原文
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):
'''去除邊框
'''
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):
'''
干擾線降噪
'''
filename = './out_img/' + img_name.split('.')[0] + '-interferenceline.jpg'
h, w = img.shape[:2]
# !!!opencv矩陣點是反的
# img[1,2] 1:圖片的高度,2:圖片的寬度
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):
"""點降噪
9鄰域框,以當前點為中心的田字框,黑點個數
:param x:
:param y:
:return:
"""
filename = './out_img/' + img_name.split('.')[0] + '-interferencePoint.jpg'
# todo 判斷圖片的長寬度下限
cur_pixel = img[x,y]# 當前圖元點的值
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: # 左上頂點,4鄰域
# 中心點旁邊3個點
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: # 右上頂點
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: # 最上非頂點,6鄰域
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: # 最下麵一行
if x == 0: # 左下頂點
# 中心點旁邊3個點
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: # 右下頂點
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: # 最下非頂點,6鄰域
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不在邊界
if x == 0: # 左邊非頂點
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: # 右邊非頂點
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: # 具備9領域條件的
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):
'''
自我調整閥值二值化
'''
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):
'''
手動二值化
'''
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):
'''用佇列和集合記錄遍歷過的圖元座標代替單純遞迴以解決cfs訪問過深問題
'''
# 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)]#四鄰域
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 # 已經訪問過了
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):
'''搜索區塊起點
'''
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):
'''切割字元位置
'''
zoneL=[]#各區塊長度L列表
zoneWB=[]#各區塊的X軸[起始,終點]列表
zoneHB=[]#各區塊的Y軸[起始,終點]列表
xmax=0#上一區塊結束黑點橫坐標,這裡是初始化
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]
# 識別出的字元個數
im_number = len(im_position[1])
# 切割字元
for i in range(im_number):
im_start_X = im_position[1][0] - xoffset
im_end_X = im_position[1][1] + xoffset
im_start_Y = im_position[2][0] - yoffset
im_end_Y = im_position[2][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 = './easy_img'
for file in os.listdir(filedir):
if fnmatch(file, '*.jpeg'):
img_name = file
# 自我調整閾值二值化
im = _get_dynamic_binary_image(filedir, img_name)
# 去除邊框
im = clear_border(im,img_name)
# 對圖片進行干擾線降噪
im = interference_line(im,img_name)
# 對圖片進行點降噪
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])
# 如果有粘連字號,如果一個字元的長度過長就認為是粘連字號,並從中間進行切割
if(maxL > minL + minL * 0.7):
maxL_index = im_position[0].index(maxL)
minL_index = im_position[0].index(minL)
# 設置字元的寬度
im_position[0][maxL_index] = maxL // 2
im_position[0].insert(maxL_index + 1, maxL // 2)
# 設置字元X軸[起始,終點]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])
# 設置字元的Y軸[起始,終點]位置
im_position[2].insert(maxL_index + 1, im_position[2][maxL_index])
# 切割字元,要想切得好就得配置參數,通常 1 or 2 就可以
cutting_img(im,im_position,img_name,1,1)
# 識別驗證碼
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)
# 識別驗證碼
str_img = str_img + image_to_string(Image.open(file),lang = 'eng', config='-psm 10') #單個字元是10,一行文本是7
except Exception as err:
pass
print('切圖:%s' % cutting_img_num)
print('識別為:%s' % str_img)
if __name__ == '__main__':
main() |