Python图像处理二值化方法实例汇总

我会带着你远行 2022-12-21 09:27 139阅读 0赞

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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:

![Image 1][]

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][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:

![Image 1][]

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][https_github.com_Liang-yc_IntegralThreshold]

结果图:

![Image 1][]

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[Link 1]: https://www.piaodoo.com/
[Link 2]: http://www.nrso.net/
[Image 1]: 
[https_scikit-image.org_docs_dev_auto_examples_segmentation_plot_niblack_sauvola.html]: https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_niblack_sauvola.html
[https_github.com_Liang-yc_IntegralThreshold]: https://github.com/Liang-yc/IntegralThreshold