def main():
"""Main method"""
from xbob.db.replay import Database
from .. import utils
protocols = [k.name for k in Database().protocols()]
parser = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('inputdir', metavar='DIR', type=str, help='Base directory containing the scores to be loaded and merged')
parser.add_argument('outputdir', metavar='DIR', type=str, help='Base output directory for every file created by this procedure')
parser.add_argument('-p', '--protocol', metavar='PROTOCOL', type=str,
default='grandtest', choices=protocols, dest="protocol",
help="The protocol type may be specified to subselect a smaller number of files to operate on (one of '%s'; defaults to '%%(default)s')" % '|'.join(sorted(protocols)))
supports = ('fixed', 'hand', 'hand+fixed')
parser.add_argument('-s', '--support', metavar='SUPPORT', type=str,
default='hand+fixed', dest='support', choices=supports, help="If you would like to select a specific support to be used, use this option (one of '%s'; defaults to '%%(default)s')" % '|'.join(sorted(supports)))
parser.add_argument('-S', '--skip-frames', metavar='INT', type=int,
default=10, dest='skip', help="Number of frames to skip once an eye-blink has been detected (defaults to %(default)s)")
parser.add_argument('-T', '--threshold-ratio', metavar='FLOAT', type=float,
default=3.0, dest='thres_ratio', help="How many standard deviations to use for counting positive blink picks to %(default)s)")
parser.add_argument('-v', '--verbose', action='store_true', dest='verbose',
default=False, help='Increases this script verbosity')
args = parser.parse_args()
if not os.path.exists(args.inputdir):
parser.error("input directory `%s' does not exist" % args.inputdir)
if args.support == 'hand+fixed': args.support = ('hand', 'fixed')
if not os.path.exists(args.outputdir):
if args.verbose: print "Creating output directory %s..." % args.outputdir
os.makedirs(args.outputdir)
db = Database()
objs = db.objects(protocol=args.protocol, support=args.support,
cls=('real', 'attack', 'enroll'))
counter = 0
for obj in objs:
counter += 1
arr = obj.load(args.inputdir, '.hdf5')
nb = utils.count_blinks(arr, args.thres_ratio, args.skip)
if args.verbose:
print "Processed file %s [%d/%d]... %d blink(s)" % \
(obj.path, counter, len(objs), nb[-1])
obj.save(nb, args.outputdir, '.hdf5')
def main():
import os, sys
from xbob.db.replay import Database, File
from .. import utils
basedir = os.path.dirname(os.path.dirname(os.path.realpath(sys.argv[0])))
ANNOTATIONS = os.path.join(basedir, 'annotations')
FEATURES = os.path.join(basedir, 'framediff')
parser = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('annotations', metavar='DIR', type=str,
default=ANNOTATIONS, nargs='?', help='Base directory containing the (flandmark) annotations to be treated by this procedure (defaults to "%(default)s")')
parser.add_argument('path', metavar='PATH', type=str,
help='Base path to the movie file you need plotting')
parser.add_argument('output', metavar='FILE', type=str,
help='Name of the output file to save the video')
parser.add_argument('-M', '--maximum-displacement', metavar='FLOAT',
type=float, dest="max_displacement", default=0.2, help="Maximum displacement (w.r.t. to the eye width) between eye-centers to consider the eye for calculating eye-differences")
parser.add_argument('-S', '--skip-frames', metavar='INT', type=int,
default=10, dest='skip', help="Number of frames to skip once an eye-blink has been detected (defaults to %(default)s)")
parser.add_argument('-T', '--threshold-ratio', metavar='FLOAT', type=float,
default=3.0, dest='thres_ratio', help="How many standard deviations to use for counting positive blink picks to %(default)s)")
args = parser.parse_args()
db = Database()
# Gets the information concerning the input path or id
db.assert_validity()
splitted = args.path.split(os.sep)
k = [splitted.index(k) for k in splitted if k in \
('train', 'test', 'devel', 'enroll')][0]
splitted[-1] = os.path.splitext(splitted[-1])[0]
path_query = os.sep.join(splitted[k:])
obj = db.session.query(File).filter(File.path == path_query).one()
video = bob.io.VideoReader(args.path)
print "Opened movie file %s (%d frames), id = %d" % \
(args.path, len(video), obj.id)
# Choose the printed frames here.
start = 0
end = 225
# Loads the input video
frames = [bob.ip.rgb_to_gray(k) for k in video[start:end]]
# Recalculates the features
annotations = utils.flandmark_load_annotations(obj, args.annotations,
verbose=True)
# Light-normalizes detected faces
#utils.light_normalize_tantriggs(frames, annotations, start, end)
utils.light_normalize_histogram(frames, annotations, start, end)
features = numpy.zeros((len(frames), 2), dtype='float64')
sys.stdout.write("Computing features ")
sys.stdout.flush()
for k in range(start+1, end):
curr_annot = annotations[k] if annotations.has_key(k) else None
prev_annot = annotations[k-1] if annotations.has_key(k-1) else None
use_annotation = (prev_annot, curr_annot)
use_frames = (frames[k-1], frames[k])
# maximum of 5 pixel displacement acceptable
eye_diff, eye_pixels = utils.eval_eyes_difference(use_frames,
use_annotation, max_center_displacement=args.max_displacement)
facerem_diff, facerem_pixels = utils.eval_face_remainder_difference(
use_frames, use_annotation, eye_diff, eye_pixels)
if eye_pixels != 0: features[k][0] = eye_diff/float(eye_pixels)
else: features[k][0] = 0.
if facerem_pixels != 0: features[k][1] = facerem_diff/float(facerem_pixels)
else: features[k][1] = 1.
if eye_diff == 0: sys.stdout.write('x')
else: sys.stdout.write('.')
sys.stdout.flush()
scores = utils.score(features)
sys.stdout.write('\n')
sys.stdout.flush()
# plot N sequential images containing the video on the top and the advancing
# graph of the features of choice on the bottom
fig = mpl.figure()
sys.stdout.write("Writing %d frames " % (end-start))
sys.stdout.flush()
outv = None #output video place holder
orows, ocolumns = None, None #the size of every frame in outv
old_blinks = 0
for k in range(start,end):
mpl.subplot(211)
mpl.title("Frame %05d" % k)
use_annotation = annotations[k] if annotations.has_key(k) else None
if use_annotation:
x, y, width, height = use_annotation['bbox']
bob.ip.draw_box(frames[k], x, y, width, height, 255)
x, y, width, height = use_annotation['eyes'][0]
bob.ip.draw_box(frames[k], x, y, width, height, 255)
x, y, width, height = use_annotation['eyes'][1]
bob.ip.draw_box(frames[k], x, y, width, height, 255)
x, y, width, height = use_annotation['face_remainder']
bob.ip.draw_box(frames[k], x, y, width, height, 255)
mpl.imshow(frames[k], cmap=GrayColorMap) #top plot
mpl.subplot(212)
score_set = scores[start:k+1]
blinks = utils.count_blinks(score_set, args.thres_ratio, args.skip)
rmean = utils.rmean(score_set)[-1]
rstd = utils.rstd(score_set)[-1]
threshold = (args.thres_ratio * rstd) + rmean
mpl.plot(numpy.arange(start, k+1), score_set, linewidth=2, label='score')
mpl.hlines(rmean, start, end, color='red',
linestyles='dashed', alpha=0.8, label='mean')
mpl.hlines(threshold, start, end, color='red',
linestyles='solid', alpha=0.8, label='threshold')
yrange = scores.max() - scores.min()
mpl.axis((start, end, scores.min(), (0.2*yrange) + scores.max()))
mpl.grid(True)
mpl.xlabel("Frames | Blinks = %d" % blinks[-1])
mpl.ylabel("Magnitude")
figure = fig2array(fig)
if outv is None:
orows = 2*(figure.shape[1]/2)
ocolumns = 2*(figure.shape[2]/2)
outv = bob.io.VideoWriter(args.output, orows, ocolumns, video.frame_rate)
outv.append(figure[:,0:orows,0:ocolumns])
mpl.clf()
if blinks[-1] != old_blinks:
old_blinks = blinks[-1]
sys.stdout.write('%d' % old_blinks)
else:
sys.stdout.write('.')
sys.stdout.flush()
sys.stdout.write('\n')
def main():
"""Main method"""
from xbob.db.replay import Database
protocols = [k.name for k in Database().protocols()]
basedir = os.path.dirname(os.path.dirname(os.path.realpath(sys.argv[0])))
INPUTDIR = os.path.join(basedir, "framediff")
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 frame differences 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"
)
parser.add_argument(
"-p",
"--protocol",
metavar="PROTOCOL",
type=str,
default="grandtest",
choices=protocols,
dest="protocol",
help="The protocol type may be specified to subselect a smaller number of files to operate on (one of '%s'; defaults to '%%(default)s')"
% "|".join(sorted(protocols)),
)
supports = ("fixed", "hand", "hand+fixed")
parser.add_argument(
"-s",
"--support",
metavar="SUPPORT",
type=str,
default="hand+fixed",
dest="support",
choices=supports,
help="If you would like to select a specific support to be used, use this option (one of '%s'; defaults to '%%(default)s')"
% "|".join(sorted(supports)),
)
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.outputdir):
if args.verbose:
print "Creating output directory `%s'..." % args.outputdir
bob.db.utils.makedirs_safe(args.outputdir)
if args.support == "hand+fixed":
args.support = ("hand", "fixed")
db = Database()
process = db.objects(protocol=args.protocol, support=args.support, cls=("real", "attack", "enroll"))
counter = 0
for obj in process:
counter += 1
if args.verbose:
sys.stdout.write("Processing file %s [%d/%d] " % (obj.path, counter, len(process)))
input = obj.load(args.inputdir, ".hdf5")
obj.save(score(input), directory=args.outputdir, extension=".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 os, sys
from xbob.db.replay import Database, File
from .. import utils
basedir = os.path.dirname(os.path.dirname(os.path.realpath(sys.argv[0])))
ANNOTATIONS = os.path.join(basedir, 'annotations')
FEATURES = os.path.join(basedir, 'framediff')
parser = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('annotations', metavar='DIR', type=str,
default=ANNOTATIONS, nargs='?', help='Base directory containing the (flandmark) annotations to be treated by this procedure (defaults to "%(default)s")')
parser.add_argument('path', metavar='PATH', type=str,
help='Base path to the movie file you need plotting')
parser.add_argument('output', metavar='FILE', type=str,
help='Name of the output file to save the video')
parser.add_argument('-M', '--maximum-displacement', metavar='FLOAT',
type=float, dest="max_displacement", default=0.2, help="Maximum displacement (w.r.t. to the eye width) between eye-centers to consider the eye for calculating eye-differences")
parser.add_argument('-S', '--skip-frames', metavar='INT', type=int,
default=10, dest='skip', help="Number of frames to skip once an eye-blink has been detected (defaults to %(default)s)")
parser.add_argument('-T', '--threshold-ratio', metavar='FLOAT', type=float,
default=3.0, dest='thres_ratio', help="How many standard deviations to use for counting positive blink picks to %(default)s)")
args = parser.parse_args()
db = Database()
# Gets the information concerning the input path or id
db.assert_validity()
splitted = args.path.split(os.sep)
k = [splitted.index(k) for k in splitted if k in \
('train', 'test', 'devel')][0]
splitted[-1] = os.path.splitext(splitted[-1])[0]
path_query = os.sep.join(splitted[k:])
obj = db.session.query(File).filter(File.path == path_query).one()
video = bob.io.VideoReader(args.path)
print "Opened movie file %s (%d frames), id = %d" % \
(args.path, len(video), obj.id)
# Choose the printed frames here.
start = 0
end = 225
# Loads the input video
frames = [bob.ip.rgb_to_gray(k) for k in video[start:end]]
# Recalculates the features
annotations = utils.flandmark_load_annotations(obj, args.annotations,
verbose=True)
# Light-normalizes detected faces
#utils.light_normalize_tantriggs(frames, annotations, start, end)
utils.light_normalize_histogram(frames, annotations, start, end)
features = numpy.zeros((len(frames), 2), dtype='float64')
sys.stdout.write("Computing features ")
sys.stdout.flush()
for k in range(start+1, end):
curr_annot = annotations[k] if annotations.has_key(k) else None
prev_annot = annotations[k-1] if annotations.has_key(k-1) else None
use_annotation = (prev_annot, curr_annot)
use_frames = (frames[k-1], frames[k])
# maximum of 5 pixel displacement acceptable
eye_diff, eye_pixels = utils.eval_eyes_difference(use_frames,
use_annotation, max_center_displacement=args.max_displacement)
facerem_diff, facerem_pixels = utils.eval_face_remainder_difference(
use_frames, use_annotation, eye_diff, eye_pixels)
if eye_pixels != 0: features[k][0] = eye_diff/float(eye_pixels)
else: features[k][0] = 0.
if facerem_pixels != 0: features[k][1] = facerem_diff/float(facerem_pixels)
else: features[k][1] = 1.
if eye_diff == 0: sys.stdout.write('x')
else: sys.stdout.write('.')
sys.stdout.flush()
scores = utils.score(features)
sys.stdout.write('\n')
sys.stdout.flush()
# plot N sequential images containing the video on the top and the advancing
# graph of the features of choice on the bottom
fig = mpl.figure()
sys.stdout.write("Writing %d frames " % (end-start))
sys.stdout.flush()
outv = None #output video place holder
orows, ocolumns = None, None #the size of every frame in outv
old_blinks = 0
for k in range(start,end):
mpl.subplot(211)
mpl.title("Frame %05d" % k)
use_annotation = annotations[k] if annotations.has_key(k) else None
if use_annotation:
x, y, width, height = use_annotation['bbox']
bob.ip.draw_box(frames[k], x, y, width, height, 255)
x, y, width, height = use_annotation['eyes'][0]
bob.ip.draw_box(frames[k], x, y, width, height, 255)
x, y, width, height = use_annotation['eyes'][1]
bob.ip.draw_box(frames[k], x, y, width, height, 255)
x, y, width, height = use_annotation['face_remainder']
bob.ip.draw_box(frames[k], x, y, width, height, 255)
mpl.imshow(frames[k], cmap=GrayColorMap) #top plot
mpl.subplot(212)
score_set = scores[start:k+1]
blinks = utils.count_blinks(score_set, args.thres_ratio, args.skip)
rmean = utils.rmean(score_set)[-1]
rstd = utils.rstd(score_set)[-1]
threshold = (args.thres_ratio * rstd) + rmean
mpl.plot(numpy.arange(start, k+1), score_set, linewidth=2, label='score')
mpl.hlines(rmean, start, end, color='red',
linestyles='dashed', alpha=0.8, label='mean')
mpl.hlines(threshold, start, end, color='red',
linestyles='solid', alpha=0.8, label='threshold')
yrange = scores.max() - scores.min()
mpl.axis((start, end, scores.min(), (0.2*yrange) + scores.max()))
mpl.grid(True)
mpl.xlabel("Frames | Blinks = %d" % blinks[-1])
mpl.ylabel("Magnitude")
figure = fig2array(fig)
if outv is None:
orows = 2*(figure.shape[1]/2)
ocolumns = 2*(figure.shape[2]/2)
outv = bob.io.VideoWriter(args.output, orows, ocolumns, video.frame_rate)
outv.append(figure[:,0:orows,0:ocolumns])
mpl.clf()
if blinks[-1] != old_blinks:
old_blinks = blinks[-1]
sys.stdout.write('%d' % old_blinks)
else:
sys.stdout.write('.')
sys.stdout.flush()
sys.stdout.write('\n')
def main():
import bob
import numpy
from xbob.db.replay import Database
protocols = [k.name for k in Database().protocols()]
basedir = os.path.dirname(os.path.dirname(os.path.realpath(sys.argv[0])))
INPUTDIR = os.path.join(basedir, 'database')
ANNOTATIONS = os.path.join(basedir, 'annotations')
OUTPUTDIR = os.path.join(basedir, 'framediff')
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 videos to be treated by this procedure (defaults to "%(default)s")')
parser.add_argument('annotations', metavar='DIR', type=str,
default=ANNOTATIONS, nargs='?', help='Base directory containing the (flandmark) annotations 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('-p', '--protocol', metavar='PROTOCOL', type=str,
default='grandtest', choices=protocols, dest="protocol",
help='The protocol type may be specified instead of the the id switch to subselect a smaller number of files to operate on (one of "%s"; defaults to "%%(default)s")' % '|'.join(sorted(protocols)))
parser.add_argument('-M', '--maximum-displacement', metavar='FLOAT',
type=float, dest="max_displacement", default=0.2, help="Maximum displacement (w.r.t. to the eye width) between eye-centers to consider the eye for calculating eye-differences (defaults to %(default)s)")
supports = ('fixed', 'hand', 'hand+fixed')
parser.add_argument('-s', '--support', metavar='SUPPORT', type=str,
default='hand+fixed', dest='support', choices=supports, help="If you would like to select a specific support to be used, use this option (one of '%s'; defaults to '%%(default)s')" % '|'.join(sorted(supports)))
# The next option just returns the total number of cases we will be running
# It can be used to set jman --array option.
parser.add_argument('--grid-count', dest='grid_count', action='store_true',
default=False, help=argparse.SUPPRESS)
args = parser.parse_args()
if args.support == 'hand+fixed': args.support = ('hand', 'fixed')
from .. import utils
db = Database()
process = db.objects(protocol=args.protocol, support=args.support,
cls=('real', 'attack', 'enroll'))
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'):
key = int(os.environ['SGE_TASK_ID']) - 1
if key >= len(process):
raise RuntimeError, "Grid request for job %d on a setup with %d jobs" % \
(key, len(process))
process = [process[key]]
for counter, obj in enumerate(process):
filename = str(obj.videofile(args.inputdir))
input = bob.io.VideoReader(filename)
annotations = utils.flandmark_load_annotations(obj, args.annotations,
verbose=True)
sys.stdout.write("Processing file %s (%d frames) [%d/%d]..." % (filename,
input.number_of_frames, counter+1, len(process)))
# start the work here...
frames = [bob.ip.rgb_to_gray(k) for k in input]
#utils.light_normalize_tantriggs(frames, annotations, 0, len(frames))
utils.light_normalize_histogram(frames, annotations, 0, len(frames))
features = numpy.ndarray((input.number_of_frames, 2), dtype='float64')
features[:] = numpy.NaN
for k in range(1, len(frames)):
curr_annot = annotations[k] if annotations.has_key(k) else None
prev_annot = annotations[k-1] if annotations.has_key(k-1) else None
use_annotation = (prev_annot, curr_annot)
use_frames = (frames[k-1], frames[k])
# maximum of 5 pixel displacement acceptable
eye_diff, eye_pixels = utils.eval_eyes_difference(use_frames,
use_annotation, args.max_displacement)
facerem_diff, facerem_pixels = utils.eval_face_remainder_difference(
use_frames, use_annotation, eye_diff, eye_pixels)
if eye_pixels != 0:
features[k][0] = eye_diff/float(eye_pixels)
else:
features[k][0] = 0.
if facerem_pixels != 0:
features[k][1] = facerem_diff/float(facerem_pixels)
else:
features[k][1] = 1.
if eye_diff == 0: sys.stdout.write('x')
else: sys.stdout.write('.')
sys.stdout.flush()
obj.save(features, directory=args.outputdir, extension='.hdf5')
sys.stdout.write('\n')
sys.stdout.flush()
return 0