使用机器学习识别简单的验证码
识别验证码
在我们的生活中，验证码是经常使用到的一个东西，那么有没有一种程序可以自动识别验证码呢？
准备步骤：生成验证码
首先我们生成一部分的验证码用以识别。
import random
from PIL import Image, ImageDraw, ImageFont
def getRandomStr():
random_num = str(random.randint(0, 9))
return random_num
def getRandomColor():
R = random.randint(0, 255)
G = random.randint(0, 255)
B = random.randint(0, 255)
if R == 255 and G == 255 and B == 255:
R = G = B = 0
return (R, G, B)
def generate_captcha():
image = Image.new('RGB', (150, 50), (255, 255, 255))
draw = ImageDraw.Draw(image)
font = ImageFont.truetype('LiberationSans-Bold.ttf', size=32)
label = ''
for i in range(5):
random_char = getRandomStr()
label += random_char
draw.text((10 + i * 30, 0), random_char, getRandomColor(), font=font)
width = 150
height = 30
for i in range(3):
x1 = random.randint(0, width)
x2 = random.randint(0, width)
y1 = random.randint(0, height)
y2 = random.randint(0, height)
draw.line((x1, y1, x2, y2), fill=(0, 0, 0))
for i in range(5):
draw.point([random.randint(0, width), random.randint(0, height)], fill=getRandomColor())
x = random.randint(0, width)
y = random.randint(0, height)
draw.arc((x, y, x + 4, y + 4), 0, 90, fill=(0, 0, 0))
image.save(open(''.join(['captcha_predict/', 'unknown.png']), 'wb'), 'png')
if __name__ == '__main__':
generate_captcha()
进行识别：首先对验证码进行降噪
降噪：顾名思义，降低噪音，也就是将验证码中那些无用的东西去除掉，比如一些点、线之类的
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import os
def binarization(path):
img = Image.open(path)
img_gray = img.convert('L')
img_gray = np.array(img_gray)
w, h = img_gray.shape
for x in range(w):
for y in range(h):
gray = img_gray[x, y]
if gray <= 250:
img_gray[x, y] = 0
else:
img_gray[x, y] = 1
return img_gray
def noiseReduction(img_gray, label):
width, height = img_gray.shape
for x in range(width):
for y in range(height):
count = 0
if img_gray[x, y] == 1:
continue
try:
if img_gray[x - 1, y - 1] == 0:
count += 1
except:
pass
try:
if img_gray[x - 1, y] == 0:
count += 1
except:
pass
try:
if img_gray[x - 1, y + 1] == 0:
count += 1
except:
pass
try:
if img_gray[x, y + 1] == 0:
count += 1
except:
pass
try:
if img_gray[x + 1, y + 1] == 0:
count += 1
except:
pass
try:
if img_gray[x + 1, y] == 0:
count += 1
except:
pass
try:
if img_gray[x + 1, y - 1] == 0:
count += 1
except:
pass
try:
if img_gray[x, y - 1] == 0:
count += 1
except:
pass
if count < 4:
img_gray[x, y] = 1
plt.figure('')
plt.imshow(img_gray, cmap='gray')
plt.axis('off')
plt.savefig(''.join(['降噪image/', label, '.png']))
def cutImg(label):
labels = list(label)
img = Image.open(''.join(['降噪image/', label, '.png']))
for i in range(5):
pic = img.crop((100 * (1 + i), 170, 100 * (2 + i), 280))
plt.imshow(pic)
seq = get_save_seq(label[i])
pic.save(''.join(['cut_number/', str(label[i]), '/', str(seq), '.png']))
def get_save_seq(num):
numlist = os.listdir(''.join(['cut_number/', num, '/']))
if len(numlist) == 0 or numlist is None:
return 0
else:
max_file = 0
for file in numlist:
if int(file.split('.')[0]) > max_file:
max_file = int(file.split('.')[0])
return int(max_file + 1)
if __name__ == '__main__':
img_list = os.listdir('captcha_images/')
label_list = []
for img in img_list:
img = str(img).replace('.png', '')
label_list.append(img)
for label in label_list:
img = 'captcha_images/' + label + '.png'
img_gray = binarization(img)
noiseReduction(img_gray, label)
cutImg(label)
降噪后的效果为黑白灰度图片
进行识别：切片建模
from sklearn.externals import joblib
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from 机器学习.降噪 import *
def load_train_data():
X = []
Y = []
cut_list = os.listdir('cut_number')
for numC in cut_list:
num_list_dir = ''.join(['cut_number/', str(numC), '/'])
nums_dir = os.listdir((num_list_dir))
for num_file in nums_dir:
img = Image.open(''.join((['cut_number/', str(numC), '/', num_file])))
img_gray = img.convert('L')
img_array = np.array(img_gray)
w, h = img_array.shape
for x in range(w):
for y in range(h):
gray = img_array[x, y]
if gray <= 250:
img_array[x, y] = 0
else:
img_array[x, y] = 1
img_re = img_array.reshape(1, -1)
X.append(img_re[0])
Y.append(int(numC))
# print(np.array(X), np.array(Y))
return np.array(X), np.array(Y)
def generate_model(X, Y):
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3)
log_clf = LogisticRegression(multi_class='ovr', solver='sag', max_iter=10000)
log_clf.fit(X_train, Y_train)
joblib.dump(log_clf, 'captcha_model/captcha_model.model')
def get_model():
model = joblib.load('captcha_model/captcha_model.model')
return model
def captcha_predict():
path = 'captcha_predict/unknown.png'
pre_img_gray = binarization(path)
noiseReduction(pre_img_gray,'unknown')
labels = ['0','1','2','3','4']
img = Image.open(''.join(['降噪image/unknown.png']))
for i in range(5):
pic = img.crop((100 * (1 + i), 170, 100 * (2 + i), 280))
plt.imshow(pic)
pic.save(''.join(['captcha_predict/',labels[i],'.png']))
result = ''
model = get_model()
for i in range(5):
path = ''.join(['captcha_predict/',labels[i],'.png'])
img = Image.open(path)
img_gray = img.convert('L')
img_array = np.array(img_gray)
w, h = img_array.shape
for x in range(w):
for y in range(h):
gray = img_array[x, y]
if gray <= 250:
img_array[x, y] = 0
else:
img_array[x, y] = 1
img_re = img_array.reshape(1.-1)
X = img_re[0]
Y_pre = model.predict([X])
result = ''.join([result,str(Y_pre[0])])
return result
if __name__ == '__main__':
X,Y=load_train_data()
generate_model(X,Y)
# print(captcha_predict())
数据处理，建模及测试代码实现
# encoding=utf-8
"""
Date:2019-08-10 13:43
User:LiYu
Email:liyu_5498@163.com
"""
import os
from PIL import Image
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.externals import joblib
from 处理切分验证码图片 import *
def img2data(num):
X = []
Y = []
labels = getAllFileLabels(''.join(['cut_number/', str(num)]))
for label in labels:
img = Image.open(''.join(['cut_number/', str(num), '/', label, '.png']))
# convert("L")变为灰度图
img_gray = img.convert('L')
img_gray = np.array(img_gray)
w, h = img_gray.shape
for x in range(w):
for y in range(h):
gray = img_gray[x, y]
if gray <= 250:
img_gray[x, y] = 0
else:
img_gray[x, y] = 1
img_gray = np.reshape(img_gray, (1, -1))[0]
# print("img_gray: ", img_gray)
X.append(img_gray)
Y.append(num)
# print('X: ', X)
# print('Y: ', Y)
return X, Y
def getAllFileLabels(dir):
"""获取文件夹里的所有图片名，只保留数字部分"""
imgFileList = os.listdir(dir)
labels = []
for imgFile in imgFileList:
labels.append(imgFile.rstrip('.png'))
return labels
def loadData():
X = []
Y = []
for i in range(10):
Xi, Yi = img2data(i)
for x in Xi:
X.append(x)
for y in Yi:
Y.append(y)
X = np.array(X)
Y = np.array(Y)
print("X: ", X)
print("Y: ", Y)
return X, Y
def generatrModel(X, Y):
# X_train, X_test, Y_train, Y_test = train_test_split(X, Y)
logReg = LogisticRegression(multi_class='ovr', solver='sag')
logReg.fit(X, Y)
print(logReg.score(X, Y))
joblib.dump(logReg, 'captcha_model/captcha_model.model')
def getModel():
model = joblib.load('captcha_model/captcha_model.model')
return model
def captcha_predict(path):
img_gray = binarization('captcha_images_test/' + path)
noiseReduction(img_gray, path)
labels = ['0', '1', '2', '3', '4']
img = Image.open('captcha_images_test/' + path)
for i in range(5):
pic = img.crop((100 * (1 + i), 170, 100 * (1 + i) + 100, 280))
plt.imshow(pic)
pic.save(''.join(['captcha_images_test/', labels[i], '.png']))
result = ''
model = getModel()
for i in range(5):
path = ''.join(['captcha_images_test/', labels[i], '.png'])
img = Image.open(path)
img_gray = img.convert('L')
img_gray = np.array(img_gray)
w, h = img_gray.shape
for x in range(w):
for y in range(h):
gray = img_gray[x, y]
if gray <= 250:
img_gray[x, y] = 0
else:
img_gray[x, y] = 1
img_gray = np.reshape(img_gray, (1, -1))
X = img_gray[0]
Y_pre = model.predict([X])
result = ''.join([result, str(Y_pre[0])])
return result
if __name__ == '__main__':
# # 建模过程
# X, Y = loadData()
# generatrModel(X, Y)
# 测试
imgs = ['unknow2.png', 'unknow3.png', 'unknow4.png']
for img in imgs:
result = captcha_predict(img)
print(result)
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