opencv图像常规修改

canny 边缘检测

# 步骤：灰度图，高斯滤波，canny
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) 

#soble边缘检测
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)


# style transform 风格转换
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)

SAVE

import numpy as np
import cv2

img = cv2.imread('X.jpg',0)
cv2.imshow('image',img)
k = cv2.waitKey(0)
k = cv2.waitKey(0) & 0xFF  # 64位机器
if k == 27:         # 按下esc时，退出
    cv2.destroyAllWindows()
elif k == ord('s'): # 按下s键时保存并退出
    cv2.imwrite('M.png',img)
    cv2.destroyAllWindows()

图像修改

#缩放1
import cv2
img=cv2.imread('E:/python_cv/01.jpg',1)
img_info=img.shape
print(img_info)
image_height=img_info[0]
image_weight=img_info[1]
image_mode=img_info[2]
 
dstHeight=int(0.5*image_height)
dstWeight=int(0.5*image_weight)
dst=cv2.resize(img,(dstHeight,dstWeight))
cv2.imshow('image_dst',dst)
cv2.waitKey(0)

#缩放2
import cv2
import numpy as np
img=cv2.imread('E:/python_cv/01.jpg',1)
img_info=img.shape
print(img_info)
image_height=img_info[0]
image_weight=img_info[1]
image_mode=img_info[2]
 
dstHeight=int(0.5*image_height)
dstWeight=int(0.5*image_weight)
 
dst=np.zeros((dstHeight,dstWeight,3),np.uint8)  #0-255
for i in range(dstHeight):
    for j in range(dstWeight):
        img_i=int(i*(image_height)/dstHeight)
        img_j=int(j*(image_weight)/dstWeight)
        dst[i,j]=img[img_i,img_j]
cv2.imshow('image_dst',dst)
cv2.waitKey(0)

#缩放3
import cv2
import numpy as np
img=cv2.imread('E:/python_cv/01.jpg',1)
img_info=img.shape
print(img_info)
image_height=img_info[0]
image_weight=img_info[1]
image_mode=img_info[2]
 
mat_scale=np.float32([[0.5,0,0],[0,0.5,0]])
dst=cv2.warpAffine(img,mat_scale,(int(image_height/2),int(image_weight/2)))
cv2.imshow('image_dst',dst)
cv2.waitKey(0)

#图像剪切(x-->100到200，y-->100到300
import cv2
import numpy as np
img=cv2.imread('E:/python_cv/01.jpg',1)
img_info=img.shape
dst=img[100:200,100:300]
cv2.imshow('image_dst',dst)
cv2.waitKey(0)


#图像移位
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]
cv2.imshow('image_src',img)
#移位矩阵
mat_shift=np.float32([[1,0,100],[0,1,200]])
dst=cv2.warpAffine(img,mat_shift,(image_height,image_weight))
cv2.imshow('image_dst',dst)
cv2.waitKey(0)

#图像移位
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]
cv2.imshow('image_src',img)
dst=np.zeros(img.shape,np.uint8)
for i in range(image_height-200):
    for j in range(image_weight-100):
        dst[i+200,j+100]=img[i,j]
cv2.imshow('image_dst',dst)
cv2.waitKey(0)


#图像镜像
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]
cv2.imshow('image_src',img)
dst=np.zeros(img.shape,np.uint8)
for i in range(image_height):
    for j in range(image_weight):
        dst[i,j]=img[image_height-i-1,j]
cv2.imshow('image_dst',dst)
cv2.waitKey(0)



#仿射变换：位移 旋转 缩放
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]
#src3-->dst3(左上角，左下角，右上角）
mat_src=np.float32([[0,0],[0,image_height-1],[image_weight-1,0]])
mat_dst=np.float32([[50,50],[300,image_height-200],[image_weight-300,100]])
mat_Affine=cv2.getAffineTransform(mat_src,mat_dst)
dst=cv2.warpAffine(img,mat_Affine,(image_height,image_weight))
cv2.imshow('image_dst',dst)
cv2.waitKey(0)

#旋转
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]
mat_rotate=cv2.getRotationMatrix2D((image_height*0.5,image_weight*0.5),45,1)    #center angle 3scale
dst=cv2.warpAffine(img,mat_rotate,(image_height,image_weight))
cv2.imshow('image_dst',dst)
cv2.waitKey(0)

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)

#定点操作比浮点操作要快  +-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)

图像翻转

import cv2
import numpy as np
#读取彩色原图   灰度图翻转
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)


import cv2
import numpy as np
#读取彩色原图   彩色图翻转
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)


import cv2
import numpy as np
#读取彩色原图    图像融合
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)