Python实现PS滤镜功能之波浪特效示例

这篇文章主要介绍了Python实现PS滤镜功能之波浪特效,结合实例形式分析了Python实现PS滤镜波浪特效的原理与相关操作技巧,需要的朋友可以参考下
这里用 Python 实现 PS 滤镜的波浪特效，具体效果可以参考附录说明    
import numpy as np
from skimage import img_as_float
import matplotlib.pyplot as plt
from skimage import io
import numpy.matlib
import math
file_name2='D:/Visual Effects/PS Algorithm/4.jpg'
img=io.imread(file_name2)
img = img_as_float(img)
row, col, channel = img.shape
img_out = img * 1.0
alpha = 70.0
beta = 30.0
degree = 20.0
center_x = (col-1)/2.0
center_y = (row-1)/2.0
xx = np.arange(col)
yy = np.arange(row)
x_mask = numpy.matlib.repmat (xx, row, 1)
y_mask = numpy.matlib.repmat (yy, col, 1)
y_mask = np.transpose(y_mask)
xx_dif = x_mask - center_x
yy_dif = center_y - y_mask
x = degree * np.sin(2 * math.pi * yy_dif / alpha) + xx_dif
y = degree * np.cos(2 * math.pi * xx_dif / beta) + yy_dif
x_new = x + center_x
y_new = center_y - y
int_x = np.floor (x_new)
int_x = int_x.astype(int)
int_y = np.floor (y_new)
int_y = int_y.astype(int)
for ii in range(row):
  for jj in range (col):
    new_xx = int_x [ii, jj]
    new_yy = int_y [ii, jj]
    if x_new [ii, jj] < 0 or x_new [ii, jj] > col -1 :
      continue
    if y_new [ii, jj] < 0 or y_new [ii, jj] > row -1 :
      continue
    img_out[ii, jj, :] = img[new_yy, new_xx, :]
plt.figure (1)
plt.title('www.jb51.net')
plt.imshow (img)
plt.axis('off')
plt.figure (2)
plt.title('www.jb51.net')
plt.imshow (img_out)
plt.axis('off')
plt.show()
附录：PS 滤镜——波浪 wave    
%%% Wave
%%% 波浪效果
clc;
clear all;
close all;
addpath('E:\PhotoShop Algortihm\Image Processing\PS Algorithm');
I=imread('4.jpg');
Image=double(I);
% Image=0.2989 * I(:,:,1) + 0.5870 * I(:,:,2) + 0.1140 * I(:,:,3);
[row, col,channel]=size(Image);
R=floor(max(row, col)/2);
Image_new=Image;
Degree=30;  % 控制扭曲的程度
Center_X=(col+1)/2;
Center_Y=(row+1)/2;
for i=1:row
  for j=1:col
    x0=j-Center_X;
    y0=Center_Y-i;
    x=Degree*sin(2*pi*y0/128)+x0;
    y=Degree*cos(2*pi*x0/128)+y0;
    x=x+col/2;
    y=row/2-y;
    if(x>1 && x<col && y<row && y>1)
      x1=floor(x);
      y1=floor(y);
      p=x-x1;
      q=y-y1;
       Image_new(i,j,:)=(1-p)*(1-q)*Image(y1,x1,:)+p*(1-q)*Image(y1,x1+1,:)...
              +q*(1-p)*Image(y1+1,x1,:)+p*q*Image(y1+1,x1+1,:);
    end
  end
end
figure, imshow(Image_new/255);
本例Python运行效果：