基于OpenCV的混凝土裂纹检测 |
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基于OpenCV的混凝土裂纹检测
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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 np Type = 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 T T = Type def 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 V V = Value o = 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: break elif k == ord('s'): realType = T realValue = V retval, 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： 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： 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()效果如图： 最后想说几句，为了更好的学习，所以来这里分享我的学习成果，更好地发现自己的不足之处，还希望看到这篇文章的大佬们有什么建议还请给我留言，感激不尽！！！ |
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。） 
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