python 验证码识别示例（二）复杂验证码识别

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在这篇博文中手把手教你如何去分割验证，然后进行识别。

一：下载验证码

验证码分析，图片上有折线，验证码有数字，有英文字母大小写，分类的时候需要更多的样本，验证码的字母是彩色的，图片上有雪花等噪点，因此识别改验证码难度较大

二：二值化和降噪：

三：切割：

四：分类：

五：测试识别率

六：总结：

综合识别率在70%左右，对于这个识别率我觉得还是挺高的，因为这个验证码的识别难度还是很大

代码：

一. 下载图片：

#-*-coding:utf-8-*-
import requestsdef spider():
url = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
for i in range(1, 101):
print("正在下载的张数是：",i)
with open("./1__get_image/{}.png".format(i), "wb") as f:
f.write(requests.get(url).content)
spider()

二：验证码二值化和降噪：

#-*-coding:utf-8-*-
# coding:utf-8
import sys, os
from PIL import Image, ImageDraw# 二值数组
t2val = {}def twoValue(image, G):
for y in range(0, image.size[1]):
for x in range(0, image.size[0]):
g = image.getpixel((x, y))
if g > G:
t2val[(x, y)] = 1
else:
t2val[(x, y)] = 0# 根据一个点A的RGB值，与周围的8个点的RBG值比较，设定一个值N（0 <N <8），当A的RGB值与周围8个点的RGB相等数小于N时，此点为噪点
# G: Integer 图像二值化阀值
# N: Integer 降噪率 0 <N <8
# Z: Integer 降噪次数
# 输出
# 0：降噪成功
# 1：降噪失败
def clearNoise(image, N, Z):
for i in range(0, Z):
t2val[(0, 0)] = 1
t2val[(image.size[0] - 1, image.size[1] - 1)] = 1 for x in range(1, image.size[0] - 1):
for y in range(1, image.size[1] - 1):
nearDots = 0
L = t2val[(x, y)]
if L == t2val[(x - 1, y - 1)]:
nearDots += 1
if L == t2val[(x - 1, y)]:
nearDots += 1
if L == t2val[(x - 1, y + 1)]:
nearDots += 1
if L == t2val[(x, y - 1)]:
nearDots += 1
if L == t2val[(x, y + 1)]:
nearDots += 1
if L == t2val[(x + 1, y - 1)]:
nearDots += 1
if L == t2val[(x + 1, y)]:
nearDots += 1
if L == t2val[(x + 1, y + 1)]:
nearDots += 1 if nearDots < N:
t2val[(x, y)] = 1def saveImage(filename, size):
image = Image.new("", size)
draw = ImageDraw.Draw(image) for x in range(0, size[0]):
for y in range(0, size[1]):
draw.point((x, y), t2val[(x, y)]) image.save(filename)for i in range(1, 101): path = "1__get_image/" + str(i) + ".png"
image = Image.open(path) image = image.convert('L')
twoValue(image, 198)
clearNoise(image, 3, 1)
path1 = "2__erzhihua_jiangzao/" + str(i) + ".jpg"
saveImage(path1, image.size)

三：切割验证码：

#-*-coding:utf-8-*-from PIL import Imagedef smartSliceImg(img, outDir, ii,count=4, p_w=3):
'''
:param img:
:param outDir:
:param count: 图片中有多少个图片
:param p_w: 对切割地方多少像素内进行判断
:return:
'''
w, h = img.size
pixdata = img.load()
eachWidth = int(w / count)
beforeX = 0
for i in range(count): allBCount = []
nextXOri = (i + 1) * eachWidth for x in range(nextXOri - p_w, nextXOri + p_w):
if x >= w:
x = w - 1
if x < 0:
x = 0
b_count = 0
for y in range(h):
if pixdata[x, y] == 0:
b_count += 1
allBCount.append({'x_pos': x, 'count': b_count})
sort = sorted(allBCount, key=lambda e: e.get('count')) nextX = sort[0]['x_pos']
box = (beforeX, 0, nextX, h)
img.crop(box).save(outDir + str(ii) + "_" + str(i) + ".png")
beforeX = nextXfor ii in range(1, 101):
path = "2__erzhihua_jiangzao/" + str(ii) + ".jpg"
img = Image.open(path)
outDir = '3__qiege/'
smartSliceImg(img, outDir, ii,count=4, p_w=3)

四：训练：

#-*-coding:utf-8-*-import numpy as np
import os
import timefrom PIL import Image
from sklearn.externals import joblib
from sklearn.neighbors import KNeighborsClassifierdef load_dataset():
X = []
y = []
for i in "23456789ABVDEFGHKMNPRSTUVWXYZ":
target_path = "fenlei/" + i
print(target_path)
for title in os.listdir(target_path):
pix = np.asarray(Image.open(os.path.join(target_path, title)).convert('L'))
X.append(pix.reshape(25 * 30))
y.append(target_path.split('/')[-1]) X = np.asarray(X)
y = np.asarray(y)
return X, ydef check_everyone(model):
pre_list = []
y_list = []
for i in "23456789ABCDEFGHKMNPRSTUVWXYZ":
part_path = "part/" + i
for title in os.listdir(part_path):
pix = np.asarray(Image.open(os.path.join(part_path, title)).convert('L'))
pix = pix.reshape(25 * 30)
pre_list.append(pix)
y_list.append(part_path.split('/')[-1])
pre_list = np.asarray(pre_list)
y_list = np.asarray(y_list) result_list = model.predict(pre_list)
acc = 0
for i in result_list == y_list:
print(result_list,y_list,) if i == np.bool(True):
acc += 1
print(acc, acc / len(result_list))X, y = load_dataset()
knn = KNeighborsClassifier()
knn.fit(X, y)
joblib.dump(knn, 'yipai.model')
check_everyone(knn)

五：模型测试：

# -*- coding: utf-8 -*-import numpy as np
from PIL import Image
from sklearn.externals import joblib
import ostarget_path = "1__get_image/"
source_result = []
for title in os.listdir(target_path):
source_result.append(title.replace('.png',''))def predict(model):
predict_result = []
for q in range(1,101):
pre_list = []
y_list = []
for i in range(0,4):
part_path = "part1/" + str(q) + "_" + str(i) + ".png"
# print(part_path)
pix = np.asarray(Image.open(os.path.join(part_path)))
pix = pix.reshape(25 * 30)
pre_list.append(pix)
y_list.append(part_path.split('/')[-1])
pre_list = np.asarray(pre_list)
y_list = np.asarray(y_list) result_list = model.predict(pre_list)
print(result_list,q) predict_result.append(str(result_list[0] + result_list[1] + result_list[2] + result_list[3])) return predict_resultmodel = joblib.load('yipai.model')
predict_result = predict(model)
# print(source_result)
# print(predict_result)