How to obtain a dynamic threshold for contour detection in OpenCV

Question

In my image database, there is a need to 1) detect if there is a flake (a very black contour) or not in an image and also 2) find a minimum closing circle to measure the radius of the flake.

However, the images come with slightly different illuminations.

Here are some examples:

This one is very easy to either detect and measure:

But these ones are more difficult:

My initial thought is to use a threshold related to the average value of pixels of images.

Is there any other way of computing such a dynamic threshold in OpenCV?

Answer 1

I think what you're looking for is cv2.adaptiveThreshold() or Otsu's thresholding. To satisfy your requirements for #1, we can use a minimum threshold area to determine if the flake exists. For #2, once we detect the contour, we can use moments to determine the radius. Here's a simple approach

• Convert image to grayscale and median blur
• Adaptive threshold
• Morph close to smooth image
• Dilate to enhance contour
• Find contours and sort using contour area

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The main idea is to use a large median blur to remove the noise then adaptive threshold. Here's the results for each for your four pictures. For some of your pictures, the black spot was not actually a circle, it was more of a oval shape. You can decide what you want to do with that situation.

import cv2

image = cv2.imread('4.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
blur = cv2.medianBlur(gray, 25)
thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,27,6)

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=1)
dilate = cv2.dilate(close, kernel, iterations=2)

cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:10]

minimum_area = 500
for c in cnts:
    area = cv2.contourArea(c)
    if area > minimum_area:
        # Find centroid
        M = cv2.moments(c)
        cX = int(M["m10"] / M["m00"])
        cY = int(M["m01"] / M["m00"])
        cv2.circle(image, (cX, cY), 20, (36, 255, 12), 2) 
        x,y,w,h = cv2.boundingRect(c)
        cv2.putText(image, 'Radius: {}'.format(w/2), (10,20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (36,255,12), 2)
        break

cv2.imshow('thresh', thresh)
cv2.imshow('close', close)
cv2.imshow('image', image)
cv2.waitKey()

Answer 2

You must start with a thresholding

Here you have some thresholding, you can choose the one you want, with good parameters, most of the noise will go.

Then you can do en edge detection

Finally, hough transform seems to best the best approach to detect circles (noise will be remove by the parameters of the hough circle transform).

You can set a minimal and a maximal radius, so if you have an idea of the average radius, you can adjust it this way.