python 图像插值 最近邻、双线性、双三次实例

import cv2
import numpy as np
def function(img):
 height,width,channels =img.shape
 emptyImage=np.zeros((2048,2048,channels),np.uint8)
 sh=2048/height
 sw=2048/width
 for i in range(2048):
  for j in range(2048):
   x=int(i/sh)
   y=int(j/sw)
   emptyImage[i,j]=img[x,y]
 return emptyImage
 
img=cv2.imread("e:\\lena.bmp")
zoom=function(img)
cv2.imshow("nearest neighbor",zoom)
cv2.imshow("image",img)
cv2.waitKey(0)

import cv2
import numpy as np
import math
def function(img,m,n):
 height,width,channels =img.shape
 emptyImage=np.zeros((m,n,channels),np.uint8)
 value=[0,0,0]
 sh=m/height
 sw=n/width
 for i in range(m):
  for j in range(n):
   x = i/sh
   y = j/sw
   p=(i+0.0)/sh-x
   q=(j+0.0)/sw-y
   x=int(x)-1
   y=int(y)-1
   for k in range(3):
    if x+1<m and y+1<n:
     value[k]=int(img[x,y][k]*(1-p)*(1-q)+img[x,y+1][k]*q*(1-p)+img[x+1,y][k]*(1-q)*p+img[x+1,y+1][k]*p*q)
   emptyImage[i, j] = (value[0], value[1], value[2])
 return emptyImage
 
img=cv2.imread("e:\\lena.bmp")
zoom=function(img,2048,2048)
cv2.imshow("Bilinear Interpolation",zoom)
cv2.imshow("image",img)
cv2.waitKey(0)

import cv2
import numpy as np
import math
 
def S(x):
 x = np.abs(x)
 if 0 <= x < 1:
  return 1 - 2 * x * x + x * x * x
 if 1 <= x < 2:
  return 4 - 8 * x + 5 * x * x - x * x * x
 else:
  return 0
def function(img,m,n):
 height,width,channels =img.shape
 emptyImage=np.zeros((m,n,channels),np.uint8)
 sh=m/height
 sw=n/width
 for i in range(m):
  for j in range(n):
   x = i/sh
   y = j/sw
   p=(i+0.0)/sh-x
   q=(j+0.0)/sw-y
   x=int(x)-2
   y=int(y)-2
   A = np.array([
    [S(1 + p), S(p), S(1 - p), S(2 - p)]
   ])
   if x>=m-3:
    m-1
   if y>=n-3:
    n-1
   if x>=1 and x<=(m-3) and y>=1 and y<=(n-3):
    B = np.array([
     [img[x-1, y-1], img[x-1, y],
      img[x-1, y+1],
      img[x-1, y+1]],
     [img[x, y-1], img[x, y],
      img[x, y+1], img[x, y+2]],
     [img[x+1, y-1], img[x+1, y],
      img[x+1, y+1], img[x+1, y+2]],
     [img[x+2, y-1], img[x+2, y],
      img[x+2, y+1], img[x+2, y+1]],
 
     ])
    C = np.array([
     [S(1 + q)],
     [S(q)],
     [S(1 - q)],
     [S(2 - q)]
    ])
    blue = np.dot(np.dot(A, B[:, :, 0]), C)[0, 0]
    green = np.dot(np.dot(A, B[:, :, 1]), C)[0, 0]
    red = np.dot(np.dot(A, B[:, :, 2]), C)[0, 0]
 
    # ajust the value to be in [0,255]
    def adjust(value):
     if value > 255:
      value = 255
     elif value < 0:
      value = 0
     return value
 
    blue = adjust(blue)
    green = adjust(green)
    red = adjust(red)
    emptyImage[i, j] = np.array([blue, green, red], dtype=np.uint8)
 
 return emptyImage
 
img=cv2.imread("e:\\lena.bmp")
zoom=function(img,1024,1024)
cv2.imshow("cubic",zoom)
cv2.imshow("image",img)
cv2.waitKey(0)

function re_im = nearest(im, p, q)
%最邻近插值法，输入目标图像和行缩放、纵缩放倍数
%ziheng 2016.3.27
[m,n] = size(im);
im_R = im(:,:,1);
im_G = im(:,:,2);
im_B = im(:,:,3);
l = round(m*p);
h = round(n*q)/3;
re_R = uint8(zeros(l,h));
re_G = uint8(zeros(l,h));
re_B = uint8(zeros(l,h));
for dstx = 1:l
 for dsty = 1:h
   srcx = max(1,min(m,round(dstx/p)));
   srcy = max(1,min(n/3,round(dsty/q)));
   re_R(dstx,dsty) = im_R(srcx,srcy);
   re_G(dstx,dsty) = im_G(srcx,srcy);
   re_B(dstx,dsty) = im_B(srcx,srcy);
 end
end
re_im = cat(3,re_R,re_G,re_B);
figure,imshow(re_im);