├── .gitignore ├── LICENSE ├── README.md ├── data ├── download │ ├── all │ │ └── .gitignore │ ├── image │ │ └── .gitignore │ └── words │ │ └── .gitignore ├── image │ ├── test │ │ └── .gitignore │ └── train │ │ └── .gitignore └── words │ ├── test │ └── .gitignore │ └── train │ └── .gitignore ├── label └── synset ├── model ├── image │ └── .gitignore └── words │ └── .gitignore ├── src ├── __init__.py ├── classify_demo.py ├── config.py ├── image │ ├── image-classify.py │ ├── model │ │ ├── image_deploy.prototxt │ │ ├── image_solver.prototxt │ │ └── image_train_val.prototxt │ └── scripts │ │ ├── calc_mean.sh │ │ ├── create_data.py │ │ ├── create_lmdb.sh │ │ ├── image_finetune_train.sh │ │ └── image_train.sh ├── tools │ ├── __init__.py │ ├── cut_image.py │ ├── download_image.py │ ├── remove_noisy.py │ └── words.py ├── web │ ├── README.md │ ├── __init__.py │ ├── index.py │ ├── static │ │ ├── bootstrap │ │ │ ├── css │ │ │ │ ├── bootstrap-datetimepicker.css │ │ │ │ ├── bootstrap-datetimepicker.min.css │ │ │ │ ├── bootstrap-theme.css │ │ │ │ ├── bootstrap-theme.min.css │ │ │ │ ├── bootstrap.css │ │ │ │ └── bootstrap.min.css │ │ │ ├── fonts │ │ │ │ ├── glyphicons-halflings-regular.eot │ │ │ │ ├── glyphicons-halflings-regular.svg │ │ │ │ ├── glyphicons-halflings-regular.ttf │ │ │ │ └── glyphicons-halflings-regular.woff │ │ │ └── js │ │ │ │ ├── bootstrap-datetimepicker.js │ │ │ │ ├── bootstrap-datetimepicker.min.js │ │ │ │ ├── bootstrap.js │ │ │ │ ├── bootstrap.min.js │ │ │ │ ├── bootstrap2-typeahead.min.js │ │ │ │ └── locales │ │ │ │ └── bootstrap-datetimepicker.zh-CN.js │ │ ├── css │ │ │ └── index.css │ │ ├── js │ │ │ ├── index.js │ │ │ └── jquery-2.1.1.min.js │ │ └── tmp │ │ │ └── .gitignore │ ├── templates │ │ └── 12306.html │ └── utils │ │ ├── Img12306.py │ │ └── __init__.py └── words │ ├── model │ ├── words_deploy.prototxt │ ├── words_solver.prototxt │ └── words_train_val.prototxt │ └── scripts │ ├── calc_mean.sh │ ├── create_data.py │ ├── create_lmdb.sh │ ├── words_finetune_train.sh │ └── words_train.sh └── web-demo.png /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | 49 | # Translations 50 | *.mo 51 | *.pot 52 | 53 | # Django stuff: 54 | *.log 55 | local_settings.py 56 | 57 | # Flask stuff: 58 | instance/ 59 | .webassets-cache 60 | 61 | # Scrapy stuff: 62 | .scrapy 63 | 64 | # Sphinx documentation 65 | docs/_build/ 66 | 67 | # PyBuilder 68 | target/ 69 | 70 | # Jupyter Notebook 71 | .ipynb_checkpoints 72 | 73 | # pyenv 74 | .python-version 75 | 76 | # celery beat schedule file 77 | celerybeat-schedule 78 | 79 | # SageMath parsed files 80 | *.sage.py 81 | 82 | # dotenv 83 | .env 84 | 85 | # virtualenv 86 | .venv 87 | venv/ 88 | ENV/ 89 | 90 | # Spyder project settings 91 | .spyderproject 92 | .spyproject 93 | 94 | # Rope project settings 95 | .ropeproject 96 | 97 | # mkdocs documentation 98 | /site 99 | 100 | # mypy 101 | .mypy_cache/ 102 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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-------------------------------------------------------------------------------- 1 | # 12306-captcha [![Author](https://img.shields.io/badge/Author-%E4%B8%AD%E9%BE%84%E7%A8%8B%E5%BA%8F%E5%91%98-blue.svg)](https://www.shanruifeng.win) [![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE) [![Stars](https://img.shields.io/github/stars/aaronshan/12306-captcha.svg?label=Stars&style=social)](https://github.com/aaronshan/12306-captcha) 2 | 3 | 12306验证码识别 4 | 5 | ## 1. 训练 6 | 7 | ### 1.1 准备工作 8 | 9 | * 下载caffe并编译, 具体可参考官方文档, 此处不再赘述. 10 | * 修改`src/config.py`中的caffe根目录和项目根目录. 11 | * pip安装easydict, skimage等. 12 | 13 | ### 1.2 数据 14 | 15 | * 通过运行`src/tools/download_image.py`, 会将12306验证码下载至`data/download/all`目录. 16 | * 下载完成后, 通过运行`src/tools/cut_image.py`, 会将其裁剪为图片和文字两部分, 分别放在`data/download/image`目录和`data/download/words`目录. 17 | * 修改`src/image/scripts/words.py`文件main方法中cut方法的参数(其参数为`data/download/words`中子目录的`words_*`中的数字),　它的目的是处理`data/download/words`中的所有子文件, 对多个词语进行分割并调整大小为固定值. 18 | * 然后手工对其进行分类, 分别放至`data/image`和`data/words`目录. 可以将其分为两部分，分别放在对应的train和test目录.比如,一个示例目录如下: 19 | ``` 20 | -image 21 | --test 22 | ---蜡烛 23 | ----1-1.jpg 24 | ---沙漠 25 | ----2-1.jpg 26 | --train 27 | ---蜡烛 28 | ----1-2.jpg 29 | ---沙漠 30 | ----2-2.jpg 31 | ``` 32 | 33 | #### 图片部分 34 | 35 | * 运行`src/image/scripts/create_data.py`, 将会生成图片部分对应的train.txt和test.txt, 里面包含着训练和测试文件及其类别列表. 36 | * 运行`src/image/scripts/create_lmdb.sh`, 将会生成图片部分对应的lmdb文件. 37 | 38 | #### 文字部分 39 | 40 | * 运行`src/words/scripts/create_data.py`, 将会生成文字部分对应的train.txt和test.txt, 里面包含着训练和测试文件及其类别列表. 41 | * 运行`src/words/scripts/create_lmdb.sh`, 将会生成文字部分对应的lmdb文件. 42 | 43 | ### 1.3 参数 44 | 可以根据实际情况对`src/image/model/image_solver.prototxt`和`src/words/model/words_solver.prototxt`文件进行修改.具体修改方法可参考其他模型. 45 | 46 | ### 1.4 开始训练 47 | `src/image/scripts/image_train.sh`和`src/image/scripts/image_finetune_train.sh`脚本分别用来进行从头训练/微调训练, 训练方法可参考caffe模型训练方法. 48 | 49 | 同理： 50 | 51 | `src/words/scripts/words_train.sh`和`src/words/scripts/words_finetune_train.sh`脚本分别用来进行从头训练/微调训练, 训练方法可参考caffe模型训练方法. 52 | 53 | 54 | ## 测试 55 | `src/web`提供了一个web测试界面, 运行index.py即可.　运行前, 可以更改对应的模型文件名称.　一个简单示例如下: 56 | 57 | ![web-demo](https://github.com/aaronshan/12306-captcha/blob/master/web-demo.png) 58 | 59 | ## 其他 60 | 61 | 1. 在实际应用中, 会使用从百度/搜狗/谷歌等图片搜索引擎中爬取图片并做处理的方式来完成图片分类收集工作. 比如爬取关键词为档案袋的图片, 再进一步做处理. 以解决从12306下载并裁剪-手工分类效率太低及样本量不足的问题, 提升效率。 62 | 63 | 2. 此外, 项目里对文字部分的分割也不是很完美. 对图片的分类也是裁剪并逐个进行的, 这样的响应效率不会很高. 可以使用目标检测的方式, 对整个验证码图片做目标检测, 同时检测8个图片及文字部分. 以加快检测速度. 64 | -------------------------------------------------------------------------------- /data/download/all/.gitignore: -------------------------------------------------------------------------------- 1 | # 忽略所有文件 2 | * 3 | # 除了这个文件 4 | !.gitignore 5 | -------------------------------------------------------------------------------- /data/download/image/.gitignore: -------------------------------------------------------------------------------- 1 | # 忽略所有文件 2 | * 3 | # 除了这个文件 4 | !.gitignore 5 | -------------------------------------------------------------------------------- /data/download/words/.gitignore: -------------------------------------------------------------------------------- 1 | # 忽略所有文件 2 | * 3 | # 除了这个文件 4 | !.gitignore 5 | -------------------------------------------------------------------------------- /data/image/test/.gitignore: -------------------------------------------------------------------------------- 1 | # 忽略所有文件 2 | * 3 | # 除了这个文件 4 | !.gitignore 5 | -------------------------------------------------------------------------------- /data/image/train/.gitignore: -------------------------------------------------------------------------------- 1 | # 忽略所有文件 2 | * 3 | # 除了这个文件 4 | !.gitignore 5 | -------------------------------------------------------------------------------- /data/words/test/.gitignore: -------------------------------------------------------------------------------- 1 | # 忽略所有文件 2 | * 3 | # 除了这个文件 4 | !.gitignore 5 | -------------------------------------------------------------------------------- /data/words/train/.gitignore: -------------------------------------------------------------------------------- 1 | # 忽略所有文件 2 | * 3 | # 除了这个文件 4 | !.gitignore 5 | -------------------------------------------------------------------------------- /label/synset: -------------------------------------------------------------------------------- 1 | 蜡烛 2 | 沙漠 3 | 游泳圈 4 | 手掌印 5 | 订书机 6 | 扳手 7 | 七星瓢虫 8 | 漏斗 9 | 花轿 10 | 帐篷 11 | 喷泉 12 | 菠萝 13 | 报纸 14 | 老虎 15 | 紫砂壶 16 | 盘子 17 | 狗 18 | 星星 19 | 海鸥 20 | 鲨鱼 21 | 斑马 22 | 卷尺 23 | 灭火器 24 | 土豆 25 | 糖葫芦 26 | 手电筒 27 | 红绿灯 28 | 鹰 29 | 手套 30 | 杏仁 31 | 狮子 32 | 太阳能 33 | 键盘 34 | 烤鸭 35 | 秋千 36 | 箱子 37 | 桥 38 | 天线 39 | 葱 40 | 人民币 41 | 骆驼 42 | 榨汁机 43 | 收音机 44 | 梳子 45 | 航母 46 | 萝卜 47 | 粽子 48 | 袋鼠 49 | 茶几 50 | 电话亭 51 | 戒指 52 | 加湿器 53 | 盒子 54 | 风筝 55 | 档案袋 56 | 自行车 57 | 电视机 58 | 仪表盘 59 | 仙人球 60 | 山楂 61 | 药片 62 | 蚊香 63 | 电饭煲 64 | 绿豆 65 | 西红柿 66 | 花瓶 67 | 苍蝇拍 68 | 算盘 69 | 矿泉水 70 | 游泳池 71 | 辣椒酱 72 | 摩天轮 73 | 球拍 74 | 纽扣 75 | 光盘 76 | 铃铛 77 | 海豹 78 | 中国结 79 | 话梅 80 | 安全帽 81 | 白菜 82 | 保温杯 83 | 本子 84 | 鞭炮 85 | 冰箱 86 | 创可贴 87 | 刺猬 88 | 蛋挞 89 | 灯笼 90 | 电话机 91 | 电线 92 | 电子秤 93 | 雕像 94 | 调色板 95 | 鹅蛋 96 | 饭盒 97 | 肥皂盒 98 | 蜂蜜 99 | 公路 100 | 核桃 101 | 荷叶 102 | 红豆 103 | 红酒 104 | 红枣 105 | 花生 106 | 馄饨 107 | 剪纸 108 | 奖状 109 | 胶卷 110 | 警示牌 111 | 恐龙 112 | 垃圾桶 113 | 拉链 114 | 篮球 115 | 龙舟 116 | 楼梯 117 | 轮胎 118 | 锣 119 | 蚂蚁 120 | 芒果 121 | 猫 122 | 猫头鹰 123 | 毛线 124 | 煤油灯 125 | 蒙古包 126 | 蜜蜂 127 | 奶瓶 128 | 南瓜 129 | 排风机 130 | 螃蟹 131 | 披萨 132 | 啤酒 133 | 皮球 134 | 气球 135 | 热水瓶 136 | 人参 137 | 薯条 138 | 树叶 139 | 水管 140 | 塑料杯 141 | 天鹅 142 | 兔子 143 | 围巾 144 | 西装 145 | 鞋刷 146 | 鸭蛋 147 | 牙膏 148 | 洋葱 149 | 椰子 150 | 衣架 151 | 印章 152 | 樱桃 153 | 油 154 | 鱼缸 155 | 熨斗 156 | 蒸笼 157 | 纸牌 158 | 钟表 159 | 钻石 160 | 贝壳 161 | 拖把 162 | 挂钟 163 | 锅铲 164 | 烛台 165 | 珊瑚 166 | 沙拉 167 | 口哨 168 | 金字塔 169 | 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970 | 970 971 | 971 972 | 972 973 | 973 974 | 974 975 | 975 976 | 976 977 | 977 978 | 978 979 | 979 980 | 980 981 | 981 982 | 982 983 | 983 984 | 984 985 | 985 986 | 986 987 | 987 988 | 988 989 | 989 990 | 990 991 | 991 992 | 992 993 | 993 994 | 994 995 | 995 996 | 996 997 | 997 998 | 998 999 | 999 1000 | 1000 -------------------------------------------------------------------------------- /model/image/.gitignore: -------------------------------------------------------------------------------- 1 | # 忽略所有文件 2 | * 3 | # 除了这个文件 4 | !.gitignore 5 | -------------------------------------------------------------------------------- /model/words/.gitignore: -------------------------------------------------------------------------------- 1 | # 忽略所有文件 2 | * 3 | # 除了这个文件 4 | !.gitignore 5 | -------------------------------------------------------------------------------- /src/__init__.py: -------------------------------------------------------------------------------- 1 | #! _*_ coding:utf-8 _*_ 2 | 3 | # -------------------------------------------------------- 4 | # 12306-captcha 5 | # Copyright (c) 2017 6 | # Written by ruifengshan 7 | # -------------------------------------------------------- 8 | 9 | -------------------------------------------------------------------------------- /src/classify_demo.py: -------------------------------------------------------------------------------- 1 | # -*-coding:utf-8-*- 2 | 3 | # -------------------------------------------------------- 4 | # 12306-captcha 5 | # Copyright (c) 2017 6 | # Licensed under The Apache License [see LICENSE for details] 7 | # Written by ruifengshan 8 | # -------------------------------------------------------- 9 | 10 | """验证码识别""" 11 | 12 | import os 13 | import sys 14 | 15 | import cv2 16 | import numpy as np 17 | from tools import words 18 | from tools import remove_noisy 19 | import skimage 20 | import skimage.io 21 | import skimage.transform 22 | 23 | from tools import cut_image 24 | from src.config import cfg 25 | 26 | # =================================== common start ============================================ 27 | caffe_root = cfg.CAFFE_ROOT 28 | sys.path.insert(0, caffe_root + 'python') 29 | 30 | import caffe 31 | 32 | imagenet_labels_filename = cfg.ROOT + 'label/synset' 33 | labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\t') 34 | # =================================== common end ============================================= 35 | 36 | # ====================================image start================================================= 37 | image_net_file = cfg.ROOT + '/src/image/model/image_deploy.prototxt' 38 | image_caffe_model = cfg.ROOT + 'model/image/f18_snapshot_iter_84600.caffemodel' 39 | image_net = caffe.Net(image_net_file, image_caffe_model, caffe.TEST) 40 | image_transformer = caffe.io.Transformer({'data': image_net.blobs['data'].data.shape}) 41 | "读取的图片文件格式为H×W×K，需转化为K×H×W" 42 | image_transformer.set_transpose('data', (2, 0, 1)) 43 | # transformer.set_mean('data', mean=np.float32([103.939, 116.779, 123.68])) 44 | "将图片存储为[0, 1]，而caffe中将图片存储为[0, 255" 45 | image_transformer.set_raw_scale('data', 255) 46 | "caffe中图片是BGR格式，而原始格式是RGB，所以要转化" 47 | image_transformer.set_channel_swap('data', (2, 1, 0)) 48 | # ====================================image end================================================= 49 | 50 | 51 | # ====================================words start================================================= 52 | words_net_file = cfg.ROOT + '/src/words/model/words_deploy.prototxt' 53 | words_caffe_model = cfg.ROOT + 'model/words/words_4_snapshot_iter_160000.caffemodel' 54 | words_net = caffe.Net(words_net_file, words_caffe_model, caffe.TEST) 55 | words_transformer = caffe.io.Transformer({'data': words_net.blobs['data'].data.shape}) 56 | "读取的图片文件格式为H×W×K，需转化为K×H×W" 57 | words_transformer.set_transpose('data', (2, 0, 1)) 58 | # transformer.set_mean('data', mean=np.float32([103.939, 116.779, 123.68])) 59 | "将图片存储为[0, 1]，而caffe中将图片存储为[0, 255" 60 | words_transformer.set_raw_scale('data', 255) 61 | "caffe中图片是BGR格式，而原始格式是RGB，所以要转化" 62 | words_transformer.set_channel_swap('data', (2, 1, 0)) 63 | 64 | 65 | # ====================================words end================================================= 66 | def judge_words(img): 67 | """返回文件坐标""" 68 | list_img = words.get_test(img) 69 | _text = [] 70 | if list_img is None: 71 | return None 72 | for im in list_img: 73 | # 灰度图转化为多通道RGB 74 | im = cv2.merge([im, im, im]) # 前面分离出来的三个通道 75 | words_net.blobs['data'].data[...] = words_transformer.preprocess('data', im.astype(np.int)) 76 | out = words_net.forward() 77 | prob = out['softmax'][0] 78 | top_k = words_net.blobs['softmax'].data[0].flatten().argsort()[-1:-6:-1] 79 | _text.append(labels[top_k[0]]) 80 | return _text 81 | 82 | 83 | def judge_image(list_img, list_label): 84 | """判断是不是文件夹""" 85 | if list_img is None: 86 | return None 87 | _index = 1 88 | for im in list_img: 89 | 90 | img = remove_noisy.rm_blackNoisy(im) 91 | img = cut_image.load_image(img) 92 | 93 | image_net.blobs['data'].data[...] = image_transformer.preprocess('data', img) 94 | out = image_net.forward() 95 | prob = out['softmax'][0] 96 | top_k = image_net.blobs['softmax'].data[0].flatten().argsort()[-1:-6:-1] 97 | print "第", _index, " 图预测是：", labels[top_k[0]] 98 | for label in list_label: 99 | if labels[top_k[0]] == label: 100 | print "++++++++++++++++++图片位置：", _index, label, "+++++++++++++++++" 101 | _index += 1 102 | 103 | 104 | def train_main(): 105 | path_dir = '/home/ruifengshan/github/12306-captcha/data/download/' 106 | img_names = filter(lambda s: not s.startswith("."), os.listdir(path_dir + '/all')) 107 | 108 | for img_name in img_names: 109 | im = cut_image.read_image(os.path.join(path_dir + '/all', img_name)) 110 | if im is None: 111 | print "该图片{ %s }处理异常: " % img_name 112 | continue 113 | # 转为灰度图 114 | list_text = judge_words(cut_image.get_text(cv2.cvtColor(im, cv2.COLOR_BGR2GRAY))) 115 | print "文字部分的内容：" 116 | for text in list_text: 117 | print text 118 | judge_image(cut_image.get_image(im), list_text) 119 | skimage.io.imshow(im) 120 | 121 | 122 | if __name__ == '__main__': 123 | train_main() 124 | -------------------------------------------------------------------------------- /src/config.py: -------------------------------------------------------------------------------- 1 | #! _*_ coding:utf-8 _*_ 2 | 3 | # -------------------------------------------------------- 4 | # 12306-captcha 5 | # Copyright (c) 2017 6 | # Written by ruifengshan 7 | # -------------------------------------------------------- 8 | 9 | # `pip install easydict` if you don't have it 10 | from easydict import EasyDict as edict 11 | 12 | __C = edict() 13 | # Consumers can get config by: 14 | # from src.config import cfg 15 | cfg = __C 16 | 17 | # caffe根目录 18 | __C.CAFFE_ROOT = '/home/ruifengshan/caffe-ssd/' 19 | 20 | # 项目根目录 21 | __C.ROOT = '/home/ruifengshan/github/12306-captcha/' -------------------------------------------------------------------------------- /src/image/image-classify.py: -------------------------------------------------------------------------------- 1 | # -*-coding:utf-8-*- 2 | 3 | # -------------------------------------------------------- 4 | # 12306-captcha 5 | # Copyright (c) 2017 6 | # Licensed under The Apache License [see LICENSE for details] 7 | # Written by ruifengshan 8 | # -------------------------------------------------------- 9 | 10 | import os 11 | import sys 12 | 13 | import numpy as np 14 | from src.tools import remove_noisy 15 | from src.tools import cut_image as cimg 16 | import logging 17 | from src.config import cfg 18 | 19 | caffe_root = cfg.CAFFE_ROOT 20 | sys.path.insert(0, caffe_root + 'python') 21 | 22 | import caffe 23 | 24 | net_file = cfg.ROOT + '/src/image/model/image_deploy.prototxt' 25 | caffe_model = cfg.ROOT + 'model/image/f16_snapshot_iter_208000.caffemodel' 26 | synset_file = cfg.ROOT + 'label/synset' 27 | 28 | logger = logging.getLogger(__name__) 29 | 30 | 31 | def judge(path, file_path=synset_file): 32 | """判断是不是文件夹""" 33 | img_names = os.listdir(path) 34 | for img_name in img_names: 35 | logger.info("文件名：　%s", img_name) 36 | tmp_img_name = os.path.join(path, img_name) 37 | if os.path.isdir(tmp_img_name): 38 | judge(tmp_img_name) 39 | os.removedirs(tmp_img_name) 40 | print '$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$文件夹已经被删除', tmp_img_name 41 | elif tmp_img_name.split('.')[1] != "DS_Store": 42 | im1 = cimg.read_image(tmp_img_name) 43 | img = remove_noisy.rm_blackNoisy(im1) 44 | im = cimg.load_image(img) 45 | net.blobs['data'].data[...] = transformer.preprocess('data', im) 46 | out = net.forward() 47 | 48 | imagenet_labels_filename = synset_file 49 | labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\t') 50 | prob = out['softmax'][0] 51 | top_k = net.blobs['softmax'].data[0].flatten().argsort()[-1:-6:-1] 52 | for i in np.arange(top_k.size): 53 | print '照片名: %s , 对应的类：%s , 概率 %%%3f' % (img_name, labels[top_k[i]], prob[top_k[i]] * 100) 54 | print top_k[i], labels[top_k[i]] 55 | new_path = os.path.join("/home/ruifengshan/github/12306-captcha/judge-image-result/", labels[top_k[0]]) 56 | cimg.makeDir(new_path) 57 | cimg.write_image(im1, os.path.join(new_path, img_name)) 58 | # '删除' 59 | os.remove(tmp_img_name) 60 | print '++++++++++++++++++++++++++++++++++++文件已经被删除', tmp_img_name 61 | # time.sleep(1) 62 | 63 | 64 | if __name__ == '__main__': 65 | net = caffe.Net(net_file, caffe_model, caffe.TEST) 66 | transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape}) 67 | "读取的图片文件格式为H×W×K，需转化为K×H×W" 68 | transformer.set_transpose('data', (2, 0, 1)) 69 | # transformer.set_mean('data', mean=np.float32([103.939, 116.779, 123.68])) 70 | "将图片存储为[0, 1]，而caffe中将图片存储为[0, 255" 71 | transformer.set_raw_scale('data', 255) 72 | "caffe中图片是BGR格式，而原始格式是RGB，所以要转化" 73 | transformer.set_channel_swap('data', (2, 1, 0)) 74 | image_dir='/home/ruifengshan/github/12306-captcha/judge-image' 75 | judge(image_dir) 76 | -------------------------------------------------------------------------------- /src/image/model/image_deploy.prototxt: -------------------------------------------------------------------------------- 1 | input: "data" 2 | input_shape { 3 | dim: 1 4 | dim: 3 5 | dim: 68 6 | dim: 68 7 | } 8 | layer { 9 | name: "conv1_1" 10 | type: "Convolution" 11 | bottom: "data" 12 | top: "conv1_1" 13 | convolution_param { 14 | num_output: 64 15 | pad: 1 16 | kernel_size: 3 17 | weight_filler { 18 | type: "xavier" 19 | } 20 | bias_filler { 21 | type: "constant" 22 | value: 0.0 23 | } 24 | } 25 | } 26 | layer { 27 | name: "relu1_1" 28 | type: "ReLU" 29 | bottom: "conv1_1" 30 | top: "conv1_1" 31 | } 32 | layer { 33 | name: "conv1_2" 34 | type: "Convolution" 35 | bottom: "conv1_1" 36 | top: "conv1_2" 37 | convolution_param { 38 | num_output: 64 39 | pad: 1 40 | kernel_size: 3 41 | weight_filler { 42 | type: "xavier" 43 | } 44 | bias_filler { 45 | type: "constant" 46 | value: 0.0 47 | } 48 | } 49 | } 50 | layer { 51 | name: "relu1_2" 52 | type: "ReLU" 53 | bottom: "conv1_2" 54 | top: "conv1_2" 55 | } 56 | layer { 57 | name: "pool1" 58 | type: "Pooling" 59 | bottom: "conv1_2" 60 | top: "pool1" 61 | pooling_param { 62 | pool: MAX 63 | kernel_size: 2 64 | stride: 2 65 | } 66 | } 67 | layer { 68 | name: "conv2_1" 69 | type: "Convolution" 70 | bottom: "pool1" 71 | top: "conv2_1" 72 | convolution_param { 73 | num_output: 128 74 | pad: 1 75 | kernel_size: 3 76 | weight_filler { 77 | type: "xavier" 78 | } 79 | bias_filler { 80 | type: "constant" 81 | value: 0.0 82 | } 83 | } 84 | } 85 | layer { 86 | name: "relu2_1" 87 | type: "ReLU" 88 | bottom: "conv2_1" 89 | top: "conv2_1" 90 | } 91 | layer { 92 | name: "conv2_2" 93 | type: "Convolution" 94 | bottom: "conv2_1" 95 | top: "conv2_2" 96 | convolution_param { 97 | num_output: 128 98 | pad: 1 99 | kernel_size: 3 100 | weight_filler { 101 | type: "xavier" 102 | } 103 | bias_filler { 104 | type: "constant" 105 | value: 0.0 106 | } 107 | } 108 | } 109 | layer { 110 | name: "relu2_2" 111 | type: "ReLU" 112 | bottom: "conv2_2" 113 | top: "conv2_2" 114 | } 115 | layer { 116 | name: "pool2" 117 | type: "Pooling" 118 | bottom: "conv2_2" 119 | top: "pool2" 120 | pooling_param { 121 | pool: MAX 122 | kernel_size: 2 123 | stride: 2 124 | } 125 | } 126 | layer { 127 | name: "conv3_1" 128 | type: "Convolution" 129 | bottom: "pool2" 130 | top: "conv3_1" 131 | convolution_param { 132 | num_output: 256 133 | pad: 1 134 | kernel_size: 3 135 | weight_filler { 136 | type: "xavier" 137 | } 138 | bias_filler { 139 | type: "constant" 140 | value: 0.0 141 | } 142 | } 143 | } 144 | layer { 145 | name: "relu3_1" 146 | type: "ReLU" 147 | bottom: "conv3_1" 148 | top: "conv3_1" 149 | } 150 | layer { 151 | name: "conv3_2" 152 | type: "Convolution" 153 | bottom: "conv3_1" 154 | top: "conv3_2" 155 | convolution_param { 156 | num_output: 256 157 | pad: 1 158 | kernel_size: 3 159 | weight_filler { 160 | type: "xavier" 161 | } 162 | bias_filler { 163 | type: "constant" 164 | value: 0.0 165 | } 166 | } 167 | } 168 | layer { 169 | name: "relu3_2" 170 | type: "ReLU" 171 | bottom: "conv3_2" 172 | top: "conv3_2" 173 | } 174 | layer { 175 | name: "conv3_3" 176 | type: "Convolution" 177 | bottom: "conv3_2" 178 | top: "conv3_3" 179 | convolution_param { 180 | num_output: 256 181 | pad: 1 182 | kernel_size: 3 183 | weight_filler { 184 | type: "xavier" 185 | } 186 | bias_filler { 187 | type: "constant" 188 | value: 0.0 189 | } 190 | } 191 | } 192 | layer { 193 | name: "relu3_3" 194 | type: "ReLU" 195 | bottom: "conv3_3" 196 | top: "conv3_3" 197 | } 198 | layer { 199 | name: "pool3" 200 | type: "Pooling" 201 | bottom: "conv3_3" 202 | top: "pool3" 203 | pooling_param { 204 | pool: MAX 205 | kernel_size: 2 206 | stride: 2 207 | } 208 | } 209 | layer { 210 | name: "conv4_1" 211 | type: "Convolution" 212 | bottom: "pool3" 213 | top: "conv4_1" 214 | convolution_param { 215 | num_output: 512 216 | pad: 1 217 | kernel_size: 3 218 | weight_filler { 219 | type: "xavier" 220 | } 221 | bias_filler { 222 | type: "constant" 223 | value: 0.0 224 | } 225 | } 226 | } 227 | layer { 228 | name: "relu4_1" 229 | type: "ReLU" 230 | bottom: "conv4_1" 231 | top: "conv4_1" 232 | } 233 | layer { 234 | name: "conv4_2" 235 | type: "Convolution" 236 | bottom: "conv4_1" 237 | top: "conv4_2" 238 | convolution_param { 239 | num_output: 512 240 | pad: 1 241 | kernel_size: 3 242 | weight_filler { 243 | type: "xavier" 244 | } 245 | bias_filler { 246 | type: "constant" 247 | value: 0.0 248 | } 249 | } 250 | } 251 | layer { 252 | name: "relu4_2" 253 | type: "ReLU" 254 | bottom: "conv4_2" 255 | top: "conv4_2" 256 | } 257 | layer { 258 | name: "conv4_3" 259 | type: "Convolution" 260 | bottom: "conv4_2" 261 | top: "conv4_3" 262 | convolution_param { 263 | num_output: 512 264 | pad: 1 265 | kernel_size: 3 266 | weight_filler { 267 | type: "xavier" 268 | } 269 | bias_filler { 270 | type: "constant" 271 | value: 0.0 272 | } 273 | } 274 | } 275 | layer { 276 | name: "relu4_3" 277 | type: "ReLU" 278 | bottom: "conv4_3" 279 | top: "conv4_3" 280 | } 281 | layer { 282 | name: "pool4" 283 | type: "Pooling" 284 | bottom: "conv4_3" 285 | top: "pool4" 286 | pooling_param { 287 | pool: MAX 288 | kernel_size: 2 289 | stride: 2 290 | } 291 | } 292 | layer { 293 | name: "conv5_1" 294 | type: "Convolution" 295 | bottom: "pool4" 296 | top: "conv5_1" 297 | convolution_param { 298 | num_output: 512 299 | pad: 1 300 | kernel_size: 3 301 | weight_filler { 302 | type: "xavier" 303 | } 304 | bias_filler { 305 | type: "constant" 306 | value: 0.0 307 | } 308 | } 309 | } 310 | layer { 311 | name: "relu5_1" 312 | type: "ReLU" 313 | bottom: "conv5_1" 314 | top: "conv5_1" 315 | } 316 | layer { 317 | name: "conv5_2" 318 | type: "Convolution" 319 | bottom: "conv5_1" 320 | top: "conv5_2" 321 | convolution_param { 322 | num_output: 512 323 | pad: 1 324 | kernel_size: 3 325 | weight_filler { 326 | type: "xavier" 327 | } 328 | bias_filler { 329 | type: "constant" 330 | value: 0.0 331 | } 332 | } 333 | } 334 | layer { 335 | name: "relu5_2" 336 | type: "ReLU" 337 | bottom: "conv5_2" 338 | top: "conv5_2" 339 | } 340 | layer { 341 | name: "conv5_3" 342 | type: "Convolution" 343 | bottom: "conv5_2" 344 | top: "conv5_3" 345 | convolution_param { 346 | num_output: 512 347 | pad: 1 348 | kernel_size: 3 349 | weight_filler { 350 | type: "xavier" 351 | } 352 | bias_filler { 353 | type: "constant" 354 | value: 0.0 355 | } 356 | } 357 | } 358 | layer { 359 | name: "relu5_3" 360 | type: "ReLU" 361 | bottom: "conv5_3" 362 | top: "conv5_3" 363 | } 364 | layer { 365 | name: "pool5" 366 | type: "Pooling" 367 | bottom: "conv5_3" 368 | top: "pool5" 369 | pooling_param { 370 | pool: MAX 371 | kernel_size: 2 372 | stride: 2 373 | } 374 | } 375 | layer { 376 | name: "fc6" 377 | type: "InnerProduct" 378 | bottom: "pool5" 379 | top: "fc6" 380 | inner_product_param { 381 | num_output: 4096 382 | weight_filler { 383 | type: "xavier" 384 | } 385 | bias_filler { 386 | type: "constant" 387 | value: 0.1 388 | } 389 | } 390 | } 391 | layer { 392 | name: "relu6" 393 | type: "ReLU" 394 | bottom: "fc6" 395 | top: "fc6" 396 | } 397 | layer { 398 | name: "drop6" 399 | type: "Dropout" 400 | bottom: "fc6" 401 | top: "fc6" 402 | dropout_param { 403 | dropout_ratio: 0.5 404 | } 405 | } 406 | layer { 407 | name: "fc7" 408 | type: "InnerProduct" 409 | bottom: "fc6" 410 | top: "fc7" 411 | inner_product_param { 412 | num_output: 4096 413 | weight_filler { 414 | type: "xavier" 415 | } 416 | bias_filler { 417 | type: "constant" 418 | value: 0.1 419 | } 420 | } 421 | } 422 | layer { 423 | name: "relu7" 424 | type: "ReLU" 425 | bottom: "fc7" 426 | top: "fc7" 427 | } 428 | layer { 429 | name: "drop7" 430 | type: "Dropout" 431 | bottom: "fc7" 432 | top: "fc7" 433 | dropout_param { 434 | dropout_ratio: 0.5 435 | } 436 | } 437 | layer { 438 | name: "fc8" 439 | type: "InnerProduct" 440 | bottom: "fc7" 441 | top: "fc8" 442 | inner_product_param { 443 | num_output: 1000 444 | weight_filler { 445 | type: "xavier" 446 | } 447 | bias_filler { 448 | type: "constant" 449 | value: 0.1 450 | } 451 | } 452 | } 453 | layer { 454 | name: "softmax" 455 | type: "Softmax" 456 | bottom: "fc8" 457 | top: "softmax" 458 | } 459 | -------------------------------------------------------------------------------- /src/image/model/image_solver.prototxt: -------------------------------------------------------------------------------- 1 | net: "./image_train_val.prototxt" 2 | test_iter: 271 3 | test_interval: 100 4 | base_lr: 0.01 5 | display: 20 6 | max_iter: 25000 7 | lr_policy: "step" 8 | gamma: 0.1 9 | momentum: 0.9 10 | weight_decay: 0.0005 11 | power: 0.75 12 | stepsize: 100 13 | snapshot: 5000 14 | snapshot_prefix: "snapshot" 15 | solver_type: SGD 16 | solver_mode: GPU 17 | -------------------------------------------------------------------------------- /src/image/model/image_train_val.prototxt: -------------------------------------------------------------------------------- 1 | layer { 2 | name: "vgg-train-data" 3 | type: "Data" 4 | top: "data" 5 | top: "label" 6 | include { 7 | phase: TRAIN 8 | } 9 | transform_param { 10 | scale: 0.00390625 11 | } 12 | data_param { 13 | source: "/home/ruifengshan/github/12306-captcha/data/image/image_train_lmdb" 14 | batch_size: 64 15 | backend: LMDB 16 | } 17 | } 18 | layer { 19 | name: "val-data" 20 | type: "Data" 21 | top: "data" 22 | top: "label" 23 | include { 24 | phase: TEST 25 | } 26 | transform_param { 27 | scale: 0.00390625 28 | } 29 | data_param { 30 | source: "/home/ruifengshan/github/12306-captcha/data/image/image_test_lmdb" 31 | batch_size: 64 32 | backend: LMDB 33 | } 34 | } 35 | layer { 36 | name: "conv1_1" 37 | type: "Convolution" 38 | bottom: "data" 39 | top: "conv1_1" 40 | convolution_param { 41 | num_output: 64 42 | pad: 1 43 | kernel_size: 3 44 | weight_filler { 45 | type: "xavier" 46 | } 47 | bias_filler { 48 | type: "constant" 49 | value: 0.0 50 | } 51 | } 52 | } 53 | layer { 54 | name: "relu1_1" 55 | type: "ReLU" 56 | bottom: "conv1_1" 57 | top: "conv1_1" 58 | } 59 | layer { 60 | name: "conv1_2" 61 | type: "Convolution" 62 | bottom: "conv1_1" 63 | top: "conv1_2" 64 | convolution_param { 65 | num_output: 64 66 | pad: 1 67 | kernel_size: 3 68 | weight_filler { 69 | type: "xavier" 70 | } 71 | bias_filler { 72 | type: "constant" 73 | value: 0.0 74 | } 75 | } 76 | } 77 | layer { 78 | name: "relu1_2" 79 | type: "ReLU" 80 | bottom: "conv1_2" 81 | top: "conv1_2" 82 | } 83 | layer { 84 | name: "pool1" 85 | type: "Pooling" 86 | bottom: "conv1_2" 87 | top: "pool1" 88 | pooling_param { 89 | pool: MAX 90 | kernel_size: 2 91 | stride: 2 92 | } 93 | } 94 | layer { 95 | name: "conv2_1" 96 | type: "Convolution" 97 | bottom: "pool1" 98 | top: "conv2_1" 99 | convolution_param { 100 | num_output: 128 101 | pad: 1 102 | kernel_size: 3 103 | weight_filler { 104 | type: "xavier" 105 | } 106 | bias_filler { 107 | type: "constant" 108 | value: 0.0 109 | } 110 | } 111 | } 112 | layer { 113 | name: "relu2_1" 114 | type: "ReLU" 115 | bottom: "conv2_1" 116 | top: "conv2_1" 117 | } 118 | layer { 119 | name: "conv2_2" 120 | type: "Convolution" 121 | bottom: "conv2_1" 122 | top: "conv2_2" 123 | convolution_param { 124 | num_output: 128 125 | pad: 1 126 | kernel_size: 3 127 | weight_filler { 128 | type: "xavier" 129 | } 130 | bias_filler { 131 | type: "constant" 132 | value: 0.0 133 | } 134 | } 135 | } 136 | layer { 137 | name: "relu2_2" 138 | type: "ReLU" 139 | bottom: "conv2_2" 140 | top: "conv2_2" 141 | } 142 | layer { 143 | name: "pool2" 144 | type: "Pooling" 145 | bottom: "conv2_2" 146 | top: "pool2" 147 | pooling_param { 148 | pool: MAX 149 | kernel_size: 2 150 | stride: 2 151 | } 152 | } 153 | layer { 154 | name: "conv3_1" 155 | type: "Convolution" 156 | bottom: "pool2" 157 | top: "conv3_1" 158 | convolution_param { 159 | num_output: 256 160 | pad: 1 161 | kernel_size: 3 162 | weight_filler { 163 | type: "xavier" 164 | } 165 | bias_filler { 166 | type: "constant" 167 | value: 0.0 168 | } 169 | } 170 | } 171 | layer { 172 | name: "relu3_1" 173 | type: "ReLU" 174 | bottom: "conv3_1" 175 | top: "conv3_1" 176 | } 177 | layer { 178 | name: "conv3_2" 179 | type: "Convolution" 180 | bottom: "conv3_1" 181 | top: "conv3_2" 182 | convolution_param { 183 | num_output: 256 184 | pad: 1 185 | kernel_size: 3 186 | weight_filler { 187 | type: "xavier" 188 | } 189 | bias_filler { 190 | type: "constant" 191 | value: 0.0 192 | } 193 | } 194 | } 195 | layer { 196 | name: "relu3_2" 197 | type: "ReLU" 198 | bottom: "conv3_2" 199 | top: "conv3_2" 200 | } 201 | layer { 202 | name: "conv3_3" 203 | type: "Convolution" 204 | bottom: "conv3_2" 205 | top: "conv3_3" 206 | convolution_param { 207 | num_output: 256 208 | pad: 1 209 | kernel_size: 3 210 | weight_filler { 211 | type: "xavier" 212 | } 213 | bias_filler { 214 | type: "constant" 215 | value: 0.0 216 | } 217 | } 218 | } 219 | layer { 220 | name: "relu3_3" 221 | type: "ReLU" 222 | bottom: "conv3_3" 223 | top: "conv3_3" 224 | } 225 | layer { 226 | name: "pool3" 227 | type: "Pooling" 228 | bottom: "conv3_3" 229 | top: "pool3" 230 | pooling_param { 231 | pool: MAX 232 | kernel_size: 2 233 | stride: 2 234 | } 235 | } 236 | layer { 237 | name: "conv4_1" 238 | type: "Convolution" 239 | bottom: "pool3" 240 | top: "conv4_1" 241 | convolution_param { 242 | num_output: 512 243 | pad: 1 244 | kernel_size: 3 245 | weight_filler { 246 | type: "xavier" 247 | } 248 | bias_filler { 249 | type: "constant" 250 | value: 0.0 251 | } 252 | } 253 | } 254 | layer { 255 | name: "relu4_1" 256 | type: "ReLU" 257 | bottom: "conv4_1" 258 | top: "conv4_1" 259 | } 260 | layer { 261 | name: "conv4_2" 262 | type: "Convolution" 263 | bottom: "conv4_1" 264 | top: "conv4_2" 265 | convolution_param { 266 | num_output: 512 267 | pad: 1 268 | kernel_size: 3 269 | weight_filler { 270 | type: "xavier" 271 | } 272 | bias_filler { 273 | type: "constant" 274 | value: 0.0 275 | } 276 | } 277 | } 278 | layer { 279 | name: "relu4_2" 280 | type: "ReLU" 281 | bottom: "conv4_2" 282 | top: "conv4_2" 283 | } 284 | layer { 285 | name: "conv4_3" 286 | type: "Convolution" 287 | bottom: "conv4_2" 288 | top: "conv4_3" 289 | convolution_param { 290 | num_output: 512 291 | pad: 1 292 | kernel_size: 3 293 | weight_filler { 294 | type: "xavier" 295 | } 296 | bias_filler { 297 | type: "constant" 298 | value: 0.0 299 | } 300 | } 301 | } 302 | layer { 303 | name: "relu4_3" 304 | type: "ReLU" 305 | bottom: "conv4_3" 306 | top: "conv4_3" 307 | } 308 | layer { 309 | name: "pool4" 310 | type: "Pooling" 311 | bottom: "conv4_3" 312 | top: "pool4" 313 | pooling_param { 314 | pool: MAX 315 | kernel_size: 2 316 | stride: 2 317 | } 318 | } 319 | layer { 320 | name: "conv5_1" 321 | type: "Convolution" 322 | bottom: "pool4" 323 | top: "conv5_1" 324 | convolution_param { 325 | num_output: 512 326 | pad: 1 327 | kernel_size: 3 328 | weight_filler { 329 | type: "xavier" 330 | } 331 | bias_filler { 332 | type: "constant" 333 | value: 0.0 334 | } 335 | } 336 | } 337 | layer { 338 | name: "relu5_1" 339 | type: "ReLU" 340 | bottom: "conv5_1" 341 | top: "conv5_1" 342 | } 343 | layer { 344 | name: "conv5_2" 345 | type: "Convolution" 346 | bottom: "conv5_1" 347 | top: "conv5_2" 348 | convolution_param { 349 | num_output: 512 350 | pad: 1 351 | kernel_size: 3 352 | weight_filler { 353 | type: "xavier" 354 | } 355 | bias_filler { 356 | type: "constant" 357 | value: 0.0 358 | } 359 | } 360 | } 361 | layer { 362 | name: "relu5_2" 363 | type: "ReLU" 364 | bottom: "conv5_2" 365 | top: "conv5_2" 366 | } 367 | layer { 368 | name: "conv5_3" 369 | type: "Convolution" 370 | bottom: "conv5_2" 371 | top: "conv5_3" 372 | convolution_param { 373 | num_output: 512 374 | pad: 1 375 | kernel_size: 3 376 | weight_filler { 377 | type: "xavier" 378 | } 379 | bias_filler { 380 | type: "constant" 381 | value: 0.0 382 | } 383 | } 384 | } 385 | layer { 386 | name: "relu5_3" 387 | type: "ReLU" 388 | bottom: "conv5_3" 389 | top: "conv5_3" 390 | } 391 | layer { 392 | name: "pool5" 393 | type: "Pooling" 394 | bottom: "conv5_3" 395 | top: "pool5" 396 | pooling_param { 397 | pool: MAX 398 | kernel_size: 2 399 | stride: 2 400 | } 401 | } 402 | layer { 403 | name: "fc6" 404 | type: "InnerProduct" 405 | bottom: "pool5" 406 | top: "fc6" 407 | inner_product_param { 408 | num_output: 4096 409 | weight_filler { 410 | type: "xavier" 411 | } 412 | bias_filler { 413 | type: "constant" 414 | value: 0.1 415 | } 416 | } 417 | } 418 | layer { 419 | name: "relu6" 420 | type: "ReLU" 421 | bottom: "fc6" 422 | top: "fc6" 423 | } 424 | layer { 425 | name: "drop6" 426 | type: "Dropout" 427 | bottom: "fc6" 428 | top: "fc6" 429 | dropout_param { 430 | dropout_ratio: 0.5 431 | } 432 | } 433 | layer { 434 | name: "fc7" 435 | type: "InnerProduct" 436 | bottom: "fc6" 437 | top: "fc7" 438 | inner_product_param { 439 | num_output: 4096 440 | weight_filler { 441 | type: "xavier" 442 | } 443 | bias_filler { 444 | type: "constant" 445 | value: 0.1 446 | } 447 | } 448 | } 449 | layer { 450 | name: "relu7" 451 | type: "ReLU" 452 | bottom: "fc7" 453 | top: "fc7" 454 | } 455 | layer { 456 | name: "drop7" 457 | type: "Dropout" 458 | bottom: "fc7" 459 | top: "fc7" 460 | dropout_param { 461 | dropout_ratio: 0.5 462 | } 463 | } 464 | layer { 465 | name: "fc8" 466 | type: "InnerProduct" 467 | bottom: "fc7" 468 | top: "fc8" 469 | inner_product_param { 470 | num_output: 1000 471 | weight_filler { 472 | type: "xavier" 473 | } 474 | bias_filler { 475 | type: "constant" 476 | value: 0.1 477 | } 478 | } 479 | } 480 | layer { 481 | name: "accuracy" 482 | type: "Accuracy" 483 | bottom: "fc8" 484 | bottom: "label" 485 | top: "accuracy" 486 | include { 487 | phase: TEST 488 | }K 489 | } 490 | layer { 491 | name: "loss" 492 | type: "SoftmaxWithLoss" 493 | bottom: "fc8" 494 | bottom: "label" 495 | top: "loss" 496 | } 497 | -------------------------------------------------------------------------------- /src/image/scripts/calc_mean.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | TOOLS_DIR=/home/ruifengshan/caffe/build/tools 4 | DATA=/home/ruifengshan/github/12306-captcha/data/image 5 | 6 | $TOOLS_DIR/compute_image_mean $DATA/image_train_lmdb $DATA/mean.binaryproto 7 | 8 | -------------------------------------------------------------------------------- /src/image/scripts/create_data.py: -------------------------------------------------------------------------------- 1 | # _*_ coding:utf-8 _*_ 2 | 3 | import os 4 | import scipy.misc 5 | from src.config import cfg 6 | 7 | 8 | synset_file = cfg.ROOT + 'label/synset' 9 | 10 | 11 | # 将synset转换为train.txt文件 12 | def load_file(path=synset_file): 13 | synset = [line.strip() for line in open(path).readlines()] 14 | print synset 15 | return synset 16 | 17 | 18 | def create_file(data_path, file_list, file_name): 19 | """ 20 | 创建训练文件train.txt/test.txt 21 | 22 | :param data_path: 图片文件的上级路径 23 | :param file_list: synset生成的list 24 | :param train_file_name: 标签表 25 | :return: 26 | """ 27 | 28 | try: 29 | cnt = 0 30 | if not os.path.isfile(file_name): 31 | os.mknod(file_name) 32 | file_txt = open(file_name, mode='wr') 33 | for file_name in file_list: 34 | print file_name 35 | file_path = os.path.join(data_path, file_name) 36 | print file_path 37 | if not os.path.isdir(file_path): 38 | print file_path, '目录不存在' 39 | continue 40 | for path in os.listdir(file_path): 41 | file_txt.write(os.path.join(file_name, path) + " " + str(cnt) + "\n") 42 | cnt += 1 43 | except Exception, e: 44 | print "执行失败", e 45 | 46 | 47 | def create_train_file(data_path, file_list, train_file_name='train.txt'): 48 | create_file(data_path, file_list, train_file_name) 49 | 50 | 51 | def create_test_file(data_path, file_list, test_file_name='test.txt'): 52 | create_file(data_path, file_list, test_file_name) 53 | 54 | 55 | if __name__ == '__main__': 56 | create_train_file(cfg.ROOT + "/data/image/train/", load_file()) 57 | create_test_file(cfg.ROOT + "/data/image/test/", load_file()) -------------------------------------------------------------------------------- /src/image/scripts/create_lmdb.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | 3 | TOOLS_DIR=/home/ruifengshan/caffe/build/tools 4 | 5 | dir=`dirname "$0"` 6 | root_path=`cd "$dir/../../..">/dev/null; pwd` 7 | echo "root_path:"$root_path 8 | 9 | DATA=$root_path/data/image 10 | 11 | 12 | rm -rf $DATA/image_train_lmdb 13 | $TOOLS_DIR/convert_imageset \ 14 | --shuffle \ 15 | --resize_height=68 \ 16 | --resize_width=68 \ 17 | $DATA/train $DATA/train.txt $DATA/image_train_lmdb 18 | 19 | 20 | rm -rf $DATA/image_test_lmdb 21 | $TOOLS_DIR/convert_imageset \ 22 | --shuffle \ 23 | --resize_height=68 \ 24 | --resize_width=68 \ 25 | $DATA/test $DATA/test.txt $DATA/image_test_lmdb 26 | -------------------------------------------------------------------------------- /src/image/scripts/image_finetune_train.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | #该指令是用来在已有的模型上，进行微调，需要注意的是模型的图片大小以及layer需要保持一致. 3 | # 如果要开启GPU模式，只需要在该指令的末尾加上 -gpu 0 即可. ps 默认情况下run as cpu-only 4 | 5 | dir=`dirname "$0"` 6 | root_path=`cd "$dir/../../..">/dev/null; pwd` 7 | echo "root_path:"$root_path 8 | 9 | TOOLS_DIR=/home/ruifengshan/caffe/build/tools 10 | 11 | MODEL_CONFIG_DIR=$root_path/src/image/model 12 | MODEL_DIR=$root_path/model/image 13 | 14 | $TOOLS_DIR/caffe train \ 15 | -solver $MODEL_CONFIG_DIR/image_solver.prototxt \ 16 | -gpu 0 \ 17 | -weights $MODEL_DIR/f14_snapshot_iter_99000.caffemodel 1> $MODEL_DIR/image_train.log 2>&1 & 18 | -------------------------------------------------------------------------------- /src/image/scripts/image_train.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | dir=`dirname "$0"` 4 | root_path=`cd "$dir/../../..">/dev/null; pwd` 5 | echo "root_path:"$root_path 6 | 7 | TOOLS_DIR=/home/ruifengshan/caffe/build/tools 8 | 9 | MODEL_CONFIG_DIR=$root_path/src/image/model 10 | MODEL_DIR=$root_path/model/image 11 | 12 | $TOOLS_DIR/caffe train \ 13 | -solver $MODEL_CONFIG_DIR/image_solver.prototxt $@ 1>$MODEL_DIR/image_train.log 2>&1 & 14 | -------------------------------------------------------------------------------- /src/tools/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aaronshan/12306-captcha/af14073d15f8beb3f8c9fe30802ee52875b78bec/src/tools/__init__.py -------------------------------------------------------------------------------- /src/tools/cut_image.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf8-*- 2 | 3 | # -------------------------------------------------------- 4 | # 12306-captcha 5 | # Copyright (c) 2017 6 | # Written by ruifengshan 7 | # -------------------------------------------------------- 8 | 9 | # 功能: 分割组合图片 10 | 11 | import cv2 12 | import os 13 | import time 14 | import skimage.io 15 | import numpy as np 16 | from src.config import cfg 17 | 18 | IMG_V_POS = [4, 76, 148, 220] 19 | IMG_H_POS = [40, 108] 20 | IMG_WIDTH = 68 # 每个小图片的宽度 21 | IMG_HEIGHT = 68 # 每个小图片的高度 22 | 23 | 24 | def read_image(fn): 25 | """ 26 | 得到验证码完整图像 27 | :param fn:图像文件路径 28 | :return:图像对象 29 | """ 30 | im = None 31 | try: 32 | im = skimage.io.imread(fn, as_grey=False) 33 | except Exception: 34 | pass 35 | return im 36 | 37 | 38 | def load_image(im, color=True): 39 | img = skimage.img_as_float(im).astype(np.float32) 40 | if img.ndim == 2: 41 | img = img[:, :, np.newaxis] 42 | if color: 43 | img = np.tile(img, (1, 1, 3)) 44 | 45 | elif img.shape[2] == 4: 46 | img = img[:, :, :3] 47 | return img 48 | 49 | 50 | def write_image(im, fn): 51 | skimage.io.imsave(fn, im) 52 | 53 | 54 | def get_text(im): 55 | """ 56 | 得到图像中的文本部分 57 | """ 58 | return im[3:24, 116:288] 59 | 60 | 61 | # 分割图片 62 | def get_image(im): 63 | img = [] 64 | for v in range(2): # 图片行 65 | for h in range(4): # 图片列 66 | img.append(im[(40 + (v * 72)):(108 + (v * 72)), (4 + (h * 72)):((h + 1) * 72)]) 67 | return img 68 | 69 | 70 | # 二值化图像 71 | def binarize(im): 72 | gray = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY) 73 | (retval, dst) = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU) 74 | return dst 75 | 76 | 77 | # 获取图像值 78 | 79 | def show_image(im): 80 | print im.ndim, im.dtype 81 | cv2.imshow("image", im) 82 | cv2.waitKey(0) 83 | 84 | 85 | def make_dir(path): 86 | try: 87 | if not os.path.exists(path): 88 | if not os.path.isfile(path): 89 | os.mkdir(path) 90 | return True 91 | else: 92 | return False 93 | except Exception, e: 94 | print str(e) 95 | 96 | 97 | # 将文件写入到 98 | def cut_image(image_cnt): 99 | path_dir = cfg.ROOT + '/data/download/' 100 | img_names = filter(lambda s: not s.startswith("."), os.listdir(path_dir + '/all')) 101 | 102 | while True: 103 | ocr_path = os.path.join(path_dir, 'words/words_' + str(image_cnt)) 104 | if make_dir(ocr_path): 105 | break 106 | image_cnt += 1 107 | while True: 108 | img_path = os.path.join(path_dir, 'image/image_' + str(image_cnt)) 109 | if make_dir(img_path): 110 | break 111 | image_cnt += 1 112 | 113 | for img_name in img_names: 114 | f_name = os.path.join(path_dir + '/all', img_name) 115 | im = read_image(f_name) 116 | if im is None: 117 | print "该图片{ %s }处理异常: " % img_name 118 | continue 119 | print os.path.join(ocr_path, img_name) 120 | write_image(get_text(im), os.path.join(ocr_path, img_name)) 121 | num = 1 122 | sub_name = img_name.split('.') 123 | for sub_im in get_image(im): 124 | sub_img_name = sub_name[0] + '_' + str(num) + '.' + sub_name[1] 125 | num += 1 126 | write_image(sub_im, os.path.join(img_path, sub_img_name)) 127 | # '删除' 128 | # os.remove(os.path.join(path_dir + '/all', img_name)) 129 | return image_cnt 130 | 131 | 132 | def thread_main(): 133 | cnt = 0 134 | while True: 135 | cnt = cut_image(cnt) 136 | time.sleep(60) 137 | print '第{ %s }次开始切割图片' % cnt 138 | cnt += 1 139 | 140 | 141 | if __name__ == '__main__': 142 | cut_image(0) 143 | -------------------------------------------------------------------------------- /src/tools/download_image.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf8 -*- 2 | 3 | # -------------------------------------------------------- 4 | # 12306-captcha 5 | # Copyright (c) 2017 6 | # Written by ruifengshan 7 | # -------------------------------------------------------- 8 | 9 | # 功能: 用于下载12306的验证码图片 10 | 11 | import cookielib 12 | import hashlib 13 | import random 14 | import threading 15 | import time 16 | import urllib2 17 | 18 | from src.config import cfg 19 | from cut_image import thread_main 20 | 21 | "下载指定地方的验证码图片" 22 | 23 | # 'http格式封装' 24 | agent = 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.94 Safari/537.36' 25 | login = 'https://kyfw.12306.cn/otn/login/init' 26 | domain = 'kyfw.12306.cn' 27 | 28 | headers = { 29 | 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8', 30 | 'Accept-Encoding': 'gzip, deflate, sdch, br', 31 | 'Accept-Language': 'zh - CN, zh;q = 0.8', 32 | 'Cache-Control': 'no - cache', 33 | 'Connection': 'keep-alive', 34 | 'Host': domain, 35 | 'User-aget': agent, 36 | 'Referer': login 37 | } 38 | 39 | 40 | def download_image(download_dir=cfg.ROOT + '/data/download/all'): 41 | def get_url(): 42 | const_url = 'https://kyfw.12306.cn/otn/passcodeNew/getPassCodeNew?module=login&rand=sjrand&' 43 | random_token = random.uniform(0, 1) 44 | img_url = const_url + urllib2.quote(str(random_token)) 45 | print img_url 46 | return img_url 47 | 48 | try: 49 | cookie_j = cookielib.CookieJar() 50 | op = urllib2.build_opener(urllib2.HTTPCookieProcessor(cookie_j)) 51 | op.add_handler = [('User-agent', agent)] 52 | op.add_handler = [('Host', domain)] 53 | op.open(login) 54 | # '获取cookie' 55 | cookies = '' 56 | for Cookie in enumerate(cookie_j): 57 | ck = Cookie[1] 58 | cookies += ck.name + '=' + ck.value + ";" 59 | headers['Cookie'] = cookies 60 | 61 | # 下载30次 62 | for _ in range(random.randint(50, 200)): 63 | request = urllib2.Request(get_url(), cookies, headers=headers) 64 | raw = urllib2.urlopen(request).read() 65 | "采用十进制的md5命名文件名称" 66 | fn = hashlib.md5(raw).hexdigest() 67 | with open(download_dir + "/%s.jpg" % fn, 'wb') as fp: 68 | fp.write(raw) 69 | 70 | time.sleep(1) 71 | except Exception, e: 72 | print str(e) 73 | 74 | 75 | if __name__ == '__main__': 76 | while True: 77 | try: 78 | download_image() 79 | except Exception, e: 80 | print 'error', str(e) 81 | -------------------------------------------------------------------------------- /src/tools/remove_noisy.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | 3 | # -------------------------------------------------------- 4 | # 12306-captcha 5 | # Copyright (c) 2017 6 | # Licensed under The Apache License [see LICENSE for details] 7 | # Written by ruifengshan 8 | # -------------------------------------------------------- 9 | 10 | """设置去除图像中的黑色斑点""" 11 | import cv2 12 | import numpy as np 13 | 14 | 15 | def rm_blackNoisy(im, threshold=20): 16 | """ 17 | 该函数用来去除图像的中黑色斑点,同时采用插值法进行该点像素的补充 18 | ,param threshold, 设定黑色斑点的最大值 19 | ,param img, RGB图像的数据 20 | ,return, 返回RGB图像数据 21 | """ 22 | 23 | def min(c_val, t_val): 24 | return c_val if c_val < t_val else t_val 25 | 26 | def sum(_val, _sum, _num): 27 | return _val + _sum, _num + 1 28 | 29 | def rm_blackNoisy_component(img, threshold): 30 | y, x = img.shape[:2] # x,y表示坐标 31 | for _x in range(0, x, 1): 32 | for _y in range(0, y, 1): 33 | cnt = 0 34 | _sum = num = 0 35 | if _y > 0: 36 | _sum, num = sum(img[_y - 1, _x], _sum, num) 37 | if _y + 1 < y: 38 | _sum, num = sum(img[_y + 1, _x], _sum, num) 39 | if _x > 0: 40 | _sum, num = sum(img[_y, _x - 1], _sum, num) 41 | if _x + 1 < x: 42 | _sum, num = sum(img[_y, _x + 1], _sum, num) 43 | if _y > 0 and _x > 0: 44 | _sum, num = sum(img[_y - 1, _x - 1], _sum, num) 45 | if _y > 0 and _x + 1 < x: 46 | _sum, num = sum(img[_y - 1, _x + 1], _sum, num) 47 | if _y + 1 < y and _x > 0: 48 | _sum, num = sum(img[_y + 1, _x - 1], _sum, num) 49 | 50 | if _y + 1 < y and _x + 1 < x: 51 | _sum, num = sum(img[_y + 1, _x + 1], _sum, num) 52 | if cnt > 0 or num < 1: # 说明不是单个点 53 | continue 54 | # 如果是孤点,则可以依据权重进行插值 55 | arvage = _sum / num 56 | if img[_y, _x] + 100 <= arvage: 57 | img[_y, _x] = arvage 58 | 59 | return img 60 | 61 | if im.ndim == 3: 62 | b, g, r = cv2.split(im) 63 | _b = rm_blackNoisy_component(b, threshold) 64 | _g = rm_blackNoisy_component(g, threshold) 65 | _r = rm_blackNoisy_component(r, threshold) 66 | img = cv2.merge([_b, _g, _r]) # 前面分离出来的三个通道 67 | 68 | else: 69 | return rm_blackNoisy_component(im, threshold) 70 | 71 | return img 72 | -------------------------------------------------------------------------------- /src/tools/words.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | 3 | # -------------------------------------------------------- 4 | # 12306-captcha 5 | # Copyright (c) 2017 6 | # Licensed under The Apache License [see LICENSE for details] 7 | # Written by ruifengshan 8 | # -------------------------------------------------------- 9 | 10 | import cv2 11 | import numpy as np 12 | import cv2 13 | import os 14 | import math as mt 15 | import skimage.transform 16 | import matplotlib.pyplot as plt 17 | from scipy import ndimage as ndi 18 | from skimage import morphology, color, data 19 | import matplotlib.pyplot as plt 20 | from PIL import Image 21 | from src.config import cfg 22 | 23 | 24 | def make_dir(path): 25 | try: 26 | if not os.path.exists(path): 27 | if not os.path.isfile(path): 28 | os.makedirs(path) 29 | return True 30 | except Exception, e: 31 | print str(e) 32 | return False 33 | 34 | 35 | def int_max(a, b): 36 | """求解最大值""" 37 | return a if a > b else b 38 | 39 | 40 | def int_min(a, b): 41 | """求解最小值""" 42 | return a if a < b else b 43 | 44 | 45 | def get_mean_value(img): 46 | """ 47 | 求图像分均值 48 | :param img: 49 | :return: 平均值 50 | """ 51 | y, x = img.shape[:2] 52 | sum_value = 0 53 | for _x in range(0, x, 1): 54 | # temp_value = 0 55 | for _y in range(0, y, 1): 56 | sum_value += img[_y][_x] 57 | 58 | return sum_value / (x * y) 59 | 60 | 61 | def pretreatment_image(img, times=3): 62 | """ 63 | 预处理图片 64 | :param img: 待处理的灰度图图片 65 | :return: 处理之后的图片 66 | """ 67 | 68 | img = 255 - img 69 | y, x = img.shape[:2] 70 | for _ in xrange(times): 71 | mean_value = get_mean_value(img) 72 | for _x in range(0, x, 1): 73 | # temp_value = 0 74 | for _y in range(0, y, 1): 75 | if img[_y][_x] >= mean_value: 76 | img[_y][_x] -= mean_value 77 | else: 78 | img[_y][_x] = 0 79 | while (1): 80 | max_value = -1 81 | for _x in range(0, x, 1): 82 | # temp_value = 0 83 | for _y in range(0, y, 1): 84 | if img[_y][_x] + img[_y][_x] * 0.2 >= 255: 85 | img[_y][_x] = 255 86 | else: 87 | img[_y][_x] += img[_y][_x] * 0.2 88 | if max_value < img[_y][_x] * 0.2: 89 | max_value = img[_y][_x] * 0.2 90 | if max_value >= mean_value: 91 | break 92 | mean_value -= max_value 93 | 94 | return img 95 | 96 | 97 | def binary_text(im): 98 | """ 99 | 二值化 100 | :param im: 待处理的原始图片 101 | :return: 二值化后的图片 102 | """ 103 | dst = cv2.adaptiveThreshold(im, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 7, 0) 104 | return dst 105 | 106 | 107 | def rm_noise(img): 108 | """ 109 | 用来去除噪点 110 | :param img: 原始数据图片 111 | :return: 去噪点之后的图片 112 | """ 113 | y, x = img.shape[:2] 114 | for _x in range(0, x, 1): 115 | for _y in range(0, y, 1): 116 | cnt = 0 117 | if img[_y][_x] > 0: 118 | if _y > 0 and img[_y - 1][_x] > 0: 119 | cnt += 1 120 | if _y + 1 < y and img[_y + 1][_x] > 0: 121 | cnt += 1 122 | if _x > 0 and img[_y][_x - 1] > 0: 123 | cnt += 1 124 | if _x + 1 < x and img[_y][_x + 1] > 0: 125 | cnt += 1 126 | if _y > 0 and _x > 0 and img[_y - 1][_x - 1] > 0: 127 | cnt += 1 128 | if _y > 0 and _x + 1 < x and img[_y - 1][_x + 1] > 0: 129 | cnt += 1 130 | if _y + 1 < y and _x > 0 and img[_y + 1][_x - 1] > 0: 131 | continue 132 | if _y + 1 < y and _x + 1 < x and img[_y + 1][_x + 1] > 0: 133 | cnt += 1 134 | if cnt > 1: 135 | continue 136 | img[_y][_x] = 0 137 | return img 138 | 139 | 140 | def analyse_version(img, threshold=180): 141 | """ 142 | 分析图像类别 143 | :param threshold: 阈值 默认为180 144 | :param img: 145 | :return: 图像类别 1. 表示无背景 2.表示有背景 146 | """ 147 | "我们认为图像区域的值范围在0~130之间" 148 | if get_mean_value(img) <= threshold: 149 | return 2 150 | return 1 151 | 152 | 153 | def cut_version2(img, threshold=182): 154 | """ 155 | 对版本二进行图像切割 156 | :param img: 157 | :param threshold: 阈值 158 | :return: 返回图像开始位置链表和结束位置链表 159 | """ 160 | y, x = img.shape[:2] 161 | "避免统计到开头的部分" 162 | img_st_pos = 0 163 | """图像开始位置""" 164 | img_rect_st = [] 165 | """图像结束位置""" 166 | img_rect_en = [] 167 | mean_arr = np.zeros((x, 1)) 168 | for _x in xrange(x): 169 | tmp_sum = 0 170 | for _y in xrange(y): 171 | tmp_sum += img[_y][_x] 172 | mean_arr[_x] = tmp_sum / y 173 | 174 | for _value in xrange(x): 175 | if _value > 0 and mean_arr[_value] >= mean_arr[_value - 1] + 30 and mean_arr[_value] >= threshold: 176 | if _value > img_st_pos + 14: 177 | img_rect_st.append(img_st_pos) 178 | img_rect_en.append(_value) 179 | img_st_pos = _value 180 | break 181 | img_st_pos = _value 182 | if x > img_st_pos + 14: 183 | img_rect_st.append(img_st_pos) 184 | img_rect_en.append(x) 185 | return img_rect_st, img_rect_en 186 | 187 | 188 | def mesr_text(img, del_text_path, image_cnt, words_name, y_value=10, x_value=30): 189 | """ 190 | 分水岭算法 191 | :param deep: 192 | :param x_value: 193 | :param img: 194 | :param del_text_path: 195 | :param image_cnt: 196 | :param words_name: 197 | :param a_value: 198 | :return: 199 | """ 200 | # B, G, R = cv2.split(img) 201 | mser = cv2.MSER() 202 | regions = mser.detect(img) 203 | hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in regions] 204 | """寻找最大区域""" 205 | for index, hull in enumerate(hulls): 206 | x_min = 256 207 | x_max = -1 208 | y_min = 256 209 | y_max = -1 210 | for point in hull: 211 | x_min = int_min(x_min, point[0][0]) 212 | x_max = int_max(x_max, point[0][0]) 213 | y_min = int_min(y_min, point[0][1]) 214 | y_max = int_max(y_max, point[0][1]) 215 | tmp = img[y_min:y_max, x_min:x_max] 216 | sub_path = os.path.join(del_text_path, 'words_' + str(image_cnt)) 217 | if make_dir(sub_path): 218 | final_path = os.path.join(sub_path, words_name.split('.')[0]) 219 | if make_dir(final_path): 220 | cv2.imwrite( 221 | os.path.join(final_path, 222 | words_name.split('.')[0] + '_' + str(index) + '.' + 223 | words_name.split('.')[1]), 224 | tmp) 225 | 226 | 227 | def get_real_image(img, threshold=130): 228 | """ 229 | 截取整体文字区域 230 | :param img: 灰度图片 231 | :param threshold： 阈值 232 | :return: 文字区域图片 233 | """ 234 | y, x = img.shape[:2] 235 | real_x_start = 0 236 | real_x_end = 0 237 | for _x in xrange(x): 238 | for _y in xrange(y): 239 | if img[_y][_x] <= threshold: 240 | real_x_start = _x 241 | break 242 | if real_x_start != 0: 243 | break 244 | for _x in range(x - 1, 0, -1): 245 | for _y in xrange(y): 246 | if img[_y][_x] <= threshold: 247 | real_x_end = _x + 1 248 | break 249 | if real_x_end != 0: 250 | break 251 | return img[0:y, real_x_start:real_x_end] 252 | 253 | 254 | def get_binary_real_image(img): 255 | """ 256 | 截取整体文字区域 257 | :param img: 灰度图片 258 | :return: 文字区域图片 259 | """ 260 | y, x = img.shape[:2] 261 | real_x_st = 0 262 | real_x_en = 0 263 | for _x in xrange(x): 264 | for _y in xrange(y): 265 | if img[_y][_x] != 0: 266 | real_x_st = _x 267 | break 268 | if real_x_st != 0: 269 | break 270 | for _x in range(x - 1, 0, -1): 271 | for _y in xrange(y): 272 | if img[_y][_x] != 0: 273 | real_x_en = _x + 1 274 | break 275 | if real_x_en != 0: 276 | break 277 | return img[0:y, real_x_st:real_x_en] 278 | 279 | 280 | def get_real_image_rect(img): 281 | """ 282 | 截取整体文字区域 283 | :param img: 灰度图片 284 | :return: 文字区域图片 285 | """ 286 | y, x = img.shape[:2] 287 | real_x_st = 0 288 | real_x_en = 0 289 | """对于水平方向""" 290 | for _x in xrange(x): 291 | for _y in xrange(y): 292 | if img[_y][_x] != 0: 293 | real_x_st = _x 294 | break 295 | if real_x_st != 0: 296 | break 297 | for _x in range(x - 1, 0, -1): 298 | for _y in xrange(y): 299 | if img[_y][_x] != 0: 300 | real_x_en = _x 301 | break 302 | if real_x_en != 0: 303 | break 304 | """对于垂直方向""" 305 | real_y_st = 0 306 | real_y_en = 0 307 | for _x in range(real_x_st, real_x_en + 1, 1): 308 | for _y in range(0, y, 1): 309 | if img[_y][_x] != 0: 310 | real_y_st = _y 311 | break 312 | if real_x_st != 0: 313 | break 314 | for _x in range(real_x_st, real_x_en + 1, 1): 315 | for _y in xrange(y - 1, 0, -1): 316 | if img[_y][_x] != 0: 317 | real_y_en = _y 318 | break 319 | if real_x_en != 0: 320 | break 321 | 322 | return real_y_en - real_y_st, real_x_en - real_x_st 323 | 324 | 325 | def judge_the_image_size(img, height=24, width=60): 326 | """ 327 | 设置成固定大小 328 | :param img: 329 | :param height: 默认为24 330 | :param width: 默认为60 331 | :return: 332 | """ 333 | y_len, x_len = img.shape[:2] 334 | new_img = np.zeros((height, width)) 335 | for y_px in xrange(height): 336 | for i in xrange(width): 337 | if y_len > y_px and x_len > i: 338 | new_img[y_px][i] = img[y_px][i] 339 | else: 340 | new_img[y_px][i] = 255 341 | 342 | return new_img 343 | 344 | 345 | # 保存图片 346 | def save_img(img, n_img, del_text_path, image_cnt, words_name, y_value=10, x_value=30): 347 | """统计能量值""" 348 | y, x = n_img.shape[:2] 349 | pol = [] 350 | 351 | for _x in range(0, x, 1): 352 | temp_value = 0 353 | for _y in range(0, y, 1): 354 | temp_value += n_img[_y][_x] 355 | pol.append(temp_value) 356 | min_value_pre = 0 357 | min_value_nex = 0 358 | _len = len(pol) 359 | equal_size = 0 360 | line_pre = [] 361 | line_nex = [] 362 | img_cnt = 0 363 | for index in range(0, _len, 1): 364 | min_value_nex = index + 1 365 | if index + 1 < _len and pol[index] > pol[index + 1]: 366 | equal_size = 1 367 | elif index + 1 < _len and equal_size > 0 and pol[index] == 0 and pol[index] == pol[index + 1]: 368 | equal_size += 1 369 | 370 | elif index + 1 < _len and equal_size > 1 and pol[index] < pol[index + 1]: 371 | while min_value_pre < min_value_nex and pol[min_value_pre] == 0: 372 | min_value_pre += 1 373 | while min_value_pre < min_value_nex and pol[min_value_nex] == 0: 374 | min_value_nex -= 1 375 | if equal_size > 5: 376 | line_pre.append(0 if min_value_pre == 0 else min_value_pre - 1) 377 | line_nex.append(min_value_nex + 1) 378 | img_cnt += 1 379 | min_value_pre = min_value_nex 380 | equal_size = 0 381 | while min_value_pre < min_value_nex and pol[min_value_pre] == 0: 382 | min_value_pre += 1 383 | while min_value_pre < min_value_nex and pol[min_value_nex - 1] == 0: 384 | min_value_nex -= 1 385 | if min_value_nex > min_value_pre + 10: 386 | line_pre.append(0 if min_value_pre == 0 else min_value_pre - 1) 387 | line_nex.append(min_value_nex + 1) 388 | write_image(img, del_text_path, image_cnt, words_name, line_pre, line_nex) 389 | """对于image进行分析""" 390 | 391 | 392 | def save_img_mat(img, n_img, y_value=10, x_value=30): 393 | """ 394 | 保存图片 395 | :param n_img: 396 | :param index: 397 | :param deep: 398 | :param x_value: 399 | :param img: 400 | :param del_text_path: 401 | :param image_cnt: 402 | :param words_name: 403 | :param a_value: 404 | 405 | :return: 406 | """ 407 | """统计能量值""" 408 | y, x = n_img.shape[:2] 409 | pol = [] 410 | 411 | for _x in range(0, x, 1): 412 | temp_value = 0 413 | for _y in range(0, y, 1): 414 | temp_value += n_img[_y][_x] 415 | pol.append(temp_value) 416 | min_value_pre = 0 417 | min_value_nex = 0 418 | _len = len(pol) 419 | equal_size = 0 420 | line_pre = [] 421 | line_nex = [] 422 | img_cnt = 0 423 | for index in range(0, _len, 1): 424 | min_value_nex = index + 1 425 | if index + 1 < _len and pol[index] > pol[index + 1]: 426 | equal_size = 1 427 | elif index + 1 < _len and equal_size > 0 and pol[index] == 0 and pol[index] == pol[index + 1]: 428 | equal_size += 1 429 | 430 | elif index + 1 < _len and equal_size > 1 and pol[index] < pol[index + 1]: 431 | while min_value_pre < min_value_nex and pol[min_value_pre] == 0: 432 | min_value_pre += 1 433 | while min_value_pre < min_value_nex and pol[min_value_nex] == 0: 434 | min_value_nex -= 1 435 | if equal_size > 5: 436 | line_pre.append(0 if min_value_pre == 0 else min_value_pre - 1) 437 | line_nex.append(min_value_nex + 1) 438 | img_cnt += 1 439 | min_value_pre = min_value_nex 440 | equal_size = 0 441 | # plt.plot(pol[int(min_value_pre):int(_len)]) 442 | # plt.show() 443 | while min_value_pre < min_value_nex and pol[min_value_pre] == 0: 444 | min_value_pre += 1 445 | while min_value_pre < min_value_nex and pol[min_value_nex - 1] == 0: 446 | min_value_nex -= 1 447 | if min_value_nex > min_value_pre + 10: 448 | line_pre.append(0 if min_value_pre == 0 else min_value_pre - 1) 449 | line_nex.append(min_value_nex + 1) 450 | return get_image(img, line_pre, line_nex) 451 | """对于image进行分析""" 452 | 453 | 454 | def write_image(img, del_text_path, image_cnt, words_name, line_pre, line_nex, binary_flag=False): 455 | """ 456 | 将图片保存到磁盘中 457 | :param img: 图片数据 458 | :param del_text_path: 路径 459 | :param image_cnt: words对应编号 460 | :param words_name: 图片名称 461 | :param line_pre: 图片开始位置数组 462 | :param line_nex: 图片结束位置数组 463 | :param binary_flag: 表示是否对图片进行二值化，默认是不二值化 464 | :return: 465 | """ 466 | y, x = img.shape[:2] 467 | for _indx in xrange(len(line_pre)): 468 | 469 | t_img = img[0:y, int(line_pre[_indx]):int(line_nex[_indx] + 1)] 470 | if binary_flag: 471 | t_img = pretreatment_image(t_img, 7) 472 | t_img = binary_text(t_img) 473 | t_img = get_binary_real_image(t_img) 474 | t_img = judge_the_image_size(t_img) 475 | sub_path = os.path.join(del_text_path, 'words_' + str(image_cnt)) 476 | if make_dir(sub_path): 477 | cv2.imwrite( 478 | os.path.join(sub_path, 479 | words_name.split('.')[0] + '_' + str(_indx) + '.' + 480 | words_name.split('.')[1]), 481 | t_img) 482 | 483 | 484 | def get_image(img, line_pre, line_nex, binary_flag=False): 485 | """ 486 | 将图片保存到磁盘中 487 | :param img: 图片数据 488 | :param line_pre: 图片开始位置数组 489 | :param line_nex: 图片结束位置数组 490 | :param binary_flag: 表示是否对图片进行二值化，默认是不二值化 491 | :return: 492 | """ 493 | y, x = img.shape[:2] 494 | list_img = [] 495 | for _indx in xrange(len(line_pre)): 496 | 497 | t_img = img[0:y, int(line_pre[_indx]):int(line_nex[_indx] + 1)] 498 | if binary_flag: 499 | t_img = pretreatment_image(t_img, 7) 500 | t_img = binary_text(t_img) 501 | t_img = get_binary_real_image(t_img) 502 | t_img = judge_the_image_size(t_img) 503 | list_img.append(t_img) 504 | return list_img 505 | 506 | 507 | def cut(image_cnt, path_dir= cfg.ROOT + '/data/download'): 508 | """ 509 | :param image_cnt: words对应编号 510 | :param path_dir: 511 | :return: 512 | """ 513 | del_text_path = os.path.join(path_dir, 'words_cut_result') 514 | make_dir(del_text_path) 515 | 516 | words_path = os.path.join(path_dir, 'words/words_' + str(image_cnt)) 517 | if not os.path.exists(words_path): 518 | return 519 | for words_name in os.listdir(words_path): 520 | try: 521 | print words_name 522 | img = cv2.imread(os.path.join(words_path, words_name), 0) 523 | img = get_real_image(img, 160) 524 | if 2 == analyse_version(img): 525 | "说明有背景" 526 | img_rect_st, img_rect_en = cut_version2(img, 182) 527 | write_image(img, del_text_path, image_cnt, words_name, img_rect_st, img_rect_en, False) 528 | else: 529 | # img = rm_noise(img) 530 | n_img = pretreatment_image(img, 3) 531 | n_img = binary_text(n_img) 532 | n_img = get_binary_real_image(n_img) 533 | save_img(img, n_img, del_text_path, image_cnt, words_name, y_value=10, x_value=28) 534 | # mesr_text(img, del_text_path, image_cnt, words_name, y_value=10, x_value=28) 535 | except Exception as e: 536 | pass 537 | 538 | 539 | def get_test(img): 540 | img = get_real_image(img, 160) 541 | if 2 == analyse_version(img): 542 | "说明有背景" 543 | img_rect_st, img_rect_en = cut_version2(img, 182) 544 | return get_image(img, img_rect_st, img_rect_en, False) 545 | else: 546 | # img = rm_noise(img) 547 | n_img = pretreatment_image(img, 3) 548 | n_img = binary_text(n_img) 549 | n_img = get_binary_real_image(n_img) 550 | return save_img_mat(img, n_img, y_value=10, x_value=28) 551 | # mesr_text(img, del_text_path, image_cnt, words_name, y_value=10, x_value=28) 552 | 553 | 554 | # 调整目录中图像大小 555 | def classify_image(path_dir=cfg.ROOT + '/data/download/words', 556 | dest_path=cfg.ROOT + '/data/download/words-classify'): 557 | """ 558 | :param path_dir: 559 | :param dest_path: 560 | :return: 561 | """ 562 | temp = filter(lambda s: not s.startswith("."), os.listdir(path_dir)) 563 | for words_name in temp: 564 | print words_name 565 | if not os.path.exists(dest_path): 566 | os.makedirs(dest_path) 567 | sub_root_path = os.path.join(path_dir, words_name) 568 | if os.path.isdir(sub_root_path): 569 | classify_image(sub_root_path, os.path.join(dest_path, words_name)) 570 | continue 571 | img = cv2.imread(sub_root_path, 0) 572 | new_img = judge_the_image_size(img) 573 | cv2.imwrite(os.path.join(dest_path, words_name), new_img) 574 | return 0 575 | 576 | if __name__ == '__main__': 577 | cut(0) 578 | -------------------------------------------------------------------------------- /src/web/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aaronshan/12306-captcha/af14073d15f8beb3f8c9fe30802ee52875b78bec/src/web/README.md -------------------------------------------------------------------------------- /src/web/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aaronshan/12306-captcha/af14073d15f8beb3f8c9fe30802ee52875b78bec/src/web/__init__.py -------------------------------------------------------------------------------- /src/web/index.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | from flask import Flask 4 | from flask import render_template 5 | from flask import redirect, url_for 6 | from flask import request 7 | import urllib2 8 | import ssl 9 | import numpy as np 10 | import random 11 | import hashlib 12 | import datetime 13 | import cookielib 14 | import sys 15 | 16 | import utils.Img12306 as Img12306 17 | from src.config import cfg 18 | 19 | caffe_root = cfg.CAFFE_ROOT 20 | sys.path.insert(0, caffe_root + 'python') 21 | 22 | import caffe 23 | 24 | ### config_file_path 25 | pj_dir= cfg.ROOT + "/src/web" 26 | 27 | 28 | app = Flask(__name__) 29 | 30 | ### caffe config 31 | net_file = cfg.ROOT + '/src/image/model/image_deploy.prototxt' 32 | caffe_model = cfg.ROOT + 'model/image/f14_snapshot_iter_99000.caffemodel' 33 | labels_filename = cfg.ROOT + 'label/synset' 34 | 35 | net = caffe.Net(net_file, caffe_model, caffe.TEST) 36 | transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape}) 37 | "读取的图片文件格式为H×W×K，需转化为K×H×W" 38 | transformer.set_transpose('data', (2, 0, 1)) 39 | # transformer.set_mean('data', mean=np.float32([103.939, 116.779, 123.68])) 40 | "将图片存储为[0, 1]，而caffe中将图片存储为[0, 255" 41 | transformer.set_raw_scale('data', 255) 42 | "caffe中图片是BGR格式，而原始格式是RGB，所以要转化" 43 | transformer.set_channel_swap('data', (2, 1, 0)) 44 | 45 | ### agent config 46 | ssl._create_default_https_context = ssl._create_unverified_context 47 | agent = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Safari/537.36' 48 | login = 'https://kyfw.12306.cn/otn/login/init' 49 | domain = 'kyfw.12306.cn' 50 | cookieJ = cookielib.CookieJar() 51 | op = urllib2.build_opener(urllib2.HTTPCookieProcessor(cookieJ)) 52 | op.add_handler = [('User-agent', agent)] 53 | op.add_handler = [('Host', domain)] 54 | op.open(login) 55 | # '获取cookie' 56 | cookies = '' 57 | for Cookie in enumerate(cookieJ): 58 | ck = Cookie[1] 59 | cookies += ck.name + '=' + ck.value + ";" 60 | headers = { 61 | 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8', 62 | 'Accept-Encoding': 'gzip, deflate, sdch, br', 63 | 'Accept-Language': 'zh - CN, zh;q = 0.8', 64 | 'Cache-Control': 'no - cache', 65 | 'Connection': 'keep-alive', 66 | 'Referer': login, 67 | 'Cookie': cookies, 68 | 'Host': domain, 69 | 'User-aget': agent 70 | } 71 | 72 | 73 | @app.route('/') 74 | def hello_world(): 75 | return 'Hello World!' 76 | 77 | 78 | @app.route('/12306submit', methods=['POST']) 79 | def submit12306(): 80 | img_url = request.form['img_url'] 81 | error_idx = request.form['error_idx'] 82 | if error_idx != "": 83 | error_idx_arr = request.form['error_idx'].split("-") 84 | error_idx_arr.remove("") 85 | results=[request.form['result0'], request.form['result1'], request.form['result2'], request.form['result3'], request.form['result4'], request.form['result5'], request.form['result6'], request.form['result7']] 86 | file_path = "%s%s" % (pj_dir, img_url) 87 | img_file_name = file_path.split("/")[-1]; 88 | img = Img12306.get_img_file(file_path) 89 | img_split = Img12306.get_img(img) 90 | for idx in error_idx_arr: 91 | i = int(idx) 92 | if results[i] == "": 93 | continue 94 | out_path = "%s/error/%s/%s_%s" % (pj_dir, results[i], i, img_file_name) 95 | Img12306.save_as_img_file(img_split[i], out_path) 96 | 97 | return redirect(url_for('img')) 98 | 99 | @app.route('/12306') 100 | def img(): 101 | total = np.random.randint(1, 2) 102 | const_url = 'https://kyfw.12306.cn/otn/passcodeNew/getPassCodeNew?module=login&rand=sjrand&' 103 | random_token = random.uniform(0, 1) 104 | img_url = const_url + urllib2.quote(str(random_token)) 105 | while total > 0: 106 | resq = urllib2.Request(img_url) 107 | response = urllib2.urlopen(resq) 108 | total = total - 1 109 | 110 | raw = response.read() 111 | fn = hashlib.md5(raw).hexdigest() 112 | dt=datetime.datetime.now() 113 | dt=dt.strftime('%Y%m%d') 114 | output_file_name = "%s/static/tmp/%s_%s.jpg" % (pj_dir, dt, fn); 115 | with open(output_file_name, 'wb') as fp: 116 | fp.write(raw) 117 | 118 | no_noise_file_name = "%s/static/tmp/%s_%s_no_noise.jpg" % (pj_dir, dt, fn); 119 | Img12306.remove_file_noise(output_file_name, no_noise_file_name) 120 | 121 | # arr = np.asarray(bytearray(raw), dtype=np.uint8) 122 | # img = cv2.imdecode(arr,cv2.IMREAD_UNCHANGED) 123 | # img = img / 255. 124 | # img = img[:,:,(2,1,0)] 125 | img = caffe.io.load_image(no_noise_file_name) 126 | 127 | img_split = Img12306.get_img(img) 128 | results = [] 129 | for i in range(img_split.__len__()): 130 | im = img_split[i] 131 | results.append(judge(im).decode("utf8")) 132 | 133 | img_src = "/static/tmp/%s_%s.jpg" % (dt, fn); 134 | return render_template('12306.html', img_url=img_src, results=results) 135 | 136 | def judge(im): 137 | net.blobs['data'].data[...] = transformer.preprocess('data', im) 138 | out = net.forward() 139 | 140 | labels = np.loadtxt(labels_filename, str, delimiter='\t') 141 | # prob = out['softmax'][0] 142 | top_k = net.blobs['softmax'].data[0].flatten().argsort()[-1:-6:-1] 143 | 144 | return labels[top_k[0]] 145 | 146 | 147 | if __name__ == '__main__': 148 | app.run(host='0.0.0.0', port=5111, debug=False) 149 | -------------------------------------------------------------------------------- /src/web/static/bootstrap/css/bootstrap-datetimepicker.css: -------------------------------------------------------------------------------- 1 | /*! 2 | * Datetimepicker for Bootstrap 3 | * 4 | * Copyright 2012 Stefan Petre 5 | * Improvements by Andrew Rowls 6 | * Licensed under the Apache License v2.0 7 | * http://www.apache.org/licenses/LICENSE-2.0 8 | * 9 | */ 10 | .datetimepicker { 11 | padding: 4px; 12 | margin-top: 1px; 13 | -webkit-border-radius: 4px; 14 | -moz-border-radius: 4px; 15 | border-radius: 4px; 16 | direction: ltr; 17 | } 18 | 19 | .datetimepicker-inline { 20 | width: 220px; 21 | } 22 | 23 | .datetimepicker.datetimepicker-rtl { 24 | direction: rtl; 25 | } 26 | 27 | .datetimepicker.datetimepicker-rtl table tr td span { 28 | float: right; 29 | } 30 | 31 | .datetimepicker-dropdown, .datetimepicker-dropdown-left { 32 | top: 0; 33 | left: 0; 34 | } 35 | 36 | [class*=" datetimepicker-dropdown"]:before { 37 | content: ''; 38 | display: inline-block; 39 | border-left: 7px solid transparent; 40 | border-right: 7px solid transparent; 41 | border-bottom: 7px solid #cccccc; 42 | border-bottom-color: rgba(0, 0, 0, 0.2); 43 | position: absolute; 44 | } 45 | 46 | [class*=" datetimepicker-dropdown"]:after { 47 | content: ''; 48 | display: inline-block; 49 | border-left: 6px solid transparent; 50 | border-right: 6px solid transparent; 51 | border-bottom: 6px solid #ffffff; 52 | position: absolute; 53 | } 54 | 55 | [class*=" datetimepicker-dropdown-top"]:before { 56 | content: ''; 57 | display: inline-block; 58 | border-left: 7px solid transparent; 59 | border-right: 7px solid transparent; 60 | border-top: 7px solid #cccccc; 61 | border-top-color: rgba(0, 0, 0, 0.2); 62 | border-bottom: 0; 63 | } 64 | 65 | [class*=" datetimepicker-dropdown-top"]:after { 66 | content: ''; 67 | display: inline-block; 68 | border-left: 6px solid transparent; 69 | border-right: 6px solid transparent; 70 | border-top: 6px solid #ffffff; 71 | border-bottom: 0; 72 | } 73 | 74 | .datetimepicker-dropdown-bottom-left:before { 75 | top: -7px; 76 | right: 6px; 77 | } 78 | 79 | .datetimepicker-dropdown-bottom-left:after { 80 | top: -6px; 81 | right: 7px; 82 | } 83 | 84 | .datetimepicker-dropdown-bottom-right:before { 85 | top: -7px; 86 | left: 6px; 87 | } 88 | 89 | .datetimepicker-dropdown-bottom-right:after { 90 | top: -6px; 91 | left: 7px; 92 | } 93 | 94 | .datetimepicker-dropdown-top-left:before { 95 | bottom: -7px; 96 | right: 6px; 97 | } 98 | 99 | .datetimepicker-dropdown-top-left:after { 100 | bottom: -6px; 101 | right: 7px; 102 | } 103 | 104 | .datetimepicker-dropdown-top-right:before { 105 | bottom: -7px; 106 | left: 6px; 107 | } 108 | 109 | .datetimepicker-dropdown-top-right:after { 110 | bottom: -6px; 111 | left: 7px; 112 | } 113 | 114 | .datetimepicker > div { 115 | display: none; 116 | } 117 | 118 | .datetimepicker.minutes div.datetimepicker-minutes { 119 | display: block; 120 | } 121 | 122 | .datetimepicker.hours div.datetimepicker-hours { 123 | display: block; 124 | } 125 | 126 | .datetimepicker.days div.datetimepicker-days { 127 | display: block; 128 | } 129 | 130 | .datetimepicker.months div.datetimepicker-months { 131 | display: block; 132 | } 133 | 134 | .datetimepicker.years div.datetimepicker-years { 135 | display: block; 136 | } 137 | 138 | .datetimepicker table { 139 | margin: 0; 140 | } 141 | 142 | .datetimepicker td, 143 | .datetimepicker th { 144 | text-align: center; 145 | width: 20px; 146 | height: 20px; 147 | -webkit-border-radius: 4px; 148 | -moz-border-radius: 4px; 149 | border-radius: 4px; 150 | border: none; 151 | } 152 | 153 | .table-striped .datetimepicker table tr td, 154 | .table-striped .datetimepicker table tr th { 155 | background-color: transparent; 156 | } 157 | 158 | .datetimepicker table tr td.minute:hover { 159 | background: #eeeeee; 160 | cursor: pointer; 161 | } 162 | 163 | .datetimepicker table tr td.hour:hover { 164 | background: #eeeeee; 165 | cursor: pointer; 166 | } 167 | 168 | .datetimepicker table tr td.day:hover { 169 | background: #eeeeee; 170 | cursor: pointer; 171 | } 172 | 173 | .datetimepicker table tr td.old, 174 | .datetimepicker table tr td.new { 175 | color: #999999; 176 | } 177 | 178 | .datetimepicker table tr td.disabled, 179 | .datetimepicker table tr td.disabled:hover { 180 | background: none; 181 | color: #999999; 182 | cursor: default; 183 | } 184 | 185 | .datetimepicker table tr td.today, 186 | .datetimepicker table tr td.today:hover, 187 | .datetimepicker table tr td.today.disabled, 188 | .datetimepicker table tr td.today.disabled:hover { 189 | background-color: #fde19a; 190 | background-image: -moz-linear-gradient(top, #fdd49a, #fdf59a); 191 | background-image: -ms-linear-gradient(top, #fdd49a, #fdf59a); 192 | background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fdd49a), to(#fdf59a)); 193 | background-image: -webkit-linear-gradient(top, #fdd49a, #fdf59a); 194 | background-image: -o-linear-gradient(top, #fdd49a, #fdf59a); 195 | background-image: linear-gradient(top, #fdd49a, #fdf59a); 196 | background-repeat: repeat-x; 197 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fdd49a', endColorstr='#fdf59a', GradientType=0); 198 | border-color: #fdf59a #fdf59a #fbed50; 199 | border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); 200 | filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); 201 | } 202 | 203 | .datetimepicker table tr td.today:hover, 204 | .datetimepicker table tr td.today:hover:hover, 205 | .datetimepicker table tr td.today.disabled:hover, 206 | .datetimepicker table tr td.today.disabled:hover:hover, 207 | .datetimepicker table tr td.today:active, 208 | .datetimepicker table tr td.today:hover:active, 209 | .datetimepicker table tr td.today.disabled:active, 210 | .datetimepicker table tr td.today.disabled:hover:active, 211 | .datetimepicker table tr td.today.active, 212 | .datetimepicker table tr td.today:hover.active, 213 | .datetimepicker table tr td.today.disabled.active, 214 | .datetimepicker table tr td.today.disabled:hover.active, 215 | .datetimepicker table tr td.today.disabled, 216 | .datetimepicker table tr td.today:hover.disabled, 217 | .datetimepicker table tr td.today.disabled.disabled, 218 | .datetimepicker table tr td.today.disabled:hover.disabled, 219 | .datetimepicker table tr td.today[disabled], 220 | .datetimepicker table tr td.today:hover[disabled], 221 | .datetimepicker table tr td.today.disabled[disabled], 222 | .datetimepicker table tr td.today.disabled:hover[disabled] { 223 | background-color: #fdf59a; 224 | } 225 | 226 | .datetimepicker table tr td.today:active, 227 | .datetimepicker table tr td.today:hover:active, 228 | .datetimepicker table tr td.today.disabled:active, 229 | .datetimepicker table tr td.today.disabled:hover:active, 230 | .datetimepicker table tr td.today.active, 231 | .datetimepicker table tr td.today:hover.active, 232 | .datetimepicker table tr td.today.disabled.active, 233 | .datetimepicker table tr td.today.disabled:hover.active { 234 | background-color: #fbf069; 235 | } 236 | 237 | .datetimepicker table tr td.active, 238 | .datetimepicker table tr td.active:hover, 239 | .datetimepicker table tr td.active.disabled, 240 | .datetimepicker table tr td.active.disabled:hover { 241 | background-color: #006dcc; 242 | background-image: -moz-linear-gradient(top, #0088cc, #0044cc); 243 | background-image: -ms-linear-gradient(top, #0088cc, #0044cc); 244 | background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0044cc)); 245 | background-image: -webkit-linear-gradient(top, #0088cc, #0044cc); 246 | background-image: -o-linear-gradient(top, #0088cc, #0044cc); 247 | background-image: linear-gradient(top, #0088cc, #0044cc); 248 | background-repeat: repeat-x; 249 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#0088cc', endColorstr='#0044cc', GradientType=0); 250 | border-color: #0044cc #0044cc #002a80; 251 | border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); 252 | filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); 253 | color: #ffffff; 254 | text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); 255 | } 256 | 257 | .datetimepicker table tr td.active:hover, 258 | .datetimepicker table tr td.active:hover:hover, 259 | .datetimepicker table tr td.active.disabled:hover, 260 | .datetimepicker table tr td.active.disabled:hover:hover, 261 | .datetimepicker table tr td.active:active, 262 | .datetimepicker table tr td.active:hover:active, 263 | .datetimepicker table tr td.active.disabled:active, 264 | .datetimepicker table tr td.active.disabled:hover:active, 265 | .datetimepicker table tr td.active.active, 266 | .datetimepicker table tr td.active:hover.active, 267 | .datetimepicker table tr td.active.disabled.active, 268 | .datetimepicker table tr td.active.disabled:hover.active, 269 | .datetimepicker table tr td.active.disabled, 270 | .datetimepicker table tr td.active:hover.disabled, 271 | .datetimepicker table tr td.active.disabled.disabled, 272 | .datetimepicker table tr td.active.disabled:hover.disabled, 273 | .datetimepicker table tr td.active[disabled], 274 | .datetimepicker table tr td.active:hover[disabled], 275 | .datetimepicker table tr td.active.disabled[disabled], 276 | .datetimepicker table tr td.active.disabled:hover[disabled] { 277 | background-color: #0044cc; 278 | } 279 | 280 | .datetimepicker table tr td.active:active, 281 | .datetimepicker table tr td.active:hover:active, 282 | .datetimepicker table tr td.active.disabled:active, 283 | .datetimepicker table tr td.active.disabled:hover:active, 284 | .datetimepicker table tr td.active.active, 285 | .datetimepicker table tr td.active:hover.active, 286 | .datetimepicker table tr td.active.disabled.active, 287 | .datetimepicker table tr td.active.disabled:hover.active { 288 | background-color: #003399; 289 | } 290 | 291 | .datetimepicker table tr td span { 292 | display: block; 293 | width: 23%; 294 | height: 54px; 295 | line-height: 54px; 296 | float: left; 297 | margin: 1%; 298 | cursor: pointer; 299 | -webkit-border-radius: 4px; 300 | -moz-border-radius: 4px; 301 | border-radius: 4px; 302 | } 303 | 304 | .datetimepicker .datetimepicker-hours span { 305 | height: 26px; 306 | line-height: 26px; 307 | } 308 | 309 | .datetimepicker .datetimepicker-hours table tr td span.hour_am, 310 | .datetimepicker .datetimepicker-hours table tr td span.hour_pm { 311 | width: 14.6%; 312 | } 313 | 314 | .datetimepicker .datetimepicker-hours fieldset legend, 315 | .datetimepicker .datetimepicker-minutes fieldset legend { 316 | margin-bottom: inherit; 317 | line-height: 30px; 318 | } 319 | 320 | .datetimepicker .datetimepicker-minutes span { 321 | height: 26px; 322 | line-height: 26px; 323 | } 324 | 325 | .datetimepicker table tr td span:hover { 326 | background: #eeeeee; 327 | } 328 | 329 | .datetimepicker table tr td span.disabled, 330 | .datetimepicker table tr td span.disabled:hover { 331 | background: none; 332 | color: #999999; 333 | cursor: default; 334 | } 335 | 336 | .datetimepicker table tr td span.active, 337 | .datetimepicker table tr td span.active:hover, 338 | .datetimepicker table tr td span.active.disabled, 339 | .datetimepicker table tr td span.active.disabled:hover { 340 | background-color: #006dcc; 341 | background-image: -moz-linear-gradient(top, #0088cc, #0044cc); 342 | background-image: -ms-linear-gradient(top, #0088cc, #0044cc); 343 | background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0044cc)); 344 | background-image: -webkit-linear-gradient(top, #0088cc, #0044cc); 345 | background-image: -o-linear-gradient(top, #0088cc, #0044cc); 346 | background-image: linear-gradient(top, #0088cc, #0044cc); 347 | background-repeat: repeat-x; 348 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#0088cc', endColorstr='#0044cc', GradientType=0); 349 | border-color: #0044cc #0044cc #002a80; 350 | border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); 351 | filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); 352 | color: #ffffff; 353 | text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); 354 | } 355 | 356 | .datetimepicker table tr td span.active:hover, 357 | .datetimepicker table tr td span.active:hover:hover, 358 | .datetimepicker table tr td span.active.disabled:hover, 359 | .datetimepicker table tr td span.active.disabled:hover:hover, 360 | .datetimepicker table tr td span.active:active, 361 | .datetimepicker table tr td span.active:hover:active, 362 | .datetimepicker table tr td span.active.disabled:active, 363 | .datetimepicker table tr td span.active.disabled:hover:active, 364 | .datetimepicker table tr td span.active.active, 365 | .datetimepicker table tr td span.active:hover.active, 366 | .datetimepicker table tr td span.active.disabled.active, 367 | .datetimepicker table tr td span.active.disabled:hover.active, 368 | .datetimepicker table tr td span.active.disabled, 369 | .datetimepicker table tr td span.active:hover.disabled, 370 | .datetimepicker table tr td span.active.disabled.disabled, 371 | .datetimepicker table tr td span.active.disabled:hover.disabled, 372 | .datetimepicker table tr td span.active[disabled], 373 | .datetimepicker table tr td span.active:hover[disabled], 374 | .datetimepicker table tr td span.active.disabled[disabled], 375 | .datetimepicker table tr td span.active.disabled:hover[disabled] { 376 | background-color: #0044cc; 377 | } 378 | 379 | .datetimepicker table tr td span.active:active, 380 | .datetimepicker table tr td span.active:hover:active, 381 | .datetimepicker table tr td span.active.disabled:active, 382 | .datetimepicker table tr td span.active.disabled:hover:active, 383 | .datetimepicker table tr td span.active.active, 384 | .datetimepicker table tr td span.active:hover.active, 385 | .datetimepicker table tr td span.active.disabled.active, 386 | .datetimepicker table tr td span.active.disabled:hover.active { 387 | background-color: #003399; 388 | } 389 | 390 | .datetimepicker table tr td span.old { 391 | color: #999999; 392 | } 393 | 394 | .datetimepicker th.switch { 395 | width: 145px; 396 | } 397 | 398 | .datetimepicker th span.glyphicon { 399 | pointer-events: none; 400 | } 401 | 402 | .datetimepicker thead tr:first-child th, 403 | .datetimepicker tfoot tr:first-child th { 404 | cursor: pointer; 405 | } 406 | 407 | .datetimepicker thead tr:first-child th:hover, 408 | .datetimepicker tfoot tr:first-child th:hover { 409 | background: #eeeeee; 410 | } 411 | 412 | .input-append.date .add-on i, 413 | .input-prepend.date .add-on i, 414 | .input-group.date .input-group-addon span { 415 | cursor: pointer; 416 | width: 14px; 417 | height: 14px; 418 | } 419 | -------------------------------------------------------------------------------- /src/web/static/bootstrap/css/bootstrap-datetimepicker.min.css: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aaronshan/12306-captcha/af14073d15f8beb3f8c9fe30802ee52875b78bec/src/web/static/bootstrap/css/bootstrap-datetimepicker.min.css -------------------------------------------------------------------------------- /src/web/static/bootstrap/css/bootstrap-theme.css: -------------------------------------------------------------------------------- 1 | .btn-default, 2 | .btn-primary, 3 | .btn-success, 4 | .btn-info, 5 | .btn-warning, 6 | .btn-danger { 7 | text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2); 8 | -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075); 9 | box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075); 10 | } 11 | 12 | .btn-default:active, 13 | .btn-primary:active, 14 | .btn-success:active, 15 | .btn-info:active, 16 | .btn-warning:active, 17 | .btn-danger:active, 18 | .btn-default.active, 19 | .btn-primary.active, 20 | .btn-success.active, 21 | .btn-info.active, 22 | .btn-warning.active, 23 | .btn-danger.active { 24 | -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); 25 | box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); 26 | } 27 | 28 | .btn:active, 29 | .btn.active { 30 | background-image: none; 31 | } 32 | 33 | .btn-default { 34 | text-shadow: 0 1px 0 #fff; 35 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#ffffff), to(#e6e6e6)); 36 | background-image: -webkit-linear-gradient(top, #ffffff, 0%, #e6e6e6, 100%); 37 | background-image: -moz-linear-gradient(top, #ffffff 0%, #e6e6e6 100%); 38 | background-image: linear-gradient(to bottom, #ffffff 0%, #e6e6e6 100%); 39 | background-repeat: repeat-x; 40 | border-color: #e0e0e0; 41 | border-color: #ccc; 42 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0); 43 | } 44 | 45 | .btn-default:active, 46 | .btn-default.active { 47 | background-color: #e6e6e6; 48 | border-color: #e0e0e0; 49 | } 50 | 51 | .btn-primary { 52 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#428bca), to(#3071a9)); 53 | background-image: -webkit-linear-gradient(top, #428bca, 0%, #3071a9, 100%); 54 | background-image: -moz-linear-gradient(top, #428bca 0%, #3071a9 100%); 55 | background-image: linear-gradient(to bottom, #428bca 0%, #3071a9 100%); 56 | background-repeat: repeat-x; 57 | border-color: #2d6ca2; 58 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff428bca', endColorstr='#ff3071a9', GradientType=0); 59 | } 60 | 61 | .btn-primary:active, 62 | .btn-primary.active { 63 | background-color: #3071a9; 64 | border-color: #2d6ca2; 65 | } 66 | 67 | .btn-success { 68 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#5cb85c), to(#449d44)); 69 | background-image: -webkit-linear-gradient(top, #5cb85c, 0%, #449d44, 100%); 70 | background-image: -moz-linear-gradient(top, #5cb85c 0%, #449d44 100%); 71 | background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%); 72 | background-repeat: repeat-x; 73 | border-color: #419641; 74 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0); 75 | } 76 | 77 | .btn-success:active, 78 | .btn-success.active { 79 | background-color: #449d44; 80 | border-color: #419641; 81 | } 82 | 83 | .btn-warning { 84 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#f0ad4e), to(#ec971f)); 85 | background-image: -webkit-linear-gradient(top, #f0ad4e, 0%, #ec971f, 100%); 86 | background-image: -moz-linear-gradient(top, #f0ad4e 0%, #ec971f 100%); 87 | background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%); 88 | background-repeat: repeat-x; 89 | border-color: #eb9316; 90 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0); 91 | } 92 | 93 | .btn-warning:active, 94 | .btn-warning.active { 95 | background-color: #ec971f; 96 | border-color: #eb9316; 97 | } 98 | 99 | .btn-danger { 100 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#d9534f), to(#c9302c)); 101 | background-image: -webkit-linear-gradient(top, #d9534f, 0%, #c9302c, 100%); 102 | background-image: -moz-linear-gradient(top, #d9534f 0%, #c9302c 100%); 103 | background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%); 104 | background-repeat: repeat-x; 105 | border-color: #c12e2a; 106 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0); 107 | } 108 | 109 | .btn-danger:active, 110 | .btn-danger.active { 111 | background-color: #c9302c; 112 | border-color: #c12e2a; 113 | } 114 | 115 | .btn-info { 116 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#5bc0de), to(#31b0d5)); 117 | background-image: -webkit-linear-gradient(top, #5bc0de, 0%, #31b0d5, 100%); 118 | background-image: -moz-linear-gradient(top, #5bc0de 0%, #31b0d5 100%); 119 | background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%); 120 | background-repeat: repeat-x; 121 | border-color: #2aabd2; 122 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0); 123 | } 124 | 125 | .btn-info:active, 126 | .btn-info.active { 127 | background-color: #31b0d5; 128 | border-color: #2aabd2; 129 | } 130 | 131 | .thumbnail, 132 | .img-thumbnail { 133 | -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075); 134 | box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075); 135 | } 136 | 137 | .dropdown-menu > li > a:hover, 138 | .dropdown-menu > li > a:focus, 139 | .dropdown-menu > .active > a, 140 | .dropdown-menu > .active > a:hover, 141 | .dropdown-menu > .active > a:focus { 142 | background-color: #357ebd; 143 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#428bca), to(#357ebd)); 144 | background-image: -webkit-linear-gradient(top, #428bca, 0%, #357ebd, 100%); 145 | background-image: -moz-linear-gradient(top, #428bca 0%, #357ebd 100%); 146 | background-image: linear-gradient(to bottom, #428bca 0%, #357ebd 100%); 147 | background-repeat: repeat-x; 148 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff428bca', endColorstr='#ff357ebd', GradientType=0); 149 | } 150 | 151 | .navbar { 152 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#ffffff), to(#f8f8f8)); 153 | background-image: -webkit-linear-gradient(top, #ffffff, 0%, #f8f8f8, 100%); 154 | background-image: -moz-linear-gradient(top, #ffffff 0%, #f8f8f8 100%); 155 | background-image: linear-gradient(to bottom, #ffffff 0%, #f8f8f8 100%); 156 | background-repeat: repeat-x; 157 | border-radius: 4px; 158 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0); 159 | -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075); 160 | box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075); 161 | } 162 | 163 | .navbar .navbar-nav > .active > a { 164 | background-color: #f8f8f8; 165 | } 166 | 167 | .navbar-brand, 168 | .navbar-nav > li > a { 169 | text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25); 170 | } 171 | 172 | .navbar-inverse { 173 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#3c3c3c), to(#222222)); 174 | background-image: -webkit-linear-gradient(top, #3c3c3c, 0%, #222222, 100%); 175 | background-image: -moz-linear-gradient(top, #3c3c3c 0%, #222222 100%); 176 | background-image: linear-gradient(to bottom, #3c3c3c 0%, #222222 100%); 177 | background-repeat: repeat-x; 178 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0); 179 | } 180 | 181 | .navbar-inverse .navbar-nav > .active > a { 182 | background-color: #222222; 183 | } 184 | 185 | .navbar-inverse .navbar-brand, 186 | .navbar-inverse .navbar-nav > li > a { 187 | text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); 188 | } 189 | 190 | .navbar-static-top, 191 | .navbar-fixed-top, 192 | .navbar-fixed-bottom { 193 | border-radius: 0; 194 | } 195 | 196 | .alert { 197 | text-shadow: 0 1px 0 rgba(255, 255, 255, 0.2); 198 | -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05); 199 | box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05); 200 | } 201 | 202 | .alert-success { 203 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#dff0d8), to(#c8e5bc)); 204 | background-image: -webkit-linear-gradient(top, #dff0d8, 0%, #c8e5bc, 100%); 205 | background-image: -moz-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%); 206 | background-image: linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%); 207 | background-repeat: repeat-x; 208 | border-color: #b2dba1; 209 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0); 210 | } 211 | 212 | .alert-info { 213 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#d9edf7), to(#b9def0)); 214 | background-image: -webkit-linear-gradient(top, #d9edf7, 0%, #b9def0, 100%); 215 | background-image: -moz-linear-gradient(top, #d9edf7 0%, #b9def0 100%); 216 | background-image: linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%); 217 | background-repeat: repeat-x; 218 | border-color: #9acfea; 219 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0); 220 | } 221 | 222 | .alert-warning { 223 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#fcf8e3), to(#f8efc0)); 224 | background-image: -webkit-linear-gradient(top, #fcf8e3, 0%, #f8efc0, 100%); 225 | background-image: -moz-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%); 226 | background-image: linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%); 227 | background-repeat: repeat-x; 228 | border-color: #f5e79e; 229 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0); 230 | } 231 | 232 | .alert-danger { 233 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#f2dede), to(#e7c3c3)); 234 | background-image: -webkit-linear-gradient(top, #f2dede, 0%, #e7c3c3, 100%); 235 | background-image: -moz-linear-gradient(top, #f2dede 0%, #e7c3c3 100%); 236 | background-image: linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%); 237 | background-repeat: repeat-x; 238 | border-color: #dca7a7; 239 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0); 240 | } 241 | 242 | .progress { 243 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#ebebeb), to(#f5f5f5)); 244 | background-image: -webkit-linear-gradient(top, #ebebeb, 0%, #f5f5f5, 100%); 245 | background-image: -moz-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%); 246 | background-image: linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%); 247 | background-repeat: repeat-x; 248 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0); 249 | } 250 | 251 | .progress-bar { 252 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#428bca), to(#3071a9)); 253 | background-image: -webkit-linear-gradient(top, #428bca, 0%, #3071a9, 100%); 254 | background-image: -moz-linear-gradient(top, #428bca 0%, #3071a9 100%); 255 | background-image: linear-gradient(to bottom, #428bca 0%, #3071a9 100%); 256 | background-repeat: repeat-x; 257 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff428bca', endColorstr='#ff3071a9', GradientType=0); 258 | } 259 | 260 | .progress-bar-success { 261 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#5cb85c), to(#449d44)); 262 | background-image: -webkit-linear-gradient(top, #5cb85c, 0%, #449d44, 100%); 263 | background-image: -moz-linear-gradient(top, #5cb85c 0%, #449d44 100%); 264 | background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%); 265 | background-repeat: repeat-x; 266 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0); 267 | } 268 | 269 | .progress-bar-info { 270 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#5bc0de), to(#31b0d5)); 271 | background-image: -webkit-linear-gradient(top, #5bc0de, 0%, #31b0d5, 100%); 272 | background-image: -moz-linear-gradient(top, #5bc0de 0%, #31b0d5 100%); 273 | background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%); 274 | background-repeat: repeat-x; 275 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0); 276 | } 277 | 278 | .progress-bar-warning { 279 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#f0ad4e), to(#ec971f)); 280 | background-image: -webkit-linear-gradient(top, #f0ad4e, 0%, #ec971f, 100%); 281 | background-image: -moz-linear-gradient(top, #f0ad4e 0%, #ec971f 100%); 282 | background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%); 283 | background-repeat: repeat-x; 284 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0); 285 | } 286 | 287 | .progress-bar-danger { 288 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#d9534f), to(#c9302c)); 289 | background-image: -webkit-linear-gradient(top, #d9534f, 0%, #c9302c, 100%); 290 | background-image: -moz-linear-gradient(top, #d9534f 0%, #c9302c 100%); 291 | background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%); 292 | background-repeat: repeat-x; 293 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0); 294 | } 295 | 296 | .list-group { 297 | border-radius: 4px; 298 | -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075); 299 | box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075); 300 | } 301 | 302 | .list-group-item.active, 303 | .list-group-item.active:hover, 304 | .list-group-item.active:focus { 305 | text-shadow: 0 -1px 0 #3071a9; 306 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#428bca), to(#3278b3)); 307 | background-image: -webkit-linear-gradient(top, #428bca, 0%, #3278b3, 100%); 308 | background-image: -moz-linear-gradient(top, #428bca 0%, #3278b3 100%); 309 | background-image: linear-gradient(to bottom, #428bca 0%, #3278b3 100%); 310 | background-repeat: repeat-x; 311 | border-color: #3278b3; 312 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff428bca', endColorstr='#ff3278b3', GradientType=0); 313 | } 314 | 315 | .panel { 316 | -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); 317 | box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); 318 | } 319 | 320 | .panel-default > .panel-heading { 321 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#f5f5f5), to(#e8e8e8)); 322 | background-image: -webkit-linear-gradient(top, #f5f5f5, 0%, #e8e8e8, 100%); 323 | background-image: -moz-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%); 324 | background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%); 325 | background-repeat: repeat-x; 326 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0); 327 | } 328 | 329 | .panel-primary > .panel-heading { 330 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#428bca), to(#357ebd)); 331 | background-image: -webkit-linear-gradient(top, #428bca, 0%, #357ebd, 100%); 332 | background-image: -moz-linear-gradient(top, #428bca 0%, #357ebd 100%); 333 | background-image: linear-gradient(to bottom, #428bca 0%, #357ebd 100%); 334 | background-repeat: repeat-x; 335 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff428bca', endColorstr='#ff357ebd', GradientType=0); 336 | } 337 | 338 | .panel-success > .panel-heading { 339 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#dff0d8), to(#d0e9c6)); 340 | background-image: -webkit-linear-gradient(top, #dff0d8, 0%, #d0e9c6, 100%); 341 | background-image: -moz-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%); 342 | background-image: linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%); 343 | background-repeat: repeat-x; 344 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0); 345 | } 346 | 347 | .panel-info > .panel-heading { 348 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#d9edf7), to(#c4e3f3)); 349 | background-image: -webkit-linear-gradient(top, #d9edf7, 0%, #c4e3f3, 100%); 350 | background-image: -moz-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%); 351 | background-image: linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%); 352 | background-repeat: repeat-x; 353 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0); 354 | } 355 | 356 | .panel-warning > .panel-heading { 357 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#fcf8e3), to(#faf2cc)); 358 | background-image: -webkit-linear-gradient(top, #fcf8e3, 0%, #faf2cc, 100%); 359 | background-image: -moz-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%); 360 | background-image: linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%); 361 | background-repeat: repeat-x; 362 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0); 363 | } 364 | 365 | .panel-danger > .panel-heading { 366 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#f2dede), to(#ebcccc)); 367 | background-image: -webkit-linear-gradient(top, #f2dede, 0%, #ebcccc, 100%); 368 | background-image: -moz-linear-gradient(top, #f2dede 0%, #ebcccc 100%); 369 | background-image: linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%); 370 | background-repeat: repeat-x; 371 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0); 372 | } 373 | 374 | .well { 375 | background-image: -webkit-gradient(linear, left 0%, left 100%, from(#e8e8e8), to(#f5f5f5)); 376 | background-image: -webkit-linear-gradient(top, #e8e8e8, 0%, #f5f5f5, 100%); 377 | background-image: -moz-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%); 378 | background-image: linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%); 379 | background-repeat: repeat-x; 380 | border-color: #dcdcdc; 381 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0); 382 | -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1); 383 | box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1); 384 | } -------------------------------------------------------------------------------- /src/web/static/bootstrap/css/bootstrap-theme.min.css: -------------------------------------------------------------------------------- 1 | .btn-default, .btn-primary, .btn-success, .btn-info, .btn-warning, .btn-danger { 2 | text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2); 3 | -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075); 4 | box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075) 5 | } 6 | 7 | .btn-default:active, .btn-primary:active, .btn-success:active, .btn-info:active, .btn-warning:active, .btn-danger:active, .btn-default.active, .btn-primary.active, .btn-success.active, .btn-info.active, .btn-warning.active, .btn-danger.active { 8 | -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); 9 | box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125) 10 | } 11 | 12 | .btn:active, .btn.active { 13 | background-image: none 14 | } 15 | 16 | .btn-default { 17 | text-shadow: 0 1px 0 #fff; 18 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#fff), to(#e6e6e6)); 19 | background-image: -webkit-linear-gradient(top, #fff, 0%, #e6e6e6, 100%); 20 | background-image: -moz-linear-gradient(top, #fff 0, #e6e6e6 100%); 21 | background-image: linear-gradient(to bottom, #fff 0, #e6e6e6 100%); 22 | background-repeat: repeat-x; 23 | border-color: #e0e0e0; 24 | border-color: #ccc; 25 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0) 26 | } 27 | 28 | .btn-default:active, .btn-default.active { 29 | background-color: #e6e6e6; 30 | border-color: #e0e0e0 31 | } 32 | 33 | .btn-primary { 34 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#428bca), to(#3071a9)); 35 | background-image: -webkit-linear-gradient(top, #428bca, 0%, #3071a9, 100%); 36 | background-image: -moz-linear-gradient(top, #428bca 0, #3071a9 100%); 37 | background-image: linear-gradient(to bottom, #428bca 0, #3071a9 100%); 38 | background-repeat: repeat-x; 39 | border-color: #2d6ca2; 40 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff428bca', endColorstr='#ff3071a9', GradientType=0) 41 | } 42 | 43 | .btn-primary:active, .btn-primary.active { 44 | background-color: #3071a9; 45 | border-color: #2d6ca2 46 | } 47 | 48 | .btn-success { 49 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#5cb85c), to(#449d44)); 50 | background-image: -webkit-linear-gradient(top, #5cb85c, 0%, #449d44, 100%); 51 | background-image: -moz-linear-gradient(top, #5cb85c 0, #449d44 100%); 52 | background-image: linear-gradient(to bottom, #5cb85c 0, #449d44 100%); 53 | background-repeat: repeat-x; 54 | border-color: #419641; 55 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0) 56 | } 57 | 58 | .btn-success:active, .btn-success.active { 59 | background-color: #449d44; 60 | border-color: #419641 61 | } 62 | 63 | .btn-warning { 64 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#f0ad4e), to(#ec971f)); 65 | background-image: -webkit-linear-gradient(top, #f0ad4e, 0%, #ec971f, 100%); 66 | background-image: -moz-linear-gradient(top, #f0ad4e 0, #ec971f 100%); 67 | background-image: linear-gradient(to bottom, #f0ad4e 0, #ec971f 100%); 68 | background-repeat: repeat-x; 69 | border-color: #eb9316; 70 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0) 71 | } 72 | 73 | .btn-warning:active, .btn-warning.active { 74 | background-color: #ec971f; 75 | border-color: #eb9316 76 | } 77 | 78 | .btn-danger { 79 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#d9534f), to(#c9302c)); 80 | background-image: -webkit-linear-gradient(top, #d9534f, 0%, #c9302c, 100%); 81 | background-image: -moz-linear-gradient(top, #d9534f 0, #c9302c 100%); 82 | background-image: linear-gradient(to bottom, #d9534f 0, #c9302c 100%); 83 | background-repeat: repeat-x; 84 | border-color: #c12e2a; 85 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0) 86 | } 87 | 88 | .btn-danger:active, .btn-danger.active { 89 | background-color: #c9302c; 90 | border-color: #c12e2a 91 | } 92 | 93 | .btn-info { 94 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#5bc0de), to(#31b0d5)); 95 | background-image: -webkit-linear-gradient(top, #5bc0de, 0%, #31b0d5, 100%); 96 | background-image: -moz-linear-gradient(top, #5bc0de 0, #31b0d5 100%); 97 | background-image: linear-gradient(to bottom, #5bc0de 0, #31b0d5 100%); 98 | background-repeat: repeat-x; 99 | border-color: #2aabd2; 100 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0) 101 | } 102 | 103 | .btn-info:active, .btn-info.active { 104 | background-color: #31b0d5; 105 | border-color: #2aabd2 106 | } 107 | 108 | .thumbnail, .img-thumbnail { 109 | -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075); 110 | box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075) 111 | } 112 | 113 | .dropdown-menu > li > a:hover, .dropdown-menu > li > a:focus, .dropdown-menu > .active > a, .dropdown-menu > .active > a:hover, .dropdown-menu > .active > a:focus { 114 | background-color: #357ebd; 115 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#428bca), to(#357ebd)); 116 | background-image: -webkit-linear-gradient(top, #428bca, 0%, #357ebd, 100%); 117 | background-image: -moz-linear-gradient(top, #428bca 0, #357ebd 100%); 118 | background-image: linear-gradient(to bottom, #428bca 0, #357ebd 100%); 119 | background-repeat: repeat-x; 120 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff428bca', endColorstr='#ff357ebd', GradientType=0) 121 | } 122 | 123 | .navbar { 124 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#fff), to(#f8f8f8)); 125 | background-image: -webkit-linear-gradient(top, #fff, 0%, #f8f8f8, 100%); 126 | background-image: -moz-linear-gradient(top, #fff 0, #f8f8f8 100%); 127 | background-image: linear-gradient(to bottom, #fff 0, #f8f8f8 100%); 128 | background-repeat: repeat-x; 129 | border-radius: 4px; 130 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0); 131 | -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075); 132 | box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075) 133 | } 134 | 135 | .navbar .navbar-nav > .active > a { 136 | background-color: #f8f8f8 137 | } 138 | 139 | .navbar-brand, .navbar-nav > li > a { 140 | text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25) 141 | } 142 | 143 | .navbar-inverse { 144 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#3c3c3c), to(#222)); 145 | background-image: -webkit-linear-gradient(top, #3c3c3c, 0%, #222, 100%); 146 | background-image: -moz-linear-gradient(top, #3c3c3c 0, #222 100%); 147 | background-image: linear-gradient(to bottom, #3c3c3c 0, #222 100%); 148 | background-repeat: repeat-x; 149 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0) 150 | } 151 | 152 | .navbar-inverse .navbar-nav > .active > a { 153 | background-color: #222 154 | } 155 | 156 | .navbar-inverse .navbar-brand, .navbar-inverse .navbar-nav > li > a { 157 | text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25) 158 | } 159 | 160 | .navbar-static-top, .navbar-fixed-top, .navbar-fixed-bottom { 161 | border-radius: 0 162 | } 163 | 164 | .alert { 165 | text-shadow: 0 1px 0 rgba(255, 255, 255, 0.2); 166 | -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05); 167 | box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05) 168 | } 169 | 170 | .alert-success { 171 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#dff0d8), to(#c8e5bc)); 172 | background-image: -webkit-linear-gradient(top, #dff0d8, 0%, #c8e5bc, 100%); 173 | background-image: -moz-linear-gradient(top, #dff0d8 0, #c8e5bc 100%); 174 | background-image: linear-gradient(to bottom, #dff0d8 0, #c8e5bc 100%); 175 | background-repeat: repeat-x; 176 | border-color: #b2dba1; 177 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0) 178 | } 179 | 180 | .alert-info { 181 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#d9edf7), to(#b9def0)); 182 | background-image: -webkit-linear-gradient(top, #d9edf7, 0%, #b9def0, 100%); 183 | background-image: -moz-linear-gradient(top, #d9edf7 0, #b9def0 100%); 184 | background-image: linear-gradient(to bottom, #d9edf7 0, #b9def0 100%); 185 | background-repeat: repeat-x; 186 | border-color: #9acfea; 187 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0) 188 | } 189 | 190 | .alert-warning { 191 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#fcf8e3), to(#f8efc0)); 192 | background-image: -webkit-linear-gradient(top, #fcf8e3, 0%, #f8efc0, 100%); 193 | background-image: -moz-linear-gradient(top, #fcf8e3 0, #f8efc0 100%); 194 | background-image: linear-gradient(to bottom, #fcf8e3 0, #f8efc0 100%); 195 | background-repeat: repeat-x; 196 | border-color: #f5e79e; 197 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0) 198 | } 199 | 200 | .alert-danger { 201 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#f2dede), to(#e7c3c3)); 202 | background-image: -webkit-linear-gradient(top, #f2dede, 0%, #e7c3c3, 100%); 203 | background-image: -moz-linear-gradient(top, #f2dede 0, #e7c3c3 100%); 204 | background-image: linear-gradient(to bottom, #f2dede 0, #e7c3c3 100%); 205 | background-repeat: repeat-x; 206 | border-color: #dca7a7; 207 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0) 208 | } 209 | 210 | .progress { 211 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#ebebeb), to(#f5f5f5)); 212 | background-image: -webkit-linear-gradient(top, #ebebeb, 0%, #f5f5f5, 100%); 213 | background-image: -moz-linear-gradient(top, #ebebeb 0, #f5f5f5 100%); 214 | background-image: linear-gradient(to bottom, #ebebeb 0, #f5f5f5 100%); 215 | background-repeat: repeat-x; 216 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0) 217 | } 218 | 219 | .progress-bar { 220 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#428bca), to(#3071a9)); 221 | background-image: -webkit-linear-gradient(top, #428bca, 0%, #3071a9, 100%); 222 | background-image: -moz-linear-gradient(top, #428bca 0, #3071a9 100%); 223 | background-image: linear-gradient(to bottom, #428bca 0, #3071a9 100%); 224 | background-repeat: repeat-x; 225 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff428bca', endColorstr='#ff3071a9', GradientType=0) 226 | } 227 | 228 | .progress-bar-success { 229 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#5cb85c), to(#449d44)); 230 | background-image: -webkit-linear-gradient(top, #5cb85c, 0%, #449d44, 100%); 231 | background-image: -moz-linear-gradient(top, #5cb85c 0, #449d44 100%); 232 | background-image: linear-gradient(to bottom, #5cb85c 0, #449d44 100%); 233 | background-repeat: repeat-x; 234 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0) 235 | } 236 | 237 | .progress-bar-info { 238 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#5bc0de), to(#31b0d5)); 239 | background-image: -webkit-linear-gradient(top, #5bc0de, 0%, #31b0d5, 100%); 240 | background-image: -moz-linear-gradient(top, #5bc0de 0, #31b0d5 100%); 241 | background-image: linear-gradient(to bottom, #5bc0de 0, #31b0d5 100%); 242 | background-repeat: repeat-x; 243 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0) 244 | } 245 | 246 | .progress-bar-warning { 247 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#f0ad4e), to(#ec971f)); 248 | background-image: -webkit-linear-gradient(top, #f0ad4e, 0%, #ec971f, 100%); 249 | background-image: -moz-linear-gradient(top, #f0ad4e 0, #ec971f 100%); 250 | background-image: linear-gradient(to bottom, #f0ad4e 0, #ec971f 100%); 251 | background-repeat: repeat-x; 252 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0) 253 | } 254 | 255 | .progress-bar-danger { 256 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#d9534f), to(#c9302c)); 257 | background-image: -webkit-linear-gradient(top, #d9534f, 0%, #c9302c, 100%); 258 | background-image: -moz-linear-gradient(top, #d9534f 0, #c9302c 100%); 259 | background-image: linear-gradient(to bottom, #d9534f 0, #c9302c 100%); 260 | background-repeat: repeat-x; 261 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0) 262 | } 263 | 264 | .list-group { 265 | border-radius: 4px; 266 | -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075); 267 | box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075) 268 | } 269 | 270 | .list-group-item.active, .list-group-item.active:hover, .list-group-item.active:focus { 271 | text-shadow: 0 -1px 0 #3071a9; 272 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#428bca), to(#3278b3)); 273 | background-image: -webkit-linear-gradient(top, #428bca, 0%, #3278b3, 100%); 274 | background-image: -moz-linear-gradient(top, #428bca 0, #3278b3 100%); 275 | background-image: linear-gradient(to bottom, #428bca 0, #3278b3 100%); 276 | background-repeat: repeat-x; 277 | border-color: #3278b3; 278 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff428bca', endColorstr='#ff3278b3', GradientType=0) 279 | } 280 | 281 | .panel { 282 | -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); 283 | box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05) 284 | } 285 | 286 | .panel-default > .panel-heading { 287 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#f5f5f5), to(#e8e8e8)); 288 | background-image: -webkit-linear-gradient(top, #f5f5f5, 0%, #e8e8e8, 100%); 289 | background-image: -moz-linear-gradient(top, #f5f5f5 0, #e8e8e8 100%); 290 | background-image: linear-gradient(to bottom, #f5f5f5 0, #e8e8e8 100%); 291 | background-repeat: repeat-x; 292 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0) 293 | } 294 | 295 | .panel-primary > .panel-heading { 296 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#428bca), to(#357ebd)); 297 | background-image: -webkit-linear-gradient(top, #428bca, 0%, #357ebd, 100%); 298 | background-image: -moz-linear-gradient(top, #428bca 0, #357ebd 100%); 299 | background-image: linear-gradient(to bottom, #428bca 0, #357ebd 100%); 300 | background-repeat: repeat-x; 301 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff428bca', endColorstr='#ff357ebd', GradientType=0) 302 | } 303 | 304 | .panel-success > .panel-heading { 305 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#dff0d8), to(#d0e9c6)); 306 | background-image: -webkit-linear-gradient(top, #dff0d8, 0%, #d0e9c6, 100%); 307 | background-image: -moz-linear-gradient(top, #dff0d8 0, #d0e9c6 100%); 308 | background-image: linear-gradient(to bottom, #dff0d8 0, #d0e9c6 100%); 309 | background-repeat: repeat-x; 310 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0) 311 | } 312 | 313 | .panel-info > .panel-heading { 314 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#d9edf7), to(#c4e3f3)); 315 | background-image: -webkit-linear-gradient(top, #d9edf7, 0%, #c4e3f3, 100%); 316 | background-image: -moz-linear-gradient(top, #d9edf7 0, #c4e3f3 100%); 317 | background-image: linear-gradient(to bottom, #d9edf7 0, #c4e3f3 100%); 318 | background-repeat: repeat-x; 319 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0) 320 | } 321 | 322 | .panel-warning > .panel-heading { 323 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#fcf8e3), to(#faf2cc)); 324 | background-image: -webkit-linear-gradient(top, #fcf8e3, 0%, #faf2cc, 100%); 325 | background-image: -moz-linear-gradient(top, #fcf8e3 0, #faf2cc 100%); 326 | background-image: linear-gradient(to bottom, #fcf8e3 0, #faf2cc 100%); 327 | background-repeat: repeat-x; 328 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0) 329 | } 330 | 331 | .panel-danger > .panel-heading { 332 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#f2dede), to(#ebcccc)); 333 | background-image: -webkit-linear-gradient(top, #f2dede, 0%, #ebcccc, 100%); 334 | background-image: -moz-linear-gradient(top, #f2dede 0, #ebcccc 100%); 335 | background-image: linear-gradient(to bottom, #f2dede 0, #ebcccc 100%); 336 | background-repeat: repeat-x; 337 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0) 338 | } 339 | 340 | .well { 341 | background-image: -webkit-gradient(linear, left 0, left 100%, from(#e8e8e8), to(#f5f5f5)); 342 | background-image: -webkit-linear-gradient(top, #e8e8e8, 0%, #f5f5f5, 100%); 343 | background-image: -moz-linear-gradient(top, #e8e8e8 0, #f5f5f5 100%); 344 | background-image: linear-gradient(to bottom, #e8e8e8 0, #f5f5f5 100%); 345 | background-repeat: repeat-x; 346 | border-color: #dcdcdc; 347 | filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0); 348 | -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1); 349 | box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1) 350 | } -------------------------------------------------------------------------------- /src/web/static/bootstrap/fonts/glyphicons-halflings-regular.eot: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aaronshan/12306-captcha/af14073d15f8beb3f8c9fe30802ee52875b78bec/src/web/static/bootstrap/fonts/glyphicons-halflings-regular.eot -------------------------------------------------------------------------------- /src/web/static/bootstrap/fonts/glyphicons-halflings-regular.ttf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aaronshan/12306-captcha/af14073d15f8beb3f8c9fe30802ee52875b78bec/src/web/static/bootstrap/fonts/glyphicons-halflings-regular.ttf -------------------------------------------------------------------------------- /src/web/static/bootstrap/fonts/glyphicons-halflings-regular.woff: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aaronshan/12306-captcha/af14073d15f8beb3f8c9fe30802ee52875b78bec/src/web/static/bootstrap/fonts/glyphicons-halflings-regular.woff -------------------------------------------------------------------------------- /src/web/static/bootstrap/js/bootstrap.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Bootstrap v3.1.1 (http://getbootstrap.com) 3 | * Copyright 2011-2014 Twitter, Inc. 4 | * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) 5 | */ 6 | if ("undefined" == typeof jQuery)throw new Error("Bootstrap's JavaScript requires jQuery"); 7 | +function (a) { 8 | "use strict"; 9 | function b() { 10 | var a = document.createElement("bootstrap"), b = { 11 | WebkitTransition: "webkitTransitionEnd", 12 | MozTransition: "transitionend", 13 | OTransition: "oTransitionEnd otransitionend", 14 | transition: "transitionend" 15 | }; 16 | for (var c in b)if (void 0 !== a.style[c])return {end: b[c]}; 17 | return !1 18 | } 19 | 20 | a.fn.emulateTransitionEnd = function (b) { 21 | var c = !1, d = this; 22 | a(this).one(a.support.transition.end, function () { 23 | c = !0 24 | }); 25 | var e = function () { 26 | c || a(d).trigger(a.support.transition.end) 27 | }; 28 | return setTimeout(e, b), this 29 | }, a(function () { 30 | a.support.transition = b() 31 | }) 32 | }(jQuery), +function (a) { 33 | "use strict"; 34 | var b = '[data-dismiss="alert"]', c = function (c) { 35 | a(c).on("click", b, this.close) 36 | }; 37 | c.prototype.close = function (b) { 38 | function c() { 39 | f.trigger("closed.bs.alert").remove() 40 | } 41 | 42 | var d = a(this), e = d.attr("data-target"); 43 | e || (e = d.attr("href"), e = e && e.replace(/.*(?=#[^\s]*$)/, "")); 44 | var f = a(e); 45 | b && b.preventDefault(), f.length || (f = d.hasClass("alert") ? d : d.parent()), f.trigger(b = a.Event("close.bs.alert")), b.isDefaultPrevented() || (f.removeClass("in"), a.support.transition && f.hasClass("fade") ? f.one(a.support.transition.end, c).emulateTransitionEnd(150) : c()) 46 | }; 47 | var d = a.fn.alert; 48 | a.fn.alert = function (b) { 49 | return this.each(function () { 50 | var d = a(this), e = d.data("bs.alert"); 51 | e || d.data("bs.alert", e = new c(this)), "string" == typeof b && e[b].call(d) 52 | }) 53 | }, a.fn.alert.Constructor = c, a.fn.alert.noConflict = function () { 54 | return a.fn.alert = d, this 55 | }, a(document).on("click.bs.alert.data-api", b, c.prototype.close) 56 | }(jQuery), +function (a) { 57 | "use strict"; 58 | var b = function (c, d) { 59 | this.$element = a(c), this.options = a.extend({}, b.DEFAULTS, d), this.isLoading = !1 60 | }; 61 | b.DEFAULTS = {loadingText: "loading..."}, b.prototype.setState = function (b) { 62 | var c = "disabled", d = this.$element, e = d.is("input") ? "val" : "html", f = d.data(); 63 | b += "Text", f.resetText || d.data("resetText", d[e]()), d[e](f[b] || this.options[b]), setTimeout(a.proxy(function () { 64 | "loadingText" == b ? (this.isLoading = !0, d.addClass(c).attr(c, c)) : this.isLoading && (this.isLoading = !1, d.removeClass(c).removeAttr(c)) 65 | }, this), 0) 66 | }, b.prototype.toggle = function () { 67 | var a = !0, b = this.$element.closest('[data-toggle="buttons"]'); 68 | if (b.length) { 69 | var c = this.$element.find("input"); 70 | "radio" == c.prop("type") && (c.prop("checked") && this.$element.hasClass("active") ? a = !1 : b.find(".active").removeClass("active")), a && c.prop("checked", !this.$element.hasClass("active")).trigger("change") 71 | } 72 | a && this.$element.toggleClass("active") 73 | }; 74 | var c = a.fn.button; 75 | a.fn.button = function (c) { 76 | return this.each(function () { 77 | var d = a(this), e = d.data("bs.button"), f = "object" == typeof c && c; 78 | e || d.data("bs.button", e = new b(this, f)), "toggle" == c ? e.toggle() : c && e.setState(c) 79 | }) 80 | }, a.fn.button.Constructor = b, a.fn.button.noConflict = function () { 81 | return a.fn.button = c, this 82 | }, a(document).on("click.bs.button.data-api", "[data-toggle^=button]", function (b) { 83 | var c = a(b.target); 84 | c.hasClass("btn") || (c = c.closest(".btn")), c.button("toggle"), b.preventDefault() 85 | }) 86 | }(jQuery), +function (a) { 87 | "use strict"; 88 | var b = function (b, c) { 89 | this.$element = a(b), this.$indicators = this.$element.find(".carousel-indicators"), this.options = c, this.paused = this.sliding = this.interval = this.$active = this.$items = null, "hover" == this.options.pause && this.$element.on("mouseenter", a.proxy(this.pause, this)).on("mouseleave", a.proxy(this.cycle, this)) 90 | }; 91 | b.DEFAULTS = {interval: 5e3, pause: "hover", wrap: !0}, b.prototype.cycle = function (b) { 92 | return b || (this.paused = !1), this.interval && clearInterval(this.interval), this.options.interval && !this.paused && (this.interval = setInterval(a.proxy(this.next, this), this.options.interval)), this 93 | }, b.prototype.getActiveIndex = function () { 94 | return this.$active = this.$element.find(".item.active"), this.$items = this.$active.parent().children(), this.$items.index(this.$active) 95 | }, b.prototype.to = function (b) { 96 | var c = this, d = this.getActiveIndex(); 97 | return b > this.$items.length - 1 || 0 > b ? void 0 : this.sliding ? this.$element.one("slid.bs.carousel", function () { 98 | c.to(b) 99 | }) : d == b ? this.pause().cycle() : this.slide(b > d ? "next" : "prev", a(this.$items[b])) 100 | }, b.prototype.pause = function (b) { 101 | return b || (this.paused = !0), this.$element.find(".next, .prev").length && a.support.transition && (this.$element.trigger(a.support.transition.end), this.cycle(!0)), this.interval = clearInterval(this.interval), this 102 | }, b.prototype.next = function () { 103 | return this.sliding ? void 0 : this.slide("next") 104 | }, b.prototype.prev = function () { 105 | return this.sliding ? void 0 : this.slide("prev") 106 | }, b.prototype.slide = function (b, c) { 107 | var d = this.$element.find(".item.active"), e = c || d[b](), f = this.interval, g = "next" == b ? "left" : "right", h = "next" == b ? "first" : "last", i = this; 108 | if (!e.length) { 109 | if (!this.options.wrap)return; 110 | e = this.$element.find(".item")[h]() 111 | } 112 | if (e.hasClass("active"))return this.sliding = !1; 113 | var j = a.Event("slide.bs.carousel", {relatedTarget: e[0], direction: g}); 114 | return this.$element.trigger(j), j.isDefaultPrevented() ? void 0 : (this.sliding = !0, f && this.pause(), this.$indicators.length && (this.$indicators.find(".active").removeClass("active"), this.$element.one("slid.bs.carousel", function () { 115 | var b = a(i.$indicators.children()[i.getActiveIndex()]); 116 | b && b.addClass("active") 117 | })), a.support.transition && this.$element.hasClass("slide") ? (e.addClass(b), e[0].offsetWidth, d.addClass(g), e.addClass(g), d.one(a.support.transition.end, function () { 118 | e.removeClass([b, g].join(" ")).addClass("active"), d.removeClass(["active", g].join(" ")), i.sliding = !1, setTimeout(function () { 119 | i.$element.trigger("slid.bs.carousel") 120 | }, 0) 121 | }).emulateTransitionEnd(1e3 * d.css("transition-duration").slice(0, -1))) : (d.removeClass("active"), e.addClass("active"), this.sliding = !1, this.$element.trigger("slid.bs.carousel")), f && this.cycle(), this) 122 | }; 123 | var c = a.fn.carousel; 124 | a.fn.carousel = function (c) { 125 | return this.each(function () { 126 | var d = a(this), e = d.data("bs.carousel"), f = a.extend({}, b.DEFAULTS, d.data(), "object" == typeof c && c), g = "string" == typeof c ? c : f.slide; 127 | e || d.data("bs.carousel", e = new b(this, f)), "number" == typeof c ? e.to(c) : g ? e[g]() : f.interval && e.pause().cycle() 128 | }) 129 | }, a.fn.carousel.Constructor = b, a.fn.carousel.noConflict = function () { 130 | return a.fn.carousel = c, this 131 | }, a(document).on("click.bs.carousel.data-api", "[data-slide], [data-slide-to]", function (b) { 132 | var c, d = a(this), e = a(d.attr("data-target") || (c = d.attr("href")) && c.replace(/.*(?=#[^\s]+$)/, "")), f = a.extend({}, e.data(), d.data()), g = d.attr("data-slide-to"); 133 | g && (f.interval = !1), e.carousel(f), (g = d.attr("data-slide-to")) && e.data("bs.carousel").to(g), b.preventDefault() 134 | }), a(window).on("load", function () { 135 | a('[data-ride="carousel"]').each(function () { 136 | var b = a(this); 137 | b.carousel(b.data()) 138 | }) 139 | }) 140 | }(jQuery), +function (a) { 141 | "use strict"; 142 | var b = function (c, d) { 143 | this.$element = a(c), this.options = a.extend({}, b.DEFAULTS, d), this.transitioning = null, this.options.parent && (this.$parent = a(this.options.parent)), this.options.toggle && this.toggle() 144 | }; 145 | b.DEFAULTS = {toggle: !0}, b.prototype.dimension = function () { 146 | var a = this.$element.hasClass("width"); 147 | return a ? "width" : "height" 148 | }, b.prototype.show = function () { 149 | if (!this.transitioning && !this.$element.hasClass("in")) { 150 | var b = a.Event("show.bs.collapse"); 151 | if (this.$element.trigger(b), !b.isDefaultPrevented()) { 152 | var c = this.$parent && this.$parent.find("> .panel > .in"); 153 | if (c && c.length) { 154 | var d = c.data("bs.collapse"); 155 | if (d && d.transitioning)return; 156 | c.collapse("hide"), d || c.data("bs.collapse", null) 157 | } 158 | var e = this.dimension(); 159 | this.$element.removeClass("collapse").addClass("collapsing")[e](0), this.transitioning = 1; 160 | var f = function () { 161 | this.$element.removeClass("collapsing").addClass("collapse in")[e]("auto"), this.transitioning = 0, this.$element.trigger("shown.bs.collapse") 162 | }; 163 | if (!a.support.transition)return f.call(this); 164 | var g = a.camelCase(["scroll", e].join("-")); 165 | this.$element.one(a.support.transition.end, a.proxy(f, this)).emulateTransitionEnd(350)[e](this.$element[0][g]) 166 | } 167 | } 168 | }, b.prototype.hide = function () { 169 | if (!this.transitioning && this.$element.hasClass("in")) { 170 | var b = a.Event("hide.bs.collapse"); 171 | if (this.$element.trigger(b), !b.isDefaultPrevented()) { 172 | var c = this.dimension(); 173 | this.$element[c](this.$element[c]())[0].offsetHeight, this.$element.addClass("collapsing").removeClass("collapse").removeClass("in"), this.transitioning = 1; 174 | var d = function () { 175 | this.transitioning = 0, this.$element.trigger("hidden.bs.collapse").removeClass("collapsing").addClass("collapse") 176 | }; 177 | return a.support.transition ? void this.$element[c](0).one(a.support.transition.end, a.proxy(d, this)).emulateTransitionEnd(350) : d.call(this) 178 | } 179 | } 180 | }, b.prototype.toggle = function () { 181 | this[this.$element.hasClass("in") ? "hide" : "show"]() 182 | }; 183 | var c = a.fn.collapse; 184 | a.fn.collapse = function (c) { 185 | return this.each(function () { 186 | var d = a(this), e = d.data("bs.collapse"), f = a.extend({}, b.DEFAULTS, d.data(), "object" == typeof c && c); 187 | !e && f.toggle && "show" == c && (c = !c), e || d.data("bs.collapse", e = new b(this, f)), "string" == typeof c && e[c]() 188 | }) 189 | }, a.fn.collapse.Constructor = b, a.fn.collapse.noConflict = function () { 190 | return a.fn.collapse = c, this 191 | }, a(document).on("click.bs.collapse.data-api", "[data-toggle=collapse]", function (b) { 192 | var c, d = a(this), e = d.attr("data-target") || b.preventDefault() || (c = d.attr("href")) && c.replace(/.*(?=#[^\s]+$)/, ""), f = a(e), g = f.data("bs.collapse"), h = g ? "toggle" : d.data(), i = d.attr("data-parent"), j = i && a(i); 193 | g && g.transitioning || (j && j.find('[data-toggle=collapse][data-parent="' + i + '"]').not(d).addClass("collapsed"), d[f.hasClass("in") ? "addClass" : "removeClass"]("collapsed")), f.collapse(h) 194 | }) 195 | }(jQuery), +function (a) { 196 | "use strict"; 197 | function b(b) { 198 | a(d).remove(), a(e).each(function () { 199 | var d = c(a(this)), e = {relatedTarget: this}; 200 | d.hasClass("open") && (d.trigger(b = a.Event("hide.bs.dropdown", e)), b.isDefaultPrevented() || d.removeClass("open").trigger("hidden.bs.dropdown", e)) 201 | }) 202 | } 203 | 204 | function c(b) { 205 | var c = b.attr("data-target"); 206 | c || (c = b.attr("href"), c = c && /#[A-Za-z]/.test(c) && c.replace(/.*(?=#[^\s]*$)/, "")); 207 | var d = c && a(c); 208 | return d && d.length ? d : b.parent() 209 | } 210 | 211 | var d = ".dropdown-backdrop", e = "[data-toggle=dropdown]", f = function (b) { 212 | a(b).on("click.bs.dropdown", this.toggle) 213 | }; 214 | f.prototype.toggle = function (d) { 215 | var e = a(this); 216 | if (!e.is(".disabled, :disabled")) { 217 | var f = c(e), g = f.hasClass("open"); 218 | if (b(), !g) { 219 | "ontouchstart" in document.documentElement && !f.closest(".navbar-nav").length && a('
').insertAfter(a(this)).on("click", b); 220 | var h = {relatedTarget: this}; 221 | if (f.trigger(d = a.Event("show.bs.dropdown", h)), d.isDefaultPrevented())return; 222 | f.toggleClass("open").trigger("shown.bs.dropdown", h), e.focus() 223 | } 224 | return !1 225 | } 226 | }, f.prototype.keydown = function (b) { 227 | if (/(38|40|27)/.test(b.keyCode)) { 228 | var d = a(this); 229 | if (b.preventDefault(), b.stopPropagation(), !d.is(".disabled, :disabled")) { 230 | var f = c(d), g = f.hasClass("open"); 231 | if (!g || g && 27 == b.keyCode)return 27 == b.which && f.find(e).focus(), d.click(); 232 | var h = " li:not(.divider):visible a", i = f.find("[role=menu]" + h + ", [role=listbox]" + h); 233 | if (i.length) { 234 | var j = i.index(i.filter(":focus")); 235 | 38 == b.keyCode && j > 0 && j--, 40 == b.keyCode && j < i.length - 1 && j++, ~j || (j = 0), i.eq(j).focus() 236 | } 237 | } 238 | } 239 | }; 240 | var g = a.fn.dropdown; 241 | a.fn.dropdown = function (b) { 242 | return this.each(function () { 243 | var c = a(this), d = c.data("bs.dropdown"); 244 | d || c.data("bs.dropdown", d = new f(this)), "string" == typeof b && d[b].call(c) 245 | }) 246 | }, a.fn.dropdown.Constructor = f, a.fn.dropdown.noConflict = function () { 247 | return a.fn.dropdown = g, this 248 | }, a(document).on("click.bs.dropdown.data-api", b).on("click.bs.dropdown.data-api", ".dropdown form", function (a) { 249 | a.stopPropagation() 250 | }).on("click.bs.dropdown.data-api", e, f.prototype.toggle).on("keydown.bs.dropdown.data-api", e + ", [role=menu], [role=listbox]", f.prototype.keydown) 251 | }(jQuery), +function (a) { 252 | "use strict"; 253 | var b = function (b, c) { 254 | this.options = c, this.$element = a(b), this.$backdrop = this.isShown = null, this.options.remote && this.$element.find(".modal-content").load(this.options.remote, a.proxy(function () { 255 | this.$element.trigger("loaded.bs.modal") 256 | }, this)) 257 | }; 258 | b.DEFAULTS = {backdrop: !0, keyboard: !0, show: !0}, b.prototype.toggle = function (a) { 259 | return this[this.isShown ? "hide" : "show"](a) 260 | }, b.prototype.show = function (b) { 261 | var c = this, d = a.Event("show.bs.modal", {relatedTarget: b}); 262 | this.$element.trigger(d), this.isShown || d.isDefaultPrevented() || (this.isShown = !0, this.escape(), this.$element.on("click.dismiss.bs.modal", '[data-dismiss="modal"]', a.proxy(this.hide, this)), this.backdrop(function () { 263 | var d = a.support.transition && c.$element.hasClass("fade"); 264 | c.$element.parent().length || c.$element.appendTo(document.body), c.$element.show().scrollTop(0), d && c.$element[0].offsetWidth, c.$element.addClass("in").attr("aria-hidden", !1), c.enforceFocus(); 265 | var e = a.Event("shown.bs.modal", {relatedTarget: b}); 266 | d ? c.$element.find(".modal-dialog").one(a.support.transition.end, function () { 267 | c.$element.focus().trigger(e) 268 | }).emulateTransitionEnd(300) : c.$element.focus().trigger(e) 269 | })) 270 | }, b.prototype.hide = function (b) { 271 | b && b.preventDefault(), b = a.Event("hide.bs.modal"), this.$element.trigger(b), this.isShown && !b.isDefaultPrevented() && (this.isShown = !1, this.escape(), a(document).off("focusin.bs.modal"), this.$element.removeClass("in").attr("aria-hidden", !0).off("click.dismiss.bs.modal"), a.support.transition && this.$element.hasClass("fade") ? this.$element.one(a.support.transition.end, a.proxy(this.hideModal, this)).emulateTransitionEnd(300) : this.hideModal()) 272 | }, b.prototype.enforceFocus = function () { 273 | a(document).off("focusin.bs.modal").on("focusin.bs.modal", a.proxy(function (a) { 274 | this.$element[0] === a.target || this.$element.has(a.target).length || this.$element.focus() 275 | }, this)) 276 | }, b.prototype.escape = function () { 277 | this.isShown && this.options.keyboard ? this.$element.on("keyup.dismiss.bs.modal", a.proxy(function (a) { 278 | 27 == a.which && this.hide() 279 | }, this)) : this.isShown || this.$element.off("keyup.dismiss.bs.modal") 280 | }, b.prototype.hideModal = function () { 281 | var a = this; 282 | this.$element.hide(), this.backdrop(function () { 283 | a.removeBackdrop(), a.$element.trigger("hidden.bs.modal") 284 | }) 285 | }, b.prototype.removeBackdrop = function () { 286 | this.$backdrop && this.$backdrop.remove(), this.$backdrop = null 287 | }, b.prototype.backdrop = function (b) { 288 | var c = this.$element.hasClass("fade") ? "fade" : ""; 289 | if (this.isShown && this.options.backdrop) { 290 | var d = a.support.transition && c; 291 | if (this.$backdrop = a('
').appendTo(document.body), this.$element.on("click.dismiss.bs.modal", a.proxy(function (a) { 292 | a.target === a.currentTarget && ("static" == this.options.backdrop ? this.$element[0].focus.call(this.$element[0]) : this.hide.call(this)) 293 | }, this)), d && this.$backdrop[0].offsetWidth, this.$backdrop.addClass("in"), !b)return; 294 | d ? this.$backdrop.one(a.support.transition.end, b).emulateTransitionEnd(150) : b() 295 | } else!this.isShown && this.$backdrop ? (this.$backdrop.removeClass("in"), a.support.transition && this.$element.hasClass("fade") ? this.$backdrop.one(a.support.transition.end, b).emulateTransitionEnd(150) : b()) : b && b() 296 | }; 297 | var c = a.fn.modal; 298 | a.fn.modal = function (c, d) { 299 | return this.each(function () { 300 | var e = a(this), f = e.data("bs.modal"), g = a.extend({}, b.DEFAULTS, e.data(), "object" == typeof c && c); 301 | f || e.data("bs.modal", f = new b(this, g)), "string" == typeof c ? f[c](d) : g.show && f.show(d) 302 | }) 303 | }, a.fn.modal.Constructor = b, a.fn.modal.noConflict = function () { 304 | return a.fn.modal = c, this 305 | }, a(document).on("click.bs.modal.data-api", '[data-toggle="modal"]', function (b) { 306 | var c = a(this), d = c.attr("href"), e = a(c.attr("data-target") || d && d.replace(/.*(?=#[^\s]+$)/, "")), f = e.data("bs.modal") ? "toggle" : a.extend({remote: !/#/.test(d) && d}, e.data(), c.data()); 307 | c.is("a") && b.preventDefault(), e.modal(f, this).one("hide", function () { 308 | c.is(":visible") && c.focus() 309 | }) 310 | }), a(document).on("show.bs.modal", ".modal", function () { 311 | a(document.body).addClass("modal-open") 312 | }).on("hidden.bs.modal", ".modal", function () { 313 | a(document.body).removeClass("modal-open") 314 | }) 315 | }(jQuery), +function (a) { 316 | "use strict"; 317 | var b = function (a, b) { 318 | this.type = this.options = this.enabled = this.timeout = this.hoverState = this.$element = null, this.init("tooltip", a, b) 319 | }; 320 | b.DEFAULTS = { 321 | animation: !0, 322 | placement: "top", 323 | selector: !1, 324 | template: '
', 325 | trigger: "hover focus", 326 | title: "", 327 | delay: 0, 328 | html: !1, 329 | container: !1 330 | }, b.prototype.init = function (b, c, d) { 331 | this.enabled = !0, this.type = b, this.$element = a(c), this.options = this.getOptions(d); 332 | for (var e = this.options.trigger.split(" "), f = e.length; f--;) { 333 | var g = e[f]; 334 | if ("click" == g)this.$element.on("click." + this.type, this.options.selector, a.proxy(this.toggle, this)); else if ("manual" != g) { 335 | var h = "hover" == g ? "mouseenter" : "focusin", i = "hover" == g ? "mouseleave" : "focusout"; 336 | this.$element.on(h + "." + this.type, this.options.selector, a.proxy(this.enter, this)), this.$element.on(i + "." + this.type, this.options.selector, a.proxy(this.leave, this)) 337 | } 338 | } 339 | this.options.selector ? this._options = a.extend({}, this.options, { 340 | trigger: "manual", 341 | selector: "" 342 | }) : this.fixTitle() 343 | }, b.prototype.getDefaults = function () { 344 | return b.DEFAULTS 345 | }, b.prototype.getOptions = function (b) { 346 | return b = a.extend({}, this.getDefaults(), this.$element.data(), b), b.delay && "number" == typeof b.delay && (b.delay = { 347 | show: b.delay, 348 | hide: b.delay 349 | }), b 350 | }, b.prototype.getDelegateOptions = function () { 351 | var b = {}, c = this.getDefaults(); 352 | return this._options && a.each(this._options, function (a, d) { 353 | c[a] != d && (b[a] = d) 354 | }), b 355 | }, b.prototype.enter = function (b) { 356 | var c = b instanceof this.constructor ? b : a(b.currentTarget)[this.type](this.getDelegateOptions()).data("bs." + this.type); 357 | return clearTimeout(c.timeout), c.hoverState = "in", c.options.delay && c.options.delay.show ? void(c.timeout = setTimeout(function () { 358 | "in" == c.hoverState && c.show() 359 | }, c.options.delay.show)) : c.show() 360 | }, b.prototype.leave = function (b) { 361 | var c = b instanceof this.constructor ? b : a(b.currentTarget)[this.type](this.getDelegateOptions()).data("bs." + this.type); 362 | return clearTimeout(c.timeout), c.hoverState = "out", c.options.delay && c.options.delay.hide ? void(c.timeout = setTimeout(function () { 363 | "out" == c.hoverState && c.hide() 364 | }, c.options.delay.hide)) : c.hide() 365 | }, b.prototype.show = function () { 366 | var b = a.Event("show.bs." + this.type); 367 | if (this.hasContent() && this.enabled) { 368 | if (this.$element.trigger(b), b.isDefaultPrevented())return; 369 | var c = this, d = this.tip(); 370 | this.setContent(), this.options.animation && d.addClass("fade"); 371 | var e = "function" == typeof this.options.placement ? this.options.placement.call(this, d[0], this.$element[0]) : this.options.placement, f = /\s?auto?\s?/i, g = f.test(e); 372 | g && (e = e.replace(f, "") || "top"), d.detach().css({ 373 | top: 0, 374 | left: 0, 375 | display: "block" 376 | }).addClass(e), this.options.container ? d.appendTo(this.options.container) : d.insertAfter(this.$element); 377 | var h = this.getPosition(), i = d[0].offsetWidth, j = d[0].offsetHeight; 378 | if (g) { 379 | var k = this.$element.parent(), l = e, m = document.documentElement.scrollTop || document.body.scrollTop, n = "body" == this.options.container ? window.innerWidth : k.outerWidth(), o = "body" == this.options.container ? window.innerHeight : k.outerHeight(), p = "body" == this.options.container ? 0 : k.offset().left; 380 | e = "bottom" == e && h.top + h.height + j - m > o ? "top" : "top" == e && h.top - m - j < 0 ? "bottom" : "right" == e && h.right + i > n ? "left" : "left" == e && h.left - i < p ? "right" : e, d.removeClass(l).addClass(e) 381 | } 382 | var q = this.getCalculatedOffset(e, h, i, j); 383 | this.applyPlacement(q, e), this.hoverState = null; 384 | var r = function () { 385 | c.$element.trigger("shown.bs." + c.type) 386 | }; 387 | a.support.transition && this.$tip.hasClass("fade") ? d.one(a.support.transition.end, r).emulateTransitionEnd(150) : r() 388 | } 389 | }, b.prototype.applyPlacement = function (b, c) { 390 | var d, e = this.tip(), f = e[0].offsetWidth, g = e[0].offsetHeight, h = parseInt(e.css("margin-top"), 10), i = parseInt(e.css("margin-left"), 10); 391 | isNaN(h) && (h = 0), isNaN(i) && (i = 0), b.top = b.top + h, b.left = b.left + i, a.offset.setOffset(e[0], a.extend({ 392 | using: function (a) { 393 | e.css({top: Math.round(a.top), left: Math.round(a.left)}) 394 | } 395 | }, b), 0), e.addClass("in"); 396 | var j = e[0].offsetWidth, k = e[0].offsetHeight; 397 | if ("top" == c && k != g && (d = !0, b.top = b.top + g - k), /bottom|top/.test(c)) { 398 | var l = 0; 399 | b.left < 0 && (l = -2 * b.left, b.left = 0, e.offset(b), j = e[0].offsetWidth, k = e[0].offsetHeight), this.replaceArrow(l - f + j, j, "left") 400 | } else this.replaceArrow(k - g, k, "top"); 401 | d && e.offset(b) 402 | }, b.prototype.replaceArrow = function (a, b, c) { 403 | this.arrow().css(c, a ? 50 * (1 - a / b) + "%" : "") 404 | }, b.prototype.setContent = function () { 405 | var a = this.tip(), b = this.getTitle(); 406 | a.find(".tooltip-inner")[this.options.html ? "html" : "text"](b), a.removeClass("fade in top bottom left right") 407 | }, b.prototype.hide = function () { 408 | function b() { 409 | "in" != c.hoverState && d.detach(), c.$element.trigger("hidden.bs." + c.type) 410 | } 411 | 412 | var c = this, d = this.tip(), e = a.Event("hide.bs." + this.type); 413 | return this.$element.trigger(e), e.isDefaultPrevented() ? void 0 : (d.removeClass("in"), a.support.transition && this.$tip.hasClass("fade") ? d.one(a.support.transition.end, b).emulateTransitionEnd(150) : b(), this.hoverState = null, this) 414 | }, b.prototype.fixTitle = function () { 415 | var a = this.$element; 416 | (a.attr("title") || "string" != typeof a.attr("data-original-title")) && a.attr("data-original-title", a.attr("title") || "").attr("title", "") 417 | }, b.prototype.hasContent = function () { 418 | return this.getTitle() 419 | }, b.prototype.getPosition = function () { 420 | var b = this.$element[0]; 421 | return a.extend({}, "function" == typeof b.getBoundingClientRect ? b.getBoundingClientRect() : { 422 | width: b.offsetWidth, 423 | height: b.offsetHeight 424 | }, this.$element.offset()) 425 | }, b.prototype.getCalculatedOffset = function (a, b, c, d) { 426 | return "bottom" == a ? { 427 | top: b.top + b.height, 428 | left: b.left + b.width / 2 - c / 2 429 | } : "top" == a ? { 430 | top: b.top - d, 431 | left: b.left + b.width / 2 - c / 2 432 | } : "left" == a ? {top: b.top + b.height / 2 - d / 2, left: b.left - c} : { 433 | top: b.top + b.height / 2 - d / 2, 434 | left: b.left + b.width 435 | } 436 | }, b.prototype.getTitle = function () { 437 | var a, b = this.$element, c = this.options; 438 | return a = b.attr("data-original-title") || ("function" == typeof c.title ? c.title.call(b[0]) : c.title) 439 | }, b.prototype.tip = function () { 440 | return this.$tip = this.$tip || a(this.options.template) 441 | }, b.prototype.arrow = function () { 442 | return this.$arrow = this.$arrow || this.tip().find(".tooltip-arrow") 443 | }, b.prototype.validate = function () { 444 | this.$element[0].parentNode || (this.hide(), this.$element = null, this.options = null) 445 | }, b.prototype.enable = function () { 446 | this.enabled = !0 447 | }, b.prototype.disable = function () { 448 | this.enabled = !1 449 | }, b.prototype.toggleEnabled = function () { 450 | this.enabled = !this.enabled 451 | }, b.prototype.toggle = function (b) { 452 | var c = b ? a(b.currentTarget)[this.type](this.getDelegateOptions()).data("bs." + this.type) : this; 453 | c.tip().hasClass("in") ? c.leave(c) : c.enter(c) 454 | }, b.prototype.destroy = function () { 455 | clearTimeout(this.timeout), this.hide().$element.off("." + this.type).removeData("bs." + this.type) 456 | }; 457 | var c = a.fn.tooltip; 458 | a.fn.tooltip = function (c) { 459 | return this.each(function () { 460 | var d = a(this), e = d.data("bs.tooltip"), f = "object" == typeof c && c; 461 | (e || "destroy" != c) && (e || d.data("bs.tooltip", e = new b(this, f)), "string" == typeof c && e[c]()) 462 | }) 463 | }, a.fn.tooltip.Constructor = b, a.fn.tooltip.noConflict = function () { 464 | return a.fn.tooltip = c, this 465 | } 466 | }(jQuery), +function (a) { 467 | "use strict"; 468 | var b = function (a, b) { 469 | this.init("popover", a, b) 470 | }; 471 | if (!a.fn.tooltip)throw new Error("Popover requires tooltip.js"); 472 | b.DEFAULTS = a.extend({}, a.fn.tooltip.Constructor.DEFAULTS, { 473 | placement: "right", 474 | trigger: "click", 475 | content: "", 476 | template: '

' 477 | }), b.prototype = a.extend({}, a.fn.tooltip.Constructor.prototype), b.prototype.constructor = b, b.prototype.getDefaults = function () { 478 | return b.DEFAULTS 479 | }, b.prototype.setContent = function () { 480 | var a = this.tip(), b = this.getTitle(), c = this.getContent(); 481 | a.find(".popover-title")[this.options.html ? "html" : "text"](b), a.find(".popover-content")[this.options.html ? "string" == typeof c ? "html" : "append" : "text"](c), a.removeClass("fade top bottom left right in"), a.find(".popover-title").html() || a.find(".popover-title").hide() 482 | }, b.prototype.hasContent = function () { 483 | return this.getTitle() || this.getContent() 484 | }, b.prototype.getContent = function () { 485 | var a = this.$element, b = this.options; 486 | return a.attr("data-content") || ("function" == typeof b.content ? b.content.call(a[0]) : b.content) 487 | }, b.prototype.arrow = function () { 488 | return this.$arrow = this.$arrow || this.tip().find(".arrow") 489 | }, b.prototype.tip = function () { 490 | return this.$tip || (this.$tip = a(this.options.template)), this.$tip 491 | }; 492 | var c = a.fn.popover; 493 | a.fn.popover = function (c) { 494 | return this.each(function () { 495 | var d = a(this), e = d.data("bs.popover"), f = "object" == typeof c && c; 496 | (e || "destroy" != c) && (e || d.data("bs.popover", e = new b(this, f)), "string" == typeof c && e[c]()) 497 | }) 498 | }, a.fn.popover.Constructor = b, a.fn.popover.noConflict = function () { 499 | return a.fn.popover = c, this 500 | } 501 | }(jQuery), +function (a) { 502 | "use strict"; 503 | function b(c, d) { 504 | var e, f = a.proxy(this.process, this); 505 | this.$element = a(a(c).is("body") ? window : c), this.$body = a("body"), this.$scrollElement = this.$element.on("scroll.bs.scroll-spy.data-api", f), this.options = a.extend({}, b.DEFAULTS, d), this.selector = (this.options.target || (e = a(c).attr("href")) && e.replace(/.*(?=#[^\s]+$)/, "") || "") + " .nav li > a", this.offsets = a([]), this.targets = a([]), this.activeTarget = null, this.refresh(), this.process() 506 | } 507 | 508 | b.DEFAULTS = {offset: 10}, b.prototype.refresh = function () { 509 | var b = this.$element[0] == window ? "offset" : "position"; 510 | this.offsets = a([]), this.targets = a([]); 511 | { 512 | var c = this; 513 | this.$body.find(this.selector).map(function () { 514 | var d = a(this), e = d.data("target") || d.attr("href"), f = /^#./.test(e) && a(e); 515 | return f && f.length && f.is(":visible") && [ 516 | [f[b]().top + (!a.isWindow(c.$scrollElement.get(0)) && c.$scrollElement.scrollTop()), e] 517 | ] || null 518 | }).sort(function (a, b) { 519 | return a[0] - b[0] 520 | }).each(function () { 521 | c.offsets.push(this[0]), c.targets.push(this[1]) 522 | }) 523 | } 524 | }, b.prototype.process = function () { 525 | var a, b = this.$scrollElement.scrollTop() + this.options.offset, c = this.$scrollElement[0].scrollHeight || this.$body[0].scrollHeight, d = c - this.$scrollElement.height(), e = this.offsets, f = this.targets, g = this.activeTarget; 526 | if (b >= d)return g != (a = f.last()[0]) && this.activate(a); 527 | if (g && b <= e[0])return g != (a = f[0]) && this.activate(a); 528 | for (a = e.length; a--;)g != f[a] && b >= e[a] && (!e[a + 1] || b <= e[a + 1]) && this.activate(f[a]) 529 | }, b.prototype.activate = function (b) { 530 | this.activeTarget = b, a(this.selector).parentsUntil(this.options.target, ".active").removeClass("active"); 531 | var c = this.selector + '[data-target="' + b + '"],' + this.selector + '[href="' + b + '"]', d = a(c).parents("li").addClass("active"); 532 | d.parent(".dropdown-menu").length && (d = d.closest("li.dropdown").addClass("active")), d.trigger("activate.bs.scrollspy") 533 | }; 534 | var c = a.fn.scrollspy; 535 | a.fn.scrollspy = function (c) { 536 | return this.each(function () { 537 | var d = a(this), e = d.data("bs.scrollspy"), f = "object" == typeof c && c; 538 | e || d.data("bs.scrollspy", e = new b(this, f)), "string" == typeof c && e[c]() 539 | }) 540 | }, a.fn.scrollspy.Constructor = b, a.fn.scrollspy.noConflict = function () { 541 | return a.fn.scrollspy = c, this 542 | }, a(window).on("load", function () { 543 | a('[data-spy="scroll"]').each(function () { 544 | var b = a(this); 545 | b.scrollspy(b.data()) 546 | }) 547 | }) 548 | }(jQuery), +function (a) { 549 | "use strict"; 550 | var b = function (b) { 551 | this.element = a(b) 552 | }; 553 | b.prototype.show = function () { 554 | var b = this.element, c = b.closest("ul:not(.dropdown-menu)"), d = b.data("target"); 555 | if (d || (d = b.attr("href"), d = d && d.replace(/.*(?=#[^\s]*$)/, "")), !b.parent("li").hasClass("active")) { 556 | var e = c.find(".active:last a")[0], f = a.Event("show.bs.tab", {relatedTarget: e}); 557 | if (b.trigger(f), !f.isDefaultPrevented()) { 558 | var g = a(d); 559 | this.activate(b.parent("li"), c), this.activate(g, g.parent(), function () { 560 | b.trigger({type: "shown.bs.tab", relatedTarget: e}) 561 | }) 562 | } 563 | } 564 | }, b.prototype.activate = function (b, c, d) { 565 | function e() { 566 | f.removeClass("active").find("> .dropdown-menu > .active").removeClass("active"), b.addClass("active"), g ? (b[0].offsetWidth, b.addClass("in")) : b.removeClass("fade"), b.parent(".dropdown-menu") && b.closest("li.dropdown").addClass("active"), d && d() 567 | } 568 | 569 | var f = c.find("> .active"), g = d && a.support.transition && f.hasClass("fade"); 570 | g ? f.one(a.support.transition.end, e).emulateTransitionEnd(150) : e(), f.removeClass("in") 571 | }; 572 | var c = a.fn.tab; 573 | a.fn.tab = function (c) { 574 | return this.each(function () { 575 | var d = a(this), e = d.data("bs.tab"); 576 | e || d.data("bs.tab", e = new b(this)), "string" == typeof c && e[c]() 577 | }) 578 | }, a.fn.tab.Constructor = b, a.fn.tab.noConflict = function () { 579 | return a.fn.tab = c, this 580 | }, a(document).on("click.bs.tab.data-api", '[data-toggle="tab"], [data-toggle="pill"]', function (b) { 581 | b.preventDefault(), a(this).tab("show") 582 | }) 583 | }(jQuery), +function (a) { 584 | "use strict"; 585 | var b = function (c, d) { 586 | this.options = a.extend({}, b.DEFAULTS, d), this.$window = a(window).on("scroll.bs.affix.data-api", a.proxy(this.checkPosition, this)).on("click.bs.affix.data-api", a.proxy(this.checkPositionWithEventLoop, this)), this.$element = a(c), this.affixed = this.unpin = this.pinnedOffset = null, this.checkPosition() 587 | }; 588 | b.RESET = "affix affix-top affix-bottom", b.DEFAULTS = {offset: 0}, b.prototype.getPinnedOffset = function () { 589 | if (this.pinnedOffset)return this.pinnedOffset; 590 | this.$element.removeClass(b.RESET).addClass("affix"); 591 | var a = this.$window.scrollTop(), c = this.$element.offset(); 592 | return this.pinnedOffset = c.top - a 593 | }, b.prototype.checkPositionWithEventLoop = function () { 594 | setTimeout(a.proxy(this.checkPosition, this), 1) 595 | }, b.prototype.checkPosition = function () { 596 | if (this.$element.is(":visible")) { 597 | var c = a(document).height(), d = this.$window.scrollTop(), e = this.$element.offset(), f = this.options.offset, g = f.top, h = f.bottom; 598 | "top" == this.affixed && (e.top += d), "object" != typeof f && (h = g = f), "function" == typeof g && (g = f.top(this.$element)), "function" == typeof h && (h = f.bottom(this.$element)); 599 | var i = null != this.unpin && d + this.unpin <= e.top ? !1 : null != h && e.top + this.$element.height() >= c - h ? "bottom" : null != g && g >= d ? "top" : !1; 600 | if (this.affixed !== i) { 601 | this.unpin && this.$element.css("top", ""); 602 | var j = "affix" + (i ? "-" + i : ""), k = a.Event(j + ".bs.affix"); 603 | this.$element.trigger(k), k.isDefaultPrevented() || (this.affixed = i, this.unpin = "bottom" == i ? this.getPinnedOffset() : null, this.$element.removeClass(b.RESET).addClass(j).trigger(a.Event(j.replace("affix", "affixed"))), "bottom" == i && this.$element.offset({top: c - h - this.$element.height()})) 604 | } 605 | } 606 | }; 607 | var c = a.fn.affix; 608 | a.fn.affix = function (c) { 609 | return this.each(function () { 610 | var d = a(this), e = d.data("bs.affix"), f = "object" == typeof c && c; 611 | e || d.data("bs.affix", e = new b(this, f)), "string" == typeof c && e[c]() 612 | }) 613 | }, a.fn.affix.Constructor = b, a.fn.affix.noConflict = function () { 614 | return a.fn.affix = c, this 615 | }, a(window).on("load", function () { 616 | a('[data-spy="affix"]').each(function () { 617 | var b = a(this), c = b.data(); 618 | c.offset = c.offset || {}, c.offsetBottom && (c.offset.bottom = c.offsetBottom), c.offsetTop && (c.offset.top = c.offsetTop), b.affix(c) 619 | }) 620 | }) 621 | }(jQuery); -------------------------------------------------------------------------------- /src/web/static/bootstrap/js/bootstrap2-typeahead.min.js: -------------------------------------------------------------------------------- 1 | /* ============================================================= 2 | * bootstrap-typeahead.js v2.3.2 3 | * http://twitter.github.com/bootstrap/javascript.html#typeahead 4 | * ============================================================= 5 | * Copyright 2012 Twitter, Inc. 6 | * 7 | * Licensed under the Apache License, Version 2.0 (the "License"); 8 | * you may not use this file except in compliance with the License. 9 | * You may obtain a copy of the License at 10 | * 11 | * http://www.apache.org/licenses/LICENSE-2.0 12 | * 13 | * Unless required by applicable law or agreed to in writing, software 14 | * distributed under the License is distributed on an "AS IS" BASIS, 15 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 16 | * See the License for the specific language governing permissions and 17 | * limitations under the License. 18 | * ============================================================ */ 19 | 20 | 21 | !function (t) { 22 | "use strict"; 23 | var e = function (e, s) { 24 | this.$element = t(e), this.options = t.extend({}, t.fn.typeahead.defaults, s), this.matcher = this.options.matcher || this.matcher, this.sorter = this.options.sorter || this.sorter, this.highlighter = this.options.highlighter || this.highlighter, this.updater = this.options.updater || this.updater, this.source = this.options.source, this.$menu = t(this.options.menu), this.shown = !1, this.listen() 25 | }; 26 | e.prototype = { 27 | constructor: e, select: function () { 28 | var t = this.$menu.find(".active").attr("data-value"); 29 | return this.$element.val(this.updater(t)).change(), this.hide() 30 | }, updater: function (t) { 31 | return t 32 | }, show: function () { 33 | var e = t.extend({}, this.$element.position(), {height: this.$element[0].offsetHeight}); 34 | return this.$menu.insertAfter(this.$element).css({ 35 | top: e.top + e.height, 36 | left: e.left 37 | }).show(), this.shown = !0, this 38 | }, hide: function () { 39 | return this.$menu.hide(), this.shown = !1, this 40 | }, lookup: function () { 41 | var e; 42 | return this.query = this.$element.val(), !this.query || this.query.length < this.options.minLength ? this.shown ? this.hide() : this : (e = t.isFunction(this.source) ? this.source(this.query, t.proxy(this.process, this)) : this.source, e ? this.process(e) : this) 43 | }, process: function (e) { 44 | var s = this; 45 | return e = t.grep(e, function (t) { 46 | return s.matcher(t) 47 | }), e = this.sorter(e), e.length ? this.render(e.slice(0, this.options.items)).show() : this.shown ? this.hide() : this 48 | }, matcher: function (t) { 49 | return ~t.toLowerCase().indexOf(this.query.toLowerCase()) 50 | }, sorter: function (t) { 51 | for (var e, s = [], i = [], n = []; e = t.shift();)e.toLowerCase().indexOf(this.query.toLowerCase()) ? ~e.indexOf(this.query) ? i.push(e) : n.push(e) : s.push(e); 52 | return s.concat(i, n) 53 | }, highlighter: function (t) { 54 | var e = this.query.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g, "\\$&"); 55 | return t.replace(new RegExp("(" + e + ")", "ig"), function (t, e) { 56 | return "" + e + "" 57 | }) 58 | }, render: function (e) { 59 | var s = this; 60 | return e = t(e).map(function (e, i) { 61 | return e = t(s.options.item).attr("data-value", i), e.find("a").html(s.highlighter(i)), e[0] 62 | }), e.first().addClass("active"), this.$menu.html(e), this 63 | }, next: function () { 64 | var e = this.$menu.find(".active").removeClass("active"), s = e.next(); 65 | s.length || (s = t(this.$menu.find("li")[0])), s.addClass("active") 66 | }, prev: function () { 67 | var t = this.$menu.find(".active").removeClass("active"), e = t.prev(); 68 | e.length || (e = this.$menu.find("li").last()), e.addClass("active") 69 | }, listen: function () { 70 | this.$element.on("focus", t.proxy(this.focus, this)).on("blur", t.proxy(this.blur, this)).on("keypress", t.proxy(this.keypress, this)).on("keyup", t.proxy(this.keyup, this)), this.eventSupported("keydown") && this.$element.on("keydown", t.proxy(this.keydown, this)), this.$menu.on("click", t.proxy(this.click, this)).on("mouseenter", "li", t.proxy(this.mouseenter, this)).on("mouseleave", "li", t.proxy(this.mouseleave, this)) 71 | }, eventSupported: function (t) { 72 | var e = t in this.$element; 73 | return e || (this.$element.setAttribute(t, "return;"), e = "function" == typeof this.$element[t]), e 74 | }, move: function (t) { 75 | if (this.shown) { 76 | switch (t.keyCode) { 77 | case 9: 78 | case 13: 79 | case 27: 80 | t.preventDefault(); 81 | break; 82 | case 38: 83 | t.preventDefault(), this.prev(); 84 | break; 85 | case 40: 86 | t.preventDefault(), this.next() 87 | } 88 | t.stopPropagation() 89 | } 90 | }, keydown: function (e) { 91 | this.suppressKeyPressRepeat = ~t.inArray(e.keyCode, [40, 38, 9, 13, 27]), this.move(e) 92 | }, keypress: function (t) { 93 | this.suppressKeyPressRepeat || this.move(t) 94 | }, keyup: function (t) { 95 | switch (t.keyCode) { 96 | case 40: 97 | case 38: 98 | case 16: 99 | case 17: 100 | case 18: 101 | break; 102 | case 9: 103 | case 13: 104 | if (!this.shown)return; 105 | this.select(); 106 | break; 107 | case 27: 108 | if (!this.shown)return; 109 | this.hide(); 110 | break; 111 | default: 112 | this.lookup() 113 | } 114 | t.stopPropagation(), t.preventDefault() 115 | }, focus: function () { 116 | this.focused = !0 117 | }, blur: function () { 118 | this.focused = !1, !this.mousedover && this.shown && this.hide() 119 | }, click: function (t) { 120 | t.stopPropagation(), t.preventDefault(), this.select(), this.$element.focus() 121 | }, mouseenter: function (e) { 122 | this.mousedover = !0, this.$menu.find(".active").removeClass("active"), t(e.currentTarget).addClass("active") 123 | }, mouseleave: function () { 124 | this.mousedover = !1, !this.focused && this.shown && this.hide() 125 | } 126 | }; 127 | var s = t.fn.typeahead; 128 | t.fn.typeahead = function (s) { 129 | return this.each(function () { 130 | var i = t(this), n = i.data("typeahead"), h = "object" == typeof s && s; 131 | n || i.data("typeahead", n = new e(this, h)), "string" == typeof s && n[s]() 132 | }) 133 | }, t.fn.typeahead.defaults = { 134 | source: [], 135 | items: 8, 136 | menu: '', 137 | item: '
    • ', 138 | minLength: 1 139 | }, t.fn.typeahead.Constructor = e, t.fn.typeahead.noConflict = function () { 140 | return t.fn.typeahead = s, this 141 | }, t(document).on("focus.typeahead.data-api", '[data-provide="typeahead"]', function () { 142 | var e = t(this); 143 | e.data("typeahead") || e.typeahead(e.data()) 144 | }) 145 | }(window.jQuery); -------------------------------------------------------------------------------- /src/web/static/bootstrap/js/locales/bootstrap-datetimepicker.zh-CN.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Simplified Chinese translation for bootstrap-datetimepicker 3 | * Yuan Cheung 4 | */ 5 | ; 6 | (function ($) { 7 | $.fn.datetimepicker.dates['zh-CN'] = { 8 | days: ["星期日", "星期一", "星期二", "星期三", "星期四", "星期五", "星期六", "星期日"], 9 | daysShort: ["周日", "周一", "周二", "周三", "周四", "周五", "周六", "周日"], 10 | daysMin: ["日", "一", "二", "三", "四", "五", "六", "日"], 11 | months: ["一月", "二月", "三月", "四月", "五月", "六月", "七月", "八月", "九月", "十月", "十一月", "十二月"], 12 | monthsShort: ["一月", "二月", "三月", "四月", "五月", "六月", "七月", "八月", "九月", "十月", "十一月", "十二月"], 13 | today: "今天", 14 | suffix: [], 15 | meridiem: ["上午", "下午"] 16 | }; 17 | }(jQuery)); 18 | -------------------------------------------------------------------------------- /src/web/static/css/index.css: -------------------------------------------------------------------------------- 1 | body { 2 | text-align: center 3 | } 4 | 5 | .div { 6 | margin: 0 auto; 7 | width: 800px; 8 | height: 200px; 9 | border: 2px solid #b32141 10 | } 11 | 12 | .result { 13 | margin: 0 auto; 14 | width: 250px; 15 | height: 150px; 16 | } 17 | 18 | .bol { 19 | font-weight: bold; 20 | } -------------------------------------------------------------------------------- /src/web/static/js/index.js: -------------------------------------------------------------------------------- 1 | $(document).ready(function() { 2 | $("#error_idx").val(""); 3 | $("#result0").keyup(function() { 4 | var value = $("#error_idx").val(); 5 | if (value.indexOf("-0") == -1) { 6 | value = value + "-0"; 7 | $("#error_idx").val(value) 8 | } 9 | }); 10 | $("#result1").keyup(function() { 11 | var value = $("#error_idx").val(); 12 | if (value.indexOf("-1") == -1) { 13 | value = value + "-1"; 14 | $("#error_idx").val(value) 15 | } 16 | }); 17 | $("#result2").keyup(function() { 18 | var value = $("#error_idx").val(); 19 | if (value.indexOf("-2") == -1) { 20 | value = value + "-2"; 21 | $("#error_idx").val(value) 22 | } 23 | }); 24 | $("#result3").keyup(function() { 25 | var value = $("#error_idx").val(); 26 | if (value.indexOf("-3") == -1) { 27 | value = value + "-3"; 28 | $("#error_idx").val(value) 29 | } 30 | }); 31 | $("#result4").keyup(function() { 32 | var value = $("#error_idx").val(); 33 | if (value.indexOf("-4") == -1) { 34 | value = value + "-4"; 35 | $("#error_idx").val(value) 36 | } 37 | }); 38 | $("#result5").keyup(function() { 39 | var value = $("#error_idx").val(); 40 | if (value.indexOf("-5") == -1) { 41 | value = value + "-5"; 42 | $("#error_idx").val(value) 43 | } 44 | }); 45 | $("#result6").keyup(function() { 46 | var value = $("#error_idx").val(); 47 | if (value.indexOf("-6") == -1) { 48 | value = value + "-6"; 49 | $("#error_idx").val(value) 50 | } 51 | }); 52 | $("#result7").keyup(function() { 53 | var value = $("#error_idx").val(); 54 | if (value.indexOf("-7") == -1) { 55 | value = value + "-7"; 56 | $("#error_idx").val(value) 57 | } 58 | }); 59 | }); -------------------------------------------------------------------------------- /src/web/static/tmp/.gitignore: -------------------------------------------------------------------------------- 1 | # 忽略所有文件 2 | * 3 | # 除了这个文件 4 | !.gitignore 5 | -------------------------------------------------------------------------------- /src/web/templates/12306.html: -------------------------------------------------------------------------------- 1 | 2 | 12306验证码模型测试页面 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |

    验证码图片

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    模型判断结果

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    {{ results[0] }}{{ results[1] }}{{ results[2] }}{{ results[3] }}
    {{ results[4] }}{{ results[5] }}{{ results[6] }}{{ results[7] }}
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    55 | -------------------------------------------------------------------------------- /src/web/utils/Img12306.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | import cv2 4 | import os, sys 5 | 6 | reload(sys) 7 | sys.setdefaultencoding('utf-8') 8 | 9 | """ 10 | 该函数用来去除图像的中黑色斑点,同时采用插值法进行该点像素的补充 11 | ,param threshold, 设定黑色斑点的最大值 12 | ,param img, RGB图像的数据 13 | ,return, 返回RGB图像数据 14 | """ 15 | def rm_blackNoisy(im, threshold=20): 16 | def min(c_val, t_val): 17 | return c_val if c_val < t_val else t_val 18 | 19 | def sum(_val, _sum, _num): 20 | return _val + _sum, _num + 1 21 | 22 | def rm_blackNoisy_component(img, threshold): 23 | y, x = img.shape[:2] # x,y表示坐标 24 | for _x in range(0, x, 1): 25 | for _y in range(0, y, 1): 26 | cnt = 0 27 | _sum = num = 0 28 | if _y > 0: 29 | _sum, num = sum(img[_y - 1, _x], _sum, num) 30 | if _y + 1 < y: 31 | _sum, num = sum(img[_y + 1, _x], _sum, num) 32 | if _x > 0: 33 | _sum, num = sum(img[_y, _x - 1], _sum, num) 34 | if _x + 1 < x: 35 | _sum, num = sum(img[_y, _x + 1], _sum, num) 36 | if _y > 0 and _x > 0: 37 | _sum, num = sum(img[_y - 1, _x - 1], _sum, num) 38 | if _y > 0 and _x + 1 < x: 39 | _sum, num = sum(img[_y - 1, _x + 1], _sum, num) 40 | if _y + 1 < y and _x > 0: 41 | _sum, num = sum(img[_y + 1, _x - 1], _sum, num) 42 | 43 | if _y + 1 < y and _x + 1 < x: 44 | _sum, num = sum(img[_y + 1, _x + 1], _sum, num) 45 | if cnt > 0 or num < 1: # 说明不是单个点 46 | continue 47 | # 如果是孤点,则可以依据权重进行插值 48 | average = _sum / num 49 | if img[_y, _x] + 100 <= average: 50 | img[_y, _x] = average 51 | 52 | return img 53 | 54 | if im.ndim == 3: 55 | b, g, r = cv2.split(im) 56 | _b = rm_blackNoisy_component(b, threshold) 57 | _g = rm_blackNoisy_component(g, threshold) 58 | _r = rm_blackNoisy_component(r, threshold) 59 | img = cv2.merge([_b, _g, _r]) # 前面分离出来的三个通道 60 | 61 | else: 62 | return rm_blackNoisy_component(im, threshold) 63 | 64 | return img 65 | 66 | def remove_file_noise(input_path, output_path): 67 | im_in = cv2.imread(input_path, 1); 68 | im_out = rm_blackNoisy(im_in); 69 | cv2.imwrite(output_path, im_out) 70 | 71 | 72 | # 分割图片12306图片 73 | def get_img(im): 74 | img = [] 75 | for v in range(2): # 图片行 76 | for h in range(4): # 图片列 77 | img.append(im[(40 + (v * 72)):(108 + (v * 72)), (4 + (h * 72)):((h + 1) * 72)]) 78 | return img 79 | 80 | # 得到图像中的文本部分 81 | def get_text(im): 82 | return im[3:24, 116:288] 83 | 84 | def get_img_file(input_path): 85 | return cv2.imread(input_path, 1); 86 | 87 | def save_as_img_file(im, output_path): 88 | if output_path.endswith(".jpg"): 89 | dir_path = "/".join(output_path.split("/")[0:-1]) 90 | if not os.path.exists(dir_path): 91 | os.makedirs(dir_path) 92 | 93 | # output_path= output_path.decode("utf8") 94 | cv2.imwrite(output_path, im) -------------------------------------------------------------------------------- /src/web/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aaronshan/12306-captcha/af14073d15f8beb3f8c9fe30802ee52875b78bec/src/web/utils/__init__.py -------------------------------------------------------------------------------- /src/words/model/words_deploy.prototxt: -------------------------------------------------------------------------------- 1 | input: "data" 2 | input_shape { 3 | dim: 1 4 | dim: 3 5 | dim: 24 6 | dim: 60 7 | } 8 | layer { 9 | name: "conv1_1" 10 | type: "Convolution" 11 | bottom: "data" 12 | top: "conv1_1" 13 | convolution_param { 14 | num_output: 64 15 | pad: 1 16 | kernel_size: 3 17 | weight_filler { 18 | type: "xavier" 19 | } 20 | bias_filler { 21 | type: "constant" 22 | value: 0.0 23 | } 24 | } 25 | } 26 | layer { 27 | name: "relu1_1" 28 | type: "ReLU" 29 | bottom: "conv1_1" 30 | top: "conv1_1" 31 | } 32 | layer { 33 | name: "conv1_2" 34 | type: "Convolution" 35 | bottom: "conv1_1" 36 | top: "conv1_2" 37 | convolution_param { 38 | num_output: 64 39 | pad: 1 40 | kernel_size: 3 41 | weight_filler { 42 | type: "xavier" 43 | } 44 | bias_filler { 45 | type: "constant" 46 | value: 0.0 47 | } 48 | } 49 | } 50 | layer { 51 | name: "relu1_2" 52 | type: "ReLU" 53 | bottom: "conv1_2" 54 | top: "conv1_2" 55 | } 56 | layer { 57 | name: "pool1" 58 | type: "Pooling" 59 | bottom: "conv1_2" 60 | top: "pool1" 61 | pooling_param { 62 | pool: MAX 63 | kernel_size: 2 64 | stride: 2 65 | } 66 | } 67 | layer { 68 | name: "conv2_1" 69 | type: "Convolution" 70 | bottom: "pool1" 71 | top: "conv2_1" 72 | convolution_param { 73 | num_output: 128 74 | pad: 1 75 | kernel_size: 3 76 | weight_filler { 77 | type: "xavier" 78 | } 79 | bias_filler { 80 | type: "constant" 81 | value: 0.0 82 | } 83 | } 84 | } 85 | layer { 86 | name: "relu2_1" 87 | type: "ReLU" 88 | bottom: "conv2_1" 89 | top: "conv2_1" 90 | } 91 | layer { 92 | name: "conv2_2" 93 | type: "Convolution" 94 | bottom: "conv2_1" 95 | top: "conv2_2" 96 | convolution_param { 97 | num_output: 128 98 | pad: 1 99 | kernel_size: 3 100 | weight_filler { 101 | type: "xavier" 102 | } 103 | bias_filler { 104 | type: "constant" 105 | value: 0.0 106 | } 107 | } 108 | } 109 | layer { 110 | name: "relu2_2" 111 | type: "ReLU" 112 | bottom: "conv2_2" 113 | top: "conv2_2" 114 | } 115 | layer { 116 | name: "pool2" 117 | type: "Pooling" 118 | bottom: "conv2_2" 119 | top: "pool2" 120 | pooling_param { 121 | pool: MAX 122 | kernel_size: 2 123 | stride: 2 124 | } 125 | } 126 | layer { 127 | name: "conv3_1" 128 | type: "Convolution" 129 | bottom: "pool2" 130 | top: "conv3_1" 131 | convolution_param { 132 | num_output: 256 133 | pad: 1 134 | kernel_size: 3 135 | weight_filler { 136 | type: "xavier" 137 | } 138 | bias_filler { 139 | type: "constant" 140 | value: 0.0 141 | } 142 | } 143 | } 144 | layer { 145 | name: "relu3_1" 146 | type: "ReLU" 147 | bottom: "conv3_1" 148 | top: "conv3_1" 149 | } 150 | layer { 151 | name: "conv3_2" 152 | type: "Convolution" 153 | bottom: "conv3_1" 154 | top: "conv3_2" 155 | convolution_param { 156 | num_output: 256 157 | pad: 1 158 | kernel_size: 3 159 | weight_filler { 160 | type: "xavier" 161 | } 162 | bias_filler { 163 | type: "constant" 164 | value: 0.0 165 | } 166 | } 167 | } 168 | layer { 169 | name: "relu3_2" 170 | type: "ReLU" 171 | bottom: "conv3_2" 172 | top: "conv3_2" 173 | } 174 | layer { 175 | name: "conv3_3" 176 | type: "Convolution" 177 | bottom: "conv3_2" 178 | top: "conv3_3" 179 | convolution_param { 180 | num_output: 256 181 | pad: 1 182 | kernel_size: 3 183 | weight_filler { 184 | type: "xavier" 185 | } 186 | bias_filler { 187 | type: "constant" 188 | value: 0.0 189 | } 190 | } 191 | } 192 | layer { 193 | name: "relu3_3" 194 | type: "ReLU" 195 | bottom: "conv3_3" 196 | top: "conv3_3" 197 | } 198 | layer { 199 | name: "pool3" 200 | type: "Pooling" 201 | bottom: "conv3_3" 202 | top: "pool3" 203 | pooling_param { 204 | pool: MAX 205 | kernel_size: 2 206 | stride: 2 207 | } 208 | } 209 | layer { 210 | name: "conv4_1" 211 | type: "Convolution" 212 | bottom: "pool3" 213 | top: "conv4_1" 214 | convolution_param { 215 | num_output: 512 216 | pad: 1 217 | kernel_size: 3 218 | weight_filler { 219 | type: "xavier" 220 | } 221 | bias_filler { 222 | type: "constant" 223 | value: 0.0 224 | } 225 | } 226 | } 227 | layer { 228 | name: "relu4_1" 229 | type: "ReLU" 230 | bottom: "conv4_1" 231 | top: "conv4_1" 232 | } 233 | layer { 234 | name: "conv4_2" 235 | type: "Convolution" 236 | bottom: "conv4_1" 237 | top: "conv4_2" 238 | convolution_param { 239 | num_output: 512 240 | pad: 1 241 | kernel_size: 3 242 | weight_filler { 243 | type: "xavier" 244 | } 245 | bias_filler { 246 | type: "constant" 247 | value: 0.0 248 | } 249 | } 250 | } 251 | layer { 252 | name: "relu4_2" 253 | type: "ReLU" 254 | bottom: "conv4_2" 255 | top: "conv4_2" 256 | } 257 | layer { 258 | name: "conv4_3" 259 | type: "Convolution" 260 | bottom: "conv4_2" 261 | top: "conv4_3" 262 | convolution_param { 263 | num_output: 512 264 | pad: 1 265 | kernel_size: 3 266 | weight_filler { 267 | type: "xavier" 268 | } 269 | bias_filler { 270 | type: "constant" 271 | value: 0.0 272 | } 273 | } 274 | } 275 | layer { 276 | name: "relu4_3" 277 | type: "ReLU" 278 | bottom: "conv4_3" 279 | top: "conv4_3" 280 | } 281 | layer { 282 | name: "pool4" 283 | type: "Pooling" 284 | bottom: "conv4_3" 285 | top: "pool4" 286 | pooling_param { 287 | pool: MAX 288 | kernel_size: 2 289 | stride: 2 290 | } 291 | } 292 | layer { 293 | name: "conv5_1" 294 | type: "Convolution" 295 | bottom: "pool4" 296 | top: "conv5_1" 297 | convolution_param { 298 | num_output: 512 299 | pad: 1 300 | kernel_size: 3 301 | weight_filler { 302 | type: "xavier" 303 | } 304 | bias_filler { 305 | type: "constant" 306 | value: 0.0 307 | } 308 | } 309 | } 310 | layer { 311 | name: "relu5_1" 312 | type: "ReLU" 313 | bottom: "conv5_1" 314 | top: "conv5_1" 315 | } 316 | layer { 317 | name: "conv5_2" 318 | type: "Convolution" 319 | bottom: "conv5_1" 320 | top: "conv5_2" 321 | convolution_param { 322 | num_output: 512 323 | pad: 1 324 | kernel_size: 3 325 | weight_filler { 326 | type: "xavier" 327 | } 328 | bias_filler { 329 | type: "constant" 330 | value: 0.0 331 | } 332 | } 333 | } 334 | layer { 335 | name: "relu5_2" 336 | type: "ReLU" 337 | bottom: "conv5_2" 338 | top: "conv5_2" 339 | } 340 | layer { 341 | name: "conv5_3" 342 | type: "Convolution" 343 | bottom: "conv5_2" 344 | top: "conv5_3" 345 | convolution_param { 346 | num_output: 512 347 | pad: 1 348 | kernel_size: 3 349 | weight_filler { 350 | type: "xavier" 351 | } 352 | bias_filler { 353 | type: "constant" 354 | value: 0.0 355 | } 356 | } 357 | } 358 | layer { 359 | name: "relu5_3" 360 | type: "ReLU" 361 | bottom: "conv5_3" 362 | top: "conv5_3" 363 | } 364 | layer { 365 | name: "pool5" 366 | type: "Pooling" 367 | bottom: "conv5_3" 368 | top: "pool5" 369 | pooling_param { 370 | pool: MAX 371 | kernel_size: 2 372 | stride: 2 373 | } 374 | } 375 | layer { 376 | name: "fc6" 377 | type: "InnerProduct" 378 | bottom: "pool5" 379 | top: "fc6" 380 | inner_product_param { 381 | num_output: 4096 382 | weight_filler { 383 | type: "xavier" 384 | } 385 | bias_filler { 386 | type: "constant" 387 | value: 0.1 388 | } 389 | } 390 | } 391 | layer { 392 | name: "relu6" 393 | type: "ReLU" 394 | bottom: "fc6" 395 | top: "fc6" 396 | } 397 | layer { 398 | name: "drop6" 399 | type: "Dropout" 400 | bottom: "fc6" 401 | top: "fc6" 402 | dropout_param { 403 | dropout_ratio: 0.5 404 | } 405 | } 406 | layer { 407 | name: "fc7" 408 | type: "InnerProduct" 409 | bottom: "fc6" 410 | top: "fc7" 411 | inner_product_param { 412 | num_output: 4096 413 | weight_filler { 414 | type: "xavier" 415 | } 416 | bias_filler { 417 | type: "constant" 418 | value: 0.1 419 | } 420 | } 421 | } 422 | layer { 423 | name: "relu7" 424 | type: "ReLU" 425 | bottom: "fc7" 426 | top: "fc7" 427 | } 428 | layer { 429 | name: "drop7" 430 | type: "Dropout" 431 | bottom: "fc7" 432 | top: "fc7" 433 | dropout_param { 434 | dropout_ratio: 0.5 435 | } 436 | } 437 | layer { 438 | name: "fc8" 439 | type: "InnerProduct" 440 | bottom: "fc7" 441 | top: "fc8" 442 | inner_product_param { 443 | num_output: 1000 444 | weight_filler { 445 | type: "xavier" 446 | } 447 | bias_filler { 448 | type: "constant" 449 | value: 0.1 450 | } 451 | } 452 | } 453 | layer { 454 | name: "softmax" 455 | type: "Softmax" 456 | bottom: "fc8" 457 | top: "softmax" 458 | } 459 | -------------------------------------------------------------------------------- /src/words/model/words_solver.prototxt: -------------------------------------------------------------------------------- 1 | net: "./words_train_val.prototxt" 2 | test_iter: 14 3 | test_interval: 1000 4 | base_lr: 0.01 5 | momentum: 0.9 6 | weight_decay: 0.004 7 | lr_policy: "step" 8 | gamma: 0.1 9 | stepsize: 10000 10 | display: 200 11 | max_iter: 160000 12 | snapshot: 40000 13 | snapshot_prefix: "../words_4_snapshot" 14 | solver_mode: GPU 15 | 16 | -------------------------------------------------------------------------------- /src/words/model/words_train_val.prototxt: -------------------------------------------------------------------------------- 1 | layer { 2 | name: "words-vgg-train-data" 3 | type: "Data" 4 | top: "data" 5 | top: "label" 6 | include { 7 | phase: TRAIN 8 | } 9 | transform_param { 10 | scale: 0.00390625 11 | } 12 | data_param { 13 | source: "../words_train_lmdb" 14 | batch_size: 100 15 | backend: LMDB 16 | } 17 | } 18 | layer { 19 | name: "words-val-data" 20 | type: "Data" 21 | top: "data" 22 | top: "label" 23 | include { 24 | phase: TEST 25 | } 26 | transform_param { 27 | scale: 0.00390625 28 | } 29 | data_param { 30 | source: "../words_test_lmdb" 31 | batch_size: 50 32 | backend: LMDB 33 | } 34 | } 35 | layer { 36 | name: "conv1_1" 37 | type: "Convolution" 38 | bottom: "data" 39 | top: "conv1_1" 40 | convolution_param { 41 | num_output: 64 42 | pad: 1 43 | kernel_size: 3 44 | weight_filler { 45 | type: "xavier" 46 | } 47 | bias_filler { 48 | type: "constant" 49 | value: 0.0 50 | } 51 | } 52 | } 53 | layer { 54 | name: "relu1_1" 55 | type: "ReLU" 56 | bottom: "conv1_1" 57 | top: "conv1_1" 58 | } 59 | layer { 60 | name: "conv1_2" 61 | type: "Convolution" 62 | bottom: "conv1_1" 63 | top: "conv1_2" 64 | convolution_param { 65 | num_output: 64 66 | pad: 1 67 | kernel_size: 3 68 | weight_filler { 69 | type: "xavier" 70 | } 71 | bias_filler { 72 | type: "constant" 73 | value: 0.0 74 | } 75 | } 76 | } 77 | layer { 78 | name: "relu1_2" 79 | type: "ReLU" 80 | bottom: "conv1_2" 81 | top: "conv1_2" 82 | } 83 | layer { 84 | name: "pool1" 85 | type: "Pooling" 86 | bottom: "conv1_2" 87 | top: "pool1" 88 | pooling_param { 89 | pool: MAX 90 | kernel_size: 2 91 | stride: 2 92 | } 93 | } 94 | layer { 95 | name: "conv2_1" 96 | type: "Convolution" 97 | bottom: "pool1" 98 | top: "conv2_1" 99 | convolution_param { 100 | num_output: 128 101 | pad: 1 102 | kernel_size: 3 103 | weight_filler { 104 | type: "xavier" 105 | } 106 | bias_filler { 107 | type: "constant" 108 | value: 0.0 109 | } 110 | } 111 | } 112 | layer { 113 | name: "relu2_1" 114 | type: "ReLU" 115 | bottom: "conv2_1" 116 | top: "conv2_1" 117 | } 118 | layer { 119 | name: "conv2_2" 120 | type: "Convolution" 121 | bottom: "conv2_1" 122 | top: "conv2_2" 123 | convolution_param { 124 | num_output: 128 125 | pad: 1 126 | kernel_size: 3 127 | weight_filler { 128 | type: "xavier" 129 | } 130 | bias_filler { 131 | type: "constant" 132 | value: 0.0 133 | } 134 | } 135 | } 136 | layer { 137 | name: "relu2_2" 138 | type: "ReLU" 139 | bottom: "conv2_2" 140 | top: "conv2_2" 141 | } 142 | layer { 143 | name: "pool2" 144 | type: "Pooling" 145 | bottom: "conv2_2" 146 | top: "pool2" 147 | pooling_param { 148 | pool: MAX 149 | kernel_size: 2 150 | stride: 2 151 | } 152 | } 153 | layer { 154 | name: "conv3_1" 155 | type: "Convolution" 156 | bottom: "pool2" 157 | top: "conv3_1" 158 | convolution_param { 159 | num_output: 256 160 | pad: 1 161 | kernel_size: 3 162 | weight_filler { 163 | type: "xavier" 164 | } 165 | bias_filler { 166 | type: "constant" 167 | value: 0.0 168 | } 169 | } 170 | } 171 | layer { 172 | name: "relu3_1" 173 | type: "ReLU" 174 | bottom: "conv3_1" 175 | top: "conv3_1" 176 | } 177 | layer { 178 | name: "conv3_2" 179 | type: "Convolution" 180 | bottom: "conv3_1" 181 | top: "conv3_2" 182 | convolution_param { 183 | num_output: 256 184 | pad: 1 185 | kernel_size: 3 186 | weight_filler { 187 | type: "xavier" 188 | } 189 | bias_filler { 190 | type: "constant" 191 | value: 0.0 192 | } 193 | } 194 | } 195 | layer { 196 | name: "relu3_2" 197 | type: "ReLU" 198 | bottom: "conv3_2" 199 | top: "conv3_2" 200 | } 201 | layer { 202 | name: "conv3_3" 203 | type: "Convolution" 204 | bottom: "conv3_2" 205 | top: "conv3_3" 206 | convolution_param { 207 | num_output: 256 208 | pad: 1 209 | kernel_size: 3 210 | weight_filler { 211 | type: "xavier" 212 | } 213 | bias_filler { 214 | type: "constant" 215 | value: 0.0 216 | } 217 | } 218 | } 219 | layer { 220 | name: "relu3_3" 221 | type: "ReLU" 222 | bottom: "conv3_3" 223 | top: "conv3_3" 224 | } 225 | layer { 226 | name: "pool3" 227 | type: "Pooling" 228 | bottom: "conv3_3" 229 | top: "pool3" 230 | pooling_param { 231 | pool: MAX 232 | kernel_size: 2 233 | stride: 2 234 | } 235 | } 236 | layer { 237 | name: "conv4_1" 238 | type: "Convolution" 239 | bottom: "pool3" 240 | top: "conv4_1" 241 | convolution_param { 242 | num_output: 512 243 | pad: 1 244 | kernel_size: 3 245 | weight_filler { 246 | type: "xavier" 247 | } 248 | bias_filler { 249 | type: "constant" 250 | value: 0.0 251 | } 252 | } 253 | } 254 | layer { 255 | name: "relu4_1" 256 | type: "ReLU" 257 | bottom: "conv4_1" 258 | top: "conv4_1" 259 | } 260 | layer { 261 | name: "conv4_2" 262 | type: "Convolution" 263 | bottom: "conv4_1" 264 | top: "conv4_2" 265 | convolution_param { 266 | num_output: 512 267 | pad: 1 268 | kernel_size: 3 269 | weight_filler { 270 | type: "xavier" 271 | } 272 | bias_filler { 273 | type: "constant" 274 | value: 0.0 275 | } 276 | } 277 | } 278 | layer { 279 | name: "relu4_2" 280 | type: "ReLU" 281 | bottom: "conv4_2" 282 | top: "conv4_2" 283 | } 284 | layer { 285 | name: "conv4_3" 286 | type: "Convolution" 287 | bottom: "conv4_2" 288 | top: "conv4_3" 289 | convolution_param { 290 | num_output: 512 291 | pad: 1 292 | kernel_size: 3 293 | weight_filler { 294 | type: "xavier" 295 | } 296 | bias_filler { 297 | type: "constant" 298 | value: 0.0 299 | } 300 | } 301 | } 302 | layer { 303 | name: "relu4_3" 304 | type: "ReLU" 305 | bottom: "conv4_3" 306 | top: "conv4_3" 307 | } 308 | layer { 309 | name: "pool4" 310 | type: "Pooling" 311 | bottom: "conv4_3" 312 | top: "pool4" 313 | pooling_param { 314 | pool: MAX 315 | kernel_size: 2 316 | stride: 2 317 | } 318 | } 319 | layer { 320 | name: "conv5_1" 321 | type: "Convolution" 322 | bottom: "pool4" 323 | top: "conv5_1" 324 | convolution_param { 325 | num_output: 512 326 | pad: 1 327 | kernel_size: 3 328 | weight_filler { 329 | type: "xavier" 330 | } 331 | bias_filler { 332 | type: "constant" 333 | value: 0.0 334 | } 335 | } 336 | } 337 | layer { 338 | name: "relu5_1" 339 | type: "ReLU" 340 | bottom: "conv5_1" 341 | top: "conv5_1" 342 | } 343 | layer { 344 | name: "conv5_2" 345 | type: "Convolution" 346 | bottom: "conv5_1" 347 | top: "conv5_2" 348 | convolution_param { 349 | num_output: 512 350 | pad: 1 351 | kernel_size: 3 352 | weight_filler { 353 | type: "xavier" 354 | } 355 | bias_filler { 356 | type: "constant" 357 | value: 0.0 358 | } 359 | } 360 | } 361 | layer { 362 | name: "relu5_2" 363 | type: "ReLU" 364 | bottom: "conv5_2" 365 | top: "conv5_2" 366 | } 367 | layer { 368 | name: "conv5_3" 369 | type: "Convolution" 370 | bottom: "conv5_2" 371 | top: "conv5_3" 372 | convolution_param { 373 | num_output: 512 374 | pad: 1 375 | kernel_size: 3 376 | weight_filler { 377 | type: "xavier" 378 | } 379 | bias_filler { 380 | type: "constant" 381 | value: 0.0 382 | } 383 | } 384 | } 385 | layer { 386 | name: "relu5_3" 387 | type: "ReLU" 388 | bottom: "conv5_3" 389 | top: "conv5_3" 390 | } 391 | layer { 392 | name: "pool5" 393 | type: "Pooling" 394 | bottom: "conv5_3" 395 | top: "pool5" 396 | pooling_param { 397 | pool: MAX 398 | kernel_size: 2 399 | stride: 2 400 | } 401 | } 402 | layer { 403 | name: "fc6" 404 | type: "InnerProduct" 405 | bottom: "pool5" 406 | top: "fc6" 407 | inner_product_param { 408 | num_output: 4096 409 | weight_filler { 410 | type: "xavier" 411 | } 412 | bias_filler { 413 | type: "constant" 414 | value: 0.1 415 | } 416 | } 417 | } 418 | layer { 419 | name: "relu6" 420 | type: "ReLU" 421 | bottom: "fc6" 422 | top: "fc6" 423 | } 424 | layer { 425 | name: "drop6" 426 | type: "Dropout" 427 | bottom: "fc6" 428 | top: "fc6" 429 | dropout_param { 430 | dropout_ratio: 0.5 431 | } 432 | } 433 | layer { 434 | name: "fc7" 435 | type: "InnerProduct" 436 | bottom: "fc6" 437 | top: "fc7" 438 | inner_product_param { 439 | num_output: 4096 440 | weight_filler { 441 | type: "xavier" 442 | } 443 | bias_filler { 444 | type: "constant" 445 | value: 0.1 446 | } 447 | } 448 | } 449 | layer { 450 | name: "relu7" 451 | type: "ReLU" 452 | bottom: "fc7" 453 | top: "fc7" 454 | } 455 | layer { 456 | name: "drop7" 457 | type: "Dropout" 458 | bottom: "fc7" 459 | top: "fc7" 460 | dropout_param { 461 | dropout_ratio: 0.5 462 | } 463 | } 464 | layer { 465 | name: "fc8" 466 | type: "InnerProduct" 467 | bottom: "fc7" 468 | top: "fc8" 469 | inner_product_param { 470 | num_output: 1000 471 | weight_filler { 472 | type: "xavier" 473 | } 474 | bias_filler { 475 | type: "constant" 476 | value: 0.1 477 | } 478 | } 479 | } 480 | layer { 481 | name: "accuracy" 482 | type: "Accuracy" 483 | bottom: "fc8" 484 | bottom: "label" 485 | top: "accuracy" 486 | include { 487 | phase: TEST 488 | } 489 | } 490 | layer { 491 | name: "loss" 492 | type: "SoftmaxWithLoss" 493 | bottom: "fc8" 494 | bottom: "label" 495 | top: "loss" 496 | } 497 | -------------------------------------------------------------------------------- /src/words/scripts/calc_mean.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | TOOLS_DIR=/home/ruifengshan/caffe/build/tools 4 | DATA=/home/ruifengshan/github/12306-captcha/data/words 5 | 6 | $TOOLS_DIR/compute_image_mean $DATA/words_train_lmdb $DATA/mean.binaryproto 7 | -------------------------------------------------------------------------------- /src/words/scripts/create_data.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | 3 | # -------------------------------------------------------- 4 | # 12306-captcha 5 | # Copyright (c) 2017 6 | # Licensed under The Apache License [see LICENSE for details] 7 | # Written by ruifengshan 8 | # -------------------------------------------------------- 9 | 10 | import os 11 | import scipy.misc 12 | from src.config import cfg 13 | 14 | 15 | synset_file = cfg.ROOT + 'label/synset' 16 | 17 | 18 | # 将synset转换为train.txt文件 19 | def load_file(path=synset_file): 20 | synset = [line.strip() for line in open(path).readlines()] 21 | print synset 22 | return synset 23 | 24 | 25 | def create_file(data_path, file_list, file_name): 26 | """ 27 | 创建训练文件train.txt/test.txt 28 | 29 | :param data_path: 图片文件的上级路径 30 | :param file_list: synset生成的list 31 | :param train_file_name: 标签表 32 | :return: 33 | """ 34 | 35 | try: 36 | cnt = 0 37 | if not os.path.isfile(file_name): 38 | os.mknod(file_name) 39 | file_txt = open(file_name, mode='wr') 40 | for file_name in file_list: 41 | print file_name 42 | file_path = os.path.join(data_path, file_name) 43 | print file_path 44 | if not os.path.isdir(file_path): 45 | print file_path, '目录不存在' 46 | continue 47 | for path in os.listdir(file_path): 48 | file_txt.write(os.path.join(file_name, path) + " " + str(cnt) + "\n") 49 | cnt += 1 50 | except Exception, e: 51 | print "执行失败", e 52 | 53 | 54 | def create_train_file(data_path, file_list, train_file_name='train.txt'): 55 | create_file(data_path, file_list, train_file_name) 56 | 57 | 58 | def create_test_file(data_path, file_list, test_file_name='test.txt'): 59 | create_file(data_path, file_list, test_file_name) 60 | 61 | 62 | if __name__ == '__main__': 63 | create_train_file(cfg.ROOT + "/data/words/train/", load_file()) 64 | create_test_file(cfg.ROOT + "/data/words/test/", load_file()) -------------------------------------------------------------------------------- /src/words/scripts/create_lmdb.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | 3 | TOOLS_DIR=/home/ruifengshan/caffe/build/tools 4 | 5 | dir=`dirname "$0"` 6 | root_path=`cd "$dir/../../..">/dev/null; pwd` 7 | echo "root_path:"$root_path 8 | 9 | DATA=$root_path/data/words 10 | 11 | rm -rf $DATA/words_train_lmdb 12 | $TOOLS_DIR/convert_imageset \ 13 | --shuffle \ 14 | --resize_height=24 \ 15 | --resize_width=60 \ 16 | $DATA/train $DATA/train.txt $DATA/words_train_lmdb 17 | 18 | 19 | rm -rf $DATA/words_test_lmdb 20 | $TOOLS_DIR/convert_imageset \ 21 | --shuffle \ 22 | --resize_height=24 \ 23 | --resize_width=60 \ 24 | $DATA/test $DATA/test.txt $DATA/words_test_lmdb 25 | -------------------------------------------------------------------------------- /src/words/scripts/words_finetune_train.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env sh 2 | #该指令是用来在已有的模型上，进行微调，需要注意的是模型的图片大小以及layer需要保持一致. 3 | # 如果要开启GPU模式，只需要在该指令的末尾加上 -gpu 0 即可. ps 默认情况下run as cpu-only 4 | 5 | dir=`dirname "$0"` 6 | root_path=`cd "$dir/../../..">/dev/null; pwd` 7 | echo "root_path:"$root_path 8 | 9 | TOOLS_DIR=/home/ruifengshan/caffe/build/tools 10 | 11 | MODEL_CONFIG_DIR=$root_path/src/image/model 12 | MODEL_DIR=$root_path/model/image 13 | 14 | $TOOLS_DIR/caffe train \ 15 | -solver $MODEL_CONFIG_DIR/words_solver.prototxt \ 16 | -gpu 0 \ 17 | -weights $MODEL_DIR/words_4_snapshot_iter_160000.caffemodel 1> $MODEL_DIR/words_train.log 2>&1 & 18 | -------------------------------------------------------------------------------- /src/words/scripts/words_train.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | dir=`dirname "$0"` 4 | root_path=`cd "$dir/../../..">/dev/null; pwd` 5 | echo "root_path:"$root_path 6 | 7 | TOOLS_DIR=/home/ruifengshan/caffe/build/tools 8 | 9 | MODEL_CONFIG_DIR=$root_path/src/image/model 10 | MODEL_DIR=$root_path/model/image 11 | 12 | $TOOLS_DIR/caffe train \ 13 | -solver $MODEL_CONFIG_DIR/words_solver.prototxt $@ 1>$MODEL_DIR/words_train.log 2>&1 & 14 | -------------------------------------------------------------------------------- /web-demo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aaronshan/12306-captcha/af14073d15f8beb3f8c9fe30802ee52875b78bec/web-demo.png 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