图像形变（插值方法）

类似于iphone相册的水平矫正

双线性插值

from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import math
def BiLinear_interpolation(img,dstH,dstW):
    scrH,scrW,_=img.shape
    print(img)
    img=np.pad(img,((0,1),(0,1),(0,0)),'constant')
    print(img)
    retimg=np.zeros((dstH,dstW,3),dtype=np.uint8)
    for i in range(dstH):
        for j in range(dstW):
            scrx=(i+1)*(scrH/dstH)-1
            scry=(j+1)*(scrW/dstW)-1
            x=math.floor(scrx)
            y=math.floor(scry)
            u=scrx-x
            v=scry-y
            retimg[i,j]=(1-u)*(1-v)*img[x,y]+u*(1-v)*img[x+1,y]+(1-u)*v*img[x,y+1]+u*v*img[x+1,y+1]
    return retimg
im_path='1.jpg'
image=np.array(Image.open(im_path))
#image2=BiLinear_interpolation(image,1,300) 调整图像大小
image2=BiLinear_interpolation(image,image.shape[0]*2,image.shape[1]*2) 
image2=Image.fromarray(image2.astype('uint8')).convert('RGB')
image2.save('out.png')

利用双线性插值进行形变

本想resize本质是插值方法，应该效率高一些，但是没有好好梳理代码，做了很多重复的插值（一张图像所有像素列都进行了插值，可我只需要其中第 i 列），以后有时间再去调整吧。效率太慢，弃用！

from tkinter import N, W, Y
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import math

#双线性插值
def BiLinear_interpolation(img,dstH,dstW):
    scrH,scrW,_=img.shape
    # img[1]，代表第一行 存在第0行
    # img[x,y]，x代表行。y代表列
    img=np.pad(img,((0,1),(0,1),(0,0)),'constant') # 最后一行，最后一列填充
    retimg=np.zeros((dstH,dstW,3),dtype=np.uint8)
    #for k in range(img.shape[0]):
    for i in range(dstH):
        for j in range(dstW):
            scrx=(i+1)*(scrH/dstH)-1
            scry=(j+1)*(scrW/dstW)-1
            x=math.floor(scrx)
            y=math.floor(scry)
            u=scrx-x
            v=scry-y
            retimg[i,j]=(1-u)*(1-v)*img[x,y]+u*(1-v)*img[x+1,y]+(1-u)*v*img[x,y+1]+u*v*img[x+1,y+1]
    return retimg

#构成梯形缩放
def Ladder_shaped(image,y):
    H=image.shape[0]
    WW=image.shape[1]
    # print(H)
    # print(W)
    P=np.zeros((y,WW,3,),dtype=np.uint8)
#    print(P)
    for x in range(WW):
        h=(x*(y-H)/WW)+H
#        print(h)
#        print(int(h))
        image2=BiLinear_interpolation(image,int(h),WW)
        for k in range(int(h)):
            P[k,x]=image2[k,x]
            print(x)
            print(k)
            # print(image2[x,k])
#        print(P)

    return P

im_path='2.jpg'
image=np.array(Image.open(im_path))

#image2=BiLinear_interpolation(image,1,300)
# image2=BiLinear_interpolation(image,image.shape[0]*2,image.shape[1]*2)
# image2=Image.fromarray(image2.astype('uint8')).convert('RGB')
# image2.save('out.png')
image3=Ladder_shaped(image,4000)
image3=Image.fromarray(image3.astype('uint8')).convert('RGB')
image3.save('test.png')

最后还是用resize吧

from tkinter import N, W, Y
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import math

#构成梯形缩放
def Ladder_shaped(img,image,y):
    H=image.shape[0]
    WW=image.shape[1]
    # print(H)
    # print(W)
    P=np.zeros((y,WW,3,),dtype=np.uint8)
#    print(P)
    for x in range(WW):
        h=(x*(y-H)/WW)+H
#        print(h)WW
#        print(int(h))
        image2 = img.resize((WW, int(h)),Image.ANTIALIAS)
        image2 = np.array(image2)

        for k in range(int(h)):
            P[k,x]=image2[k,x]
            print(x)
            print(k)
            # print(image2[x,k])
#        print(P)

    return P

im_path='3.jpg'
img=Image.open(im_path)
image=np.array(Image.open(im_path))
image3=Ladder_shaped(img,image,2000)
image3=Image.fromarray(image3.astype('uint8')).convert('RGB')
image3.save('testresize7.png')