在用python進行影象處理時，二值化是非常重要的一步，現總結了自己遇到過的6種 影象二值化的方法（當然這個絕對不是全部的二值化方法，若發現新的方法會繼續新增）。

1. opencv 簡單閾值 cv2.threshold

2. opencv 自適應閾值 cv2.adaptiveThreshold (自適應閾值中計算閾值的方法有兩種：mean_c 和 guassian_c ，可以嘗試用下哪種效果好)

3. Otsu's 二值化

例子:

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('scratch.png',0)
# global thresholding
ret1,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
# Otsu's thresholding
th2 = cv2.adaptiveThreshold(img,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2)
# Otsu's thresholding
# 閾值一定要設為 0 !
ret3,th3 = cv2.threshold(img,cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# plot all the images and their histograms
images = [img,th1,img,th2,th3]
titles = [
  'Original Noisy Image','Histogram','Global Thresholding (v=127)','Original Noisy Image',"Adaptive Thresholding","Otsu's Thresholding"
]
# 這裡使用了 pyplot 中畫直方圖的方法,plt.hist,要注意的是它的引數是一維陣列
# 所以這裡使用了( numpy ) ravel 方法,將多維陣列轉換成一維,也可以使用 flatten 方法
# ndarray.flat 1-D iterator over an array.
# ndarray.flatten 1-D array copy of the elements of an array in row-major order.
for i in range(3):
  plt.subplot(3,3,i * 3 + 1),plt.imshow(images[i * 3],'gray')
  plt.title(titles[i * 3]),plt.xticks([]),plt.yticks([])
  plt.subplot(3,i * 3 + 2),plt.hist(images[i * 3].ravel(),256)
  plt.title(titles[i * 3 + 1]),i * 3 + 3),plt.imshow(images[i * 3 + 2],'gray')
  plt.title(titles[i * 3 + 2]),plt.yticks([])
plt.show()

結果圖:

4. skimage niblack閾值

5. skimage sauvola閾值 (主要用於文字檢測)

例子:

https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_niblack_sauvola.html

import matplotlib
import matplotlib.pyplot as plt

from skimage.data import page
from skimage.filters import (threshold_otsu,threshold_niblack,threshold_sauvola)


matplotlib.rcParams['font.size'] = 9


image = page()
binary_global = image > threshold_otsu(image)

window_size = 25
thresh_niblack = threshold_niblack(image,window_size=window_size,k=0.8)
thresh_sauvola = threshold_sauvola(image,window_size=window_size)

binary_niblack = image > thresh_niblack
binary_sauvola = image > thresh_sauvola

plt.figure(figsize=(8,7))
plt.subplot(2,2,1)
plt.imshow(image,cmap=plt.cm.gray)
plt.title('Original')
plt.axis('off')

plt.subplot(2,2)
plt.title('Global Threshold')
plt.imshow(binary_global,cmap=plt.cm.gray)
plt.axis('off')

plt.subplot(2,3)
plt.imshow(binary_niblack,cmap=plt.cm.gray)
plt.title('Niblack Threshold')
plt.axis('off')

plt.subplot(2,4)
plt.imshow(binary_sauvola,cmap=plt.cm.gray)
plt.title('Sauvola Threshold')
plt.axis('off')

plt.show()

結果圖:

6.IntegralThreshold(主要用於文字檢測)

使用方法: 執行下面網址的util.py檔案

https://github.com/Liang-yc/IntegralThreshold

結果圖:

以上就是本文的全部內容，希望對大家的學習有所幫助，也希望大家多多支援我們。