# WARNING: DO NOT EDIT, AUTO-GENERATED CODE!
# See https://github.com/aws-beam/aws-codegen for more details.
defmodule AWS.Rekognition do
@moduledoc """
This is the API Reference for [Amazon Rekognition Image](https://docs.aws.amazon.com/rekognition/latest/dg/images.html), [Amazon Rekognition Custom
Labels](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/what-is.html),
[Amazon Rekognition Stored Video](https://docs.aws.amazon.com/rekognition/latest/dg/video.html), [Amazon Rekognition Streaming
Video](https://docs.aws.amazon.com/rekognition/latest/dg/streaming-video.html).
It provides descriptions of actions, data types, common
parameters, and common errors.
## Amazon Rekognition Image
*
[AssociateFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_AssociateFaces.html)
*
[CompareFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CompareFaces.html)
*
[CreateCollection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateCollection.html)
*
[CreateUser](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateUser.html)
*
[DeleteCollection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteCollection.html)
*
[DeleteFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteFaces.html)
*
[DeleteUser](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteUser.html)
*
[DescribeCollection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeCollection.html)
*
[DetectFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectFaces.html)
*
[DetectLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectLabels.html)
*
[DetectModerationLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectModerationLabels.html)
*
[DetectProtectiveEquipment](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectProtectiveEquipment.html)
*
[DetectText](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectText.html)
*
[DisassociateFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DisassociateFaces.html)
*
[GetCelebrityInfo](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetCelebrityInfo.html)
*
[GetMediaAnalysisJob](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetMediaAnalysisJob.html)
*
[IndexFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_IndexFaces.html)
*
[ListCollections](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListCollections.html)
*
[ListMediaAnalysisJob](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListMediaAnalysisJob.html)
*
[ListFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListFaces.html)
*
[ListUsers](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListFaces.html)
*
[RecognizeCelebrities](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_RecognizeCelebrities.html)
*
[SearchFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchFaces.html)
*
[SearchFacesByImage](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchFacesByImage.html)
*
[SearchUsers](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchUsers.html)
*
[SearchUsersByImage](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchUsersByImage.html)
*
[StartMediaAnalysisJob](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartMediaAnalysisJob.html)
## Amazon Rekognition Custom Labels
*
[CopyProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CopyProjectVersion.html)
*
[CreateDataset](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateDataset.html)
*
[CreateProject](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateProject.html)
*
[CreateProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateProjectVersion.html)
*
[DeleteDataset](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteDataset.html)
*
[DeleteProject](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteProject.html)
*
[DeleteProjectPolicy](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteProjectPolicy.html)
*
[DeleteProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteProjectVersion.html)
*
[DescribeDataset](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeDataset.html)
*
[DescribeProjects](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeProjects.html)
*
[DescribeProjectVersions](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeProjectVersions.html)
*
[DetectCustomLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectCustomLabels.html)
*
[DistributeDatasetEntries](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DistributeDatasetEntries.html)
*
[ListDatasetEntries](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListDatasetEntries.html)
*
[ListDatasetLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListDatasetLabels.html)
*
[ListProjectPolicies](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListProjectPolicies.html)
*
[PutProjectPolicy](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_PutProjectPolicy.html)
*
[StartProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartProjectVersion.html)
*
[StopProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StopProjectVersion.html)
*
[UpdateDatasetEntries](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_UpdateDatasetEntries.html)
## Amazon Rekognition Video Stored Video
*
[GetCelebrityRecognition](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetCelebrityRecognition.html)
*
[GetContentModeration](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetContentModeration.html)
*
[GetFaceDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetFaceDetection.html)
*
[GetFaceSearch](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetFaceSearch.html)
*
[GetLabelDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetLabelDetection.html)
*
[GetPersonTracking](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetPersonTracking.html)
*
[GetSegmentDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetSegmentDetection.html)
*
[GetTextDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetTextDetection.html)
*
[StartCelebrityRecognition](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartCelebrityRecognition.html)
*
[StartContentModeration](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartContentModeration.html)
*
[StartFaceDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartFaceDetection.html)
*
[StartFaceSearch](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartFaceSearch.html)
*
[StartLabelDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartLabelDetection.html)
*
[StartPersonTracking](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartPersonTracking.html)
*
[StartSegmentDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartSegmentDetection.html)
*
[StartTextDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartTextDetection.html)
## Amazon Rekognition Video Streaming Video
*
[CreateStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateStreamProcessor.html)
*
[DeleteStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteStreamProcessor.html)
*
[DescribeStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeStreamProcessor.html)
*
[ListStreamProcessors](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListStreamProcessors.html)
*
[StartStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartStreamProcessor.html)
*
[StopStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StopStreamProcessor.html)
*
[UpdateStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_UpdateStreamProcessor.html)
"""
alias AWS.Client
alias AWS.Request
@typedoc """
## Example:
bounding_box() :: %{
"Height" => float(),
"Left" => float(),
"Top" => float(),
"Width" => float()
}
"""
@type bounding_box() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_celebrity_recognition_response() :: %{
"Celebrities" => list(celebrity_recognition()),
"JobId" => String.t() | atom(),
"JobStatus" => list(any()),
"JobTag" => String.t() | atom(),
"NextToken" => String.t() | atom(),
"StatusMessage" => String.t() | atom(),
"Video" => video(),
"VideoMetadata" => video_metadata()
}
"""
@type get_celebrity_recognition_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_project_policies_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
required("ProjectArn") => String.t() | atom()
}
"""
@type list_project_policies_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_face_detection_response() :: %{
"JobId" => String.t() | atom()
}
"""
@type start_face_detection_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
face_detection() :: %{
"Face" => face_detail(),
"Timestamp" => float()
}
"""
@type face_detection() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_project_response() :: %{
"Status" => list(any())
}
"""
@type delete_project_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
dominant_color() :: %{
"Blue" => integer(),
"CSSColor" => String.t() | atom(),
"Green" => integer(),
"HexCode" => String.t() | atom(),
"PixelPercent" => float(),
"Red" => integer(),
"SimplifiedColor" => String.t() | atom()
}
"""
@type dominant_color() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_stream_processor_response() :: %{}
"""
@type delete_stream_processor_response() :: %{}
@typedoc """
## Example:
describe_projects_request() :: %{
optional("Features") => list(list(any())()),
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
optional("ProjectNames") => list(String.t() | atom())
}
"""
@type describe_projects_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_media_analysis_jobs_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom()
}
"""
@type list_media_analysis_jobs_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_stream_processor_request() :: %{
required("Name") => String.t() | atom()
}
"""
@type delete_stream_processor_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
distribute_dataset_entries_request() :: %{
required("Datasets") => list(distribute_dataset())
}
"""
@type distribute_dataset_entries_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
output_config() :: %{
"S3Bucket" => String.t() | atom(),
"S3KeyPrefix" => String.t() | atom()
}
"""
@type output_config() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
ground_truth_manifest() :: %{
"S3Object" => s3_object()
}
"""
@type ground_truth_manifest() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_media_analysis_job_request() :: %{
required("JobId") => String.t() | atom()
}
"""
@type get_media_analysis_job_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_segment_detection_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
optional("Filters") => start_segment_detection_filters(),
optional("JobTag") => String.t() | atom(),
optional("NotificationChannel") => notification_channel(),
required("SegmentTypes") => list(list(any())()),
required("Video") => video()
}
"""
@type start_segment_detection_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
beard() :: %{
"Confidence" => float(),
"Value" => boolean()
}
"""
@type beard() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
content_moderation_detection() :: %{
"ContentTypes" => list(content_type()),
"DurationMillis" => float(),
"EndTimestampMillis" => float(),
"ModerationLabel" => moderation_label(),
"StartTimestampMillis" => float(),
"Timestamp" => float()
}
"""
@type content_moderation_detection() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
index_faces_response() :: %{
"FaceModelVersion" => String.t() | atom(),
"FaceRecords" => list(face_record()),
"OrientationCorrection" => list(any()),
"UnindexedFaces" => list(unindexed_face())
}
"""
@type index_faces_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
stream_processing_stop_selector() :: %{
"MaxDurationInSeconds" => float()
}
"""
@type stream_processing_stop_selector() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_labels_image_background() :: %{
"DominantColors" => list(dominant_color()),
"Quality" => detect_labels_image_quality()
}
"""
@type detect_labels_image_background() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
parent() :: %{
"Name" => String.t() | atom()
}
"""
@type parent() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
face_record() :: %{
"Face" => face(),
"FaceDetail" => face_detail()
}
"""
@type face_record() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_collections_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom()
}
"""
@type list_collections_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
general_labels_settings() :: %{
"LabelCategoryExclusionFilters" => list(String.t() | atom()),
"LabelCategoryInclusionFilters" => list(String.t() | atom()),
"LabelExclusionFilters" => list(String.t() | atom()),
"LabelInclusionFilters" => list(String.t() | atom())
}
"""
@type general_labels_settings() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
s3_object() :: %{
"Bucket" => String.t() | atom(),
"Name" => String.t() | atom(),
"Version" => String.t() | atom()
}
"""
@type s3_object() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
put_project_policy_response() :: %{
"PolicyRevisionId" => String.t() | atom()
}
"""
@type put_project_policy_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
known_gender() :: %{
"Type" => list(any())
}
"""
@type known_gender() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_faces_response() :: %{
"FaceModelVersion" => String.t() | atom(),
"Faces" => list(face()),
"NextToken" => String.t() | atom()
}
"""
@type list_faces_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
dataset_changes() :: %{
"GroundTruth" => binary()
}
"""
@type dataset_changes() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
validation_data() :: %{
"Assets" => list(asset())
}
"""
@type validation_data() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_collection_request() :: %{
required("CollectionId") => String.t() | atom()
}
"""
@type delete_collection_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_face_liveness_session_response() :: %{
"SessionId" => String.t() | atom()
}
"""
@type create_face_liveness_session_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
throttling_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type throttling_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_project_request() :: %{
required("ProjectArn") => String.t() | atom()
}
"""
@type delete_project_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
describe_project_versions_response() :: %{
"NextToken" => String.t() | atom(),
"ProjectVersionDescriptions" => list(project_version_description())
}
"""
@type describe_project_versions_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
put_project_policy_request() :: %{
optional("PolicyRevisionId") => String.t() | atom(),
required("PolicyDocument") => String.t() | atom(),
required("PolicyName") => String.t() | atom(),
required("ProjectArn") => String.t() | atom()
}
"""
@type put_project_policy_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
kinesis_data_stream() :: %{
"Arn" => String.t() | atom()
}
"""
@type kinesis_data_stream() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_stream_processor_response() :: %{
"SessionId" => String.t() | atom()
}
"""
@type start_stream_processor_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
index_faces_request() :: %{
optional("DetectionAttributes") => list(list(any())()),
optional("ExternalImageId") => String.t() | atom(),
optional("MaxFaces") => integer(),
optional("QualityFilter") => list(any()),
required("CollectionId") => String.t() | atom(),
required("Image") => image()
}
"""
@type index_faces_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
label_category() :: %{
"Name" => String.t() | atom()
}
"""
@type label_category() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
human_loop_data_attributes() :: %{
"ContentClassifiers" => list(list(any())())
}
"""
@type human_loop_data_attributes() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
describe_dataset_request() :: %{
required("DatasetArn") => String.t() | atom()
}
"""
@type describe_dataset_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
access_denied_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type access_denied_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
challenge() :: %{
"Type" => list(any()),
"Version" => String.t() | atom()
}
"""
@type challenge() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
project_description() :: %{
"AutoUpdate" => list(any()),
"CreationTimestamp" => non_neg_integer(),
"Datasets" => list(dataset_metadata()),
"Feature" => list(any()),
"ProjectArn" => String.t() | atom(),
"Status" => list(any())
}
"""
@type project_description() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
emotion() :: %{
"Confidence" => float(),
"Type" => list(any())
}
"""
@type emotion() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
media_analysis_detect_moderation_labels_config() :: %{
"MinConfidence" => float(),
"ProjectVersion" => String.t() | atom()
}
"""
@type media_analysis_detect_moderation_labels_config() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_project_policy_response() :: %{}
"""
@type delete_project_policy_response() :: %{}
@typedoc """
## Example:
detect_labels_response() :: %{
"ImageProperties" => detect_labels_image_properties(),
"LabelModelVersion" => String.t() | atom(),
"Labels" => list(label()),
"OrientationCorrection" => list(any())
}
"""
@type detect_labels_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
search_faces_by_image_request() :: %{
optional("FaceMatchThreshold") => float(),
optional("MaxFaces") => integer(),
optional("QualityFilter") => list(any()),
required("CollectionId") => String.t() | atom(),
required("Image") => image()
}
"""
@type search_faces_by_image_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
liveness_output_config() :: %{
"S3Bucket" => String.t() | atom(),
"S3KeyPrefix" => String.t() | atom()
}
"""
@type liveness_output_config() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
searched_face() :: %{
"FaceId" => String.t() | atom()
}
"""
@type searched_face() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
video_metadata() :: %{
"Codec" => String.t() | atom(),
"ColorRange" => list(any()),
"DurationMillis" => float(),
"Format" => String.t() | atom(),
"FrameHeight" => float(),
"FrameRate" => float(),
"FrameWidth" => float()
}
"""
@type video_metadata() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
person_detail() :: %{
"BoundingBox" => bounding_box(),
"Face" => face_detail(),
"Index" => float()
}
"""
@type person_detail() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_project_policies_response() :: %{
"NextToken" => String.t() | atom(),
"ProjectPolicies" => list(project_policy())
}
"""
@type list_project_policies_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_labels_image_foreground() :: %{
"DominantColors" => list(dominant_color()),
"Quality" => detect_labels_image_quality()
}
"""
@type detect_labels_image_foreground() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_collection_request() :: %{
optional("Tags") => map(),
required("CollectionId") => String.t() | atom()
}
"""
@type create_collection_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
celebrity() :: %{
"Face" => compared_face(),
"Id" => String.t() | atom(),
"KnownGender" => known_gender(),
"MatchConfidence" => float(),
"Name" => String.t() | atom(),
"Urls" => list(String.t() | atom())
}
"""
@type celebrity() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
disassociate_faces_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
required("CollectionId") => String.t() | atom(),
required("FaceIds") => list(String.t() | atom()),
required("UserId") => String.t() | atom()
}
"""
@type disassociate_faces_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
text_detection() :: %{
"Confidence" => float(),
"DetectedText" => String.t() | atom(),
"Geometry" => geometry(),
"Id" => integer(),
"ParentId" => integer(),
"Type" => list(any())
}
"""
@type text_detection() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_labels_image_properties_settings() :: %{
"MaxDominantColors" => integer()
}
"""
@type detect_labels_image_properties_settings() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_labels_image_quality() :: %{
"Brightness" => float(),
"Contrast" => float(),
"Sharpness" => float()
}
"""
@type detect_labels_image_quality() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_project_version_response() :: %{
"Status" => list(any())
}
"""
@type delete_project_version_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_media_analysis_job_response() :: %{
"CompletionTimestamp" => non_neg_integer(),
"CreationTimestamp" => non_neg_integer(),
"FailureDetails" => media_analysis_job_failure_details(),
"Input" => media_analysis_input(),
"JobId" => String.t() | atom(),
"JobName" => String.t() | atom(),
"KmsKeyId" => String.t() | atom(),
"ManifestSummary" => media_analysis_manifest_summary(),
"OperationsConfig" => media_analysis_operations_config(),
"OutputConfig" => media_analysis_output_config(),
"Results" => media_analysis_results(),
"Status" => list(any())
}
"""
@type get_media_analysis_job_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
limit_exceeded_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type limit_exceeded_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
protective_equipment_person() :: %{
"BodyParts" => list(protective_equipment_body_part()),
"BoundingBox" => bounding_box(),
"Confidence" => float(),
"Id" => integer()
}
"""
@type protective_equipment_person() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
search_faces_response() :: %{
"FaceMatches" => list(face_match()),
"FaceModelVersion" => String.t() | atom(),
"SearchedFaceId" => String.t() | atom()
}
"""
@type search_faces_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
describe_project_versions_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
optional("VersionNames") => list(String.t() | atom()),
required("ProjectArn") => String.t() | atom()
}
"""
@type describe_project_versions_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
recognize_celebrities_response() :: %{
"CelebrityFaces" => list(celebrity()),
"OrientationCorrection" => list(any()),
"UnrecognizedFaces" => list(compared_face())
}
"""
@type recognize_celebrities_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
landmark() :: %{
"Type" => list(any()),
"X" => float(),
"Y" => float()
}
"""
@type landmark() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
custom_label() :: %{
"Confidence" => float(),
"Geometry" => geometry(),
"Name" => String.t() | atom()
}
"""
@type custom_label() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_project_response() :: %{
"ProjectArn" => String.t() | atom()
}
"""
@type create_project_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_project_request() :: %{
optional("AutoUpdate") => list(any()),
optional("Feature") => list(any()),
optional("Tags") => map(),
required("ProjectName") => String.t() | atom()
}
"""
@type create_project_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
describe_projects_response() :: %{
"NextToken" => String.t() | atom(),
"ProjectDescriptions" => list(project_description())
}
"""
@type describe_projects_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_collections_response() :: %{
"CollectionIds" => list(String.t() | atom()),
"FaceModelVersions" => list(String.t() | atom()),
"NextToken" => String.t() | atom()
}
"""
@type list_collections_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
face() :: %{
"BoundingBox" => bounding_box(),
"Confidence" => float(),
"ExternalImageId" => String.t() | atom(),
"FaceId" => String.t() | atom(),
"ImageId" => String.t() | atom(),
"IndexFacesModelVersion" => String.t() | atom(),
"UserId" => String.t() | atom()
}
"""
@type face() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_content_moderation_request_metadata() :: %{
"AggregateBy" => list(any()),
"SortBy" => list(any())
}
"""
@type get_content_moderation_request_metadata() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
protective_equipment_summary() :: %{
"PersonsIndeterminate" => list(integer()),
"PersonsWithRequiredEquipment" => list(integer()),
"PersonsWithoutRequiredEquipment" => list(integer())
}
"""
@type protective_equipment_summary() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_faces_request() :: %{
optional("Attributes") => list(list(any())()),
required("Image") => image()
}
"""
@type detect_faces_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
idempotent_parameter_mismatch_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type idempotent_parameter_mismatch_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
resource_not_ready_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type resource_not_ready_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
stream_processor_input() :: %{
"KinesisVideoStream" => kinesis_video_stream()
}
"""
@type stream_processor_input() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_text_detection_filters() :: %{
"RegionsOfInterest" => list(region_of_interest()),
"WordFilter" => detection_filter()
}
"""
@type start_text_detection_filters() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
human_loop_activation_output() :: %{
"HumanLoopActivationConditionsEvaluationResults" => String.t() | atom(),
"HumanLoopActivationReasons" => list(String.t() | atom()),
"HumanLoopArn" => String.t() | atom()
}
"""
@type human_loop_activation_output() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
describe_stream_processor_response() :: %{
"CreationTimestamp" => non_neg_integer(),
"DataSharingPreference" => stream_processor_data_sharing_preference(),
"Input" => stream_processor_input(),
"KmsKeyId" => String.t() | atom(),
"LastUpdateTimestamp" => non_neg_integer(),
"Name" => String.t() | atom(),
"NotificationChannel" => stream_processor_notification_channel(),
"Output" => stream_processor_output(),
"RegionsOfInterest" => list(region_of_interest()),
"RoleArn" => String.t() | atom(),
"Settings" => stream_processor_settings(),
"Status" => list(any()),
"StatusMessage" => String.t() | atom(),
"StreamProcessorArn" => String.t() | atom()
}
"""
@type describe_stream_processor_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
copy_project_version_request() :: %{
optional("KmsKeyId") => String.t() | atom(),
optional("Tags") => map(),
required("DestinationProjectArn") => String.t() | atom(),
required("OutputConfig") => output_config(),
required("SourceProjectArn") => String.t() | atom(),
required("SourceProjectVersionArn") => String.t() | atom(),
required("VersionName") => String.t() | atom()
}
"""
@type copy_project_version_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
internal_server_error() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type internal_server_error() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
smile() :: %{
"Confidence" => float(),
"Value" => boolean()
}
"""
@type smile() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
segment_type_info() :: %{
"ModelVersion" => String.t() | atom(),
"Type" => list(any())
}
"""
@type segment_type_info() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
eyeglasses() :: %{
"Confidence" => float(),
"Value" => boolean()
}
"""
@type eyeglasses() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
stop_stream_processor_request() :: %{
required("Name") => String.t() | atom()
}
"""
@type stop_stream_processor_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
invalid_image_format_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type invalid_image_format_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_dataset_response() :: %{}
"""
@type delete_dataset_response() :: %{}
@typedoc """
## Example:
start_media_analysis_job_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
optional("JobName") => String.t() | atom(),
optional("KmsKeyId") => String.t() | atom(),
required("Input") => media_analysis_input(),
required("OperationsConfig") => media_analysis_operations_config(),
required("OutputConfig") => media_analysis_output_config()
}
"""
@type start_media_analysis_job_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
testing_data_result() :: %{
"Input" => testing_data(),
"Output" => testing_data(),
"Validation" => validation_data()
}
"""
@type testing_data_result() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
notification_channel() :: %{
"RoleArn" => String.t() | atom(),
"SNSTopicArn" => String.t() | atom()
}
"""
@type notification_channel() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
associate_faces_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
optional("UserMatchThreshold") => float(),
required("CollectionId") => String.t() | atom(),
required("FaceIds") => list(String.t() | atom()),
required("UserId") => String.t() | atom()
}
"""
@type associate_faces_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_shot_detection_filter() :: %{
"MinSegmentConfidence" => float()
}
"""
@type start_shot_detection_filter() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_collection_response() :: %{
"CollectionArn" => String.t() | atom(),
"FaceModelVersion" => String.t() | atom(),
"StatusCode" => integer()
}
"""
@type create_collection_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
project_policy() :: %{
"CreationTimestamp" => non_neg_integer(),
"LastUpdatedTimestamp" => non_neg_integer(),
"PolicyDocument" => String.t() | atom(),
"PolicyName" => String.t() | atom(),
"PolicyRevisionId" => String.t() | atom(),
"ProjectArn" => String.t() | atom()
}
"""
@type project_policy() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
search_faces_by_image_response() :: %{
"FaceMatches" => list(face_match()),
"FaceModelVersion" => String.t() | atom(),
"SearchedFaceBoundingBox" => bounding_box(),
"SearchedFaceConfidence" => float()
}
"""
@type search_faces_by_image_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_label_detection_request() :: %{
optional("AggregateBy") => list(any()),
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
optional("SortBy") => list(any()),
required("JobId") => String.t() | atom()
}
"""
@type get_label_detection_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
person_match() :: %{
"FaceMatches" => list(face_match()),
"Person" => person_detail(),
"Timestamp" => float()
}
"""
@type person_match() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
matched_user() :: %{
"UserId" => String.t() | atom(),
"UserStatus" => list(any())
}
"""
@type matched_user() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
compared_face() :: %{
"BoundingBox" => bounding_box(),
"Confidence" => float(),
"Emotions" => list(emotion()),
"Landmarks" => list(landmark()),
"Pose" => pose(),
"Quality" => image_quality(),
"Smile" => smile()
}
"""
@type compared_face() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_labels_settings() :: %{
"GeneralLabels" => general_labels_settings(),
"ImageProperties" => detect_labels_image_properties_settings()
}
"""
@type detect_labels_settings() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
invalid_pagination_token_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type invalid_pagination_token_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_face_search_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
optional("FaceMatchThreshold") => float(),
optional("JobTag") => String.t() | atom(),
optional("NotificationChannel") => notification_channel(),
required("CollectionId") => String.t() | atom(),
required("Video") => video()
}
"""
@type start_face_search_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
connected_home_settings() :: %{
"Labels" => list(String.t() | atom()),
"MinConfidence" => float()
}
"""
@type connected_home_settings() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
unsuccessful_face_deletion() :: %{
"FaceId" => String.t() | atom(),
"Reasons" => list(list(any())()),
"UserId" => String.t() | atom()
}
"""
@type unsuccessful_face_deletion() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
person_detection() :: %{
"Person" => person_detail(),
"Timestamp" => float()
}
"""
@type person_detection() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_stream_processor_response() :: %{
"StreamProcessorArn" => String.t() | atom()
}
"""
@type create_stream_processor_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_content_moderation_response() :: %{
"GetRequestMetadata" => get_content_moderation_request_metadata(),
"JobId" => String.t() | atom(),
"JobStatus" => list(any()),
"JobTag" => String.t() | atom(),
"ModerationLabels" => list(content_moderation_detection()),
"ModerationModelVersion" => String.t() | atom(),
"NextToken" => String.t() | atom(),
"StatusMessage" => String.t() | atom(),
"Video" => video(),
"VideoMetadata" => video_metadata()
}
"""
@type get_content_moderation_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_content_moderation_response() :: %{
"JobId" => String.t() | atom()
}
"""
@type start_content_moderation_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
challenge_preference() :: %{
"Type" => list(any()),
"Versions" => versions()
}
"""
@type challenge_preference() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_faces_request() :: %{
optional("FaceIds") => list(String.t() | atom()),
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
optional("UserId") => String.t() | atom(),
required("CollectionId") => String.t() | atom()
}
"""
@type list_faces_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
dataset_label_stats() :: %{
"BoundingBoxCount" => integer(),
"EntryCount" => integer()
}
"""
@type dataset_label_stats() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
age_range() :: %{
"High" => integer(),
"Low" => integer()
}
"""
@type age_range() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_moderation_labels_request() :: %{
optional("HumanLoopConfig") => human_loop_config(),
optional("MinConfidence") => float(),
optional("ProjectVersion") => String.t() | atom(),
required("Image") => image()
}
"""
@type detect_moderation_labels_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_segment_detection_response() :: %{
"AudioMetadata" => list(audio_metadata()),
"JobId" => String.t() | atom(),
"JobStatus" => list(any()),
"JobTag" => String.t() | atom(),
"NextToken" => String.t() | atom(),
"Segments" => list(segment_detection()),
"SelectedSegmentTypes" => list(segment_type_info()),
"StatusMessage" => String.t() | atom(),
"Video" => video(),
"VideoMetadata" => list(video_metadata())
}
"""
@type get_segment_detection_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
customization_feature_content_moderation_config() :: %{
"ConfidenceThreshold" => float()
}
"""
@type customization_feature_content_moderation_config() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
connected_home_settings_for_update() :: %{
"Labels" => list(String.t() | atom()),
"MinConfidence" => float()
}
"""
@type connected_home_settings_for_update() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
black_frame() :: %{
"MaxPixelThreshold" => float(),
"MinCoveragePercentage" => float()
}
"""
@type black_frame() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
training_data() :: %{
"Assets" => list(asset())
}
"""
@type training_data() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_text_filters() :: %{
"RegionsOfInterest" => list(region_of_interest()),
"WordFilter" => detection_filter()
}
"""
@type detect_text_filters() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_technical_cue_detection_filter() :: %{
"BlackFrame" => black_frame(),
"MinSegmentConfidence" => float()
}
"""
@type start_technical_cue_detection_filter() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_celebrity_recognition_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
optional("JobTag") => String.t() | atom(),
optional("NotificationChannel") => notification_channel(),
required("Video") => video()
}
"""
@type start_celebrity_recognition_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_project_policy_request() :: %{
optional("PolicyRevisionId") => String.t() | atom(),
required("PolicyName") => String.t() | atom(),
required("ProjectArn") => String.t() | atom()
}
"""
@type delete_project_policy_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
point() :: %{
"X" => float(),
"Y" => float()
}
"""
@type point() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
eye_direction() :: %{
"Confidence" => float(),
"Pitch" => float(),
"Yaw" => float()
}
"""
@type eye_direction() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
media_analysis_results() :: %{
"ModelVersions" => media_analysis_model_versions(),
"S3Object" => s3_object()
}
"""
@type media_analysis_results() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
stream_processing_start_selector() :: %{
"KVSStreamStartSelector" => kinesis_video_stream_start_selector()
}
"""
@type stream_processing_start_selector() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
resource_not_found_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type resource_not_found_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
label() :: %{
"Aliases" => list(label_alias()),
"Categories" => list(label_category()),
"Confidence" => float(),
"Instances" => list(instance()),
"Name" => String.t() | atom(),
"Parents" => list(parent())
}
"""
@type label() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
label_detection_settings() :: %{
"GeneralLabels" => general_labels_settings()
}
"""
@type label_detection_settings() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_faces_response() :: %{
"FaceDetails" => list(face_detail()),
"OrientationCorrection" => list(any())
}
"""
@type detect_faces_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
stream_processor_settings() :: %{
"ConnectedHome" => connected_home_settings(),
"FaceSearch" => face_search_settings()
}
"""
@type stream_processor_settings() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_text_detection_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
required("JobId") => String.t() | atom()
}
"""
@type get_text_detection_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
searched_user() :: %{
"UserId" => String.t() | atom()
}
"""
@type searched_user() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_face_liveness_session_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
optional("KmsKeyId") => String.t() | atom(),
optional("Settings") => create_face_liveness_session_request_settings()
}
"""
@type create_face_liveness_session_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
user() :: %{
"UserId" => String.t() | atom(),
"UserStatus" => list(any())
}
"""
@type user() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
human_loop_config() :: %{
"DataAttributes" => human_loop_data_attributes(),
"FlowDefinitionArn" => String.t() | atom(),
"HumanLoopName" => String.t() | atom()
}
"""
@type human_loop_config() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
tag_resource_request() :: %{
required("ResourceArn") => String.t() | atom(),
required("Tags") => map()
}
"""
@type tag_resource_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
describe_dataset_response() :: %{
"DatasetDescription" => dataset_description()
}
"""
@type describe_dataset_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_dataset_entries_response() :: %{
"DatasetEntries" => list(String.t() | atom()),
"NextToken" => String.t() | atom()
}
"""
@type list_dataset_entries_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
celebrity_recognition() :: %{
"Celebrity" => celebrity_detail(),
"Timestamp" => float()
}
"""
@type celebrity_recognition() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_moderation_labels_response() :: %{
"ContentTypes" => list(content_type()),
"HumanLoopActivationOutput" => human_loop_activation_output(),
"ModerationLabels" => list(moderation_label()),
"ModerationModelVersion" => String.t() | atom(),
"ProjectVersion" => String.t() | atom()
}
"""
@type detect_moderation_labels_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
label_detection() :: %{
"DurationMillis" => float(),
"EndTimestampMillis" => float(),
"Label" => label(),
"StartTimestampMillis" => float(),
"Timestamp" => float()
}
"""
@type label_detection() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_text_response() :: %{
"TextDetections" => list(text_detection()),
"TextModelVersion" => String.t() | atom()
}
"""
@type detect_text_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_user_response() :: %{}
"""
@type delete_user_response() :: %{}
@typedoc """
## Example:
unindexed_face() :: %{
"FaceDetail" => face_detail(),
"Reasons" => list(list(any())())
}
"""
@type unindexed_face() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
media_analysis_job_description() :: %{
"CompletionTimestamp" => non_neg_integer(),
"CreationTimestamp" => non_neg_integer(),
"FailureDetails" => media_analysis_job_failure_details(),
"Input" => media_analysis_input(),
"JobId" => String.t() | atom(),
"JobName" => String.t() | atom(),
"KmsKeyId" => String.t() | atom(),
"ManifestSummary" => media_analysis_manifest_summary(),
"OperationsConfig" => media_analysis_operations_config(),
"OutputConfig" => media_analysis_output_config(),
"Results" => media_analysis_results(),
"Status" => list(any())
}
"""
@type media_analysis_job_description() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_celebrity_recognition_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
optional("SortBy") => list(any()),
required("JobId") => String.t() | atom()
}
"""
@type get_celebrity_recognition_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_collection_response() :: %{
"StatusCode" => integer()
}
"""
@type delete_collection_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
moderation_label() :: %{
"Confidence" => float(),
"Name" => String.t() | atom(),
"ParentName" => String.t() | atom(),
"TaxonomyLevel" => integer()
}
"""
@type moderation_label() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
media_analysis_manifest_summary() :: %{
"S3Object" => s3_object()
}
"""
@type media_analysis_manifest_summary() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
user_match() :: %{
"Similarity" => float(),
"User" => matched_user()
}
"""
@type user_match() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
content_type() :: %{
"Confidence" => float(),
"Name" => String.t() | atom()
}
"""
@type content_type() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
media_analysis_input() :: %{
"S3Object" => s3_object()
}
"""
@type media_analysis_input() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_dataset_labels_response() :: %{
"DatasetLabelDescriptions" => list(dataset_label_description()),
"NextToken" => String.t() | atom()
}
"""
@type list_dataset_labels_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
tag_resource_response() :: %{}
"""
@type tag_resource_response() :: %{}
@typedoc """
## Example:
compared_source_image_face() :: %{
"BoundingBox" => bounding_box(),
"Confidence" => float()
}
"""
@type compared_source_image_face() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
invalid_manifest_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type invalid_manifest_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
image_quality() :: %{
"Brightness" => float(),
"Sharpness" => float()
}
"""
@type image_quality() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
instance() :: %{
"BoundingBox" => bounding_box(),
"Confidence" => float(),
"DominantColors" => list(dominant_color())
}
"""
@type instance() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_dataset_request() :: %{
optional("DatasetSource") => dataset_source(),
optional("Tags") => map(),
required("DatasetType") => list(any()),
required("ProjectArn") => String.t() | atom()
}
"""
@type create_dataset_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
search_users_response() :: %{
"FaceModelVersion" => String.t() | atom(),
"SearchedFace" => searched_face(),
"SearchedUser" => searched_user(),
"UserMatches" => list(user_match())
}
"""
@type search_users_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
compare_faces_response() :: %{
"FaceMatches" => list(compare_faces_match()),
"SourceImageFace" => compared_source_image_face(),
"SourceImageOrientationCorrection" => list(any()),
"TargetImageOrientationCorrection" => list(any()),
"UnmatchedFaces" => list(compared_face())
}
"""
@type compare_faces_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
resource_in_use_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type resource_in_use_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
stream_processor() :: %{
"Name" => String.t() | atom(),
"Status" => list(any())
}
"""
@type stream_processor() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
provisioned_throughput_exceeded_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type provisioned_throughput_exceeded_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
image_too_large_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type image_too_large_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
media_analysis_operations_config() :: %{
"DetectModerationLabels" => media_analysis_detect_moderation_labels_config()
}
"""
@type media_analysis_operations_config() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_face_liveness_session_request_settings() :: %{
"AuditImagesLimit" => integer(),
"ChallengePreferences" => list(challenge_preference()),
"OutputConfig" => liveness_output_config()
}
"""
@type create_face_liveness_session_request_settings() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
asset() :: %{
"GroundTruthManifest" => ground_truth_manifest()
}
"""
@type asset() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
describe_collection_request() :: %{
required("CollectionId") => String.t() | atom()
}
"""
@type describe_collection_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
segment_detection() :: %{
"DurationFrames" => float(),
"DurationMillis" => float(),
"DurationSMPTE" => String.t() | atom(),
"EndFrameNumber" => float(),
"EndTimecodeSMPTE" => String.t() | atom(),
"EndTimestampMillis" => float(),
"ShotSegment" => shot_segment(),
"StartFrameNumber" => float(),
"StartTimecodeSMPTE" => String.t() | atom(),
"StartTimestampMillis" => float(),
"TechnicalCueSegment" => technical_cue_segment(),
"Type" => list(any())
}
"""
@type segment_detection() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
stream_processor_data_sharing_preference() :: %{
"OptIn" => boolean()
}
"""
@type stream_processor_data_sharing_preference() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_text_request() :: %{
optional("Filters") => detect_text_filters(),
required("Image") => image()
}
"""
@type detect_text_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
summary() :: %{
"S3Object" => s3_object()
}
"""
@type summary() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
malformed_policy_document_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type malformed_policy_document_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
unsuccessful_face_association() :: %{
"Confidence" => float(),
"FaceId" => String.t() | atom(),
"Reasons" => list(list(any())()),
"UserId" => String.t() | atom()
}
"""
@type unsuccessful_face_association() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_user_response() :: %{}
"""
@type create_user_response() :: %{}
@typedoc """
## Example:
face_match() :: %{
"Face" => face(),
"Similarity" => float()
}
"""
@type face_match() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
human_loop_quota_exceeded_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom(),
"QuotaCode" => String.t() | atom(),
"ResourceType" => String.t() | atom(),
"ServiceCode" => String.t() | atom()
}
"""
@type human_loop_quota_exceeded_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
conflict_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type conflict_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
untag_resource_response() :: %{}
"""
@type untag_resource_response() :: %{}
@typedoc """
## Example:
pose() :: %{
"Pitch" => float(),
"Roll" => float(),
"Yaw" => float()
}
"""
@type pose() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
customization_feature_config() :: %{
"ContentModeration" => customization_feature_content_moderation_config()
}
"""
@type customization_feature_config() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
gender() :: %{
"Confidence" => float(),
"Value" => list(any())
}
"""
@type gender() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_labels_request() :: %{
optional("Features") => list(list(any())()),
optional("MaxLabels") => integer(),
optional("MinConfidence") => float(),
optional("Settings") => detect_labels_settings(),
required("Image") => image()
}
"""
@type detect_labels_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_project_version_response() :: %{
"ProjectVersionArn" => String.t() | atom()
}
"""
@type create_project_version_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
stop_project_version_request() :: %{
required("ProjectVersionArn") => String.t() | atom()
}
"""
@type stop_project_version_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
dataset_source() :: %{
"DatasetArn" => String.t() | atom(),
"GroundTruthManifest" => ground_truth_manifest()
}
"""
@type dataset_source() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
update_stream_processor_response() :: %{}
"""
@type update_stream_processor_response() :: %{}
@typedoc """
## Example:
shot_segment() :: %{
"Confidence" => float(),
"Index" => float()
}
"""
@type shot_segment() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
untag_resource_request() :: %{
required("ResourceArn") => String.t() | atom(),
required("TagKeys") => list(String.t() | atom())
}
"""
@type untag_resource_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_media_analysis_job_response() :: %{
"JobId" => String.t() | atom()
}
"""
@type start_media_analysis_job_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
update_dataset_entries_request() :: %{
required("Changes") => dataset_changes(),
required("DatasetArn") => String.t() | atom()
}
"""
@type update_dataset_entries_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_text_detection_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
optional("Filters") => start_text_detection_filters(),
optional("JobTag") => String.t() | atom(),
optional("NotificationChannel") => notification_channel(),
required("Video") => video()
}
"""
@type start_text_detection_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_users_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
required("CollectionId") => String.t() | atom()
}
"""
@type list_users_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
video_too_large_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type video_too_large_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_person_tracking_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
optional("SortBy") => list(any()),
required("JobId") => String.t() | atom()
}
"""
@type get_person_tracking_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_labels_image_properties() :: %{
"Background" => detect_labels_image_background(),
"DominantColors" => list(dominant_color()),
"Foreground" => detect_labels_image_foreground(),
"Quality" => detect_labels_image_quality()
}
"""
@type detect_labels_image_properties() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_person_tracking_response() :: %{
"JobId" => String.t() | atom()
}
"""
@type start_person_tracking_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_user_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
required("CollectionId") => String.t() | atom(),
required("UserId") => String.t() | atom()
}
"""
@type delete_user_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
stream_processor_output() :: %{
"KinesisDataStream" => kinesis_data_stream(),
"S3Destination" => s3_destination()
}
"""
@type stream_processor_output() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
kinesis_video_stream_start_selector() :: %{
"FragmentNumber" => String.t() | atom(),
"ProducerTimestamp" => float()
}
"""
@type kinesis_video_stream_start_selector() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
testing_data() :: %{
"Assets" => list(asset()),
"AutoCreate" => boolean()
}
"""
@type testing_data() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_label_detection_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
optional("Features") => list(list(any())()),
optional("JobTag") => String.t() | atom(),
optional("MinConfidence") => float(),
optional("NotificationChannel") => notification_channel(),
optional("Settings") => label_detection_settings(),
required("Video") => video()
}
"""
@type start_label_detection_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
evaluation_result() :: %{
"F1Score" => float(),
"Summary" => summary()
}
"""
@type evaluation_result() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
kinesis_video_stream() :: %{
"Arn" => String.t() | atom()
}
"""
@type kinesis_video_stream() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
disassociate_faces_response() :: %{
"DisassociatedFaces" => list(disassociated_face()),
"UnsuccessfulFaceDisassociations" => list(unsuccessful_face_disassociation()),
"UserStatus" => list(any())
}
"""
@type disassociate_faces_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
image() :: %{
"Bytes" => binary(),
"S3Object" => s3_object()
}
"""
@type image() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
geometry() :: %{
"BoundingBox" => bounding_box(),
"Polygon" => list(point())
}
"""
@type geometry() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_face_liveness_session_results_response() :: %{
"AuditImages" => list(audit_image()),
"Challenge" => challenge(),
"Confidence" => float(),
"ReferenceImage" => audit_image(),
"SessionId" => String.t() | atom(),
"Status" => list(any())
}
"""
@type get_face_liveness_session_results_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
dataset_stats() :: %{
"ErrorEntries" => integer(),
"LabeledEntries" => integer(),
"TotalEntries" => integer(),
"TotalLabels" => integer()
}
"""
@type dataset_stats() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_label_detection_response() :: %{
"JobId" => String.t() | atom()
}
"""
@type start_label_detection_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
search_faces_request() :: %{
optional("FaceMatchThreshold") => float(),
optional("MaxFaces") => integer(),
required("CollectionId") => String.t() | atom(),
required("FaceId") => String.t() | atom()
}
"""
@type search_faces_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
celebrity_detail() :: %{
"BoundingBox" => bounding_box(),
"Confidence" => float(),
"Face" => face_detail(),
"Id" => String.t() | atom(),
"KnownGender" => known_gender(),
"Name" => String.t() | atom(),
"Urls" => list(String.t() | atom())
}
"""
@type celebrity_detail() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
stop_stream_processor_response() :: %{}
"""
@type stop_stream_processor_response() :: %{}
@typedoc """
## Example:
face_search_settings() :: %{
"CollectionId" => String.t() | atom(),
"FaceMatchThreshold" => float()
}
"""
@type face_search_settings() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_person_tracking_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
optional("JobTag") => String.t() | atom(),
optional("NotificationChannel") => notification_channel(),
required("Video") => video()
}
"""
@type start_person_tracking_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
associated_face() :: %{
"FaceId" => String.t() | atom()
}
"""
@type associated_face() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
dataset_description() :: %{
"CreationTimestamp" => non_neg_integer(),
"DatasetStats" => dataset_stats(),
"LastUpdatedTimestamp" => non_neg_integer(),
"Status" => list(any()),
"StatusMessage" => String.t() | atom(),
"StatusMessageCode" => list(any())
}
"""
@type dataset_description() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
versions() :: %{
"Maximum" => String.t() | atom(),
"Minimum" => String.t() | atom()
}
"""
@type versions() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
disassociated_face() :: %{
"FaceId" => String.t() | atom()
}
"""
@type disassociated_face() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_stream_processor_request() :: %{
optional("DataSharingPreference") => stream_processor_data_sharing_preference(),
optional("KmsKeyId") => String.t() | atom(),
optional("NotificationChannel") => stream_processor_notification_channel(),
optional("RegionsOfInterest") => list(region_of_interest()),
optional("Tags") => map(),
required("Input") => stream_processor_input(),
required("Name") => String.t() | atom(),
required("Output") => stream_processor_output(),
required("RoleArn") => String.t() | atom(),
required("Settings") => stream_processor_settings()
}
"""
@type create_stream_processor_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
equipment_detection() :: %{
"BoundingBox" => bounding_box(),
"Confidence" => float(),
"CoversBodyPart" => covers_body_part(),
"Type" => list(any())
}
"""
@type equipment_detection() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_label_detection_response() :: %{
"GetRequestMetadata" => get_label_detection_request_metadata(),
"JobId" => String.t() | atom(),
"JobStatus" => list(any()),
"JobTag" => String.t() | atom(),
"LabelModelVersion" => String.t() | atom(),
"Labels" => list(label_detection()),
"NextToken" => String.t() | atom(),
"StatusMessage" => String.t() | atom(),
"Video" => video(),
"VideoMetadata" => video_metadata()
}
"""
@type get_label_detection_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_text_detection_response() :: %{
"JobId" => String.t() | atom()
}
"""
@type start_text_detection_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
search_users_by_image_request() :: %{
optional("MaxUsers") => integer(),
optional("QualityFilter") => list(any()),
optional("UserMatchThreshold") => float(),
required("CollectionId") => String.t() | atom(),
required("Image") => image()
}
"""
@type search_users_by_image_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_label_detection_request_metadata() :: %{
"AggregateBy" => list(any()),
"SortBy" => list(any())
}
"""
@type get_label_detection_request_metadata() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
update_dataset_entries_response() :: %{}
"""
@type update_dataset_entries_response() :: %{}
@typedoc """
## Example:
delete_faces_request() :: %{
required("CollectionId") => String.t() | atom(),
required("FaceIds") => list(String.t() | atom())
}
"""
@type delete_faces_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_content_moderation_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
optional("JobTag") => String.t() | atom(),
optional("MinConfidence") => float(),
optional("NotificationChannel") => notification_channel(),
required("Video") => video()
}
"""
@type start_content_moderation_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
region_of_interest() :: %{
"BoundingBox" => bounding_box(),
"Polygon" => list(point())
}
"""
@type region_of_interest() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
video() :: %{
"S3Object" => s3_object()
}
"""
@type video() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
protective_equipment_body_part() :: %{
"Confidence" => float(),
"EquipmentDetections" => list(equipment_detection()),
"Name" => list(any())
}
"""
@type protective_equipment_body_part() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_tags_for_resource_response() :: %{
"Tags" => map()
}
"""
@type list_tags_for_resource_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
text_detection_result() :: %{
"TextDetection" => text_detection(),
"Timestamp" => float()
}
"""
@type text_detection_result() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_text_detection_response() :: %{
"JobId" => String.t() | atom(),
"JobStatus" => list(any()),
"JobTag" => String.t() | atom(),
"NextToken" => String.t() | atom(),
"StatusMessage" => String.t() | atom(),
"TextDetections" => list(text_detection_result()),
"TextModelVersion" => String.t() | atom(),
"Video" => video(),
"VideoMetadata" => video_metadata()
}
"""
@type get_text_detection_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_face_liveness_session_results_request() :: %{
required("SessionId") => String.t() | atom()
}
"""
@type get_face_liveness_session_results_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
searched_face_details() :: %{
"FaceDetail" => face_detail()
}
"""
@type searched_face_details() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_stream_processors_response() :: %{
"NextToken" => String.t() | atom(),
"StreamProcessors" => list(stream_processor())
}
"""
@type list_stream_processors_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detection_filter() :: %{
"MinBoundingBoxHeight" => float(),
"MinBoundingBoxWidth" => float(),
"MinConfidence" => float()
}
"""
@type detection_filter() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
distribute_dataset_entries_response() :: %{}
"""
@type distribute_dataset_entries_response() :: %{}
@typedoc """
## Example:
media_analysis_model_versions() :: %{
"Moderation" => String.t() | atom()
}
"""
@type media_analysis_model_versions() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
project_version_description() :: %{
"BaseModelVersion" => String.t() | atom(),
"BillableTrainingTimeInSeconds" => float(),
"CreationTimestamp" => non_neg_integer(),
"EvaluationResult" => evaluation_result(),
"Feature" => list(any()),
"FeatureConfig" => customization_feature_config(),
"KmsKeyId" => String.t() | atom(),
"ManifestSummary" => ground_truth_manifest(),
"MaxInferenceUnits" => integer(),
"MinInferenceUnits" => integer(),
"OutputConfig" => output_config(),
"ProjectVersionArn" => String.t() | atom(),
"SourceProjectVersionArn" => String.t() | atom(),
"Status" => list(any()),
"StatusMessage" => String.t() | atom(),
"TestingDataResult" => testing_data_result(),
"TrainingDataResult" => training_data_result(),
"TrainingEndTimestamp" => non_neg_integer(),
"VersionDescription" => String.t() | atom()
}
"""
@type project_version_description() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_segment_detection_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
required("JobId") => String.t() | atom()
}
"""
@type get_segment_detection_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_face_search_response() :: %{
"JobId" => String.t() | atom()
}
"""
@type start_face_search_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_project_version_request() :: %{
required("ProjectVersionArn") => String.t() | atom()
}
"""
@type delete_project_version_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_dataset_response() :: %{
"DatasetArn" => String.t() | atom()
}
"""
@type create_dataset_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
resource_already_exists_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type resource_already_exists_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_users_response() :: %{
"NextToken" => String.t() | atom(),
"Users" => list(user())
}
"""
@type list_users_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_faces_response() :: %{
"DeletedFaces" => list(String.t() | atom()),
"UnsuccessfulFaceDeletions" => list(unsuccessful_face_deletion())
}
"""
@type delete_faces_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_custom_labels_request() :: %{
optional("MaxResults") => integer(),
optional("MinConfidence") => float(),
required("Image") => image(),
required("ProjectVersionArn") => String.t() | atom()
}
"""
@type detect_custom_labels_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_face_detection_response() :: %{
"Faces" => list(face_detection()),
"JobId" => String.t() | atom(),
"JobStatus" => list(any()),
"JobTag" => String.t() | atom(),
"NextToken" => String.t() | atom(),
"StatusMessage" => String.t() | atom(),
"Video" => video(),
"VideoMetadata" => video_metadata()
}
"""
@type get_face_detection_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
training_data_result() :: %{
"Input" => training_data(),
"Output" => training_data(),
"Validation" => validation_data()
}
"""
@type training_data_result() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
eye_open() :: %{
"Confidence" => float(),
"Value" => boolean()
}
"""
@type eye_open() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
mouth_open() :: %{
"Confidence" => float(),
"Value" => boolean()
}
"""
@type mouth_open() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
describe_stream_processor_request() :: %{
required("Name") => String.t() | atom()
}
"""
@type describe_stream_processor_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_segment_detection_response() :: %{
"JobId" => String.t() | atom()
}
"""
@type start_segment_detection_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
media_analysis_job_failure_details() :: %{
"Code" => list(any()),
"Message" => String.t() | atom()
}
"""
@type media_analysis_job_failure_details() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_celebrity_info_request() :: %{
required("Id") => String.t() | atom()
}
"""
@type get_celebrity_info_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_protective_equipment_response() :: %{
"Persons" => list(protective_equipment_person()),
"ProtectiveEquipmentModelVersion" => String.t() | atom(),
"Summary" => protective_equipment_summary()
}
"""
@type detect_protective_equipment_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_dataset_entries_request() :: %{
optional("ContainsLabels") => list(String.t() | atom()),
optional("HasErrors") => boolean(),
optional("Labeled") => boolean(),
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
optional("SourceRefContains") => String.t() | atom(),
required("DatasetArn") => String.t() | atom()
}
"""
@type list_dataset_entries_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_protective_equipment_request() :: %{
optional("SummarizationAttributes") => protective_equipment_summarization_attributes(),
required("Image") => image()
}
"""
@type detect_protective_equipment_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
protective_equipment_summarization_attributes() :: %{
"MinConfidence" => float(),
"RequiredEquipmentTypes" => list(list(any())())
}
"""
@type protective_equipment_summarization_attributes() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_stream_processors_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom()
}
"""
@type list_stream_processors_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
update_stream_processor_request() :: %{
optional("DataSharingPreferenceForUpdate") => stream_processor_data_sharing_preference(),
optional("ParametersToDelete") => list(list(any())()),
optional("RegionsOfInterestForUpdate") => list(region_of_interest()),
optional("SettingsForUpdate") => stream_processor_settings_for_update(),
required("Name") => String.t() | atom()
}
"""
@type update_stream_processor_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
compare_faces_request() :: %{
optional("QualityFilter") => list(any()),
optional("SimilarityThreshold") => float(),
required("SourceImage") => image(),
required("TargetImage") => image()
}
"""
@type compare_faces_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
covers_body_part() :: %{
"Confidence" => float(),
"Value" => boolean()
}
"""
@type covers_body_part() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
invalid_parameter_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type invalid_parameter_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
search_users_request() :: %{
optional("FaceId") => String.t() | atom(),
optional("MaxUsers") => integer(),
optional("UserId") => String.t() | atom(),
optional("UserMatchThreshold") => float(),
required("CollectionId") => String.t() | atom()
}
"""
@type search_users_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
stop_project_version_response() :: %{
"Status" => list(any())
}
"""
@type stop_project_version_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
session_not_found_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type session_not_found_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_face_search_response() :: %{
"JobId" => String.t() | atom(),
"JobStatus" => list(any()),
"JobTag" => String.t() | atom(),
"NextToken" => String.t() | atom(),
"Persons" => list(person_match()),
"StatusMessage" => String.t() | atom(),
"Video" => video(),
"VideoMetadata" => video_metadata()
}
"""
@type get_face_search_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_content_moderation_request() :: %{
optional("AggregateBy") => list(any()),
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
optional("SortBy") => list(any()),
required("JobId") => String.t() | atom()
}
"""
@type get_content_moderation_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
search_users_by_image_response() :: %{
"FaceModelVersion" => String.t() | atom(),
"SearchedFace" => searched_face_details(),
"UnsearchedFaces" => list(unsearched_face()),
"UserMatches" => list(user_match())
}
"""
@type search_users_by_image_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
technical_cue_segment() :: %{
"Confidence" => float(),
"Type" => list(any())
}
"""
@type technical_cue_segment() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_project_version_request() :: %{
optional("MaxInferenceUnits") => integer(),
required("MinInferenceUnits") => integer(),
required("ProjectVersionArn") => String.t() | atom()
}
"""
@type start_project_version_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
invalid_s3_object_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type invalid_s3_object_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_stream_processor_request() :: %{
optional("StartSelector") => stream_processing_start_selector(),
optional("StopSelector") => stream_processing_stop_selector(),
required("Name") => String.t() | atom()
}
"""
@type start_stream_processor_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_celebrity_recognition_response() :: %{
"JobId" => String.t() | atom()
}
"""
@type start_celebrity_recognition_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
invalid_policy_revision_id_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type invalid_policy_revision_id_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
stream_processor_notification_channel() :: %{
"SNSTopicArn" => String.t() | atom()
}
"""
@type stream_processor_notification_channel() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
audit_image() :: %{
"BoundingBox" => bounding_box(),
"Bytes" => binary(),
"S3Object" => s3_object()
}
"""
@type audit_image() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_segment_detection_filters() :: %{
"ShotFilter" => start_shot_detection_filter(),
"TechnicalCueFilter" => start_technical_cue_detection_filter()
}
"""
@type start_segment_detection_filters() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
compare_faces_match() :: %{
"Face" => compared_face(),
"Similarity" => float()
}
"""
@type compare_faces_match() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
face_detail() :: %{
"AgeRange" => age_range(),
"Beard" => beard(),
"BoundingBox" => bounding_box(),
"Confidence" => float(),
"Emotions" => list(emotion()),
"EyeDirection" => eye_direction(),
"Eyeglasses" => eyeglasses(),
"EyesOpen" => eye_open(),
"FaceOccluded" => face_occluded(),
"Gender" => gender(),
"Landmarks" => list(landmark()),
"MouthOpen" => mouth_open(),
"Mustache" => mustache(),
"Pose" => pose(),
"Quality" => image_quality(),
"Smile" => smile(),
"Sunglasses" => sunglasses()
}
"""
@type face_detail() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
media_analysis_output_config() :: %{
"S3Bucket" => String.t() | atom(),
"S3KeyPrefix" => String.t() | atom()
}
"""
@type media_analysis_output_config() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
recognize_celebrities_request() :: %{
required("Image") => image()
}
"""
@type recognize_celebrities_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_face_search_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
optional("SortBy") => list(any()),
required("JobId") => String.t() | atom()
}
"""
@type get_face_search_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_face_detection_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
optional("FaceAttributes") => list(any()),
optional("JobTag") => String.t() | atom(),
optional("NotificationChannel") => notification_channel(),
required("Video") => video()
}
"""
@type start_face_detection_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
stream_processor_settings_for_update() :: %{
"ConnectedHomeForUpdate" => connected_home_settings_for_update()
}
"""
@type stream_processor_settings_for_update() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
distribute_dataset() :: %{
"Arn" => String.t() | atom()
}
"""
@type distribute_dataset() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_project_version_request() :: %{
optional("FeatureConfig") => customization_feature_config(),
optional("KmsKeyId") => String.t() | atom(),
optional("Tags") => map(),
optional("TestingData") => testing_data(),
optional("TrainingData") => training_data(),
optional("VersionDescription") => String.t() | atom(),
required("OutputConfig") => output_config(),
required("ProjectArn") => String.t() | atom(),
required("VersionName") => String.t() | atom()
}
"""
@type create_project_version_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_dataset_request() :: %{
required("DatasetArn") => String.t() | atom()
}
"""
@type delete_dataset_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
label_alias() :: %{
"Name" => String.t() | atom()
}
"""
@type label_alias() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_person_tracking_response() :: %{
"JobId" => String.t() | atom(),
"JobStatus" => list(any()),
"JobTag" => String.t() | atom(),
"NextToken" => String.t() | atom(),
"Persons" => list(person_detection()),
"StatusMessage" => String.t() | atom(),
"Video" => video(),
"VideoMetadata" => video_metadata()
}
"""
@type get_person_tracking_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
unsuccessful_face_disassociation() :: %{
"FaceId" => String.t() | atom(),
"Reasons" => list(list(any())()),
"UserId" => String.t() | atom()
}
"""
@type unsuccessful_face_disassociation() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
dataset_metadata() :: %{
"CreationTimestamp" => non_neg_integer(),
"DatasetArn" => String.t() | atom(),
"DatasetType" => list(any()),
"Status" => list(any()),
"StatusMessage" => String.t() | atom(),
"StatusMessageCode" => list(any())
}
"""
@type dataset_metadata() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_user_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
required("CollectionId") => String.t() | atom(),
required("UserId") => String.t() | atom()
}
"""
@type create_user_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
unsearched_face() :: %{
"FaceDetails" => face_detail(),
"Reasons" => list(list(any())())
}
"""
@type unsearched_face() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_project_version_response() :: %{
"Status" => list(any())
}
"""
@type start_project_version_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_media_analysis_jobs_response() :: %{
"MediaAnalysisJobs" => list(media_analysis_job_description()),
"NextToken" => String.t() | atom()
}
"""
@type list_media_analysis_jobs_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_tags_for_resource_request() :: %{
required("ResourceArn") => String.t() | atom()
}
"""
@type list_tags_for_resource_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
audio_metadata() :: %{
"Codec" => String.t() | atom(),
"DurationMillis" => float(),
"NumberOfChannels" => float(),
"SampleRate" => float()
}
"""
@type audio_metadata() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
copy_project_version_response() :: %{
"ProjectVersionArn" => String.t() | atom()
}
"""
@type copy_project_version_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
associate_faces_response() :: %{
"AssociatedFaces" => list(associated_face()),
"UnsuccessfulFaceAssociations" => list(unsuccessful_face_association()),
"UserStatus" => list(any())
}
"""
@type associate_faces_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_face_detection_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
required("JobId") => String.t() | atom()
}
"""
@type get_face_detection_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_celebrity_info_response() :: %{
"KnownGender" => known_gender(),
"Name" => String.t() | atom(),
"Urls" => list(String.t() | atom())
}
"""
@type get_celebrity_info_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
dataset_label_description() :: %{
"LabelName" => String.t() | atom(),
"LabelStats" => dataset_label_stats()
}
"""
@type dataset_label_description() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
face_occluded() :: %{
"Confidence" => float(),
"Value" => boolean()
}
"""
@type face_occluded() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_dataset_labels_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
required("DatasetArn") => String.t() | atom()
}
"""
@type list_dataset_labels_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
service_quota_exceeded_exception() :: %{
"Code" => String.t() | atom(),
"Logref" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type service_quota_exceeded_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
mustache() :: %{
"Confidence" => float(),
"Value" => boolean()
}
"""
@type mustache() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
describe_collection_response() :: %{
"CollectionARN" => String.t() | atom(),
"CreationTimestamp" => non_neg_integer(),
"FaceCount" => float(),
"FaceModelVersion" => String.t() | atom(),
"UserCount" => float()
}
"""
@type describe_collection_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
s3_destination() :: %{
"Bucket" => String.t() | atom(),
"KeyPrefix" => String.t() | atom()
}
"""
@type s3_destination() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
sunglasses() :: %{
"Confidence" => float(),
"Value" => boolean()
}
"""
@type sunglasses() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_custom_labels_response() :: %{
"CustomLabels" => list(custom_label())
}
"""
@type detect_custom_labels_response() :: %{(String.t() | atom()) => any()}
@type associate_faces_errors() ::
service_quota_exceeded_exception()
| invalid_parameter_exception()
| conflict_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| idempotent_parameter_mismatch_exception()
| access_denied_exception()
| throttling_exception()
@type compare_faces_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| image_too_large_exception()
| provisioned_throughput_exceeded_exception()
| invalid_image_format_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type copy_project_version_errors() ::
service_quota_exceeded_exception()
| invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| resource_not_found_exception()
| internal_server_error()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type create_collection_errors() ::
service_quota_exceeded_exception()
| invalid_parameter_exception()
| resource_already_exists_exception()
| provisioned_throughput_exceeded_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type create_dataset_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| resource_already_exists_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type create_face_liveness_session_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type create_project_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| internal_server_error()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type create_project_version_errors() ::
service_quota_exceeded_exception()
| invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| resource_not_found_exception()
| internal_server_error()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type create_stream_processor_errors() ::
service_quota_exceeded_exception()
| invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| internal_server_error()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type create_user_errors() ::
service_quota_exceeded_exception()
| invalid_parameter_exception()
| conflict_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| idempotent_parameter_mismatch_exception()
| access_denied_exception()
| throttling_exception()
@type delete_collection_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type delete_dataset_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| resource_not_found_exception()
| internal_server_error()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type delete_faces_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type delete_project_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type delete_project_policy_errors() ::
invalid_policy_revision_id_exception()
| invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type delete_project_version_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type delete_stream_processor_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type delete_user_errors() ::
invalid_parameter_exception()
| conflict_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| idempotent_parameter_mismatch_exception()
| access_denied_exception()
| throttling_exception()
@type describe_collection_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type describe_dataset_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type describe_project_versions_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type describe_projects_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type describe_stream_processor_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type detect_custom_labels_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| image_too_large_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_image_format_exception()
| internal_server_error()
| resource_not_ready_exception()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type detect_faces_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| image_too_large_exception()
| provisioned_throughput_exceeded_exception()
| invalid_image_format_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type detect_labels_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| image_too_large_exception()
| provisioned_throughput_exceeded_exception()
| invalid_image_format_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type detect_moderation_labels_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| human_loop_quota_exceeded_exception()
| image_too_large_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_image_format_exception()
| internal_server_error()
| resource_not_ready_exception()
| access_denied_exception()
| throttling_exception()
@type detect_protective_equipment_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| image_too_large_exception()
| provisioned_throughput_exceeded_exception()
| invalid_image_format_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type detect_text_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| image_too_large_exception()
| provisioned_throughput_exceeded_exception()
| invalid_image_format_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type disassociate_faces_errors() ::
invalid_parameter_exception()
| conflict_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| idempotent_parameter_mismatch_exception()
| access_denied_exception()
| throttling_exception()
@type distribute_dataset_entries_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| resource_not_ready_exception()
| access_denied_exception()
| throttling_exception()
@type get_celebrity_info_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type get_celebrity_recognition_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type get_content_moderation_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type get_face_detection_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type get_face_liveness_session_results_errors() ::
session_not_found_exception()
| invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type get_face_search_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type get_label_detection_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type get_media_analysis_job_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type get_person_tracking_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type get_segment_detection_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type get_text_detection_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type index_faces_errors() ::
service_quota_exceeded_exception()
| invalid_s3_object_exception()
| invalid_parameter_exception()
| image_too_large_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_image_format_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type list_collections_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type list_dataset_entries_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| resource_not_found_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| resource_not_ready_exception()
| access_denied_exception()
| throttling_exception()
@type list_dataset_labels_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| resource_not_found_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| resource_not_ready_exception()
| access_denied_exception()
| throttling_exception()
@type list_faces_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type list_media_analysis_jobs_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type list_project_policies_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type list_stream_processors_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type list_tags_for_resource_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type list_users_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_pagination_token_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type put_project_policy_errors() ::
service_quota_exceeded_exception()
| invalid_policy_revision_id_exception()
| invalid_parameter_exception()
| resource_already_exists_exception()
| malformed_policy_document_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type recognize_celebrities_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| image_too_large_exception()
| provisioned_throughput_exceeded_exception()
| invalid_image_format_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type search_faces_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type search_faces_by_image_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| image_too_large_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_image_format_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type search_users_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type search_users_by_image_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| image_too_large_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| invalid_image_format_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type start_celebrity_recognition_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| video_too_large_exception()
| provisioned_throughput_exceeded_exception()
| internal_server_error()
| idempotent_parameter_mismatch_exception()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type start_content_moderation_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| video_too_large_exception()
| provisioned_throughput_exceeded_exception()
| internal_server_error()
| idempotent_parameter_mismatch_exception()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type start_face_detection_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| video_too_large_exception()
| provisioned_throughput_exceeded_exception()
| internal_server_error()
| idempotent_parameter_mismatch_exception()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type start_face_search_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| video_too_large_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| idempotent_parameter_mismatch_exception()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type start_label_detection_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| video_too_large_exception()
| provisioned_throughput_exceeded_exception()
| internal_server_error()
| idempotent_parameter_mismatch_exception()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type start_media_analysis_job_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| invalid_manifest_exception()
| resource_not_found_exception()
| internal_server_error()
| resource_not_ready_exception()
| idempotent_parameter_mismatch_exception()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type start_person_tracking_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| video_too_large_exception()
| provisioned_throughput_exceeded_exception()
| internal_server_error()
| idempotent_parameter_mismatch_exception()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type start_project_version_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| resource_not_found_exception()
| internal_server_error()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type start_segment_detection_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| video_too_large_exception()
| provisioned_throughput_exceeded_exception()
| internal_server_error()
| idempotent_parameter_mismatch_exception()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type start_stream_processor_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type start_text_detection_errors() ::
invalid_s3_object_exception()
| invalid_parameter_exception()
| video_too_large_exception()
| provisioned_throughput_exceeded_exception()
| internal_server_error()
| idempotent_parameter_mismatch_exception()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type stop_project_version_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type stop_stream_processor_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type tag_resource_errors() ::
service_quota_exceeded_exception()
| invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type untag_resource_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
@type update_dataset_entries_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| resource_not_found_exception()
| internal_server_error()
| limit_exceeded_exception()
| access_denied_exception()
| throttling_exception()
@type update_stream_processor_errors() ::
invalid_parameter_exception()
| provisioned_throughput_exceeded_exception()
| resource_in_use_exception()
| resource_not_found_exception()
| internal_server_error()
| access_denied_exception()
| throttling_exception()
def metadata do
%{
api_version: "2016-06-27",
content_type: "application/x-amz-json-1.1",
credential_scope: nil,
endpoint_prefix: "rekognition",
global?: false,
hostname: nil,
protocol: "json",
service_id: "Rekognition",
signature_version: "v4",
signing_name: "rekognition",
target_prefix: "RekognitionService"
}
end
@doc """
Associates one or more faces with an existing UserID.
Takes an array of
`FaceIds`. Each `FaceId` that are present in the `FaceIds`
list is associated with the provided UserID. The number of FaceIds that can be
used as input
in a single request is limited to 100.
Note that the total number of faces that can be associated with a single
`UserID` is also limited to 100. Once a `UserID` has 100 faces
associated with it, no additional faces can be added. If more API calls are made
after the
limit is reached, a `ServiceQuotaExceededException` will result.
The `UserMatchThreshold` parameter specifies the minimum user match confidence
required for the face to be associated with a UserID that has at least one
`FaceID`
already associated. This ensures that the `FaceIds` are associated with the
right
UserID. The value ranges from 0-100 and default value is 75.
If successful, an array of `AssociatedFace` objects containing the associated
`FaceIds` is returned. If a given face is already associated with the given
`UserID`, it will be ignored and will not be returned in the response. If a
given
face is already associated to a different `UserID`, isn't found in the
collection,
doesn’t meet the `UserMatchThreshold`, or there are already 100 faces associated
with the `UserID`, it will be returned as part of an array of
`UnsuccessfulFaceAssociations.`
The `UserStatus` reflects the status of an operation which updates a UserID
representation with a list of given faces. The `UserStatus` can be:
*
ACTIVE - All associations or disassociations of FaceID(s) for a UserID are
complete.
*
CREATED - A UserID has been created, but has no FaceID(s) associated with it.
*
UPDATING - A UserID is being updated and there are current associations or
disassociations of FaceID(s) taking place.
"""
@spec associate_faces(map(), associate_faces_request(), list()) ::
{:ok, associate_faces_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, associate_faces_errors()}
def associate_faces(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "AssociateFaces", input, options)
end
@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.
"""
@spec compare_faces(map(), compare_faces_request(), list()) ::
{:ok, compare_faces_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, compare_faces_errors()}
def compare_faces(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "CompareFaces", input, options)
end
@doc """
This operation applies only to Amazon Rekognition Custom Labels.
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.
Copying project versions is supported only for Custom Labels models.
To copy a model, the destination project, source project, and source model
version
must already exist.
Copying a model version takes a while to complete. To get the current status,
call `DescribeProjectVersions` and check the value of `Status` in the
`ProjectVersionDescription` object. The copy operation has finished when
the value of `Status` is `COPYING_COMPLETED`.
This operation requires permissions to perform the
`rekognition:CopyProjectVersion` action.
"""
@spec copy_project_version(map(), copy_project_version_request(), list()) ::
{:ok, copy_project_version_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, copy_project_version_errors()}
def copy_project_version(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "CopyProjectVersion", input, options)
end
@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.
"""
@spec create_collection(map(), create_collection_request(), list()) ::
{:ok, create_collection_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, create_collection_errors()}
def create_collection(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "CreateCollection", input, options)
end
@doc """
This operation applies only to Amazon Rekognition Custom Labels.
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.
"""
@spec create_dataset(map(), create_dataset_request(), list()) ::
{:ok, create_dataset_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, create_dataset_errors()}
def create_dataset(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "CreateDataset", input, options)
end
@doc """
This API operation initiates a Face Liveness session.
It returns a `SessionId`,
which you can use to start streaming Face Liveness video and get the results for
a Face
Liveness session.
You can use the `OutputConfig` option in the Settings parameter to provide an
Amazon S3 bucket location. The Amazon S3 bucket stores reference images and
audit images. If no Amazon S3
bucket is defined, raw bytes are sent instead.
You can use `AuditImagesLimit` to limit the number of audit images returned
when `GetFaceLivenessSessionResults` is called. This number is between 0 and 4.
By
default, it is set to 0. The limit is best effort and based on the duration of
the
selfie-video.
"""
@spec create_face_liveness_session(map(), create_face_liveness_session_request(), list()) ::
{:ok, create_face_liveness_session_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, create_face_liveness_session_errors()}
def create_face_liveness_session(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "CreateFaceLivenessSession", input, options)
end
@doc """
Creates a new Amazon Rekognition project.
A project is a group of resources (datasets, model
versions) that you use to create and manage a Amazon Rekognition Custom Labels
Model or custom adapter. You can
specify a feature to create the project with, if no feature is specified then
Custom Labels
is used by default. For adapters, you can also choose whether or not to have the
project
auto update by using the AutoUpdate argument. This operation requires
permissions to
perform the `rekognition:CreateProject` action.
"""
@spec create_project(map(), create_project_request(), list()) ::
{:ok, create_project_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, create_project_errors()}
def create_project(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "CreateProject", input, options)
end
@doc """
Creates a new version of Amazon Rekognition project (like a Custom Labels model
or a custom adapter)
and begins training.
Models and adapters are managed as part of a Rekognition project. The
response from `CreateProjectVersion` is an Amazon Resource Name (ARN) for the
project version.
The FeatureConfig operation argument allows you to configure specific model or
adapter
settings. You can provide a description to the project version by using the
VersionDescription argment. Training can take a while to complete. You can get
the current
status by calling `DescribeProjectVersions`. Training completed
successfully if the value of the `Status` field is
`TRAINING_COMPLETED`. Once training has successfully completed, call
`DescribeProjectVersions` to get the training results and evaluate the
model.
This operation requires permissions to perform the
`rekognition:CreateProjectVersion` action.
*The following applies only to projects with Amazon Rekognition Custom Labels as
the chosen
feature:*
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.
"""
@spec create_project_version(map(), create_project_version_request(), list()) ::
{:ok, create_project_version_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, create_project_version_errors()}
def create_project_version(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "CreateProjectVersion", input, options)
end
@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.
*
If you are creating a stream processor for detecting faces, you provide as input
a Kinesis video stream
(`Input`) and a Kinesis data stream (`Output`) stream for receiving
the output. You must use the `FaceSearch` option in
`Settings`, specifying the collection that contains the faces you
want to recognize. After you have finished analyzing a streaming video, use
`StopStreamProcessor` to stop processing.
*
If you are creating a stream processor to detect labels, you provide as input a
Kinesis video stream
(`Input`), Amazon S3 bucket information (`Output`), and an
Amazon SNS topic ARN (`NotificationChannel`). You can also provide a KMS
key ID to encrypt the data sent to your Amazon S3 bucket. You specify what you
want
to detect by using the `ConnectedHome` option in settings, and
selecting one of the following: `PERSON`, `PET`,
`PACKAGE`, `ALL` You can also specify where in the
frame you want Amazon Rekognition to monitor 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.
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.
"""
@spec create_stream_processor(map(), create_stream_processor_request(), list()) ::
{:ok, create_stream_processor_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, create_stream_processor_errors()}
def create_stream_processor(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "CreateStreamProcessor", input, options)
end
@doc """
Creates a new User within a collection specified by `CollectionId`.
Takes
`UserId` as a parameter, which is a user provided ID which should be unique
within the collection. The provided `UserId` will alias the system generated
UUID
to make the `UserId` more user friendly.
Uses a `ClientToken`, an idempotency token that ensures a call to
`CreateUser` completes only once. If the value is not supplied, the AWS SDK
generates an idempotency token for the requests. This prevents retries after a
network error
results from making multiple `CreateUser` calls.
"""
@spec create_user(map(), create_user_request(), list()) ::
{:ok, create_user_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, create_user_errors()}
def create_user(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "CreateUser", input, options)
end
@doc """
Deletes the specified collection.
Note that this operation removes all faces in the
collection. For an example, see [Deleting a collection](https://docs.aws.amazon.com/rekognition/latest/dg/delete-collection-procedure.html).
This operation requires permissions to perform the
`rekognition:DeleteCollection` action.
"""
@spec delete_collection(map(), delete_collection_request(), list()) ::
{:ok, delete_collection_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, delete_collection_errors()}
def delete_collection(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DeleteCollection", input, options)
end
@doc """
This operation applies only to Amazon Rekognition Custom Labels.
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.
"""
@spec delete_dataset(map(), delete_dataset_request(), list()) ::
{:ok, delete_dataset_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, delete_dataset_errors()}
def delete_dataset(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DeleteDataset", input, options)
end
@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.
"""
@spec delete_faces(map(), delete_faces_request(), list()) ::
{:ok, delete_faces_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, delete_faces_errors()}
def delete_faces(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DeleteFaces", input, options)
end
@doc """
Deletes a Amazon Rekognition project.
To delete a project you must first delete all models or
adapters associated with the project. To delete a model or adapter, see
`DeleteProjectVersion`.
`DeleteProject` is an asynchronous operation. To check if the project is
deleted, call `DescribeProjects`. The project is deleted when the project
no longer appears in the response. Be aware that deleting a given project will
also delete
any `ProjectPolicies` associated with that project.
This operation requires permissions to perform the
`rekognition:DeleteProject` action.
"""
@spec delete_project(map(), delete_project_request(), list()) ::
{:ok, delete_project_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, delete_project_errors()}
def delete_project(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DeleteProject", input, options)
end
@doc """
This operation applies only to Amazon Rekognition Custom Labels.
Deletes an existing project policy.
To get a list of project policies attached to a project, call
`ListProjectPolicies`. To attach a project policy to a project, call
`PutProjectPolicy`.
This operation requires permissions to perform the
`rekognition:DeleteProjectPolicy` action.
"""
@spec delete_project_policy(map(), delete_project_policy_request(), list()) ::
{:ok, delete_project_policy_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, delete_project_policy_errors()}
def delete_project_policy(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DeleteProjectPolicy", input, options)
end
@doc """
Deletes a Rekognition project model or project version, like a Amazon
Rekognition Custom Labels model or a custom
adapter.
You can't delete a project version if it is running or if it is training. To
check
the status of a project version, use the Status field returned from
`DescribeProjectVersions`. To stop a project version call `StopProjectVersion`.
If the project version is training, wait until it
finishes.
This operation requires permissions to perform the
`rekognition:DeleteProjectVersion` action.
"""
@spec delete_project_version(map(), delete_project_version_request(), list()) ::
{:ok, delete_project_version_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, delete_project_version_errors()}
def delete_project_version(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DeleteProjectVersion", input, options)
end
@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`.
"""
@spec delete_stream_processor(map(), delete_stream_processor_request(), list()) ::
{:ok, delete_stream_processor_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, delete_stream_processor_errors()}
def delete_stream_processor(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DeleteStreamProcessor", input, options)
end
@doc """
Deletes the specified UserID within the collection.
Faces that are associated with the
UserID are disassociated from the UserID before deleting the specified UserID.
If the
specified `Collection` or `UserID` is already deleted or not found, a
`ResourceNotFoundException` will be thrown. If the action is successful with a
200 response, an empty HTTP body is returned.
"""
@spec delete_user(map(), delete_user_request(), list()) ::
{:ok, delete_user_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, delete_user_errors()}
def delete_user(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DeleteUser", input, options)
end
@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.
"""
@spec describe_collection(map(), describe_collection_request(), list()) ::
{:ok, describe_collection_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, describe_collection_errors()}
def describe_collection(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DescribeCollection", input, options)
end
@doc """
This operation applies only to Amazon Rekognition Custom Labels.
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.
"""
@spec describe_dataset(map(), describe_dataset_request(), list()) ::
{:ok, describe_dataset_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, describe_dataset_errors()}
def describe_dataset(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DescribeDataset", input, options)
end
@doc """
Lists and describes the versions of an Amazon Rekognition project.
You can specify up to 10 model or
adapter versions in `ProjectVersionArns`. If you don't specify a value,
descriptions for all model/adapter versions in the project are returned.
This operation requires permissions to perform the
`rekognition:DescribeProjectVersions`
action.
"""
@spec describe_project_versions(map(), describe_project_versions_request(), list()) ::
{:ok, describe_project_versions_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, describe_project_versions_errors()}
def describe_project_versions(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DescribeProjectVersions", input, options)
end
@doc """
Gets information about your Rekognition projects.
This operation requires permissions to perform the
`rekognition:DescribeProjects` action.
"""
@spec describe_projects(map(), describe_projects_request(), list()) ::
{:ok, describe_projects_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, describe_projects_errors()}
def describe_projects(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DescribeProjects", input, options)
end
@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.
"""
@spec describe_stream_processor(map(), describe_stream_processor_request(), list()) ::
{:ok, describe_stream_processor_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, describe_stream_processor_errors()}
def describe_stream_processor(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DescribeStreamProcessor", input, options)
end
@doc """
This operation applies only to Amazon Rekognition Custom Labels.
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.
"""
@spec detect_custom_labels(map(), detect_custom_labels_request(), list()) ::
{:ok, detect_custom_labels_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, detect_custom_labels_errors()}
def detect_custom_labels(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DetectCustomLabels", input, options)
end
@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),
pose,
presence of facial occlusion, and so on.
The face-detection algorithm is most effective on frontal faces. For non-frontal
or
obscured faces, the algorithm might not detect the faces or might detect faces
with lower
confidence.
You pass the input image either as base64-encoded image bytes or as a reference
to an
image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition
operations,
passing image bytes is not supported. The image must be either a PNG or JPEG
formatted file.
This is a stateless API operation. That is, the operation does not persist any
data.
This operation requires permissions to perform the `rekognition:DetectFaces`
action.
"""
@spec detect_faces(map(), detect_faces_request(), list()) ::
{:ok, detect_faces_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, detect_faces_errors()}
def detect_faces(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DetectFaces", input, options)
end
@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](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html).
When getting labels, you can specify `MinConfidence` to control the
confidence threshold for the labels returned. The default is 55%. You can also
add the
`MaxLabels` parameter to limit the number of labels returned. The default and
upper limit is 1000 labels. These arguments are only valid when supplying
GENERAL_LABELS as a
feature type.
## Response Elements
For each object, scene, and concept the API returns one or more labels. The API
returns the following types of information about labels:
*
Name - The name of the detected label.
*
Confidence - The level of confidence in the label assigned to a detected object.
*
Parents - The ancestor labels for a detected label. DetectLabels returns a
hierarchical taxonomy of detected labels. For example, a detected car might be
assigned
the label car. The label car has two parent labels: Vehicle (its parent) and
Transportation (its grandparent). The response includes the all ancestors for a
label,
where every ancestor is a unique label. In the previous example, Car, Vehicle,
and
Transportation are returned as unique labels in the response.
*
Aliases - Possible Aliases for the label.
*
Categories - The label categories that the detected label belongs to.
*
BoundingBox — Bounding boxes are described for all instances of detected common
object labels, returned in an array of Instance objects. An Instance object
contains a
BoundingBox object, describing the location of the label on the input image. It
also
includes the confidence for the accuracy of the detected bounding box.
The API returns the following information regarding the image, as part of the
ImageProperties structure:
*
Quality - Information about the Sharpness, Brightness, and Contrast of the input
image, scored between 0 to 100. Image quality is returned for the entire image,
as well as
the background and the foreground.
*
Dominant Color - An array of the dominant colors in the image.
*
Foreground - Information about the sharpness, brightness, and dominant colors of
the
input image’s foreground.
*
Background - Information about the sharpness, brightness, and dominant colors of
the
input image’s background.
The list of returned labels will include at least one label for every detected
object,
along with information about that label. In the following example, suppose the
input image has
a lighthouse, the sea, and a rock. The response includes all three labels, one
for each
object, as well as the confidence in the label:
`{Name: lighthouse, Confidence: 98.4629}`
`{Name: rock,Confidence: 79.2097}`
` {Name: sea,Confidence: 75.061}`
The list of labels can include multiple labels for the same object. For example,
if the
input image shows a flower (for example, a tulip), the operation might return
the following
three labels.
`{Name: flower,Confidence: 99.0562}`
`{Name: plant,Confidence: 99.0562}`
`{Name: tulip,Confidence: 99.0562}`
In this example, the detection algorithm more precisely identifies the flower as
a
tulip.
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 doesn't return any data.
This operation requires permissions to perform the
`rekognition:DetectLabels` action.
"""
@spec detect_labels(map(), detect_labels_request(), list()) ::
{:ok, detect_labels_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, detect_labels_errors()}
def detect_labels(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DetectLabels", input, options)
end
@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.
You can specify an adapter to use when retrieving label predictions by providing
a
`ProjectVersionArn` to the `ProjectVersion` argument.
"""
@spec detect_moderation_labels(map(), detect_moderation_labels_request(), list()) ::
{:ok, detect_moderation_labels_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, detect_moderation_labels_errors()}
def detect_moderation_labels(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DetectModerationLabels", input, options)
end
@doc """
Detects Personal Protective Equipment (PPE) worn by people detected in an image.
Amazon Rekognition can detect the
following types of PPE.
*
Face cover
*
Hand cover
*
Head cover
You pass the input image as base64-encoded image bytes or as a reference to an
image in an Amazon S3 bucket.
The image must be either a PNG or JPG formatted file.
`DetectProtectiveEquipment` detects PPE worn by up to 15 persons detected in an
image.
For each person detected in the image the API returns an array of body parts
(face, head, left-hand, right-hand).
For each body part, an array of detected items of PPE is returned, including an
indicator of whether or not the PPE
covers the body part. The API returns the confidence it has in each detection
(person, PPE, body part and body part coverage). It also returns a bounding box
(`BoundingBox`) for each detected
person and each detected item of PPE.
You can optionally request a summary of detected PPE items with the
`SummarizationAttributes` input parameter.
The summary provides the following information.
*
The persons detected as wearing all of the types of PPE that you specify.
*
The persons detected as not wearing all of the types PPE that you specify.
*
The persons detected where PPE adornment could not be determined.
This is a stateless API operation. That is, the operation does not persist any
data.
This operation requires permissions to perform the
`rekognition:DetectProtectiveEquipment` action.
"""
@spec detect_protective_equipment(map(), detect_protective_equipment_request(), list()) ::
{:ok, detect_protective_equipment_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, detect_protective_equipment_errors()}
def detect_protective_equipment(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DetectProtectiveEquipment", input, options)
end
@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.
"""
@spec detect_text(map(), detect_text_request(), list()) ::
{:ok, detect_text_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, detect_text_errors()}
def detect_text(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DetectText", input, options)
end
@doc """
Removes the association between a `Face` supplied in an array of
`FaceIds` and the User.
If the User is not present already, then a
`ResourceNotFound` exception is thrown. If successful, an array of faces that
are
disassociated from the User is returned. If a given face is already
disassociated from the
given UserID, it will be ignored and not be returned in the response. If a given
face is
already associated with a different User or not found in the collection it will
be returned as
part of `UnsuccessfulDisassociations`. You can remove 1 - 100 face IDs from a
user
at one time.
"""
@spec disassociate_faces(map(), disassociate_faces_request(), list()) ::
{:ok, disassociate_faces_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, disassociate_faces_errors()}
def disassociate_faces(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DisassociateFaces", input, options)
end
@doc """
This operation applies only to Amazon Rekognition Custom Labels.
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.
"""
@spec distribute_dataset_entries(map(), distribute_dataset_entries_request(), list()) ::
{:ok, distribute_dataset_entries_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, distribute_dataset_entries_errors()}
def distribute_dataset_entries(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "DistributeDatasetEntries", input, options)
end
@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.
"""
@spec get_celebrity_info(map(), get_celebrity_info_request(), list()) ::
{:ok, get_celebrity_info_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_celebrity_info_errors()}
def get_celebrity_info(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "GetCelebrityInfo", input, options)
end
@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`.
"""
@spec get_celebrity_recognition(map(), get_celebrity_recognition_request(), list()) ::
{:ok, get_celebrity_recognition_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_celebrity_recognition_errors()}
def get_celebrity_recognition(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "GetCelebrityRecognition", input, options)
end
@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](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api).
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.
"""
@spec get_content_moderation(map(), get_content_moderation_request(), list()) ::
{:ok, get_content_moderation_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_content_moderation_errors()}
def get_content_moderation(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "GetContentModeration", input, options)
end
@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`.
Note that for the `GetFaceDetection` operation, the returned values for
`FaceOccluded` and `EyeDirection` will always be "null".
"""
@spec get_face_detection(map(), get_face_detection_request(), list()) ::
{:ok, get_face_detection_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_face_detection_errors()}
def get_face_detection(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "GetFaceDetection", input, options)
end
@doc """
Retrieves the results of a specific Face Liveness session.
It requires the
`sessionId` as input, which was created using
`CreateFaceLivenessSession`. Returns the corresponding Face Liveness confidence
score, a reference image that includes a face bounding box, and audit images
that also contain
face bounding boxes. The Face Liveness confidence score ranges from 0 to 100.
The number of audit images returned by `GetFaceLivenessSessionResults` is
defined by the `AuditImagesLimit` paramater when calling
`CreateFaceLivenessSession`. Reference images are always returned when
possible.
"""
@spec get_face_liveness_session_results(
map(),
get_face_liveness_session_results_request(),
list()
) ::
{:ok, get_face_liveness_session_results_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_face_liveness_session_results_errors()}
def get_face_liveness_session_results(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "GetFaceLivenessSessionResults", input, options)
end
@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.
"""
@spec get_face_search(map(), get_face_search_request(), list()) ::
{:ok, get_face_search_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_face_search_errors()}
def get_face_search(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "GetFaceSearch", input, options)
end
@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:
*
Name - The name of the detected label.
*
Confidence - The level of confidence in the label assigned to a detected object.
*
Parents - The ancestor labels for a detected label. GetLabelDetection returns a
hierarchical
taxonomy of detected labels. For example, a detected car might be assigned the
label car.
The label car has two parent labels: Vehicle (its parent) and Transportation
(its
grandparent). The response includes the all ancestors for a label, where every
ancestor is
a unique label. In the previous example, Car, Vehicle, and Transportation are
returned as
unique labels in the response.
*
Aliases - Possible Aliases for the label.
*
Categories - The label categories that the detected label belongs to.
*
BoundingBox — Bounding boxes are described for all instances of detected common
object labels,
returned in an array of Instance objects. An Instance object contains a
BoundingBox object, describing
the location of the label on the input image. It also includes the confidence
for the accuracy of the detected bounding box.
*
Timestamp - Time, in milliseconds from the start of the video, that the label
was detected.
For aggregation by `SEGMENTS`, 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`.
Timestamp and Bounding box information are returned for detected Instances, only
if
aggregation is done by `TIMESTAMPS`. If aggregating by `SEGMENTS`,
information about detected instances isn’t returned.
The version of the label model used for the detection is also returned.
## Note `DominantColors` isn't returned for `Instances`,
although it is shown as part of the response in the sample seen below.
Use `MaxResults` parameter to limit the number of labels returned. If
there are more results than specified in `MaxResults`, the value of
`NextToken` in the operation response contains a pagination token for getting
the
next set of results. To get the next page of results, call `GetlabelDetection`
and
populate the `NextToken` request parameter with the token value returned from
the
previous call to `GetLabelDetection`.
If you are retrieving results while using the Amazon Simple Notification
Service, note that you will receive an
"ERROR" notification if the job encounters an issue.
"""
@spec get_label_detection(map(), get_label_detection_request(), list()) ::
{:ok, get_label_detection_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_label_detection_errors()}
def get_label_detection(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "GetLabelDetection", input, options)
end
@doc """
Retrieves the results for a given media analysis job.
Takes a `JobId` returned by StartMediaAnalysisJob.
"""
@spec get_media_analysis_job(map(), get_media_analysis_job_request(), list()) ::
{:ok, get_media_analysis_job_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_media_analysis_job_errors()}
def get_media_analysis_job(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "GetMediaAnalysisJob", input, options)
end
@doc """
*End of support notice:* On October 31, 2025, AWS will discontinue
support for Amazon Rekognition People Pathing.
After October 31, 2025, you will no
longer be able to use the Rekognition People Pathing capability. For more
information,
visit this [blog post](https://aws.amazon.com/blogs/machine-learning/transitioning-from-amazon-rekognition-people-pathing-exploring-other-alternatives/).
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`.
"""
@spec get_person_tracking(map(), get_person_tracking_request(), list()) ::
{:ok, get_person_tracking_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_person_tracking_errors()}
def get_person_tracking(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "GetPersonTracking", input, options)
end
@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.
"""
@spec get_segment_detection(map(), get_segment_detection_request(), list()) ::
{:ok, get_segment_detection_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_segment_detection_errors()}
def get_segment_detection(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "GetSegmentDetection", input, options)
end
@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 100 words per frame of video.
Each element of the array includes the detected text, the precentage confidence
in the acuracy
of the detected text, the time the text was detected, bounding box information
for where the text
was located, and unique identifiers for words and their lines.
Use MaxResults parameter to limit the number of text detections returned. If
there are more results than
specified in `MaxResults`, the value of `NextToken` in the operation response
contains
a pagination token for getting the next set of results. To get the next page of
results, call `GetTextDetection`
and populate the `NextToken` request parameter with the token value returned
from the previous
call to `GetTextDetection`.
"""
@spec get_text_detection(map(), get_text_detection_request(), list()) ::
{:ok, get_text_detection_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_text_detection_errors()}
def get_text_detection(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "GetTextDetection", input, options)
end
@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:
*
The number of faces detected exceeds the value of the `MaxFaces` request
parameter.
*
The face is too small compared to the image dimensions.
*
The face is too blurry.
*
The image is too dark.
*
The face has an extreme pose.
*
The face doesn’t have enough detail to be suitable for face search.
In response, the `IndexFaces` operation returns an array of metadata for all
detected faces, `FaceRecords`. This includes:
*
The bounding box, `BoundingBox`, of the detected face.
*
A confidence value, `Confidence`, which indicates the confidence that the
bounding box contains a face.
*
A face ID, `FaceId`, assigned by the service for each face that's detected
and stored.
*
An image ID, `ImageId`, assigned by the service for the input image.
If you request `ALL` or specific facial attributes (e.g.,
`FACE_OCCLUDED`) by using the detectionAttributes parameter, Amazon Rekognition
returns detailed facial attributes, such as facial landmarks (for example,
location of eye and
mouth), facial occlusion, and other facial attributes.
If you provide the same image, specify the same collection, and use the same
external ID
in the `IndexFaces` operation, Amazon Rekognition doesn't save duplicate face
metadata.
The input image is passed either as base64-encoded image bytes, or as a
reference to an
image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition
operations,
passing image bytes isn't supported. The image must be formatted as a PNG or
JPEG file.
This operation requires permissions to perform the `rekognition:IndexFaces`
action.
"""
@spec index_faces(map(), index_faces_request(), list()) ::
{:ok, index_faces_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, index_faces_errors()}
def index_faces(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "IndexFaces", input, options)
end
@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.
"""
@spec list_collections(map(), list_collections_request(), list()) ::
{:ok, list_collections_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, list_collections_errors()}
def list_collections(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "ListCollections", input, options)
end
@doc """
This operation applies only to Amazon Rekognition Custom Labels.
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](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-manifest-files.html).
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.
"""
@spec list_dataset_entries(map(), list_dataset_entries_request(), list()) ::
{:ok, list_dataset_entries_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, list_dataset_entries_errors()}
def list_dataset_entries(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "ListDatasetEntries", input, options)
end
@doc """
This operation applies only to Amazon Rekognition Custom Labels.
Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to
describe images. For more information, see
[Labeling images](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-labeling-images.html).
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*.
"""
@spec list_dataset_labels(map(), list_dataset_labels_request(), list()) ::
{:ok, list_dataset_labels_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, list_dataset_labels_errors()}
def list_dataset_labels(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "ListDatasetLabels", input, options)
end
@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.
"""
@spec list_faces(map(), list_faces_request(), list()) ::
{:ok, list_faces_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, list_faces_errors()}
def list_faces(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "ListFaces", input, options)
end
@doc """
Returns a list of media analysis jobs.
Results are sorted by `CreationTimestamp` in descending order.
"""
@spec list_media_analysis_jobs(map(), list_media_analysis_jobs_request(), list()) ::
{:ok, list_media_analysis_jobs_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, list_media_analysis_jobs_errors()}
def list_media_analysis_jobs(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "ListMediaAnalysisJobs", input, options)
end
@doc """
This operation applies only to Amazon Rekognition Custom Labels.
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`.
This operation requires permissions to perform the
`rekognition:ListProjectPolicies` action.
"""
@spec list_project_policies(map(), list_project_policies_request(), list()) ::
{:ok, list_project_policies_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, list_project_policies_errors()}
def list_project_policies(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "ListProjectPolicies", input, options)
end
@doc """
Gets a list of stream processors that you have created with
`CreateStreamProcessor`.
"""
@spec list_stream_processors(map(), list_stream_processors_request(), list()) ::
{:ok, list_stream_processors_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, list_stream_processors_errors()}
def list_stream_processors(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "ListStreamProcessors", input, options)
end
@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.
"""
@spec list_tags_for_resource(map(), list_tags_for_resource_request(), list()) ::
{:ok, list_tags_for_resource_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, list_tags_for_resource_errors()}
def list_tags_for_resource(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "ListTagsForResource", input, options)
end
@doc """
Returns metadata of the User such as `UserID` in the specified collection.
Anonymous User (to reserve faces without any identity) is not returned as part
of this
request. The results are sorted by system generated primary key ID. If the
response is
truncated, `NextToken` is returned in the response that can be used in the
subsequent request to retrieve the next set of identities.
"""
@spec list_users(map(), list_users_request(), list()) ::
{:ok, list_users_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, list_users_errors()}
def list_users(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "ListUsers", input, options)
end
@doc """
This operation applies only to Amazon Rekognition Custom Labels.
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. Only applies to Custom Labels
projects.
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`.
This operation requires permissions to perform the
`rekognition:PutProjectPolicy` action.
"""
@spec put_project_policy(map(), put_project_policy_request(), list()) ::
{:ok, put_project_policy_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, put_project_policy_errors()}
def put_project_policy(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "PutProjectPolicy", input, options)
end
@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.
"""
@spec recognize_celebrities(map(), recognize_celebrities_request(), list()) ::
{:ok, recognize_celebrities_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, recognize_celebrities_errors()}
def recognize_celebrities(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "RecognizeCelebrities", input, options)
end
@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.
"""
@spec search_faces(map(), search_faces_request(), list()) ::
{:ok, search_faces_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, search_faces_errors()}
def search_faces(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "SearchFaces", input, options)
end
@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.
"""
@spec search_faces_by_image(map(), search_faces_by_image_request(), list()) ::
{:ok, search_faces_by_image_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, search_faces_by_image_errors()}
def search_faces_by_image(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "SearchFacesByImage", input, options)
end
@doc """
Searches for UserIDs within a collection based on a `FaceId` or
`UserId`.
This API can be used to find the closest UserID (with a highest
similarity) to associate a face. The request must be provided with either
`FaceId`
or `UserId`. The operation returns an array of UserID that match the
`FaceId` or `UserId`, ordered by similarity score with the highest
similarity first.
"""
@spec search_users(map(), search_users_request(), list()) ::
{:ok, search_users_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, search_users_errors()}
def search_users(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "SearchUsers", input, options)
end
@doc """
Searches for UserIDs using a supplied image.
It first detects the largest face in the
image, and then searches a specified collection for matching UserIDs.
The operation returns an array of UserIDs that match the face in the supplied
image,
ordered by similarity score with the highest similarity first. It also returns a
bounding box
for the face found in the input image.
Information about faces detected in the supplied image, but not used for the
search, is
returned in an array of `UnsearchedFace` objects. If no valid face is detected
in
the image, the response will contain an empty `UserMatches` list and no
`SearchedFace` object.
"""
@spec search_users_by_image(map(), search_users_by_image_request(), list()) ::
{:ok, search_users_by_image_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, search_users_by_image_errors()}
def search_users_by_image(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "SearchUsersByImage", input, options)
end
@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.
"""
@spec start_celebrity_recognition(map(), start_celebrity_recognition_request(), list()) ::
{:ok, start_celebrity_recognition_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, start_celebrity_recognition_errors()}
def start_celebrity_recognition(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "StartCelebrityRecognition", input, options)
end
@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](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api).
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.
"""
@spec start_content_moderation(map(), start_content_moderation_request(), list()) ::
{:ok, start_content_moderation_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, start_content_moderation_errors()}
def start_content_moderation(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "StartContentModeration", input, options)
end
@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.
"""
@spec start_face_detection(map(), start_face_detection_request(), list()) ::
{:ok, start_face_detection_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, start_face_detection_errors()}
def start_face_detection(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "StartFaceDetection", input, options)
end
@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](https://docs.aws.amazon.com/rekognition/latest/dg/procedure-person-search-videos.html).
"""
@spec start_face_search(map(), start_face_search_request(), list()) ::
{:ok, start_face_search_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, start_face_search_errors()}
def start_face_search(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "StartFaceSearch", input, options)
end
@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](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detecting-labels-video.html).
You can specify `MinConfidence` to control the confidence threshold for the
labels returned. The default is 50.
"""
@spec start_label_detection(map(), start_label_detection_request(), list()) ::
{:ok, start_label_detection_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, start_label_detection_errors()}
def start_label_detection(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "StartLabelDetection", input, options)
end
@doc """
Initiates a new media analysis job.
Accepts a manifest file in an Amazon S3 bucket. The
output is a manifest file and a summary of the manifest stored in the Amazon S3
bucket.
"""
@spec start_media_analysis_job(map(), start_media_analysis_job_request(), list()) ::
{:ok, start_media_analysis_job_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, start_media_analysis_job_errors()}
def start_media_analysis_job(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "StartMediaAnalysisJob", input, options)
end
@doc """
*End of support notice:* On October 31, 2025, AWS will discontinue
support for Amazon Rekognition People Pathing.
After October 31, 2025, you will no
longer be able to use the Rekognition People Pathing capability. For more
information,
visit this [blog post](https://aws.amazon.com/blogs/machine-learning/transitioning-from-amazon-rekognition-people-pathing-exploring-other-alternatives/).
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`.
"""
@spec start_person_tracking(map(), start_person_tracking_request(), list()) ::
{:ok, start_person_tracking_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, start_person_tracking_errors()}
def start_person_tracking(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "StartPersonTracking", input, options)
end
@doc """
This operation applies only to Amazon Rekognition Custom Labels.
Starts the running of the version of a model. Starting a model takes a while to
complete. To check the current state of the model, use
`DescribeProjectVersions`.
Once the model is running, you can detect custom labels in new images by calling
`DetectCustomLabels`.
You are charged for the amount of time that the model is running. To stop a
running
model, call `StopProjectVersion`.
This operation requires permissions to perform the
`rekognition:StartProjectVersion` action.
"""
@spec start_project_version(map(), start_project_version_request(), list()) ::
{:ok, start_project_version_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, start_project_version_errors()}
def start_project_version(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "StartProjectVersion", input, options)
end
@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.
"""
@spec start_segment_detection(map(), start_segment_detection_request(), list()) ::
{:ok, start_segment_detection_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, start_segment_detection_errors()}
def start_segment_detection(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "StartSegmentDetection", input, options)
end
@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.
"""
@spec start_stream_processor(map(), start_stream_processor_request(), list()) ::
{:ok, start_stream_processor_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, start_stream_processor_errors()}
def start_stream_processor(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "StartStreamProcessor", input, options)
end
@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`.
"""
@spec start_text_detection(map(), start_text_detection_request(), list()) ::
{:ok, start_text_detection_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, start_text_detection_errors()}
def start_text_detection(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "StartTextDetection", input, options)
end
@doc """
This operation applies only to Amazon Rekognition Custom Labels.
Stops a running model. The operation might take a while to complete. To check
the
current status, call `DescribeProjectVersions`. Only applies to Custom
Labels projects.
This operation requires permissions to perform the
`rekognition:StopProjectVersion` action.
"""
@spec stop_project_version(map(), stop_project_version_request(), list()) ::
{:ok, stop_project_version_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, stop_project_version_errors()}
def stop_project_version(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "StopProjectVersion", input, options)
end
@doc """
Stops a running stream processor that was created by `CreateStreamProcessor`.
"""
@spec stop_stream_processor(map(), stop_stream_processor_request(), list()) ::
{:ok, stop_stream_processor_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, stop_stream_processor_errors()}
def stop_stream_processor(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "StopStreamProcessor", input, options)
end
@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](https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html).
This operation requires permissions to perform the `rekognition:TagResource`
action.
"""
@spec tag_resource(map(), tag_resource_request(), list()) ::
{:ok, tag_resource_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, tag_resource_errors()}
def tag_resource(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "TagResource", input, options)
end
@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.
"""
@spec untag_resource(map(), untag_resource_request(), list()) ::
{:ok, untag_resource_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, untag_resource_errors()}
def untag_resource(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "UntagResource", input, options)
end
@doc """
This operation applies only to Amazon Rekognition Custom Labels.
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.
"""
@spec update_dataset_entries(map(), update_dataset_entries_request(), list()) ::
{:ok, update_dataset_entries_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, update_dataset_entries_errors()}
def update_dataset_entries(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "UpdateDatasetEntries", input, options)
end
@doc """
Allows you to update a stream processor.
You can change some settings and regions of interest and delete certain
parameters.
"""
@spec update_stream_processor(map(), update_stream_processor_request(), list()) ::
{:ok, update_stream_processor_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, update_stream_processor_errors()}
def update_stream_processor(%Client{} = client, input, options \\ []) do
meta =
metadata()
Request.request_post(client, meta, "UpdateStreamProcessor", input, options)
end
end