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With the advancement of technology, our lives have become increasingly digitized. However, this has also brought forth new challenges to personal privacy. In order to address this issue, we take pride in introducing AutoMosaic, an intelligent privacy protection software specifically developed

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AutoMosaic:对图像中的车牌和行人进行马赛克处理

概述

AutoMosaic 是一个用于解决隐私问题的 Python 脚本,通过自动对图像中的敏感信息(如车牌和行人)进行马赛克处理,保护个体隐私。该脚本利用AI模型执行目标检测,然后应用马赛克滤镜来遮蔽检测到的区域,确保个体信息的隐私安全。

Local GIF

主要特性

车牌匿名化:检测并匿名化图像中存在的车牌。

行人隐私:识别并对行人进行马赛克处理,以保护他们的隐私。

安装

  • 使用 pip 安装所需的依赖项:
pip install ultralytics
git clone https://github.com/JiayuXu0/AutoMosaic.git
cd AutoMosaic
  • 运行脚本并提供目标图像的路径:
python detect.py

脚本将生成一个输出图像,其中包含经过马赛克处理的车牌和行人。

贡献与问题

微信联系:littleeyu

AutoMosaic: Anonymize License Plates and Pedestrians in Images

Overview

AutoMosaic is a Python script designed to address privacy concerns by automatically anonymizing sensitive information such as license plates and pedestrians in images. The script utilizes the model from Ultralytics to perform object detection, and then applies a mosaic filter to mask the detected regions, ensuring the privacy of individuals and their identifiable information.

Key Features

License Plate Anonymization: Detects and anonymizes license plates present in images. Pedestrian Privacy: Identifies and masks pedestrians to protect their privacy.

Installation

Install the required dependencies using pip:

pip install ultralytics

Download Model

Download model.pt

Clone the repository:

git clone https://github.com/JiayuXu0/AutoMosaic.git
cd AutoMosaic

Run the script with the path to the target image:

python detect.py

The script will generate an output image with anonymized license plates and pedestrians.

Contributions and Issues

wechat: littleeyu

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With the advancement of technology, our lives have become increasingly digitized. However, this has also brought forth new challenges to personal privacy. In order to address this issue, we take pride in introducing AutoMosaic, an intelligent privacy protection software specifically developed

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