cv2.connectedComponents not detecting components

Question

I am on Ubuntu, python 2.7. Working with OpenCV.

I was trying to understand exactly what the function cv2.connectedComponents is doing. This is the image:

The code:

import cv2
import numpy as np

img = cv2.imread('BN.tif', 0)

img = np.uint8(img)
_, markers = cv2.connectedComponents(img)

From what I understood, this funtion creates an array with same size than the provided image. For each component detected assign the same number for all the (y,x) positions for that component. If the background is all '0', then the circle would be all '1', the next square all '2', etc. The last component should be all '19'. I am reading the numbers of components by getting the highest number defining a component:

np.amax(markers)

I should get the 19, but I am getting 1.

My question: why I am getting only 1 component?

Because the foreground objects should be white, and the background black. Invert the image! img = 255 - img; — Miki
– Miki, Commented Apr 21, 2017 at 16:19

Jeru Luke · Accepted Answer · 2017-04-21 16:31:42Z

13

This is because cv2.connectedComponents() considers only the white portion as a component. Hence you are getting a single component.

You have to invert your image. You can do so by using cv2.bitwise_not() function.

CODE:

import cv2
import numpy as np

img = cv2.imread('cc.png', 0)
ret, thresh = cv2.threshold(img, 127, 255, 0)

#---- Inverting the image here ----
img = cv2.bitwise_not(thresh)     
_, markers = cv2.connectedComponents(img)
print np.amax(markers)

RESULT:

edited Apr 21, 2017 at 16:31

answered Apr 21, 2017 at 16:26

Jeru Luke

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4 Comments

daniel_hck Over a year ago

YEs, you are right. Thank you to Miki and Jeru Luke for the right answers. Now it works.

beaker Over a year ago

It would seem to me more logical to use cv2.THRESH_BINARY_INV as the type rather than thresholding and then inverting.

Jeru Luke Over a year ago

@beaker Point well noted. But the highlight of this question was to find the connected components upon inversion. So that is why I actually wrote an extra line to highlight image inversion

Rahul Over a year ago

for python3 change last line to print(np.max(markers))

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