diff --git a/bob/bio/face/script/ijba_collect_results.py b/bob/bio/face/script/ijba_collect_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f28ddec8e963f3772df1429a8c66284822dc9ec
--- /dev/null
+++ b/bob/bio/face/script/ijba_collect_results.py
@@ -0,0 +1,209 @@
+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+
+from __future__ import print_function
+
+"""
+This script parses through the given directory, collects all results of
+experiments using bob.db.ijba and dumps a nice copy/paste rst text.
+
+It supports comparison (--report-type comparison) and search (--report-type search) experiments
+
+For comparison, we have a RST table like this:
+
++-----------------+-----------------+-----------------+-----------------+--------------------------+
+| CMC% (R=1) | TPIR% (FAR=0.1) | TPIR% (FAR=0.01)|TPIR% (FAR=0.001)| split |
++=================+=================+=================+=================+==========================+
+| 00000 |0000000 |00000000 |00000 |split 0 |
++-----------------+-----------------+-----------------+-----------------+--------------------------+
+| |
+
+
+For search, we have a RST table like this:
+
++-----------------+-----------------+-----------------+--------------------------+
+| DIR% (FAR=0.1) | DIR% (FAR=0.01) | DIR% (FAR=0.001)| split |
++=================+=================+=================+==========================+
+| 00000 |000000 |0000000 |00000000 |
+
+
+"""
+
+
+import sys, os, glob
+import argparse
+import numpy
+
+import bob.measure
+import bob.core
+logger = bob.core.log.setup("bob.bio.base")
+
+from bob.bio.base.script.collect_results import Result, recurse, add_results
+
+far_thresholds = [0.1, 0.01, 0.001]
+
+
+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', '--directory', default=".", help = "The directory where the results should be collected from; might include search patterns as '*'.")
+ parser.add_argument('-o', '--output', help = "Name of the output file that will contain the EER/HTER scores")
+ parser.add_argument('-r', '--report-type', default="comparison", choices=("comparison", "search"), help = "Type of the report. For `comparison`, CMC(rank=1) and TPIR (FAR=[0.1, 0.01 and 0.001] ) will be reported. For the search DIR (rank=1) and TPIR (FAR=[0.1, 0.01 and 0.001] is reported")
+
+ parser.add_argument('--self-test', action='store_true', help=argparse.SUPPRESS)
+
+ bob.core.log.add_command_line_option(parser)
+
+ # parse arguments
+ args = parser.parse_args(command_line_parameters)
+
+ bob.core.log.set_verbosity_level(logger, args.verbose)
+
+ return args
+
+
+def search_results(args, directories):
+ """
+ Navigates throught the directories collection evaluation results
+ """
+
+ results = []
+ for directory in directories:
+ r = recurse(args, directory)
+ if r is not None:
+ results += r
+
+ return results
+
+
+def compute_mean_std(results):
+ return numpy.mean([r.nonorm_dev for r in results]), numpy.std([r.nonorm_dev for r in results])
+
+
+def compute_comparison(args, directories):
+ """
+ Plot evaluation table for the comparison protocol
+ """
+
+ def plot_comparison_table(cmc_r1, fnmr):
+
+ grid = "+-----------------+-----------------+-----------------+-----------------+--------------------------+\n"
+ grid += "| CMC% (R=1) | TPIR% (FAR=0.1) | TPIR% (FAR=0.01)|TPIR% (FAR=0.001)| split |\n"
+ grid += "+=================+=================+=================+=================+==========================+\n"
+
+ for cmc, fnmr_0, fnmr_1, fnmr_2, split in zip(cmc_r1, fnmr[0], fnmr[1], fnmr[2], range(len(cmc_r1))):
+ grid += "|{:17s}|{:17s}|{:17s}|{:17s}|{:26s}|\n".format(str(round(cmc.nonorm_dev,5)*100),
+ str(round(fnmr_0.nonorm_dev,5)*100),
+ str(round(fnmr_1.nonorm_dev,5)*100),
+ str(round(fnmr_2.nonorm_dev,5)*100),
+ "split {0}".format(split))
+ grid += "+-----------------+-----------------+-----------------+-----------------+--------------------------+\n"
+
+ cmc = compute_mean_std(cmc_r1)
+ fnmr_0 = compute_mean_std(fnmr[0])
+ fnmr_1 = compute_mean_std(fnmr[1])
+ fnmr_2 = compute_mean_std(fnmr[2])
+ grid += "|**{:6s}({:5s})**|**{:6s}({:5s})**|**{:6s}({:5s})**|**{:6s}({:5s})**|{:26s}|\n".format(
+ str(round(cmc[0],4)*100),str(round(cmc[1],4)*100),
+ str(round(fnmr_0[0],4)*100),str(round(fnmr_0[1],4)*100),
+ str(round(fnmr_1[0],4)*100),str(round(fnmr_1[1],4)*100),
+ str(round(fnmr_2[0],4)*100),str(round(fnmr_2[1],4)*100),
+ "mean(std)")
+ grid += "+-----------------+-----------------+-----------------+-----------------+--------------------------+\n"
+
+ return grid
+
+ def compute_fnmr(args, directories):
+ fnmr = []
+ for f in far_thresholds:
+ args.criterion = "FAR"
+ args.far_threshold = f
+
+ # Computing TPIR
+ frr = search_results(args, directories)
+ for rr in frr:
+ rr.nonorm_dev = 1.-rr.nonorm_dev
+ fnmr.append(frr)
+
+ return fnmr
+
+
+ args = args
+ args.rank = 1
+ args.criterion = "RR"
+ cmc_r1 = search_results(args, directories)
+ fnmr = compute_fnmr(args, directories)
+ return plot_comparison_table(cmc_r1, fnmr)
+
+
+def compute_search(args, directories):
+ """
+ Plot evaluation table for the search protocol
+ """
+
+ def plot_search_table(dira):
+ grid = "+-----------------+-----------------+-----------------+--------------------------+\n"
+ grid += "| DIR% (FAR=0.1) | DIR% (FAR=0.01) | DIR% (FAR=0.001)| split |\n"
+ grid += "+=================+=================+=================+==========================+\n"
+
+ n_splits = len(dira[0])
+ for dira_0, dira_1, dira_2, split in zip(dira[0], dira[1], dira[2], range(n_splits)):
+ grid += "|{:17s}|{:17s}|{:17s}|{:26s}|\n".format(str(round(dira_0.nonorm_dev,5)*100),
+ str(round(dira_1.nonorm_dev,5)*100),
+ str(round(dira_2.nonorm_dev,5)*100),
+ "split {0}".format(split))
+ grid += "+-----------------+-----------------+-----------------+--------------------------+\n"
+
+ dira_0 = compute_mean_std(dira[0])
+ dira_1 = compute_mean_std(dira[1])
+ dira_2 = compute_mean_std(dira[2])
+ grid += "|**{:6s}({:5s})**|**{:6s}({:5s})**|**{:6s}({:5s})**|{:26s}|\n".format(
+ str(round(dira_0[0],4)*100),str(round(dira_0[1],4)*100),
+ str(round(dira_1[0],4)*100),str(round(dira_1[1],4)*100),
+ str(round(dira_2[0],4)*100),str(round(dira_2[1],4)*100),
+ "mean(std)")
+ grid += "+-----------------+-----------------+-----------------+--------------------------+\n"
+
+ return grid
+
+ def compute_dir(args, directories):
+ dira = []
+ for f in far_thresholds:
+ args.criterion = "DIR"
+ args.far_threshold = f
+ dira.append(search_results(args, directories))
+
+ return dira
+
+
+ args = args
+ args.rank = 1
+ args.criterion = "DIR"
+
+ dira = compute_dir(args, directories)
+ return plot_search_table(dira)
+
+
+def main(command_line_parameters = None):
+ """Iterates through the desired directory and collects all result files."""
+ args = command_line_arguments(command_line_parameters)
+
+ # collect results
+ directories = glob.glob(args.directory)
+
+ # Injecting some variables
+ args.dev = "scores-dev"
+ args.eval = "scores-eval"
+ args.nonorm = "nonorm"
+ args.ztnorm = "ztnorm"
+
+ if args.report_type == "comparison":
+ print(compute_comparison(args, directories))
+ else:
+ print(compute_search(args, directories))
+
diff --git a/setup.py b/setup.py
index efa163beab83bc71120d469b61c47c366bcfdfcc..18a9859f1fe906caf03108a13227470ad9d069a3 100644
--- a/setup.py
+++ b/setup.py
@@ -104,7 +104,8 @@ setup(
# scripts should be declared using this entry:
'console_scripts': [
'baselines.py = bob.bio.face.script.baselines:main',
- 'display_face_annotations.py = bob.bio.face.script.display_face_annotations:main'
+ 'display_face_annotations.py = bob.bio.face.script.display_face_annotations:main',
+ 'bob_ijba_collect_results.py = bob.bio.face.script.ijba_collect_results:main',
],
'bob.bio.database': [