自学教程：python简单实现图片文字分割

# 图片二值化def binarization(img,threshold):    #图片二值化操作    width,height=img.size    im_new = img.copy()    for i in range(width):        for j in range(height):            a = img.getpixel((i, j))            aa = 0.30 * a[0] + 0.59 * a[1] + 0.11 * a[2]            if (aa <= threshold):                im_new.putpixel((i, j), (0, 0, 0))            else:                im_new.putpixel((i, j), (255, 255, 255))    # im_new.show()  # 显示图像    return im_new

# 图片降噪处理def clear_noise(img):    # 图片降噪处理    x, y = img.width, img.height    for i in range(x-1):        for j in range(y-1):            if sum_9_region(img, i, j) < 600:                # 改变像素点颜色，白色                img.putpixel((i, j), (255,255,255))    # img = np.array(img)    #     # cv2.imwrite('handle_two.png', img)    #     # img = Image.open('handle_two.png')    img.show()    return img# 获取田字格内当前像素点的像素值def sum_9_region(img, x, y):    """    田字格    """    # 获取当前像素点的像素值    a1 = img.getpixel((x - 1, y - 1))[0]    a2 = img.getpixel((x - 1, y))[0]    a3 = img.getpixel((x - 1, y+1 ))[0]    a4 = img.getpixel((x, y - 1))[0]    a5 = img.getpixel((x, y))[0]    a6 = img.getpixel((x, y+1 ))[0]    a7 = img.getpixel((x+1 , y - 1))[0]    a8 = img.getpixel((x+1 , y))[0]    a9 = img.getpixel((x+1 , y+1))[0]    width = img.width    height = img.height    if a5 == 255:  # 如果当前点为白色区域,则不统计邻域值        return 2550    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_1 = a5 + a6 + a8 + a9            return 4*255 - sum_1        elif x == width - 1:  # 右上顶点            sum_2 = a5 + a6 + a2 + a3            return 4*255 - sum_2        else:  # 最上非顶点,6邻域            sum_3 = a2 + a3+ a5 + a6 + a8 + a9            return 6*255 - sum_3    elif y == height - 1:  # 最下面一行        if x == 0:  # 左下顶点            # 中心点旁边3个点            sum_4 = a5 + a8 + a7 + a4            return 4*255 - sum_4        elif x == width - 1:  # 右下顶点            sum_5 = a5 + a4 + a2 + a1            return 4*255 - sum_5        else:  # 最下非顶点,6邻域            sum_6 = a5+ a2 + a8 + a4 +a1 + a7            return 6*255 - sum_6    else:  # y不在边界        if x == 0:  # 左边非顶点            sum_7 = a4 + a5 + a6 + a7 + a8 + a9            return 6*255 - sum_7        elif x == width - 1:  # 右边非顶点            sum_8 = a4 + a5 + a6 + a1 + a2 + a3            return 6*255 - sum_8        else:  # 具备9领域条件的            sum_9 = a1 + a2 + a3 + a4 + a5 + a6 + a7 + a8 + a9            return 9*255 - sum_9

# 传入二值化后的图片进行垂直投影def vertical(img):    """传入二值化后的图片进行垂直投影"""    pixdata = img.load()    w,h = img.size    ver_list = []    # 开始投影    for x in range(w):        black = 0        for y in range(h):            if pixdata[x,y][0] == 0:                black += 1        ver_list.append(black)    # 判断边界    l,r = 0,0    flag = False    t=0#判断分割数量    cuts = []    for i,count in enumerate(ver_list):        # 阈值这里为0        if flag is False and count > 0:            l = i            flag = True        if flag and count == 0:            r = i-1            flag = False            cuts.append((l,r))#记录边界点            t += 1    #print(t)    return cuts,t# 传入二值化后的图片进行水平投影def horizontal(img):    """传入二值化后的图片进行水平投影"""    pixdata = img.load()    w,h = img.size    ver_list = []    # 开始投影    for y in range(h):        black = 0        for x in range(w):            if pixdata[x,y][0] == 0:                black += 1        ver_list.append(black)    # 判断边界    l,r = 0,0    flag = False    # 分割区域数    t=0    cuts = []    for i,count in enumerate(ver_list):        # 阈值这里为0        if flag is False and count > 0:            l = i            flag = True        if flag and count == 0:            r = i-1            flag = False            cuts.append((l,r))            t += 1    return cuts,t

# 创建获得图片路径并处理图片函数def get_im_path():    OpenFile = tk.Tk()#创建新窗口    OpenFile.withdraw()    file_path = filedialog.askopenfilename()    im = Image.open(file_path)    # 阈值    th = getthreshold(im) - 16    print(th)    # 原图直接二值化    im_new1 = binarization(im, th)    im_new1.show()    # 直方图均衡化    im1 = his_bal(im)    im1.show()    im_new_np = np.array(his_bal(im))    th1 = getthreshold(im1) - 16    print(th1)    # 二值化    im_new = binarization(im1, th1)    # 降噪    im_new_cn = clear_noise(im_new)    height = im_new_cn.size[1]    print(height)    # 算出水平投影和垂直投影的数值    v, vt = vertical(im_new1)    h, ht = horizontal(im_new1)    # 算出分割区域    a = []    for i in range(vt):        a.append((v[i][0], 0, v[i][1], height))    print(a)    im_new.show()  # 直方图均衡化后再二值化    # 切割    for i, n in enumerate(a, 1):        temp = im_new_cn.crop(n)  # 调用crop函数进行切割        temp.show()        temp.save("c/%s.png" % i)

import numpy as npfrom PIL import Imageimport queueimport  matplotlib.pyplot as pltimport  tkinter as tkfrom tkinter import filedialog#导入文件对话框函数库window = tk.Tk()window.title('图片选择界面')window.geometry('400x100')var = tk.StringVar()# 创建获得图片路径并处理图片函数def get_im_path():    OpenFile = tk.Tk()#创建新窗口    OpenFile.withdraw()    file_path = filedialog.askopenfilename()    im = Image.open(file_path)    # 阈值    th = getthreshold(im) - 16    print(th)    # 原图直接二值化    im_new1 = binarization(im, th)    im_new1.show()    # 直方图均衡化    im1 = his_bal(im)    im1.show()    im_new_np = np.array(his_bal(im))    th1 = getthreshold(im1) - 16    print(th1)    # 二值化    im_new = binarization(im1, th1)    # 降噪    im_new_cn = clear_noise(im_new)    height = im_new_cn.size[1]    print(height)    # 算出水平投影和垂直投影的数值    v, vt = vertical(im_new1)    h, ht = horizontal(im_new1)    # 算出分割区域    a = []    for i in range(vt):        a.append((v[i][0], 0, v[i][1], height))    print(a)    im_new.show()  # 直方图均衡化后再二值化    # 切割    for i, n in enumerate(a, 1):        temp = im_new_cn.crop(n)  # 调用crop函数进行切割        temp.show()        temp.save("c/%s.png" % i)# 传入二值化后的图片进行垂直投影def vertical(img):    """传入二值化后的图片进行垂直投影"""    pixdata = img.load()    w,h = img.size    ver_list = []    # 开始投影    for x in range(w):        black = 0        for y in range(h):            if pixdata[x,y][0] == 0:                black += 1        ver_list.append(black)    # 判断边界    l,r = 0,0    flag = False    t=0#判断分割数量    cuts = []    for i,count in enumerate(ver_list):        # 阈值这里为0        if flag is False and count > 0:            l = i            flag = True        if flag and count == 0:            r = i-1            flag = False            cuts.append((l,r))#记录边界点            t += 1    #print(t)    return cuts,t# 传入二值化后的图片进行水平投影def horizontal(img):    """传入二值化后的图片进行水平投影"""    pixdata = img.load()    w,h = img.size    ver_list = []    # 开始投影    for y in range(h):        black = 0        for x in range(w):            if pixdata[x,y][0] == 0:                black += 1        ver_list.append(black)    # 判断边界    l,r = 0,0    flag = False    # 分割区域数    t=0    cuts = []    for i,count in enumerate(ver_list):        # 阈值这里为0        if flag is False and count > 0:            l = i            flag = True        if flag and count == 0:            r = i-1            flag = False            cuts.append((l,r))            t += 1    return cuts,t# 获得阈值算出平均像素def getthreshold(im):    #获得阈值 算出平均像素    wid, hei = im.size    hist = [0] * 256    th = 0    for i in range(wid):        for j in range(hei):            gray = int(0.3 * im.getpixel((i, j))[0] + 0.59 * im.getpixel((i, j))[1] + 0.11 * im.getpixel((i, j))[2])            th = gray + th            hist[gray] += 1    threshold = int(th/(wid*hei))    return threshold# 直方图均衡化 提高对比度def his_bal(im):    #直方图均衡化 提高对比度    # 统计灰度直方图    im_new = im.copy()    wid, hei = im.size    hist = [0] * 256    for i in range(wid):        for j in range(hei):            gray = int(0.3*im.getpixel((i,j))[0]+0.59*im.getpixel((i,j))[1]+0.11*im.getpixel((i,j))[2])            hist[gray] += 1    # 计算累积分布函数    cdf = [0] * 256    for i in range(256):        if i == 0:            cdf[i] = hist[i]        else:            cdf[i] = cdf[i - 1] + hist[i]    # 用累积分布函数计算输出灰度映射函数LUT    new_gray = [0] * 256    for i in range(256):        new_gray[i] = int(cdf[i] / (wid * hei) * 255 + 0.5)    # 遍历原图像，通过LUT逐点计算新图像对应的像素值    for i in range(wid):        for j in range(hei):            gray = int(0.3*im.getpixel((i,j))[0]+0.59*im.getpixel((i,j))[1]+0.11*im.getpixel((i,j))[2])            im_new.putpixel((i, j), new_gray[gray])    return im_new# 图片二值化def binarization(img,threshold):    #图片二值化操作    width,height=img.size    im_new = img.copy()    for i in range(width):        for j in range(height):            a = img.getpixel((i, j))            aa = 0.30 * a[0] + 0.59 * a[1] + 0.11 * a[2]            if (aa <= threshold):                im_new.putpixel((i, j), (0, 0, 0))            else:                im_new.putpixel((i, j), (255, 255, 255))    # im_new.show()  # 显示图像    return im_new# 图片降噪处理def clear_noise(img):    # 图片降噪处理    x, y = img.width, img.height    for i in range(x-1):        for j in range(y-1):            if sum_9_region(img, i, j) < 600:                # 改变像素点颜色，白色                img.putpixel((i, j), (255,255,255))    # img = np.array(img)    #     # cv2.imwrite('handle_two.png', img)    #     # img = Image.open('handle_two.png')    img.show()    return img# 获取田字格内当前像素点的像素值def sum_9_region(img, x, y):    """    田字格    """    # 获取当前像素点的像素值    a1 = img.getpixel((x - 1, y - 1))[0]    a2 = img.getpixel((x - 1, y))[0]    a3 = img.getpixel((x - 1, y+1 ))[0]    a4 = img.getpixel((x, y - 1))[0]    a5 = img.getpixel((x, y))[0]    a6 = img.getpixel((x, y+1 ))[0]    a7 = img.getpixel((x+1 , y - 1))[0]    a8 = img.getpixel((x+1 , y))[0]    a9 = img.getpixel((x+1 , y+1))[0]    width = img.width    height = img.height    if a5 == 255:  # 如果当前点为白色区域,则不统计邻域值        return 2550    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_1 = a5 + a6 + a8 + a9            return 4*255 - sum_1        elif x == width - 1:  # 右上顶点            sum_2 = a5 + a6 + a2 + a3            return 4*255 - sum_2        else:  # 最上非顶点,6邻域            sum_3 = a2 + a3+ a5 + a6 + a8 + a9            return 6*255 - sum_3    elif y == height - 1:  # 最下面一行        if x == 0:  # 左下顶点            # 中心点旁边3个点            sum_4 = a5 + a8 + a7 + a4            return 4*255 - sum_4        elif x == width - 1:  # 右下顶点            sum_5 = a5 + a4 + a2 + a1            return 4*255 - sum_5        else:  # 最下非顶点,6邻域            sum_6 = a5+ a2 + a8 + a4 +a1 + a7            return 6*255 - sum_6    else:  # y不在边界        if x == 0:  # 左边非顶点            sum_7 = a4 + a5 + a6 + a7 + a8 + a9            return 6*255 - sum_7        elif x == width - 1:  # 右边非顶点            sum_8 = a4 + a5 + a6 + a1 + a2 + a3            return 6*255 - sum_8        else:  # 具备9领域条件的            sum_9 = a1 + a2 + a3 + a4 + a5 + a6 + a7 + a8 + a9            return 9*255 - sum_9btn_Open = tk.Button(window,    text='打开图像',      # 显示在按钮上的文字    width=15, height=2,    command=get_im_path)     # 点击按钮式执行的命令btn_Open.pack()# 运行整体窗口window.mainloop()