diff options
Diffstat (limited to 'release/scripts/modules/mocap_tools.py')
| -rw-r--r-- | release/scripts/modules/mocap_tools.py | 90 |
1 files changed, 58 insertions, 32 deletions
diff --git a/release/scripts/modules/mocap_tools.py b/release/scripts/modules/mocap_tools.py index 3f821270e3c..f4b6a93f531 100644 --- a/release/scripts/modules/mocap_tools.py +++ b/release/scripts/modules/mocap_tools.py @@ -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))]) - - diff = [] - - for i in range(len(data) - flm): - diff.append((data[i] - data[i + flm]).lengthSq) + 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 = [] + + for i in range(len(dataA) - flm): + diff.append((dataA[i] - dataB[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
\ No newline at end of file |