通过简单的卷积神经网络直接预测出验证码图片中滑块的位置

Last update: Mar 08, 2022

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Miscellaneous jd_joy_verification

Overview

使用说明

1. 在本地测试

运行python3 prdict_one.py即可，默认需要预测的图片路径位于testImg文件夹下的test1.png
运行python3 predict_folder.py预测testImg下的所有图片

2. 部署到服务器

运行python3 run_a_server.py 8888即在端口8888部署api，也可以在本地运行测试，如果不填写端口，默认端口为7000

3. 通过所部署API进行预测

运行python3 predict_by_server.py即可调用所部署的api进行testImg文件夹下所有图片的预测

4. 打包为可执行文件

pyinstaller -F run_a_server.py

即可打包为可执行文件，打包结束后会在dist文件夹下生成可执行文件。直接运行不需要python环境

注意！运行打包后的二进制文件时需要将trained_weights.h5放在同级目录下

其他说明

通过测试，调用本地API预测一张图平均所需约0.05s，服务器1C2G占用内存200~400mb之间
我所用的tensorflo和Keras版本分别为：1.15.2和2.3.1
model.py 存放网络结构
trained_weights.h5 训练的权重文件
简单curl命令测试

curl -X POST -F [email protected]/test1.png 'http://localhost:7000/predict'

成功后会返回

{"predictions":0.553,"success":true}

本模型所针对的输入图片尺寸大小必须为140x360，没有适配其他图片大小，有能力的可以根据网络结构进行修改从而适应所需。

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Releases(publish)

publish(Sep 5, 2021)

执行二进制文件前需要将trained_weights.h5放在二进制文件同级目录下
Source code(tar.gz)
Source code(zip)
run(157.02 MB)
trained_weights.h5(2.04 MB)

通过简单的卷积神经网络直接预测出验证码图片中滑块的位置

Related tags

Overview

使用说明

1. 在本地测试

2. 部署到服务器

3. 通过所部署API进行预测

4. 打包为可执行文件

其他说明

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Releases(publish)

publish(Sep 5, 2021)

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