Revision: 38116
http://projects.blender.org/scm/viewvc.php?view=rev&root=bf-blender&revision=38116
Author: benjycook
Date: 2011-07-05 10:55:35 +0000 (Tue, 05 Jul 2011)
Log Message:
-----------
Added denoising function. Uses a type of median filter to smooth out spikes,
typical of sensor noise in motion capture data
Modified Paths:
--------------
branches/soc-2011-pepper/release/scripts/modules/mocap_tools.py
Modified: branches/soc-2011-pepper/release/scripts/modules/mocap_tools.py
===================================================================
--- branches/soc-2011-pepper/release/scripts/modules/mocap_tools.py
2011-07-05 10:35:48 UTC (rev 38115)
+++ branches/soc-2011-pepper/release/scripts/modules/mocap_tools.py
2011-07-05 10:55:35 UTC (rev 38116)
@@ -513,3 +513,37 @@
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
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