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('3water.com')
plt.imshow(img)
plt.axis('off');
plt.figure(2)
plt.title('3water.com')
plt.imshow(img_out)
plt.axis('off');
plt.show();

运行效果：