Classical Computer Vision: Value Domain II: Threshold

 Thresholding

    Thresholding is the simplest segmentation method. 

    Imagine that I have an object in an image and I want to segment it from the background. The thresholding is based on the variation of intensity between that object and the background.

    Basically, we compare the pixel value to a threshold value and take an action. For example, we could put 0 for every pixel that is equal or lower than the threshold.

import matplotlib.pyplot as plt

import skimage.io as io

from skimage import data, color

from skimage.transform import rescale, resize, downscale_local_mean

from skimage.filters import threshold_mean

import numpy as np

apple_original = io.imread("red-apple-1326427.jpg")

print(apple_original.shape)

plt.imshow(apple_original)

plt.show()

def image_binary_threshold(image, min_thr, max_thr):

res = image.copy()

for i in range(0, 3):

k = res[:, :, i] < min_thr[i]

res[k] = [0, 0, 0]

for i in range(0, 3):

k = res[:, :, i] > max_thr[i]

res[k] = [0, 0, 0]

return res

def image_cut_by_diff(image, mean, mean_diff):

res = image.copy()

k1 = (res[:, :, 0] < (mean + mean_diff))

k2 = (res[:, :, 0] > (mean - mean_diff))

k3 = (res[:, :, 1] < (mean + mean_diff))

k4 = (res[:, :, 1] > (mean - mean_diff))

k5 = (res[:, :, 2] < (mean + mean_diff))

k6 = (res[:, :, 2] > (mean - mean_diff))

k7 = (k1 & k2) & (k3 & k4) & (k5 & k6)

res[k7] = [0, 0, 0]

return res

thr_img = image_cut_by_diff(apple_original, 250, 10)

plt.imshow(thr_img)