Struct DetectAnomalyResultBuilder 

#[non_exhaustive]
pub struct DetectAnomalyResultBuilder { /* private fields */ }

A builder for DetectAnomalyResult.

Implementations

impl DetectAnomalyResultBuilder

pub fn source(self, input: ImageSource) -> Self

The source of the image that was analyzed. direct means that the images was supplied from the local computer. No other values are supported.

pub fn set_source(self, input: Option<ImageSource>) -> Self

The source of the image that was analyzed. direct means that the images was supplied from the local computer. No other values are supported.

pub fn get_source(&self) -> &Option<ImageSource>

The source of the image that was analyzed. direct means that the images was supplied from the local computer. No other values are supported.

pub fn is_anomalous(self, input: bool) -> Self

True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.

pub fn set_is_anomalous(self, input: Option<bool>) -> Self

True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.

pub fn get_is_anomalous(&self) -> &Option<bool>

True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.

pub fn confidence(self, input: f32) -> Self

The confidence that Lookout for Vision has in the accuracy of the classification in IsAnomalous.

pub fn set_confidence(self, input: Option<f32>) -> Self

The confidence that Lookout for Vision has in the accuracy of the classification in IsAnomalous.

pub fn get_confidence(&self) -> &Option<f32>

The confidence that Lookout for Vision has in the accuracy of the classification in IsAnomalous.

pub fn anomalies(self, input: Anomaly) -> Self

Appends an item to anomalies.

To override the contents of this collection use set_anomalies.

If the model is an image segmentation model, Anomalies contains a list of anomaly types found in the image. There is one entry for each type of anomaly found (even if multiple instances of an anomaly type exist on the image). The first element in the list is always an anomaly type representing the image background ('background') and shouldn't be considered an anomaly. Amazon Lookout for Vision automatically add the background anomaly type to the response, and you don't need to declare a background anomaly type in your dataset.

If the list has one entry ('background'), no anomalies were found on the image.

An image classification model doesn't return an Anomalies list.

pub fn set_anomalies(self, input: Option<Vec<Anomaly>>) -> Self

If the model is an image segmentation model, Anomalies contains a list of anomaly types found in the image. There is one entry for each type of anomaly found (even if multiple instances of an anomaly type exist on the image). The first element in the list is always an anomaly type representing the image background ('background') and shouldn't be considered an anomaly. Amazon Lookout for Vision automatically add the background anomaly type to the response, and you don't need to declare a background anomaly type in your dataset.

If the list has one entry ('background'), no anomalies were found on the image.

An image classification model doesn't return an Anomalies list.

pub fn get_anomalies(&self) -> &Option<Vec<Anomaly>>

If the model is an image segmentation model, Anomalies contains a list of anomaly types found in the image. There is one entry for each type of anomaly found (even if multiple instances of an anomaly type exist on the image). The first element in the list is always an anomaly type representing the image background ('background') and shouldn't be considered an anomaly. Amazon Lookout for Vision automatically add the background anomaly type to the response, and you don't need to declare a background anomaly type in your dataset.

If the list has one entry ('background'), no anomalies were found on the image.

An image classification model doesn't return an Anomalies list.

pub fn anomaly_mask(self, input: Blob) -> Self

If the model is an image segmentation model, AnomalyMask contains pixel masks that covers all anomaly types found on the image. Each anomaly type has a different mask color. To map a color to an anomaly type, see the color field of the PixelAnomaly object.

An image classification model doesn't return an Anomalies list.

pub fn set_anomaly_mask(self, input: Option<Blob>) -> Self

If the model is an image segmentation model, AnomalyMask contains pixel masks that covers all anomaly types found on the image. Each anomaly type has a different mask color. To map a color to an anomaly type, see the color field of the PixelAnomaly object.

An image classification model doesn't return an Anomalies list.

pub fn get_anomaly_mask(&self) -> &Option<Blob>

If the model is an image segmentation model, AnomalyMask contains pixel masks that covers all anomaly types found on the image. Each anomaly type has a different mask color. To map a color to an anomaly type, see the color field of the PixelAnomaly object.

An image classification model doesn't return an Anomalies list.

pub fn build(self) -> DetectAnomalyResult

Consumes the builder and constructs a DetectAnomalyResult.

Trait Implementations

impl Clone for DetectAnomalyResultBuilder

fn clone(&self) -> DetectAnomalyResultBuilder

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for DetectAnomalyResultBuilder

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for DetectAnomalyResultBuilder

fn default() -> DetectAnomalyResultBuilder

Returns the “default value” for a type.

impl PartialEq for DetectAnomalyResultBuilder

fn eq(&self, other: &DetectAnomalyResultBuilder) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for DetectAnomalyResultBuilder