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1.原理介绍: 人脸检测利用opencv进行图像处理,借用keras框架搭建卷积神经网络,对图像进行特征提取与特征训练,再利用训练好的分类模型进行预测,再将预测标签实时打在每一帧图片上,实现实时人脸检测与人脸标注. 2.具体步骤: 2.2数据获取:借由opencv自带的人脸检测haarcascade_frontalface_alt2.xml进行人脸检测划分,构建起2*1000数据集. 2.3数据处理:利用opencv对图片进行读取,剪切,转为矩阵;划分好图片和标签集 2.4模型训练与保存: 搭建CNN神经网络模型; 对标签进行one-hot编码(用不同位置数组表示不同类型),对图像矩阵进行归一化; 将处理好的数据分批量fit给模型,进行训练. 保存训练好的模型,以便调用. 2.5模型预测:调用训练完成的模型进行识别.

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amazon-cloudwatch-log-centralizer

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ansibled

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apigee-anthos-service-mesh-demo

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auto_clone

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aws-account-automation

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aws-api-gateway-developer-portal

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aws-automation

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aws-big-data-blog-dmscdc-walkthrough

aws-cloudformation-templates

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aws-codecommit-serverless-backup

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aws-config-rdk

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aws-emr-cost-calculator

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aws-emr-launch

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aws-instance-scheduler

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aws-key-rotation-scripts

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aws-lambda-developer-guide

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aws-lambda-ebs-backups

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aws-pipeline-to-service-catalog

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