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python 圖像插值最近鄰、雙線性、雙三次實(shí)例

更新時間：2020年07月05日 10:05:21 作者：cc0587

這篇文章主要介紹了python 圖像插值最近鄰、雙線性、雙三次實(shí)例，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧

最近鄰：

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)

雙線性：

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)

雙三次：

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)

補(bǔ)充知識：最鄰近插值法(The nearest interpolation)實(shí)現(xiàn)圖像縮放

也稱零階插值。它輸出的像素灰度值就等于距離它映射到的位置最近的輸入像素的灰度值。但當(dāng)圖像中包含像素之間灰度級有變化的細(xì)微結(jié)構(gòu)時,最鄰近算法會在圖像中產(chǎn)生人為加工的痕跡。

具體計算方法：對于一個目的坐標(biāo)，設(shè)為 M(x,y)，通過向后映射法得到其在原始圖像的對應(yīng)的浮點(diǎn)坐標(biāo)，設(shè)為 m(i+u,j+v)，其中 i,j 為正整數(shù)，u,v 為大于零小于1的小數(shù)(下同)，則待求象素灰度的值 f(m)。利用浮點(diǎn) m 相鄰的四個像素求f(m)的值。

function re_im = nearest(im, p, q)
%最鄰近插值法，輸入目標(biāo)圖像和行縮放、縱縮放倍數(shù)
%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);

以上這篇python 圖像插值最近鄰、雙線性、雙三次實(shí)例就是小編分享給大家的全部內(nèi)容了，希望能給大家一個參考，也希望大家多多支持腳本之家。

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