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theano-simplex-naive.py
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theano-simplex-naive.py
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#!/usr/bin/python3
# -*- coding: utf-8 -*-
from __future__ import division, print_function, unicode_literals
import numpy as np
import theano.tensor as T
import theano
from time import time
from input import get_input_vectors
from image_helpers import sum_phases, show
np_perm = [151, 160, 137, 91, 90, 15, 131, 13, 201, 95, 96, 53, 194, 233, 7, 225, 140, 36, 103, 30, 69, 142, 8, 99,
37, 240, 21, 10, 23, 190, 6, 148, 247, 120, 234, 75, 0, 26, 197, 62, 94, 252, 219, 203, 117, 35, 11, 32,
57, 177, 33, 88, 237, 149, 56, 87, 174, 20, 125, 136, 171, 168, 68, 175, 74, 165, 71, 134, 139, 48, 27,
166, 77, 146, 158, 231, 83, 111, 229, 122, 60, 211, 133, 230, 220, 105, 92, 41, 55, 46, 245, 40, 244, 102,
143, 54, 65, 25, 63, 161, 1, 216, 80, 73, 209, 76, 132, 187, 208, 89, 18, 169, 200, 196, 135, 130, 116,
188, 159, 86, 164, 100, 109, 198, 173, 186, 3, 64, 52, 217, 226, 250, 124, 123, 5, 202, 38, 147, 118, 126,
255, 82, 85, 212, 207, 206, 59, 227, 47, 16, 58, 17, 182, 189, 28, 42, 223, 183, 170, 213, 119, 248, 152,
2, 44, 154, 163, 70, 221, 153, 101, 155, 167, 43, 172, 9, 129, 22, 39, 253, 19, 98, 108, 110, 79, 113, 224,
232, 178, 185, 112, 104, 218, 246, 97, 228, 251, 34, 242, 193, 238, 210, 144, 12, 191, 179, 162, 241, 81,
51, 145, 235, 249, 14, 239, 107, 49, 192, 214, 31, 181, 199, 106, 157, 184, 84, 204, 176, 115, 121, 50,
45, 127, 4, 150, 254, 138, 236, 205, 93, 222, 114, 67, 29, 24, 72, 243, 141, 128, 195, 78, 66, 215, 61,
156, 180]
np_perm = np.array(np_perm + np_perm, dtype=np.int32)
np_grad3 = np.array([[1, 1, 0], [-1, 1, 0], [1, -1, 0], [-1, -1, 0],
[1, 0, 1], [-1, 0, 1], [1, 0, -1], [-1, 0, -1],
[0, 1, 1], [0, -1, 1], [0, 1, -1], [0, -1, -1]], dtype=np.float32)
vertices_options = np.array([
[[1, 0, 0], [1, 1, 0]],
[[1, 0, 0], [1, 0, 1]],
[[0, 0, 1], [1, 0, 1]],
[[0, 0, 1], [0, 1, 1]],
[[0, 1, 0], [0, 1, 1]],
[[0, 1, 0], [1, 1, 0]]
], dtype=np.int32)
# Dimensions are: x0 >= y0, y0 >= z0, x0 >= z0
vertices_table = np.array([
[[[vertices_options[3]], [vertices_options[3]]],
[[vertices_options[4]], [vertices_options[5]]]],
[[[vertices_options[2]], [vertices_options[1]]],
[[vertices_options[2]], [vertices_options[0]]]]
], dtype=np.uint8)
def t_noise3d(v, perm, grad3):
x = v[0]
y = v[1]
z = v[2]
skew_factor = (x + y + z) * 1.0 / 3.0
i = T.floor(x + skew_factor)
j = T.floor(y + skew_factor)
k = T.floor(z + skew_factor)
unskew_factor = (i + j + k) * 1.0 / 6.0
x0 = x - (i - unskew_factor)
y0 = y - (j - unskew_factor)
z0 = z - (k - unskew_factor)
vertices = T.switch(T.ge(x0, y0),
T.switch(T.ge(y0, z0), vertices_options[0],
T.switch(T.ge(x0, z0), vertices_options[1],
vertices_options[2])),
T.switch(T.lt(y0, z0), vertices_options[3],
T.switch(T.lt(x0, z0), vertices_options[4],
vertices_options[5]))
)
x1 = x0 - vertices[0][0] + 1.0 / 6.0
y1 = y0 - vertices[0][1] + 1.0 / 6.0
z1 = z0 - vertices[0][2] + 1.0 / 6.0
x2 = x0 - vertices[1][0] + 1.0 / 3.0
y2 = y0 - vertices[1][1] + 1.0 / 3.0
z2 = z0 - vertices[1][2] + 1.0 / 3.0
x3 = x0 - 0.5
y3 = y0 - 0.5
z3 = z0 - 0.5
ii = T.bitwise_and(i.astype('int32'), 255)
jj = T.bitwise_and(j.astype('int32'), 255)
kk = T.bitwise_and(k.astype('int32'), 255)
gi0 = perm[ii + perm[
jj + perm[
kk].astype('int32')].astype('int32')] % 12
gi1 = perm[ii + vertices[0][0] + perm[
jj + vertices[0][1] + perm[
kk + vertices[0][2]].astype('int32')].astype('int32')] % 12
gi2 = perm[ii + vertices[1][0] + perm[
jj + vertices[1][1] + perm[
kk + vertices[1][2]].astype('int32')].astype('int32')] % 12
gi3 = perm[ii + 1 + perm[
jj + 1 + perm[
kk + 1].astype('int32')].astype('int32')] % 12
t0 = 0.5 - x0 ** 2 - y0 ** 2 - z0 ** 2
n0 = T.switch(
T.lt(t0, 0),
0.0,
t0 ** 4 * T.dot(grad3[gi0.astype('int32')], [x0, y0, z0]))
t1 = 0.5 - x1 ** 2 - y1 ** 2 - z1 ** 2
n1 = T.switch(
T.lt(t1, 0),
0.0,
t1 ** 4 * T.dot(grad3[gi1.astype('int32')], [x1, y1, z1])),
t2 = 0.5 - x2 ** 2 - y2 ** 2 - z2 ** 2
n2 = T.switch(
T.lt(t2, 0),
0.0,
t2 ** 4 * T.dot(grad3[gi2.astype('int32')], [x2, y2, z2]))
t3 = 0.5 - x3 ** 2 - y3 ** 2 - z3 ** 2
n3 = T.switch(
T.lt(t3, 0),
0.0,
t3 ** 4 * T.dot(grad3[gi3.astype('int32')], [x3, y3, z3]))
return 23.0 * (n0 + n1 + n2 + n3)
if __name__ == "__main__":
theano.config.openmp = True
theano.config.openmp_elemwise_minsize = 200
perm = T.vector('perm', dtype='int32')
grad3 = T.matrix('grad3', dtype='float32')
vl = T.matrix('vl', dtype='float32')
output, updates = theano.map(fn=t_noise3d,
sequences=[vl],
non_sequences=[perm, grad3],
name="noise_all_pixels")
simplex_noise = theano.function([vl, perm, grad3], output)
print("Compiled")
shape = (512, 512)
phases = 5
scaling = 200.0
input_vectors = get_input_vectors(shape, phases, scaling)
start_time = time()
raw_noise = simplex_noise(input_vectors, np_perm, np_grad3)
print("The calculation took " + str(time() - start_time) + " seconds.")
image_data = sum_phases(raw_noise, phases, shape)
show(image_data)