How to find the largest white pixel region from a binary image

Question

I have been working on a binary image on opencv python. I need to get the largest region. I have used following code, but I am not getting desired output.

edged = cv2.Canny(im_bw, 35, 125)
(cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
c = max(cnts, key = cv2.contourArea)

Answer 1

You don't need to use the canny output to do this. Just do findContours on im_bw directly and you should get the desired results. If still not what you want, try to use different threshold values (given that your original image isn't BW itself)

(_, im_bw) = threshold(frame, 100, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
(cnts, _) = cv2.findContours(im_bw.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
c = max(cnts, key = cv2.contourArea)

Answer 2

You really didn't explain what are you looking for,"largest region"? The code you posted will give you the largest contour found but you need to understand what is an OpenCV contour here . Now depending of your image you can have a lot of noise and that makes OpenCV gives you not the "region" you are expecting, so you need to reduce the noise. Before apply the Canny or the threshold you can apply BLUR to the image, EROTION and/or DILATION .

The algorithm should be like this:

• Get the frame / image
• Grayscale it
• Apply Blur / Erode / Dilate to reduce noise
• Apply Canny or threshold
• Find contours
• Get the largest
• Do what you need

Here you'll find good documentation in Python.

Answer 3

I am using the scikit-image package of python which measures the area of the islands and chooses the largest area as follows -

import skimage
from skimage import measure

labels_mask = measure.label(input_mask)                       
regions = measure.regionprops(labels_mask)
regions.sort(key=lambda x: x.area, reverse=True)
if len(regions) > 1:
    for rg in regions[1:]:
        labels_mask[rg.coords[:,0], rg.coords[:,1]] = 0
labels_mask[labels_mask!=0] = 1
mask = labels_mask

Input image -

Output image -