最近鄰：

import cv2import numpy as npdef 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 cv2import numpy as npimport mathdef 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 cv2import numpy as npimport 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 0def 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)

補充知識：最鄰近插值法(The nearest interpolation)實現圖像縮放

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。但當圖像中包含像素之間灰度級有變化的細微結構時,最鄰近算法會在圖像中產生人為加工的痕跡。

具體計算方法：對于一個目的坐標，設為 M(x,y)，通過向后映射法得到其在原始圖像的對應的浮點坐標，設為 m(i+u,j+v)，其中 i,j 為正整數，u,v 為大于零小于1的小數(下同)，則待求象素灰度的值 f(m)。利用浮點 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，輸入目標圖像和行縮放、縱縮放倍數%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); endendre_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、雙三次實例就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持好吧啦網。