def evaluate_all_datasets_combination():
results = [['Origin', 'Target', 'Feature', 'HTER', 'APCER', 'BPCER']]
datasets_origin = file_helper.get_dirs_from_folder(BASE_PATH_COMBINATION)
for dataset_origin in datasets_origin:
print('Origin: ', dataset_origin)
datasets_target = file_helper.get_dirs_from_folder(
os.path.join(BASE_PATH_COMBINATION, dataset_origin))
for dataset_target in datasets_target:
print(' Target: ', dataset_target)
features = file_helper.get_dirs_from_folder(
os.path.join(BASE_PATH_COMBINATION, dataset_origin,
dataset_target))
for feature in features:
full_path_features = os.path.join(BASE_PATH_COMBINATION,
dataset_origin,
dataset_target, feature)
try:
hter, apcer, bpcer = evaluate_predictions(
full_path_features)
row = [
dataset_origin, dataset_target, feature, hter, apcer,
bpcer
]
results.append(row)
except Exception as e:
print(e)
df = DataFrame(results)
print(df)
df.to_csv('results_hter_combinations.csv', sep=' ')
def classify_all_datasets(organized_attack_path, base_output_path):
datasets = file_helper.get_dirs_from_folder(organized_attack_path)
for dataset in datasets:
print("Dataset: %s" % dataset)
dataset_path = os.path.join(organized_attack_path, dataset)
types_attacks = file_helper.get_dirs_from_folder(dataset_path)
for type_attack in types_attacks:
print("Type attack: %s" % type_attack)
attack_path = os.path.join(dataset_path, type_attack)
features = file_helper.get_dirs_from_folder(attack_path)
for feature in features:
print("Feature: %s" % feature)
if feature != 'features': # this folder is where we keep the results from the extraction
feature_path = os.path.join(dataset_path, type_attack,
feature)
output_path = os.path.join(base_output_path, dataset,
type_attack, feature)
print(feature_path)
try:
run(feature_path, output_path, 'jpg')
except Exception as e:
print(e)
def evaluate_all_cross(base_features, base_output):
datasets = file_helper.get_dirs_from_folder(base_features)
for dataset_origin in datasets:
origin_path = os.path.join(base_features, dataset_origin)
# types_attack = all, cut, print, tablet, etc.
types_attack = file_helper.get_dirs_from_folder(origin_path)
for attack_origin in types_attack:
full_path_origin = os.path.join(origin_path, attack_origin)
features_origin = file_helper.get_dirs_from_folder(
full_path_origin)
for feature_origin in features_origin:
full_path_origin = os.path.join(origin_path, attack_origin,
feature_origin)
print(full_path_origin)
targets_datasets = file_helper.get_dirs_from_folder(
base_features)
for dataset_target in targets_datasets:
if dataset_target != dataset_origin and attack_origin != 'all':
continue
attacks_target = os.path.join(base_features,
dataset_target)
for attack_target in os.listdir(attacks_target):
features_target = os.path.join(base_features,
dataset_target,
attack_target)
# if (dataset_target == dataset_origin and attack_target != attack_origin):
# continue
for feature_target in os.listdir(features_target):
if feature_target == feature_origin:
full_path_target = os.path.join(
features_target, feature_target)
output_path = os.path.join(
base_output, dataset_origin, attack_origin,
dataset_target, attack_target,
feature_target)
print(' target: %s' % (full_path_target))
try:
evaluate(full_path_origin,
full_path_target, output_path)
except Exception as e:
print(e)
def classify_all_datasets():
datasets = file_helper.get_dirs_from_folder(BASE_PATH)
for dataset in datasets:
dataset_path = os.path.join(BASE_PATH, dataset)
types_attacks = file_helper.get_dirs_from_folder(dataset_path)
for type_attack in types_attacks:
attack_path = os.path.join(dataset_path, type_attack)
features = file_helper.get_dirs_from_folder(attack_path)
for feature in features:
if feature != 'features': # this folder is where we keep the results from the extraction
feature_path = os.path.join(dataset_path, type_attack, feature)
print(feature_path)
run(feature_path, 'jpg')
def evaluate_all_datasets():
results = [[
'Origin', 'Target', 'Origin Type', 'Target Type', 'Feature', 'HTER',
'APCER', 'BPCER'
]]
datasets_origin = file_helper.get_dirs_from_folder(BASE_PATH_INTRA)
for dataset_origin in datasets_origin:
attacks_origin = os.listdir(
os.path.join(BASE_PATH_INTRA, dataset_origin))
for attack_origin in attacks_origin:
datasets_target = file_helper.get_dirs_from_folder(
(os.path.join(BASE_PATH_INTRA, dataset_origin, attack_origin)))
for dataset_target in datasets_target:
attacks = file_helper.get_dirs_from_folder(
os.path.join(BASE_PATH_INTRA, dataset_origin,
attack_origin, dataset_target))
for attack_target in attacks:
features = os.listdir(
os.path.join(BASE_PATH_INTRA, dataset_origin,
attack_origin, dataset_target,
attack_target))
for feature in features:
full_path_features = os.path.join(
BASE_PATH_INTRA, dataset_origin, attack_origin,
dataset_target, attack_target, feature)
try:
hter, apcer, bpcer = evaluate_predictions(
full_path_features)
row = [
dataset_origin, dataset_target, attack_origin,
attack_target, feature, hter, apcer, bpcer
]
results.append(row)
except Exception as e:
print(e)
df = DataFrame(results)
print(df)
df.to_csv('results_hter.csv', sep=' ')
def run_pipeline(dataset_alias, pai, dataset_type, dataset_root, data_type,
output_path):
l.log("Run pipeline (run_pipeline()) method invocation. Parameters below:")
l.log(" Fetching items from dataset")
l.log(" Dataset root: " + dataset_root)
l.log(" Dataset alias: " + dataset_alias)
l.log(" PAI: " + pai)
l.log(" Data type: " + data_type)
detector, fa = face_aligner.align_faces.make_face_aligner()
items_dataset = file_helper.get_dirs_from_folder(dataset_root)
for index, item in enumerate(items_dataset):
try:
if is_item_processed(output_path, dataset_alias, pai, dataset_type,
dataset_root, data_type, item):
print("Item % already processed" % item)
else:
print("Processing %s" % item)
process_item(output_path, dataset_alias, pai, dataset_type,
dataset_root, data_type, item, detector, fa)
except Exception as exception:
l.logE(exception)