主要内容

图像灰度处理1

算法优化

马赛克

毛玻璃效果

图片融合

边缘检测

浮雕效果

颜色风格

油画效果

主要内容

图像灰度处理1

#imread
#方法1 imread
import cv2
img0 = cv2.imread('image0.jpg',0)
img1 = cv2.imread('image0.jpg',1)
print(img0.shape)
print(img1.shape)
cv2.imshow('src',img0)
cv2.waitKey(0)

#方法2 cvtColor
import cv2
img = cv2.imread('image0.jpg',1)
dst = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)# 颜色空间转换 1 data 2 BGR gray
cv2.imshow('dst',dst)
cv2.waitKey(0)

import cv2
import numpy as np
img = cv2.imread('image0.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
# RGB R=G=B = gray  (R+G+B)/3
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]gray = (int(b)+int(g)+int(r))/3dst[i,j] = np.uint8(gray)
cv2.imshow('dst',dst)
cv2.waitKey(0)

#方法4 gray = r*0.299+g*0.587+b*0.114
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]b = int(b)g = int(g)r = int(r)gray = r*0.299+g*0.587+b*0.114dst[i,j] = np.uint8(gray)
cv2.imshow('dst',dst)
cv2.waitKey(0)

算法优化

# 1 灰度 最重要 2 基础 3 实时性
# 定点-》浮点 +- */ >>
# r*0.299+g*0.587+b*0.114
import cv2
import numpy as np
img = cv2.imread('image0.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
# RGB R=G=B = gray  (R+G+B)/3
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]b = int(b)g = int(g)r = int(r)##gray = (r*1+g*2+b*1)/4gray = (r+(g<<1)+b)>>2dst[i,j] = np.uint8(gray)
cv2.imshow('dst',dst)
cv2.waitKey(0)

乘以2相当于左移一位，除以4相当于右移二位

马赛克

用右上角一个像素替换所有的

每隔10个像素替换一个像素

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)

获取bgr时一个10*10取一次值就好，上面这个运算量会增大

可以将获取bgr的代码提前写赋值循环之前

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:(b,g,r) = img[m,n]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('dst1',img)
cv2.waitKey(0)

毛玻璃效果

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)
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

其实质是图像的卷积

import cv2
import numpy as np
import random
img = cv2.imread('image0.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)

import cv2
import numpy as np
import random
import math
img = cv2.imread('image0.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
cv2.imshow('src',img)
# sobel 1 算子模版 2 图片卷积 3 阈值判决
# [1 2 1          [ 1 0 -1
#  0 0 0            2 0 -2
# -1 -2 -1 ]       1 0 -1 ]# [1 2 3 4] [a b c d] a*1+b*2+c*3+d*4 = dst
# sqrt(a*a+b*b) = f>th
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dst = np.zeros((height,width,1),np.uint8)
for i in range(0,height-2):for j in range(0,width-2):gy = gray[i,j]*1+gray[i,j+1]*2+gray[i,j+2]*1-gray[i+2,j]*1-gray[i+2,j+1]*2-gray[i+2,j+2]*1gx = gray[i,j]+gray[i+1,j]*2+gray[i+2,j]-gray[i,j+2]-gray[i+1,j+2]*2-gray[i+2,j+2]grad = math.sqrt(gx*gx+gy*gy)if grad>50:dst[i,j] = 255else:dst[i,j] = 0
cv2.imshow('dst',dst)
cv2.waitKey(0)

浮雕效果

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)
# newP = gray0-gray1+150
dst = np.zeros((height,width,1),np.uint8)
for i in range(0,height):for j in range(0,width-1):grayP0 = int(gray[i,j])grayP1 = int(gray[i,j+1])newP = grayP0-grayP1+150if newP > 255:newP = 255if newP < 0:newP = 0dst[i,j] = newP
cv2.imshow('dst',dst)
cv2.waitKey(0)

颜色风格

import cv2
import numpy as np
img = cv2.imread('image0.jpg',1)
cv2.imshow('src',img)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
#rgb -》RGB new “蓝色”
# b=b*1.5
# g = g*1.3
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]b = b*1.5g = g*1.3if b>255:b = 255if g>255:g = 255dst[i,j]=(b,g,r)
cv2.imshow('dst',dst)
cv2.waitKey(0)

油画效果

#1 gray 2 7*7 10*10 3 0-255 256 4 640-63 64-127
# 3 10 0-63 99
# 4 count 5 dst = resultimport cv2
import numpy as np
img = cv2.imread('image00.jpg',1)
cv2.imshow('src',img)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dst = np.zeros((height,width,3),np.uint8)
for i in range(4,height-4):for j in range(4,width-4):array1 = np.zeros(8,np.uint8)for m in range(-4,4):for n in range(-4,4):p1 = int(gray[i+m,j+n]/32)array1[p1] = array1[p1]+1currentMax = array1[0]l = 0for k in range(0,8):if currentMax<array1[k]:currentMax = array1[k]l = k# 简化 均值for m in range(-4,4):for n in range(-4,4):if gray[i+m,j+n]>=(l*32) and gray[i+m,j+n]<=((l+1)*32):(b,g,r) = img[i+m,j+n]dst[i,j] = (b,g,r)
cv2.imshow('dst',dst)
cv2.waitKey(0)

灰度等级划分

2020-11-30 OpenCV人工智能图像处理学习笔记第4章计算机视觉加强之图像特效相关推荐

数字图像处理学习笔记（六）——数字图像处理中用到的数学操作
数字图像处理(Digital Image Processing)是通过计算机对图像进行去除噪声.增强.复原.分割.提取特征等处理的方法和技术.本专栏将以学习笔记形式对数字图像处理的重点基础知识进行总结 ...
人工智能导论学习笔记02
人工智能导论学习笔记02--第二章 :人工智能哲学基础人工智能导论学习笔记人工智能导论学习笔记02--第二章 :人工智能哲学基础第一讲大历史观下的人工智能宇宙演化人类与生命进化机器进化 ...
OpenCV与图像处理学习五——图像滤波与增强：线性、非线性滤波、直方图均衡化与Gamma变换
OpenCV与图像处理学习五--图像滤波与增强:线性.非线性滤波.直方图均衡化与Gamma变换三.图像滤波与增强 3.1 线性滤波 3.1.1 方框滤波 3.1.2 均值滤波 3.1.3 高斯滤波 ...
数字图像处理学习笔记（三）：ORB算法（尺度不变特征变换）Oriented FAST and Rotated BRIEF
数字图像处理学习笔记(三):ORB算法(尺度不变特征变换)Oriented FAST and Rotated BRIEF 一.概述参考:特征点匹配+特征检测方法汇总 ORB的全称是Oriented ...
OpenCV与图像处理学习七——传统图像分割之阈值法（固定阈值、自适应阈值、大津阈值）
OpenCV与图像处理学习七--传统图像分割之阈值法(固定阈值.自适应阈值.大津阈值) 一.固定阈值图像分割 1.1 直方图双峰法 1.2 OpenCV中的固定阈值分割二.自动阈值图像分割 2.1 ...
OpenCV与图像处理学习六——图像形态学操作：腐蚀、膨胀、开、闭运算、形态学梯度、顶帽和黑帽
OpenCV与图像处理学习六--图像形态学操作:腐蚀.膨胀.开.闭运算.形态学梯度.顶帽和黑帽四.图像形态学操作 4.1 腐蚀和膨胀 4.1.1 图像腐蚀 4.1.2 图像膨胀 4.2 开运算与闭运 ...
OpenCV与图像处理学习三——线段、矩形、圆、椭圆、多边形的绘制以及文字的添加
OpenCV与图像处理学习三--线段.矩形.圆.椭圆.多边形的绘制以及文字的添加一.OpenCV中的绘图函数 1.1 线段绘制 1.2 矩形绘制 1.3 圆绘制 1.4 椭圆的绘制 1.5 多边形绘 ...
OpenCV之Python学习笔记
RSS订阅登陆注册原文链接地址:http://www.itozi.net/19477.html OpenCV之Python学习笔记 ITOZI 发布于 2015-08-06 分类:OpenSta ...
图像处理学习笔记（二）
图像处理学习笔记(二) 4 OpenCV图像处理: 4.2 形态学操作: 4.2.1 连通性: 4.2.1 腐蚀和膨胀: 4.2.2 开闭运算: 4.2.3 礼帽和黑帽: 4.2.4 形态学操作总结: ...
OpenCV之Python学习笔记（1）（2）：图像的载入、显示和保存图像元素的访问、通道分离与合并
OpenCV之Python学习笔记一直都在用Python+OpenCV做一些算法的原型.本来想留下发布一些文章的,可是整理一下就有点无奈了,都是写零散不成系统的小片段.现在看到一本国外的新书< ...

2020-11-30 OpenCV人工智能图像处理学习笔记第4章计算机视觉加强之图像特效

主要内容

图像灰度处理1

算法优化

马赛克

毛玻璃效果

图片融合

边缘检测

浮雕效果

颜色风格

油画效果

2020-11-30 OpenCV人工智能图像处理学习笔记第4章计算机视觉加强之图像特效相关推荐

最新文章

热门文章

2020-11-30 OpenCV人工智能图像处理学习笔记 第4章 计算机视觉加强之图像特效

主要内容

图像灰度处理1

算法优化

马赛克

毛玻璃效果

图片融合

边缘检测

浮雕效果

颜色风格

油画效果

2020-11-30 OpenCV人工智能图像处理学习笔记 第4章 计算机视觉加强之图像特效相关推荐

最新文章

热门文章

2020-11-30 OpenCV人工智能图像处理学习笔记第4章计算机视觉加强之图像特效

2020-11-30 OpenCV人工智能图像处理学习笔记第4章计算机视觉加强之图像特效相关推荐