def run():
candlist = pd.read_csv(LABELS_PATH)
filelist, filepathlist = getFilelist(DATA_PATH)
fout = open(DATA_CANDIDATES_PATH[:-1] + '.status', 'w')
for i in range(STARTFILE, len(filelist)):
print "FILE NUMBER: " + str(i) + " FILENAME: " + str(filelist[i])
time1 = time.time()
currentNodules = candlist[candlist['seriesuid'] == filelist[i]]
test = currentNodules.as_matrix()
if test.shape[0] == 0:
continue
fpath = filepathlist[i]
itk = sitk.ReadImage(fpath)
origin = np.array(itk.GetOrigin())
image, _ = loadItk(filepathlist[i], None)
segmentation = cd.segmentLung(image)
mask = cd.applyMask(image, segmentation)
segmentedImage = copy.deepcopy(image)
segmentedImage[mask == 0]
zmin, zmax, ymin, ymax, xmin, xmax = cd.findROI(segmentedImage)
roi = cd.crop(segmentedImage)
t1 = time.time()
cands = cd.filterHessian(cd.multiscaleHessian(roi))
t2 = time.time()
print "SEGMENTED NODULES IN: " + str(t2 - t1) + 's'
#NEW CODE
#Masking the ROI prior to volume thresholding
maskROI = mask[zmin:zmax, ymin:ymax, xmin:xmax]
cands[maskROI == 0] = 0
#END NEW CODE
t1 = time.time()
cands = cd.optVolumeThresholding(cands, mn=8, mx=20000)
t2 = time.time()
print "THREHOLDING APPLIED TO CANDIDATES: " + str(t2 - t1) + 's'
coords = getCurrentNodulesCoords(currentNodules, origin)
labels = measure.label(cands, connectivity=1)
com = scipy.ndimage.measurements.center_of_mass(
labels, labels, list(range(1,
np.max(labels) + 1)))
generateCandidates(segmentedImage, labels, coords, (xmin, ymin, zmin),
com, DATA_CANDIDATES_PATH, filelist[i])
time2 = time.time()
print "TOTAL TIME FOR FILE NUMBER " + str(i) + " IS " + str(
time2 - time1) + "s"
fout.write('FINISHED FILE NUMBER ' + str(i) + ' IN ' +
str(time2 - time1) + 's\n')
def run():
global MODEL
global TEST
global VAL
global DATAPATH
global WEIGHTSPATH
global OUTPATH
global OUTFILE
print "TEST: " + str(TEST) + " VAL: " + str(VAL) + " MODEL: " + str(
MODEL)
filelist, filepathlist = getFilelist(DATAPATH)
model = seg3d.getModel()
model.load_weights(WEIGHTSPATH)
candlist = pd.read_csv(ANNOTATIONS)
time1 = time.time()
flag = False
#for i in range(len(filelist)):
# print filelist[i]
#return
for n in range(START, len(filelist)):
if scanids is not None:
for scan in scanids:
#print n
flag = True
if filelist[n] == scan:
flag = False
break
if flag:
flag = False
continue
t1 = time.time()
print "FILE NO " + str(n) + " UID: " + filelist[n]
currentNodules = candlist[candlist['seriesuid'] == filelist[n]]
itk = sitk.ReadImage(filepathlist[n])
image, _ = cg.loadItk(filepathlist[n], None)
origin = np.array(itk.GetOrigin())
spacing = np.array(itk.GetSpacing())
coords, d = getCoords(currentNodules, origin, spacing)
print coords
#TODO: ADD TO OTHER SEGMENT_x_.PY
if len(coords) == 0:
continue
segmentation = cd.segmentLung(image, reseg=False)
if cd.checkSeg(segmentation):
segmentation = cd.segmentLung(image, reseg=True)
mask = cd.applyMask(image, segmentation)
segmentedImage = copy.deepcopy(image)
segmentedImage[mask == 0] = 0
zmin, zmax, ymin, ymax, xmin, xmax = cd.findROI(segmentedImage)
roi = cd.crop(segmentedImage)
if roi.shape[0] < 100 or roi.shape[1] < 100 or roi.shape[2] < 100:
segmentation = cd.segmentLung(image, error=True)
mask = cd.applyMask(image, segmentation)
segmentedImage = copy.deepcopy(image)
segmentedImage[mask == 0] = 0
zmin, zmax, ymin, ymax, xmin, xmax = cd.findROI(segmentedImage)
roi = cd.crop(segmentedImage)
shift = (xmin, ymin, zmin) #**XYZ NOT ZYX
segShape = getSegmentationShape(
roi.shape) #, sixteen=SIXTEEN)#image.shape)
lpad, hpad = getPadding(segShape, roi) #image)
segImage = np.zeros(segShape)
segImage[lpad[0]:segShape[0] - hpad[0], lpad[1]:segShape[1] - hpad[1],
lpad[2]:segShape[2] - hpad[2]] = roi #image
segImage = np.clip(segImage, -1000, 1000)
segImage += 1000
segImage *= (255. / 2000.)
print "SEGMENTING ..."
stride = STRIDE
strides = copy.deepcopy(segShape)
strides /= 32
print segShape
print strides
segmentation = segmentImage(segImage, model, strides, stride)
#if stride == 32:
lpad -= 16
hpad -= 16
#elif stride == 16:
# lpad -= 8
# hpad -= 8
#print segmentation.shape
#print mask.shape
#segmentation[mask==0] = 0
tmpseg = segmentation[lpad[0]:segmentation.shape[0] - hpad[0],
lpad[1]:segmentation.shape[1] - hpad[1],
lpad[2]:segmentation.shape[2] - hpad[2]]
#tmpseg[mask==0] = 0
pseg = np.zeros(image.shape)
pseg[zmin:zmax, ymin:ymax, xmin:xmax] = tmpseg
seg = np.zeros(pseg.shape)
seg = np.where(pseg > THRESH, 1, 0)
#pseg[mask==0] = 0
#seg[mask==0] = 0
print "ASSESSING ..."
labels = measure.label(seg, connectivity=1)
#if np.max(labels) >= 315:
# print "TOO MANY: " + str(np.max(labels))
# continue
assess(OUTFILE, labels, coords, filelist[n], OUTPATH, pseg, image, d)
t2 = time.time()
print "LAST SCAN TIME: " + str((t2 - t1) / 60) + 'm ' + str(
(t2 - t1) % 60) + 's'
time2 = time.time()
print "TOTAL TIME: " + str((time2 - time1) / 60) + 'm ' + str(
(time2 - time1) % 60) + 's'