效果图如下：
对于任意图标都不需要自定义模板，直接程序生成，不过需要注意，图中的表格必须是水平的，无法适配倾斜的表格。
直接上代码：
import cv2
import numpy as np
import math
import xlwt
src='图片路径'
raw = cv2.imread(src, 1)
# 灰度图片
gray = cv2.cvtColor(raw, cv2.COLOR_BGR2GRAY)
binary = cv2.adaptiveThreshold(~gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 35, -5)
# 展示图片
rows, cols = binary.shape
scale2=15
scale = 20
# 自适应获取核值
# 识别横线:
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (cols // scale, 1))
kernel1 = cv2.getStructuringElement(cv2.MORPH_RECT, (cols // scale2, 1))
eroded = cv2.erode(binary, kernel, iterations=1)
dilated_col = cv2.dilate(eroded, kernel1, iterations=1)
# cv2.imwrite("横线图.jpg", dilated_col)
# 识别竖线：
# scale = 40#scale越大，越能检测出不存在的线
kernel2 = cv2.getStructuringElement(cv2.MORPH_RECT, (1, rows // scale2))
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, rows // scale))
eroded = cv2.erode(binary, kernel, iterations=1)
dilated_row = cv2.dilate(eroded, kernel2, iterations=1)
# cv2.imwrite("竖线图.jpg", dilated_row)
# cv2.imwrite("3.png", dilated_row)
# 将识别出来的横竖线合起来
bitwise_and = cv2.bitwise_and(dilated_col, dilated_row)#对二值图进行与操作
# cv2.imwrite("交点二值图.jpg", bitwise_and)
# 标识表格轮廓
merge = cv2.add(dilated_col, dilated_row)
ret,binary = cv2.threshold(merge, 127, 255, cv2.THRESH_BINARY)
_,contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
area=[]
for k in range(len(contours)):
area.append(cv2.contourArea(contours[k]))
max_idx = np.argmax(np.array(area))
m_d_r=[]
m_u_l=[]
max_p=0
min_p=1e6
for l1 in contours[max_idx]:
for l2 in l1:
if sum(l2)>max_p:
max_p=sum(l2)
d_r=l2
if sum(l2)<min_p:
min_p=sum(l2)
u_l=l2
m_d_r=d_r
m_u_l=u_l
padding=5
x0=max(m_u_l[0]-padding,0)
x1=min(m_d_r[0]+padding,raw.shape[1])
y0=max(m_u_l[1]-padding,0)
y1=min(m_d_r[1]+padding,raw.shape[0])
bitwise_and_crop=bitwise_and[y0:y1,x0:x1]
merge=merge[y0:y1,x0:x1]
raw=raw[y0:y1,x0:x1]
# # 两张图片进行减法运算，去掉表格框线
# merge2 = cv2.subtract(binary, merge)
# cv2.imwrite("去表格图.jpg", merge2)
# new_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
# erode_image = cv2.morphologyEx(merge2, cv2.MORPH_OPEN, new_kernel)#先腐蚀，再膨胀
# cv2.imwrite('腐蚀膨胀后的图.jpg', erode_image)
# merge3 = cv2.add(erode_image, bitwise_and)
# cv2.imwrite('角点带字.jpg', merge3)
# 将焦点标识取出来
ys, xs = np.where(bitwise_and_crop > 0)
# print(xs)
# # print('---------------------------------')
# print(ys)
# 横纵坐标数组
y_point_arr = []
x_point_arr = []
# 通过排序，排除掉相近的像素点，只取相近值的最后一点
# 这个10就是两个像素点的距离，不是固定的，根据不同的图片会有调整，基本上为单元格表格的高度（y坐标跳变）和长度（x坐标跳变）
i = 0
sort_x_point = np.sort(xs)
# print(sort_x_point)
for i in range(len(sort_x_point) - 1):
if sort_x_point[i + 1] - sort_x_point[i] > 3:
x_point_arr.append(sort_x_point[i])
i = i + 1
# 要将最后一个点加入
x_point_arr.append(sort_x_point[i])
i = 0
sort_y_point = np.sort(ys)
for i in range(len(sort_y_point) - 1):
if sort_y_point[i + 1] - sort_y_point[i] > 3:
y_point_arr.append(sort_y_point[i])
i = i + 1
y_point_arr.append(sort_y_point[i])
h_list=[y_point_arr[i+1]-y_point_arr[i] for i in range(len(y_point_arr)-1)]
w_list=[x_point_arr[i+1]-x_point_arr[i] for i in range(len(x_point_arr)-1)]
col_alpha=['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z']
print(h_list)
print(w_list)
import xlsxwriter
workbook = xlsxwriter.Workbook('chineseQA.xlsx') #创建工作簿
worksheet = workbook.add_worksheet()
for i in range(len(w_list)):
worksheet.set_column('{}:{}'.format(col_alpha[i],col_alpha[i]),w_list[i]/6)
for j in range(len(h_list)):
worksheet.set_row(j,h_list[j])
def islianjie(p1,p2,img):#p的格式是先y后x
if p1[0]==p2[0]:
for i in range(min(p1[1],p2[1]),max(p1[1],p2[1])+1):
if sum([img[j,i] for j in range(max(p1[0]-5,0),min(p1[0]+5,img.shape[0]))])==0:
return False
return True
elif p1[1]==p2[1]:
for i in range(min(p1[0],p2[0]),max(p1[0],p2[0])+1):
if sum([img[i,j] for j in range(max(p1[1]-5,0),min(p1[1]+5,img.shape[1]))])==0:
return False
return True
else:
return False
class cell:
def __init__(self,lt,rd,belong):
self.lt=lt
self.rd=rd
self.belong=belong
lt_list_x=x_point_arr[:-1]
lt_list_y=y_point_arr[:-1]
rd_list_x=x_point_arr[1:]
rd_list_y=y_point_arr[1:]
d={ }
for i in range(len(lt_list_x)):
for j in range(len(lt_list_y)):
d['cell_{}_{}'.format(i,j)]=cell([lt_list_x[i],lt_list_y[j]],[rd_list_x[i],rd_list_y[j]],[lt_list_x[i],lt_list_y[j]])
for i in range(len(lt_list_x)):
for j in range(len(lt_list_y)):
p1=[d['cell_{}_{}'.format(i,j)].rd[1],d['cell_{}_{}'.format(i,j)].lt[0]]#左下点
p2=[d['cell_{}_{}'.format(i,j)].rd[1],d['cell_{}_{}'.format(i,j)].rd[0]]#右下点
p3=[d['cell_{}_{}'.format(i,j)].lt[1],d['cell_{}_{}'.format(i,j)].rd[0]]#右上点
if not islianjie(p1,p2,merge):
d['cell_{}_{}'.format(i,j+1)].belong=d['cell_{}_{}'.format(i,j)].belong
if not islianjie(p2,p3,merge):
d['cell_{}_{}'.format(i+1,j)].belong=d['cell_{}_{}'.format(i,j)].belong
crop_list={ }
for i in range(len(lt_list_x)):
for j in range(len(lt_list_y)):
crop_list['{},{}'.format(d['cell_{}_{}'.format(i,j)].belong[0],d['cell_{}_{}'.format(i,j)].belong[1])]=d['cell_{}_{}'.format(i,j)].rd
w_h_list=[]
zmax=0
zmin=1e6
zlt=[]
zrd=[]
for key in crop_list.keys():
lt=[int(i) for i in key.split(',')]
rd=crop_list[key]
# print(lt,rd)
if sum(rd)>zmax:
zrd=rd
zmax=sum(rd)
if sum(lt)<zmin:
zlt=lt
zmin=sum(lt)
cv2.imwrite('crop/{}.jpg'.format(key),raw[lt[1]:rd[1],lt[0]:rd[0]])
merge_format = workbook.add_format({
'bold': True,
'border': 6,
'align': 'center',#水平居中
'valign': 'vcenter',#垂直居中
'fg_color': '#D7E4BC',#颜色填充
})
for key in crop_list.keys():
lt=[int(i) for i in key.split(',')]
rd=crop_list[key]
lt_=[lt[0]-zlt[0],lt[1]-zlt[1]]
rd_=[rd[0]-zlt[0],rd[1]-zlt[1]]
print(lt_)
print(rd_)
for i in range(len(w_list)+1):
if lt_[0]==sum(w_list[:i]):
lt_col=chr(ord('A')+i)
if rd_[0]==sum(w_list[:i]):
rd_col=chr(ord('A')+i-1)
for i in range(len(h_list)+1):
if lt_[1]==sum(h_list[:i]):
lt_row=i+1
if rd_[1]==sum(h_list[:i]):
rd_row=i
if lt_col==rd_col and lt_row==rd_row:
worksheet.write('{}{}'.format(lt_col,lt_row),'',merge_format)
else:
worksheet.merge_range('{}{}:{}{}'.format(lt_col,lt_row,rd_col,rd_row),'',merge_format)
workbook.close()
本文地址：https://blog.csdn.net/sxl1399504891/article/details/110880866
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