在用python進(jìn)行圖像處理時(shí)，二值化是非常重要的一步，現(xiàn)總結(jié)了自己遇到過(guò)的6種 圖像二值化的方法（當(dāng)然這個(gè)絕對(duì)不是全部的二值化方法，若發(fā)現(xiàn)新的方法會(huì)繼續(xù)新增）。

相關(guān)學(xué)習(xí)推薦：python視頻教程

1. opencv 簡(jiǎn)單閾值 cv2.threshold

2. opencv 自適應(yīng)閾值 cv2.adaptiveThreshold (自適應(yīng)閾值中計(jì)算閾值的方法有兩種：mean_c 和 guassian_c ，可以嘗試用下哪種效果好)

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 # 閾值一定要設(shè)為 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" ] # 這里使用了 pyplot 中畫(huà)直方圖的方法, plt.hist, 要注意的是它的參數(shù)是一維數(shù)組 # 所以這里使用了( numpy ) ravel 方法,將多維數(shù)組轉(zhuǎn)換成一維,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()

結(jié)果圖:

4. skimage niblack閾值

5. skimage sauvola閾值 (主要用于文本檢測(cè))

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()

結(jié)果圖:

6.IntegralThreshold(主要用于文本檢測(cè))

使用方法: 運(yùn)行下面網(wǎng)址的util.py文件

https://github.com/Liang-yc/IntegralThreshold

結(jié)果圖: