Geometric Transformations of Images
扩展缩放
- cv2.resize()
# -*- coding: utf-8 -*-
# 改变图像尺寸
import cv2
import numpy as np
img=cv2.imread('demo.jpg')
# src 输入图像
# dsize 输出图像的尺寸，为空时的计算逻辑是 Size(round(fx*src.cols), round(fy*src.rows)), dsize 和 fx,fy不能同时为0
# fx x轴的缩放因子，为0时的计算逻辑是(double)dsize.width/src.cols
# fy y轴的缩放因子，为0时的计算逻辑是(double)dsize.height/src.rows
# interpolation 插值方法
res = cv2.resize(img,None,fx=0.5, fy=0.5, interpolation = cv2.INTER_AREA)
#或者直接设置输出图像的尺寸，不设置缩放因子，达到的效果是一样的
#height,width=img.shape[:2]
#res=cv2.resize(img,(width/2,height/2),interpolation=cv2.INTER_AREA)
cv2.imshow('res',res)
cv2.imshow('img',img)
cv2.waitKey(0)
image.png
平移
- cv2.warpAffine()
平移矩阵.jpg
# -*- coding: utf-8 -*-
import cv2
import numpy as np
img = cv2.imread('demo.jpg',0)
rows,cols = img.shape # 默认返回行数，列数，通道数
# 构建平移矩阵
M = np.float32([[1,0,100],[0,1,50]])
# 调用warpAffine进行平移
# img 图像
# M 平移矩阵
# (width,height) 输出图像大小
dst = cv2.warpAffine(img,M,(cols,rows))
cv2.imshow('img',dst)
cv2.waitKey(0)
result.jpg
旋转
- cv2.getRotationMatrix2D()与cv2.warpAffine()
# -*- coding: utf-8 -*-
# 旋转
import cv2
import numpy as np
img=cv2.imread('demo.jpg',0)
rows,cols = img.shape
# cv2.getRotationMatrix2D()用于构建旋转矩阵
# 参数一：旋转中心
# 参数二：旋转角度
# 参数三：缩放因子
M = cv2.getRotationMatrix2D((cols/2,rows/2),90,1)
# 参数三是输出图像的尺寸
dst = cv2.warpAffine(img,M,(cols,rows))
cv2.imshow('dst',dst)
cv2.waitKey(0)
result.jpg
仿射变换
# -*- coding: utf-8 -*-
# 仿射变换
import cv2
import numpy as np
from matplotlib import pyplot as plt
img=cv2.imread('demo.jpg',0)
rows,cols = img.shape
pts1 = np.float32([[50,50],[200,50],[50,200]])
pts2 = np.float32([[10,100],[200,50],[100,250]])
M = cv2.getAffineTransform(pts1,pts2)
dst = cv2.warpAffine(img,M,(cols,rows))
plt.subplot(121),plt.imshow(img),plt.title('Input')
plt.subplot(122),plt.imshow(dst),plt.title('Output')
plt.show()
result.jpg
上图可以看出matplotlib并没有显示出正常的颜色色彩，这是因为opencv的cv2库中的色彩空间和matplotlib库中的色彩空间的排布方式是不一样导致的。cv2中的色彩排列是(b,g,r)，而matplotlib库中的排列方式是(r,g,b)。可以通过以下代码对色彩空间进行转换之后再显示：
# -*- coding: utf-8 -*-
# 放射变换，色彩空间转换
import cv2
import numpy as np
from matplotlib import pyplot as plt
img_bgr = cv2.imread('demo.jpg')
img_rgb = np.zeros(img_bgr.shape, img_bgr.dtype)
img_rgb[:,:,0] = img_bgr[:,:,2]
img_rgb[:,:,1] = img_bgr[:,:,1]
img_rgb[:,:,2] = img_bgr[:,:,0]
rows,cols,ch = img_rgb.shape
pts1 = np.float32([[50,50],[200,50],[50,200]])
pts2 = np.float32([[10,100],[200,50],[100,250]])
M = cv2.getAffineTransform(pts1,pts2)
dst = cv2.warpAffine(img_rgb,M,(cols,rows))
plt.subplot(121),plt.imshow(img_rgb),plt.title('Input')
plt.subplot(122),plt.imshow(dst),plt.title('Output')
plt.show()
Capture.PNG
透视变换
# -*- coding: utf-8 -*-
# 透视变换
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('demo.jpg')
rows,cols,ch = img.shape
pts1 = np.float32([[56,65],[368,52],[28,387],[389,390]])
pts2 = np.float32([[0,0],[300,0],[0,300],[300,300]])
M = cv2.getPerspectiveTransform(pts1,pts2)
dst = cv2.warpPerspective(img,M,(300,300))
plt.subplot(121),plt.imshow(img),plt.title('Input')
plt.subplot(122),plt.imshow(dst),plt.title('Output')
plt.show()
result.jpg