Using a mask with an adaptive threshold?

Question

I am writing a small program in C++ using OpenCV-2.3 API. I have an issue processing an adaptive threshold using a non rectangular mask.

So far, I was performing the adaptive threshold on the whole image and masking afterwards. I realise that,in my case , this was a mistake since the masked pixels would be used to calculate the threshold of my pixels of interest (while I simply want to exclude the former from the analysis)... However, unlike functions such as cv:: norm, cv::adaptiveThreshold does not seem to support explicitly a mask.

Do you know any obvious solution or workaround? Thank you very muck for your suggestions, Quentin

Answer 1

I've written some Python (sorry not c++) code that will allow for masked adaptive thresholding. Its not very fast, but it does what you want, and you may be able to use it as a basis for C++ code. It works as follows:

1. Sets masked pixels in the image to zero.
2. Determines the number of unmasked neighbours within the convolution block for each pixel.
3. Performs a convolution, and averages it by the number of unmasked neighbours within the block. This yields the average value within a pixels neighbourhood block.
4. Thresholds, by comparing the image to the mean neighbourhood values, mean_conv
5. Adds the masked off (non-thresholded) part of the image back on.

The images show, the initial image, the mask, the final processed image.

Here's the code:

import cv
import numpy
from scipy import signal

def thresh(a, b, max_value, C):
    return max_value if a > b - C else 0

def mask(a,b):
    return a if b > 100 else 0

def unmask(a,b,c):
    return b if c > 100 else a

v_unmask = numpy.vectorize(unmask)
v_mask = numpy.vectorize(mask)
v_thresh = numpy.vectorize(thresh)

def block_size(size):
    block = numpy.ones((size, size), dtype='d')
    block[(size - 1 ) / 2, (size - 1 ) / 2] = 0
    return block

def get_number_neighbours(mask,block):
    '''returns number of unmasked neighbours of every element within block'''
    mask = mask / 255.0
    return signal.convolve2d(mask, block, mode='same', boundary='symm')

def masked_adaptive_threshold(image,mask,max_value,size,C):
    '''thresholds only using the unmasked elements'''
    block = block_size(size)
    conv = signal.convolve2d(image, block, mode='same', boundary='symm')
    mean_conv = conv / get_number_neighbours(mask,block)
    return v_thresh(image, mean_conv, max_value,C)

image = cv.LoadImageM("image.png", cv.CV_LOAD_IMAGE_GRAYSCALE)
mask = cv.LoadImageM("mask.png", cv.CV_LOAD_IMAGE_GRAYSCALE)

#change the images to numpy arrays
original_image = numpy.asarray(image)
mask = numpy.asarray(mask)
# Masks the image, by removing all masked pixels.
# Elements for mask > 100, will be processed
image = v_mask(original_image, mask)
# convolution parameters, size and C are crucial. See discussion in link below.
image = masked_adaptive_threshold(image,mask,max_value=255,size=7,C=5)
# puts the original masked off region of the image back
image = v_unmask(original_image, image, mask)
#change to suitable type for opencv
image = image.astype(numpy.uint8)
#convert back to cvmat
image = cv.fromarray(image)

cv.ShowImage('image', image)
#cv.SaveImage('final.png',image)
cv.WaitKey(0)

After writing this I found this great link that has a good explanation with plenty of image examples, I used their text image for the above example.

Note. Numpy masks do not seem to be respected by scipy signal.convolve2d(), so the above workarounds were necessary.