Interface DetectAnomalyResult.Builder
- All Superinterfaces:
Buildable
,CopyableBuilder<DetectAnomalyResult.Builder,
,DetectAnomalyResult> SdkBuilder<DetectAnomalyResult.Builder,
,DetectAnomalyResult> SdkPojo
- Enclosing class:
DetectAnomalyResult
-
Method Summary
Modifier and TypeMethodDescriptionanomalies
(Collection<Anomaly> anomalies) If the model is an image segmentation model,Anomalies
contains a list of anomaly types found in the image.anomalies
(Consumer<Anomaly.Builder>... anomalies) If the model is an image segmentation model,Anomalies
contains a list of anomaly types found in the image.If the model is an image segmentation model,Anomalies
contains a list of anomaly types found in the image.anomalyMask
(SdkBytes anomalyMask) If the model is an image segmentation model,AnomalyMask
contains pixel masks that covers all anomaly types found on the image.confidence
(Float confidence) The confidence that Lookout for Vision has in the accuracy of the classification inIsAnomalous
.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.source
(ImageSource source) The source of the image that was analyzed.Methods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuilder
copy
Methods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder
applyMutation, build
Methods inherited from interface software.amazon.awssdk.core.SdkPojo
equalsBySdkFields, sdkFields
-
Method Details
-
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.- Parameters:
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.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
source
The source of the image that was analyzed.
This is a convenience method that creates an instance of thedirect
means that the images was supplied from the local computer. No other values are supported.ImageSource.Builder
avoiding the need to create one manually viaImageSource.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed tosource(ImageSource)
.- Parameters:
source
- a consumer that will call methods onImageSource.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
isAnomalous
True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.
- Parameters:
isAnomalous
- True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
confidence
The confidence that Lookout for Vision has in the accuracy of the classification in
IsAnomalous
.- Parameters:
confidence
- The confidence that Lookout for Vision has in the accuracy of the classification inIsAnomalous
.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
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.- Parameters:
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.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
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.- Parameters:
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.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
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
This is a convenience method that creates an instance of theAnomalies
list.Anomaly.Builder
avoiding the need to create one manually viaAnomaly.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toanomalies(List<Anomaly>)
.- Parameters:
anomalies
- a consumer that will call methods onAnomaly.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
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 thecolor
field of the PixelAnomaly object.An image classification model doesn't return an
Anomalies
list.- Parameters:
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 thecolor
field of the PixelAnomaly object.An image classification model doesn't return an
Anomalies
list.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-