Otsu thresholding inside mask

Question

I'm working with Python and trying to do Otsu thresholding on an image but only inside the mask (yes, I have an image and a mask image). It means less pixel on the image will be included in the histogram for calculating the Otsu threshold.

I'm currently using the cv2.threshold function without the mask image and have no idea how to do this kind of job.

ret, OtsuMat = cv2.threshold(GaborMat, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

Since this function also incorporates the pixels outside the mask, I think it will give a less accurate threshold.

This is the example of the image and its mask:

https://drive.google.com/drive/folders/1p8JMhncJs19oOWO9RdkWuEADVGqE-gzQ?usp=sharing

Hope there is a OpenCV or other lib function to do it easily (and also with fast computing), but any kind of help will be appreciated.

Answer 1

I had a try at this using the threshold_otsu() method from skimage and a Numpy masked array. I don't know if there are faster ways - the skimage is normally pretty well optimised. If anyone else wants to take my sample data and try other ideas on it, please feel free - although there is a service charge of one upvote ;-)

#!/usr/bin/env python3

import cv2
import numpy as np
import numpy.ma as ma
from skimage.filters import threshold_otsu

# Set up some repeatable test data, 4 blocks 100x100 pixels each of random normal np.uint8s centred on 32, 64, 160,192
np.random.seed(42)
a=np.random.normal(size = (100,100), loc = 32,scale=10).astype(np.uint8)
b=np.random.normal(size = (100,100), loc = 64,scale=10).astype(np.uint8)
c=np.random.normal(size = (100,100), loc = 160,scale=10).astype(np.uint8)
d=np.random.normal(size = (100,100), loc = 192,scale=10).astype(np.uint8)
# Stack (concatenate) the 4 squares horizontally across the page
im = np.hstack((a,b,c,d))
# Next line is just for debug
cv2.imwrite('start.png',im)

That gives us this:

# Now make a mask revealing only left half of image, centred on 32 and 64
mask=np.zeros((100,400))
mask[:,200:]=1
masked = ma.masked_array(im,mask)
print(threshold_otsu(masked.compressed()))       # Prints 47

# Now do same revealing only right half of image, centred on 160 and 192
masked = ma.masked_array(im,1-mask)
print(threshold_otsu(masked.compressed()))       # Prints 175

The histogram of the test data looks like this, x-axis is 0..255

---

Adapting to your own sample data, I get this:

#!/usr/bin/env python3

import cv2
import numpy as np
import numpy.ma as ma
from skimage.filters import threshold_otsu

# Load images
im   = cv2.imread('eye.tif', cv2.IMREAD_UNCHANGED)
mask = cv2.imread('mask.tif', cv2.IMREAD_UNCHANGED)

# Calculate Otsu threshold on entire image
print(threshold_otsu(im))                       # prints 130

# Now do same for masked image
masked = ma.masked_array(im,mask>0)
print(threshold_otsu(masked.compressed())).     # prints 124