parser.add_option("-t", dest="refImg")
parser.add_option("-i", dest="tFiles", action="store", nargs=4)
parser.add_option("-h", dest="doHelp", action="store_true", default=False)
parser.add_option("-x", dest="recomp", action="store_true", default=False)
options, args = parser.parse_args()
if options.doHelp:
usage()
sys.exit(-1)
if (options.vFiles is None or options.config is None
or options.tFiles is None or options.refImg is None):
usage()
sys.exit(-1)
helper = regtools.regtools(options.config)
vesselFileList = options.vFiles
vList0 = open(vesselFileList).readlines()
vList0 = [c.strip() for c in vList0]
T0ListFile, T1ListFile, FDListFile, IDListFile = options.tFiles
T0 = pickle.load(open(T0ListFile))
T1 = pickle.load(open(T1ListFile))
FD = pickle.load(open(FDListFile))
ID = pickle.load(open(IDListFile))
vList1 = helper.treeApplyTfm(vList0, T0, "RigidMRAToMRI", options.recomp)
vList2 = helper.treeApplyTfm(vList1, T1, "AffineMRIToRef", options.recomp)
vList3 = helper.treeApplyDfm(vList2, ID, "DeformRefToRef", options.recomp)
parser.add_option("-h", dest="doHelp", action="store_true", default=False)
parser.add_option("-x", dest="recomp", action="store_true", default=False)
options, args = parser.parse_args()
if options.doHelp:
usage()
sys.exit(-1)
if (options.vFiles is None or
options.config is None or
options.tFiles is None or
options.refImg is None):
usage()
sys.exit(-1)
helper = regtools.regtools(options.config)
vesselFileList = options.vFiles
vList0 = open(vesselFileList).readlines()
vList0 = [c.strip() for c in vList0]
T0ListFile, T1ListFile, FDListFile, IDListFile = options.tFiles
T0 = pickle.load(open(T0ListFile))
T1 = pickle.load(open(T1ListFile))
FD = pickle.load(open(FDListFile))
ID = pickle.load(open(IDListFile))
vList1 = helper.treeApplyTfm(vList0, T0, "RigidMRAToMRI", options.recomp)
vList2 = helper.treeApplyTfm(vList1, T1, "AffineMRIToRef", options.recomp)
vList3 = helper.treeApplyDfm(vList2, ID, "DeformRefToRef", options.recomp)
def main(argv=None):
if argv is None:
argv=sys.argv
parser = OptionParser(add_help_option=False)
parser.add_option("-i", dest="imgJSON")
parser.add_option("-c", dest="cfgJSON")
parser.add_option("-a", dest="outAtomFile")
parser.add_option("-d", dest="outDiffFile")
parser.add_option("-x", dest="outImagFile")
parser.add_option("-s", dest="imSlice", type="int")
parser.add_option("-r", dest="imScale", type="float")
parser.add_option("-D", dest="dictSiz", type="int", default=5)
parser.add_option("-k", dest="nearest", type="int", default=5)
parser.add_option("-h", dest="doHelp", action="store_true", default=False)
options, _ = parser.parse_args()
if options.doHelp:
usage()
sys.exit(-1)
imgJSON = options.imgJSON
cfgJSON = options.cfgJSON
outAtomFile = options.outAtomFile
outImagFile = options.outImagFile
outDiffFile = options.outDiffFile
imSlice = options.imSlice
dictSiz = options.dictSiz
imScale = options.imScale
nearest = options.nearest
imData = json.load(open(imgJSON))
helper = regtools.regtools(cfgJSON)
groupSet = set()
groupMap = dict()
groupLab = []
# generate numeric labels for each image
for entry in imData["Data"]: groupSet.add(entry["Group"])
for cnt, group in enumerate(groupSet): groupMap[group] = cnt
for entry in imData["Data"]: groupLab.append(groupMap[entry["Group"]])
imgFiles = []
[imgFiles.append(str(e["Source"])) for e in imData["Data"]]
dataList = []
for i, imFile in enumerate(imgFiles):
im0 = sitk.ReadImage(imFile)
im1 = pbmutils.imResize(im0, imScale)
imSz = sitk.GetArrayFromImage(im1).shape
helper.infoMsg("Image size : (%d,%d,%d)" % imSz)
if not imSlice is None:
sl0 = pbmutils.imSlice(im1, [0, 0, imSlice])
dataList.append(sl0.ravel())
else:
dataList.append(sitk.GetArrayFromImage(im1).ravel())
helper.infoMsg("Done with image %d!" % i)
# write raw image data
if not outImagFile is None:
tfid = open(outImgFile, 'w')
np.reshape(np.asmatrix(dataList).T,-1).astype('float32').tofile(tfid)
tfid.close()
# build difference images
diffIm = pbmutils.groupDiff(np.asmatrix(dataList).T, groupLab, nearest)
helper.infoMsg("Difference image matrix (%d x %d)" % diffIm.shape)
# write raw difference data
if not outDiffFile is None:
outFid = open(outDiffFile, 'w')
np.reshape(diffIm, -1).ravel().astype('float32').tofile(outFid)
outFid.close()
# create the dictionary learner and run (alpha=1)
lrnObj = MiniBatchDictionaryLearning(dictSiz, 1, verbose=True)
lrnRes = lrnObj.fit(np.asmatrix(diffIm).T).components_
# write dictionary atoms
if not outAtomFile is None:
outFid = open(outAtomFile, 'w')
np.reshape(lrnRes.T, -1).ravel().astype('float32').tofile(outFid)
outFid.close()
def main(argv=None):
if argv is None:
argv = sys.argv
parser = OptionParser(add_help_option=False)
parser.add_option("-i", dest="imgJSON")
parser.add_option("-c", dest="cfgJSON")
parser.add_option("-a", dest="outAtomFile")
parser.add_option("-d", dest="outDiffFile")
parser.add_option("-x", dest="outImagFile")
parser.add_option("-s", dest="imSlice", type="int")
parser.add_option("-r", dest="imScale", type="float")
parser.add_option("-D", dest="dictSiz", type="int", default=5)
parser.add_option("-k", dest="nearest", type="int", default=5)
parser.add_option("-h", dest="doHelp", action="store_true", default=False)
options, _ = parser.parse_args()
if options.doHelp:
usage()
sys.exit(-1)
imgJSON = options.imgJSON
cfgJSON = options.cfgJSON
outAtomFile = options.outAtomFile
outImagFile = options.outImagFile
outDiffFile = options.outDiffFile
imSlice = options.imSlice
dictSiz = options.dictSiz
imScale = options.imScale
nearest = options.nearest
imData = json.load(open(imgJSON))
helper = regtools.regtools(cfgJSON)
groupSet = set()
groupMap = dict()
groupLab = []
# generate numeric labels for each image
for entry in imData["Data"]:
groupSet.add(entry["Group"])
for cnt, group in enumerate(groupSet):
groupMap[group] = cnt
for entry in imData["Data"]:
groupLab.append(groupMap[entry["Group"]])
imgFiles = []
[imgFiles.append(str(e["Source"])) for e in imData["Data"]]
dataList = []
for i, imFile in enumerate(imgFiles):
im0 = sitk.ReadImage(imFile)
im1 = pbmutils.imResize(im0, imScale)
imSz = sitk.GetArrayFromImage(im1).shape
helper.infoMsg("Image size : (%d,%d,%d)" % imSz)
if not imSlice is None:
sl0 = pbmutils.imSlice(im1, [0, 0, imSlice])
dataList.append(sl0.ravel())
else:
dataList.append(sitk.GetArrayFromImage(im1).ravel())
helper.infoMsg("Done with image %d!" % i)
# write raw image data
if not outImagFile is None:
tfid = open(outImgFile, 'w')
np.reshape(np.asmatrix(dataList).T, -1).astype('float32').tofile(tfid)
tfid.close()
# build difference images
diffIm = pbmutils.groupDiff(np.asmatrix(dataList).T, groupLab, nearest)
helper.infoMsg("Difference image matrix (%d x %d)" % diffIm.shape)
# write raw difference data
if not outDiffFile is None:
outFid = open(outDiffFile, 'w')
np.reshape(diffIm, -1).ravel().astype('float32').tofile(outFid)
outFid.close()
# create the dictionary learner and run (alpha=1)
lrnObj = MiniBatchDictionaryLearning(dictSiz, 1, verbose=True)
lrnRes = lrnObj.fit(np.asmatrix(diffIm).T).components_
# write dictionary atoms
if not outAtomFile is None:
outFid = open(outAtomFile, 'w')
np.reshape(lrnRes.T, -1).ravel().astype('float32').tofile(outFid)
outFid.close()