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 npimport mathimport numpy.matlibfrom skimage import ioimport randomfrom skimage import img_as_floatimport matplotlib.pyplot as pltdef 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, g3def 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 outfile_name='D:/Visual Effects/PS Algorithm/4.jpg';img=io.imread(file_name)img = img_as_float(img)row, col, channel = img.shapexScale = 25.0yScale = 25.0turbulence =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 / xScaley_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] = 255Dis[Dis<0] = 0Dis = 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();