def prepared_texture_features_by_string(names):
"""
There are two possible feature_fcn formats
* method
* object with method 'features' and attribute 'description'
"""
gf = tfeat.GaborFeatures() # noqa
glcmf = tfeat.GlcmFeatures() # noqa
haralick = tfeat.HaralickFeatures() # noqa
hist_gf = tfeat.FeaturesCombinedFeatures(feat_hist, gf.feats_gabor)
hist_glcm = tfeat.FeaturesCombinedFeatures(feat_hist, glcmf.feats_glcm)
hist_glcm_gf = tfeat.FeaturesCombinedFeatures(feat_hist, glcmf.feats_glcm,
gf.feats_gabor)
dict_of_feature_fcn = {
'hist': [feat_hist, []],
'gf': [gf.feats_gabor, []],
'glcm': [glcmf.feats_glcm, []],
'haralick': [haralick.feats_haralick, [True]],
'hist_gf': [hist_gf, []],
'hist_glcm': [hist_glcm, []],
'hist_glcm_gf': [hist_glcm_gf, []],
}
selected_classifiers = [dict_of_feature_fcn[name] for name in names]
return selected_classifiers
def main():
# # logger = logging.getLogger()
# logger = logging.getLogger()
logger.setLevel(logging.WARNING)
ch = logging.StreamHandler()
logger.addHandler(ch)
# logger.debug('input params')
parser = argparse.ArgumentParser(
description='Compute features on liver and other tissue.')
parser.add_argument('-tr',
'--training_yaml_path',
help='Input yaml file.' +
" You can check sample with -si parameter.",
default="20130919_liver_statistics.yaml")
parser.add_argument('-te',
'--testing_yaml_path',
help='Input yaml file.' +
" You can check sample with -si parameter.",
default=None)
parser.add_argument('-si',
'--sampleInput',
action='store_true',
help='generate sample intput data',
default=False)
parser.add_argument('-v',
'--visualization',
action='store_true',
help='Turn on visualization',
default=False)
parser.add_argument('-fc',
'--features_classifs',
action='store_true',
help='Read features and classifs list from file',
default=False)
parser.add_argument('-o',
'--output',
help='output file',
default="20130919_liver_statistics_results.pkl")
parser.add_argument('-t',
'--train',
help='Training',
default=False,
action='store_true')
parser.add_argument(
'-cl',
'--classifers',
help='classifer by string: "SVC", or "GaussianNB", ...',
nargs='+',
type=str,
default=['SVC'])
parser.add_argument('-fe',
'--features',
help='features by string: "hist", or "glcm", ...',
nargs='+',
type=str,
default=['hist'])
args = parser.parse_args()
if args.sampleInput:
sample_input_data()
# input parser
# path_to_yaml = os.path.join(path_to_script, args.input)
# training_yaml_path = args.training_yaml_path
# testing_yaml_path = args.testing_yaml_path
if args.testing_yaml_path is None:
print('testing is same as training')
args.testing_yaml_path = args.training_yaml_path
# write_csv(fvall)
gf = tfeat.GaborFeatures() # noqa
glcmf = tfeat.GlcmFeatures() # noqa
haralick = tfeat.HaralickFeatures() # noqa
list_of_feature_fcn = prepared_texture_features_by_string(args.features)
list_of_classifiers = prepared_classifiers_by_string(args.classifers)
tile_shape = [10, 50, 50]
if args.features_classifs:
import features_classifs
featrs_plus_classifs = features_classifs.fc
else:
featrs_plus_classifs = make_product_list(list_of_feature_fcn,
list_of_classifiers)
result = experiment(args.training_yaml_path,
args.testing_yaml_path,
featrs_plus_classifs,
tile_shape=tile_shape,
use_voxelsize_norm=True,
working_voxelsize_mm=[1, 1, 1],
visualization=args.visualization,
train=args.train)
# Ukládání výsledku do souboru
output_file = os.path.join(path_to_script, args.output)
misc.obj_to_file(result, output_file, filetype='pickle')