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Commit 00382740 authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira Committed by Amir MOHAMMADI
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Removed openBR scoring 

Removed openBR scoring
parent dbac940d
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1 merge request!180[dask] Preparing bob.bio.base for dask pipelines
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bob/bio/base/score/__init__.py
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− 2
View file @ 00382740
...@@ -2,5 +2,3 @@ from .load import (open_file, split, cmc, split_four_column, four_column, ...@@ -2,5 +2,3 @@ from .load import (open_file, split, cmc, split_four_column, four_column,
split_five_column, five_column, scores, load_score, split_five_column, five_column, scores, load_score,
dump_score, cmc_four_column, cmc_five_column dump_score, cmc_four_column, cmc_five_column
) )
from .openbr import (write_matrix, write_score_file)
bob/bio/base/score/openbr.py deleted 100644 → 0
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#!/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))
bob/bio/base/test/test_io.py
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...@@ -163,164 +163,3 @@ def _check_binary_identical(name1, name2): ...@@ -163,164 +163,3 @@ def _check_binary_identical(name1, name2):
with open(name1, 'rb') as f1: with open(name1, 'rb') as f1:
with open(name2, 'rb') as f2: with open(name2, 'rb') as f2:
assert md5(f1.read()).digest() == md5(f2.read()).digest() 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)
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