diff --git a/bob/bio/base/score/__init__.py b/bob/bio/base/score/__init__.py
index df3dd386139b9bf99822c93b8424dcc76fcff968..b7ac4bebc0db3081673d86d5d1a0e15f4d62caac 100644
--- a/bob/bio/base/score/__init__.py
+++ b/bob/bio/base/score/__init__.py
@@ -2,5 +2,3 @@ from .load import (open_file, split, cmc, split_four_column, four_column,
split_five_column, five_column, scores, load_score,
dump_score, cmc_four_column, cmc_five_column
)
-from .openbr import (write_matrix, write_score_file)
-
diff --git a/bob/bio/base/score/openbr.py b/bob/bio/base/score/openbr.py
deleted file mode 100644
index 51010f7df69e4e2f34782508aa258f46db01d68d..0000000000000000000000000000000000000000
--- a/bob/bio/base/score/openbr.py
+++ /dev/null
@@ -1,368 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-
-
-"""This file includes functionality to convert between Bob's four column or
- five column score files and the Matrix files used in OpenBR."""
-
-
-import numpy
-import sys
-import logging
-logger = logging.getLogger("bob.measure")
-
-from .load import open_file, four_column, five_column
-
-
-def write_matrix(
- score_file,
- matrix_file,
- mask_file,
- model_names=None,
- probe_names=None,
- score_file_format='4column',
- gallery_file_name='unknown-gallery.lst',
- probe_file_name='unknown-probe.lst',
- search=None):
- """Writes the OpenBR matrix and mask files (version 2), given a score file.
-
- If gallery and probe names are provided, the matrices in both files will be
- sorted by gallery and probe names. Otherwise, the order will be the same as
- given in the score file.
-
- If ``search`` is given (as an integer), the resulting matrix files will be in
- the *search* format, keeping the given number of gallery scores with the
- highest values for each probe.
-
- .. warning::
-
- When provided with a 4-column score file, this function will work only, if
- there is only a single model id for each client.
-
- Parameters:
-
- score_file (str): The 4 or 5 column style score file written by bob.
-
- matrix_file (str): The OpenBR matrix file that should be written.
- Usually, the file name extension is ``.mtx``
-
- mask_file (str): The OpenBR mask file that should be written.
- The mask file defines, which values are positives, negatives or to be
- ignored. Usually, the file name extension is ``.mask``
-
- model_names (:py:class:`str`, optional): If given, the matrix will be
- written in the same order as the given model names. The model names must
- be identical with the second column in the 5-column ``score_file``.
-
- .. note::
-
- If the score file is in four column format, the model_names must be
- the client ids stored in the first column. In this case, there might
- be only a single model per client
-
- Only the scores of the given models will be considered.
-
- probe_names (:any:`list`, optional): A list of strings. If given,
- the matrix will be written in the same order as the given probe names
- (the ``path`` of the probe). The probe names are identical to the third
- column of the 4-column (or the fourth column of the 5-column)
- ``score_file``. Only the scores of the given probe names will be
- considered in this case.
-
- score_file_format (:py:class:`str`, optional): One of ``('4column',
- '5column')``. The format, in which the ``score_file`` is; defaults to
- ``'4column'``
-
- gallery_file_name (:py:class:`str`, optional): The name of the gallery file
- that will be written in the header of the OpenBR files.
-
- probe_file_name (:py:class:`str`, optional): The name of the probe file that
- will be written in the header of the OpenBR files.
-
- search (:py:class:`int`, optional): If given, the scores will be sorted per
- probe, keeping the specified number of highest scores. If the given
- number is higher than the models, ``NaN`` values will be added, and the
- mask will contain ``0x00`` values.
-
- """
-
- def _write_matrix(filename, matrix):
- # Helper function to write a matrix file as required by OpenBR
- with open(filename, 'wb') as f:
- # write the first four lines
- header = "S2\n%s\n%s\nM%s %d %d " % (
- gallery_file_name, probe_file_name, 'B' if matrix.dtype == numpy.uint8 else 'F', matrix.shape[0], matrix.shape[1])
- footer = "\n"
- if sys.version_info[0] > 2:
- header, footer = header.encode('utf-8'), footer.encode('utf-8')
- f.write(header)
- # write magic number
- numpy.array(0x12345678, numpy.int32).tofile(f)
- f.write(footer)
- # write the matrix
- matrix.tofile(f)
-
- # define read functions, and which information should be read
- read_function = {'4column': four_column,
- '5column': five_column}[score_file_format]
- offset = {'4column': 0, '5column': 1}[score_file_format]
-
- # first, read the score file and estimate model and probe names, if not given
- if model_names is None or probe_names is None:
- model_names, probe_names = [], []
- model_set, probe_set = set(), set()
-
- # read the score file
- for line in read_function(score_file):
- model, probe = line[offset], line[2 + offset]
- if model not in model_set:
- model_names.append(model)
- model_set.add(model)
- if probe not in probe_set:
- probe_names.append(probe)
- probe_set.add(probe)
-
- if search is None:
- # create a shortcut to get indices for client and probe subset (to
- # increase speed)
- model_dict, probe_dict = {}, {}
- for i, m in enumerate(model_names):
- model_dict[m] = i
- for i, p in enumerate(probe_names):
- probe_dict[p] = i
-
- # create the matrices in the desired size
- matrix = numpy.ndarray((len(probe_names), len(model_names)), numpy.float32)
- matrix[:] = numpy.nan
- mask = numpy.zeros(matrix.shape, numpy.uint8)
-
- # now, iterate through the score file and fill in the matrix
- for line in read_function(score_file):
- client, model, id, probe, score = line[0], line[offset], line[
- 1 + offset], line[2 + offset], line[3 + offset]
-
- assert model in model_dict, "model " + model + " unknown"
- assert probe in probe_dict, "probe " + probe + " unknown"
-
- model_index = model_dict[model]
- probe_index = probe_dict[probe]
-
- # check, if we have already written something into that matrix element
- if mask[probe_index, model_index]:
- logger.warn("Overwriting existing matrix '%f' element of client '%s' and probe '%s' with '%f'", matrix[
- probe_index, model_index], client, probe, score)
-
- matrix[probe_index, model_index] = score
- mask[probe_index, model_index] = 0xff if client == id else 0x7f
-
- else:
- # get the correct search parameter, if negative
- if search < 0:
- search = len(model_names)
-
- # create the matrices in the desired size
- matrix = numpy.ndarray((len(probe_names), search), numpy.float32)
- matrix[:] = numpy.nan
- mask = numpy.zeros(matrix.shape, numpy.uint8)
-
- # get the scores, sorted by probe
- scores = {}
- for line in read_function(score_file):
- client, model, id, probe, score = line[0], line[offset], line[
- 1 + offset], line[2 + offset], line[3 + offset]
-
- if probe not in scores:
- scores[probe] = []
- scores[probe].append((score, 0xff if client == id else 0x7f))
-
- # go ahead and sort the scores per probe
- sorted_scores = {}
- for k, v in scores.items():
- sorted_scores[k] = sorted(v, key=lambda x: x[0], reverse=True)
-
- # now, write matrix
- for p, probe in enumerate(probe_names):
- if probe in scores:
- for m in range(min(search, len(sorted_scores[probe]))):
- matrix[p, m], mask[p, m] = sorted_scores[probe][m]
-
- # OK, now finally write the file in the desired format
- _write_matrix(mask_file, mask)
- _write_matrix(matrix_file, matrix)
-
-
-def write_score_file(
- matrix_file,
- mask_file,
- score_file,
- models_ids=None,
- probes_ids=None,
- model_names=None,
- probe_names=None,
- score_file_format='4column',
- replace_nan=None
-):
- """Writes the Bob score file in the desired format from OpenBR files.
-
- Writes a Bob score file in the desired format (four or five column), given
- the OpenBR matrix and mask files.
-
- In principle, the score file can be written based on the matrix and mask
- files, and the format suffice the requirements to compute CMC curves.
- However, the contents of the score files can be adapted. If given, the
- ``models_ids`` and ``probes_ids`` define the **client ids** of model and
- probe, and they have to be in the same order as used to compute the OpenBR
- matrix. The ``model_names`` and ``probe_names`` define the **paths** of
- model and probe, and they should be in the same order as the ids.
-
- In rare cases, the OpenBR matrix contains NaN values, which Bob's score files
- cannot handle. You can use the ``replace_nan`` parameter to decide, what to
- do with these values. By default (``None``), these values are ignored, i.e.,
- not written into the score file. This is, what OpenBR is doing as well.
- However, you can also set ``replace_nan`` to any value, which will be written
- instead of the NaN values.
-
-
- Parameters:
-
- matrix_file (str): The OpenBR matrix file that should be read. Usually, the
- file name extension is ``.mtx``
-
- mask_file (str): The OpenBR mask file that should be read. Usually, the
- file name extension is ``.mask``
-
- score_file (str): Path to the 4 or 5 column style score file that should be
- written.
-
- models_ids (:any:`list`, optional): A list of strings with the client
- ids of the models that will be written in the first column of the score
- file. If given, the size must be identical to the number of models
- (gallery templates) in the OpenBR files. If not given, client ids of the
- model will be identical to the **gallery index** in the matrix file.
-
- probes_ids (:any:`list`, optional): A list of strings with the client
- ids of the probes that will be written in the second/third column of the
- four/five column score file. If given, the size must be identical to the
- number of probe templates in the OpenBR files. It will be checked that
- the OpenBR mask fits to the model/probe client ids. If not given, the
- probe ids will be estimated automatically, i.e., to fit the OpenBR
- matrix.
-
- model_names (:any:`list`, optional): A list of strings with the model
- path written in the second column of the five column score file. If not
- given, the model index in the OpenBR file will be used.
-
- .. note::
-
- This entry is ignored in the four column score file format.
-
- probe_names (:any:`list`, optional): A list of probe path to be
- written in the third/fourth column in the four/five column score file. If
- given, the size must be identical to the number of probe templates in the
- OpenBR files. If not given, the probe index in the OpenBR file will be
- used.
-
- score_file_format (:py:class:`str`, optional): One of ``('4column',
- '5column')``. The format, in which the ``score_file`` is; defaults to
- ``'4column'``
-
- replace_nan (:py:class:`float`, optional): If NaN values are encountered in
- the OpenBR matrix (which are not ignored due to the mask being non-NULL),
- this value will be written instead. If ``None``, the values will not be
- written in the score file at all.
-
- """
-
- def _read_matrix(filename):
- py3 = sys.version_info[0] >= 3
- # Helper function to read a matrix file as written by OpenBR
- with open(filename, 'rb') as f:
- # get version
- header = f.readline()
- if py3:
- header = header.decode("utf-8")
- assert header[:2] == "S2"
- # skip gallery and probe files
- f.readline()
- f.readline()
- # read size and type of matrix
- size = f.readline()
- if py3:
- size = size.decode("utf-8")
- splits = size.rstrip().split()
- # TODO: check the endianess of the magic number stored in split[3]
- assert splits[0][0] == 'M'
- w, h = int(splits[1]), int(splits[2])
- # read matrix data
- data = numpy.fromfile(
- f, dtype={'B': numpy.uint8, 'F': numpy.float32}[splits[0][1]])
- assert data.shape[0] == w * h
- data.shape = (w, h)
- return data
-
- # check parameters
- if score_file_format not in ("4column", "5column"):
- raise ValueError(
- "The given score file format %s is not known; choose one of ('4column', '5column')" % score_file_format)
- # get type of score file
- four_col = score_file_format == "4column"
-
- # read the two matrices
- scores = _read_matrix(matrix_file)
- mask = _read_matrix(mask_file)
-
- # generate the id lists, if not given
- if models_ids is None:
- models_ids = [str(g + 1) for g in range(mask.shape[1])]
- assert len(models_ids) == mask.shape[1]
-
- if probes_ids is None:
- probes_ids = []
- # iterate over all probes
- for p in range(mask.shape[0]):
- # get indices, where model and probe id should be identical
- equal_indices = numpy.where(mask[p] == 0xff)
- if len(equal_indices):
- # model id found, use the first one
- probes_ids.append(models_ids[equal_indices[0][0]])
- else:
- # no model found; add non-existing id
- probes_ids.append("unknown")
- else:
- assert len(probes_ids) == mask.shape[0]
- # check that the probes client ids are in the correct order
- for p in range(mask.shape[0]):
- for g in range(mask.shape[1]):
- if mask[p, g] == 0x7f:
- if models_ids[g] == probes_ids[p]:
- raise ValueError("The probe id %s with index %d should not be identical to model id %s with index %d" % (
- probes_ids[p], p, models_ids[g], g))
- elif mask[p, g] == 0xff:
- if models_ids[g] != probes_ids[p]:
- raise ValueError("The probe id %s with index %d should be identical to model id %s with index %d" % (
- probes_ids[p], p, models_ids[g], g))
-
- # generate model and probe names, if not given
- if not four_col and model_names is None:
- model_names = [str(g + 1) for g in range(mask.shape[1])]
- if probe_names is None:
- probe_names = [str(p + 1) for p in range(mask.shape[0])]
-
- # iterate through the files and write scores
- with open(score_file, 'w') as f:
- for g in range(mask.shape[1]):
- for p in range(mask.shape[0]):
- if mask[p, g]:
- score = scores[p, g]
- # handle NaN values
- if numpy.isnan(score):
- if replace_nan is None:
- continue
- score = replace_nan
- # write score file
- if four_col:
- f.write("%s %s %s %3.8f\n" %
- (models_ids[g], probes_ids[p], probe_names[p], score))
- else:
- f.write("%s %s %s %s %3.8f\n" % (models_ids[g], model_names[
- g], probes_ids[p], probe_names[p], score))
diff --git a/bob/bio/base/test/test_io.py b/bob/bio/base/test/test_io.py
index fa908d46bf2b6c49c56edbc159ac0efc898cf6da..b75ef225a62086154c525dbc9e7499009522bd0d 100644
--- a/bob/bio/base/test/test_io.py
+++ b/bob/bio/base/test/test_io.py
@@ -163,164 +163,3 @@ def _check_binary_identical(name1, name2):
with open(name1, 'rb') as f1:
with open(name2, 'rb') as f2:
assert md5(f1.read()).digest() == md5(f2.read()).digest()
-
-
-def test_openbr_verify():
- # This function tests that the conversion to the OpenBR verify file works
- # as expected
- temp_dir = tempfile.mkdtemp(prefix='bob_test')
-
- # define output files
- openbr_extensions = ('.mtx', '.mask')
- matrix_file, mask_file = [os.path.join(
- temp_dir, "scores%s") % ext for ext in openbr_extensions]
-
- try:
- for variant in ('4col', '5col'):
- # get score file
- score_file = pkg_resources.resource_filename(
- 'bob.bio.base.test', 'data/scores-cmc-%s.txt' % variant)
-
- # first round, do not define keyword arguments -- let the file get the
- # gallery and probe ids automatically
- kwargs = {}
- for i in range(2):
- # get the files by automatically obtaining the identities
- score.write_matrix(
- score_file, matrix_file, mask_file, score_file_format="%sumn" % variant, **kwargs)
-
- assert os.path.isfile(matrix_file) and os.path.isfile(mask_file)
-
- # check that they are binary identical to the reference files (which
- # are tested to work and give the same results with OpenBR)
- matrix_ref, mask_ref = [pkg_resources.resource_filename(
- 'bob.bio.base.test', 'data/scores%s' % ext) for ext in openbr_extensions]
- _check_binary_identical(matrix_file, matrix_ref)
- _check_binary_identical(mask_file, mask_ref)
-
- # define new kwargs for second round, i.e., define model and probe names
- # these names are identical to what is found in the score file, which
- # in turn comes from the AT&T database
- model_type = {"4col": "%d", "5col": "s%d"}[variant]
- dev_ids = (3, 4, 7, 8, 9, 13, 15, 18, 19, 22, 23,
- 25, 28, 30, 31, 32, 35, 37, 38, 40)
- kwargs['model_names'] = [model_type % c for c in dev_ids]
- kwargs['probe_names'] = ["s%d/%d" %
- (c, i) for c in dev_ids for i in (1, 3, 6, 8, 10)]
-
- finally:
- shutil.rmtree(temp_dir)
-
-
-def test_openbr_search():
- # This function tests that the conversion to the OpenBR search file works
- # as expected
- temp_dir = tempfile.mkdtemp(prefix='bob_test')
-
- # define output files
- openbr_extensions = ('.mtx', '.mask')
- matrix_file, mask_file = [os.path.join(
- temp_dir, "search%s") % ext for ext in openbr_extensions]
-
- try:
- for variant in ('4col', '5col'):
- # get score file
- score_file = pkg_resources.resource_filename(
- 'bob.bio.base.test', 'data/scores-cmc-%s.txt' % variant)
-
- # first round, do not define keyword arguments -- let the file get the
- # gallery and probe ids automatically
- kwargs = {}
- for i in range(2):
- # get the files by automatically obtaining the identities
- score.write_matrix(
- score_file, matrix_file, mask_file, score_file_format="%sumn" % variant, search=50, **kwargs)
-
- assert os.path.isfile(matrix_file) and os.path.isfile(mask_file)
-
- # check that they are binary identical to the reference files (which
- # are tested to work and give the same results with OpenBR)
- matrix_ref, mask_ref = [pkg_resources.resource_filename(
- 'bob.bio.base.test', 'data/search%s' % ext) for ext in openbr_extensions]
- _check_binary_identical(matrix_file, matrix_ref)
- _check_binary_identical(mask_file, mask_ref)
-
- # define new kwargs for second round, i.e., define model and probe names
- # these names are identical to what is found in the score file, which
- # in turn comes from the AT&T database
- model_type = {"4col": "%d", "5col": "s%d"}[variant]
- dev_ids = (3, 4, 7, 8, 9, 13, 15, 18, 19, 22, 23,
- 25, 28, 30, 31, 32, 35, 37, 38, 40)
- kwargs['model_names'] = [model_type % c for c in dev_ids]
- kwargs['probe_names'] = ["s%d/%d" %
- (c, i) for c in dev_ids for i in (1, 3, 6, 8, 10)]
-
- finally:
- shutil.rmtree(temp_dir)
-
-
-def test_from_openbr():
- # This function tests that the conversion from the OpenBR matrices work as
- # expected
- temp_dir = tempfile.mkdtemp(prefix='bob_test')
-
- # define input files
- openbr_extensions = ('.mtx', '.mask')
- matrix_file, mask_file = [pkg_resources.resource_filename(
- 'bob.bio.base.test', 'data/scores%s' % ext) for ext in openbr_extensions]
-
- score_file = os.path.join(temp_dir, "scores")
- load_functions = {'4col': score.four_column,
- '5col': score.five_column}
-
- try:
- for variant in load_functions:
- # first, do not define keyword arguments -- let the file get the model
- # and probe ids being created automatically
- score.write_score_file(
- matrix_file, mask_file, score_file, score_file_format="%sumn" % variant)
- assert os.path.exists(score_file)
- # read the score file with bobs functionality
- columns = list(load_functions[variant](score_file))
-
- # check the contents
- assert len(columns) == 2000
-
- # now, generate model and probe names and ids
- model_type = {"4col": "%d", "5col": "s%d"}[variant]
- dev_ids = (3, 4, 7, 8, 9, 13, 15, 18, 19, 22, 23,
- 25, 28, 30, 31, 32, 35, 37, 38, 40)
- model_names = ["s%d" % c for c in dev_ids]
- probe_names = ["s%d/%d" % (c, i)
- for c in dev_ids for i in (1, 3, 6, 8, 10)]
- models_ids = ["%d" % c for c in dev_ids]
- probes_ids = ["%d" % c for c in dev_ids for i in (1, 3, 6, 8, 10)]
-
- score.write_score_file(matrix_file, mask_file, score_file, models_ids=models_ids, probes_ids=probes_ids,
- model_names=model_names, probe_names=probe_names, score_file_format="%sumn" % variant)
-
- # check that we re-generated the bob score file
- reference_file = pkg_resources.resource_filename(
- 'bob.bio.base.test', 'data/scores-cmc-%s.txt' % variant)
-
- # assert that we can (almost) reproduce the score file
- # ... read both files
- columns = list(load_functions[variant](score_file))
- reference = list(load_functions[variant](reference_file))
- assert len(columns) == len(reference)
- for i in range(len(columns)):
- for j in range(len(columns[i]) - 1):
- # check that the model and probe names are fine
- assert columns[i][j] == reference[i][j], str(
- columns[i]) + " != " + str(reference[i])
- # check that the score is close (OpenBR write scores in float32
- # precision only)
- assert abs(columns[i][-1] - numpy.float32(reference[i][-1])
- ) <= 1e-8, str(columns[i][-1]) + " != " + str(reference[i][-1])
-
- #assert numpy.isclose(columns[i][-1], reference[i][-1], atol = 1e-3, rtol=1e-8), str(columns[i][-1]) + " != " + str(reference[i][-1])
- assert numpy.allclose(columns[i][-1], reference[i][-1], atol=1e-3,
- rtol=1e-8), str(columns[i][-1]) + " != " + str(reference[i][-1])
-
- finally:
- shutil.rmtree(temp_dir)