def main(model, metrics):
if metrics:
old_metrics = {}
new_metrics = {}
files = f'data/predict/{model}/*_0000.nii.gz'
files = f'data/predict/{model}/*/*.nii.gz'
for file in glob.glob(files):
sample, i = os.path.basename(file).split('_')
i = i[:4]
header = util.header(file)
volume = util.read_vol(file)
if metrics:
label = util.read_vol(
glob.glob(f'data/labels/{sample}/{sample}_0_all_brains.nii.gz')
[0])
old_metrics[sample] = util.dice_coef(label, volume)
n = 2 if sample in constants.TWINS else 1
volume = remove_artifacts(volume, n)
if metrics:
new_metrics[sample] = util.dice_coef(label, volume)
os.makedirs(f'data/predict_cleaned/{model}/{sample}', exist_ok=True)
util.save_vol(
volume,
f'data/predict_cleaned/{model}/{sample}/{sample}_{i}.nii.gz',
header)
if metrics:
print('before: ', old_metrics)
print('after: ', new_metrics)
def predict(self, generator, subj_dirs=False):
n = len(generator)
for i in range(n):
#TODO: why is this hard coded here.
input_file = generator.input_files[i]
subj_dir = input_file.split("/")[-2]
path = f'{self.output_location}/{subj_dir}'
os.makedirs(path, exist_ok=True)
fname = input_file.split('/')[-1]
header = util.header(input_file)
#vol = nib.load(input_file)
vol = generator[i]
#vol = vol[0,:,:,:]
sub_vols, sub_codes = simple_preproc.get_subvolumes(
vol[0], self.input_size)
preds = []
for sub_vol in sub_vols:
preds.append(
self.model.predict(sub_vol[np.newaxis, :, :, :, :])[0])
pred = simple_preproc.sub_vols_to_original_shape(
preds, sub_codes, vol.shape[1:])
util.save_vol(
pred,
os.path.join(path, fname),
)
def predict(self, generator, path):
preds = self.model.predict_generator(generator, verbose=1)
# FIXME
for i in range(preds.shape[0]):
fname = generator.inputs[i].split('/')[-1]
header = util.header(generator.inputs[i])
util.save_vol(uncrop(preds[i], generator.shape),
os.path.join(path, fname), header)
def save_prediction(pred, input_file, tile, path, scale=False):
fname = input_file.split('/')[-1]
sample = fname.split('_')[0]
path = os.path.join(path, sample)
os.makedirs(path, exist_ok=True)
shape = util.shape(input_file)
header = util.header(input_file)
vol = process.postprocess(pred, shape, resize=True, tile=tile)
util.save_vol(vol, os.path.join(path, fname), header, scale)
print(fname, flush=True)
def save_predictions(preds, generator, path, scale=False):
os.makedirs(path, exist_ok=True)
if generator.tile_inputs:
preds = np.reshape(preds, (-1, 8) + preds.shape[1:])
for i in range(preds.shape[0]):
input_file = generator.input_files[i]
fname = input_file.split('/')[-1]
header = util.header(input_file)
shape = util.shape(input_file)
volume = process.postprocess(preds[i],
shape,
resize=True,
tile=generator.tile_inputs)
util.save_vol(volume, os.path.join(path, fname), header, scale)
def main(model):
folder = options.sample if options.sample else '*'
files = f'data/predict/{model}/{folder}/*.nii.gz'
for file in glob.glob(files):
sample, i = os.path.basename(file).split('_')
i = i[:4]
header = util.header(file)
volume = util.read_vol(file)
n = 2 if sample in constants.TWINS else 1
volume = remove_artifacts(volume, n)
os.makedirs(f'data/predict_cleaned/{model}/{sample}', exist_ok=True)
util.save_vol(
volume,
f'data/predict_cleaned/{model}/{sample}/{sample}_{i}.nii.gz',
header)
def dilate(mask, sample, iterations=4):
struct = sp.generate_binary_structure(3, 1)
dil = sp.binary_dilation(mask, iterations=iterations)
util.save_vol(dil, '{}mask/{}.nii.gz'.format(registration_dir, sample))
return dil
import glob
import numpy as np
import util
for sample in [
'010918L', '010918S', '012115', '013018L', '013018S', '013118L',
'013118S', '021015', '021218L', '021218S', '022318L', '022318S',
'022415', '022618', '030217', '030315', '031317L', '031317T', '031516',
'031615', '031616', '031716', '032217', '032318a', '032318b',
'032318c', '032318d', '032818', '040218', '040417'
]:
files = glob.glob('data/labels/{}/{}_0_*_brain.nii.gz'.format(
sample, sample))
volume = np.zeros(
util.shape('data/raw/{}/{}_0.nii.gz'.format(sample, sample)))
header = util.header(files[0])
for file in files:
volume += util.read_vol(file)
util.save_vol(
volume, 'data/labels/{}/{}_0_all_brains.nii.gz'.format(sample, sample),
header)
def main(sample, order):
files = sorted(glob.glob(f'data/nifti/{sample}/*.nii.gz'))
vols = np.concatenate([util.read_vol(file) for file in files], axis=-1)
if vols.shape[0] == vols.shape[1] == vols.shape[2]:
axis = int(input(f'shape: {vols.shape}\n> '))
elif vols.shape[0] == vols.shape[1]:
axis = 2
elif vols.shape[0] == vols.shape[2]:
axis = 1
elif vols.shape[1] == vols.shape[2]:
axis = 0
else:
axis = int(input(f'shape: {vols.shape}\n> '))
vols = np.moveaxis(vols, axis, 0)
shape = vols.shape
even_i = np.arange(0, shape[0], 2)
even_slices = vols[::2, ...]
even_interpolator = interp1d(even_i, even_slices, kind='linear', axis=0)
odd_i = np.arange(1, shape[0], 2)
odd_slices = vols[1::2, ...]
odd_interpolator = interp1d(odd_i, odd_slices, kind='linear', axis=0)
if shape[0] % 2 == 0:
evens = even_interpolator(np.arange(0, shape[0] - 1))
odds = odd_interpolator(np.arange(1, shape[0]))
evens = np.concatenate((evens, evens[-1:, ...]))
else:
evens = even_interpolator(np.arange(0, shape[0]))
odds = odd_interpolator(np.arange(1, shape[0] - 1))
odds = np.concatenate((odds, odds[-1:, ...]))
odds = np.concatenate((odds[:1, ...], odds))
new_shape = list(shape)
new_shape[-1] *= 2
series = np.zeros(new_shape)
if order is None:
import matplotlib.pyplot as plt
even_1 = evens[shape[0] // 3, shape[1] // 2, ...]
even_2 = evens[shape[0] * 2 // 3, shape[1] // 2, ...]
odd_1 = odds[shape[0] // 3, shape[1] // 2, ...]
odd_2 = odds[shape[0] * 2 // 3, shape[1] // 2, ...]
even_img_1 = np.zeros((shape[2], shape[3] * 2))
even_img_1[:, ::2] = even_1
even_img_1[:, 1::2] = odd_1
even_img_2 = np.zeros((shape[2], shape[3] * 2))
even_img_2[:, ::2] = even_2
even_img_2[:, 1::2] = odd_2
odd_img_1 = np.zeros((shape[2], shape[3] * 2))
odd_img_1[:, ::2] = odd_1
odd_img_1[:, 1::2] = even_1
odd_img_2 = np.zeros((shape[2], shape[3] * 2))
odd_img_2[:, ::2] = odd_2
odd_img_2[:, 1::2] = even_2
img_1 = np.concatenate((odd_img_1, even_img_1), axis=0)
img_2 = np.concatenate((odd_img_2, even_img_2), axis=0)
fig = plt.figure(figsize=(9, 9))
fig.add_subplot(2, 1, 1)
plt.imshow(img_1)
plt.axis('off')
fig.add_subplot(2, 1, 2)
plt.imshow(img_2)
plt.axis('off')
plt.suptitle(sample)
plt.show(block=False)
order = input('0. 3D\n1. odd\n2. even\n> ')
if order == '0':
even = evens[shape[0] // 2, ...]
odd = odds[shape[0] // 2, ...]
even_img = np.zeros((shape[1], shape[2], shape[3] * 2))
even_img[..., ::2] = even
even_img[..., 1::2] = odd
odd_img = np.zeros((shape[1], shape[2], shape[3] * 2))
odd_img[..., ::2] = odd
odd_img[..., 1::2] = even
temp_folder = 'data/temp'
os.makedirs(temp_folder, exist_ok=True)
util.save_vol(even_img, temp_folder + 'even.nii.gz')
util.save_vol(odd_img, temp_folder + 'odd.nii.gz')
os.system(f'open {temp_folder}/even.nii.gz')
os.system(f'open {temp_folder}/odd.nii.gz')
order = input('1. odd\n2. even\n> ')
plt.close()
if order == '1' or order == 'o':
series[..., ::2] = odds
series[..., 1::2] = evens
elif order == '2' or order == 'e':
series[..., ::2] = evens
series[..., 1::2] = odds
else:
raise ValueError('Must be even or odd slice.')
series = np.moveaxis(series, 0, axis)
new_folder = f'data/raw/{sample}/'
os.makedirs(new_folder, exist_ok=True)
for i in range(new_shape[-1]):
util.save_vol(series[..., i],
new_folder + sample + f'_{str(i).zfill(4)}.nii.gz')
def main(folder, volume):
files = glob.glob(folder + '*.nii.gz')
vols = np.concatenate([util.read_vol(file) for file in files], axis=-1)
if vols.shape[0] == vols.shape[1] == vols.shape[2]:
axis = int(input('shape: {}\n> '.format(vols.shape)))
elif vols.shape[0] == vols.shape[1]:
axis = 2
elif vols.shape[0] == vols.shape[2]:
axis = 1
elif vols.shape[1] == vols.shape[2]:
axis = 0
else:
axis = int(input('shape: {}\n> '.format(vols.shape)))
vols = np.moveaxis(vols, axis, 0)
shape = vols.shape
even_i = np.arange(0, shape[0], 2)
even_slices = vols[::2, ...]
even_interpolator = interp1d(even_i, even_slices, kind='linear', axis=0)
odd_i = np.arange(1, shape[0], 2)
odd_slices = vols[1::2, ...]
odd_interpolator = interp1d(odd_i, odd_slices, kind='linear', axis=0)
if shape[0] % 2 == 0:
evens = even_interpolator(np.arange(0, shape[0] - 1))
odds = odd_interpolator(np.arange(1, shape[0]))
evens = np.concatenate((evens, evens[np.newaxis, -1, ...]))
else:
evens = even_interpolator(np.arange(0, shape[0]))
odds = odd_interpolator(np.arange(1, shape[0] - 1))
odds = np.concatenate((odds, odds[np.newaxis, -1, ...]))
odds = np.concatenate((np.zeros([
1,
] + list(shape[1:])), odds))
if volume:
even_1 = evens[shape[0] // 3, ...]
even_2 = evens[shape[0] * 2 // 3, ...]
odd_1 = odds[shape[0] // 3, ...]
odd_2 = odds[shape[0] * 2 // 3, ...]
even_img_1 = np.zeros((shape[1], shape[2], shape[3] * 2))
even_img_1[..., ::2] = even_1
even_img_1[..., 1::2] = odd_1
even_img_2 = np.zeros((shape[1], shape[2], shape[3] * 2))
even_img_2[..., ::2] = even_2
even_img_2[..., 1::2] = odd_2
odd_img_1 = np.zeros((shape[1], shape[2], shape[3] * 2))
odd_img_1[..., ::2] = odd_1
odd_img_1[..., 1::2] = even_1
odd_img_2 = np.zeros((shape[1], shape[2], shape[3] * 2))
odd_img_2[..., ::2] = odd_2
odd_img_2[..., 1::2] = even_2
temp_folder = 'data/temp/'
os.makedirs(temp_folder, exist_ok=True)
util.save_vol(even_img_1, temp_folder + 'even_1.nii.gz')
util.save_vol(even_img_2, temp_folder + 'even_2.nii.gz')
util.save_vol(odd_img_1, temp_folder + 'odd_1.nii.gz')
util.save_vol(odd_img_2, temp_folder + 'odd_2.nii.gz')
else:
even_1 = evens[shape[0] // 3, shape[1] // 2, ...]
even_2 = evens[shape[0] * 2 // 3, shape[1] // 2, ...]
odd_1 = odds[shape[0] // 3, shape[1] // 2, ...]
odd_2 = odds[shape[0] * 2 // 3, shape[1] // 2, ...]
even_img_1 = np.zeros((shape[2], shape[3] * 2))
even_img_1[:, ::2] = even_1
even_img_1[:, 1::2] = odd_1
even_img_2 = np.zeros((shape[2], shape[3] * 2))
even_img_2[:, ::2] = even_2
even_img_2[:, 1::2] = odd_2
odd_img_1 = np.zeros((shape[2], shape[3] * 2))
odd_img_1[:, ::2] = odd_1
odd_img_1[:, 1::2] = even_1
odd_img_2 = np.zeros((shape[2], shape[3] * 2))
odd_img_2[:, ::2] = odd_2
odd_img_2[:, 1::2] = even_2
fig = plt.figure(figsize=(16, 8))
fig.add_subplot(2, 2, 1)
plt.imshow(odd_img_1)
plt.axis('off')
plt.title('odd')
fig.add_subplot(2, 2, 2)
plt.imshow(even_img_1)
plt.axis('off')
plt.title('even')
fig.add_subplot(2, 2, 3)
plt.imshow(odd_img_2)
plt.axis('off')
plt.title('odd')
fig.add_subplot(2, 2, 4)
plt.imshow(even_img_2)
plt.axis('off')
plt.title('even')
plt.show(block=False)
order = input('1. odd\n2. even\n> ')
plt.close()
new_shape = list(shape)
new_shape[-1] *= 2
series = np.zeros(new_shape)
if order == '1':
series[..., ::2] = odds
series[..., 1::2] = evens
elif order == '2':
series[..., ::2] = evens
series[..., 1::2] = odds
else:
raise ValueError('Must be even or odd slice.')
series = np.moveaxis(series, 0, axis)
sample = folder.split('/')[2]
new_folder = 'data/originals/{}/'.format(folder.split('/')[2])
os.makedirs(new_folder, exist_ok=True)
for i in range(new_shape[-1]):
util.save_vol(series[..., i],
new_folder + sample + '_{}.nii.gz'.format(i))