114 lines
3.4 KiB
C
114 lines
3.4 KiB
C
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/**
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* Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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* SPDX-License-Identifier: Apache-2.0.
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*/
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#pragma once
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#include <aws/rekognition/Rekognition_EXPORTS.h>
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#include <aws/rekognition/model/GenderType.h>
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#include <utility>
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namespace Aws
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{
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namespace Utils
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{
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namespace Json
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{
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class JsonValue;
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class JsonView;
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} // namespace Json
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} // namespace Utils
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namespace Rekognition
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{
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namespace Model
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{
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/**
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* <p>The predicted gender of a detected face. </p> <p>Amazon Rekognition makes
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* gender binary (male/female) predictions based on the physical appearance of a
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* face in a particular image. This kind of prediction is not designed to
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* categorize a person’s gender identity, and you shouldn't use Amazon Rekognition
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* to make such a determination. For example, a male actor wearing a long-haired
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* wig and earrings for a role might be predicted as female.</p> <p>Using Amazon
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* Rekognition to make gender binary predictions is best suited for use cases where
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* aggregate gender distribution statistics need to be analyzed without identifying
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* specific users. For example, the percentage of female users compared to male
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* users on a social media platform. </p> <p>We don't recommend using gender binary
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* predictions to make decisions that impact
 an individual's rights,
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* privacy, or access to services.</p><p><h3>See Also:</h3> <a
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* href="http://docs.aws.amazon.com/goto/WebAPI/rekognition-2016-06-27/Gender">AWS
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* API Reference</a></p>
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*/
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class AWS_REKOGNITION_API Gender
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{
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public:
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Gender();
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Gender(Aws::Utils::Json::JsonView jsonValue);
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Gender& operator=(Aws::Utils::Json::JsonView jsonValue);
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Aws::Utils::Json::JsonValue Jsonize() const;
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/**
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* <p>The predicted gender of the face.</p>
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*/
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inline const GenderType& GetValue() const{ return m_value; }
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/**
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* <p>The predicted gender of the face.</p>
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*/
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inline bool ValueHasBeenSet() const { return m_valueHasBeenSet; }
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/**
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* <p>The predicted gender of the face.</p>
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*/
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inline void SetValue(const GenderType& value) { m_valueHasBeenSet = true; m_value = value; }
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/**
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* <p>The predicted gender of the face.</p>
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*/
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inline void SetValue(GenderType&& value) { m_valueHasBeenSet = true; m_value = std::move(value); }
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/**
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* <p>The predicted gender of the face.</p>
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*/
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inline Gender& WithValue(const GenderType& value) { SetValue(value); return *this;}
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/**
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* <p>The predicted gender of the face.</p>
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*/
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inline Gender& WithValue(GenderType&& value) { SetValue(std::move(value)); return *this;}
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/**
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* <p>Level of confidence in the prediction.</p>
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*/
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inline double GetConfidence() const{ return m_confidence; }
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/**
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* <p>Level of confidence in the prediction.</p>
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*/
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inline bool ConfidenceHasBeenSet() const { return m_confidenceHasBeenSet; }
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/**
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* <p>Level of confidence in the prediction.</p>
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*/
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inline void SetConfidence(double value) { m_confidenceHasBeenSet = true; m_confidence = value; }
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/**
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* <p>Level of confidence in the prediction.</p>
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*/
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inline Gender& WithConfidence(double value) { SetConfidence(value); return *this;}
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private:
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GenderType m_value;
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bool m_valueHasBeenSet;
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double m_confidence;
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bool m_confidenceHasBeenSet;
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};
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} // namespace Model
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} // namespace Rekognition
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} // namespace Aws
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