def _BezierInterpolate(pts, n, cache, npts, steps, scale):
"""Interpolate points cache to bezier curve
pts:
numpy.ndarray([x, y, 3], dtype='f')
array for bezier interpolated control points
n:
int
position in pts array
cache:
ctypes.POINTER(_ParticleCacheKey)
points cache
npts:
int
points number in cache.
scale:
float
interpolation scale factor
"""
#TODO: First Render -> Cache is Empty, causes Hair only to appear in subsequent renders.
#try:
for i in range(npts):
#print(npts)
if cache:
c = cache[i]
a = _NDARRAY([steps, 3], dtype='f')
for j in range(steps):
a[j] = c[j].co
s = a[1:-1]
t = a[2:] - a[:-2]
t *= scale / _NORM(t, axis=1)[_S] # tangents
m = _NORM(a[1:] - a[:-1], axis=1)[_S] # magnitudes
pts[n, ::3] = a
pts[n, 1] = a[0] + (a[1] - a[0]) * scale
pts[n, -2] = a[-1] - (a[-1] - a[-2]) * scale
pts[n, 2:-3:3] = s - t * m[:-1]
pts[n, 4::3] = s + t * m[1:]
n += 1
# else:
# cache = []
# for i in range(npts):
# cache.append(i)
return n
def _BezierInterpolate(pts, n, cache, npts, steps, scale):
"""Interpolate points cache to bezier curve
pts:
numpy.ndarray([x, y, 3], dtype='f')
array for bezier interpolated control points
n:
int
position in pts array
cache:
ctypes.POINTER(_ParticleCacheKey)
points cache
npts:
int
points number in cache.
scale:
float
interpolation scale factor
"""
for i in range(npts):
c = cache[i]
a = _NDARRAY([steps, 3], dtype='f')
for j in range(steps):
a[j] = c[j].co
s = a[1:-1]
t = a[2:] - a[:-2]
t *= scale / _NORM(t, axis=1)[_S] # tangents
m = _NORM(a[1:] - a[:-1], axis=1)[_S] # magnitudes
pts[n, ::3] = a
pts[n, 1] = a[0] + (a[1] - a[0]) * scale
pts[n, -2] = a[-1] - (a[-1] - a[-2]) * scale
pts[n, 2:-3:3] = s - t * m[:-1]
pts[n, 4::3] = s + t * m[1:]
n += 1
return n
def _BezierInterpolate(pts, n, cache, npts, steps, scale):
"""Interpolate points cache to bezier curve
pts:
numpy.ndarray([x, y, 3], dtype='f')
array for bezier interpolated control points
n:
int
position in pts array
cache:
ctypes.POINTER(_ParticleCacheKey)
points cache
npts:
int
points number in cache.
scale:
float
interpolation scale factor
"""
for i in range(npts):
c = cache[i]
a = _NDARRAY([steps, 3], dtype='f')
for j in range(steps):
a[j] = c[j].co
s = a[1:-1]
t = a[2:] - a[:-2]
t *= scale / _NORM(t, axis=1)[_S] # tangents
m = _NORM(a[1:] - a[:-1], axis=1)[_S] # magnitudes
pts[n, ::3] = a
pts[n, 1] = a[0] + (a[1] - a[0]) * scale
pts[n, -2] = a[-1] - (a[-1] - a[-2]) * scale
pts[n, 2:-3:3] = s - t * m[:-1]
pts[n, 4::3] = s + t * m[1:]
n += 1
return n
def psys_get_curves(ps, steps, use_parent_particles, props):
nch = len(ps.child_particles)
if nch == 0 or use_parent_particles:
np = len(ps.particles)
tot = np + nch
if tot <= 0:
return None
use_parent_particles = True
elif nch > 0:
tot = nch
use_parent_particles = False
else:
return None
_ps = _ParticleSystem.from_address(ps.as_pointer())
n = 0
if props.basis == 'bezier':
nsteps = steps * 3 - 2
points = _NDARRAY([tot, nsteps, 3], dtype=numpy.float32)
#print(points)
scale = props.bezier_scale
if use_parent_particles:
n = _BezierInterpolate(points, n, _ps.pathcache, np, steps, scale)
_BezierInterpolate(points, n, _ps.childcache, nch, steps, scale)
radius = numpy.linspace(props.radius_root,
props.radius_tip,
steps,
dtype=numpy.float32)
return (points.reshape(-1, 3), numpy.tile(radius, tot), nsteps)
if props.basis in {'b-spline', 'catmull-rom'}:
points = _NDARRAY([tot * (steps + 4), 3], dtype=numpy.float32)
if use_parent_particles:
_cache = _ps.pathcache
for i in range(np):
if _cache:
c = _cache[i]
points[n:n + 2] = c[0].co
n += 2
for j in range(steps):
points[n] = c[j].co
n += 1
points[n:n + 2] = points[n - 1]
n += 2
_cache = _ps.childcache
for i in range(nch):
if _cache:
c = _cache[i]
points[n:n + 2] = c[0].co
n += 2
for j in range(steps):
points[n] = c[j].co
n += 1
points[n:n + 2] = points[n - 1]
n += 2
radius = numpy.ndarray(steps + 2, dtype=numpy.float32)
radius[1:-1] = numpy.linspace(props.radius_root,
props.radius_tip,
steps,
dtype=numpy.float32)
radius[0] = 0
radius[-1] = 0
return (points, numpy.tile(radius, tot), steps + 4)
if props.basis == 'linear':
points = _NDARRAY([tot * steps, 3], dtype=numpy.float32)
if use_parent_particles:
_cache = _ps.pathcache
for i in range(np):
if _cache:
c = _cache[i]
for j in range(steps):
points[n] = c[j].co
n += 1
_cache = _ps.childcache
for i in range(nch):
if _cache:
c = _cache[i]
for j in range(steps):
points[n] = c[j].co
n += 1
radius = numpy.linspace(props.radius_root,
props.radius_tip,
steps,
dtype=numpy.float32)
return (points, numpy.tile(radius, tot), steps)
return None
def psys_get_curves(ps, steps, use_parent_particles, props):
nch = len(ps.child_particles)
if nch == 0 or use_parent_particles:
np = len(ps.particles)
tot = np + nch
if tot <= 0:
return None
use_parent_particles = True
elif nch > 0:
tot = nch
use_parent_particles = False
else:
return None
_ps = _ParticleSystem.from_address(ps.as_pointer())
n = 0
if props.basis == 'bezier':
nsteps = steps * 3 - 2
points = _NDARRAY([tot, nsteps, 3], dtype=numpy.float32)
scale = props.bezier_scale
if use_parent_particles:
n = _BezierInterpolate(points, n, _ps.pathcache, np, steps, scale)
_BezierInterpolate(points, n, _ps.childcache, nch, steps, scale)
radius = numpy.linspace(props.radius_root, props.radius_tip, steps, dtype=numpy.float32)
return (points.reshape(-1, 3), numpy.tile(radius, tot), nsteps)
if props.basis in {'b-spline', 'catmull-rom'}:
points = _NDARRAY([tot * (steps + 4), 3], dtype=numpy.float32)
if use_parent_particles:
_cache = _ps.pathcache
for i in range(np):
c = _cache[i]
points[n:n + 2] = c[0].co
n += 2
for j in range(steps):
points[n] = c[j].co
n += 1
points[n:n + 2] = points[n - 1]
n += 2
_cache = _ps.childcache
for i in range(nch):
c = _cache[i]
points[n:n + 2] = c[0].co
n += 2
for j in range(steps):
points[n] = c[j].co
n += 1
points[n: n + 2] = points[n - 1]
n += 2
radius = numpy.ndarray(steps + 2, dtype=numpy.float32)
radius[1:-1] = numpy.linspace(props.radius_root, props.radius_tip, steps, dtype=numpy.float32)
radius[0] = 0
radius[-1] = 0
return (points, numpy.tile(radius, tot), steps + 4)
if props.basis == 'linear':
points = _NDARRAY([tot * steps, 3], dtype=numpy.float32)
if use_parent_particles:
_cache = _ps.pathcache
for i in range(np):
c = _cache[i]
for j in range(steps):
points[n] = c[j].co
n += 1
_cache = _ps.childcache
for i in range(nch):
c = _cache[i]
for j in range(steps):
points[n] = c[j].co
n += 1
radius = numpy.linspace(props.radius_root, props.radius_tip, steps, dtype=numpy.float32)
return (points, numpy.tile(radius, tot), steps)
return None
