OpenCV学习笔记之四:图像阈值与平滑处理

一、图像阈值

ret, dst = cv2.threshold(src, thresh, maxval, type)

  • src: 输入图,只能输入单通道图像,通常来说为灰度图
  • dst: 输出图
  • thresh: 阈值
  • maxval: 当像素值超过了阈值(或者小于阈值,根据type来决定),所赋予的值
  • type:二值化操作的类型,包含以下5种类型: cv2.THRESH_BINARY; cv2.THRESH_BINARY_INV; cv2.THRESH_TRUNC; cv2.THRESH_TOZERO;cv2.THRESH_TOZERO_INV
  • cv2.THRESH_BINARY 超过阈值部分取maxval(最大值),否则取0
  • cv2.THRESH_BINARY_INV THRESH_BINARY的反转
  • cv2.THRESH_TRUNC 大于阈值部分设为阈值,否则不变
  • cv2.THRESH_TOZERO 大于阈值部分不改变,否则设为0
  • cv2.THRESH_TOZERO_INV THRESH_TOZERO的反转
#!/usr/bin/env python 
# -*- coding: utf-8 -*-
# @Time    : 2019/12/31 14:03
# @Author  : King110108
# @File    : threshold_filter.py
# @Description: 
# @IDE     : PyCharm

import cv2 #opencv读取的格式是BGR
import matplotlib.pyplot as plt #Matplotlib是RGB
import numpy as np

#显示一张图片,第一个参数是窗口名字,第二个参数是要显示的图片
def cv_show(name,img):
    cv2.imshow(name,img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

#灰度处理
img=cv2.imread('jay1.jpg')
img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
print(img_gray.shape)

ret, thresh1 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)
ret, thresh2 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)
ret, thresh3 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TRUNC)
ret, thresh4 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO)
ret, thresh5 = cv2.threshold(img_gray, 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()

二、图像平滑处理

图片平滑处理即对图片滤波降噪的过程。

均值滤波--简单的平均卷积操作

blur = cv2.blur(img, (3, 3))
	
cv2.imshow('blur', blur)
cv2.waitKey(0)
cv2.destroyAllWindows()

方框滤波--基本和均值一样,可以选择归一化,False越界会产生高亮图

boxFilter= cv2.boxFilter(img,-1,(3,3), normalize=True)  
	
cv2.imshow('boxFilter', boxFilter)
cv2.waitKey(0)
cv2.destroyAllWindows()

高斯滤波--高斯模糊的卷积核里的数值是满足高斯分布,相当于更重视中间的

aussianBlur = cv2.GaussianBlur(img, (5, 5), 1)  
	
cv2.imshow('aussianBlur ', aussianBlur )
cv2.waitKey(0)
cv2.destroyAllWindows()

中值滤波--相当于用中值代替

medianBlur= cv2.medianBlur(img, 5)  # 中值滤波
	
cv2.imshow('medianBlur', medianBlur)
cv2.waitKey(0)
cv2.destroyAllWindows()

 

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