class AbstractParallelRoutine:
def __init__(self, config, in_folder, file_list=None):
self._config = config
self._in_data_folder = DataFolder(in_folder, file_list)
# self._num_processes = config['num_processes']
self._num_processes = 1
def run_parallel(self):
pool = Pool(processes=self._num_processes)
chunk_list = self._in_data_folder.get_chunks_list(self._num_processes)
result_obj_list = [
pool.apply_async(self._run_chunk, (file_idx_chunk, ))
for file_idx_chunk in chunk_list
]
result_list = []
for thread_idx in range(len(result_obj_list)):
result_obj = result_obj_list[thread_idx]
result_obj.wait()
logger.info(
f'Thread with idx {thread_idx} / {len(result_obj_list)} is completed'
)
result_list = result_list + result_obj.get()
return result_list
def run_non_parallel(self, idx_list=None):
run_idx_list = idx_list if idx_list else range(self.num_files())
for idx in run_idx_list:
self._in_data_folder.print_idx(idx)
self._run_single_scan(idx)
# print('Done', flush=True)
def _run_single_scan(self, idx):
raise NotImplementedError
def _run_chunk(self, chunk_list):
result_list = []
for idx in chunk_list:
self._in_data_folder.print_idx(idx)
result = self._run_single_scan(idx)
result_list.append(result)
return result_list
def num_files(self):
return self._in_data_folder.num_files()
class AverageScans:
def __init__(self,
config,
in_folder=None,
data_file_txt=None,
in_data_folder_obj=None):
self._data_folder = None
if in_data_folder_obj is None:
self._data_folder = DataFolder(in_folder, data_file_txt)
else:
self._data_folder = in_data_folder_obj
self._standard_ref = ScanWrapper(self._data_folder.get_first_path())
self._num_processes = config['num_processes']
def get_average_image_union(self, save_path):
im_shape = self._get_std_shape()
average_union = np.zeros(im_shape)
average_union.fill(np.nan)
non_null_mask_count_image = np.zeros(im_shape)
chunk_list = self._data_folder.get_chunks_list(self._num_processes)
pool = Pool(processes=self._num_processes)
print('Average in union')
print('Step.1 Summation')
image_average_union_result_list = [
pool.apply_async(self._sum_images_union, (file_idx_chunk, ))
for file_idx_chunk in chunk_list
]
for thread_idx in range(len(image_average_union_result_list)):
result = image_average_union_result_list[thread_idx]
result.wait()
print(
f'Thread with idx {thread_idx} / {len(image_average_union_result_list)} is completed'
)
print('Adding to averaged_image...')
averaged_image_chunk = result.get()
average_union = self._add_image_union(average_union,
averaged_image_chunk)
print('Done.')
print('Step.2 Non-nan counter')
non_null_mask_count_result = [
pool.apply_async(self._sum_non_null_count, (file_idx_chunk, ))
for file_idx_chunk in chunk_list
]
for thread_idx in range(len(non_null_mask_count_result)):
result = non_null_mask_count_result[thread_idx]
result.wait()
print(
f'Thread with idx {thread_idx} / {len(non_null_mask_count_result)} is completed'
)
print('Adding to averaged_image...')
averaged_image_chunk = result.get()
non_null_mask_count_image = np.add(non_null_mask_count_image,
averaged_image_chunk)
print('Done.')
average_union = np.divide(average_union,
non_null_mask_count_image,
out=average_union,
where=non_null_mask_count_image > 0)
self._standard_ref.save_scan_same_space(save_path, average_union)
print('Done.')
def _sum_images_union(self, chunk_list):
print('Sum images, union non-null region. Loading images...')
im_shape = self._get_std_shape()
sum_image = np.zeros(im_shape)
sum_image.fill(np.nan)
for id_file in chunk_list:
file_path = self._data_folder.get_file_path(id_file)
self._data_folder.print_idx(id_file)
im = nib.load(file_path)
im_data = im.get_data()
sum_image = self._add_image_union(sum_image, im_data)
return sum_image
def _sum_non_null_count(self, chunk_list):
print('Count non-null per voxel. Loading images...')
im_shape = self._get_std_shape()
sum_image = np.zeros(im_shape)
for id_file in chunk_list:
file_path = self._data_folder.get_file_path(id_file)
self._data_folder.print_idx(id_file)
im = nib.load(file_path)
im_data = im.get_data()
sum_image = np.add(sum_image,
1,
out=sum_image,
where=np.logical_not(np.isnan(im_data)))
return sum_image
def _get_std_shape(self):
return self._standard_ref.get_data().shape
@staticmethod
def _add_image_inter(image1, image2):
return np.add(image1,
image2,
out=np.full_like(image1, np.nan),
where=np.logical_not(
np.logical_or(np.isnan(image1), np.isnan(image2))))
@staticmethod
def _add_image_union(image1, image2):
add_image = np.full_like(image1, np.nan)
add_image[np.logical_not(
np.logical_and(np.isnan(image1), np.isnan(image2)))] = 0
add_image = np.add(add_image,
image1,
out=add_image,
where=np.logical_not(np.isnan(image1)))
add_image = np.add(add_image,
image2,
out=add_image,
where=np.logical_not(np.isnan(image2)))
return add_image
@staticmethod
def sum_non_null_count(file_list, in_folder):
print('Count non-null per voxel. Loading images...')
im_temp = nib.load(os.path.join(in_folder, file_list[0]))
im_temp_data = im_temp.get_data()
sum_image = np.zeros_like(im_temp_data)
for id_file in range(len(file_list)):
file_name = file_list[id_file]
print('%s (%d/%d)' % (file_name, id_file, len(file_list)))
file_path = os.path.join(in_folder, file_name)
im = nib.load(file_path)
im_data = im.get_data()
sum_image = np.add(sum_image,
1,
out=sum_image,
where=np.logical_not(np.isnan(im_data)))
return sum_image