939 lines
37 KiB
Python
939 lines
37 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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from math import sqrt, radians, floor, ceil
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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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# A Python implementation of n sized Vectors.
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# Mathutils has a max size of 4, and we need at least 5 for Simplify Curves and even more for Cross Correlation.
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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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# assume anything with list access is a vector
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if isinstance(otherMember, NdVector):
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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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else:
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# int/float
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return NdVector([otherMember * x for x in self.vec])
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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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@property
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def length(self):
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return sqrt(self * self)
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@property
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def lengthSq(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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if len > 0:
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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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@property
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def x(self):
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return self.vec[0]
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@property
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def y(self):
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return self.vec[1]
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def resize_2d(self):
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return Vector((self.x, self.y))
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#Sampled Data Point class for Simplify Curves
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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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# Helper to convert from a sampled fcurve back to editable keyframes one.
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def make_editable_fcurves(fcurves):
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for fc in fcurves:
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if fc.sampled_points:
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fc.convert_to_keyframes(floor(fc.sampled_points[0].co[0]), ceil(fc.sampled_points[-1].co[0]) + 1)
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#Cross Correlation Function
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#http://en.wikipedia.org/wiki/Cross_correlation
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#IN: curvesA, curvesB - bpy_collection/list of fcurves to analyze. Auto-Correlation is when they are the same.
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# margin - When searching for the best "start" frame, how large a neighborhood of frames should we inspect (similar to epsilon in Calculus)
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#OUT: startFrame, length of new anim, and curvesA
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def crossCorrelationMatch(curvesA, curvesB, margin):
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dataA = []
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dataB = []
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start = int(max(curvesA[0].range()[0], curvesB[0].range()[0]))
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end = int(min(curvesA[0].range()[1], curvesB[0].range()[1]))
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#transfer all fcurves data on each frame to a single NdVector.
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for i in range(1, end):
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vec = []
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for fcurve in curvesA:
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if fcurve.data_path in [otherFcurve.data_path for otherFcurve in curvesB]:
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vec.append(fcurve.evaluate(i))
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dataA.append(NdVector(vec))
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vec = []
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for fcurve in curvesB:
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if fcurve.data_path in [otherFcurve.data_path for otherFcurve in curvesA]:
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vec.append(fcurve.evaluate(i))
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dataB.append(NdVector(vec))
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#Comparator for Cross Correlation. "Classic" implementation uses dot product, as do we.
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def comp(a, b):
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return a * b
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#Create Rxy, which holds the Cross Correlation data.
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N = len(dataA)
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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(dataA[j], dataB[j - i])
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for j in range(i):
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Rxy[i] += comp(dataA[j], dataB[j - i + N])
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Rxy[i] /= float(N)
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#Find the Local maximums in the Cross Correlation data via numerical derivative.
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def LocalMaximums(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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#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 [x[0] for x in maxs]
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#~ return max(maxs, key=lambda x: x[1])[0]
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#flms - the possible offsets of the first part of the animation. In Auto-Corr, this is the length of the loop.
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flms = LocalMaximums(Rxy[0:int(len(Rxy))])
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ss = []
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#for every local maximum, find the best one - i.e. also has the best start frame.
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for flm in flms:
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diff = []
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for i in range(len(dataA) - flm):
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diff.append((dataA[i] - dataB[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, flm)
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return bestSlice
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s = lowerErrorSlice(diff, margin)
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ss.append(s)
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#Find the best result and return it.
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ss.sort(key=lambda x: x[1])
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return ss[0][2], ss[0][0], dataA
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#Uses auto correlation (cross correlation of the same set of curves) and trims the active_object's fcurves
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#Except for location curves (which in mocap tend to be not cyclic, e.g. a walk cycle forward)
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#Transfers the fcurve data to a list of NdVector (length of list is number of fcurves), and calls the cross correlation function.
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#Then trims the fcurve accordingly.
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#IN: Nothing, set the object you want as active and call. Assumes object has animation_data.action!
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#OUT: Trims the object's fcurves (except location curves).
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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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def locCurve(x):
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x.data_path == "location"
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fcurves = [x for x in obj.animation_data.action.fcurves if not locCurve(x)]
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margin = 10
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flm, s, data = crossCorrelationMatch(fcurves, fcurves, margin)
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loop = data[s:s + flm]
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#performs blending with a root falloff on the seam's neighborhood to ensure good tiling.
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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 + 2, loop[i][c])
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context.scene.frame_end = flm
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#simplifyCurves: performs the bulk of the samples to bezier conversion.
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#IN: curveGroup - which can be a collection of singleFcurves, or grouped (via nested lists) .
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# error - threshold of permittable error (max distance) of the new beziers to the original data
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# reparaError - threshold of error where we should try to fix the parameterization rather than split the existing curve. > error, usually by a small constant factor for best performance.
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# maxIterations - maximum number of iterations of reparameterizations we should attempt. (Newton-Rahpson is not guaranteed to converge, so this is needed).
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# group_mode - boolean, indicating whether we should place bezier keyframes on the same x (frame), or optimize each individual curve.
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#OUT: None. Deletes the existing curves and creates the new beziers.
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def simplifyCurves(curveGroup, error, reparaError, maxIterations, group_mode):
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#Calculates the unit tangent of point v
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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, via relative arc length
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#http://en.wikipedia.org/wiki/Arc_length
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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 lookup table, 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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}
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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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normalize_error = pt.co.length
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if normalize_error == 0:
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normalize_error = 1
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tmpError = (pt.co - bezVal).length / normalize_error
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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 parameterization 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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#Create data_pts, a list of dataPoint type, each is assigned index i, and an NdVector
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def createDataPts(curveGroup, group_mode):
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make_editable_fcurves(curveGroup if group_mode else (curveGroup,))
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if group_mode:
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print([x.data_path for x in curveGroup])
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comp_cos = (0,) * (4 - len(curveGroup)) # We need to add that number of null cos to get our 5D vector.
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kframes = sorted(set(kf.co.x for fc in curveGroup for kf in fc.keyframe_points))
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data_pts = [dataPoint(i, NdVector((fra,) + tuple(fc.evaluate(fra) for fc in curveGroup) + comp_cos))
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for i, fra in enumerate(kframes)]
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else:
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data_pts = [dataPoint(i, NdVector((kf.co.x, kf.co.y, 0, 0, 0)))
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for i, kf in enumerate(curveGroup.keyframe_points)]
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return data_pts
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#Recursively fit cubic beziers to the data_pts between s and e
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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):
|
|
newtonRaphson(data_pts, s, e, bez)
|
|
if e - s < 3:
|
|
bez = fitSingleCubic2Pts(data_pts, s, e)
|
|
else:
|
|
bez = fitSingleCubic(data_pts, s, e)
|
|
|
|
#recalculate max error and point where it occurs
|
|
maxError, maxErrorPt = maxErrorAmount(data_pts, bez, s, e)
|
|
|
|
#repara wasn't enough, we need 2 beziers for this range.
|
|
#Split the bezier at point of maximum error
|
|
if maxError > error:
|
|
fitCubic(data_pts, s, maxErrorPt)
|
|
fitCubic(data_pts, maxErrorPt, e)
|
|
else:
|
|
#error is small enough, return the beziers.
|
|
beziers.append(bez)
|
|
return
|
|
|
|
# deletes the sampled points and creates beziers.
|
|
def createNewCurves(curveGroup, beziers, group_mode):
|
|
#remove all existing data points
|
|
if group_mode:
|
|
for fcurve in curveGroup:
|
|
for i in range(len(fcurve.keyframe_points) - 1, 0, -1):
|
|
fcurve.keyframe_points.remove(fcurve.keyframe_points[i], fast=True)
|
|
else:
|
|
fcurve = curveGroup
|
|
for i in range(len(fcurve.keyframe_points) - 1, 0, -1):
|
|
fcurve.keyframe_points.remove(fcurve.keyframe_points[i], fast=True)
|
|
|
|
#insert the calculated beziers to blender data.
|
|
if group_mode:
|
|
for fullbez in beziers:
|
|
for i, fcurve in enumerate(curveGroup):
|
|
bez = [Vector((vec[0], vec[i + 1])) for vec in fullbez]
|
|
newKey = fcurve.keyframe_points.insert(frame=bez[0].x, value=bez[0].y, options={'FAST'})
|
|
newKey.handle_right = (bez[1].x, bez[1].y)
|
|
|
|
newKey = fcurve.keyframe_points.insert(frame=bez[3].x, value=bez[3].y, options={'FAST'})
|
|
newKey.handle_left = (bez[2].x, bez[2].y)
|
|
else:
|
|
for bez in beziers:
|
|
for vec in bez:
|
|
vec.resize_2d()
|
|
newKey = fcurve.keyframe_points.insert(frame=bez[0].x, value=bez[0].y, options={'FAST'})
|
|
newKey.handle_right = (bez[1].x, bez[1].y)
|
|
|
|
newKey = fcurve.keyframe_points.insert(frame=bez[3].x, value=bez[3].y, options={'FAST'})
|
|
newKey.handle_left = (bez[2].x, bez[2].y)
|
|
|
|
# We used fast remove/insert, time to update the curves!
|
|
for fcurve in (curveGroup if group_mode else (curveGroup,)):
|
|
fcurve.update()
|
|
|
|
# indices are detached from data point's frame (x) value and
|
|
# stored in the dataPoint object, represent a range
|
|
|
|
data_pts = createDataPts(curveGroup, group_mode)
|
|
|
|
if not data_pts:
|
|
return
|
|
|
|
s = 0 # start
|
|
e = len(data_pts) - 1 # end
|
|
|
|
beziers = []
|
|
|
|
#begin the recursive fitting algorithm.
|
|
fitCubic(data_pts, s, e)
|
|
|
|
#remove old Fcurves and insert the new ones
|
|
createNewCurves(curveGroup, beziers, group_mode)
|
|
|
|
|
|
#Main function of simplification, which called by Operator
|
|
#IN:
|
|
# sel_opt- either "sel" (selected) or "all" for which curves to effect
|
|
# error- maximum error allowed, in fraction (20% = 0.0020, which is the default),
|
|
# i.e. divide by 10000 from percentage wanted.
|
|
# group_mode- boolean, to analyze each curve separately or in groups,
|
|
# where a group is all curves that effect the same property/RNA path
|
|
def fcurves_simplify(context, obj, sel_opt="all", error=0.002, group_mode=True):
|
|
# main vars
|
|
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
|
|
|
|
|
|
def detect_min_max(v):
|
|
"""
|
|
Converted from MATLAB script at http://billauer.co.il/peakdet.html
|
|
|
|
Yields indices of peaks, i.e. local minima/maxima.
|
|
|
|
% Eli Billauer, 3.4.05 (Explicitly not copyrighted).
|
|
% This function is released to the public domain; Any use is allowed.
|
|
"""
|
|
|
|
min_val, max_val = float('inf'), -float('inf')
|
|
|
|
check_max = True
|
|
|
|
for i, val in enumerate(v):
|
|
if val > max_val:
|
|
max_val = val
|
|
if val < min_val:
|
|
min_val = val
|
|
|
|
if check_max:
|
|
if val < max_val:
|
|
yield i
|
|
min_val = val
|
|
check_max = False
|
|
else:
|
|
if val > min_val:
|
|
yield i
|
|
max_val = val
|
|
check_max = True
|
|
|
|
|
|
def denoise(obj, fcurves):
|
|
"""
|
|
Implementation of non-linear blur filter.
|
|
Finds spikes in the fcurve, and replaces spikes that are too big with the average of the surrounding keyframes.
|
|
"""
|
|
make_editable_fcurves(fcurves)
|
|
|
|
for fcurve in fcurves:
|
|
org_pts = fcurve.keyframe_points[:]
|
|
|
|
for idx in detect_min_max(pt.co.y for pt in fcurve.keyframe_points[1:-1]):
|
|
# Find the neighbours
|
|
prev_pt = org_pts[idx - 1].co.y
|
|
next_pt = org_pts[idx + 1].co.y
|
|
this_pt = org_pts[idx]
|
|
|
|
# Check the distance from the min/max to the average of the surrounding points.
|
|
avg = (prev_pt + next_pt) / 2
|
|
is_peak = abs(this_pt.co.y - avg) > avg * 0.02
|
|
|
|
if is_peak:
|
|
diff = avg - fcurve.keyframe_points[idx].co.y
|
|
fcurve.keyframe_points[idx].co.y = avg
|
|
fcurve.keyframe_points[idx].handle_left.y += diff
|
|
fcurve.keyframe_points[idx].handle_right.y += diff
|
|
|
|
# Important to update the curve after modifying it!
|
|
fcurve.update()
|
|
|
|
|
|
# Receives armature, and rotations all bones by 90 degrees along the X axis
|
|
# This fixes the common axis issue BVH files have when importing.
|
|
# IN: Armature (bpy.types.Armature)
|
|
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)
|
|
|
|
|
|
#Roughly scales the performer armature to match the enduser armature
|
|
#IN: perfromer_obj, enduser_obj, Blender objects whose .data is an armature.
|
|
def scale_fix_armature(performer_obj, enduser_obj):
|
|
perf_bones = performer_obj.data.bones
|
|
end_bones = enduser_obj.data.bones
|
|
|
|
def calculateBoundingRadius(bones):
|
|
# Calculate the average position of each bone
|
|
center = sum((bone.head_local for bone in bones), Vector())
|
|
center /= len(bones)
|
|
|
|
# The radius is defined as the max distance from the center.
|
|
radius = max((bone.head_local - center).length for bone in bones)
|
|
return radius
|
|
|
|
perf_rad = calculateBoundingRadius(performer_obj.data.bones)
|
|
end_rad = calculateBoundingRadius(enduser_obj.data.bones)
|
|
|
|
factor = end_rad / perf_rad
|
|
performer_obj.scale = factor * Vector((1, 1, 1))
|
|
|
|
|
|
#Guess Mapping
|
|
#Given a performer and enduser armature, attempts to guess the hierarchy mapping
|
|
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 - receives 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
|
|
#~ elif match == 1 and not perf_bone.map:
|
|
#~ oppo = perf_bones[oppositeBone(perf_bone)].map
|
|
# if oppo:
|
|
# perf_bone = oppo
|
|
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)
|
|
|
|
|
|
# Creates limit rotation constraints on the enduser armature based on range of motion (max min of fcurves) of the performer.
|
|
# IN: context (bpy.context, etc.), and 2 blender objects which are armatures
|
|
# OUT: creates the limit constraints.
|
|
def limit_dof(context, performer_obj, enduser_obj):
|
|
limitDict = {}
|
|
perf_bones = [bone for bone in performer_obj.pose.bones if bone.bone.map]
|
|
c_frame = context.scene.frame_current
|
|
for bone in perf_bones:
|
|
limitDict[bone.bone.map] = [1000, 1000, 1000, -1000, -1000, -1000]
|
|
for t in range(context.scene.frame_start, context.scene.frame_end):
|
|
context.scene.frame_set(t)
|
|
for bone in perf_bones:
|
|
end_bone = enduser_obj.pose.bones[bone.bone.map]
|
|
bake_matrix = bone.matrix
|
|
rest_matrix = end_bone.bone.matrix_local
|
|
|
|
if end_bone.parent and end_bone.bone.use_inherit_rotation:
|
|
srcParent = bone.parent
|
|
parent_mat = srcParent.matrix
|
|
parent_rest = end_bone.parent.bone.matrix_local
|
|
parent_rest_inv = parent_rest.inverted()
|
|
parent_mat_inv = parent_mat.inverted()
|
|
bake_matrix = parent_mat_inv @ bake_matrix
|
|
rest_matrix = parent_rest_inv @ rest_matrix
|
|
|
|
rest_matrix_inv = rest_matrix.inverted()
|
|
bake_matrix = rest_matrix_inv @ bake_matrix
|
|
|
|
mat = bake_matrix
|
|
euler = mat.to_euler()
|
|
limitDict[bone.bone.map][0] = min(limitDict[bone.bone.map][0], euler.x)
|
|
limitDict[bone.bone.map][1] = min(limitDict[bone.bone.map][1], euler.y)
|
|
limitDict[bone.bone.map][2] = min(limitDict[bone.bone.map][2], euler.z)
|
|
limitDict[bone.bone.map][3] = max(limitDict[bone.bone.map][3], euler.x)
|
|
limitDict[bone.bone.map][4] = max(limitDict[bone.bone.map][4], euler.y)
|
|
limitDict[bone.bone.map][5] = max(limitDict[bone.bone.map][5], euler.z)
|
|
for bone in enduser_obj.pose.bones:
|
|
existingConstraint = [constraint for constraint in bone.constraints if constraint.name == "DOF Limitation"]
|
|
if existingConstraint:
|
|
bone.constraints.remove(existingConstraint[0])
|
|
end_bones = [bone for bone in enduser_obj.pose.bones if bone.name in limitDict.keys()]
|
|
for bone in end_bones:
|
|
#~ if not bone.is_in_ik_chain:
|
|
newCons = bone.constraints.new("LIMIT_ROTATION")
|
|
newCons.name = "DOF Limitation"
|
|
newCons.owner_space = "LOCAL"
|
|
newCons.min_x, newCons.min_y, newCons.min_z, newCons.max_x, newCons.max_y, newCons.max_z = limitDict[bone.name]
|
|
newCons.use_limit_x = True
|
|
newCons.use_limit_y = True
|
|
newCons.use_limit_z = True
|
|
context.scene.frame_set(c_frame)
|
|
|
|
|
|
# Removes the constraints that were added by limit_dof on the enduser_obj
|
|
def limit_dof_toggle_off(context, enduser_obj):
|
|
for bone in enduser_obj.pose.bones:
|
|
existingConstraint = [constraint for constraint in bone.constraints if constraint.name == "DOF Limitation"]
|
|
if existingConstraint:
|
|
bone.constraints.remove(existingConstraint[0])
|
|
|
|
|
|
# Reparameterizes a blender path via keyframing it's eval_time to match a stride_object's forward velocity.
|
|
# IN: Context, stride object (blender object with location keyframes), path object.
|
|
def path_editing(context, stride_obj, path):
|
|
y_fcurve = [fcurve for fcurve in stride_obj.animation_data.action.fcurves if fcurve.data_path == "location"][1]
|
|
s, e = context.scene.frame_start, context.scene.frame_end # y_fcurve.range()
|
|
s = int(s)
|
|
e = int(e)
|
|
y_s = y_fcurve.evaluate(s)
|
|
y_e = y_fcurve.evaluate(e)
|
|
direction = (y_e - y_s) / abs(y_e - y_s)
|
|
existing_cons = [constraint for constraint in stride_obj.constraints if constraint.type == "FOLLOW_PATH"]
|
|
for cons in existing_cons:
|
|
stride_obj.constraints.remove(cons)
|
|
path_cons = stride_obj.constraints.new("FOLLOW_PATH")
|
|
if direction < 0:
|
|
path_cons.forward_axis = "TRACK_NEGATIVE_Y"
|
|
else:
|
|
path_cons.forward_axis = "FORWARD_Y"
|
|
path_cons.target = path
|
|
path_cons.use_curve_follow = True
|
|
path.data.path_duration = e - s
|
|
try:
|
|
path.data.animation_data.action.fcurves
|
|
except AttributeError:
|
|
path.data.keyframe_insert("eval_time", frame=0)
|
|
eval_time_fcurve = [fcurve for fcurve in path.data.animation_data.action.fcurves if fcurve.data_path == "eval_time"]
|
|
eval_time_fcurve = eval_time_fcurve[0]
|
|
totalLength = 0
|
|
parameterization = {}
|
|
print("evaluating curve")
|
|
for t in range(s, e - 1):
|
|
if s == t:
|
|
chordLength = 0
|
|
else:
|
|
chordLength = (y_fcurve.evaluate(t) - y_fcurve.evaluate(t + 1))
|
|
totalLength += chordLength
|
|
parameterization[t] = totalLength
|
|
for t in range(s + 1, e - 1):
|
|
if totalLength == 0:
|
|
print("no forward motion")
|
|
parameterization[t] /= totalLength
|
|
parameterization[t] *= e - s
|
|
parameterization[e] = e - s
|
|
for t in parameterization.keys():
|
|
eval_time_fcurve.keyframe_points.insert(frame=t, value=parameterization[t])
|
|
y_fcurve.mute = True
|
|
print("finished path editing")
|
|
|
|
|
|
#Animation Stitching
|
|
#Stitches two retargeted animations together via NLA settings.
|
|
#IN: enduser_obj, a blender armature that has had two retargets applied.
|
|
def anim_stitch(context, enduser_obj):
|
|
stitch_settings = enduser_obj.data.stitch_settings
|
|
action_1 = stitch_settings.first_action
|
|
action_2 = stitch_settings.second_action
|
|
if stitch_settings.stick_bone != "":
|
|
selected_bone = enduser_obj.pose.bones[stitch_settings.stick_bone]
|
|
else:
|
|
selected_bone = enduser_obj.pose.bones[0]
|
|
scene = context.scene
|
|
TrackNamesA = enduser_obj.data.mocapNLATracks[action_1]
|
|
TrackNamesB = enduser_obj.data.mocapNLATracks[action_2]
|
|
enduser_obj.data.active_mocap = action_1
|
|
anim_data = enduser_obj.animation_data
|
|
# add tracks for action 2
|
|
mocapAction = bpy.data.actions[TrackNamesB.base_track]
|
|
mocapTrack = anim_data.nla_tracks.new()
|
|
mocapTrack.name = TrackNamesB.base_track
|
|
mocapStrip = mocapTrack.strips.new(TrackNamesB.base_track, stitch_settings.blend_frame, mocapAction)
|
|
mocapStrip.extrapolation = "HOLD_FORWARD"
|
|
mocapStrip.blend_in = stitch_settings.blend_amount
|
|
mocapStrip.action_frame_start += stitch_settings.second_offset
|
|
mocapStrip.action_frame_end += stitch_settings.second_offset
|
|
constraintTrack = anim_data.nla_tracks.new()
|
|
constraintTrack.name = TrackNamesB.auto_fix_track
|
|
constraintAction = bpy.data.actions[TrackNamesB.auto_fix_track]
|
|
constraintStrip = constraintTrack.strips.new(TrackNamesB.auto_fix_track, stitch_settings.blend_frame, constraintAction)
|
|
constraintStrip.extrapolation = "HOLD_FORWARD"
|
|
constraintStrip.blend_in = stitch_settings.blend_amount
|
|
userTrack = anim_data.nla_tracks.new()
|
|
userTrack.name = TrackNamesB.manual_fix_track
|
|
userAction = bpy.data.actions[TrackNamesB.manual_fix_track]
|
|
userStrip = userTrack.strips.new(TrackNamesB.manual_fix_track, stitch_settings.blend_frame, userAction)
|
|
userStrip.extrapolation = "HOLD_FORWARD"
|
|
userStrip.blend_in = stitch_settings.blend_amount
|
|
#stride bone
|
|
if enduser_obj.parent:
|
|
if enduser_obj.parent.name == "stride_bone":
|
|
stride_bone = enduser_obj.parent
|
|
stride_anim_data = stride_bone.animation_data
|
|
stride_anim_data.use_nla = True
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stride_anim_data.action = None
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for track in stride_anim_data.nla_tracks:
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stride_anim_data.nla_tracks.remove(track)
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actionATrack = stride_anim_data.nla_tracks.new()
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actionATrack.name = TrackNamesA.stride_action
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actionAStrip = actionATrack.strips.new(TrackNamesA.stride_action, 0, bpy.data.actions[TrackNamesA.stride_action])
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actionAStrip.extrapolation = "NOTHING"
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actionBTrack = stride_anim_data.nla_tracks.new()
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actionBTrack.name = TrackNamesB.stride_action
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actionBStrip = actionBTrack.strips.new(TrackNamesB.stride_action, stitch_settings.blend_frame, bpy.data.actions[TrackNamesB.stride_action])
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actionBStrip.action_frame_start += stitch_settings.second_offset
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actionBStrip.action_frame_end += stitch_settings.second_offset
|
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actionBStrip.extrapolation = "NOTHING"
|
|
#we need to change the stride_bone's action to add the offset
|
|
aStrideCurves = [fcurve for fcurve in bpy.data.actions[TrackNamesA.stride_action].fcurves if fcurve.data_path == "location"]
|
|
bStrideCurves = [fcurve for fcurve in bpy.data.actions[TrackNamesB.stride_action].fcurves if fcurve.data_path == "location"]
|
|
scene.frame_set(stitch_settings.blend_frame - 1)
|
|
desired_pos = (enduser_obj.matrix_world * selected_bone.matrix.to_translation())
|
|
scene.frame_set(stitch_settings.blend_frame)
|
|
actual_pos = (enduser_obj.matrix_world * selected_bone.matrix.to_translation())
|
|
print(desired_pos, actual_pos)
|
|
offset = Vector(actual_pos) - Vector(desired_pos)
|
|
|
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for i, fcurve in enumerate(bStrideCurves):
|
|
print(offset[i], i, fcurve.array_index)
|
|
for pt in fcurve.keyframe_points:
|
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pt.co.y -= offset[i]
|
|
pt.handle_left.y -= offset[i]
|
|
pt.handle_right.y -= offset[i]
|
|
|
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#actionBStrip.blend_in = stitch_settings.blend_amount
|
|
|
|
|
|
#Guesses setting for animation stitching via Cross Correlation
|
|
def guess_anim_stitch(context, enduser_obj):
|
|
stitch_settings = enduser_obj.data.stitch_settings
|
|
action_1 = stitch_settings.first_action
|
|
action_2 = stitch_settings.second_action
|
|
TrackNamesA = enduser_obj.data.mocapNLATracks[action_1]
|
|
TrackNamesB = enduser_obj.data.mocapNLATracks[action_2]
|
|
mocapA = bpy.data.actions[TrackNamesA.base_track]
|
|
mocapB = bpy.data.actions[TrackNamesB.base_track]
|
|
curvesA = mocapA.fcurves
|
|
curvesB = mocapB.fcurves
|
|
flm, s, data = crossCorrelationMatch(curvesA, curvesB, 10)
|
|
print("Guessed the following for start and offset: ", s, flm)
|
|
enduser_obj.data.stitch_settings.blend_frame = flm
|
|
enduser_obj.data.stitch_settings.second_offset = s
|