Add adaptive threshold algorithm using mean method by miralshah365 · Pull Request #341

Add adaptive threshold algorithm using mean method by miralshah365 · Pull Request #341 · boostorg/gil

@miralshah365
I've been playing with the algorithm against this little funny sample of 16x16 pixel greyscale gradient background with two 'barely visible rectangles.
You can download it here .

OpenCV

The 16x16 sample above passed through the adaptive mean threshold by OpenCV:

import cv2
import numpy as np
import matplotlib.pyplot as plt
path = 'D:\\workshop\\opencv\\'
path_original = path + "rectangles.png"

img = cv2.imread(path_original, 0)

ret, th1 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
th2 = cv2.adaptiveThreshold(
    img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 0)
th3 = cv2.adaptiveThreshold(
    img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 0)

titles = ['Input', 'Global Thresholding (t=127)',
          'Adaptive Mean', 'Adaptive Gaussian']
images = [img, th1, th2, th3]
for i in range(4):
    plt.subplot(2, 2, i+1), plt.imshow(images[i], 'gray')
    plt.title(titles[i])
    plt.xticks([]), plt.yticks([])
plt.show()

identifies both rectangles:

GIL

Using threshold_adaptive:

std::string image_dir = "d:\\workshop\\opencv\\";
{
    gray8_image_t img;
    read_image(image_dir + "rectangles.png", img, png_tag{});
    gray8_image_t img_out(img.dimensions());

    boost::gil::threshold_adaptive(const_view(img), view(img_out), 11);
    write_view(image_dir + "out-threshold-adaptive.png", view(img_out), png_tag{});
}

identifies only one, the upper rectangle:

Here is dump of the convoluted view generated with this

write_view("d:\\workshop\\opencv\\out-threshold-adaptive-convoluted-view.png", temp_view, png_tag{});

Unfortunately, I have no way to generate equivalent for OpenCV.

Conclusion

There still may be some fine tuning of the algorithm necessary.
At least, it may be worth to test it a bit more, also with your impl. of convolution.