diff --git a/bob/bio/base/algorithm/Algorithm.py b/bob/bio/base/algorithm/Algorithm.py
index 38834b8fa45d6484f1c9927d250f2cd5e654bb78..c15cc9e0fd80af78842e6c4b4a655ec32d2588d9 100644
--- a/bob/bio/base/algorithm/Algorithm.py
+++ b/bob/bio/base/algorithm/Algorithm.py
@@ -172,7 +172,8 @@ class Algorithm (object):
probe : object
The probe object to compare the model with.
- The ``probe`` was read using the :py:meth:`read_probe` function.
+ The ``probe`` was read using the :py:meth:`read_feature` function
+ (or the :py:meth:`bob.bio.base.extractor.Extractor.read_feature` function, if this algorithm does not perform projection.
**Returns:**
@@ -330,29 +331,6 @@ class Algorithm (object):
return utils.load(model_file)
- def read_probe(self, probe_file):
- """read_probe(probe_file) -> probe
-
- Reads the probe feature from file.
- By default, the probe feature is identical to the projected feature.
- Hence, this base class implementation simply calls :py:meth:`read_feature`.
-
- If your algorithm requires different behavior, please overwrite this function.
-
- **Parameters:**
-
- probe_file : str or :py:class:`bob.io.base.HDF5File`
- The file open for reading, or the file name to read from.
-
- **Returns:**
-
- probe : object
- The probe that was read from file.
- """
- return self.read_feature(probe_file)
-
-
-
def train_projector(self, training_features, projector_file):
"""This function can be overwritten to train the feature projector.
If you do this, please also register the function by calling this base class constructor
diff --git a/bob/bio/base/algorithm/BIC.py b/bob/bio/base/algorithm/BIC.py
index c15b5e58c04ec064db1c14886e4e1febc8799326..42aa785c53af8fd36898f6f4818567b2bb633782 100644
--- a/bob/bio/base/algorithm/BIC.py
+++ b/bob/bio/base/algorithm/BIC.py
@@ -230,25 +230,6 @@ class BIC(Algorithm):
i += 1
return model
- def read_probe(self, probe_file):
- """read_probe(probe_file) -> probe
-
- Reads the probe feature from the given HDF5 file.
-
- To read the feature, the ``read_function`` specified in the constructor is employed.
-
- **Parameters:**
-
- probe_file : str or :py:class:`bob.io.base.HDF5File`
- The file (open for reading) or the name of an existing file to read from.
-
- **Returns:**
-
- probe : object
- The read probe, which is a feature.
- """
- return self.read_function(bob.io.base.HDF5File(probe_file))
-
def score(self, model, probe):
"""score(model, probe) -> float
diff --git a/bob/bio/base/algorithm/Distance.py b/bob/bio/base/algorithm/Distance.py
index f902dcd25c559204b8ab331eb05a0f0e97f9a17d..c6a235960d97f7691a2c01631125261ed2bf782e 100644
--- a/bob/bio/base/algorithm/Distance.py
+++ b/bob/bio/base/algorithm/Distance.py
@@ -73,24 +73,6 @@ class Distance (Algorithm):
return numpy.vstack([f.flatten() for f in enroll_features])
- def read_probe(self, probe_file):
- """read_probe(probe_file) -> probe
-
- Reads the probe feature from the given HDF5 file.
-
- **Parameters:**
-
- probe_file : str or :py:class:`bob.io.base.HDF5File`
- The file (open for reading) or the name of an existing file to read from.
-
- **Returns:**
-
- probe : object
- The probe.
- """
- return utils.load(probe_file)
-
-
def score(self, model, probe):
"""score(model, probe) -> float
diff --git a/bob/bio/base/algorithm/PLDA.py b/bob/bio/base/algorithm/PLDA.py
index e2350d6b2bb4e2dc4c68f7adebbbd3bccc1f0aa1..712b9d0f352be4e90c4399ab1285718de7847e77 100644
--- a/bob/bio/base/algorithm/PLDA.py
+++ b/bob/bio/base/algorithm/PLDA.py
@@ -168,10 +168,6 @@ class PLDA (Algorithm):
plda_machine = bob.learn.em.PLDAMachine(bob.io.base.HDF5File(model_file), self.plda_base)
return plda_machine
- def read_probe(self, probe_file):
- """Reads the probe using :py:func:`bob.bio.base.load`."""
- return bob.bio.base.load(probe_file)
-
def score(self, model, probe):
"""Computes the PLDA score for the given model and probe"""
diff --git a/bob/bio/base/script/score.py b/bob/bio/base/script/score.py
index 224df9d237af0ffead6c0eba2eeb92ca2f8e36d0..3432ffa74862d96ddfe52663dd0ca583e59d47f1 100644
--- a/bob/bio/base/script/score.py
+++ b/bob/bio/base/script/score.py
@@ -17,9 +17,9 @@ def command_line_arguments(command_line_parameters):
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-a', '--algorithm', metavar = 'x', nargs = '+', required = True, help = 'Biometric recognition; registered algorithms are: %s' % bob.bio.base.resource_keys('algorithm'))
+ parser.add_argument('-e', '--extractor', metavar = 'x', nargs = '+', required = True, help = 'Feature extraction; registered feature extractors are: %s' % bob.bio.base.resource_keys('extractor'))
parser.add_argument('-P', '--projector-file', metavar = 'FILE', help = 'The pre-trained projector file, if the algorithm performs projection')
parser.add_argument('-E', '--enroller-file' , metavar = 'FILE', help = 'The pre-trained enroller file, if the extractor requires enroller training')
- parser.add_argument('-e', '--extractor', metavar = 'x', nargs = '+', help = 'Feature extraction -- required when algorithm performs projection; registered feature extractors are: %s' % bob.bio.base.resource_keys('extractor'))
parser.add_argument('-m', '--model-files', metavar = 'MODEL', nargs='+', required = True, help = "A list of enrolled model files")
parser.add_argument('-p', '--probe-files', metavar = 'PROBE', nargs='+', required = True, help = "A list of extracted feature files used as probes")
@@ -37,6 +37,8 @@ def main(command_line_parameters=None):
"""Preprocesses the given image with the given preprocessor."""
args = command_line_arguments(command_line_parameters)
+ logger.debug("Loading extractor")
+ extractor = bob.bio.base.load_resource(' '.join(args.extractor), "extractor")
logger.debug("Loading algorithm")
algorithm = bob.bio.base.load_resource(' '.join(args.algorithm), "algorithm")
if algorithm.requires_projector_training:
@@ -52,18 +54,11 @@ def main(command_line_parameters=None):
models, probes = {}, {}
logger.debug("Loading %d models", len(args.model_files))
for m in args.model_files: models[m] = algorithm.read_model(m)
+ logger.debug("Loading %d probes", len(args.probe_files))
+ for p in args.probe_files: probes[p] = extractor.read_feature(p)
if algorithm.performs_projection:
- if args.extractor is None:
- raise ValueError("The --extractor is required when the algorithm requires projection")
- logger.debug("Loading extractor")
- extractor = bob.bio.base.load_resource(' '.join(args.extractor), "extractor")
- logger.debug("Loading %d probes", len(args.probe_files))
- for p in args.probe_files: probes[p] = extractor.read_feature(p)
logger.debug("Projecting %d probes", len(args.probe_files))
for p in probes: probes[p] = algorithm.project(probes[p])
- else:
- logger.debug("Loading %d probes", len(args.probe_files))
- for p in args.probe_files: probes[p] = algorithm.read_probe(p)
logger.info("Computing scores")
for p in args.probe_files:
diff --git a/bob/bio/base/script/verify.py b/bob/bio/base/script/verify.py
index f26bf699ddeb68d18a3dcf26849bb111c6143b0e..eb67f3ead971d6debc75a311362fb93efc526f9a 100644
--- a/bob/bio/base/script/verify.py
+++ b/bob/bio/base/script/verify.py
@@ -336,6 +336,7 @@ def execute(args):
if args.score_type in ['A', 'B']:
tools.compute_scores(
args.algorithm,
+ args.extractor,
args.zt_norm,
indices = tools.indices(fs.model_ids(args.group), None if args.grid is None else args.grid.number_of_scoring_jobs),
groups = [args.group],
@@ -347,6 +348,7 @@ def execute(args):
elif args.score_type in ['C', 'D']:
tools.compute_scores(
args.algorithm,
+ args.extractor,
args.zt_norm,
indices = tools.indices(fs.t_model_ids(args.group), None if args.grid is None else args.grid.number_of_scoring_jobs),
groups = [args.group],
diff --git a/bob/bio/base/test/test_algorithms.py b/bob/bio/base/test/test_algorithms.py
index 08e117dc4657d53b8db4793636059dc6523c527e..8b03c5e32599244677fe5e2ed5db437680f7a3b7 100644
--- a/bob/bio/base/test/test_algorithms.py
+++ b/bob/bio/base/test/test_algorithms.py
@@ -109,7 +109,7 @@ def test_pca():
_compare(model, pkg_resources.resource_filename('bob.bio.base.test', 'data/pca_model.hdf5'), pca1.write_model, pca1.read_model)
# compare model with probe
- probe = pca1.read_probe(pkg_resources.resource_filename('bob.bio.base.test', 'data/pca_projected.hdf5'))
+ probe = pca1.read_feature(pkg_resources.resource_filename('bob.bio.base.test', 'data/pca_projected.hdf5'))
reference_score = -251.53563107
assert abs(pca1.score(model, probe) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (pca1.score(model, probe), reference_score)
assert abs(pca1.score_for_multiple_probes(model, [probe, probe]) - reference_score) < 1e-5
@@ -183,7 +183,7 @@ def test_lda():
_compare(model, pkg_resources.resource_filename('bob.bio.base.test', 'data/lda_model.hdf5'), lda1.write_model, lda1.read_model)
# compare model with probe
- probe = lda1.read_probe(pkg_resources.resource_filename('bob.bio.base.test', 'data/lda_projected.hdf5'))
+ probe = lda1.read_feature(pkg_resources.resource_filename('bob.bio.base.test', 'data/lda_projected.hdf5'))
reference_score = -233.30450012
assert abs(lda1.score(model, probe) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (lda1.score(model, probe), reference_score)
assert abs(lda1.score_for_multiple_probes(model, [probe, probe]) - reference_score) < 1e-5
@@ -226,7 +226,7 @@ def test_distance():
# compare model with probe
enroll = utils.random_training_set(5, 5, 0., 255., seed=21);
model = numpy.mean(distance.enroll(enroll),axis=0)
- probe = distance.read_probe(pkg_resources.resource_filename('bob.bio.base.test', 'data/lda_projected.hdf5'))
+ probe = bob.io.base.load(pkg_resources.resource_filename('bob.bio.base.test', 'data/lda_projected.hdf5'))
reference_score = -0.1873371
assert abs(distance.score(model, probe) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (distance.score(model, probe), reference_score)
@@ -358,7 +358,7 @@ def test_plda():
reference_score = 0.
assert abs(plda1.score(model, feature) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (plda1.score(model, feature), reference_score)
assert abs(plda1.score_for_multiple_probes(model, [feature, feature]) - reference_score) < 1e-5
-
+
def test_plda_nopca():
temp_file = bob.io.base.test_utils.temporary_filename()
plda_ref = bob.bio.base.load_resource("plda", "algorithm", preferred_package = 'bob.bio.base')
@@ -373,7 +373,7 @@ def test_plda_nopca():
# train the projector
try:
# train projector
- plda.train_enroller(train_set, temp_file)
+ plda.train_enroller(train_set, temp_file)
assert os.path.exists(temp_file)
if regenerate_refs: shutil.copy(temp_file, reference_file)
@@ -396,4 +396,3 @@ def test_plda_nopca():
reference = plda.read_model(reference)
assert model.is_similar_to(reference)
-
diff --git a/bob/bio/base/test/test_scripts.py b/bob/bio/base/test/test_scripts.py
index e028cb1e8eeb792f504b66983ba9cf9dc37a8cef..67d0a8d51d50a460e0835d1a1615d0bb0f2e6bb3 100644
--- a/bob/bio/base/test/test_scripts.py
+++ b/bob/bio/base/test/test_scripts.py
@@ -337,12 +337,60 @@ def test_internal_raises():
'--imports', 'bob.bio.base.test.dummy'
]
- from bob.bio.base.script.verify import main
- for option, value in (("--group", "dev"), ("--model-type", "N"), ("--score-type", "A")):
- internal = parameters + [option, value]
+ try:
+ from bob.bio.base.script.verify import main
+ for option, value in (("--group", "dev"), ("--model-type", "N"), ("--score-type", "A")):
+ internal = parameters + [option, value]
+
+ nose.tools.assert_raises(ValueError, main, internal)
+ finally:
+ shutil.rmtree(test_dir)
+
+
+def test_verify_generate_config():
+ # tests the config file generation
+ test_dir = tempfile.mkdtemp(prefix='bobtest_')
+ config_file = os.path.join(test_dir, 'config.py')
+ # define dummy parameters
+ parameters = [
+ '-H', config_file
+ ]
+ try:
+ from bob.bio.base.script.verify import main
+ nose.tools.assert_raises(SystemExit, main, parameters)
+ assert os.path.exists(config_file)
+ from bob.bio.base.tools.command_line import _required_list, _common_list, _optional_list
+ assert all(a in _required_list for a in ['database', 'preprocessor', 'extractor', 'algorithm', 'sub_directory'])
+ assert all(a in _common_list for a in ['protocol', 'grid', 'parallel', 'verbose', 'groups', 'temp_directory', 'result_directory', 'zt_norm', 'allow_missing_files', 'dry_run', 'force'])
+ assert all(a in _optional_list for a in ['preprocessed_directory', 'extracted_directory', 'projected_directory', 'model_directories', 'extractor_file', 'projector_file', 'enroller_file'])
+ # todo: this list is actually much longer...
+ _rare_list = ['imports', 'experiment_info_file', 'write_compressed_score_files', 'skip_preprocessing', 'skip_calibration', 'execute_only']
+
+ lines = open(config_file).readlines()
+
+ # split into four lists (required, common, optional, rare)
+ last_lines = None
+ split_lines = []
+ for line in lines:
+ if line.startswith("#####"):
+ if last_lines:
+ split_lines.append(last_lines)
+ last_lines = []
+ else:
+ if last_lines is not None:
+ last_lines.append(line)
+ split_lines.append(last_lines)
+ assert len(split_lines) == 4
+
+ for _list, lines in zip((_required_list, _common_list, _optional_list, _rare_list), split_lines):
+ for a in _list:
+ assert any(l.startswith("#%s =" %a) for l in lines), a
+ finally:
+ shutil.rmtree(test_dir)
+
+
+
- nose.tools.assert_raises(ValueError, main, internal)
- shutil.rmtree(test_dir)
def test_fusion():
diff --git a/bob/bio/base/tools/scoring.py b/bob/bio/base/tools/scoring.py
index 3700a58da8020dcf6bcc81195f4e3fad1f67966c..bc90d5b2b85d99eb30b0e0309a65ec2f931d9a45 100644
--- a/bob/bio/base/tools/scoring.py
+++ b/bob/bio/base/tools/scoring.py
@@ -13,7 +13,7 @@ from .FileSelector import FileSelector
from .extractor import read_features
from .. import utils
-def _scores(algorithm, model, probes, allow_missing_files):
+def _scores(algorithm, reader, model, probes, allow_missing_files):
"""Compute scores for the given model and a list of probes.
"""
# the file selector object
@@ -36,7 +36,7 @@ def _scores(algorithm, model, probes, allow_missing_files):
# we keep the NaN score
continue
# read probe from probe_set
- probe = [algorithm.read_probe(probe_file) for probe_file in probe_element]
+ probe = [reader.read_feature(probe_file) for probe_file in probe_element]
# compute score
scores[0,i] = algorithm.score_for_multiple_probes(model, probe)
else:
@@ -44,7 +44,7 @@ def _scores(algorithm, model, probes, allow_missing_files):
# we keep the NaN score
continue
# read probe
- probe = algorithm.read_probe(probe_element)
+ probe = reader.read_feature(probe_element)
# compute score
scores[0,i] = algorithm.score(model, probe)
# Returns the scores
@@ -111,7 +111,7 @@ def _save_scores(score_file, scores, probe_objects, client_id, write_compressed)
_close_written(score_file, f, write_compressed)
-def _scores_a(algorithm, model_ids, group, compute_zt_norm, force, write_compressed, allow_missing_files):
+def _scores_a(algorithm, reader, model_ids, group, compute_zt_norm, force, write_compressed, allow_missing_files):
"""Computes A scores for the models with the given model_ids. If ``compute_zt_norm = False``, these are the only scores that are actually computed."""
# the file selector object
fs = FileSelector.instance()
@@ -138,7 +138,7 @@ def _scores_a(algorithm, model_ids, group, compute_zt_norm, force, write_compres
# get the probe files
current_probe_files = fs.get_paths(current_probe_objects, 'projected' if algorithm.performs_projection else 'extracted')
# compute scores
- a = _scores(algorithm, model, current_probe_files, allow_missing_files)
+ a = _scores(algorithm, reader, model, current_probe_files, allow_missing_files)
if compute_zt_norm:
# write A matrix only when you want to compute zt norm afterwards
@@ -148,7 +148,7 @@ def _scores_a(algorithm, model_ids, group, compute_zt_norm, force, write_compres
_save_scores(fs.no_norm_file(model_id, group), a, current_probe_objects, fs.client_id(model_id, group), write_compressed)
-def _scores_b(algorithm, model_ids, group, force, allow_missing_files):
+def _scores_b(algorithm, reader, model_ids, group, force, allow_missing_files):
"""Computes B scores for the given model ids."""
# the file selector object
fs = FileSelector.instance()
@@ -171,10 +171,10 @@ def _scores_b(algorithm, model_ids, group, force, allow_missing_files):
model = None
else:
model = algorithm.read_model(model_file)
- b = _scores(algorithm, model, z_probe_files, allow_missing_files)
+ b = _scores(algorithm, reader, model, z_probe_files, allow_missing_files)
bob.io.base.save(b, score_file, True)
-def _scores_c(algorithm, t_model_ids, group, force, allow_missing_files):
+def _scores_c(algorithm, reader, t_model_ids, group, force, allow_missing_files):
"""Computes C scores for the given t-norm model ids."""
# the file selector object
fs = FileSelector.instance()
@@ -197,10 +197,10 @@ def _scores_c(algorithm, t_model_ids, group, force, allow_missing_files):
t_model = None
else:
t_model = algorithm.read_model(t_model_file)
- c = _scores(algorithm, t_model, probe_files, allow_missing_files)
+ c = _scores(algorithm, reader, t_model, probe_files, allow_missing_files)
bob.io.base.save(c, score_file, True)
-def _scores_d(algorithm, t_model_ids, group, force, allow_missing_files):
+def _scores_d(algorithm, reader, t_model_ids, group, force, allow_missing_files):
"""Computes D scores for the given t-norm model ids. Both the D matrix and the D-samevalue matrix are written."""
# the file selector object
fs = FileSelector.instance()
@@ -227,7 +227,7 @@ def _scores_d(algorithm, t_model_ids, group, force, allow_missing_files):
t_model = None
else:
t_model = algorithm.read_model(t_model_file)
- d = _scores(algorithm, t_model, z_probe_files, allow_missing_files)
+ d = _scores(algorithm, reader, t_model, z_probe_files, allow_missing_files)
bob.io.base.save(d, score_file, True)
t_client_id = [fs.client_id(t_model_id, group, True)]
@@ -235,7 +235,7 @@ def _scores_d(algorithm, t_model_ids, group, force, allow_missing_files):
bob.io.base.save(d_same_value_tm, same_score_file, True)
-def compute_scores(algorithm, compute_zt_norm, indices = None, groups = ['dev', 'eval'], types = ['A', 'B', 'C', 'D'], write_compressed = False, allow_missing_files = False, force = False):
+def compute_scores(algorithm, extractor, compute_zt_norm, indices = None, groups = ['dev', 'eval'], types = ['A', 'B', 'C', 'D'], write_compressed = False, allow_missing_files = False, force = False):
"""Computes the scores for the given groups.
This function computes all scores for the experiment, and writes them to files, one per model.
@@ -247,6 +247,10 @@ def compute_scores(algorithm, compute_zt_norm, indices = None, groups = ['dev',
algorithm : py:class:`bob.bio.base.algorithm.Algorithm` or derived
The algorithm, used for enrolling model and writing them to file.
+ extractor : py:class:`bob.bio.base.extractor.Extractor` or derived
+ The extractor, used for extracting the features.
+ The extractor is only used to read features, if the algorithm does not perform projection.
+
compute_zt_norm : bool
If set to ``True``, also ZT-norm scores are computed.
@@ -280,6 +284,14 @@ def compute_scores(algorithm, compute_zt_norm, indices = None, groups = ['dev',
algorithm.load_projector(fs.projector_file)
algorithm.load_enroller(fs.enroller_file)
+ # which tool to use to read the probes
+ if algorithm.performs_projection:
+ reader = algorithm
+ else:
+ reader = extractor
+ # make sure that the extractor is loaded
+ extractor.load(fs.extractor_file)
+
for group in groups:
# get model ids
model_ids = fs.model_ids(group)
@@ -293,20 +305,20 @@ def compute_scores(algorithm, compute_zt_norm, indices = None, groups = ['dev',
# compute A scores
if 'A' in types:
- _scores_a(algorithm, model_ids, group, compute_zt_norm, force, write_compressed, allow_missing_files)
+ _scores_a(algorithm, reader, model_ids, group, compute_zt_norm, force, write_compressed, allow_missing_files)
if compute_zt_norm:
# compute B scores
if 'B' in types:
- _scores_b(algorithm, model_ids, group, force, allow_missing_files)
+ _scores_b(algorithm, reader, model_ids, group, force, allow_missing_files)
# compute C scores
if 'C' in types:
- _scores_c(algorithm, t_model_ids, group, force, allow_missing_files)
+ _scores_c(algorithm, reader, t_model_ids, group, force, allow_missing_files)
# compute D scores
if 'D' in types:
- _scores_d(algorithm, t_model_ids, group, force, allow_missing_files)
+ _scores_d(algorithm, reader, t_model_ids, group, force, allow_missing_files)
diff --git a/doc/implementation.rst b/doc/implementation.rst
index 142cd551de228bcb4c734691af7b8e2f33854fbb..4d9221fc5160b9b8a0f1f8cabeb7473f84065f94 100644
--- a/doc/implementation.rst
+++ b/doc/implementation.rst
@@ -173,10 +173,6 @@ If the ``score`` function expects models and probe features to be of a different
* ``write_model(self, model, model_file)``: writes the model (as returned by the ``enroll`` function).
* ``read_model(self, model_file) -> model``: reads the model (as written by the ``write_model`` function) from file.
-* ``read_probe(self, probe_file) -> feature``: reads the probe feature from file.
-
- .. note::
- In many cases, the ``read_feature`` and ``read_probe`` functions are identical (if both are present).
Finally, the :py:class:`bob.bio.base.algorithm.Algorithm` class provides default implementations for the case that models store several features, or that several probe features should be combined into one score.
These two functions are: