for roi in rois:
md = max(roi)
if md > max_dim:
max_dim = md
distance = sum((roi-nodule)**2)**(0.5)
if distance < min_distance:
min_distance = distance
closest_roi = roi
print 'max_dim', max_dim
print 'n', n
print 'closest_roi', closest_roi
print 'min_distance', min_distance
print 'diameter', diameter_in_mm
if min_distance < diameter_in_mm:
n_found += 1
print 'found', n_found, '/', n_nodules
print 'n_regions', n_regions
if __name__ == "__main__":
# model = segnet1
set_configuration("configurations/elias/roi_luna_1.py")
config.data_loader.prepare()
for set in [VALIDATION]:
check_nodules(set)
pickle.dump(d_out,
open(output_folder + patient_id + ".pkl", "wb"),
protocol=pickle.HIGHEST_PROTOCOL)
print 'n_regions', n_regions
print 'n_regions_in_mask', n_regions_in_mask
if __name__ == "__main__":
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"config",
help='configuration to run',
)
args = parser.parse_args()
set_configuration(args.config)
roi_config, roi_config_name = get_configuration(config.roi_model_config)
roi_config.data_loader.prepare()
fpr_config, fpr_config_name = get_configuration(config.fpr_model_config)
prediction_folder = paths.MODEL_PREDICTIONS_PATH + '/' + config.prediction_loc + '/'
output_folder = paths.MODEL_PREDICTIONS_PATH + '/' + config.output_name + '/'
ensure_dir(output_folder)
x_shared, iter_predict = load_and_build_model(fpr_config,
config.saved_model_loc)
for set in [TRAIN, VALIDATION, TEST]: