# Decaptcha
English version Readme is available [here](README-EN.md) .
通过简单的图像识别算法来完成验证码识别，打算把机器学习中的分类算法全部使用一遍。
## 使用方法
1. 爬取验证码
2. 对图像做处理并切分
3. 手工标注数据
4. 导入训练集
5. 使用测试集
## 前期准备
1. Image (图像处理库)
- [jpeg](http://www.ijg.org/files/)
- [zlib](http://www.zlib.net)
- [PIL](http://effbot.org/imagingbook/introduction.htm#using-the-image-class)
2. numpy (数学处理库)
3. ImageEnhance (图像处理库)
```Python
enhancer = ImageEnhance.Contrast(img) # 增加对比对
img = enhancer.enhance(2)
enhancer = ImageEnhance.Sharpness(img) # 锐化
img = enhancer.enhance(2)
enhancer = ImageEnhance.Brightness(img) # 增加亮度
img = enhancer.enhance(2)
```
## 图像处理
### 静态图片
1. 清除图片噪点
2. 清除图片干扰线
3. 切割图片
4. 信息输出
### 动态图片
1. 按帧转存 GIF
2. 读取每个 GIF 的 Duration 属性
3. 找到 Duration 最长的图片，后同静态图片处理
## 识别算法
### KNN
```Python
# kNN algorithm
def classify0(inX, dataSet, labels, k):
dataSetSize = dataSet.shape[0]
diffMat = tile(inX, (dataSetSize, 1)) - dataSet
sqDiffMat = diffMat ** 2
sqDistances = sqDiffMat.sum(axis=1)
distances = sqDistances ** 0.5
sortedDistIndicies = distances.argsort()
classCount = {}
for i in range(k):
voteIlabel = labels[sortedDistIndicies[i]] # changed
classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)
return sortedClassCount[0][0]
```
### SVM
根据算法的性质，可以问题设定成一个二分类问题：识别数字1和2（当然也可以是其他的任意两个数字)。
## 参考
- http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html?js=1#svm-toy-js
- http://www.pami.sjtu.edu.cn/people/gpliu/document/libsvm_src.pdf
- Machine Learning in Action
## License
MIT
机器学习识别图片验证码（专门应付本科毕业设计）.zip
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2023-08-03
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机器学习识别图片验证码（专门应付本科毕业设计）.zip （2000个子文件） 
README.md 2KB
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