Python代码库OpenCV之11提取碑文文字并分割
代码参考
-
https://www.cnblogs.com/phil-chow/p/5612270.html
我在作者基础做了简单修改，目前适用于python3
素材
20180302103750800.jpeg
目录结构
image.png
代码
import numpy as np
import cv2
from matplotlib import pyplot as plt
import os
project_path = os.path.dirname(os.path.abspath('__file__'))
class PrCalligraph(object):
filename = 0
dirname = ""
def cut_img(self, img, flag_pi):
row, col = img.shape
for i in range(row-1):
if img[i, int(col/2)] <= flag_pi:
new_up_row = i
break
for i in range(col-1):
if img[int(row/2), i] <= flag_pi:
new_left_col = i
break
for i in range(row-1, 0, -1):
if img[i, int(col/2)] <= flag_pi:
new_down_row = i
break
for i in range(col-1, 0, -1):
if img[int(row/2), i] <= flag_pi:
new_right_col = i
break
print (new_up_row, new_left_col, new_down_row, new_right_col)
return new_up_row, new_left_col, new_down_row, new_right_col
# deal the image with binaryzation
def thresh_binary(self, img):
blur = cv2.GaussianBlur(img, (9, 9), 0)
# OTSU's binaryzation
ret3, th3 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
kernel = np.ones((2, 2), np.uint8)
opening = cv2.morphologyEx(th3, cv2.MORPH_OPEN, kernel)
return opening
# sum the black pixel numbers in each cols
def hist_col(self, img):
list=[]
row, col = img.shape
for i in range(col):
list.append((img[:, i] < 200).sum())
return list
# find the each segmentatoin cols
def cut_col(self, img, hist_list):
minlist = []
images = []
row, col = img.shape
np_list = np.array(hist_list)
avg = col/8
i = 0
# print np_list
while i < col-1:
if i >= col-10:
if np_list[i] < 40 and np_list[i] <= np_list[i+1: col].min():
minlist.append(i)
break
if i == col-1:
minlist.append(i)
break
else:
i=int(i)
if np_list[i] < 40 and np_list[i] < np_list[i+1: i+10].min():
minlist.append(i)
i += avg
i += 1
print (minlist)
for j in range(len(minlist)-1):
print (j)
images.append(img[0:row, minlist[j]:minlist[j+1]])
return images
# sum the black pixel numbers in each rows
def hist_row(self, img):
list=[]
row, col = img.shape
for i in range(row):
list.append((img[i, :] < 200).sum())
return self.cut_row(img, list)
# find each segmentation rows
def cut_row(self, img, row_list):
minlist = []
single_images_with_rect = []
row, col = img.shape
np_list = np.array(row_list)
avg = row/16
i = 0
while i <= row-1:
i=int(i)
if i >= row-10 and np_list[i] == 0:
minlist.append(i)
break
elif np_list[i] == 0 and (np_list[i+1: i+10] < 200).sum() >= 5:
minlist.append(i)
i += avg
i += 1
print (minlist)
for j in range(len(minlist)-1):
single_img = img[minlist[j]:minlist[j+1], 0:col]
single_img_with_rect = self.single_cut(single_img)
if single_img_with_rect is not None:
single_images_with_rect.append(single_img_with_rect)
return single_images_with_rect
# find the single word's contours and take off the redundant margin
def single_cut(self, img):
blur = cv2.GaussianBlur(img, (9, 9), 0)
ret3, th3 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
contours, hierarchy = cv2.findContours(th3, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
up, left = img.shape
down, right = 0, 0
for i in range(len(contours)):
cnt = contours[i]
x, y, w, h = cv2.boundingRect(cnt)
if w < 6 and h < 6:
continue
if x < up:
up = x
if y < left:
left = y
if x+w > down:
down = x+w
if y+h > right:
right = y+h
if down-up >= 40 and right-left >= 40:
word = img[left:right, up:down]
cv2.imwrite(self.dirname+str(self.filename)+'.png', word)
cv2.rectangle(img,(up, left), (down, right), (0, 255, 0), 2)
self.filename += 1
return img
else:
return None
if __name__ == '__main__':
prcaligraphy = PrCalligraph()
# read sys origin dir
origin_images = os.listdir('./calligraphies/')
# handle each picture
for im in origin_images:
# use for new single word filename
prcaligraphy.filename = 0
# take out the original picture's name
outdir = os.path.splitext(im)[0]
# mkdir output dir name/path
#prcaligraphy.dirname = "./split/"+outdir+'/'
prcaligraphy.dirname =project_path+ "/split/"+outdir+'/'
# if exists path ,delete it
outpath_str=prcaligraphy.dirname
if os.path.exists(outpath_str):
os.remove(outpath_str )
os.mkdir(outpath_str)
#os.makedirs(outpath_str, False)
# use opencv read images
img = cv2.imread('./calligraphies/'+im, cv2.IMREAD_GRAYSCALE)
# preprocess original picture, cutout the redundant margin
row, col = img.shape
# row/2 col/2
middle_pi = img[int(row/2), int(col/2)]
if middle_pi > 220:
middle_pi = 220
else:
middle_pi += 10
new_up_row, new_left_col, new_down_row, new_right_col = prcaligraphy.cut_img(img, middle_pi)
cutedimg = img[new_up_row:new_down_row, new_left_col:new_right_col]
# deal the image with binaryzation
opening = prcaligraphy.thresh_binary(cutedimg)
# split the image into pieces with cols
hist_list = prcaligraphy.hist_col(opening)
images = prcaligraphy.cut_col(opening, hist_list)
# create two plt
fig, axes = plt.subplots(1, len(images), sharex=True, sharey=True)
fig2, axes2 = plt.subplots(len(images), 12, sharex=True, sharey=True)
# split the pieces into single words by rows
for i in range(len(images)):
axes[i].imshow(images[i], 'gray')
single_images_with_rect = prcaligraphy.hist_row(images[i])
for j in range(len(single_images_with_rect)):
axes2[i, j].imshow(single_images_with_rect[j], 'gray')
fig.savefig(prcaligraphy.dirname+'cut_col.png')
fig2.savefig(prcaligraphy.dirname+'single.png')
plt.clf()
# plt.show()
# cv2.imshow('image', imageee)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
