Class: Aws::Rekognition::Client
- Inherits:
-
Seahorse::Client::Base
- Object
- Seahorse::Client::Base
- Aws::Rekognition::Client
- Includes:
- ClientStubs
- Defined in:
- gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb
Overview
An API client for Rekognition. To construct a client, you need to configure a :region
and :credentials
.
client = Aws::Rekognition::Client.new(
region: region_name,
credentials: credentials,
# ...
)
For details on configuring region and credentials see the developer guide.
See #initialize for a full list of supported configuration options.
Instance Attribute Summary
Attributes inherited from Seahorse::Client::Base
API Operations collapse
-
#compare_faces(params = {}) ⇒ Types::CompareFacesResponse
Compares a face in the source input image with each of the 100 largest faces detected in the target input image.
-
#create_collection(params = {}) ⇒ Types::CreateCollectionResponse
Creates a collection in an AWS Region.
-
#create_project(params = {}) ⇒ Types::CreateProjectResponse
Creates a new Amazon Rekognition Custom Labels project.
-
#create_project_version(params = {}) ⇒ Types::CreateProjectVersionResponse
Creates a new version of a model and begins training.
-
#create_stream_processor(params = {}) ⇒ Types::CreateStreamProcessorResponse
Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces in a streaming video.
-
#delete_collection(params = {}) ⇒ Types::DeleteCollectionResponse
Deletes the specified collection.
-
#delete_faces(params = {}) ⇒ Types::DeleteFacesResponse
Deletes faces from a collection.
-
#delete_project(params = {}) ⇒ Types::DeleteProjectResponse
Deletes an Amazon Rekognition Custom Labels project.
-
#delete_project_version(params = {}) ⇒ Types::DeleteProjectVersionResponse
Deletes an Amazon Rekognition Custom Labels model.
-
#delete_stream_processor(params = {}) ⇒ Struct
Deletes the stream processor identified by
Name
. -
#describe_collection(params = {}) ⇒ Types::DescribeCollectionResponse
Describes the specified collection.
-
#describe_project_versions(params = {}) ⇒ Types::DescribeProjectVersionsResponse
Lists and describes the models in an Amazon Rekognition Custom Labels project.
-
#describe_projects(params = {}) ⇒ Types::DescribeProjectsResponse
Lists and gets information about your Amazon Rekognition Custom Labels projects.
-
#describe_stream_processor(params = {}) ⇒ Types::DescribeStreamProcessorResponse
Provides information about a stream processor created by CreateStreamProcessor.
-
#detect_custom_labels(params = {}) ⇒ Types::DetectCustomLabelsResponse
Detects custom labels in a supplied image by using an Amazon Rekognition Custom Labels model.
-
#detect_faces(params = {}) ⇒ Types::DetectFacesResponse
Detects faces within an image that is provided as input.
-
#detect_labels(params = {}) ⇒ Types::DetectLabelsResponse
Detects instances of real-world entities within an image (JPEG or PNG) provided as input.
-
#detect_moderation_labels(params = {}) ⇒ Types::DetectModerationLabelsResponse
Detects unsafe content in a specified JPEG or PNG format image.
-
#detect_protective_equipment(params = {}) ⇒ Types::DetectProtectiveEquipmentResponse
Detects Personal Protective Equipment (PPE) worn by people detected in an image.
-
#detect_text(params = {}) ⇒ Types::DetectTextResponse
Detects text in the input image and converts it into machine-readable text.
-
#get_celebrity_info(params = {}) ⇒ Types::GetCelebrityInfoResponse
Gets the name and additional information about a celebrity based on his or her Amazon Rekognition ID.
-
#get_celebrity_recognition(params = {}) ⇒ Types::GetCelebrityRecognitionResponse
Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by StartCelebrityRecognition.
-
#get_content_moderation(params = {}) ⇒ Types::GetContentModerationResponse
Gets the unsafe content analysis results for a Amazon Rekognition Video analysis started by StartContentModeration.
-
#get_face_detection(params = {}) ⇒ Types::GetFaceDetectionResponse
Gets face detection results for a Amazon Rekognition Video analysis started by StartFaceDetection.
-
#get_face_search(params = {}) ⇒ Types::GetFaceSearchResponse
Gets the face search results for Amazon Rekognition Video face search started by StartFaceSearch.
-
#get_label_detection(params = {}) ⇒ Types::GetLabelDetectionResponse
Gets the label detection results of a Amazon Rekognition Video analysis started by StartLabelDetection.
-
#get_person_tracking(params = {}) ⇒ Types::GetPersonTrackingResponse
Gets the path tracking results of a Amazon Rekognition Video analysis started by StartPersonTracking.
-
#get_segment_detection(params = {}) ⇒ Types::GetSegmentDetectionResponse
Gets the segment detection results of a Amazon Rekognition Video analysis started by StartSegmentDetection.
-
#get_text_detection(params = {}) ⇒ Types::GetTextDetectionResponse
Gets the text detection results of a Amazon Rekognition Video analysis started by StartTextDetection.
-
#index_faces(params = {}) ⇒ Types::IndexFacesResponse
Detects faces in the input image and adds them to the specified collection.
-
#list_collections(params = {}) ⇒ Types::ListCollectionsResponse
Returns list of collection IDs in your account.
-
#list_faces(params = {}) ⇒ Types::ListFacesResponse
Returns metadata for faces in the specified collection.
-
#list_stream_processors(params = {}) ⇒ Types::ListStreamProcessorsResponse
Gets a list of stream processors that you have created with CreateStreamProcessor.
-
#list_tags_for_resource(params = {}) ⇒ Types::ListTagsForResourceResponse
Returns a list of tags in an Amazon Rekognition collection, stream processor, or Custom Labels model.
-
#recognize_celebrities(params = {}) ⇒ Types::RecognizeCelebritiesResponse
Returns an array of celebrities recognized in the input image.
-
#search_faces(params = {}) ⇒ Types::SearchFacesResponse
For a given input face ID, searches for matching faces in the collection the face belongs to.
-
#search_faces_by_image(params = {}) ⇒ Types::SearchFacesByImageResponse
For a given input image, first detects the largest face in the image, and then searches the specified collection for matching faces.
-
#start_celebrity_recognition(params = {}) ⇒ Types::StartCelebrityRecognitionResponse
Starts asynchronous recognition of celebrities in a stored video.
-
#start_content_moderation(params = {}) ⇒ Types::StartContentModerationResponse
Starts asynchronous detection of unsafe content in a stored video.
-
#start_face_detection(params = {}) ⇒ Types::StartFaceDetectionResponse
Starts asynchronous detection of faces in a stored video.
-
#start_face_search(params = {}) ⇒ Types::StartFaceSearchResponse
Starts the asynchronous search for faces in a collection that match the faces of persons detected in a stored video.
-
#start_label_detection(params = {}) ⇒ Types::StartLabelDetectionResponse
Starts asynchronous detection of labels in a stored video.
-
#start_person_tracking(params = {}) ⇒ Types::StartPersonTrackingResponse
Starts the asynchronous tracking of a person's path in a stored video.
-
#start_project_version(params = {}) ⇒ Types::StartProjectVersionResponse
Starts the running of the version of a model.
-
#start_segment_detection(params = {}) ⇒ Types::StartSegmentDetectionResponse
Starts asynchronous detection of segment detection in a stored video.
-
#start_stream_processor(params = {}) ⇒ Struct
Starts processing a stream processor.
-
#start_text_detection(params = {}) ⇒ Types::StartTextDetectionResponse
Starts asynchronous detection of text in a stored video.
-
#stop_project_version(params = {}) ⇒ Types::StopProjectVersionResponse
Stops a running model.
-
#stop_stream_processor(params = {}) ⇒ Struct
Stops a running stream processor that was created by CreateStreamProcessor.
-
#tag_resource(params = {}) ⇒ Struct
Adds one or more key-value tags to an Amazon Rekognition collection, stream processor, or Custom Labels model.
-
#untag_resource(params = {}) ⇒ Struct
Removes one or more tags from an Amazon Rekognition collection, stream processor, or Custom Labels model.
Instance Method Summary collapse
-
#initialize(options) ⇒ Client
constructor
A new instance of Client.
-
#wait_until(waiter_name, params = {}, options = {}) {|w.waiter| ... } ⇒ Boolean
Polls an API operation until a resource enters a desired state.
Methods included from ClientStubs
#api_requests, #stub_data, #stub_responses
Methods inherited from Seahorse::Client::Base
add_plugin, api, clear_plugins, define, new, #operation_names, plugins, remove_plugin, set_api, set_plugins
Methods included from Seahorse::Client::HandlerBuilder
#handle, #handle_request, #handle_response
Constructor Details
#initialize(options) ⇒ Client
Returns a new instance of Client.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 334 def initialize(*args) super end |
Instance Method Details
#compare_faces(params = {}) ⇒ Types::CompareFacesResponse
Compares a face in the source input image with each of the 100 largest faces detected in the target input image.
If the source image contains multiple faces, the service detects the largest face and compares it with each face detected in the target image.
CompareFaces
to
make a decision that impacts an individual's rights, privacy, or
access to services, we recommend that you pass the result to a human
for review and further validation before taking action.
You pass the input and target images either as base64-encoded image bytes or as references to images in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file.
In response, the operation returns an array of face matches ordered by similarity score in descending order. For each face match, the response provides a bounding box of the face, facial landmarks, pose details (pitch, 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.
SimilarityThreshold
parameter.
CompareFaces
also returns an array of faces that don't match the
source image. For each face, it returns a bounding box, confidence
value, landmarks, pose details, and quality. The response also returns
information about the face in the source image, including the bounding
box of the face and confidence value.
The QualityFilter
input parameter allows you to filter out detected
faces that don’t meet a required quality bar. The quality bar is based
on a variety of common use cases. Use QualityFilter
to set the
quality bar by specifying LOW
, MEDIUM
, or HIGH
. If you do not
want to filter detected faces, specify NONE
. The default value is
NONE
.
If the image doesn't contain Exif metadata, CompareFaces
returns
orientation information for the source and target images. Use these
values to display the images with the correct image orientation.
If no faces are detected in the source or target images,
CompareFaces
returns an InvalidParameterException
error.
For an example, see Comparing Faces in Images in the Amazon Rekognition Developer Guide.
This operation requires permissions to perform the
rekognition:CompareFaces
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 570 def compare_faces(params = {}, = {}) req = build_request(:compare_faces, params) req.send_request() end |
#create_collection(params = {}) ⇒ Types::CreateCollectionResponse
Creates a collection in an AWS Region. You can add faces to the collection using the IndexFaces operation.
For example, you might create collections, one for each of your
application users. A user can then index faces using the IndexFaces
operation and persist results in a specific collection. Then, a user
can search the collection for faces in the user-specific container.
When you create a collection, it is associated with the latest version of the face model version.
This operation requires permissions to perform the
rekognition:CreateCollection
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 638 def create_collection(params = {}, = {}) req = build_request(:create_collection, params) req.send_request() end |
#create_project(params = {}) ⇒ Types::CreateProjectResponse
Creates a new Amazon Rekognition Custom Labels project. A project is a logical grouping of resources (images, Labels, models) and operations (training, evaluation and detection).
This operation requires permissions to perform the
rekognition:CreateProject
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 669 def create_project(params = {}, = {}) req = build_request(:create_project, params) req.send_request() end |
#create_project_version(params = {}) ⇒ Types::CreateProjectVersionResponse
Creates a new version of a model and begins training. Models are
managed as part of an Amazon Rekognition Custom Labels project. You
can specify one training dataset and one testing dataset. The response
from CreateProjectVersion
is an Amazon Resource Name (ARN) for the
version of the model.
Training takes a while to complete. You can get the current status by calling DescribeProjectVersions.
Once training has successfully completed, call DescribeProjectVersions to get the training results and evaluate the model.
After evaluating the model, you start the model by calling StartProjectVersion.
This operation requires permissions to perform the
rekognition:CreateProjectVersion
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 762 def create_project_version(params = {}, = {}) req = build_request(:create_project_version, params) req.send_request() end |
#create_stream_processor(params = {}) ⇒ Types::CreateStreamProcessorResponse
Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces in a streaming video.
Amazon Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. Amazon Rekognition Video sends analysis results to Amazon Kinesis Data Streams.
You provide as input a Kinesis video stream (Input
) and a Kinesis
data stream (Output
) stream. You also specify the face recognition
criteria in Settings
. For example, the collection containing faces
that you want to recognize. Use Name
to assign an identifier for the
stream processor. You use Name
to manage the stream processor. For
example, you can start processing the source video by calling
StartStreamProcessor with the Name
field.
After you have finished analyzing a streaming video, use StopStreamProcessor to stop processing. You can delete the stream processor by calling DeleteStreamProcessor.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 850 def create_stream_processor(params = {}, = {}) req = build_request(:create_stream_processor, params) req.send_request() end |
#delete_collection(params = {}) ⇒ Types::DeleteCollectionResponse
Deletes the specified collection. Note that this operation removes all faces in the collection. For an example, see delete-collection-procedure.
This operation requires permissions to perform the
rekognition:DeleteCollection
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 895 def delete_collection(params = {}, = {}) req = build_request(:delete_collection, params) req.send_request() end |
#delete_faces(params = {}) ⇒ Types::DeleteFacesResponse
Deletes faces from a collection. You specify a collection ID and an array of face IDs to remove from the collection.
This operation requires permissions to perform the
rekognition:DeleteFaces
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 949 def delete_faces(params = {}, = {}) req = build_request(:delete_faces, params) req.send_request() end |
#delete_project(params = {}) ⇒ Types::DeleteProjectResponse
Deletes an Amazon Rekognition Custom Labels project. To delete a project you must first delete all models associated with the project. To delete a model, see DeleteProjectVersion.
This operation requires permissions to perform the
rekognition:DeleteProject
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 980 def delete_project(params = {}, = {}) req = build_request(:delete_project, params) req.send_request() end |
#delete_project_version(params = {}) ⇒ Types::DeleteProjectVersionResponse
Deletes an Amazon Rekognition Custom Labels model.
You can't delete a model if it is running or if it is training. To
check the status of a model, use the Status
field returned from
DescribeProjectVersions. To stop a running model call
StopProjectVersion. If the model is training, wait until it finishes.
This operation requires permissions to perform the
rekognition:DeleteProjectVersion
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1015 def delete_project_version(params = {}, = {}) req = build_request(:delete_project_version, params) req.send_request() end |
#delete_stream_processor(params = {}) ⇒ Struct
Deletes the stream processor identified by Name
. You assign the
value for Name
when you create the stream processor with
CreateStreamProcessor. You might not be able to use the same name for
a stream processor for a few seconds after calling
DeleteStreamProcessor
.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1039 def delete_stream_processor(params = {}, = {}) req = build_request(:delete_stream_processor, params) req.send_request() end |
#describe_collection(params = {}) ⇒ Types::DescribeCollectionResponse
Describes the specified collection. You can use DescribeCollection
to get information, such as the number of faces indexed into a
collection and the version of the model used by the collection for
face detection.
For more information, see Describing a Collection in the Amazon Rekognition Developer Guide.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1077 def describe_collection(params = {}, = {}) req = build_request(:describe_collection, params) req.send_request() end |
#describe_project_versions(params = {}) ⇒ Types::DescribeProjectVersionsResponse
Lists and describes the models in an Amazon Rekognition Custom Labels
project. You can specify up to 10 model versions in
ProjectVersionArns
. If you don't specify a value, descriptions for
all models are returned.
This operation requires permissions to perform the
rekognition:DescribeProjectVersions
action.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
The following waiters are defined for this operation (see #wait_until for detailed usage):
- project_version_running
- project_version_training_completed
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1186 def describe_project_versions(params = {}, = {}) req = build_request(:describe_project_versions, params) req.send_request() end |
#describe_projects(params = {}) ⇒ Types::DescribeProjectsResponse
Lists and gets information about your Amazon Rekognition Custom Labels projects.
This operation requires permissions to perform the
rekognition:DescribeProjects
action.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1233 def describe_projects(params = {}, = {}) req = build_request(:describe_projects, params) req.send_request() end |
#describe_stream_processor(params = {}) ⇒ Types::DescribeStreamProcessorResponse
Provides information about a stream processor created by CreateStreamProcessor. You can get information about the input and output streams, the input parameters for the face recognition being performed, and the current status of the stream processor.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1281 def describe_stream_processor(params = {}, = {}) req = build_request(:describe_stream_processor, params) req.send_request() end |
#detect_custom_labels(params = {}) ⇒ Types::DetectCustomLabelsResponse
Detects custom labels in a supplied image by using an Amazon Rekognition Custom Labels model.
You specify which version of a model version to use by using the
ProjectVersionArn
input parameter.
You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.
For each object that the model version detects on an image, the API
returns a (CustomLabel
) object in an array (CustomLabels
). Each
CustomLabel
object provides the label name (Name
), the level of
confidence that the image contains the object (Confidence
), and
object location information, if it exists, for the label on the image
(Geometry
).
During training model calculates a threshold value that determines if
a prediction for a label is true. By default, DetectCustomLabels
doesn't return labels whose confidence value is below the model's
calculated threshold value. To filter labels that are returned,
specify a value for MinConfidence
that is higher than the model's
calculated threshold. You can get the model's calculated threshold
from the model's training results shown in the Amazon Rekognition
Custom Labels console. To get all labels, regardless of confidence,
specify a MinConfidence
value of 0.
You can also add the MaxResults
parameter to limit the number of
labels returned.
This is a stateless API operation. That is, the operation does not persist any data.
This operation requires permissions to perform the
rekognition:DetectCustomLabels
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1402 def detect_custom_labels(params = {}, = {}) req = build_request(:detect_custom_labels, params) req.send_request() end |
#detect_faces(params = {}) ⇒ Types::DetectFacesResponse
Detects faces within an image that is provided as input.
DetectFaces
detects the 100 largest faces in the image. For each
face detected, the operation returns face details. These details
include a bounding box of the face, a confidence value (that the
bounding box contains a face), and a fixed set of attributes such as
facial landmarks (for example, coordinates of eye and mouth), presence
of beard, sunglasses, and so on.
The face-detection algorithm is most effective on frontal faces. For non-frontal or obscured faces, the algorithm might not detect the faces or might detect faces with lower confidence.
You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.
This operation requires permissions to perform the
rekognition:DetectFaces
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1583 def detect_faces(params = {}, = {}) req = build_request(:detect_faces, params) req.send_request() end |
#detect_labels(params = {}) ⇒ Types::DetectLabelsResponse
Detects instances of real-world entities within an image (JPEG or PNG) provided as input. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; and concepts like landscape, evening, and nature.
For an example, see Analyzing Images Stored in an Amazon S3 Bucket in the Amazon Rekognition Developer Guide.
DetectLabels
does not support the detection of activities. However,
activity detection is supported for label detection in videos. For
more information, see StartLabelDetection in the Amazon Rekognition
Developer Guide.
You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.
For each object, scene, and concept the API returns one or more labels. Each label provides the object name, and the level of confidence that the image contains the object. For example, suppose the input image has a lighthouse, the sea, and a rock. The response includes all three labels, one for each object.
\{Name: lighthouse, Confidence: 98.4629\}
\{Name: rock,Confidence: 79.2097\}
\{Name: sea,Confidence: 75.061\}
In the preceding example, the operation returns one label for each of the three objects. The operation can also return multiple labels for the same object in the image. For example, if the input image shows a flower (for example, a tulip), the operation might return the following three labels.
\{Name: flower,Confidence: 99.0562\}
\{Name: plant,Confidence: 99.0562\}
\{Name: tulip,Confidence: 99.0562\}
In this example, the detection algorithm more precisely identifies the flower as a tulip.
In response, the API returns an array of labels. In addition, the
response also includes the orientation correction. Optionally, you can
specify MinConfidence
to control the confidence threshold for the
labels returned. The default is 55%. You can also add the MaxLabels
parameter to limit the number of labels returned.
DetectLabels
returns bounding boxes for instances of common object
labels in an array of Instance objects. An Instance
object contains
a BoundingBox object, for the location of the label on the image. It
also includes the confidence by which the bounding box was detected.
DetectLabels
also returns a hierarchical taxonomy of detected
labels. For example, a detected car might be assigned the label car.
The label car has two parent labels: Vehicle (its parent) and
Transportation (its grandparent). The response returns the entire
list of ancestors for a label. Each ancestor is a unique label in the
response. In the previous example, Car, Vehicle, and
Transportation are returned as unique labels in the response.
This is a stateless API operation. That is, the operation does not persist any data.
This operation requires permissions to perform the
rekognition:DetectLabels
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1758 def detect_labels(params = {}, = {}) req = build_request(:detect_labels, params) req.send_request() end |
#detect_moderation_labels(params = {}) ⇒ Types::DetectModerationLabelsResponse
Detects unsafe content in a specified JPEG or PNG format image. Use
DetectModerationLabels
to moderate images depending on your
requirements. For example, you might want to filter images that
contain nudity, but not images containing suggestive content.
To filter images, use the labels returned by DetectModerationLabels
to determine which types of content are appropriate.
For information about moderation labels, see Detecting Unsafe Content in the Amazon Rekognition Developer Guide.
You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1842 def detect_moderation_labels(params = {}, = {}) req = build_request(:detect_moderation_labels, params) req.send_request() end |
#detect_protective_equipment(params = {}) ⇒ Types::DetectProtectiveEquipmentResponse
Detects Personal Protective Equipment (PPE) worn by people detected in an image. Amazon Rekognition can detect the following types of PPE.
Face cover
Hand cover
Head cover
You pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. The image must be either a PNG or JPG formatted file.
DetectProtectiveEquipment
detects PPE worn by up to 15 persons
detected in an image.
For each person detected in the image the API returns an array of body parts (face, head, left-hand, right-hand). For each body part, an array of detected items of PPE is returned, including an indicator of whether or not the PPE covers the body part. The API returns the confidence it has in each detection (person, PPE, body part and body part coverage). It also returns a bounding box (BoundingBox) for each detected person and each detected item of PPE.
You can optionally request a summary of detected PPE items with the
SummarizationAttributes
input parameter. The summary provides the
following information.
The persons detected as wearing all of the types of PPE that you specify.
The persons detected as not wearing all of the types PPE that you specify.
The persons detected where PPE adornment could not be determined.
This is a stateless API operation. That is, the operation does not persist any data.
This operation requires permissions to perform the
rekognition:DetectProtectiveEquipment
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 1951 def detect_protective_equipment(params = {}, = {}) req = build_request(:detect_protective_equipment, params) req.send_request() end |
#detect_text(params = {}) ⇒ Types::DetectTextResponse
Detects text in the input image and converts it into machine-readable text.
Pass the input image as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, you must pass it as a reference to an image in an Amazon S3 bucket. For the AWS CLI, passing image bytes is not supported. The image must be either a .png or .jpeg formatted file.
The DetectText
operation returns text in an array of TextDetection
elements, TextDetections
. Each TextDetection
element provides
information about a single word or line of text that was detected in
the image.
A word is one or more ISO basic latin script characters that are not
separated by spaces. DetectText
can detect up to 50 words in an
image.
A line is a string of equally spaced words. A line isn't necessarily
a complete sentence. For example, a driver's license number is
detected as a line. A line ends when there is no aligned text after
it. Also, a line ends when there is a large gap between words,
relative to the length of the words. This means, depending on the gap
between words, Amazon Rekognition may detect multiple lines in text
aligned in the same direction. Periods don't represent the end of a
line. If a sentence spans multiple lines, the DetectText
operation
returns multiple lines.
To determine whether a TextDetection
element is a line of text or a
word, use the TextDetection
object Type
field.
To be detected, text must be within +/- 90 degrees orientation of the horizontal axis.
For more information, see DetectText in the Amazon Rekognition Developer Guide.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2062 def detect_text(params = {}, = {}) req = build_request(:detect_text, params) req.send_request() end |
#get_celebrity_info(params = {}) ⇒ Types::GetCelebrityInfoResponse
Gets the name and additional information about a celebrity based on his or her Amazon Rekognition ID. The additional information is returned as an array of URLs. If there is no additional information about the celebrity, this list is empty.
For more information, see Recognizing Celebrities in an Image in the Amazon Rekognition Developer Guide.
This operation requires permissions to perform the
rekognition:GetCelebrityInfo
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2102 def get_celebrity_info(params = {}, = {}) req = build_request(:get_celebrity_info, params) req.send_request() end |
#get_celebrity_recognition(params = {}) ⇒ Types::GetCelebrityRecognitionResponse
Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by StartCelebrityRecognition.
Celebrity recognition in a video is an asynchronous operation.
Analysis is started by a call to StartCelebrityRecognition which
returns a job identifier (JobId
). When the celebrity recognition
operation finishes, Amazon Rekognition Video publishes a completion
status to the Amazon Simple Notification Service topic registered in
the initial call to StartCelebrityRecognition
. To get the results of
the celebrity recognition analysis, first check that the status value
published to the Amazon SNS topic is SUCCEEDED
. If so, call
GetCelebrityDetection
and pass the job identifier (JobId
) from the
initial call to StartCelebrityDetection
.
For more information, see Working With Stored Videos in the Amazon Rekognition Developer Guide.
GetCelebrityRecognition
returns detected celebrities and the time(s)
they are detected in an array (Celebrities
) of CelebrityRecognition
objects. Each CelebrityRecognition
contains information about the
celebrity in a CelebrityDetail object and the time, Timestamp
, the
celebrity was detected.
GetCelebrityRecognition
only returns the default facial attributes
(BoundingBox
, Confidence
, Landmarks
, Pose
, and Quality
). The
other facial attributes listed in the Face
object of the following
response syntax are not returned. For more information, see FaceDetail
in the Amazon Rekognition Developer Guide.
By default, the Celebrities
array is sorted by time (milliseconds
from the start of the video). You can also sort the array by celebrity
by specifying the value ID
in the SortBy
input parameter.
The CelebrityDetail
object includes the celebrity identifer and
additional information urls. If you don't store the additional
information urls, you can get them later by calling GetCelebrityInfo
with the celebrity identifer.
No information is returned for faces not recognized as celebrities.
Use MaxResults parameter to limit the number of labels returned. If
there are more results than specified in MaxResults
, the value of
NextToken
in the operation response contains a pagination token for
getting the next set of results. To get the next page of results, call
GetCelebrityDetection
and populate the NextToken
request parameter
with the token value returned from the previous call to
GetCelebrityRecognition
.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2257 def get_celebrity_recognition(params = {}, = {}) req = build_request(:get_celebrity_recognition, params) req.send_request() end |
#get_content_moderation(params = {}) ⇒ Types::GetContentModerationResponse
Gets the unsafe content analysis results for a Amazon Rekognition Video analysis started by StartContentModeration.
Unsafe content analysis of a video is an asynchronous operation. You
start analysis by calling StartContentModeration which returns a job
identifier (JobId
). When analysis finishes, Amazon Rekognition Video
publishes a completion status to the Amazon Simple Notification
Service topic registered in the initial call to
StartContentModeration
. To get the results of the unsafe content
analysis, first check that the status value published to the Amazon
SNS topic is SUCCEEDED
. If so, call GetContentModeration
and pass
the job identifier (JobId
) from the initial call to
StartContentModeration
.
For more information, see Working with Stored Videos in the Amazon Rekognition Devlopers Guide.
GetContentModeration
returns detected unsafe content labels, and the
time they are detected, in an array, ModerationLabels
, of
ContentModerationDetection objects.
By default, the moderated labels are returned sorted by time, in
milliseconds from the start of the video. You can also sort them by
moderated label by specifying NAME
for the SortBy
input parameter.
Since video analysis can return a large number of results, use the
MaxResults
parameter to limit the number of labels returned in a
single call to GetContentModeration
. If there are more results than
specified in MaxResults
, the value of NextToken
in the operation
response contains a pagination token for getting the next set of
results. To get the next page of results, call GetContentModeration
and populate the NextToken
request parameter with the value of
NextToken
returned from the previous call to GetContentModeration
.
For more information, see Detecting Unsafe Content in the Amazon Rekognition Developer Guide.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2362 def get_content_moderation(params = {}, = {}) req = build_request(:get_content_moderation, params) req.send_request() end |
#get_face_detection(params = {}) ⇒ Types::GetFaceDetectionResponse
Gets face detection results for a Amazon Rekognition Video analysis started by StartFaceDetection.
Face detection with Amazon Rekognition Video is an asynchronous
operation. You start face detection by calling StartFaceDetection
which returns a job identifier (JobId
). When the face detection
operation finishes, Amazon Rekognition Video publishes a completion
status to the Amazon Simple Notification Service topic registered in
the initial call to StartFaceDetection
. To get the results of the
face detection operation, first check that the status value published
to the Amazon SNS topic is SUCCEEDED
. If so, call GetFaceDetection
and pass the job identifier (JobId
) from the initial call to
StartFaceDetection
.
GetFaceDetection
returns an array of detected faces (Faces
) sorted
by the time the faces were detected.
Use MaxResults parameter to limit the number of labels returned. If
there are more results than specified in MaxResults
, the value of
NextToken
in the operation response contains a pagination token for
getting the next set of results. To get the next page of results, call
GetFaceDetection
and populate the NextToken
request parameter with
the token value returned from the previous call to GetFaceDetection
.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2476 def get_face_detection(params = {}, = {}) req = build_request(:get_face_detection, params) req.send_request() end |
#get_face_search(params = {}) ⇒ Types::GetFaceSearchResponse
Gets the face search results for Amazon Rekognition Video face search started by StartFaceSearch. The search returns faces in a collection that match the faces of persons detected in a video. It also includes the time(s) that faces are matched in the video.
Face search in a video is an asynchronous operation. You start face
search by calling to StartFaceSearch which returns a job identifier
(JobId
). When the search operation finishes, Amazon Rekognition
Video publishes a completion status to the Amazon Simple Notification
Service topic registered in the initial call to StartFaceSearch
. To
get the search results, first check that the status value published to
the Amazon SNS topic is SUCCEEDED
. If so, call GetFaceSearch
and
pass the job identifier (JobId
) from the initial call to
StartFaceSearch
.
For more information, see Searching Faces in a Collection in the Amazon Rekognition Developer Guide.
The search results are retured in an array, Persons
, of PersonMatch
objects. EachPersonMatch
element contains details about the matching
faces in the input collection, person information (facial attributes,
bounding boxes, and person identifer) for the matched person, and the
time the person was matched in the video.
GetFaceSearch
only returns the default facial attributes
(BoundingBox
, Confidence
, Landmarks
, Pose
, and Quality
). The
other facial attributes listed in the Face
object of the following
response syntax are not returned. For more information, see FaceDetail
in the Amazon Rekognition Developer Guide.
By default, the Persons
array is sorted by the time, in milliseconds
from the start of the video, persons are matched. You can also sort by
persons by specifying INDEX
for the SORTBY
input parameter.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2623 def get_face_search(params = {}, = {}) req = build_request(:get_face_search, params) req.send_request() end |
#get_label_detection(params = {}) ⇒ Types::GetLabelDetectionResponse
Gets the label detection results of a Amazon Rekognition Video analysis started by StartLabelDetection.
The label detection operation is started by a call to
StartLabelDetection which returns a job identifier (JobId
). When the
label detection operation finishes, Amazon Rekognition publishes a
completion status to the Amazon Simple Notification Service topic
registered in the initial call to StartlabelDetection
. To get the
results of the label detection operation, first check that the status
value published to the Amazon SNS topic is SUCCEEDED
. If so, call
GetLabelDetection and pass the job identifier (JobId
) from the
initial call to StartLabelDetection
.
GetLabelDetection
returns an array of detected labels (Labels
)
sorted by the time the labels were detected. You can also sort by the
label name by specifying NAME
for the SortBy
input parameter.
The labels returned include the label name, the percentage confidence in the accuracy of the detected label, and the time the label was detected in the video.
The returned labels also include bounding box information for common objects, a hierarchical taxonomy of detected labels, and the version of the label model used for detection.
Use MaxResults parameter to limit the number of labels returned. If
there are more results than specified in MaxResults
, the value of
NextToken
in the operation response contains a pagination token for
getting the next set of results. To get the next page of results, call
GetlabelDetection
and populate the NextToken
request parameter
with the token value returned from the previous call to
GetLabelDetection
.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2732 def get_label_detection(params = {}, = {}) req = build_request(:get_label_detection, params) req.send_request() end |
#get_person_tracking(params = {}) ⇒ Types::GetPersonTrackingResponse
Gets the path tracking results of a Amazon Rekognition Video analysis started by StartPersonTracking.
The person path tracking operation is started by a call to
StartPersonTracking
which returns a job identifier (JobId
). When
the operation finishes, Amazon Rekognition Video publishes a
completion status to the Amazon Simple Notification Service topic
registered in the initial call to StartPersonTracking
.
To get the results of the person path tracking operation, first check
that the status value published to the Amazon SNS topic is
SUCCEEDED
. If so, call GetPersonTracking and pass the job identifier
(JobId
) from the initial call to StartPersonTracking
.
GetPersonTracking
returns an array, Persons
, of tracked persons
and the time(s) their paths were tracked in the video.
GetPersonTracking
only returns the default facial attributes
(BoundingBox
, Confidence
, Landmarks
, Pose
, and Quality
). The
other facial attributes listed in the Face
object of the following
response syntax are not returned.
For more information, see FaceDetail in the Amazon Rekognition Developer Guide.
By default, the array is sorted by the time(s) a person's path is
tracked in the video. You can sort by tracked persons by specifying
INDEX
for the SortBy
input parameter.
Use the MaxResults
parameter to limit the number of items returned.
If there are more results than specified in MaxResults
, the value of
NextToken
in the operation response contains a pagination token for
getting the next set of results. To get the next page of results, call
GetPersonTracking
and populate the NextToken
request parameter
with the token value returned from the previous call to
GetPersonTracking
.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2874 def get_person_tracking(params = {}, = {}) req = build_request(:get_person_tracking, params) req.send_request() end |
#get_segment_detection(params = {}) ⇒ Types::GetSegmentDetectionResponse
Gets the segment detection results of a Amazon Rekognition Video analysis started by StartSegmentDetection.
Segment detection with Amazon Rekognition Video is an asynchronous
operation. You start segment detection by calling
StartSegmentDetection which returns a job identifier (JobId
). When
the segment detection operation finishes, Amazon Rekognition publishes
a completion status to the Amazon Simple Notification Service topic
registered in the initial call to StartSegmentDetection
. To get the
results of the segment detection operation, first check that the
status value published to the Amazon SNS topic is SUCCEEDED
. if so,
call GetSegmentDetection
and pass the job identifier (JobId
) from
the initial call of StartSegmentDetection
.
GetSegmentDetection
returns detected segments in an array
(Segments
) of SegmentDetection objects. Segments
is sorted by the
segment types specified in the SegmentTypes
input parameter of
StartSegmentDetection
. Each element of the array includes the
detected segment, the precentage confidence in the acuracy of the
detected segment, the type of the segment, and the frame in which the
segment was detected.
Use SelectedSegmentTypes
to find out the type of segment detection
requested in the call to StartSegmentDetection
.
Use the MaxResults
parameter to limit the number of segment
detections returned. If there are more results than specified in
MaxResults
, the value of NextToken
in the operation response
contains a pagination token for getting the next set of results. To
get the next page of results, call GetSegmentDetection
and populate
the NextToken
request parameter with the token value returned from
the previous call to GetSegmentDetection
.
For more information, see Detecting Video Segments in Stored Video in the Amazon Rekognition Developer Guide.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 2984 def get_segment_detection(params = {}, = {}) req = build_request(:get_segment_detection, params) req.send_request() end |
#get_text_detection(params = {}) ⇒ Types::GetTextDetectionResponse
Gets the text detection results of a Amazon Rekognition Video analysis started by StartTextDetection.
Text detection with Amazon Rekognition Video is an asynchronous
operation. You start text detection by calling StartTextDetection
which returns a job identifier (JobId
) When the text detection
operation finishes, Amazon Rekognition publishes a completion status
to the Amazon Simple Notification Service topic registered in the
initial call to StartTextDetection
. To get the results of the text
detection operation, first check that the status value published to
the Amazon SNS topic is SUCCEEDED
. if so, call GetTextDetection
and pass the job identifier (JobId
) from the initial call of
StartLabelDetection
.
GetTextDetection
returns an array of detected text
(TextDetections
) sorted by the time the text was detected, up to 50
words per frame of video.
Each element of the array includes the detected text, the precentage confidence in the acuracy of the detected text, the time the text was detected, bounding box information for where the text was located, and unique identifiers for words and their lines.
Use MaxResults parameter to limit the number of text detections
returned. If there are more results than specified in MaxResults
,
the value of NextToken
in the operation response contains a
pagination token for getting the next set of results. To get the next
page of results, call GetTextDetection
and populate the NextToken
request parameter with the token value returned from the previous call
to GetTextDetection
.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3083 def get_text_detection(params = {}, = {}) req = build_request(:get_text_detection, params) req.send_request() end |
#index_faces(params = {}) ⇒ Types::IndexFacesResponse
Detects faces in the input image and adds them to the specified collection.
Amazon Rekognition doesn't save the actual faces that are detected. Instead, the underlying detection algorithm first detects the faces in the input image. For each face, the algorithm extracts facial features into a feature vector, and stores it in the backend database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the SearchFaces and SearchFacesByImage operations.
For more information, see Adding Faces to a Collection in the Amazon Rekognition Developer Guide.
To get the number of faces in a collection, call DescribeCollection.
If you're using version 1.0 of the face detection model, IndexFaces
indexes the 15 largest faces in the input image. Later versions of the
face detection model index the 100 largest faces in the input image.
If you're using version 4 or later of the face model, image
orientation information is not returned in the OrientationCorrection
field.
To determine which version of the model you're using, call
DescribeCollection and supply the collection ID. You can also get the
model version from the value of FaceModelVersion
in the response
from IndexFaces
For more information, see Model Versioning in the Amazon Rekognition Developer Guide.
If you provide the optional ExternalImageId
for the input image you
provided, Amazon Rekognition associates this ID with all faces that it
detects. When you call the ListFaces operation, the response returns
the external ID. You can use this external image ID to create a
client-side index to associate the faces with each image. You can then
use the index to find all faces in an image.
You can specify the maximum number of faces to index with the
MaxFaces
input parameter. This is useful when you want to index the
largest faces in an image and don't want to index smaller faces, such
as those belonging to people standing in the background.
The QualityFilter
input parameter allows you to filter out detected
faces that don’t meet a required quality bar. The quality bar is based
on a variety of common use cases. By default, IndexFaces
chooses the
quality bar that's used to filter faces. You can also explicitly
choose the quality bar. Use QualityFilter
, to set the quality bar by
specifying LOW
, MEDIUM
, or HIGH
. If you do not want to filter
detected faces, specify NONE
.
Information about faces detected in an image, but not indexed, is
returned in an array of UnindexedFace objects, UnindexedFaces
. Faces
aren't indexed for reasons such as:
The number of faces detected exceeds the value of the
MaxFaces
request parameter.The face is too small compared to the image dimensions.
The face is too blurry.
The image is too dark.
The face has an extreme pose.
The face doesn’t have enough detail to be suitable for face search.
In response, the IndexFaces
operation returns an array of metadata
for all detected faces, FaceRecords
. This includes:
The bounding box,
BoundingBox
, of the detected face.A confidence value,
Confidence
, which indicates the confidence that the bounding box contains a face.A face ID,
FaceId
, assigned by the service for each face that's detected and stored.An image ID,
ImageId
, assigned by the service for the input image.
If you request all facial attributes (by using the
detectionAttributes
parameter), Amazon Rekognition returns detailed
facial attributes, such as facial landmarks (for example, location of
eye and mouth) and other facial attributes. If you provide the same
image, specify the same collection, and use the same external ID in
the IndexFaces
operation, Amazon Rekognition doesn't save duplicate
face metadata.
The input image is passed either as base64-encoded image bytes, or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file.
This operation requires permissions to perform the
rekognition:IndexFaces
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3513 def index_faces(params = {}, = {}) req = build_request(:index_faces, params) req.send_request() end |
#list_collections(params = {}) ⇒ Types::ListCollectionsResponse
Returns list of collection IDs in your account. If the result is
truncated, the response also provides a NextToken
that you can use
in the subsequent request to fetch the next set of collection IDs.
For an example, see Listing Collections in the Amazon Rekognition Developer Guide.
This operation requires permissions to perform the
rekognition:ListCollections
action.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3574 def list_collections(params = {}, = {}) req = build_request(:list_collections, params) req.send_request() end |
#list_faces(params = {}) ⇒ Types::ListFacesResponse
Returns metadata for faces in the specified collection. This metadata includes information such as the bounding box coordinates, the confidence (that the bounding box contains a face), and face ID. For an example, see Listing Faces in a Collection in the Amazon Rekognition Developer Guide.
This operation requires permissions to perform the
rekognition:ListFaces
action.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3769 def list_faces(params = {}, = {}) req = build_request(:list_faces, params) req.send_request() end |
#list_stream_processors(params = {}) ⇒ Types::ListStreamProcessorsResponse
Gets a list of stream processors that you have created with CreateStreamProcessor.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3810 def list_stream_processors(params = {}, = {}) req = build_request(:list_stream_processors, params) req.send_request() end |
#list_tags_for_resource(params = {}) ⇒ Types::ListTagsForResourceResponse
Returns a list of tags in an Amazon Rekognition collection, stream processor, or Custom Labels model.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3839 def (params = {}, = {}) req = build_request(:list_tags_for_resource, params) req.send_request() end |
#recognize_celebrities(params = {}) ⇒ Types::RecognizeCelebritiesResponse
Returns an array of celebrities recognized in the input image. For more information, see Recognizing Celebrities in the Amazon Rekognition Developer Guide.
RecognizeCelebrities
returns the 64 largest faces in the image. It
lists recognized celebrities in the CelebrityFaces
array and
unrecognized faces in the UnrecognizedFaces
array.
RecognizeCelebrities
doesn't return celebrities whose faces aren't
among the largest 64 faces in the image.
For each celebrity recognized, RecognizeCelebrities
returns a
Celebrity
object. The Celebrity
object contains the celebrity
name, ID, URL links to additional information, match confidence, and a
ComparedFace
object that you can use to locate the celebrity's face
on the image.
Amazon Rekognition doesn't retain information about which images a
celebrity has been recognized in. Your application must store this
information and use the Celebrity
ID property as a unique identifier
for the celebrity. If you don't store the celebrity name or
additional information URLs returned by RecognizeCelebrities
, you
will need the ID to identify the celebrity in a call to the
GetCelebrityInfo operation.
You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.
For an example, see Recognizing Celebrities in an Image in the Amazon Rekognition Developer Guide.
This operation requires permissions to perform the
rekognition:RecognizeCelebrities
operation.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 3949 def recognize_celebrities(params = {}, = {}) req = build_request(:recognize_celebrities, params) req.send_request() end |
#search_faces(params = {}) ⇒ Types::SearchFacesResponse
For a given input face ID, searches for matching faces in the collection the face belongs to. You get a face ID when you add a face to the collection using the IndexFaces operation. The operation compares the features of the input face with faces in the specified collection.
SearchFacesByImage
operation.
The operation response returns an array of faces that match, ordered
by similarity score with the highest similarity first. More
specifically, it is an array of metadata for each face match that is
found. Along with the metadata, the response also includes a
confidence
value for each face match, indicating the confidence that
the specific face matches the input face.
For an example, see Searching for a Face Using Its Face ID in the Amazon Rekognition Developer Guide.
This operation requires permissions to perform the
rekognition:SearchFaces
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4086 def search_faces(params = {}, = {}) req = build_request(:search_faces, params) req.send_request() end |
#search_faces_by_image(params = {}) ⇒ Types::SearchFacesByImageResponse
For a given input image, first detects the largest face in the image, and then searches the specified collection for matching faces. The operation compares the features of the input face with faces in the specified collection.
You can also call the DetectFaces
operation and use the bounding
boxes in the response to make face crops, which then you can pass in
to the SearchFacesByImage
operation.
You pass the input image either as base64-encoded image bytes or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. The image must be either a PNG or JPEG formatted file.
The response returns an array of faces that match, ordered by
similarity score with the highest similarity first. More specifically,
it is an array of metadata for each face match found. Along with the
metadata, the response also includes a similarity
indicating how
similar the face is to the input face. In the response, the operation
also returns the bounding box (and a confidence level that the
bounding box contains a face) of the face that Amazon Rekognition used
for the input image.
For an example, Searching for a Face Using an Image in the Amazon Rekognition Developer Guide.
The QualityFilter
input parameter allows you to filter out detected
faces that don’t meet a required quality bar. The quality bar is based
on a variety of common use cases. Use QualityFilter
to set the
quality bar for filtering by specifying LOW
, MEDIUM
, or HIGH
. If
you do not want to filter detected faces, specify NONE
. The default
value is NONE
.
This operation requires permissions to perform the
rekognition:SearchFacesByImage
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4265 def search_faces_by_image(params = {}, = {}) req = build_request(:search_faces_by_image, params) req.send_request() end |
#start_celebrity_recognition(params = {}) ⇒ Types::StartCelebrityRecognitionResponse
Starts asynchronous recognition of celebrities in a stored video.
Amazon Rekognition Video can detect celebrities in a video must be
stored in an Amazon S3 bucket. Use Video to specify the bucket name
and the filename of the video. StartCelebrityRecognition
returns a
job identifier (JobId
) which you use to get the results of the
analysis. When celebrity recognition analysis is finished, Amazon
Rekognition Video publishes a completion status to the Amazon Simple
Notification Service topic that you specify in NotificationChannel
.
To get the results of the celebrity recognition analysis, first check
that the status value published to the Amazon SNS topic is
SUCCEEDED
. If so, call GetCelebrityRecognition and pass the job
identifier (JobId
) from the initial call to
StartCelebrityRecognition
.
For more information, see Recognizing Celebrities in the Amazon Rekognition Developer Guide.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4337 def start_celebrity_recognition(params = {}, = {}) req = build_request(:start_celebrity_recognition, params) req.send_request() end |
#start_content_moderation(params = {}) ⇒ Types::StartContentModerationResponse
Starts asynchronous detection of unsafe content in a stored video.
Amazon Rekognition Video can moderate content in a video stored in an
Amazon S3 bucket. Use Video to specify the bucket name and the
filename of the video. StartContentModeration
returns a job
identifier (JobId
) which you use to get the results of the analysis.
When unsafe content analysis is finished, Amazon Rekognition Video
publishes a completion status to the Amazon Simple Notification
Service topic that you specify in NotificationChannel
.
To get the results of the unsafe content analysis, first check that
the status value published to the Amazon SNS topic is SUCCEEDED
. If
so, call GetContentModeration and pass the job identifier (JobId
)
from the initial call to StartContentModeration
.
For more information, see Detecting Unsafe Content in the Amazon Rekognition Developer Guide.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4419 def start_content_moderation(params = {}, = {}) req = build_request(:start_content_moderation, params) req.send_request() end |
#start_face_detection(params = {}) ⇒ Types::StartFaceDetectionResponse
Starts asynchronous detection of faces in a stored video.
Amazon Rekognition Video can detect faces in a video stored in an
Amazon S3 bucket. Use Video to specify the bucket name and the
filename of the video. StartFaceDetection
returns a job identifier
(JobId
) that you use to get the results of the operation. When face
detection is finished, Amazon Rekognition Video publishes a completion
status to the Amazon Simple Notification Service topic that you
specify in NotificationChannel
. To get the results of the face
detection operation, first check that the status value published to
the Amazon SNS topic is SUCCEEDED
. If so, call GetFaceDetection and
pass the job identifier (JobId
) from the initial call to
StartFaceDetection
.
For more information, see Detecting Faces in a Stored Video in the Amazon Rekognition Developer Guide.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4499 def start_face_detection(params = {}, = {}) req = build_request(:start_face_detection, params) req.send_request() end |
#start_face_search(params = {}) ⇒ Types::StartFaceSearchResponse
Starts the asynchronous search for faces in a collection that match the faces of persons detected in a stored video.
The video must be stored in an Amazon S3 bucket. Use Video to specify
the bucket name and the filename of the video. StartFaceSearch
returns a job identifier (JobId
) which you use to get the search
results once the search has completed. When searching is finished,
Amazon Rekognition Video publishes a completion status to the Amazon
Simple Notification Service topic that you specify in
NotificationChannel
. To get the search results, first check that the
status value published to the Amazon SNS topic is SUCCEEDED
. If so,
call GetFaceSearch and pass the job identifier (JobId
) from the
initial call to StartFaceSearch
. For more information, see
procedure-person-search-videos.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4577 def start_face_search(params = {}, = {}) req = build_request(:start_face_search, params) req.send_request() end |
#start_label_detection(params = {}) ⇒ Types::StartLabelDetectionResponse
Starts asynchronous detection of labels in a stored video.
Amazon Rekognition Video can detect labels in a video. Labels are instances of real-world entities. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; concepts like landscape, evening, and nature; and activities like a person getting out of a car or a person skiing.
The video must be stored in an Amazon S3 bucket. Use Video to specify
the bucket name and the filename of the video. StartLabelDetection
returns a job identifier (JobId
) which you use to get the results of
the operation. When label detection is finished, Amazon Rekognition
Video publishes a completion status to the Amazon Simple Notification
Service topic that you specify in NotificationChannel
.
To get the results of the label detection operation, first check that
the status value published to the Amazon SNS topic is SUCCEEDED
. If
so, call GetLabelDetection and pass the job identifier (JobId
) from
the initial call to StartLabelDetection
.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4662 def start_label_detection(params = {}, = {}) req = build_request(:start_label_detection, params) req.send_request() end |
#start_person_tracking(params = {}) ⇒ Types::StartPersonTrackingResponse
Starts the asynchronous tracking of a person's path in a stored video.
Amazon Rekognition Video can track the path of people in a video
stored in an Amazon S3 bucket. Use Video to specify the bucket name
and the filename of the video. StartPersonTracking
returns a job
identifier (JobId
) which you use to get the results of the
operation. When label detection is finished, Amazon Rekognition
publishes a completion status to the Amazon Simple Notification
Service topic that you specify in NotificationChannel
.
To get the results of the person detection operation, first check that
the status value published to the Amazon SNS topic is SUCCEEDED
. If
so, call GetPersonTracking and pass the job identifier (JobId
) from
the initial call to StartPersonTracking
.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4731 def start_person_tracking(params = {}, = {}) req = build_request(:start_person_tracking, params) req.send_request() end |
#start_project_version(params = {}) ⇒ Types::StartProjectVersionResponse
Starts the running of the version of a model. Starting a model takes a while to complete. To check the current state of the model, use DescribeProjectVersions.
Once the model is running, you can detect custom labels in new images by calling DetectCustomLabels.
This operation requires permissions to perform the
rekognition:StartProjectVersion
action.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4779 def start_project_version(params = {}, = {}) req = build_request(:start_project_version, params) req.send_request() end |
#start_segment_detection(params = {}) ⇒ Types::StartSegmentDetectionResponse
Starts asynchronous detection of segment detection in a stored video.
Amazon Rekognition Video can detect segments in a video stored in an
Amazon S3 bucket. Use Video to specify the bucket name and the
filename of the video. StartSegmentDetection
returns a job
identifier (JobId
) which you use to get the results of the
operation. When segment detection is finished, Amazon Rekognition
Video publishes a completion status to the Amazon Simple Notification
Service topic that you specify in NotificationChannel
.
You can use the Filters
(StartSegmentDetectionFilters) input
parameter to specify the minimum detection confidence returned in the
response. Within Filters
, use ShotFilter
(StartShotDetectionFilter) to filter detected shots. Use
TechnicalCueFilter
(StartTechnicalCueDetectionFilter) to filter
technical cues.
To get the results of the segment detection operation, first check
that the status value published to the Amazon SNS topic is
SUCCEEDED
. if so, call GetSegmentDetection and pass the job
identifier (JobId
) from the initial call to StartSegmentDetection
.
For more information, see Detecting Video Segments in Stored Video in the Amazon Rekognition Developer Guide.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4876 def start_segment_detection(params = {}, = {}) req = build_request(:start_segment_detection, params) req.send_request() end |
#start_stream_processor(params = {}) ⇒ Struct
Starts processing a stream processor. You create a stream processor by
calling CreateStreamProcessor. To tell StartStreamProcessor
which
stream processor to start, use the value of the Name
field specified
in the call to CreateStreamProcessor
.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4899 def start_stream_processor(params = {}, = {}) req = build_request(:start_stream_processor, params) req.send_request() end |
#start_text_detection(params = {}) ⇒ Types::StartTextDetectionResponse
Starts asynchronous detection of text in a stored video.
Amazon Rekognition Video can detect text in a video stored in an
Amazon S3 bucket. Use Video to specify the bucket name and the
filename of the video. StartTextDetection
returns a job identifier
(JobId
) which you use to get the results of the operation. When text
detection is finished, Amazon Rekognition Video publishes a completion
status to the Amazon Simple Notification Service topic that you
specify in NotificationChannel
.
To get the results of the text detection operation, first check that
the status value published to the Amazon SNS topic is SUCCEEDED
. if
so, call GetTextDetection and pass the job identifier (JobId
) from
the initial call to StartTextDetection
.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 4991 def start_text_detection(params = {}, = {}) req = build_request(:start_text_detection, params) req.send_request() end |
#stop_project_version(params = {}) ⇒ Types::StopProjectVersionResponse
Stops a running model. The operation might take a while to complete. To check the current status, call DescribeProjectVersions.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5022 def stop_project_version(params = {}, = {}) req = build_request(:stop_project_version, params) req.send_request() end |
#stop_stream_processor(params = {}) ⇒ Struct
Stops a running stream processor that was created by CreateStreamProcessor.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5043 def stop_stream_processor(params = {}, = {}) req = build_request(:stop_stream_processor, params) req.send_request() end |
#tag_resource(params = {}) ⇒ Struct
Adds one or more key-value tags to an Amazon Rekognition collection, stream processor, or Custom Labels model. For more information, see Tagging AWS Resources.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5076 def tag_resource(params = {}, = {}) req = build_request(:tag_resource, params) req.send_request() end |
#untag_resource(params = {}) ⇒ Struct
Removes one or more tags from an Amazon Rekognition collection, stream processor, or Custom Labels model.
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5102 def untag_resource(params = {}, = {}) req = build_request(:untag_resource, params) req.send_request() end |
#wait_until(waiter_name, params = {}, options = {}) {|w.waiter| ... } ⇒ Boolean
Polls an API operation until a resource enters a desired state.
Basic Usage
A waiter will call an API operation until:
- It is successful
- It enters a terminal state
- It makes the maximum number of attempts
In between attempts, the waiter will sleep.
# polls in a loop, sleeping between attempts
client.wait_until(waiter_name, params)
Configuration
You can configure the maximum number of polling attempts, and the delay (in seconds) between each polling attempt. You can pass configuration as the final arguments hash.
# poll for ~25 seconds
client.wait_until(waiter_name, params, {
max_attempts: 5,
delay: 5,
})
Callbacks
You can be notified before each polling attempt and before each
delay. If you throw :success
or :failure
from these callbacks,
it will terminate the waiter.
started_at = Time.now
client.wait_until(waiter_name, params, {
# disable max attempts
max_attempts: nil,
# poll for 1 hour, instead of a number of attempts
before_wait: -> (attempts, response) do
throw :failure if Time.now - started_at > 3600
end
})
Handling Errors
When a waiter is unsuccessful, it will raise an error. All of the failure errors extend from Waiters::Errors::WaiterFailed.
begin
client.wait_until(...)
rescue Aws::Waiters::Errors::WaiterFailed
# resource did not enter the desired state in time
end
Valid Waiters
The following table lists the valid waiter names, the operations they call,
and the default :delay
and :max_attempts
values.
waiter_name | params | :delay | :max_attempts |
---|---|---|---|
project_version_running | #describe_project_versions | 30 | 40 |
project_version_training_completed | #describe_project_versions | 120 | 360 |
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# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/client.rb', line 5213 def wait_until(waiter_name, params = {}, = {}) w = waiter(waiter_name, ) yield(w.waiter) if block_given? # deprecated w.wait(params) end |