def __init__(self, size, seed=0, population=1000000):
self.size = size
self.population = population
noise = generate_perlin_noise_2d(size, (4, 4), (False, False))
noise = np.where(noise < 0, 0, noise)
noise *= population / np.sum(noise)
self.population_distribution = noise
def _instantiate_grid_(self):
self._hex_model = loader.loadModel("models/simple_hex.obj")
# NOTE: higher resolution has steeper gradients per area.
# The generation of terrain with perlin noise should be done elsewhere.
np.random.seed(12345)
self.terrain_noise = generate_perlin_noise_2d((512, 512), (32, 32),
tileable=(True, True))
# Set the initial position and scale.
self._hex_model.setPos(0, 0, 0)
self._hex_model.setScale(1)
self._hex_model.setHpr(0, 0, 0)
self._hex_model.setDepthOffset(1)
center_chunk = np.array((0, 0, 0))
chunk_id = self.data_model.chunk_vec_to_buf(center_chunk)
starting_chunk = self.data_model.load_chunk(chunk_id)
chunk_radius_to_render = 2
radius_to_render = (self.data_model.chunk_radius * 3 +
1) * chunk_radius_to_render
# BFS Queue
neighbors_to_explore = [starting_chunk]
seen_neighbors = set()
loaded_chunks = {chunk_id}
while len(neighbors_to_explore) != 0:
chunk = neighbors_to_explore.pop(0)
if chunk in seen_neighbors:
continue
seen_neighbors.add(chunk)
# Only render chunk if the chunk's radius is less than or equal to R away
origin_d = cubic_manhattan(starting_chunk.chunk_center,
chunk.chunk_center)
if origin_d > radius_to_render:
continue
# Convert the list of neighbor positions to ids, then load the neighbors.
for neighbor_vector in chunk.neighbor_chunk_vec:
neighbor_id = self.data_model.chunk_vec_to_buf(neighbor_vector)
if neighbor_id not in loaded_chunks:
self.data_model.load_chunk(neighbor_id)
loaded_chunks.add(neighbor_id)
self._render_chunk(chunk)
neighbors_to_explore.extend(
filter(
lambda n: n is not None and n not in neighbors_to_explore,
chunk.neighbors))
def __init__(self, shape, res, seed=0):
"""
+ shape: shape of the generated array (tuple of 2 ints)
+ res: number of periods of noise to generate along each axis (tuple of 2 ints)
+ seed: rng seed (numpy)
Note: shape must be a multiple of res
"""
random.seed(seed)
noise = generate_perlin_noise_2d(shape, res, (True, True))
# [-1, 1] -> [-pi, pi]
# field = noise * 2 * pi
field = noise
super().__init__(field)
self.shape = shape
self.res = res
def generate_z_vect_from_perlin_noise(seed=1001, size_z=1, scale=1.0):
np.random.seed(seed)
x = generate_perlin_noise_2d((size_z, VECTOR_DIM), (1, 1))
x = x * scale
return x
def random_image(array_shape,
shapes_number,
shapes_min_size=(1, 1),
shapes_max_size=(None, None)):
"""
return an image filled with random shapes with noise and texture.
to avoid using to much parameters in the function, most of them are fixed at the start of the function.
"""
background_noise = True
shape_texture = True
shape_noise = True
final_noise = True
fill_range = (0, 255)
background_noise_mean = 128
background_noise_std = 32
texture_noise_res_min = 2
texture_noise_res_max = 16
texture_noise_amp = 42
tnrmin = texture_noise_res_min
tnrmax = texture_noise_res_max
shape_gaussian_noise_mean = 0
shape_gaussian_noise_std = 10
final_added_gaussian_noise_mean = 0
final_added_gaussian_noise_std = 20
# check and set shapes_max_size
sms = np.asarray(array_shape)
for i in range(len(shapes_max_size)):
if shapes_max_size[i] is not None:
sms[i] = min(shapes_max_size[i], array_shape[i])
shapes_max_size = tuple(sms)
# check and set shapes_min_size
sms = np.asarray(shapes_max_size)
for i in range(len(shapes_min_size)):
if shapes_min_size[i] is not None:
sms[i] = min(shapes_min_size[i], array_shape[i])
shapes_min_size = tuple(sms)
# background noise
if background_noise:
array = (
np.random.standard_normal(array_shape) * background_noise_std +
background_noise_mean).astype(dtype)
array = scipy.ndimage.gaussian_filter(array, 0.5)
else:
array = np.zeros(array_shape, dtype=dtype)
print(array.dtype)
# add random shapes
for _ in range(shapes_number):
# create new shape
fill = randint(fill_range[0], fill_range[1])
mask = elastic_deformation(
random_ellipsoid(array.shape, shapes_min_size, shapes_max_size))
# mask = (elasticdeform.deform_random_grid(random_spheroid(array.shape, shapes_max_size), sigma=4, points=3) > 0) * 1
# mask = random_shape_multi_resolution(array.shape, shapes_max_size, resolution=2, iteration=3)
# mask = random_shape(array.shape, shapes_max_size, iteration=10)
# skip if void shape
if np.sum(mask) == 0:
continue
shape = np.copy(mask)
if shape_texture:
# add perlin / fractal noise to create texture for the shape
# get mask bounding box for texture and noise
x, y = np.where(mask)
x_min, y_min = x.min(), y.min()
x_max, y_max = x.max(), y.max()
x_size, y_size = x_max - x_min, y_max - y_min
# apply noise to mask
noise_res_x, noise_res_y = randint(tnrmin, tnrmax), \
randint(tnrmin, tnrmax)
x_size, y_size = ((x_size//noise_res_x)+1)*noise_res_x,\
((y_size//noise_res_y)+1)*noise_res_y
try:
texture_noise = generate_perlin_noise_2d(
(x_size, y_size), (noise_res_x, noise_res_y),
tileable=(False, False))
except:
print("generate_perlin_noise_2d failed, alternative used")
sX, sY = randint(1, 50) / 100, randint(1, 50) / 100
rX, rY = (x_size * sX) // 2, (y_size * sY) // 2
arX, arY = np.arange(-rX - 1, rX + 1, sX), \
np.arange(-rY - 1, rY + 1, sY)
xx, yy = np.meshgrid(arY, arX)
texture_noise = (np.sin(xx) / 2 + np.sin(yy) / 2 +
np.tanh(xx + yy) / 2) / 4
texture_noise = texture_noise[0:x_size, 0:y_size]
shape_subpart = shape[x_min:x_min + x_size,
y_min:y_min + y_size].shape
shape[x_min:x_min+x_size, y_min:y_min+y_size] = \
np.clip(((texture_noise[0:shape_subpart[0], 0:shape_subpart[1]] * 2)
* texture_noise_amp) + fill, 0, 255)
else:
shape = shape * fill
if shape_noise:
# add gaussian noise
gaussian_noise = np.random.normal(shape_gaussian_noise_mean,
shape_gaussian_noise_std,
shape.shape)
shape[mask == 1] = np.clip(
shape[mask == 1] + gaussian_noise[mask == 1], 0, 255)
array[mask == 1] = shape[mask == 1]
if final_noise:
# add gaussian noise
array = np.clip(
array +
np.random.normal(final_added_gaussian_noise_mean,
final_added_gaussian_noise_std, array.shape), 0,
255)
return array, mask
