摘要：對于非雙峰圖像，二值化不準確。如果未使用閾值法，則與之前使用的閾值相同在第一種情況下，將全局閾值應用為值在第二種情況下，直接應用了的閾值在第三種情況下，使用高斯內核過濾圖像以消除噪聲，然后應用閾值處理代碼

Image Thresholding

cv2.threshold第一個參數是源圖像，它應該是灰度圖像. 第二個參數是用于對像素值進行分類的閾值, 第三個參數是maxVal，它表示如果像素值大于（有時小于）閾值則要給出的值. OpenCV提供不同類型的閾值，它由函數的第四個參數決定. 不同的類型是：

代碼：

import cv2
import numpy as np
import matplotlib.pylab  as plt

img = cv2.imread("img.jpg",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 range(6):
    plt.subplot(2,3,i+1),plt.imshow(images[i],"gray")
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])

plt.show()

圖像在不同區域具有不同照明條件時，應進行自適應閾值處理.因此，我們為同一圖像的不同區域獲得不同的閾值，并且它為具有不同照明的圖像提供了更好的結果.
cv2.adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst])
adaptiveMethod:決定如何計算閾值

cv2.ADAPTIVE_THRESH_MEAN_C:閾值是鄰域的平均值

cv2.ADAPTIVE_THRESH_GAUSSIAN_C:閾值是鄰域值的加權和，其中權重是高斯窗口

blockSize:決定了鄰域的大小
C:從計算的平均值或加權平均值中減去的常數

代碼：

import cv2
import numpy as np
import matplotlib.pylab  as plt

img = cv2.imread("img.jpg",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 range(4):
    plt.subplot(2,2,i+1),plt.imshow(images[i],"gray")
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])
plt.show()

[lz是密集恐懼癥，有點不忍直視……]

根據雙峰圖像的圖像直方圖自動計算閾值。 （對于非雙峰圖像，二值化不準確。）

使用cv.threshold（）但是傳遞了一個額外的標志v.THRESH_OTSU.對于閾值，只需傳遞零.然后算法找到最佳閾值并返回為第二個輸出retVal。如果未使用Otsu閾值法，則retVal與之前使用的閾值相同.

在第一種情況下，將全局閾值應用為值127.在第二種情況下，直接應用了Otsu的閾值.在第三種情況下，使用5x5高斯內核過濾圖像以消除噪聲，然后應用Otsu閾值處理.
代碼：

import cv2
import numpy as np
import matplotlib.pylab  as plt

img = cv2.imread("img.jpg",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 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()