python卡通滤镜_Python: PS 滤镜特效 -- Marble Filter
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 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.imshow(img)
plt.axis('off');
plt.figure(2)
plt.imshow(img_out)
plt.axis('off');
plt.show();