channel2 = iiImage
channels3 = channels2 = channels1 = [channel1,channel2]
# anisotropy factor is the ratio between z voxel size and x/y voxel size.
# if Isotropic -> 1.0
zAnisotropyFactor = 1.0;
# this is typically a good value, but it depends on the voxel size of the data
hessianSigma = 3.5
eigV1 = computeEigenVectorsOfHessianImage( img1, zAnisotropyFactor, hessianSigma )
eigV2 = computeEigenVectorsOfHessianImage( img2, zAnisotropyFactor, hessianSigma )
eigV3 = computeEigenVectorsOfHessianImage( img3, zAnisotropyFactor, hessianSigma )
# Train: note that we pass a list of stacks
model.trainWithChannels( [img1,img2,img3], [eigV1, eigV2, eigV3], [gt1,gt2,gt3], [channels1,channels2,channels3],
zAnisotropyFactor, numStumps=100, gtNegativeLabel=1, gtPositiveLabel=2, debugOutput=True)
pred = model.predictWithChannels( img, eigV1, channels1, zAnisotropyFactor, useEarlyStopping=True)
roi = ROICoordinates()
roi.x2 = img.shape[2] - 1
roi.y2 = img.shape[1] - 1
roi.z1 = roi.z2 = img.shape[0] / 2
predSingleSlice = model.predictWithChannels( img, eigV1, channels1, zAnisotropyFactor, useEarlyStopping=True, subROI=roi)
# show image & prediction side by side
plt.ion()
plt.figure()
