Module sverchok.utils.surface.populate
Expand source code
# This file is part of project Sverchok. It's copyrighted by the contributors
# recorded in the version control history of the file, available from
# its original location https://github.com/nortikin/sverchok/commit/master
#
# SPDX-License-Identifier: GPL3
# License-Filename: LICENSE
import numpy as np
import random
from mathutils.kdtree import KDTree
from sverchok.utils.sv_logging import sv_logger
def random_point(min_x, max_x, min_y, max_y):
x = random.uniform(min_x, max_x)
y = random.uniform(min_y, max_y)
return x,y
def _check_min_distance(v_new, vs_old, min_r):
kdt = KDTree(len(vs_old))
for i, v in enumerate(vs_old):
kdt.insert(v, i)
kdt.balance()
nearest, idx, dist = kdt.find(v_new)
if dist is None:
return True
return (dist >= min_r)
# for v_old in vs_old:
# distance = np.linalg.norm(v_new - v_old)
# if distance < min_r:
# return False
# return True
def _check_min_radius(point, old_points, old_radiuses, min_r):
if not old_points:
return True
old_points = np.array(old_points)
old_radiuses = np.array(old_radiuses)
point = np.array(point)
distances = np.linalg.norm(old_points - point, axis=1)
ok = (old_radiuses + min_r < distances).all()
return ok
BATCH_SIZE = 100
MAX_ITERATIONS = 1000
def populate_surface(surface, field, count, threshold,
proportional=False, field_min=None, field_max=None,
min_r=0, min_r_field = None,
random_radius = False,
avoid_spheres = None,
seed=0, predicate=None):
"""
Generate random points on the surface, with distribution controlled (optionally) by scalar field.
inputs:
* surface : SvSurface
* field : SvScalarField. Pass None to use even (uniform) distribution.
* count: number of points to generate.
* threshold: do not generate points where value of scalar field is less than this value.
* proportional: if True, then density of points will be proportional to
scalar field value. Otherwise, values of the field will be used only to not
generate points in places where scalar field is less than threshold.
* field_min: (expected) minimum value of scalar field in the area of the
surface. Mandatory if `proportional` is set to True.
* field_max: (expected) maximum value of scalar field in the area of the
surface. Mandatory if `proportional` is set to True.
* min_r: minimum distance between generated points. Set to zero to disable this check.
* seed: random generator seed value.
* predicate: additional predicate to check if generated point is valid.
Takes two arguments: point in UV space and the same point in 3D space.
Optional.
outputs: tuple:
* Coordinates of points in surface's UV space
* Coordinates of points in 3D space.
"""
if min_r != 0 and min_r_field is not None:
raise Exception("min_r and min_r_field can not be specified simultaneously")
u_min, u_max = surface.get_u_min(), surface.get_u_max()
v_min, v_max = surface.get_v_min(), surface.get_v_max()
if avoid_spheres is not None:
old_points = [s[0] for s in avoid_spheres]
old_radiuses = [s[1] for s in avoid_spheres]
else:
old_points = []
old_radiuses = []
if seed == 0:
seed = 12345
if seed is not None:
random.seed(seed)
done = 0
generated_verts = []
generated_uv = []
generated_radiuses = []
iterations = 0
if field is None and avoid_spheres is None and min_r == 0 and min_r_field is None and predicate is None:
batch_size = count
else:
batch_size = BATCH_SIZE
while done < count:
iterations += 1
if iterations > MAX_ITERATIONS:
sv_logger.error("Maximum number of iterations (%s) reached, stop.", MAX_ITERATIONS)
break
batch_us = []
batch_vs = []
left = count - done
max_size = min(batch_size, left)
for i in range(max_size):
u = random.uniform(u_min, u_max)
v = random.uniform(v_min, v_max)
batch_us.append(u)
batch_vs.append(v)
batch_us = np.array(batch_us)
batch_vs = np.array(batch_vs)
batch_ws = np.zeros_like(batch_us)
batch_uvs = np.stack((batch_us, batch_vs, batch_ws)).T
batch_verts = surface.evaluate_array(batch_us, batch_vs)
batch_xs = batch_verts[:,0]
batch_ys = batch_verts[:,1]
batch_zs = batch_verts[:,2]
if field is not None:
values = field.evaluate_grid(batch_xs, batch_ys, batch_zs)
good_idxs = values >= threshold
if not proportional:
candidates = batch_verts[good_idxs]
candidate_uvs = batch_uvs[good_idxs]
else:
candidates = []
candidate_uvs = []
for uv, vert, value in zip(batch_uvs[good_idxs].tolist(), batch_verts[good_idxs].tolist(), values[good_idxs].tolist()):
probe = random.uniform(field_min, field_max)
if probe <= value:
candidates.append(vert)
candidate_uvs.append(uv)
candidates = np.array(candidates)
candidate_uvs = np.array(candidate_uvs)
else:
candidates = batch_verts
candidate_uvs = batch_uvs
good_radiuses = []
if len(candidates) > 0:
if min_r == 0 and min_r_field is None:
good_verts = candidates.tolist()
good_uvs = candidate_uvs.tolist()
good_radiuses = [0 for i in range(len(good_verts))]
elif min_r_field is not None:
xs = np.array([p[0] for p in candidates])
ys = np.array([p[1] for p in candidates])
zs = np.array([p[2] for p in candidates])
min_rs = min_r_field.evaluate_grid(xs, ys, zs).tolist()
good_verts = []
good_uvs = []
for candidate_uv, candidate, min_r in zip(candidate_uvs, candidates, min_rs):
if random_radius:
min_r = random.uniform(0, min_r)
if _check_min_radius(candidate, old_points + generated_verts + good_verts, old_radiuses + generated_radiuses + good_radiuses, min_r):
good_verts.append(tuple(candidate))
good_uvs.append(tuple(candidate_uv))
good_radiuses.append(min_r)
else: # min_r != 0
good_verts = []
good_uvs = []
for candidate_uv, candidate in zip(candidate_uvs, candidates):
distance_ok = _check_min_distance(candidate, old_points + generated_verts + good_verts, min_r)
if distance_ok:
good_verts.append(tuple(candidate))
good_uvs.append(tuple(candidate_uv))
good_radiuses.append(0)
if predicate is not None:
results = [(uv, vert, radius) for uv, vert, radius in zip(good_uvs, good_verts, good_radiuses) if predicate(uv, vert)]
good_uvs = [r[0] for r in results]
good_verts = [r[1] for r in results]
good_radiuses = [r[2] for r in results]
generated_verts.extend(good_verts)
generated_uv.extend(good_uvs)
generated_radiuses.extend(good_radiuses)
done += len(good_verts)
return generated_uv, generated_verts, generated_radiusesFunctions
def populate_surface(surface, field, count, threshold, proportional=False, field_min=None, field_max=None, min_r=0, min_r_field=None, random_radius=False, avoid_spheres=None, seed=0, predicate=None)-
Generate random points on the surface, with distribution controlled (optionally) by scalar field.
inputs: * surface : SvSurface * field : SvScalarField. Pass None to use even (uniform) distribution. * count: number of points to generate. * threshold: do not generate points where value of scalar field is less than this value. * proportional: if True, then density of points will be proportional to scalar field value. Otherwise, values of the field will be used only to not generate points in places where scalar field is less than threshold. * field_min: (expected) minimum value of scalar field in the area of the surface. Mandatory if
proportionalis set to True. * field_max: (expected) maximum value of scalar field in the area of the surface. Mandatory ifproportionalis set to True. * min_r: minimum distance between generated points. Set to zero to disable this check. * seed: random generator seed value. * predicate: additional predicate to check if generated point is valid. Takes two arguments: point in UV space and the same point in 3D space. Optional.outputs: tuple: * Coordinates of points in surface's UV space * Coordinates of points in 3D space.
Expand source code
def populate_surface(surface, field, count, threshold, proportional=False, field_min=None, field_max=None, min_r=0, min_r_field = None, random_radius = False, avoid_spheres = None, seed=0, predicate=None): """ Generate random points on the surface, with distribution controlled (optionally) by scalar field. inputs: * surface : SvSurface * field : SvScalarField. Pass None to use even (uniform) distribution. * count: number of points to generate. * threshold: do not generate points where value of scalar field is less than this value. * proportional: if True, then density of points will be proportional to scalar field value. Otherwise, values of the field will be used only to not generate points in places where scalar field is less than threshold. * field_min: (expected) minimum value of scalar field in the area of the surface. Mandatory if `proportional` is set to True. * field_max: (expected) maximum value of scalar field in the area of the surface. Mandatory if `proportional` is set to True. * min_r: minimum distance between generated points. Set to zero to disable this check. * seed: random generator seed value. * predicate: additional predicate to check if generated point is valid. Takes two arguments: point in UV space and the same point in 3D space. Optional. outputs: tuple: * Coordinates of points in surface's UV space * Coordinates of points in 3D space. """ if min_r != 0 and min_r_field is not None: raise Exception("min_r and min_r_field can not be specified simultaneously") u_min, u_max = surface.get_u_min(), surface.get_u_max() v_min, v_max = surface.get_v_min(), surface.get_v_max() if avoid_spheres is not None: old_points = [s[0] for s in avoid_spheres] old_radiuses = [s[1] for s in avoid_spheres] else: old_points = [] old_radiuses = [] if seed == 0: seed = 12345 if seed is not None: random.seed(seed) done = 0 generated_verts = [] generated_uv = [] generated_radiuses = [] iterations = 0 if field is None and avoid_spheres is None and min_r == 0 and min_r_field is None and predicate is None: batch_size = count else: batch_size = BATCH_SIZE while done < count: iterations += 1 if iterations > MAX_ITERATIONS: sv_logger.error("Maximum number of iterations (%s) reached, stop.", MAX_ITERATIONS) break batch_us = [] batch_vs = [] left = count - done max_size = min(batch_size, left) for i in range(max_size): u = random.uniform(u_min, u_max) v = random.uniform(v_min, v_max) batch_us.append(u) batch_vs.append(v) batch_us = np.array(batch_us) batch_vs = np.array(batch_vs) batch_ws = np.zeros_like(batch_us) batch_uvs = np.stack((batch_us, batch_vs, batch_ws)).T batch_verts = surface.evaluate_array(batch_us, batch_vs) batch_xs = batch_verts[:,0] batch_ys = batch_verts[:,1] batch_zs = batch_verts[:,2] if field is not None: values = field.evaluate_grid(batch_xs, batch_ys, batch_zs) good_idxs = values >= threshold if not proportional: candidates = batch_verts[good_idxs] candidate_uvs = batch_uvs[good_idxs] else: candidates = [] candidate_uvs = [] for uv, vert, value in zip(batch_uvs[good_idxs].tolist(), batch_verts[good_idxs].tolist(), values[good_idxs].tolist()): probe = random.uniform(field_min, field_max) if probe <= value: candidates.append(vert) candidate_uvs.append(uv) candidates = np.array(candidates) candidate_uvs = np.array(candidate_uvs) else: candidates = batch_verts candidate_uvs = batch_uvs good_radiuses = [] if len(candidates) > 0: if min_r == 0 and min_r_field is None: good_verts = candidates.tolist() good_uvs = candidate_uvs.tolist() good_radiuses = [0 for i in range(len(good_verts))] elif min_r_field is not None: xs = np.array([p[0] for p in candidates]) ys = np.array([p[1] for p in candidates]) zs = np.array([p[2] for p in candidates]) min_rs = min_r_field.evaluate_grid(xs, ys, zs).tolist() good_verts = [] good_uvs = [] for candidate_uv, candidate, min_r in zip(candidate_uvs, candidates, min_rs): if random_radius: min_r = random.uniform(0, min_r) if _check_min_radius(candidate, old_points + generated_verts + good_verts, old_radiuses + generated_radiuses + good_radiuses, min_r): good_verts.append(tuple(candidate)) good_uvs.append(tuple(candidate_uv)) good_radiuses.append(min_r) else: # min_r != 0 good_verts = [] good_uvs = [] for candidate_uv, candidate in zip(candidate_uvs, candidates): distance_ok = _check_min_distance(candidate, old_points + generated_verts + good_verts, min_r) if distance_ok: good_verts.append(tuple(candidate)) good_uvs.append(tuple(candidate_uv)) good_radiuses.append(0) if predicate is not None: results = [(uv, vert, radius) for uv, vert, radius in zip(good_uvs, good_verts, good_radiuses) if predicate(uv, vert)] good_uvs = [r[0] for r in results] good_verts = [r[1] for r in results] good_radiuses = [r[2] for r in results] generated_verts.extend(good_verts) generated_uv.extend(good_uvs) generated_radiuses.extend(good_radiuses) done += len(good_verts) return generated_uv, generated_verts, generated_radiuses def random_point(min_x, max_x, min_y, max_y)-
Expand source code
def random_point(min_x, max_x, min_y, max_y): x = random.uniform(min_x, max_x) y = random.uniform(min_y, max_y) return x,y