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
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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)
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demo很简单,就是将图像向右平移了100个像素.如图:
2.镜像
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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)
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demo生成一个如下效果:
3.缩放
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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)
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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:
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# 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)
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4.旋转
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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)
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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
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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)
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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服务器之家。
原文链接：https://blog.csdn.net/missyougoon/article/details/81092512