diff --git a/README.md b/README.md
index 0bd210ea214751a040c402a9f357f8825fc9ca43..25573e270598ccf6d6104985705d3de0a89ac536 100644
--- a/README.md
+++ b/README.md
@@ -43,38 +43,51 @@ This repository is organised as follows:
1. Download the database from the following link: https://www.idiap.ch/en/dataset/phymatt
-2. Create a list of the videos to be used for the experiment. The list should contain the path for each video you want in the experiment. If you want all videos, you can use the following command:
+2. Update conda using:
+
+ ``` bash
+ mamba update -n base -c conda-forge conda mamba
+ ```
+
+3. Create a new conda environment using the provided environment file `environment.yml`:
+
+ ``` bash
+ mamba env create -f environment.yml
+ ``````
+
+4. Activate the environment:
+
+ ``` bash
+ conda activate bob_hyg_mask
+ ```
+
+5. Create a list of the videos to be used for the experiment. The list should contain the path for each video you want in the experiment. If you want all videos, you can use the following command:
+
``` bash
find <path_to_database> -name "*.mp4" > <path_to_list>
```
-3. Run the frames extraction code as follows:
+6. Run the frames extraction code as follows:
``` bash
python preprocessor/extract_frames.py -l <path_to_list> -o <path_to_output_folder>`
```
-4. Run the database organization code as follows:
+7. Run the database organization code as follows:
``` bash
python database/create_database_dataframe.py --frames_list --output_path -metadata_filename -save_mode --min_face_size`
```
-5. Run the pipeline as follows:
+8. Run the pipeline as follows:
``` bash
python pipeline_vuln.py --database_path --output_path --metadata_filename --save_mode --min_face_size --attack_type --attack_params --attac
```
-6. Once you have the score files, namely the `score-dev.csv`, you can use the script `utils/split_scores.sh` to split the scores into bona-fide and attack scores. The script will create three files: `scores-dev_print-attack.csv`, `scores-dev_replay-attack.csv` and `scores-dev_hyg-maks.csv`.
-
-7. Activate your `bob` environment:
-
- ```bash
- conda activate bob
- ```
+9. Once you have the score files, namely the `score-dev.csv`, you can use the script `utils/split_scores.sh` to split the scores into bona-fide and attack scores. The script will create three files: `scores-dev_print-attack.csv`, `scores-dev_replay-attack.csv` and `scores-dev_hyg-maks.csv`.
-8. You can then use these files to compute the metrics as follows:
+10. You can then use these files to compute the metrics as follows:
```bash
bob vuln metrics scores-dev_print-attack.csv scores-dev_replay-attack.csv scores-dev_hyg-maks.csv
diff --git a/database/create_database_dataframe.py b/database/create_database_dataframe.py
index 85323b1ac427be4a2a090bcd8849a6a2f32dfc54..05bfedb90ab97bbbf39bf160fb9e4a2831d1d1f9 100644
--- a/database/create_database_dataframe.py
+++ b/database/create_database_dataframe.py
@@ -23,8 +23,6 @@ import pandas as pd
from bob.bio.face.annotator import MTCNN
from bob.io.base import load
-SCENARIOS=["normal_light", "low_light", "angle_var_right_left", "angle_var_up_down"]
-
def detect_faces(frame, min_face_size):
"""_summary_
This function detects faces in frames of videos, it fills them into the list 'nb_detected_faces'.
@@ -38,6 +36,11 @@ def detect_faces(frame, min_face_size):
return nb_detected_faces, probs, boxes, landmarks
def create_metadata_file(frames_list, output_path, metadata_filename, save_mode, min_face_size):
+ # Check if output path exists
+ if not os.path.exists(output_path):
+ os.makedirs(output_path)
+
+ # Open the file containing the list of frames
my_file = open(frames_list, "r")
metadata = []
@@ -55,7 +58,7 @@ def create_metadata_file(frames_list, output_path, metadata_filename, save_mode,
session = filename_split[7]
frame_nb = filename_split[-1].split(".")[0].split("-")[-1]
reference_id = '_'.join((filename_split[0])) # Should be replaced by template_id
- frame = load(os.path.join(path, filename))
+ frame = load(line)
nb_detected_faces, probs, boxes, landmarks = detect_faces(frame, min_face_size)
metadata.append(
@@ -79,21 +82,12 @@ def create_metadata_file(frames_list, output_path, metadata_filename, save_mode,
'landmarks': landmarks,
}
)
- if save_mode == 'frame':
- df_frame = pd.DataFrame(metadata[-1])
- metadata_filename = filename.split('.')[0]
- # Save the dataframe into pickle
- df.to_pickle(os.path.join(os.path.join(output_path, "frames"), '.'.join((metadata_filename, 'pkl'))))
my_file.close()
# Put your list in a dataframe
df = pd.DataFrame(metadata)
-
- if save_mode == 'batch':
- batch_idx = frames_list.split('/')[-1].split('.')[0].split('_')[-1]
- metadata_filename = metadata_filename + '_'+ str(batch_idx)
- # Save the dataframe into pickle
- df.to_pickle(os.path.join(output_path, '.'.join((metadata_filename, 'pkl'))))
+ # Save the dataframe into pickle
+ df.to_pickle(os.path.join(output_path, '.'.join((metadata_filename, 'pkl'))))
if __name__ == '__main__':
@@ -101,10 +95,9 @@ if __name__ == '__main__':
parser = argparse.ArgumentParser()
# Add an argument with a default value
- parser.add_argument('-f', '--frames_list', default='/idiap/user/akomaty/projects/soteria/scripts/data/all_print_attack_frames.txt', help='A path to a file containing the list of frames.')
- parser.add_argument( '-o' ,'--output_path', default='/idiap/project/soteria/soteria_database_frames', help='The output path where you want to save your pickled metadata.')
+ parser.add_argument('-f', '--frames_list', help='A path to a file containing the list of frames.')
+ parser.add_argument( '-o' ,'--output_path', help='The output path where you want to save your pickled metadata.')
parser.add_argument('-m' ,'--metadata_filename', default='metadata_df_frames',help='name of the pickled metadata file.')
- parser.add_argument('-s' ,'--save_mode', default='batch', choices=['batch', 'frame'], help='Choose if you want to save one pickled file per list of frames ("batch"), or you want to save one pickled file per frame ("frame").')
parser.add_argument('-z' ,'--min_face_size', default=40, help=' Minimum face size to be detected.')
# Parse the arguments
diff --git a/environment.yml b/environment.yml
new file mode 100644
index 0000000000000000000000000000000000000000..23c9a91ce467ecc15311f582c91851ca20073f97
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,440 @@
+name: bob_hyg_mask
+channels:
+ - https://www.idiap.ch/software/bob/conda
+ - conda-forge
+dependencies:
+ - _libgcc_mutex=0.1=conda_forge
+ - _openmp_mutex=4.5=2_kmp_llvm
+ - abseil-cpp=20220623.0=h93e1e8c_5
+ - absl-py=1.2.0=pyhd8ed1ab_0
+ - aiohttp=3.8.1=py39hb9d737c_1
+ - aiosignal=1.2.0=pyhd8ed1ab_0
+ - alsa-lib=1.2.8=h166bdaf_0
+ - anyio=3.6.2=pyhd8ed1ab_0
+ - aom=3.4.0=h27087fc_1
+ - argon2-cffi=21.3.0=pyhd8ed1ab_0
+ - argon2-cffi-bindings=21.2.0=py39hb9d737c_3
+ - arrow-cpp=10.0.1=h27aab58_4_cpu
+ - asttokens=2.1.0=pyhd8ed1ab_0
+ - astunparse=1.6.3=pyhd8ed1ab_0
+ - async-timeout=4.0.2=pyhd8ed1ab_0
+ - attr=2.5.1=h166bdaf_1
+ - attrs=22.1.0=pyh71513ae_1
+ - aws-c-auth=0.6.21=hd93a3ba_3
+ - aws-c-cal=0.5.20=hff2c3d7_3
+ - aws-c-common=0.8.5=h166bdaf_0
+ - aws-c-compression=0.2.16=hf5f93bc_0
+ - aws-c-event-stream=0.2.17=h57874a7_1
+ - aws-c-http=0.7.0=h96ef541_0
+ - aws-c-io=0.13.12=h57ca295_1
+ - aws-c-mqtt=0.7.13=h0b5698f_12
+ - aws-c-s3=0.2.1=h82cbbf9_4
+ - aws-c-sdkutils=0.1.7=hf5f93bc_0
+ - aws-checksums=0.1.14=h6027aba_0
+ - aws-crt-cpp=0.18.16=h26430d7_8
+ - aws-sdk-cpp=1.9.379=h32f6703_7
+ - babel=2.11.0=pyhd8ed1ab_0
+ - backcall=0.2.0=pyh9f0ad1d_0
+ - backports=1.0=py_2
+ - backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
+ - beautifulsoup4=4.11.1=pyha770c72_0
+ - bleach=5.0.1=pyhd8ed1ab_0
+ - blinker=1.4=py_1
+ - blosc=1.21.1=h83bc5f7_3
+ - bob.bio.base=7.1.0=np122py39ha9b4201_0
+ - bob.bio.face=7.1.2=py39hb6b4a9b_0
+ - bob.bio.video=6.1.1=py39h5e91936_0
+ - bob.extension=7.0.3=py39h5300674_0
+ - bob.io.base=5.0.2=py39h2761e7f_0
+ - bob.learn.em=3.2.0=np122py39h2cb5f17_0
+ - bob.measure=6.0.2=py39ha6266be_0
+ - bob.pad.base=5.0.4=py39hba019c8_0
+ - bob.pad.face=4.1.1=py39heb51839_0
+ - bob.pipelines=3.0.3=np122py39ha758e9a_0
+ - bokeh=2.4.3=pyhd8ed1ab_3
+ - brotli=1.0.9=h166bdaf_7
+ - brotli-bin=1.0.9=h166bdaf_7
+ - brotlipy=0.7.0=py39hb9d737c_1004
+ - brunsli=0.1=h9c3ff4c_0
+ - bzip2=1.0.8=h7f98852_4
+ - c-ares=1.18.1=h7f98852_0
+ - c-blosc2=2.3.1=h7a311fb_0
+ - ca-certificates=2022.12.7=ha878542_0
+ - cached-property=1.5.2=hd8ed1ab_1
+ - cached_property=1.5.2=pyha770c72_1
+ - cachetools=5.2.0=pyhd8ed1ab_0
+ - cairo=1.16.0=ha61ee94_1012
+ - certifi=2022.12.7=pyhd8ed1ab_0
+ - cffi=1.15.1=py39he91dace_0
+ - cfitsio=4.1.0=hd9d235c_0
+ - charls=2.3.4=h9c3ff4c_0
+ - charset-normalizer=2.1.1=pyhd8ed1ab_0
+ - click=8.1.3=py39hf3d152e_0
+ - click-plugins=1.1.1=py_0
+ - cloudpickle=2.1.0=pyhd8ed1ab_0
+ - colorama=0.4.5=pyhd8ed1ab_0
+ - contourpy=1.0.6=py39hf939315_0
+ - cryptography=39.0.0=py39h079d5ae_0
+ - cudatoolkit=11.7.0=hd8887f6_10
+ - cudnn=8.4.1.50=hed8a83a_0
+ - cycler=0.11.0=pyhd8ed1ab_0
+ - cytoolz=0.12.0=py39hb9d737c_0
+ - dask=2022.9.2=pyhd8ed1ab_0
+ - dask-core=2022.9.2=pyhd8ed1ab_0
+ - dask-glm=0.2.0=py_1
+ - dask-jobqueue=0.8.1=pyhd8ed1ab_0
+ - dask-labextension=6.0.0=pyhd8ed1ab_0
+ - dask-ml=2022.5.27=pyhd8ed1ab_0
+ - dav1d=1.0.0=h166bdaf_1
+ - dbus=1.13.6=h5008d03_3
+ - debugpy=1.6.3=py39h5a03fae_1
+ - decorator=5.1.1=pyhd8ed1ab_0
+ - defusedxml=0.7.1=pyhd8ed1ab_0
+ - distributed=2022.9.2=pyhd8ed1ab_0
+ - entrypoints=0.4=pyhd8ed1ab_0
+ - executing=1.2.0=pyhd8ed1ab_0
+ - expat=2.5.0=h27087fc_0
+ - ffmpeg=4.4.2=gpl_hfe78399_107
+ - fftw=3.3.10=nompi_ha7695d1_103
+ - flatbuffers=22.12.06=hcb278e6_2
+ - flit-core=3.8.0=pyhd8ed1ab_0
+ - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
+ - font-ttf-inconsolata=3.000=h77eed37_0
+ - font-ttf-source-code-pro=2.038=h77eed37_0
+ - font-ttf-ubuntu=0.83=hab24e00_0
+ - fontconfig=2.14.1=hc2a2eb6_0
+ - fonts-conda-ecosystem=1=0
+ - fonts-conda-forge=1=0
+ - fonttools=4.37.1=py39hb9d737c_0
+ - freeglut=3.2.2=h9c3ff4c_1
+ - freetype=2.12.1=hca18f0e_0
+ - frozenlist=1.3.1=py39hb9d737c_0
+ - fsspec=2022.7.1=pyhd8ed1ab_0
+ - future=0.18.2=py39hf3d152e_5
+ - gast=0.4.0=pyh9f0ad1d_0
+ - gettext=0.21.1=h27087fc_0
+ - gflags=2.2.2=he1b5a44_1004
+ - giflib=5.2.1=h36c2ea0_2
+ - glib=2.74.1=h6239696_1
+ - glib-tools=2.74.1=h6239696_1
+ - glog=0.6.0=h6f12383_0
+ - gmp=6.2.1=h58526e2_0
+ - gnutls=3.7.7=hf3e180e_0
+ - google-auth=2.11.0=pyh6c4a22f_0
+ - google-auth-oauthlib=0.4.6=pyhd8ed1ab_0
+ - google-pasta=0.2.0=pyh8c360ce_0
+ - graphite2=1.3.13=h58526e2_1001
+ - greenlet=1.1.2=py39h5a03fae_2
+ - grpc-cpp=1.51.1=h27aab58_0
+ - grpcio=1.51.1=py39h8c60046_0
+ - gst-plugins-base=1.21.3=h4243ec0_1
+ - gstreamer=1.21.3=h25f0c4b_1
+ - gstreamer-orc=0.4.33=h166bdaf_0
+ - h5py=3.7.0=nompi_py39hd51670d_101
+ - harfbuzz=5.1.0=hf9f4e7c_0
+ - hdf5=1.12.2=nompi_h4df4325_101
+ - heapdict=1.0.1=py_0
+ - icu=70.1=h27087fc_0
+ - idna=3.3=pyhd8ed1ab_0
+ - imagecodecs=2022.8.8=py39h6e07900_4
+ - imageio=2.22.4=pyhfa7a67d_1
+ - imageio-ffmpeg=0.4.7=pyhd8ed1ab_0
+ - importlib-metadata=4.11.4=py39hf3d152e_0
+ - importlib_metadata=4.11.4=hd8ed1ab_0
+ - importlib_resources=5.10.0=pyhd8ed1ab_0
+ - ipdb=0.13.9=pyhd8ed1ab_0
+ - ipykernel=6.17.1=pyh210e3f2_0
+ - ipython=8.6.0=pyh41d4057_1
+ - ipython_genutils=0.2.0=py_1
+ - jack=1.9.21=h583fa2b_2
+ - jasper=2.0.33=ha77e612_0
+ - jedi=0.18.1=pyhd8ed1ab_2
+ - jinja2=3.1.2=pyhd8ed1ab_1
+ - joblib=1.1.0=pyhd8ed1ab_0
+ - jpeg=9e=h166bdaf_2
+ - json5=0.9.5=pyh9f0ad1d_0
+ - jsonschema=4.17.1=pyhd8ed1ab_0
+ - jupyter-server-proxy=3.2.2=pyhd8ed1ab_0
+ - jupyter_client=7.3.4=pyhd8ed1ab_0
+ - jupyter_core=5.0.0=py39hf3d152e_0
+ - jupyter_server=1.23.3=pyhd8ed1ab_0
+ - jupyterlab=3.5.0=pyhd8ed1ab_0
+ - jupyterlab_pygments=0.2.2=pyhd8ed1ab_0
+ - jupyterlab_server=2.16.3=pyhd8ed1ab_0
+ - jxrlib=1.1=h7f98852_2
+ - keras=2.11.0=pyhd8ed1ab_0
+ - keras-preprocessing=1.1.2=pyhd8ed1ab_0
+ - keyutils=1.6.1=h166bdaf_0
+ - kiwisolver=1.4.4=py39hf939315_0
+ - krb5=1.20.1=h81ceb04_0
+ - lame=3.100=h7f98852_1001
+ - lcms2=2.12=hddcbb42_0
+ - ld_impl_linux-64=2.36.1=hea4e1c9_2
+ - lerc=4.0.0=h27087fc_0
+ - libabseil=20220623.0=cxx17_h48a1fff_5
+ - libaec=1.0.6=h9c3ff4c_0
+ - libarrow=10.0.1=h86614e7_4_cpu
+ - libavif=0.10.1=h166bdaf_1
+ - libblas=3.9.0=16_linux64_mkl
+ - libbrotlicommon=1.0.9=h166bdaf_7
+ - libbrotlidec=1.0.9=h166bdaf_7
+ - libbrotlienc=1.0.9=h166bdaf_7
+ - libcap=2.66=ha37c62d_0
+ - libcblas=3.9.0=16_linux64_mkl
+ - libclang=15.0.7=default_had23c3d_0
+ - libclang13=15.0.7=default_h3e3d535_0
+ - libcrc32c=1.1.2=h9c3ff4c_0
+ - libcups=2.3.3=h36d4200_3
+ - libcurl=7.87.0=hdc1c0ab_0
+ - libdb=6.2.32=h9c3ff4c_0
+ - libdeflate=1.13=h166bdaf_0
+ - libdrm=2.4.112=h166bdaf_0
+ - libedit=3.1.20191231=he28a2e2_2
+ - libev=4.33=h516909a_1
+ - libevent=2.1.10=h28343ad_4
+ - libffi=3.4.2=h7f98852_5
+ - libflac=1.4.2=h27087fc_0
+ - libgcc-ng=12.1.0=h8d9b700_16
+ - libgcrypt=1.10.1=h166bdaf_0
+ - libgfortran-ng=12.1.0=h69a702a_16
+ - libgfortran5=12.1.0=hdcd56e2_16
+ - libglib=2.74.1=h606061b_1
+ - libglu=9.0.0=he1b5a44_1001
+ - libgoogle-cloud=2.5.0=h21dfe5b_1
+ - libgpg-error=1.46=h620e276_0
+ - libgrpc=1.51.1=h30feacc_0
+ - libiconv=1.17=h166bdaf_0
+ - libidn2=2.3.3=h166bdaf_0
+ - liblapack=3.9.0=16_linux64_mkl
+ - liblapacke=3.9.0=16_linux64_mkl
+ - libllvm11=11.1.0=hf817b99_3
+ - libllvm14=14.0.6=he0ac6c6_0
+ - libllvm15=15.0.7=hadd5161_0
+ - libnghttp2=1.51.0=hff17c54_0
+ - libnsl=2.0.0=h7f98852_0
+ - libogg=1.3.4=h7f98852_1
+ - libopencv=4.6.0=py39h04bf7ee_4
+ - libopus=1.3.1=h7f98852_1
+ - libpciaccess=0.16=h516909a_0
+ - libpng=1.6.39=h753d276_0
+ - libpq=15.1=hb675445_3
+ - libprotobuf=3.21.12=h3eb15da_0
+ - libsndfile=1.2.0=hb75c966_0
+ - libsodium=1.0.18=h36c2ea0_1
+ - libsqlite=3.40.0=h753d276_0
+ - libssh2=1.10.0=hf14f497_3
+ - libstdcxx-ng=12.1.0=ha89aaad_16
+ - libsystemd0=252=h2a991cd_0
+ - libtasn1=4.19.0=h166bdaf_0
+ - libthrift=0.16.0=he500d00_2
+ - libtiff=4.4.0=h0e0dad5_3
+ - libtool=2.4.7=h27087fc_0
+ - libudev1=252=h166bdaf_0
+ - libunistring=0.9.10=h7f98852_0
+ - libutf8proc=2.8.0=h166bdaf_0
+ - libuuid=2.32.1=h7f98852_1000
+ - libuv=1.44.2=h166bdaf_0
+ - libva=2.15.0=h166bdaf_0
+ - libvorbis=1.3.7=h9c3ff4c_0
+ - libvpx=1.11.0=h9c3ff4c_3
+ - libwebp-base=1.2.4=h166bdaf_0
+ - libxcb=1.13=h7f98852_1004
+ - libxkbcommon=1.0.3=he3ba5ed_0
+ - libxml2=2.10.3=h7463322_0
+ - libzlib=1.2.13=h166bdaf_4
+ - libzopfli=1.0.3=h9c3ff4c_0
+ - llvm-openmp=14.0.4=he0ac6c6_0
+ - llvmlite=0.38.1=py39h7d9a04d_0
+ - locket=1.0.0=pyhd8ed1ab_0
+ - lz4=4.0.0=py39h029007f_2
+ - lz4-c=1.9.3=h9c3ff4c_1
+ - lzo=2.10=h516909a_1000
+ - magma=2.5.4=h6103c52_2
+ - markdown=3.4.1=pyhd8ed1ab_0
+ - markupsafe=2.1.1=py39hb9d737c_1
+ - matplotlib=3.6.2=py39hf3d152e_0
+ - matplotlib-base=3.6.2=py39hf9fd14e_0
+ - matplotlib-inline=0.1.6=pyhd8ed1ab_0
+ - mistune=2.0.4=pyhd8ed1ab_0
+ - mkl=2022.1.0=h84fe81f_915
+ - mpg123=1.31.2=hcb278e6_0
+ - msgpack-python=1.0.4=py39hf939315_0
+ - multidict=6.0.2=py39hb9d737c_1
+ - multipledispatch=0.6.0=py_0
+ - munkres=1.1.4=pyh9f0ad1d_0
+ - mysql-common=8.0.32=ha901b37_0
+ - mysql-libs=8.0.32=hd7da12d_0
+ - nbclassic=0.4.8=pyhd8ed1ab_0
+ - nbclient=0.7.0=pyhd8ed1ab_0
+ - nbconvert=7.2.5=pyhd8ed1ab_0
+ - nbconvert-core=7.2.5=pyhd8ed1ab_0
+ - nbconvert-pandoc=7.2.5=pyhd8ed1ab_0
+ - nbformat=5.7.0=pyhd8ed1ab_0
+ - nccl=2.14.3.1=h0800d71_0
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diff --git a/frames.csv b/frames.csv
new file mode 100644
index 0000000000000000000000000000000000000000..047f6ac245813fa01c4deff25b40b01efd8a03ce
--- /dev/null
+++ b/frames.csv
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diff --git a/preprocessor/extract_frames.py b/preprocessor/extract_frames.py
index a11be1fb84ade4a9c09dceb51455683135fdb423..30b0cd3d32f121e42d7d2f54abb65809814ad428 100644
--- a/preprocessor/extract_frames.py
+++ b/preprocessor/extract_frames.py
@@ -29,11 +29,15 @@ def extract_frames(video_list, output_path, max_number_of_frames):
for video_path in fp:
video_path = video_path.strip()
myvid = VideoAsArray(video_path, max_number_of_frames=max_number_of_frames)
+ type_ = video_path.split("/")[-2]
+ id = video_path.split("/")[-1].split("_")[0]
+ filename=video_path.split("/")[-1].split(".")[0]
+ id_path = os.path.join(output_path, type_, id)
+ if not os.path.exists(id_path):
+ os.makedirs(id_path)
index = 0
for frame in myvid:
- id = video_path.split("/")[-2]
- filename=video_path.split("/")[-1].split(".")[0]
- dest_file_path = os.path.join(os.path.join(output_path, id), filename + "_frame-" + str(index)+ '.jpg')
+ dest_file_path = os.path.join(id_path, filename + "_frame-" + str(index)+ '.jpg')
matplotlib.image.imsave(dest_file_path, to_matplotlib(frame))
index +=1
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index e7c197346d7d941f7fcc862d7d0c2c1fbd3ffed3..0000000000000000000000000000000000000000
--- a/requirements.txt
+++ /dev/null
@@ -1,12 +0,0 @@
-pandas
-sklearn
-numpy
-cv2
-typing
-matplotlib
-pickle
-bob.bio.video
-bob.io.base
-bob.pipelines
-bob.bio.face
-bob.io.image
\ No newline at end of file
diff --git a/videos.txt b/videos.txt
new file mode 100644
index 0000000000000000000000000000000000000000..91e2d14905ec5cfa974960b6d67b456984a933c9
--- /dev/null
+++ b/videos.txt
@@ -0,0 +1,16 @@
+/idiap/resource/database/PHyMAtt/attack/hyg_mask_attack/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15.mp4
+/idiap/resource/database/PHyMAtt/attack/hyg_mask_attack/2_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-21-40.mp4
+/idiap/resource/database/PHyMAtt/attack/hyg_mask_attack/3_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-23-35.mp4
+/idiap/resource/database/PHyMAtt/attack/hyg_mask_attack/4_iPhone12_Front_ATTACK_hyg-mask_set-4_2023-02-10T14-25-54.mp4
+/idiap/resource/database/PHyMAtt/attack/print_attack/1_iPhone12_Front_ATTACK_a1-print_2022-12-14T14-13-05.mp4
+/idiap/resource/database/PHyMAtt/attack/print_attack/2_iPhone12_Front_ATTACK_a1-print_2022-12-14T14-13-21.mp4
+/idiap/resource/database/PHyMAtt/attack/print_attack/3_iPhone12_Front_ATTACK_a1-print_2022-12-14T14-13-36.mp4
+/idiap/resource/database/PHyMAtt/attack/print_attack/4_iPhone12_Front_ATTACK_a1-print_2022-12-14T14-13-51.mp4
+/idiap/resource/database/PHyMAtt/attack/replay_attack/1_iPhone6s_front_ATTACK_replay-attack_1_Redmi6pro_iPhone12_s1.mp4
+/idiap/resource/database/PHyMAtt/attack/replay_attack/2_iPhone12_front_ATTACK_replay-attack_1_iPhone6s_Redmi9A_s1.mp4
+/idiap/resource/database/PHyMAtt/attack/replay_attack/3_iPhone6s_front_ATTACK_replay-attack_1_iPhone12_Redmi9A_s1.mp4
+/idiap/resource/database/PHyMAtt/attack/replay_attack/4_iPhone12_front_ATTACK_replay-attack_1_iPhone6s_Redmi9A_s1.mp4
+/idiap/resource/database/PHyMAtt/bonafide/1_iPhone12_Front_BONAFIDE_indoor-normal-light_s1_2022-09-09T12-07-58.mp4
+/idiap/resource/database/PHyMAtt/bonafide/2_iPhone12_Front_BONAFIDE_indoor-normal-light_s1_2022-09-09T12-21-16.mp4
+/idiap/resource/database/PHyMAtt/bonafide/3_iPhone12_Front_BONAFIDE_indoor-normal-light_s1_2022-09-09T12-33-49.mp4
+/idiap/resource/database/PHyMAtt/bonafide/4_iPhone12_Front_BONAFIDE_indoor-normal-light_s1_2022-09-12T07-20-18.mp4
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