Image processing for crack detection and length estimation

121 次查看（过去 30 天）

显示更早的评论

BB BSB 2015-4-9

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/196886-image-processing-for-crack-detection-and-length-estimation

评论： Image Analyst 2023-5-12

采纳的回答： Image Analyst

two.jpg

Hi, I have written the following matlab code to do the following:-

load rgb image of surface
contrast stretch
convert rgb to gray scale
image segmentation
morphological operations (thin, clean , fill, etc...)
imtool for pixel length determination
Calculation of crack length based on calibration of image and above determined pixel lenght.

My aim is to develop the SIMPLEST matlab code for automatic detection of cracks and estimate the length of the crack (if possible other geometrical properties) from a sample image.

The code is shown below:

%%load image
I=imread('two.jpg');
figure,imshow(I)
title('Original image')
%%Image adjust 
Istrech = imadjust(I,stretchlim(I));
figure,imshow(Istrech)
title('Contrast stretched image')
%%Convert RGB image to gray
Igray_s = rgb2gray(Istrech);
figure,imshow(Igray_s,[])
title('RGB to gray (contrast stretched) ')
%%Image segmentation by thresholding
%use incremental value to run this selection till required threshold 'level' is          
%achieved
level = 0.08;
Ithres = im2bw(Igray_h,level);
figure,imshow(Ithres)
title('Segmented cracks')
%%Image morphological operation
BW = bwmorph(gradmag,'clean',10);
figure,imshow(BW)
title('Cleaned image')
BW = bwmorph(gradmag,'thin', inf);
figure,imshow(BW)
title('Thinned image')
BW = imfill(gradmag, 'holes')
figure,imshow(BW)
title('Filled image')
%%Image tool
figure,imtool(BW1)
figure,imtool(I)
%%Calaculate crack length
calibration_length=0.001;   
calibration_pixels=1000;
crack_pixel=35;
crack_length=(crack_pixel *calibration_length)/calibration_pixels;

Please, I need help from image specialist to improve the code from above to meet my aim. I have also attached a sample picture that I am using for this code.

Picture two.jpg is attached below:

Thanks

3 个评论
显示 1更早的评论隐藏 1更早的评论

Hari krishna 2019-11-3

Could someone please help me to measure the crack and sketch a line on a diagram .

Image Analyst 2019-11-3

We'll try, after you've read this link

请先登录，再进行评论。

请先登录，再回答此问题。

采纳的回答

Image Analyst 2015-4-9

3
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/196886-image-processing-for-crack-detection-and-length-estimation#answer_174562

farthest_points.m

You're just arbitrarily setting

crack_pixel=35;

You're not even doing it manually (with user assistance) - you're just setting some arbitrary number. What's up with that? If you need code to find the distance between the farthest points in a binary blob, see my attached demo.

15 个评论
显示 13更早的评论隐藏 13更早的评论

BB BSB 2015-4-9

Thanks for your blob demo, helped alot! In line with your example I have started to tweak my code as below and would like to ask the following questions before.

i) Is the bwlabel function labelling ALL 4-5 cracks in the image under one label? if yes how do I make sure it labels each crack separately?

ii) the area returned from regionprop function is it for ALL 4-5 cracks in image? if yes how do get the area for each crack separately?

iii) the bwboundaries function returns 34 boundaries how do plot these boundaries such that the edges of each crack is highlighted.

iv) finally based on all these can you clarify me on how to determine the length of each crack (4-5 as shown in the sample image)? It was not clear from your example.

%% load image

I=imread('two.jpg');

Igray = rgb2gray(I);

figure,imshow(Igray)

title('Gray image')

%% Binarize

level = graythresh(Igray);

binaryImage = im2bw(Igray, level);

figure,imshow(binaryImage)

title('Binarized image')

%% Labeling & regionprop

labeledImage = bwlabel(binaryImage);

measurements = regionprops(labeledImage, 'Area');

%% Boundaries

boundaries = bwboundaries(binaryImage);

numberOfBoundaries = size(boundaries, 1);

BB BSB 2015-4-10

YES! I have managed to label and plot out each crack and also get its boundaries and area. see code below:

%%load image
I=imread('GCC4.jpg');
figure,imshow(I)
title('Original Image')
%%Binarize
Igray = rgb2gray(I);
level = 0.599;
binaryImage = im2bw(Igray,level);
figure,imshow(binaryImage)
title('Binarized image')
%%Sawp background and foreground pixels
[row col] = size(binaryImage);
binaryImage2=zeros(row,col);
  for i=1:row
      for j=1:col
          if binaryImage(i,j)==0
              binaryImage2(i,j)=1;
          else 
              binaryImage2(i,j)=0;
          end
    end
end
figure,imshow(binaryImage2)
title('Inverse binary Image')
%%Cleaning thining skel fill open close etc
binaryImage2 = bwmorph(binaryImage2,'clean',inf);
figure,imshow(binaryImage2)
title('Cleaned image')
binaryImage2 = imfill(binaryImage2, 'holes');
figure,imshow(binaryImage2)
title('Filled image')
% Get rid of anything less than 1% of the image
binaryImage2 = bwareaopen(binaryImage2, round(0.001*numel(binaryImage2)));
figure,imshow(binaryImage2)
%%Labeling image and regionprop for area
labeledImage = bwlabel(binaryImage2);
number_of_labeles = max(max(labeledImage))
for i= 1:number_of_labeles
      Im = (labeledImage==i)
    figure, imshow(Im)
end
measurements = regionprops(labeledImage, 'Area');
%%Boundaries
boundaries = bwboundaries(binaryImage);
numberOfBoundaries = size(boundaries, 1);

I would like to know if I can apply farthest boundaries points demo on each label to determine the length of each crack???? Thanks alot!

Pilli Lalvinnu 2022-5-7

ma'am I am doing the same project. I would reaaly appreciate it if you could help me out!

Image Analyst 2022-5-7

@Pilli Lalvinnu, sure. What sort of help do you need? I'm assuming you started with the solutions given here but they didn't work for your image(s) so start a new question with your code and images attached, and ask a specific question and we'll help.

请先登录，再进行评论。

更多回答（5 个）

Dan 2015-4-13

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/196886-image-processing-for-crack-detection-and-length-estimation#answer_175076

I just glanced at your code and if you need a completely automated system, you need to either define what constitutes a crack beforehand OR have the system learn these rules using something like a neural net.

But... that's a bigger project. In the short term, you might want to play with the graythresh function that will automate your selection of a threshold.

Just my $0.02.

Dan

1 个评论
显示 -1更早的评论隐藏 -1更早的评论

BB BSB 2015-4-14

编辑：BB BSB 2015-4-14

I see, for timeline limits Id rather attempt the easier of the 3 methods you outlined which I guess is playing around with graythresh function.

Any hints as to how or what to look out for in doing so? Because my first approach would be to scale by a factor??? make any sense? Thanks.

请先登录，再进行评论。

Yashwanth G M 2019-3-25

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/196886-image-processing-for-crack-detection-and-length-estimation#answer_367170

Sir help me to find out the area of a solar panel interms of Sq.mm....

1 个评论
显示 -1更早的评论隐藏 -1更早的评论

Image Analyst 2019-3-25

Then post a new Question, not an Answer to BB's question.

请先登录，再进行评论。

Ram kumar R 2020-2-12

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/196886-image-processing-for-crack-detection-and-length-estimation#answer_415141

i got error in line no.21 as Ithres = im2bw(Igray_h,level) can u explain it ?

4 个评论
显示 2更早的评论隐藏 2更早的评论

Ayoub MOSSLIH 2021-2-28

Undefined function or variable 'gradmag'.

Error in Untitled (line 30)

BW = bwmorph(gradmag,'clean',10);

Image Analyst 2021-2-28

@Ayoub MOSSLIH, we have no idea what code you ran. Please start a new question and attach your image and m-file there.

请先登录，再进行评论。

Image Analyst 2021-1-2

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/196886-image-processing-for-crack-detection-and-length-estimation#answer_589238

See my attached demo that computes both the tortuosity and mean width.

10 个评论
显示 8更早的评论隐藏 8更早的评论

AMAR SAISH 2023-1-2

sir, i have done image segmentation for crack images. Now, i want to find the length and width of crack, so i should use this code on original images or segmented images ??