Python图像处理二值化方法实例云集

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）。

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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.thresholdimg, 127, 255, cv2.THRESH_BINARY)
# Otsu's thresholding
th2 = cv2.adaptiveThresholdimg, 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.thresholdimg, 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 range3):
  plt.subplot3, 3, i * 3 + 1), plt.imshowimages[i * 3], 'gray')
  plt.titletitles[i * 3]), plt.xticks[]), plt.yticks[])
  plt.subplot3, 3, i * 3 + 2), plt.histimages[i * 3].ravel), 256)
  plt.titletitles[i * 3 + 1]), plt.xticks[]), plt.yticks[])
  plt.subplot3, 3, i * 3 + 3), plt.imshowimages[i * 3 + 2], 'gray')
  plt.titletitles[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:

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_otsuimage)

window_size = 25
thresh_niblack = threshold_niblackimage, window_size=window_size, k=0.8)
thresh_sauvola = threshold_sauvolaimage, window_size=window_size)

binary_niblack = image > thresh_niblack
binary_sauvola = image > thresh_sauvola

plt.figurefigsize=8, 7))
plt.subplot2, 2, 1)
plt.imshowimage, cmap=plt.cm.gray)
plt.title'Original')
plt.axis'off')

plt.subplot2, 2, 2)
plt.title'Global Threshold')
plt.imshowbinary_global, cmap=plt.cm.gray)
plt.axis'off')

plt.subplot2, 2, 3)
plt.imshowbinary_niblack, cmap=plt.cm.gray)
plt.title'Niblack Threshold')
plt.axis'off')

plt.subplot2, 2, 4)
plt.imshowbinary_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: