def main():
"""Main method"""
basedir = os.path.dirname(os.path.dirname(os.path.realpath(sys.argv[0])))
INPUTDIR = os.path.join(basedir, 'quantities')
MACHINE = os.path.join(basedir, 'mlp/mlp.hdf5')
OUTPUTDIR = os.path.join(basedir, 'scores')
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument(
'inputdir',
metavar='DIR',
type=str,
default=INPUTDIR,
nargs='?',
help=
'Base directory containing the feature vectors to be inspected by this procedure (defaults to "%(default)s")'
)
parser.add_argument(
'machine',
metavar='FILE',
type=str,
default=MACHINE,
nargs='?',
help=
'Name of the machine containing the trained MLP or Linear Machine that will be used to produce the scores (defaults to "%(default)s").'
)
parser.add_argument(
'outputdir',
metavar='DIR',
type=str,
default=OUTPUTDIR,
nargs='?',
help=
'Base directory that will be used to save the results (defaults to "%(default)s").'
)
parser.add_argument('-v',
'--verbose',
action='store_true',
dest='verbose',
default=False,
help='Increases this script verbosity')
# Adds database support using the common infrastructure
# N.B.: Only databases with 'video' support
import antispoofing.utils.db
antispoofing.utils.db.Database.create_parser(parser, 'video')
args = parser.parse_args()
if not os.path.exists(args.inputdir):
parser.error("input directory `%s' does not exist" % args.inputdir)
if not os.path.exists(args.machine):
parser.error("Machine file `%s' does not exist" % args.machine)
if not os.path.exists(args.outputdir):
if args.verbose:
print "Creating output directory `%s'..." % args.outputdir
bob.db.utils.makedirs_safe(args.outputdir)
# Creates an instance of the database
db = args.cls(args)
real, attack = db.get_all_data()
process = real + attack
macfile = bob.io.HDF5File(args.machine)
try:
machine = bob.machine.MLP(macfile)
except:
try:
machine = bob.machine.LinearMachine(macfile)
except:
print "Cannot load Linear or MLP machine from file %s" % args.machine
raise
counter = 0
for obj in process:
counter += 1
if args.verbose:
filename = obj.make_path(args.inputdir, extension='.hdf5')
sys.stdout.write("Processing file %s [%d/%d] " %
(filename, counter, len(process)))
input = obj.load(args.inputdir, '.hdf5')
valid_index = ~numpy.isnan(input.sum(axis=1))
valid_data = input[valid_index, :]
valid_output = machine(valid_data)
output = numpy.ndarray((len(input), 1), dtype='float64')
output[valid_index] = valid_output
output[~valid_index] = numpy.NaN
obj.save(output, args.outputdir, '.hdf5')
if args.verbose:
sys.stdout.write('Saving results to "%s"...\n' % args.outputdir)
sys.stdout.flush()
if args.verbose: print "All done, bye!"
def main():
import bob
import numpy
basedir = os.path.dirname(os.path.dirname(os.path.realpath(sys.argv[0])))
INPUTDIR = os.path.join(basedir, 'framediff')
OUTPUTDIR = os.path.join(basedir, 'clustered')
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument(
'inputdir',
metavar='DIR',
type=str,
default=INPUTDIR,
nargs='?',
help=
'Base directory containing the frame differences to be treated by this procedure (defaults to "%(default)s")'
)
parser.add_argument(
'outputdir',
metavar='DIR',
type=str,
default=OUTPUTDIR,
nargs='?',
help=
'Base output directory for every file created by this procedure (defaults to "%(default)s")'
)
parser.add_argument(
'-n',
'--window-size',
dest="window_size",
default=20,
type=int,
help=
"determines the window size to be used when clustering frame-difference observations (defaults to %(default)s)"
),
parser.add_argument(
'-o',
'--overlap',
dest="overlap",
default=0,
type=int,
help=
"determines the window overlapping; this number has to be between 0 (no overlapping) and 'window-size'-1 (defaults to %(default)s)"
),
# The next option just returns the total number of cases we will be running
# It can be used to set jman --array option. To avoid user confusion, this
# option is suppressed # from the --help menu
parser.add_argument('--grid-count',
dest='grid_count',
action='store_true',
default=False,
help=argparse.SUPPRESS)
# Adds database support using the common infrastructure
# N.B.: Only databases with 'video' support
import antispoofing.utils.db
antispoofing.utils.db.Database.create_parser(parser, 'video')
args = parser.parse_args()
# checks window size and overlap
if args.window_size <= 0:
parser.error("window-size has to be greater than 0")
if args.overlap >= args.window_size or args.overlap < 0:
parser.error(
"overlap has to be smaller than window-size and greater or equal zero"
)
from .. import cluster_5quantities
# Creates an instance of the database
db = args.cls(args)
real, attack = db.get_all_data()
process = real + attack
if args.grid_count:
print len(process)
sys.exit(0)
# if we are on a grid environment, just find what I have to process.
if os.environ.has_key('SGE_TASK_ID'):
pos = int(os.environ['SGE_TASK_ID']) - 1
if pos >= len(process):
raise RuntimeError, "Grid request for job %d on a setup with %d jobs" % \
(pos, len(process))
process = [process[pos]]
sys.stdout.write('Processing %d file(s)\n' % len(process))
sys.stdout.flush()
counter = 0
for obj in process:
counter += 1
sys.stdout.write(
"Processing file %s [%d/%d] " %
(obj.make_path(args.inputdir, '.hdf5'), counter, len(process)))
input = obj.load(args.inputdir, '.hdf5')
d_face = cluster_5quantities(input[:, 0], args.window_size,
args.overlap)
d_bg = cluster_5quantities(input[:, 1], args.window_size, args.overlap)
sys.stdout.write('Saving results to "%s"...\n' % args.outputdir)
obj.save(numpy.hstack((d_face, d_bg)), args.outputdir, '.hdf5')
sys.stdout.flush()
sys.stdout.write('\n')
sys.stdout.flush()
return 0
def main():
import bob
import numpy
basedir = os.path.dirname(os.path.dirname(os.path.realpath(sys.argv[0])))
INPUTDIR = os.path.join(basedir, 'database')
OUTPUTDIR = os.path.join(basedir, 'framediff')
parser = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('-m', '--mininum-face-size', dest='min_face_size',
metavar='PIXELS', type=int, default=0,
help='Minimum face size to be considered when pre-processing and extrapolating detections (defaults to %(default)s)')
parser.add_argument('inputdir', metavar='DIR', type=str, default=INPUTDIR,
nargs='?', help='Base directory containing the videos to be treated by this procedure (defaults to "%(default)s")')
parser.add_argument('outputdir', metavar='DIR', type=str, default=OUTPUTDIR,
nargs='?', help='Base output directory for every file created by this procedure defaults to "%(default)s")')
# The next option just returns the total number of cases we will be running
# It can be used to set jman --array option. To avoid user confusion, this
# option is suppressed # from the --help menu
parser.add_argument('--grid-count', dest='grid_count', action='store_true',
default=False, help=argparse.SUPPRESS)
# Adds database support using the common infrastructure
# N.B.: Only databases with 'video' support
import antispoofing.utils.db
antispoofing.utils.db.Database.create_parser(parser, 'video')
args = parser.parse_args()
# checks face sizes
if args.min_face_size < 0:
parser.error('Face size cannot be less than zero. Give a value in pixels')
from antispoofing.utils.faceloc import preprocess_detections
from .. import eval_face_differences, eval_background_differences
# Creates an instance of the database
db = args.cls(args)
real, attack = db.get_all_data()
process = real + attack
if args.grid_count:
print len(process)
sys.exit(0)
# if we are on a grid environment, just find what I have to process.
if os.environ.has_key('SGE_TASK_ID'):
pos = int(os.environ['SGE_TASK_ID']) - 1
if pos >= len(process):
raise RuntimeError, "Grid request for job %d on a setup with %d jobs" % \
(pos, len(process))
process = [process[pos]]
counter = 0
for obj in process:
counter += 1
filename = obj.videofile(args.inputdir)
input = bob.io.VideoReader(filename)
facefile = obj.facefile(args.inputdir)
locations = preprocess_detections(facefile, len(input),
facesize_filter=args.min_face_size)
sys.stdout.write("Processing file %s (%d frames) [%d/%d]..." % (filename,
input.number_of_frames, counter, len(process)))
# start the work here...
vin = input.load() # load all in one shot.
prev = bob.ip.rgb_to_gray(vin[0,:,:,:])
curr = numpy.empty_like(prev)
data = numpy.zeros((len(vin), 2), dtype='float64')
data[0] = (numpy.NaN, numpy.NaN)
for k in range(1, vin.shape[0]):
bob.ip.rgb_to_gray(vin[k,:,:,:], curr)
if locations[k] and locations[k].is_valid():
sys.stdout.write('.')
data[k][0] = eval_face_differences(prev, curr, locations[k])
data[k][1] = eval_background_differences(prev, curr, locations[k], None)
else:
sys.stdout.write('x')
data[k] = (numpy.NaN, numpy.NaN)
sys.stdout.flush()
# swap buffers: curr <=> prev
tmp = prev
prev = curr
curr = tmp
obj.save(data, args.outputdir, '.hdf5')
sys.stdout.write('\n')
sys.stdout.flush()
return 0
def main():
import bob
import numpy
basedir = os.path.dirname(os.path.dirname(os.path.realpath(sys.argv[0])))
INPUTDIR = os.path.join(basedir, 'database')
OUTPUTDIR = os.path.join(basedir, 'framediff')
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument(
'-m',
'--mininum-face-size',
dest='min_face_size',
metavar='PIXELS',
type=int,
default=0,
help=
'Minimum face size to be considered when pre-processing and extrapolating detections (defaults to %(default)s)'
)
parser.add_argument(
'inputdir',
metavar='DIR',
type=str,
default=INPUTDIR,
nargs='?',
help=
'Base directory containing the videos to be treated by this procedure (defaults to "%(default)s")'
)
parser.add_argument(
'outputdir',
metavar='DIR',
type=str,
default=OUTPUTDIR,
nargs='?',
help=
'Base output directory for every file created by this procedure defaults to "%(default)s")'
)
# The next option just returns the total number of cases we will be running
# It can be used to set jman --array option. To avoid user confusion, this
# option is suppressed # from the --help menu
parser.add_argument('--grid-count',
dest='grid_count',
action='store_true',
default=False,
help=argparse.SUPPRESS)
# Adds database support using the common infrastructure
# N.B.: Only databases with 'video' support
import antispoofing.utils.db
antispoofing.utils.db.Database.create_parser(parser, 'video')
args = parser.parse_args()
# checks face sizes
if args.min_face_size < 0:
parser.error(
'Face size cannot be less than zero. Give a value in pixels')
from antispoofing.utils.faceloc import preprocess_detections
from .. import eval_face_differences, eval_background_differences
# Creates an instance of the database
db = args.cls(args)
real, attack = db.get_all_data()
process = real + attack
if args.grid_count:
print len(process)
sys.exit(0)
# if we are on a grid environment, just find what I have to process.
if os.environ.has_key('SGE_TASK_ID'):
pos = int(os.environ['SGE_TASK_ID']) - 1
if pos >= len(process):
raise RuntimeError, "Grid request for job %d on a setup with %d jobs" % \
(pos, len(process))
process = [process[pos]]
counter = 0
for obj in process:
counter += 1
filename = obj.videofile(args.inputdir)
input = bob.io.VideoReader(filename)
facefile = obj.facefile(args.inputdir)
locations = preprocess_detections(facefile,
len(input),
facesize_filter=args.min_face_size)
sys.stdout.write(
"Processing file %s (%d frames) [%d/%d]..." %
(filename, input.number_of_frames, counter, len(process)))
# start the work here...
vin = input.load() # load all in one shot.
prev = bob.ip.rgb_to_gray(vin[0, :, :, :])
curr = numpy.empty_like(prev)
data = numpy.zeros((len(vin), 2), dtype='float64')
data[0] = (numpy.NaN, numpy.NaN)
for k in range(1, vin.shape[0]):
bob.ip.rgb_to_gray(vin[k, :, :, :], curr)
if locations[k] and locations[k].is_valid():
sys.stdout.write('.')
data[k][0] = eval_face_differences(compute_lbp(prev),
compute_lbp(curr),
locations[k])
data[k][1] = eval_background_differences(
compute_lbp(prev), compute_lbp(curr), locations[k], None)
else:
sys.stdout.write('x')
data[k] = (numpy.NaN, numpy.NaN)
sys.stdout.flush()
# swap buffers: curr <=> prev
tmp = prev
prev = curr
curr = tmp
obj.save(data, args.outputdir, '.hdf5')
sys.stdout.write('\n')
sys.stdout.flush()
return 0
def main():
import bob
import numpy
basedir = os.path.dirname(os.path.dirname(os.path.realpath(sys.argv[0])))
INPUTDIR = os.path.join(basedir, 'framediff')
OUTPUTDIR = os.path.join(basedir, 'clustered')
parser = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('inputdir', metavar='DIR', type=str, default=INPUTDIR,
nargs='?', help='Base directory containing the frame differences to be treated by this procedure (defaults to "%(default)s")')
parser.add_argument('outputdir', metavar='DIR', type=str, default=OUTPUTDIR,
nargs='?', help='Base output directory for every file created by this procedure (defaults to "%(default)s")')
parser.add_argument('-n', '--window-size', dest="window_size", default=20,
type=int, help="determines the window size to be used when clustering frame-difference observations (defaults to %(default)s)"),
parser.add_argument('-o', '--overlap', dest="overlap", default=0, type=int,
help="determines the window overlapping; this number has to be between 0 (no overlapping) and 'window-size'-1 (defaults to %(default)s)"),
# The next option just returns the total number of cases we will be running
# It can be used to set jman --array option. To avoid user confusion, this
# option is suppressed # from the --help menu
parser.add_argument('--grid-count', dest='grid_count', action='store_true',
default=False, help=argparse.SUPPRESS)
# Adds database support using the common infrastructure
# N.B.: Only databases with 'video' support
import antispoofing.utils.db
antispoofing.utils.db.Database.create_parser(parser, 'video')
args = parser.parse_args()
# checks window size and overlap
if args.window_size <= 0:
parser.error("window-size has to be greater than 0")
if args.overlap >= args.window_size or args.overlap < 0:
parser.error("overlap has to be smaller than window-size and greater or equal zero")
from .. import cluster_5quantities
# Creates an instance of the database
db = args.cls(args)
real, attack = db.get_all_data()
process = real + attack
if args.grid_count:
print len(process)
sys.exit(0)
# if we are on a grid environment, just find what I have to process.
if os.environ.has_key('SGE_TASK_ID'):
pos = int(os.environ['SGE_TASK_ID']) - 1
if pos >= len(process):
raise RuntimeError, "Grid request for job %d on a setup with %d jobs" % \
(pos, len(process))
process = [process[pos]]
sys.stdout.write('Processing %d file(s)\n' % len(process))
sys.stdout.flush()
counter = 0
for obj in process:
counter += 1
sys.stdout.write("Processing file %s [%d/%d] " % (obj.make_path(args.inputdir, '.hdf5'), counter, len(process)))
input = obj.load(args.inputdir, '.hdf5')
d_face = cluster_5quantities(input[:,0], args.window_size, args.overlap)
d_bg = cluster_5quantities(input[:,1], args.window_size, args.overlap)
sys.stdout.write('Saving results to "%s"...\n' % args.outputdir)
obj.save(numpy.hstack((d_face, d_bg)), args.outputdir, '.hdf5')
sys.stdout.flush()
sys.stdout.write('\n')
sys.stdout.flush()
return 0
