def main(argv=None):
if argv is None:
argv = sys.argv
parser = OptionParser(add_help_option=False)
parser.add_option("-N", dest="nNeighbor", type="int", default=3)
parser.add_option("-f", dest="filesList")
parser.add_option("-l", dest="labelList")
parser.add_option("-o", dest="outDiffFile")
parser.add_option("-h", dest="doHelp", action="store_true", default=False)
options, _ = parser.parse_args()
if options.doHelp:
usage()
sys.exit(-1)
filesList = options.filesList
labelList = options.labelList
nNeighbor = options.nNeighbor
outDiffFile = options.outDiffFile
if filesList is None or labelList is None or outDiffFile is None:
usage()
sys.exit(-1)
with open(filesList) as fid:
files = fid.readlines()
with open(labelList) as fid:
label = fid.readlines()
numLab = []
[numLab.append(int(x)) for x in label]
dataLst = []
for img in files:
sitkIm = sitk.ReadImage(img.rstrip())
x = sitk.GetArrayFromImage(sitkIm)
dataLst.append(sitk.GetArrayFromImage(sitkIm).ravel())
# build difference image matrix (using Euclidean distance as similarity)
diffImg = pbmutils.groupDiff(np.asmatrix(dataLst).T, numLab, nNeighbor)
print "Difference image matrix (%d x %d)" % diffImg.shape
outFid = open(outDiffFile, 'w')
np.reshape(diffImg, -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("-N", dest="nNeighbor", type="int", default=3)
parser.add_option("-f", dest="filesList")
parser.add_option("-l", dest="labelList")
parser.add_option("-o", dest="outDiffFile")
parser.add_option("-h", dest="doHelp", action="store_true", default=False)
options, _ = parser.parse_args()
if options.doHelp:
usage()
sys.exit(-1)
filesList = options.filesList
labelList = options.labelList
nNeighbor = options.nNeighbor
outDiffFile = options.outDiffFile
if filesList is None or labelList is None or outDiffFile is None:
usage()
sys.exit(-1)
with open(filesList) as fid: files = fid.readlines()
with open(labelList) as fid: label = fid.readlines()
numLab = []
[numLab.append(int(x)) for x in label]
dataLst = []
for img in files:
sitkIm = sitk.ReadImage(img.rstrip())
x = sitk.GetArrayFromImage(sitkIm)
dataLst.append(sitk.GetArrayFromImage(sitkIm).ravel())
# build difference image matrix (using Euclidean distance as similarity)
diffImg = pbmutils.groupDiff(np.asmatrix(dataLst).T, numLab, nNeighbor)
print "Difference image matrix (%d x %d)" % diffImg.shape
outFid = open(outDiffFile, 'w')
np.reshape(diffImg, -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()
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()
