opencv 学习 图像形态学处理

import cv2#默认bgr
import matplotlib.pyplot as plt
import numpy as np

"""
ret,dst = cv2.threshold(src,thresh,maxcal,type)
四个参数：src:原始的图像， threshold:实际值-如127，maxcal:255(最大值)，type：处理的功能和方法

cv2.THRESH_BINARY——二值法 大于某个阈值取一个值 小于阈值取某一个值
cv2.THRESH_BINARY_INV)——方法的反转 inverse

"""
#test图片
way = 'C:\Users\ZKY\Desktop\images\'
# img1 = cv2.imread(way+'2.png')
img = cv2.imread(way+'jks.jpg')

#
def cv_show(image):
    cv2.imshow('',image)# cv2.imshow(winname 窗口的名称.  mat 要显示的图像)
    cv2.waitKey(0)
def change(img):
    img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#转换图像颜色
    w,thr_img = cv2.threshold(img_gray,120,255,cv2.THRESH_BINARY_INV)
    return thr_img
# img = change(img)

腐蚀操作

cv2.imshow('',img1)
cv2.waitKey(0)
thr_img1 = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)#灰化
w,thr_img1 = cv2.threshold(thr_img1,127,255,cv2.THRESH_BINARY_INV)#变黑#这里发现INV起到了去毛刺的效果 否则反而变粗
cv2.imshow('',thr_img1)
cv2.waitKey(0)
kernel = np.ones((4,4),np.uint8)#核
erosion1 = cv2.erode(thr_img1,kernel,iterations = 1)#会让暗色  大数值 腐蚀小数值
erosion2 = cv2.erode(thr_img1,kernel,iterations = 2)
erosion3 = cv2.erode(thr_img1,kernel,iterations = 3)#腐蚀次数越多
res = np.hstack((erosion1,erosion2,erosion3))
cv2.imshow('',res)#传两个参
cv2.waitKey(0)

膨胀操作：

cv2.imshow('',img1)
cv2.waitKey(0)
thr_img1 = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)#灰化
w,thr_img1 = cv2.threshold(thr_img1,127,255,cv2.THRESH_BINARY_INV)#变黑#这里发现INV起到了去毛刺的效果 否则反而变粗
cv2.imshow('',thr_img1)
cv2.waitKey(0)
kernel = np.ones((4,4),np.uint8)#核
erosion = cv2.erode(thr_img1,kernel,iterations = 1)#腐蚀！！！会让暗色  大数值 腐蚀小数值
cv2.imshow('',erosion)#传两个参   发现变细了
cv2.waitKey(0)
#膨胀.....
kernel = np.ones((4,4),np.uint8)#核
dige_erosian = cv2.dilate(erosion,kernel,iterations=1)#膨胀！！！会让亮色 变大
cv2.imshow('',dige_erosian)
cv2.waitKey(0)
cv2.destroyWindow('')

开运算与闭运算：

开运算 —— 先腐蚀 再膨胀 MORPH_OPEN

cv2.imshow('',img)
cv2.waitKey(0)#原图


kernel = np.ones(3,np.uint8)
opening = cv2.morphologyEx(img,cv2.MORPH_OPEN,kernel)

cv2.imshow('opening',opening)
cv2.waitKey(0)

闭运算 —— 先膨胀 再腐蚀 MORPH_CLOSE

cv2.imshow('',img)
cv2.waitKey(0)#原图


kernel = np.ones(3,np.uint8)
opening = cv2.morphologyEx(img,cv2.MORPH_CLOSE,kernel)

cv2.imshow('opening',opening)
cv2.waitKey(0)

梯度运算  梯度 = 膨胀 - 腐蚀：

img = cv2.GaussianBlur(img,(5,5),1)#高斯滤波

kernel = np.ones((3,3),np.uint8)
img = cv2.morphologyEx(img,cv2.MORPH_CLOSE,kernel,iterations=10)#效果好像不如高通滤波

cv2.imshow('',img)
cv2.waitKey(0)#原图


kernel = np.ones(3,np.uint8)

dilate = cv2.dilate(img,kernel)
erosion = cv2.erode(img,kernel)
res = np.hstack((dilate,erosion))#这里输出两个值 分别是膨胀和腐蚀后的图像
cv2.imshow('',res)
cv2.waitKey(0)

gradient = cv2.morphologyEx(img,cv2.MORPH_GRADIENT,kernel)#这里是梯度操作 原理是膨胀 - 腐蚀
cv2.imshow('gradient',gradient)