OpenCV:图像的普通二值化

首先我们来看看图像二值化的过程，opencv一共有好几种不同的二值化算法可以使用，一般来说图像的像素，亮度等条件如果超过了某个或者低于了某个阈值，就会恒等于某个值，可以用于某些物体轮廓的监测：

导包：

import numpy as np

import cv2

import matplotlib.pyplot as plt

def show(image):

    plt.imshow(image)

    plt.axis('off')

    plt.show()

def imread(image):

    image=cv2.imread(image)

    image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)

    return image

读入图像：

image=imread('123.jpg')

show(image)

gray=cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)

plt.imshow(gray,'gray')#这里需要加一个参数之后才可以显示灰度图

plt.axis('off')

plt.show()

进行二值化的参数设定：

ret1,thresh1 = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)

ret2,thresh2 = cv2.threshold(gray,127,255,cv2.THRESH_BINARY_INV)

ret3,thresh3 = cv2.threshold(gray,127,255,cv2.THRESH_TRUNC)

ret4,thresh4 = cv2.threshold(gray,127,255,cv2.THRESH_TOZERO)

ret5,thresh5 = cv2.threshold(gray,127,255,cv2.THRESH_TOZERO_INV)

进行二值化：

titles = ['original','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']

images = [gray, thresh1, thresh2, thresh3, thresh4, thresh5]

plt.figure(figsize=(15,5))

for i in range(6):

    plt.subplot(2,3,i+1)

    plt.imshow(images[i],'gray')

    plt.title(titles[i])

    plt.axis('off')

plt.show()