碑文书法汉字拆分

Posted 2020-07-28 Phil Chow

废话不多说，先上图：

 

此程序的主要目的，就是将碑文图片上的汉字截取出来，并且将文字周围多余边距去除，完成此后模式识别的先前准备工作。

用的是opencv的库，在处理噪音和二值化处理的时候方便一点。

其中涉及了一些在是使用opencv可能遇到的问题，比如矩形轮廓怎么画，用opencv提取出轮廓之后，怎么取舍这些轮廓……

利用如上图所示的方法，对每个字进行切分，即寻找每个谷点。

目录结构如下：

calligraphies里面放的是原始碑文图片，split放的是切分后得到的图片，子文件夹以每个原始图片名命名。

calligraphy_split.py为主程序。

代码如下，有点长，步骤写得应该还算清楚，英文注释：

  1 import numpy as np
  2 import cv2
  3 from matplotlib import pyplot as plt
  4 import os
  5 
  6 
  7 class PrCalligraph(object):
  8 
  9     filename = 0
 10     dirname = ""
 11 
 12     def cut_img(self, img, flag_pi):
 13         row, col = img.shape
 14         for i in range(row-1):
 15             if img[i, col/2] <= flag_pi:
 16                 new_up_row = i
 17                 break
 18         for i in range(col-1):
 19             if img[row/2, i] <= flag_pi:
 20                 new_left_col = i
 21                 break
 22         for i in range(row-1, 0, -1):
 23             if img[i, col/2] <= flag_pi:
 24                 new_down_row = i
 25                 break
 26         for i in range(col-1, 0, -1):
 27             if img[row/2, i] <= flag_pi:
 28                 new_right_col = i
 29                 break
 30         print new_up_row, new_left_col, new_down_row, new_right_col
 31         return new_up_row, new_left_col, new_down_row, new_right_col
 32 
 33     # deal the image with binaryzation
 34     def thresh_binary(self, img):
 35         blur = cv2.GaussianBlur(img, (9, 9), 0)
 36         # OTSU\'s binaryzation
 37         ret3, th3 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
 38         kernel = np.ones((2, 2), np.uint8)
 39         opening = cv2.morphologyEx(th3, cv2.MORPH_OPEN, kernel)
 40         return opening
 41 
 42     # sum the black pixel numbers in each cols
 43     def hist_col(self, img):
 44         list=[]
 45         row, col = img.shape
 46         for i in xrange(col):
 47             list.append((img[:, i] < 200).sum())
 48         return list
 49 
 50     # find the each segmentatoin cols
 51     def cut_col(self, img, hist_list):
 52         minlist = []
 53         images = []
 54         row, col = img.shape
 55         np_list = np.array(hist_list)
 56         avg = col/8
 57         i = 0
 58         # print np_list
 59         while i < col-1:
 60             if i >= col-10:
 61                 if np_list[i] < 40 and np_list[i] <= np_list[i+1: col].min():
 62                     minlist.append(i)
 63                     break
 64                 if i == col-1:
 65                     minlist.append(i)
 66                     break
 67             else:
 68                 if np_list[i] < 40 and np_list[i] < np_list[i+1: i+10].min():
 69                     minlist.append(i)
 70                     i += avg
 71             i += 1
 72         print minlist
 73         for j in xrange(len(minlist)-1):
 74             print j
 75             images.append(img[0:row, minlist[j]:minlist[j+1]])
 76         return images
 77 
 78     # sum the black pixel numbers in each rows
 79     def hist_row(self, img):
 80         list=[]
 81         row, col = img.shape
 82         for i in xrange(row):
 83             list.append((img[i, :] < 200).sum())
 84         return self.cut_row(img, list)
 85 
 86     # find each segmentation rows
 87     def cut_row(self, img, row_list):
 88         minlist = []
 89         single_images_with_rect = []
 90         row, col = img.shape
 91         np_list = np.array(row_list)
 92         avg = row/16
 93         i = 0
 94         while i <= row-1:
 95             if i >= row-10 and np_list[i] == 0:
 96                 minlist.append(i)
 97                 break
 98             elif np_list[i] == 0 and (np_list[i+1: i+10] < 200).sum() >= 5:
 99                 minlist.append(i)
100                 i += avg
101             i += 1
102         print minlist
103         for j in xrange(len(minlist)-1):
104             single_img = img[minlist[j]:minlist[j+1], 0:col]
105             single_img_with_rect = self.single_cut(single_img)
106             if single_img_with_rect is not None:
107                 single_images_with_rect.append(single_img_with_rect)
108         return single_images_with_rect
109 
110     # find the single word\'s contours and take off the redundant margin
111     def single_cut(self, img):
112         blur = cv2.GaussianBlur(img, (9, 9), 0)
113         ret3, th3 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
114         contours, hierarchy = cv2.findContours(th3, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
115         up, left = img.shape
116         down, right = 0, 0
117         for i in range(len(contours)):
118             cnt = contours[i]
119             x, y, w, h = cv2.boundingRect(cnt)
120             if w < 6 and h < 6:
121                 continue
122             if x < up:
123                 up = x
124             if y < left:
125                 left = y
126             if x+w > down:
127                 down = x+w
128             if y+h > right:
129                 right = y+h
130         if down-up >= 40 and right-left >= 40:
131             word = img[left:right, up:down]
132             cv2.imwrite(self.dirname+str(self.filename)+\'.png\', word)
133             cv2.rectangle(img,(up, left), (down, right), (0, 255, 0), 2)
134             self.filename += 1
135             return img
136         else:
137             return None
138 
139 if __name__ == \'__main__\':
140 
141     prcaligraphy = PrCalligraph()
142     # read sys origin dir
143     origin_images = os.listdir(\'./calligraphies/\')
144     # handle each picture
145     for im in origin_images:
146         # use for new single word filename
147         prcaligraphy.filename = 0
148         # take out the original picture\'s name
149         outdir = os.path.splitext(im)[0]
150         # mkdir  output dir name/path
151         prcaligraphy.dirname = "./split/"+outdir+\'/\'
152         os.makedirs(prcaligraphy.dirname, False)
153         # use opencv read images
154         img = cv2.imread(\'./calligraphies/\'+im, cv2.IMREAD_GRAYSCALE)
155         # preprocess original picture, cutout the redundant margin
156         row, col = img.shape
157         middle_pi = img[row/2, col/2]
158         if middle_pi > 220:
159             middle_pi = 220
160         else:
161             middle_pi += 10
162         new_up_row, new_left_col, new_down_row, new_right_col = prcaligraphy.cut_img(img, middle_pi)
163         cutedimg = img[new_up_row:new_down_row, new_left_col:new_right_col]
164         # deal the image with binaryzation
165         opening = prcaligraphy.thresh_binary(cutedimg)
166         # split the image into pieces with cols
167         hist_list = prcaligraphy.hist_col(opening)
168         images = prcaligraphy.cut_col(opening, hist_list)
169         # create two plt
170         fig, axes = plt.subplots(1, len(images), sharex=True, sharey=True)
171         fig2, axes2 = plt.subplots(len(images), 12, sharex=True, sharey=True)
172         # split the pieces into single words by rows
173         for i in range(len(images)):
174             axes[i].imshow(images[i], \'gray\')
175             single_images_with_rect = prcaligraphy.hist_row(images[i])
176             for j in range(len(single_images_with_rect)):
177                 axes2[i, j].imshow(single_images_with_rect[j], \'gray\')
178         fig.savefig(prcaligraphy.dirname+\'cut_col.png\')
179         fig2.savefig(prcaligraphy.dirname+\'single.png\')
180         plt.clf()
181     # plt.show()
182 
183 # cv2.imshow(\'image\', imageee)
184 # cv2.waitKey(0)
185 # cv2.destroyAllWindows()