:

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

Returns an array of celebrities recognized in the input image. For more information, see celebrities.

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 are not amongst the largest 100 faces in the image.

For each celebrity recognized, the 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.

Rekognition does not 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 operation.

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

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"
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"
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"

Options Hash (options):

  • :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.

Returns:

#search_faces(options = {}) ⇒ 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 search-face-with-id-procedure.

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

Options Hash (options):

  • :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%.

Returns:

#search_faces_by_image(options = {}) ⇒ 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 operation, and then use the face IDs returned in subsequent calls to the 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 Amazon 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, see search-face-with-image-procedure.

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,
})

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

Options Hash (options):

  • :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.

  • :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%.

Returns:

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

Starts asynchronous recognition of celebrities in a stored video.

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, 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 and pass the job identifier (JobId) from the initial call to StartCelebrityRecognition. For more information, see celebrities.

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

Options Hash (options):

  • :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 Rekognition Video to publish the completion status of the celebrity recognition analysis to.

  • :job_tag (String)

    Unique identifier you specify to identify the job in the completion status published to the Amazon Simple Notification Service topic.

Returns:

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

Starts asynchronous detection of explicit or suggestive adult content in a stored video.

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

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

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

Options Hash (options):

  • :video (required, Types::Video)

    The video in which you want to moderate 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.

  • :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 Rekognition Video to publish the completion status of the content moderation analysis to.

  • :job_tag (String)

    Unique identifier you specify to identify the job in the completion status published to the Amazon Simple Notification Service topic.

Returns:

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

Starts asynchronous detection of faces in a stored video.

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, 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 and pass the job identifier (JobId) from the initial call to StartFaceDetection. For more information, see faces-video.

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

Options Hash (options):

  • :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 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)

    Unique identifier you specify to identify the job in the completion status published to the Amazon Simple Notification Service topic.

Returns:

#start_face_search(options = {}) ⇒ 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, 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 and pass the job identifier (JobId) from the initial call to StartFaceSearch. For more information, see collections-search-person.

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

Options Hash (options):

  • :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%.

  • :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 Rekognition Video to publish the completion status of the search.

  • :job_tag (String)

    Unique identifier you specify to identify the job in the completion status published to the Amazon Simple Notification Service topic.

Returns:

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

Starts asynchronous detection of labels in a stored video.

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, 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 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

Options Hash (options):

  • :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 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. 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 Rekognition Video to publish the completion status of the label detection operation to.

  • :job_tag (String)

    Unique identifier you specify to identify the job in the completion status published to the Amazon Simple Notification Service topic.

Returns:

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

Starts the asynchronous tracking of persons in a stored video.

Rekognition Video can track persons 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 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

Options Hash (options):

  • :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 Rekognition Video to publish the completion status of the people detection operation to.

  • :job_tag (String)

    Unique identifier you specify to identify the job in the completion status published to the Amazon Simple Notification Service topic.

Returns:

#start_stream_processor(options = {}) ⇒ Struct

Starts processing a stream processor. You create a stream processor by calling . 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
})

Options Hash (options):

  • :name (required, String)

    The name of the stream processor to start processing.

Returns:

  • (Struct)

    Returns an empty response.

#stop_stream_processor(options = {}) ⇒ Struct

Stops a running stream processor that was created by .

Examples:

Request syntax with placeholder values


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

Options Hash (options):

  • :name (required, String)

    The name of a stream processor created by .

Returns:

  • (Struct)

    Returns an empty response.

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

Waiters polls an API operation until a resource enters a desired state.

Basic Usage

Waiters will poll until they are succesful, they fail by entering a terminal state, or until a maximum number of attempts are made.

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

Configuration

You can configure the maximum number of polling attempts, and the delay (in seconds) between each polling attempt. You configure waiters by passing a block to #wait_until:

# poll for ~25 seconds
client.wait_until(...) do |w|
  w.max_attempts = 5
  w.delay = 5
end

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(...) do |w|

  # disable max attempts
  w.max_attempts = nil

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

end

Handling Errors

When a waiter is successful, it returns true. When a waiter fails, it raises an error. All errors raised extend from Waiters::Errors::WaiterFailed.

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

Parameters:

  • waiter_name (Symbol)

    The name of the waiter. See #waiter_names for a full list of supported waiters.

  • params (Hash) (defaults to: {})

    Additional request parameters. See the #waiter_names for a list of supported waiters and what request they call. The called request determines the list of accepted parameters.

Yield Parameters:

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.

#waiter_namesArray<Symbol>

Returns the list of supported waiters. The following table lists the supported waiters and the client method they call:

Waiter NameClient MethodDefault Delay:Default Max Attempts:

Returns:

  • (Array<Symbol>)

    the list of supported waiters.