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:

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。