hua1.jpg

OpenCV图片缩放

方法一、

# 1 load 2 info 3 resize 4 check
import cv2
img = cv2.imread('hua1.jpg',1)
cv2.imshow("hua1",img)
imgInfo = img.shape
print(imgInfo)
height = imgInfo[0]
width = imgInfo[1]
mode = imgInfo[2]
# 1 放大 缩小 2 同比缩放   等比例   非同比缩放   非等比例   2:3 
dstHeight = int(height*0.5)
dstWidth = int(width*0.5)
#最近临域插值 双线性插值 像素关系重采样 立方插值
dst = cv2.resize(img,(dstWidth,dstHeight))
cv2.imshow('hua2',dst)
cv2.waitKey(0)

#最近临域插值 双线性插值 原理
# src 10*20 dst 5*10
# dst<-src
# (1,2) <- (2,4)
# dst x 1 -> src x 2 newX
# newX = x*(src 行/目标 行) newX = 1*（10/5） = 2
# newY = y*(src 列/目标 列) newY = 2*（20/10）= 4
# 12.3 = 12

# 双线性插值
# A1 = 20% 上+80%下 A2
# B1 = 30% 左+70%右 B2
# 1 最终点  = A1 30% + A2 70%
# 2 最终点  = B1 20% + B2 80%

#实质：矩阵运算 

方法二、

# 1 info 2 空白模版 3 xy
import cv2
import numpy as np
img = cv2.imread('hua1.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
dstHeight = int(height/2)
dstWidth = int(width/2)
dstImage = np.zeros((dstHeight,dstWidth,3),np.uint8)#0-255 
for i in range(0,dstHeight):#行
    for j in range(0,dstWidth):#列 
        iNew = int(i*(height*1.0/dstHeight))
        jNew = int(j*(width*1.0/dstWidth))
        dstImage[i,j] = img[iNew,jNew]
cv2.imshow('hua',dstImage)
cv2.waitKey(0)
# 1 opencv API resize 2 算法原理 3 源码 

方法三、

#[[A1 A2 B1],[A3 A4 B2]]
# [[A1 A2],[A3 A4]]  [[B1],[B2]]
# newX = A1*x + A2*y+B1
# newY = A3*x +A4*y+B2
# x->x*0.5 y->y*0.5
# newX = 0.5*x
import cv2
import numpy as np
img = cv2.imread('hua1.jpg',1)
cv2.imshow('hua1',img)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
matScale = np.float32([[0.5,0,0],[0,0.5,0]])
dst = cv2.warpAffine(img,matScale,(int(width/2),int(height/2)))
cv2.imshow('hua2',dst)
cv2.waitKey(0)

图片剪切

#100 -》200 x
#100-》300 y
import cv2
img = cv2.imread('hua1.jpg',1)
imgInfo = img.shape
dst = img[100:200,100:300]
cv2.imshow('hua',dst)
cv2.waitKey(0)

图片位移

方法一、

# 1 API 2 算法原理 3 源代码
import cv2
import numpy as np
img = cv2.imread('hua1.jpg',1)
cv2.imshow('hua1',img)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
####
matShift = np.float32([[1,0,100],[0,1,200]])# 2*3
dst = cv2.warpAffine(img,matShift,(height,width))#1 data 2 mat 3 info
# 移位 矩阵
cv2.imshow('hua2',dst)
cv2.waitKey(0)

# [1,0,100],[0,1,200] 2*2 2*1 
# [[1,0],[0,1]]  2*2  A
# [[100],[200]] 2*1   B
# xy C
# A*C+B = [[1*x+0*y],[0*x+1*y]]+[[100],[200]]
# = [[x+100],[y+200]]

#(10,20)->(110,120)

方法二、

import cv2
import numpy as np
img = cv2.imread('hua1.jpg',1)
cv2.imshow('hua1',img)
imgInfo = img.shape
dst = np.zeros(img.shape,np.uint8)
height = imgInfo[0]
width = imgInfo[1]
for i in range(0,height):
    for j in range(0,width-100):
        dst[i,j+100]=img[i,j]
cv2.imshow('hua2',dst)
cv2.waitKey(0)

图片镜像

import cv2
import numpy as np
img = cv2.imread('hua1.jpg',1)
cv2.imshow('hua1',img)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
deep = imgInfo[2]
newImgInfo = (height*2,width,deep)
dst = np.zeros(newImgInfo,np.uint8)#uint8 
for i in range(0,height):
    for j in range(0,width):
        dst[i,j] = img[i,j]
        #x y = 2*h - y -1
        dst[height*2-i-1,j] = img[i,j]
for i in range(0,width):
    dst[height,i] = (0,0,255)#BGR
cv2.imshow('hua2',dst)
cv2.waitKey(0)

仿射变换

import cv2
import numpy as np
img = cv2.imread('hua1.jpg',1)
cv2.imshow('hua1',img)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
#src 3->dst 3 (左上角 左下角 右上角)
matSrc = np.float32([[0,0],[0,height-1],[width-1,0]])
matDst = np.float32([[50,50],[300,height-200],[width-300,100]])
#组合
matAffine = cv2.getAffineTransform(matSrc,matDst)# mat 1 src 2 dst
dst = cv2.warpAffine(img,matAffine,(width,height))
cv2.imshow('hua2',dst)
cv2.waitKey(0)

图片旋转

import cv2
import numpy as np
img = cv2.imread('hua1.jpg',1)
cv2.imshow('hua1',img)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
# 2*3 
matRotate = cv2.getRotationMatrix2D((height*0.5,width*0.5),45,1)# mat rotate 1 center 2 angle 3 scale
#100*100 25 
dst = cv2.warpAffine(img,matRotate,(height,width))
cv2.imshow('hua2',dst)
cv2.waitKey(0)