diff --git a/bob/bio/base/database/DatabaseBob.py b/bob/bio/base/database/DatabaseBob.py
index f6c36cfeabac1a1463fbcb2cb4698365ffab4bde..31bafe5a7b0f491fd99d774ef8ae29cd1d438119 100644
--- a/bob/bio/base/database/DatabaseBob.py
+++ b/bob/bio/base/database/DatabaseBob.py
@@ -362,9 +362,8 @@ class DatabaseBob (Database):
**Returns:**
- paths : [str] or [[str]]
+ paths : [str]
The paths extracted for the files, in the same order.
- If this database provides file sets, a list of lists of file names is returned, one sub-list for each file set.
"""
return self.database.original_file_names(files, self.check_existence)
diff --git a/bob/bio/base/tools/algorithm.py b/bob/bio/base/tools/algorithm.py
index c132ca24aad7379bad2b7bf416a1ecef5ec7844f..5a3af5b6444c011b8246dd4d05e9bb5f32f49c12 100644
--- a/bob/bio/base/tools/algorithm.py
+++ b/bob/bio/base/tools/algorithm.py
@@ -103,8 +103,8 @@ def project(algorithm, extractor, groups = None, indices = None, force = False):
logger.info("- Projection: projecting %d features from directory '%s' to directory '%s'", len(index_range), fs.directories['extracted'], fs.directories['projected'])
# extract the features
for i in index_range:
- feature_file = str(feature_files[i])
- projected_file = str(projected_files[i])
+ feature_file = feature_files[i]
+ projected_file = projected_files[i]
if not utils.check_file(projected_file, force, 1000):
logger.debug("... Projecting features for file '%s'", feature_file)
@@ -224,7 +224,7 @@ def enroll(algorithm, extractor, compute_zt_norm, indices = None, groups = ['dev
logger.info("- Enrollment: enrolling models of group '%s'", group)
for model_id in model_ids:
# Path to the model
- model_file = str(fs.model_file(model_id, group))
+ model_file = fs.model_file(model_id, group)
# Removes old file if required
if not utils.check_file(model_file, force, 1000):
@@ -232,7 +232,7 @@ def enroll(algorithm, extractor, compute_zt_norm, indices = None, groups = ['dev
logger.debug("... Enrolling model from %d features to file '%s'", len(enroll_files), model_file)
# load all files into memory
- enroll_features = [reader.read_feature(str(enroll_file)) for enroll_file in enroll_files]
+ enroll_features = [reader.read_feature(enroll_file) for enroll_file in enroll_files]
model = algorithm.enroll(enroll_features)
# save the model
@@ -255,7 +255,7 @@ def enroll(algorithm, extractor, compute_zt_norm, indices = None, groups = ['dev
logger.info("- Enrollment: enrolling T-models of group '%s'", group)
for t_model_id in t_model_ids:
# Path to the model
- t_model_file = str(fs.t_model_file(t_model_id, group))
+ t_model_file = fs.t_model_file(t_model_id, group)
# Removes old file if required
if not utils.check_file(t_model_file, force, 1000):
@@ -263,7 +263,7 @@ def enroll(algorithm, extractor, compute_zt_norm, indices = None, groups = ['dev
logger.debug("... Enrolling T-model from %d features to file '%s'", len(t_enroll_files), t_model_file)
# load all files into memory
- t_enroll_features = [reader.read_feature(str(t_enroll_file)) for t_enroll_file in t_enroll_files]
+ t_enroll_features = [reader.read_feature(t_enroll_file) for t_enroll_file in t_enroll_files]
t_model = algorithm.enroll(t_enroll_features)
# save model
diff --git a/bob/bio/base/tools/extractor.py b/bob/bio/base/tools/extractor.py
index 1fe941b6a4ded1cd1a38b9ab121beaaaf8dd9b46..87e7d8ef22e260704e2936b3071d3191b06ead1c 100644
--- a/bob/bio/base/tools/extractor.py
+++ b/bob/bio/base/tools/extractor.py
@@ -93,8 +93,8 @@ def extract(extractor, preprocessor, groups=None, indices = None, force = False)
logger.info("- Extraction: extracting %d features from directory '%s' to directory '%s'", len(index_range), fs.directories['preprocessed'], fs.directories['extracted'])
for i in index_range:
- data_file = str(data_files[i])
- feature_file = str(feature_files[i])
+ data_file = data_files[i]
+ feature_file = feature_files[i]
if not utils.check_file(feature_file, force, 1000):
logger.debug("... Extracting features for data file '%s'", data_file)
@@ -133,6 +133,6 @@ def read_features(file_names, extractor, split_by_client = False):
The list of extracted features, in the same order as in the ``file_names``.
"""
if split_by_client:
- return [[extractor.read_feature(str(f)) for f in client_files] for client_files in file_names]
+ return [[extractor.read_feature(f) for f in client_files] for client_files in file_names]
else:
- return [extractor.read_feature(str(f)) for f in file_names]
+ return [extractor.read_feature(f) for f in file_names]
diff --git a/bob/bio/base/tools/preprocessor.py b/bob/bio/base/tools/preprocessor.py
index c155630e409c3578cd66f22213c6ef294a1bb891..067b9ccd4461b6d0253ee655ab095b445b356fcd 100644
--- a/bob/bio/base/tools/preprocessor.py
+++ b/bob/bio/base/tools/preprocessor.py
@@ -50,8 +50,8 @@ def preprocess(preprocessor, groups = None, indices = None, force = False):
# iterate over the selected files
for i in index_range:
- preprocessed_data_file = str(preprocessed_data_files[i])
- file_name = str(data_files[i])
+ preprocessed_data_file = preprocessed_data_files[i]
+ file_name = data_files[i]
# check for existence
if not utils.check_file(preprocessed_data_file, force, 1000):
@@ -99,6 +99,6 @@ def read_preprocessed_data(file_names, preprocessor, split_by_client = False):
The list of preprocessed data, in the same order as in the ``file_names``.
"""
if split_by_client:
- return [[preprocessor.read_data(str(f)) for f in client_files] for client_files in file_names]
+ return [[preprocessor.read_data(f) for f in client_files] for client_files in file_names]
else:
- return [preprocessor.read_data(str(f)) for f in file_names]
+ return [preprocessor.read_data(f) for f in file_names]
diff --git a/bob/bio/base/tools/scoring.py b/bob/bio/base/tools/scoring.py
index b22191fe078e0e216eca55772b0002e1357b934a..c55149a9eb2a86bccb9beb63ac65bfa64727a7e2 100644
--- a/bob/bio/base/tools/scoring.py
+++ b/bob/bio/base/tools/scoring.py
@@ -26,7 +26,7 @@ def _scores(algorithm, model, probes):
if fs.uses_probe_file_sets():
assert isinstance(probe_element, list)
# read probe from probe_set
- probe = [algorithm.read_probe(str(probe_file)) for probe_file in probe_element]
+ probe = [algorithm.read_probe(probe_file) for probe_file in probe_element]
# compute score
scores[0,i] = algorithm.score_for_multiple_probes(model, probe)
else:
@@ -244,7 +244,7 @@ def compute_scores(algorithm, compute_zt_norm, force = False, indices = None, gr
fs = FileSelector.instance()
# load the projector and the enroller, if needed
- if algorithm.requires_projector_training:
+ if algorithm.requires_projector_training:
algorithm.load_projector(fs.projector_file)
algorithm.load_enroller(fs.enroller_file)