com.amazonaws.services.lookoutforvision.model.DetectAnomalyResult Maven / Gradle / Ivy
/*
* Copyright 2019-2024 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
* CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package com.amazonaws.services.lookoutforvision.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
*
* 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.
*
*
* @see AWS
* API Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class DetectAnomalyResult implements Serializable, Cloneable, StructuredPojo {
/**
*
* The source of the image that was analyzed. direct means that the images was supplied from the local
* computer. No other values are supported.
*
*/
private ImageSource source;
/**
*
* True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.
*
*/
private Boolean isAnomalous;
/**
*
* The confidence that Lookout for Vision has in the accuracy of the classification in IsAnomalous.
*
*/
private Float confidence;
/**
*
* 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.
*
*/
private java.util.List anomalies;
/**
*
* 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.
*
*/
private java.nio.ByteBuffer anomalyMask;
/**
*
* The source of the image that was analyzed. direct means that the images was supplied from the local
* computer. No other values are supported.
*
*
* @param 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.
*/
public void setSource(ImageSource source) {
this.source = 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.
*
*
* @return The source of the image that was analyzed. direct means that the images was supplied from
* the local computer. No other values are supported.
*/
public ImageSource getSource() {
return this.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.
*
*
* @param 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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DetectAnomalyResult withSource(ImageSource source) {
setSource(source);
return this;
}
/**
*
* True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.
*
*
* @param isAnomalous
* True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.
*/
public void setIsAnomalous(Boolean isAnomalous) {
this.isAnomalous = isAnomalous;
}
/**
*
* True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.
*
*
* @return True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.
*/
public Boolean getIsAnomalous() {
return this.isAnomalous;
}
/**
*
* True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.
*
*
* @param isAnomalous
* True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DetectAnomalyResult withIsAnomalous(Boolean isAnomalous) {
setIsAnomalous(isAnomalous);
return this;
}
/**
*
* True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.
*
*
* @return True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.
*/
public Boolean isAnomalous() {
return this.isAnomalous;
}
/**
*
* The confidence that Lookout for Vision has in the accuracy of the classification in IsAnomalous.
*
*
* @param confidence
* The confidence that Lookout for Vision has in the accuracy of the classification in
* IsAnomalous.
*/
public void setConfidence(Float confidence) {
this.confidence = confidence;
}
/**
*
* The confidence that Lookout for Vision has in the accuracy of the classification in IsAnomalous.
*
*
* @return The confidence that Lookout for Vision has in the accuracy of the classification in
* IsAnomalous.
*/
public Float getConfidence() {
return this.confidence;
}
/**
*
* The confidence that Lookout for Vision has in the accuracy of the classification in IsAnomalous.
*
*
* @param confidence
* The confidence that Lookout for Vision has in the accuracy of the classification in
* IsAnomalous.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DetectAnomalyResult withConfidence(Float confidence) {
setConfidence(confidence);
return this;
}
/**
*
* 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.
*
*
* @return 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.
*/
public java.util.List getAnomalies() {
return 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.
*
*
* @param 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.
*/
public void setAnomalies(java.util.Collection anomalies) {
if (anomalies == null) {
this.anomalies = null;
return;
}
this.anomalies = new java.util.ArrayList(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.
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setAnomalies(java.util.Collection)} or {@link #withAnomalies(java.util.Collection)} if you want to
* override the existing values.
*
*
* @param 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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DetectAnomalyResult withAnomalies(Anomaly... anomalies) {
if (this.anomalies == null) {
setAnomalies(new java.util.ArrayList(anomalies.length));
}
for (Anomaly ele : anomalies) {
this.anomalies.add(ele);
}
return this;
}
/**
*
* 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.
*
*
* @param 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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DetectAnomalyResult withAnomalies(java.util.Collection anomalies) {
setAnomalies(anomalies);
return this;
}
/**
*
* 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.
*
*
* The AWS SDK for Java performs a Base64 encoding on this field before sending this request to the AWS service.
* Users of the SDK should not perform Base64 encoding on this field.
*
*
* Warning: ByteBuffers returned by the SDK are mutable. Changes to the content or position of the byte buffer will
* be seen by all objects that have a reference to this object. It is recommended to call ByteBuffer.duplicate() or
* ByteBuffer.asReadOnlyBuffer() before using or reading from the buffer. This behavior will be changed in a future
* major version of the SDK.
*
*
* @param 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.
*/
public void setAnomalyMask(java.nio.ByteBuffer anomalyMask) {
this.anomalyMask = 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.
*
*
* {@code ByteBuffer}s are stateful. Calling their {@code get} methods changes their {@code position}. We recommend
* using {@link java.nio.ByteBuffer#asReadOnlyBuffer()} to create a read-only view of the buffer with an independent
* {@code position}, and calling {@code get} methods on this rather than directly on the returned {@code ByteBuffer}.
* Doing so will ensure that anyone else using the {@code ByteBuffer} will not be affected by changes to the
* {@code position}.
*
*
* @return 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.
*/
public java.nio.ByteBuffer getAnomalyMask() {
return this.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.
*
*
* The AWS SDK for Java performs a Base64 encoding on this field before sending this request to the AWS service.
* Users of the SDK should not perform Base64 encoding on this field.
*
*
* Warning: ByteBuffers returned by the SDK are mutable. Changes to the content or position of the byte buffer will
* be seen by all objects that have a reference to this object. It is recommended to call ByteBuffer.duplicate() or
* ByteBuffer.asReadOnlyBuffer() before using or reading from the buffer. This behavior will be changed in a future
* major version of the SDK.
*
*
* @param 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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DetectAnomalyResult withAnomalyMask(java.nio.ByteBuffer anomalyMask) {
setAnomalyMask(anomalyMask);
return this;
}
/**
* Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be
* redacted from this string using a placeholder value.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getSource() != null)
sb.append("Source: ").append(getSource()).append(",");
if (getIsAnomalous() != null)
sb.append("IsAnomalous: ").append(getIsAnomalous()).append(",");
if (getConfidence() != null)
sb.append("Confidence: ").append(getConfidence()).append(",");
if (getAnomalies() != null)
sb.append("Anomalies: ").append(getAnomalies()).append(",");
if (getAnomalyMask() != null)
sb.append("AnomalyMask: ").append(getAnomalyMask());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof DetectAnomalyResult == false)
return false;
DetectAnomalyResult other = (DetectAnomalyResult) obj;
if (other.getSource() == null ^ this.getSource() == null)
return false;
if (other.getSource() != null && other.getSource().equals(this.getSource()) == false)
return false;
if (other.getIsAnomalous() == null ^ this.getIsAnomalous() == null)
return false;
if (other.getIsAnomalous() != null && other.getIsAnomalous().equals(this.getIsAnomalous()) == false)
return false;
if (other.getConfidence() == null ^ this.getConfidence() == null)
return false;
if (other.getConfidence() != null && other.getConfidence().equals(this.getConfidence()) == false)
return false;
if (other.getAnomalies() == null ^ this.getAnomalies() == null)
return false;
if (other.getAnomalies() != null && other.getAnomalies().equals(this.getAnomalies()) == false)
return false;
if (other.getAnomalyMask() == null ^ this.getAnomalyMask() == null)
return false;
if (other.getAnomalyMask() != null && other.getAnomalyMask().equals(this.getAnomalyMask()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getSource() == null) ? 0 : getSource().hashCode());
hashCode = prime * hashCode + ((getIsAnomalous() == null) ? 0 : getIsAnomalous().hashCode());
hashCode = prime * hashCode + ((getConfidence() == null) ? 0 : getConfidence().hashCode());
hashCode = prime * hashCode + ((getAnomalies() == null) ? 0 : getAnomalies().hashCode());
hashCode = prime * hashCode + ((getAnomalyMask() == null) ? 0 : getAnomalyMask().hashCode());
return hashCode;
}
@Override
public DetectAnomalyResult clone() {
try {
return (DetectAnomalyResult) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
@com.amazonaws.annotation.SdkInternalApi
@Override
public void marshall(ProtocolMarshaller protocolMarshaller) {
com.amazonaws.services.lookoutforvision.model.transform.DetectAnomalyResultMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}