from functools import partial
import lasagne.layers.dnn as dnn
import theano.tensor as T
import utils
import utils_lung
import os
# TODO: import correct config here
candidates_config = 'dsb_c3_s2_p8a1_ls_elias'
restart_from_save = None
rng = np.random.RandomState(42)
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
candidates_path = predictions_dir + '/%s' % candidates_config
id2candidates_path = utils_lung.get_candidates_paths(candidates_path)
# transformations
p_transform = {'patch_size': (48, 48, 48),
'mm_patch_size': (48, 48, 48),
'pixel_spacing': (1., 1., 1.)
}
p_transform_augment = {
'translation_range_z': [-5, 5],
'translation_range_y': [-5, 5],
'translation_range_x': [-5, 5],
'rotation_range_z': [-10, 10],
'rotation_range_y': [-10, 10],
'rotation_range_x': [-10, 10]
}
n_candidates_per_patient = 8
import utils
import utils_lung
import os
from configs_luna_props_patch import r_elias_10 as cfg_prop
# TODO: import correct config here
candidates_config = 'dsb_relias10_s5_p8a1'
restart_from_save = None
rng = np.random.RandomState(42)
predictions_dir = utils.get_dir_path('model-predictions',
pathfinder.METADATA_PATH)
candidates_path = predictions_dir + '/%s' % candidates_config
id2candidates_path = utils_lung.get_candidates_paths(candidates_path)
# transformations
p_transform = {
'patch_size': (48, 48, 48),
'mm_patch_size': (48, 48, 48),
'pixel_spacing': (1., 1., 1.)
}
p_transform_augment = {
'translation_range_z': [-5, 5],
'translation_range_y': [-5, 5],
'translation_range_x': [-5, 5],
'rotation_range_z': [-10, 10],
'rotation_range_y': [-10, 10],
'rotation_range_x': [-10, 10]
}
p_transform = patch_class_config.p_transform
data_prep_function = patch_class_config.partial(
patch_class_config.data_prep_function,
p_transform_augment=None,
p_transform=p_transform,
world_coord_system=False,
luna_origin=None)
rng = patch_class_config.rng
# candidates after segmentations path
predictions_dir = utils.get_dir_path('model-predictions',
pathfinder.METADATA_PATH)
segmentation_outputs_path = predictions_dir + '/%s' % seg_config_name
id2candidates_path = utils_lung.get_candidates_paths(segmentation_outputs_path)
# filter our those, who are already generated
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH) \
+ '/' + utils.get_script_name(__file__)
exclude_pids = utils_lung.get_generated_pids(predictions_dir)
data_iterator = data_iterators.CandidatesDSBDataGenerator(
data_path=pathfinder.DATA_PATH,
transform_params=p_transform,
data_prep_fun=data_prep_function,
id2candidates_path=id2candidates_path,
exclude_pids=exclude_pids)
def build_model():
import configs_fpred_patch.luna_c3 as patch_class_config
p_transform = patch_class_config.p_transform
data_prep_function = patch_class_config.partial(patch_class_config.data_prep_function,
p_transform_augment=None,
p_transform=p_transform,
world_coord_system=False,
luna_origin=None)
rng = patch_class_config.rng
# candidates after segmentations path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
segmentation_outputs_path = predictions_dir + '/%s' % seg_config_name
id2candidates_path = utils_lung.get_candidates_paths(segmentation_outputs_path)
# filter our those, who are already generated
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/dsb_c3_s2_p8a1_ls_elias' # TODO write it here correctly
exclude_pids = []
if os.path.isdir(outputs_path):
exclude_pids = os.listdir(outputs_path)
exclude_pids = [utils_lung.extract_pid_filename(p) for p in exclude_pids]
for pid in exclude_pids:
id2candidates_path.pop(pid, None)
data_iterator = data_iterators.CandidatesDSBDataGenerator(data_path=pathfinder.DATA_PATH,
transform_params=p_transform,
data_prep_fun=data_prep_function,
id2candidates_path=id2candidates_path)
import utils
from configuration import set_configuration
import utils_lung
theano.config.warn_float64 = 'raise'
if len(sys.argv) < 2:
sys.exit("Usage: test_luna_scan.py <configuration_name>")
config_name = sys.argv[1]
set_configuration('configs_fpred_scan', config_name)
# predictions path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/%s' % config_name
pid2candidates_path = utils_lung.get_candidates_paths(outputs_path)
pid2candidates = {}
for k, v in pid2candidates_path.iteritems():
pid2candidates[k] = utils.load_pkl(v)
pid2annotations = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
n_top = 1
tp_top_n = 0
fp_top_n = 0
tp = 0
n_pos = 0
idx1 = []
for k in pid2candidates.iterkeys():
print '----------------------------------------'
print k
from configuration import set_configuration
import utils_lung
theano.config.warn_float64 = 'raise'
if len(sys.argv) < 2:
sys.exit("Usage: test_luna_scan.py <configuration_name>")
config_name = sys.argv[1]
set_configuration('configs_fpred_scan', config_name)
# predictions path
predictions_dir = utils.get_dir_path('model-predictions',
pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/%s' % config_name
pid2candidates_path = utils_lung.get_candidates_paths(outputs_path)
pid2candidates = {}
for k, v in pid2candidates_path.iteritems():
pid2candidates[k] = utils.load_pkl(v)
pid2annotations = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
n_top = 1
tp_top_n = 0
fp_top_n = 0
tp = 0
n_pos = 0
idx1 = []
for k in pid2candidates.iterkeys():
print('----------------------------------------')
print(k)