// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct UpdateStreamProcessorOutput {}
/// See [`UpdateStreamProcessorOutput`](crate::output::UpdateStreamProcessorOutput).
pub mod update_stream_processor_output {
/// A builder for [`UpdateStreamProcessorOutput`](crate::output::UpdateStreamProcessorOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {}
impl Builder {
/// Consumes the builder and constructs a [`UpdateStreamProcessorOutput`](crate::output::UpdateStreamProcessorOutput).
pub fn build(self) -> crate::output::UpdateStreamProcessorOutput {
crate::output::UpdateStreamProcessorOutput {}
}
}
}
impl UpdateStreamProcessorOutput {
/// Creates a new builder-style object to manufacture [`UpdateStreamProcessorOutput`](crate::output::UpdateStreamProcessorOutput).
pub fn builder() -> crate::output::update_stream_processor_output::Builder {
crate::output::update_stream_processor_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct UpdateDatasetEntriesOutput {}
/// See [`UpdateDatasetEntriesOutput`](crate::output::UpdateDatasetEntriesOutput).
pub mod update_dataset_entries_output {
/// A builder for [`UpdateDatasetEntriesOutput`](crate::output::UpdateDatasetEntriesOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {}
impl Builder {
/// Consumes the builder and constructs a [`UpdateDatasetEntriesOutput`](crate::output::UpdateDatasetEntriesOutput).
pub fn build(self) -> crate::output::UpdateDatasetEntriesOutput {
crate::output::UpdateDatasetEntriesOutput {}
}
}
}
impl UpdateDatasetEntriesOutput {
/// Creates a new builder-style object to manufacture [`UpdateDatasetEntriesOutput`](crate::output::UpdateDatasetEntriesOutput).
pub fn builder() -> crate::output::update_dataset_entries_output::Builder {
crate::output::update_dataset_entries_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct UntagResourceOutput {}
/// See [`UntagResourceOutput`](crate::output::UntagResourceOutput).
pub mod untag_resource_output {
/// A builder for [`UntagResourceOutput`](crate::output::UntagResourceOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {}
impl Builder {
/// Consumes the builder and constructs a [`UntagResourceOutput`](crate::output::UntagResourceOutput).
pub fn build(self) -> crate::output::UntagResourceOutput {
crate::output::UntagResourceOutput {}
}
}
}
impl UntagResourceOutput {
/// Creates a new builder-style object to manufacture [`UntagResourceOutput`](crate::output::UntagResourceOutput).
pub fn builder() -> crate::output::untag_resource_output::Builder {
crate::output::untag_resource_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct TagResourceOutput {}
/// See [`TagResourceOutput`](crate::output::TagResourceOutput).
pub mod tag_resource_output {
/// A builder for [`TagResourceOutput`](crate::output::TagResourceOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {}
impl Builder {
/// Consumes the builder and constructs a [`TagResourceOutput`](crate::output::TagResourceOutput).
pub fn build(self) -> crate::output::TagResourceOutput {
crate::output::TagResourceOutput {}
}
}
}
impl TagResourceOutput {
/// Creates a new builder-style object to manufacture [`TagResourceOutput`](crate::output::TagResourceOutput).
pub fn builder() -> crate::output::tag_resource_output::Builder {
crate::output::tag_resource_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StopStreamProcessorOutput {}
/// See [`StopStreamProcessorOutput`](crate::output::StopStreamProcessorOutput).
pub mod stop_stream_processor_output {
/// A builder for [`StopStreamProcessorOutput`](crate::output::StopStreamProcessorOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {}
impl Builder {
/// Consumes the builder and constructs a [`StopStreamProcessorOutput`](crate::output::StopStreamProcessorOutput).
pub fn build(self) -> crate::output::StopStreamProcessorOutput {
crate::output::StopStreamProcessorOutput {}
}
}
}
impl StopStreamProcessorOutput {
/// Creates a new builder-style object to manufacture [`StopStreamProcessorOutput`](crate::output::StopStreamProcessorOutput).
pub fn builder() -> crate::output::stop_stream_processor_output::Builder {
crate::output::stop_stream_processor_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StopProjectVersionOutput {
/// <p>The current status of the stop operation. </p>
#[doc(hidden)]
pub status: std::option::Option<crate::model::ProjectVersionStatus>,
}
impl StopProjectVersionOutput {
/// <p>The current status of the stop operation. </p>
pub fn status(&self) -> std::option::Option<&crate::model::ProjectVersionStatus> {
self.status.as_ref()
}
}
/// See [`StopProjectVersionOutput`](crate::output::StopProjectVersionOutput).
pub mod stop_project_version_output {
/// A builder for [`StopProjectVersionOutput`](crate::output::StopProjectVersionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) status: std::option::Option<crate::model::ProjectVersionStatus>,
}
impl Builder {
/// <p>The current status of the stop operation. </p>
pub fn status(mut self, input: crate::model::ProjectVersionStatus) -> Self {
self.status = Some(input);
self
}
/// <p>The current status of the stop operation. </p>
pub fn set_status(
mut self,
input: std::option::Option<crate::model::ProjectVersionStatus>,
) -> Self {
self.status = input;
self
}
/// Consumes the builder and constructs a [`StopProjectVersionOutput`](crate::output::StopProjectVersionOutput).
pub fn build(self) -> crate::output::StopProjectVersionOutput {
crate::output::StopProjectVersionOutput {
status: self.status,
}
}
}
}
impl StopProjectVersionOutput {
/// Creates a new builder-style object to manufacture [`StopProjectVersionOutput`](crate::output::StopProjectVersionOutput).
pub fn builder() -> crate::output::stop_project_version_output::Builder {
crate::output::stop_project_version_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StartTextDetectionOutput {
/// <p>Identifier for the text detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetTextDetection</code>.</p>
#[doc(hidden)]
pub job_id: std::option::Option<std::string::String>,
}
impl StartTextDetectionOutput {
/// <p>Identifier for the text detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetTextDetection</code>.</p>
pub fn job_id(&self) -> std::option::Option<&str> {
self.job_id.as_deref()
}
}
/// See [`StartTextDetectionOutput`](crate::output::StartTextDetectionOutput).
pub mod start_text_detection_output {
/// A builder for [`StartTextDetectionOutput`](crate::output::StartTextDetectionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>Identifier for the text detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetTextDetection</code>.</p>
pub fn job_id(mut self, input: impl Into<std::string::String>) -> Self {
self.job_id = Some(input.into());
self
}
/// <p>Identifier for the text detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetTextDetection</code>.</p>
pub fn set_job_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_id = input;
self
}
/// Consumes the builder and constructs a [`StartTextDetectionOutput`](crate::output::StartTextDetectionOutput).
pub fn build(self) -> crate::output::StartTextDetectionOutput {
crate::output::StartTextDetectionOutput {
job_id: self.job_id,
}
}
}
}
impl StartTextDetectionOutput {
/// Creates a new builder-style object to manufacture [`StartTextDetectionOutput`](crate::output::StartTextDetectionOutput).
pub fn builder() -> crate::output::start_text_detection_output::Builder {
crate::output::start_text_detection_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StartStreamProcessorOutput {
/// <p> A unique identifier for the stream processing session. </p>
#[doc(hidden)]
pub session_id: std::option::Option<std::string::String>,
}
impl StartStreamProcessorOutput {
/// <p> A unique identifier for the stream processing session. </p>
pub fn session_id(&self) -> std::option::Option<&str> {
self.session_id.as_deref()
}
}
/// See [`StartStreamProcessorOutput`](crate::output::StartStreamProcessorOutput).
pub mod start_stream_processor_output {
/// A builder for [`StartStreamProcessorOutput`](crate::output::StartStreamProcessorOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) session_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p> A unique identifier for the stream processing session. </p>
pub fn session_id(mut self, input: impl Into<std::string::String>) -> Self {
self.session_id = Some(input.into());
self
}
/// <p> A unique identifier for the stream processing session. </p>
pub fn set_session_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.session_id = input;
self
}
/// Consumes the builder and constructs a [`StartStreamProcessorOutput`](crate::output::StartStreamProcessorOutput).
pub fn build(self) -> crate::output::StartStreamProcessorOutput {
crate::output::StartStreamProcessorOutput {
session_id: self.session_id,
}
}
}
}
impl StartStreamProcessorOutput {
/// Creates a new builder-style object to manufacture [`StartStreamProcessorOutput`](crate::output::StartStreamProcessorOutput).
pub fn builder() -> crate::output::start_stream_processor_output::Builder {
crate::output::start_stream_processor_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StartSegmentDetectionOutput {
/// <p>Unique identifier for the segment detection job. The <code>JobId</code> is returned from <code>StartSegmentDetection</code>. </p>
#[doc(hidden)]
pub job_id: std::option::Option<std::string::String>,
}
impl StartSegmentDetectionOutput {
/// <p>Unique identifier for the segment detection job. The <code>JobId</code> is returned from <code>StartSegmentDetection</code>. </p>
pub fn job_id(&self) -> std::option::Option<&str> {
self.job_id.as_deref()
}
}
/// See [`StartSegmentDetectionOutput`](crate::output::StartSegmentDetectionOutput).
pub mod start_segment_detection_output {
/// A builder for [`StartSegmentDetectionOutput`](crate::output::StartSegmentDetectionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>Unique identifier for the segment detection job. The <code>JobId</code> is returned from <code>StartSegmentDetection</code>. </p>
pub fn job_id(mut self, input: impl Into<std::string::String>) -> Self {
self.job_id = Some(input.into());
self
}
/// <p>Unique identifier for the segment detection job. The <code>JobId</code> is returned from <code>StartSegmentDetection</code>. </p>
pub fn set_job_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_id = input;
self
}
/// Consumes the builder and constructs a [`StartSegmentDetectionOutput`](crate::output::StartSegmentDetectionOutput).
pub fn build(self) -> crate::output::StartSegmentDetectionOutput {
crate::output::StartSegmentDetectionOutput {
job_id: self.job_id,
}
}
}
}
impl StartSegmentDetectionOutput {
/// Creates a new builder-style object to manufacture [`StartSegmentDetectionOutput`](crate::output::StartSegmentDetectionOutput).
pub fn builder() -> crate::output::start_segment_detection_output::Builder {
crate::output::start_segment_detection_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StartProjectVersionOutput {
/// <p>The current running status of the model. </p>
#[doc(hidden)]
pub status: std::option::Option<crate::model::ProjectVersionStatus>,
}
impl StartProjectVersionOutput {
/// <p>The current running status of the model. </p>
pub fn status(&self) -> std::option::Option<&crate::model::ProjectVersionStatus> {
self.status.as_ref()
}
}
/// See [`StartProjectVersionOutput`](crate::output::StartProjectVersionOutput).
pub mod start_project_version_output {
/// A builder for [`StartProjectVersionOutput`](crate::output::StartProjectVersionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) status: std::option::Option<crate::model::ProjectVersionStatus>,
}
impl Builder {
/// <p>The current running status of the model. </p>
pub fn status(mut self, input: crate::model::ProjectVersionStatus) -> Self {
self.status = Some(input);
self
}
/// <p>The current running status of the model. </p>
pub fn set_status(
mut self,
input: std::option::Option<crate::model::ProjectVersionStatus>,
) -> Self {
self.status = input;
self
}
/// Consumes the builder and constructs a [`StartProjectVersionOutput`](crate::output::StartProjectVersionOutput).
pub fn build(self) -> crate::output::StartProjectVersionOutput {
crate::output::StartProjectVersionOutput {
status: self.status,
}
}
}
}
impl StartProjectVersionOutput {
/// Creates a new builder-style object to manufacture [`StartProjectVersionOutput`](crate::output::StartProjectVersionOutput).
pub fn builder() -> crate::output::start_project_version_output::Builder {
crate::output::start_project_version_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StartPersonTrackingOutput {
/// <p>The identifier for the person detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetPersonTracking</code>.</p>
#[doc(hidden)]
pub job_id: std::option::Option<std::string::String>,
}
impl StartPersonTrackingOutput {
/// <p>The identifier for the person detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetPersonTracking</code>.</p>
pub fn job_id(&self) -> std::option::Option<&str> {
self.job_id.as_deref()
}
}
/// See [`StartPersonTrackingOutput`](crate::output::StartPersonTrackingOutput).
pub mod start_person_tracking_output {
/// A builder for [`StartPersonTrackingOutput`](crate::output::StartPersonTrackingOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The identifier for the person detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetPersonTracking</code>.</p>
pub fn job_id(mut self, input: impl Into<std::string::String>) -> Self {
self.job_id = Some(input.into());
self
}
/// <p>The identifier for the person detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetPersonTracking</code>.</p>
pub fn set_job_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_id = input;
self
}
/// Consumes the builder and constructs a [`StartPersonTrackingOutput`](crate::output::StartPersonTrackingOutput).
pub fn build(self) -> crate::output::StartPersonTrackingOutput {
crate::output::StartPersonTrackingOutput {
job_id: self.job_id,
}
}
}
}
impl StartPersonTrackingOutput {
/// Creates a new builder-style object to manufacture [`StartPersonTrackingOutput`](crate::output::StartPersonTrackingOutput).
pub fn builder() -> crate::output::start_person_tracking_output::Builder {
crate::output::start_person_tracking_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StartLabelDetectionOutput {
/// <p>The identifier for the label detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetLabelDetection</code>. </p>
#[doc(hidden)]
pub job_id: std::option::Option<std::string::String>,
}
impl StartLabelDetectionOutput {
/// <p>The identifier for the label detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetLabelDetection</code>. </p>
pub fn job_id(&self) -> std::option::Option<&str> {
self.job_id.as_deref()
}
}
/// See [`StartLabelDetectionOutput`](crate::output::StartLabelDetectionOutput).
pub mod start_label_detection_output {
/// A builder for [`StartLabelDetectionOutput`](crate::output::StartLabelDetectionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The identifier for the label detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetLabelDetection</code>. </p>
pub fn job_id(mut self, input: impl Into<std::string::String>) -> Self {
self.job_id = Some(input.into());
self
}
/// <p>The identifier for the label detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetLabelDetection</code>. </p>
pub fn set_job_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_id = input;
self
}
/// Consumes the builder and constructs a [`StartLabelDetectionOutput`](crate::output::StartLabelDetectionOutput).
pub fn build(self) -> crate::output::StartLabelDetectionOutput {
crate::output::StartLabelDetectionOutput {
job_id: self.job_id,
}
}
}
}
impl StartLabelDetectionOutput {
/// Creates a new builder-style object to manufacture [`StartLabelDetectionOutput`](crate::output::StartLabelDetectionOutput).
pub fn builder() -> crate::output::start_label_detection_output::Builder {
crate::output::start_label_detection_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StartFaceSearchOutput {
/// <p>The identifier for the search job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetFaceSearch</code>. </p>
#[doc(hidden)]
pub job_id: std::option::Option<std::string::String>,
}
impl StartFaceSearchOutput {
/// <p>The identifier for the search job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetFaceSearch</code>. </p>
pub fn job_id(&self) -> std::option::Option<&str> {
self.job_id.as_deref()
}
}
/// See [`StartFaceSearchOutput`](crate::output::StartFaceSearchOutput).
pub mod start_face_search_output {
/// A builder for [`StartFaceSearchOutput`](crate::output::StartFaceSearchOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The identifier for the search job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetFaceSearch</code>. </p>
pub fn job_id(mut self, input: impl Into<std::string::String>) -> Self {
self.job_id = Some(input.into());
self
}
/// <p>The identifier for the search job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetFaceSearch</code>. </p>
pub fn set_job_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_id = input;
self
}
/// Consumes the builder and constructs a [`StartFaceSearchOutput`](crate::output::StartFaceSearchOutput).
pub fn build(self) -> crate::output::StartFaceSearchOutput {
crate::output::StartFaceSearchOutput {
job_id: self.job_id,
}
}
}
}
impl StartFaceSearchOutput {
/// Creates a new builder-style object to manufacture [`StartFaceSearchOutput`](crate::output::StartFaceSearchOutput).
pub fn builder() -> crate::output::start_face_search_output::Builder {
crate::output::start_face_search_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StartFaceDetectionOutput {
/// <p>The identifier for the face detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetFaceDetection</code>.</p>
#[doc(hidden)]
pub job_id: std::option::Option<std::string::String>,
}
impl StartFaceDetectionOutput {
/// <p>The identifier for the face detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetFaceDetection</code>.</p>
pub fn job_id(&self) -> std::option::Option<&str> {
self.job_id.as_deref()
}
}
/// See [`StartFaceDetectionOutput`](crate::output::StartFaceDetectionOutput).
pub mod start_face_detection_output {
/// A builder for [`StartFaceDetectionOutput`](crate::output::StartFaceDetectionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The identifier for the face detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetFaceDetection</code>.</p>
pub fn job_id(mut self, input: impl Into<std::string::String>) -> Self {
self.job_id = Some(input.into());
self
}
/// <p>The identifier for the face detection job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetFaceDetection</code>.</p>
pub fn set_job_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_id = input;
self
}
/// Consumes the builder and constructs a [`StartFaceDetectionOutput`](crate::output::StartFaceDetectionOutput).
pub fn build(self) -> crate::output::StartFaceDetectionOutput {
crate::output::StartFaceDetectionOutput {
job_id: self.job_id,
}
}
}
}
impl StartFaceDetectionOutput {
/// Creates a new builder-style object to manufacture [`StartFaceDetectionOutput`](crate::output::StartFaceDetectionOutput).
pub fn builder() -> crate::output::start_face_detection_output::Builder {
crate::output::start_face_detection_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StartContentModerationOutput {
/// <p>The identifier for the content analysis job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetContentModeration</code>.</p>
#[doc(hidden)]
pub job_id: std::option::Option<std::string::String>,
}
impl StartContentModerationOutput {
/// <p>The identifier for the content analysis job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetContentModeration</code>.</p>
pub fn job_id(&self) -> std::option::Option<&str> {
self.job_id.as_deref()
}
}
/// See [`StartContentModerationOutput`](crate::output::StartContentModerationOutput).
pub mod start_content_moderation_output {
/// A builder for [`StartContentModerationOutput`](crate::output::StartContentModerationOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The identifier for the content analysis job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetContentModeration</code>.</p>
pub fn job_id(mut self, input: impl Into<std::string::String>) -> Self {
self.job_id = Some(input.into());
self
}
/// <p>The identifier for the content analysis job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetContentModeration</code>.</p>
pub fn set_job_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_id = input;
self
}
/// Consumes the builder and constructs a [`StartContentModerationOutput`](crate::output::StartContentModerationOutput).
pub fn build(self) -> crate::output::StartContentModerationOutput {
crate::output::StartContentModerationOutput {
job_id: self.job_id,
}
}
}
}
impl StartContentModerationOutput {
/// Creates a new builder-style object to manufacture [`StartContentModerationOutput`](crate::output::StartContentModerationOutput).
pub fn builder() -> crate::output::start_content_moderation_output::Builder {
crate::output::start_content_moderation_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StartCelebrityRecognitionOutput {
/// <p>The identifier for the celebrity recognition analysis job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetCelebrityRecognition</code>.</p>
#[doc(hidden)]
pub job_id: std::option::Option<std::string::String>,
}
impl StartCelebrityRecognitionOutput {
/// <p>The identifier for the celebrity recognition analysis job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetCelebrityRecognition</code>.</p>
pub fn job_id(&self) -> std::option::Option<&str> {
self.job_id.as_deref()
}
}
/// See [`StartCelebrityRecognitionOutput`](crate::output::StartCelebrityRecognitionOutput).
pub mod start_celebrity_recognition_output {
/// A builder for [`StartCelebrityRecognitionOutput`](crate::output::StartCelebrityRecognitionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The identifier for the celebrity recognition analysis job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetCelebrityRecognition</code>.</p>
pub fn job_id(mut self, input: impl Into<std::string::String>) -> Self {
self.job_id = Some(input.into());
self
}
/// <p>The identifier for the celebrity recognition analysis job. Use <code>JobId</code> to identify the job in a subsequent call to <code>GetCelebrityRecognition</code>.</p>
pub fn set_job_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_id = input;
self
}
/// Consumes the builder and constructs a [`StartCelebrityRecognitionOutput`](crate::output::StartCelebrityRecognitionOutput).
pub fn build(self) -> crate::output::StartCelebrityRecognitionOutput {
crate::output::StartCelebrityRecognitionOutput {
job_id: self.job_id,
}
}
}
}
impl StartCelebrityRecognitionOutput {
/// Creates a new builder-style object to manufacture [`StartCelebrityRecognitionOutput`](crate::output::StartCelebrityRecognitionOutput).
pub fn builder() -> crate::output::start_celebrity_recognition_output::Builder {
crate::output::start_celebrity_recognition_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct SearchFacesByImageOutput {
/// <p>The bounding box around the face in the input image that Amazon Rekognition used for the search.</p>
#[doc(hidden)]
pub searched_face_bounding_box: std::option::Option<crate::model::BoundingBox>,
/// <p>The level of confidence that the <code>searchedFaceBoundingBox</code>, contains a face.</p>
#[doc(hidden)]
pub searched_face_confidence: std::option::Option<f32>,
/// <p>An array of faces that match the input face, along with the confidence in the match.</p>
#[doc(hidden)]
pub face_matches: std::option::Option<std::vec::Vec<crate::model::FaceMatch>>,
/// <p>Version number of the face detection model associated with the input collection (<code>CollectionId</code>).</p>
#[doc(hidden)]
pub face_model_version: std::option::Option<std::string::String>,
}
impl SearchFacesByImageOutput {
/// <p>The bounding box around the face in the input image that Amazon Rekognition used for the search.</p>
pub fn searched_face_bounding_box(&self) -> std::option::Option<&crate::model::BoundingBox> {
self.searched_face_bounding_box.as_ref()
}
/// <p>The level of confidence that the <code>searchedFaceBoundingBox</code>, contains a face.</p>
pub fn searched_face_confidence(&self) -> std::option::Option<f32> {
self.searched_face_confidence
}
/// <p>An array of faces that match the input face, along with the confidence in the match.</p>
pub fn face_matches(&self) -> std::option::Option<&[crate::model::FaceMatch]> {
self.face_matches.as_deref()
}
/// <p>Version number of the face detection model associated with the input collection (<code>CollectionId</code>).</p>
pub fn face_model_version(&self) -> std::option::Option<&str> {
self.face_model_version.as_deref()
}
}
/// See [`SearchFacesByImageOutput`](crate::output::SearchFacesByImageOutput).
pub mod search_faces_by_image_output {
/// A builder for [`SearchFacesByImageOutput`](crate::output::SearchFacesByImageOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) searched_face_bounding_box: std::option::Option<crate::model::BoundingBox>,
pub(crate) searched_face_confidence: std::option::Option<f32>,
pub(crate) face_matches: std::option::Option<std::vec::Vec<crate::model::FaceMatch>>,
pub(crate) face_model_version: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The bounding box around the face in the input image that Amazon Rekognition used for the search.</p>
pub fn searched_face_bounding_box(mut self, input: crate::model::BoundingBox) -> Self {
self.searched_face_bounding_box = Some(input);
self
}
/// <p>The bounding box around the face in the input image that Amazon Rekognition used for the search.</p>
pub fn set_searched_face_bounding_box(
mut self,
input: std::option::Option<crate::model::BoundingBox>,
) -> Self {
self.searched_face_bounding_box = input;
self
}
/// <p>The level of confidence that the <code>searchedFaceBoundingBox</code>, contains a face.</p>
pub fn searched_face_confidence(mut self, input: f32) -> Self {
self.searched_face_confidence = Some(input);
self
}
/// <p>The level of confidence that the <code>searchedFaceBoundingBox</code>, contains a face.</p>
pub fn set_searched_face_confidence(mut self, input: std::option::Option<f32>) -> Self {
self.searched_face_confidence = input;
self
}
/// Appends an item to `face_matches`.
///
/// To override the contents of this collection use [`set_face_matches`](Self::set_face_matches).
///
/// <p>An array of faces that match the input face, along with the confidence in the match.</p>
pub fn face_matches(mut self, input: crate::model::FaceMatch) -> Self {
let mut v = self.face_matches.unwrap_or_default();
v.push(input);
self.face_matches = Some(v);
self
}
/// <p>An array of faces that match the input face, along with the confidence in the match.</p>
pub fn set_face_matches(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::FaceMatch>>,
) -> Self {
self.face_matches = input;
self
}
/// <p>Version number of the face detection model associated with the input collection (<code>CollectionId</code>).</p>
pub fn face_model_version(mut self, input: impl Into<std::string::String>) -> Self {
self.face_model_version = Some(input.into());
self
}
/// <p>Version number of the face detection model associated with the input collection (<code>CollectionId</code>).</p>
pub fn set_face_model_version(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.face_model_version = input;
self
}
/// Consumes the builder and constructs a [`SearchFacesByImageOutput`](crate::output::SearchFacesByImageOutput).
pub fn build(self) -> crate::output::SearchFacesByImageOutput {
crate::output::SearchFacesByImageOutput {
searched_face_bounding_box: self.searched_face_bounding_box,
searched_face_confidence: self.searched_face_confidence,
face_matches: self.face_matches,
face_model_version: self.face_model_version,
}
}
}
}
impl SearchFacesByImageOutput {
/// Creates a new builder-style object to manufacture [`SearchFacesByImageOutput`](crate::output::SearchFacesByImageOutput).
pub fn builder() -> crate::output::search_faces_by_image_output::Builder {
crate::output::search_faces_by_image_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct SearchFacesOutput {
/// <p>ID of the face that was searched for matches in a collection.</p>
#[doc(hidden)]
pub searched_face_id: std::option::Option<std::string::String>,
/// <p>An array of faces that matched the input face, along with the confidence in the match.</p>
#[doc(hidden)]
pub face_matches: std::option::Option<std::vec::Vec<crate::model::FaceMatch>>,
/// <p>Version number of the face detection model associated with the input collection (<code>CollectionId</code>).</p>
#[doc(hidden)]
pub face_model_version: std::option::Option<std::string::String>,
}
impl SearchFacesOutput {
/// <p>ID of the face that was searched for matches in a collection.</p>
pub fn searched_face_id(&self) -> std::option::Option<&str> {
self.searched_face_id.as_deref()
}
/// <p>An array of faces that matched the input face, along with the confidence in the match.</p>
pub fn face_matches(&self) -> std::option::Option<&[crate::model::FaceMatch]> {
self.face_matches.as_deref()
}
/// <p>Version number of the face detection model associated with the input collection (<code>CollectionId</code>).</p>
pub fn face_model_version(&self) -> std::option::Option<&str> {
self.face_model_version.as_deref()
}
}
/// See [`SearchFacesOutput`](crate::output::SearchFacesOutput).
pub mod search_faces_output {
/// A builder for [`SearchFacesOutput`](crate::output::SearchFacesOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) searched_face_id: std::option::Option<std::string::String>,
pub(crate) face_matches: std::option::Option<std::vec::Vec<crate::model::FaceMatch>>,
pub(crate) face_model_version: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>ID of the face that was searched for matches in a collection.</p>
pub fn searched_face_id(mut self, input: impl Into<std::string::String>) -> Self {
self.searched_face_id = Some(input.into());
self
}
/// <p>ID of the face that was searched for matches in a collection.</p>
pub fn set_searched_face_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.searched_face_id = input;
self
}
/// Appends an item to `face_matches`.
///
/// To override the contents of this collection use [`set_face_matches`](Self::set_face_matches).
///
/// <p>An array of faces that matched the input face, along with the confidence in the match.</p>
pub fn face_matches(mut self, input: crate::model::FaceMatch) -> Self {
let mut v = self.face_matches.unwrap_or_default();
v.push(input);
self.face_matches = Some(v);
self
}
/// <p>An array of faces that matched the input face, along with the confidence in the match.</p>
pub fn set_face_matches(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::FaceMatch>>,
) -> Self {
self.face_matches = input;
self
}
/// <p>Version number of the face detection model associated with the input collection (<code>CollectionId</code>).</p>
pub fn face_model_version(mut self, input: impl Into<std::string::String>) -> Self {
self.face_model_version = Some(input.into());
self
}
/// <p>Version number of the face detection model associated with the input collection (<code>CollectionId</code>).</p>
pub fn set_face_model_version(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.face_model_version = input;
self
}
/// Consumes the builder and constructs a [`SearchFacesOutput`](crate::output::SearchFacesOutput).
pub fn build(self) -> crate::output::SearchFacesOutput {
crate::output::SearchFacesOutput {
searched_face_id: self.searched_face_id,
face_matches: self.face_matches,
face_model_version: self.face_model_version,
}
}
}
}
impl SearchFacesOutput {
/// Creates a new builder-style object to manufacture [`SearchFacesOutput`](crate::output::SearchFacesOutput).
pub fn builder() -> crate::output::search_faces_output::Builder {
crate::output::search_faces_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct RecognizeCelebritiesOutput {
/// <p>Details about each celebrity found in the image. Amazon Rekognition can detect a maximum of 64 celebrities in an image. Each celebrity object includes the following attributes: <code>Face</code>, <code>Confidence</code>, <code>Emotions</code>, <code>Landmarks</code>, <code>Pose</code>, <code>Quality</code>, <code>Smile</code>, <code>Id</code>, <code>KnownGender</code>, <code>MatchConfidence</code>, <code>Name</code>, <code>Urls</code>.</p>
#[doc(hidden)]
pub celebrity_faces: std::option::Option<std::vec::Vec<crate::model::Celebrity>>,
/// <p>Details about each unrecognized face in the image.</p>
#[doc(hidden)]
pub unrecognized_faces: std::option::Option<std::vec::Vec<crate::model::ComparedFace>>,
/// <note>
/// <p>Support for estimating image orientation using the the OrientationCorrection field has ceased as of August 2021. Any returned values for this field included in an API response will always be NULL.</p>
/// </note>
/// <p>The orientation of the input image (counterclockwise direction). If your application displays the image, you can use this value to correct the orientation. The bounding box coordinates returned in <code>CelebrityFaces</code> and <code>UnrecognizedFaces</code> represent face locations before the image orientation is corrected. </p> <note>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image (Exif) metadata that includes the image's orientation. If so, and the Exif metadata for the input image populates the orientation field, the value of <code>OrientationCorrection</code> is null. The <code>CelebrityFaces</code> and <code>UnrecognizedFaces</code> bounding box coordinates represent face locations after Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata. </p>
/// </note>
#[doc(hidden)]
pub orientation_correction: std::option::Option<crate::model::OrientationCorrection>,
}
impl RecognizeCelebritiesOutput {
/// <p>Details about each celebrity found in the image. Amazon Rekognition can detect a maximum of 64 celebrities in an image. Each celebrity object includes the following attributes: <code>Face</code>, <code>Confidence</code>, <code>Emotions</code>, <code>Landmarks</code>, <code>Pose</code>, <code>Quality</code>, <code>Smile</code>, <code>Id</code>, <code>KnownGender</code>, <code>MatchConfidence</code>, <code>Name</code>, <code>Urls</code>.</p>
pub fn celebrity_faces(&self) -> std::option::Option<&[crate::model::Celebrity]> {
self.celebrity_faces.as_deref()
}
/// <p>Details about each unrecognized face in the image.</p>
pub fn unrecognized_faces(&self) -> std::option::Option<&[crate::model::ComparedFace]> {
self.unrecognized_faces.as_deref()
}
/// <note>
/// <p>Support for estimating image orientation using the the OrientationCorrection field has ceased as of August 2021. Any returned values for this field included in an API response will always be NULL.</p>
/// </note>
/// <p>The orientation of the input image (counterclockwise direction). If your application displays the image, you can use this value to correct the orientation. The bounding box coordinates returned in <code>CelebrityFaces</code> and <code>UnrecognizedFaces</code> represent face locations before the image orientation is corrected. </p> <note>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image (Exif) metadata that includes the image's orientation. If so, and the Exif metadata for the input image populates the orientation field, the value of <code>OrientationCorrection</code> is null. The <code>CelebrityFaces</code> and <code>UnrecognizedFaces</code> bounding box coordinates represent face locations after Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata. </p>
/// </note>
pub fn orientation_correction(
&self,
) -> std::option::Option<&crate::model::OrientationCorrection> {
self.orientation_correction.as_ref()
}
}
/// See [`RecognizeCelebritiesOutput`](crate::output::RecognizeCelebritiesOutput).
pub mod recognize_celebrities_output {
/// A builder for [`RecognizeCelebritiesOutput`](crate::output::RecognizeCelebritiesOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) celebrity_faces: std::option::Option<std::vec::Vec<crate::model::Celebrity>>,
pub(crate) unrecognized_faces:
std::option::Option<std::vec::Vec<crate::model::ComparedFace>>,
pub(crate) orientation_correction: std::option::Option<crate::model::OrientationCorrection>,
}
impl Builder {
/// Appends an item to `celebrity_faces`.
///
/// To override the contents of this collection use [`set_celebrity_faces`](Self::set_celebrity_faces).
///
/// <p>Details about each celebrity found in the image. Amazon Rekognition can detect a maximum of 64 celebrities in an image. Each celebrity object includes the following attributes: <code>Face</code>, <code>Confidence</code>, <code>Emotions</code>, <code>Landmarks</code>, <code>Pose</code>, <code>Quality</code>, <code>Smile</code>, <code>Id</code>, <code>KnownGender</code>, <code>MatchConfidence</code>, <code>Name</code>, <code>Urls</code>.</p>
pub fn celebrity_faces(mut self, input: crate::model::Celebrity) -> Self {
let mut v = self.celebrity_faces.unwrap_or_default();
v.push(input);
self.celebrity_faces = Some(v);
self
}
/// <p>Details about each celebrity found in the image. Amazon Rekognition can detect a maximum of 64 celebrities in an image. Each celebrity object includes the following attributes: <code>Face</code>, <code>Confidence</code>, <code>Emotions</code>, <code>Landmarks</code>, <code>Pose</code>, <code>Quality</code>, <code>Smile</code>, <code>Id</code>, <code>KnownGender</code>, <code>MatchConfidence</code>, <code>Name</code>, <code>Urls</code>.</p>
pub fn set_celebrity_faces(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::Celebrity>>,
) -> Self {
self.celebrity_faces = input;
self
}
/// Appends an item to `unrecognized_faces`.
///
/// To override the contents of this collection use [`set_unrecognized_faces`](Self::set_unrecognized_faces).
///
/// <p>Details about each unrecognized face in the image.</p>
pub fn unrecognized_faces(mut self, input: crate::model::ComparedFace) -> Self {
let mut v = self.unrecognized_faces.unwrap_or_default();
v.push(input);
self.unrecognized_faces = Some(v);
self
}
/// <p>Details about each unrecognized face in the image.</p>
pub fn set_unrecognized_faces(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::ComparedFace>>,
) -> Self {
self.unrecognized_faces = input;
self
}
/// <note>
/// <p>Support for estimating image orientation using the the OrientationCorrection field has ceased as of August 2021. Any returned values for this field included in an API response will always be NULL.</p>
/// </note>
/// <p>The orientation of the input image (counterclockwise direction). If your application displays the image, you can use this value to correct the orientation. The bounding box coordinates returned in <code>CelebrityFaces</code> and <code>UnrecognizedFaces</code> represent face locations before the image orientation is corrected. </p> <note>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image (Exif) metadata that includes the image's orientation. If so, and the Exif metadata for the input image populates the orientation field, the value of <code>OrientationCorrection</code> is null. The <code>CelebrityFaces</code> and <code>UnrecognizedFaces</code> bounding box coordinates represent face locations after Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata. </p>
/// </note>
pub fn orientation_correction(
mut self,
input: crate::model::OrientationCorrection,
) -> Self {
self.orientation_correction = Some(input);
self
}
/// <note>
/// <p>Support for estimating image orientation using the the OrientationCorrection field has ceased as of August 2021. Any returned values for this field included in an API response will always be NULL.</p>
/// </note>
/// <p>The orientation of the input image (counterclockwise direction). If your application displays the image, you can use this value to correct the orientation. The bounding box coordinates returned in <code>CelebrityFaces</code> and <code>UnrecognizedFaces</code> represent face locations before the image orientation is corrected. </p> <note>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image (Exif) metadata that includes the image's orientation. If so, and the Exif metadata for the input image populates the orientation field, the value of <code>OrientationCorrection</code> is null. The <code>CelebrityFaces</code> and <code>UnrecognizedFaces</code> bounding box coordinates represent face locations after Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata. </p>
/// </note>
pub fn set_orientation_correction(
mut self,
input: std::option::Option<crate::model::OrientationCorrection>,
) -> Self {
self.orientation_correction = input;
self
}
/// Consumes the builder and constructs a [`RecognizeCelebritiesOutput`](crate::output::RecognizeCelebritiesOutput).
pub fn build(self) -> crate::output::RecognizeCelebritiesOutput {
crate::output::RecognizeCelebritiesOutput {
celebrity_faces: self.celebrity_faces,
unrecognized_faces: self.unrecognized_faces,
orientation_correction: self.orientation_correction,
}
}
}
}
impl RecognizeCelebritiesOutput {
/// Creates a new builder-style object to manufacture [`RecognizeCelebritiesOutput`](crate::output::RecognizeCelebritiesOutput).
pub fn builder() -> crate::output::recognize_celebrities_output::Builder {
crate::output::recognize_celebrities_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct PutProjectPolicyOutput {
/// <p>The ID of the project policy.</p>
#[doc(hidden)]
pub policy_revision_id: std::option::Option<std::string::String>,
}
impl PutProjectPolicyOutput {
/// <p>The ID of the project policy.</p>
pub fn policy_revision_id(&self) -> std::option::Option<&str> {
self.policy_revision_id.as_deref()
}
}
/// See [`PutProjectPolicyOutput`](crate::output::PutProjectPolicyOutput).
pub mod put_project_policy_output {
/// A builder for [`PutProjectPolicyOutput`](crate::output::PutProjectPolicyOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) policy_revision_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The ID of the project policy.</p>
pub fn policy_revision_id(mut self, input: impl Into<std::string::String>) -> Self {
self.policy_revision_id = Some(input.into());
self
}
/// <p>The ID of the project policy.</p>
pub fn set_policy_revision_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.policy_revision_id = input;
self
}
/// Consumes the builder and constructs a [`PutProjectPolicyOutput`](crate::output::PutProjectPolicyOutput).
pub fn build(self) -> crate::output::PutProjectPolicyOutput {
crate::output::PutProjectPolicyOutput {
policy_revision_id: self.policy_revision_id,
}
}
}
}
impl PutProjectPolicyOutput {
/// Creates a new builder-style object to manufacture [`PutProjectPolicyOutput`](crate::output::PutProjectPolicyOutput).
pub fn builder() -> crate::output::put_project_policy_output::Builder {
crate::output::put_project_policy_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct ListTagsForResourceOutput {
/// <p> A list of key-value tags assigned to the resource. </p>
#[doc(hidden)]
pub tags:
std::option::Option<std::collections::HashMap<std::string::String, std::string::String>>,
}
impl ListTagsForResourceOutput {
/// <p> A list of key-value tags assigned to the resource. </p>
pub fn tags(
&self,
) -> std::option::Option<&std::collections::HashMap<std::string::String, std::string::String>>
{
self.tags.as_ref()
}
}
/// See [`ListTagsForResourceOutput`](crate::output::ListTagsForResourceOutput).
pub mod list_tags_for_resource_output {
/// A builder for [`ListTagsForResourceOutput`](crate::output::ListTagsForResourceOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) tags: std::option::Option<
std::collections::HashMap<std::string::String, std::string::String>,
>,
}
impl Builder {
/// Adds a key-value pair to `tags`.
///
/// To override the contents of this collection use [`set_tags`](Self::set_tags).
///
/// <p> A list of key-value tags assigned to the resource. </p>
pub fn tags(
mut self,
k: impl Into<std::string::String>,
v: impl Into<std::string::String>,
) -> Self {
let mut hash_map = self.tags.unwrap_or_default();
hash_map.insert(k.into(), v.into());
self.tags = Some(hash_map);
self
}
/// <p> A list of key-value tags assigned to the resource. </p>
pub fn set_tags(
mut self,
input: std::option::Option<
std::collections::HashMap<std::string::String, std::string::String>,
>,
) -> Self {
self.tags = input;
self
}
/// Consumes the builder and constructs a [`ListTagsForResourceOutput`](crate::output::ListTagsForResourceOutput).
pub fn build(self) -> crate::output::ListTagsForResourceOutput {
crate::output::ListTagsForResourceOutput { tags: self.tags }
}
}
}
impl ListTagsForResourceOutput {
/// Creates a new builder-style object to manufacture [`ListTagsForResourceOutput`](crate::output::ListTagsForResourceOutput).
pub fn builder() -> crate::output::list_tags_for_resource_output::Builder {
crate::output::list_tags_for_resource_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct ListStreamProcessorsOutput {
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of stream processors. </p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
/// <p>List of stream processors that you have created.</p>
#[doc(hidden)]
pub stream_processors: std::option::Option<std::vec::Vec<crate::model::StreamProcessor>>,
}
impl ListStreamProcessorsOutput {
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of stream processors. </p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
/// <p>List of stream processors that you have created.</p>
pub fn stream_processors(&self) -> std::option::Option<&[crate::model::StreamProcessor]> {
self.stream_processors.as_deref()
}
}
/// See [`ListStreamProcessorsOutput`](crate::output::ListStreamProcessorsOutput).
pub mod list_stream_processors_output {
/// A builder for [`ListStreamProcessorsOutput`](crate::output::ListStreamProcessorsOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) next_token: std::option::Option<std::string::String>,
pub(crate) stream_processors:
std::option::Option<std::vec::Vec<crate::model::StreamProcessor>>,
}
impl Builder {
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of stream processors. </p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of stream processors. </p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Appends an item to `stream_processors`.
///
/// To override the contents of this collection use [`set_stream_processors`](Self::set_stream_processors).
///
/// <p>List of stream processors that you have created.</p>
pub fn stream_processors(mut self, input: crate::model::StreamProcessor) -> Self {
let mut v = self.stream_processors.unwrap_or_default();
v.push(input);
self.stream_processors = Some(v);
self
}
/// <p>List of stream processors that you have created.</p>
pub fn set_stream_processors(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::StreamProcessor>>,
) -> Self {
self.stream_processors = input;
self
}
/// Consumes the builder and constructs a [`ListStreamProcessorsOutput`](crate::output::ListStreamProcessorsOutput).
pub fn build(self) -> crate::output::ListStreamProcessorsOutput {
crate::output::ListStreamProcessorsOutput {
next_token: self.next_token,
stream_processors: self.stream_processors,
}
}
}
}
impl ListStreamProcessorsOutput {
/// Creates a new builder-style object to manufacture [`ListStreamProcessorsOutput`](crate::output::ListStreamProcessorsOutput).
pub fn builder() -> crate::output::list_stream_processors_output::Builder {
crate::output::list_stream_processors_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct ListProjectPoliciesOutput {
/// <p>A list of project policies attached to the project.</p>
#[doc(hidden)]
pub project_policies: std::option::Option<std::vec::Vec<crate::model::ProjectPolicy>>,
/// <p>If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of project policies.</p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
}
impl ListProjectPoliciesOutput {
/// <p>A list of project policies attached to the project.</p>
pub fn project_policies(&self) -> std::option::Option<&[crate::model::ProjectPolicy]> {
self.project_policies.as_deref()
}
/// <p>If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of project policies.</p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
}
/// See [`ListProjectPoliciesOutput`](crate::output::ListProjectPoliciesOutput).
pub mod list_project_policies_output {
/// A builder for [`ListProjectPoliciesOutput`](crate::output::ListProjectPoliciesOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) project_policies:
std::option::Option<std::vec::Vec<crate::model::ProjectPolicy>>,
pub(crate) next_token: std::option::Option<std::string::String>,
}
impl Builder {
/// Appends an item to `project_policies`.
///
/// To override the contents of this collection use [`set_project_policies`](Self::set_project_policies).
///
/// <p>A list of project policies attached to the project.</p>
pub fn project_policies(mut self, input: crate::model::ProjectPolicy) -> Self {
let mut v = self.project_policies.unwrap_or_default();
v.push(input);
self.project_policies = Some(v);
self
}
/// <p>A list of project policies attached to the project.</p>
pub fn set_project_policies(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::ProjectPolicy>>,
) -> Self {
self.project_policies = input;
self
}
/// <p>If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of project policies.</p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of project policies.</p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Consumes the builder and constructs a [`ListProjectPoliciesOutput`](crate::output::ListProjectPoliciesOutput).
pub fn build(self) -> crate::output::ListProjectPoliciesOutput {
crate::output::ListProjectPoliciesOutput {
project_policies: self.project_policies,
next_token: self.next_token,
}
}
}
}
impl ListProjectPoliciesOutput {
/// Creates a new builder-style object to manufacture [`ListProjectPoliciesOutput`](crate::output::ListProjectPoliciesOutput).
pub fn builder() -> crate::output::list_project_policies_output::Builder {
crate::output::list_project_policies_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct ListFacesOutput {
/// <p>An array of <code>Face</code> objects. </p>
#[doc(hidden)]
pub faces: std::option::Option<std::vec::Vec<crate::model::Face>>,
/// <p>If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces.</p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
/// <p>Version number of the face detection model associated with the input collection (<code>CollectionId</code>).</p>
#[doc(hidden)]
pub face_model_version: std::option::Option<std::string::String>,
}
impl ListFacesOutput {
/// <p>An array of <code>Face</code> objects. </p>
pub fn faces(&self) -> std::option::Option<&[crate::model::Face]> {
self.faces.as_deref()
}
/// <p>If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces.</p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
/// <p>Version number of the face detection model associated with the input collection (<code>CollectionId</code>).</p>
pub fn face_model_version(&self) -> std::option::Option<&str> {
self.face_model_version.as_deref()
}
}
/// See [`ListFacesOutput`](crate::output::ListFacesOutput).
pub mod list_faces_output {
/// A builder for [`ListFacesOutput`](crate::output::ListFacesOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) faces: std::option::Option<std::vec::Vec<crate::model::Face>>,
pub(crate) next_token: std::option::Option<std::string::String>,
pub(crate) face_model_version: std::option::Option<std::string::String>,
}
impl Builder {
/// Appends an item to `faces`.
///
/// To override the contents of this collection use [`set_faces`](Self::set_faces).
///
/// <p>An array of <code>Face</code> objects. </p>
pub fn faces(mut self, input: crate::model::Face) -> Self {
let mut v = self.faces.unwrap_or_default();
v.push(input);
self.faces = Some(v);
self
}
/// <p>An array of <code>Face</code> objects. </p>
pub fn set_faces(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::Face>>,
) -> Self {
self.faces = input;
self
}
/// <p>If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces.</p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces.</p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// <p>Version number of the face detection model associated with the input collection (<code>CollectionId</code>).</p>
pub fn face_model_version(mut self, input: impl Into<std::string::String>) -> Self {
self.face_model_version = Some(input.into());
self
}
/// <p>Version number of the face detection model associated with the input collection (<code>CollectionId</code>).</p>
pub fn set_face_model_version(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.face_model_version = input;
self
}
/// Consumes the builder and constructs a [`ListFacesOutput`](crate::output::ListFacesOutput).
pub fn build(self) -> crate::output::ListFacesOutput {
crate::output::ListFacesOutput {
faces: self.faces,
next_token: self.next_token,
face_model_version: self.face_model_version,
}
}
}
}
impl ListFacesOutput {
/// Creates a new builder-style object to manufacture [`ListFacesOutput`](crate::output::ListFacesOutput).
pub fn builder() -> crate::output::list_faces_output::Builder {
crate::output::list_faces_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct ListDatasetLabelsOutput {
/// <p> A list of the labels in the dataset. </p>
#[doc(hidden)]
pub dataset_label_descriptions:
std::option::Option<std::vec::Vec<crate::model::DatasetLabelDescription>>,
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
}
impl ListDatasetLabelsOutput {
/// <p> A list of the labels in the dataset. </p>
pub fn dataset_label_descriptions(
&self,
) -> std::option::Option<&[crate::model::DatasetLabelDescription]> {
self.dataset_label_descriptions.as_deref()
}
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
}
/// See [`ListDatasetLabelsOutput`](crate::output::ListDatasetLabelsOutput).
pub mod list_dataset_labels_output {
/// A builder for [`ListDatasetLabelsOutput`](crate::output::ListDatasetLabelsOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) dataset_label_descriptions:
std::option::Option<std::vec::Vec<crate::model::DatasetLabelDescription>>,
pub(crate) next_token: std::option::Option<std::string::String>,
}
impl Builder {
/// Appends an item to `dataset_label_descriptions`.
///
/// To override the contents of this collection use [`set_dataset_label_descriptions`](Self::set_dataset_label_descriptions).
///
/// <p> A list of the labels in the dataset. </p>
pub fn dataset_label_descriptions(
mut self,
input: crate::model::DatasetLabelDescription,
) -> Self {
let mut v = self.dataset_label_descriptions.unwrap_or_default();
v.push(input);
self.dataset_label_descriptions = Some(v);
self
}
/// <p> A list of the labels in the dataset. </p>
pub fn set_dataset_label_descriptions(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::DatasetLabelDescription>>,
) -> Self {
self.dataset_label_descriptions = input;
self
}
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Consumes the builder and constructs a [`ListDatasetLabelsOutput`](crate::output::ListDatasetLabelsOutput).
pub fn build(self) -> crate::output::ListDatasetLabelsOutput {
crate::output::ListDatasetLabelsOutput {
dataset_label_descriptions: self.dataset_label_descriptions,
next_token: self.next_token,
}
}
}
}
impl ListDatasetLabelsOutput {
/// Creates a new builder-style object to manufacture [`ListDatasetLabelsOutput`](crate::output::ListDatasetLabelsOutput).
pub fn builder() -> crate::output::list_dataset_labels_output::Builder {
crate::output::list_dataset_labels_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct ListDatasetEntriesOutput {
/// <p> A list of entries (images) in the dataset. </p>
#[doc(hidden)]
pub dataset_entries: std::option::Option<std::vec::Vec<std::string::String>>,
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
}
impl ListDatasetEntriesOutput {
/// <p> A list of entries (images) in the dataset. </p>
pub fn dataset_entries(&self) -> std::option::Option<&[std::string::String]> {
self.dataset_entries.as_deref()
}
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
}
/// See [`ListDatasetEntriesOutput`](crate::output::ListDatasetEntriesOutput).
pub mod list_dataset_entries_output {
/// A builder for [`ListDatasetEntriesOutput`](crate::output::ListDatasetEntriesOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) dataset_entries: std::option::Option<std::vec::Vec<std::string::String>>,
pub(crate) next_token: std::option::Option<std::string::String>,
}
impl Builder {
/// Appends an item to `dataset_entries`.
///
/// To override the contents of this collection use [`set_dataset_entries`](Self::set_dataset_entries).
///
/// <p> A list of entries (images) in the dataset. </p>
pub fn dataset_entries(mut self, input: impl Into<std::string::String>) -> Self {
let mut v = self.dataset_entries.unwrap_or_default();
v.push(input.into());
self.dataset_entries = Some(v);
self
}
/// <p> A list of entries (images) in the dataset. </p>
pub fn set_dataset_entries(
mut self,
input: std::option::Option<std::vec::Vec<std::string::String>>,
) -> Self {
self.dataset_entries = input;
self
}
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Consumes the builder and constructs a [`ListDatasetEntriesOutput`](crate::output::ListDatasetEntriesOutput).
pub fn build(self) -> crate::output::ListDatasetEntriesOutput {
crate::output::ListDatasetEntriesOutput {
dataset_entries: self.dataset_entries,
next_token: self.next_token,
}
}
}
}
impl ListDatasetEntriesOutput {
/// Creates a new builder-style object to manufacture [`ListDatasetEntriesOutput`](crate::output::ListDatasetEntriesOutput).
pub fn builder() -> crate::output::list_dataset_entries_output::Builder {
crate::output::list_dataset_entries_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct ListCollectionsOutput {
/// <p>An array of collection IDs.</p>
#[doc(hidden)]
pub collection_ids: std::option::Option<std::vec::Vec<std::string::String>>,
/// <p>If the result is truncated, the response provides a <code>NextToken</code> that you can use in the subsequent request to fetch the next set of collection IDs.</p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
/// <p>Version numbers of the face detection models associated with the collections in the array <code>CollectionIds</code>. For example, the value of <code>FaceModelVersions[2]</code> is the version number for the face detection model used by the collection in <code>CollectionId[2]</code>.</p>
#[doc(hidden)]
pub face_model_versions: std::option::Option<std::vec::Vec<std::string::String>>,
}
impl ListCollectionsOutput {
/// <p>An array of collection IDs.</p>
pub fn collection_ids(&self) -> std::option::Option<&[std::string::String]> {
self.collection_ids.as_deref()
}
/// <p>If the result is truncated, the response provides a <code>NextToken</code> that you can use in the subsequent request to fetch the next set of collection IDs.</p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
/// <p>Version numbers of the face detection models associated with the collections in the array <code>CollectionIds</code>. For example, the value of <code>FaceModelVersions[2]</code> is the version number for the face detection model used by the collection in <code>CollectionId[2]</code>.</p>
pub fn face_model_versions(&self) -> std::option::Option<&[std::string::String]> {
self.face_model_versions.as_deref()
}
}
/// See [`ListCollectionsOutput`](crate::output::ListCollectionsOutput).
pub mod list_collections_output {
/// A builder for [`ListCollectionsOutput`](crate::output::ListCollectionsOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) collection_ids: std::option::Option<std::vec::Vec<std::string::String>>,
pub(crate) next_token: std::option::Option<std::string::String>,
pub(crate) face_model_versions: std::option::Option<std::vec::Vec<std::string::String>>,
}
impl Builder {
/// Appends an item to `collection_ids`.
///
/// To override the contents of this collection use [`set_collection_ids`](Self::set_collection_ids).
///
/// <p>An array of collection IDs.</p>
pub fn collection_ids(mut self, input: impl Into<std::string::String>) -> Self {
let mut v = self.collection_ids.unwrap_or_default();
v.push(input.into());
self.collection_ids = Some(v);
self
}
/// <p>An array of collection IDs.</p>
pub fn set_collection_ids(
mut self,
input: std::option::Option<std::vec::Vec<std::string::String>>,
) -> Self {
self.collection_ids = input;
self
}
/// <p>If the result is truncated, the response provides a <code>NextToken</code> that you can use in the subsequent request to fetch the next set of collection IDs.</p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the result is truncated, the response provides a <code>NextToken</code> that you can use in the subsequent request to fetch the next set of collection IDs.</p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Appends an item to `face_model_versions`.
///
/// To override the contents of this collection use [`set_face_model_versions`](Self::set_face_model_versions).
///
/// <p>Version numbers of the face detection models associated with the collections in the array <code>CollectionIds</code>. For example, the value of <code>FaceModelVersions[2]</code> is the version number for the face detection model used by the collection in <code>CollectionId[2]</code>.</p>
pub fn face_model_versions(mut self, input: impl Into<std::string::String>) -> Self {
let mut v = self.face_model_versions.unwrap_or_default();
v.push(input.into());
self.face_model_versions = Some(v);
self
}
/// <p>Version numbers of the face detection models associated with the collections in the array <code>CollectionIds</code>. For example, the value of <code>FaceModelVersions[2]</code> is the version number for the face detection model used by the collection in <code>CollectionId[2]</code>.</p>
pub fn set_face_model_versions(
mut self,
input: std::option::Option<std::vec::Vec<std::string::String>>,
) -> Self {
self.face_model_versions = input;
self
}
/// Consumes the builder and constructs a [`ListCollectionsOutput`](crate::output::ListCollectionsOutput).
pub fn build(self) -> crate::output::ListCollectionsOutput {
crate::output::ListCollectionsOutput {
collection_ids: self.collection_ids,
next_token: self.next_token,
face_model_versions: self.face_model_versions,
}
}
}
}
impl ListCollectionsOutput {
/// Creates a new builder-style object to manufacture [`ListCollectionsOutput`](crate::output::ListCollectionsOutput).
pub fn builder() -> crate::output::list_collections_output::Builder {
crate::output::list_collections_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct IndexFacesOutput {
/// <p>An array of faces detected and added to the collection. For more information, see Searching Faces in a Collection in the Amazon Rekognition Developer Guide. </p>
#[doc(hidden)]
pub face_records: std::option::Option<std::vec::Vec<crate::model::FaceRecord>>,
/// <p>If your collection is associated with a face detection model that's later than version 3.0, the value of <code>OrientationCorrection</code> is always null and no orientation information is returned.</p>
/// <p>If your collection is associated with a face detection model that's version 3.0 or earlier, the following applies:</p>
/// <ul>
/// <li> <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction - the bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata. The value of <code>OrientationCorrection</code> is null.</p> </li>
/// <li> <p>If the image doesn't contain orientation information in its Exif metadata, Amazon Rekognition returns an estimated orientation (ROTATE_0, ROTATE_90, ROTATE_180, ROTATE_270). Amazon Rekognition doesn’t perform image correction for images. The bounding box coordinates aren't translated and represent the object locations before the image is rotated.</p> </li>
/// </ul>
/// <p>Bounding box information is returned in the <code>FaceRecords</code> array. You can get the version of the face detection model by calling <code>DescribeCollection</code>. </p>
#[doc(hidden)]
pub orientation_correction: std::option::Option<crate::model::OrientationCorrection>,
/// <p>The version number of the face detection model that's associated with the input collection (<code>CollectionId</code>).</p>
#[doc(hidden)]
pub face_model_version: std::option::Option<std::string::String>,
/// <p>An array of faces that were detected in the image but weren't indexed. They weren't indexed because the quality filter identified them as low quality, or the <code>MaxFaces</code> request parameter filtered them out. To use the quality filter, you specify the <code>QualityFilter</code> request parameter.</p>
#[doc(hidden)]
pub unindexed_faces: std::option::Option<std::vec::Vec<crate::model::UnindexedFace>>,
}
impl IndexFacesOutput {
/// <p>An array of faces detected and added to the collection. For more information, see Searching Faces in a Collection in the Amazon Rekognition Developer Guide. </p>
pub fn face_records(&self) -> std::option::Option<&[crate::model::FaceRecord]> {
self.face_records.as_deref()
}
/// <p>If your collection is associated with a face detection model that's later than version 3.0, the value of <code>OrientationCorrection</code> is always null and no orientation information is returned.</p>
/// <p>If your collection is associated with a face detection model that's version 3.0 or earlier, the following applies:</p>
/// <ul>
/// <li> <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction - the bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata. The value of <code>OrientationCorrection</code> is null.</p> </li>
/// <li> <p>If the image doesn't contain orientation information in its Exif metadata, Amazon Rekognition returns an estimated orientation (ROTATE_0, ROTATE_90, ROTATE_180, ROTATE_270). Amazon Rekognition doesn’t perform image correction for images. The bounding box coordinates aren't translated and represent the object locations before the image is rotated.</p> </li>
/// </ul>
/// <p>Bounding box information is returned in the <code>FaceRecords</code> array. You can get the version of the face detection model by calling <code>DescribeCollection</code>. </p>
pub fn orientation_correction(
&self,
) -> std::option::Option<&crate::model::OrientationCorrection> {
self.orientation_correction.as_ref()
}
/// <p>The version number of the face detection model that's associated with the input collection (<code>CollectionId</code>).</p>
pub fn face_model_version(&self) -> std::option::Option<&str> {
self.face_model_version.as_deref()
}
/// <p>An array of faces that were detected in the image but weren't indexed. They weren't indexed because the quality filter identified them as low quality, or the <code>MaxFaces</code> request parameter filtered them out. To use the quality filter, you specify the <code>QualityFilter</code> request parameter.</p>
pub fn unindexed_faces(&self) -> std::option::Option<&[crate::model::UnindexedFace]> {
self.unindexed_faces.as_deref()
}
}
/// See [`IndexFacesOutput`](crate::output::IndexFacesOutput).
pub mod index_faces_output {
/// A builder for [`IndexFacesOutput`](crate::output::IndexFacesOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) face_records: std::option::Option<std::vec::Vec<crate::model::FaceRecord>>,
pub(crate) orientation_correction: std::option::Option<crate::model::OrientationCorrection>,
pub(crate) face_model_version: std::option::Option<std::string::String>,
pub(crate) unindexed_faces: std::option::Option<std::vec::Vec<crate::model::UnindexedFace>>,
}
impl Builder {
/// Appends an item to `face_records`.
///
/// To override the contents of this collection use [`set_face_records`](Self::set_face_records).
///
/// <p>An array of faces detected and added to the collection. For more information, see Searching Faces in a Collection in the Amazon Rekognition Developer Guide. </p>
pub fn face_records(mut self, input: crate::model::FaceRecord) -> Self {
let mut v = self.face_records.unwrap_or_default();
v.push(input);
self.face_records = Some(v);
self
}
/// <p>An array of faces detected and added to the collection. For more information, see Searching Faces in a Collection in the Amazon Rekognition Developer Guide. </p>
pub fn set_face_records(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::FaceRecord>>,
) -> Self {
self.face_records = input;
self
}
/// <p>If your collection is associated with a face detection model that's later than version 3.0, the value of <code>OrientationCorrection</code> is always null and no orientation information is returned.</p>
/// <p>If your collection is associated with a face detection model that's version 3.0 or earlier, the following applies:</p>
/// <ul>
/// <li> <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction - the bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata. The value of <code>OrientationCorrection</code> is null.</p> </li>
/// <li> <p>If the image doesn't contain orientation information in its Exif metadata, Amazon Rekognition returns an estimated orientation (ROTATE_0, ROTATE_90, ROTATE_180, ROTATE_270). Amazon Rekognition doesn’t perform image correction for images. The bounding box coordinates aren't translated and represent the object locations before the image is rotated.</p> </li>
/// </ul>
/// <p>Bounding box information is returned in the <code>FaceRecords</code> array. You can get the version of the face detection model by calling <code>DescribeCollection</code>. </p>
pub fn orientation_correction(
mut self,
input: crate::model::OrientationCorrection,
) -> Self {
self.orientation_correction = Some(input);
self
}
/// <p>If your collection is associated with a face detection model that's later than version 3.0, the value of <code>OrientationCorrection</code> is always null and no orientation information is returned.</p>
/// <p>If your collection is associated with a face detection model that's version 3.0 or earlier, the following applies:</p>
/// <ul>
/// <li> <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction - the bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata. The value of <code>OrientationCorrection</code> is null.</p> </li>
/// <li> <p>If the image doesn't contain orientation information in its Exif metadata, Amazon Rekognition returns an estimated orientation (ROTATE_0, ROTATE_90, ROTATE_180, ROTATE_270). Amazon Rekognition doesn’t perform image correction for images. The bounding box coordinates aren't translated and represent the object locations before the image is rotated.</p> </li>
/// </ul>
/// <p>Bounding box information is returned in the <code>FaceRecords</code> array. You can get the version of the face detection model by calling <code>DescribeCollection</code>. </p>
pub fn set_orientation_correction(
mut self,
input: std::option::Option<crate::model::OrientationCorrection>,
) -> Self {
self.orientation_correction = input;
self
}
/// <p>The version number of the face detection model that's associated with the input collection (<code>CollectionId</code>).</p>
pub fn face_model_version(mut self, input: impl Into<std::string::String>) -> Self {
self.face_model_version = Some(input.into());
self
}
/// <p>The version number of the face detection model that's associated with the input collection (<code>CollectionId</code>).</p>
pub fn set_face_model_version(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.face_model_version = input;
self
}
/// Appends an item to `unindexed_faces`.
///
/// To override the contents of this collection use [`set_unindexed_faces`](Self::set_unindexed_faces).
///
/// <p>An array of faces that were detected in the image but weren't indexed. They weren't indexed because the quality filter identified them as low quality, or the <code>MaxFaces</code> request parameter filtered them out. To use the quality filter, you specify the <code>QualityFilter</code> request parameter.</p>
pub fn unindexed_faces(mut self, input: crate::model::UnindexedFace) -> Self {
let mut v = self.unindexed_faces.unwrap_or_default();
v.push(input);
self.unindexed_faces = Some(v);
self
}
/// <p>An array of faces that were detected in the image but weren't indexed. They weren't indexed because the quality filter identified them as low quality, or the <code>MaxFaces</code> request parameter filtered them out. To use the quality filter, you specify the <code>QualityFilter</code> request parameter.</p>
pub fn set_unindexed_faces(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::UnindexedFace>>,
) -> Self {
self.unindexed_faces = input;
self
}
/// Consumes the builder and constructs a [`IndexFacesOutput`](crate::output::IndexFacesOutput).
pub fn build(self) -> crate::output::IndexFacesOutput {
crate::output::IndexFacesOutput {
face_records: self.face_records,
orientation_correction: self.orientation_correction,
face_model_version: self.face_model_version,
unindexed_faces: self.unindexed_faces,
}
}
}
}
impl IndexFacesOutput {
/// Creates a new builder-style object to manufacture [`IndexFacesOutput`](crate::output::IndexFacesOutput).
pub fn builder() -> crate::output::index_faces_output::Builder {
crate::output::index_faces_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetTextDetectionOutput {
/// <p>Current status of the text detection job.</p>
#[doc(hidden)]
pub job_status: std::option::Option<crate::model::VideoJobStatus>,
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
#[doc(hidden)]
pub status_message: std::option::Option<std::string::String>,
/// <p>Information about a video that Amazon Rekognition analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition video operation.</p>
#[doc(hidden)]
pub video_metadata: std::option::Option<crate::model::VideoMetadata>,
/// <p>An array of text detected in the video. Each element contains the detected text, the time in milliseconds from the start of the video that the text was detected, and where it was detected on the screen.</p>
#[doc(hidden)]
pub text_detections: std::option::Option<std::vec::Vec<crate::model::TextDetectionResult>>,
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of text.</p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
/// <p>Version number of the text detection model that was used to detect text.</p>
#[doc(hidden)]
pub text_model_version: std::option::Option<std::string::String>,
}
impl GetTextDetectionOutput {
/// <p>Current status of the text detection job.</p>
pub fn job_status(&self) -> std::option::Option<&crate::model::VideoJobStatus> {
self.job_status.as_ref()
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(&self) -> std::option::Option<&str> {
self.status_message.as_deref()
}
/// <p>Information about a video that Amazon Rekognition analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition video operation.</p>
pub fn video_metadata(&self) -> std::option::Option<&crate::model::VideoMetadata> {
self.video_metadata.as_ref()
}
/// <p>An array of text detected in the video. Each element contains the detected text, the time in milliseconds from the start of the video that the text was detected, and where it was detected on the screen.</p>
pub fn text_detections(&self) -> std::option::Option<&[crate::model::TextDetectionResult]> {
self.text_detections.as_deref()
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of text.</p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
/// <p>Version number of the text detection model that was used to detect text.</p>
pub fn text_model_version(&self) -> std::option::Option<&str> {
self.text_model_version.as_deref()
}
}
/// See [`GetTextDetectionOutput`](crate::output::GetTextDetectionOutput).
pub mod get_text_detection_output {
/// A builder for [`GetTextDetectionOutput`](crate::output::GetTextDetectionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_status: std::option::Option<crate::model::VideoJobStatus>,
pub(crate) status_message: std::option::Option<std::string::String>,
pub(crate) video_metadata: std::option::Option<crate::model::VideoMetadata>,
pub(crate) text_detections:
std::option::Option<std::vec::Vec<crate::model::TextDetectionResult>>,
pub(crate) next_token: std::option::Option<std::string::String>,
pub(crate) text_model_version: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>Current status of the text detection job.</p>
pub fn job_status(mut self, input: crate::model::VideoJobStatus) -> Self {
self.job_status = Some(input);
self
}
/// <p>Current status of the text detection job.</p>
pub fn set_job_status(
mut self,
input: std::option::Option<crate::model::VideoJobStatus>,
) -> Self {
self.job_status = input;
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(mut self, input: impl Into<std::string::String>) -> Self {
self.status_message = Some(input.into());
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn set_status_message(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.status_message = input;
self
}
/// <p>Information about a video that Amazon Rekognition analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition video operation.</p>
pub fn video_metadata(mut self, input: crate::model::VideoMetadata) -> Self {
self.video_metadata = Some(input);
self
}
/// <p>Information about a video that Amazon Rekognition analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition video operation.</p>
pub fn set_video_metadata(
mut self,
input: std::option::Option<crate::model::VideoMetadata>,
) -> Self {
self.video_metadata = input;
self
}
/// Appends an item to `text_detections`.
///
/// To override the contents of this collection use [`set_text_detections`](Self::set_text_detections).
///
/// <p>An array of text detected in the video. Each element contains the detected text, the time in milliseconds from the start of the video that the text was detected, and where it was detected on the screen.</p>
pub fn text_detections(mut self, input: crate::model::TextDetectionResult) -> Self {
let mut v = self.text_detections.unwrap_or_default();
v.push(input);
self.text_detections = Some(v);
self
}
/// <p>An array of text detected in the video. Each element contains the detected text, the time in milliseconds from the start of the video that the text was detected, and where it was detected on the screen.</p>
pub fn set_text_detections(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::TextDetectionResult>>,
) -> Self {
self.text_detections = input;
self
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of text.</p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of text.</p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// <p>Version number of the text detection model that was used to detect text.</p>
pub fn text_model_version(mut self, input: impl Into<std::string::String>) -> Self {
self.text_model_version = Some(input.into());
self
}
/// <p>Version number of the text detection model that was used to detect text.</p>
pub fn set_text_model_version(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.text_model_version = input;
self
}
/// Consumes the builder and constructs a [`GetTextDetectionOutput`](crate::output::GetTextDetectionOutput).
pub fn build(self) -> crate::output::GetTextDetectionOutput {
crate::output::GetTextDetectionOutput {
job_status: self.job_status,
status_message: self.status_message,
video_metadata: self.video_metadata,
text_detections: self.text_detections,
next_token: self.next_token,
text_model_version: self.text_model_version,
}
}
}
}
impl GetTextDetectionOutput {
/// Creates a new builder-style object to manufacture [`GetTextDetectionOutput`](crate::output::GetTextDetectionOutput).
pub fn builder() -> crate::output::get_text_detection_output::Builder {
crate::output::get_text_detection_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetSegmentDetectionOutput {
/// <p>Current status of the segment detection job.</p>
#[doc(hidden)]
pub job_status: std::option::Option<crate::model::VideoJobStatus>,
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
#[doc(hidden)]
pub status_message: std::option::Option<std::string::String>,
/// <p>Currently, Amazon Rekognition Video returns a single object in the <code>VideoMetadata</code> array. The object contains information about the video stream in the input file that Amazon Rekognition Video chose to analyze. The <code>VideoMetadata</code> object includes the video codec, video format and other information. Video metadata is returned in each page of information returned by <code>GetSegmentDetection</code>.</p>
#[doc(hidden)]
pub video_metadata: std::option::Option<std::vec::Vec<crate::model::VideoMetadata>>,
/// <p>An array of objects. There can be multiple audio streams. Each <code>AudioMetadata</code> object contains metadata for a single audio stream. Audio information in an <code>AudioMetadata</code> objects includes the audio codec, the number of audio channels, the duration of the audio stream, and the sample rate. Audio metadata is returned in each page of information returned by <code>GetSegmentDetection</code>.</p>
#[doc(hidden)]
pub audio_metadata: std::option::Option<std::vec::Vec<crate::model::AudioMetadata>>,
/// <p>If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of text.</p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
/// <p>An array of segments detected in a video. The array is sorted by the segment types (TECHNICAL_CUE or SHOT) specified in the <code>SegmentTypes</code> input parameter of <code>StartSegmentDetection</code>. Within each segment type the array is sorted by timestamp values.</p>
#[doc(hidden)]
pub segments: std::option::Option<std::vec::Vec<crate::model::SegmentDetection>>,
/// <p>An array containing the segment types requested in the call to <code>StartSegmentDetection</code>. </p>
#[doc(hidden)]
pub selected_segment_types: std::option::Option<std::vec::Vec<crate::model::SegmentTypeInfo>>,
}
impl GetSegmentDetectionOutput {
/// <p>Current status of the segment detection job.</p>
pub fn job_status(&self) -> std::option::Option<&crate::model::VideoJobStatus> {
self.job_status.as_ref()
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(&self) -> std::option::Option<&str> {
self.status_message.as_deref()
}
/// <p>Currently, Amazon Rekognition Video returns a single object in the <code>VideoMetadata</code> array. The object contains information about the video stream in the input file that Amazon Rekognition Video chose to analyze. The <code>VideoMetadata</code> object includes the video codec, video format and other information. Video metadata is returned in each page of information returned by <code>GetSegmentDetection</code>.</p>
pub fn video_metadata(&self) -> std::option::Option<&[crate::model::VideoMetadata]> {
self.video_metadata.as_deref()
}
/// <p>An array of objects. There can be multiple audio streams. Each <code>AudioMetadata</code> object contains metadata for a single audio stream. Audio information in an <code>AudioMetadata</code> objects includes the audio codec, the number of audio channels, the duration of the audio stream, and the sample rate. Audio metadata is returned in each page of information returned by <code>GetSegmentDetection</code>.</p>
pub fn audio_metadata(&self) -> std::option::Option<&[crate::model::AudioMetadata]> {
self.audio_metadata.as_deref()
}
/// <p>If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of text.</p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
/// <p>An array of segments detected in a video. The array is sorted by the segment types (TECHNICAL_CUE or SHOT) specified in the <code>SegmentTypes</code> input parameter of <code>StartSegmentDetection</code>. Within each segment type the array is sorted by timestamp values.</p>
pub fn segments(&self) -> std::option::Option<&[crate::model::SegmentDetection]> {
self.segments.as_deref()
}
/// <p>An array containing the segment types requested in the call to <code>StartSegmentDetection</code>. </p>
pub fn selected_segment_types(&self) -> std::option::Option<&[crate::model::SegmentTypeInfo]> {
self.selected_segment_types.as_deref()
}
}
/// See [`GetSegmentDetectionOutput`](crate::output::GetSegmentDetectionOutput).
pub mod get_segment_detection_output {
/// A builder for [`GetSegmentDetectionOutput`](crate::output::GetSegmentDetectionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_status: std::option::Option<crate::model::VideoJobStatus>,
pub(crate) status_message: std::option::Option<std::string::String>,
pub(crate) video_metadata: std::option::Option<std::vec::Vec<crate::model::VideoMetadata>>,
pub(crate) audio_metadata: std::option::Option<std::vec::Vec<crate::model::AudioMetadata>>,
pub(crate) next_token: std::option::Option<std::string::String>,
pub(crate) segments: std::option::Option<std::vec::Vec<crate::model::SegmentDetection>>,
pub(crate) selected_segment_types:
std::option::Option<std::vec::Vec<crate::model::SegmentTypeInfo>>,
}
impl Builder {
/// <p>Current status of the segment detection job.</p>
pub fn job_status(mut self, input: crate::model::VideoJobStatus) -> Self {
self.job_status = Some(input);
self
}
/// <p>Current status of the segment detection job.</p>
pub fn set_job_status(
mut self,
input: std::option::Option<crate::model::VideoJobStatus>,
) -> Self {
self.job_status = input;
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(mut self, input: impl Into<std::string::String>) -> Self {
self.status_message = Some(input.into());
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn set_status_message(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.status_message = input;
self
}
/// Appends an item to `video_metadata`.
///
/// To override the contents of this collection use [`set_video_metadata`](Self::set_video_metadata).
///
/// <p>Currently, Amazon Rekognition Video returns a single object in the <code>VideoMetadata</code> array. The object contains information about the video stream in the input file that Amazon Rekognition Video chose to analyze. The <code>VideoMetadata</code> object includes the video codec, video format and other information. Video metadata is returned in each page of information returned by <code>GetSegmentDetection</code>.</p>
pub fn video_metadata(mut self, input: crate::model::VideoMetadata) -> Self {
let mut v = self.video_metadata.unwrap_or_default();
v.push(input);
self.video_metadata = Some(v);
self
}
/// <p>Currently, Amazon Rekognition Video returns a single object in the <code>VideoMetadata</code> array. The object contains information about the video stream in the input file that Amazon Rekognition Video chose to analyze. The <code>VideoMetadata</code> object includes the video codec, video format and other information. Video metadata is returned in each page of information returned by <code>GetSegmentDetection</code>.</p>
pub fn set_video_metadata(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::VideoMetadata>>,
) -> Self {
self.video_metadata = input;
self
}
/// Appends an item to `audio_metadata`.
///
/// To override the contents of this collection use [`set_audio_metadata`](Self::set_audio_metadata).
///
/// <p>An array of objects. There can be multiple audio streams. Each <code>AudioMetadata</code> object contains metadata for a single audio stream. Audio information in an <code>AudioMetadata</code> objects includes the audio codec, the number of audio channels, the duration of the audio stream, and the sample rate. Audio metadata is returned in each page of information returned by <code>GetSegmentDetection</code>.</p>
pub fn audio_metadata(mut self, input: crate::model::AudioMetadata) -> Self {
let mut v = self.audio_metadata.unwrap_or_default();
v.push(input);
self.audio_metadata = Some(v);
self
}
/// <p>An array of objects. There can be multiple audio streams. Each <code>AudioMetadata</code> object contains metadata for a single audio stream. Audio information in an <code>AudioMetadata</code> objects includes the audio codec, the number of audio channels, the duration of the audio stream, and the sample rate. Audio metadata is returned in each page of information returned by <code>GetSegmentDetection</code>.</p>
pub fn set_audio_metadata(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::AudioMetadata>>,
) -> Self {
self.audio_metadata = input;
self
}
/// <p>If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of text.</p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the previous response was incomplete (because there are more labels to retrieve), Amazon Rekognition Video returns a pagination token in the response. You can use this pagination token to retrieve the next set of text.</p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Appends an item to `segments`.
///
/// To override the contents of this collection use [`set_segments`](Self::set_segments).
///
/// <p>An array of segments detected in a video. The array is sorted by the segment types (TECHNICAL_CUE or SHOT) specified in the <code>SegmentTypes</code> input parameter of <code>StartSegmentDetection</code>. Within each segment type the array is sorted by timestamp values.</p>
pub fn segments(mut self, input: crate::model::SegmentDetection) -> Self {
let mut v = self.segments.unwrap_or_default();
v.push(input);
self.segments = Some(v);
self
}
/// <p>An array of segments detected in a video. The array is sorted by the segment types (TECHNICAL_CUE or SHOT) specified in the <code>SegmentTypes</code> input parameter of <code>StartSegmentDetection</code>. Within each segment type the array is sorted by timestamp values.</p>
pub fn set_segments(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::SegmentDetection>>,
) -> Self {
self.segments = input;
self
}
/// Appends an item to `selected_segment_types`.
///
/// To override the contents of this collection use [`set_selected_segment_types`](Self::set_selected_segment_types).
///
/// <p>An array containing the segment types requested in the call to <code>StartSegmentDetection</code>. </p>
pub fn selected_segment_types(mut self, input: crate::model::SegmentTypeInfo) -> Self {
let mut v = self.selected_segment_types.unwrap_or_default();
v.push(input);
self.selected_segment_types = Some(v);
self
}
/// <p>An array containing the segment types requested in the call to <code>StartSegmentDetection</code>. </p>
pub fn set_selected_segment_types(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::SegmentTypeInfo>>,
) -> Self {
self.selected_segment_types = input;
self
}
/// Consumes the builder and constructs a [`GetSegmentDetectionOutput`](crate::output::GetSegmentDetectionOutput).
pub fn build(self) -> crate::output::GetSegmentDetectionOutput {
crate::output::GetSegmentDetectionOutput {
job_status: self.job_status,
status_message: self.status_message,
video_metadata: self.video_metadata,
audio_metadata: self.audio_metadata,
next_token: self.next_token,
segments: self.segments,
selected_segment_types: self.selected_segment_types,
}
}
}
}
impl GetSegmentDetectionOutput {
/// Creates a new builder-style object to manufacture [`GetSegmentDetectionOutput`](crate::output::GetSegmentDetectionOutput).
pub fn builder() -> crate::output::get_segment_detection_output::Builder {
crate::output::get_segment_detection_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetPersonTrackingOutput {
/// <p>The current status of the person tracking job.</p>
#[doc(hidden)]
pub job_status: std::option::Option<crate::model::VideoJobStatus>,
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
#[doc(hidden)]
pub status_message: std::option::Option<std::string::String>,
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition Video operation.</p>
#[doc(hidden)]
pub video_metadata: std::option::Option<crate::model::VideoMetadata>,
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of persons. </p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
/// <p>An array of the persons detected in the video and the time(s) their path was tracked throughout the video. An array element will exist for each time a person's path is tracked. </p>
#[doc(hidden)]
pub persons: std::option::Option<std::vec::Vec<crate::model::PersonDetection>>,
}
impl GetPersonTrackingOutput {
/// <p>The current status of the person tracking job.</p>
pub fn job_status(&self) -> std::option::Option<&crate::model::VideoJobStatus> {
self.job_status.as_ref()
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(&self) -> std::option::Option<&str> {
self.status_message.as_deref()
}
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition Video operation.</p>
pub fn video_metadata(&self) -> std::option::Option<&crate::model::VideoMetadata> {
self.video_metadata.as_ref()
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of persons. </p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
/// <p>An array of the persons detected in the video and the time(s) their path was tracked throughout the video. An array element will exist for each time a person's path is tracked. </p>
pub fn persons(&self) -> std::option::Option<&[crate::model::PersonDetection]> {
self.persons.as_deref()
}
}
/// See [`GetPersonTrackingOutput`](crate::output::GetPersonTrackingOutput).
pub mod get_person_tracking_output {
/// A builder for [`GetPersonTrackingOutput`](crate::output::GetPersonTrackingOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_status: std::option::Option<crate::model::VideoJobStatus>,
pub(crate) status_message: std::option::Option<std::string::String>,
pub(crate) video_metadata: std::option::Option<crate::model::VideoMetadata>,
pub(crate) next_token: std::option::Option<std::string::String>,
pub(crate) persons: std::option::Option<std::vec::Vec<crate::model::PersonDetection>>,
}
impl Builder {
/// <p>The current status of the person tracking job.</p>
pub fn job_status(mut self, input: crate::model::VideoJobStatus) -> Self {
self.job_status = Some(input);
self
}
/// <p>The current status of the person tracking job.</p>
pub fn set_job_status(
mut self,
input: std::option::Option<crate::model::VideoJobStatus>,
) -> Self {
self.job_status = input;
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(mut self, input: impl Into<std::string::String>) -> Self {
self.status_message = Some(input.into());
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn set_status_message(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.status_message = input;
self
}
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition Video operation.</p>
pub fn video_metadata(mut self, input: crate::model::VideoMetadata) -> Self {
self.video_metadata = Some(input);
self
}
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition Video operation.</p>
pub fn set_video_metadata(
mut self,
input: std::option::Option<crate::model::VideoMetadata>,
) -> Self {
self.video_metadata = input;
self
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of persons. </p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of persons. </p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Appends an item to `persons`.
///
/// To override the contents of this collection use [`set_persons`](Self::set_persons).
///
/// <p>An array of the persons detected in the video and the time(s) their path was tracked throughout the video. An array element will exist for each time a person's path is tracked. </p>
pub fn persons(mut self, input: crate::model::PersonDetection) -> Self {
let mut v = self.persons.unwrap_or_default();
v.push(input);
self.persons = Some(v);
self
}
/// <p>An array of the persons detected in the video and the time(s) their path was tracked throughout the video. An array element will exist for each time a person's path is tracked. </p>
pub fn set_persons(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::PersonDetection>>,
) -> Self {
self.persons = input;
self
}
/// Consumes the builder and constructs a [`GetPersonTrackingOutput`](crate::output::GetPersonTrackingOutput).
pub fn build(self) -> crate::output::GetPersonTrackingOutput {
crate::output::GetPersonTrackingOutput {
job_status: self.job_status,
status_message: self.status_message,
video_metadata: self.video_metadata,
next_token: self.next_token,
persons: self.persons,
}
}
}
}
impl GetPersonTrackingOutput {
/// Creates a new builder-style object to manufacture [`GetPersonTrackingOutput`](crate::output::GetPersonTrackingOutput).
pub fn builder() -> crate::output::get_person_tracking_output::Builder {
crate::output::get_person_tracking_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetLabelDetectionOutput {
/// <p>The current status of the label detection job.</p>
#[doc(hidden)]
pub job_status: std::option::Option<crate::model::VideoJobStatus>,
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
#[doc(hidden)]
pub status_message: std::option::Option<std::string::String>,
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition video operation.</p>
#[doc(hidden)]
pub video_metadata: std::option::Option<crate::model::VideoMetadata>,
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of labels.</p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
/// <p>An array of labels detected in the video. Each element contains the detected label and the time, in milliseconds from the start of the video, that the label was detected. </p>
#[doc(hidden)]
pub labels: std::option::Option<std::vec::Vec<crate::model::LabelDetection>>,
/// <p>Version number of the label detection model that was used to detect labels.</p>
#[doc(hidden)]
pub label_model_version: std::option::Option<std::string::String>,
}
impl GetLabelDetectionOutput {
/// <p>The current status of the label detection job.</p>
pub fn job_status(&self) -> std::option::Option<&crate::model::VideoJobStatus> {
self.job_status.as_ref()
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(&self) -> std::option::Option<&str> {
self.status_message.as_deref()
}
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition video operation.</p>
pub fn video_metadata(&self) -> std::option::Option<&crate::model::VideoMetadata> {
self.video_metadata.as_ref()
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of labels.</p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
/// <p>An array of labels detected in the video. Each element contains the detected label and the time, in milliseconds from the start of the video, that the label was detected. </p>
pub fn labels(&self) -> std::option::Option<&[crate::model::LabelDetection]> {
self.labels.as_deref()
}
/// <p>Version number of the label detection model that was used to detect labels.</p>
pub fn label_model_version(&self) -> std::option::Option<&str> {
self.label_model_version.as_deref()
}
}
/// See [`GetLabelDetectionOutput`](crate::output::GetLabelDetectionOutput).
pub mod get_label_detection_output {
/// A builder for [`GetLabelDetectionOutput`](crate::output::GetLabelDetectionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_status: std::option::Option<crate::model::VideoJobStatus>,
pub(crate) status_message: std::option::Option<std::string::String>,
pub(crate) video_metadata: std::option::Option<crate::model::VideoMetadata>,
pub(crate) next_token: std::option::Option<std::string::String>,
pub(crate) labels: std::option::Option<std::vec::Vec<crate::model::LabelDetection>>,
pub(crate) label_model_version: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The current status of the label detection job.</p>
pub fn job_status(mut self, input: crate::model::VideoJobStatus) -> Self {
self.job_status = Some(input);
self
}
/// <p>The current status of the label detection job.</p>
pub fn set_job_status(
mut self,
input: std::option::Option<crate::model::VideoJobStatus>,
) -> Self {
self.job_status = input;
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(mut self, input: impl Into<std::string::String>) -> Self {
self.status_message = Some(input.into());
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn set_status_message(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.status_message = input;
self
}
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition video operation.</p>
pub fn video_metadata(mut self, input: crate::model::VideoMetadata) -> Self {
self.video_metadata = Some(input);
self
}
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition video operation.</p>
pub fn set_video_metadata(
mut self,
input: std::option::Option<crate::model::VideoMetadata>,
) -> Self {
self.video_metadata = input;
self
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of labels.</p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of labels.</p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Appends an item to `labels`.
///
/// To override the contents of this collection use [`set_labels`](Self::set_labels).
///
/// <p>An array of labels detected in the video. Each element contains the detected label and the time, in milliseconds from the start of the video, that the label was detected. </p>
pub fn labels(mut self, input: crate::model::LabelDetection) -> Self {
let mut v = self.labels.unwrap_or_default();
v.push(input);
self.labels = Some(v);
self
}
/// <p>An array of labels detected in the video. Each element contains the detected label and the time, in milliseconds from the start of the video, that the label was detected. </p>
pub fn set_labels(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::LabelDetection>>,
) -> Self {
self.labels = input;
self
}
/// <p>Version number of the label detection model that was used to detect labels.</p>
pub fn label_model_version(mut self, input: impl Into<std::string::String>) -> Self {
self.label_model_version = Some(input.into());
self
}
/// <p>Version number of the label detection model that was used to detect labels.</p>
pub fn set_label_model_version(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.label_model_version = input;
self
}
/// Consumes the builder and constructs a [`GetLabelDetectionOutput`](crate::output::GetLabelDetectionOutput).
pub fn build(self) -> crate::output::GetLabelDetectionOutput {
crate::output::GetLabelDetectionOutput {
job_status: self.job_status,
status_message: self.status_message,
video_metadata: self.video_metadata,
next_token: self.next_token,
labels: self.labels,
label_model_version: self.label_model_version,
}
}
}
}
impl GetLabelDetectionOutput {
/// Creates a new builder-style object to manufacture [`GetLabelDetectionOutput`](crate::output::GetLabelDetectionOutput).
pub fn builder() -> crate::output::get_label_detection_output::Builder {
crate::output::get_label_detection_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetFaceSearchOutput {
/// <p>The current status of the face search job.</p>
#[doc(hidden)]
pub job_status: std::option::Option<crate::model::VideoJobStatus>,
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
#[doc(hidden)]
pub status_message: std::option::Option<std::string::String>,
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of search results. </p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
/// <p>Information about a video that Amazon Rekognition analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition Video operation. </p>
#[doc(hidden)]
pub video_metadata: std::option::Option<crate::model::VideoMetadata>,
/// <p>An array of persons, <code>PersonMatch</code>, in the video whose face(s) match the face(s) in an Amazon Rekognition collection. It also includes time information for when persons are matched in the video. You specify the input collection in an initial call to <code>StartFaceSearch</code>. Each <code>Persons</code> element includes a time the person was matched, face match details (<code>FaceMatches</code>) for matching faces in the collection, and person information (<code>Person</code>) for the matched person. </p>
#[doc(hidden)]
pub persons: std::option::Option<std::vec::Vec<crate::model::PersonMatch>>,
}
impl GetFaceSearchOutput {
/// <p>The current status of the face search job.</p>
pub fn job_status(&self) -> std::option::Option<&crate::model::VideoJobStatus> {
self.job_status.as_ref()
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(&self) -> std::option::Option<&str> {
self.status_message.as_deref()
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of search results. </p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
/// <p>Information about a video that Amazon Rekognition analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition Video operation. </p>
pub fn video_metadata(&self) -> std::option::Option<&crate::model::VideoMetadata> {
self.video_metadata.as_ref()
}
/// <p>An array of persons, <code>PersonMatch</code>, in the video whose face(s) match the face(s) in an Amazon Rekognition collection. It also includes time information for when persons are matched in the video. You specify the input collection in an initial call to <code>StartFaceSearch</code>. Each <code>Persons</code> element includes a time the person was matched, face match details (<code>FaceMatches</code>) for matching faces in the collection, and person information (<code>Person</code>) for the matched person. </p>
pub fn persons(&self) -> std::option::Option<&[crate::model::PersonMatch]> {
self.persons.as_deref()
}
}
/// See [`GetFaceSearchOutput`](crate::output::GetFaceSearchOutput).
pub mod get_face_search_output {
/// A builder for [`GetFaceSearchOutput`](crate::output::GetFaceSearchOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_status: std::option::Option<crate::model::VideoJobStatus>,
pub(crate) status_message: std::option::Option<std::string::String>,
pub(crate) next_token: std::option::Option<std::string::String>,
pub(crate) video_metadata: std::option::Option<crate::model::VideoMetadata>,
pub(crate) persons: std::option::Option<std::vec::Vec<crate::model::PersonMatch>>,
}
impl Builder {
/// <p>The current status of the face search job.</p>
pub fn job_status(mut self, input: crate::model::VideoJobStatus) -> Self {
self.job_status = Some(input);
self
}
/// <p>The current status of the face search job.</p>
pub fn set_job_status(
mut self,
input: std::option::Option<crate::model::VideoJobStatus>,
) -> Self {
self.job_status = input;
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(mut self, input: impl Into<std::string::String>) -> Self {
self.status_message = Some(input.into());
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn set_status_message(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.status_message = input;
self
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of search results. </p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of search results. </p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// <p>Information about a video that Amazon Rekognition analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition Video operation. </p>
pub fn video_metadata(mut self, input: crate::model::VideoMetadata) -> Self {
self.video_metadata = Some(input);
self
}
/// <p>Information about a video that Amazon Rekognition analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition Video operation. </p>
pub fn set_video_metadata(
mut self,
input: std::option::Option<crate::model::VideoMetadata>,
) -> Self {
self.video_metadata = input;
self
}
/// Appends an item to `persons`.
///
/// To override the contents of this collection use [`set_persons`](Self::set_persons).
///
/// <p>An array of persons, <code>PersonMatch</code>, in the video whose face(s) match the face(s) in an Amazon Rekognition collection. It also includes time information for when persons are matched in the video. You specify the input collection in an initial call to <code>StartFaceSearch</code>. Each <code>Persons</code> element includes a time the person was matched, face match details (<code>FaceMatches</code>) for matching faces in the collection, and person information (<code>Person</code>) for the matched person. </p>
pub fn persons(mut self, input: crate::model::PersonMatch) -> Self {
let mut v = self.persons.unwrap_or_default();
v.push(input);
self.persons = Some(v);
self
}
/// <p>An array of persons, <code>PersonMatch</code>, in the video whose face(s) match the face(s) in an Amazon Rekognition collection. It also includes time information for when persons are matched in the video. You specify the input collection in an initial call to <code>StartFaceSearch</code>. Each <code>Persons</code> element includes a time the person was matched, face match details (<code>FaceMatches</code>) for matching faces in the collection, and person information (<code>Person</code>) for the matched person. </p>
pub fn set_persons(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::PersonMatch>>,
) -> Self {
self.persons = input;
self
}
/// Consumes the builder and constructs a [`GetFaceSearchOutput`](crate::output::GetFaceSearchOutput).
pub fn build(self) -> crate::output::GetFaceSearchOutput {
crate::output::GetFaceSearchOutput {
job_status: self.job_status,
status_message: self.status_message,
next_token: self.next_token,
video_metadata: self.video_metadata,
persons: self.persons,
}
}
}
}
impl GetFaceSearchOutput {
/// Creates a new builder-style object to manufacture [`GetFaceSearchOutput`](crate::output::GetFaceSearchOutput).
pub fn builder() -> crate::output::get_face_search_output::Builder {
crate::output::get_face_search_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetFaceDetectionOutput {
/// <p>The current status of the face detection job.</p>
#[doc(hidden)]
pub job_status: std::option::Option<crate::model::VideoJobStatus>,
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
#[doc(hidden)]
pub status_message: std::option::Option<std::string::String>,
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition video operation.</p>
#[doc(hidden)]
pub video_metadata: std::option::Option<crate::model::VideoMetadata>,
/// <p>If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces. </p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
/// <p>An array of faces detected in the video. Each element contains a detected face's details and the time, in milliseconds from the start of the video, the face was detected. </p>
#[doc(hidden)]
pub faces: std::option::Option<std::vec::Vec<crate::model::FaceDetection>>,
}
impl GetFaceDetectionOutput {
/// <p>The current status of the face detection job.</p>
pub fn job_status(&self) -> std::option::Option<&crate::model::VideoJobStatus> {
self.job_status.as_ref()
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(&self) -> std::option::Option<&str> {
self.status_message.as_deref()
}
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition video operation.</p>
pub fn video_metadata(&self) -> std::option::Option<&crate::model::VideoMetadata> {
self.video_metadata.as_ref()
}
/// <p>If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces. </p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
/// <p>An array of faces detected in the video. Each element contains a detected face's details and the time, in milliseconds from the start of the video, the face was detected. </p>
pub fn faces(&self) -> std::option::Option<&[crate::model::FaceDetection]> {
self.faces.as_deref()
}
}
/// See [`GetFaceDetectionOutput`](crate::output::GetFaceDetectionOutput).
pub mod get_face_detection_output {
/// A builder for [`GetFaceDetectionOutput`](crate::output::GetFaceDetectionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_status: std::option::Option<crate::model::VideoJobStatus>,
pub(crate) status_message: std::option::Option<std::string::String>,
pub(crate) video_metadata: std::option::Option<crate::model::VideoMetadata>,
pub(crate) next_token: std::option::Option<std::string::String>,
pub(crate) faces: std::option::Option<std::vec::Vec<crate::model::FaceDetection>>,
}
impl Builder {
/// <p>The current status of the face detection job.</p>
pub fn job_status(mut self, input: crate::model::VideoJobStatus) -> Self {
self.job_status = Some(input);
self
}
/// <p>The current status of the face detection job.</p>
pub fn set_job_status(
mut self,
input: std::option::Option<crate::model::VideoJobStatus>,
) -> Self {
self.job_status = input;
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(mut self, input: impl Into<std::string::String>) -> Self {
self.status_message = Some(input.into());
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn set_status_message(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.status_message = input;
self
}
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition video operation.</p>
pub fn video_metadata(mut self, input: crate::model::VideoMetadata) -> Self {
self.video_metadata = Some(input);
self
}
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition video operation.</p>
pub fn set_video_metadata(
mut self,
input: std::option::Option<crate::model::VideoMetadata>,
) -> Self {
self.video_metadata = input;
self
}
/// <p>If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces. </p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the response is truncated, Amazon Rekognition returns this token that you can use in the subsequent request to retrieve the next set of faces. </p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Appends an item to `faces`.
///
/// To override the contents of this collection use [`set_faces`](Self::set_faces).
///
/// <p>An array of faces detected in the video. Each element contains a detected face's details and the time, in milliseconds from the start of the video, the face was detected. </p>
pub fn faces(mut self, input: crate::model::FaceDetection) -> Self {
let mut v = self.faces.unwrap_or_default();
v.push(input);
self.faces = Some(v);
self
}
/// <p>An array of faces detected in the video. Each element contains a detected face's details and the time, in milliseconds from the start of the video, the face was detected. </p>
pub fn set_faces(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::FaceDetection>>,
) -> Self {
self.faces = input;
self
}
/// Consumes the builder and constructs a [`GetFaceDetectionOutput`](crate::output::GetFaceDetectionOutput).
pub fn build(self) -> crate::output::GetFaceDetectionOutput {
crate::output::GetFaceDetectionOutput {
job_status: self.job_status,
status_message: self.status_message,
video_metadata: self.video_metadata,
next_token: self.next_token,
faces: self.faces,
}
}
}
}
impl GetFaceDetectionOutput {
/// Creates a new builder-style object to manufacture [`GetFaceDetectionOutput`](crate::output::GetFaceDetectionOutput).
pub fn builder() -> crate::output::get_face_detection_output::Builder {
crate::output::get_face_detection_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetContentModerationOutput {
/// <p>The current status of the content moderation analysis job.</p>
#[doc(hidden)]
pub job_status: std::option::Option<crate::model::VideoJobStatus>,
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
#[doc(hidden)]
pub status_message: std::option::Option<std::string::String>,
/// <p>Information about a video that Amazon Rekognition analyzed. <code>Videometadata</code> is returned in every page of paginated responses from <code>GetContentModeration</code>. </p>
#[doc(hidden)]
pub video_metadata: std::option::Option<crate::model::VideoMetadata>,
/// <p>The detected inappropriate, unwanted, or offensive content moderation labels and the time(s) they were detected.</p>
#[doc(hidden)]
pub moderation_labels:
std::option::Option<std::vec::Vec<crate::model::ContentModerationDetection>>,
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of content moderation labels. </p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
/// <p>Version number of the moderation detection model that was used to detect inappropriate, unwanted, or offensive content.</p>
#[doc(hidden)]
pub moderation_model_version: std::option::Option<std::string::String>,
}
impl GetContentModerationOutput {
/// <p>The current status of the content moderation analysis job.</p>
pub fn job_status(&self) -> std::option::Option<&crate::model::VideoJobStatus> {
self.job_status.as_ref()
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(&self) -> std::option::Option<&str> {
self.status_message.as_deref()
}
/// <p>Information about a video that Amazon Rekognition analyzed. <code>Videometadata</code> is returned in every page of paginated responses from <code>GetContentModeration</code>. </p>
pub fn video_metadata(&self) -> std::option::Option<&crate::model::VideoMetadata> {
self.video_metadata.as_ref()
}
/// <p>The detected inappropriate, unwanted, or offensive content moderation labels and the time(s) they were detected.</p>
pub fn moderation_labels(
&self,
) -> std::option::Option<&[crate::model::ContentModerationDetection]> {
self.moderation_labels.as_deref()
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of content moderation labels. </p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
/// <p>Version number of the moderation detection model that was used to detect inappropriate, unwanted, or offensive content.</p>
pub fn moderation_model_version(&self) -> std::option::Option<&str> {
self.moderation_model_version.as_deref()
}
}
/// See [`GetContentModerationOutput`](crate::output::GetContentModerationOutput).
pub mod get_content_moderation_output {
/// A builder for [`GetContentModerationOutput`](crate::output::GetContentModerationOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_status: std::option::Option<crate::model::VideoJobStatus>,
pub(crate) status_message: std::option::Option<std::string::String>,
pub(crate) video_metadata: std::option::Option<crate::model::VideoMetadata>,
pub(crate) moderation_labels:
std::option::Option<std::vec::Vec<crate::model::ContentModerationDetection>>,
pub(crate) next_token: std::option::Option<std::string::String>,
pub(crate) moderation_model_version: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The current status of the content moderation analysis job.</p>
pub fn job_status(mut self, input: crate::model::VideoJobStatus) -> Self {
self.job_status = Some(input);
self
}
/// <p>The current status of the content moderation analysis job.</p>
pub fn set_job_status(
mut self,
input: std::option::Option<crate::model::VideoJobStatus>,
) -> Self {
self.job_status = input;
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(mut self, input: impl Into<std::string::String>) -> Self {
self.status_message = Some(input.into());
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn set_status_message(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.status_message = input;
self
}
/// <p>Information about a video that Amazon Rekognition analyzed. <code>Videometadata</code> is returned in every page of paginated responses from <code>GetContentModeration</code>. </p>
pub fn video_metadata(mut self, input: crate::model::VideoMetadata) -> Self {
self.video_metadata = Some(input);
self
}
/// <p>Information about a video that Amazon Rekognition analyzed. <code>Videometadata</code> is returned in every page of paginated responses from <code>GetContentModeration</code>. </p>
pub fn set_video_metadata(
mut self,
input: std::option::Option<crate::model::VideoMetadata>,
) -> Self {
self.video_metadata = input;
self
}
/// Appends an item to `moderation_labels`.
///
/// To override the contents of this collection use [`set_moderation_labels`](Self::set_moderation_labels).
///
/// <p>The detected inappropriate, unwanted, or offensive content moderation labels and the time(s) they were detected.</p>
pub fn moderation_labels(
mut self,
input: crate::model::ContentModerationDetection,
) -> Self {
let mut v = self.moderation_labels.unwrap_or_default();
v.push(input);
self.moderation_labels = Some(v);
self
}
/// <p>The detected inappropriate, unwanted, or offensive content moderation labels and the time(s) they were detected.</p>
pub fn set_moderation_labels(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::ContentModerationDetection>>,
) -> Self {
self.moderation_labels = input;
self
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of content moderation labels. </p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of content moderation labels. </p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// <p>Version number of the moderation detection model that was used to detect inappropriate, unwanted, or offensive content.</p>
pub fn moderation_model_version(mut self, input: impl Into<std::string::String>) -> Self {
self.moderation_model_version = Some(input.into());
self
}
/// <p>Version number of the moderation detection model that was used to detect inappropriate, unwanted, or offensive content.</p>
pub fn set_moderation_model_version(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.moderation_model_version = input;
self
}
/// Consumes the builder and constructs a [`GetContentModerationOutput`](crate::output::GetContentModerationOutput).
pub fn build(self) -> crate::output::GetContentModerationOutput {
crate::output::GetContentModerationOutput {
job_status: self.job_status,
status_message: self.status_message,
video_metadata: self.video_metadata,
moderation_labels: self.moderation_labels,
next_token: self.next_token,
moderation_model_version: self.moderation_model_version,
}
}
}
}
impl GetContentModerationOutput {
/// Creates a new builder-style object to manufacture [`GetContentModerationOutput`](crate::output::GetContentModerationOutput).
pub fn builder() -> crate::output::get_content_moderation_output::Builder {
crate::output::get_content_moderation_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetCelebrityRecognitionOutput {
/// <p>The current status of the celebrity recognition job.</p>
#[doc(hidden)]
pub job_status: std::option::Option<crate::model::VideoJobStatus>,
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
#[doc(hidden)]
pub status_message: std::option::Option<std::string::String>,
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition Video operation.</p>
#[doc(hidden)]
pub video_metadata: std::option::Option<crate::model::VideoMetadata>,
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of celebrities.</p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
/// <p>Array of celebrities recognized in the video.</p>
#[doc(hidden)]
pub celebrities: std::option::Option<std::vec::Vec<crate::model::CelebrityRecognition>>,
}
impl GetCelebrityRecognitionOutput {
/// <p>The current status of the celebrity recognition job.</p>
pub fn job_status(&self) -> std::option::Option<&crate::model::VideoJobStatus> {
self.job_status.as_ref()
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(&self) -> std::option::Option<&str> {
self.status_message.as_deref()
}
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition Video operation.</p>
pub fn video_metadata(&self) -> std::option::Option<&crate::model::VideoMetadata> {
self.video_metadata.as_ref()
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of celebrities.</p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
/// <p>Array of celebrities recognized in the video.</p>
pub fn celebrities(&self) -> std::option::Option<&[crate::model::CelebrityRecognition]> {
self.celebrities.as_deref()
}
}
/// See [`GetCelebrityRecognitionOutput`](crate::output::GetCelebrityRecognitionOutput).
pub mod get_celebrity_recognition_output {
/// A builder for [`GetCelebrityRecognitionOutput`](crate::output::GetCelebrityRecognitionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_status: std::option::Option<crate::model::VideoJobStatus>,
pub(crate) status_message: std::option::Option<std::string::String>,
pub(crate) video_metadata: std::option::Option<crate::model::VideoMetadata>,
pub(crate) next_token: std::option::Option<std::string::String>,
pub(crate) celebrities:
std::option::Option<std::vec::Vec<crate::model::CelebrityRecognition>>,
}
impl Builder {
/// <p>The current status of the celebrity recognition job.</p>
pub fn job_status(mut self, input: crate::model::VideoJobStatus) -> Self {
self.job_status = Some(input);
self
}
/// <p>The current status of the celebrity recognition job.</p>
pub fn set_job_status(
mut self,
input: std::option::Option<crate::model::VideoJobStatus>,
) -> Self {
self.job_status = input;
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn status_message(mut self, input: impl Into<std::string::String>) -> Self {
self.status_message = Some(input.into());
self
}
/// <p>If the job fails, <code>StatusMessage</code> provides a descriptive error message.</p>
pub fn set_status_message(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.status_message = input;
self
}
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition Video operation.</p>
pub fn video_metadata(mut self, input: crate::model::VideoMetadata) -> Self {
self.video_metadata = Some(input);
self
}
/// <p>Information about a video that Amazon Rekognition Video analyzed. <code>Videometadata</code> is returned in every page of paginated responses from a Amazon Rekognition Video operation.</p>
pub fn set_video_metadata(
mut self,
input: std::option::Option<crate::model::VideoMetadata>,
) -> Self {
self.video_metadata = input;
self
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of celebrities.</p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the response is truncated, Amazon Rekognition Video returns this token that you can use in the subsequent request to retrieve the next set of celebrities.</p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Appends an item to `celebrities`.
///
/// To override the contents of this collection use [`set_celebrities`](Self::set_celebrities).
///
/// <p>Array of celebrities recognized in the video.</p>
pub fn celebrities(mut self, input: crate::model::CelebrityRecognition) -> Self {
let mut v = self.celebrities.unwrap_or_default();
v.push(input);
self.celebrities = Some(v);
self
}
/// <p>Array of celebrities recognized in the video.</p>
pub fn set_celebrities(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::CelebrityRecognition>>,
) -> Self {
self.celebrities = input;
self
}
/// Consumes the builder and constructs a [`GetCelebrityRecognitionOutput`](crate::output::GetCelebrityRecognitionOutput).
pub fn build(self) -> crate::output::GetCelebrityRecognitionOutput {
crate::output::GetCelebrityRecognitionOutput {
job_status: self.job_status,
status_message: self.status_message,
video_metadata: self.video_metadata,
next_token: self.next_token,
celebrities: self.celebrities,
}
}
}
}
impl GetCelebrityRecognitionOutput {
/// Creates a new builder-style object to manufacture [`GetCelebrityRecognitionOutput`](crate::output::GetCelebrityRecognitionOutput).
pub fn builder() -> crate::output::get_celebrity_recognition_output::Builder {
crate::output::get_celebrity_recognition_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetCelebrityInfoOutput {
/// <p>An array of URLs pointing to additional celebrity information. </p>
#[doc(hidden)]
pub urls: std::option::Option<std::vec::Vec<std::string::String>>,
/// <p>The name of the celebrity.</p>
#[doc(hidden)]
pub name: std::option::Option<std::string::String>,
/// <p>Retrieves the known gender for the celebrity.</p>
#[doc(hidden)]
pub known_gender: std::option::Option<crate::model::KnownGender>,
}
impl GetCelebrityInfoOutput {
/// <p>An array of URLs pointing to additional celebrity information. </p>
pub fn urls(&self) -> std::option::Option<&[std::string::String]> {
self.urls.as_deref()
}
/// <p>The name of the celebrity.</p>
pub fn name(&self) -> std::option::Option<&str> {
self.name.as_deref()
}
/// <p>Retrieves the known gender for the celebrity.</p>
pub fn known_gender(&self) -> std::option::Option<&crate::model::KnownGender> {
self.known_gender.as_ref()
}
}
/// See [`GetCelebrityInfoOutput`](crate::output::GetCelebrityInfoOutput).
pub mod get_celebrity_info_output {
/// A builder for [`GetCelebrityInfoOutput`](crate::output::GetCelebrityInfoOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) urls: std::option::Option<std::vec::Vec<std::string::String>>,
pub(crate) name: std::option::Option<std::string::String>,
pub(crate) known_gender: std::option::Option<crate::model::KnownGender>,
}
impl Builder {
/// Appends an item to `urls`.
///
/// To override the contents of this collection use [`set_urls`](Self::set_urls).
///
/// <p>An array of URLs pointing to additional celebrity information. </p>
pub fn urls(mut self, input: impl Into<std::string::String>) -> Self {
let mut v = self.urls.unwrap_or_default();
v.push(input.into());
self.urls = Some(v);
self
}
/// <p>An array of URLs pointing to additional celebrity information. </p>
pub fn set_urls(
mut self,
input: std::option::Option<std::vec::Vec<std::string::String>>,
) -> Self {
self.urls = input;
self
}
/// <p>The name of the celebrity.</p>
pub fn name(mut self, input: impl Into<std::string::String>) -> Self {
self.name = Some(input.into());
self
}
/// <p>The name of the celebrity.</p>
pub fn set_name(mut self, input: std::option::Option<std::string::String>) -> Self {
self.name = input;
self
}
/// <p>Retrieves the known gender for the celebrity.</p>
pub fn known_gender(mut self, input: crate::model::KnownGender) -> Self {
self.known_gender = Some(input);
self
}
/// <p>Retrieves the known gender for the celebrity.</p>
pub fn set_known_gender(
mut self,
input: std::option::Option<crate::model::KnownGender>,
) -> Self {
self.known_gender = input;
self
}
/// Consumes the builder and constructs a [`GetCelebrityInfoOutput`](crate::output::GetCelebrityInfoOutput).
pub fn build(self) -> crate::output::GetCelebrityInfoOutput {
crate::output::GetCelebrityInfoOutput {
urls: self.urls,
name: self.name,
known_gender: self.known_gender,
}
}
}
}
impl GetCelebrityInfoOutput {
/// Creates a new builder-style object to manufacture [`GetCelebrityInfoOutput`](crate::output::GetCelebrityInfoOutput).
pub fn builder() -> crate::output::get_celebrity_info_output::Builder {
crate::output::get_celebrity_info_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DistributeDatasetEntriesOutput {}
/// See [`DistributeDatasetEntriesOutput`](crate::output::DistributeDatasetEntriesOutput).
pub mod distribute_dataset_entries_output {
/// A builder for [`DistributeDatasetEntriesOutput`](crate::output::DistributeDatasetEntriesOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {}
impl Builder {
/// Consumes the builder and constructs a [`DistributeDatasetEntriesOutput`](crate::output::DistributeDatasetEntriesOutput).
pub fn build(self) -> crate::output::DistributeDatasetEntriesOutput {
crate::output::DistributeDatasetEntriesOutput {}
}
}
}
impl DistributeDatasetEntriesOutput {
/// Creates a new builder-style object to manufacture [`DistributeDatasetEntriesOutput`](crate::output::DistributeDatasetEntriesOutput).
pub fn builder() -> crate::output::distribute_dataset_entries_output::Builder {
crate::output::distribute_dataset_entries_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DetectTextOutput {
/// <p>An array of text that was detected in the input image.</p>
#[doc(hidden)]
pub text_detections: std::option::Option<std::vec::Vec<crate::model::TextDetection>>,
/// <p>The model version used to detect text.</p>
#[doc(hidden)]
pub text_model_version: std::option::Option<std::string::String>,
}
impl DetectTextOutput {
/// <p>An array of text that was detected in the input image.</p>
pub fn text_detections(&self) -> std::option::Option<&[crate::model::TextDetection]> {
self.text_detections.as_deref()
}
/// <p>The model version used to detect text.</p>
pub fn text_model_version(&self) -> std::option::Option<&str> {
self.text_model_version.as_deref()
}
}
/// See [`DetectTextOutput`](crate::output::DetectTextOutput).
pub mod detect_text_output {
/// A builder for [`DetectTextOutput`](crate::output::DetectTextOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) text_detections: std::option::Option<std::vec::Vec<crate::model::TextDetection>>,
pub(crate) text_model_version: std::option::Option<std::string::String>,
}
impl Builder {
/// Appends an item to `text_detections`.
///
/// To override the contents of this collection use [`set_text_detections`](Self::set_text_detections).
///
/// <p>An array of text that was detected in the input image.</p>
pub fn text_detections(mut self, input: crate::model::TextDetection) -> Self {
let mut v = self.text_detections.unwrap_or_default();
v.push(input);
self.text_detections = Some(v);
self
}
/// <p>An array of text that was detected in the input image.</p>
pub fn set_text_detections(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::TextDetection>>,
) -> Self {
self.text_detections = input;
self
}
/// <p>The model version used to detect text.</p>
pub fn text_model_version(mut self, input: impl Into<std::string::String>) -> Self {
self.text_model_version = Some(input.into());
self
}
/// <p>The model version used to detect text.</p>
pub fn set_text_model_version(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.text_model_version = input;
self
}
/// Consumes the builder and constructs a [`DetectTextOutput`](crate::output::DetectTextOutput).
pub fn build(self) -> crate::output::DetectTextOutput {
crate::output::DetectTextOutput {
text_detections: self.text_detections,
text_model_version: self.text_model_version,
}
}
}
}
impl DetectTextOutput {
/// Creates a new builder-style object to manufacture [`DetectTextOutput`](crate::output::DetectTextOutput).
pub fn builder() -> crate::output::detect_text_output::Builder {
crate::output::detect_text_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DetectProtectiveEquipmentOutput {
/// <p>The version number of the PPE detection model used to detect PPE in the image.</p>
#[doc(hidden)]
pub protective_equipment_model_version: std::option::Option<std::string::String>,
/// <p>An array of persons detected in the image (including persons not wearing PPE).</p>
#[doc(hidden)]
pub persons: std::option::Option<std::vec::Vec<crate::model::ProtectiveEquipmentPerson>>,
/// <p>Summary information for the types of PPE specified in the <code>SummarizationAttributes</code> input parameter.</p>
#[doc(hidden)]
pub summary: std::option::Option<crate::model::ProtectiveEquipmentSummary>,
}
impl DetectProtectiveEquipmentOutput {
/// <p>The version number of the PPE detection model used to detect PPE in the image.</p>
pub fn protective_equipment_model_version(&self) -> std::option::Option<&str> {
self.protective_equipment_model_version.as_deref()
}
/// <p>An array of persons detected in the image (including persons not wearing PPE).</p>
pub fn persons(&self) -> std::option::Option<&[crate::model::ProtectiveEquipmentPerson]> {
self.persons.as_deref()
}
/// <p>Summary information for the types of PPE specified in the <code>SummarizationAttributes</code> input parameter.</p>
pub fn summary(&self) -> std::option::Option<&crate::model::ProtectiveEquipmentSummary> {
self.summary.as_ref()
}
}
/// See [`DetectProtectiveEquipmentOutput`](crate::output::DetectProtectiveEquipmentOutput).
pub mod detect_protective_equipment_output {
/// A builder for [`DetectProtectiveEquipmentOutput`](crate::output::DetectProtectiveEquipmentOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) protective_equipment_model_version: std::option::Option<std::string::String>,
pub(crate) persons:
std::option::Option<std::vec::Vec<crate::model::ProtectiveEquipmentPerson>>,
pub(crate) summary: std::option::Option<crate::model::ProtectiveEquipmentSummary>,
}
impl Builder {
/// <p>The version number of the PPE detection model used to detect PPE in the image.</p>
pub fn protective_equipment_model_version(
mut self,
input: impl Into<std::string::String>,
) -> Self {
self.protective_equipment_model_version = Some(input.into());
self
}
/// <p>The version number of the PPE detection model used to detect PPE in the image.</p>
pub fn set_protective_equipment_model_version(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.protective_equipment_model_version = input;
self
}
/// Appends an item to `persons`.
///
/// To override the contents of this collection use [`set_persons`](Self::set_persons).
///
/// <p>An array of persons detected in the image (including persons not wearing PPE).</p>
pub fn persons(mut self, input: crate::model::ProtectiveEquipmentPerson) -> Self {
let mut v = self.persons.unwrap_or_default();
v.push(input);
self.persons = Some(v);
self
}
/// <p>An array of persons detected in the image (including persons not wearing PPE).</p>
pub fn set_persons(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::ProtectiveEquipmentPerson>>,
) -> Self {
self.persons = input;
self
}
/// <p>Summary information for the types of PPE specified in the <code>SummarizationAttributes</code> input parameter.</p>
pub fn summary(mut self, input: crate::model::ProtectiveEquipmentSummary) -> Self {
self.summary = Some(input);
self
}
/// <p>Summary information for the types of PPE specified in the <code>SummarizationAttributes</code> input parameter.</p>
pub fn set_summary(
mut self,
input: std::option::Option<crate::model::ProtectiveEquipmentSummary>,
) -> Self {
self.summary = input;
self
}
/// Consumes the builder and constructs a [`DetectProtectiveEquipmentOutput`](crate::output::DetectProtectiveEquipmentOutput).
pub fn build(self) -> crate::output::DetectProtectiveEquipmentOutput {
crate::output::DetectProtectiveEquipmentOutput {
protective_equipment_model_version: self.protective_equipment_model_version,
persons: self.persons,
summary: self.summary,
}
}
}
}
impl DetectProtectiveEquipmentOutput {
/// Creates a new builder-style object to manufacture [`DetectProtectiveEquipmentOutput`](crate::output::DetectProtectiveEquipmentOutput).
pub fn builder() -> crate::output::detect_protective_equipment_output::Builder {
crate::output::detect_protective_equipment_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DetectModerationLabelsOutput {
/// <p>Array of detected Moderation labels and the time, in milliseconds from the start of the video, they were detected.</p>
#[doc(hidden)]
pub moderation_labels: std::option::Option<std::vec::Vec<crate::model::ModerationLabel>>,
/// <p>Version number of the moderation detection model that was used to detect unsafe content.</p>
#[doc(hidden)]
pub moderation_model_version: std::option::Option<std::string::String>,
/// <p>Shows the results of the human in the loop evaluation.</p>
#[doc(hidden)]
pub human_loop_activation_output: std::option::Option<crate::model::HumanLoopActivationOutput>,
}
impl DetectModerationLabelsOutput {
/// <p>Array of detected Moderation labels and the time, in milliseconds from the start of the video, they were detected.</p>
pub fn moderation_labels(&self) -> std::option::Option<&[crate::model::ModerationLabel]> {
self.moderation_labels.as_deref()
}
/// <p>Version number of the moderation detection model that was used to detect unsafe content.</p>
pub fn moderation_model_version(&self) -> std::option::Option<&str> {
self.moderation_model_version.as_deref()
}
/// <p>Shows the results of the human in the loop evaluation.</p>
pub fn human_loop_activation_output(
&self,
) -> std::option::Option<&crate::model::HumanLoopActivationOutput> {
self.human_loop_activation_output.as_ref()
}
}
/// See [`DetectModerationLabelsOutput`](crate::output::DetectModerationLabelsOutput).
pub mod detect_moderation_labels_output {
/// A builder for [`DetectModerationLabelsOutput`](crate::output::DetectModerationLabelsOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) moderation_labels:
std::option::Option<std::vec::Vec<crate::model::ModerationLabel>>,
pub(crate) moderation_model_version: std::option::Option<std::string::String>,
pub(crate) human_loop_activation_output:
std::option::Option<crate::model::HumanLoopActivationOutput>,
}
impl Builder {
/// Appends an item to `moderation_labels`.
///
/// To override the contents of this collection use [`set_moderation_labels`](Self::set_moderation_labels).
///
/// <p>Array of detected Moderation labels and the time, in milliseconds from the start of the video, they were detected.</p>
pub fn moderation_labels(mut self, input: crate::model::ModerationLabel) -> Self {
let mut v = self.moderation_labels.unwrap_or_default();
v.push(input);
self.moderation_labels = Some(v);
self
}
/// <p>Array of detected Moderation labels and the time, in milliseconds from the start of the video, they were detected.</p>
pub fn set_moderation_labels(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::ModerationLabel>>,
) -> Self {
self.moderation_labels = input;
self
}
/// <p>Version number of the moderation detection model that was used to detect unsafe content.</p>
pub fn moderation_model_version(mut self, input: impl Into<std::string::String>) -> Self {
self.moderation_model_version = Some(input.into());
self
}
/// <p>Version number of the moderation detection model that was used to detect unsafe content.</p>
pub fn set_moderation_model_version(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.moderation_model_version = input;
self
}
/// <p>Shows the results of the human in the loop evaluation.</p>
pub fn human_loop_activation_output(
mut self,
input: crate::model::HumanLoopActivationOutput,
) -> Self {
self.human_loop_activation_output = Some(input);
self
}
/// <p>Shows the results of the human in the loop evaluation.</p>
pub fn set_human_loop_activation_output(
mut self,
input: std::option::Option<crate::model::HumanLoopActivationOutput>,
) -> Self {
self.human_loop_activation_output = input;
self
}
/// Consumes the builder and constructs a [`DetectModerationLabelsOutput`](crate::output::DetectModerationLabelsOutput).
pub fn build(self) -> crate::output::DetectModerationLabelsOutput {
crate::output::DetectModerationLabelsOutput {
moderation_labels: self.moderation_labels,
moderation_model_version: self.moderation_model_version,
human_loop_activation_output: self.human_loop_activation_output,
}
}
}
}
impl DetectModerationLabelsOutput {
/// Creates a new builder-style object to manufacture [`DetectModerationLabelsOutput`](crate::output::DetectModerationLabelsOutput).
pub fn builder() -> crate::output::detect_moderation_labels_output::Builder {
crate::output::detect_moderation_labels_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DetectLabelsOutput {
/// <p>An array of labels for the real-world objects detected. </p>
#[doc(hidden)]
pub labels: std::option::Option<std::vec::Vec<crate::model::Label>>,
/// <p>The value of <code>OrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
#[doc(hidden)]
pub orientation_correction: std::option::Option<crate::model::OrientationCorrection>,
/// <p>Version number of the label detection model that was used to detect labels.</p>
#[doc(hidden)]
pub label_model_version: std::option::Option<std::string::String>,
/// <p>Information about the properties of the input image, such as brightness, sharpness, contrast, and dominant colors.</p>
#[doc(hidden)]
pub image_properties: std::option::Option<crate::model::DetectLabelsImageProperties>,
}
impl DetectLabelsOutput {
/// <p>An array of labels for the real-world objects detected. </p>
pub fn labels(&self) -> std::option::Option<&[crate::model::Label]> {
self.labels.as_deref()
}
/// <p>The value of <code>OrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
pub fn orientation_correction(
&self,
) -> std::option::Option<&crate::model::OrientationCorrection> {
self.orientation_correction.as_ref()
}
/// <p>Version number of the label detection model that was used to detect labels.</p>
pub fn label_model_version(&self) -> std::option::Option<&str> {
self.label_model_version.as_deref()
}
/// <p>Information about the properties of the input image, such as brightness, sharpness, contrast, and dominant colors.</p>
pub fn image_properties(
&self,
) -> std::option::Option<&crate::model::DetectLabelsImageProperties> {
self.image_properties.as_ref()
}
}
/// See [`DetectLabelsOutput`](crate::output::DetectLabelsOutput).
pub mod detect_labels_output {
/// A builder for [`DetectLabelsOutput`](crate::output::DetectLabelsOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) labels: std::option::Option<std::vec::Vec<crate::model::Label>>,
pub(crate) orientation_correction: std::option::Option<crate::model::OrientationCorrection>,
pub(crate) label_model_version: std::option::Option<std::string::String>,
pub(crate) image_properties: std::option::Option<crate::model::DetectLabelsImageProperties>,
}
impl Builder {
/// Appends an item to `labels`.
///
/// To override the contents of this collection use [`set_labels`](Self::set_labels).
///
/// <p>An array of labels for the real-world objects detected. </p>
pub fn labels(mut self, input: crate::model::Label) -> Self {
let mut v = self.labels.unwrap_or_default();
v.push(input);
self.labels = Some(v);
self
}
/// <p>An array of labels for the real-world objects detected. </p>
pub fn set_labels(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::Label>>,
) -> Self {
self.labels = input;
self
}
/// <p>The value of <code>OrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
pub fn orientation_correction(
mut self,
input: crate::model::OrientationCorrection,
) -> Self {
self.orientation_correction = Some(input);
self
}
/// <p>The value of <code>OrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
pub fn set_orientation_correction(
mut self,
input: std::option::Option<crate::model::OrientationCorrection>,
) -> Self {
self.orientation_correction = input;
self
}
/// <p>Version number of the label detection model that was used to detect labels.</p>
pub fn label_model_version(mut self, input: impl Into<std::string::String>) -> Self {
self.label_model_version = Some(input.into());
self
}
/// <p>Version number of the label detection model that was used to detect labels.</p>
pub fn set_label_model_version(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.label_model_version = input;
self
}
/// <p>Information about the properties of the input image, such as brightness, sharpness, contrast, and dominant colors.</p>
pub fn image_properties(
mut self,
input: crate::model::DetectLabelsImageProperties,
) -> Self {
self.image_properties = Some(input);
self
}
/// <p>Information about the properties of the input image, such as brightness, sharpness, contrast, and dominant colors.</p>
pub fn set_image_properties(
mut self,
input: std::option::Option<crate::model::DetectLabelsImageProperties>,
) -> Self {
self.image_properties = input;
self
}
/// Consumes the builder and constructs a [`DetectLabelsOutput`](crate::output::DetectLabelsOutput).
pub fn build(self) -> crate::output::DetectLabelsOutput {
crate::output::DetectLabelsOutput {
labels: self.labels,
orientation_correction: self.orientation_correction,
label_model_version: self.label_model_version,
image_properties: self.image_properties,
}
}
}
}
impl DetectLabelsOutput {
/// Creates a new builder-style object to manufacture [`DetectLabelsOutput`](crate::output::DetectLabelsOutput).
pub fn builder() -> crate::output::detect_labels_output::Builder {
crate::output::detect_labels_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DetectFacesOutput {
/// <p>Details of each face found in the image. </p>
#[doc(hidden)]
pub face_details: std::option::Option<std::vec::Vec<crate::model::FaceDetail>>,
/// <p>The value of <code>OrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
#[doc(hidden)]
pub orientation_correction: std::option::Option<crate::model::OrientationCorrection>,
}
impl DetectFacesOutput {
/// <p>Details of each face found in the image. </p>
pub fn face_details(&self) -> std::option::Option<&[crate::model::FaceDetail]> {
self.face_details.as_deref()
}
/// <p>The value of <code>OrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
pub fn orientation_correction(
&self,
) -> std::option::Option<&crate::model::OrientationCorrection> {
self.orientation_correction.as_ref()
}
}
/// See [`DetectFacesOutput`](crate::output::DetectFacesOutput).
pub mod detect_faces_output {
/// A builder for [`DetectFacesOutput`](crate::output::DetectFacesOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) face_details: std::option::Option<std::vec::Vec<crate::model::FaceDetail>>,
pub(crate) orientation_correction: std::option::Option<crate::model::OrientationCorrection>,
}
impl Builder {
/// Appends an item to `face_details`.
///
/// To override the contents of this collection use [`set_face_details`](Self::set_face_details).
///
/// <p>Details of each face found in the image. </p>
pub fn face_details(mut self, input: crate::model::FaceDetail) -> Self {
let mut v = self.face_details.unwrap_or_default();
v.push(input);
self.face_details = Some(v);
self
}
/// <p>Details of each face found in the image. </p>
pub fn set_face_details(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::FaceDetail>>,
) -> Self {
self.face_details = input;
self
}
/// <p>The value of <code>OrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
pub fn orientation_correction(
mut self,
input: crate::model::OrientationCorrection,
) -> Self {
self.orientation_correction = Some(input);
self
}
/// <p>The value of <code>OrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
pub fn set_orientation_correction(
mut self,
input: std::option::Option<crate::model::OrientationCorrection>,
) -> Self {
self.orientation_correction = input;
self
}
/// Consumes the builder and constructs a [`DetectFacesOutput`](crate::output::DetectFacesOutput).
pub fn build(self) -> crate::output::DetectFacesOutput {
crate::output::DetectFacesOutput {
face_details: self.face_details,
orientation_correction: self.orientation_correction,
}
}
}
}
impl DetectFacesOutput {
/// Creates a new builder-style object to manufacture [`DetectFacesOutput`](crate::output::DetectFacesOutput).
pub fn builder() -> crate::output::detect_faces_output::Builder {
crate::output::detect_faces_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DetectCustomLabelsOutput {
/// <p>An array of custom labels detected in the input image.</p>
#[doc(hidden)]
pub custom_labels: std::option::Option<std::vec::Vec<crate::model::CustomLabel>>,
}
impl DetectCustomLabelsOutput {
/// <p>An array of custom labels detected in the input image.</p>
pub fn custom_labels(&self) -> std::option::Option<&[crate::model::CustomLabel]> {
self.custom_labels.as_deref()
}
}
/// See [`DetectCustomLabelsOutput`](crate::output::DetectCustomLabelsOutput).
pub mod detect_custom_labels_output {
/// A builder for [`DetectCustomLabelsOutput`](crate::output::DetectCustomLabelsOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) custom_labels: std::option::Option<std::vec::Vec<crate::model::CustomLabel>>,
}
impl Builder {
/// Appends an item to `custom_labels`.
///
/// To override the contents of this collection use [`set_custom_labels`](Self::set_custom_labels).
///
/// <p>An array of custom labels detected in the input image.</p>
pub fn custom_labels(mut self, input: crate::model::CustomLabel) -> Self {
let mut v = self.custom_labels.unwrap_or_default();
v.push(input);
self.custom_labels = Some(v);
self
}
/// <p>An array of custom labels detected in the input image.</p>
pub fn set_custom_labels(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::CustomLabel>>,
) -> Self {
self.custom_labels = input;
self
}
/// Consumes the builder and constructs a [`DetectCustomLabelsOutput`](crate::output::DetectCustomLabelsOutput).
pub fn build(self) -> crate::output::DetectCustomLabelsOutput {
crate::output::DetectCustomLabelsOutput {
custom_labels: self.custom_labels,
}
}
}
}
impl DetectCustomLabelsOutput {
/// Creates a new builder-style object to manufacture [`DetectCustomLabelsOutput`](crate::output::DetectCustomLabelsOutput).
pub fn builder() -> crate::output::detect_custom_labels_output::Builder {
crate::output::detect_custom_labels_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DescribeStreamProcessorOutput {
/// <p>Name of the stream processor. </p>
#[doc(hidden)]
pub name: std::option::Option<std::string::String>,
/// <p>ARN of the stream processor.</p>
#[doc(hidden)]
pub stream_processor_arn: std::option::Option<std::string::String>,
/// <p>Current status of the stream processor.</p>
#[doc(hidden)]
pub status: std::option::Option<crate::model::StreamProcessorStatus>,
/// <p>Detailed status message about the stream processor.</p>
#[doc(hidden)]
pub status_message: std::option::Option<std::string::String>,
/// <p>Date and time the stream processor was created</p>
#[doc(hidden)]
pub creation_timestamp: std::option::Option<aws_smithy_types::DateTime>,
/// <p>The time, in Unix format, the stream processor was last updated. For example, when the stream processor moves from a running state to a failed state, or when the user starts or stops the stream processor.</p>
#[doc(hidden)]
pub last_update_timestamp: std::option::Option<aws_smithy_types::DateTime>,
/// <p>Kinesis video stream that provides the source streaming video.</p>
#[doc(hidden)]
pub input: std::option::Option<crate::model::StreamProcessorInput>,
/// <p>Kinesis data stream to which Amazon Rekognition Video puts the analysis results.</p>
#[doc(hidden)]
pub output: std::option::Option<crate::model::StreamProcessorOutput>,
/// <p>ARN of the IAM role that allows access to the stream processor.</p>
#[doc(hidden)]
pub role_arn: std::option::Option<std::string::String>,
/// <p>Input parameters used in a streaming video analyzed by a stream processor. You can use <code>FaceSearch</code> to recognize faces in a streaming video, or you can use <code>ConnectedHome</code> to detect labels.</p>
#[doc(hidden)]
pub settings: std::option::Option<crate::model::StreamProcessorSettings>,
/// <p>The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.</p>
/// <p>Amazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications, one for a person at second 2 and one for a pet at second 4.</p>
/// <p>Amazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.</p>
#[doc(hidden)]
pub notification_channel: std::option::Option<crate::model::StreamProcessorNotificationChannel>,
/// <p> The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors. </p>
#[doc(hidden)]
pub kms_key_id: std::option::Option<std::string::String>,
/// <p> Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors. </p>
#[doc(hidden)]
pub regions_of_interest: std::option::Option<std::vec::Vec<crate::model::RegionOfInterest>>,
/// <p> Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams. </p>
#[doc(hidden)]
pub data_sharing_preference:
std::option::Option<crate::model::StreamProcessorDataSharingPreference>,
}
impl DescribeStreamProcessorOutput {
/// <p>Name of the stream processor. </p>
pub fn name(&self) -> std::option::Option<&str> {
self.name.as_deref()
}
/// <p>ARN of the stream processor.</p>
pub fn stream_processor_arn(&self) -> std::option::Option<&str> {
self.stream_processor_arn.as_deref()
}
/// <p>Current status of the stream processor.</p>
pub fn status(&self) -> std::option::Option<&crate::model::StreamProcessorStatus> {
self.status.as_ref()
}
/// <p>Detailed status message about the stream processor.</p>
pub fn status_message(&self) -> std::option::Option<&str> {
self.status_message.as_deref()
}
/// <p>Date and time the stream processor was created</p>
pub fn creation_timestamp(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.creation_timestamp.as_ref()
}
/// <p>The time, in Unix format, the stream processor was last updated. For example, when the stream processor moves from a running state to a failed state, or when the user starts or stops the stream processor.</p>
pub fn last_update_timestamp(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.last_update_timestamp.as_ref()
}
/// <p>Kinesis video stream that provides the source streaming video.</p>
pub fn input(&self) -> std::option::Option<&crate::model::StreamProcessorInput> {
self.input.as_ref()
}
/// <p>Kinesis data stream to which Amazon Rekognition Video puts the analysis results.</p>
pub fn output(&self) -> std::option::Option<&crate::model::StreamProcessorOutput> {
self.output.as_ref()
}
/// <p>ARN of the IAM role that allows access to the stream processor.</p>
pub fn role_arn(&self) -> std::option::Option<&str> {
self.role_arn.as_deref()
}
/// <p>Input parameters used in a streaming video analyzed by a stream processor. You can use <code>FaceSearch</code> to recognize faces in a streaming video, or you can use <code>ConnectedHome</code> to detect labels.</p>
pub fn settings(&self) -> std::option::Option<&crate::model::StreamProcessorSettings> {
self.settings.as_ref()
}
/// <p>The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.</p>
/// <p>Amazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications, one for a person at second 2 and one for a pet at second 4.</p>
/// <p>Amazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.</p>
pub fn notification_channel(
&self,
) -> std::option::Option<&crate::model::StreamProcessorNotificationChannel> {
self.notification_channel.as_ref()
}
/// <p> The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors. </p>
pub fn kms_key_id(&self) -> std::option::Option<&str> {
self.kms_key_id.as_deref()
}
/// <p> Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors. </p>
pub fn regions_of_interest(&self) -> std::option::Option<&[crate::model::RegionOfInterest]> {
self.regions_of_interest.as_deref()
}
/// <p> Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams. </p>
pub fn data_sharing_preference(
&self,
) -> std::option::Option<&crate::model::StreamProcessorDataSharingPreference> {
self.data_sharing_preference.as_ref()
}
}
/// See [`DescribeStreamProcessorOutput`](crate::output::DescribeStreamProcessorOutput).
pub mod describe_stream_processor_output {
/// A builder for [`DescribeStreamProcessorOutput`](crate::output::DescribeStreamProcessorOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) name: std::option::Option<std::string::String>,
pub(crate) stream_processor_arn: std::option::Option<std::string::String>,
pub(crate) status: std::option::Option<crate::model::StreamProcessorStatus>,
pub(crate) status_message: std::option::Option<std::string::String>,
pub(crate) creation_timestamp: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) last_update_timestamp: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) input: std::option::Option<crate::model::StreamProcessorInput>,
pub(crate) output: std::option::Option<crate::model::StreamProcessorOutput>,
pub(crate) role_arn: std::option::Option<std::string::String>,
pub(crate) settings: std::option::Option<crate::model::StreamProcessorSettings>,
pub(crate) notification_channel:
std::option::Option<crate::model::StreamProcessorNotificationChannel>,
pub(crate) kms_key_id: std::option::Option<std::string::String>,
pub(crate) regions_of_interest:
std::option::Option<std::vec::Vec<crate::model::RegionOfInterest>>,
pub(crate) data_sharing_preference:
std::option::Option<crate::model::StreamProcessorDataSharingPreference>,
}
impl Builder {
/// <p>Name of the stream processor. </p>
pub fn name(mut self, input: impl Into<std::string::String>) -> Self {
self.name = Some(input.into());
self
}
/// <p>Name of the stream processor. </p>
pub fn set_name(mut self, input: std::option::Option<std::string::String>) -> Self {
self.name = input;
self
}
/// <p>ARN of the stream processor.</p>
pub fn stream_processor_arn(mut self, input: impl Into<std::string::String>) -> Self {
self.stream_processor_arn = Some(input.into());
self
}
/// <p>ARN of the stream processor.</p>
pub fn set_stream_processor_arn(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.stream_processor_arn = input;
self
}
/// <p>Current status of the stream processor.</p>
pub fn status(mut self, input: crate::model::StreamProcessorStatus) -> Self {
self.status = Some(input);
self
}
/// <p>Current status of the stream processor.</p>
pub fn set_status(
mut self,
input: std::option::Option<crate::model::StreamProcessorStatus>,
) -> Self {
self.status = input;
self
}
/// <p>Detailed status message about the stream processor.</p>
pub fn status_message(mut self, input: impl Into<std::string::String>) -> Self {
self.status_message = Some(input.into());
self
}
/// <p>Detailed status message about the stream processor.</p>
pub fn set_status_message(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.status_message = input;
self
}
/// <p>Date and time the stream processor was created</p>
pub fn creation_timestamp(mut self, input: aws_smithy_types::DateTime) -> Self {
self.creation_timestamp = Some(input);
self
}
/// <p>Date and time the stream processor was created</p>
pub fn set_creation_timestamp(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.creation_timestamp = input;
self
}
/// <p>The time, in Unix format, the stream processor was last updated. For example, when the stream processor moves from a running state to a failed state, or when the user starts or stops the stream processor.</p>
pub fn last_update_timestamp(mut self, input: aws_smithy_types::DateTime) -> Self {
self.last_update_timestamp = Some(input);
self
}
/// <p>The time, in Unix format, the stream processor was last updated. For example, when the stream processor moves from a running state to a failed state, or when the user starts or stops the stream processor.</p>
pub fn set_last_update_timestamp(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.last_update_timestamp = input;
self
}
/// <p>Kinesis video stream that provides the source streaming video.</p>
pub fn input(mut self, input: crate::model::StreamProcessorInput) -> Self {
self.input = Some(input);
self
}
/// <p>Kinesis video stream that provides the source streaming video.</p>
pub fn set_input(
mut self,
input: std::option::Option<crate::model::StreamProcessorInput>,
) -> Self {
self.input = input;
self
}
/// <p>Kinesis data stream to which Amazon Rekognition Video puts the analysis results.</p>
pub fn output(mut self, input: crate::model::StreamProcessorOutput) -> Self {
self.output = Some(input);
self
}
/// <p>Kinesis data stream to which Amazon Rekognition Video puts the analysis results.</p>
pub fn set_output(
mut self,
input: std::option::Option<crate::model::StreamProcessorOutput>,
) -> Self {
self.output = input;
self
}
/// <p>ARN of the IAM role that allows access to the stream processor.</p>
pub fn role_arn(mut self, input: impl Into<std::string::String>) -> Self {
self.role_arn = Some(input.into());
self
}
/// <p>ARN of the IAM role that allows access to the stream processor.</p>
pub fn set_role_arn(mut self, input: std::option::Option<std::string::String>) -> Self {
self.role_arn = input;
self
}
/// <p>Input parameters used in a streaming video analyzed by a stream processor. You can use <code>FaceSearch</code> to recognize faces in a streaming video, or you can use <code>ConnectedHome</code> to detect labels.</p>
pub fn settings(mut self, input: crate::model::StreamProcessorSettings) -> Self {
self.settings = Some(input);
self
}
/// <p>Input parameters used in a streaming video analyzed by a stream processor. You can use <code>FaceSearch</code> to recognize faces in a streaming video, or you can use <code>ConnectedHome</code> to detect labels.</p>
pub fn set_settings(
mut self,
input: std::option::Option<crate::model::StreamProcessorSettings>,
) -> Self {
self.settings = input;
self
}
/// <p>The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.</p>
/// <p>Amazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications, one for a person at second 2 and one for a pet at second 4.</p>
/// <p>Amazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.</p>
pub fn notification_channel(
mut self,
input: crate::model::StreamProcessorNotificationChannel,
) -> Self {
self.notification_channel = Some(input);
self
}
/// <p>The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.</p>
/// <p>Amazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications, one for a person at second 2 and one for a pet at second 4.</p>
/// <p>Amazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.</p>
pub fn set_notification_channel(
mut self,
input: std::option::Option<crate::model::StreamProcessorNotificationChannel>,
) -> Self {
self.notification_channel = input;
self
}
/// <p> The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors. </p>
pub fn kms_key_id(mut self, input: impl Into<std::string::String>) -> Self {
self.kms_key_id = Some(input.into());
self
}
/// <p> The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors. </p>
pub fn set_kms_key_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.kms_key_id = input;
self
}
/// Appends an item to `regions_of_interest`.
///
/// To override the contents of this collection use [`set_regions_of_interest`](Self::set_regions_of_interest).
///
/// <p> Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors. </p>
pub fn regions_of_interest(mut self, input: crate::model::RegionOfInterest) -> Self {
let mut v = self.regions_of_interest.unwrap_or_default();
v.push(input);
self.regions_of_interest = Some(v);
self
}
/// <p> Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors. </p>
pub fn set_regions_of_interest(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::RegionOfInterest>>,
) -> Self {
self.regions_of_interest = input;
self
}
/// <p> Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams. </p>
pub fn data_sharing_preference(
mut self,
input: crate::model::StreamProcessorDataSharingPreference,
) -> Self {
self.data_sharing_preference = Some(input);
self
}
/// <p> Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams. </p>
pub fn set_data_sharing_preference(
mut self,
input: std::option::Option<crate::model::StreamProcessorDataSharingPreference>,
) -> Self {
self.data_sharing_preference = input;
self
}
/// Consumes the builder and constructs a [`DescribeStreamProcessorOutput`](crate::output::DescribeStreamProcessorOutput).
pub fn build(self) -> crate::output::DescribeStreamProcessorOutput {
crate::output::DescribeStreamProcessorOutput {
name: self.name,
stream_processor_arn: self.stream_processor_arn,
status: self.status,
status_message: self.status_message,
creation_timestamp: self.creation_timestamp,
last_update_timestamp: self.last_update_timestamp,
input: self.input,
output: self.output,
role_arn: self.role_arn,
settings: self.settings,
notification_channel: self.notification_channel,
kms_key_id: self.kms_key_id,
regions_of_interest: self.regions_of_interest,
data_sharing_preference: self.data_sharing_preference,
}
}
}
}
impl DescribeStreamProcessorOutput {
/// Creates a new builder-style object to manufacture [`DescribeStreamProcessorOutput`](crate::output::DescribeStreamProcessorOutput).
pub fn builder() -> crate::output::describe_stream_processor_output::Builder {
crate::output::describe_stream_processor_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DescribeProjectVersionsOutput {
/// <p>A list of model descriptions. The list is sorted by the creation date and time of the model versions, latest to earliest.</p>
#[doc(hidden)]
pub project_version_descriptions:
std::option::Option<std::vec::Vec<crate::model::ProjectVersionDescription>>,
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
}
impl DescribeProjectVersionsOutput {
/// <p>A list of model descriptions. The list is sorted by the creation date and time of the model versions, latest to earliest.</p>
pub fn project_version_descriptions(
&self,
) -> std::option::Option<&[crate::model::ProjectVersionDescription]> {
self.project_version_descriptions.as_deref()
}
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
}
/// See [`DescribeProjectVersionsOutput`](crate::output::DescribeProjectVersionsOutput).
pub mod describe_project_versions_output {
/// A builder for [`DescribeProjectVersionsOutput`](crate::output::DescribeProjectVersionsOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) project_version_descriptions:
std::option::Option<std::vec::Vec<crate::model::ProjectVersionDescription>>,
pub(crate) next_token: std::option::Option<std::string::String>,
}
impl Builder {
/// Appends an item to `project_version_descriptions`.
///
/// To override the contents of this collection use [`set_project_version_descriptions`](Self::set_project_version_descriptions).
///
/// <p>A list of model descriptions. The list is sorted by the creation date and time of the model versions, latest to earliest.</p>
pub fn project_version_descriptions(
mut self,
input: crate::model::ProjectVersionDescription,
) -> Self {
let mut v = self.project_version_descriptions.unwrap_or_default();
v.push(input);
self.project_version_descriptions = Some(v);
self
}
/// <p>A list of model descriptions. The list is sorted by the creation date and time of the model versions, latest to earliest.</p>
pub fn set_project_version_descriptions(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::ProjectVersionDescription>>,
) -> Self {
self.project_version_descriptions = input;
self
}
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Consumes the builder and constructs a [`DescribeProjectVersionsOutput`](crate::output::DescribeProjectVersionsOutput).
pub fn build(self) -> crate::output::DescribeProjectVersionsOutput {
crate::output::DescribeProjectVersionsOutput {
project_version_descriptions: self.project_version_descriptions,
next_token: self.next_token,
}
}
}
}
impl DescribeProjectVersionsOutput {
/// Creates a new builder-style object to manufacture [`DescribeProjectVersionsOutput`](crate::output::DescribeProjectVersionsOutput).
pub fn builder() -> crate::output::describe_project_versions_output::Builder {
crate::output::describe_project_versions_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DescribeProjectsOutput {
/// <p>A list of project descriptions. The list is sorted by the date and time the projects are created.</p>
#[doc(hidden)]
pub project_descriptions: std::option::Option<std::vec::Vec<crate::model::ProjectDescription>>,
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
}
impl DescribeProjectsOutput {
/// <p>A list of project descriptions. The list is sorted by the date and time the projects are created.</p>
pub fn project_descriptions(&self) -> std::option::Option<&[crate::model::ProjectDescription]> {
self.project_descriptions.as_deref()
}
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
}
/// See [`DescribeProjectsOutput`](crate::output::DescribeProjectsOutput).
pub mod describe_projects_output {
/// A builder for [`DescribeProjectsOutput`](crate::output::DescribeProjectsOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) project_descriptions:
std::option::Option<std::vec::Vec<crate::model::ProjectDescription>>,
pub(crate) next_token: std::option::Option<std::string::String>,
}
impl Builder {
/// Appends an item to `project_descriptions`.
///
/// To override the contents of this collection use [`set_project_descriptions`](Self::set_project_descriptions).
///
/// <p>A list of project descriptions. The list is sorted by the date and time the projects are created.</p>
pub fn project_descriptions(mut self, input: crate::model::ProjectDescription) -> Self {
let mut v = self.project_descriptions.unwrap_or_default();
v.push(input);
self.project_descriptions = Some(v);
self
}
/// <p>A list of project descriptions. The list is sorted by the date and time the projects are created.</p>
pub fn set_project_descriptions(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::ProjectDescription>>,
) -> Self {
self.project_descriptions = input;
self
}
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results. </p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Consumes the builder and constructs a [`DescribeProjectsOutput`](crate::output::DescribeProjectsOutput).
pub fn build(self) -> crate::output::DescribeProjectsOutput {
crate::output::DescribeProjectsOutput {
project_descriptions: self.project_descriptions,
next_token: self.next_token,
}
}
}
}
impl DescribeProjectsOutput {
/// Creates a new builder-style object to manufacture [`DescribeProjectsOutput`](crate::output::DescribeProjectsOutput).
pub fn builder() -> crate::output::describe_projects_output::Builder {
crate::output::describe_projects_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DescribeDatasetOutput {
/// <p> The description for the dataset. </p>
#[doc(hidden)]
pub dataset_description: std::option::Option<crate::model::DatasetDescription>,
}
impl DescribeDatasetOutput {
/// <p> The description for the dataset. </p>
pub fn dataset_description(&self) -> std::option::Option<&crate::model::DatasetDescription> {
self.dataset_description.as_ref()
}
}
/// See [`DescribeDatasetOutput`](crate::output::DescribeDatasetOutput).
pub mod describe_dataset_output {
/// A builder for [`DescribeDatasetOutput`](crate::output::DescribeDatasetOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) dataset_description: std::option::Option<crate::model::DatasetDescription>,
}
impl Builder {
/// <p> The description for the dataset. </p>
pub fn dataset_description(mut self, input: crate::model::DatasetDescription) -> Self {
self.dataset_description = Some(input);
self
}
/// <p> The description for the dataset. </p>
pub fn set_dataset_description(
mut self,
input: std::option::Option<crate::model::DatasetDescription>,
) -> Self {
self.dataset_description = input;
self
}
/// Consumes the builder and constructs a [`DescribeDatasetOutput`](crate::output::DescribeDatasetOutput).
pub fn build(self) -> crate::output::DescribeDatasetOutput {
crate::output::DescribeDatasetOutput {
dataset_description: self.dataset_description,
}
}
}
}
impl DescribeDatasetOutput {
/// Creates a new builder-style object to manufacture [`DescribeDatasetOutput`](crate::output::DescribeDatasetOutput).
pub fn builder() -> crate::output::describe_dataset_output::Builder {
crate::output::describe_dataset_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DescribeCollectionOutput {
/// <p>The number of faces that are indexed into the collection. To index faces into a collection, use <code>IndexFaces</code>.</p>
#[doc(hidden)]
pub face_count: std::option::Option<i64>,
/// <p>The version of the face model that's used by the collection for face detection.</p>
/// <p>For more information, see Model versioning in the Amazon Rekognition Developer Guide.</p>
#[doc(hidden)]
pub face_model_version: std::option::Option<std::string::String>,
/// <p>The Amazon Resource Name (ARN) of the collection.</p>
#[doc(hidden)]
pub collection_arn: std::option::Option<std::string::String>,
/// <p>The number of milliseconds since the Unix epoch time until the creation of the collection. The Unix epoch time is 00:00:00 Coordinated Universal Time (UTC), Thursday, 1 January 1970.</p>
#[doc(hidden)]
pub creation_timestamp: std::option::Option<aws_smithy_types::DateTime>,
}
impl DescribeCollectionOutput {
/// <p>The number of faces that are indexed into the collection. To index faces into a collection, use <code>IndexFaces</code>.</p>
pub fn face_count(&self) -> std::option::Option<i64> {
self.face_count
}
/// <p>The version of the face model that's used by the collection for face detection.</p>
/// <p>For more information, see Model versioning in the Amazon Rekognition Developer Guide.</p>
pub fn face_model_version(&self) -> std::option::Option<&str> {
self.face_model_version.as_deref()
}
/// <p>The Amazon Resource Name (ARN) of the collection.</p>
pub fn collection_arn(&self) -> std::option::Option<&str> {
self.collection_arn.as_deref()
}
/// <p>The number of milliseconds since the Unix epoch time until the creation of the collection. The Unix epoch time is 00:00:00 Coordinated Universal Time (UTC), Thursday, 1 January 1970.</p>
pub fn creation_timestamp(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.creation_timestamp.as_ref()
}
}
/// See [`DescribeCollectionOutput`](crate::output::DescribeCollectionOutput).
pub mod describe_collection_output {
/// A builder for [`DescribeCollectionOutput`](crate::output::DescribeCollectionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) face_count: std::option::Option<i64>,
pub(crate) face_model_version: std::option::Option<std::string::String>,
pub(crate) collection_arn: std::option::Option<std::string::String>,
pub(crate) creation_timestamp: std::option::Option<aws_smithy_types::DateTime>,
}
impl Builder {
/// <p>The number of faces that are indexed into the collection. To index faces into a collection, use <code>IndexFaces</code>.</p>
pub fn face_count(mut self, input: i64) -> Self {
self.face_count = Some(input);
self
}
/// <p>The number of faces that are indexed into the collection. To index faces into a collection, use <code>IndexFaces</code>.</p>
pub fn set_face_count(mut self, input: std::option::Option<i64>) -> Self {
self.face_count = input;
self
}
/// <p>The version of the face model that's used by the collection for face detection.</p>
/// <p>For more information, see Model versioning in the Amazon Rekognition Developer Guide.</p>
pub fn face_model_version(mut self, input: impl Into<std::string::String>) -> Self {
self.face_model_version = Some(input.into());
self
}
/// <p>The version of the face model that's used by the collection for face detection.</p>
/// <p>For more information, see Model versioning in the Amazon Rekognition Developer Guide.</p>
pub fn set_face_model_version(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.face_model_version = input;
self
}
/// <p>The Amazon Resource Name (ARN) of the collection.</p>
pub fn collection_arn(mut self, input: impl Into<std::string::String>) -> Self {
self.collection_arn = Some(input.into());
self
}
/// <p>The Amazon Resource Name (ARN) of the collection.</p>
pub fn set_collection_arn(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.collection_arn = input;
self
}
/// <p>The number of milliseconds since the Unix epoch time until the creation of the collection. The Unix epoch time is 00:00:00 Coordinated Universal Time (UTC), Thursday, 1 January 1970.</p>
pub fn creation_timestamp(mut self, input: aws_smithy_types::DateTime) -> Self {
self.creation_timestamp = Some(input);
self
}
/// <p>The number of milliseconds since the Unix epoch time until the creation of the collection. The Unix epoch time is 00:00:00 Coordinated Universal Time (UTC), Thursday, 1 January 1970.</p>
pub fn set_creation_timestamp(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.creation_timestamp = input;
self
}
/// Consumes the builder and constructs a [`DescribeCollectionOutput`](crate::output::DescribeCollectionOutput).
pub fn build(self) -> crate::output::DescribeCollectionOutput {
crate::output::DescribeCollectionOutput {
face_count: self.face_count,
face_model_version: self.face_model_version,
collection_arn: self.collection_arn,
creation_timestamp: self.creation_timestamp,
}
}
}
}
impl DescribeCollectionOutput {
/// Creates a new builder-style object to manufacture [`DescribeCollectionOutput`](crate::output::DescribeCollectionOutput).
pub fn builder() -> crate::output::describe_collection_output::Builder {
crate::output::describe_collection_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DeleteStreamProcessorOutput {}
/// See [`DeleteStreamProcessorOutput`](crate::output::DeleteStreamProcessorOutput).
pub mod delete_stream_processor_output {
/// A builder for [`DeleteStreamProcessorOutput`](crate::output::DeleteStreamProcessorOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {}
impl Builder {
/// Consumes the builder and constructs a [`DeleteStreamProcessorOutput`](crate::output::DeleteStreamProcessorOutput).
pub fn build(self) -> crate::output::DeleteStreamProcessorOutput {
crate::output::DeleteStreamProcessorOutput {}
}
}
}
impl DeleteStreamProcessorOutput {
/// Creates a new builder-style object to manufacture [`DeleteStreamProcessorOutput`](crate::output::DeleteStreamProcessorOutput).
pub fn builder() -> crate::output::delete_stream_processor_output::Builder {
crate::output::delete_stream_processor_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DeleteProjectVersionOutput {
/// <p>The status of the deletion operation.</p>
#[doc(hidden)]
pub status: std::option::Option<crate::model::ProjectVersionStatus>,
}
impl DeleteProjectVersionOutput {
/// <p>The status of the deletion operation.</p>
pub fn status(&self) -> std::option::Option<&crate::model::ProjectVersionStatus> {
self.status.as_ref()
}
}
/// See [`DeleteProjectVersionOutput`](crate::output::DeleteProjectVersionOutput).
pub mod delete_project_version_output {
/// A builder for [`DeleteProjectVersionOutput`](crate::output::DeleteProjectVersionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) status: std::option::Option<crate::model::ProjectVersionStatus>,
}
impl Builder {
/// <p>The status of the deletion operation.</p>
pub fn status(mut self, input: crate::model::ProjectVersionStatus) -> Self {
self.status = Some(input);
self
}
/// <p>The status of the deletion operation.</p>
pub fn set_status(
mut self,
input: std::option::Option<crate::model::ProjectVersionStatus>,
) -> Self {
self.status = input;
self
}
/// Consumes the builder and constructs a [`DeleteProjectVersionOutput`](crate::output::DeleteProjectVersionOutput).
pub fn build(self) -> crate::output::DeleteProjectVersionOutput {
crate::output::DeleteProjectVersionOutput {
status: self.status,
}
}
}
}
impl DeleteProjectVersionOutput {
/// Creates a new builder-style object to manufacture [`DeleteProjectVersionOutput`](crate::output::DeleteProjectVersionOutput).
pub fn builder() -> crate::output::delete_project_version_output::Builder {
crate::output::delete_project_version_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DeleteProjectPolicyOutput {}
/// See [`DeleteProjectPolicyOutput`](crate::output::DeleteProjectPolicyOutput).
pub mod delete_project_policy_output {
/// A builder for [`DeleteProjectPolicyOutput`](crate::output::DeleteProjectPolicyOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {}
impl Builder {
/// Consumes the builder and constructs a [`DeleteProjectPolicyOutput`](crate::output::DeleteProjectPolicyOutput).
pub fn build(self) -> crate::output::DeleteProjectPolicyOutput {
crate::output::DeleteProjectPolicyOutput {}
}
}
}
impl DeleteProjectPolicyOutput {
/// Creates a new builder-style object to manufacture [`DeleteProjectPolicyOutput`](crate::output::DeleteProjectPolicyOutput).
pub fn builder() -> crate::output::delete_project_policy_output::Builder {
crate::output::delete_project_policy_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DeleteProjectOutput {
/// <p>The current status of the delete project operation.</p>
#[doc(hidden)]
pub status: std::option::Option<crate::model::ProjectStatus>,
}
impl DeleteProjectOutput {
/// <p>The current status of the delete project operation.</p>
pub fn status(&self) -> std::option::Option<&crate::model::ProjectStatus> {
self.status.as_ref()
}
}
/// See [`DeleteProjectOutput`](crate::output::DeleteProjectOutput).
pub mod delete_project_output {
/// A builder for [`DeleteProjectOutput`](crate::output::DeleteProjectOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) status: std::option::Option<crate::model::ProjectStatus>,
}
impl Builder {
/// <p>The current status of the delete project operation.</p>
pub fn status(mut self, input: crate::model::ProjectStatus) -> Self {
self.status = Some(input);
self
}
/// <p>The current status of the delete project operation.</p>
pub fn set_status(
mut self,
input: std::option::Option<crate::model::ProjectStatus>,
) -> Self {
self.status = input;
self
}
/// Consumes the builder and constructs a [`DeleteProjectOutput`](crate::output::DeleteProjectOutput).
pub fn build(self) -> crate::output::DeleteProjectOutput {
crate::output::DeleteProjectOutput {
status: self.status,
}
}
}
}
impl DeleteProjectOutput {
/// Creates a new builder-style object to manufacture [`DeleteProjectOutput`](crate::output::DeleteProjectOutput).
pub fn builder() -> crate::output::delete_project_output::Builder {
crate::output::delete_project_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DeleteFacesOutput {
/// <p>An array of strings (face IDs) of the faces that were deleted.</p>
#[doc(hidden)]
pub deleted_faces: std::option::Option<std::vec::Vec<std::string::String>>,
}
impl DeleteFacesOutput {
/// <p>An array of strings (face IDs) of the faces that were deleted.</p>
pub fn deleted_faces(&self) -> std::option::Option<&[std::string::String]> {
self.deleted_faces.as_deref()
}
}
/// See [`DeleteFacesOutput`](crate::output::DeleteFacesOutput).
pub mod delete_faces_output {
/// A builder for [`DeleteFacesOutput`](crate::output::DeleteFacesOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) deleted_faces: std::option::Option<std::vec::Vec<std::string::String>>,
}
impl Builder {
/// Appends an item to `deleted_faces`.
///
/// To override the contents of this collection use [`set_deleted_faces`](Self::set_deleted_faces).
///
/// <p>An array of strings (face IDs) of the faces that were deleted.</p>
pub fn deleted_faces(mut self, input: impl Into<std::string::String>) -> Self {
let mut v = self.deleted_faces.unwrap_or_default();
v.push(input.into());
self.deleted_faces = Some(v);
self
}
/// <p>An array of strings (face IDs) of the faces that were deleted.</p>
pub fn set_deleted_faces(
mut self,
input: std::option::Option<std::vec::Vec<std::string::String>>,
) -> Self {
self.deleted_faces = input;
self
}
/// Consumes the builder and constructs a [`DeleteFacesOutput`](crate::output::DeleteFacesOutput).
pub fn build(self) -> crate::output::DeleteFacesOutput {
crate::output::DeleteFacesOutput {
deleted_faces: self.deleted_faces,
}
}
}
}
impl DeleteFacesOutput {
/// Creates a new builder-style object to manufacture [`DeleteFacesOutput`](crate::output::DeleteFacesOutput).
pub fn builder() -> crate::output::delete_faces_output::Builder {
crate::output::delete_faces_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DeleteDatasetOutput {}
/// See [`DeleteDatasetOutput`](crate::output::DeleteDatasetOutput).
pub mod delete_dataset_output {
/// A builder for [`DeleteDatasetOutput`](crate::output::DeleteDatasetOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {}
impl Builder {
/// Consumes the builder and constructs a [`DeleteDatasetOutput`](crate::output::DeleteDatasetOutput).
pub fn build(self) -> crate::output::DeleteDatasetOutput {
crate::output::DeleteDatasetOutput {}
}
}
}
impl DeleteDatasetOutput {
/// Creates a new builder-style object to manufacture [`DeleteDatasetOutput`](crate::output::DeleteDatasetOutput).
pub fn builder() -> crate::output::delete_dataset_output::Builder {
crate::output::delete_dataset_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DeleteCollectionOutput {
/// <p>HTTP status code that indicates the result of the operation.</p>
#[doc(hidden)]
pub status_code: std::option::Option<i32>,
}
impl DeleteCollectionOutput {
/// <p>HTTP status code that indicates the result of the operation.</p>
pub fn status_code(&self) -> std::option::Option<i32> {
self.status_code
}
}
/// See [`DeleteCollectionOutput`](crate::output::DeleteCollectionOutput).
pub mod delete_collection_output {
/// A builder for [`DeleteCollectionOutput`](crate::output::DeleteCollectionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) status_code: std::option::Option<i32>,
}
impl Builder {
/// <p>HTTP status code that indicates the result of the operation.</p>
pub fn status_code(mut self, input: i32) -> Self {
self.status_code = Some(input);
self
}
/// <p>HTTP status code that indicates the result of the operation.</p>
pub fn set_status_code(mut self, input: std::option::Option<i32>) -> Self {
self.status_code = input;
self
}
/// Consumes the builder and constructs a [`DeleteCollectionOutput`](crate::output::DeleteCollectionOutput).
pub fn build(self) -> crate::output::DeleteCollectionOutput {
crate::output::DeleteCollectionOutput {
status_code: self.status_code,
}
}
}
}
impl DeleteCollectionOutput {
/// Creates a new builder-style object to manufacture [`DeleteCollectionOutput`](crate::output::DeleteCollectionOutput).
pub fn builder() -> crate::output::delete_collection_output::Builder {
crate::output::delete_collection_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct CreateStreamProcessorOutput {
/// <p>Amazon Resource Number for the newly created stream processor.</p>
#[doc(hidden)]
pub stream_processor_arn: std::option::Option<std::string::String>,
}
impl CreateStreamProcessorOutput {
/// <p>Amazon Resource Number for the newly created stream processor.</p>
pub fn stream_processor_arn(&self) -> std::option::Option<&str> {
self.stream_processor_arn.as_deref()
}
}
/// See [`CreateStreamProcessorOutput`](crate::output::CreateStreamProcessorOutput).
pub mod create_stream_processor_output {
/// A builder for [`CreateStreamProcessorOutput`](crate::output::CreateStreamProcessorOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) stream_processor_arn: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>Amazon Resource Number for the newly created stream processor.</p>
pub fn stream_processor_arn(mut self, input: impl Into<std::string::String>) -> Self {
self.stream_processor_arn = Some(input.into());
self
}
/// <p>Amazon Resource Number for the newly created stream processor.</p>
pub fn set_stream_processor_arn(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.stream_processor_arn = input;
self
}
/// Consumes the builder and constructs a [`CreateStreamProcessorOutput`](crate::output::CreateStreamProcessorOutput).
pub fn build(self) -> crate::output::CreateStreamProcessorOutput {
crate::output::CreateStreamProcessorOutput {
stream_processor_arn: self.stream_processor_arn,
}
}
}
}
impl CreateStreamProcessorOutput {
/// Creates a new builder-style object to manufacture [`CreateStreamProcessorOutput`](crate::output::CreateStreamProcessorOutput).
pub fn builder() -> crate::output::create_stream_processor_output::Builder {
crate::output::create_stream_processor_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct CreateProjectVersionOutput {
/// <p>The ARN of the model version that was created. Use <code>DescribeProjectVersion</code> to get the current status of the training operation.</p>
#[doc(hidden)]
pub project_version_arn: std::option::Option<std::string::String>,
}
impl CreateProjectVersionOutput {
/// <p>The ARN of the model version that was created. Use <code>DescribeProjectVersion</code> to get the current status of the training operation.</p>
pub fn project_version_arn(&self) -> std::option::Option<&str> {
self.project_version_arn.as_deref()
}
}
/// See [`CreateProjectVersionOutput`](crate::output::CreateProjectVersionOutput).
pub mod create_project_version_output {
/// A builder for [`CreateProjectVersionOutput`](crate::output::CreateProjectVersionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) project_version_arn: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The ARN of the model version that was created. Use <code>DescribeProjectVersion</code> to get the current status of the training operation.</p>
pub fn project_version_arn(mut self, input: impl Into<std::string::String>) -> Self {
self.project_version_arn = Some(input.into());
self
}
/// <p>The ARN of the model version that was created. Use <code>DescribeProjectVersion</code> to get the current status of the training operation.</p>
pub fn set_project_version_arn(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.project_version_arn = input;
self
}
/// Consumes the builder and constructs a [`CreateProjectVersionOutput`](crate::output::CreateProjectVersionOutput).
pub fn build(self) -> crate::output::CreateProjectVersionOutput {
crate::output::CreateProjectVersionOutput {
project_version_arn: self.project_version_arn,
}
}
}
}
impl CreateProjectVersionOutput {
/// Creates a new builder-style object to manufacture [`CreateProjectVersionOutput`](crate::output::CreateProjectVersionOutput).
pub fn builder() -> crate::output::create_project_version_output::Builder {
crate::output::create_project_version_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct CreateProjectOutput {
/// <p>The Amazon Resource Name (ARN) of the new project. You can use the ARN to configure IAM access to the project. </p>
#[doc(hidden)]
pub project_arn: std::option::Option<std::string::String>,
}
impl CreateProjectOutput {
/// <p>The Amazon Resource Name (ARN) of the new project. You can use the ARN to configure IAM access to the project. </p>
pub fn project_arn(&self) -> std::option::Option<&str> {
self.project_arn.as_deref()
}
}
/// See [`CreateProjectOutput`](crate::output::CreateProjectOutput).
pub mod create_project_output {
/// A builder for [`CreateProjectOutput`](crate::output::CreateProjectOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) project_arn: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The Amazon Resource Name (ARN) of the new project. You can use the ARN to configure IAM access to the project. </p>
pub fn project_arn(mut self, input: impl Into<std::string::String>) -> Self {
self.project_arn = Some(input.into());
self
}
/// <p>The Amazon Resource Name (ARN) of the new project. You can use the ARN to configure IAM access to the project. </p>
pub fn set_project_arn(mut self, input: std::option::Option<std::string::String>) -> Self {
self.project_arn = input;
self
}
/// Consumes the builder and constructs a [`CreateProjectOutput`](crate::output::CreateProjectOutput).
pub fn build(self) -> crate::output::CreateProjectOutput {
crate::output::CreateProjectOutput {
project_arn: self.project_arn,
}
}
}
}
impl CreateProjectOutput {
/// Creates a new builder-style object to manufacture [`CreateProjectOutput`](crate::output::CreateProjectOutput).
pub fn builder() -> crate::output::create_project_output::Builder {
crate::output::create_project_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct CreateDatasetOutput {
/// <p> The ARN of the created Amazon Rekognition Custom Labels dataset. </p>
#[doc(hidden)]
pub dataset_arn: std::option::Option<std::string::String>,
}
impl CreateDatasetOutput {
/// <p> The ARN of the created Amazon Rekognition Custom Labels dataset. </p>
pub fn dataset_arn(&self) -> std::option::Option<&str> {
self.dataset_arn.as_deref()
}
}
/// See [`CreateDatasetOutput`](crate::output::CreateDatasetOutput).
pub mod create_dataset_output {
/// A builder for [`CreateDatasetOutput`](crate::output::CreateDatasetOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) dataset_arn: std::option::Option<std::string::String>,
}
impl Builder {
/// <p> The ARN of the created Amazon Rekognition Custom Labels dataset. </p>
pub fn dataset_arn(mut self, input: impl Into<std::string::String>) -> Self {
self.dataset_arn = Some(input.into());
self
}
/// <p> The ARN of the created Amazon Rekognition Custom Labels dataset. </p>
pub fn set_dataset_arn(mut self, input: std::option::Option<std::string::String>) -> Self {
self.dataset_arn = input;
self
}
/// Consumes the builder and constructs a [`CreateDatasetOutput`](crate::output::CreateDatasetOutput).
pub fn build(self) -> crate::output::CreateDatasetOutput {
crate::output::CreateDatasetOutput {
dataset_arn: self.dataset_arn,
}
}
}
}
impl CreateDatasetOutput {
/// Creates a new builder-style object to manufacture [`CreateDatasetOutput`](crate::output::CreateDatasetOutput).
pub fn builder() -> crate::output::create_dataset_output::Builder {
crate::output::create_dataset_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct CreateCollectionOutput {
/// <p>HTTP status code indicating the result of the operation.</p>
#[doc(hidden)]
pub status_code: std::option::Option<i32>,
/// <p>Amazon Resource Name (ARN) of the collection. You can use this to manage permissions on your resources. </p>
#[doc(hidden)]
pub collection_arn: std::option::Option<std::string::String>,
/// <p>Version number of the face detection model associated with the collection you are creating.</p>
#[doc(hidden)]
pub face_model_version: std::option::Option<std::string::String>,
}
impl CreateCollectionOutput {
/// <p>HTTP status code indicating the result of the operation.</p>
pub fn status_code(&self) -> std::option::Option<i32> {
self.status_code
}
/// <p>Amazon Resource Name (ARN) of the collection. You can use this to manage permissions on your resources. </p>
pub fn collection_arn(&self) -> std::option::Option<&str> {
self.collection_arn.as_deref()
}
/// <p>Version number of the face detection model associated with the collection you are creating.</p>
pub fn face_model_version(&self) -> std::option::Option<&str> {
self.face_model_version.as_deref()
}
}
/// See [`CreateCollectionOutput`](crate::output::CreateCollectionOutput).
pub mod create_collection_output {
/// A builder for [`CreateCollectionOutput`](crate::output::CreateCollectionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) status_code: std::option::Option<i32>,
pub(crate) collection_arn: std::option::Option<std::string::String>,
pub(crate) face_model_version: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>HTTP status code indicating the result of the operation.</p>
pub fn status_code(mut self, input: i32) -> Self {
self.status_code = Some(input);
self
}
/// <p>HTTP status code indicating the result of the operation.</p>
pub fn set_status_code(mut self, input: std::option::Option<i32>) -> Self {
self.status_code = input;
self
}
/// <p>Amazon Resource Name (ARN) of the collection. You can use this to manage permissions on your resources. </p>
pub fn collection_arn(mut self, input: impl Into<std::string::String>) -> Self {
self.collection_arn = Some(input.into());
self
}
/// <p>Amazon Resource Name (ARN) of the collection. You can use this to manage permissions on your resources. </p>
pub fn set_collection_arn(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.collection_arn = input;
self
}
/// <p>Version number of the face detection model associated with the collection you are creating.</p>
pub fn face_model_version(mut self, input: impl Into<std::string::String>) -> Self {
self.face_model_version = Some(input.into());
self
}
/// <p>Version number of the face detection model associated with the collection you are creating.</p>
pub fn set_face_model_version(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.face_model_version = input;
self
}
/// Consumes the builder and constructs a [`CreateCollectionOutput`](crate::output::CreateCollectionOutput).
pub fn build(self) -> crate::output::CreateCollectionOutput {
crate::output::CreateCollectionOutput {
status_code: self.status_code,
collection_arn: self.collection_arn,
face_model_version: self.face_model_version,
}
}
}
}
impl CreateCollectionOutput {
/// Creates a new builder-style object to manufacture [`CreateCollectionOutput`](crate::output::CreateCollectionOutput).
pub fn builder() -> crate::output::create_collection_output::Builder {
crate::output::create_collection_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct CopyProjectVersionOutput {
/// <p>The ARN of the copied model version in the destination project. </p>
#[doc(hidden)]
pub project_version_arn: std::option::Option<std::string::String>,
}
impl CopyProjectVersionOutput {
/// <p>The ARN of the copied model version in the destination project. </p>
pub fn project_version_arn(&self) -> std::option::Option<&str> {
self.project_version_arn.as_deref()
}
}
/// See [`CopyProjectVersionOutput`](crate::output::CopyProjectVersionOutput).
pub mod copy_project_version_output {
/// A builder for [`CopyProjectVersionOutput`](crate::output::CopyProjectVersionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) project_version_arn: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The ARN of the copied model version in the destination project. </p>
pub fn project_version_arn(mut self, input: impl Into<std::string::String>) -> Self {
self.project_version_arn = Some(input.into());
self
}
/// <p>The ARN of the copied model version in the destination project. </p>
pub fn set_project_version_arn(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.project_version_arn = input;
self
}
/// Consumes the builder and constructs a [`CopyProjectVersionOutput`](crate::output::CopyProjectVersionOutput).
pub fn build(self) -> crate::output::CopyProjectVersionOutput {
crate::output::CopyProjectVersionOutput {
project_version_arn: self.project_version_arn,
}
}
}
}
impl CopyProjectVersionOutput {
/// Creates a new builder-style object to manufacture [`CopyProjectVersionOutput`](crate::output::CopyProjectVersionOutput).
pub fn builder() -> crate::output::copy_project_version_output::Builder {
crate::output::copy_project_version_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct CompareFacesOutput {
/// <p>The face in the source image that was used for comparison.</p>
#[doc(hidden)]
pub source_image_face: std::option::Option<crate::model::ComparedSourceImageFace>,
/// <p>An array of faces in the target image that match the source image face. Each <code>CompareFacesMatch</code> object provides the bounding box, the confidence level that the bounding box contains a face, and the similarity score for the face in the bounding box and the face in the source image.</p>
#[doc(hidden)]
pub face_matches: std::option::Option<std::vec::Vec<crate::model::CompareFacesMatch>>,
/// <p>An array of faces in the target image that did not match the source image face.</p>
#[doc(hidden)]
pub unmatched_faces: std::option::Option<std::vec::Vec<crate::model::ComparedFace>>,
/// <p>The value of <code>SourceImageOrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
#[doc(hidden)]
pub source_image_orientation_correction:
std::option::Option<crate::model::OrientationCorrection>,
/// <p>The value of <code>TargetImageOrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
#[doc(hidden)]
pub target_image_orientation_correction:
std::option::Option<crate::model::OrientationCorrection>,
}
impl CompareFacesOutput {
/// <p>The face in the source image that was used for comparison.</p>
pub fn source_image_face(&self) -> std::option::Option<&crate::model::ComparedSourceImageFace> {
self.source_image_face.as_ref()
}
/// <p>An array of faces in the target image that match the source image face. Each <code>CompareFacesMatch</code> object provides the bounding box, the confidence level that the bounding box contains a face, and the similarity score for the face in the bounding box and the face in the source image.</p>
pub fn face_matches(&self) -> std::option::Option<&[crate::model::CompareFacesMatch]> {
self.face_matches.as_deref()
}
/// <p>An array of faces in the target image that did not match the source image face.</p>
pub fn unmatched_faces(&self) -> std::option::Option<&[crate::model::ComparedFace]> {
self.unmatched_faces.as_deref()
}
/// <p>The value of <code>SourceImageOrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
pub fn source_image_orientation_correction(
&self,
) -> std::option::Option<&crate::model::OrientationCorrection> {
self.source_image_orientation_correction.as_ref()
}
/// <p>The value of <code>TargetImageOrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
pub fn target_image_orientation_correction(
&self,
) -> std::option::Option<&crate::model::OrientationCorrection> {
self.target_image_orientation_correction.as_ref()
}
}
/// See [`CompareFacesOutput`](crate::output::CompareFacesOutput).
pub mod compare_faces_output {
/// A builder for [`CompareFacesOutput`](crate::output::CompareFacesOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) source_image_face: std::option::Option<crate::model::ComparedSourceImageFace>,
pub(crate) face_matches:
std::option::Option<std::vec::Vec<crate::model::CompareFacesMatch>>,
pub(crate) unmatched_faces: std::option::Option<std::vec::Vec<crate::model::ComparedFace>>,
pub(crate) source_image_orientation_correction:
std::option::Option<crate::model::OrientationCorrection>,
pub(crate) target_image_orientation_correction:
std::option::Option<crate::model::OrientationCorrection>,
}
impl Builder {
/// <p>The face in the source image that was used for comparison.</p>
pub fn source_image_face(mut self, input: crate::model::ComparedSourceImageFace) -> Self {
self.source_image_face = Some(input);
self
}
/// <p>The face in the source image that was used for comparison.</p>
pub fn set_source_image_face(
mut self,
input: std::option::Option<crate::model::ComparedSourceImageFace>,
) -> Self {
self.source_image_face = input;
self
}
/// Appends an item to `face_matches`.
///
/// To override the contents of this collection use [`set_face_matches`](Self::set_face_matches).
///
/// <p>An array of faces in the target image that match the source image face. Each <code>CompareFacesMatch</code> object provides the bounding box, the confidence level that the bounding box contains a face, and the similarity score for the face in the bounding box and the face in the source image.</p>
pub fn face_matches(mut self, input: crate::model::CompareFacesMatch) -> Self {
let mut v = self.face_matches.unwrap_or_default();
v.push(input);
self.face_matches = Some(v);
self
}
/// <p>An array of faces in the target image that match the source image face. Each <code>CompareFacesMatch</code> object provides the bounding box, the confidence level that the bounding box contains a face, and the similarity score for the face in the bounding box and the face in the source image.</p>
pub fn set_face_matches(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::CompareFacesMatch>>,
) -> Self {
self.face_matches = input;
self
}
/// Appends an item to `unmatched_faces`.
///
/// To override the contents of this collection use [`set_unmatched_faces`](Self::set_unmatched_faces).
///
/// <p>An array of faces in the target image that did not match the source image face.</p>
pub fn unmatched_faces(mut self, input: crate::model::ComparedFace) -> Self {
let mut v = self.unmatched_faces.unwrap_or_default();
v.push(input);
self.unmatched_faces = Some(v);
self
}
/// <p>An array of faces in the target image that did not match the source image face.</p>
pub fn set_unmatched_faces(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::ComparedFace>>,
) -> Self {
self.unmatched_faces = input;
self
}
/// <p>The value of <code>SourceImageOrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
pub fn source_image_orientation_correction(
mut self,
input: crate::model::OrientationCorrection,
) -> Self {
self.source_image_orientation_correction = Some(input);
self
}
/// <p>The value of <code>SourceImageOrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
pub fn set_source_image_orientation_correction(
mut self,
input: std::option::Option<crate::model::OrientationCorrection>,
) -> Self {
self.source_image_orientation_correction = input;
self
}
/// <p>The value of <code>TargetImageOrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
pub fn target_image_orientation_correction(
mut self,
input: crate::model::OrientationCorrection,
) -> Self {
self.target_image_orientation_correction = Some(input);
self
}
/// <p>The value of <code>TargetImageOrientationCorrection</code> is always null.</p>
/// <p>If the input image is in .jpeg format, it might contain exchangeable image file format (Exif) metadata that includes the image's orientation. Amazon Rekognition uses this orientation information to perform image correction. The bounding box coordinates are translated to represent object locations after the orientation information in the Exif metadata is used to correct the image orientation. Images in .png format don't contain Exif metadata.</p>
/// <p>Amazon Rekognition doesn’t perform image correction for images in .png format and .jpeg images without orientation information in the image Exif metadata. The bounding box coordinates aren't translated and represent the object locations before the image is rotated. </p>
pub fn set_target_image_orientation_correction(
mut self,
input: std::option::Option<crate::model::OrientationCorrection>,
) -> Self {
self.target_image_orientation_correction = input;
self
}
/// Consumes the builder and constructs a [`CompareFacesOutput`](crate::output::CompareFacesOutput).
pub fn build(self) -> crate::output::CompareFacesOutput {
crate::output::CompareFacesOutput {
source_image_face: self.source_image_face,
face_matches: self.face_matches,
unmatched_faces: self.unmatched_faces,
source_image_orientation_correction: self.source_image_orientation_correction,
target_image_orientation_correction: self.target_image_orientation_correction,
}
}
}
}
impl CompareFacesOutput {
/// Creates a new builder-style object to manufacture [`CompareFacesOutput`](crate::output::CompareFacesOutput).
pub fn builder() -> crate::output::compare_faces_output::Builder {
crate::output::compare_faces_output::Builder::default()
}
}