Python 使用scikit图像标签修改图像，检测图像上的裂纹

这里是我的背景：我有一个混凝土墙的图像，我想在上面检测裂缝。基于这个想法（）和其他最先进的方法，我已经应用到我的灰度图像Otsu阈值和形态学操作（关闭），因此图像的像素从0（背景）到1

现在，我想做的是：我想从图像中删除垂直或水平连接的像素（例如墙上的管道）。 为此，我使用以下代码：

import cv2
import numpy as np
from skimage.measure import label, regionprops, regionprops_table
from skimage import filters
from math import *

img = cv2.imread('C:/Users/Caroline/Documents/myimage.tif', -1) 

label_img = label(img) #Label the different regions of the image
regions = regionprops_table(label_img, properties=('label', 'orientation')) #Compute the properties "orientation" of each regions

orientinter = list(regions.values())
orientradian = orientinter[1] #kind of a conversion to only get the orientation with an array and not a dictionary
orientationdeg = orientradian * 180/pi #conversion in degrees from radians

v1=np.where(abs(orientationdeg)<1) #getting the index of the objects that are horizontals
v2=np.where(abs(orientationdeg)>89) #getting the index of the objects that are verticals

#eventually merging v1 and v2 to have a triple of "every index of the regions I want to get rid of"

感谢您的帮助和评论

（该网站是新的，所以我不知道当你找到答案时，回答你自己的问题是否是公共程序）

这是我用来完成任务的代码！它可能需要更新以提高效率，以及预处理步骤。希望这对其他人有帮助

import cv2
import numpy as np
from numpy import array
from skimage.measure import label, regionprops, regionprops_table
from skimage import filters
from math import *

img = cv2.imread('C:/Users/Caroline/Documents/myimg.tif', -1) 

label_img = label(img) #Label the image
regions = regionprops_table(label_img, properties=('label', 'orientation')) #Compute properties 'label' et 'orientation' of each regions

orientinter = list(regions.values())
orientradian = orientinter[1] #conversion in array
orientationdeg = orientradian * 180/pi #conversion en degré

L = [(abs(orientationdeg) < 89) & (abs(orientationdeg) >1)] #return list with true ou false
#True beeing the region that are NOT horizontal/vertical
V = newList[0] #conversion of the list in array
r = V*1 #there will be zeros for the regions horizontal/vertical, and the orientation otherwise

Vprime= np.argwhere(r)+1 #get the non zeros index of. +1 because index starts at 0, and we want labels, they start at 1
#now Vprime is all the labels of non vertical/horizontal regions

a=np.in1d(label_img, Vprime)*1 #the ismember matlab fonction, for this application
img_final=np.reshape(a,label_img.shape)

#Export final image

导入cv2
将numpy作为np导入
从numpy导入数组
从skimage.measure导入标签、regionprops、regionprops\u表
从撇渣导入过滤器
从数学导入*
img=cv2.imread（'C:/Users/Caroline/Documents/myimg.tif'，-1）
label_img=label（img）#为图像添加标签
regions=regionprops_表（label_img，properties=（'label'，'orientation'））#计算每个区域的属性'label'和'orientation'
OrientText=列表（regions.values（））
orientradian=定向器[1]#数组中的转换
方向dG=方向半径*180/pi#换算
L=[（abs（orientationdeg）<89）和（abs（orientationdeg）>1）]#返回带有true和false的列表
#在非水平/垂直的区域中显示True Bee
V=newList[0]#数组中列表的转换
r=V*1#水平/垂直区域为零，否则方向为零
Vprime=np.argwhere（r）+1#获取的非零索引+1因为索引从0开始，我们需要标签，它们从1开始
#现在Vprime是所有非垂直/水平区域的标签
a=np.inad（label_img，Vprime）*1#此应用程序的ismember matlab函数
img\u final=np.重塑（a，标签\u img.形状）
#导出最终图像

如果你在得到答复之前就想清楚了自己的问题，那么就回答这些问题是非常受鼓励的。

import cv2
import numpy as np
from numpy import array
from skimage.measure import label, regionprops, regionprops_table
from skimage import filters
from math import *

img = cv2.imread('C:/Users/Caroline/Documents/myimg.tif', -1) 

label_img = label(img) #Label the image
regions = regionprops_table(label_img, properties=('label', 'orientation')) #Compute properties 'label' et 'orientation' of each regions

orientinter = list(regions.values())
orientradian = orientinter[1] #conversion in array
orientationdeg = orientradian * 180/pi #conversion en degré

L = [(abs(orientationdeg) < 89) & (abs(orientationdeg) >1)] #return list with true ou false
#True beeing the region that are NOT horizontal/vertical
V = newList[0] #conversion of the list in array
r = V*1 #there will be zeros for the regions horizontal/vertical, and the orientation otherwise

Vprime= np.argwhere(r)+1 #get the non zeros index of. +1 because index starts at 0, and we want labels, they start at 1
#now Vprime is all the labels of non vertical/horizontal regions

a=np.in1d(label_img, Vprime)*1 #the ismember matlab fonction, for this application
img_final=np.reshape(a,label_img.shape)

#Export final image