fixed the environemnt issue

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

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

environment.yml

+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
+  - ncurses=6.3=h27087fc_1
+  - nest-asyncio=1.5.6=pyhd8ed1ab_0
+  - nettle=3.8.1=hc379101_1
+  - networkx=2.8.6=pyhd8ed1ab_0
+  - ninja=1.11.0=h924138e_0
+  - nodejs=18.12.1=h8839609_0
+  - notebook=6.5.2=pyha770c72_1
+  - notebook-shim=0.2.2=pyhd8ed1ab_0
+  - nspr=4.35=h27087fc_0
+  - nss=3.82=he02c5a1_0
+  - numba=0.55.2=py39h66db6d7_0
+  - numexpr=2.7.3=py39hde0f152_1
+  - numpy=1.22.4=py39hc58783e_0
+  - oauthlib=3.2.0=pyhd8ed1ab_0
+  - opencv=4.6.0=py39hf3d152e_4
+  - openh264=2.3.0=h27087fc_0
+  - openjpeg=2.5.0=h7d73246_1
+  - openssl=3.0.7=h0b41bf4_1
+  - opt_einsum=3.3.0=pyhd8ed1ab_1
+  - orc=1.8.2=hfdbbad2_0
+  - p11-kit=0.24.1=hc5aa10d_0
+  - packaging=21.3=pyhd8ed1ab_0
+  - pandas=1.5.2=py39h4661b88_0
+  - pandoc=2.19.2=ha770c72_0
+  - pandocfilters=1.5.0=pyhd8ed1ab_0
+  - parquet-cpp=1.5.1=2
+  - parso=0.8.3=pyhd8ed1ab_0
+  - partd=1.3.0=pyhd8ed1ab_0
+  - pcre=8.45=h9c3ff4c_0
+  - pcre2=10.40=hc3806b6_0
+  - pexpect=4.8.0=pyh1a96a4e_2
+  - pickleshare=0.7.5=py_1003
+  - pillow=9.2.0=py39hd5dbb17_2
+  - pip=22.2.2=pyhd8ed1ab_0
+  - pixman=0.40.0=h36c2ea0_0
+  - pkgutil-resolve-name=1.3.10=pyhd8ed1ab_0
+  - platformdirs=2.5.2=pyhd8ed1ab_1
+  - ply=3.11=py_1
+  - portaudio=19.6.0=h583fa2b_7
+  - prometheus_client=0.15.0=pyhd8ed1ab_0
+  - prompt-toolkit=3.0.32=pyha770c72_0
+  - protobuf=4.21.12=py39h227be39_0
+  - psutil=5.9.1=py39hb9d737c_0
+  - pthread-stubs=0.4=h36c2ea0_1001
+  - ptyprocess=0.7.0=pyhd3deb0d_0
+  - pulseaudio=16.1=ha8d29e2_1
+  - pure_eval=0.2.2=pyhd8ed1ab_0
+  - py-opencv=4.6.0=py39hef51801_4
+  - pyarrow=10.0.1=py39h94a43e9_4_cpu
+  - pyasn1=0.4.8=py_0
+  - pyasn1-modules=0.2.7=py_0
+  - pycparser=2.21=pyhd8ed1ab_0
+  - pygments=2.13.0=pyhd8ed1ab_0
+  - pyjwt=2.4.0=pyhd8ed1ab_0
+  - pyparsing=3.0.9=pyhd8ed1ab_0
+  - pyqt=5.15.7=py39h18e9c17_0
+  - pyqt5-sip=12.11.0=py39h5a03fae_0
+  - pyrsistent=0.19.2=py39hb9d737c_0
+  - pysocks=1.7.1=py39hf3d152e_5
+  - pytables=3.7.0=py39h6a7961f_3
+  - python=3.9.15=hba424b6_0_cpython
+  - python-dateutil=2.8.2=pyhd8ed1ab_0
+  - python-fastjsonschema=2.16.2=pyhd8ed1ab_0
+  - python-flatbuffers=2.0=pyhd8ed1ab_0
+  - python_abi=3.9=2_cp39
+  - pytorch=1.12.1=cpu_py39h3439074_1
+  - pytorch-lightning=1.5.10=pyhd8ed1ab_0
+  - pytz=2022.2.1=pyhd8ed1ab_0
+  - pyu2f=0.1.5=pyhd8ed1ab_0
+  - pywavelets=1.3.0=py39hd257fcd_1
+  - pyyaml=6.0=py39hb9d737c_4
+  - pyzmq=24.0.1=py39headdf64_1
+  - qt-main=5.15.6=hf6cd601_5
+  - re2=2022.06.01=h27087fc_0
+  - readline=8.1.2=h0f457ee_0
+  - requests=2.28.1=pyhd8ed1ab_0
+  - requests-oauthlib=1.3.1=pyhd8ed1ab_0
+  - rsa=4.9=pyhd8ed1ab_0
+  - s2n=1.3.31=h3358134_0
+  - scikit-image=0.19.3=py39h1832856_1
+  - scikit-learn=1.1.2=py39he5e8d7e_0
+  - scipy=1.9.3=py39hddc5342_2
+  - send2trash=1.8.0=pyhd8ed1ab_0
+  - setuptools=59.5.0=py39hf3d152e_0
+  - simpervisor=0.4=pyhd8ed1ab_0
+  - sip=6.6.2=py39h5a03fae_0
+  - six=1.16.0=pyh6c4a22f_0
+  - sleef=3.5.1=h9b69904_2
+  - snappy=1.1.9=hbd366e4_1
+  - sniffio=1.3.0=pyhd8ed1ab_0
+  - sortedcontainers=2.4.0=pyhd8ed1ab_0
+  - soupsieve=2.3.2.post1=pyhd8ed1ab_0
+  - sqlalchemy=1.4.44=py39hb9d737c_0
+  - sqlite=3.40.0=h4ff8645_0
+  - stack_data=0.6.0=pyhd8ed1ab_0
+  - svt-av1=1.2.1=h27087fc_0
+  - tabulate=0.8.10=pyhd8ed1ab_0
+  - tbb=2021.5.0=h924138e_1
+  - tblib=1.7.0=pyhd8ed1ab_0
+  - tensorboard=2.11.2=pyhd8ed1ab_0
+  - tensorboard-data-server=0.6.1=py39h3ccb8fc_4
+  - tensorboard-plugin-wit=1.8.1=pyhd8ed1ab_0
+  - tensorflow=2.11.0=cpu_py39h4655687_0
+  - tensorflow-base=2.11.0=cpu_py39h9b4020c_0
+  - tensorflow-estimator=2.11.0=cpu_py39hf050123_0
+  - termcolor=1.1.0=pyhd8ed1ab_3
+  - terminado=0.17.0=pyh41d4057_0
+  - threadpoolctl=3.1.0=pyh8a188c0_0
+  - tifffile=2022.8.12=pyhd8ed1ab_0
+  - tinycss2=1.2.1=pyhd8ed1ab_0
+  - tk=8.6.12=h27826a3_0
+  - toml=0.10.2=pyhd8ed1ab_0
+  - tomli=2.0.1=pyhd8ed1ab_0
+  - toolz=0.12.0=pyhd8ed1ab_0
+  - torchmetrics=0.9.3=pyhd8ed1ab_0
+  - torchvision=0.13.0=cpu_py39hedfcc36_0
+  - tornado=6.1=py39hb9d737c_3
+  - tqdm=4.64.0=pyhd8ed1ab_0
+  - traitlets=5.5.0=pyhd8ed1ab_0
+  - typing-extensions=4.3.0=hd8ed1ab_0
+  - typing_extensions=4.3.0=pyha770c72_0
+  - tzdata=2022c=h191b570_0
+  - unicodedata2=14.0.0=py39hb9d737c_1
+  - urllib3=1.26.11=pyhd8ed1ab_0
+  - wcwidth=0.2.5=pyh9f0ad1d_2
+  - webencodings=0.5.1=py_1
+  - websocket-client=1.4.2=pyhd8ed1ab_0
+  - werkzeug=2.2.2=pyhd8ed1ab_0
+  - wheel=0.37.1=pyhd8ed1ab_0
+  - wrapt=1.14.1=py39hb9d737c_0
+  - x264=1!164.3095=h166bdaf_2
+  - x265=3.5=h924138e_3
+  - xarray=2022.6.0=pyhd8ed1ab_1
+  - xcb-util=0.4.0=h166bdaf_0
+  - xcb-util-image=0.4.0=h166bdaf_0
+  - xcb-util-keysyms=0.4.0=h166bdaf_0
+  - xcb-util-renderutil=0.3.9=h166bdaf_0
+  - xcb-util-wm=0.4.1=h166bdaf_0
+  - xorg-fixesproto=5.0=h7f98852_1002
+  - xorg-inputproto=2.3.2=h7f98852_1002
+  - xorg-kbproto=1.0.7=h7f98852_1002
+  - xorg-libice=1.0.10=h7f98852_0
+  - xorg-libsm=1.2.3=hd9c2040_1000
+  - xorg-libx11=1.7.2=h7f98852_0
+  - xorg-libxau=1.0.9=h7f98852_0
+  - xorg-libxdmcp=1.1.3=h7f98852_0
+  - xorg-libxext=1.3.4=h7f98852_1
+  - xorg-libxfixes=5.0.3=h7f98852_1004
+  - xorg-libxi=1.7.10=h7f98852_0
+  - xorg-libxrender=0.9.10=h7f98852_1003
+  - xorg-renderproto=0.11.1=h7f98852_1002
+  - xorg-xextproto=7.3.0=h7f98852_1002
+  - xorg-xproto=7.0.31=h7f98852_1007
+  - xz=5.2.6=h166bdaf_0
+  - yaml=0.2.5=h7f98852_2
+  - yarl=1.7.2=py39hb9d737c_2
+  - zeromq=4.3.4=h9c3ff4c_1
+  - zfp=1.0.0=h27087fc_1
+  - zict=2.2.0=pyhd8ed1ab_0
+  - zipp=3.8.1=pyhd8ed1ab_0
+  - zlib=1.2.13=h166bdaf_4
+  - zlib-ng=2.0.6=h166bdaf_0
+  - zstd=1.5.2=h6239696_4
+  - pip:
+    - av==10.0.0
+    - ffmpeg-python==0.2.0
+    - pydeprecate==0.3.1

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
 

requirements.txt

-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

videos.txt

+/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
\ No newline at end of file