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src/aws_rekognition.erl
%% WARNING: DO NOT EDIT, AUTO-GENERATED CODE!
%% See https://github.com/aws-beam/aws-codegen for more details.
%% @doc This is the API Reference for Amazon Rekognition Image, Amazon
%% Rekognition Custom Labels, Amazon Rekognition Stored Video, Amazon
%% Rekognition Streaming Video.
%%
%% It provides descriptions of actions, data types, common parameters, and
%% common errors.
%%
%% Amazon Rekognition Image
%%
%% <ul> <li> CompareFaces
%%
%% </li> <li> CreateCollection
%%
%% </li> <li> DeleteCollection
%%
%% </li> <li> DeleteFaces
%%
%% </li> <li> DescribeCollection
%%
%% </li> <li> DetectFaces
%%
%% </li> <li> DetectLabels
%%
%% </li> <li> DetectModerationLabels
%%
%% </li> <li> DetectProtectiveEquipment
%%
%% </li> <li> DetectText
%%
%% </li> <li> GetCelebrityInfo
%%
%% </li> <li> IndexFaces
%%
%% </li> <li> ListCollections
%%
%% </li> <li> ListFaces
%%
%% </li> <li> RecognizeCelebrities
%%
%% </li> <li> SearchFaces
%%
%% </li> <li> SearchFacesByImage
%%
%% </li> </ul> Amazon Rekognition Custom Labels
%%
%% <ul> <li> CopyProjectVersion
%%
%% </li> <li> CreateDataset
%%
%% </li> <li> CreateProject
%%
%% </li> <li> CreateProjectVersion
%%
%% </li> <li> DeleteDataset
%%
%% </li> <li> DeleteProject
%%
%% </li> <li> DeleteProjectPolicy
%%
%% </li> <li> DeleteProjectVersion
%%
%% </li> <li> DescribeDataset
%%
%% </li> <li> DescribeProjects
%%
%% </li> <li> DescribeProjectVersions
%%
%% </li> <li> DetectCustomLabels
%%
%% </li> <li> DistributeDatasetEntries
%%
%% </li> <li> ListDatasetEntries
%%
%% </li> <li> ListDatasetLabels
%%
%% </li> <li> ListProjectPolicies
%%
%% </li> <li> PutProjectPolicy
%%
%% </li> <li> StartProjectVersion
%%
%% </li> <li> StopProjectVersion
%%
%% </li> <li> UpdateDatasetEntries
%%
%% </li> </ul> Amazon Rekognition Video Stored Video
%%
%% <ul> <li> GetCelebrityRecognition
%%
%% </li> <li> GetContentModeration
%%
%% </li> <li> GetFaceDetection
%%
%% </li> <li> GetFaceSearch
%%
%% </li> <li> GetLabelDetection
%%
%% </li> <li> GetPersonTracking
%%
%% </li> <li> GetSegmentDetection
%%
%% </li> <li> GetTextDetection
%%
%% </li> <li> StartCelebrityRecognition
%%
%% </li> <li> StartContentModeration
%%
%% </li> <li> StartFaceDetection
%%
%% </li> <li> StartFaceSearch
%%
%% </li> <li> StartLabelDetection
%%
%% </li> <li> StartPersonTracking
%%
%% </li> <li> StartSegmentDetection
%%
%% </li> <li> StartTextDetection
%%
%% </li> </ul> Amazon Rekognition Video Streaming Video
%%
%% <ul> <li> CreateStreamProcessor
%%
%% </li> <li> DeleteStreamProcessor
%%
%% </li> <li> DescribeStreamProcessor
%%
%% </li> <li> ListStreamProcessors
%%
%% </li> <li> StartStreamProcessor
%%
%% </li> <li> StopStreamProcessor
%%
%% </li> <li> UpdateStreamProcessor
%%
%% </li> </ul>
-module(aws_rekognition).
-export([compare_faces/2,
compare_faces/3,
copy_project_version/2,
copy_project_version/3,
create_collection/2,
create_collection/3,
create_dataset/2,
create_dataset/3,
create_project/2,
create_project/3,
create_project_version/2,
create_project_version/3,
create_stream_processor/2,
create_stream_processor/3,
delete_collection/2,
delete_collection/3,
delete_dataset/2,
delete_dataset/3,
delete_faces/2,
delete_faces/3,
delete_project/2,
delete_project/3,
delete_project_policy/2,
delete_project_policy/3,
delete_project_version/2,
delete_project_version/3,
delete_stream_processor/2,
delete_stream_processor/3,
describe_collection/2,
describe_collection/3,
describe_dataset/2,
describe_dataset/3,
describe_project_versions/2,
describe_project_versions/3,
describe_projects/2,
describe_projects/3,
describe_stream_processor/2,
describe_stream_processor/3,
detect_custom_labels/2,
detect_custom_labels/3,
detect_faces/2,
detect_faces/3,
detect_labels/2,
detect_labels/3,
detect_moderation_labels/2,
detect_moderation_labels/3,
detect_protective_equipment/2,
detect_protective_equipment/3,
detect_text/2,
detect_text/3,
distribute_dataset_entries/2,
distribute_dataset_entries/3,
get_celebrity_info/2,
get_celebrity_info/3,
get_celebrity_recognition/2,
get_celebrity_recognition/3,
get_content_moderation/2,
get_content_moderation/3,
get_face_detection/2,
get_face_detection/3,
get_face_search/2,
get_face_search/3,
get_label_detection/2,
get_label_detection/3,
get_person_tracking/2,
get_person_tracking/3,
get_segment_detection/2,
get_segment_detection/3,
get_text_detection/2,
get_text_detection/3,
index_faces/2,
index_faces/3,
list_collections/2,
list_collections/3,
list_dataset_entries/2,
list_dataset_entries/3,
list_dataset_labels/2,
list_dataset_labels/3,
list_faces/2,
list_faces/3,
list_project_policies/2,
list_project_policies/3,
list_stream_processors/2,
list_stream_processors/3,
list_tags_for_resource/2,
list_tags_for_resource/3,
put_project_policy/2,
put_project_policy/3,
recognize_celebrities/2,
recognize_celebrities/3,
search_faces/2,
search_faces/3,
search_faces_by_image/2,
search_faces_by_image/3,
start_celebrity_recognition/2,
start_celebrity_recognition/3,
start_content_moderation/2,
start_content_moderation/3,
start_face_detection/2,
start_face_detection/3,
start_face_search/2,
start_face_search/3,
start_label_detection/2,
start_label_detection/3,
start_person_tracking/2,
start_person_tracking/3,
start_project_version/2,
start_project_version/3,
start_segment_detection/2,
start_segment_detection/3,
start_stream_processor/2,
start_stream_processor/3,
start_text_detection/2,
start_text_detection/3,
stop_project_version/2,
stop_project_version/3,
stop_stream_processor/2,
stop_stream_processor/3,
tag_resource/2,
tag_resource/3,
untag_resource/2,
untag_resource/3,
update_dataset_entries/2,
update_dataset_entries/3,
update_stream_processor/2,
update_stream_processor/3]).
-include_lib("hackney/include/hackney_lib.hrl").
%%====================================================================
%% API
%%====================================================================
%% @doc Compares a face in the source input image with each of the 100
%% largest faces detected in the target input image.
%%
%% If the source image contains multiple faces, the service detects the
%% largest face and compares it with each face detected in the target image.
%%
%% CompareFaces uses machine learning algorithms, which are probabilistic. A
%% false negative is an incorrect prediction that a face in the target image
%% has a low similarity confidence score when compared to the face in the
%% source image. To reduce the probability of false negatives, we recommend
%% that you compare the target image against multiple source images. If you
%% plan to use `CompareFaces' to make a decision that impacts an
%% individual's rights, privacy, or access to services, we recommend that
%% you pass the result to a human for review and further validation before
%% taking action.
%%
%% You pass the input and target images either as base64-encoded image bytes
%% or as references to images in an Amazon S3 bucket. If you use the AWS CLI
%% to call Amazon Rekognition operations, passing image bytes isn't
%% supported. The image must be formatted as a PNG or JPEG file.
%%
%% In response, the operation returns an array of face matches ordered by
%% similarity score in descending order. For each face match, the response
%% provides a bounding box of the face, facial landmarks, pose details
%% (pitch, roll, and yaw), quality (brightness and sharpness), and confidence
%% value (indicating the level of confidence that the bounding box contains a
%% face). The response also provides a similarity score, which indicates how
%% closely the faces match.
%%
%% By default, only faces with a similarity score of greater than or equal to
%% 80% are returned in the response. You can change this value by specifying
%% the `SimilarityThreshold' parameter.
%%
%% `CompareFaces' also returns an array of faces that don't match the
%% source image. For each face, it returns a bounding box, confidence value,
%% landmarks, pose details, and quality. The response also returns
%% information about the face in the source image, including the bounding box
%% of the face and confidence value.
%%
%% The `QualityFilter' input parameter allows you to filter out detected
%% faces that don’t meet a required quality bar. The quality bar is based on
%% a variety of common use cases. Use `QualityFilter' to set the quality
%% bar by specifying `LOW', `MEDIUM', or `HIGH'. If you do not
%% want to filter detected faces, specify `NONE'. The default value is
%% `NONE'.
%%
%% If the image doesn't contain Exif metadata, `CompareFaces' returns
%% orientation information for the source and target images. Use these values
%% to display the images with the correct image orientation.
%%
%% If no faces are detected in the source or target images,
%% `CompareFaces' returns an `InvalidParameterException' error.
%%
%% This is a stateless API operation. That is, data returned by this
%% operation doesn't persist.
%%
%% For an example, see Comparing Faces in Images in the Amazon Rekognition
%% Developer Guide.
%%
%% This operation requires permissions to perform the
%% `rekognition:CompareFaces' action.
compare_faces(Client, Input)
when is_map(Client), is_map(Input) ->
compare_faces(Client, Input, []).
compare_faces(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"CompareFaces">>, Input, Options).
%% @doc Copies a version of an Amazon Rekognition Custom Labels model from a
%% source project to a destination project.
%%
%% The source and destination projects can be in different AWS accounts but
%% must be in the same AWS Region. You can't copy a model to another AWS
%% service.
%%
%% To copy a model version to a different AWS account, you need to create a
%% resource-based policy known as a project policy. You attach the project
%% policy to the source project by calling `PutProjectPolicy'. The
%% project policy gives permission to copy the model version from a trusting
%% AWS account to a trusted account.
%%
%% For more information creating and attaching a project policy, see
%% Attaching a project policy (SDK) in the Amazon Rekognition Custom Labels
%% Developer Guide.
%%
%% If you are copying a model version to a project in the same AWS account,
%% you don't need to create a project policy.
%%
%% To copy a model, the destination project, source project, and source model
%% version must already exist.
%%
%% Copying a model version takes a while to complete. To get the current
%% status, call `DescribeProjectVersions' and check the value of
%% `Status' in the `ProjectVersionDescription' object. The copy
%% operation has finished when the value of `Status' is
%% `COPYING_COMPLETED'.
copy_project_version(Client, Input)
when is_map(Client), is_map(Input) ->
copy_project_version(Client, Input, []).
copy_project_version(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"CopyProjectVersion">>, Input, Options).
%% @doc Creates a collection in an AWS Region.
%%
%% You can add faces to the collection using the `IndexFaces' operation.
%%
%% For example, you might create collections, one for each of your
%% application users. A user can then index faces using the `IndexFaces'
%% operation and persist results in a specific collection. Then, a user can
%% search the collection for faces in the user-specific container.
%%
%% When you create a collection, it is associated with the latest version of
%% the face model version.
%%
%% Collection names are case-sensitive.
%%
%% This operation requires permissions to perform the
%% `rekognition:CreateCollection' action. If you want to tag your
%% collection, you also require permission to perform the
%% `rekognition:TagResource' operation.
create_collection(Client, Input)
when is_map(Client), is_map(Input) ->
create_collection(Client, Input, []).
create_collection(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"CreateCollection">>, Input, Options).
%% @doc Creates a new Amazon Rekognition Custom Labels dataset.
%%
%% You can create a dataset by using an Amazon Sagemaker format manifest file
%% or by copying an existing Amazon Rekognition Custom Labels dataset.
%%
%% To create a training dataset for a project, specify `train' for the
%% value of `DatasetType'. To create the test dataset for a project,
%% specify `test' for the value of `DatasetType'.
%%
%% The response from `CreateDataset' is the Amazon Resource Name (ARN)
%% for the dataset. Creating a dataset takes a while to complete. Use
%% `DescribeDataset' to check the current status. The dataset created
%% successfully if the value of `Status' is `CREATE_COMPLETE'.
%%
%% To check if any non-terminal errors occurred, call
%% `ListDatasetEntries' and check for the presence of `errors' lists
%% in the JSON Lines.
%%
%% Dataset creation fails if a terminal error occurs (`Status' =
%% `CREATE_FAILED'). Currently, you can't access the terminal error
%% information.
%%
%% For more information, see Creating dataset in the Amazon Rekognition
%% Custom Labels Developer Guide.
%%
%% This operation requires permissions to perform the
%% `rekognition:CreateDataset' action. If you want to copy an existing
%% dataset, you also require permission to perform the
%% `rekognition:ListDatasetEntries' action.
create_dataset(Client, Input)
when is_map(Client), is_map(Input) ->
create_dataset(Client, Input, []).
create_dataset(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"CreateDataset">>, Input, Options).
%% @doc Creates a new Amazon Rekognition Custom Labels project.
%%
%% A project is a group of resources (datasets, model versions) that you use
%% to create and manage Amazon Rekognition Custom Labels models.
%%
%% This operation requires permissions to perform the
%% `rekognition:CreateProject' action.
create_project(Client, Input)
when is_map(Client), is_map(Input) ->
create_project(Client, Input, []).
create_project(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"CreateProject">>, Input, Options).
%% @doc Creates a new version of a model and begins training.
%%
%% Models are managed as part of an Amazon Rekognition Custom Labels project.
%% The response from `CreateProjectVersion' is an Amazon Resource Name
%% (ARN) for the version of the model.
%%
%% Training uses the training and test datasets associated with the project.
%% For more information, see Creating training and test dataset in the Amazon
%% Rekognition Custom Labels Developer Guide.
%%
%% You can train a model in a project that doesn't have associated
%% datasets by specifying manifest files in the `TrainingData' and
%% `TestingData' fields.
%%
%% If you open the console after training a model with manifest files, Amazon
%% Rekognition Custom Labels creates the datasets for you using the most
%% recent manifest files. You can no longer train a model version for the
%% project by specifying manifest files.
%%
%% Instead of training with a project without associated datasets, we
%% recommend that you use the manifest files to create training and test
%% datasets for the project.
%%
%% Training takes a while to complete. You can get the current status by
%% calling `DescribeProjectVersions'. Training completed successfully if
%% the value of the `Status' field is `TRAINING_COMPLETED'.
%%
%% If training fails, see Debugging a failed model training in the Amazon
%% Rekognition Custom Labels developer guide.
%%
%% Once training has successfully completed, call
%% `DescribeProjectVersions' to get the training results and evaluate the
%% model. For more information, see Improving a trained Amazon Rekognition
%% Custom Labels model in the Amazon Rekognition Custom Labels developers
%% guide.
%%
%% After evaluating the model, you start the model by calling
%% `StartProjectVersion'.
%%
%% This operation requires permissions to perform the
%% `rekognition:CreateProjectVersion' action.
create_project_version(Client, Input)
when is_map(Client), is_map(Input) ->
create_project_version(Client, Input, []).
create_project_version(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"CreateProjectVersion">>, Input, Options).
%% @doc Creates an Amazon Rekognition stream processor that you can use to
%% detect and recognize faces or to detect labels in a streaming video.
%%
%% Amazon Rekognition Video is a consumer of live video from Amazon Kinesis
%% Video Streams. There are two different settings for stream processors in
%% Amazon Rekognition: detecting faces and detecting labels.
%%
%% <ul> <li> If you are creating a stream processor for detecting faces, you
%% provide as input a Kinesis video stream (`Input') and a Kinesis data
%% stream (`Output') stream. You also specify the face recognition
%% criteria in `Settings'. For example, the collection containing faces
%% that you want to recognize. After you have finished analyzing a streaming
%% video, use `StopStreamProcessor' to stop processing.
%%
%% </li> <li> If you are creating a stream processor to detect labels, you
%% provide as input a Kinesis video stream (`Input'), Amazon S3 bucket
%% information (`Output'), and an Amazon SNS topic ARN
%% (`NotificationChannel'). You can also provide a KMS key ID to encrypt
%% the data sent to your Amazon S3 bucket. You specify what you want to
%% detect in `ConnectedHomeSettings', such as people, packages and
%% people, or pets, people, and packages. You can also specify where in the
%% frame you want Amazon Rekognition to monitor with `RegionsOfInterest'.
%% When you run the `StartStreamProcessor' operation on a label detection
%% stream processor, you input start and stop information to determine the
%% length of the processing time.
%%
%% </li> </ul> Use `Name' to assign an identifier for the stream
%% processor. You use `Name' to manage the stream processor. For example,
%% you can start processing the source video by calling
%% `StartStreamProcessor' with the `Name' field.
%%
%% This operation requires permissions to perform the
%% `rekognition:CreateStreamProcessor' action. If you want to tag your
%% stream processor, you also require permission to perform the
%% `rekognition:TagResource' operation.
create_stream_processor(Client, Input)
when is_map(Client), is_map(Input) ->
create_stream_processor(Client, Input, []).
create_stream_processor(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"CreateStreamProcessor">>, Input, Options).
%% @doc Deletes the specified collection.
%%
%% Note that this operation removes all faces in the collection. For an
%% example, see Deleting a collection.
%%
%% This operation requires permissions to perform the
%% `rekognition:DeleteCollection' action.
delete_collection(Client, Input)
when is_map(Client), is_map(Input) ->
delete_collection(Client, Input, []).
delete_collection(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DeleteCollection">>, Input, Options).
%% @doc Deletes an existing Amazon Rekognition Custom Labels dataset.
%%
%% Deleting a dataset might take while. Use `DescribeDataset' to check
%% the current status. The dataset is still deleting if the value of
%% `Status' is `DELETE_IN_PROGRESS'. If you try to access the dataset
%% after it is deleted, you get a `ResourceNotFoundException' exception.
%%
%% You can't delete a dataset while it is creating (`Status' =
%% `CREATE_IN_PROGRESS') or if the dataset is updating (`Status' =
%% `UPDATE_IN_PROGRESS').
%%
%% This operation requires permissions to perform the
%% `rekognition:DeleteDataset' action.
delete_dataset(Client, Input)
when is_map(Client), is_map(Input) ->
delete_dataset(Client, Input, []).
delete_dataset(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DeleteDataset">>, Input, Options).
%% @doc 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.
delete_faces(Client, Input)
when is_map(Client), is_map(Input) ->
delete_faces(Client, Input, []).
delete_faces(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DeleteFaces">>, Input, Options).
%% @doc Deletes an Amazon Rekognition Custom Labels project.
%%
%% To delete a project you must first delete all models associated with the
%% project. To delete a model, see `DeleteProjectVersion'.
%%
%% `DeleteProject' is an asynchronous operation. To check if the project
%% is deleted, call `DescribeProjects'. The project is deleted when the
%% project no longer appears in the response. Be aware that deleting a given
%% project will also delete any `ProjectPolicies' associated with that
%% project.
%%
%% This operation requires permissions to perform the
%% `rekognition:DeleteProject' action.
delete_project(Client, Input)
when is_map(Client), is_map(Input) ->
delete_project(Client, Input, []).
delete_project(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DeleteProject">>, Input, Options).
%% @doc Deletes an existing project policy.
%%
%% To get a list of project policies attached to a project, call
%% `ListProjectPolicies'. To attach a project policy to a project, call
%% `PutProjectPolicy'.
delete_project_policy(Client, Input)
when is_map(Client), is_map(Input) ->
delete_project_policy(Client, Input, []).
delete_project_policy(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DeleteProjectPolicy">>, Input, Options).
%% @doc 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.
delete_project_version(Client, Input)
when is_map(Client), is_map(Input) ->
delete_project_version(Client, Input, []).
delete_project_version(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DeleteProjectVersion">>, Input, Options).
%% @doc 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'.
delete_stream_processor(Client, Input)
when is_map(Client), is_map(Input) ->
delete_stream_processor(Client, Input, []).
delete_stream_processor(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DeleteStreamProcessor">>, Input, Options).
%% @doc 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.
describe_collection(Client, Input)
when is_map(Client), is_map(Input) ->
describe_collection(Client, Input, []).
describe_collection(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DescribeCollection">>, Input, Options).
%% @doc Describes an Amazon Rekognition Custom Labels dataset.
%%
%% You can get information such as the current status of a dataset and
%% statistics about the images and labels in a dataset.
%%
%% This operation requires permissions to perform the
%% `rekognition:DescribeDataset' action.
describe_dataset(Client, Input)
when is_map(Client), is_map(Input) ->
describe_dataset(Client, Input, []).
describe_dataset(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DescribeDataset">>, Input, Options).
%% @doc Lists and describes the versions of a model in an Amazon Rekognition
%% Custom Labels project.
%%
%% You can specify up to 10 model versions in `ProjectVersionArns'. If
%% you don't specify a value, descriptions for all model versions in the
%% project are returned.
%%
%% This operation requires permissions to perform the
%% `rekognition:DescribeProjectVersions' action.
describe_project_versions(Client, Input)
when is_map(Client), is_map(Input) ->
describe_project_versions(Client, Input, []).
describe_project_versions(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DescribeProjectVersions">>, Input, Options).
%% @doc Gets information about your Amazon Rekognition Custom Labels
%% projects.
%%
%% This operation requires permissions to perform the
%% `rekognition:DescribeProjects' action.
describe_projects(Client, Input)
when is_map(Client), is_map(Input) ->
describe_projects(Client, Input, []).
describe_projects(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DescribeProjects">>, Input, Options).
%% @doc 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.
describe_stream_processor(Client, Input)
when is_map(Client), is_map(Input) ->
describe_stream_processor(Client, Input, []).
describe_stream_processor(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DescribeStreamProcessor">>, Input, Options).
%% @doc Detects custom labels in a supplied image by using an Amazon
%% Rekognition Custom Labels model.
%%
%% You specify which version of a model version to use by using the
%% `ProjectVersionArn' input parameter.
%%
%% You pass the input image as base64-encoded image bytes or as a reference
%% to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon
%% Rekognition operations, passing image bytes is not supported. The image
%% must be either a PNG or JPEG formatted file.
%%
%% For each object that the model version detects on an image, the API
%% returns a (`CustomLabel') object in an array (`CustomLabels').
%% Each `CustomLabel' object provides the label name (`Name'), the
%% level of confidence that the image contains the object (`Confidence'),
%% and object location information, if it exists, for the label on the image
%% (`Geometry').
%%
%% To filter labels that are returned, specify a value for
%% `MinConfidence'. `DetectCustomLabelsLabels' only returns labels
%% with a confidence that's higher than the specified value. The value of
%% `MinConfidence' maps to the assumed threshold values created during
%% training. For more information, see Assumed threshold in the Amazon
%% Rekognition Custom Labels Developer Guide. Amazon Rekognition Custom
%% Labels metrics expresses an assumed threshold as a floating point value
%% between 0-1. The range of `MinConfidence' normalizes the threshold
%% value to a percentage value (0-100). Confidence responses from
%% `DetectCustomLabels' are also returned as a percentage. You can use
%% `MinConfidence' to change the precision and recall or your model. For
%% more information, see Analyzing an image in the Amazon Rekognition Custom
%% Labels Developer Guide.
%%
%% If you don't specify a value for `MinConfidence',
%% `DetectCustomLabels' returns labels based on the assumed threshold of
%% each label.
%%
%% This is a stateless API operation. That is, the operation does not persist
%% any data.
%%
%% This operation requires permissions to perform the
%% `rekognition:DetectCustomLabels' action.
%%
%% For more information, see Analyzing an image in the Amazon Rekognition
%% Custom Labels Developer Guide.
detect_custom_labels(Client, Input)
when is_map(Client), is_map(Input) ->
detect_custom_labels(Client, Input, []).
detect_custom_labels(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DetectCustomLabels">>, Input, Options).
%% @doc 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 is a stateless API operation. That is, the operation does not persist
%% any data.
%%
%% This operation requires permissions to perform the
%% `rekognition:DetectFaces' action.
detect_faces(Client, Input)
when is_map(Client), is_map(Input) ->
detect_faces(Client, Input, []).
detect_faces(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DetectFaces">>, Input, Options).
%% @doc Detects instances of real-world entities within an image (JPEG or
%% PNG) provided as input.
%%
%% This includes objects like flower, tree, and table; events like wedding,
%% graduation, and birthday party; and concepts like landscape, evening, and
%% nature.
%%
%% For an example, see Analyzing images stored in an Amazon S3 bucket in the
%% Amazon Rekognition Developer Guide.
%%
%% You pass the input image as base64-encoded image bytes or as a reference
%% to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon
%% Rekognition operations, passing image bytes is not supported. The image
%% must be either a PNG or JPEG formatted file.
%%
%% Optional Parameters
%%
%% You can specify one or both of the `GENERAL_LABELS' and
%% `IMAGE_PROPERTIES' feature types when calling the DetectLabels API.
%% Including `GENERAL_LABELS' will ensure the response includes the
%% labels detected in the input image, while including `IMAGE_PROPERTIES
%% 'will ensure the response includes information about the image quality
%% and color.
%%
%% When using `GENERAL_LABELS' and/or `IMAGE_PROPERTIES' you can
%% provide filtering criteria to the Settings parameter. You can filter with
%% sets of individual labels or with label categories. You can specify
%% inclusive filters, exclusive filters, or a combination of inclusive and
%% exclusive filters. For more information on filtering see Detecting Labels
%% in an Image.
%%
%% You can specify `MinConfidence' to control the confidence threshold
%% for the labels returned. The default is 55%. You can also add the
%% `MaxLabels' parameter to limit the number of labels returned. The
%% default and upper limit is 1000 labels.
%%
%% Response Elements
%%
%% For each object, scene, and concept the API returns one or more labels.
%% The API returns the following types of information regarding labels:
%%
%% <ul> <li> Name - The name of the detected label.
%%
%% </li> <li> Confidence - The level of confidence in the label assigned to a
%% detected object.
%%
%% </li> <li> Parents - The ancestor labels for a detected label.
%% DetectLabels returns a hierarchical taxonomy of detected labels. For
%% example, a detected car might be assigned the label car. The label car has
%% two parent labels: Vehicle (its parent) and Transportation (its
%% grandparent). The response includes the all ancestors for a label, where
%% every ancestor is a unique label. In the previous example, Car, Vehicle,
%% and Transportation are returned as unique labels in the response.
%%
%% </li> <li> Aliases - Possible Aliases for the label.
%%
%% </li> <li> Categories - The label categories that the detected label
%% belongs to.
%%
%% </li> <li> BoundingBox — Bounding boxes are described for all instances of
%% detected common object labels, returned in an array of Instance objects.
%% An Instance object contains a BoundingBox object, describing the location
%% of the label on the input image. It also includes the confidence for the
%% accuracy of the detected bounding box.
%%
%% </li> </ul> The API returns the following information regarding the image,
%% as part of the ImageProperties structure:
%%
%% <ul> <li> Quality - Information about the Sharpness, Brightness, and
%% Contrast of the input image, scored between 0 to 100. Image quality is
%% returned for the entire image, as well as the background and the
%% foreground.
%%
%% </li> <li> Dominant Color - An array of the dominant colors in the image.
%%
%% </li> <li> Foreground - Information about the sharpness, brightness, and
%% dominant colors of the input image’s foreground.
%%
%% </li> <li> Background - Information about the sharpness, brightness, and
%% dominant colors of the input image’s background.
%%
%% </li> </ul> The list of returned labels will include at least one label
%% for every detected object, along with information about that label. In the
%% following example, suppose the input image has a lighthouse, the sea, and
%% a rock. The response includes all three labels, one for each object, as
%% well as the confidence in the label:
%%
%% `{Name: lighthouse, Confidence: 98.4629}'
%%
%% `{Name: rock,Confidence: 79.2097}'
%%
%% ` {Name: sea,Confidence: 75.061}'
%%
%% The list of labels can include multiple labels for the same object. For
%% example, if the input image shows a flower (for example, a tulip), the
%% operation might return the following three labels.
%%
%% `{Name: flower,Confidence: 99.0562}'
%%
%% `{Name: plant,Confidence: 99.0562}'
%%
%% `{Name: tulip,Confidence: 99.0562}'
%%
%% In this example, the detection algorithm more precisely identifies the
%% flower as a tulip.
%%
%% If the object detected is a person, the operation doesn't provide the
%% same facial details that the `DetectFaces' operation provides.
%%
%% This is a stateless API operation. That is, the operation does not persist
%% any data.
%%
%% This operation requires permissions to perform the
%% `rekognition:DetectLabels' action.
detect_labels(Client, Input)
when is_map(Client), is_map(Input) ->
detect_labels(Client, Input, []).
detect_labels(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DetectLabels">>, Input, Options).
%% @doc 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.
detect_moderation_labels(Client, Input)
when is_map(Client), is_map(Input) ->
detect_moderation_labels(Client, Input, []).
detect_moderation_labels(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DetectModerationLabels">>, Input, Options).
%% @doc Detects Personal Protective Equipment (PPE) worn by people detected
%% in an image.
%%
%% Amazon Rekognition can detect the following types of PPE.
%%
%% <ul> <li> Face cover
%%
%% </li> <li> Hand cover
%%
%% </li> <li> Head cover
%%
%% </li> </ul> 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.
%%
%% <ul> <li> The persons detected as wearing all of the types of PPE that you
%% specify.
%%
%% </li> <li> The persons detected as not wearing all of the types PPE that
%% you specify.
%%
%% </li> <li> The persons detected where PPE adornment could not be
%% determined.
%%
%% </li> </ul> 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.
detect_protective_equipment(Client, Input)
when is_map(Client), is_map(Input) ->
detect_protective_equipment(Client, Input, []).
detect_protective_equipment(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DetectProtectiveEquipment">>, Input, Options).
%% @doc Detects text in the input image and converts it into machine-readable
%% text.
%%
%% Pass the input image as base64-encoded image bytes or as a reference to an
%% image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon
%% Rekognition operations, you must pass it as a reference to an image in an
%% Amazon S3 bucket. For the AWS CLI, passing image bytes is not supported.
%% The image must be either a .png or .jpeg formatted file.
%%
%% The `DetectText' operation returns text in an array of
%% `TextDetection' elements, `TextDetections'. Each
%% `TextDetection' element provides information about a single word or
%% line of text that was detected in the image.
%%
%% A word is one or more script characters that are not separated by spaces.
%% `DetectText' can detect up to 100 words in an image.
%%
%% A line is a string of equally spaced words. A line isn't necessarily a
%% complete sentence. For example, a driver's license number is detected
%% as a line. A line ends when there is no aligned text after it. Also, a
%% line ends when there is a large gap between words, relative to the length
%% of the words. This means, depending on the gap between words, Amazon
%% Rekognition may detect multiple lines in text aligned in the same
%% direction. Periods don't represent the end of a line. If a sentence
%% spans multiple lines, the `DetectText' operation returns multiple
%% lines.
%%
%% To determine whether a `TextDetection' element is a line of text or a
%% word, use the `TextDetection' object `Type' field.
%%
%% To be detected, text must be within +/- 90 degrees orientation of the
%% horizontal axis.
%%
%% For more information, see Detecting text in the Amazon Rekognition
%% Developer Guide.
detect_text(Client, Input)
when is_map(Client), is_map(Input) ->
detect_text(Client, Input, []).
detect_text(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DetectText">>, Input, Options).
%% @doc Distributes the entries (images) in a training dataset across the
%% training dataset and the test dataset for a project.
%%
%% `DistributeDatasetEntries' moves 20% of the training dataset images to
%% the test dataset. An entry is a JSON Line that describes an image.
%%
%% You supply the Amazon Resource Names (ARN) of a project's training
%% dataset and test dataset. The training dataset must contain the images
%% that you want to split. The test dataset must be empty. The datasets must
%% belong to the same project. To create training and test datasets for a
%% project, call `CreateDataset'.
%%
%% Distributing a dataset takes a while to complete. To check the status call
%% `DescribeDataset'. The operation is complete when the `Status'
%% field for the training dataset and the test dataset is
%% `UPDATE_COMPLETE'. If the dataset split fails, the value of
%% `Status' is `UPDATE_FAILED'.
%%
%% This operation requires permissions to perform the
%% `rekognition:DistributeDatasetEntries' action.
distribute_dataset_entries(Client, Input)
when is_map(Client), is_map(Input) ->
distribute_dataset_entries(Client, Input, []).
distribute_dataset_entries(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"DistributeDatasetEntries">>, Input, Options).
%% @doc Gets the name and additional information about a celebrity based on
%% their Amazon Rekognition ID.
%%
%% The additional information is returned as an array of URLs. If there is no
%% additional information about the celebrity, this list is empty.
%%
%% For more information, see Getting information about a celebrity in the
%% Amazon Rekognition Developer Guide.
%%
%% This operation requires permissions to perform the
%% `rekognition:GetCelebrityInfo' action.
get_celebrity_info(Client, Input)
when is_map(Client), is_map(Input) ->
get_celebrity_info(Client, Input, []).
get_celebrity_info(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"GetCelebrityInfo">>, Input, Options).
%% @doc Gets the celebrity recognition results for a Amazon Rekognition Video
%% analysis started by `StartCelebrityRecognition'.
%%
%% Celebrity recognition in a video is an asynchronous operation. Analysis is
%% started by a call to `StartCelebrityRecognition' which returns a job
%% identifier (`JobId').
%%
%% When the celebrity recognition operation finishes, Amazon Rekognition
%% Video publishes a completion status to the Amazon Simple Notification
%% Service topic registered in the initial call to
%% `StartCelebrityRecognition'. To get the results of the celebrity
%% recognition analysis, first check that the status value published to the
%% Amazon SNS topic is `SUCCEEDED'. If so, call
%% `GetCelebrityDetection' and pass the job identifier (`JobId') from
%% the initial call to `StartCelebrityDetection'.
%%
%% For more information, see Working With Stored Videos in the Amazon
%% Rekognition Developer Guide.
%%
%% `GetCelebrityRecognition' returns detected celebrities and the time(s)
%% they are detected in an array (`Celebrities') of
%% `CelebrityRecognition' objects. Each `CelebrityRecognition'
%% contains information about the celebrity in a `CelebrityDetail' object
%% and the time, `Timestamp', the celebrity was detected. This
%% `CelebrityDetail' object stores information about the detected
%% celebrity's face attributes, a face bounding box, known gender, the
%% celebrity's name, and a confidence estimate.
%%
%% `GetCelebrityRecognition' only returns the default facial attributes
%% (`BoundingBox', `Confidence', `Landmarks', `Pose', and
%% `Quality'). The `BoundingBox' field only applies to the detected
%% face instance. The other facial attributes listed in the `Face' object
%% of the following response syntax are not returned. For more information,
%% see FaceDetail in the Amazon Rekognition Developer Guide.
%%
%% By default, the `Celebrities' array is sorted by time (milliseconds
%% from the start of the video). You can also sort the array by celebrity by
%% specifying the value `ID' in the `SortBy' input parameter.
%%
%% The `CelebrityDetail' object includes the celebrity identifer and
%% additional information urls. If you don't store the additional
%% information urls, you can get them later by calling `GetCelebrityInfo'
%% with the celebrity identifer.
%%
%% No information is returned for faces not recognized as celebrities.
%%
%% Use MaxResults parameter to limit the number of labels returned. If there
%% are more results than specified in `MaxResults', the value of
%% `NextToken' in the operation response contains a pagination token for
%% getting the next set of results. To get the next page of results, call
%% `GetCelebrityDetection' and populate the `NextToken' request
%% parameter with the token value returned from the previous call to
%% `GetCelebrityRecognition'.
get_celebrity_recognition(Client, Input)
when is_map(Client), is_map(Input) ->
get_celebrity_recognition(Client, Input, []).
get_celebrity_recognition(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"GetCelebrityRecognition">>, Input, Options).
%% @doc Gets the inappropriate, unwanted, or offensive content analysis
%% results for a Amazon Rekognition Video analysis started by
%% `StartContentModeration'.
%%
%% For a list of moderation labels in Amazon Rekognition, see Using the image
%% and video moderation APIs.
%%
%% Amazon Rekognition Video inappropriate or offensive content detection in a
%% stored video is an asynchronous operation. You start analysis by calling
%% `StartContentModeration' which returns a job identifier (`JobId').
%% When analysis finishes, Amazon Rekognition Video publishes a completion
%% status to the Amazon Simple Notification Service topic registered in the
%% initial call to `StartContentModeration'. To get the results of the
%% content analysis, first check that the status value published to the
%% Amazon SNS topic is `SUCCEEDED'. If so, call
%% `GetContentModeration' and pass the job identifier (`JobId') from
%% the initial call to `StartContentModeration'.
%%
%% For more information, see Working with Stored Videos in the Amazon
%% Rekognition Devlopers Guide.
%%
%% `GetContentModeration' returns detected inappropriate, unwanted, or
%% offensive content moderation labels, and the time they are detected, in an
%% array, `ModerationLabels', of `ContentModerationDetection'
%% objects.
%%
%% By default, the moderated labels are returned sorted by time, in
%% milliseconds from the start of the video. You can also sort them by
%% moderated label by specifying `NAME' for the `SortBy' input
%% parameter.
%%
%% Since video analysis can return a large number of results, use the
%% `MaxResults' parameter to limit the number of labels returned in a
%% single call to `GetContentModeration'. If there are more results than
%% specified in `MaxResults', the value of `NextToken' in the
%% operation response contains a pagination token for getting the next set of
%% results. To get the next page of results, call `GetContentModeration'
%% and populate the `NextToken' request parameter with the value of
%% `NextToken' returned from the previous call to
%% `GetContentModeration'.
%%
%% For more information, see moderating content in the Amazon Rekognition
%% Developer Guide.
get_content_moderation(Client, Input)
when is_map(Client), is_map(Input) ->
get_content_moderation(Client, Input, []).
get_content_moderation(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"GetContentModeration">>, Input, Options).
%% @doc 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'.
get_face_detection(Client, Input)
when is_map(Client), is_map(Input) ->
get_face_detection(Client, Input, []).
get_face_detection(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"GetFaceDetection">>, Input, Options).
%% @doc 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. Each`PersonMatch' 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.
get_face_search(Client, Input)
when is_map(Client), is_map(Input) ->
get_face_search(Client, Input, []).
get_face_search(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"GetFaceSearch">>, Input, Options).
%% @doc Gets the label detection results of a Amazon Rekognition Video
%% analysis started by `StartLabelDetection'.
%%
%% The label detection operation is started by a call to
%% `StartLabelDetection' which returns a job identifier (`JobId').
%% When the label detection operation finishes, Amazon Rekognition publishes
%% a completion status to the Amazon Simple Notification Service topic
%% registered in the initial call to `StartlabelDetection'.
%%
%% To get the results of the label detection operation, first check that the
%% status value published to the Amazon SNS topic is `SUCCEEDED'. If so,
%% call `GetLabelDetection' and pass the job identifier (`JobId')
%% from the initial call to `StartLabelDetection'.
%%
%% `GetLabelDetection' returns an array of detected labels (`Labels')
%% sorted by the time the labels were detected. You can also sort by the
%% label name by specifying `NAME' for the `SortBy' input parameter.
%% If there is no `NAME' specified, the default sort is by timestamp.
%%
%% You can select how results are aggregated by using the `AggregateBy'
%% input parameter. The default aggregation method is `TIMESTAMPS'. You
%% can also aggregate by `SEGMENTS', which aggregates all instances of
%% labels detected in a given segment.
%%
%% The returned Labels array may include the following attributes:
%%
%% <ul> <li> Name - The name of the detected label.
%%
%% </li> <li> Confidence - The level of confidence in the label assigned to a
%% detected object.
%%
%% </li> <li> Parents - The ancestor labels for a detected label.
%% GetLabelDetection returns a hierarchical taxonomy of detected labels. For
%% example, a detected car might be assigned the label car. The label car has
%% two parent labels: Vehicle (its parent) and Transportation (its
%% grandparent). The response includes the all ancestors for a label, where
%% every ancestor is a unique label. In the previous example, Car, Vehicle,
%% and Transportation are returned as unique labels in the response.
%%
%% </li> <li> Aliases - Possible Aliases for the label.
%%
%% </li> <li> Categories - The label categories that the detected label
%% belongs to.
%%
%% </li> <li> BoundingBox — Bounding boxes are described for all instances of
%% detected common object labels, returned in an array of Instance objects.
%% An Instance object contains a BoundingBox object, describing the location
%% of the label on the input image. It also includes the confidence for the
%% accuracy of the detected bounding box.
%%
%% </li> <li> Timestamp - Time, in milliseconds from the start of the video,
%% that the label was detected. For aggregation by `SEGMENTS', the
%% `StartTimestampMillis', `EndTimestampMillis', and
%% `DurationMillis' structures are what define a segment. Although the
%% “Timestamp” structure is still returned with each label, its value is set
%% to be the same as `StartTimestampMillis'.
%%
%% </li> </ul> Timestamp and Bounding box information are returned for
%% detected Instances, only if aggregation is done by `TIMESTAMPS'. If
%% aggregating by `SEGMENTS', information about detected instances isn’t
%% returned.
%%
%% The version of the label model used for the detection is also returned.
%%
%% Note `DominantColors' isn't returned for `Instances', although
%% it is shown as part of the response in the sample seen below.
%%
%% Use `MaxResults' parameter to limit the number of labels returned. If
%% there are more results than specified in `MaxResults', the value of
%% `NextToken' in the operation response contains a pagination token for
%% getting the next set of results. To get the next page of results, call
%% `GetlabelDetection' and populate the `NextToken' request parameter
%% with the token value returned from the previous call to
%% `GetLabelDetection'.
get_label_detection(Client, Input)
when is_map(Client), is_map(Input) ->
get_label_detection(Client, Input, []).
get_label_detection(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"GetLabelDetection">>, Input, Options).
%% @doc 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'.
get_person_tracking(Client, Input)
when is_map(Client), is_map(Input) ->
get_person_tracking(Client, Input, []).
get_person_tracking(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"GetPersonTracking">>, Input, Options).
%% @doc 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.
get_segment_detection(Client, Input)
when is_map(Client), is_map(Input) ->
get_segment_detection(Client, Input, []).
get_segment_detection(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"GetSegmentDetection">>, Input, Options).
%% @doc 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'.
get_text_detection(Client, Input)
when is_map(Client), is_map(Input) ->
get_text_detection(Client, Input, []).
get_text_detection(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"GetTextDetection">>, Input, Options).
%% @doc Detects faces in the input image and adds them to the specified
%% collection.
%%
%% Amazon Rekognition doesn't save the actual faces that are detected.
%% Instead, the underlying detection algorithm first detects the faces in the
%% input image. For each face, the algorithm extracts facial features into a
%% feature vector, and stores it in the backend database. Amazon Rekognition
%% uses feature vectors when it performs face match and search operations
%% using the `SearchFaces' and `SearchFacesByImage' operations.
%%
%% For more information, see Adding faces to a collection in the Amazon
%% Rekognition Developer Guide.
%%
%% To get the number of faces in a collection, call `DescribeCollection'.
%%
%% If you're using version 1.0 of the face detection model,
%% `IndexFaces' indexes the 15 largest faces in the input image. Later
%% versions of the face detection model index the 100 largest faces in the
%% input image.
%%
%% If you're using version 4 or later of the face model, image
%% orientation information is not returned in the `OrientationCorrection'
%% field.
%%
%% To determine which version of the model you're using, call
%% `DescribeCollection' and supply the collection ID. You can also get
%% the model version from the value of `FaceModelVersion' in the response
%% from `IndexFaces'
%%
%% For more information, see Model Versioning in the Amazon Rekognition
%% Developer Guide.
%%
%% If you provide the optional `ExternalImageId' for the input image you
%% provided, Amazon Rekognition associates this ID with all faces that it
%% detects. When you call the `ListFaces' operation, the response returns
%% the external ID. You can use this external image ID to create a
%% client-side index to associate the faces with each image. You can then use
%% the index to find all faces in an image.
%%
%% You can specify the maximum number of faces to index with the
%% `MaxFaces' input parameter. This is useful when you want to index the
%% largest faces in an image and don't want to index smaller faces, such
%% as those belonging to people standing in the background.
%%
%% The `QualityFilter' input parameter allows you to filter out detected
%% faces that don’t meet a required quality bar. The quality bar is based on
%% a variety of common use cases. By default, `IndexFaces' chooses the
%% quality bar that's used to filter faces. You can also explicitly
%% choose the quality bar. Use `QualityFilter', to set the quality bar by
%% specifying `LOW', `MEDIUM', or `HIGH'. If you do not want to
%% filter detected faces, specify `NONE'.
%%
%% To use quality filtering, you need a collection associated with version 3
%% of the face model or higher. To get the version of the face model
%% associated with a collection, call `DescribeCollection'.
%%
%% Information about faces detected in an image, but not indexed, is returned
%% in an array of `UnindexedFace' objects, `UnindexedFaces'. Faces
%% aren't indexed for reasons such as:
%%
%% <ul> <li> The number of faces detected exceeds the value of the
%% `MaxFaces' request parameter.
%%
%% </li> <li> The face is too small compared to the image dimensions.
%%
%% </li> <li> The face is too blurry.
%%
%% </li> <li> The image is too dark.
%%
%% </li> <li> The face has an extreme pose.
%%
%% </li> <li> The face doesn’t have enough detail to be suitable for face
%% search.
%%
%% </li> </ul> In response, the `IndexFaces' operation returns an array
%% of metadata for all detected faces, `FaceRecords'. This includes:
%%
%% <ul> <li> The bounding box, `BoundingBox', of the detected face.
%%
%% </li> <li> A confidence value, `Confidence', which indicates the
%% confidence that the bounding box contains a face.
%%
%% </li> <li> A face ID, `FaceId', assigned by the service for each face
%% that's detected and stored.
%%
%% </li> <li> An image ID, `ImageId', assigned by the service for the
%% input image.
%%
%% </li> </ul> 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.
index_faces(Client, Input)
when is_map(Client), is_map(Input) ->
index_faces(Client, Input, []).
index_faces(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"IndexFaces">>, Input, Options).
%% @doc 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.
list_collections(Client, Input)
when is_map(Client), is_map(Input) ->
list_collections(Client, Input, []).
list_collections(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"ListCollections">>, Input, Options).
%% @doc Lists the entries (images) within a dataset.
%%
%% An entry is a JSON Line that contains the information for a single image,
%% including the image location, assigned labels, and object location
%% bounding boxes. For more information, see Creating a manifest file.
%%
%% JSON Lines in the response include information about non-terminal errors
%% found in the dataset. Non terminal errors are reported in `errors'
%% lists within each JSON Line. The same information is reported in the
%% training and testing validation result manifests that Amazon Rekognition
%% Custom Labels creates during model training.
%%
%% You can filter the response in variety of ways, such as choosing which
%% labels to return and returning JSON Lines created after a specific date.
%%
%% This operation requires permissions to perform the
%% `rekognition:ListDatasetEntries' action.
list_dataset_entries(Client, Input)
when is_map(Client), is_map(Input) ->
list_dataset_entries(Client, Input, []).
list_dataset_entries(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"ListDatasetEntries">>, Input, Options).
%% @doc Lists the labels in a dataset.
%%
%% Amazon Rekognition Custom Labels uses labels to describe images. For more
%% information, see Labeling images.
%%
%% Lists the labels in a dataset. Amazon Rekognition Custom Labels uses
%% labels to describe images. For more information, see Labeling images in
%% the Amazon Rekognition Custom Labels Developer Guide.
list_dataset_labels(Client, Input)
when is_map(Client), is_map(Input) ->
list_dataset_labels(Client, Input, []).
list_dataset_labels(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"ListDatasetLabels">>, Input, Options).
%% @doc 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.
list_faces(Client, Input)
when is_map(Client), is_map(Input) ->
list_faces(Client, Input, []).
list_faces(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"ListFaces">>, Input, Options).
%% @doc Gets a list of the project policies attached to a project.
%%
%% To attach a project policy to a project, call `PutProjectPolicy'. To
%% remove a project policy from a project, call `DeleteProjectPolicy'.
list_project_policies(Client, Input)
when is_map(Client), is_map(Input) ->
list_project_policies(Client, Input, []).
list_project_policies(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"ListProjectPolicies">>, Input, Options).
%% @doc Gets a list of stream processors that you have created with
%% `CreateStreamProcessor'.
list_stream_processors(Client, Input)
when is_map(Client), is_map(Input) ->
list_stream_processors(Client, Input, []).
list_stream_processors(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"ListStreamProcessors">>, Input, Options).
%% @doc Returns a list of tags in an Amazon Rekognition collection, stream
%% processor, or Custom Labels model.
%%
%% This operation requires permissions to perform the
%% `rekognition:ListTagsForResource' action.
list_tags_for_resource(Client, Input)
when is_map(Client), is_map(Input) ->
list_tags_for_resource(Client, Input, []).
list_tags_for_resource(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"ListTagsForResource">>, Input, Options).
%% @doc Attaches a project policy to a Amazon Rekognition Custom Labels
%% project in a trusting AWS account.
%%
%% A project policy specifies that a trusted AWS account can copy a model
%% version from a trusting AWS account to a project in the trusted AWS
%% account. To copy a model version you use the `CopyProjectVersion'
%% operation.
%%
%% For more information about the format of a project policy document, see
%% Attaching a project policy (SDK) in the Amazon Rekognition Custom Labels
%% Developer Guide.
%%
%% The response from `PutProjectPolicy' is a revision ID for the project
%% policy. You can attach multiple project policies to a project. You can
%% also update an existing project policy by specifying the policy revision
%% ID of the existing policy.
%%
%% To remove a project policy from a project, call `DeleteProjectPolicy'.
%% To get a list of project policies attached to a project, call
%% `ListProjectPolicies'.
%%
%% You copy a model version by calling `CopyProjectVersion'.
put_project_policy(Client, Input)
when is_map(Client), is_map(Input) ->
put_project_policy(Client, Input, []).
put_project_policy(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"PutProjectPolicy">>, Input, Options).
%% @doc Returns an array of celebrities recognized in the input image.
%%
%% For more information, see Recognizing celebrities in the Amazon
%% Rekognition Developer Guide.
%%
%% `RecognizeCelebrities' returns the 64 largest faces in the image. It
%% lists the recognized celebrities in the `CelebrityFaces' array and any
%% unrecognized faces in the `UnrecognizedFaces' array.
%% `RecognizeCelebrities' doesn't return celebrities whose faces
%% aren't among the largest 64 faces in the image.
%%
%% For each celebrity recognized, `RecognizeCelebrities' returns a
%% `Celebrity' object. The `Celebrity' object contains the celebrity
%% name, ID, URL links to additional information, match confidence, and a
%% `ComparedFace' object that you can use to locate the celebrity's
%% face on the image.
%%
%% Amazon Rekognition doesn't retain information about which images a
%% celebrity has been recognized in. Your application must store this
%% information and use the `Celebrity' ID property as a unique identifier
%% for the celebrity. If you don't store the celebrity name or additional
%% information URLs returned by `RecognizeCelebrities', you will need the
%% ID to identify the celebrity in a call to the `GetCelebrityInfo'
%% operation.
%%
%% You pass the input image either as base64-encoded image bytes or as a
%% reference to an image in an Amazon S3 bucket. If you use the AWS CLI to
%% call Amazon Rekognition operations, passing image bytes is not supported.
%% The image must be either a PNG or JPEG formatted file.
%%
%% For an example, see Recognizing celebrities in an image in the Amazon
%% Rekognition Developer Guide.
%%
%% This operation requires permissions to perform the
%% `rekognition:RecognizeCelebrities' operation.
recognize_celebrities(Client, Input)
when is_map(Client), is_map(Input) ->
recognize_celebrities(Client, Input, []).
recognize_celebrities(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"RecognizeCelebrities">>, Input, Options).
%% @doc For a given input face ID, searches for matching faces in the
%% collection the face belongs to.
%%
%% You get a face ID when you add a face to the collection using the
%% `IndexFaces' operation. The operation compares the features of the
%% input face with faces in the specified collection.
%%
%% You can also search faces without indexing faces by using the
%% `SearchFacesByImage' operation.
%%
%% The operation response returns an array of faces that match, ordered by
%% similarity score with the highest similarity first. More specifically, it
%% is an array of metadata for each face match that is found. Along with the
%% metadata, the response also includes a `confidence' value for each
%% face match, indicating the confidence that the specific face matches the
%% input face.
%%
%% For an example, see Searching for a face using its face ID in the Amazon
%% Rekognition Developer Guide.
%%
%% This operation requires permissions to perform the
%% `rekognition:SearchFaces' action.
search_faces(Client, Input)
when is_map(Client), is_map(Input) ->
search_faces(Client, Input, []).
search_faces(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"SearchFaces">>, Input, Options).
%% @doc For a given input image, first detects the largest face in the image,
%% and then searches the specified collection for matching faces.
%%
%% The operation compares the features of the input face with faces in the
%% specified collection.
%%
%% To search for all faces in an input image, you might first call the
%% `IndexFaces' operation, and then use the face IDs returned in
%% subsequent calls to the `SearchFaces' operation.
%%
%% You can also call the `DetectFaces' operation and use the bounding
%% boxes in the response to make face crops, which then you can pass in to
%% the `SearchFacesByImage' operation.
%%
%% You pass the input image either as base64-encoded image bytes or as a
%% reference to an image in an Amazon S3 bucket. If you use the AWS CLI to
%% call Amazon Rekognition operations, passing image bytes is not supported.
%% The image must be either a PNG or JPEG formatted file.
%%
%% The response returns an array of faces that match, ordered by similarity
%% score with the highest similarity first. More specifically, it is an array
%% of metadata for each face match found. Along with the metadata, the
%% response also includes a `similarity' indicating how similar the face
%% is to the input face. In the response, the operation also returns the
%% bounding box (and a confidence level that the bounding box contains a
%% face) of the face that Amazon Rekognition used for the input image.
%%
%% If no faces are detected in the input image, `SearchFacesByImage'
%% returns an `InvalidParameterException' error.
%%
%% For an example, Searching for a Face Using an Image in the Amazon
%% Rekognition Developer Guide.
%%
%% The `QualityFilter' input parameter allows you to filter out detected
%% faces that don’t meet a required quality bar. The quality bar is based on
%% a variety of common use cases. Use `QualityFilter' to set the quality
%% bar for filtering by specifying `LOW', `MEDIUM', or `HIGH'. If
%% you do not want to filter detected faces, specify `NONE'. The default
%% value is `NONE'.
%%
%% To use quality filtering, you need a collection associated with version 3
%% of the face model or higher. To get the version of the face model
%% associated with a collection, call `DescribeCollection'.
%%
%% This operation requires permissions to perform the
%% `rekognition:SearchFacesByImage' action.
search_faces_by_image(Client, Input)
when is_map(Client), is_map(Input) ->
search_faces_by_image(Client, Input, []).
search_faces_by_image(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"SearchFacesByImage">>, Input, Options).
%% @doc 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.
start_celebrity_recognition(Client, Input)
when is_map(Client), is_map(Input) ->
start_celebrity_recognition(Client, Input, []).
start_celebrity_recognition(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"StartCelebrityRecognition">>, Input, Options).
%% @doc Starts asynchronous detection of inappropriate, unwanted, or
%% offensive content in a stored video.
%%
%% For a list of moderation labels in Amazon Rekognition, see Using the image
%% and video moderation APIs.
%%
%% Amazon Rekognition Video can moderate content in a video stored in an
%% Amazon S3 bucket. Use `Video' to specify the bucket name and the
%% filename of the video. `StartContentModeration' returns a job
%% identifier (`JobId') which you use to get the results of the analysis.
%% When content analysis is finished, Amazon Rekognition Video publishes a
%% completion status to the Amazon Simple Notification Service topic that you
%% specify in `NotificationChannel'.
%%
%% To get the results of the content analysis, first check that the status
%% value published to the Amazon SNS topic is `SUCCEEDED'. If so, call
%% `GetContentModeration' and pass the job identifier (`JobId') from
%% the initial call to `StartContentModeration'.
%%
%% For more information, see Moderating content in the Amazon Rekognition
%% Developer Guide.
start_content_moderation(Client, Input)
when is_map(Client), is_map(Input) ->
start_content_moderation(Client, Input, []).
start_content_moderation(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"StartContentModeration">>, Input, Options).
%% @doc 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.
start_face_detection(Client, Input)
when is_map(Client), is_map(Input) ->
start_face_detection(Client, Input, []).
start_face_detection(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"StartFaceDetection">>, Input, Options).
%% @doc Starts the asynchronous search for faces in a collection that match
%% the faces of persons detected in a stored video.
%%
%% The video must be stored in an Amazon S3 bucket. Use `Video' to
%% specify the bucket name and the filename of the video.
%% `StartFaceSearch' returns a job identifier (`JobId') which you use
%% to get the search results once the search has completed. When searching is
%% finished, Amazon Rekognition Video publishes a completion status to the
%% Amazon Simple Notification Service topic that you specify in
%% `NotificationChannel'. To get the search results, first check that the
%% status value published to the Amazon SNS topic is `SUCCEEDED'. If so,
%% call `GetFaceSearch' and pass the job identifier (`JobId') from
%% the initial call to `StartFaceSearch'. For more information, see
%% Searching stored videos for faces.
start_face_search(Client, Input)
when is_map(Client), is_map(Input) ->
start_face_search(Client, Input, []).
start_face_search(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"StartFaceSearch">>, Input, Options).
%% @doc Starts asynchronous detection of labels in a stored video.
%%
%% Amazon Rekognition Video can detect labels in a video. Labels are
%% instances of real-world entities. This includes objects like flower, tree,
%% and table; events like wedding, graduation, and birthday party; concepts
%% like landscape, evening, and nature; and activities like a person getting
%% out of a car or a person skiing.
%%
%% The video must be stored in an Amazon S3 bucket. Use `Video' to
%% specify the bucket name and the filename of the video.
%% `StartLabelDetection' returns a job identifier (`JobId') which you
%% use to get the results of the operation. When label detection is finished,
%% Amazon Rekognition Video publishes a completion status to the Amazon
%% Simple Notification Service topic that you specify in
%% `NotificationChannel'.
%%
%% To get the results of the label detection operation, first check that the
%% status value published to the Amazon SNS topic is `SUCCEEDED'. If so,
%% call `GetLabelDetection' and pass the job identifier (`JobId')
%% from the initial call to `StartLabelDetection'.
%%
%% Optional Parameters
%%
%% `StartLabelDetection' has the `GENERAL_LABELS' Feature applied by
%% default. This feature allows you to provide filtering criteria to the
%% `Settings' parameter. You can filter with sets of individual labels or
%% with label categories. You can specify inclusive filters, exclusive
%% filters, or a combination of inclusive and exclusive filters. For more
%% information on filtering, see Detecting labels in a video.
%%
%% You can specify `MinConfidence' to control the confidence threshold
%% for the labels returned. The default is 50.
start_label_detection(Client, Input)
when is_map(Client), is_map(Input) ->
start_label_detection(Client, Input, []).
start_label_detection(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"StartLabelDetection">>, Input, Options).
%% @doc 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'.
start_person_tracking(Client, Input)
when is_map(Client), is_map(Input) ->
start_person_tracking(Client, Input, []).
start_person_tracking(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"StartPersonTracking">>, Input, Options).
%% @doc Starts the running of the version of a model.
%%
%% Starting a model takes a while to complete. To check the current state of
%% the model, use `DescribeProjectVersions'.
%%
%% Once the model is running, you can detect custom labels in new images by
%% calling `DetectCustomLabels'.
%%
%% You are charged for the amount of time that the model is running. To stop
%% a running model, call `StopProjectVersion'.
%%
%% For more information, see Running a trained Amazon Rekognition Custom
%% Labels model in the Amazon Rekognition Custom Labels Guide.
%%
%% This operation requires permissions to perform the
%% `rekognition:StartProjectVersion' action.
start_project_version(Client, Input)
when is_map(Client), is_map(Input) ->
start_project_version(Client, Input, []).
start_project_version(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"StartProjectVersion">>, Input, Options).
%% @doc 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.
start_segment_detection(Client, Input)
when is_map(Client), is_map(Input) ->
start_segment_detection(Client, Input, []).
start_segment_detection(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"StartSegmentDetection">>, Input, Options).
%% @doc Starts processing a stream processor.
%%
%% You create a stream processor by calling `CreateStreamProcessor'. To
%% tell `StartStreamProcessor' which stream processor to start, use the
%% value of the `Name' field specified in the call to
%% `CreateStreamProcessor'.
%%
%% If you are using a label detection stream processor to detect labels, you
%% need to provide a `Start selector' and a `Stop selector' to
%% determine the length of the stream processing time.
start_stream_processor(Client, Input)
when is_map(Client), is_map(Input) ->
start_stream_processor(Client, Input, []).
start_stream_processor(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"StartStreamProcessor">>, Input, Options).
%% @doc 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'.
start_text_detection(Client, Input)
when is_map(Client), is_map(Input) ->
start_text_detection(Client, Input, []).
start_text_detection(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"StartTextDetection">>, Input, Options).
%% @doc Stops a running model.
%%
%% The operation might take a while to complete. To check the current status,
%% call `DescribeProjectVersions'.
stop_project_version(Client, Input)
when is_map(Client), is_map(Input) ->
stop_project_version(Client, Input, []).
stop_project_version(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"StopProjectVersion">>, Input, Options).
%% @doc Stops a running stream processor that was created by
%% `CreateStreamProcessor'.
stop_stream_processor(Client, Input)
when is_map(Client), is_map(Input) ->
stop_stream_processor(Client, Input, []).
stop_stream_processor(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"StopStreamProcessor">>, Input, Options).
%% @doc Adds one or more key-value tags to an Amazon Rekognition collection,
%% stream processor, or Custom Labels model.
%%
%% For more information, see Tagging AWS Resources.
%%
%% This operation requires permissions to perform the
%% `rekognition:TagResource' action.
tag_resource(Client, Input)
when is_map(Client), is_map(Input) ->
tag_resource(Client, Input, []).
tag_resource(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"TagResource">>, Input, Options).
%% @doc Removes one or more tags from an Amazon Rekognition collection,
%% stream processor, or Custom Labels model.
%%
%% This operation requires permissions to perform the
%% `rekognition:UntagResource' action.
untag_resource(Client, Input)
when is_map(Client), is_map(Input) ->
untag_resource(Client, Input, []).
untag_resource(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"UntagResource">>, Input, Options).
%% @doc Adds or updates one or more entries (images) in a dataset.
%%
%% An entry is a JSON Line which contains the information for a single image,
%% including the image location, assigned labels, and object location
%% bounding boxes. For more information, see Image-Level labels in manifest
%% files and Object localization in manifest files in the Amazon Rekognition
%% Custom Labels Developer Guide.
%%
%% If the `source-ref' field in the JSON line references an existing
%% image, the existing image in the dataset is updated. If `source-ref'
%% field doesn't reference an existing image, the image is added as a new
%% image to the dataset.
%%
%% You specify the changes that you want to make in the `Changes' input
%% parameter. There isn't a limit to the number JSON Lines that you can
%% change, but the size of `Changes' must be less than 5MB.
%%
%% `UpdateDatasetEntries' returns immediatly, but the dataset update
%% might take a while to complete. Use `DescribeDataset' to check the
%% current status. The dataset updated successfully if the value of
%% `Status' is `UPDATE_COMPLETE'.
%%
%% To check if any non-terminal errors occured, call `ListDatasetEntries'
%% and check for the presence of `errors' lists in the JSON Lines.
%%
%% Dataset update fails if a terminal error occurs (`Status' =
%% `UPDATE_FAILED'). Currently, you can't access the terminal error
%% information from the Amazon Rekognition Custom Labels SDK.
%%
%% This operation requires permissions to perform the
%% `rekognition:UpdateDatasetEntries' action.
update_dataset_entries(Client, Input)
when is_map(Client), is_map(Input) ->
update_dataset_entries(Client, Input, []).
update_dataset_entries(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"UpdateDatasetEntries">>, Input, Options).
%% @doc Allows you to update a stream processor.
%%
%% You can change some settings and regions of interest and delete certain
%% parameters.
update_stream_processor(Client, Input)
when is_map(Client), is_map(Input) ->
update_stream_processor(Client, Input, []).
update_stream_processor(Client, Input, Options)
when is_map(Client), is_map(Input), is_list(Options) ->
request(Client, <<"UpdateStreamProcessor">>, Input, Options).
%%====================================================================
%% Internal functions
%%====================================================================
-spec request(aws_client:aws_client(), binary(), map(), list()) ->
{ok, Result, {integer(), list(), hackney:client()}} |
{error, Error, {integer(), list(), hackney:client()}} |
{error, term()} when
Result :: map() | undefined,
Error :: map().
request(Client, Action, Input, Options) ->
RequestFun = fun() -> do_request(Client, Action, Input, Options) end,
aws_request:request(RequestFun, Options).
do_request(Client, Action, Input0, Options) ->
Client1 = Client#{service => <<"rekognition">>},
Host = build_host(<<"rekognition">>, Client1),
URL = build_url(Host, Client1),
Headers = [
{<<"Host">>, Host},
{<<"Content-Type">>, <<"application/x-amz-json-1.1">>},
{<<"X-Amz-Target">>, <<"RekognitionService.", Action/binary>>}
],
Input = Input0,
Payload = jsx:encode(Input),
SignedHeaders = aws_request:sign_request(Client1, <<"POST">>, URL, Headers, Payload),
Response = hackney:request(post, URL, SignedHeaders, Payload, Options),
handle_response(Response).
handle_response({ok, 200, ResponseHeaders, Client}) ->
case hackney:body(Client) of
{ok, <<>>} ->
{ok, undefined, {200, ResponseHeaders, Client}};
{ok, Body} ->
Result = jsx:decode(Body),
{ok, Result, {200, ResponseHeaders, Client}}
end;
handle_response({ok, StatusCode, ResponseHeaders, Client}) ->
{ok, Body} = hackney:body(Client),
Error = jsx:decode(Body),
{error, Error, {StatusCode, ResponseHeaders, Client}};
handle_response({error, Reason}) ->
{error, Reason}.
build_host(_EndpointPrefix, #{region := <<"local">>, endpoint := Endpoint}) ->
Endpoint;
build_host(_EndpointPrefix, #{region := <<"local">>}) ->
<<"localhost">>;
build_host(EndpointPrefix, #{region := Region, endpoint := Endpoint}) ->
aws_util:binary_join([EndpointPrefix, Region, Endpoint], <<".">>).
build_url(Host, Client) ->
Proto = maps:get(proto, Client),
Port = maps:get(port, Client),
aws_util:binary_join([Proto, <<"://">>, Host, <<":">>, Port, <<"/">>], <<"">>).