ThinkPHP验证码类库 think-captcha 图片验证码识别

所用项目及服务器

ThinkPHPhttps://github.com/top-think/framework
think-captchahttps://github.com/top-think/think-captcha
captcha_trainer (训练):https://github.com/kerlomz/captcha_trainer
captcha_platform (部署):https://github.com/kerlomz/captcha_platform
MuggleOCRhttps://pypi.org/project/muggle-ocr
captcha_trainer 作者介绍:https://www.jianshu.com/p/80ef04b16efc
矩池云 GPU服务器:https://matpool.com/


captcha_trainer 使用过程

  1. 使用 ThinkPHP 调用 think-captcha 生成验证码

    composer create-project topthink/think tp
    cd tp
    composer require topthink/think-captcha
    php think run
  2. 修改 think-captcha 源码,将验证码存储到 Session
    vendor/topthink/think-captcha/src/Captcha.php:195 插入以下代码

    Session::set('captcha', implode('', $code), '');
  3. 在控制器内打印验证码

    public function code() {
        $code = Session::get('captcha', '');
        echo $code;
    }
  4. 使用 Python 抓取图片验证码样本,按照 captcha_trainer 默认规则重命名保存

    import requests
    import threading
    import os
    import hashlib
    #
    def md5(s, salt=''):
        new_s = str(s) + salt
        m = hashlib.md5(new_s.encode())
        return m.hexdigest()
    #
    def get_captcha():
        session = requests.session()
        for i in range(0, 100000):
            try:
                content = session.get('http://x.com/captcha?'+str(i))
                if content.status_code != 200:
                    continue
                code = session.get('http://x.com/index/Home/code')
                if code.status_code != 200:
                    continue
                filename = '{}_{}.png'.format(code.text, md5(content.content))
                with open(os.path.join('captcha_images', filename), 'wb') as f:
                    f.write(content.content)
                    f.close()
            except Exception as e:
                print(str(e))
    #
    for i in range(1, 20):
        t = threading.Thread(target=get_captcha, args=())
        t.start()
  5. 租用矩池云 RTX 2080 Ti 的GPU服务器进行训练
    16085313396385.jpg
  6. 使用 captcha_trainer 进行训练

    配置训练环境及项目

    pip3 install -r requirements.txt
    pip3 install tensorflow-gpu # tensorflow模块需独立安装
    mkdir -p projects/{project name}/
    vi projects/{project name}/model.yaml

    model.yaml 模板参数介绍请参考 captcha_trainer 项目介绍,可以用 Windows 编译版生成后上传至服务器,需保持项目名一致。

    项目名 TP-CNNX-GRU-H64-CTC-C1
    ./projects/TP-CNNX-GRU-H64-CTC-C1/model.yaml

    # - requirement.txt  -  GPU: tensorflow-gpu, CPU: tensorflow
    # - If you use the GPU version, you need to install some additional applications.
    System:
      MemoryUsage: 0.8
      Version: 2
    
    # CNNNetwork: [CNN5, ResNet, DenseNet]
    # RecurrentNetwork: [CuDNNBiLSTM, CuDNNLSTM, CuDNNGRU, BiLSTM, LSTM, GRU, BiGRU, NoRecurrent]
    # - The recommended configuration is CNN5+GRU
    # UnitsNum: [16, 64, 128, 256, 512]
    # - This parameter indicates the number of nodes used to remember and store past states.
    # Optimizer: Loss function algorithm for calculating gradient.
    # - [AdaBound, Adam, Momentum]
    # OutputLayer: [LossFunction, Decoder]
    # - LossFunction: [CTC, CrossEntropy]
    # - Decoder: [CTC, CrossEntropy]
    NeuralNet:
      CNNNetwork: CNNX
      RecurrentNetwork: GRU
      UnitsNum: 64
      Optimizer: RAdam
      OutputLayer:
        LossFunction: CTC
        Decoder: CTC
    
    
    # ModelName: Corresponding to the model file in the model directory
    # ModelField: [Image, Text]
    # ModelScene: [Classification]
    # - Currently only Image-Classification is supported.
    Model:
      ModelName: TP-CNNX-GRU-H64-CTC-C1
      ModelField: Image
      ModelScene: Classification
    
    # FieldParam contains the Image, Text.
    # When you filed to Image:
    # - Category: Provides a default optional built-in solution:
    # -- [ALPHANUMERIC, ALPHANUMERIC_LOWER, ALPHANUMERIC_UPPER,
    # -- NUMERIC, ALPHABET_LOWER, ALPHABET_UPPER, ALPHABET, ALPHANUMERIC_CHS_3500_LOWER]
    # - or can be customized by:
    # -- ['Cat', 'Lion', 'Tiger', 'Fish', 'BigCat']
    # - Resize: [ImageWidth, ImageHeight/-1, ImageChannel]
    # - ImageChannel: [1, 3]
    # - In order to automatically select models using image size, when multiple models are deployed at the same time:
    # -- ImageWidth: The width of the image.
    # -- ImageHeight: The height of the image.
    # - MaxLabelNum: You can fill in -1, or any integer, where -1 means not defining the value.
    # -- Used when the number of label is fixed
    # When you filed to Text:
    # This type is temporarily not supported.
    FieldParam:
      Category: ['2', '3', '4', '5', '6', '7', '8', 'a', 'b', 'c', 'd', 'e', 'f', 'h', 'i', 'j', 'k', 'm', 'n', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'T', 'U', 'V', 'W', 'X', 'Y']
      Resize: [224, 70]
      ImageChannel: 1
      ImageWidth: 224
      ImageHeight: 70
      MaxLabelNum: 4
      OutputSplit: 
      AutoPadding: True
    
    
    # The configuration is applied to the label of the data source.
    # LabelFrom: [FileName, XML, LMDB]
    # ExtractRegex: Only for methods extracted from FileName:
    # - Default matching apple_20181010121212.jpg file.
    # - The Default is .*?(?=_.*\.)
    # LabelSplit: Only for methods extracted from FileName:
    # - The split symbol in the file name is like: cat&big cat&lion_20181010121212.png
    # - The Default is null.
    Label:
      LabelFrom: FileName
      ExtractRegex: .*?(?=_)
      LabelSplit: 
    
    
    # DatasetPath: [Training/Validation], The local absolute path of a packed training or validation set.
    # SourcePath:  [Training/Validation], The local absolute path to the source folder of the training or validation set.
    # ValidationSetNum: This is an optional parameter that is used when you want to extract some of the validation set
    # - from the training set when you are not preparing the validation set separately.
    # SavedSteps: A Session.run() execution is called a Step,
    # - Used to save training progress, Default value is 100.
    # ValidationSteps: Used to calculate accuracy, Default value is 500.
    # EndAcc: Finish the training when the accuracy reaches [EndAcc*100]% and other conditions.
    # EndCost: Finish the training when the cost reaches EndCost and other conditions.
    # EndEpochs: Finish the training when the epoch is greater than the defined epoch and other conditions.
    # BatchSize: Number of samples selected for one training step.
    # ValidationBatchSize: Number of samples selected for one validation step.
    # LearningRate: [0.1, 0.01, 0.001, 0.0001]
    # - Use a smaller learning rate for fine-tuning.
    Trains:
      DatasetPath:
        Training: 
          - ./projects/TP-CNNX-GRU-H64-CTC-C1/dataset/Trains.0.tfrecords
        Validation: 
          - ./projects/TP-CNNX-GRU-H64-CTC-C1/dataset/Validation.0.tfrecords
      SourcePath:
        Training: 
          - /root/captcha_images
        Validation: 
      ValidationSetNum: 300
      SavedSteps: 100
      ValidationSteps: 500
      EndAcc: 0.95
      EndCost: 0.5
      EndEpochs: 2
      BatchSize: 64
      ValidationBatchSize: 300
      LearningRate: 0.001
    
    # Binaryzation: The argument is of type list and contains the range of int values, -1 is not enabled.
    # MedianBlur: The parameter is an int value, -1 is not enabled.
    # GaussianBlur: The parameter is an int value, -1 is not enabled.
    # EqualizeHist: The parameter is an bool value.
    # Laplace: The parameter is an bool value.
    # WarpPerspective: The parameter is an bool value.
    # Rotate: The parameter is a positive integer int type greater than 0, -1 is not enabled.
    # PepperNoise: This parameter is a float type less than 1, -1 is not enabled.
    # Brightness: The parameter is an bool value.
    # Saturation: The parameter is an bool value.
    # Hue: The parameter is an bool value.
    # Gamma: The parameter is an bool value.
    # ChannelSwap: The parameter is an bool value.
    # RandomBlank: The parameter is a positive integer int type greater than 0, -1 is not enabled.
    # RandomTransition: The parameter is a positive integer int type greater than 0, -1 is not enabled.
    DataAugmentation:
      Binaryzation: -1
      MedianBlur: -1
      GaussianBlur: -1
      EqualizeHist: False
      Laplace: False
      WarpPerspective: False
      Rotate: -1
      PepperNoise: -1.0
      Brightness: False
      Saturation: False
      Hue: False
      Gamma: False
      ChannelSwap: False
      RandomBlank: -1
      RandomTransition: -1
      RandomCaptcha: 
         Enable: False
         FontPath: 
    
    # Binaryzation: The parameter is an integer number between 0 and 255, -1 is not enabled.
    # ReplaceTransparent: Transparent background replacement, bool type.
    # HorizontalStitching: Horizontal stitching, bool type.
    # ConcatFrames: Horizontally merge two frames according to the provided frame index list, -1 is not enabled.
    # BlendFrames: Fusion corresponding frames according to the provided frame index list, -1 is not enabled.
    # - [-1] means all frames
    Pretreatment:
      Binaryzation: -1
      ReplaceTransparent: True
      HorizontalStitching: False
      ConcatFrames: -1
      BlendFrames: -1
      ExecuteMap: {}

    打包训练集后开始训练

    python3 make_dataset.py 
    python3 trains.py
  7. Linux 训练环境强制结束任务

    因为 think-captcha 随机调用系统字体生成图片验证码,部分字体显示只有大写字母,与 think-captcha 生成的验证码存在大小写偏差,导致 captcha_trainer 训练正确率只能维持在 0.6 左右,无法满足结束训练任务的 0.95 正确率,但忽略大小写后的正确率已满足需求,所以需要强制结束训练任务,编译模型。

    Windows 环境下可以直接点击 Stop 按钮结束任务后再点击 Compile 按钮编译模型。
    16085360475051.jpg

    Linux 环境结束任务需要修改 trains.py 代码,简单分析后发现 trains.py 共有两处正确率判断

    trains.py:308
    
    # 满足终止条件但尚未完成当前epoch时跳出epoch循环
    if self.achieve_cond(acc=accuracy, cost=batch_cost, epoch=epoch_count):
        break
    trains.py:314
    if self.achieve_cond(acc=accuracy, cost=batch_cost, epoch=epoch_count):
        # sess.close()
        tf.compat.v1.keras.backend.clear_session()
        sess.close()
        self.compile_graph(accuracy)
        tf.compat.v1.logging.info('Total Time: {} sec.'.format(time.time() - start_time))

    找到代码后直接简单粗暴的把判断条件改成 1==1 让条件成立即可结束任务

    trains.py:308
    # 满足终止条件但尚未完成当前epoch时跳出epoch循环
    if 1==1:
        break
    trains.py:314
    if 1==1:
        # sess.close()
        tf.compat.v1.keras.backend.clear_session()
        sess.close()
        self.compile_graph(accuracy)
        tf.compat.v1.logging.info('Total Time: {} sec.'.format(time.time() - start_time))

    captcha_trainer 支持中断任务恢复,修改代码后按 Ctrl+C 结束任务,重新执行 python3 trains.py,初始化后将直接结束训练任务,编译模型。


部署验证码识别接口

使用 captcha_platform 项目进行 docker 部署
  1. captcha_trainer 训练后编译生成的模型复制到 captcha_platform 项目中

    mv -rf captcha_trainer/out/* captcha_platform/
  2. 构建并启动 docker

    cd captcha_platform/
    docker build . 
    docker run -d -p 19952:19952 [image:tag]


识别测试

13万 think-captcha 图片验证码样本经过约 11 个小时的训练后,使用未经过训练的新样本进行识别测试,识别成功率在 95% 左右
16085387497778.jpg

使用 Python 脚本进行识别测试

import requests
import base64
import os
import re
#
dir = 'test-1' # 未经过训练的图片验证码样本目录
success = 0
error = 0
count = 0
file_list = os.listdir(dir)
#
for i in range(0, 1000):
    filename = file_list[i]
    origin = re.match(r'.*?(?=_.*\.)', filename).group()
    if not origin:
        continue
    src = os.path.join(dir, filename)
    img = base64.b64encode(open(src, 'rb').read())
    post_data = {
        'image': img,
        'model_name': 'TP-CNNX-GRU-H64-CTC-C1'
    }
    res = requests.post('http://localhost:19952/captcha/v1', data=post_data)
    json_res = res.json()
    # 忽略大小写
    if json_res['code'] == 0 and str(json_res['message']).upper() == origin.upper():
        print('Sample: {}, Identify: {}, Status: {}'.format(org, json_res['message'], 'success'))
        success += 1
    else:
        print('Sample: {}, Identify: {}, Status: {}'.format(org, json_res['message'], 'error'))
        error += 1
    count += 1
print('Count: {}, Success: {}, Error: {}'.format(count, success, error))

测试结果
16085386229942.jpg


模型下载

链接: https://pan.baidu.com/s/1e0quRSqMV8lP6XXoS_Ty1g 提取码:sg6c

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