# captcha_trainer **Repository Path**: skygit/captcha_trainer ## Basic Information - **Project Name**: captcha_trainer - **Description**: 验证码识别 - 该项目是基于 CNN5/DenseNet+BLSTM/LSTM+CTC 来实现验证码识别. 该项目仅用于训练 - **Primary Language**: Python - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 59 - **Created**: 2019-03-14 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # 项目介绍 验证码识别 - 该项目是基于 CNN5/DenseNet+BLSTM/LSTM+CTC 来实现验证码识别. 该项目仅用于训练，如果需要部署模型请移步： https://github.com/kerlomz/captcha_platform （通用WEB服务，HTTP请求调用） https://github.com/kerlomz/captcha_library_c （动态链接库，DLL调用，基于TensoFlow C++） https://github.com/kerlomz/captcha_demo_csharp （C#源码调用，基于TensorFlowSharp） 许多人问我，部署识别也需要GPU吗？我的答案是，完全没必要。理想中是用GPU训练，使用CPU部署识别服务，部署如果也需要这么高的成本，那还有什么现实意义和应用场景呢，实测阿里云最低配1核1G的配置识别1次大约30ms，我的i7-8700k大约10-15ms之间。 # 注意事项 1. **如何使用CPU训练：** 本项目默认安装TensorFlow-GPU版，建议使用GPU进行训练，如需换用CPU训练请替换 ```requirements.txt``` 文件中的```tensorflow-gpu==1.6.0``` 为```tensorflow==1.6.0```，其他无需改动。 2. **关于LSTM网络**: 保证CNN得到的featuremap输入到LSTM时的宽度至少大于等于最大字符数的3倍左右，即time_step大于等于最大字符数3倍。 3. **No valid path found 问题解决**： 在```model.yaml```中修改```Pretreatment```->```Resize```的参数，自行调整为合适的值，总结了百来个验证码训练经验，可以尝试这个较为通用的值：```Resize: [150, 50]```，或者使用代码```tutorial.py``` （自动生成配置文件、打包样本、训练一体化），填写训练集路径执行。 4. **参数修改：** 切记，ModelName 是绑定一个模型的唯一标志，如果修改了训练参数如：ImageWidth，ImageHeight，Resize，CharSet，CNNNetwork，RecurrentNetwork，HiddenNum 这类影响计算图的参数，需要删除model路径下的旧文件，重新训练，或者使用新的ModelName 重新训练，否则默认作为断点续练。 # 准备工作 如果你准备使用GPU训练，请先安装CUDA和cuDNN，可以了解下官方测试过的编译版本对应: https://www.tensorflow.org/install/install_sources#tested_source_configurations Github上可以下载到第三方编译好的TensorFlow的WHL安装包： https://github.com/fo40225/tensorflow-windows-wheel CUDA下载地址：https://developer.nvidia.com/cuda-downloads cuDNN下载地址：https://developer.nvidia.com/rdp/form/cudnn-download-survey （需要注册账号） **笔者使用的版本为：CUDA10+cuDNN7.3.1+TensorFlow 1.12** # 环境安装 1. 安装Python 3.6 环境（包含pip） 2. 安装虚拟环境 virtualenv ```pip3 install virtualenv``` 3. 为该项目创建独立的虚拟环境: ```bash virtualenv -p /usr/bin/python3 venv # venv is the name of the virtual environment. cd venv/ # venv is the name of the virtual environment. source bin/activate # to activate the current virtual environment. cd captcha_trainer # captcha_trainer is the project path. ``` 4. 安装本项目的依赖列表：```pip install -r requirements.txt``` # 开始 ## 1. 架构与流程 本项目依赖于训练配置```config.yaml```和模型配置```model.yaml```，初始化项目的时候请复制```config_demo.yaml```到当前目录下命名为```config.yaml```，```model_demo.yaml```同理。或者可以使用```tutorial.py``` 自动设置模型配置。 **训练流程**：配置好两个配置文件后，执行```trains.py``` 中的代码，读取配置，根据```model.yaml```配置文件构建神经网络计算图，依据```config.yaml```的配置参数进行训练。 **关于```config.yaml```中的训练参数有几点建议：** 1. BatchSize（训练批次大小）与TestBatchSize（测试批次大小）是需要大家关注的，建议根据显卡条件进行调整，显存小的建议BatchSize不要太大，TestBatchSize也是，我提供的默认配置是基于显存8G，使用率50%设置的，请悉知。 2. LearningRate（学习率）也是需要关注的，深度学习本质就是调参，一般的模型可以保持默认的配置无需调整，有些模型想要获得更高的识别精度可以先使用0.01快速收敛，准确率差不多95%左右再使用0.001/0.0001提高精度。 3. TestSetNum（测试集数目），这个是专门为懒人（说我自己）设计提供的，根据给定的测试集数目切割训练集，有一个前提，测试集必须是随机的，随机的，随机的，重要的事说三遍，有些人用Windows资源管理器打开，一拖动选择几百个，默认都是按名称排序的，如果名称是标注，那么就不是随机了，也就是很可能你取的测试集是标注为0~3之间的图片，这样可能导致永远无法收敛。 4. TrainRegex 和 TestRegex，正则匹配，请各位采集样本的时候，尽量和我给的示例保持一致吧，正则问题请谷歌，如果是为1111.jpg这种命名的话，这里提供了一个批量转换的代码： ```python import re import os import hashlib # 训练集路径 root = r"D:\TrainSet\***" all_files = os.listdir(root) for file in all_files: old_path = os.path.join(root, file) # 已被修改过忽略 if len(file.split(".")[0]) > 32: continue # 采用标注_文件md5码.图片后缀 进行命名 with open(old_path, "rb") as f: _id = hashlib.md5(f.read()).hexdigest() new_path = os.path.join(root, file.replace(".", "_{}.".format(_id))) # 重复标签的时候会出现形如：abcd (1).jpg 这种形式的文件名 new_path = re.sub(" \(\d+\)", "", new_path) print(new_path) os.rename(old_path, new_path) ``` ## 2. 配置化 1. config.yaml - System Config ```yaml # - requirement.txt - GPU: tensorflow-gpu, CPU: tensorflow # - If you use the GPU version, you need to install some additional applications. # TrainRegex and TestRegex: Default matching apple_20181010121212.jpg file. # - The Default is .*?(?=_.*\.) # TrainsPath and TestPath: The local absolute path of your training and testing set. # TestSetNum: This is an optional parameter that is used when you want to extract some of the test set # - from the training set when you are not preparing the test set separately. System: DeviceUsage: 0.5 TrainRegex: '.*?(?=_)' TestRegex: '.*?(?=_)' TestSetNum: 300 # CNNNetwork: [CNN5, DenseNet] # RecurrentNetwork: [BLSTM, LSTM] # - The recommended configuration is CNN5+BLSTM / DenseNet+BLSTM # HiddenNum: [64, 128, 256] # - This parameter indicates the number of nodes used to remember and store past states. NeuralNet: CNNNetwork: CNN5 RecurrentNetwork: BLSTM HiddenNum: 64 KeepProb: 0.99 # SavedEpochs: A Session.run() execution is called a Epochs, # - Used to save traininsed to calculate accuracy, Default value is 100. # TestNum: The number of samples for each test batch. # - A test for every saved steps. # CompileAcc: When the accuracy reaches the set threshold, # - the model will be compiled together each time it is archived. # - Available for specific usage scenarios. # EndAcc: Finish the training when the accuracy reaches [EndAcc*100]%. # EndEpochs: Finish the training when the epoch is greater than the defined epoch. # PreprocessCollapseRepe ated: If True, then a preprocessing step runs # - before loss calculation, wherein repeated labels passed to the loss # - are merged into single labels. This is useful if the training labels come # - from, e.g., forced alignments and therefore have unnecessary repetitions. # CTCMergeRepeated: If False, then deep within the CTC calculation, # - repeated non-blank labels will not be merged and are interpreted # - as individual labels. This is a simplified (non-standard) version of CTC. Trains: SavedSteps: 100 ValidationSteps: 500 EndAcc: 0.98 EndEpochs: 1 BatchSize: 64 TestBatchSize: 300 LearningRate: 0.01 DecayRate: 0.98 DecaySteps: 100000 PreprocessCollapseRepeated: False CTCMergeRepeated: True ``` There are several common examples of TrainRegex: i. apple_20181010121212.jpg ``` .*?(?=_.*\.) ``` ii apple.png ``` .*?(?=\.) ``` 2. model.yaml - Model Config ```yaml # Sites: A bindable parameter used to select a model. # - If this parameter is defined, # - it can be identified by using the model_site parameter # - to identify a model that is inconsistent with the actual size of the current model. # ModelName: Corresponding to the model file in the model directory, # - such as YourModelName.pb, fill in YourModelName here. # ModelType: This parameter is also used to locate the model. # - The difference from the sites is that if there is no corresponding site, # - the size will be used to assign the model. # - If a model of the corresponding size and corresponding to the ModelType is not found, # - the model belonging to the category is preferentially selected. # CharSet: Provides a default optional built-in solution: # - [ALPHANUMERIC, ALPHANUMERIC_LOWER, ALPHANUMERIC_UPPER, # -- NUMERIC, ALPHABET_LOWER, ALPHABET_UPPER, ALPHABET] # - Or you can use your own customized character set like: ['a', '1', '2']. # CharExclude: CharExclude should be a list, like: ['a', '1', '2'] # - which is convenient for users to freely combine character sets. # - If you don't want to manually define the character set manually, # - you can choose a built-in character set # - and set the characters to be excluded by CharExclude parameter. Model: Sites: [] ModelName: YourModelName ModelType: 150x50 CharSet: ALPHANUMERIC_LOWER CharExclude: [] CharReplace: {} ImageWidth: 150 ImageHeight: 50 # Binaryzation: [-1: Off, >0 and < 255: On]. # Smoothing: [-1: Off, >0: On]. # Blur: [-1: Off, >0: On]. Pretreatment: Binaryzation: -1 Smoothing: -1 Blur: -1 Resize: [150, 50] ``` # 工具集 1. 预处理预览工具，只支持为打包的训练集查看 ```python -m tools.preview``` 2. 新手指南 （只支持字符集推荐，我觉得是个鸡肋各位请忽略） ```python -m tools.navigator``` 3. PyInstaller 一键打包（训练的话支持不好，部署的打包效果不错） ``` pip install pyinstaller python -m tools.package ``` # 运行 1. 命令行或终端运行：```python trains.py``` 2. 使用 PyCharm 运行，右键 Run # 开源许可 遵循SATA原则，使用本项目请不要吝啬给颗星 # 详细指南 之前专门为该项目写的文章，欢迎大家点评 https://www.jianshu.com/p/80ef04b16efc # 捐赠 手机保存下来就是这么大的图，有点尴尬，感谢有你们的支持让我更有动力贡献社区。 ![赞助](https://kerlomz-business.oss-cn-hangzhou.aliyuncs.com/WeChat%20Image_20190212111824.jpg)