clf_heart = joblib.load( args.detector + '/clf_heart' )
reg_heart = joblib.load( args.detector + '/reg_heart' )
clf_heart.set_params(n_jobs=args.n_jobs)
reg_heart.set_params(n_jobs=args.n_jobs)
print "done loading detectors"
print "preprocessing..."
img = irtk.imread( args.input, dtype='float32', force_neurological=True )
grad = irtk.Image(nd.gaussian_gradient_magnitude( img, 0.5 ),
img.get_header())
sat = integral_image(img)
sat_grad = integral_image(grad)
blurred_img = nd.gaussian_filter(img,0.5)
gradZ = nd.sobel( blurred_img, axis=0 ).astype('float32')
gradY = nd.sobel( blurred_img, axis=1 ).astype('float32')
gradX = nd.sobel( blurred_img, axis=2 ).astype('float32')
irtk.imwrite(args.output + "/img.nii.gz", img)
irtk.imwrite(args.output + "/grad.nii.gz", grad)
print "done preprocessing"
del blurred_img
del grad
def get_training_data_regression( img, seg ):
seg = irtk.imread( seg, dtype='int32', force_neurological=True )
img = irtk.imread( img, dtype='int32', force_neurological=True )
#u0,v0,w0 = get_orientation_training(seg)
brain_center, heart_center, left_lung, right_lung = get_centers(seg)
grad = irtk.Image(nd.gaussian_gradient_magnitude( img, 0.5 ),
img.get_header())
blurred_img = nd.gaussian_filter(img,0.5)
gradZ = nd.sobel( blurred_img, axis=0 ).astype('float32')
gradY = nd.sobel( blurred_img, axis=1 ).astype('float32')
gradX = nd.sobel( blurred_img, axis=2 ).astype('float32')
new_seg = irtk.zeros( seg.get_header() )
new_seg[seg==3] = 1 # lung 1
new_seg[seg==4] = 1 # lung 2
new_seg[seg==5] = 2 # heart
new_seg[seg==8] = 3 # liver
seg = new_seg
center1 = np.array(nd.center_of_mass( (seg == 1).view(np.ndarray) ),
dtype='float32')
center2 = np.array(nd.center_of_mass( (seg == 2).view(np.ndarray) ),
dtype='float32')
center3 = np.array(nd.center_of_mass( (seg == 3).view(np.ndarray) ),
dtype='float32')
sat = integral_image(img)
sat_grad = integral_image(grad)
m = np.zeros(img.shape, dtype='uint8')
m[brain_center[0],
brain_center[1],
brain_center[2]] = 1
X = []
Y = []
for l in range(1,nb_labels):
coords = np.argwhere(seg==l)
coords = coords[np.random.randint( 0,
coords.shape[0],
args.n_samples)].astype('int32')
if args.not_centered:
x1 = get_block_comparisons_cpp( sat, coords,
offsets1, sizes1,
offsets2, sizes2,n_jobs=args.n_jobs )
x2 = get_block_comparisons_cpp( sat_grad, coords,
offsets3, sizes3,
offsets4, sizes4,
n_jobs=args.n_jobs )
x_grad1 = get_grad( coords.astype('int32'), offsets5.astype('int32'),
gradZ.astype('float32'), gradY.astype('float32'), gradX.astype('float32') )
x_grad2 = get_grad( coords.astype('int32'), offsets6.astype('int32'),
gradZ.astype('float32'), gradY.astype('float32'), gradX.astype('float32') )
else:
# u = np.tile(u0,(coords.shape[0],1))
# v = np.tile(v0,(coords.shape[0],1))
# w = np.tile(w0,(coords.shape[0],1))
u,v,w = get_orientation_training_jitter( brain_center,
heart_center,
left_lung,
right_lung,
coords.shape[0],
brain_jitter=args.brain_jitter,
heart_jitter=args.heart_jitter,
lung_jitter=args.lung_jitter )
x1 = get_block_comparisons_cpp_uvw( sat, coords,
w,v,u,
offsets1, sizes1,
offsets2, sizes2,n_jobs=args.n_jobs )
x2 = get_block_comparisons_cpp_uvw( sat_grad, coords,
w,v,u,
offsets3, sizes3,
offsets4, sizes4,
n_jobs=args.n_jobs )
x3 = get_grad_comparisons_cpp_uvw( gradZ, gradY, gradX,
coords,
offsets5, offsets6,
w,v,u,
n_jobs=args.n_jobs )
# x_grad1 = get_grad_uvw( coords.astype('int32'), offsets5.astype('int32'),
# gradZ.astype('float32'), gradY.astype('float32'), gradX.astype('float32'),
# w.astype('float32'), v.astype('float32'), u.astype('float32') )
# x_grad2 = get_grad_uvw( coords.astype('int32'), offsets6.astype('int32'),
# gradZ.astype('float32'), gradY.astype('float32'), gradX.astype('float32'),
# w.astype('float32'), v.astype('float32'), u.astype('float32') )
#x = np.concatenate( ( x1, x2, x_grad1 > x_grad2 ), axis=1 )
x = np.concatenate( ( x1, x2, x3 ), axis=1 )
if l == 1:
y = center1[np.newaxis,...] - coords.astype('float32')
if not args.not_centered:
y = np.concatenate( ( (y*w).sum(axis=1)[...,np.newaxis],
(y*v).sum(axis=1)[...,np.newaxis],
(y*u).sum(axis=1)[...,np.newaxis] ),
axis=1 )
elif l == 2:
y = center2[np.newaxis,...] - coords.astype('float32')
if not args.not_centered:
y = np.concatenate( ( (y*w).sum(axis=1)[...,np.newaxis],
(y*v).sum(axis=1)[...,np.newaxis],
(y*u).sum(axis=1)[...,np.newaxis] ),
axis=1 )
else:
y = center3[np.newaxis,...] - coords.astype('float32')
if not args.not_centered:
y = np.concatenate( ( (y*w).sum(axis=1)[...,np.newaxis],
(y*v).sum(axis=1)[...,np.newaxis],
(y*u).sum(axis=1)[...,np.newaxis] ),
axis=1 )
X.append(x)
Y.append(y)
return X,Y
def run( o_size, d_size):
offsets1 = np.random.randint( -o_size, o_size+1, size=(n_tests,3) ).astype('int32')
sizes1 = np.random.randint( 0, d_size+1, size=(n_tests,1) ).astype('int32')
offsets2 = np.random.randint( -o_size, o_size+1, size=(n_tests,3) ).astype('int32')
sizes2 = np.random.randint( 0, d_size+1, size=(n_tests,1) ).astype('int32')
offsets3 = np.random.randint( -o_size, o_size+1, size=(n_tests,3) ).astype('int32')
sizes3 = np.random.randint( 0, d_size+1, size=(n_tests,1) ).astype('int32')
offsets4 = np.random.randint( -o_size, o_size+1, size=(n_tests,3) ).astype('int32')
sizes4 = np.random.randint( 0, d_size+1, size=(n_tests,1) ).astype('int32')
offsets5 = np.random.randint( -o_size, o_size+1, size=(n_tests/3,3) ).astype('int32')
offsets6 = np.random.randint( -o_size, o_size+1, size=(n_tests/3,3) ).astype('int32')
# we use only squares for rotation invariance
sizes1 = np.tile(sizes1,(1,3))
sizes2 = np.tile(sizes2,(1,3))
sizes3 = np.tile(sizes3,(1,3))
sizes4 = np.tile(sizes4,(1,3))
X_train = []
Y_train = []
X_test = []
Y_test = []
n = 0
for f in all_files:
n += 1
patient_id = os.path.basename(f)[:-len("_img.nii.gz")]
img = irtk.imread( f, dtype='int32', force_neurological=True )
seg = irtk.imread( myfolder + "/" + patient_id + "_seg.nii.gz", dtype='int32', force_neurological=True )
brain_center, heart_center, left_lung, right_lung = get_centers(seg)
grad = irtk.Image(nd.gaussian_gradient_magnitude( img, 0.5 ),
img.get_header())
blurred_img = nd.gaussian_filter(img,0.5)
gradZ = nd.sobel( blurred_img, axis=0 )
gradY = nd.sobel( blurred_img, axis=1 )
gradX = nd.sobel( blurred_img, axis=2 )
new_seg = irtk.zeros( seg.get_header() )
new_seg[seg==5] = 1 # heart
seg = new_seg
sat = integral_image(img)
sat_grad = integral_image(grad)
m = np.zeros(img.shape, dtype='uint8')
m[brain_center[0],
brain_center[1],
brain_center[2]] = 1
narrow_band = nd.distance_transform_edt(np.logical_not(m))
narrow_band[narrow_band<30] = 0
narrow_band[narrow_band>120] = 0
narrow_band[img==0] = 0
X = []
Y = []
for l in range(2):
coords = np.argwhere(np.logical_and(narrow_band>0,seg==l))
if l==0:
coords = coords[np.random.randint( 0,
coords.shape[0],
n_samples)].astype('int32')
else:
coords = coords[np.random.randint( 0,
coords.shape[0],
n_samples)].astype('int32')
if l == 0:
u,v,w = get_random_orientation(coords.shape[0])
else:
u,v,w = get_orientation_training_jitter( brain_center,
heart_center,
left_lung,
right_lung,
coords.shape[0],
brain_jitter=10,
heart_jitter=5,
lung_jitter=5 )
x1 = get_block_comparisons_cpp_uvw( sat, coords,
w,v,u,
offsets1, sizes1,
offsets2, sizes2,
n_jobs=10 )
x2 = get_block_comparisons_cpp_uvw( sat_grad, coords,
w,v,u,
offsets3, sizes3,
offsets4, sizes4,
n_jobs=10 )
x_grad1 = get_grad_uvw( coords.astype('int32'), offsets5.astype('int32'),
gradZ.astype('float32'), gradY.astype('float32'), gradX.astype('float32'),
w.astype('float32'), v.astype('float32'), u.astype('float32') )
x_grad2 = get_grad_uvw( coords.astype('int32'), offsets6.astype('int32'),
gradZ.astype('float32'), gradY.astype('float32'), gradX.astype('float32'),
w.astype('float32'), v.astype('float32'), u.astype('float32') )
x = np.concatenate( ( x1, x2, x_grad1 > x_grad2 ), axis=1 )
#x = np.concatenate( ( x1, x2 ), axis=1 )
y = seg[coords[:,0],
coords[:,1],
coords[:,2]]
if n < 30:
X_train.extend(x)
Y_train.extend(y)
else:
X_test.extend(x)
Y_test.extend(y)
print "train:",len(X_train),len(Y_train)
forest = RandomForestClassifier( n_estimators=100,
oob_score=True,
n_jobs=10 )#,min_samples_leaf=10)
forest.fit(X_train,Y_train)
oob_score = forest.oob_score_
print "test:",len(X_test),len(Y_test)
test_score = forest.score( X_test, Y_test)
print [oob_score,test_score,o_size,d_size]
return [oob_score,test_score,o_size,d_size]
def get_training_data_classification( img, seg, label_name ):
seg = irtk.imread( seg, dtype='int32', force_neurological=True )
img = irtk.imread( img, dtype='int32', force_neurological=True )
#u0,v0,w0 = get_orientation_training(seg)
brain_center, heart_center, left_lung, right_lung = get_centers(seg)
grad = irtk.Image(nd.gaussian_gradient_magnitude( img, 0.5 ),
img.get_header())
blurred_img = nd.gaussian_filter(img,0.5)
gradZ = nd.sobel( blurred_img, axis=0 ).astype('float32')
gradY = nd.sobel( blurred_img, axis=1 ).astype('float32')
gradX = nd.sobel( blurred_img, axis=2 ).astype('float32')
new_seg = irtk.zeros( seg.get_header() )
if label_name == "lungs":
new_seg[seg==3] = 1 # lung 1
new_seg[seg==4] = 1 # lung 2
elif label_name == "heart":
new_seg[seg==5] = 1 # heart
elif label_name == "liver":
new_seg[seg==8] = 1 # liver
seg = new_seg
sat = integral_image(img)
sat_grad = integral_image(grad)
m = np.zeros(img.shape, dtype='uint8')
m[brain_center[0],
brain_center[1],
brain_center[2]] = 1
narrow_band = nd.distance_transform_edt(np.logical_not(m))
if args.narrow_band:
narrow_band[narrow_band<30] = 0
narrow_band[narrow_band>120] = 0
narrow_band[img==0] = 0
X = []
Y = []
for l in range(2):
coords = np.argwhere(np.logical_and(narrow_band>0,seg==l))
if l==0:
coords = coords[np.random.randint( 0,
coords.shape[0],
int(args.factor_background*args.n_samples))].astype('int32')
else:
coords = coords[np.random.randint( 0,
coords.shape[0],
args.n_samples)].astype('int32')
if args.not_centered:
x1 = get_block_comparisons_cpp( sat, coords,
offsets1, sizes1,
offsets2, sizes2,
n_jobs=args.n_jobs )
x2 = get_block_comparisons_cpp( sat_grad, coords,
offsets3, sizes3,
offsets4, sizes4,
n_jobs=args.n_jobs )
x_grad1 = get_grad( coords.astype('int32'), offsets5.astype('int32'),
gradZ.astype('float32'), gradY.astype('float32'), gradX.astype('float32'))
x_grad2 = get_grad( coords.astype('int32'), offsets6.astype('int32'),
gradZ.astype('float32'), gradY.astype('float32'), gradX.astype('float32'))
else:
if l == 0:
u,v,w = get_random_orientation(coords.shape[0])
else:
# u = np.tile(u0,(coords.shape[0],1))
# v = np.tile(v0,(coords.shape[0],1))
# w = np.tile(w0,(coords.shape[0],1))
u,v,w = get_orientation_training_jitter( brain_center,
heart_center,
left_lung,
right_lung,
coords.shape[0],
brain_jitter=args.brain_jitter,
heart_jitter=args.heart_jitter,
lung_jitter=args.lung_jitter )
x1 = get_block_comparisons_cpp_uvw( sat, coords,
w,v,u,
offsets1, sizes1,
offsets2, sizes2,
n_jobs=args.n_jobs )
x2 = get_block_comparisons_cpp_uvw( sat_grad, coords,
w,v,u,
offsets3, sizes3,
offsets4, sizes4,
n_jobs=args.n_jobs )
x3 = get_grad_comparisons_cpp_uvw( gradZ, gradY, gradX,
coords,
offsets5, offsets6,
w,v,u,
n_jobs=args.n_jobs )
# x_grad1 = get_grad_uvw( coords.astype('int32'), offsets5.astype('int32'),
# gradZ.astype('float32'), gradY.astype('float32'), gradX.astype('float32'),
# w.astype('float32'), v.astype('float32'), u.astype('float32') )
# x_grad2 = get_grad_uvw( coords.astype('int32'), offsets6.astype('int32'),
# gradZ.astype('float32'), gradY.astype('float32'), gradX.astype('float32'),
# w.astype('float32'), v.astype('float32'), u.astype('float32') )
R = np.linalg.norm( coords - brain_center, axis=1 )
#x = np.concatenate( ( x1, x2, x_grad1 > x_grad2, R[...,np.newaxis] ), axis=1 )
x = np.concatenate( ( x1, x2, x3, R[...,np.newaxis] ), axis=1 )
y = seg[coords[:,0],
coords[:,1],
coords[:,2]]
X.extend(x)
Y.extend(y)
return (X,Y)
