def point_poisson():
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 * 2))
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)
ggrid = gcol + grow * cols
if gcol > -1 and grow > -1 and gcol < cols and grow < rows:
ok = True
#check distance between selected point and points in the around grids
nums = [-1, 0, 1]
num1s = [-1, 0, 1]
for num in nums:
for num1 in num1s:
if 0 <= gcol + num1 < cols and 0 <= grow + num < rows:
index = (gcol + num1) + (grow + num) * cols
if grid[index] is None:
pass
else:
d = _distance2d.dist(grid[index][0],
grid[index][1], sample[0],
sample[1])
if d < r:
ok = None
if ok is True:
found = True
grid[gcol + grow * 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:
if (grid[i][0] < width - roundRadius[0]) and (
grid[i][0] > roundRadius[0]) and (
grid[i][1] < height - roundRadius[0]) and (
grid[i][1] > roundRadius[0]):
Circles.append(
_Circle.Circle(grid[i][0], grid[i][1], roundRadius[0],
width, height))
occupation_poisson = occupation_poisson + 1
# enlarge the particles
_generateParticle.generateCircles_p(ranges[0], (maximums[0] - 2), Circles)
print('poisson disk sampling', occupation_poisson)
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)
