基于OpenCV的混凝土裂纹检测

前言

这是我发的第一次博客，有什么建议大家可以给我留言，感激不尽!
接下来，我们进入正题。

一、使用函数库

numpy, opencv, heapq, skimage.morphology

二、使用步骤

1.初步预处理

初步预处理包括：灰度阈值处理，高斯滤波平滑处理，滤波除燥。代码如下：

import cv2
import numpy as npType = 0   # 阈值处理方式
Value = 0  # 使用的阈值
T = 0
V = 0
def onType(a):Type = cv2.getTrackbarPos(tType, windowName)Value = cv2.getTrackbarPos(tValue, windowName)ret, dst = cv2.threshold(o, Value, 255, Type)cv2.imshow(windowName, dst)global TT = Typedef onValue(a):Type = cv2.getTrackbarPos(tType, windowName)Value = cv2.getTrackbarPos(tValue, windowName)ret, dst = cv2.threshold(o, Value, 255, Type)cv2.imshow(windowName, dst)global VV = Valueo = cv2.imread(r'E:\database\CrackForest-dataset-master\image\001.jpg')
# 灰度转换
gray = cv2.cvtColor(o, cv2.COLOR_BGR2GRAY)
# cv2.imshow('gray', gray)
windowName = 'output'
cv2.namedWindow(windowName)
cv2.imshow(windowName, gray)
# 构建滚动条
tType = 'Type'
tValue = 'tValue'
cv2.createTrackbar(tType, windowName, 0, 4, onType)
cv2.createTrackbar(tValue, windowName, 0, 255, onValue)
while 1:k = 0xFF & cv2.waitKey(1)if k == 27:breakelif k == ord('s'):realType = TrealValue = Vretval, img = cv2.threshold(gray, realValue, 255, realType)cv2.imshow('output.jpg', img)cv2.imwrite('output.jpg', img)output = cv2.imread('E:/bishe/daima/output.jpg')print(output.shape)# 高斯滤波平滑处理gauss = cv2.GaussianBlur(output, (3, 3), 0, 0)# cv2.imshow('gauss',gauss)# 滤波除噪k = np.ones((5, 5), np.uint8)_open = cv2.morphologyEx(gauss, cv2.MORPH_OPEN, k)_close = cv2.morphologyEx(_open, cv2.MORPH_CLOSE, k)cv2.imshow('_close', _close)cv2.imwrite('output2.jpg', _close)
cv2.destroyAllWindows()

直接运行代码后，会出现一个窗口。拖动Type，Value滚动条，将图片进行阈值处理，其中Type是阈值处理方式，Value是阈值，如下图
原始图像：在这里插入图片描述处理后（Type=1：处理方式为cv2.THRESH_BINARY_INV；Value=53：可以根据不同的照片自行调整参数，直到自己满意为止。）

2.提取最大连通域

代码如下：

import cv2
import numpy as np
import heapq
o = cv2.imread('E:/bishe/daima/output2.jpg')
gray = cv2.cvtColor(o, cv2.COLOR_BGR2GRAY)
ret, binary = cv2.threshold(gray, 50, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(binary, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cv2.imshow('original', o)
n = len(contours)
area = []
for i in range(n):area.append(cv2.contourArea(contours[i]))
max_num_index_list = map(area.index, heapq.nlargest(1, area))
Max = list(max_num_index_list)[0]
print(Max)
contoursImg = np.zeros(o.shape, np.uint8)
contoursImg = cv2.drawContours(contoursImg, contours, Max, (255, 255, 255), -1)
cv2.imshow('the crack', contoursImg)
cv2.imwrite('the crack.jpg', contoursImg)
cv2.waitKey()
cv2.destroyAllWindows()

效果如图：
在这里插入图片描述

3.提取裂纹骨架

代码如下：

import cv2
from skimage import morphology
import numpy as np
img = cv2.imread(r'E:\bishe\daima\the crack.jpg', 0)
kernel = np.ones((3, 3), np.uint8)
dilation = cv2.dilate(img, kernel)
cv2.imshow('dilation', dilation)
erosion = cv2.erode(dilation, kernel, iterations=4)
cv2.imshow('erosion',erosion)
dst = cv2.bitwise_not(erosion)
_, binary = cv2.threshold(dst, 100, 255, cv2.THRESH_BINARY_INV)
# cv2.imwrite("binary.png",binary)
binary[binary == 255] = 1
skeleton0 = morphology.skeletonize(binary)
skeleton = skeleton0.astype(np.uint8)*255
cv2.imshow('bone', skeleton)
cv2.imwrite('bone.jpg', skeleton)
cv2.waitKey()
cv2.destroyAllWindows()