def main():
basedir = os.path.dirname(os.path.dirname(os.path.realpath(sys.argv[0])))
INPUT_DIR = os.path.join(basedir, 'database')
OUTPUT_DIR = os.path.join(basedir, 'lbp_features')
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('-v', '--input-dir', metavar='DIR', type=str, dest='inputdir', default=INPUT_DIR, help='Base directory containing the videos to be treated by this procedure (defaults to "%(default)s")')
parser.add_argument('-d', '--directory', dest="directory", default=OUTPUT_DIR, help="This path will be prepended to every file output by this procedure (defaults to '%(default)s')")
parser.add_argument('-n', '--normface-size', dest="normfacesize", default=64, type=int, help="this is the size of the normalized face box if face normalization is used (defaults to '%(default)s')")
parser.add_argument('--ff', '--facesize_filter', dest="facesize_filter", default=0, type=int, help="all the frames with faces smaller then this number, will be discarded (defaults to '%(default)s')")
parser.add_argument('-l', '--lbptype', metavar='LBPTYPE', type=str, choices=('regular', 'riu2', 'uniform'), default='uniform', dest='lbptype', help='Choose the type of LBP to use (defaults to "%(default)s")')
parser.add_argument('--el', '--elbptype', metavar='ELBPTYPE', type=str, choices=('regular', 'transitional', 'direction_coded', 'modified'), default='regular', dest='elbptype', help='Choose the type of extended LBP features to compute (defaults to "%(default)s")')
parser.add_argument('-b', '--blocks', metavar='BLOCKS', type=int, default=1, dest='blocks', help='The region over which the LBP is calculated will be divided into the given number of blocks squared. The histograms of the individial blocks will be concatenated.(defaults to "%(default)s")')
parser.add_argument('-c', dest='circular', action='store_true', default=False, help='If set, circular LBP will be computed')
parser.add_argument('-o', dest='overlap', action='store_true', default=False, help='If set, the blocks on which the image is divided will be overlapping')
parser.add_argument('-e', '--enrollment', action='store_true', default=False, dest='enrollment', help='If True, will do the processing of the enrollment data of the database (defaults to "%(default)s")')
parser.add_argument('--nn', '--nonorm', dest='nonorm', action='store_true', default=False, help='If True, normalization on the bounding box will NOT be perfomed. If False, normalization will be done depending on the -n parameter.')
parser.add_argument('--bbx', '--boundingbox', action='store_true', default=False, dest='boundingbox', help='If True, will read the face locations using the bbx function of the File class of the database. If False, will use faceloc.read_face utility to read the faceloc. For MSU-MFSD only (defaults to "%(default)s")')
#######
# Database especific configuration
#######
#Database.create_parser(parser)
Database.create_parser(parser, implements_any_of='video')
args = parser.parse_args()
lbphistlength = {'regular':256, 'riu2':10, 'uniform':59} # hardcoding the number of bins for the LBP variants
from .. import spoof
########################
#Querying the database
########################
#database = new_database(databaseName,args=args)
database = args.cls(args)
if args.enrollment:
process = database.get_enroll_data()
else:
realObjects, attackObjects = database.get_all_data()
process = realObjects + attackObjects
counter = 0
# process each video
for obj in process:
counter += 1
input = bob.io.video.reader(obj.videofile(directory=args.inputdir))
# loading the face locations
if string.find(database.short_description(), "CASIA") != -1:
locations = faceloc.read_face(obj.facefile())
elif string.find(database.short_description(), "MSU") != -1:
if args.boundingbox: # load the locations based on the bbx function of the File object
locations = obj.bbx(directory=args.inputdir)
locations = {x[0]:faceloc.BoundingBox(x[1], x[2], x[3], x[4]) for x in locations} # for MSU MFSD
else:
locations = faceloc.read_face(obj.facefile(args.inputdir)) # for MSU MFSD
else:
locations = faceloc.read_face(obj.facefile(args.inputdir)) # for Replay-Attack
locations = faceloc.expand_detections(locations, input.number_of_frames)
sz = args.normfacesize # the size of the normalized face box
sys.stdout.write("Processing file %s (%d frames) [%d/%d] " % (obj.make_path(),
input.number_of_frames, counter, len(process)))
# start the work here...
vin = input.load() # load the video
data = numpy.array(args.blocks * args.blocks * lbphistlength[args.lbptype] * [0.]) # initialize the accumulated histogram for uniform LBP
numvf = 0 # number of valid frames in the video (will be smaller then the total number of frames if a face is not detected or a very small face is detected in a frame when face lbp are calculated
for k in range(0, vin.shape[0]):
frame = bob.ip.color.rgb_to_gray(vin[k,:,:,:])
sys.stdout.write('.')
sys.stdout.flush()
if args.nonorm:
hist, vf = spoof.lbphist_face(frame, args.lbptype, locations[k], args.elbptype, numbl=args.blocks, circ=args.circular, overlap=args.overlap, bbxsize_filter=args.facesize_filter) # vf = 1 if it was a valid frame, 0 otherwise
else:
hist, vf = spoof.lbphist_facenorm(frame, args.lbptype, locations[k], sz, args.elbptype, numbl=args.blocks, circ=args.circular, overlap=args.overlap, bbxsize_filter=args.facesize_filter) # vf = 1 if it was a valid frame, 0 otherwise
numvf = numvf + vf
if vf == 1: # if it is a valid frame, add this histogram into the accumulated histogram
data = data + hist # accumulate the histograms of all the frames one by one
data = data / numvf # averaging over the number of valied frames
sys.stdout.write('\n')
sys.stdout.flush()
# saves the output
obj.save(data.reshape([1,data.size]), directory = args.directory, extension='.hdf5')
return 0
def main():
basedir = os.path.dirname(os.path.dirname(os.path.realpath(sys.argv[0])))
INPUT_DIR = os.path.join(basedir, 'database')
OUTPUT_DIR = os.path.join(basedir, 'lbp_features')
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('-v', '--input-dir', metavar='DIR', type=str,
dest='inputdir', default=INPUT_DIR, help='Base directory containing the videos to be treated by this procedure (defaults to "%(default)s")')
parser.add_argument('-d', '--directory', dest="directory", default=OUTPUT_DIR, help="This path will be prepended to every file output by this procedure (defaults to '%(default)s')")
parser.add_argument('-n', '--normface-size', dest="normfacesize", default=64, type=int, help="this is the size of the normalized face box if face normalization is used (defaults to '%(default)s')")
parser.add_argument('--ff', '--facesize_filter', dest="facesize_filter", default=0, type=int, help="all the frames with faces smaller then this number, will be discarded (defaults to '%(default)s')")
parser.add_argument('-l', '--lbptype', metavar='LBPTYPE', type=str, choices=('regular', 'riu2', 'uniform'), default='uniform', dest='lbptype', help='Choose the type of LBP to use (defaults to "%(default)s")')
parser.add_argument('--el', '--elbptype', metavar='ELBPTYPE', type=str, choices=('regular', 'transitional', 'direction_coded', 'modified'), default='regular', dest='elbptype', help='Choose the type of extended LBP features to compute (defaults to "%(default)s")')
parser.add_argument('-e', '--enrollment', action='store_true', default=False, dest='enrollment', help='If True, will do the processing on the enrollment data of the database (defaults to "%(default)s")')
parser.add_argument('--nn', '--nonorm', dest='nonorm', action='store_true', default=False, help='If True, normalization on the bounding box will NOT be perfomed. If False, normalization will be done depending on the -n parameter.')
parser.add_argument('--bbx', '--boundingbox', action='store_true', default=False, dest='boundingbox', help='If True, will read the face locations using the bbx function of the File class of the database. If False, will use faceloc.read_face utility to read the faceloc. For MSU-MFSD only (defaults to "%(default)s")')
#######
# Database especific configuration
#######
#Database.create_parser(parser)
Database.create_parser(parser, implements_any_of='video')
args = parser.parse_args()
from .. import spoof
########################
#Querying the database
########################
#database = new_database(databaseName,args=args)
database = args.cls(args)
if args.enrollment:
process = database.get_enroll_data()
else:
realObjects, attackObjects = database.get_all_data()
process = realObjects + attackObjects
counter = 0
# process each video
for obj in process:
counter += 1
input = bob.io.video.reader(obj.videofile(directory=args.inputdir))
# loading the face locations
if string.find(database.short_description(), "CASIA") != -1:
locations = faceloc.read_face(obj.facefile())
elif string.find(database.short_description(), "MSU") != -1:
if args.boundingbox: # load the locations based on the bbx function of the File object
locations = obj.bbx(directory=args.inputdir)
locations = {x[0]:faceloc.BoundingBox(x[1], x[2], x[3], x[4]) for x in locations}
else:
locations = faceloc.read_face(obj.facefile(args.inputdir)) # for MSU MFSD
else:
locations = faceloc.read_face(obj.facefile(args.inputdir)) # for Replay-Attack
locations = faceloc.expand_detections(locations, input.number_of_frames)
sz = args.normfacesize # the size of the normalized face box
sys.stdout.write("Processing file %s (%d frames) [%d/%d] " % (obj.make_path(),
input.number_of_frames, counter, len(process)))
# start the work here...
vin = input.load() # load the video
histdata = None
numvf = 0 # number of valid frames in the video (will be smaller then the total number of frames if a face is not detected or a very small face is detected in a frame when face lbp are calculated
validframes = [] # list with the indices of the valid frames
for k in range(0, vin.shape[0]):
frame = bob.ip.color.rgb_to_gray(vin[k,:,:,:])
if string.find(database.short_description(), "MSU") != -1 and obj.is_rotated(): # rotate the frame by 180 degrees if needed
frame = numpy.rot90(numpy.rot90(frame))
sys.stdout.write('.')
sys.stdout.flush()
if args.nonorm:
hist1, _ = spoof.lbphist_face(frame, args.lbptype, locations[k], args.elbptype, radius=2, neighbors=16, numbl=1, circ=True, overlap=False, bbxsize_filter=args.facesize_filter) # vf = 1 if it was a valid frame, 0 otherwise
hist2, _ = spoof.lbphist_face(frame, args.lbptype, locations[k], args.elbptype, radius=1, neighbors=8, numbl=3, circ=True, overlap=True, bbxsize_filter=args.facesize_filter) # vf = 1 if it was a valid frame, 0 otherwise
hist3, vf = spoof.lbphist_face(frame, args.lbptype, locations[k], args.elbptype, radius=2, neighbors=8, numbl=1, circ=True, overlap=False, bbxsize_filter=args.facesize_filter) # vf = 1 if it was a valid frame, 0 otherwise
hist = numpy.hstack((hist1, hist2, hist3))
else:
hist1, _ = spoof.lbphist_facenorm(frame, args.lbptype, locations[k], sz, args.elbptype, radius=2, neighbors=16, numbl=1, circ=True, overlap=False, bbxsize_filter=args.facesize_filter) # vf = 1 if it was a valid frame, 0 otherwise
hist2, _ = spoof.lbphist_facenorm(frame, args.lbptype, locations[k], sz, args.elbptype, radius=1, neighbors=8, numbl=3, circ=True, overlap=True, bbxsize_filter=args.facesize_filter) # vf = 1 if it was a valid frame, 0 otherwise
hist3, vf = spoof.lbphist_facenorm(frame, args.lbptype, locations[k], sz, args.elbptype, radius=2, neighbors=8, numbl=1, circ=True, overlap=False, bbxsize_filter=args.facesize_filter) # vf = 1 if it was a valid frame, 0 otherwise
hist = numpy.hstack((hist1, hist2, hist3))
numvf = numvf + vf
validframes.append(vf) # add 0 if it is not a valid frame, 1 in contrary
if histdata is None:
histdata = hist.reshape([1, hist.size])
else:
histdata = numpy.append(histdata, hist.reshape([1, hist.size]), axis = 0) # add the histogram into the list of frame feature vectors
sys.stdout.write('\n')
sys.stdout.flush()
# saves the output
obj.save(histdata, directory = args.directory, extension='.hdf5')
obj.save(numpy.array(validframes), directory = os.path.join(args.directory, 'validframes'), extension='.hdf5')
return 0
def main():
basedir = os.path.dirname(os.path.dirname(os.path.realpath(sys.argv[0])))
INPUT_DIR = os.path.join(basedir, 'database')
OUTPUT_DIR = os.path.join(basedir, 'lbp_features')
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('-v', '--input-dir', metavar='DIR', type=str,
dest='inputdir', default=INPUT_DIR, help='Base directory containing the videos to be treated by this procedure (defaults to "%(default)s")')
parser.add_argument('-d', '--directory', dest="directory", default=OUTPUT_DIR, help="This path will be prepended to every file output by this procedure (defaults to '%(default)s')")
parser.add_argument('-n', '--normface-size', dest="normfacesize", default=64, type=int, help="this is the size of the normalized face box if face normalization is used (defaults to '%(default)s')")
parser.add_argument('--ff', '--facesize_filter', dest="facesize_filter", default=0, type=int, help="all the frames with faces smaller then this number, will be discarded (defaults to '%(default)s')")
parser.add_argument('-e', '--enrollment', action='store_true', default=False, dest='enrollment', help='If True, will do the processing on the enrollment data of the database (defaults to "%(default)s")')
parser.add_argument('-c', '--cell', dest='cell', type=int, default=16, help='The size of the cells (defaults to "%(default)s")')
parser.add_argument('--co', '--cell-overlap', dest="cell_overlap", type=int, default=8, help='The overlap size of the cells (defaults to "%(default)s")')
parser.add_argument('-b', '--block', dest='block', type=int, default=4, help='The size of the blocks (defaults to "%(default)s")')
parser.add_argument('--bo', '--block-overlap', dest="block_overlap", type=int, default=1, help='The overlap size of the blocks (defaults to "%(default)s")')
parser.add_argument('--nn', '--nonorm', dest='nonorm', action='store_true', default=False, help='If True, block normalization of the HOG featurs will NOT be perfomed.')
parser.add_argument('--bbx', '--boundingbox', action='store_true', default=False, dest='boundingbox', help='If True, will read the face locations using the bbx function of the File class of the database. If False, will use faceloc.read_face utility to read the faceloc. For MSU-MFSD only (defaults to "%(default)s")')
#######
# Database especific configuration
#######
#Database.create_parser(parser)
Database.create_parser(parser, implements_any_of='video')
args = parser.parse_args()
from .. import spoof
########################
#Querying the database
########################
#database = new_database(databaseName,args=args)
database = args.cls(args)
if args.enrollment:
process = database.get_enroll_data()
else:
realObjects, attackObjects = database.get_all_data()
process = realObjects + attackObjects
counter = 0
# process each video
for obj in process:
counter += 1
input = bob.io.video.reader(obj.videofile(directory=args.inputdir))
# loading the face locations
if string.find(database.short_description(), "CASIA") != -1:
locations = faceloc.read_face(obj.facefile())
elif string.find(database.short_description(), "MSU") != -1:
if args.boundingbox: # load the locations based on the bbx function of the File object
locations = obj.bbx(directory=args.inputdir)
locations = {x[0]:faceloc.BoundingBox(x[1], x[2], x[3], x[4]) for x in locations} # for MSU MFSD
else:
locations = faceloc.read_face(obj.facefile(args.inputdir)) # for MSU MFSD
else:
locations = faceloc.read_face(obj.facefile(args.inputdir)) # for Replay-Attack
locations = faceloc.expand_detections(locations, input.number_of_frames)
sz = args.normfacesize # the size of the normalized face box
sys.stdout.write("Processing file %s (%d frames) [%d/%d] " % (obj.make_path(),
input.number_of_frames, counter, len(process)))
# start the work here...
vin = input.load() # load the video
hog = bob.ip.base.HOG((sz,sz), cell_size=(args.cell, args.cell), cell_overlap=(args.cell_overlap, args.cell_overlap), block_size=(args.block,args.block), block_overlap=(args.block_overlap, args.block_overlap))
if args.nonorm:
hog.disable_block_normalization()
hog_feat_shape = hog.output_shape()
histdata = numpy.ndarray((0, hog_feat_shape[0] * hog_feat_shape[1] * hog_feat_shape[2]), 'float64')
numvf = 0 # number of valid frames in the video (will be smaller then the total number of frames if a face is not detected or a very small face is detected in a frame when face lbp are calculated
validframes = [] # list with the indices of the valid frames
for k in range(0, vin.shape[0]):
frame = bob.ip.color.rgb_to_gray(vin[k,:,:,:])
if string.find(database.short_description(), "MSU") != -1 and obj.is_rotated(): # rotate the frame by 180 degrees if needed
frame = numpy.rot90(numpy.rot90(frame))
#sys.stdout.write("File %s is rotated " % (obj.make_path()))
sys.stdout.write('.')
sys.stdout.flush()
normbbx, vf = cut_face_bbx(frame, locations[k], sz, bbxsize_filter=0)
numvf = numvf + vf
validframes.append(vf)
if vf == 1:
hogfeats = hog.extract(normbbx)
else:
numpy.array(hog_feat_shape[0] * hog_feat_shape[1] * hog_feat_shape[2] * [numpy.NaN])
histdata = numpy.append(histdata, hogfeats.reshape([1, hog_feat_shape[0] * hog_feat_shape[1] * hog_feat_shape[2]]), axis = 0) # add the histogram into the list of frame feature vectors
sys.stdout.write('\n')
sys.stdout.flush()
# saves the output
obj.save(histdata, directory = args.directory, extension='.hdf5')
obj.save(numpy.array(validframes), directory = os.path.join(args.directory, 'validframes'), extension='.hdf5')
return 0
