OpenCV图像处理技术(Python)——阈值与平滑处理

©FuXianjun

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阈值处理

阈值处理类似于分段函数处理，设定一个阈值，若图像中的像素点灰度值大于阈值，对其做一定处理；对低于阈值的像素点做另一类处理。如对于一幅灰度图，我们设定阈值为125，大于125的像素点灰度值设为255，小于255的像素点设为0，这样我们就可以得到一副二值图像。
在OpenCV中提供了cv2.threshold()和cv2.adaptiveThreshold()来实现阈值处理。

参数说明：

import cv2
Type=0  #阈值处理类型值
Value=0 #使用的阈值
def onType(a):
    Type= cv2.getTrackbarPos(tType, windowName)
    Value= cv2.getTrackbarPos(tValue, windowName)
    ret, dst = cv2.threshold(img, Value,255, Type) 
    cv2.imshow(windowName,dst)
    
def onValue(a):
    Type= cv2.getTrackbarPos(tType, windowName)
    Value= cv2.getTrackbarPos(tValue, windowName)
    ret, dst = cv2.threshold(img, Value, 255, Type) 
    cv2.imshow(windowName,dst)

img = cv2.imread("peppa.jpg",0)
windowName = "Peppa"  #窗体名
cv2.namedWindow(windowName)
cv2.imshow(windowName,img)
#创建两个滑动条
tType = "Type"  #用来选取阈值处理类型的滚动条
tValue = "Value"    #用来选取阈值的滚动条
cv2.createTrackbar(tType, windowName, 0, 4, onType)
cv2.createTrackbar(tValue, windowName,0, 255, onValue) 
cv2.waitKey()
cv2.destroyAllWindows()

运行结果

使用滑动条来调整阈值

import cv2
Type=0  #阈值处理类型值
Value=0 #使用的阈值
def onType(a):
    Type= cv2.getTrackbarPos(tType, windowName)
    Value= cv2.getTrackbarPos(tValue, windowName)
    ret, dst = cv2.threshold(img, Value,255, Type) 
    cv2.imshow(windowName,dst)
    
def onValue(a):
    Type= cv2.getTrackbarPos(tType, windowName)
    Value= cv2.getTrackbarPos(tValue, windowName)
    ret, dst = cv2.threshold(img, Value, 255, Type) 
    cv2.imshow(windowName,dst)

img = cv2.imread("peppa.jpg",0)
windowName = "Peppa"  #窗体名
cv2.namedWindow(windowName)
cv2.imshow(windowName,img)
#创建两个滑动条
tType = "Type"  #用来选取阈值处理类型的滚动条
tValue = "Value"    #用来选取阈值的滚动条
cv2.createTrackbar(tType, windowName, 0, 4, onType)
cv2.createTrackbar(tValue, windowName,0, 255, onValue) 
cv2.waitKey()
cv2.destroyAllWindows()

运行结果

自适应阈值

cv2.adaptiveThreshold()

img=cv2.imread('peppa.jpg',0)
athdMEAN=cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,7,5)
athdGAUS=cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,5,3)
cv2.imshow("athMEAN",athdMEAN)
cv2.imshow("athGAUS",athdGAUS)
cv2.waitKey(0)
cv2.destroyAllWindows()

Otsu阈值处理

img=cv2.imread('peppa.jpg',0)
ret,otsu=cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
cv2.imshow("otsu",otsu)
cv2.waitKey(0)
cv2.destroyAllWindows()

平滑处理 模糊处理

import cv2
import numpy as np
img = cv2.imread("peppa_gaussian.jpg")
blur = cv2.blur(img, (7, 7))
box = cv2.boxFilter(img,-1,(7,7), normalize=True)  
gaussian = cv2.GaussianBlur(img, (7, 7), 10) 
median = cv2.medianBlur(img, 7)
bilater=cv2.bilateralFilter(img,9,75,75)
kernel = np.array((
        [-2, -1, 0],
        [-1,1,1],
        [0, 1, 2]), dtype="float32")
filter2D=cv2.filter2D(img,-1,kernel)#https://my.oschina.net/u/4306156/blog/3598055
cv2.imshow('img',img)
cv2.imshow('blur',blur)
cv2.imshow('box',box)
cv2.imshow('gaussian',gaussian)
cv2.imshow('median',median)
cv2.imshow('bilater',bilater)
cv2.imshow('filter2D',filter2D)
cv2.waitKey()
cv2.destroyAllWindows()