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()