opencv python 图像二值化/简单阈值化/大津阈值法

摘要：对于非双峰图像，二值化不准确。如果未使用阈值法，则与之前使用的阈值相同在第一种情况下，将全局阈值应用为值在第二种情况下，直接应用了的阈值在第三种情况下，使用高斯内核过滤图像以消除噪声，然后应用阈值处理代码

Image Thresholding

cv2.threshold第一个参数是源图像，它应该是灰度图像. 第二个参数是用于对像素值进行分类的阈值, 第三个参数是maxVal，它表示如果像素值大于（有时小于）阈值则要给出的值. OpenCV提供不同类型的阈值，它由函数的第四个参数决定. 不同的类型是：

代码：

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()

图像在不同区域具有不同照明条件时，应进行自适应阈值处理.因此，我们为同一图像的不同区域获得不同的阈值，并且它为具有不同照明的图像提供了更好的结果.
cv2.adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst])
adaptiveMethod:决定如何计算阈值

cv2.ADAPTIVE_THRESH_MEAN_C:阈值是邻域的平均值

cv2.ADAPTIVE_THRESH_GAUSSIAN_C:阈值是邻域值的加权和，其中权重是高斯窗口

blockSize:决定了邻域的大小
C:从计算的平均值或加权平均值中减去的常数

代码：

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是密集恐惧症，有点不忍直视……]

根据双峰图像的图像直方图自动计算阈值。 （对于非双峰图像，二值化不准确。）

使用cv.threshold（）但是传递了一个额外的标志v.THRESH_OTSU.对于阈值，只需传递零.然后算法找到最佳阈值并返回为第二个输出retVal。如果未使用Otsu阈值法，则retVal与之前使用的阈值相同.

在第一种情况下，将全局阈值应用为值127.在第二种情况下，直接应用了Otsu的阈值.在第三种情况下，使用5x5高斯内核过滤图像以消除噪声，然后应用Otsu阈值处理.
代码：

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()