验证码操作完整流程，可选其中一部分进行识别

from PIL import Image
from pytesseract import *
from fnmatch import fnmatch
from queue import Queue
import matplotlib.pyplot as plt
import cv2
import time
import os


def clear_border(img, img_name):
    ‘‘‘去除边框
    ‘‘‘

    filename = ‘./out_img/‘ + img_name.split(‘.‘)[0] + ‘-clearBorder.jpg‘
    h, w = img.shape[:2]
    for y in range(0, w):
        for x in range(0, h):
            # if y ==0 or y == w -1 or y == w - 2:
            if y < 4 or y > w - 4:
                img[x, y] = 255
            # if x == 0 or x == h - 1 or x == h - 2:
            if x < 4 or x > h - 4:
                img[x, y] = 255

    cv2.imwrite(filename, img)
    return img


def interference_line(img, img_name):
    ‘‘‘
    干扰线降噪
    ‘‘‘

    filename = ‘./out_img/‘ + img_name.split(‘.‘)[0] + ‘-interferenceline.jpg‘
    h, w = img.shape[:2]
    # ！！！opencv矩阵点是反的
    # img[1,2] 1:图片的高度，2：图片的宽度
    for y in range(1, w - 1):
        for x in range(1, h - 1):
            count = 0
            if img[x, y - 1] > 245:
                count = count + 1
            if img[x, y + 1] > 245:
                count = count + 1
            if img[x - 1, y] > 245:
                count = count + 1
            if img[x + 1, y] > 245:
                count = count + 1
            if count > 2:
                img[x, y] = 255
    cv2.imwrite(filename, img)
    return img


def interference_point(img, img_name, x=0, y=0):
    """点降噪
    9邻域框,以当前点为中心的田字框,黑点个数
    :param x:
    :param y:
    :return:
    """
    filename = ‘./out_img/‘ + img_name.split(‘.‘)[0] + ‘-interferencePoint.jpg‘
    # todo 判断图片的长宽度下限
    cur_pixel = img[x, y]  # 当前像素点的值
    height, width = img.shape[:2]

    for y in range(0, width - 1):
        for x in range(0, height - 1):
            if y == 0:  # 第一行
                if x == 0:  # 左上顶点,4邻域
                    # 中心点旁边3个点
                    sum = int(cur_pixel)                           + int(img[x, y + 1])                           + int(img[x + 1, y])                           + int(img[x + 1, y + 1])
                    if sum <= 2 * 245:
                        img[x, y] = 0
                elif x == height - 1:  # 右上顶点
                    sum = int(cur_pixel)                           + int(img[x, y + 1])                           + int(img[x - 1, y])                           + int(img[x - 1, y + 1])
                    if sum <= 2 * 245:
                        img[x, y] = 0
                else:  # 最上非顶点,6邻域
                    sum = int(img[x - 1, y])                           + int(img[x - 1, y + 1])                           + int(cur_pixel)                           + int(img[x, y + 1])                           + int(img[x + 1, y])                           + int(img[x + 1, y + 1])
                    if sum <= 3 * 245:
                        img[x, y] = 0
            elif y == width - 1:  # 最下面一行
                if x == 0:  # 左下顶点
                    # 中心点旁边3个点
                    sum = int(cur_pixel)                           + int(img[x + 1, y])                           + int(img[x + 1, y - 1])                           + int(img[x, y - 1])
                    if sum <= 2 * 245:
                        img[x, y] = 0
                elif x == height - 1:  # 右下顶点
                    sum = int(cur_pixel)                           + int(img[x, y - 1])                           + int(img[x - 1, y])                           + int(img[x - 1, y - 1])

                    if sum <= 2 * 245:
                        img[x, y] = 0
                else:  # 最下非顶点,6邻域
                    sum = int(cur_pixel)                           + int(img[x - 1, y])                           + int(img[x + 1, y])                           + int(img[x, y - 1])                           + int(img[x - 1, y - 1])                           + int(img[x + 1, y - 1])
                    if sum <= 3 * 245:
                        img[x, y] = 0
            else:  # y不在边界
                if x == 0:  # 左边非顶点
                    sum = int(img[x, y - 1])                           + int(cur_pixel)                           + int(img[x, y + 1])                           + int(img[x + 1, y - 1])                           + int(img[x + 1, y])                           + int(img[x + 1, y + 1])

                    if sum <= 3 * 245:
                        img[x, y] = 0
                elif x == height - 1:  # 右边非顶点
                    sum = int(img[x, y - 1])                           + int(cur_pixel)                           + int(img[x, y + 1])                           + int(img[x - 1, y - 1])                           + int(img[x - 1, y])                           + int(img[x - 1, y + 1])

                    if sum <= 3 * 245:
                        img[x, y] = 0
                else:  # 具备9领域条件的
                    sum = int(img[x - 1, y - 1])                           + int(img[x - 1, y])                           + int(img[x - 1, y + 1])                           + int(img[x, y - 1])                           + int(cur_pixel)                           + int(img[x, y + 1])                           + int(img[x + 1, y - 1])                           + int(img[x + 1, y])                           + int(img[x + 1, y + 1])
                    if sum <= 4 * 245:
                        img[x, y] = 0
    cv2.imwrite(filename, img)
    return img


def _get_dynamic_binary_image(filedir, img_name):
    ‘‘‘
    自适应阀值二值化
    ‘‘‘

    filename = ‘./out_img/‘ + img_name.split(‘.‘)[0] + ‘-binary.jpg‘
    img_name = filedir + ‘/‘ + img_name
    print(‘.....‘ + img_name)
    im = cv2.imread(img_name)
    im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)

    th1 = cv2.adaptiveThreshold(im, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 21, 1)
    cv2.imwrite(filename, th1)
    return th1


def _get_static_binary_image(img, threshold=140):
    ‘‘‘
    手动二值化
    ‘‘‘

    img = Image.open(img)
    img = img.convert(‘L‘)
    pixdata = img.load()
    w, h = img.size
    for y in range(h):
        for x in range(w):
            if pixdata[x, y] < threshold:
                pixdata[x, y] = 0
            else:
                pixdata[x, y] = 255

    return img


def cfs(im, x_fd, y_fd):
    ‘‘‘用队列和集合记录遍历过的像素坐标代替单纯递归以解决cfs访问过深问题
    ‘‘‘

    # print(‘**********‘)

    xaxis = []
    yaxis = []
    visited = set()
    q = Queue()
    q.put((x_fd, y_fd))
    visited.add((x_fd, y_fd))
    offsets = [(1, 0), (0, 1), (-1, 0), (0, -1)]  # 四邻域

    while not q.empty():
        x, y = q.get()

        for xoffset, yoffset in offsets:
            x_neighbor, y_neighbor = x + xoffset, y + yoffset

            if (x_neighbor, y_neighbor) in (visited):
                continue  # 已经访问过了

            visited.add((x_neighbor, y_neighbor))

            try:
                if im[x_neighbor, y_neighbor] == 0:
                    xaxis.append(x_neighbor)
                    yaxis.append(y_neighbor)
                    q.put((x_neighbor, y_neighbor))

            except IndexError:
                pass
    # print(xaxis)
    if (len(xaxis) == 0 | len(yaxis) == 0):
        xmax = x_fd + 1
        xmin = x_fd
        ymax = y_fd + 1
        ymin = y_fd

    else:
        xmax = max(xaxis)
        xmin = min(xaxis)
        ymax = max(yaxis)
        ymin = min(yaxis)
        # ymin,ymax=sort(yaxis)

    return ymax, ymin, xmax, xmin


def detectFgPix(im, xmax):
    ‘‘‘搜索区块起点
    ‘‘‘

    h, w = im.shape[:2]
    for y_fd in range(xmax + 1, w):
        for x_fd in range(h):
            if im[x_fd, y_fd] == 0:
                return x_fd, y_fd
def CFS(im):
    ‘‘‘切割字符位置
    ‘‘‘

    zoneL = []  # 各区块长度L列表
    zoneWB = []  # 各区块的X轴[起始，终点]列表
    zoneHB = []  # 各区块的Y轴[起始，终点]列表

    xmax = 0  # 上一区块结束黑点横坐标,这里是初始化
    for i in range(10):

        try:
            x_fd, y_fd = detectFgPix(im, xmax)
            # print(y_fd,x_fd)
            xmax, xmin, ymax, ymin = cfs(im, x_fd, y_fd)
            L = xmax - xmin
            H = ymax - ymin
            zoneL.append(L)
            zoneWB.append([xmin, xmax])
            zoneHB.append([ymin, ymax])

        except TypeError:
            return zoneL, zoneWB, zoneHB

    return zoneL, zoneWB, zoneHB
def cutting_img(im, im_position, img, xoffset=1, yoffset=1):
    filename = ‘./out_img/‘ + img.split(‘.‘)[0]
    # 识别出的字符个数
    im_number = len(im_position[1])
    # 切割字符
    for i in range(im_number):
        im_start_X = im_position[1][i][0] - xoffset
        im_end_X = im_position[1][i][1] + xoffset
        im_start_Y = im_position[2][i][0] - yoffset
        im_end_Y = im_position[2][i][1] + yoffset
        cropped = im[im_start_Y:im_end_Y, im_start_X:im_end_X]
        cv2.imwrite(filename + ‘-cutting-‘ + str(i) + ‘.jpg‘, cropped)
def main():
    filedir = ‘./img‘
    for file in os.listdir(filedir):
        if fnmatch(file, ‘*.jpg‘):
            img_name = file
            # 自适应阈值二值化
            im = _get_dynamic_binary_image(filedir, img_name)
            # 去除边框
            im = clear_border(im, img_name)
            对图片进行干扰线降噪
            im = interference_line(im, img_name)
            对图片进行点降噪
            im = interference_point(im, img_name)
            切割的位置
            im_position = CFS(im)
            maxL = max(im_position[0])
            minL = min(im_position[0])
            # 如果有粘连字符，如果一个字符的长度过长就认为是粘连字符，并从中间进行切割
            if (maxL > minL + minL * 0.7):
                maxL_index = im_position[0].index(maxL)
                minL_index = im_position[0].index(minL)
                # 设置字符的宽度
                im_position[0][maxL_index] = maxL // 2
                im_position[0].insert(maxL_index + 1, maxL // 2)
                # 设置字符X轴[起始，终点]位置
                im_position[1][maxL_index][1] = im_position[1][maxL_index][0] + maxL // 2
                im_position[1].insert(maxL_index + 1, [im_position[1][maxL_index][1] + 1,
                                                       im_position[1][maxL_index][1] + 1 + maxL // 2])
                # 设置字符的Y轴[起始，终点]位置
                im_position[2].insert(maxL_index + 1, im_position[2][maxL_index])
            # 切割字符，要想切得好就得配置参数，通常 1 or 2 就可以
            cutting_img(im, im_position, img_name, 1, 1)
            # 识别验证码
            cutting_img_num = 0
            for file in os.listdir(‘./out_img‘):
                str_img = ‘‘
                if fnmatch(file, ‘%s-cutting-*.jpg‘ % img_name.split(‘.‘)[0]):
                    cutting_img_num += 1
            for i in range(cutting_img_num):
                try:
                    file = ‘./out_img/%s-cutting-%s.jpg‘ % (img_name.split(‘.‘)[0], i)
                    # 识别验证码
                    str_img = str_img + image_to_string(Image.open(file), lang=‘eng‘,
                                                        config=‘-psm 10‘)  # 单个字符是10，一行文本是7
                except Exception as err:
                    pass
            print(‘识别为：%s‘ % str_img)


if __name__ == ‘__main__‘:
    main()