Python3+OpenCV2实现图像的几何变换(平移、镜像、缩放、旋转、仿射)


						前言

总结一下最近看的关于opencv图像几何变换的一些笔记. 



这是原图: 





1.平移





import cv2

import numpy as np



img = cv2.imread("image0.jpg", 1)

imgInfo = img.shape

height = imgInfo[0]

width = imgInfo[1]

mode = imgInfo[2]



dst = np.zeros(imgInfo, np.uint8)



for i in range( height ):

  for j in range( width - 100 ):

    dst[i, j + 100] = img[i, j]



cv2.imshow('image', dst)

cv2.waitKey(0)



demo很简单,就是将图像向右平移了100个像素.如图: 





2.镜像





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]

deep = imgInfo[2]



dst = np.zeros([height*2, width, deep], np.uint8)



for i in range( height ):

  for j in range( width ):

    dst[i,j] = img[i,j]

    dst[height*2-i-1,j] = img[i,j]



for i in range(width):

  dst[height, i] = (0, 0, 255)

cv2.imshow('image', dst)

cv2.waitKey(0)



demo生成一个如下效果: 





3.缩放





import cv2

img = cv2.imread("image0.jpg", 1)

imgInfo = img.shape

print( imgInfo )

height = imgInfo[0]

width = imgInfo[1]

mode = imgInfo[2]



# 1 放大 缩小 2 等比例 非等比例

dstHeight = int(height * 0.5)

dstWeight = int(width * 0.5)



# 最近邻域插值 双线性插值 像素关系重采样 立方插值

dst = cv2.resize(img, (dstWeight,dstHeight))

print(dst.shape)

cv2.imshow('image', dst)

cv2.waitKey(0)



使用resize直接进行缩放操作,同时还可以使用邻域插值法进行缩放,代码如下:





# 1 info 2 空白模板 3 重新计算x, y

import cv2

import numpy as np

img = cv2.imread('image0.jpg', 1)

imgInfo = img.shape # 先高度,后宽度

height = imgInfo[0]

width = imgInfo[1]

dstHeight = int(height/2)

dstWidth = int(width/2)



dstImage = np.zeros([dstHeight, dstWidth, 3], np.uint8)

for i in range( dstHeight ):

  for j in range(dstWidth):

    iNew = i * ( height * 1.0 / dstHeight )

    jNew = j * ( width * 1.0 / dstWidth )



    dstImage[i,j] = img[int(iNew),int(jNew)]



cv2.imshow('image', dstImage)

cv2.waitKey(0)



4.旋转





import cv2



img = cv2.imread('image0.jpg', 1)

cv2.imshow('src', img)

imgInfo = img.shape

height= imgInfo[0]

width = imgInfo[1]

deep = imgInfo[2]



# 定义一个旋转矩阵

matRotate = cv2.getRotationMatrix2D((height*0.5, width*0.5), 45, 0.7) # mat rotate 1 center 2 angle 3 缩放系数



dst = cv2.warpAffine(img, matRotate, (height, width))



cv2.imshow('image',dst)

cv2.waitKey(0)



旋转需要先定义一个旋转矩阵,cv2.getRotationMatrix2D(),参数1:需要旋转的中心点.参数2:需要旋转的角度.参数三:需要缩放的比例.效果如下图: 





5.仿射





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]

deep = imgInfo[2]

# src 3 -> dst 3 (左上角, 左下角,右上角)

matSrc = np.float32([[0,0],[0,height-1],[width-1, 0]]) # 需要注意的是 行列 和 坐标 是不一致的

matDst = np.float32([[50,50],[100, height-50],[width-200,100]])



matAffine = cv2.getAffineTransform(matSrc,matDst) #mat 1 src 2 dst 形成组合矩阵

dst = cv2.warpAffine(img, matAffine,(height, width))

cv2.imshow('image',dst)

cv2.waitKey(0)



需要确定图像矩阵的三个点坐标,及(左上角, 左下角,右上角).定义两个矩阵,matSrc 为原图的三个点坐标,matDst为进行仿射的三个点坐标,通过cv2.getAffineTransform()形成组合矩阵.效果如下: 





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