Python版本图像处理学习2-效果篇

1.马赛克效果：

import cv2
image = cv2.imread("lena.tiff") #读取一张图片
imgInfo = image.shape  #获取图像的宽高信息
height = imgInfo[0] #图像宽度值
width = imgInfo[1]  #图像高度值

mashiroLeft = 0   #马赛克起始X坐标
mashiroBottom = 0  #马赛克起始Y坐标
mashiroWidth = width  #马赛克区域宽
mashiroHeight = height  #马赛克区域高
masiroUnit = 15   #马赛克单元长
for i in range(mashiroLeft,mashiroWidth-masiroUnit):
    for j in range(mashiroBottom,mashiroHeight-masiroUnit):
        if i%masiroUnit==0 and j%masiroUnit==0:
            for m in range(0,masiroUnit):
                for n in range(0,masiroUnit):
                    b,g,r = image[i,j]
                    image[i+m,j+n] = [b,g,r]

cv2.imshow('mashiro',image)
k = cv2.waitKey(0)
if k == 27:
    cv2.destroyAllWindows()

效果图：
马赛克单位长4与马赛克单位长15效果如下：

2.毛玻璃效果

import cv2,random
import numpy as np
image = cv2.imread("lena.tiff") #读取一张图片
imgInfo = image.shape  #获取图像的宽高信息
height = imgInfo[0] #图像宽度值
width = imgInfo[1]  #图像高度值
dst = np.zeros((height,width,3),np.uint8)
mm = 16
for m in range(0,height-mm):
    for n in range(0,width-mm):
        index = int(random.random()*mm)
        b,g,r = image[m+index,n+index]
        dst[m,n] = b,g,r

cv2.imshow('glassBlur',dst)
k = cv2.waitKey(0)
if k == 27:
    cv2.destroyAllWindows()

效果图：

3.反色

import cv2,random
import numpy as np
image = cv2.imread("lena.tiff") #读取一张图片
imgInfo = image.shape  #获取图像的宽高信息
height = imgInfo[0] #图像宽度值
width = imgInfo[1]  #图像高度值
dst = np.zeros((height,width,3),np.uint8)
mm = 16
for m in range(0,height-mm):
    for n in range(0,width-mm):
        b,g,r = image[m,n]
        gray = (int(b)+int(g)+int(r))/3
        image[m,n] = [255-gray,255-gray,255-gray]

cv2.imshow('glassBlur',image)
k = cv2.waitKey(0)
if k == 27:
    cv2.destroyAllWindows()

效果图：

4.浮雕效果

import cv2,random
import numpy as np
image = cv2.imread("lena.tiff") #读取一张图片
imgInfo = image.shape  #获取图像的宽高信息
height = imgInfo[0] #图像宽度值
width = imgInfo[1]  #图像高度值
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
dst = np.zeros((height,width,3),np.uint8)
for i in range(0,width):
    for j in range(0,height-1):
        gray1 = int(gray[i,j])
        gray2 = int(gray[i,j+1])
        graySub = gray2 - gray1 + 100
        if graySub>255:
            graySub = 255
        elif graySub<0:
            graySub = 0
        dst[i,j] = graySub
cv2.imshow('Gray Image',dst)
k = cv2.waitKey(0)
if k == 27:
    cv2.destroyAllWindows()

效果图：

5.边缘检测

import cv2,random
import numpy as np
image = cv2.imread("lena.tiff") #读取一张图片
imgInfo = image.shape  #获取图像的宽高信息
height = imgInfo[0] #图像宽度值
width = imgInfo[1]  #图像高度值
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
imgG = cv2.GaussianBlur(gray,(5,5),0)
dst = cv2.Canny(imgG,50,50)

cv2.imshow('EdgeDetect',dst)
k = cv2.waitKey(0)
if k == 27:
    cv2.destroyAllWindows()

效果图：

标签：Python,image,cv2,height,width,图像处理,版本,np,imgInfo
来源： https://blog.csdn.net/sky_person/article/details/93407933