Struct DetectAnomalyResult 

#[non_exhaustive]
pub struct DetectAnomalyResult {
    pub source: Option<ImageSource>,
    pub is_anomalous: Option<bool>,
    pub confidence: Option<f32>,
    pub anomalies: Option<Vec<Anomaly>>,
    pub anomaly_mask: Option<Blob>,
}

The prediction results from a call to DetectAnomalies. DetectAnomalyResult includes classification information for the prediction (IsAnomalous and Confidence). If the model you use is an image segementation model, DetectAnomalyResult also includes segmentation information (Anomalies and AnomalyMask). Classification information is calculated separately from segmentation information and you shouldn't assume a relationship between them.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

source: 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.

is_anomalous: Option<bool>

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

confidence: Option<f32>

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

anomalies: 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.

anomaly_mask: 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.

Implementations

impl DetectAnomalyResult

pub fn 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) -> Option<bool>

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

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

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

pub fn anomalies(&self) -> &[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.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .anomalies.is_none().

pub fn 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.

impl DetectAnomalyResult

pub fn builder() -> DetectAnomalyResultBuilder

Creates a new builder-style object to manufacture DetectAnomalyResult.

Trait Implementations

impl Clone for DetectAnomalyResult

fn clone(&self) -> DetectAnomalyResult

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for DetectAnomalyResult

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

Formats the value using the given formatter.

impl PartialEq for DetectAnomalyResult

fn eq(&self, other: &DetectAnomalyResult) -> 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 DetectAnomalyResult