Removed str() conversion of file names as file.make_path() is supposed to return str

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)
 

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

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]

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]

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)