python之基于libsvm识别数字验证码

时间：2023-12-28 20:03:14

1. 参考

字符型图片验证码识别完整过程及Python实现

2.图片预处理和手动分类

(1)分析图片

from PIL import Image

img = Image.open('nums/ttt.png')
gray = img.convert('L')

img.show()

windows图片查看器可以放大像素级别：从左到右，从上到下依次为原图，灰度图，阈值为100的二值图，分割图。

python之基于libsvm识别数字验证码

# 输出为(count,(R,G,B,A))   alpha透明度一般为255

In [366]: sorted(img.getcolors())

(22, (251, 0, 0, 255)),

(24, (251, 184, 245, 255)),

(41, (192, 192, 192, 255)),   #没有交叉的灰色干扰线

(102, (255, 0, 0, 255)),      #红色数字

(490, (245, 245, 245, 255))]  #背景色白色

# img.convert帮助显示 L = R * 299/1000 + G * 587/1000 + B * 114/1000

# 所以可以确定干扰线灰色RGB 192 192 192的灰度为192

In [367]: sorted(gray.getcolors())

(24, 210),

(41, 75),

(41, 192),

(102, 76),

(505, 245)]

# 按照灰度排序，基本确定阈值为100以下全黑

In [369]: sorted(gray.getcolors(),key=lambda x:x[1])

[(1, 70),

 (2, 73),

 (41, 75),

 (102, 76),

 (2, 82),

 (11, 83),

 (10, 88),

 (5, 98),

# getdata也可以查看数据

In [371]: list(img.getdata())

Out[371]:

[(245, 245, 245, 255),

(2)批量下载图片

# 批量下载100张验证码

# urllib.urlretrieve(url,'nums/ttt.png')  #也行，不支持https

# 如 urls='https://bytebucket.org/wswp/code/raw/9e6b82b47087c2ada0e9fdf4f5e037e151975f0f/chapter07/samples/sample1.png'

# http://blog.csdn.net/zyz511919766/article/details/25049365

# python内置的urllib模块不支持https协议的解决办法

# 编译安装python之前没有编译安装类似于openssl这样的SSL库,以至于python不支持SSL

# [Errno socket error] [SSL: UNKNOWN_PROTOCOL] unknown protocol (_ssl.c:590)

url='http://jbywcg.lnypcg.com.cn/CommonPage/Code.aspx?0.10330188674268' #后面添加任意数字即可

for i in range(100):

    with open('nums/%s.png'%i,'wb') as f:

        f.write(urllib2.urlopen(url+str(i)).read())

# 新建分类文件夹，0~9

for i in range(10):

    os.mkdir('nums/%s'%i)

(3)对100张验证码进行预处理，数字分割，然后手动分类并保存到相应文件夹

import time

for index in range(100):

    img = Image.open('nums/%s.png'%index)

    gray = img.convert('L')

    gray_array = np.array(gray)

    # 阈值100以下黑色标记为1，方便确定边缘

    bilevel_array = np.where(gray_array<100,1,0)  

    left_list = []
    # 从左到右按列求和

    vertical = bilevel_array.sum(0)

    # 验证码图片规律：左右留白，上下留白3和4，每个数字占据w8h13，总共4个数字

    # 从左到右按列扫描，2白1黑确定为数字左边缘

    for i,c in enumerate(vertical[:-2]):

        if vertical[i] == 0 and vertical[i+1] == 0 and vertical[i+2] != 0:

            left_list.append(i+2)

        if len(left_list) == 4:

            break

    # 分割为肉眼可分辨的图片

    bilevel = Image.fromarray(np.where(gray_array<100,0,255))

    children = [bilevel.crop((left,3,left+8,img.height-4)) for left in left_list]

    for child in children:

        child.show()

        result = raw_input(':')

        child.save('nums/%s/%s_%s.png'%(result,result,time.strftime('%H%M%S')))

    print index

(4)确认分类结果

# 分割图片尺寸太小w8h13，windows看图软件显示为小黑块，img.show()则正常

# 将所有分割图片按行排列合并一图
import os
# 确定新建图片最大宽度

count_max = max(len(os.listdir('nums/%s'%i)) for i in range(10))

img_merge = Image.new('',(8*count_max,13*10))

for h in range(10):

    for w,f in enumerate(os.listdir('nums/%s'%h)):

        img_merge.paste(Image.open('nums/%s/%s'%(h,f)),(w*8,h*13))

img_merge.show()

3.libsvm训练

# 1.官网页面搜索download下载压缩包 http://www.csie.ntu.edu.tw/~cjlin/libsvm/#download

# 2.将压缩包所有文件解压到 Lib\site-packages\libsvm

# 3.将 libsvm\windows 的 libsvm.dll 复制到 C:\WINDOWS\system32\ http://blog.csdn.net/yearningseeker/article/details/49018015

# 4.在 libsvm 根目录和 libsvm\python 子目录下中分别新建名为__init__.py的空文件即可 http://www.cnblogs.com/Finley/p/5329417.html
def get_feature(num, picpath):

    img = Image.open(picpath)

    # 纯黑白转为01二值

    img_array = np.array(img)/255

    # 先遍历w，再遍历h，8+13总共21维度

    # 这里以每一维上的0个数为特征值，img_array.shape[0]是总行数

    return (num, list(img_array.shape[0]-img_array.sum(0)) + list(img_array.shape[1]-img_array.sum(1)))

def write_features(feature_list, filepath='nums/result_temp.txt'):

    with open(filepath,'w') as fp:

        # LIBSVM 对样本文件的格式要求为：<label> <index1>:<value1> <index2>:<value2> ...

        # 1 1:1 2:2 3:2 4:3 5:4 6:13 7:2 8:2 9:1 10:2 11:2 12:2 13:1 14:2 15:2 16:1 17:8 18:1 19:1 20:1 21:5

        for num, data in feature_list:

            temp = ' '.join([str(num)]  +  [str(i)+':'+str(j) for (i,j) in zip(range(1,len(data)+1), data)])

            fp.write(temp+'\n')

# 批量获取0~9十个文件夹所有分割数字的特征值并写入features.txt

feature_list = []

for num in range(10):

    for filename in os.listdir('nums/%s'%num):

        feature_list.append(get_feature(num, 'nums/%s/%s'%(num,filename)))

write_features(feature_list, 'nums/features.txt')      

from libsvm.python.svmutil import *

from libsvm.python.svm import *

# 训练得到分类模型model文件

def train_svm_model():

    y, x = svm_read_problem('nums/features.txt')

    model = svm_train(y, x)

    svm_save_model('nums/model', model)

4.libsvm测试

重复之前批量下载和手动分类得到features_test.txt，测试正确率。

def svm_model_test(filepath='nums/features_test.txt'):

    yt, xt = svm_read_problem(filepath)

    model = svm_load_model('nums/model')

    p_label, p_acc, p_val = svm_predict(yt, xt, model)#p_label即为识别的结果

    return ''.join(str(int(p)) for p in p_label)

5.完整应用

#!/usr/bin/env python

#coding: UTF-8

import os, time

import urllib, urllib2

from PIL import Image

import numpy as np

from libsvm.python.svmutil import *

from libsvm.python.svm import *

def get_image(url=''):

    url='http://jbywcg.lnypcg.com.cn/CommonPage/Code.aspx?0.10330188674268'

    temp = time.strftime('%H%M%S')

    picpath = 'nums/temp/%s.png'%(temp)

    with open(picpath,'wb') as f:

        f.write(urllib2.urlopen(url+str(temp)).read())

        return picpath

def split_image(filepath):

    img = Image.open(filepath)

    # img.show()

    gray = img.convert('L')

    gray_array = np.array(gray)

    bilevel_array = np.where(gray_array<100,1,0)

    left_list = []

    vertical = bilevel_array.sum(0)

    for i,c in enumerate(vertical[:-2]):

        if vertical[i] == 0 and vertical[i+1] == 0 and vertical[i+2] != 0:

            left_list.append(i+2)

        if len(left_list) == 4:

            break

    bilevel = Image.fromarray(np.where(gray_array<100,0,255))

    children = [bilevel.crop((left,3,left+8,img.height-4)) for left in left_list]

    filepath_list = []

    for i,child in enumerate(children):

        filepath = 'nums/temp/%s_%s.png'%(time.strftime('%H%M%S'),i+1)

        filepath_list.append(filepath)

        child.save(filepath)

    return filepath_list    

def get_feature(num, picpath):

    img = Image.open(picpath)

    img_array = np.array(img)/255

    #先遍历w，再遍历h

    return (num, list(img_array.shape[0]-img_array.sum(0)) + list(img_array.shape[1]-img_array.sum(1)))

def write_features(feature_list, filepath='nums/features_test.txt'):

    with open(filepath,'w') as fp:

        for num, data in feature_list:

            temp = ' '.join([str(num)]  +  [str(i)+':'+str(j) for (i,j) in zip(range(1,len(data)+1), data)])

            fp.write(temp+'\n')

def svm_model_test(filepath='nums/features_test.txt'):

    yt, xt = svm_read_problem(filepath)

    model = svm_load_model('nums/model')

    p_label, p_acc, p_val = svm_predict(yt, xt, model)  #p_label即为识别的结果

    return ''.join(str(int(p)) for p in p_label)

def main():

    while True:

        picpath = get_image()

        splitpath_list = split_image(picpath)

        feature_list = []

        for splitpath in splitpath_list:

            feature_list.append(get_feature(1, splitpath))  #1为任意预设整数值

            os.remove(splitpath)

        write_features(feature_list)

        result = svm_model_test()

        print result

        (dirname, filename) = os.path.split(picpath)

        (shortname, extension) = os.path.splitext(picpath)

        try:

            os.rename(picpath, os.path.join(dirname,result+extension))

        except:

            os.rename(picpath, os.path.join(dirname,result+'_'+time.strftime('%H%M%S')+extension))

if __name__ == '__main__':

    main()

6.运行结果

python之基于libsvm识别数字验证码

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