public static interface DetectAnomalyResult.Builder extends SdkPojo, CopyableBuilder<DetectAnomalyResult.Builder,DetectAnomalyResult>
| Modifier and Type | Method and Description |
|---|---|
DetectAnomalyResult.Builder |
anomalies(Anomaly... anomalies)
If the model is an image segmentation model,
Anomalies contains a list of anomaly types found in
the image. |
DetectAnomalyResult.Builder |
anomalies(Collection<Anomaly> anomalies)
If the model is an image segmentation model,
Anomalies contains a list of anomaly types found in
the image. |
DetectAnomalyResult.Builder |
anomalies(Consumer<Anomaly.Builder>... anomalies)
If the model is an image segmentation model,
Anomalies contains a list of anomaly types found in
the image. |
DetectAnomalyResult.Builder |
anomalyMask(SdkBytes anomalyMask)
If the model is an image segmentation model,
AnomalyMask contains pixel masks that covers all
anomaly types found on the image. |
DetectAnomalyResult.Builder |
confidence(Float confidence)
The confidence that Lookout for Vision has in the accuracy of the classification in
IsAnomalous. |
DetectAnomalyResult.Builder |
isAnomalous(Boolean isAnomalous)
True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.
|
default DetectAnomalyResult.Builder |
source(Consumer<ImageSource.Builder> source)
The source of the image that was analyzed.
|
DetectAnomalyResult.Builder |
source(ImageSource source)
The source of the image that was analyzed.
|
equalsBySdkFields, sdkFieldscopyapplyMutation, buildDetectAnomalyResult.Builder source(ImageSource source)
The source of the image that was analyzed. direct means that the images was supplied from the
local computer. No other values are supported.
source - The source of the image that was analyzed. direct means that the images was supplied from
the local computer. No other values are supported.default DetectAnomalyResult.Builder source(Consumer<ImageSource.Builder> source)
The source of the image that was analyzed. direct means that the images was supplied from the
local computer. No other values are supported.
ImageSource.Builder avoiding the need to
create one manually via ImageSource.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its result
is passed to source(ImageSource).source - a consumer that will call methods on ImageSource.Buildersource(ImageSource)DetectAnomalyResult.Builder isAnomalous(Boolean isAnomalous)
True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.
isAnomalous - True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.DetectAnomalyResult.Builder confidence(Float confidence)
The confidence that Lookout for Vision has in the accuracy of the classification in IsAnomalous.
confidence - The confidence that Lookout for Vision has in the accuracy of the classification in
IsAnomalous.DetectAnomalyResult.Builder anomalies(Collection<Anomaly> 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.
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.
DetectAnomalyResult.Builder anomalies(Anomaly... 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.
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.
DetectAnomalyResult.Builder anomalies(Consumer<Anomaly.Builder>... 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.
List.Builder avoiding the need
to create one manually via List#builder() .
When the Consumer completes, List.Builder#build() is called immediately and its
result is passed to #anomalies(List) .anomalies - a consumer that will call methods on List.Builder #anomalies(List) DetectAnomalyResult.Builder anomalyMask(SdkBytes anomalyMask)
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.
anomalyMask - 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.
Copyright © 2022. All rights reserved.