Python图像处理二值化方法实例汇总_Python

Python图像处理二值化方法实例汇总

2020-07-25 00:07ttweixiao9999 Python

这篇文章主要介绍了Python图像处理二值化方法实例汇总,文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值,需要的朋友可以参考下

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:

Python图像处理二值化方法实例汇总

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()