Python+OpenCV实现阈值分割的方法详解

目录

一、全局阈值

1.效果图

2.源码

二、滑动改变阈值(滑动条)

1.效果图

2.源码

三、自适应阈值分割

1.效果图

2.源码

3.GaussianBlur()函数去噪

四、参数解释

一、全局阈值

原图:

整幅图采用一个阈值,与图片的每一个像素灰度进行比较,重新赋值;

1.效果图

2.源码 import cv2 import matplotlib.pyplot as plt #设定阈值 thresh=130 #载入原图,并转化为灰度图像 img_original=cv2.imread(r'E:\py\python3.7\test2\test14yuzhi\cell.webp',0) img_original=cv2.resize(img_original,(0,0),fx=0.3,fy=0.3) #采用5种阈值类型(thresholding type)分割图像 retval1,img_binary=cv2.threshold(img_original,thresh,255,cv2.THRESH_BINARY) retval2,img_binary_invertion=cv2.threshold(img_original,thresh,255,cv2.THRESH_BINARY_INV) retval3,img_trunc=cv2.threshold(img_original,thresh,255,cv2.THRESH_TRUNC) retval4,img_tozero=cv2.threshold(img_original,thresh,255,cv2.THRESH_TOZERO) retval5,img_tozero_inversion=cv2.threshold(img_original,thresh,255,cv2.THRESH_TOZERO_INV) #采用plt.imshow()显示图像 imgs=[img_original,img_binary,img_binary_invertion,img_trunc,img_tozero,img_tozero_inversion] titles=['original','binary','binary_inv','trunc','tozero','tozero_inv'] for i in range(6): plt.subplot(2,3,i+1) plt.imshow(imgs[i],'gray') plt.title(titles[i]) plt.xticks([]) plt.yticks([]) plt.show() 二、滑动改变阈值(滑动条) 1.效果图

2.源码

代码如下(示例):

import cv2 import numpy as np import matplotlib.pyplot as plt #载入原图,转化为灰度图像,并通过cv2.resize()等比调整图像大小 img_original=cv2.imread(r'E:\py\python3.7\test2\test14yuzhi\cell.webp',0) img_original=cv2.resize(img_original,(0,0),fx=0.3,fy=0.3) #初始化阈值,定义全局变量imgs thresh=130 imgs=0 #创建滑动条回调函数,参数thresh为滑动条对应位置的数值 def threshold_segmentation(thresh): #采用5种阈值类型(thresholding type)分割图像 retval1,img_binary=cv2.threshold(img_original,thresh,255,cv2.THRESH_BINARY) retval2,img_binary_invertion=cv2.threshold(img_original,thresh,255,cv2.THRESH_BINARY_INV) retval3,img_trunc=cv2.threshold(img_original,thresh,255,cv2.THRESH_TRUNC) retval4,img_tozero=cv2.threshold(img_original,thresh,255,cv2.THRESH_TOZERO) retval5,img_tozero_inversion=cv2.threshold(img_original,thresh,255,cv2.THRESH_TOZERO_INV) #由于cv2.imshow()显示的是多维数组(ndarray),因此我们通过np.hstack(数组水平拼接) #和np.vstack(竖直拼接)拼接数组,达到同时显示多幅图的目的 img1=np.hstack([img_original,img_binary,img_binary_invertion]) img2=np.hstack([img_trunc,img_tozero,img_tozero_inversion]) global imgs imgs=np.vstack([img1,img2]) #新建窗口 cv2.namedWindow('Images') #新建滑动条,初始位置为130 cv2.createTrackbar('threshold value','Images',130,255,threshold_segmentation) #第一次调用函数 threshold_segmentation(thresh) #显示图像 while(1): cv2.imshow('Images',imgs) if cv2.waitKey(1)==ord('q'): break cv2.destroyAllWindows() 三、自适应阈值分割 1.效果图

2.源码

代码如下(示例):

import cv2 import matplotlib.pyplot as plt #载入原图 img_original=cv2.imread(r'E:\py\python3.7\test2\test14yuzhi\cell.webp',0) #全局阈值分割 retval,img_global=cv2.threshold(img_original,130,255,cv2.THRESH_BINARY) #自适应阈值分割 img_ada_mean=cv2.adaptiveThreshold(img_original,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,15,3) img_ada_gaussian=cv2.adaptiveThreshold(img_original,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,15,3) imgs=[img_original,img_global,img_ada_mean,img_ada_gaussian] titles=['Original Image','Global Thresholding(130)','Adaptive Mean','Adaptive Guassian',] #显示图片 for i in range(4): plt.subplot(2,2,i+1) plt.imshow(imgs[i],'gray') plt.title(titles[i]) plt.xticks([]) plt.yticks([]) plt.show() 3.GaussianBlur()函数去噪

代码如下(示例):

import cv2 import matplotlib.pyplot as plt #载入原图 img_original=cv2.imread(r'E:\py\python3.7\test2\test14yuzhi\cell.webp',0) #高斯滤波 img_blur=cv2.GaussianBlur(img_original,(13,13),13) #根据情况修改参数 #自适应阈值分割 img_thresh=cv2.adaptiveThreshold(img_original,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,15,3) img_thresh_blur=cv2.adaptiveThreshold(img_blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,15,3) #显示图像 imgs=[img_thresh,img_thresh_blur] titles=['img_thresh','img_thresh_blur'] for i in range(2): plt.subplot(1,2,i+1) plt.imshow(imgs[i],'gray') plt.title(titles[i]) plt.xticks([]) plt.yticks([]) plt.show() 四、参数解释

1.cv2.threshold(src, thresh, maxval, type)

参数:

src:输入的图像

thresh:图像分割所用的阈值(threshold value)

maxval:当阈值类型(thresholding type)采用cv2.THRESH_BINARY和cv2.THRESH_BINARY_INV时像素点被赋予的新值

type:介绍6种类型:

cv2.THRESH_BINARY(当图像某点像素值大于thresh(阈值)时赋予maxval,反之为0。注:最常用)

cv2.THRESH_BINARY_INV(当图像某点像素值小于thresh时赋予maxval,反之为0)

cv2.THRESH_TRUNC(当图像某点像素值大于thresh时赋予thresh,反之不变。注:虽然maxval没用了,但是调用函数不能省略)

cv2.THRESH_TOZERO(当图像某点像素值小于thresh时赋予0,反之不变。注:同上)

cv2.THRESH_TOZERO_INV(当图像某点像素值大于thresh时赋予0,反之不变。注:同上)

cv2.THRESH_OTSU(该方法自动寻找最优阈值,并返回给retval,见下文)

返回值:

retval:设定的thresh值,或者是通过cv2.THRESH_OTSU算出的最优阈值

dst:阈值分割后的图像

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