def process_dataset(args, num_tries=3):
""" pre process and registrate volume"""
# pylint: disable= unused-argument
moving_image_id = args[0]
print(moving_image_id)
conn = sqlite3.connect(util.DB_PATH)
conn.text_factory = str
cursor = conn.execute('''SELECT pid from Images where id = ?''', (moving_image_id,))
pid = cursor.fetchone()[0]
cursor = conn.execute('''SELECT id from Images where pid = ? AND diag_pre_post = ?''',
(pid, "pre"))
db_temp = cursor.fetchone()
pre_image_id = db_temp[0]
cursor.close()
conn.close()
import datetime
start_time = datetime.datetime.now()
pre_img = img_data(pre_image_id, util.DATA_FOLDER, util.TEMP_FOLDER_PATH)
post_img = img_data(moving_image_id, util.DATA_FOLDER, util.TEMP_FOLDER_PATH)
pre_img = image_registration.pre_process(pre_img, False)
post_img = image_registration.pre_process(post_img, False)
img = image_registration.registration(post_img, pre_img.pre_processed_filepath,
image_registration.RIGID)
print("\n\n\n\n -- Total run time: ")
print(datetime.datetime.now() - start_time)
img.fixed_image = pre_image_id
return img
def process_dataset(args):
""" pre process and registrate volume"""
(moving_image_id, reg_type, save_to_db, be_method, reg_type_be) = args
util.LOGGER.info(moving_image_id)
for k in range(3):
try:
start_time = datetime.datetime.now()
img = img_data(moving_image_id, util.DATA_FOLDER,
util.TEMP_FOLDER_PATH)
img = pre_process(img, reg_type=reg_type_be, be_method=be_method)
util.LOGGER.info("-- Run time preprocess: ")
util.LOGGER.info(datetime.datetime.now() - start_time)
img = registration(img, util.TEMPLATE_MASKED_VOLUME, reg_type)
break
# pylint: disable= broad-except
except Exception as exp:
util.LOGGER.error('Crashed during ' + str(k + 1) + ' of 3 ' +
str(exp))
util.LOGGER.info(" -- Run time: " +
str(datetime.datetime.now() - start_time))
if save_to_db:
save_transform_to_database([img])
del img
else:
transform_labels_temp([img])
del img
def post_calculations(moving_dataset_image_ids, result=None):
""" Transform images and calculate avg"""
if result is None:
result = {}
for _id in moving_dataset_image_ids:
img = img_data(_id, util.DATA_FOLDER, util.TEMP_FOLDER_PATH)
img.load_db_transforms()
img_pre = img_data(img.fixed_image, util.DATA_FOLDER,
util.TEMP_FOLDER_PATH)
img_pre.load_db_transforms()
reg_vol = util.transform_volume(img.reg_img_filepath,
img_pre.get_transforms())
vol = util.TEMP_FOLDER_PATH + util.get_basename(
basename(reg_vol)) + '_BE.nii.gz'
mult = ants.MultiplyImages()
mult.inputs.dimension = 3
mult.inputs.first_input = reg_vol
mult.inputs.second_input = util.TEMPLATE_MASK
mult.inputs.output_product_image = vol
mult.run()
label = "img"
if label in result:
result[label].append(vol)
else:
result[label] = [vol]
for (segmentation, label) in util.find_reg_label_images(_id):
segmentation = util.transform_volume(segmentation,
img_pre.get_transforms(),
label_img=True)
if label in result:
result[label].append(segmentation)
else:
result[label] = [segmentation]
return result
def process_dataset(args, num_tries=3):
""" pre process and registrate volume"""
# pylint: disable= unused-argument
(moving_image_id, _, save_to_db, _) = args
print(moving_image_id)
conn = sqlite3.connect(util.DB_PATH)
conn.text_factory = str
cursor = conn.execute('''SELECT pid from Images where id = ?''',
(moving_image_id, ))
pid = cursor.fetchone()[0]
cursor = conn.execute(
'''SELECT id from Images where pid = ? AND diag_pre_post = ?''',
(pid, "pre"))
db_temp = cursor.fetchone()
pre_image_id = db_temp[0]
cursor.close()
conn.close()
start_time = datetime.datetime.now()
pre_img = img_data(pre_image_id, util.DATA_FOLDER, util.TEMP_FOLDER_PATH)
post_img = img_data(moving_image_id, util.DATA_FOLDER,
util.TEMP_FOLDER_PATH)
post_img.fixed_image = pre_image_id
pre_img = image_registration.pre_process(pre_img, False)
post_img = image_registration.pre_process(post_img, False)
img = image_registration.registration(post_img,
pre_img.pre_processed_filepath,
image_registration.RIGID)
print("\n\n\n\n -- Total run time: ")
print(datetime.datetime.now() - start_time)
img.fixed_image = pre_image_id
if save_to_db:
image_registration.save_transform_to_database([img])
def save_to_db(image_ids, ny_image_ids):
print("----here")
data_transforms = []
for (img_id, ny_img_id) in zip(image_ids, ny_image_ids):
print(img_id)
_img = img_data(img_id, db_path, util.TEMP_FOLDER_PATH)
_img.load_db_transforms()
print(_img.transform)
if _img.transform is None:
continue
_img.processed_filepath = image_registration.move_vol(_img.img_filepath, _img.get_transforms())
_img.image_id = ny_img_id
data_transforms.append(_img)
image_registration.save_transform_to_database(data_transforms)
def save_to_db(image_ids, ny_image_ids):
print("----here")
data_transforms = []
for (img_id, ny_img_id) in zip(image_ids, ny_image_ids):
print(img_id)
_img = img_data(img_id, db_path, util.TEMP_FOLDER_PATH)
_img.load_db_transforms()
print(_img.transform)
if _img.transform is None:
continue
_img.processed_filepath = image_registration.move_vol(
_img.img_filepath, _img.get_transforms())
_img.image_id = ny_img_id
data_transforms.append(_img)
image_registration.save_transform_to_database(data_transforms)
def move_vol(moving, transform, label_img=False):
""" Move data with transform """
if label_img:
# resample volume to 1 mm slices
target_affine_3x3 = np.eye(3) * 1
img_3d_affine = resample_img(moving, target_affine=target_affine_3x3,
interpolation='nearest')
resampled_file = util.TEMP_FOLDER_PATH + util.get_basename(moving) + '_resample.nii.gz'
# pylint: disable= no-member
img_3d_affine.to_filename(resampled_file)
else:
img = img_data(-1, util.DATA_FOLDER, util.TEMP_FOLDER_PATH)
img.set_img_filepath(moving)
resampled_file = pre_process(img, False).pre_processed_filepath
result = util.transform_volume(moving, transform, label_img)
util.generate_image(result, util.TEMPLATE_VOLUME)
return result
def move_vol(moving, transform, label_img=False, slice_size=1, ref_img=None):
""" Move data with transform """
if label_img:
# resample volume to 1 mm slices
target_affine_3x3 = np.eye(3) * slice_size
img_3d_affine = resample_img(moving,
target_affine=target_affine_3x3,
interpolation='nearest')
resampled_file = util.TEMP_FOLDER_PATH + util.get_basename(
moving) + '_resample.nii.gz'
# pylint: disable= no-member
img_3d_affine.to_filename(resampled_file)
del img_3d_affine
else:
img = img_data(-1, util.DATA_FOLDER, util.TEMP_FOLDER_PATH)
img.set_img_filepath(moving)
resampled_file = pre_process(img, False).pre_processed_filepath
result = util.transform_volume(resampled_file,
transform,
label_img,
ref_img=ref_img)
util.generate_image(result, util.TEMPLATE_VOLUME)
return result
def process_dataset(args):
""" pre process and registrate volume"""
(moving_image_id, reg_type, save_to_db) = args
print(moving_image_id)
start_time = datetime.datetime.now()
img = img_data(moving_image_id, util.DATA_FOLDER, util.TEMP_FOLDER_PATH)
img = pre_process(img)
print("\n\n\n\n -- Run time preprocess: ")
print(datetime.datetime.now() - start_time)
for k in range(3):
try:
img = registration(img, util.TEMPLATE_MASKED_VOLUME, reg_type)
break
# pylint: disable= broad-except
except Exception as exp:
print('Crashed during' + str(k+1) + ' of 3 \n' + str(exp))
print("\n\n\n\n -- Run time: ")
print(datetime.datetime.now() - start_time)
if save_to_db:
save_transform_to_database([img])
return img
conn = sqlite3.connect(util.DB_PATH)
cursor = conn.cursor()
modified_patients = [
subfolder_name
for subfolder_name in os.listdir(new_segmentations_folder) if
os.path.isdir(os.path.join(new_segmentations_folder, subfolder_name))
]
for pid in modified_patients:
#print('PATIENT ' + pid)
cursor.execute("SELECT id FROM Images WHERE pid = ?", (int(pid), ))
image_id = cursor.fetchone()[0]
print(image_id)
img = img_data(image_id, util.DATA_FOLDER, util.TEMP_FOLDER_PATH)
img.load_db_transforms()
#cursor.execute("SELECT filepath, filepath_reg FROM Labels WHERE image_id IN "
# "(SELECT id FROM Images WHERE pid = ?)", (int(pid),))
#(label_path, label_reg_path) = cursor.fetchone()
#cursor.execute("SELECT transform FROM Images WHERE pid = ?", (int(pid),))
#transform = cursor.fetchone()[0]
new_segmentation_path = glob.glob(new_segmentations_folder + str(pid) +
'/*label.nii')[0]
#print(new_segmentation_path)
#temp = util.compress_vol(move_vol(new_segmentation_path, img.get_transforms(), True))
#shutil.copy(temp, img.reg_label_filepath)
shutil.copy(new_segmentation_path, img.label_filepath)
#path = '/Volumes/Neuro/Segmentations/oppdaterte_filer/**/*label.nii'
def main():
Data = img_data.img_data('train')
images, labels = Data.get()
print("images {} labels {}".format(np.shape(images), np.shape(labels)))
