threshold：固定閾值二值化，
ret, dst = cv2.threshold(src, thresh, maxval, type)
- src： 輸入圖，只能輸入單通道圖像，通常來說為灰度圖
- dst： 輸出圖
- thresh： 閾值
- maxval： 當(dāng)像素值超過了閾值（或者小于閾值，根據(jù)type來決定），所賦予的值
- type：二值化操作的類型，包含以下5種類型： cv2.THRESH_BINARY； cv2.THRESH_BINARY_INV； cv2.THRESH_TRUNC； cv2.THRESH_TOZERO；cv2.THRESH_TOZERO_INV
官方文檔的示例代碼：
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('gradient.png',0)
ret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
ret,thresh2 = cv2.threshold(img,127,255,cv2.THRESH_BINARY_INV)
ret,thresh3 = cv2.threshold(img,127,255,cv2.THRESH_TRUNC)
ret,thresh4 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO)
ret,thresh5 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO_INV)
titles = ['Original Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]
for i in xrange(6):
plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
plt.show()
結(jié)果為：
adaptiveThreshold：自適應(yīng)閾值二值化
自適應(yīng)閾值二值化函數(shù)根據(jù)圖片一小塊區(qū)域的值來計(jì)算對應(yīng)區(qū)域的閾值，從而得到也許更為合適的圖片。
dst = cv2.adaptiveThreshold(src, maxval, thresh_type, type, Block Size, C)
- src： 輸入圖，只能輸入單通道圖像，通常來說為灰度圖
- dst： 輸出圖
- maxval： 當(dāng)像素值超過了閾值（或者小于閾值，根據(jù)type來決定），所賦予的值
- thresh_type： 閾值的計(jì)算方法，包含以下2種類型：cv2.ADAPTIVE_THRESH_MEAN_C； cv2.ADAPTIVE_THRESH_GAUSSIAN_C.
- type：二值化操作的類型，與固定閾值函數(shù)相同，包含以下5種類型： cv2.THRESH_BINARY； cv2.THRESH_BINARY_INV； cv2.THRESH_TRUNC； cv2.THRESH_TOZERO；cv2.THRESH_TOZERO_INV.
- Block Size： 圖片中分塊的大小
- C ：閾值計(jì)算方法中的常數(shù)項(xiàng)
官方文檔的示例代碼：
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('sudoku.png',0)
img = cv2.medianBlur(img,5)
ret,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
cv2.THRESH_BINARY,11,2)
th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
cv2.THRESH_BINARY,11,2)
titles = ['Original Image', 'Global Thresholding (v = 127)',
'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']
images = [img, th1, th2, th3]
for i in xrange(4):
plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
plt.show()
結(jié)果為：
Otsu's Binarization: 基于直方圖的二值化
Otsu's Binarization是一種基于直方圖的二值化方法，它需要和threshold函數(shù)配合使用。
Otsu過程：
1. 計(jì)算圖像直方圖；
2. 設(shè)定一閾值，把直方圖強(qiáng)度大于閾值的像素分成一組，把小于閾值的像素分成另外一組；
3. 分別計(jì)算兩組內(nèi)的偏移數(shù)，并把偏移數(shù)相加；
4. 把0~255依照順序多為閾值，重復(fù)1-3的步驟，直到得到最小偏移數(shù)，其所對應(yīng)的值即為結(jié)果閾值。
官方文檔的示例代碼：
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('noisy2.png',0)
# global thresholding
ret1,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
# Otsu's thresholding
ret2,th2 = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# Otsu's thresholding after Gaussian filtering
blur = cv2.GaussianBlur(img,(5,5),0)
ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# plot all the images and their histograms
images = [img, 0, th1,
img, 0, th2,
blur, 0, th3]
titles = ['Original Noisy Image','Histogram','Global Thresholding (v=127)',
'Original Noisy Image','Histogram',"Otsu's Thresholding",
'Gaussian filtered Image','Histogram',"Otsu's Thresholding"]
for i in xrange(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)果為：
參考文獻(xiàn)：http://docs.opencv.org/3.2.0/d7/d4d/tutorial_py_thresholding.html
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