def simplex(shape, time=0.0, seed=None, speed=1.0, as_np=False):
"""Return simplex noise values. Lives in its own module to avoid circular dependencies."""
tensor = np.empty(shape, dtype=np.float32)
if seed is None:
seed = get_seed()
# h/t Etienne Jacob
# https://necessarydisorder.wordpress.com/2017/11/15/drawing-from-noise-and-then-making-animated-loopy-gifs-from-there/
two_pi_times_time = math.tau * time
z = math.cos(two_pi_times_time) * speed
w = math.sin(two_pi_times_time) * speed
if len(shape) == 2:
simplex = OpenSimplex(seed=seed)
for y in range(shape[0]):
for x in range(shape[1]):
tensor[y][x] = simplex.noise4d(x, y, z, w)
else:
for c in range(shape[2]):
simplex = OpenSimplex(seed=seed + c * 65535)
for y in range(shape[0]):
for x in range(shape[1]):
tensor[y][x][c] = simplex.noise4d(x, y, z, w)
tensor = (tensor + 1.0) * .5
if not as_np:
tensor = tf.stack(tensor)
return tensor
class Benchmark:
def __init__(self):
self.simplex = OpenSimplex(seed=0)
def run(self, number=100000):
for i in _range(number):
self.simplex.noise2d(0.1, 0.1)
self.simplex.noise3d(0.1, 0.1, 0.1)
self.simplex.noise4d(0.1, 0.1, 0.1, 0.1)
class Benchmark:
def __init__(self):
self.simplex = OpenSimplex(seed=0)
def run(self, number=100000):
for i in _range(number):
self.simplex.noise2d(0.1, 0.1)
self.simplex.noise3d(0.1, 0.1, 0.1)
self.simplex.noise4d(0.1, 0.1, 0.1, 0.1)
class Benchmark:
def __init__(self):
self.simplex = OpenSimplex(seed=0)
# trigger compilation
x = np.linspace(0, 1, 10)
self.simplex.noise2d(x, x)
self.simplex.noise3d(x, x, x)
self.simplex.noise4d(x, x, x, x)
def run(self, number=1000000):
x = np.linspace(0, 1, number)
self.simplex.noise2d(x, x)
self.simplex.noise3d(x, x, x)
self.simplex.noise4d(x, x, x, x)
def main():
simplex = OpenSimplex()
print('Generating 2D image...')
im = Image.new('L', (WIDTH, HEIGHT))
for y in range(0, HEIGHT):
for x in range(0, WIDTH):
value = simplex.noise2d(x / FEATURE_SIZE, y / FEATURE_SIZE)
color = int((value + 1) * 128)
im.putpixel((x, y), color)
im.save('noise2d.png')
print('Generating 2D slice of 3D...')
im = Image.new('L', (WIDTH, HEIGHT))
for y in range(0, HEIGHT):
for x in range(0, WIDTH):
value = simplex.noise3d(x / FEATURE_SIZE, y / FEATURE_SIZE, 0.0)
color = int((value + 1) * 128)
im.putpixel((x, y), color)
im.save('noise3d.png')
print('Generating 2D slice of 4D...')
im = Image.new('L', (WIDTH, HEIGHT))
for y in range(0, HEIGHT):
for x in range(0, WIDTH):
value = simplex.noise4d(x / FEATURE_SIZE, y / FEATURE_SIZE, 0.0,
0.0)
color = int((value + 1) * 128)
im.putpixel((x, y), color)
im.save('noise4d.png')
def main():
simplex = OpenSimplex()
print("Generating 2D image...")
im = Image.new("L", (WIDTH, HEIGHT))
for y in range(0, HEIGHT):
for x in range(0, WIDTH):
value = simplex.noise2d(x / FEATURE_SIZE, y / FEATURE_SIZE)
color = int((value + 1) * 128)
im.putpixel((x, y), color)
im.save("noise2d.png")
print("Generating 2D slice of 3D...")
im = Image.new("L", (WIDTH, HEIGHT))
for y in range(0, HEIGHT):
for x in range(0, WIDTH):
value = simplex.noise3d(x / FEATURE_SIZE, y / FEATURE_SIZE, 0.0)
color = int((value + 1) * 128)
im.putpixel((x, y), color)
im.save("noise3d.png")
print("Generating 2D slice of 4D...")
im = Image.new("L", (WIDTH, HEIGHT))
for y in range(0, HEIGHT):
for x in range(0, WIDTH):
value = simplex.noise4d(x / FEATURE_SIZE, y / FEATURE_SIZE, 0.0, 0.0)
color = int((value + 1) * 128)
im.putpixel((x, y), color)
im.save("noise4d.png")
class OpenSimplexWrapper(NoiseLibWrapper):
def __init__(self, seed=0):
super().__init__(seed)
self.corelib = OpenSimplex(seed)
def eval2d(self, x, y):
return self.corelib.noise2d(x, y)
def eval3d(self, x, y, z):
return self.corelib.noise3d(x, y, z)
def eval4d(self, x, y, z, w):
return self.corelib.noise4d(x, y, z, w)
def simplex_noise():
FEATURE_SIZE = 4.0
freq_sine = 36
simplex = OpenSimplex()
print('Generating 2D image...')
noise_color = []
noise = Image.new('L', (32, 32))
for y in range(0, 32):
for x in range(0, 32):
value = simplex.noise4d(x / FEATURE_SIZE, y / FEATURE_SIZE, 0.0,
0.0)
color = int((value + 1) * 128)
noise.putpixel((x, y), color)
noise = normalize(np.array(noise))
noise = np.sin(noise * freq_sine * np.pi)
noise_color = colorize(normalize(noise))
return noise_color
def plot_arrow(x, y, dir):
plt.plot([x, x + dir[0]], [y, y + dir[1]], c='b')
simp = OpenSimplex()
n = 16
T_MAX = 2 * np.pi
NUM_FRAMES = 180
x = np.linspace(0, 1, n)
y = np.linspace(0, 1, n)
z = np.linspace(0, T_MAX, NUM_FRAMES)
t = np.linspace(0, T_MAX, NUM_FRAMES)
canvas = np.zeros((len(x), len(y)))
for k in range(len(t)):
print(k)
for i in range(len(x)):
for j in range(len(y)):
dirX = simp.noise4d(x[i], y[j], np.cos(t[k]), np.sin(z[k]))
dirY = simp.noise4d(x[i], y[j], np.cos(t[k]) + 3, np.sin(z[k]) + 3)
plot_arrow(10 * x[i], 10 * y[j], [dirX, dirY])
plt.axis('equal')
plt.axis('off')
plt.xlim(-1, 11)
plt.ylim(-1, 11)
#plt.show(block=False)
#plt.pause(0.1)
plt.savefig('ADFrames/' + str(k) + '.png')
plt.clf()
gif.gifFolder('ADFrames', 'arrows2D.gif')
model = VGG19(weights = 'imagenet')
size = 224
j = 0
FEATURE_SIZE = 40.0
simplex = OpenSimplex()
print('Generating 2D image...')
noise_color = []
freq_sine = 36
noise = Image.new('L', (size, size))
for y in range(0, size):
for x in range(0, size):
value = simplex.noise4d(x / FEATURE_SIZE, y / FEATURE_SIZE, 0.0)
color = int((value + 1) * 128)
noise.putpixel((x, y), color)
noise = normalize(np.array(noise))
noise = np.sin(noise * freq_sine * np.pi)
noise_color = colorize(normalize(noise))
max_norm = 12
i = 0
for filename in os.listdir(img_dir):
if not filename.startswith('.'):
index = (int)(filename.split(".JPEG")[0].split("_")[2])
#print(index)
img = image.load_img(img_dir + filename, target_size = (size, size)) # We assume all images have the same dimensions
img = image.img_to_array(img)
label = val_annotations_map[index-1]
i = i + 1
#setup
background_colour = (0, 0, 0)
(width, height) = (300, 300)
screen = pygame.display.set_mode((width, height)) #,pygame.FULLSCREEN)
screen.fill(background_colour)
pygame.display.set_caption('Simplex Noise')
pygame.font.init()
myfont = pygame.font.SysFont("monospace", 20)
clock = pygame.time.Clock()
simp = OpenSimplex()
print(simp.noise4d(x=10, y=10, z=5, w=2))
screen.fill(background_colour)
maxv = 0
minv = 255
values = []
for i in range(width):
values.append([])
for j in range(height):
s = i / width
t = j / height
nx = 5 + 2 * math.cos(2 * math.pi * s)
ny = 5.5 + 2 * math.cos(2 * math.pi * t)
palette = {
0: [73, 89, 103],
1: [189, 213, 234],
2: [87, 115, 153],
3: [255, 79, 121]
}
frames = []
fig = plt.Figure(figsize=(5, 4), dpi=100)
imData = io.BytesIO()
for k in range(len(t)):
print(k, end='\r')
for i in range(len(x)):
for j in range(len(y)):
for h in range(NUM_SAMPLE_LAYERS):
samples[i][j][h] = int(
simp.noise4d(x[i], y[j], t[k] + 2 * h, z[k]) + 1)
canvas[i][j] = palette[samples[i][j][0] + 2 * samples[i][j][1]]
plt.axis('off')
plt.imshow(canvas, interpolation="bicubic")
# plt.show(block=False)
# plt.pause(0.001)
plt.savefig(imData, format='png')
plt.clf()
imData.seek(0)
frames.append((255 * mpimg.imread(imData)).astype(np.uint8))
imData.seek(0)
print('Done, Saving gif...')
imageio.mimsave('lamp.gif', frames, duration=0.05)
fill='-',
zfill=' ')
print("Your animation is in process : ")
for step in range(NB_FRAMES):
stepPercent = step / NB_FRAMES
pb.print_progress_bar(step)
tx = dT * math.cos(stepPercent * 2 * math.pi)
ty = dT * math.sin(stepPercent * 2 * math.pi)
# Create the noise plane
for x in range(noise.shape[0]):
for y in range(noise.shape[1]):
n = simplex.noise4d(x * dX, y * dY, tx, ty)
noise[x, y] = n
# Resize it to fit the image
resized_noise = cv2.resize(noise, (img.shape[1], img.shape[0]))
# apply the effect to the hue
hue_effect = h + resized_noise * EFFECT_STRENGTH
np.clip(hue_effect, 0, 255, out=hue_effect)
hue_effect = hue_effect.astype('uint8')
# create the image back to be displayed
effect = cv2.merge((hue_effect, s, v))
effect = cv2.cvtColor(effect, cv2.COLOR_HSV2BGR)
if SAVE:
