Beispiel #1
0
def slicer(patientDir):
    '''
    Split NRRD CT Scan file into 64/64/64 disjoint cubes for inference
    '''
    multiple = 4
    desired = np.multiply(IN_SIZE, multiple)
    newshape = np.array(IN_SIZE)
    cuber = Cubify(oldshape=desired, newshape=newshape)

    sliceDir = patientDir + "sliced/"
    if not os.path.exists(sliceDir):
        os.mkdir(sliceDir)

    patient = patientDir + "/orig.nrrd"
    scan = sitk.ReadImage(patient)
    scan = sitk.GetArrayFromImage(scan)
    shape = np.array(scan.shape)

    # crop the image to fit desired --- NOTE WE ARE LOSING INFO (EDGE) HERE BE CAREFUL
    excess = np.abs(desired - shape)
    excess1 = excess / 2
    excess2 = excess - excess1
    scan = scan[excess1[0]:-excess2[0], excess1[1]:-excess2[1],
                excess1[2]:-excess2[2]]

    scan = pad(scan, desired)

    sitk.WriteImage(sitk.GetImageFromArray(scan), sliceDir + "orig.nrrd")

    scan = cuber.cubify(scan)
    nCubes = scan.shape[0]

    for arrNo in range(nCubes):
        wp = sliceDir + "sliced_{0}.bin".format(arrNo)
        scan[arrNo].tofile(wp)
    paths = glob.glob(sliceDir + "sliced_[0-9].bin")
    paths = [os.path.abspath(path) for path in paths]
    y = ["dummy.bin" for x in paths]
    csv = pd.DataFrame({"x": paths, "y": y})
    csv.to_csv(sliceDir + "csv.csv", index=0)
Beispiel #2
0
def grouper(patientDir):
    multiple = 4
    desired = np.multiply(IN_SIZE, multiple)
    newshape = np.array(IN_SIZE)
    cuber = Cubify(oldshape=desired, newshape=newshape)

    patientDir += "sliced/"
    nCubes = len(glob.glob(patientDir + "sliced_*_yPred.bin"))
    scan = np.empty((nCubes, IN_SIZE[0], IN_SIZE[1], IN_SIZE[2]))
    for i in xrange(nCubes):

        path = patientDir + "sliced_{0}_yPred.bin".format(i)
        img = np.fromfile(path, dtype=np.float32).reshape(IN_SIZE)
        scan[i] = img

    scan = np.array(scan)
    scan = cuber.uncubify(scan)
    mass = scan.sum()
    mass.tofile(patientDir + "predictedMass.bin")
    sitk.WriteImage(sitk.GetImageFromArray(scan),
                    patientDir + "predicted.nrrd")

    # Original image
    orig = sitk.ReadImage(patientDir + "../orig.nrrd")

    orig = sitk.GetArrayFromImage(orig)
    desired = np.array(orig.shape)
    difference = desired - np.array(scan.shape)
    difference = difference / 2
    try:
        padding = ((difference[0], difference[0]),
                   (difference[1], difference[1]), (difference[2],
                                                    difference[2]))
        scanPad = np.pad(scan, padding, "constant")
        sitk.WriteImage(sitk.GetImageFromArray(scanPad),
                        patientDir + "predictedPad.nrrd")
    except ValueError:
        print("Couldn't pad this one")
Beispiel #3
0
#
# Cubify Tutorial Part 2
#
# This tutorial shows you how to use CubeSets 
#

from cubify import Cubify
import json

# Instantiate Cubify
cubify= Cubify()

#
# Do cleanup from previous runs of this tutorial
#
cubify.deleteCubeSet('purchasesCubeSet')
cubify.deleteCubeSet('purchasesCubeSet2')

# Create a cube set called 'purchasesCubeSet' (with automatic binning)
cubeSet = cubify.createCubeSet('tutorial', 'purchasesCubeSet', 'purchases.csv')

print ""
print "CubeSet purchasesCubeSet created successfully"
print ""

cubeRows = cubify.getSourceCubeRows(cubeSet)
binnedCubeRows = cubify.getBinnedCubeRows(cubeSet)

print ""
print "Cube rows in purchasesCubeSet's source cube:"
for cubeRow in cubeRows:
Beispiel #4
0
#
# This tutorial shows you how to use Cubify to:
#
#   1. Create a cube 
#   2. Export a cube
#   3. Query cube rows
#   4. Add columns to a cube 
#   5. Bin a cube
#   6. Aggregate a cube
#

from cubify import Cubify
import json

# Instantiate Cubify
cubify= Cubify()

#
# Do cleanup from previous runs of this tutorial
#
cubify.deleteCube('purchases')
cubify.deleteCube('purchases_autobinned_1')
cubify.deleteCube('purchases_autobinned_2')
cubify.deleteCube('purchases_binned_1')
cubify.deleteCube('purchases_binned_2')
cubify.deleteCube('purchases_binned_2_CustomerId')
cubify.deleteCube('purchases_binned_2_CustomerState-ProductCategory')
cubify.deleteCube('purchases_binned_2_ProductId')
cubify.deleteCube('purchases_binned_2_CustomerId-TransactionDate')
cubify.deleteCube('purchases_binned_2_agg1')
cubify.deleteCube('purchases_binned_2_agg2')