python 破解验证码

#-*-coding:utf-8-*-
# coding:utf-8
import sys, os
from PIL.Image import *
from PIL.ImageDraw import *
import shutil
import numpy as np
import os
import time

from sklearn.externals import joblib
from sklearn.neighbors import KNeighborsClassifier

# 二值数组
t2val = {}
save_path="2.jpg"
#
# 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)] = 1
#
#
# def saveImage(filename, size):
#     image = Image.new("1", size)
#     draw = 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 = r"C:\Users\Administrator\Desktop\11.png"
#     image = Image.open(path)
#
#     image = image.convert('L')
#     twoValue(image, 198)
#     clearNoise(image, 3, 1)
#     path1 = save_path
#     saveImage(path1, image.size)
#
# #切割验证码
# shutil.rmtree('test1')
# os.makedir('test1')
# def 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 = nextX
#
# for ii in range(1, 101):
#     path = save_path
#     img = Image.open(path)
#     outDir = 'test1/'
#     smartSliceImg(img, outDir, ii,count=4, p_w=3)


# 训练

def 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, y

def 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)