diff --git a/MANIFEST.in b/MANIFEST.in
index 6154f1db0f6bdba5fc6a689dfe5e99f3ef4513a2..7e877d8ac59d89a3ea540ee0bc0dbc89dbcdbb73 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,3 +1,3 @@
include LICENSE README.rst buildout.cfg develop.cfg version.txt requirements.txt test-requirements.txt
recursive-include doc *.py *.rst
-recursive-include bob/bio/base/test/data *.lst *.hdf5 *-dev *.pos
+recursive-include bob/bio/base/test/data *.* *-dev
diff --git a/bob/bio/base/score/__init__.py b/bob/bio/base/score/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..df3dd386139b9bf99822c93b8424dcc76fcff968
--- /dev/null
+++ b/bob/bio/base/score/__init__.py
@@ -0,0 +1,6 @@
+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/load.py b/bob/bio/base/score/load.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a5d76cacba631b1ab09f8049773e43f9910cb7f
--- /dev/null
+++ b/bob/bio/base/score/load.py
@@ -0,0 +1,553 @@
+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# Mon 23 May 2011 16:23:05 CEST
+
+"""A set of utilities to load score files with different formats.
+"""
+
+import numpy
+import csv
+import tarfile
+import os
+import sys
+
+import logging
+logger = logging.getLogger('bob.bio.base')
+
+
+def open_file(filename, mode='rt'):
+ """Opens the given score file for reading.
+
+ Score files might be raw text files, or a tar-file including a single score
+ file inside.
+
+
+ Parameters:
+
+ filename (:py:class:`str`, ``file-like``): The name of the score file to
+ open, or a file-like object open for reading. If a file name is given,
+ the according file might be a raw text file or a (compressed) tar file
+ containing a raw text file.
+
+
+ Returns:
+
+
+ ``file-like``: A read-only file-like object as it would be returned by
+ :py:func:`open`.
+
+ """
+
+ if not isinstance(filename, str) and hasattr(filename, 'read'):
+ # It seems that this is an open file
+ return filename
+
+ if not os.path.isfile(filename):
+ raise IOError("Score file '%s' does not exist." % filename)
+ if not tarfile.is_tarfile(filename):
+ return open(filename, mode)
+
+ # open the tar file for reading
+ tar = tarfile.open(filename, 'r')
+ # get the first file in the tar file
+ tar_info = tar.next()
+ while tar_info is not None and not tar_info.isfile():
+ tar_info = tar.next()
+ # check that one file was found in the archive
+ if tar_info is None:
+ raise IOError(
+ "The given file is a .tar file, but it does not contain any file.")
+
+ # open the file for reading
+ return tar.extractfile(tar_info)
+
+
+def four_column(filename):
+ """Loads a score set from a single file and yield its lines
+
+ Loads a score set from a single file and yield its lines (to avoid loading
+ the score file at once into memory). This function verifies that all fields
+ are correctly placed and contain valid fields. The score file must contain
+ the following information in each line:
+
+ .. code-block:: text
+
+ claimed_id real_id test_label score
+
+
+ Parameters:
+
+ filename (:py:class:`str`, ``file-like``): The file object that will be
+ opened with :py:func:`open_file` containing the scores.
+
+
+ Yields:
+
+ str: The claimed identity -- the client name of the model that was used in
+ the comparison
+
+ str: The real identity -- the client name of the probe that was used in the
+ comparison
+
+ str: A label of the probe -- usually the probe file name, or the probe id
+
+ float: The result of the comparison of the model and the probe
+
+ """
+ return _iterate_score_file(filename)
+
+
+def split_four_column(filename):
+ """Loads a score set from a single file and splits the scores
+
+ Loads a score set from a single file and splits the scores between negatives
+ and positives. The score file has to respect the 4 column format as defined
+ in the method :py:func:`four_column`.
+
+ This method avoids loading and allocating memory for the strings present in
+ the file. We only keep the scores.
+
+
+ Parameters:
+
+ filename (:py:class:`str`, ``file-like``): The file object that will be
+ opened with :py:func:`open_file` containing the scores.
+
+
+ Returns:
+
+ array: negatives, 1D float array containing the list of scores, for which
+ the ``claimed_id`` and the ``real_id`` are different
+ (see :py:func:`four_column`)
+
+ array: positives, 1D float array containing the list of scores, for which
+ the ``claimed_id`` and the ``real_id`` are identical
+ (see :py:func:`four_column`)
+
+ """
+
+ score_lines = four_column(filename)
+ return _split_scores(score_lines, 1)
+
+
+def cmc_four_column(filename):
+ """Loads scores to compute CMC curves from a file in four column format.
+
+ The four column file needs to be in the same format as described in
+ :py:func:`four_column`, and the ``test_label`` (column 3) has to contain the
+ test/probe file name or a probe id.
+
+ This function returns a list of tuples. For each probe file, the tuple
+ consists of a list of negative scores and a list of positive scores.
+ Usually, the list of positive scores should contain only one element, but
+ more are allowed. The result of this function can directly be passed to,
+ e.g., the :py:func:`bob.measure.cmc` function.
+
+
+ Parameters:
+
+ filename (:py:class:`str`, ``file-like``): The file object that will be
+ opened with :py:func:`open_file` containing the scores.
+
+
+ Returns:
+
+ :any:`list`: A list of tuples, where each tuple contains the
+ ``negative`` and ``positive`` scores for one probe of the database. Both
+ ``negatives`` and ``positives`` can be either an 1D
+ :py:class:`numpy.ndarray` of type ``float``, or ``None``.
+
+ """
+
+ score_lines = four_column(filename)
+ return _split_cmc_scores(score_lines, 1)
+
+
+def five_column(filename):
+ """Loads a score set from a single file and yield its lines
+
+ Loads a score set from a single file and yield its lines (to avoid loading
+ the score file at once into memory). This function verifies that all fields
+ are correctly placed and contain valid fields. The score file must contain
+ the following information in each line:
+
+ .. code-block:: text
+
+ claimed_id model_label real_id test_label score
+
+
+ Parameters:
+
+ filename (:py:class:`str`, ``file-like``): The file object that will be
+ opened with :py:func:`open_file` containing the scores.
+
+
+ Yields:
+
+ str: The claimed identity -- the client name of the model that was used in
+ the comparison
+
+ str: A label for the model -- usually the model file name, or the model id
+
+ str: The real identity -- the client name of the probe that was used in the
+ comparison
+
+ str: A label of the probe -- usually the probe file name, or the probe id
+
+ float: The result of the comparison of the model and the probe
+
+ """
+
+ return _iterate_score_file(filename)
+
+
+def split_five_column(filename):
+ """Loads a score set from a single file and splits the scores
+
+ Loads a score set from a single file in five column format and splits the
+ scores between negatives and positives. The score file has to respect the 5
+ column format as defined in the method :py:func:`five_column`.
+
+ This method avoids loading and allocating memory for the strings present in
+ the file. We only keep the scores.
+
+
+ Parameters:
+
+ filename (:py:class:`str`, ``file-like``): The file object that will be
+ opened with :py:func:`open_file` containing the scores.
+
+
+ Returns:
+
+ array: negatives, 1D float array containing the list of scores, for which
+ the ``claimed_id`` and the ``real_id`` are different
+ (see :py:func:`four_column`)
+
+ array: positives, 1D float array containing the list of scores, for which
+ the ``claimed_id`` and the ``real_id`` are identical
+ (see :py:func:`four_column`)
+
+ """
+
+ score_lines = four_column(filename)
+ return _split_scores(score_lines, 2)
+
+
+def cmc_five_column(filename):
+ """Loads scores to compute CMC curves from a file in five column format.
+
+ The five column file needs to be in the same format as described in
+ :py:func:`five_column`, and the ``test_label`` (column 4) has to contain the
+ test/probe file name or a probe id.
+
+ This function returns a list of tuples. For each probe file, the tuple
+ consists of a list of negative scores and a list of positive scores.
+ Usually, the list of positive scores should contain only one element, but
+ more are allowed. The result of this function can directly be passed to,
+ e.g., the :py:func:`bob.measure.cmc` function.
+
+
+ Parameters:
+
+ filename (:py:class:`str`, ``file-like``): The file object that will be
+ opened with :py:func:`open_file` containing the scores.
+
+
+ Returns:
+
+ :any:`list`: A list of tuples, where each tuple contains the
+ ``negative`` and ``positive`` scores for one probe of the database.
+
+ """
+ score_lines = four_column(filename)
+ return _split_cmc_scores(score_lines, 2)
+
+
+def scores(filename, ncolumns=None):
+ """scores(filename, ncolumns=None) -> tuple
+
+ Loads the scores from the given score file and yield its lines.
+ Depending on the score file format, four or five elements are yielded, see
+ :py:func:`bob.bio.base.score.load.four_column` and
+ :py:func:`bob.bio.base.score.load.five_column` for details.
+
+ Parameters:
+
+ filename: :py:class:`str`, ``file-like``:
+ The file object that will be opened with :py:func:`open_file` containing the scores.
+
+ ncolumns: any
+ ignored
+
+ Yields:
+
+ tuple:
+ see :py:func:`bob.bio.base.score.load.four_column` or
+ :py:func:`bob.bio.base.score.load.five_column`
+ """
+ return _iterate_score_file(filename)
+
+
+def split(filename, ncolumns=None):
+ """split(filename, ncolumns=None) -> negatives, positives
+
+ Loads the scores from the given score file and splits them into positives and negatives.
+
+ Depending on the score file format, it calls see :py:func:`split_four_column`
+ and :py:func:`split_five_column` for details.
+
+ Parameters:
+
+ filename: :py:class:`str`, ``file-like``:
+ The file object that will be opened with :py:func:`open_file` containing the scores.
+
+ ncolumns: int or ``None``
+ If specified to be ``4`` or ``5``, the score file will be assumed to be in the given format.
+ If not specified, the score file format will be estimated automatically
+
+ Returns:
+
+ negatives: 1D :py:class:`numpy.ndarray` of type float
+ This array contains the list of scores, for which the ``claimed_id`` and the ``real_id`` are different (see :py:func:`four_column`)
+
+ positives: 1D :py:class:`numpy.ndarray` of type float
+ This array contains the list of scores, for which the ``claimed_id`` and the ``real_id`` are identical (see :py:func:`four_column`)
+
+ """
+ ncolumns = _estimate_score_file_format(filename, ncolumns)
+ if ncolumns == 4:
+ return split_four_column(filename)
+ else:
+ assert ncolumns == 5
+ return split_five_column(filename)
+
+
+def cmc(filename, ncolumns=None):
+ """cmc(filename, ncolumns=None) -> list
+
+ Loads scores to compute CMC curves.
+
+ Depending on the score file format, it calls see
+ :py:func:`bob.bio.base.score.load.cmc_four_column` and
+ `:py:func:`bob.bio.base.score.load.cmc_five_column` for details.
+
+ Parameters:
+
+ filename (:py:class:`str` or ``file-like``): The file object that will be
+ opened with :py:func:`open_file` containing the scores.
+
+ ncolumns: (:py:class:`int`, Optional): If specified to be ``4`` or ``5``,
+ the score file will be assumed to be in the given format. If not
+ specified, the score file format will be estimated automatically
+
+ Returns:
+
+ :any:`list`: [(neg,pos)] A list of tuples, where each tuple contains the
+ ``negative`` and ``positive`` scores for one probe of the database.
+
+ """
+ ncolumns = _estimate_score_file_format(filename, ncolumns)
+ if ncolumns == 4:
+ return cmc_four_column(filename)
+ else:
+ assert ncolumns == 5
+ return cmc_five_column(filename)
+
+
+def load_score(filename, ncolumns=None, minimal=False, **kwargs):
+ """Load scores using numpy.loadtxt and return the data as a numpy array.
+
+ Parameters:
+
+ filename (:py:class:`str`, ``file-like``): The file object that will be
+ opened with :py:func:`open_file` containing the scores.
+
+ ncolumns (:py:class:`int`, optional): 4, 5 or None (the default),
+ specifying the number of columns in the score file. If None is provided,
+ the number of columns will be guessed.
+
+ minimal (:py:class:`bool`, optional): If True, only loads ``claimed_id``, ``real_id``,
+ and ``scores``.
+
+ **kwargs: Keyword arguments passed to :py:func:`numpy.genfromtxt`
+
+
+ Returns:
+
+ array: An array which contains not only the actual scores but also the
+ ``claimed_id``, ``real_id``, ``test_label`` and ``['model_label']``
+
+ """
+
+ def convertfunc(x):
+ return x
+
+ ncolumns = _estimate_score_file_format(filename, ncolumns)
+
+ usecols = kwargs.pop('usecols', None)
+ if ncolumns == 4:
+ names = ('claimed_id', 'real_id', 'test_label', 'score')
+ converters = {
+ 0: convertfunc,
+ 1: convertfunc,
+ 2: convertfunc,
+ 3: float}
+ if minimal:
+ usecols = (0, 1, 3)
+
+ elif ncolumns == 5:
+ names = ('claimed_id', 'model_label', 'real_id', 'test_label', 'score')
+ converters = {
+ 0: convertfunc,
+ 1: convertfunc,
+ 2: convertfunc,
+ 3: convertfunc,
+ 4: float}
+ if minimal:
+ usecols = (0, 2, 4)
+ else:
+ raise ValueError("ncolumns of 4 and 5 are supported only.")
+
+ score_lines = numpy.genfromtxt(
+ open_file(filename, mode='rb'), dtype=None, names=names,
+ converters=converters, invalid_raise=True, usecols=usecols, **kwargs)
+ new_dtype = []
+ for name in score_lines.dtype.names[:-1]:
+ new_dtype.append((name, str(score_lines.dtype[name]).replace('S', 'U')))
+ new_dtype.append(('score', float))
+ score_lines = numpy.array(score_lines, new_dtype)
+ return score_lines
+
+
+def get_negatives_positives(score_lines):
+ """Take the output of load_score and return negatives and positives. This
+ function aims to replace split_four_column and split_five_column but takes a
+ different input. It's up to you to use which one.
+ """
+
+ pos_mask = score_lines['claimed_id'] == score_lines['real_id']
+ positives = score_lines['score'][pos_mask]
+ negatives = score_lines['score'][numpy.logical_not(pos_mask)]
+ return (negatives, positives)
+
+
+def get_negatives_positives_from_file(filename, **kwargs):
+ """Loads the scores first efficiently and then calls
+ get_negatives_positives"""
+ score_lines = load_score(filename, minimal=True, **kwargs)
+ return get_negatives_positives(score_lines)
+
+
+def get_negatives_positives_all(score_lines_list):
+ """Take a list of outputs of load_score and return stacked negatives and
+ positives.
+ """
+
+ negatives, positives = [], []
+ for score_lines in score_lines_list:
+ neg_pos = get_negatives_positives(score_lines)
+ negatives.append(neg_pos[0])
+ positives.append(neg_pos[1])
+ negatives = numpy.vstack(negatives).T
+ positives = numpy.vstack(positives).T
+ return (negatives, positives)
+
+
+def get_all_scores(score_lines_list):
+ """Take a list of outputs of load_score and return stacked scores"""
+
+ return numpy.vstack([score_lines['score']
+ for score_lines in score_lines_list]).T
+
+
+def dump_score(filename, score_lines):
+ """Dump scores that were loaded using :py:func:`load_score`
+ The number of columns is automatically detected.
+ """
+
+ if len(score_lines.dtype) == 5:
+ fmt = '%s %s %s %s %.9f'
+ elif len(score_lines.dtype) == 4:
+ fmt = '%s %s %s %.9f'
+ else:
+ raise ValueError("Only scores with 4 and 5 columns are supported.")
+ numpy.savetxt(filename, score_lines, fmt=fmt)
+
+
+def _estimate_score_file_format(filename, ncolumns=None):
+ """Estimates the score file format from the given score file.
+ If ``ncolumns`` is in ``(4,5)``, then ``ncolumns`` is returned instead.
+ """
+ if ncolumns in (4, 5):
+ return ncolumns
+
+ f = open_file(filename, 'rb')
+ try:
+ line = f.readline()
+ ncolumns = len(line.split())
+ except Exception:
+ logger.warn('Could not guess the number of columns in file: {}. '
+ 'Assuming 4 column format.'.format(filename))
+ ncolumns = 4
+ finally:
+ f.close()
+ return ncolumns
+
+
+def _iterate_score_file(filename):
+ """Opens the score file for reading and yields the score file line by line in a tuple/list.
+
+ The last element of the line (which is the score) will be transformed to float, the other elements will be str
+ """
+ opened = open_file(filename, 'rb')
+ if sys.version_info.major > 2:
+ import io
+ if not isinstance(opened, io.TextIOWrapper):
+ opened = io.TextIOWrapper(opened, newline="")
+
+ reader = csv.reader(opened, delimiter=' ')
+ for splits in reader:
+ splits[-1] = float(splits[-1])
+ yield splits
+
+
+def _split_scores(score_lines, real_id_index, claimed_id_index=0, score_index=-1):
+ """Take the output of :py:func:`four_column` or :py:func:`five_column` and return negatives and positives.
+ """
+ positives, negatives = [], []
+ for line in score_lines:
+ which = positives if line[claimed_id_index] == line[
+ real_id_index] else negatives
+ which.append(line[score_index])
+
+ return (numpy.array(negatives), numpy.array(positives))
+
+
+def _split_cmc_scores(score_lines, real_id_index, probe_name_index=None, claimed_id_index=0, score_index=-1):
+ """Takes the output of :py:func:`four_column` or :py:func:`five_column` and return cmc scores.
+ """
+ if probe_name_index is None:
+ probe_name_index = real_id_index + 1
+ # extract positives and negatives
+ pos_dict = {}
+ neg_dict = {}
+ # read four column list
+ for line in score_lines:
+ which = pos_dict if line[claimed_id_index] == line[
+ real_id_index] else neg_dict
+ probe_name = line[probe_name_index]
+ # append score
+ if probe_name not in which:
+ which[probe_name] = []
+ which[probe_name].append(line[score_index])
+
+ # convert to lists of tuples of ndarrays (or None)
+ probe_names = sorted(set(neg_dict.keys()).union(set(pos_dict.keys())))
+ # get all scores in the desired format
+ return [(
+ numpy.array(neg_dict[probe_name],
+ numpy.float64) if probe_name in neg_dict else None,
+ numpy.array(pos_dict[probe_name],
+ numpy.float64) if probe_name in pos_dict else None
+ ) for probe_name in probe_names]
diff --git a/bob/bio/base/score/openbr.py b/bob/bio/base/score/openbr.py
new file mode 100644
index 0000000000000000000000000000000000000000..51010f7df69e4e2f34782508aa258f46db01d68d
--- /dev/null
+++ b/bob/bio/base/score/openbr.py
@@ -0,0 +1,368 @@
+#!/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/script/collect_results.py b/bob/bio/base/script/collect_results.py
index 6e1cc0681258b967339dc466bff96bc7470185b9..df08375cf7202a6086df0803f353a2abfd9f6dd9 100644
--- a/bob/bio/base/script/collect_results.py
+++ b/bob/bio/base/script/collect_results.py
@@ -29,8 +29,9 @@ import sys, os, glob
import argparse
import numpy
-import bob.measure
import bob.core
+from .. import score
+
logger = bob.core.log.setup("bob.bio.base")
def command_line_arguments(command_line_parameters):
@@ -80,9 +81,9 @@ class Result:
def _calculate(self, dev_file, eval_file = None):
"""Calculates the EER and HTER or FRR based on the threshold criterion."""
if self.m_args.criterion in ("RR", "DIR"):
- scores_dev = bob.measure.load.cmc(dev_file)
+ scores_dev = score.cmc(dev_file)
if eval_file is not None:
- scores_eval = bob.measure.load.cmc(eval_file)
+ scores_eval = score.cmc(eval_file)
if self.m_args.criterion == "DIR":
# get negatives without positives
@@ -110,7 +111,7 @@ class Result:
else:
- dev_neg, dev_pos = bob.measure.load.split(dev_file)
+ dev_neg, dev_pos = score.split(dev_file)
# switch which threshold function to use
if self.m_args.criterion == 'EER':
@@ -127,7 +128,7 @@ class Result:
dev_hter = (dev_far + dev_frr)/2.0
if eval_file:
- eval_neg, eval_pos = bob.measure.load.split(eval_file)
+ eval_neg, eval_pos = score.split(eval_file)
eval_far, eval_frr = bob.measure.farfrr(eval_neg, eval_pos, threshold)
eval_hter = (eval_far + eval_frr)/2.0
else:
diff --git a/bob/bio/base/script/evaluate.py b/bob/bio/base/script/evaluate.py
index 21c27ee876fac1eb0c6f9748b79993dd73f10c71..8d285f70953599bb36cb1eed02e8cee5a046d6c7 100644
--- a/bob/bio/base/script/evaluate.py
+++ b/bob/bio/base/script/evaluate.py
@@ -1,465 +1,467 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-
-"""This script evaluates the given score files and computes EER, HTER.
-It also is able to plot CMC and ROC curves.
-You can set the environment variable BOB_NO_STYLE_CHANGES to any value to avoid
-this script from changing the matplotlib style values. """
-
-from __future__ import print_function
-
-# matplotlib stuff
-import matplotlib
-from matplotlib import pyplot
-pyplot.switch_backend('pdf') # switch to non-X backend
-from matplotlib.backends.backend_pdf import PdfPages
-
-# import bob.measure after matplotlib, so that it cannot define the backend
-import bob.measure
-
-import argparse
-import numpy
-import math
-import os
-
-
-if not os.environ.get('BOB_NO_STYLE_CHANGES'):
- # make the fig size smaller so that everything becomes bigger
- matplotlib.rc('figure', figsize=(4, 3))
-
-
-import bob.core
-logger = bob.core.log.setup("bob.bio.base")
-
-
-def command_line_arguments(command_line_parameters):
- """Parse the program options"""
-
- # set up command line parser
- parser = argparse.ArgumentParser(description=__doc__,
- formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
- parser.add_argument('-d', '--dev-files', required=True, nargs='+', help = "A list of score files of the development set.")
- parser.add_argument('-e', '--eval-files', nargs='+', help = "A list of score files of the evaluation set; if given it must be the same number of files as the --dev-files.")
-
- parser.add_argument('-s', '--directory', default = '.', help = "A directory, where to find the --dev-files and the --eval-files")
-
- parser.add_argument('-c', '--criterion', choices = ('EER', 'HTER', 'FAR'), help = "If given, the threshold of the development set will be computed with this criterion.")
- parser.add_argument('-f', '--far-value', type=float, default=0.001, help = "The FAR value for which to evaluate (only for --criterion FAR)")
- parser.add_argument('-x', '--cllr', action = 'store_true', help = "If given, Cllr and minCllr will be computed.")
- parser.add_argument('-m', '--mindcf', action = 'store_true', help = "If given, minDCF will be computed.")
- parser.add_argument('--cost', default=0.99, help='Cost for FAR in minDCF')
- parser.add_argument('-r', '--rr', action = 'store_true', help = "If given, the Recognition Rate will be computed.")
- parser.add_argument('-o', '--rank', type=int, default=1, help = "The rank for which to plot the DIR curve")
- parser.add_argument('-t', '--thresholds', type=float, nargs='+', help = "If given, the Recognition Rate will incorporate an Open Set handling, rejecting all scores that are below the given threshold; when multiple thresholds are given, they are applied in the same order as the --dev-files.")
- parser.add_argument('-l', '--legends', nargs='+', help = "A list of legend strings used for ROC, CMC and DET plots; if given, must be the same number than --dev-files.")
- parser.add_argument('-F', '--legend-font-size', type=int, default=10, help = "Set the font size of the legends.")
- parser.add_argument('-P', '--legend-position', type=int, help = "Set the font size of the legends.")
- parser.add_argument('-T', '--title', nargs = '+', help = "Overwrite the default title of the plot for development (and evaluation) set")
- parser.add_argument('-R', '--roc', help = "If given, ROC curves will be plotted into the given pdf file.")
- parser.add_argument('-D', '--det', help = "If given, DET curves will be plotted into the given pdf file.")
- parser.add_argument('-C', '--cmc', help = "If given, CMC curves will be plotted into the given pdf file.")
- parser.add_argument('-O', '--dir', help = "If given, DIR curves will be plotted into the given pdf file; This is an open-set measure, which cannot be applied to closed set score files.")
- parser.add_argument('-E', '--epc', help = "If given, EPC curves will be plotted into the given pdf file. For this plot --eval-files is mandatory.")
- parser.add_argument('-M', '--min-far-value', type=float, default=1e-4, help = "Select the minimum FAR value used in ROC plots; should be a power of 10.")
- parser.add_argument('-L', '--far-line-at', type=float, help = "If given, draw a veritcal line at this FAR value in the ROC plots.")
-
- # add verbose option
- bob.core.log.add_command_line_option(parser)
-
- # parse arguments
- args = parser.parse_args(command_line_parameters)
-
- # set verbosity level
- bob.core.log.set_verbosity_level(logger, args.verbose)
-
- # some sanity checks:
- for f in args.dev_files + (args.eval_files or []):
- real_file = os.path.join(args.directory, f)
- if not os.path.exists(real_file):
- raise ValueError("The provided score file '%s' does not exist" % real_file)
-
- if args.eval_files is not None and len(args.dev_files) != len(args.eval_files):
- logger.error("The number of --dev-files (%d) and --eval-files (%d) are not identical", len(args.dev_files), len(args.eval_files))
-
- # update legends when they are not specified on command line
- if args.legends is None:
- args.legends = [f.replace('_', '-') for f in args.dev_files]
- logger.warn("Legends are not specified; using legends estimated from --dev-files: %s", args.legends)
-
- # check that the legends have the same length as the dev-files
- if len(args.dev_files) != len(args.legends):
- logger.error("The number of --dev-files (%d) and --legends (%d) are not identical", len(args.dev_files), len(args.legends))
-
- if args.thresholds is not None:
- if len(args.thresholds) == 1:
- args.thresholds = args.thresholds * len(args.dev_files)
- elif len(args.thresholds) != len(args.dev_files):
- logger.error("If given, the number of --thresholds imust be either 1, or the same as --dev-files (%d), but it is %d", len(args.dev_files), len(args.thresholds))
- else:
- args.thresholds = [None] * len(args.dev_files)
-
- if args.title is not None:
- if args.eval_files is None and len(args.title) != 1:
- logger.warning("Ignoring the title for the evaluation set, as no evaluation set is given")
- if args.eval_files is not None and len(args.title) < 2:
- logger.error("The title for the evaluation set is not specified")
-
- return args
-
-def _add_far_labels(min_far):
- # compute and apply tick marks
- assert min_far > 0
- ticks = [min_far]
- while ticks[-1] < 1.: ticks.append(ticks[-1] * 10.)
- pyplot.xticks(ticks)
- pyplot.axis([min_far, 1., -0.01, 1.01])
-
-
-
-def _plot_roc(frrs, colors, labels, title, fontsize=10, position=None, farfrrs=None, min_far=None):
- if position is None: position = 'lower right'
- figure = pyplot.figure()
-
- # plot FAR and CAR for each algorithm
- for i in range(len(frrs)):
- pyplot.semilogx([f for f in frrs[i][0]], [1. - f for f in frrs[i][1]], color=colors[i], label=labels[i])
- if isinstance(farfrrs, list):
- pyplot.plot(farfrrs[i][0], (1.-farfrrs[i][1]), 'o', color=colors[i], markeredgecolor=colors[i])
-
- # plot vertical bar, if desired
- if farfrrs is not None:
- if isinstance(farfrrs, float):
- pyplot.plot([farfrrs,farfrrs],[0.,1.], "--", color='black')
- else:
- pyplot.plot([x[0] for x in farfrrs], [(1.-x[1]) for x in farfrrs], '--', color='black')
-
- _add_far_labels(min_far)
-
- # set label, legend and title
- pyplot.xlabel('FMR')
- pyplot.ylabel('1 - FNMR')
- pyplot.grid(True, color=(0.6,0.6,0.6))
- pyplot.legend(loc=position, prop = {'size':fontsize})
- pyplot.title(title)
-
- return figure
-
-
-def _plot_det(dets, colors, labels, title, fontsize=10, position=None):
- if position is None: position = 'upper right'
- # open new page for current plot
- figure = pyplot.figure(figsize=(matplotlib.rcParams['figure.figsize'][0],
- matplotlib.rcParams['figure.figsize'][0] * 0.975))
- pyplot.grid(True)
-
- # plot the DET curves
- for i in range(len(dets)):
- pyplot.plot(dets[i][0], dets[i][1], color=colors[i], label=labels[i])
-
- # change axes accordingly
- det_list = [0.0002, 0.001, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 0.7, 0.9, 0.95]
- ticks = [bob.measure.ppndf(d) for d in det_list]
- labels = [("%.5f" % d).rstrip('0').rstrip('.') for d in det_list]
- pyplot.xticks(ticks, [l if i % 2 else "" for i,l in enumerate(labels)])
- pyplot.yticks(ticks, labels)
- pyplot.axis((ticks[0], ticks[-1], ticks[0], ticks[-1]))
-
- pyplot.xlabel('FMR')
- pyplot.ylabel('FNMR')
- pyplot.legend(loc=position, prop = {'size':fontsize})
- pyplot.title(title)
-
- return figure
-
-
-def _plot_cmc(cmcs, colors, labels, title, fontsize=10, position=None):
- if position is None: position = 'lower right'
- # open new page for current plot
- figure = pyplot.figure()
-
- max_R = 0
- # plot the CMC curves
- for i in range(len(cmcs)):
- probs = bob.measure.cmc(cmcs[i])
- R = len(probs)
- pyplot.semilogx(range(1, R+1), probs, figure=figure, color=colors[i], label=labels[i])
- max_R = max(R, max_R)
-
- # change axes accordingly
- ticks = [int(t) for t in pyplot.xticks()[0]]
- pyplot.xlabel('Rank')
- pyplot.ylabel('Probability')
- pyplot.xticks(ticks, [str(t) for t in ticks])
- pyplot.axis([0, max_R, -0.01, 1.01])
- pyplot.legend(loc=position, prop = {'size':fontsize})
- pyplot.title(title)
-
- return figure
-
-
-def _plot_dir(cmc_scores, far_values, rank, colors, labels, title, fontsize=10, position=None):
- if position is None: position = 'lower right'
- # open new page for current plot
- figure = pyplot.figure()
-
- # for each probe, for which no positives exists, get the highest negative
- # score; and sort them to compute the FAR thresholds
- for i, cmcs in enumerate(cmc_scores):
- negatives = sorted(max(neg) for neg, pos in cmcs if (pos is None or not numpy.array(pos).size) and neg is not None)
- if not negatives:
- raise ValueError("There need to be at least one pair with only negative scores")
-
- # compute thresholds based on FAR values
- thresholds = [bob.measure.far_threshold(negatives, [], v, True) for v in far_values]
-
- # compute detection and identification rate based on the thresholds for
- # the given rank
- rates = [bob.measure.detection_identification_rate(cmcs, t, rank) for t in thresholds]
-
- # plot DIR curve
- pyplot.semilogx(far_values, rates, figure=figure, color=colors[i], label=labels[i])
-
- # finalize plot
- _add_far_labels(far_values[0])
-
- pyplot.xlabel('FAR')
- pyplot.ylabel('DIR')
- pyplot.legend(loc=position, prop = {'size':fontsize})
- pyplot.title(title)
-
- return figure
-
-
-def _plot_epc(scores_dev, scores_eval, colors, labels, title, fontsize=10, position=None):
- if position is None: position = 'upper center'
- # open new page for current plot
- figure = pyplot.figure()
-
- # plot the DET curves
- for i in range(len(scores_dev)):
- x,y = bob.measure.epc(scores_dev[i][0], scores_dev[i][1], scores_eval[i][0], scores_eval[i][1], 100)
- pyplot.plot(x, y, color=colors[i], label=labels[i])
-
- # change axes accordingly
- pyplot.xlabel('alpha')
- pyplot.ylabel('HTER')
- pyplot.title(title)
- pyplot.axis([-0.01, 1.01, -0.01, 0.51])
- pyplot.grid(True)
- pyplot.legend(loc=position, prop = {'size':fontsize})
- pyplot.title(title)
-
- return figure
-
-
-def remove_nan(scores):
- """removes the NaNs from the scores"""
- nans = numpy.isnan(scores)
- sum_nans = sum(nans)
- total = len(scores)
- return scores[numpy.where(~nans)], sum_nans, total
-
-
-def get_fta(scores):
- """calculates the Failure To Acquire (FtA) rate"""
- fta_sum, fta_total = 0, 0
- neg, sum_nans, total = remove_nan(scores[0])
- fta_sum += sum_nans
- fta_total += total
- pos, sum_nans, total = remove_nan(scores[1])
- fta_sum += sum_nans
- fta_total += total
- return (neg, pos, fta_sum * 100 / float(fta_total))
-
-
-def main(command_line_parameters=None):
- """Reads score files, computes error measures and plots curves."""
-
- args = command_line_arguments(command_line_parameters)
-
- # get some colors for plotting
- if len(args.dev_files) > 10:
- cmap = pyplot.cm.get_cmap(name='magma')
- colors = [cmap(i) for i in numpy.linspace(0, 1.0, len(args.dev_files) + 1)]
- else:
- # matplotlib 2.0 default color cycler list: Vega category10 palette
- colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728',
- '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',
- '#bcbd22', '#17becf']
-
- if args.criterion or args.roc or args.det or args.epc or args.cllr or args.mindcf:
-
- # First, read the score files
- logger.info("Loading %d score files of the development set", len(args.dev_files))
- scores_dev = [bob.measure.load.split(os.path.join(args.directory, f)) for f in args.dev_files]
- # remove nans
- scores_dev = [get_fta(s) for s in scores_dev]
-
- if args.eval_files:
- logger.info("Loading %d score files of the evaluation set", len(args.eval_files))
- scores_eval = [bob.measure.load.split(os.path.join(args.directory, f)) for f in args.eval_files]
- # remove nans
- scores_eval = [get_fta(s) for s in scores_eval]
-
-
- if args.criterion:
- logger.info("Computing %s on the development " % args.criterion + ("and HTER on the evaluation set" if args.eval_files else "set"))
- for i in range(len(scores_dev)):
- # compute threshold on development set
- if args.criterion == 'FAR':
- threshold = bob.measure.far_threshold(scores_dev[i][0], scores_dev[i][1], args.far_value/100.)
- else:
- threshold = {'EER': bob.measure.eer_threshold, 'HTER' : bob.measure.min_hter_threshold} [args.criterion](scores_dev[i][0], scores_dev[i][1])
- # apply threshold to development set
- far, frr = bob.measure.farfrr(scores_dev[i][0], scores_dev[i][1], threshold)
- if args.criterion == 'FAR':
- print("The FRR at FAR=%.1E of the development set of '%s' is %2.3f%% (CAR: %2.3f%%)" % (args.far_value, args.legends[i], frr * 100., 100.*(1-frr)))
- else:
- print("The %s of the development set of '%s' is %2.3f%%" % (args.criterion, args.legends[i], (far + frr) * 50.)) # / 2 * 100%
- if args.eval_files:
- # apply threshold to evaluation set
- far, frr = bob.measure.farfrr(scores_eval[i][0], scores_eval[i][1], threshold)
- if args.criterion == 'FAR':
- print("The FRR of the evaluation set of '%s' is %2.3f%% (CAR: %2.3f%%)" % (args.legends[i], frr * 100., 100.*(1-frr))) # / 2 * 100%
- else:
- print("The HTER of the evaluation set of '%s' is %2.3f%%" % (args.legends[i], (far + frr) * 50.)) # / 2 * 100%
-
-
- if args.mindcf:
- logger.info("Computing minDCF on the development " + ("and on the evaluation set" if args.eval_files else "set"))
- for i in range(len(scores_dev)):
- # compute threshold on development set
- threshold = bob.measure.min_weighted_error_rate_threshold(scores_dev[i][0], scores_dev[i][1], args.cost)
- # apply threshold to development set
- far, frr = bob.measure.farfrr(scores_dev[i][0], scores_dev[i][1], threshold)
- print("The minDCF of the development set of '%s' is %2.3f%%" % (args.legends[i], (args.cost * far + (1-args.cost) * frr) * 100. ))
- if args.eval_files:
- # compute threshold on evaluation set
- threshold = bob.measure.min_weighted_error_rate_threshold(scores_eval[i][0], scores_eval[i][1], args.cost)
- # apply threshold to evaluation set
- far, frr = bob.measure.farfrr(scores_eval[i][0], scores_eval[i][1], threshold)
- print("The minDCF of the evaluation set of '%s' is %2.3f%%" % (args.legends[i], (args.cost * far + (1-args.cost) * frr) * 100. ))
-
-
- if args.cllr:
- logger.info("Computing Cllr and minCllr on the development " + ("and on the evaluation set" if args.eval_files else "set"))
- for i in range(len(scores_dev)):
- cllr = bob.measure.calibration.cllr(scores_dev[i][0], scores_dev[i][1])
- min_cllr = bob.measure.calibration.min_cllr(scores_dev[i][0], scores_dev[i][1])
- print("Calibration performance on development set of '%s' is Cllr %1.5f and minCllr %1.5f " % (args.legends[i], cllr, min_cllr))
- if args.eval_files:
- cllr = bob.measure.calibration.cllr(scores_eval[i][0], scores_eval[i][1])
- min_cllr = bob.measure.calibration.min_cllr(scores_eval[i][0], scores_eval[i][1])
- print("Calibration performance on evaluation set of '%s' is Cllr %1.5f and minCllr %1.5f" % (args.legends[i], cllr, min_cllr))
-
-
- if args.roc:
- logger.info("Computing CAR curves on the development " + ("and on the evaluation set" if args.eval_files else "set"))
- min_far = int(math.floor(math.log(args.min_far_value, 10)))
- fars = [math.pow(10., i * 0.25) for i in range(min_far * 4, 0)] + [1.]
- frrs_dev = [bob.measure.roc_for_far(scores[0], scores[1], fars) for scores in scores_dev]
- if args.eval_files:
- frrs_eval = [bob.measure.roc_for_far(scores[0], scores[1], fars) for scores in scores_eval]
-
- logger.info("Plotting ROC curves to file '%s'", args.roc)
- try:
- # create a multi-page PDF for the ROC curve
- pdf = PdfPages(args.roc)
- # create a separate figure for dev and eval
- pdf.savefig(_plot_roc(frrs_dev, colors, args.legends, args.title[0] if args.title is not None else "ROC for development set", args.legend_font_size, args.legend_position, args.far_line_at, min_far=args.min_far_value), bbox_inches='tight')
- del frrs_dev
- if args.eval_files:
- if args.far_line_at is not None:
- farfrrs = []
- for i in range(len(scores_dev)):
- threshold = bob.measure.far_threshold(scores_dev[i][0], scores_dev[i][1], args.far_line_at)
- farfrrs.append(bob.measure.farfrr(scores_eval[i][0], scores_eval[i][1], threshold))
- else:
- farfrrs = None
- pdf.savefig(_plot_roc(frrs_eval, colors, args.legends, args.title[1] if args.title is not None else "ROC for evaluation set", args.legend_font_size, args.legend_position, farfrrs, min_far=args.min_far_value), bbox_inches='tight')
- del frrs_eval
- pdf.close()
- except RuntimeError as e:
- raise RuntimeError("During plotting of ROC curves, the following exception occured:\n%s" % e)
-
- if args.det:
- logger.info("Computing DET curves on the development " + ("and on the evaluation set" if args.eval_files else "set"))
- dets_dev = [bob.measure.det(scores[0], scores[1], 1000) for scores in scores_dev]
- if args.eval_files:
- dets_eval = [bob.measure.det(scores[0], scores[1], 1000) for scores in scores_eval]
-
- logger.info("Plotting DET curves to file '%s'", args.det)
- try:
- # create a multi-page PDF for the DET curve
- pdf = PdfPages(args.det)
- # create a separate figure for dev and eval
- pdf.savefig(_plot_det(dets_dev, colors, args.legends, args.title[0] if args.title is not None else "DET for development set", args.legend_font_size, args.legend_position), bbox_inches='tight')
- del dets_dev
- if args.eval_files:
- pdf.savefig(_plot_det(dets_eval, colors, args.legends, args.title[1] if args.title is not None else "DET for evaluation set", args.legend_font_size, args.legend_position), bbox_inches='tight')
- del dets_eval
- pdf.close()
- except RuntimeError as e:
- raise RuntimeError("During plotting of DET curves, the following exception occured:\n%s" % e)
-
-
- if args.epc:
- logger.info("Plotting EPC curves to file '%s'", args.epc)
-
- if not args.eval_files:
- raise ValueError("To plot the EPC curve the evaluation scores are necessary. Please, set it with the --eval-files option.")
-
- try:
- # create a multi-page PDF for the EPC curve
- pdf = PdfPages(args.epc)
- pdf.savefig(_plot_epc(scores_dev, scores_eval, colors, args.legends, args.title[0] if args.title is not None else "" , args.legend_font_size, args.legend_position), bbox_inches='tight')
- pdf.close()
- except RuntimeError as e:
- raise RuntimeError("During plotting of EPC curves, the following exception occured:\n%s" % e)
-
-
-
- if args.cmc or args.rr or args.dir:
- logger.info("Loading CMC data on the development " + ("and on the evaluation set" if args.eval_files else "set"))
- cmcs_dev = [bob.measure.load.cmc(os.path.join(args.directory, f)) for f in args.dev_files]
- if args.eval_files:
- cmcs_eval = [bob.measure.load.cmc(os.path.join(args.directory, f)) for f in args.eval_files]
-
- if args.cmc:
- logger.info("Plotting CMC curves to file '%s'", args.cmc)
- try:
- # create a multi-page PDF for the CMC curve
- pdf = PdfPages(args.cmc)
- # create a separate figure for dev and eval
- pdf.savefig(_plot_cmc(cmcs_dev, colors, args.legends, args.title[0] if args.title is not None else "CMC curve for development set", args.legend_font_size, args.legend_position), bbox_inches='tight')
- if args.eval_files:
- pdf.savefig(_plot_cmc(cmcs_eval, colors, args.legends, args.title[1] if args.title is not None else "CMC curve for evaluation set", args.legend_font_size, args.legend_position), bbox_inches='tight')
- pdf.close()
- except RuntimeError as e:
- raise RuntimeError("During plotting of CMC curves, the following exception occured:\n%s\nUsually this happens when the label contains characters that LaTeX cannot parse." % e)
-
- if args.rr:
- logger.info("Computing recognition rate on the development " + ("and on the evaluation set" if args.eval_files else "set"))
- for i in range(len(cmcs_dev)):
- rr = bob.measure.recognition_rate(cmcs_dev[i], args.thresholds[i])
- print("The Recognition Rate of the development set of '%s' is %2.3f%%" % (args.legends[i], rr * 100.))
- if args.eval_files:
- rr = bob.measure.recognition_rate(cmcs_eval[i], args.thresholds[i])
- print("The Recognition Rate of the development set of '%s' is %2.3f%%" % (args.legends[i], rr * 100.))
-
- if args.dir:
- # compute false alarm values to evaluate
- min_far = int(math.floor(math.log(args.min_far_value, 10)))
- fars = [math.pow(10., i * 0.25) for i in range(min_far * 4, 0)] + [1.]
- logger.info("Plotting DIR curves to file '%s'", args.dir)
- try:
- # create a multi-page PDF for the DIR curve
- pdf = PdfPages(args.dir)
- # create a separate figure for dev and eval
- pdf.savefig(_plot_dir(cmcs_dev, fars, args.rank, colors, args.legends, args.title[0] if args.title is not None else "DIR curve for development set", args.legend_font_size, args.legend_position), bbox_inches='tight')
- if args.eval_files:
- pdf.savefig(_plot_dir(cmcs_eval, fars, args.rank, colors, args.legends, args.title[1] if args.title is not None else "DIR curve for evaluation set", args.legend_font_size, args.legend_position), bbox_inches='tight')
- pdf.close()
- except RuntimeError as e:
- raise RuntimeError("During plotting of DIR curves, the following exception occured:\n%s" % e)
+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+
+"""This script evaluates the given score files and computes EER, HTER.
+It also is able to plot CMC and ROC curves.
+You can set the environment variable BOB_NO_STYLE_CHANGES to any value to avoid
+this script from changing the matplotlib style values. """
+
+from __future__ import print_function
+
+# matplotlib stuff
+import matplotlib
+from matplotlib import pyplot
+pyplot.switch_backend('pdf') # switch to non-X backend
+from matplotlib.backends.backend_pdf import PdfPages
+
+# import bob.measure after matplotlib, so that it cannot define the backend
+
+import argparse
+import numpy
+import math
+import os
+
+import bob.measure
+from .. import score
+
+
+if not os.environ.get('BOB_NO_STYLE_CHANGES'):
+ # make the fig size smaller so that everything becomes bigger
+ matplotlib.rc('figure', figsize=(4, 3))
+
+
+import bob.core
+logger = bob.core.log.setup("bob.bio.base")
+
+
+def command_line_arguments(command_line_parameters):
+ """Parse the program options"""
+
+ # set up command line parser
+ parser = argparse.ArgumentParser(description=__doc__,
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+ parser.add_argument('-d', '--dev-files', required=True, nargs='+', help = "A list of score files of the development set.")
+ parser.add_argument('-e', '--eval-files', nargs='+', help = "A list of score files of the evaluation set; if given it must be the same number of files as the --dev-files.")
+
+ parser.add_argument('-s', '--directory', default = '.', help = "A directory, where to find the --dev-files and the --eval-files")
+
+ parser.add_argument('-c', '--criterion', choices = ('EER', 'HTER', 'FAR'), help = "If given, the threshold of the development set will be computed with this criterion.")
+ parser.add_argument('-f', '--far-value', type=float, default=0.001, help = "The FAR value for which to evaluate (only for --criterion FAR)")
+ parser.add_argument('-x', '--cllr', action = 'store_true', help = "If given, Cllr and minCllr will be computed.")
+ parser.add_argument('-m', '--mindcf', action = 'store_true', help = "If given, minDCF will be computed.")
+ parser.add_argument('--cost', default=0.99, help='Cost for FAR in minDCF')
+ parser.add_argument('-r', '--rr', action = 'store_true', help = "If given, the Recognition Rate will be computed.")
+ parser.add_argument('-o', '--rank', type=int, default=1, help = "The rank for which to plot the DIR curve")
+ parser.add_argument('-t', '--thresholds', type=float, nargs='+', help = "If given, the Recognition Rate will incorporate an Open Set handling, rejecting all scores that are below the given threshold; when multiple thresholds are given, they are applied in the same order as the --dev-files.")
+ parser.add_argument('-l', '--legends', nargs='+', help = "A list of legend strings used for ROC, CMC and DET plots; if given, must be the same number than --dev-files.")
+ parser.add_argument('-F', '--legend-font-size', type=int, default=10, help = "Set the font size of the legends.")
+ parser.add_argument('-P', '--legend-position', type=int, help = "Set the font size of the legends.")
+ parser.add_argument('-T', '--title', nargs = '+', help = "Overwrite the default title of the plot for development (and evaluation) set")
+ parser.add_argument('-R', '--roc', help = "If given, ROC curves will be plotted into the given pdf file.")
+ parser.add_argument('-D', '--det', help = "If given, DET curves will be plotted into the given pdf file.")
+ parser.add_argument('-C', '--cmc', help = "If given, CMC curves will be plotted into the given pdf file.")
+ parser.add_argument('-O', '--dir', help = "If given, DIR curves will be plotted into the given pdf file; This is an open-set measure, which cannot be applied to closed set score files.")
+ parser.add_argument('-E', '--epc', help = "If given, EPC curves will be plotted into the given pdf file. For this plot --eval-files is mandatory.")
+ parser.add_argument('-M', '--min-far-value', type=float, default=1e-4, help = "Select the minimum FAR value used in ROC plots; should be a power of 10.")
+ parser.add_argument('-L', '--far-line-at', type=float, help = "If given, draw a veritcal line at this FAR value in the ROC plots.")
+
+ # add verbose option
+ bob.core.log.add_command_line_option(parser)
+
+ # parse arguments
+ args = parser.parse_args(command_line_parameters)
+
+ # set verbosity level
+ bob.core.log.set_verbosity_level(logger, args.verbose)
+
+ # some sanity checks:
+ for f in args.dev_files + (args.eval_files or []):
+ real_file = os.path.join(args.directory, f)
+ if not os.path.exists(real_file):
+ raise ValueError("The provided score file '%s' does not exist" % real_file)
+
+ if args.eval_files is not None and len(args.dev_files) != len(args.eval_files):
+ logger.error("The number of --dev-files (%d) and --eval-files (%d) are not identical", len(args.dev_files), len(args.eval_files))
+
+ # update legends when they are not specified on command line
+ if args.legends is None:
+ args.legends = [f.replace('_', '-') for f in args.dev_files]
+ logger.warn("Legends are not specified; using legends estimated from --dev-files: %s", args.legends)
+
+ # check that the legends have the same length as the dev-files
+ if len(args.dev_files) != len(args.legends):
+ logger.error("The number of --dev-files (%d) and --legends (%d) are not identical", len(args.dev_files), len(args.legends))
+
+ if args.thresholds is not None:
+ if len(args.thresholds) == 1:
+ args.thresholds = args.thresholds * len(args.dev_files)
+ elif len(args.thresholds) != len(args.dev_files):
+ logger.error("If given, the number of --thresholds imust be either 1, or the same as --dev-files (%d), but it is %d", len(args.dev_files), len(args.thresholds))
+ else:
+ args.thresholds = [None] * len(args.dev_files)
+
+ if args.title is not None:
+ if args.eval_files is None and len(args.title) != 1:
+ logger.warning("Ignoring the title for the evaluation set, as no evaluation set is given")
+ if args.eval_files is not None and len(args.title) < 2:
+ logger.error("The title for the evaluation set is not specified")
+
+ return args
+
+def _add_far_labels(min_far):
+ # compute and apply tick marks
+ assert min_far > 0
+ ticks = [min_far]
+ while ticks[-1] < 1.: ticks.append(ticks[-1] * 10.)
+ pyplot.xticks(ticks)
+ pyplot.axis([min_far, 1., -0.01, 1.01])
+
+
+
+def _plot_roc(frrs, colors, labels, title, fontsize=10, position=None, farfrrs=None, min_far=None):
+ if position is None: position = 'lower right'
+ figure = pyplot.figure()
+
+ # plot FAR and CAR for each algorithm
+ for i in range(len(frrs)):
+ pyplot.semilogx([f for f in frrs[i][0]], [1. - f for f in frrs[i][1]], color=colors[i], label=labels[i])
+ if isinstance(farfrrs, list):
+ pyplot.plot(farfrrs[i][0], (1.-farfrrs[i][1]), 'o', color=colors[i], markeredgecolor=colors[i])
+
+ # plot vertical bar, if desired
+ if farfrrs is not None:
+ if isinstance(farfrrs, float):
+ pyplot.plot([farfrrs,farfrrs],[0.,1.], "--", color='black')
+ else:
+ pyplot.plot([x[0] for x in farfrrs], [(1.-x[1]) for x in farfrrs], '--', color='black')
+
+ _add_far_labels(min_far)
+
+ # set label, legend and title
+ pyplot.xlabel('FMR')
+ pyplot.ylabel('1 - FNMR')
+ pyplot.grid(True, color=(0.6,0.6,0.6))
+ pyplot.legend(loc=position, prop = {'size':fontsize})
+ pyplot.title(title)
+
+ return figure
+
+
+def _plot_det(dets, colors, labels, title, fontsize=10, position=None):
+ if position is None: position = 'upper right'
+ # open new page for current plot
+ figure = pyplot.figure(figsize=(matplotlib.rcParams['figure.figsize'][0],
+ matplotlib.rcParams['figure.figsize'][0] * 0.975))
+ pyplot.grid(True)
+
+ # plot the DET curves
+ for i in range(len(dets)):
+ pyplot.plot(dets[i][0], dets[i][1], color=colors[i], label=labels[i])
+
+ # change axes accordingly
+ det_list = [0.0002, 0.001, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 0.7, 0.9, 0.95]
+ ticks = [bob.measure.ppndf(d) for d in det_list]
+ labels = [("%.5f" % d).rstrip('0').rstrip('.') for d in det_list]
+ pyplot.xticks(ticks, [l if i % 2 else "" for i,l in enumerate(labels)])
+ pyplot.yticks(ticks, labels)
+ pyplot.axis((ticks[0], ticks[-1], ticks[0], ticks[-1]))
+
+ pyplot.xlabel('FMR')
+ pyplot.ylabel('FNMR')
+ pyplot.legend(loc=position, prop = {'size':fontsize})
+ pyplot.title(title)
+
+ return figure
+
+
+def _plot_cmc(cmcs, colors, labels, title, fontsize=10, position=None):
+ if position is None: position = 'lower right'
+ # open new page for current plot
+ figure = pyplot.figure()
+
+ max_R = 0
+ # plot the CMC curves
+ for i in range(len(cmcs)):
+ probs = bob.measure.cmc(cmcs[i])
+ R = len(probs)
+ pyplot.semilogx(range(1, R+1), probs, figure=figure, color=colors[i], label=labels[i])
+ max_R = max(R, max_R)
+
+ # change axes accordingly
+ ticks = [int(t) for t in pyplot.xticks()[0]]
+ pyplot.xlabel('Rank')
+ pyplot.ylabel('Probability')
+ pyplot.xticks(ticks, [str(t) for t in ticks])
+ pyplot.axis([0, max_R, -0.01, 1.01])
+ pyplot.legend(loc=position, prop = {'size':fontsize})
+ pyplot.title(title)
+
+ return figure
+
+
+def _plot_dir(cmc_scores, far_values, rank, colors, labels, title, fontsize=10, position=None):
+ if position is None: position = 'lower right'
+ # open new page for current plot
+ figure = pyplot.figure()
+
+ # for each probe, for which no positives exists, get the highest negative
+ # score; and sort them to compute the FAR thresholds
+ for i, cmcs in enumerate(cmc_scores):
+ negatives = sorted(max(neg) for neg, pos in cmcs if (pos is None or not numpy.array(pos).size) and neg is not None)
+ if not negatives:
+ raise ValueError("There need to be at least one pair with only negative scores")
+
+ # compute thresholds based on FAR values
+ thresholds = [bob.measure.far_threshold(negatives, [], v, True) for v in far_values]
+
+ # compute detection and identification rate based on the thresholds for
+ # the given rank
+ rates = [bob.measure.detection_identification_rate(cmcs, t, rank) for t in thresholds]
+
+ # plot DIR curve
+ pyplot.semilogx(far_values, rates, figure=figure, color=colors[i], label=labels[i])
+
+ # finalize plot
+ _add_far_labels(far_values[0])
+
+ pyplot.xlabel('FAR')
+ pyplot.ylabel('DIR')
+ pyplot.legend(loc=position, prop = {'size':fontsize})
+ pyplot.title(title)
+
+ return figure
+
+
+def _plot_epc(scores_dev, scores_eval, colors, labels, title, fontsize=10, position=None):
+ if position is None: position = 'upper center'
+ # open new page for current plot
+ figure = pyplot.figure()
+
+ # plot the DET curves
+ for i in range(len(scores_dev)):
+ x,y = bob.measure.epc(scores_dev[i][0], scores_dev[i][1], scores_eval[i][0], scores_eval[i][1], 100)
+ pyplot.plot(x, y, color=colors[i], label=labels[i])
+
+ # change axes accordingly
+ pyplot.xlabel('alpha')
+ pyplot.ylabel('HTER')
+ pyplot.title(title)
+ pyplot.axis([-0.01, 1.01, -0.01, 0.51])
+ pyplot.grid(True)
+ pyplot.legend(loc=position, prop = {'size':fontsize})
+ pyplot.title(title)
+
+ return figure
+
+
+def remove_nan(scores):
+ """removes the NaNs from the scores"""
+ nans = numpy.isnan(scores)
+ sum_nans = sum(nans)
+ total = len(scores)
+ return scores[numpy.where(~nans)], sum_nans, total
+
+
+def get_fta(scores):
+ """calculates the Failure To Acquire (FtA) rate"""
+ fta_sum, fta_total = 0, 0
+ neg, sum_nans, total = remove_nan(scores[0])
+ fta_sum += sum_nans
+ fta_total += total
+ pos, sum_nans, total = remove_nan(scores[1])
+ fta_sum += sum_nans
+ fta_total += total
+ return (neg, pos, fta_sum * 100 / float(fta_total))
+
+
+def main(command_line_parameters=None):
+ """Reads score files, computes error measures and plots curves."""
+
+ args = command_line_arguments(command_line_parameters)
+
+ # get some colors for plotting
+ if len(args.dev_files) > 10:
+ cmap = pyplot.cm.get_cmap(name='magma')
+ colors = [cmap(i) for i in numpy.linspace(0, 1.0, len(args.dev_files) + 1)]
+ else:
+ # matplotlib 2.0 default color cycler list: Vega category10 palette
+ colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728',
+ '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',
+ '#bcbd22', '#17becf']
+
+ if args.criterion or args.roc or args.det or args.epc or args.cllr or args.mindcf:
+
+ # First, read the score files
+ logger.info("Loading %d score files of the development set", len(args.dev_files))
+ scores_dev = [score.split(os.path.join(args.directory, f)) for f in args.dev_files]
+ # remove nans
+ scores_dev = [get_fta(s) for s in scores_dev]
+
+ if args.eval_files:
+ logger.info("Loading %d score files of the evaluation set", len(args.eval_files))
+ scores_eval = [score.split(os.path.join(args.directory, f)) for f in args.eval_files]
+ # remove nans
+ scores_eval = [get_fta(s) for s in scores_eval]
+
+
+ if args.criterion:
+ logger.info("Computing %s on the development " % args.criterion + ("and HTER on the evaluation set" if args.eval_files else "set"))
+ for i in range(len(scores_dev)):
+ # compute threshold on development set
+ if args.criterion == 'FAR':
+ threshold = bob.measure.far_threshold(scores_dev[i][0], scores_dev[i][1], args.far_value/100.)
+ else:
+ threshold = {'EER': bob.measure.eer_threshold, 'HTER' : bob.measure.min_hter_threshold} [args.criterion](scores_dev[i][0], scores_dev[i][1])
+ # apply threshold to development set
+ far, frr = bob.measure.farfrr(scores_dev[i][0], scores_dev[i][1], threshold)
+ if args.criterion == 'FAR':
+ print("The FRR at FAR=%.1E of the development set of '%s' is %2.3f%% (CAR: %2.3f%%)" % (args.far_value, args.legends[i], frr * 100., 100.*(1-frr)))
+ else:
+ print("The %s of the development set of '%s' is %2.3f%%" % (args.criterion, args.legends[i], (far + frr) * 50.)) # / 2 * 100%
+ if args.eval_files:
+ # apply threshold to evaluation set
+ far, frr = bob.measure.farfrr(scores_eval[i][0], scores_eval[i][1], threshold)
+ if args.criterion == 'FAR':
+ print("The FRR of the evaluation set of '%s' is %2.3f%% (CAR: %2.3f%%)" % (args.legends[i], frr * 100., 100.*(1-frr))) # / 2 * 100%
+ else:
+ print("The HTER of the evaluation set of '%s' is %2.3f%%" % (args.legends[i], (far + frr) * 50.)) # / 2 * 100%
+
+
+ if args.mindcf:
+ logger.info("Computing minDCF on the development " + ("and on the evaluation set" if args.eval_files else "set"))
+ for i in range(len(scores_dev)):
+ # compute threshold on development set
+ threshold = bob.measure.min_weighted_error_rate_threshold(scores_dev[i][0], scores_dev[i][1], args.cost)
+ # apply threshold to development set
+ far, frr = bob.measure.farfrr(scores_dev[i][0], scores_dev[i][1], threshold)
+ print("The minDCF of the development set of '%s' is %2.3f%%" % (args.legends[i], (args.cost * far + (1-args.cost) * frr) * 100. ))
+ if args.eval_files:
+ # compute threshold on evaluation set
+ threshold = bob.measure.min_weighted_error_rate_threshold(scores_eval[i][0], scores_eval[i][1], args.cost)
+ # apply threshold to evaluation set
+ far, frr = bob.measure.farfrr(scores_eval[i][0], scores_eval[i][1], threshold)
+ print("The minDCF of the evaluation set of '%s' is %2.3f%%" % (args.legends[i], (args.cost * far + (1-args.cost) * frr) * 100. ))
+
+
+ if args.cllr:
+ logger.info("Computing Cllr and minCllr on the development " + ("and on the evaluation set" if args.eval_files else "set"))
+ for i in range(len(scores_dev)):
+ cllr = bob.measure.calibration.cllr(scores_dev[i][0], scores_dev[i][1])
+ min_cllr = bob.measure.calibration.min_cllr(scores_dev[i][0], scores_dev[i][1])
+ print("Calibration performance on development set of '%s' is Cllr %1.5f and minCllr %1.5f " % (args.legends[i], cllr, min_cllr))
+ if args.eval_files:
+ cllr = bob.measure.calibration.cllr(scores_eval[i][0], scores_eval[i][1])
+ min_cllr = bob.measure.calibration.min_cllr(scores_eval[i][0], scores_eval[i][1])
+ print("Calibration performance on evaluation set of '%s' is Cllr %1.5f and minCllr %1.5f" % (args.legends[i], cllr, min_cllr))
+
+
+ if args.roc:
+ logger.info("Computing CAR curves on the development " + ("and on the evaluation set" if args.eval_files else "set"))
+ min_far = int(math.floor(math.log(args.min_far_value, 10)))
+ fars = [math.pow(10., i * 0.25) for i in range(min_far * 4, 0)] + [1.]
+ frrs_dev = [bob.measure.roc_for_far(scores[0], scores[1], fars) for scores in scores_dev]
+ if args.eval_files:
+ frrs_eval = [bob.measure.roc_for_far(scores[0], scores[1], fars) for scores in scores_eval]
+
+ logger.info("Plotting ROC curves to file '%s'", args.roc)
+ try:
+ # create a multi-page PDF for the ROC curve
+ pdf = PdfPages(args.roc)
+ # create a separate figure for dev and eval
+ pdf.savefig(_plot_roc(frrs_dev, colors, args.legends, args.title[0] if args.title is not None else "ROC for development set", args.legend_font_size, args.legend_position, args.far_line_at, min_far=args.min_far_value), bbox_inches='tight')
+ del frrs_dev
+ if args.eval_files:
+ if args.far_line_at is not None:
+ farfrrs = []
+ for i in range(len(scores_dev)):
+ threshold = bob.measure.far_threshold(scores_dev[i][0], scores_dev[i][1], args.far_line_at)
+ farfrrs.append(bob.measure.farfrr(scores_eval[i][0], scores_eval[i][1], threshold))
+ else:
+ farfrrs = None
+ pdf.savefig(_plot_roc(frrs_eval, colors, args.legends, args.title[1] if args.title is not None else "ROC for evaluation set", args.legend_font_size, args.legend_position, farfrrs, min_far=args.min_far_value), bbox_inches='tight')
+ del frrs_eval
+ pdf.close()
+ except RuntimeError as e:
+ raise RuntimeError("During plotting of ROC curves, the following exception occured:\n%s" % e)
+
+ if args.det:
+ logger.info("Computing DET curves on the development " + ("and on the evaluation set" if args.eval_files else "set"))
+ dets_dev = [bob.measure.det(scores[0], scores[1], 1000) for scores in scores_dev]
+ if args.eval_files:
+ dets_eval = [bob.measure.det(scores[0], scores[1], 1000) for scores in scores_eval]
+
+ logger.info("Plotting DET curves to file '%s'", args.det)
+ try:
+ # create a multi-page PDF for the DET curve
+ pdf = PdfPages(args.det)
+ # create a separate figure for dev and eval
+ pdf.savefig(_plot_det(dets_dev, colors, args.legends, args.title[0] if args.title is not None else "DET for development set", args.legend_font_size, args.legend_position), bbox_inches='tight')
+ del dets_dev
+ if args.eval_files:
+ pdf.savefig(_plot_det(dets_eval, colors, args.legends, args.title[1] if args.title is not None else "DET for evaluation set", args.legend_font_size, args.legend_position), bbox_inches='tight')
+ del dets_eval
+ pdf.close()
+ except RuntimeError as e:
+ raise RuntimeError("During plotting of DET curves, the following exception occured:\n%s" % e)
+
+
+ if args.epc:
+ logger.info("Plotting EPC curves to file '%s'", args.epc)
+
+ if not args.eval_files:
+ raise ValueError("To plot the EPC curve the evaluation scores are necessary. Please, set it with the --eval-files option.")
+
+ try:
+ # create a multi-page PDF for the EPC curve
+ pdf = PdfPages(args.epc)
+ pdf.savefig(_plot_epc(scores_dev, scores_eval, colors, args.legends, args.title[0] if args.title is not None else "" , args.legend_font_size, args.legend_position), bbox_inches='tight')
+ pdf.close()
+ except RuntimeError as e:
+ raise RuntimeError("During plotting of EPC curves, the following exception occured:\n%s" % e)
+
+
+
+ if args.cmc or args.rr or args.dir:
+ logger.info("Loading CMC data on the development " + ("and on the evaluation set" if args.eval_files else "set"))
+ cmcs_dev = [score.cmc(os.path.join(args.directory, f)) for f in args.dev_files]
+ if args.eval_files:
+ cmcs_eval = [score.cmc(os.path.join(args.directory, f)) for f in args.eval_files]
+
+ if args.cmc:
+ logger.info("Plotting CMC curves to file '%s'", args.cmc)
+ try:
+ # create a multi-page PDF for the CMC curve
+ pdf = PdfPages(args.cmc)
+ # create a separate figure for dev and eval
+ pdf.savefig(_plot_cmc(cmcs_dev, colors, args.legends, args.title[0] if args.title is not None else "CMC curve for development set", args.legend_font_size, args.legend_position), bbox_inches='tight')
+ if args.eval_files:
+ pdf.savefig(_plot_cmc(cmcs_eval, colors, args.legends, args.title[1] if args.title is not None else "CMC curve for evaluation set", args.legend_font_size, args.legend_position), bbox_inches='tight')
+ pdf.close()
+ except RuntimeError as e:
+ raise RuntimeError("During plotting of CMC curves, the following exception occured:\n%s\nUsually this happens when the label contains characters that LaTeX cannot parse." % e)
+
+ if args.rr:
+ logger.info("Computing recognition rate on the development " + ("and on the evaluation set" if args.eval_files else "set"))
+ for i in range(len(cmcs_dev)):
+ rr = bob.measure.recognition_rate(cmcs_dev[i], args.thresholds[i])
+ print("The Recognition Rate of the development set of '%s' is %2.3f%%" % (args.legends[i], rr * 100.))
+ if args.eval_files:
+ rr = bob.measure.recognition_rate(cmcs_eval[i], args.thresholds[i])
+ print("The Recognition Rate of the development set of '%s' is %2.3f%%" % (args.legends[i], rr * 100.))
+
+ if args.dir:
+ # compute false alarm values to evaluate
+ min_far = int(math.floor(math.log(args.min_far_value, 10)))
+ fars = [math.pow(10., i * 0.25) for i in range(min_far * 4, 0)] + [1.]
+ logger.info("Plotting DIR curves to file '%s'", args.dir)
+ try:
+ # create a multi-page PDF for the DIR curve
+ pdf = PdfPages(args.dir)
+ # create a separate figure for dev and eval
+ pdf.savefig(_plot_dir(cmcs_dev, fars, args.rank, colors, args.legends, args.title[0] if args.title is not None else "DIR curve for development set", args.legend_font_size, args.legend_position), bbox_inches='tight')
+ if args.eval_files:
+ pdf.savefig(_plot_dir(cmcs_eval, fars, args.rank, colors, args.legends, args.title[1] if args.title is not None else "DIR curve for evaluation set", args.legend_font_size, args.legend_position), bbox_inches='tight')
+ pdf.close()
+ except RuntimeError as e:
+ raise RuntimeError("During plotting of DIR curves, the following exception occured:\n%s" % e)
diff --git a/bob/bio/base/script/fuse_scores.py b/bob/bio/base/script/fuse_scores.py
index 29152981af9d3e823da7c04ca0a3474b0f8ef1d1..220791dc0086d83c4190c20d6fd06e07c7b0054f 100755
--- a/bob/bio/base/script/fuse_scores.py
+++ b/bob/bio/base/script/fuse_scores.py
@@ -17,6 +17,7 @@ import bob, os, sys
import bob.learn.linear
import bob.core
+from .. import score
logger = bob.core.log.setup("bob.bio.base")
def parse_command_line(command_line_options):
@@ -66,7 +67,7 @@ def main(command_line_options = None):
for i in range(n_systems):
logger.info("Loading development set score file '%s'", args.dev_files[i])
# pythonic way: create inline dictionary "{...}", index with desired value "[...]", execute function "(...)"
- data.append({'4column' : bob.measure.load.split_four_column, '5column' : bob.measure.load.split_five_column}[args.parser](args.dev_files[i]))
+ data.append({'4column' : score.split_four_column, '5column' : score.split_five_column}[args.parser](args.dev_files[i]))
import numpy
trainer = bob.learn.linear.CGLogRegTrainer(0.5, args.convergence_threshold, args.max_iterations, mean_std_norm=not args.no_whitening)
@@ -78,7 +79,7 @@ def main(command_line_options = None):
gen_data_dev = []
for i in range(n_systems):
logger.info("Loading development set score file '%s'", args.dev_files[i])
- gen_data_dev.append({'4column' : bob.measure.load.four_column, '5column' : bob.measure.load.five_column}[args.parser](args.dev_files[i]))
+ gen_data_dev.append({'4column' : score.four_column, '5column' : score.five_column}[args.parser](args.dev_files[i]))
logger.info("Writing fused development set score file '%s'", args.fused_dev_file)
outf = open(args.fused_dev_file, 'w')
@@ -99,7 +100,7 @@ def main(command_line_options = None):
gen_data_eval = []
for i in range(n_systems):
logger.info("Loading evaluation set score file '%s'", args.eval_files[i])
- gen_data_eval.append({'4column' : bob.measure.load.four_column, '5column' : bob.measure.load.five_column}[args.parser](args.eval_files[i]))
+ gen_data_eval.append({'4column' : score.four_column, '5column' : score.five_column}[args.parser](args.eval_files[i]))
logger.info("Writing fused evaluation set score file '%s'", args.fused_eval_file)
outf = open(args.fused_eval_file, 'w')
diff --git a/bob/bio/base/test/data/dev-4col.tar.gz b/bob/bio/base/test/data/dev-4col.tar.gz
new file mode 100644
index 0000000000000000000000000000000000000000..a903eaa18e8f219b24a59933ef43987cb5f9fe76
Binary files /dev/null and b/bob/bio/base/test/data/dev-4col.tar.gz differ
diff --git a/bob/bio/base/test/data/dev-4col.txt b/bob/bio/base/test/data/dev-4col.txt
new file mode 100644
index 0000000000000000000000000000000000000000..be5f2d89447b7c47ff2a99c26fea82d3b4aeb8a8
--- /dev/null
+++ b/bob/bio/base/test/data/dev-4col.txt
@@ -0,0 +1,910 @@
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diff --git a/bob/bio/base/test/data/dev-5col.tar.gz b/bob/bio/base/test/data/dev-5col.tar.gz
new file mode 100644
index 0000000000000000000000000000000000000000..65a1cefd846ffdf691101b842ee66c1d383df1c8
Binary files /dev/null and b/bob/bio/base/test/data/dev-5col.tar.gz differ
diff --git a/bob/bio/base/test/data/dev-5col.txt b/bob/bio/base/test/data/dev-5col.txt
new file mode 100644
index 0000000000000000000000000000000000000000..02cd05efea52fcfac85b9a3f856c0475f2412354
--- /dev/null
+++ b/bob/bio/base/test/data/dev-5col.txt
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diff --git a/bob/bio/base/test/data/scores-cmc-4col-open-set-one-error.txt b/bob/bio/base/test/data/scores-cmc-4col-open-set-one-error.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ab3f4a187d8592df4460f29ed09e0a655972732b
--- /dev/null
+++ b/bob/bio/base/test/data/scores-cmc-4col-open-set-one-error.txt
@@ -0,0 +1,27 @@
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diff --git a/bob/bio/base/test/data/scores-cmc-4col-open-set-two-errors.txt b/bob/bio/base/test/data/scores-cmc-4col-open-set-two-errors.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f8f7ba24c5de55cb904a70f64501615c404416f1
--- /dev/null
+++ b/bob/bio/base/test/data/scores-cmc-4col-open-set-two-errors.txt
@@ -0,0 +1,27 @@
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diff --git a/bob/bio/base/test/data/scores-cmc-4col-open-set.txt b/bob/bio/base/test/data/scores-cmc-4col-open-set.txt
new file mode 100644
index 0000000000000000000000000000000000000000..75ed810189de655dce5d9d3314568ea1c09c8bb5
--- /dev/null
+++ b/bob/bio/base/test/data/scores-cmc-4col-open-set.txt
@@ -0,0 +1,27 @@
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diff --git a/bob/bio/base/test/data/scores-cmc-4col.txt b/bob/bio/base/test/data/scores-cmc-4col.txt
new file mode 100644
index 0000000000000000000000000000000000000000..33a76cab1bd5b61b29ff0975b3cdca9eb4bebf6d
--- /dev/null
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diff --git a/bob/bio/base/test/data/scores-cmc-5col.txt b/bob/bio/base/test/data/scores-cmc-5col.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f69a0a26dffc648dc131d2093dddbd2588289186
--- /dev/null
+++ b/bob/bio/base/test/data/scores-cmc-5col.txt
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diff --git a/bob/bio/base/test/data/test-5col.txt b/bob/bio/base/test/data/test-5col.txt
new file mode 100644
index 0000000000000000000000000000000000000000..430047a567c7fbd0ab3ec68ab5888732324b9b8c
--- /dev/null
+++ b/bob/bio/base/test/data/test-5col.txt
@@ -0,0 +1,910 @@
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+m_1041 x m_1042 x -0.097618
+m_1041 x m_1042 x -0.021077
+m_1042 x m_1043 x -0.037937
+m_1042 x m_1043 x -0.002371
+m_1042 x m_1043 x 0.012176
+m_1042 x m_1043 x -0.093591
+m_1042 x m_1043 x -0.038423
+m_1043 x m_1044 x -0.101432
+m_1043 x m_1044 x -0.039491
+m_1043 x m_1044 x -0.11965
+m_1043 x m_1044 x -0.109702
+m_1043 x m_1044 x -0.156936
+m_1044 x m_1045 x -0.085479
+m_1044 x m_1045 x -0.13362
+m_1044 x m_1045 x -0.12149
+m_1044 x m_1045 x -0.075713
+m_1044 x m_1045 x -0.08801
+m_1045 x m_1046 x -0.106102
+m_1045 x m_1046 x -0.141614
+m_1045 x m_1046 x -0.150365
+m_1045 x m_1046 x -0.123084
+m_1045 x m_1046 x -0.201897
+m_1046 x m_1047 x -0.125069
+m_1046 x m_1047 x -0.168533
+m_1046 x m_1047 x -0.112757
+m_1046 x m_1047 x -0.1736
+m_1046 x m_1047 x -0.172724
+m_1047 x m_1048 x -0.166386
+m_1047 x m_1048 x -0.170671
+m_1047 x m_1048 x -0.058329
+m_1047 x m_1048 x -0.109204
+m_1047 x m_1048 x -0.122232
+m_1048 x m_1049 x -0.204613
+m_1048 x m_1049 x -0.219919
+m_1048 x m_1049 x -0.146424
+m_1048 x m_1049 x -0.16871
+m_1048 x m_1049 x -0.142253
+m_1049 x m_1050 x -0.051034
+m_1049 x m_1050 x -0.103298
+m_1049 x m_1050 x -0.035702
+m_1049 x m_1050 x -0.100288
+m_1049 x m_1050 x -0.00913
+m_1050 x m_1051 x -0.073954
+m_1050 x m_1051 x -0.099813
+m_1050 x m_1051 x -0.044652
+m_1050 x m_1051 x -0.043802
+m_1050 x m_1051 x -0.139938
+m_1051 x m_1052 x -0.109771
+m_1051 x m_1052 x -0.121056
+m_1051 x m_1052 x -0.102811
+m_1051 x m_1052 x -0.097827
+m_1051 x m_1052 x -0.106235
+m_1052 x m_1040 x -0.129523
+m_1052 x m_1040 x -0.167863
+m_1052 x m_1040 x -0.179332
+m_1052 x m_1040 x -0.169263
+m_1052 x m_1040 x -0.188729
diff --git a/bob/bio/base/test/test_io.py b/bob/bio/base/test/test_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa908d46bf2b6c49c56edbc159ac0efc898cf6da
--- /dev/null
+++ b/bob/bio/base/test/test_io.py
@@ -0,0 +1,326 @@
+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# Andre Anjos <andre.anjos@idiap.ch>
+# Wed 11 Dec 15:14:08 2013 CET
+#
+# Copyright (C) 2011-2013 Idiap Research Institute, Martigny, Switzerland
+
+"""Tests the IO functionality of bob.bio.base.score."""
+
+import numpy
+import tempfile
+import os
+import shutil
+import pkg_resources
+
+from .. import score
+
+
+def test_load_scores():
+ # This function tests the IO functionality of loading score files in
+ # different ways
+
+ load_functions = {'4col': score.four_column,
+ '5col': score.five_column}
+ cols = {'4col': 4, '5col': 5}
+
+ for variant in cols:
+ normal_score_file = pkg_resources.resource_filename(
+ 'bob.bio.base.test', 'data/dev-%s.txt' % variant)
+ normal_scores = list(load_functions[variant](normal_score_file))
+
+ assert len(normal_scores) == 910
+ assert all(len(s) == cols[variant] for s in normal_scores)
+
+ # read the compressed score file
+ compressed_score_file = pkg_resources.resource_filename(
+ 'bob.bio.base.test', 'data/dev-%s.tar.gz' % variant)
+ compressed_scores = list(load_functions[variant](compressed_score_file))
+
+ assert len(compressed_scores) == len(normal_scores)
+ assert all(len(c) == cols[variant] for c in compressed_scores)
+ assert all(c[i] == s[i] for c, s in zip(compressed_scores,
+ normal_scores) for i in range(cols[variant]))
+
+ # Use auto-estimated score file contents
+ # read score file in normal way
+ normal_scores = list(score.scores(normal_score_file))
+
+ assert len(normal_scores) == 910
+ assert all(len(s) == cols[variant] for s in normal_scores)
+
+ # read the compressed score file
+ compressed_scores = list(score.scores(compressed_score_file))
+
+ assert len(compressed_scores) == len(normal_scores)
+ assert all(len(c) == cols[variant] for c in compressed_scores)
+ assert all(c[i] == s[i] for c, s in zip(compressed_scores,
+ normal_scores) for i in range(cols[variant]))
+
+
+def test_split_scores():
+ # This function tests the IO functionality of loading score files in
+ # different ways
+
+ split_functions = {'4col': score.split_four_column,
+ '5col': score.split_five_column}
+ cols = {'4col': 4, '5col': 5}
+
+ for variant in cols:
+ # read score file in normal way
+ normal_score_file = pkg_resources.resource_filename(
+ 'bob.bio.base.test', 'data/dev-%s.txt' % variant)
+ negatives, positives = split_functions[variant](normal_score_file)
+
+ assert len(negatives) == 520, len(negatives)
+ assert len(positives) == 390, len(positives)
+
+ # read the compressed score file
+ compressed_score_file = pkg_resources.resource_filename(
+ 'bob.bio.base.test', 'data/dev-%s.tar.gz' % variant)
+ negatives, positives = split_functions[variant](compressed_score_file)
+
+ assert len(negatives) == 520, len(negatives)
+ assert len(positives) == 390, len(positives)
+
+ # Use auto-estimated score file contents
+ # read score file in normal way
+ negatives, positives = score.split(normal_score_file)
+
+ assert len(negatives) == 520, len(negatives)
+ assert len(positives) == 390, len(positives)
+
+ # read the compressed score file
+ negatives, positives = score.split(compressed_score_file)
+
+ assert len(negatives) == 520, len(negatives)
+ assert len(positives) == 390, len(positives)
+
+
+def test_load_score():
+ # This function tests the IO functionality of loading score files in
+ # different ways
+
+ scores = []
+ cols = {'4col': 4, '5col': 5}
+
+ for variant in cols:
+ compressed_score_file = pkg_resources.resource_filename(
+ 'bob.bio.base.test', 'data/dev-%s.tar.gz' % variant)
+ normal_score_file = pkg_resources.resource_filename(
+ 'bob.bio.base.test', 'data/dev-%s.txt' % variant)
+ normal_scores = score.load_score(
+ normal_score_file, cols[variant])
+
+ assert len(normal_scores) == 910
+ assert len(normal_scores.dtype) == cols[variant]
+
+ # read the compressed score file
+ compressed_score_file = pkg_resources.resource_filename(
+ 'bob.bio.base.test', 'data/dev-%s.tar.gz' % variant)
+ compressed_scores = score.load_score(
+ compressed_score_file, cols[variant])
+
+ assert len(compressed_scores) == len(normal_scores)
+ assert len(compressed_scores.dtype) == cols[variant]
+ for name in normal_scores.dtype.names:
+ assert all(normal_scores[name] == compressed_scores[name])
+
+ # test minimal loading
+ minimal_scores = score.load_score(
+ normal_score_file, minimal=True)
+ assert len(minimal_scores) == 910
+ assert len(minimal_scores.dtype) == 3
+ assert minimal_scores.dtype.names == ('claimed_id', 'real_id', 'score')
+
+
+def test_dump_score():
+ # This function tests the IO functionality of dumping score files
+
+ scores = []
+ cols = {'4col': 4, '5col': 5}
+
+ for variant in cols:
+ # read score file
+ normal_score_file = pkg_resources.resource_filename(
+ 'bob.bio.base.test', 'data/dev-%s.txt' % variant)
+ normal_scores = score.load_score(
+ normal_score_file, cols[variant])
+
+ with tempfile.TemporaryFile() as f:
+ score.dump_score(f, normal_scores)
+ f.seek(0)
+ loaded_scores = score.load_score(f, cols[variant])
+
+ for name in normal_scores.dtype.names:
+ assert all(normal_scores[name] == loaded_scores[name])
+
+
+def _check_binary_identical(name1, name2):
+ # see: http://www.peterbe.com/plog/using-md5-to-check-equality-between-files
+ from hashlib import md5
+ # tests if two files are binary identical
+ 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)
diff --git a/bob/bio/base/test/test_scripts.py b/bob/bio/base/test/test_scripts.py
index 072a431b03af1eea9afa23d96ddb211a1ec9e183..912dd325fdf635ee1cf1cf957317fbf6d00800ee 100644
--- a/bob/bio/base/test/test_scripts.py
+++ b/bob/bio/base/test/test_scripts.py
@@ -8,6 +8,7 @@ import nose
import bob.io.image
import bob.bio.base
from . import utils
+from .. import score
from nose.plugins.skip import SkipTest
@@ -20,7 +21,6 @@ data_dir = pkg_resources.resource_filename('bob.bio.base', 'test/data')
def _verify(parameters, test_dir, sub_dir, ref_modifier="", score_modifier=('scores',''), counts=3, check_zt=True):
from bob.bio.base.script.verify import main
- import bob.measure
try:
main(parameters)
@@ -42,7 +42,7 @@ def _verify(parameters, test_dir, sub_dir, ref_modifier="", score_modifier=('sco
d = []
# read reference and new data
for score_file in (score_files[i], reference_files[i]):
- f = bob.measure.load.open_file(score_file)
+ f = score.open_file(score_file)
d_ = []
for line in f:
if isinstance(line, bytes): line = line.decode('utf-8')
@@ -278,7 +278,6 @@ def test_verify_filelist():
]
from bob.bio.base.script.verify import main
- import bob.measure
try:
main(parameters)
@@ -292,8 +291,8 @@ def test_verify_filelist():
for i in (0,1):
# load scores
- a1, b1 = bob.measure.load.split_four_column(score_files[i])
- a2, b2 = bob.measure.load.split_four_column(reference_files[i])
+ a1, b1 = score.split_four_column(score_files[i])
+ a2, b2 = score.split_four_column(reference_files[i])
# sort scores
a1 = sorted(a1); a2 = sorted(a2); b1 = sorted(b1); b2 = sorted(b2)
@@ -323,7 +322,6 @@ def test_verify_missing():
]
from bob.bio.base.script.verify import main
- import bob.measure
try:
main(parameters)
@@ -336,7 +334,7 @@ def test_verify_missing():
for i in (0,1):
# load scores
- a, b = bob.measure.load.split_four_column(score_files[i])
+ a, b = score.split_four_column(score_files[i])
assert numpy.all(numpy.isnan(a))
assert numpy.all(numpy.isnan(b))
@@ -479,15 +477,14 @@ def test_fusion():
# execute the script
from bob.bio.base.script.fuse_scores import main
- import bob.measure
try:
main(parameters)
# assert that we can read the two files, and that they contain the same number of lines as the original file
for i in (0,1):
assert os.path.exists(output_files[i])
- r = bob.measure.load.four_column(reference_files[i])
- o = bob.measure.load.four_column(output_files[i])
+ r = score.four_column(reference_files[i])
+ o = score.four_column(output_files[i])
assert len(list(r)) == len(list(o))
finally:
shutil.rmtree(test_dir)
diff --git a/bob/bio/base/tools/scoring.py b/bob/bio/base/tools/scoring.py
index 18aed256117d7c06dcb5dab84b50c25c5305ded0..eb634b96d9f286dffa9a588effa0cd2a204b4719 100644
--- a/bob/bio/base/tools/scoring.py
+++ b/bob/bio/base/tools/scoring.py
@@ -1,7 +1,6 @@
import bob.io.base
import bob.learn.em
import bob.learn.linear
-import bob.measure
import numpy
import os, sys
import tarfile
@@ -12,6 +11,7 @@ logger = logging.getLogger("bob.bio.base")
from .FileSelector import FileSelector
from .. import utils
+from .. import score
def _scores(algorithm, reader, model, probe_objects, allow_missing_files):
"""Compute scores for the given model and a list of probes.
@@ -62,12 +62,12 @@ def _scores(algorithm, reader, model, probe_objects, allow_missing_files):
def _open_to_read(score_file):
- """Checks for the existence of the normal and the compressed version of the file, and calls :py:func:`bob.measure.load.open_file` for the existing one."""
+ """Checks for the existence of the normal and the compressed version of the file, and calls :py:func:`bob.bio.base.score.open_file` for the existing one."""
if not os.path.exists(score_file):
score_file += '.tar.bz2'
if not os.path.exists(score_file):
raise IOError("The score file '%s' cannot be found. Aborting!" % score_file)
- return bob.measure.load.open_file(score_file)
+ return score.open_file(score_file)
def _open_to_write(score_file, write_compressed):
@@ -115,7 +115,8 @@ def _delete(score_file, write_compressed):
def _save_scores(score_file, scores, probe_objects, client_id, write_compressed):
- """Saves the scores of one model into a text file that can be interpreted by :py:func:`bob.measure.load.split_four_column`."""
+ """Saves the scores of one model into a text file that can be interpreted by
+ :py:func:`bob.bio.base.score.split_four_column`."""
assert len(probe_objects) == scores.shape[1]
# open file for writing
@@ -493,7 +494,9 @@ def _concat(score_files, output, write_compressed, model_ids):
def concatenate(compute_zt_norm, groups = ['dev', 'eval'], write_compressed = False, add_model_id = False):
"""Concatenates all results into one (or two) score files per group.
- Score files, which were generated per model, are concatenated into a single score file, which can be interpreter by :py:func:`bob.measure.load.split_four_column`.
+ Score files, which were generated per model, are concatenated into a single
+ score file, which can be interpreter by
+ :py:func:`bob.bio.base.score.load.split_four_column`.
The score files are always re-computed, regardless if they exist or not.
**Parameters:**
@@ -563,7 +566,7 @@ def calibrate(compute_zt_norm, groups = ['dev', 'eval'], prior = 0.5, write_comp
logger.info(" - Calibration: Training calibration for type %s from group %s", norm, groups[0])
llr_trainer = bob.learn.linear.CGLogRegTrainer(prior, 1e-16, 100000)
- training_scores = list(bob.measure.load.split_four_column(training_score_file))
+ training_scores = list(score.split_four_column(training_score_file))
for i in (0,1):
h = numpy.array(training_scores[i])
# remove NaN's
@@ -582,7 +585,7 @@ def calibrate(compute_zt_norm, groups = ['dev', 'eval'], prior = 0.5, write_comp
logger.info(" - Calibration: calibrating scores from '%s' to '%s'", score_file, calibrated_file)
# iterate through the score file and calibrate scores
- scores = bob.measure.load.four_column(_open_to_read(score_file))
+ scores = score.four_column(_open_to_read(score_file))
f = _open_to_write(calibrated_file, write_compressed)
diff --git a/doc/index.rst b/doc/index.rst
index 59c610e4569e3f3b516883877b853dbb27acaba6..2b966a5d00794c070ff4578ccbe667253e593a4f 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -78,6 +78,7 @@ Users Guide
filelist-guide
more
annotations
+ openbr
Reference Manual
diff --git a/doc/openbr.rst b/doc/openbr.rst
new file mode 100644
index 0000000000000000000000000000000000000000..348d3b77d861a7c0c29eb4aa65887d86a5b6e3c9
--- /dev/null
+++ b/doc/openbr.rst
@@ -0,0 +1,105 @@
+
+.. _bob.bio.base.openbr:
+
+=====================
+Score file conversion
+=====================
+Sometimes, it is required to export the score files generated by Bob to a
+different format, e.g., to be able to generate a plot comparing Bob's systems
+with other systems. In this package, we provide source code to convert between
+different types of score files.
+
+Bob to OpenBR
+-------------
+One of the supported formats is the matrix format that the National Institute
+of Standards and Technology (NIST) uses, and which is supported by OpenBR_.
+The scores are stored in two binary matrices, where the first matrix (usually
+with a ``.mtx`` filename extension) contains the raw scores, while a second
+mask matrix (extension ``.mask``) contains information, which scores are
+positives, and which are negatives.
+To convert from Bob's four column or five column score file to a pair of these
+matrices, you can use the :py:func:`bob.bio.base.score.openbr.write_matrix` function.
+In the simplest way, this function takes a score file
+``'five-column-sore-file'`` and writes the pair ``'openbr.mtx', 'openbr.mask'``
+of OpenBR_ compatible files:
+
+.. code-block:: py
+
+ >>> bob.bio.base.score.openbr.write_matrix('five-column-sore-file', 'openbr.mtx', 'openbr.mask', score_file_format = '5column')
+
+In this way, the score file will be parsed and the matrices will be written in
+the same order that is obtained from the score file.
+For most of the applications, this should be sufficient, but as the identity
+information is lost in the matrix files, no deeper analysis is possible anymore
+when just using the matrices. To enforce an order of the models and probes
+inside the matrices, you can use the ``model_names`` and ``probe_names``
+parameters of :py:func:`bob.bio.base.score.openbr.write_matrix`:
+
+* The ``probe_names`` parameter lists the ``path`` elements stored in the score
+ files, which are the fourth column in a ``5column`` file, and the third
+ column in a ``4column`` file, see :py:func:`bob.bio.base.score.load.five_column` and
+ :py:func:`bob.bio.base.score.load.four_column`.
+
+* The ``model_names`` parameter is a bit more complicated. In a ``5column``
+ format score file, the model names are defined by the second column of that
+ file, see :py:func:`bob.bio.base.score.load.five_column`. In a ``4column`` format
+ score file, the model information is not contained, but only the client
+ information of the model. Hence, for the ``4column`` format, the
+ ``model_names`` actually lists the client ids found in the first column, see
+ :py:func:`bob.bio.base.score.load.four_column`.
+
+.. warning::
+
+ The model information is lost, but required to write the matrix files. In
+ the ``4column`` format, we use client ids instead of the model
+ information. Hence, when several models exist per client, this function
+ will not work as expected.
+
+Additionally, there are fields in the matrix files, which define the gallery
+and probe list files that were used to generate the matrix. These file names
+can be selected with the ``gallery_file_name`` and ``probe_file_name`` keyword
+parameters of :py:func:`bob.bio.base.score.openbr.write_matrix`.
+Finally, OpenBR defines a specific ``'search'`` score file format, which is
+designed to be used to compute CMC curves. The score matrix contains
+descendingly sorted and possibly truncated list of scores, i.e., for each
+probe, a sorted list of all scores for the models is generated. To generate
+these special score file format, you can specify the ``search`` parameter. It
+specifies the number of highest scores per probe that should be kept. If the
+``search`` parameter is set to a negative value, all scores will be kept. If
+the ``search`` parameter is higher as the actual number of models, ``NaN``
+scores will be appended, and the according mask values will be set to ``0``
+(i.e., to be ignored).
+OpenBR to Bob
+-------------
+On the other hand, you might also want to generate a Bob-compatible (four or
+five column) score file based on a pair of OpenBR matrix and mask files. This
+is possible by using the :py:func:`bob.bio.base.score.openbr.write_score_file`
+function. At the basic, it takes the given pair of matrix and mask files, as
+well as the desired output score file:
+
+.. code-block:: py
+
+ >>> bob.bio.base.score.openbr.write_score_file('openbr.mtx', 'openbr.mask', 'four-column-sore-file')
+
+This score file is sufficient to compute a CMC curve (see `bob.measure`), however it
+does not contain relevant client ids or paths for models and probes.
+Particularly, it assumes that each client has exactly one associated model.
+To add/correct these information, you can use additional parameters to
+:py:func:`bob.bio.base.score.openbr.write_score_file`. Client ids of models and
+probes can be added using the ``models_ids`` and ``probes_ids`` keyword
+arguments. The length of these lists must be identical to the number of models
+and probes as given in the matrix files, **and they must be in the same order
+as used to compute the OpenBR matrix**. This includes that the same
+same-client and different-client pairs as indicated by the OpenBR mask will be
+generated, which will be checked inside the function.
+To add model and probe path information, the ``model_names`` and
+``probe_names`` parameters, which need to have the same size and order as the
+``models_ids`` and ``probes_ids``. These information are simply stored in the
+score file, and no further check is applied.
+
+.. note:: The ``model_names`` parameter is used only when writing score files in ``score_file_format='5column'``, in the ``'4column'`` format, this parameter is ignored.
+
+
+.. Place youre references here:
+.. _openbr: http://openbiometrics.org
+
diff --git a/doc/py_api.rst b/doc/py_api.rst
index c9841b2bcc2bd386e1cdf21b04a4e7a30a1dd806..4d52687cbea4d63cf1e3855f9095f9f975cec249 100644
--- a/doc/py_api.rst
+++ b/doc/py_api.rst
@@ -94,6 +94,26 @@ Scoring
bob.bio.base.tools.concatenate
bob.bio.base.tools.calibrate
+Loading data
+------------
+.. autosummary::
+ bob.bio.base.score.load.open_file
+ bob.bio.base.score.load.scores
+ bob.bio.base.score.load.split
+ bob.bio.base.score.load.cmc
+ bob.bio.base.score.load.four_column
+ bob.bio.base.score.load.split_four_column
+ bob.bio.base.score.load.cmc_four_column
+ bob.bio.base.score.load.five_column
+ bob.bio.base.score.load.split_five_column
+ bob.bio.base.score.load.cmc_five_column
+
+OpenBR conversions
+------------------
+.. autosummary::
+ bob.bio.base.score.openbr.write_matrix
+ bob.bio.base.score.openbr.write_score_file
+
Details
-------
@@ -108,5 +128,6 @@ Details
.. autoclass:: FileSelector
-
+.. automodule:: bob.bio.base.score.load
+.. automodule:: bob.bio.base.score.openbr
.. include:: links.rst