aws_sdk_rekognition/operation/start_label_detection/

builders.rs

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::start_label_detection::_start_label_detection_output::StartLabelDetectionOutputBuilder;

pub use crate::operation::start_label_detection::_start_label_detection_input::StartLabelDetectionInputBuilder;

impl crate::operation::start_label_detection::builders::StartLabelDetectionInputBuilder {
    /// Sends a request with this input using the given client.
    pub async fn send_with(
        self,
        client: &crate::Client,
    ) -> ::std::result::Result<
        crate::operation::start_label_detection::StartLabelDetectionOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::start_label_detection::StartLabelDetectionError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.start_label_detection();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `StartLabelDetection`.
///
/// <p>Starts asynchronous detection of labels in a stored video.</p>
/// <p>Amazon Rekognition Video can detect labels in a video. Labels are instances of real-world entities. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; concepts like landscape, evening, and nature; and activities like a person getting out of a car or a person skiing.</p>
/// <p>The video must be stored in an Amazon S3 bucket. Use <code>Video</code> to specify the bucket name and the filename of the video. <code>StartLabelDetection</code> returns a job identifier (<code>JobId</code>) which you use to get the results of the operation. When label detection is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic that you specify in <code>NotificationChannel</code>.</p>
/// <p>To get the results of the label detection operation, first check that the status value published to the Amazon SNS topic is <code>SUCCEEDED</code>. If so, call <code>GetLabelDetection</code> and pass the job identifier (<code>JobId</code>) from the initial call to <code>StartLabelDetection</code>.</p>
/// <p><i>Optional Parameters</i></p>
/// <p><code>StartLabelDetection</code> has the <code>GENERAL_LABELS</code> Feature applied by default. This feature allows you to provide filtering criteria to the <code>Settings</code> parameter. You can filter with sets of individual labels or with label categories. You can specify inclusive filters, exclusive filters, or a combination of inclusive and exclusive filters. For more information on filtering, see <a href="https://docs.aws.amazon.com/rekognition/latest/dg/labels-detecting-labels-video.html">Detecting labels in a video</a>.</p>
/// <p>You can specify <code>MinConfidence</code> to control the confidence threshold for the labels returned. The default is 50.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct StartLabelDetectionFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::start_label_detection::builders::StartLabelDetectionInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::start_label_detection::StartLabelDetectionOutput,
        crate::operation::start_label_detection::StartLabelDetectionError,
    > for StartLabelDetectionFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::start_label_detection::StartLabelDetectionOutput,
            crate::operation::start_label_detection::StartLabelDetectionError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl StartLabelDetectionFluentBuilder {
    /// Creates a new `StartLabelDetectionFluentBuilder`.
    pub(crate) fn new(handle: ::std::sync::Arc<crate::client::Handle>) -> Self {
        Self {
            handle,
            inner: ::std::default::Default::default(),
            config_override: ::std::option::Option::None,
        }
    }
    /// Access the StartLabelDetection as a reference.
    pub fn as_input(&self) -> &crate::operation::start_label_detection::builders::StartLabelDetectionInputBuilder {
        &self.inner
    }
    /// Sends the request and returns the response.
    ///
    /// If an error occurs, an `SdkError` will be returned with additional details that
    /// can be matched against.
    ///
    /// By default, any retryable failures will be retried twice. Retry behavior
    /// is configurable with the [RetryConfig](aws_smithy_types::retry::RetryConfig), which can be
    /// set when configuring the client.
    pub async fn send(
        self,
    ) -> ::std::result::Result<
        crate::operation::start_label_detection::StartLabelDetectionOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::start_label_detection::StartLabelDetectionError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let input = self
            .inner
            .build()
            .map_err(::aws_smithy_runtime_api::client::result::SdkError::construction_failure)?;
        let runtime_plugins = crate::operation::start_label_detection::StartLabelDetection::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::start_label_detection::StartLabelDetection::orchestrate(&runtime_plugins, input).await
    }

    /// Consumes this builder, creating a customizable operation that can be modified before being sent.
    pub fn customize(
        self,
    ) -> crate::client::customize::CustomizableOperation<
        crate::operation::start_label_detection::StartLabelDetectionOutput,
        crate::operation::start_label_detection::StartLabelDetectionError,
        Self,
    > {
        crate::client::customize::CustomizableOperation::new(self)
    }
    pub(crate) fn config_override(mut self, config_override: impl ::std::convert::Into<crate::config::Builder>) -> Self {
        self.set_config_override(::std::option::Option::Some(config_override.into()));
        self
    }

    pub(crate) fn set_config_override(&mut self, config_override: ::std::option::Option<crate::config::Builder>) -> &mut Self {
        self.config_override = config_override;
        self
    }
    /// <p>The video in which you want to detect labels. The video must be stored in an Amazon S3 bucket.</p>
    pub fn video(mut self, input: crate::types::Video) -> Self {
        self.inner = self.inner.video(input);
        self
    }
    /// <p>The video in which you want to detect labels. The video must be stored in an Amazon S3 bucket.</p>
    pub fn set_video(mut self, input: ::std::option::Option<crate::types::Video>) -> Self {
        self.inner = self.inner.set_video(input);
        self
    }
    /// <p>The video in which you want to detect labels. The video must be stored in an Amazon S3 bucket.</p>
    pub fn get_video(&self) -> &::std::option::Option<crate::types::Video> {
        self.inner.get_video()
    }
    /// <p>Idempotent token used to identify the start request. If you use the same token with multiple <code>StartLabelDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidently started more than once.</p>
    pub fn client_request_token(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.client_request_token(input.into());
        self
    }
    /// <p>Idempotent token used to identify the start request. If you use the same token with multiple <code>StartLabelDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidently started more than once.</p>
    pub fn set_client_request_token(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_client_request_token(input);
        self
    }
    /// <p>Idempotent token used to identify the start request. If you use the same token with multiple <code>StartLabelDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidently started more than once.</p>
    pub fn get_client_request_token(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_client_request_token()
    }
    /// <p>Specifies the minimum confidence that Amazon Rekognition Video must have in order to return a detected label. Confidence represents how certain Amazon Rekognition is that a label is correctly identified.0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition Video doesn't return any labels with a confidence level lower than this specified value.</p>
    /// <p>If you don't specify <code>MinConfidence</code>, the operation returns labels and bounding boxes (if detected) with confidence values greater than or equal to 50 percent.</p>
    pub fn min_confidence(mut self, input: f32) -> Self {
        self.inner = self.inner.min_confidence(input);
        self
    }
    /// <p>Specifies the minimum confidence that Amazon Rekognition Video must have in order to return a detected label. Confidence represents how certain Amazon Rekognition is that a label is correctly identified.0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition Video doesn't return any labels with a confidence level lower than this specified value.</p>
    /// <p>If you don't specify <code>MinConfidence</code>, the operation returns labels and bounding boxes (if detected) with confidence values greater than or equal to 50 percent.</p>
    pub fn set_min_confidence(mut self, input: ::std::option::Option<f32>) -> Self {
        self.inner = self.inner.set_min_confidence(input);
        self
    }
    /// <p>Specifies the minimum confidence that Amazon Rekognition Video must have in order to return a detected label. Confidence represents how certain Amazon Rekognition is that a label is correctly identified.0 is the lowest confidence. 100 is the highest confidence. Amazon Rekognition Video doesn't return any labels with a confidence level lower than this specified value.</p>
    /// <p>If you don't specify <code>MinConfidence</code>, the operation returns labels and bounding boxes (if detected) with confidence values greater than or equal to 50 percent.</p>
    pub fn get_min_confidence(&self) -> &::std::option::Option<f32> {
        self.inner.get_min_confidence()
    }
    /// <p>The Amazon SNS topic ARN you want Amazon Rekognition Video to publish the completion status of the label detection operation to. The Amazon SNS topic must have a topic name that begins with <i>AmazonRekognition</i> if you are using the AmazonRekognitionServiceRole permissions policy.</p>
    pub fn notification_channel(mut self, input: crate::types::NotificationChannel) -> Self {
        self.inner = self.inner.notification_channel(input);
        self
    }
    /// <p>The Amazon SNS topic ARN you want Amazon Rekognition Video to publish the completion status of the label detection operation to. The Amazon SNS topic must have a topic name that begins with <i>AmazonRekognition</i> if you are using the AmazonRekognitionServiceRole permissions policy.</p>
    pub fn set_notification_channel(mut self, input: ::std::option::Option<crate::types::NotificationChannel>) -> Self {
        self.inner = self.inner.set_notification_channel(input);
        self
    }
    /// <p>The Amazon SNS topic ARN you want Amazon Rekognition Video to publish the completion status of the label detection operation to. The Amazon SNS topic must have a topic name that begins with <i>AmazonRekognition</i> if you are using the AmazonRekognitionServiceRole permissions policy.</p>
    pub fn get_notification_channel(&self) -> &::std::option::Option<crate::types::NotificationChannel> {
        self.inner.get_notification_channel()
    }
    /// <p>An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use <code>JobTag</code> to group related jobs and identify them in the completion notification.</p>
    pub fn job_tag(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.job_tag(input.into());
        self
    }
    /// <p>An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use <code>JobTag</code> to group related jobs and identify them in the completion notification.</p>
    pub fn set_job_tag(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_job_tag(input);
        self
    }
    /// <p>An identifier you specify that's returned in the completion notification that's published to your Amazon Simple Notification Service topic. For example, you can use <code>JobTag</code> to group related jobs and identify them in the completion notification.</p>
    pub fn get_job_tag(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_job_tag()
    }
    ///
    /// Appends an item to `Features`.
    ///
    /// To override the contents of this collection use [`set_features`](Self::set_features).
    ///
    /// <p>The features to return after video analysis. You can specify that GENERAL_LABELS are returned.</p>
    pub fn features(mut self, input: crate::types::LabelDetectionFeatureName) -> Self {
        self.inner = self.inner.features(input);
        self
    }
    /// <p>The features to return after video analysis. You can specify that GENERAL_LABELS are returned.</p>
    pub fn set_features(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::LabelDetectionFeatureName>>) -> Self {
        self.inner = self.inner.set_features(input);
        self
    }
    /// <p>The features to return after video analysis. You can specify that GENERAL_LABELS are returned.</p>
    pub fn get_features(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::LabelDetectionFeatureName>> {
        self.inner.get_features()
    }
    /// <p>The settings for a StartLabelDetection request.Contains the specified parameters for the label detection request of an asynchronous label analysis operation. Settings can include filters for GENERAL_LABELS.</p>
    pub fn settings(mut self, input: crate::types::LabelDetectionSettings) -> Self {
        self.inner = self.inner.settings(input);
        self
    }
    /// <p>The settings for a StartLabelDetection request.Contains the specified parameters for the label detection request of an asynchronous label analysis operation. Settings can include filters for GENERAL_LABELS.</p>
    pub fn set_settings(mut self, input: ::std::option::Option<crate::types::LabelDetectionSettings>) -> Self {
        self.inner = self.inner.set_settings(input);
        self
    }
    /// <p>The settings for a StartLabelDetection request.Contains the specified parameters for the label detection request of an asynchronous label analysis operation. Settings can include filters for GENERAL_LABELS.</p>
    pub fn get_settings(&self) -> &::std::option::Option<crate::types::LabelDetectionSettings> {
        self.inner.get_settings()
    }
}