def Run(N,dim,rodrigues,prefix,J=0.00008,PtoA=1.5,Image=False,Dump=False,dovtk=False,verbose=0):
output = prefix+str(J)+"_"+str(PtoA)
rod = numpy.zeros([3]+list(rodrigues['x'].numpy_array().shape), dtype='float64')
rod[0] = rodrigues['x']
rod[1] = rodrigues['y']
rod[2] = rodrigues['z']
orientationmap = OrientationField.RodriguesToUnambiguousColor(rod[0],rod[1],rod[2])
rod = rod.transpose(range(1,len(rod.shape))+[0]).copy()
omega = rod.flatten().astype('float64')
mis = numpy.zeros((2*dim)*N**dim, dtype='float64')
grain = numpy.zeros((2*dim)*N**dim, dtype='int32')
bdlength = numpy.zeros((2*dim)*N**dim, dtype='int32')
indexmap = numpy.zeros(N**dim, dtype='int32')
status, ngrain, nbd = bp.boundary_pruning(N, dim, omega, mis, grain, bdlength, indexmap, J, PtoA, verbose)
if Image == True:
if dim == 2 :
GetReconstructionWithBoundary2D(N,orientationmap,output,indexmap=indexmap.reshape(N,N))
if dim == 3:
GetReconstructionWithBoundary3D(N,orientationmap,output,indexmap=indexmap.reshape(N,N,N),dovtk=dovtk)
if Dump == True:
pickle.dump(mis[:nbd], open("%s.mis.pickle" % output, "w"), protocol=-1)
pickle.dump(grain[:ngrain], open("%s.grain.pickle" % output, "w"), protocol=-1)
pickle.dump(bdlength[:nbd], open("%s.bdlength.pickle" % output, "w"), protocol=-1)
pickle.dump(indexmap, open("%s.indexmap.pickle" % output, "w"), protocol=-1)
if dim == 3:
return mis[:nbd], grain[:ngrain], bdlength[:nbd], indexmap.reshape(N,N,N)
return mis[:nbd], grain[:ngrain], bdlength[:nbd], indexmap.reshape(N,N)
"""
# I don't have working PIL! skip for now
#from PIL import Image
import Image
pilImage = Image.fromarray(neworientationmap.astype('uint8'), 'RGB')
pilImage.save(filename+'.png')
output = "out"
lena = True
if lena == True:
N = 128
rod = misc.imread("lena.jpg")
else:
N = 1024
rod = misc.imread("earth.jpg")
#rod = rod.transpose([1,0,2])
omega = rod.flatten().astype('float64')
mis = numpy.zeros(4*N**2, dtype='float64')
grain = numpy.zeros(4*N**2, dtype='int32')
bdlength = numpy.zeros(4*N**2, dtype='int32')
#FIXME - need to be modified
indexmap = numpy.zeros(N**2, dtype='int32')
status, ngrain, nbd = bp.boundary_pruning(N, 2, omega, mis, grain, bdlength, indexmap, 180, 1.5)
print status, ngrain, nbd
GetReconstructionWithBoundary(N,rod,output,indexmap=indexmap.reshape(N,N))
