Python: PS 滤镜特效 -- Marble Filter

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本文用 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 pltdef Init_arr(): B = 256P = 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 = 1e6for i in range(B+1):P[i] = ig1[i] = (((math.floor(random.random()*N_max)) % (2*B))-B)*1.0/Bg2[i, :] = (np.mod((np.floor(np.random.rand(1, 2)*N_max)), (2*B))-B)*1.0/Bg2[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/Bg3[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) % BP [i] = P [j]P [j] = kP[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, g3def Noise_2(x_val, y_val, P, g2):BM=255N=4096t = x_val + Nbx0 = ((np.floor(t).astype(int)) & BM) + 1bx1 = ((bx0 + 1).astype(int) & BM) + 1rx0 = t - np.floor(t)rx1 = rx0 - 1.0t = y_val + Nby0 = ((np.floor(t).astype(int)) & BM) + 1by1 = ((bx0 + 1).astype(int) & BM) + 1ry0 = t - np.floor(t)ry1 = rx0 - 1.0sx = rx0 * rx0 * (3 - 2.0 * rx0)sy = ry0 * ry0 * (3 - 2.0 * ry0)row, col = x_val.shapeq1 = 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 outfile_name='D:/Visual Effects/PS Algorithm/4.jpg';
img=io.imread(file_name)img = img_as_float(img)
row, col, channel = img.shapexScale = 25.0
yScale = 25.0
turbulence =0.25xx = 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 / yScaleIndex = 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.imshow(img)
plt.axis('off');plt.figure(2)
plt.imshow(img_out)
plt.axis('off');plt.show();