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Additional work on animation stitching, now with auto-guess capability. Only a few bugs left, regarding animations translation

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This commit is contained in:
Benjy Cook
2011-08-11 16:46:27 +00:00
parent 05b7ccb736
commit f1a8c26aa3
3 changed files with 94 additions and 39 deletions
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86
release/scripts/modules/mocap_tools.py
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@@ -105,64 +105,75 @@ class dataPoint:
self.u = u
def autoloop_anim():
context = bpy.context
obj = context.active_object
fcurves = [x for x in obj.animation_data.action.fcurves if x.select]
data = []
end = len(fcurves[0].keyframe_points)
def crossCorrelationMatch(curvesA, curvesB, margin):
dataA = []
dataB = []
end = len(curvesA[0].keyframe_points)
for i in range(1, end):
vec = []
for fcurve in fcurves:
for fcurve in curvesA:
vec.append(fcurve.evaluate(i))
data.append(NdVector(vec))
dataA.append(NdVector(vec))
vec = []
for fcurve in curvesB:
vec.append(fcurve.evaluate(i))
dataB.append(NdVector(vec))
def comp(a, b):
return a * b
N = len(data)
N = len(dataA)
Rxy = [0.0] * N
for i in range(N):
for j in range(i, min(i + N, N)):
Rxy[i] += comp(data[j], data[j - i])
Rxy[i] += comp(dataA[j], dataB[j - i])
for j in range(i):
Rxy[i] += comp(data[j], data[j - i + N])
Rxy[i] += comp(dataA[j], dataB[j - i + N])
Rxy[i] /= float(N)
def bestLocalMaximum(Rxy):
Rxyd = [Rxy[i] - Rxy[i - 1] for i in range(1, len(Rxy))]
maxs = []
for i in range(1, len(Rxyd) - 1):
a = Rxyd[i - 1]
b = Rxyd[i]
print(a, b)
#sign change (zerocrossing) at point i, denoting max point (only)
if (a >= 0 and b < 0) or (a < 0 and b >= 0):
maxs.append((i, max(Rxy[i], Rxy[i - 1])))
return max(maxs, key=lambda x: x[1])[0]
flm = bestLocalMaximum(Rxy[0:int(len(Rxy))])
return [x[0] for x in maxs]
#~ return max(maxs, key=lambda x: x[1])[0]
flms = bestLocalMaximum(Rxy[0:int(len(Rxy))])
ss = []
for flm in flms:
diff = []
diff = []
for i in range(len(dataA) - flm):
diff.append((dataA[i] - dataB[i + flm]).lengthSq)
for i in range(len(data) - flm):
diff.append((data[i] - data[i + flm]).lengthSq)
def lowerErrorSlice(diff, e):
#index, error at index
bestSlice = (0, 100000)
for i in range(e, len(diff) - e):
errorSlice = sum(diff[i - e:i + e + 1])
if errorSlice < bestSlice[1]:
bestSlice = (i, errorSlice, flm)
return bestSlice
s = lowerErrorSlice(diff, margin)
ss.append(s)
def lowerErrorSlice(diff, e):
#index, error at index
bestSlice = (0, 100000)
for i in range(e, len(diff) - e):
errorSlice = sum(diff[i - e:i + e + 1])
if errorSlice < bestSlice[1]:
bestSlice = (i, errorSlice)
return bestSlice[0]
ss.sort(key = lambda x: x[1])
return ss[0][2], ss[0][0], dataA
margin = 2
def autoloop_anim():
context = bpy.context
obj = context.active_object
fcurves = [x for x in obj.animation_data.action.fcurves if x.select]
s = lowerErrorSlice(diff, margin)
margin = 10
print(flm, s)
flm, s, data = crossCorrelationMatch(fcurves, fcurves, margin)
loop = data[s:s + flm + margin]
#find *all* loops, s:s+flm, s+flm:s+2flm, etc...
@@ -824,3 +835,18 @@ def anim_stitch(context, enduser_obj):
pt.handle_left.y-=offset[i]
pt.handle_right.y-=offset[i]
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(flm,s)
enduser_obj.data.stitch_settings.blend_frame = flm
enduser_obj.data.stitch_settings.second_offset = s