def point_poisson():
min_radius = roundRadius[0]
global occupation_poisson
while len(active) > 0:
check = np.random.randint(0, len(active))
found = None
for n in range(k):
s = _random_vector_function.random_vector_function(r, math.floor(r * 1.5))
sample = []
for i in range(0, len(s)):
sample.append(s[i] + active[check][i])
gcol = math.floor(sample[0] / w)
grow = math.floor(sample[1] / w)
gdepth = math.floor(sample[2] / w)
ggrid = gdepth + gcol * depths + grow * cols * depths
if gcol > -1 and grow > -1 and gdepth > -1 and gcol < cols and grow < rows and gdepth < depths:
ok = True
# check distances between selected point and points in the around grids
nums = [-1, 0, 1]
num1s = [-1, 0, 1]
num2s = [-1, 0, 1]
for num in nums:
for num1 in num1s:
for num2 in num2s:
if 0 <= gcol + num1 < cols and 0 <= grow + num < rows and 0 <= gdepth + num2 < depths:
index = (gcol + num1) * depths + (grow + num) * cols * depths + (gdepth + num2)
if grid[index] is None:
pass
else:
d = _distance3d.dist(grid[index][0], grid[index][1], grid[index][2], sample[0], sample[1], sample[2])
if d < r:
ok = None
if ok is True:
found = True
grid[gdepth + gcol * depths + grow * depths * cols] = sample
active.append(sample)
break
if found is None:
del active [check : (check + 1)]
for i in range(len(grid)):
if grid[i] is None:
pass
else:
#delete points which are too close to the border
if (grid[i][0] < width - min_radius) and (grid[i][0] > min_radius) and (grid[i][1] < height - min_radius) and (grid[i][1] > min_radius) and (grid[i][2] < depth - min_radius) and (grid[i][2] > min_radius):
Circles.append(_Sphere.Sphere(grid[i][0], grid[i][1], grid[i][2], min_radius, width, height, depth))
occupation_poisson = occupation_poisson + 1
# enlarge the particles
_generateParticle.generateCircles_p(ranges[0], (maximums[0] - 2), Circles)
print('poisson disk sampling', occupation_poisson)
def single_radius(q, initial_radius, range1, maximum1, Circles, cols1, depths1,
w1, width, height, depth):
valid = True
yaxis = math.floor(q / cols1 / depths1)
xaxis = math.floor((q - yaxis * cols1 * depths1) / depths1)
zaxis = q - xaxis * depths1 - yaxis * cols1 * depths1
w_use = w1
x = np.random.randint(math.floor(xaxis * w_use),
math.floor((xaxis + 1) * w_use))
y = np.random.randint(math.floor(yaxis * w_use),
math.floor((yaxis + 1) * w_use))
z = np.random.randint(math.floor(zaxis * w_use),
math.floor((zaxis + 1) * w_use))
#if the randomly selected point in the void grid is too close to borders, it will be deleted.
m = initial_radius
min_boundary = [x, y, z, (width - x), (height - y), (depth - z)]
min_b = np.amin(min_boundary)
if min_b < m:
return 0
#in the reseaching sphere area with 'mindis' radius
min_around = []
for j in range(len(Circles)):
if ((x - range1) < Circles[j].x <
(x + range1)) and ((y - range1) < Circles[j].y <
(y + range1)) and ((z - range1) < Circles[j].z <
(z + range1)):
k = _distance3d.dist(x, y, z, Circles[j].x, Circles[j].y,
Circles[j].z) - Circles[j].r
if (k < m):
return 0
#generate particles
Circles.append(_Sphere.Sphere(x, y, z, m, width, height, depth))
return m
