Python图像处理二值化方法实例汇总

在用python进行图像处理时，二值化是非常重要的一步，现总结了自己遇到过的6种 图像二值化的方法（当然这个绝对不是全部的二值化方法，若发现新的方法会继续新增）。

1. opencv 简单阈值 cv2.threshold

2. opencv 自适应阈值 cv2.adaptivethreshold (自适应阈值中计算阈值的方法有两种：mean_c 和 guassian_c ，可以尝试用下哪种效果好)

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, 255, cv2.adaptive_thresh_mean_c, cv2.thresh_binary, 11, 2)
# otsu's thresholding
# 阈值一定要设为 0 !
ret3, th3 = cv2.threshold(img, 0, 255, cv2.thresh_binary + cv2.thresh_otsu)
# plot all the images and their histograms
images = [img, 0, th1, img, 0, th2, img, 0, th3]
titles = [
  'original noisy image', 'histogram', 'global thresholding (v=127)',
  'original noisy image', 'histogram', "adaptive thresholding",
  'original noisy image', 'histogram', "otsu's thresholding"
]
# 这里使用了 pyplot 中画直方图的方法, plt.hist, 要注意的是它的参数是一维数组
# 所以这里使用了( numpy ) ravel 方法,将多维数组转换成一维,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, 3, i * 3 + 2), plt.hist(images[i * 3].ravel(), 256)
  plt.title(titles[i * 3 + 1]), plt.xticks([]), plt.yticks([])
  plt.subplot(3, 3, i * 3 + 3), plt.imshow(images[i * 3 + 2], 'gray')
  plt.title(titles[i * 3 + 2]), plt.xticks([]), plt.yticks([])
plt.show()

结果图:

4. skimage niblack阈值

5. skimage sauvola阈值 (主要用于文本检测)

例子:

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, 2)
plt.title('global threshold')
plt.imshow(binary_global, cmap=plt.cm.gray)
plt.axis('off')

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

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

plt.show()

结果图:

6.integralthreshold(主要用于文本检测)

使用方法: 运行下面网址的util.py文件

https://github.com/liang-yc/integralthreshold

结果图:

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。