def compute_ROIs(
generate_csv=False,
version=0,
patientTxtPath='/media/shared/datasets/LUNA/CSVFILES/patients_train.txt',
mode='train'):
if generate_csv:
with open('ROIs_v{}_{}.csv'.format(version, mode), 'w') as f:
f.write('patient,nodules,detected_regions')
# ## PATIENTS FILE LIST // not really useful, remove
filter_annotated = False
patients_with_annotations = pd.read_csv(
NODULES_PATH
) # filter patients with no annotations to avoid having to read them
patients_with_annotations = list(
set(patients_with_annotations['seriesuid']))
patients_with_annotations = [
"luna_%s.npz" % p.split('.')[-1] for p in patients_with_annotations
]
filenames = []
with open(patientTxtPath, 'r') as f:
for line in f:
filenames.append(line.strip())
filenames = [
os.path.join(INPUT_PATH, fp) for fp in filenames
if fp in patients_with_annotations or not filter_annotated
]
filenames = filter(os.path.isfile, filenames)
def __load_and_store(filename):
patient_data = np.load(filename)['arr_0']
patient_data = patient_data.astype(np.int16)
X, y, rois, stats = common.load_patient(patient_data,
discard_empty_nodules=True,
output_rois=True,
debug=True,
include_ground_truth=True,
thickness=1)
if not stats:
stats = {'tp': 0, 'fn': 0, 'fp': 0}
logging.info("Patient: %s, stats: %s" %
(filename.split('/')[-1], stats))
if generate_csv:
with open('ROIs_v{}_{}.csv'.format(version, mode), 'a') as f:
f.write('{},{},{}\n'.format(
filename.split('/')[-1][:-4], stats['tp'],
sum(stats.values())))
# If the patient is empty
return X, y, stats
common.multiproc_crop_generator(
filenames,
os.path.join(PATCHES_PATH, 'dl1_v{}_x_{}.npz'.format(version, mode)),
os.path.join(PATCHES_PATH, 'dl1_v{}_y_{}.npz'.format(version, mode)),
__load_and_store)
def compute_ROIs(generate_csv=False, version_dl2 = 0, SCORE_TH = 0.5,
patientTxtPath = '/media/shared/datasets/LUNA/CSVFILES/patients_train.txt',
mode = 'train', dataset = 'luna'):
"""Loads the output of DL-I and load just the 1's (TP or FN's) and the FP's for a given score (SCORE_TH) to train DL-II"""
if generate_csv:
with open('/home/shared/output/ROIs_dl2_v{}_{}.csv'.format(version_dl2, mode), 'w') as f:
f.write('patient,nodules,detected_regions')
# ## PATIENTS FILE LIST // not really useful, remove
if mode == 'train':
nodules_df = pd.read_csv(OUTPUT_DL1)
else:
nodules_df = pd.read_csv(OUTPUT_DL1_TEST)
nodules_df = nodules_df[(nodules_df['score'] > SCORE_TH) | (nodules_df['label']==1)]
nodules_df['nslice'] = nodules_df['nslice'].astype(int)
logging.info("Shape nodules df: %s" % str(nodules_df.shape))
patients = [ p + '.npz' for p in set(nodules_df['patientid'])]
files = []
with open(patientTxtPath, 'r') as f:
for line in f:
files.append(line.strip())
if dataset == 'isbi':
patients = ['/media/shared/datasets/ISBI/preprocessedNew/' + p for p in patients]
filenames = [fp for fp in files if fp in patients]
else:
filenames = [os.path.join(INPUT_PATH, fp) for fp in files if fp in patients]
def __load_and_store(filename):
patient_data = np.load(filename)['arr_0'].astype(np.int16)
ndf = nodules_df[nodules_df['patientid']==filename.split('/')[-1].split('.')[0]]
X, y, rois, stats = common.load_patient(patient_data, ndf, output_rois=True, thickness=1)
if not stats:
stats = {'tp' : 0, 'fn' : 0, 'fp' : 0}
logging.info("Patient: %s, stats: %s" % (filename.split('/')[-1], stats))
if generate_csv:
with open('/home/shared/output/ROIs_dl2_v{}_{}_{}.csv'.format(version_dl2, mode, dataset), 'a') as f:
f.write('{},{},{}\n'.format(filename.split('/')[-1][:-4], stats['tp'], sum(stats.values())))
return X, y, stats
common.multiproc_crop_generator(filenames,
os.path.join(PATCHES_PATH,'dl2_v{}_x_{}_{}.npz'.format(version_dl2, mode, dataset)),
os.path.join(PATCHES_PATH,'dl2_v{}_y_{}_{}.npz'.format(version_dl2, mode, dataset)),
__load_and_store)
filenames_train = [os.path.join(INPUT_PATH, fp) for fp in filenames if fp in patients_with_annotations]
filenames_test = [os.path.join(INPUT_PATH, fp) for fp in filenames_t if fp in patients_with_annotations]
def __load_and_store(filename):
patient_data = np.load(filename)['arr_0']
patient_data = patient_data.astype(np.int16)
X, y, rois, stats = common.load_patient(patient_data, discard_empty_nodules=True, output_rois=True, debug=True, include_ground_truth=True, thickness=1)
logging.info("Patient: %s, stats: %s" % (filename.split('/')[-1], stats))
return X, y, stats
common.multiproc_crop_generator(filenames_train,
os.path.join(PATCHES_PATH, 'dl1_v2_x_train.npz'),
os.path.join(PATCHES_PATH, 'dl1_v2_y_train.npz'),
__load_and_store)
common.multiproc_crop_generator(filenames_test,
os.path.join(PATCHES_PATH, 'dl1_v2_x_test.npz'),
os.path.join(PATCHES_PATH, 'dl1_v2_y_test.npz'),
__load_and_store)
=======
### PATCHES GENERATION -----------------------------------------------------------------
#
# ## PATIENTS FILE LIST
# patients_with_annotations = pd.read_csv(NODULES_PATH) # filter patients with no annotations to avoid having to read them
# patients_with_annotations = list(set(patients_with_annotations['seriesuid']))
# patients_with_annotations = ["luna_%s.npz" % p.split('.')[-1] for p in patients_with_annotations]
#
# filenames = os.listdir(INPUT_PATH)
patid = filename.split('/')[-1]
ndf = nodules_df[nodules_df['patientid'] == patid]
X, y, rois, stats = common.load_patient(patient_data,
ndf,
output_rois=True,
thickness=1)
label = int(label_df[label_df['id'] == patid]['cancer'])
y = [label] * len(X)
logging.info("Patient: %s, cancer:%d, stats: %s" % (patid, label, stats))
return X, y, stats
common.multiproc_crop_generator(filenames_train,
os.path.join(PATCHES_PATH,
'dl3_v1_x_train.npz'),
os.path.join(PATCHES_PATH,
'dl3_v1_y_train.npz'),
__load_and_store,
parallel=True)
common.multiproc_crop_generator(filenames_test,
os.path.join(PATCHES_PATH,
'dl3_v1_x_test.npz'),
os.path.join(PATCHES_PATH,
'dl3_v1_y_test.npz'),
__load_and_store,
parallel=True)
### TRAINING -------------------------------------------------------------------------------------------------------
# Data augmentation generator
