Hello guys,
For my graduation research into spoofing detection techniques I am trying to use the source code of https://pypi.python.org/pypi/antispoofing.lbptop/. Unfortunately, I am running into some errors that I am not able to solve myself (neither after asking help of some people who are more skilled in using python than I am). Maybe you can provide some help.
The errors I am running into are these:
1. While running lbptop_calculate_parameters.py on the MSU-MSFD or CASIA_FASD databases I get the following error:
Traceback (most recent call last):
File "./bin/lbptop_calculate_parameters.py", line 24, in <module>
sys.exit(antispoofing.lbptop.script.lbptop_calculate_parameters.main())
File "/home/jeroen/Documents/detection/antispoofing.lbptop/antispoofing/lbptop/script/lbptop_calculate_parameters.py", line 183, in main
locations = preprocess_detections(facefile, len(input), facesize_filter=args.min_face_size)
AttributeError: 'Namespace' object has no attribute 'min_face_size'
However, running lbptop_calculate_parameters.py on MSU-MSFD with the --bbx parameter does allow me to run this algorithm.
2. While running lbptop_make_scores.py on the files that are generates while running lbptop_calculate_parameters.py on MSU-MSFD with –bbx, I get the following error:
Traceback (most recent call last):
File "./bin/lbptop_make_scores.py", line 24, in <module>
sys.exit(antispoofing.lbptop.script.lbptop_make_scores.main())
File "/home/jeroen/Documents/detection/antispoofing.lbptop/antispoofing/lbptop/script/lbptop_make_scores.py", line 122, in main
dataset = calclbptop.create_full_dataset([obj],featuresDir,retrieveNanLines=True)
File "/home/jeroen/Documents/detection/antispoofing.lbptop/antispoofing/lbptop/spoof/calclbptop.py", line 303, in create_full_dataset
from bob.io import load
ImportError: cannot import name load
I have tried to import bob.io directly before this function, but even then, this code does not work.
I hope you can help me out with this problem, maybe it is something that occurs more frequently.
Kind regards,
Jeroen Fokkema
Operating system: Ubuntu 14.04.4
Installation of antispoofing.lbptop performed using zc.buildout
Installed packages according to pip:
Jinja2==2.7.2
MarkupSafe==0.18
MySQL-python==1.2.3
PAM==0.4.2
Pillow==3.2.0
Pygments==1.6
SQLAlchemy==0.8.4
Sphinx==1.2.2
Twisted-Core==13.2.0
Twisted-Web==13.2.0
adium-theme-ubuntu==0.3.4
antispoofing.lbptop==2.0.3
antispoofing.utils==2.0.7
apt-xapian-index==0.45
argparse==1.2.1
beautifulsoup4==4.2.1
bob==2.1.0
bob.ap==2.0.4
bob.blitz==2.0.8
bob.core==2.1.0
bob.db.atnt==2.0.3
bob.db.base==2.0.5
bob.db.casia-fasd==2.0.5
bob.db.iris==2.0.4
bob.db.mnist==2.0.3
bob.db.replay==2.0.5
bob.db.verification.utils==2.0.3
bob.db.wine==2.0.3
bob.extension==2.0.11
bob.io.base==2.0.7
bob.io.image==2.0.4
bob.io.matlab==2.0.4
bob.io.video==2.0.5
bob.ip.base==2.0.7
bob.ip.color==2.0.4
bob.ip.draw==2.0.3
bob.ip.facedetect==2.0.3
bob.ip.gabor==2.0.4
bob.ip.optflow.hornschunck==2.0.6
bob.ip.optflow.liu==2.0.5
bob.learn.activation==2.0.4
bob.learn.boosting==2.0.4
bob.learn.em==2.0.7
bob.learn.libsvm==2.0.3
bob.learn.linear==2.0.7
bob.learn.mlp==2.0.6
bob.math==2.0.3
bob.measure==2.1.0
bob.sp==2.0.4
chardet==2.0.1
colorama==0.2.5
command-not-found==0.3
cvxopt==1.1.4
cycler==0.10.0
debtagshw==0.1
decorator==3.4.0
defer==1.0.6
dirspec==13.10
docutils==0.11
duplicity==0.6.23
html5lib==0.999
httplib2==0.8
ipdb==0.8
ipython==1.2.1
joblib==0.7.1
lockfile==0.8
lxml==3.3.3
matplotlib==1.5.1
mercurial==2.8.2
nose==1.3.1
numexpr==2.2.2
numpy==1.11.0
oauthlib==0.6.1
oneconf==0.3.7.14.04.1
openpyxl==1.7.0
pandas==0.13.1
patsy==0.2.1
pexpect==3.1
piston-mini-client==0.7.5
pyOpenSSL==0.13
pycrypto==2.6.1
pycups==1.9.66
pycurl==7.19.3
pygobject==3.12.0
pyparsing==2.1.1
pyserial==2.6
pysmbc==1.0.14.1
python-apt==0.9.3.5ubuntu2
python-dateutil==2.5.2
python-debian==0.1.21-nmu2ubuntu2
pytz==2016.3
pyxdg==0.25
pyzmq==14.0.1
reportlab==3.0
requests==2.2.1
roman==2.0.0
scipy==0.13.3
sessioninstaller==0.0.0
simplegeneric==0.8.1
simplejson==3.3.1
six==1.5.2
software-center-aptd-plugins==0.0.0
statsmodels==0.5.0
sympy==0.7.4.1
system-service==0.1.6
tables==3.1.1
tornado==3.1.1
unity-lens-photos==1.0
urllib3==1.7.1
virtualenv==15.0.1
wheel==0.24.0
wsgiref==0.1.2
xdiagnose==3.6.3build2
xlrd==0.9.2
xlwt==0.7.5
zope.interface==4.0.5
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svm_machine.predict_class_and_scores(x)
1. running ./bin/lbptop_calculate_parameters.py on the CASIA database jams with the third file: train_release/1/HR_1.avi. I tried it on a server and in a virtual machine. The server gives a memory error, the virtual pc just stalls.
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Hello Tiago,
I have an additional question. I would like to be able to run lbp-top over other footage than the default databases. I assume creating a custom implementation of antispoofing.utils.db.Database is the simplest way to do this. However, face detection has to be performed on the video’s. In the paper about LBP-TOP you mention that a face detection using MCT features (of Froba, B. and Ernst, A.) is used. Is there any implementation available of this algorithm that I could use?
Regards,
Jeroen Fokkema
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However, face detection has to be performed on the video’s. In the paper about LBP-TOP you mention that a face detection using MCT features (of Froba, B. and Ernst, A.) is used. Is there any implementation available of this algorithm that I could use?