python 图像处理中二值化方法归纳总结

python图像处理二值化方法

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:

来自 : OpenCV-Python 中文教程

 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:

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, 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:

7.