How can I smoothly detect the welding joint using OpenCV-Python?

Question

I have tried to detect the welding joint (bead) using the codes attached below in the last part of this question. I aim to draw a contour on the joint as shown in the third image but my results are poor and don't look similar to the expected results. here is the summary of what I did but the codes are well clear: 1. Reading the image .jpg format 2. Image blurring 3. Thresholding 4. Morphological operations 5. Creating mask 6. Finding contour

But the results are not promising, how can I get out from here

img_new = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
blur_image = cv2.bilateralFilter(img_new,5,21,21)
thresh = cv2.adaptiveThreshold(blur_image,255,
                               cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                               cv2.THRESH_BINARY_INV,15,3)
kernel = np.ones((5,5),dtype='uint8')
thresh_dilated = cv2.dilate(thresh, kernel, iterations = 1)
num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(thresh_dilated, connectivity=4)
sizes = stats[1:,-1]
min_size = 5000
num_labels = num_labels -1
img2 = np.zeros((labels.shape))
for i in range(0,num_labels):
    if sizes[i]>=min_size:
        img2[labels==i+1]=255
closing = cv2.morphologyEx(img2,cv2.MORPH_CLOSE,kernel)
opening = cv2.morphologyEx(closing,cv2.MORPH_OPEN,kernel)
result = opening.copy()
new_result = result.astype(dtype=np.uint8)

black = np.full((new_result.shape[0],new_result.shape[1],3),(0,0,0),np.uint8)
black1 = cv2.ellipse(black,(700,750),(300,140),0,0,360,(255,255,255),-1)
grayscale = cv2.cvtColor(black1,cv2.COLOR_BGR2GRAY)
ret,b_mask = cv2.threshold(grayscale,127,255,0)
img_result = cv2.bitwise_and(new_result,b_mask)

contours, hierarchy = cv2.findContours(img_result, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours, -1, (0,255,0), 2)
cv2.imshow('result',img)
cv2.waitKey(0)```

Answer 1

Read any papers ?

If this for automatic processing of many welding joints on a assembly line? Does this have to work for many other similiar images? It does not make sense to fine tune an algorithm for a single picture in that case.

If yes, than you need to use more images to create the algorithm, maybe use a different light setup, maybe images taken with an IR camera right after the welding to get a "hot" mask. Or use light from left and right sight separately to combine two images for a single mark.

Another thing that would be very helpful is to get a "before" image from the parts. without the welding joint. In that case it could get easy. You would just create the difference between the images, do some filtering to remove the welding beads and the reddish layer.

Edit 1:

Another thing I forgot to mention is please look at the RGB layers separately. This is something that you should always try early on. Often there is something useful to see, eg in your case it could be that the blue layer might be interesting. Please add the layers to your question.