#!/usr/bin/env python #$ -cwd #$ -S /usr/bin/python ########################### usual importation of modules ##################### # import of standard module import sys import os from optparse import OptionParser # import the user configuration from ~/.pythonrc.py import user ######################### importation of pyVerif modules ##################### # import necessary classes from pyVerif from pyVerif.Executor import Executor from pyVerifseb.MyExperiments import DiscriminantExperiment ##################### the "main" program ################## if __name__ == '__main__': print """ pyVerif program to perform a face verification experiment using a metric. Authors: Sebastien Marcel, Johnny Mariethoz and Olivier Bornet Version: 1.0 Date: August 2006 """ # program usage usage = "usage: %prog [options]" parser = OptionParser(usage) # set options parser.set_description ("pyVerif program to perform a face verification experiment") parser.add_option ("-t", "--test", dest="test", help="Perform also test set", action="store_true", default=False) parser.add_option ("-d", "--features-dir", dest="features_dir", help="Directory containing the features files", type="string", default="none") parser.add_option ("-o", "--output-dir", dest="output_dir", help="Directory containing the results files", type="string", default="./noname") parser.add_option ("-p", "--protocol-dir", dest="protocol_dir", help="Directory containing the protocol files", type="string", default="lp") parser.add_option ("-b", "--base-dir", dest="base_dir", help="Directory containing protocols and features " + "(default xm2vts)", type="string", default="xm2vts") parser.add_option ("-v", "--variant", dest="variant", help="Variant of the protocol (default lp1)", type="string", default="lp1") parser.add_option ("-l", "--log-on-screen", dest="log_on_screen", help="Output log on screen", action="store_true", default=False) parser.add_option ("-m", "--metric", dest="metric", help="Metric " + "(default Euclidean)", type="string", default="") parser.add_option ("-i", "--index", dest="model_is_an_index", help="store a model as an index on feature data", action="store_true", default=False) parser.add_option ("", "--dim", dest="dim", help="input dimensionality to keep", type="int", default=-1) parser.add_option ("", "--width", dest="width", help="input image width", type="int", default=32) parser.add_option ("", "--height", dest="height", help="input image height", type="int", default=40) parser.add_option ("", "--perf", dest="compute_perf", help="compute the performance", action="store_true", default=False) # parse options (options,args) = parser.parse_args () # check the number of options if len(args) != 0: parser.error("Error: incorrect number of arguments, try --help") if options.features_dir == "none": parser.error("Error: no feature dir provided") # # prepare the directories # # base dir where is this script (for instance /home/vision/marcel/work/experiments/faceverification/pyverif/verification) base_dir = os.path.join ('/home', 'vision', 'marcel', 'work', 'experiments', 'faceverification', 'pyverif', options.base_dir) # directory containing the features features_dir = os.path.join (base_dir, 'features', options.features_dir) # directory containing the binary programs bin_dir = os.path.join (base_dir, 'verification','bin') # directory for the output files out_dir = os.path.join (options.output_dir, options.variant) # base dir containing the protocol files base_dir_protocols = os.path.join (base_dir, 'protocols', options.protocol_dir) # directory containing the results (created if it does'nt exist) results_dir = os.path.join (out_dir, 'results') if not os.path.isdir (results_dir): os.makedirs (results_dir) # directory containing the temporary files (created if it does'nt exist) tmp_dir = os.path.join (out_dir, 'tmp') if not os.path.isdir (tmp_dir): os.makedirs (tmp_dir) # # prepare the experiment # # create an instance of the Experiment class experiment = DiscriminantExperiment ( out_dir = out_dir, features_dir = features_dir, features_ext = '.bindata', bin_dir = bin_dir, tmp_dir = tmp_dir, log_on_screen = options.log_on_screen, log_file = os.path.join (out_dir, 'metric-' + options.variant + '.log')) # log the used options experiment.log_options (options.__dict__) # load the specified protocol experiment.load_protocols (base_dir_protocols, options.variant) # specify the main program to create a model and test using a metric cmd_test = "metric" if options.model_is_an_index: cmd_train = "cpbindata" # options to train an MLP train_opt = "%FILES %MODEL " train_opt = train_opt + " > %TMPDIR/train_model.stdout" test_opt = "-verbose -mi -usemean " else: cmd_train = "addfilebindata" # options to train an MLP train_opt = "-verbose " train_opt = train_opt + "%FILES %MODEL " train_opt = train_opt + " > %TMPDIR/train_model.stdout" test_opt = "-verbose -usemean " # options to compute the scores against a GMM (world/client model testing) # test_opt = test_opt + "-dim %d " % options.dim if options.metric: # the metric test_opt = test_opt + "-%s " % options.metric # tangent distance (td) options if options.metric == "td": test_opt = test_opt + "-width %d -height %d " % (options.width, options.height) test_opt = test_opt + "%MODEL %FILES %SCORES " test_opt = test_opt + " > %TMPDIR/test_model.stdout" # set main program and options experiment.set_cmds (cmd_train, train_opt, cmd_test, test_opt) # # run the experiment # # train all the client models experiment.train_clients_models (for_dev = True) # compute all the scores against the clients models for the development set experiment.build_clients_scores (for_dev = True) # merge scores from the world model and from the client model for the development set experiment.merge_score (os.path.join (results_dir, 'scores-dev'), for_dev = True) if options.test: # Warning: no client models to train in test with XM2VTS if options.base_dir != "xm2vts": # train all the client models experiment.train_clients_models (for_dev=False) # compute all the scores against the clients models for the test set experiment.build_clients_scores (for_dev = False) # merge scores from the world model and from the client model for the test set experiment.merge_score (os.path.join (results_dir, 'scores-test'), for_dev = False) if options.compute_perf: # compute the performance with pymeasure executor = Executor() executor.enable_log(on_screen=True,filename=experiment.executor.log_filename) cmd_perf = "pyerror" perf_opt = "-b --dev-set=" + os.path.join (results_dir, 'scores-dev') + " " + os.path.join (results_dir, 'scores-test') executor.run(cmd_perf,perf_opt) # generate DET/EPC curves cmd_perf = "pydet" perf_opt = "-b -s " + os.path.join (results_dir, 'det.eps') + " " + os.path.join (results_dir, 'scores-dev') + " " + os.path.join (results_dir, 'scores-test') executor.run(cmd_perf,perf_opt) cmd_perf = "pyepc" perf_opt = "-b -n 10 -s " + os.path.join (results_dir, 'epc.eps') + " " + os.path.join (results_dir, 'scores-dev') + " " + os.path.join (results_dir, 'scores-test') executor.run(cmd_perf,perf_opt)