Defect detection on metal Object

edit

please try again with your image, it's probably much helpful

(there's an "upload image" button, if you edit your question)

(also, what you "want to achieve" , is kinda missing)

hausdorff distance helps to compare contours of different size for similarity. is that your problem, even ?

Well, my goal is having two images, one of reference part and one of to be tested, in one way to determine if they match(no defects detected). My thought so far, was to crop ROIs, extract the edges and use Template matching with no particular success

so we need another image of something defective ?

yes, let's say a misformed(or missing) tooth from the row on the right or not a good rim because of a hit or something

no, it might just need adding an image of a defective part above.

(it is still about understanding your problem)

Can you guarantee that the image will always consist of the same distance and orientation of the metal part? Can you guarantee that there will always be the same lighting? Can you guarantee that the metal will always be the same colour?

If so, you could call absdiff() to do a comparison between the master image (the one without flaws) and the current image (the one that might have flaws). Any difference between the two images will be detected and shown in grey. It's super simple, if only you make some guarantees that make life simple for the computer. The more guarantees that you make, the better.

Here's some code that calls absdiff():

#include <opencv2/opencv.hpp>
using namespace cv;
#pragma comment(lib, "opencv_world331.lib")

#include <iostream>
using namespace std;


int main(void)
{
    Mat master = imread("master.png");
    Mat current = imread("current.png");

    if (master.empty() || current.empty())
    {
        cout << "Read error" << endl;
        return -1;
    }

    Mat a;

    absdiff(master, current, a);

    imshow("a", a);

    waitKey();

    return 0;
}

Here's the code in Python:

import numpy as np
import cv2
import sys

master = cv2.imread("master.png")
current = cv2.imread("current.png")

if master is None or current is None:
    exit()

a = cv2.absdiff(master, current)

cv2.imshow("a", a)

cv2.waitKey()

Thank you very much for your answer. Yes, the distance and orientation of the part is supposed to be the same for each part and so is the lighting as much as possible.The colour of the metal will be the same, but we cannot say the same thing for the surface quality which possible differences between two parts may be due to real defects or lighting conditions.