def controlSurfaceSmoothWeights(surface):
"""
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
DESCRIPTION:
Early version of a weight smoother for a
ARGUMENTS:
surface(string)
RETURNS:
Nada
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
"""
cvList = (mc.ls ([surface+'.cv[*][*]'],flatten=True))
print cvList
skinCluster = querySkinCluster (surface)
influenceObjects = queryInfluences (skinCluster)
print influenceObjects
#find the closest influence object to the start
startObject = (distance.returnClosestObjToCV (cvList[0], influenceObjects))
print startObject
#find the closest influence object to the end
endObject = (distance.returnClosestObjToCV (cvList[-1], influenceObjects))
print endObject
#getting the last cv list number
cvChainLength = ((len(cvList)-1)/2)
#set the skin weights for the top and bottom
mc.skinPercent (skinCluster,(surface+'.cv[0:1][0:1]'), tv = [startObject,1])
mc.skinPercent (skinCluster,('%s%s%i%s%i%s' % (surface,'.cv[0:1][',(cvChainLength-1),':',cvChainLength,']')), tv = [endObject,1])
#Blend in the nearest row to the start and end
mc.skinPercent (skinCluster,(surface+'.cv[0:1][2]'), tv = [startObject,.4])
mc.skinPercent (skinCluster,('%s%s%i%s' % (surface,'.cv[0:1][',(cvChainLength-1),']')), tv = [endObject,.4])
def controlSurfaceSmoothWeights(surface):
"""
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
DESCRIPTION:
Early version of a weight smoother for a
ARGUMENTS:
surface(string)
RETURNS:
Nada
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
"""
cvList = (mc.ls ([surface+'.cv[*][*]'],flatten=True))
print cvList
skinCluster = querySkinCluster (surface)
influenceObjects = queryInfluences (skinCluster)
print influenceObjects
#find the closest influence object to the start
startObject = (distance.returnClosestObjToCV (cvList[0], influenceObjects))
print startObject
#find the closest influence object to the end
endObject = (distance.returnClosestObjToCV (cvList[-1], influenceObjects))
print endObject
#getting the last cv list number
cvChainLength = ((len(cvList)-1)/2)
#set the skin weights for the top and bottom
mc.skinPercent (skinCluster,(surface+'.cv[0:1][0:1]'), tv = [startObject,1])
mc.skinPercent (skinCluster,('%s%s%i%s%i%s' % (surface,'.cv[0:1][',(cvChainLength-1),':',cvChainLength,']')), tv = [endObject,1])
#Blend in the nearest row to the start and end
mc.skinPercent (skinCluster,(surface+'.cv[0:1][2]'), tv = [startObject,.4])
mc.skinPercent (skinCluster,('%s%s%i%s' % (surface,'.cv[0:1][',(cvChainLength-1),']')), tv = [endObject,.4])
def nurbsCVSmoothWeights(cv):
surface = '.'.join(cv.split('.')[0:-1])
skinCluster = querySkinCluster (surface)
cvPos = mc.pointPosition (cv,world=True)
wantedName = (cv + 'loc')
actualName = mc.spaceLocator (n= wantedName)
mc.move (cvPos[0],cvPos[1],cvPos[2], [actualName[0]])
influenceObjects = queryInfluences (skinCluster)
culledList = influenceObjects
"""figure out our closest objects"""
bestChoices = []
cnt = 5
while cnt >= 0:
goodChoice = (distance.returnClosestObjToCV (cv, culledList))
culledList.remove(goodChoice)
bestChoices.append (goodChoice)
print bestChoices
cnt-=1
distanceList = []
for obj in bestChoices:
distanceList.append (distance.returnDistanceBetweenObjects(actualName[0],obj))
print distanceList
""" return normalization value """
buffer=0
y = 1 + (x-A)*(10-1)/(B-A)
for value in distanceList:
buffer += value
print buffer
normalize = ((len(distanceList))*.1)
print normalize
mc.delete (actualName[0])
def nurbsCVSmoothWeights(cv):
surface = '.'.join(cv.split('.')[0:-1])
skinCluster = querySkinCluster (surface)
cvPos = mc.pointPosition (cv,world=True)
wantedName = (cv + 'loc')
actualName = mc.spaceLocator (n= wantedName)
mc.move (cvPos[0],cvPos[1],cvPos[2], [actualName[0]])
influenceObjects = queryInfluences (skinCluster)
culledList = influenceObjects
"""figure out our closest objects"""
bestChoices = []
cnt = 5
while cnt >= 0:
goodChoice = (distance.returnClosestObjToCV (cv, culledList))
culledList.remove(goodChoice)
bestChoices.append (goodChoice)
print bestChoices
cnt-=1
distanceList = []
for obj in bestChoices:
distanceList.append (distance.returnDistanceBetweenObjects(actualName[0],obj))
print distanceList
""" return normalization value """
buffer=0
y = 1 + (x-A)*(10-1)/(B-A)
for value in distanceList:
buffer += value
print buffer
normalize = ((len(distanceList))*.1)
print normalize
mc.delete (actualName[0])
def simpleControlSurfaceSmoothWeights(surface,maxBlend = 3):
"""
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
DESCRIPTION:
Early version of a weight smoother for a
ARGUMENTS:
surface(string)
RETURNS:
Nada
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
"""
cvList = (mc.ls ([surface+'.cv[*][*]'],flatten=True))
skinCluster = querySkinCluster (surface)
influenceObjects = queryInfluences (skinCluster)
#find the closest influence object to the start
startObject = (distance.returnClosestObjToCV (cvList[0], influenceObjects))
#find the closest influence object to the end
endObject = (distance.returnClosestObjToCV (cvList[-1], influenceObjects))
#getting the last cv list number
cvChainLength = ((len(cvList)-1)/2)
#Smooth interior weights
""" get our interior CVs """
interiorCvList = []
cnt = 1
for i in range(cvChainLength):
interiorCvList.append('%s%s%i%s' % (surface,'.cv[0][',cnt,']'))
interiorCvList.append('%s%s%i%s' % (surface,'.cv[1][',cnt,']'))
cnt += 1
""" overall blend """
for cv in interiorCvList:
closestObject = (distance.returnClosestObjToCV (cv, influenceObjects))
closestObjectsDict = distance.returnClosestObjectsFromAim(closestObject,influenceObjects)
upObjects = closestObjectsDict.get('up')
downObjects = closestObjectsDict.get('dn')
objectsToCheck = []
if upObjects != None:
objectsToCheck += upObjects
if downObjects != None:
objectsToCheck += downObjects
blendInfluences =[]
blendInfluences.append(closestObject)
distances = []
cnt = 1
print objectsToCheck
while cnt < maxBlend and cnt < len(objectsToCheck):
closestSubObj = distance.returnClosestObject(closestObject, objectsToCheck)
blendInfluences.append(closestSubObj)
objectsToCheck.remove(closestSubObj)
cnt+=1
""" get our distances to normalize """
locBuffer = locators.locMeSurfaceCV(cv)
for obj in blendInfluences:
distances.append(distance.returnDistanceBetweenObjects(locBuffer,obj))
normalizedDistances = cgmMath.normList(distances, normalizeTo=1)
cnt = 0
for obj in blendInfluences:
mc.skinPercent (skinCluster,cv, tv = [obj,normalizedDistances[cnt]])
cnt +=1
mc.delete(locBuffer)
""" Set closest cv's to respective infuences to max """
cvList1 = []
cvList2 = []
for i in range(cvChainLength):
cvList1.append('%s%s%i%s' % (surface,'.cv[0][',cnt,']'))
cvList2.append('%s%s%i%s' % (surface,'.cv[1][',cnt,']'))
cnt += 1
for obj in influenceObjects:
closestCV1 = distance.returnClosestCVFromList(obj, cvList1)
mc.skinPercent (skinCluster,closestCV1, tv = [obj,1])
closestCV2 = distance.returnClosestCVFromList(obj, cvList2)
mc.skinPercent (skinCluster,closestCV2, tv = [obj,1])
#set the skin weights for the top and bottom
mc.skinPercent (skinCluster,(surface+'.cv[0:1][0:1]'), tv = [startObject,1])
mc.skinPercent (skinCluster,('%s%s%i%s%i%s' % (surface,'.cv[0:1][',(cvChainLength-1),':',cvChainLength,']')), tv = [endObject,1])
#Blend in the nearest row to the start and end
mc.skinPercent (skinCluster,(surface+'.cv[0:1][2]'), tv = [startObject,1])
mc.skinPercent (skinCluster,('%s%s%i%s' % (surface,'.cv[0:1][',(cvChainLength-2),']')), tv = [endObject,1])
mc.skinPercent (skinCluster,(surface+'.cv[0:1][3]'), tv = [startObject,.7])
mc.skinPercent (skinCluster,('%s%s%i%s' % (surface,'.cv[0:1][',(cvChainLength-3),']')), tv = [endObject,.7])
def simpleControlSurfaceSmoothWeights(surface,maxBlend = 3):
"""
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
DESCRIPTION:
Early version of a weight smoother for a
ARGUMENTS:
surface(string)
RETURNS:
Nada
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
"""
cvList = (mc.ls ([surface+'.cv[*][*]'],flatten=True))
skinCluster = querySkinCluster (surface)
influenceObjects = queryInfluences (skinCluster)
#find the closest influence object to the start
startObject = (distance.returnClosestObjToCV (cvList[0], influenceObjects))
#find the closest influence object to the end
endObject = (distance.returnClosestObjToCV (cvList[-1], influenceObjects))
#getting the last cv list number
cvChainLength = ((len(cvList)-1)/2)
#Smooth interior weights
""" get our interior CVs """
interiorCvList = []
cnt = 1
for i in range(cvChainLength):
interiorCvList.append('%s%s%i%s' % (surface,'.cv[0][',cnt,']'))
interiorCvList.append('%s%s%i%s' % (surface,'.cv[1][',cnt,']'))
cnt += 1
""" overall blend """
for cv in interiorCvList:
closestObject = (distance.returnClosestObjToCV (cv, influenceObjects))
closestObjectsDict = distance.returnClosestObjectsFromAim(closestObject,influenceObjects)
upObjects = closestObjectsDict.get('up')
downObjects = closestObjectsDict.get('dn')
objectsToCheck = []
if upObjects != None:
objectsToCheck += upObjects
if downObjects != None:
objectsToCheck += downObjects
blendInfluences =[]
blendInfluences.append(closestObject)
distances = []
cnt = 1
print objectsToCheck
while cnt < maxBlend and cnt < len(objectsToCheck):
closestSubObj = distance.returnClosestObject(closestObject, objectsToCheck)
blendInfluences.append(closestSubObj)
objectsToCheck.remove(closestSubObj)
cnt+=1
""" get our distances to normalize """
locBuffer = locators.locMeSurfaceCV(cv)
for obj in blendInfluences:
distances.append(distance.returnDistanceBetweenObjects(locBuffer,obj))
normalizedDistances = cgmMath.normList(distances, normalizeTo=1)
cnt = 0
for obj in blendInfluences:
mc.skinPercent (skinCluster,cv, tv = [obj,normalizedDistances[cnt]])
cnt +=1
mc.delete(locBuffer)
""" Set closest cv's to respective infuences to max """
cvList1 = []
cvList2 = []
for i in range(cvChainLength):
cvList1.append('%s%s%i%s' % (surface,'.cv[0][',cnt,']'))
cvList2.append('%s%s%i%s' % (surface,'.cv[1][',cnt,']'))
cnt += 1
for obj in influenceObjects:
closestCV1 = distance.returnClosestCVFromList(obj, cvList1)
mc.skinPercent (skinCluster,closestCV1, tv = [obj,1])
closestCV2 = distance.returnClosestCVFromList(obj, cvList2)
mc.skinPercent (skinCluster,closestCV2, tv = [obj,1])
#set the skin weights for the top and bottom
mc.skinPercent (skinCluster,(surface+'.cv[0:1][0:1]'), tv = [startObject,1])
mc.skinPercent (skinCluster,('%s%s%i%s%i%s' % (surface,'.cv[0:1][',(cvChainLength-1),':',cvChainLength,']')), tv = [endObject,1])
#Blend in the nearest row to the start and end
mc.skinPercent (skinCluster,(surface+'.cv[0:1][2]'), tv = [startObject,1])
mc.skinPercent (skinCluster,('%s%s%i%s' % (surface,'.cv[0:1][',(cvChainLength-2),']')), tv = [endObject,1])
mc.skinPercent (skinCluster,(surface+'.cv[0:1][3]'), tv = [startObject,.7])
mc.skinPercent (skinCluster,('%s%s%i%s' % (surface,'.cv[0:1][',(cvChainLength-3),']')), tv = [endObject,.7])
