Python实现PS滤镜特效Marble Filter玻璃条纹扭曲效果示例
本文实例讲述了Python实现PS滤镜特效Marble Filter玻璃条纹扭曲效果。分享给大家供大家参考,具体如下:
这里用 Python 实现 PS 滤镜特效,Marble Filter, 这种滤镜使图像产生不规则的扭曲,看起来像某种玻璃条纹, 具体的代码如下:
import numpy as np import math import numpy.matlib from skimage import io import random from skimage import img_as_float import matplotlib.pyplot as plt def Init_arr(): B = 256 P = np.zeros((B+B+2, 1)) g1 = np.zeros((B+B+2, 1)) g2 = np.zeros((B+B+2, 2)) g3 = np.zeros((B+B+2, 3)) N_max = 1e6 for i in range(B+1): P[i] = i g1[i] = (((math.floor(random.random()*N_max)) % (2*B))-B)*1.0/B g2[i, :] = (np.mod((np.floor(np.random.rand(1, 2)*N_max)), (2*B))-B)*1.0/B g2[i, :] = g2[i, :] / np.sum(g2[i, :] **2) g3[i, :] = (np.mod((np.floor(np.random.rand(1, 3)*N_max)), (2*B))-B)*1.0/B g3[i, :] = g3[i, :] / np.sum(g3[i, :] **2) for i in range(B, -1, -1): k = P[i] j = math.floor(random.random()*N_max) % B P [i] = P [j] P [j] = k P[B+1:2*B+2]=P[0:B+1]; g1[B+1:2*B+2]=g1[0:B+1]; g2[B+1:2*B+2, :]=g2[0:B+1, :] g3[B+1:2*B+2, :]=g3[0:B+1, :] P = P.astype(int) return P, g1, g2, g3 def Noise_2(x_val, y_val, P, g2): BM=255 N=4096 t = x_val + N bx0 = ((np.floor(t).astype(int)) & BM) + 1 bx1 = ((bx0 + 1).astype(int) & BM) + 1 rx0 = t - np.floor(t) rx1 = rx0 - 1.0 t = y_val + N by0 = ((np.floor(t).astype(int)) & BM) + 1 by1 = ((bx0 + 1).astype(int) & BM) + 1 ry0 = t - np.floor(t) ry1 = rx0 - 1.0 sx = rx0 * rx0 * (3 - 2.0 * rx0) sy = ry0 * ry0 * (3 - 2.0 * ry0) row, col = x_val.shape q1 = np.zeros((row, col ,2)) q2 = q1.copy() q3 = q1.copy() q4 = q1.copy() for i in range(row): for j in range(col): ind_i = P[bx0[i, j]] ind_j = P[bx1[i, j]] b00 = P[ind_i + by0[i, j]] b01 = P[ind_i + by1[i, j]] b10 = P[ind_j + by0[i, j]] b11 = P[ind_j + by1[i, j]] q1[i, j, :] = g2[b00, :] q2[i, j, :] = g2[b10, :] q3[i, j, :] = g2[b01, :] q4[i, j, :] = g2[b11, :] u1 = rx0 * q1[:, :, 0] + ry0 * q1[:, :, 1] v1 = rx1 * q2[:, :, 0] + ry1 * q2[:, :, 1] a = u1 + sx * (v1 - u1) u2 = rx0 * q3[:, :, 0] + ry0 * q3[:, :, 1] v2 = rx1 * q4[:, :, 0] + ry1 * q4[:, :, 1] b = u2 + sx * (v2 - u2) out = (a + sy * (b - a)) * 1.5 return out file_name='D:/Visual Effects/PS Algorithm/4.jpg'; img=io.imread(file_name) img = img_as_float(img) row, col, channel = img.shape xScale = 25.0 yScale = 25.0 turbulence =0.25 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) x_val = x_mask / xScale y_val = y_mask / yScale Index = np.arange(256) sin_T=-yScale*np.sin(2*math.pi*(Index)/255*turbulence); cos_T=xScale*np.cos(2*math.pi*(Index)/255*turbulence) P, g1, g2, g3 = Init_arr() Noise_out = Noise_2(x_val, y_val, P, g2) Noise_out = 127 * (Noise_out + 1) Dis = np.floor(Noise_out) Dis[Dis>255] = 255 Dis[Dis<0] = 0 Dis = Dis.astype(int) img_out = img.copy() for ii in range(row): for jj in range(col): new_x = jj + sin_T[Dis[ii, jj]] new_y = ii + cos_T[Dis[ii, jj]] if (new_x > 0 and new_x < col-1 and new_y > 0 and new_y < row-1): int_x = int(new_x) int_y = int(new_y) img_out[ii, jj, :] = img[int_y, int_x, :] 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();
运行效果:
附:PS 滤镜 Marble 效果原理
clc; clear all; close all; addpath('E:\PhotoShop Algortihm\Image Processing\PS Algorithm'); I=imread('4.jpg'); I=double(I); Image=I/255; xScale = 20; yScale = 20; amount = 1; turbulence =0.25; Image_new=Image; [height, width, depth]=size(Image); Index=1:256; sin_T=-yScale*sin(2*pi*(Index-1)/256*turbulence); cos_T=xScale*cos(2*pi*(Index-1)/256*turbulence); [ind, g1, g2, g3]=init_arr(); for ii=1:height % % [ind, g1, g2, g3]=init_arr(); for jj=1:width dis=min(max( floor(127*(1+Noise2(jj/xScale, ii/yScale, ind, g2))), 1), 256); x=jj+sin_T(dis); y=ii+cos_T(dis); % % if (x<=1) x=1; end % % if (x>=width) x=width-1; end; % % if (y>=height) y=height-1; end; % % if (y<1) y=1; end; % % if (x<=1) continue; end if (x>=width) continue; end; if (y>=height) continue; end; if (y<1) continue; end; x1=floor(x); y1=floor(y); p=x-x1; q=y-y1; Image_new(ii,jj,:)=(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 imshow(Image_new) imwrite(Image_new, 'out.jpg');
参考来源:http://www.jhlabs.com/index.html
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