for i in range(1,5):
tmp_seg[nd.binary_closing(tmp_seg==i,
structure=morphology.ball(2))>0] = i
background = (nd.binary_dilation( tmp_seg>0, structure=ball ) == 0).astype('int32')
header = tmp_img.get_header()
header['dim'][3] = 5
proba = irtk.zeros( header, dtype='float32' )
proba[0][tmp_seg==0] = 1.0
for i in range(1,5):
proba[i][tmp_seg==i] = 1.0
irtk.imwrite( "proba.nii.gz", proba )
print proba.header, proba.shape
tmp_seg = irtk.crf( tmp_img,
tmp_seg,
proba,
l=4.0,
sigma=50 )
res = tmp_seg.transform( target=seg, interpolation='nearest' )
print np.unique(res)
irtk.imwrite( args.output, res )
# for i in [1,2]:
# seg[proba[i] > 0.5] = i
if args.seg is None:
seg_init = irtk.Image( np.argmax(proba,axis=0).astype('int32'),
img.get_header() )
else:
seg_init = irtk.imread( args.seg, dtype='int32', force_neurological=True )
seg_init[seg_init>2] = 0
seg_init[seg_init==2] = 1
irtk.imwrite("seg_debug.nii.gz",seg_init)
seg = irtk.crf( img,
seg_init,
proba,
l=args.l,
sigma=args.sigma ) # 30,10 # 10,5
print 1, seg.shape, seg_init.shape
print np.unique(seg)
if args.remove_small_objects > 0:
seg = irtk.Image( morphology.remove_small_objects(seg.view(np.ndarray).astype('bool'),
min_size=args.remove_small_objects).astype('uint8'),
seg.get_header() )
print 2, seg.shape, seg_init.shape
if args.select:
def mask_image( file_img, file_mask, ga, r, neigh, output_dir ):
img = irtk.imread( file_img, dtype='float32' )
input_mask = irtk.imread( file_mask )
print "predicting..."
res = irtk.zeros( img.get_header(), dtype='float32' )
res2 = irtk.zeros( img.get_header(), dtype='float32' )
res3 = irtk.zeros( img.get_header(), dtype='float32' )
res4 = irtk.zeros( img.get_header(), dtype='uint8' )
mask = irtk.ones( input_mask.get_header(), dtype='uint8' )
mask[input_mask == 2] = 0
for z in xrange(img.shape[0]):
print z
YX = np.transpose( np.nonzero( mask[z] ) )
if YX.shape[0] == 0:
continue # this slice does not intersect the box
patches = extract_patches2D( img[z], r, YX )
patches = np.reshape( patches, (patches.shape[0],patches.shape[1]*patches.shape[2]) )
predictions = neigh.predict_proba(patches)[:,1]
res[z,YX[:,0],YX[:,1]] = predictions
x_min, y_min, z_min, x_max, y_max, z_max = mask.bbox()
proba = res[z_min:z_max+1,
y_min:y_max+1,
x_min:x_max+1]
if args.mass:
BV = get_BV( args.ga )
box_volume = (z_max-z_min)*img.header['pixelSize'][2]*(y_max-y_min)*img.header['pixelSize'][1]*(x_max-x_min)*img.header['pixelSize'][0]
ratio = float(BV) / float(box_volume)
print "ratio", ratio
q0,q1 = mquantiles( proba.flatten(), prob=[0.5*(1.0-ratio),
1.0-0.5*ratio] )
print "threshold", q0,q1
#threshold = max(0.5,threshold)
# labels = res[z_min:z_max+1,
# y_min:y_max+1,
# x_min:x_max+1] > threshold
#res = 1 / (np.exp(-(res-threshold)/(res.max()-res.min())))
res[res<q0] = q0
res[res>q1] = q1
res -= res.min()
res /= res.max()
labels = res[z_min:z_max+1,
y_min:y_max+1,
x_min:x_max+1] > 0.5
proba = res[z_min:z_max+1,
y_min:y_max+1,
x_min:x_max+1]
cropped_img = img[z_min:z_max+1,
y_min:y_max+1,
x_min:x_max+1]
if args.do_3D:
labels = irtk.crf( cropped_img,
labels,
proba,
l=args.l,
sigma=get_noiseXY(cropped_img),
sigmaZ=get_noiseZ(cropped_img) )
# elif args.do_patchZ:
# labels = irtk.crf_patchZ( cropped_img,
# labels,
# proba,
# l=10.0 )
# else:
# for z in xrange(z_min,z_max+1):
# labels[z] = irtk.crf( cropped_img[z],
# labels[z],
# proba[z],
# l=1.0 )
print "MAX LABEL:", labels.max()
irtk.imwrite(output_dir + "/bare_"+os.path.basename(file_img), labels )
tmp = irtk.zeros( img.get_header(), dtype='uint8' )
tmp[z_min:z_max+1,
y_min:y_max+1,
x_min:x_max+1] = labels
( min_x_bare, min_y_bare, min_z_bare,
max_x_bare, max_y_bare, max_z_bare ) = tmp.bbox()
if not args.no_cleaning:
# clean by fitting ellipses enlarged of 10%
for z in xrange(labels.shape[0]):
edges = nd.morphological_gradient( labels[z] > 0,size=5 )
points = np.transpose(edges.nonzero())[:,::-1]
if len(points) == 0:
continue
points = np.array(map(lambda x:[x],points),dtype='int32')
ellipse = cv2.fitEllipse(points)
cv2.ellipse( labels[z], (ellipse[0],
(1.1*ellipse[1][0],1.1*ellipse[1][1]),
ellipse[2]) , 1, -1 )
irtk.imwrite(output_dir + "/seg_"+os.path.basename(file_img), labels )
irtk.imwrite(output_dir + "/res_"+os.path.basename(file_img), res )
# re-read the image in case we processed it
img = irtk.imread( file_img, dtype='float32' )
cropped_img = img[z_min:z_max+1,
y_min:y_max+1,
x_min:x_max+1]
cropped_img[labels==0] = -1
masked = cropped_img.bbox(crop=True)
irtk.imwrite(output_dir + "/masked_"+os.path.basename(file_img), masked )
# re-read the image in case we processed it
img = irtk.imread( file_img, dtype='float32' )
x0 = min_x_bare + (max_x_bare - min_x_bare) / 2
y0 = min_y_bare + (max_y_bare - min_y_bare) / 2
ofd = get_OFD(ga)/img.header['pixelSize'][0]
cropped_img = img[min_z_bare:max_z_bare+1,
max(0,int(round(y0-ofd/2))):min(img.shape[1],int(round(y0+ofd/2+1))),
max(0,int(round(x0-ofd/2))):min(img.shape[2],int(round(x0+ofd/2+1)))].copy()
irtk.imwrite(output_dir + "/very_large_"+os.path.basename(file_img),
cropped_img )
cropped_proba = res[min_z_bare:max_z_bare+1,
max(0,int(round(y0-ofd/2))):min(img.shape[1],int(round(y0+ofd/2+1))),
max(0,int(round(x0-ofd/2))):min(img.shape[2],int(round(x0+ofd/2+1)))].copy()
irtk.imwrite(output_dir + "/proba_"+os.path.basename(file_img),
cropped_proba )
def mask_image(file_img, file_mask, ga, r, neigh, output_dir):
img = irtk.imread(file_img, dtype='float32')
input_mask = irtk.imread(file_mask)
print "predicting..."
res = irtk.zeros(img.get_header(), dtype='float32')
res2 = irtk.zeros(img.get_header(), dtype='float32')
res3 = irtk.zeros(img.get_header(), dtype='float32')
res4 = irtk.zeros(img.get_header(), dtype='uint8')
mask = irtk.ones(input_mask.get_header(), dtype='uint8')
mask[input_mask == 2] = 0
for z in xrange(img.shape[0]):
print z
YX = np.transpose(np.nonzero(mask[z]))
if YX.shape[0] == 0:
continue # this slice does not intersect the box
patches = extract_patches2D(img[z], r, YX)
patches = np.reshape(
patches, (patches.shape[0], patches.shape[1] * patches.shape[2]))
predictions = neigh.predict_proba(patches)[:, 1]
res[z, YX[:, 0], YX[:, 1]] = predictions
x_min, y_min, z_min, x_max, y_max, z_max = mask.bbox()
proba = res[z_min:z_max + 1, y_min:y_max + 1, x_min:x_max + 1]
if args.mass:
BV = get_BV(args.ga)
box_volume = (z_max - z_min) * img.header['pixelSize'][2] * (
y_max - y_min) * img.header['pixelSize'][1] * (
x_max - x_min) * img.header['pixelSize'][0]
ratio = float(BV) / float(box_volume)
print "ratio", ratio
q0, q1 = mquantiles(proba.flatten(),
prob=[0.5 * (1.0 - ratio), 1.0 - 0.5 * ratio])
print "threshold", q0, q1
#threshold = max(0.5,threshold)
# labels = res[z_min:z_max+1,
# y_min:y_max+1,
# x_min:x_max+1] > threshold
#res = 1 / (np.exp(-(res-threshold)/(res.max()-res.min())))
res[res < q0] = q0
res[res > q1] = q1
res -= res.min()
res /= res.max()
labels = res[z_min:z_max + 1, y_min:y_max + 1, x_min:x_max + 1] > 0.5
proba = res[z_min:z_max + 1, y_min:y_max + 1, x_min:x_max + 1]
cropped_img = img[z_min:z_max + 1, y_min:y_max + 1, x_min:x_max + 1]
if args.do_3D:
labels = irtk.crf(cropped_img,
labels,
proba,
l=args.l,
sigma=get_noiseXY(cropped_img),
sigmaZ=get_noiseZ(cropped_img))
# elif args.do_patchZ:
# labels = irtk.crf_patchZ( cropped_img,
# labels,
# proba,
# l=10.0 )
# else:
# for z in xrange(z_min,z_max+1):
# labels[z] = irtk.crf( cropped_img[z],
# labels[z],
# proba[z],
# l=1.0 )
print "MAX LABEL:", labels.max()
irtk.imwrite(output_dir + "/bare_" + os.path.basename(file_img), labels)
tmp = irtk.zeros(img.get_header(), dtype='uint8')
tmp[z_min:z_max + 1, y_min:y_max + 1, x_min:x_max + 1] = labels
(min_x_bare, min_y_bare, min_z_bare, max_x_bare, max_y_bare,
max_z_bare) = tmp.bbox()
if not args.no_cleaning:
# clean by fitting ellipses enlarged of 10%
for z in xrange(labels.shape[0]):
edges = nd.morphological_gradient(labels[z] > 0, size=5)
points = np.transpose(edges.nonzero())[:, ::-1]
if len(points) == 0:
continue
points = np.array(map(lambda x: [x], points), dtype='int32')
ellipse = cv2.fitEllipse(points)
cv2.ellipse(
labels[z],
(ellipse[0],
(1.1 * ellipse[1][0], 1.1 * ellipse[1][1]), ellipse[2]), 1,
-1)
irtk.imwrite(output_dir + "/seg_" + os.path.basename(file_img), labels)
irtk.imwrite(output_dir + "/res_" + os.path.basename(file_img), res)
# re-read the image in case we processed it
img = irtk.imread(file_img, dtype='float32')
cropped_img = img[z_min:z_max + 1, y_min:y_max + 1, x_min:x_max + 1]
cropped_img[labels == 0] = -1
masked = cropped_img.bbox(crop=True)
irtk.imwrite(output_dir + "/masked_" + os.path.basename(file_img), masked)
# re-read the image in case we processed it
img = irtk.imread(file_img, dtype='float32')
x0 = min_x_bare + (max_x_bare - min_x_bare) / 2
y0 = min_y_bare + (max_y_bare - min_y_bare) / 2
ofd = get_OFD(ga) / img.header['pixelSize'][0]
cropped_img = img[min_z_bare:max_z_bare + 1,
max(0, int(round(y0 - ofd / 2))
):min(img.shape[1], int(round(y0 + ofd / 2 + 1))),
max(0, int(round(x0 - ofd / 2))
):min(img.shape[2], int(round(x0 + ofd / 2 +
1)))].copy()
irtk.imwrite(output_dir + "/very_large_" + os.path.basename(file_img),
cropped_img)
cropped_proba = res[min_z_bare:max_z_bare + 1,
max(0, int(round(y0 - ofd / 2))
):min(img.shape[1], int(round(y0 + ofd / 2 + 1))),
max(0, int(round(x0 - ofd / 2))
):min(img.shape[2], int(round(x0 + ofd / 2 +
1)))].copy()
irtk.imwrite(output_dir + "/proba_" + os.path.basename(file_img),
cropped_proba)
