Welcome to DOFBOT-Ultra repository

Binary Image

The core idea of binarization is to set a threshold. Values greater than the threshold become 0 (black) or 255 (white), turning the image into a black-and-white image. The threshold can be fixed or adaptive. Adaptive thresholding is generally the comparison of a pixel with the average value or Gaussian distribution weighted sum of the pixels in its neighborhood region, with or without an optional offset.

Global threshold:

Python-OpenCV provides the threshold function: cv2.threshold(src, threshold, maxValue, method)

src original image: dashed line represents the value to be thresholded; dotted line represents the threshold

cv2.THRESH_BINARY: The grayscale value of pixels greater than the threshold is set to maxValue (e.g., maximum 255 for 8-bit grayscale), and the grayscale value of pixels less than the threshold is set to 0.

cv2.THRESH_BINARY_INV: The grayscale value of pixels greater than the threshold is set to 0, and the grayscale value of pixels less than the threshold is set to maxValue.

cv2.THRESH_TRUNC: The grayscale value of pixels less than the threshold remains unchanged, and the grayscale value of pixels greater than the threshold is set to the threshold value.

cv2.THRESH_TOZERO: The grayscale value of pixels less than the threshold remains unchanged, and the grayscale value of pixels greater than the threshold becomes 0.

cv2.THRESH_TOZERO_INV: The grayscale value of pixels greater than the threshold remains unchanged, and the grayscale value of pixels less than the threshold becomes 0.

Code path:

~/dofbot_opencv/scripts/3.draw_picture/02_binarization.ipynb

​x
import cv2
import numpy as np
import matplotlib.pyplot as plt
img = cv2.imread('yahboom.jpg',1)
GrayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # convert to grayscale image
​
ret,thresh1=cv2.threshold(GrayImage,10,255,cv2.THRESH_BINARY)
ret,thresh2=cv2.threshold(GrayImage,10,255,cv2.THRESH_BINARY_INV)
ret,thresh3=cv2.threshold(GrayImage,10,255,cv2.THRESH_TRUNC)
ret,thresh4=cv2.threshold(GrayImage,10,255,cv2.THRESH_TOZERO)
ret,thresh5=cv2.threshold(GrayImage,10,255,cv2.THRESH_TOZERO_INV)
titles = ['Gray Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']
images = [GrayImage, thresh1, thresh2, thresh3, thresh4, thresh5]
for i in range(6):
     plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')
   plt.title(titles[i])
   plt.xticks([]),plt.yticks([])
plt.show()