原图

图像灰度处理

#方式1

import cv2

#读取彩色原图

img0=cv2.imread('E:/python_cv/01.jpg',1)

#读取灰度图

img1=cv2.imread('E:/python_cv/01.jpg',0)

print(img0.shape)

print(img1.shape)

cv2.imshow('gary',img1)

cv2.waitKey(0)

#方式2

import cv2

#读取彩色原图

src=cv2.imread('E:/python_cv/01.jpg',1)

dst=cv2.cvtColor(src,cv2.COLOR_BGR2GRAY)

cv2.imshow('gary',dst)

cv2.waitKey(0)

#方式3

import cv2

import numpy as np

img=cv2.imread('E:/python_cv/01.jpg',1)

img_info=img.shape

image_height=img_info[0]

image_weight=img_info[1]

dst=np.zeros((image_height,image_weight,3),np.uint8)

#当彩色图像三个通道的值都相同时，即为灰度图像

for i in range(image_height):

for j in range(image_weight):

(b,g,r)=img[i][j]

gray=(int(b)+int(g)+int(r))/3 #防止越界，转换类型

dst[i,j]=np.uint8(gray)

cv2.imshow('gary',dst)

cv2.waitKey(0)

#方式4

import cv2

import numpy as np

img=cv2.imread('E:/python_cv/01.jpg',1)

img_info=img.shape

image_height=img_info[0]

image_weight=img_info[1]

dst=np.zeros((image_height,image_weight,3),np.uint8)

#当彩色图像三个通道的值都相同时，即为灰度图像

for i in range(image_height):

for j in range(image_weight):

(b,g,r)=img[i][j]

gray=0.299*int(b)+0.587*int(g)+0.114*int(r)

dst[i,j]=np.uint8(gray)

cv2.imshow('gary',dst)

cv2.waitKey(0)

灰度处理很重要，也是图像处理中的基础操做，在实际运用中要求实时性，涉及优化，好比：python

定点操做比浮点操做要快

+-比*/快，移位比*/快

import cv2

import numpy as np

img=cv2.imread('E:/python_cv/01.jpg',1)

img_info=img.shape

image_height=img_info[0]

image_weight=img_info[1]

dst=np.zeros((image_height,image_weight,3),np.uint8)

#当彩色图像三个通道的值都相同时，即为灰度图像

for i in range(image_height):

for j in range(image_weight):

(b,g,r)=img[i][j]

# gray=0.299*int(b)+0.587*int(g)+0.114*int(r)

#优化

b=int(b)

g=int(g)

r=int(r)

# gray=(b+2*g+r)/4

gray=(b+(g<<1)+r)>>2

dst[i,j]=np.uint8(gray)

cv2.imshow('gary',dst)

cv2.waitKey(0)

颜色翻转

灰度图像素值为0到255，若当前的像素值为i,翻转事后为255-i

import cv2

import numpy as np

#读取彩色原图

src=cv2.imread('E:/python_cv/01.jpg',1)

gray=cv2.cvtColor(src,cv2.COLOR_BGR2GRAY)

img_info=src.shape

image_height=img_info[0]

image_weight=img_info[1]

dst=np.zeros((image_height,image_weight,1),np.uint8)

for i in range(image_height):

for j in range(image_weight):

grayPixel=gray[i][j]

dst[i][j]=255-grayPixel

cv2.imshow('gary',dst)

cv2.waitKey(0)

原灰度图

反转灰度图

彩色图反转优化

import cv2

import numpy as np

#读取彩色原图

src=cv2.imread('E:/python_cv/01.jpg',1)

img_info=src.shape

image_height=img_info[0]

image_weight=img_info[1]

dst=np.zeros((image_height,image_weight,3),np.uint8)

for i in range(image_height):

for j in range(image_weight):

(b,g,r)=src[i][j]

dst[i][j]=(255-b,255-g,255-r)

cv2.imshow('src',src)

cv2.imshow('dst',dst)

cv2.waitKey(0)

彩色图

反转图

图片融合ui

import cv2

import numpy as np

#读取彩色原图

src=cv2.imread('E:/python_cv/01.jpg',1)

src1=cv2.imread('E:/python_cv/02.jpg',1)

img_info=src.shape

image_height=img_info[0]

image_weight=img_info[1]

roi_h=int(image_height/2)

roi_w=int(image_weight/2)

src_roi=src[0:roi_h,0:roi_w]

src1_roi=src1[0:roi_h,0:roi_w]

dst=np.zeros((roi_h,roi_w,3),np.uint8)

dst=cv2.addWeighted(src_roi,0.5,src1_roi,0.5,0)

cv2.imshow('dst',dst)

cv2.waitKey(0)

canny 边缘检测spa

步骤：灰度图，高斯滤波，canny3d

import cv2

import numpy as np

gray=cv2.imread('E:/python_cv/01.jpg',0)

img=cv2.GaussianBlur(gray,(3,3),0)

dst=cv2.Canny(img,50,50)

cv2.imshow('gray',gray)

cv2.imshow('gauss',img)

cv2.imshow('dst',dst)

cv2.waitKey(0)

灰度图

高斯滤波

canny

soble边缘检测code

import cv2

import numpy as np

import math

gray=cv2.imread('E:/python_cv/01.jpg',0)

imgInfo=gray.shape

height=imgInfo[0]

weight=imgInfo[1]

cv2.imshow('src',gray)

dst=np.zeros((height,weight,1),np.uint8)

'''

sobel

1.算子模板 2.图像卷积 3.阈值判决

竖直模板 水平模板

[1 2 1 [1 0 -1

0 0 0 2 0 -2

-1-2-1] 1 0 -1]

'''

for i in range(0,height-2):

for j in range(0,weight-2):

gy=gray[i,j]*1+gray[i,j+1]*2+gray[i,j+2]*1-gray[i+2,j]-2*gray[i+2,j+1]-gray[i+2,j+2]*1

gx=gray[i,j]*1-gray[i,j+2]+gray[i+1,j]*2-2*gray[i+1,j+2]+gray[i+2,j]-gray[i+2,j+2]

grad=math.sqrt(gx*gx+gy*gy)

if grad>50:

dst[i,j]=255

else:

dst[i,j]=0

cv2.imshow('dst',dst)

cv2.waitKey(0)

原图

sobel

import cv2

import numpy as np

gray=cv2.imread('E:/python_cv/01.jpg',0)

imgInfo=gray.shape

height=imgInfo[0]

weight=imgInfo[1]

#浮雕效果

dst=np.zeros((height,weight,1),np.uint8)

for i in range(0,height):

for j in range(0,weight-1):

gray0=gray[i,j]

gray1=gray[i,j+1]

newp=gray0-gray1+150

if newp>255:

newp=255

else:

newp=0

dst[i,j]=newp

cv2.imshow('dst',dst)

cv2.waitKey(0)

原图

浮雕效果

风格转换blog

import cv2

import numpy as np

img=cv2.imread('E:/python_cv/01.jpg',1)

imgInfo=img.shape

height=imgInfo[0]

weight=imgInfo[1]

cv2.imshow('src',img)

'''

颜色风格，rgb-->RGB

b=b*1.5

g=g*1.3

'''

dst=np.zeros((height,weight,3),np.uint8)

for i in range(0,height):

for j in range(0,weight):

(b,g,r)=img[i,j]

b=1.5*b

g=1.3*g

if b>255:

b=255

if g>255:

g=255

dst[i,j]=(b,g,r)

cv2.imshow('dst',dst)

cv2.waitKey(0)

原图

颜色风格