def get_error(self, data, model):
nr1 = beads.transform(model, self.r0[data])
err_per_point = numpy.array([
numpy.linalg.norm(self.r1[data[i]] - nr1[i])
for i in range(len(data))
])
return err_per_point
def calculateFinalWeightMatrices(params,
stacks,
axisOfRotation=1,
sigmoidHalfWidthRelativeToImageSize=0.05,
adaptiveCenterFile=None,
sampleIntensityThreshold=15):
# old function arguments:
# params,size,spacings,isotropicSpacing,outShape=None,outSpacing=None,sigmoidHalfWidthRelativeToImageSize=0.05)
"""
:rtype : object
- expects params and size, axisOfRotation, adaptiveCenterFile(sitk) relative to axes definition: (x,y,z)
- rotation around x
"""
#origins = infoDict['origins']
#positions = infoDict['positions']
#spacing = infoDict['spacing']
#centerOfRotation = infoDict['centerOfRotation']
#axisOfRotation = infoDict['axisOfRotation']
#sizes = infoDict['sizes']
params = n.array(params)
finalSize = n.ceil(stacks[0].GetSize()).astype(n.int64)
# finalSpacing = n.array(stacks[0].GetSpacing()).astype(n.float64)
# tmpMatrix = n.zeros((4,4)).astype(n.float)
# tmpMatrix[:3,:3] = params[1][:9].reshape((3,3))
# direction = transformations.rotation_from_matrix(tmpMatrix)[1]
axisOfRotation = n.argmax(n.abs(params[1][[0, 4, 8]]))
print 'determining axis of rotation from parameters... %s' % axisOfRotation
axes = range(3)
axes.remove(axisOfRotation)
firstAxis = axes[0]
lastAxis = axes[1]
def calcAngles(Y, Z):
norm = n.sqrt(n.power(Y, 2) + n.power(Z, 2))
norm[norm == 0] = 0.001
angles = []
angles.append(n.arccos(Y / norm))
angles.append(2 * n.pi - n.arccos(Y / norm))
#angles.append(n.pi-n.arccos(-Y/norm))
#angles.append(n.pi+n.arccos(-Y/norm))
finalAngles = n.zeros(Y.shape, dtype=n.float64)
#criteria = [(Y>=0)*(Z>=0),(Y>=0)*(Z<0),(Y<0)*(Z>=0),(Y<0)*(Z<0)]
criteria = [(Z >= 0), (Z < 0)]
for icrit, crit in enumerate(criteria):
finalAngles[crit] = angles[icrit][crit]
return finalAngles
if adaptiveCenterFile == None:
centers = n.array([[finalSize[lastAxis], finalSize[firstAxis]]]) / 2.
else:
print 'calculating stack centers for fusion weights (assuming sample intensity of >%s)' % sampleIntensityThreshold
#adaptiveCenterFileSpacing = n.array(adaptiveCenterFile.GetSpacing())
centers = []
Z, Y = n.mgrid[0:finalSize[lastAxis], 0:finalSize[firstAxis]]
Z, Y = sitk.GetImageFromArray(Y.astype(
n.uint16)), sitk.GetImageFromArray(Z.astype(n.uint16))
for x in range(adaptiveCenterFile.GetSize()[axisOfRotation]):
if axisOfRotation == 0:
xImage = adaptiveCenterFile[x]
elif axisOfRotation == 1:
xImage = adaptiveCenterFile[:, x]
elif axisOfRotation == 2:
xImage = adaptiveCenterFile[:, :, x]
#pdb.set_trace()
Z.SetOrigin(xImage.GetOrigin())
Z.SetSpacing(xImage.GetSpacing())
Y.SetOrigin(xImage.GetOrigin())
Y.SetSpacing(xImage.GetSpacing())
xImage = sitk.Cast(xImage > sampleIntensityThreshold, 7
) * 1000 + 0.01 #careful, could create overflow
totalIntensity = sitk.SumProjection(sitk.SumProjection(xImage, 0),
1).GetPixel(0, 0)
z = sitk.SumProjection(
sitk.SumProjection(sitk.Cast(Z, 7) * xImage, 0), 1).GetPixel(
0, 0) / totalIntensity
y = sitk.SumProjection(
sitk.SumProjection(sitk.Cast(Y, 7) * xImage, 0), 1).GetPixel(
0, 0) / totalIntensity
# totalIntensity = sitk.SumProjection(sitk.SumProjection(sitk.GetImageFromArray(n.ones(xImage.GetSize())),0),1).GetPixel(0,0)
#pdb.set_trace()
# z = sitk.SumProjection(sitk.SumProjection(Z,0),1).GetPixel(0,0)/totalIntensity
# y = sitk.SumProjection(sitk.SumProjection(Y,0),1).GetPixel(0,0)/totalIntensity
# tmp = sitk.GetArrayFromImage(xImage)
# tmp[int(y),int(z)] = 2000
# plt.imshow(tmp)
# plt.colorbar()
# plt.show()
center = [y, z]
centers.append(center)
# smoothen center line
centers = n.array(centers)
#centers = n.array([ndimage.gaussian_filter(centers[:,i],finalSize[[firstAxis,lastAxis][i]]/30.) for i in range(2)])
#centers = centers.swapaxes(0,1)
# get angles
refCenter = n.array(stacks[0].TransformIndexToPhysicalPoint(
[int(i) for i in n.array(stacks[0].GetSize()) / 2.]))
#origCenter = n.array([refCenter[i] for i in [lastAxis,firstAxis]])
bestAngles = []
refPoints = []
print 'refCenter %s' % refCenter
refPoints.append(refCenter)
for iparam, param in enumerate(params):
# point2 is refCenter in rotated view
point2 = beads.transform(param, [refCenter])[0]
# shift point2 in z (positive here since later compared to positive again)
point2[lastAxis] += 100
# transform back to reference view
refPoint = beads.backTransform(param, [point2])[0]
refPoints.append(refPoint)
# calc direction vector
refVector = refPoint - refCenter
refVector = n.array([refVector[i] for i in [lastAxis, firstAxis]])
refVector = refVector / n.linalg.norm(refVector)
# calc angle with respect to [1,1]
angle = calcAngles(n.array([refVector[0]]), n.array([refVector[1]]))
bestAngles.append(angle)
#pdb.set_trace()
refPoints = n.array(refPoints)
# plt.plot(refPoints[:,0],refPoints[:,2],'o')
# plt.show()
zCenter = finalSize[[lastAxis, firstAxis]] / 2.
lower = n.array([0, 0])
upper = finalSize[[lastAxis, firstAxis]]
templateLower = lower - n.max(centers, 0) + zCenter
templateUpper = upper - n.min(centers, 0) + zCenter
print templateLower, templateUpper
deltaplus = n.ceil(templateUpper - zCenter)
deltaminus = n.ceil(zCenter - templateLower)
templateSize = deltaminus + deltaplus
Y, Z = n.mgrid[0:templateSize[0], 0:templateSize[1]]
Y = Y - deltaminus[0]
Z = Z - deltaminus[1]
norm = n.sqrt(n.power(Y, 2) + n.power(Z, 2))
sigmoidHalfWidthInPixels = n.mean(
finalSize[[firstAxis, lastAxis]]) * sigmoidHalfWidthRelativeToImageSize
halfY = 0.9 # half width is defined as the x value corresponding to y=halfY
norm[norm == 0] = 0.0001
sigmoidA = n.log(1. / halfY - 1.) / n.arctan(
sigmoidHalfWidthInPixels / norm)
finalAngles = calcAngles(Y, Z)
# plt.imshow(finalAngles)
# plt.title('finalAngles')
# plt.colorbar()
# plt.show()
# add cameras here
viewAngles = []
for i in range(len(bestAngles)):
viewAngle = []
viewAngle.append(bestAngles[i])
viewAngle.append((bestAngles[i] + n.pi) % (2 * n.pi))
viewAngles.append(viewAngle)
print viewAngles
# calculate difference to ref angles
weights = []
for iview, viewAngle in enumerate(viewAngles):
tmp = n.min([
n.min([
n.abs(finalAngles - i),
n.abs(2 * n.pi - n.abs(finalAngles - i))
], 0) for i in viewAngle
], 0)
weights.append(tmp.astype(n.float32))
# plt.imshow(weights[iview])
# plt.title('weights1 %s' %iview)
# plt.colorbar()
# plt.show()
# #tifffile.imsave('/home/malbert/delme/test%s.tif' %(iview),weights[iview])
weightsDiff = []
for iview, view in enumerate(viewAngles):
weightsDiff.append(
n.min([
-weights[iview] + weights[i]
for i in n.delete(range(len(viewAngles)), iview, 0)
], 0))
#tifffile.imsave('/home/malbert/delme/test%s.tif' %(iview),weightsDiff[iview])
#sitk.WriteImage(sitk.Cast(sitk.GetImageFromArray(weightsDiff[iview]),3),'/home/malbert/delme/test%s.tif' %(iview))
# plt.imshow(weightsDiff[iview])
# plt.title('weightsDiff1 %s' %iview)
# plt.colorbar()
# plt.show()
for iview, view in enumerate(viewAngles):
weightsDiff[iview] = (1 / (1 + n.exp(sigmoidA * (weightsDiff[iview]))))
# plt.imshow(weightsDiff[iview])
# plt.title('weightsDiff2 %s' %iview)
# plt.colorbar()
# plt.show()
#pdb.set_trace()
#sitk.WriteImage(sitk.Cast(sitk.GetImageFromArray(weightsDiff[iview]*100),3),'/home/malbert/delme/test%s.tif' %(iview))
weights = []
weightsDiffSum = n.sum(weightsDiff, 0)
for iview, view in enumerate(viewAngles):
weights.append(weightsDiff[iview] / weightsDiffSum)
#sitk.WriteImage(sitk.Cast(sitk.GetImageFromArray(weights[iview]*1000),3),'/home/malbert/delme/test%s.tif' %(iview))
#sitk.WriteImage(sitk.Cast(finalWeights[iview]*1000,3),'/home/malbert/delme/test0.tif')
xWeights = []
for icenter, center in enumerate(centers):
xWeight = []
for iview, view in enumerate(viewAngles):
# pdb.set_trace()
# plt.imshow(weights[iview])
# plt.title('test')
# plt.colorbar()
# plt.show()
xW = sitk.GetImageFromArray(weights[iview])
#xW.SetOrigin((templateLower[0], templateLower[1]))
xW.SetOrigin([0, 0])
tmpTransform = sitk.Transform(2, 1)
tmpSize = finalSize[[lastAxis, firstAxis]].astype(n.int64)
tmpSize = [int(i) for i in tmpSize[::-1]]
tmpOrigin = n.array([-center[i] + deltaminus[i] for i in [1, 0]])
#pdb.set_trace()
tmpTransform.SetParameters(tmpOrigin)
#pdb.set_trace()
xW = sitk.Resample(
xW,
tmpSize,
tmpTransform,
sitk.sitkLinear,
#tmpOrigin
)
#pdb.set_trace()
xW = sitk.GetArrayFromImage(xW)
xWeight.append(xW)
# plt.imshow(xW)
# plt.title('weights %s %s' %(iview,center))
# plt.colorbar()
# plt.show()
xWeights.append(xWeight)
finalWeights = []
for i in range(len(params)):
#finalWeights.append(sitk.GetImageFromArray(xWeights[0][i]))
if adaptiveCenterFile is None:
tmp = n.array([xWeights[0][i]] * finalSize[axisOfRotation])
else:
tmp = n.array(
[xWeights[j][i] for j in range(finalSize[axisOfRotation])])
if axisOfRotation == 0: numpyAxisOfRotation = 2
elif axisOfRotation == 1: numpyAxisOfRotation = 1
elif axisOfRotation == 2: numpyAxisOfRotation = 0
tmp = tmp.swapaxes(0, numpyAxisOfRotation)
if numpyAxisOfRotation == 2:
otherAxes = range(3)
otherAxes.remove(numpyAxisOfRotation)
tmp = tmp.swapaxes(otherAxes[0], otherAxes[1])
#tmp = tmp.swapaxes(0, 1).swapaxes(1, 2).astype(n.float32)
tmp = tmp.astype(n.float32)
finalWeights.append(sitk.GetImageFromArray(tmp))
#sitk.WriteImage(sitk.Cast(finalWeights[iview]*1000,3),'/home/malbert/delme/test.tif')
return finalWeights
def get_error( self, data, model):
nr1 = beads.transform(model,self.r0[data])
err_per_point = numpy.array([numpy.linalg.norm(self.r1[data[i]]-nr1[i]) for i in range(len(data))])
return err_per_point
def calculateFinalWeightMatrices(params,
stacks,
axisOfRotation=1,
sigmoidHalfWidthRelativeToImageSize=0.05,
adaptiveCenterFile=None,
sampleIntensityThreshold=15
):
# old function arguments:
# params,size,spacings,isotropicSpacing,outShape=None,outSpacing=None,sigmoidHalfWidthRelativeToImageSize=0.05)
"""
:rtype : object
- expects params and size, axisOfRotation, adaptiveCenterFile(sitk) relative to axes definition: (x,y,z)
- rotation around x
"""
#origins = infoDict['origins']
#positions = infoDict['positions']
#spacing = infoDict['spacing']
#centerOfRotation = infoDict['centerOfRotation']
#axisOfRotation = infoDict['axisOfRotation']
#sizes = infoDict['sizes']
params = n.array(params)
finalSize = n.ceil(stacks[0].GetSize()).astype(n.int64)
# finalSpacing = n.array(stacks[0].GetSpacing()).astype(n.float64)
# tmpMatrix = n.zeros((4,4)).astype(n.float)
# tmpMatrix[:3,:3] = params[1][:9].reshape((3,3))
# direction = transformations.rotation_from_matrix(tmpMatrix)[1]
axisOfRotation = n.argmax(n.abs(params[1][[0,4,8]]))
print 'determining axis of rotation from parameters... %s' %axisOfRotation
axes = range(3)
axes.remove(axisOfRotation)
firstAxis = axes[0]
lastAxis = axes[1]
def calcAngles(Y,Z):
norm = n.sqrt(n.power(Y,2)+n.power(Z,2))
norm[norm==0] = 0.001
angles = []
angles.append(n.arccos(Y/norm))
angles.append(2*n.pi-n.arccos(Y/norm))
#angles.append(n.pi-n.arccos(-Y/norm))
#angles.append(n.pi+n.arccos(-Y/norm))
finalAngles = n.zeros(Y.shape,dtype=n.float64)
#criteria = [(Y>=0)*(Z>=0),(Y>=0)*(Z<0),(Y<0)*(Z>=0),(Y<0)*(Z<0)]
criteria = [(Z>=0),(Z<0)]
for icrit,crit in enumerate(criteria):
finalAngles[crit] = angles[icrit][crit]
return finalAngles
if adaptiveCenterFile == None:
centers = n.array([[finalSize[lastAxis],finalSize[firstAxis]]])/2.
else:
print 'calculating stack centers for fusion weights (assuming sample intensity of >%s)' %sampleIntensityThreshold
#adaptiveCenterFileSpacing = n.array(adaptiveCenterFile.GetSpacing())
centers = []
Z, Y = n.mgrid[0:finalSize[lastAxis], 0:finalSize[firstAxis]]
Z, Y = sitk.GetImageFromArray(Y.astype(n.uint16)), sitk.GetImageFromArray(Z.astype(n.uint16))
for x in range(adaptiveCenterFile.GetSize()[axisOfRotation]):
if axisOfRotation == 0:
xImage = adaptiveCenterFile[x]
elif axisOfRotation == 1:
xImage = adaptiveCenterFile[:,x]
elif axisOfRotation == 2:
xImage = adaptiveCenterFile[:,:,x]
#pdb.set_trace()
Z.SetOrigin(xImage.GetOrigin())
Z.SetSpacing(xImage.GetSpacing())
Y.SetOrigin(xImage.GetOrigin())
Y.SetSpacing(xImage.GetSpacing())
xImage = sitk.Cast(xImage>sampleIntensityThreshold,7)*1000+0.01 #careful, could create overflow
totalIntensity = sitk.SumProjection(sitk.SumProjection(xImage,0),1).GetPixel(0,0)
z = sitk.SumProjection(sitk.SumProjection(sitk.Cast(Z,7)*xImage,0),1).GetPixel(0,0)/totalIntensity
y = sitk.SumProjection(sitk.SumProjection(sitk.Cast(Y,7)*xImage,0),1).GetPixel(0,0)/totalIntensity
# totalIntensity = sitk.SumProjection(sitk.SumProjection(sitk.GetImageFromArray(n.ones(xImage.GetSize())),0),1).GetPixel(0,0)
#pdb.set_trace()
# z = sitk.SumProjection(sitk.SumProjection(Z,0),1).GetPixel(0,0)/totalIntensity
# y = sitk.SumProjection(sitk.SumProjection(Y,0),1).GetPixel(0,0)/totalIntensity
# tmp = sitk.GetArrayFromImage(xImage)
# tmp[int(y),int(z)] = 2000
# plt.imshow(tmp)
# plt.colorbar()
# plt.show()
center = [y,z]
centers.append(center)
# smoothen center line
centers = n.array(centers)
#centers = n.array([ndimage.gaussian_filter(centers[:,i],finalSize[[firstAxis,lastAxis][i]]/30.) for i in range(2)])
#centers = centers.swapaxes(0,1)
# get angles
refCenter = n.array(stacks[0].TransformIndexToPhysicalPoint([int(i) for i in n.array(stacks[0].GetSize())/2.]))
#origCenter = n.array([refCenter[i] for i in [lastAxis,firstAxis]])
bestAngles = []
refPoints = []
print 'refCenter %s' %refCenter
refPoints.append(refCenter)
for iparam,param in enumerate(params):
# point2 is refCenter in rotated view
point2 = beads.transform(param,[refCenter])[0]
# shift point2 in z (positive here since later compared to positive again)
point2[lastAxis] += 100
# transform back to reference view
refPoint = beads.backTransform(param,[point2])[0]
refPoints.append(refPoint)
# calc direction vector
refVector = refPoint-refCenter
refVector = n.array([refVector[i] for i in [lastAxis,firstAxis]])
refVector = refVector/n.linalg.norm(refVector)
# calc angle with respect to [1,1]
angle = calcAngles(n.array([refVector[0]]),n.array([refVector[1]]))
bestAngles.append(angle)
#pdb.set_trace()
refPoints = n.array(refPoints)
# plt.plot(refPoints[:,0],refPoints[:,2],'o')
# plt.show()
zCenter = finalSize[[lastAxis,firstAxis]]/2.
lower = n.array([0,0])
upper = finalSize[[lastAxis,firstAxis]]
templateLower = lower - n.max(centers,0)+zCenter
templateUpper = upper - n.min(centers,0)+zCenter
print templateLower,templateUpper
deltaplus = n.ceil(templateUpper-zCenter)
deltaminus = n.ceil(zCenter-templateLower)
templateSize = deltaminus+deltaplus
Y,Z = n.mgrid[0:templateSize[0],0:templateSize[1]]
Y = Y-deltaminus[0]
Z = Z-deltaminus[1]
norm = n.sqrt(n.power(Y,2)+n.power(Z,2))
sigmoidHalfWidthInPixels = n.mean(finalSize[[firstAxis,lastAxis]])*sigmoidHalfWidthRelativeToImageSize
halfY = 0.9 # half width is defined as the x value corresponding to y=halfY
norm[norm==0] = 0.0001
sigmoidA = n.log(1./halfY-1.)/n.arctan(sigmoidHalfWidthInPixels/norm)
finalAngles = calcAngles(Y,Z)
# plt.imshow(finalAngles)
# plt.title('finalAngles')
# plt.colorbar()
# plt.show()
# add cameras here
viewAngles = []
for i in range(len(bestAngles)):
viewAngle = []
viewAngle.append(bestAngles[i])
viewAngle.append((bestAngles[i]+n.pi)%(2*n.pi))
viewAngles.append(viewAngle)
print viewAngles
# calculate difference to ref angles
weights = []
for iview,viewAngle in enumerate(viewAngles):
tmp = n.min([n.min([n.abs(finalAngles-i),n.abs(2*n.pi-n.abs(finalAngles-i))],0) for i in viewAngle],0)
weights.append(tmp.astype(n.float32))
# plt.imshow(weights[iview])
# plt.title('weights1 %s' %iview)
# plt.colorbar()
# plt.show()
# #tifffile.imsave('/home/malbert/delme/test%s.tif' %(iview),weights[iview])
weightsDiff = []
for iview,view in enumerate(viewAngles):
weightsDiff.append(n.min([-weights[iview]+weights[i] for i in n.delete(range(len(viewAngles)),iview,0)],0))
#tifffile.imsave('/home/malbert/delme/test%s.tif' %(iview),weightsDiff[iview])
#sitk.WriteImage(sitk.Cast(sitk.GetImageFromArray(weightsDiff[iview]),3),'/home/malbert/delme/test%s.tif' %(iview))
# plt.imshow(weightsDiff[iview])
# plt.title('weightsDiff1 %s' %iview)
# plt.colorbar()
# plt.show()
for iview,view in enumerate(viewAngles):
weightsDiff[iview] = (1/(1+n.exp(sigmoidA*(weightsDiff[iview]))))
# plt.imshow(weightsDiff[iview])
# plt.title('weightsDiff2 %s' %iview)
# plt.colorbar()
# plt.show()
#pdb.set_trace()
#sitk.WriteImage(sitk.Cast(sitk.GetImageFromArray(weightsDiff[iview]*100),3),'/home/malbert/delme/test%s.tif' %(iview))
weights = []
weightsDiffSum = n.sum(weightsDiff,0)
for iview,view in enumerate(viewAngles):
weights.append(weightsDiff[iview]/weightsDiffSum)
#sitk.WriteImage(sitk.Cast(sitk.GetImageFromArray(weights[iview]*1000),3),'/home/malbert/delme/test%s.tif' %(iview))
#sitk.WriteImage(sitk.Cast(finalWeights[iview]*1000,3),'/home/malbert/delme/test0.tif')
xWeights = []
for icenter, center in enumerate(centers):
xWeight = []
for iview,view in enumerate(viewAngles):
# pdb.set_trace()
# plt.imshow(weights[iview])
# plt.title('test')
# plt.colorbar()
# plt.show()
xW = sitk.GetImageFromArray(weights[iview])
#xW.SetOrigin((templateLower[0], templateLower[1]))
xW.SetOrigin([0,0])
tmpTransform = sitk.Transform(2,1)
tmpSize = finalSize[[lastAxis,firstAxis]].astype(n.int64)
tmpSize = [int(i) for i in tmpSize[::-1]]
tmpOrigin = n.array([-center[i]+deltaminus[i] for i in [1,0]])
#pdb.set_trace()
tmpTransform.SetParameters(tmpOrigin)
#pdb.set_trace()
xW = sitk.Resample(xW,
tmpSize,
tmpTransform,
sitk.sitkLinear,
#tmpOrigin
)
#pdb.set_trace()
xW = sitk.GetArrayFromImage(xW)
xWeight.append(xW)
# plt.imshow(xW)
# plt.title('weights %s %s' %(iview,center))
# plt.colorbar()
# plt.show()
xWeights.append(xWeight)
finalWeights = []
for i in range(len(params)):
#finalWeights.append(sitk.GetImageFromArray(xWeights[0][i]))
if adaptiveCenterFile is None:
tmp = n.array([xWeights[0][i]]*finalSize[axisOfRotation])
else:
tmp = n.array([xWeights[j][i] for j in range(finalSize[axisOfRotation])])
if axisOfRotation == 0: numpyAxisOfRotation = 2
elif axisOfRotation == 1: numpyAxisOfRotation = 1
elif axisOfRotation == 2: numpyAxisOfRotation = 0
tmp = tmp.swapaxes(0,numpyAxisOfRotation)
if numpyAxisOfRotation == 2:
otherAxes = range(3)
otherAxes.remove(numpyAxisOfRotation)
tmp = tmp.swapaxes(otherAxes[0],otherAxes[1])
#tmp = tmp.swapaxes(0, 1).swapaxes(1, 2).astype(n.float32)
tmp = tmp.astype(n.float32)
finalWeights.append(sitk.GetImageFromArray(tmp))
#sitk.WriteImage(sitk.Cast(finalWeights[iview]*1000,3),'/home/malbert/delme/test.tif')
return finalWeights
