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python卡通滤镜_Python: PS 滤镜特效 -- Marble Filter

锺离伟彦
2023-12-01

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();

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