在全国企业信用信息系统中搜索信息时，可以看到以下验证码：
破解思路：
1.从div中或取乱序的图片及坐标，乱序图片如下图：
2.根据获取到的乱序图片及坐标将图片拼完整，如下图：
，
3.计算两张图片的像素差，并计算缺口位置，如下图：
4.根据缺口位置模拟人的行为拖动滑块
具体代码如下：
def get_merge_image(filename,location_list):
'''
根据位置对图片进行合并还原
:filename:图片
:location_list:图片位置
'''
pass
im = image.open(filename)
new_im = image.new('RGB', (260,116))
im_list_upper=[]
im_list_down=[]
for location in location_list:
if location['y']==-58:
pass
im_list_upper.append(im.crop((abs(location['x']),58,abs(location['x'])+10,166)))
if location['y']==0:
pass
im_list_down.append(im.crop((abs(location['x']),0,abs(location['x'])+10,58)))
new_im = image.new('RGB', (260,116))
x_offset = 0
for im in im_list_upper:
new_im.paste(im, (x_offset,0))
x_offset += im.size[0]
x_offset = 0
for im in im_list_down:
new_im.paste(im, (x_offset,58))
x_offset += im.size[0]
return new_im
def get_image(driver,div):
'''
下载并还原图片
:driver:webdriver
:div:图片的div
'''
pass
#找到图片所在的div
background_images=driver.find_elements_by_xpath(div)
location_list=[]
imageurl=''
for background_image in background_images:
location={}
#在html里面解析出小图片的url地址，还有长高的数值
location['x']=int(re.findall("background-image: url\((\S+)\); background-position: (.*)px (.*)px;",background_image.get_attribute('style'))[0][1])
location['y']=int(re.findall("background-image: url\((\S+)\); background-position: (.*)px (.*)px;",background_image.get_attribute('style'))[0][2])
imageurl=re.findall("background-image: url\((\S+)\); background-position: (.*)px (.*)px;",background_image.get_attribute('style'))[0][0]
location_list.append(location)
imageurl=imageurl.replace("webp","jpg")
image_bytes = urllib.request.urlopen(imageurl).read()
data_stream = BytesIO(image_bytes)
jpgfile = Image.open(data_stream)
jpgfile.save("%s.png" % 'rr')
#重新合并图片
image=get_merge_image("rr.png",location_list )
return image
# 计算缺口位置
def getlocation(image3):
for i in range(5,260):
for j in range(0,116):
pixel1 = image3.getpixel((i, j))
if(pixel1[0]>100 or pixel1[1]>100):
return i
# 获取图片并计算缺口位置
def getImgAndLoc(driver):
# 下载图片
image1 = get_image(driver, "//div[@class='gt_cut_bg gt_show']/div")
image2 = get_image(driver, "//div[@class='gt_cut_fullbg gt_show']/div")
image3 = ImageChops.difference(image1, image2)
image1.save("%s.png" % '3')
image2.save("%s.png" % '4')
image3.save("%s.png" % '5')
loc = getlocation(image3)
print("缺口位置：", loc)
return loc
# 移动滑块
def mourse(driver,element,loc):
action = ActionChains(driver)
action.move_to_element(to_element=element).perform()
action.click_and_hold(element).perform() # 鼠标左键按下不放
action.reset_actions()
id = random.randint(0, 2)
print('track==%d'%id)
#将图片划分成几个区域，对不同区域使用不同的行为数据，进一步提高通过率
if loc <= 75:
moveStep = gehostMouse.read_txt('./trackData/1_%d.txt' % id)
elif loc > 75 and loc <= 100:
moveStep = gehostMouse.read_txt('./trackData/2_%d.txt' % id)
elif loc > 100 and loc <= 150:
moveStep = gehostMouse.read_txt('./trackData/3_%d.txt' % id)
elif loc > 150 and loc <= 200:
moveStep = gehostMouse.read_txt('./trackData/4_%d.txt' % id)
else:
moveStep = gehostMouse.read_txt('./trackData/5_%d.txt' % id)
countStep = 0
idx = 0
while countStep <= loc-5 and idx < len(moveStep):
# print(moveStep[idx][0], moveStep[idx][1], moveStep[idx][2])
action.move_by_offset(moveStep[idx][0], moveStep[idx][1]).perform() # 移动一个位移
action.reset_actions()
time.sleep(moveStep[idx][2]) # 等待停顿时间
countStep += moveStep[idx][0]
idx += 1
# countStep-=moveStep[idx-1][0]
print(countStep)
subStep = loc-5-countStep
print("subStep=",subStep)
while subStep <0:
rd=random.randint(-2,-1)
action.move_by_offset(rd, 0).perform()
action.reset_actions()
subStep-=rd
time.sleep(random.randint(5, 10) / 100)
action.release().perform() # 鼠标左键松开
action.reset_actions()
说明：
1.滑块滑动距离=缺口位置-5 的原因是滑块的位置相对于缺口位置的计算距离来说，滑块初始位置离图片最左边大约距离5个像素距离。
2.验证码破解的难点是模拟人的行为数据，滑太快或太慢都不行，人的行为是一个慢-快-慢的过程，因此我通过获取自己滑动验证码的行为数据，生成了以下形式的几组数据：
第一列是x方向的偏移量，第二列是y方向的偏移量，第三列为耗时。可以使用GhostMouse获取鼠标行为数据。
GhostMouse获取鼠标偏移量数据代码如下：
import os
# xyd_typical = (1,0,0.04)
def read_rms(file_rms,file_txt):
with open(file_rms) as fp: # os.path.sep
LMouse_down = False
for line in fp:
if 'LMouse down' in line or (LMouse_down == True and 'Move' in line):
if 'LMouse down' in line:
LMouse_down = True
xyd_records = []
x_last, y_last = 0, 0 # 保证第一个偏移量为实际开始位置
xy_pos = line.split('(')[1].split(')')[0].split(',')
x_pos, y_pos = [int(i) for i in xy_pos]
continue
if LMouse_down == True and 'Delay' in line:
x_delta, y_delta = x_pos - x_last, y_pos - y_last
if x_delta == 0 and y_delta == 0 and len(xyd_records) != 0: # len 可能起点就是0，0
continue
else:
delay = float(line.split(' ')[1].split('}')[0])
xyd_records.append((x_delta, y_delta, delay))
x_last, y_last = x_pos, y_pos
continue
# {LMouse up (790,659)}
if LMouse_down == True and 'LMouse up' in line:
# x_init y_init x_change y_change
# x y d 每一次偏移量
# x y d
with open(file_txt, 'a') as fh_txt:
# x_change = sum(x for x,y,d in xyd_records)
x_init = xyd_records[0][0]
y_init = xyd_records[0][1]
x_change = sum(x for x, y, d in xyd_records[1:])
y_change = sum(y for x, y, d in xyd_records[1:])
fh_txt.write('{} {} {} {}\n'.format(x_init, y_init, x_change, y_change)) # 加os.linesep是'\r\n'
for x, y, d in xyd_records[1:]: # 第一个记录为起始位置，value记录之后的每一次偏移
fh_txt.write('{} {} {}\n'.format(x, y, d))
LMouse_down = False
xyd_records = []
def read_txt(file_txt):
with open(file_txt, 'r') as fp:
result = [] # (x_init, y_init, x_change, y_chang): [(x0,y0,d0), (x1,y1,d1)...]
# i=0;
for line in fp:
line = line.strip().split()
if len(line) == 3:
x, y, d = line
x, y, d = int(x), int(y), float(d)
tp=(x,y,d)
result.append(tp)
return result
def main():
# file_rms = os.path.join(os.path.abspath('.'), 'night.rms')
file_txt = os.path.join(os.path.abspath('.'), 'mouse.txt')
# read_rms(file_rms,file_txt)
result = read_txt(file_txt)
for k, v in result.items():
print(k,v)
if __name__ == '__main__':
pass
main()
当然也可以通过request方式模拟请求破解验证码，但需要的参数需要通过其js文件才能搞明白，现在的js文件都加密了，目前能力解决不了。（可参考：http://www.jianshu.com/p/3726581d218a）