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本文實(shí)例為大家分享了python簡單實(shí)現(xiàn)圖片文字分割的具體代碼，供大家參考，具體內(nèi)容如下

原圖：

圖片預(yù)處理：圖片二值化以及圖片降噪處理。

# 圖片二值化
def binarization(img,threshold):
 #圖片二值化操作
 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()  # 顯示圖像
 return im_new

# 圖片降噪處理
def clear_noise(img):
 # 圖片降噪處理
 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:
 # 改變像素點(diǎn)顏色，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
# 獲取田字格內(nèi)當(dāng)前像素點(diǎn)的像素值
def sum_9_region(img, x, y):
 """
 田字格
 """
 # 獲取當(dāng)前像素點(diǎn)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:  # 如果當(dāng)前點(diǎn)為白色區(qū)域,則不統(tǒng)計(jì)鄰域值
  return 2550
 if y == 0:  # 第一行
  if x == 0:  # 左上頂點(diǎn),4鄰域
# 中心點(diǎn)旁邊3個點(diǎn)
sum_1 = a5 + a6 + a8 + a9
return 4*255 - sum_1
  elif x == width - 1:  # 右上頂點(diǎn)
sum_2 = a5 + a6 + a2 + a3
return 4*255 - sum_2
  else:  # 最上非頂點(diǎn),6鄰域
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:  # 左下頂點(diǎn)
# 中心點(diǎn)旁邊3個點(diǎn)
sum_4 = a5 + a8 + a7 + a4
return 4*255 - sum_4
  elif x == width - 1:  # 右下頂點(diǎn)
sum_5 = a5 + a4 + a2 + a1
return 4*255 - sum_5
  else:  # 最下非頂點(diǎn),6鄰域
sum_6 = a5+ a2 + a8 + a4 +a1 + a7
return 6*255 - sum_6
 else:  # y不在邊界
  if x == 0:  # 左邊非頂點(diǎn)
sum_7 = a4 + a5 + a6 + a7 + a8 + a9
return 6*255 - sum_7
  elif x == width - 1:  # 右邊非頂點(diǎn)
sum_8 = a4 + a5 + a6 + a1 + a2 + a3
return 6*255 - sum_8
  else:  # 具備9領(lǐng)域條件的
sum_9 = a1 + a2 + a3 + a4 + a5 + a6 + a7 + a8 + a9
return 9*255 - sum_9

經(jīng)過二值化和降噪后得到的圖片

對圖片進(jìn)行水平投影與垂直投影：

# 傳入二值化后的圖片進(jìn)行垂直投影
def vertical(img):
 """傳入二值化后的圖片進(jìn)行垂直投影"""
 pixdata = img.load()
 w,h = img.size
 ver_list = []
 # 開始投影
 for x in range(w):
  black = 0
  for y in range(h):
if pixdata[x,y][0] == 0:
 black += 1
  ver_list.append(black)
 # 判斷邊界
 l,r = 0,0
 flag = False
 t=0#判斷分割數(shù)量
 cuts = []
 for i,count in enumerate(ver_list):
  # 閾值這里為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))#記錄邊界點(diǎn)
t += 1
 #print(t)
 return cuts,t
# 傳入二值化后的圖片進(jìn)行水平投影
def horizontal(img):
 """傳入二值化后的圖片進(jìn)行水平投影"""
 pixdata = img.load()
 w,h = img.size
 ver_list = []
 # 開始投影
 for y in range(h):
  black = 0
  for x in range(w):
if pixdata[x,y][0] == 0:
 black += 1
  ver_list.append(black)
 # 判斷邊界
 l,r = 0,0
 flag = False
 # 分割區(qū)域數(shù)
 t=0
 cuts = []
 for i,count in enumerate(ver_list):
  # 閾值這里為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

這兩段代碼目的主要是為了分割得到水平和垂直位置的每個字所占的大小，接下來就是對預(yù)處理好的圖片文字進(jìn)行分割。

# 創(chuàng)建獲得圖片路徑并處理圖片函數(shù)
def get_im_path():
 OpenFile = tk.Tk()#創(chuàng)建新窗口
 OpenFile.withdraw()
 file_path = filedialog.askopenfilename()
 im = Image.open(file_path)
 # 閾值
 th = getthreshold(im) - 16
 print(th)
 # 原圖直接二值化
 im_new1 = binarization(im, th)
 im_new1.show()
 # 直方圖均衡化
 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數(shù)值
 v, vt = vertical(im_new1)
 h, ht = horizontal(im_new1)
 # 算出分割區(qū)域
 a = []
 for i in range(vt):
  a.append((v[i][0], 0, v[i][1], height))
 print(a)
 im_new.show()  # 直方圖均衡化后再二值化
 # 切割
 for i, n in enumerate(a, 1):
  temp = im_new_cn.crop(n)  # 調(diào)用crop函數(shù)進(jìn)行切割
  temp.show()
  temp.save("c/%s.png" % i)

至此大概就完成了。

接下來是文件的全部代碼：

import numpy as np
from PIL import Image
import queue
import  matplotlib.pyplot as plt
import  tkinter as tk
from tkinter import filedialog#導(dǎo)入文件對話框函數(shù)庫
window = tk.Tk()
window.title('圖片選擇界面')
window.geometry('400x100')
var = tk.StringVar()

# 創(chuàng)建獲得圖片路徑并處理圖片函數(shù)
def get_im_path():
 OpenFile = tk.Tk()#創(chuàng)建新窗口
 OpenFile.withdraw()
 file_path = filedialog.askopenfilename()
 im = Image.open(file_path)
 # 閾值
 th = getthreshold(im) - 16
 print(th)
 # 原圖直接二值化
 im_new1 = binarization(im, th)
 im_new1.show()
 # 直方圖均衡化
 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數(shù)值
 v, vt = vertical(im_new1)
 h, ht = horizontal(im_new1)
 # 算出分割區(qū)域
 a = []
 for i in range(vt):
  a.append((v[i][0], 0, v[i][1], height))
 print(a)
 im_new.show()  # 直方圖均衡化后再二值化
 # 切割
 for i, n in enumerate(a, 1):
  temp = im_new_cn.crop(n)  # 調(diào)用crop函數(shù)進(jìn)行切割
  temp.show()
  temp.save("c/%s.png" % i)
# 傳入二值化后的圖片進(jìn)行垂直投影
def vertical(img):
 """傳入二值化后的圖片進(jìn)行垂直投影"""
 pixdata = img.load()
 w,h = img.size
 ver_list = []
 # 開始投影
 for x in range(w):
  black = 0
  for y in range(h):
if pixdata[x,y][0] == 0:
 black += 1
  ver_list.append(black)
 # 判斷邊界
 l,r = 0,0
 flag = False
 t=0#判斷分割數(shù)量
 cuts = []
 for i,count in enumerate(ver_list):
  # 閾值這里為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))#記錄邊界點(diǎn)
t += 1
 #print(t)
 return cuts,t
# 傳入二值化后的圖片進(jìn)行水平投影
def horizontal(img):
 """傳入二值化后的圖片進(jìn)行水平投影"""
 pixdata = img.load()
 w,h = img.size
 ver_list = []
 # 開始投影
 for y in range(h):
  black = 0
  for x in range(w):
if pixdata[x,y][0] == 0:
 black += 1
  ver_list.append(black)
 # 判斷邊界
 l,r = 0,0
 flag = False
 # 分割區(qū)域數(shù)
 t=0
 cuts = []
 for i,count in enumerate(ver_list):
  # 閾值這里為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
# 獲得閾值算出平均像素
def getthreshold(im):
 #獲得閾值 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
# 直方圖均衡化 提高對比度
def his_bal(im):
 #直方圖均衡化 提高對比度
 # 統(tǒng)計(jì)灰度直方圖
 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
 # 計(jì)算累積分布函數(shù)
 cdf = [0] * 256
 for i in range(256):
  if i == 0:
cdf[i] = hist[i]
  else:
cdf[i] = cdf[i - 1] + hist[i]
 # 用累積分布函數(shù)計(jì)算輸出灰度映射函數(shù)LUT
 new_gray = [0] * 256
 for i in range(256):
  new_gray[i] = int(cdf[i] / (wid * hei) * 255 + 0.5)
 # 遍歷原圖像，通過LUT逐點(diǎn)計(jì)算新圖像對應(yīng)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
# 圖片二值化
def binarization(img,threshold):
 #圖片二值化操作
 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()  # 顯示圖像
 return im_new
# 圖片降噪處理
def clear_noise(img):
 # 圖片降噪處理
 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:
 # 改變像素點(diǎn)顏色，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
# 獲取田字格內(nèi)當(dāng)前像素點(diǎn)的像素值
def sum_9_region(img, x, y):
 """
 田字格
 """
 # 獲取當(dāng)前像素點(diǎn)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:  # 如果當(dāng)前點(diǎn)為白色區(qū)域,則不統(tǒng)計(jì)鄰域值
  return 2550
 if y == 0:  # 第一行
  if x == 0:  # 左上頂點(diǎn),4鄰域
# 中心點(diǎn)旁邊3個點(diǎn)
sum_1 = a5 + a6 + a8 + a9
return 4*255 - sum_1
  elif x == width - 1:  # 右上頂點(diǎn)
sum_2 = a5 + a6 + a2 + a3
return 4*255 - sum_2
  else:  # 最上非頂點(diǎn),6鄰域
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:  # 左下頂點(diǎn)
# 中心點(diǎn)旁邊3個點(diǎn)
sum_4 = a5 + a8 + a7 + a4
return 4*255 - sum_4
  elif x == width - 1:  # 右下頂點(diǎn)
sum_5 = a5 + a4 + a2 + a1
return 4*255 - sum_5
  else:  # 最下非頂點(diǎn),6鄰域
sum_6 = a5+ a2 + a8 + a4 +a1 + a7
return 6*255 - sum_6
 else:  # y不在邊界
  if x == 0:  # 左邊非頂點(diǎn)
sum_7 = a4 + a5 + a6 + a7 + a8 + a9
return 6*255 - sum_7
  elif x == width - 1:  # 右邊非頂點(diǎn)
sum_8 = a4 + a5 + a6 + a1 + a2 + a3
return 6*255 - sum_8
  else:  # 具備9領(lǐng)域條件的
sum_9 = a1 + a2 + a3 + a4 + a5 + a6 + a7 + a8 + a9
return 9*255 - sum_9
btn_Open = tk.Button(window,
 text='打開圖像',# 顯示在按鈕上的文字
 width=15, height=2,
 command=get_im_path)  # 點(diǎn)擊按鈕式執(zhí)行的命令
btn_Open.pack()

# 運(yùn)行整體窗口
window.mainloop()

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