opencv 图片特效

特效"/>

opencv 图片特效

opencv 图片特效

更多干货

• 分布式实战（干货）

• spring cloud 实战（干货）

• mybatis 实战（干货）

• spring boot 实战（干货）

• React 入门实战（干货）

• 构建中小型互联网企业架构（干货）

• python 学习持续更新

• ElasticSearch 笔记

• kafka storm 实战 (干货)

• scala 学习持续更新

• RPC

• 深度学习

图片颜色反转

灰度图片颜色反转

#0-255 255-当前
import cv2
import numpy as np
img = cv2.imread('image0.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dst = np.zeros((height,width,1),np.uint8)
for i in range(0,height):for j in range(0,width):grayPixel = gray[i,j]dst[i,j] = 255-grayPixel
cv2.imshow('dst',dst)
cv2.waitKey(0)

彩色图片颜色反转

#RGB 255-R=newR
#0-255 255-当前
import cv2
import numpy as np
img = cv2.imread('image0.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
dst = np.zeros((height,width,3),np.uint8)
for i in range(0,height):for j in range(0,width):(b,g,r) = img[i,j]dst[i,j] = (255-b,255-g,255-r)
cv2.imshow('dst',dst)
cv2.waitKey(0)

马赛克

import cv2
import numpy as np
img = cv2.imread('image0.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
for m in range(100,300):for n in range(100,200):# pixel ->10*10if m%10 == 0 and n%10==0:for i in range(0,10):for j in range(0,10):(b,g,r) = img[m,n]img[i+m,j+n] = (b,g,r)
cv2.imshow('dst',img)
cv2.waitKey(0)

毛玻璃

import cv2
import numpy as np
import random
img = cv2.imread('image0.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
dst = np.zeros((height,width,3),np.uint8)
mm = 8
for m in range(0,height-mm):for n in range(0,width-mm):index = int(random.random()*8)#0-8(b,g,r) = img[m+index,n+index]dst[m,n] = (b,g,r)
cv2.imshow('dst',dst)
cv2.waitKey(0)

图片融合

dst = src1*a+src2*(1-a)

# dst  = src1*a+src2*(1-a)
import cv2
import numpy as np
img0 = cv2.imread('image0.jpg',1)
img1 = cv2.imread('image1.jpg',1)
imgInfo = img0.shape
height = imgInfo[0]
width = imgInfo[1]
# ROI
roiH = int(height/2)
roiW = int(width/2)
img0ROI = img0[0:roiH,0:roiW]
img1ROI = img1[0:roiH,0:roiW]
# dst
dst = np.zeros((roiH,roiW,3),np.uint8)
dst = cv2.addWeighted(img0ROI,0.5,img1ROI,0.5,0)#add src1*a+src2*(1-a)
# 1 src1 2 a 3 src2 4 1-a
cv2.imshow('dst',dst)
cv2.waitKey(0)

边缘检测

import cv2
import numpy as np
import random
img = cv2.imread('3.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
cv2.imshow('src',img)
#canny 1 gray 2 高斯 3 canny
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
imgG = cv2.GaussianBlur(gray,(3,3),0)
dst = cv2.Canny(img,50,50) #图片卷积——》th
cv2.imshow('dst',dst)
cv2.waitKey(0)