附完整python源码）基于tensorflow、opencv的入门案例_发票识别一：关键区域定位
发票识别一：从一张发票照片精确定位出“发票号码”、“发票代码”的数字区域
注：该代码适用于 “国税通用机打发票”。尽量拍摄下正常摆放的完整发票
发票识别一：区域精确定位1 具体步骤1.1 读入发票1.2 获取矩形框：将发票可能的区域定位出来边缘检测-二值化-形态学-轮廓检测-获取多个矩形框hsv颜色空间 -分别提取“红、黑、蓝”的掩膜-形态学--轮廓检测-获取多个矩形框结果：得到上百个候选框1.3 矩形框进行初步筛选。根据矩形框的长高信息、位置信息进行筛选结果：剩下十多个矩形框1.4 矩形框融合若某个矩形框与其他矩形框重叠、交叉度极高在其他矩形框内部，将其删除若两个矩形框在同一水平线上，有一定的交叉，且框内都包含数字，将两个框融合结果：这一步通常还有3-6个矩形框1.5 定位剩下的矩形框从上到下排序。根据矩形框的相对位置、尺寸，定位出“发票号码”、“发票代码”的两个区域。1.6 找了几张发票，地位区域如下。下一步“字符分割”，见发票识别二。
2.源码如下
2.1 main.py
# encoding: utf-8
import cv2
import numpy as np
import roi_merge as roi_
import util_funs as util
from get_rects import *
def main(img):
region = get_rects(img)
roi_solve = roi_.Roi_solve(region)
roi_solve.rm_inside()
roi_solve.rm_overlop()
region = roi_solve.merge_roi()
region = util.sort_region(region)
region = util.get_targetRoi(region)
for i in range(2):
rect2 = region[i]
w1,w2 = rect2[0],rect2[0]+rect2[2]
h1,h2 = rect2[1],rect2[1]+rect2[3]
box = [[w1,h2],[w1,h1],[w2,h1],[w2,h2]]
cv2.drawContours(img, np.array([box]), 0, (0, 255, 0), 1)
if i == 0:
cv2.imwrite('代码'+str(k)+'.jpg', img[h1:h2,w1:w2])
else:
cv2.imwrite('号码'+str(k)+'.jpg', img[h1:h2,w1:w2])
cv2.imshow('img', img)
cv2.waitKey(0)
if __name__ == '__main__':
img = cv2.imread("img_path")
main(img)
2.3 roi_merge.py
#encoding: utf-8
import cv2
import numpy as np
class Roi_solve:
def __init__(self,rect):
self.rect = rect #所有矩形框
self.cursor = -1 #初始化游标位置
self.rect_num = len(rect) #记录rect的实时数量
def next(self):
#将游标的位置前移一步，并返回所在检索位的矩形框
self.cursor = self.cursor+1
return self.rect[self.cursor]
def hasNext(self):
#判断是否已经检查完了所有矩形框
return self.rect_num > self.cursor + 1
def remove(self,flag = -1):
#将非优解从数据集删除
if flag == -1:
del self.rect[self.cursor]
#删除当前游标位置，游标回退一步
self.cursor = self.cursor-1
else:
#删除后面位置的rect，游标不动
del self.rect[flag]
#rect数量，减1
self.rect_num = self.rect_num - 1
def add(self,add_rect):
self.rect.append(add_rect)
self.rect_num = self.rect_num + 1
def get_u_d_l_r(self,rect_):
#获取rect的上下左右边界值
upper_,down_ = rect_[1],rect_[1] + rect_[3]
left_,right_ = rect_[0],rect_[0] + rect_[2]
return upper_,down_,left_,right_
def is_intersect(self,y01, y02 , x01, x02, y11, y12 , x11, x12):
# 判断两个矩形是否相交
lx = abs((x01 + x02) / 2 - (x11 + x12) / 2)
ly = abs((y01 + y02) / 2 - (y11 + y12) / 2)
sax = abs(x01 - x02)
sbx = abs(x11 - x12)
say = abs(y01 - y02)
sby = abs(y11 - y12)
if lx <= (sax + sbx) / 2 and ly <= (say + sby) / 2:
return True
else:
return False
def intersect_area(self,y01, y02 , x01, x02, y11, y12 , x11, x12):
#返回两个rect的交叉面积
col=min(x02,x12)-max(x01,x11)
row=min(y02,y12)-max(y01,y11)
return col*row
def intersect_height(self,y01, y02, y11, y12):
#height轴方向交叉，返回height交叉段占比
row=float(min(y02,y12)-max(y01,y11))
return max(row/float(y02-y01),row/float(y12-y11))
#remove_inside：如果“本rect”被“其他rect”包围了，则删除
def remove_inside(self,rect_curr):
#获取当前rect的上下左右边界信息
u_curr,d_curr,l_curr,r_curr = self.get_u_d_l_r(rect_curr)
#判断当前rect是否在内部
for rect_ in self.rect:
u_,d_,l_,r_ = self.get_u_d_l_r(rect_)
if u_curr>u_ and d_curr self.remove() break #remove_overlop：如果“本rect”和“其他rect”相交区域达到了95%以上，则删除 def remove_overlop(self,rect_curr): #获取当前rect的上下左右边界信息 u_curr,d_curr,l_curr,r_curr = self.get_u_d_l_r(rect_curr) area_curr = rect_curr[2] * rect_curr[3] #判断当前rect是否在内部 for rect_ in self.rect: u_,d_,l_,r_ = self.get_u_d_l_r(rect_) if self.is_intersect(u_curr,d_curr,l_curr,r_curr,u_,d_,l_,r_): if rect_ == rect_curr: continue else : area_ = self.intersect_area(u_curr,d_curr,l_curr,r_curr,u_,d_,l_,r_) if float(area_)/float(area_curr) >0.95: self.remove() break #如果“两个rect”在同一水平面上，横向坐标 def merge(self,rect_curr): #获取当前rect的上下左右边界信息 u_curr,d_curr,l_curr,r_curr = self.get_u_d_l_r(rect_curr) #判断当前rect是否在内部 for i in range(self.cursor+1,len(self.rect)): print i,self.cursor+1,len(self.rect) rect_ = self.rect[i] u_,d_,l_,r_ = self.get_u_d_l_r(rect_) #判断是否相交 if self.is_intersect(u_curr,d_curr,l_curr,r_curr,u_,d_,l_,r_): if self.intersect_height(u_curr,d_curr,u_,d_) > 0.6: if rect_curr[2] > rect_[2]: new_rect = np.array(rect_curr) else: new_rect = np.array(rect_) new_l = min(l_curr,l_) new_r = max(r_curr,r_) new_rect[0] = new_l new_rect[2] = new_r-new_l self.remove(i) self.remove() self.add(new_rect) break def rm_inside(self): self.cursor = -1 while(self.hasNext()): rect_curr = self.next() self.remove_inside(rect_curr) return self.rect def rm_overlop(self): self.cursor = -1 while(self.hasNext()): rect_curr = self.next() self.remove_overlop(rect_curr) return self.rect def merge_roi(self): self.cursor = -1 while(self.hasNext()): rect_curr = self.next() self.merge(rect_curr) return self.rect import cv2 import numpy as np class Roi_solve: def __init__(self,rect): self.rect = rect #所有矩形框 self.cursor = -1 #初始化游标位置 self.rect_num = len(rect) #记录rect的实时数量 def next(self): #将游标的位置前移一步，并返回所在检索位的矩形框 self.cursor = self.cursor+1 return self.rect[self.cursor] def hasNext(self): #判断是否已经检查完了所有矩形框 return self.rect_num > self.cursor + 1 def remove(self,flag = -1): #将非优解从数据集删除 if flag == -1: del self.rect[self.cursor] #删除当前游标位置，游标回退一步 self.cursor = self.cursor-1 else: #删除后面位置的rect，游标不动 del self.rect[flag] #rect数量，减1 self.rect_num = self.rect_num - 1 def add(self,add_rect): self.rect.append(add_rect) self.rect_num = self.rect_num + 1 def get_u_d_l_r(self,rect_): #获取rect的上下左右边界值 upper_,down_ = rect_[1],rect_[1] + rect_[3] left_,right_ = rect_[0],rect_[0] + rect_[2] return upper_,down_,left_,right_ def is_intersect(self,y01, y02 , x01, x02, y11, y12 , x11, x12): # 判断两个矩形是否相交 lx = abs((x01 + x02) / 2 - (x11 + x12) / 2) ly = abs((y01 + y02) / 2 - (y11 + y12) / 2) sax = abs(x01 - x02) sbx = abs(x11 - x12) say = abs(y01 - y02) sby = abs(y11 - y12) if lx <= (sax + sbx) / 2 and ly <= (say + sby) / 2: return True else: return False def intersect_area(self,y01, y02 , x01, x02, y11, y12 , x11, x12): #返回两个rect的交叉面积 col=min(x02,x12)-max(x01,x11) row=min(y02,y12)-max(y01,y11) return col*row def intersect_height(self,y01, y02, y11, y12): #height轴方向交叉，返回height交叉段占比 row=float(min(y02,y12)-max(y01,y11)) return max(row/float(y02-y01),row/float(y12-y11)) #remove_inside：如果“本rect”被“其他rect”包围了，则删除 def remove_inside(self,rect_curr): #获取当前rect的上下左右边界信息 u_curr,d_curr,l_curr,r_curr = self.get_u_d_l_r(rect_curr) #判断当前rect是否在内部 for rect_ in self.rect: u_,d_,l_,r_ = self.get_u_d_l_r(rect_) if u_curr>u_ and d_curr self.remove() break #remove_overlop：如果“本rect”和“其他rect”相交区域达到了95%以上，则删除 def remove_overlop(self,rect_curr): #获取当前rect的上下左右边界信息 u_curr,d_curr,l_curr,r_curr = self.get_u_d_l_r(rect_curr) area_curr = rect_curr[2] * rect_curr[3] #判断当前rect是否在内部 for rect_ in self.rect: u_,d_,l_,r_ = self.get_u_d_l_r(rect_) if self.is_intersect(u_curr,d_curr,l_curr,r_curr,u_,d_,l_,r_): if rect_ == rect_curr: continue else : area_ = self.intersect_area(u_curr,d_curr,l_curr,r_curr,u_,d_,l_,r_) if float(area_)/float(area_curr) >0.95: self.remove() break #如果“两个rect”在同一水平面上，横向坐标 def merge(self,rect_curr): #获取当前rect的上下左右边界信息 u_curr,d_curr,l_curr,r_curr = self.get_u_d_l_r(rect_curr) #判断当前rect是否在内部 for i in range(self.cursor+1,len(self.rect)): print i,self.cursor+1,len(self.rect) rect_ = self.rect[i] u_,d_,l_,r_ = self.get_u_d_l_r(rect_) #判断是否相交 if self.is_intersect(u_curr,d_curr,l_curr,r_curr,u_,d_,l_,r_): if self.intersect_height(u_curr,d_curr,u_,d_) > 0.6: if rect_curr[2] > rect_[2]: new_rect = np.array(rect_curr) else: new_rect = np.array(rect_) new_l = min(l_curr,l_) new_r = max(r_curr,r_) new_rect[0] = new_l new_rect[2] = new_r-new_l self.remove(i) self.remove() self.add(new_rect) break def rm_inside(self): self.cursor = -1 while(self.hasNext()): rect_curr = self.next() self.remove_inside(rect_curr) return self.rect def rm_overlop(self): self.cursor = -1 while(self.hasNext()): rect_curr = self.next() self.remove_overlop(rect_curr) return self.rect def merge_roi(self): self.cursor = -1 while(self.hasNext()): rect_curr = self.next() self.merge(rect_curr) return self.rect import cv2 import numpy as np def get_u_d_l_r(rect_): #获取rect的上下左右边界值 upper_,down_ = rect_[1],rect_[1] + rect_[3] left_,right_ = rect_[0],rect_[0] + rect_[2] return upper_,down_,left_,right_ #region排序。flag=1时：从上到下；flag=0时：从左到右 def sort_region(region,flag = 1): temp = [] region_new = [] for rect in region: temp.append(rect[flag]) temp_sort = sorted(temp) for height_ in temp_sort: index_ = temp.index(height_) region_new.append(region[index_]) return region_new #判断上下两个相邻框框是否为发票代码、发票号码 def judge_(rect_0,rect_1): u_d_l_r_0 = get_u_d_l_r(rect_0) u_d_l_r_1 = get_u_d_l_r(rect_1) #两个rect上边界之间的距离不超过box_height的四倍 distance_ = rect_1[1] - rect_0[1] box_height = float(rect_0[3] + rect_1[3])/2 #上边框的右边界值更大 if (distance_ > 0 and distance_ < box_height*3 and rect_0[0]+rect_0[2] > rect_1[0]+rect_1[2]): return True return False #获取 “发票代码”、“发票号码”的区域 def get_targetRoi(region): if len(region) > 1: #按照从上到下排序 new_region = sort_region(region) for i in range(len(new_region)-1): if judge_(new_region[i],new_region[i+1]): return [new_region[i],new_region[i+1]]
2.3 roi_merge.py
#encoding: utf-8
2.4 util_funs.py
#encoding:utf-8
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作者：远上寒山
来源：CSDN
原文：https://blog.csdn.net/m0_38097087/article/details/80281835
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