forked from bartvdbraak/blender
640 lines
22 KiB
Python
640 lines
22 KiB
Python
# ##### BEGIN GPL LICENSE BLOCK #####
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#
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# This program is free software; you can redistribute it and/or
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# modify it under the terms of the GNU General Public License
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# as published by the Free Software Foundation; either version 2
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# of the License, or (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with this program; if not, write to the Free Software Foundation,
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# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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#
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# ##### END GPL LICENSE BLOCK #####
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# <pep8 compliant>
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from math import hypot, sqrt, isfinite, radians
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import bpy
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import time
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from mathutils import Vector, Matrix
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#Vector utility functions
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class NdVector:
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vec = []
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def __init__(self, vec):
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self.vec = vec[:]
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def __len__(self):
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return len(self.vec)
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def __mul__(self, otherMember):
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if (isinstance(otherMember, int) or
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isinstance(otherMember, float)):
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return NdVector([otherMember * x for x in self.vec])
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else:
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a = self.vec
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b = otherMember.vec
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n = len(self)
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return sum([a[i] * b[i] for i in range(n)])
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def __sub__(self, otherVec):
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a = self.vec
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b = otherVec.vec
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n = len(self)
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return NdVector([a[i] - b[i] for i in range(n)])
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def __add__(self, otherVec):
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a = self.vec
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b = otherVec.vec
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n = len(self)
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return NdVector([a[i] + b[i] for i in range(n)])
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def __div__(self, scalar):
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return NdVector([x / scalar for x in self.vec])
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def vecLength(self):
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return sqrt(self * self)
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def vecLengthSq(self):
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return (self * self)
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def normalize(self):
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len = self.length
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self.vec = [x / len for x in self.vec]
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def copy(self):
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return NdVector(self.vec)
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def __getitem__(self, i):
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return self.vec[i]
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def x(self):
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return self.vec[0]
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def y(self):
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return self.vec[1]
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length = property(vecLength)
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lengthSq = property(vecLengthSq)
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x = property(x)
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y = property(y)
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class dataPoint:
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index = 0
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# x,y1,y2,y3 coordinate of original point
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co = NdVector((0, 0, 0, 0, 0))
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#position according to parametric view of original data, [0,1] range
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u = 0
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#use this for anything
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temp = 0
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def __init__(self, index, co, u=0):
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self.index = index
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self.co = co
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self.u = u
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def autoloop_anim():
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context = bpy.context
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obj = context.active_object
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fcurves = [x for x in obj.animation_data.action.fcurves if x.select]
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data = []
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end = len(fcurves[0].keyframe_points)
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for i in range(1, end):
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vec = []
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for fcurve in fcurves:
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vec.append(fcurve.evaluate(i))
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data.append(NdVector(vec))
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def comp(a, b):
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return a * b
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N = len(data)
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Rxy = [0.0] * N
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for i in range(N):
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for j in range(i, min(i + N, N)):
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Rxy[i] += comp(data[j], data[j - i])
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for j in range(i):
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Rxy[i] += comp(data[j], data[j - i + N])
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Rxy[i] /= float(N)
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def bestLocalMaximum(Rxy):
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Rxyd = [Rxy[i] - Rxy[i - 1] for i in range(1, len(Rxy))]
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maxs = []
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for i in range(1, len(Rxyd) - 1):
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a = Rxyd[i - 1]
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b = Rxyd[i]
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print(a, b)
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#sign change (zerocrossing) at point i, denoting max point (only)
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if (a >= 0 and b < 0) or (a < 0 and b >= 0):
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maxs.append((i, max(Rxy[i], Rxy[i - 1])))
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return max(maxs, key=lambda x: x[1])[0]
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flm = bestLocalMaximum(Rxy[0:int(len(Rxy))])
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diff = []
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for i in range(len(data) - flm):
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diff.append((data[i] - data[i + flm]).lengthSq)
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def lowerErrorSlice(diff, e):
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#index, error at index
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bestSlice = (0, 100000)
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for i in range(e, len(diff) - e):
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errorSlice = sum(diff[i - e:i + e + 1])
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if errorSlice < bestSlice[1]:
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bestSlice = (i, errorSlice)
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return bestSlice[0]
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margin = 2
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s = lowerErrorSlice(diff, margin)
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print(flm, s)
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loop = data[s:s + flm + margin]
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#find *all* loops, s:s+flm, s+flm:s+2flm, etc...
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#and interpolate between all
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# to find "the perfect loop".
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#Maybe before finding s? interp(i,i+flm,i+2flm)....
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for i in range(1, margin + 1):
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w1 = sqrt(float(i) / margin)
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loop[-i] = (loop[-i] * w1) + (loop[0] * (1 - w1))
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for curve in fcurves:
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pts = curve.keyframe_points
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for i in range(len(pts) - 1, -1, -1):
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pts.remove(pts[i])
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for c, curve in enumerate(fcurves):
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pts = curve.keyframe_points
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for i in range(len(loop)):
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pts.insert(i + 1, loop[i][c])
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context.scene.frame_end = flm + 1
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def simplifyCurves(curveGroup, error, reparaError, maxIterations, group_mode):
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def unitTangent(v, data_pts):
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tang = NdVector((0, 0, 0, 0, 0))
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if v != 0:
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#If it's not the first point, we can calculate a leftside tangent
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tang += data_pts[v].co - data_pts[v - 1].co
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if v != len(data_pts) - 1:
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#If it's not the last point, we can calculate a rightside tangent
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tang += data_pts[v + 1].co - data_pts[v].co
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tang.normalize()
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return tang
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#assign parametric u value for each point in original data
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def chordLength(data_pts, s, e):
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totalLength = 0
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for pt in data_pts[s:e + 1]:
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i = pt.index
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if i == s:
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chordLength = 0
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else:
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chordLength = (data_pts[i].co - data_pts[i - 1].co).length
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totalLength += chordLength
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pt.temp = totalLength
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for pt in data_pts[s:e + 1]:
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if totalLength == 0:
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print(s, e)
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pt.u = (pt.temp / totalLength)
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# get binomial coefficient, this function/table is only called with args
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# (3,0),(3,1),(3,2),(3,3),(2,0),(2,1),(2,2)!
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binomDict = {(3, 0): 1,
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(3, 1): 3,
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(3, 2): 3,
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(3, 3): 1,
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(2, 0): 1,
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(2, 1): 2,
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(2, 2): 1}
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#value at pt t of a single bernstein Polynomial
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def bernsteinPoly(n, i, t):
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binomCoeff = binomDict[(n, i)]
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return binomCoeff * pow(t, i) * pow(1 - t, n - i)
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# fit a single cubic to data points in range [s(tart),e(nd)].
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def fitSingleCubic(data_pts, s, e):
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# A - matrix used for calculating C matrices for fitting
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def A(i, j, s, e, t1, t2):
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if j == 1:
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t = t1
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if j == 2:
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t = t2
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u = data_pts[i].u
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return t * bernsteinPoly(3, j, u)
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# X component, used for calculating X matrices for fitting
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def xComponent(i, s, e):
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di = data_pts[i].co
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u = data_pts[i].u
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v0 = data_pts[s].co
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v3 = data_pts[e].co
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a = v0 * bernsteinPoly(3, 0, u)
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b = v0 * bernsteinPoly(3, 1, u)
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c = v3 * bernsteinPoly(3, 2, u)
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d = v3 * bernsteinPoly(3, 3, u)
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return (di - (a + b + c + d))
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t1 = unitTangent(s, data_pts)
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t2 = unitTangent(e, data_pts)
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c11 = sum([A(i, 1, s, e, t1, t2) * A(i, 1, s, e, t1, t2) for i in range(s, e + 1)])
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c12 = sum([A(i, 1, s, e, t1, t2) * A(i, 2, s, e, t1, t2) for i in range(s, e + 1)])
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c21 = c12
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c22 = sum([A(i, 2, s, e, t1, t2) * A(i, 2, s, e, t1, t2) for i in range(s, e + 1)])
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x1 = sum([xComponent(i, s, e) * A(i, 1, s, e, t1, t2) for i in range(s, e + 1)])
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x2 = sum([xComponent(i, s, e) * A(i, 2, s, e, t1, t2) for i in range(s, e + 1)])
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# calculate Determinate of the 3 matrices
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det_cc = c11 * c22 - c21 * c12
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det_cx = c11 * x2 - c12 * x1
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det_xc = x1 * c22 - x2 * c12
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# if matrix is not homogenous, fudge the data a bit
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if det_cc == 0:
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det_cc = 0.01
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# alpha's are the correct offset for bezier handles
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alpha0 = det_xc / det_cc # offset from right (first) point
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alpha1 = det_cx / det_cc # offset from left (last) point
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sRightHandle = data_pts[s].co.copy()
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sTangent = t1 * abs(alpha0)
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sRightHandle += sTangent # position of first pt's handle
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eLeftHandle = data_pts[e].co.copy()
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eTangent = t2 * abs(alpha1)
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eLeftHandle += eTangent # position of last pt's handle.
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# return a 4 member tuple representing the bezier
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return (data_pts[s].co,
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sRightHandle,
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eLeftHandle,
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data_pts[e].co)
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# convert 2 given data points into a cubic bezier.
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# handles are offset along the tangent at
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# a 3rd of the length between the points.
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def fitSingleCubic2Pts(data_pts, s, e):
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alpha0 = alpha1 = (data_pts[s].co - data_pts[e].co).length / 3
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sRightHandle = data_pts[s].co.copy()
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sTangent = unitTangent(s, data_pts) * abs(alpha0)
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sRightHandle += sTangent # position of first pt's handle
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eLeftHandle = data_pts[e].co.copy()
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eTangent = unitTangent(e, data_pts) * abs(alpha1)
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eLeftHandle += eTangent # position of last pt's handle.
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#return a 4 member tuple representing the bezier
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return (data_pts[s].co,
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sRightHandle,
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eLeftHandle,
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data_pts[e].co)
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#evaluate bezier, represented by a 4 member tuple (pts) at point t.
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def bezierEval(pts, t):
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sumVec = NdVector((0, 0, 0, 0, 0))
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for i in range(4):
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sumVec += pts[i] * bernsteinPoly(3, i, t)
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return sumVec
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#calculate the highest error between bezier and original data
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#returns the distance and the index of the point where max error occurs.
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def maxErrorAmount(data_pts, bez, s, e):
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maxError = 0
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maxErrorPt = s
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if e - s < 3:
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return 0, None
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for pt in data_pts[s:e + 1]:
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bezVal = bezierEval(bez, pt.u)
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tmpError = (pt.co - bezVal).length / pt.co.length
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if tmpError >= maxError:
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maxError = tmpError
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maxErrorPt = pt.index
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return maxError, maxErrorPt
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#calculated bezier derivative at point t.
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#That is, tangent of point t.
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def getBezDerivative(bez, t):
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n = len(bez) - 1
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sumVec = NdVector((0, 0, 0, 0, 0))
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for i in range(n - 1):
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sumVec += (bez[i + 1] - bez[i]) * bernsteinPoly(n - 1, i, t)
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return sumVec
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#use Newton-Raphson to find a better paramterization of datapoints,
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#one that minimizes the distance (or error)
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# between bezier and original data.
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def newtonRaphson(data_pts, s, e, bez):
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for pt in data_pts[s:e + 1]:
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if pt.index == s:
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pt.u = 0
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elif pt.index == e:
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pt.u = 1
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else:
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u = pt.u
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qu = bezierEval(bez, pt.u)
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qud = getBezDerivative(bez, u)
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#we wish to minimize f(u),
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#the squared distance between curve and data
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fu = (qu - pt.co).length ** 2
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fud = (2 * (qu.x - pt.co.x) * (qud.x)) - (2 * (qu.y - pt.co.y) * (qud.y))
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if fud == 0:
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fu = 0
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fud = 1
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pt.u = pt.u - (fu / fud)
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def createDataPts(curveGroup, group_mode):
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data_pts = []
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if group_mode:
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print([x.data_path for x in curveGroup])
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for i in range(len(curveGroup[0].keyframe_points)):
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x = curveGroup[0].keyframe_points[i].co.x
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y1 = curveGroup[0].keyframe_points[i].co.y
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y2 = curveGroup[1].keyframe_points[i].co.y
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y3 = curveGroup[2].keyframe_points[i].co.y
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y4 = 0
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if len(curveGroup) == 4:
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y4 = curveGroup[3].keyframe_points[i].co.y
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data_pts.append(dataPoint(i, NdVector((x, y1, y2, y3, y4))))
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else:
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for i in range(len(curveGroup.keyframe_points)):
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x = curveGroup.keyframe_points[i].co.x
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y1 = curveGroup.keyframe_points[i].co.y
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y2 = 0
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y3 = 0
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y4 = 0
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data_pts.append(dataPoint(i, NdVector((x, y1, y2, y3, y4))))
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return data_pts
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def fitCubic(data_pts, s, e):
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# if there are less than 3 points, fit a single basic bezier
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if e - s < 3:
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bez = fitSingleCubic2Pts(data_pts, s, e)
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else:
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#if there are more, parameterize the points
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# and fit a single cubic bezier
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chordLength(data_pts, s, e)
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bez = fitSingleCubic(data_pts, s, e)
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#calculate max error and point where it occurs
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maxError, maxErrorPt = maxErrorAmount(data_pts, bez, s, e)
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#if error is small enough, reparameterization might be enough
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if maxError < reparaError and maxError > error:
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for i in range(maxIterations):
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newtonRaphson(data_pts, s, e, bez)
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if e - s < 3:
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bez = fitSingleCubic2Pts(data_pts, s, e)
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else:
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bez = fitSingleCubic(data_pts, s, e)
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#recalculate max error and point where it occurs
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maxError, maxErrorPt = maxErrorAmount(data_pts, bez, s, e)
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#repara wasn't enough, we need 2 beziers for this range.
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#Split the bezier at point of maximum error
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if maxError > error:
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fitCubic(data_pts, s, maxErrorPt)
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fitCubic(data_pts, maxErrorPt, e)
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else:
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#error is small enough, return the beziers.
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beziers.append(bez)
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return
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def createNewCurves(curveGroup, beziers, group_mode):
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#remove all existing data points
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if group_mode:
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for fcurve in curveGroup:
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for i in range(len(fcurve.keyframe_points) - 1, 0, -1):
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fcurve.keyframe_points.remove(fcurve.keyframe_points[i])
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else:
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fcurve = curveGroup
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for i in range(len(fcurve.keyframe_points) - 1, 0, -1):
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fcurve.keyframe_points.remove(fcurve.keyframe_points[i])
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#insert the calculated beziers to blender data.\
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if group_mode:
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for fullbez in beziers:
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for i, fcurve in enumerate(curveGroup):
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bez = [Vector((vec[0], vec[i + 1])) for vec in fullbez]
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newKey = fcurve.keyframe_points.insert(frame=bez[0].x, value=bez[0].y)
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newKey.handle_right = (bez[1].x, bez[1].y)
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newKey = fcurve.keyframe_points.insert(frame=bez[3].x, value=bez[3].y)
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newKey.handle_left = (bez[2].x, bez[2].y)
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else:
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for bez in beziers:
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for vec in bez:
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vec.resize_2d()
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newKey = fcurve.keyframe_points.insert(frame=bez[0].x, value=bez[0].y)
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newKey.handle_right = (bez[1].x, bez[1].y)
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newKey = fcurve.keyframe_points.insert(frame=bez[3].x, value=bez[3].y)
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newKey.handle_left = (bez[2].x, bez[2].y)
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# indices are detached from data point's frame (x) value and
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# stored in the dataPoint object, represent a range
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data_pts = createDataPts(curveGroup, group_mode)
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s = 0 # start
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e = len(data_pts) - 1 # end
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beziers = []
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#begin the recursive fitting algorithm.
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fitCubic(data_pts, s, e)
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#remove old Fcurves and insert the new ones
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createNewCurves(curveGroup, beziers, group_mode)
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#Main function of simplification
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#sel_opt: either "sel" or "all" for which curves to effect
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#error: maximum error allowed, in fraction (20% = 0.0020),
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#i.e. divide by 10000 from percentage wanted.
|
|
#group_mode: boolean, to analyze each curve seperately or in groups,
|
|
#where group is all curves that effect the same property
|
|
#(e.g. a bone's x,y,z rotation)
|
|
|
|
|
|
def fcurves_simplify(sel_opt="all", error=0.002, group_mode=True):
|
|
# main vars
|
|
context = bpy.context
|
|
obj = context.active_object
|
|
fcurves = obj.animation_data.action.fcurves
|
|
|
|
if sel_opt == "sel":
|
|
sel_fcurves = [fcurve for fcurve in fcurves if fcurve.select]
|
|
else:
|
|
sel_fcurves = fcurves[:]
|
|
|
|
#Error threshold for Newton Raphson reparamatizing
|
|
reparaError = error * 32
|
|
maxIterations = 16
|
|
|
|
if group_mode:
|
|
fcurveDict = {}
|
|
#this loop sorts all the fcurves into groups of 3 or 4,
|
|
#based on their RNA Data path, which corresponds to
|
|
#which property they effect
|
|
for curve in sel_fcurves:
|
|
if curve.data_path in fcurveDict: # if this bone has been added, append the curve to its list
|
|
fcurveDict[curve.data_path].append(curve)
|
|
else:
|
|
fcurveDict[curve.data_path] = [curve] # new bone, add a new dict value with this first curve
|
|
fcurveGroups = fcurveDict.values()
|
|
else:
|
|
fcurveGroups = sel_fcurves
|
|
|
|
if error > 0.00000:
|
|
#simplify every selected curve.
|
|
totalt = 0
|
|
for i, fcurveGroup in enumerate(fcurveGroups):
|
|
print("Processing curve " + str(i + 1) + "/" + str(len(fcurveGroups)))
|
|
t = time.clock()
|
|
simplifyCurves(fcurveGroup, error, reparaError, maxIterations, group_mode)
|
|
t = time.clock() - t
|
|
print(str(t)[:5] + " seconds to process last curve")
|
|
totalt += t
|
|
print(str(totalt)[:5] + " seconds, total time elapsed")
|
|
|
|
return
|
|
|
|
# Implementation of non-linear median filter, with variable kernel size
|
|
# Double pass - one marks spikes, the other smooths one
|
|
# Expects sampled keyframes on everyframe
|
|
|
|
|
|
def denoise_median():
|
|
context = bpy.context
|
|
obj = context.active_object
|
|
fcurves = obj.animation_data.action.fcurves
|
|
medKernel = 1 # actually *2+1... since it this is offset
|
|
flagKernel = 4
|
|
highThres = (flagKernel * 2) - 1
|
|
lowThres = 0
|
|
for fcurve in fcurves:
|
|
orgPts = fcurve.keyframe_points[:]
|
|
flaggedFrames = []
|
|
# mark frames that are spikes by sorting a large kernel
|
|
for i in range(flagKernel, len(fcurve.keyframe_points) - flagKernel):
|
|
center = orgPts[i]
|
|
neighborhood = orgPts[i - flagKernel: i + flagKernel]
|
|
neighborhood.sort(key=lambda pt: pt.co[1])
|
|
weight = neighborhood.index(center)
|
|
if weight >= highThres or weight <= lowThres:
|
|
flaggedFrames.append((i, center))
|
|
# clean marked frames with a simple median filter
|
|
# averages all frames in the kernel equally, except center which has no weight
|
|
for i, pt in flaggedFrames:
|
|
newValue = 0
|
|
sumWeights = 0
|
|
neighborhood = [neighpt.co[1] for neighpt in orgPts[i - medKernel: i + medKernel + 1] if neighpt != pt]
|
|
newValue = sum(neighborhood) / len(neighborhood)
|
|
pt.co[1] = newValue
|
|
return
|
|
|
|
|
|
def rotate_fix_armature(arm_data):
|
|
global_matrix = Matrix.Rotation(radians(90), 4, "X")
|
|
bpy.ops.object.mode_set(mode='EDIT', toggle=False)
|
|
#disconnect all bones for ease of global rotation
|
|
connectedBones = []
|
|
for bone in arm_data.edit_bones:
|
|
if bone.use_connect:
|
|
connectedBones.append(bone.name)
|
|
bone.use_connect = False
|
|
|
|
#rotate all the bones around their center
|
|
for bone in arm_data.edit_bones:
|
|
bone.transform(global_matrix)
|
|
|
|
#reconnect the bones
|
|
for bone in connectedBones:
|
|
arm_data.edit_bones[bone].use_connect = True
|
|
bpy.ops.object.mode_set(mode='OBJECT', toggle=False)
|
|
|
|
|
|
def scale_fix_armature(performer_obj, enduser_obj):
|
|
perf_bones = performer_obj.data.bones
|
|
end_bones = enduser_obj.data.bones
|
|
|
|
def calculateBoundingRadius(bones):
|
|
center = Vector()
|
|
for bone in bones:
|
|
center += bone.head_local
|
|
center /= len(bones)
|
|
radius = 0
|
|
for bone in bones:
|
|
dist = (bone.head_local - center).length
|
|
if dist > radius:
|
|
radius = dist
|
|
return radius
|
|
|
|
perf_rad = calculateBoundingRadius(performer_obj.data.bones)
|
|
end_rad = calculateBoundingRadius(enduser_obj.data.bones)
|
|
#end_avg = enduser_obj.dimensions
|
|
factor = end_rad / perf_rad * 1.2
|
|
performer_obj.scale *= factor
|
|
|
|
|
|
def guessMapping(performer_obj, enduser_obj):
|
|
perf_bones = performer_obj.data.bones
|
|
end_bones = enduser_obj.data.bones
|
|
|
|
root = perf_bones[0]
|
|
|
|
def findBoneSide(bone):
|
|
if "Left" in bone:
|
|
return "Left", bone.replace("Left", "").lower().replace(".", "")
|
|
if "Right" in bone:
|
|
return "Right", bone.replace("Right", "").lower().replace(".", "")
|
|
if "L" in bone:
|
|
return "Left", bone.replace("Left", "").lower().replace(".", "")
|
|
if "R" in bone:
|
|
return "Right", bone.replace("Right", "").lower().replace(".", "")
|
|
return "", bone
|
|
|
|
def nameMatch(bone_a, bone_b):
|
|
# nameMatch - recieves two strings, returns 2 if they are relatively the same, 1 if they are the same but R and L and 0 if no match at all
|
|
side_a, noside_a = findBoneSide(bone_a)
|
|
side_b, noside_b = findBoneSide(bone_b)
|
|
if side_a == side_b:
|
|
if noside_a in noside_b or noside_b in noside_a:
|
|
return 2
|
|
else:
|
|
if noside_a in noside_b or noside_b in noside_a:
|
|
return 1
|
|
return 0
|
|
|
|
def guessSingleMapping(perf_bone):
|
|
possible_bones = [end_bones[0]]
|
|
while possible_bones:
|
|
for end_bone in possible_bones:
|
|
match = nameMatch(perf_bone.name, end_bone.name)
|
|
if match == 2 and not perf_bone.map:
|
|
perf_bone.map = end_bone.name
|
|
newPossibleBones = []
|
|
for end_bone in possible_bones:
|
|
newPossibleBones += list(end_bone.children)
|
|
possible_bones = newPossibleBones
|
|
|
|
for child in perf_bone.children:
|
|
guessSingleMapping(child)
|
|
|
|
guessSingleMapping(root)
|