Commit 8a261761 authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira

Merge branch 'dask-pipelines' into 'master'

Porting to dask pipelines

See merge request !110
parents 0b334636 1e8758bd
Pipeline #46288 passed with stages
in 10 minutes and 56 seconds
...@@ -15,3 +15,4 @@ record.txt ...@@ -15,3 +15,4 @@ record.txt
results results
submitted.sql3 submitted.sql3
temp* temp*
*.DS_Store
#!/usr/bin/env python
from bob.pad.base.algorithm import SVMCascadePCA
#=======================================================================================
# Define instances here:
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.2}
N = 2
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n2_gamma_02 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.1}
N = 2
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n2_gamma_01 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.05}
N = 2
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n2_gamma_005 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.01}
N = 2
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n2_gamma_001 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
#=======================================================================================
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.1}
N = 10
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n10_gamma_01 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.05}
N = 10
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n10_gamma_005 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.01}
N = 10
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n10_gamma_001 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.005}
N = 10
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n10_gamma_0005 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
#=======================================================================================
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.5}
N = 20
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n20_gamma_05 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.2}
N = 20
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n20_gamma_02 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.1}
N = 20
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n20_gamma_01 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.05}
N = 20
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n20_gamma_005 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.01}
N = 20
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n20_gamma_001 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.005}
N = 20
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n20_gamma_0005 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.001}
N = 20
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n20_gamma_0001 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
#=======================================================================================
MACHINE_TYPE = 'ONE_CLASS'
KERNEL_TYPE = 'RBF'
SVM_KWARGS = {'nu': 0.001, 'gamma': 0.1}
N = 2
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = False
algorithm_n2_gamma_01_video_level = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=SVM_KWARGS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
#=======================================================================================
# Test the cascade of two-class SVMs.
MACHINE_TYPE = 'C_SVC'
KERNEL_TYPE = 'RBF'
TRAINER_GRID_SEARCH_PARAMS = {'cost': 1, 'gamma': 0.01}
N = 2
POS_SCORES_SLOPE = 0.01
FRAME_LEVEL_SCORES_FLAG = True
algorithm_n2_two_class_svm_c1_gamma_001 = SVMCascadePCA(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
svm_kwargs=TRAINER_GRID_SEARCH_PARAMS,
N=N,
pos_scores_slope=POS_SCORES_SLOPE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
#!/usr/bin/env python
from bob.pad.base.algorithm import OneClassGMM
#=======================================================================================
# Define instances here:
N_COMPONENTS = 2
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_2 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 3
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_3 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 4
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_4 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 5
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_5 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 6
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_6 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 7
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_7 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 8
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_8 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 9
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_9 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 10
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_10 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
#=======================================================================================
# above 10 Gaussians:
N_COMPONENTS = 12
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_12 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 14
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_14 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 16
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_16 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 18
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_18 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 20
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_20 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
#=======================================================================================
# above 20 Gaussians:
N_COMPONENTS = 25
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_25 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 30
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_30 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 35
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_35 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 40
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_40 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 45
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_45 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 50
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_50 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
#=======================================================================================
# above 50 Gaussians:
N_COMPONENTS = 60
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_60 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 70
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_70 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 80
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_80 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 90
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_90 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 100
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_100 = OneClassGMM(
n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
#=======================================================================================
# 50 Gaussians, different random seeds:
N_COMPONENTS = 50
RANDOM_STATE = 0
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_50_0 = OneClassGMM(
n_components=N_COMPONENTS,
random_state=RANDOM_STATE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 50
RANDOM_STATE = 1
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_50_1 = OneClassGMM(
n_components=N_COMPONENTS,
random_state=RANDOM_STATE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 50
RANDOM_STATE = 2
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_50_2 = OneClassGMM(
n_components=N_COMPONENTS,
random_state=RANDOM_STATE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 50
RANDOM_STATE = 3
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_50_3 = OneClassGMM(
n_components=N_COMPONENTS,
random_state=RANDOM_STATE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 50
RANDOM_STATE = 4
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_50_4 = OneClassGMM(
n_components=N_COMPONENTS,
random_state=RANDOM_STATE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 50
RANDOM_STATE = 5
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_50_5 = OneClassGMM(
n_components=N_COMPONENTS,
random_state=RANDOM_STATE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 50
RANDOM_STATE = 6
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_50_6 = OneClassGMM(
n_components=N_COMPONENTS,
random_state=RANDOM_STATE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 50
RANDOM_STATE = 7
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_50_7 = OneClassGMM(
n_components=N_COMPONENTS,
random_state=RANDOM_STATE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 50
RANDOM_STATE = 8
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_50_8 = OneClassGMM(
n_components=N_COMPONENTS,
random_state=RANDOM_STATE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
N_COMPONENTS = 50
RANDOM_STATE = 9
FRAME_LEVEL_SCORES_FLAG = True
algorithm_gmm_50_9 = OneClassGMM(
n_components=N_COMPONENTS,
random_state=RANDOM_STATE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
#!/usr/bin/env python
from bob.pad.base.algorithm import SVM
#=======================================================================================
# Define instances here:
machine_type = 'C_SVC'
kernel_type = 'RBF'
n_samples = 10000
# trainer_grid_search_params = {'cost': [2**p for p in range(-5, 16, 2)], 'gamma': [2**p for p in range(-15, 4, 2)]}
trainer_grid_search_params = {
'cost': [2**p for p in range(-3, 14, 2)],
'gamma': [2**p for p in range(-15, 0, 2)]
}
mean_std_norm_flag = True
frame_level_scores_flag = False # one score per video(!) in this case
video_svm_pad_algorithm_10k_grid_mean_std = SVM(
machine_type=machine_type,
kernel_type=kernel_type,
n_samples=n_samples,
trainer_grid_search_params=trainer_grid_search_params,
mean_std_norm_flag=mean_std_norm_flag,
frame_level_scores_flag=frame_level_scores_flag)
frame_level_scores_flag = True # one score per frame(!) in this case
video_svm_pad_algorithm_10k_grid_mean_std_frame_level = SVM(
machine_type=machine_type,
kernel_type=kernel_type,
n_samples=n_samples,
trainer_grid_search_params=trainer_grid_search_params,
mean_std_norm_flag=mean_std_norm_flag,
frame_level_scores_flag=frame_level_scores_flag)
trainer_grid_search_params = {
'cost': [1],
'gamma': [0]
} # set the default LibSVM parameters
video_svm_pad_algorithm_default_svm_param_mean_std_frame_level = SVM(
machine_type=machine_type,
kernel_type=kernel_type,
n_samples=n_samples,
trainer_grid_search_params=trainer_grid_search_params,
mean_std_norm_flag=mean_std_norm_flag,
frame_level_scores_flag=frame_level_scores_flag)
#!/usr/bin/env python
# encoding: utf-8
from bob.pad.face.database import BRSUPadDatabase
from bob.extension import rc
database = BRSUPadDatabase(
protocol='test',
original_directory=rc['bob.db.brsu.directory'],
)
#!/usr/bin/env python
"""Config file for the CASIA FASD dataset. """Config file for the CASIA FASD dataset.
Please run ``bob config set bob.db.casia_fasd.directory /path/to/casia_fasd_files`` Please run ``bob config set bob.db.casia_fasd.directory /path/to/casia_fasd_files``
in terminal to point to the original files of the dataset on your computer.""" in terminal to point to the original files of the dataset on your computer."""
from bob.pad.face.database import CasiaFasdPadDatabase from bob.pad.face.database import CasiaFasdPadDatabase
database = CasiaFasdPadDatabase() from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
database = DatabaseConnector(CasiaFasdPadDatabase())
#!/usr/bin/env python
# encoding: utf-8
from bob.pad.face.database import CasiaSurfPadDatabase from bob.pad.face.database import CasiaSurfPadDatabase
from bob.extension import rc from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
database = CasiaSurfPadDatabase( database = DatabaseConnector(CasiaSurfPadDatabase())
protocol='all',
original_directory=rc['bob.db.casiasurf.directory'],
original_extension=".jpg",
)
#!/usr/bin/env python
# encoding: utf-8
from bob.pad.face.database import CasiaSurfPadDatabase from bob.pad.face.database import CasiaSurfPadDatabase
from bob.extension import rc from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
database = CasiaSurfPadDatabase( database = DatabaseConnector(CasiaSurfPadDatabase())
protocol='color',
original_directory=rc['bob.db.casiasurf.directory'],
original_extension=".jpg",
)
#!/usr/bin/env python
"""`CELEBA`_ is a face makeup spoofing database adapted for face PAD experiments. """`CELEBA`_ is a face makeup spoofing database adapted for face PAD experiments.
...@@ -9,48 +8,7 @@ the link. ...@@ -9,48 +8,7 @@ the link.
""" """
from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
from bob.pad.face.database.celeb_a import CELEBAPadDatabase from bob.pad.face.database.celeb_a import CELEBAPadDatabase
# Directory where the data files are stored. database = DatabaseConnector(CELEBAPadDatabase())
# This directory is given in the .bob_bio_databases.txt file located in your home directory
ORIGINAL_DIRECTORY = "[YOUR_CELEB_A_DATABASE_DIRECTORY]"
"""Value of ``~/.bob_bio_databases.txt`` for this database"""
ORIGINAL_EXTENSION = "" # extension of the data files