diff --git a/README.rst b/README.rst
index b3ee5803d55b5c5d68919b8062cccc112b83b9ea..e8d81afb0504b5a3be230a4b93edee147f6fe004 100644
--- a/README.rst
+++ b/README.rst
@@ -50,6 +50,23 @@ command::
This will install all the required software to reproduce this paper.
+Configuring the experiments
+---------------------------
+
+Once the environment is ready. The WMCA database needs to be downloaded.
+For downloading the WMCA database, please check the following link `Download Instructions <https://www.idiap.ch/dataset/wmca>`_.
+
+After downloading the database, you need to set the paths to
+those in the configuration files. Bob_ supports a configuration file
+(``~/.bob_bio_databases.txt``) in your home directory to specify where the
+databases are located. The absolute path to the database and the location store the annotations should be specified.
+Please specify the paths for the database like below::
+
+ $ cat ~/.bob_bio_databases.txt
+ [YOUR_WMCA_DB_DIRECTORY] = /databases/WMCA/
+ [YOUR_WMCA_ANNOTATIONS_DIRECTORY] = /databases/WMCA/ANNOTATIONS/
+
+
Contact
-------
diff --git a/bob/paper/mccnn/tifs2018/config/FASNet_config.py b/bob/paper/mccnn/tifs2018/config/FASNet_config.py
index 86978a92822c2744fb4d70bfef6f833dface8377..fe0fa620ae95977ef5c3506f33b9af16559ef3d1 100644
--- a/bob/paper/mccnn/tifs2018/config/FASNet_config.py
+++ b/bob/paper/mccnn/tifs2018/config/FASNet_config.py
@@ -17,6 +17,8 @@ from torchvision import transforms
from bob.learn.pytorch.datasets import ChannelSelect
+import os
+
# names of the channels to process:
_channel_names = ['color', 'depth', 'infrared', 'thermal']
@@ -98,29 +100,27 @@ from bob.bio.video.extractor import Wrapper
# If you want to use the pretrained model
-USE_PRETRAINED_MODEL=True
+USE_PRETRAINED_MODEL = True
if USE_PRETRAINED_MODEL:
- import pkg_resources
+ import pkg_resources
- MODEL_FILE = pkg_resources.resource_filename('bob.paper.mccnn.tifs2018', 'models/fasnet.pth')
+ MODEL_FILE = pkg_resources.resource_filename('bob.paper.mccnn.tifs2018', 'models/fasnet.pth')
- URL = 'https://www.idiap.ch/software/bob/data/bob/bob.paper.mccnn.tifs2018/master/fasnet.pth'
+ URL = 'https://www.idiap.ch/software/bob/data/bob/bob.paper.mccnn.tifs2018/master/fasnet.pth'
- if not os.path.exists(MODEL_FILE):
+ if not os.path.exists(MODEL_FILE):
- logger.info('Downloading the FASNet model')
+ logger.info('Downloading the FASNet model')
- bob.extension.download.download_file(URL, MODEL_FILE)
+ bob.extension.download.download_file(URL, MODEL_FILE)
- logger.info('Downloaded FASNet model to location: {}'.format(MODEL_FILE))
+ logger.info('Downloaded FASNet model to location: {}'.format(MODEL_FILE))
else:
- MODEL_FILE= None # Replace with '<PATH_TO_MODEL>'
-
-
+ MODEL_FILE = None # Replace with '<PATH_TO_MODEL>'
_img_transform = transforms.Compose([transforms.ToPILImage(), transforms.Resize(224, interpolation=2), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406],
diff --git a/bob/paper/mccnn/tifs2018/database/batl_db_color.py b/bob/paper/mccnn/tifs2018/database/batl_db_color.py
index 09060cf6b9f5fe9dd3e5728d12d45b256b814a6b..fc83b38a91cd52e868c9a4317910737458ef77e7 100644
--- a/bob/paper/mccnn/tifs2018/database/batl_db_color.py
+++ b/bob/paper/mccnn/tifs2018/database/batl_db_color.py
@@ -14,7 +14,7 @@ ORIGINAL_EXTENSION = ".h5" # extension of the data files
ANNOTATIONS_TEMP_DIR = "[YOUR_WMCA_ANNOTATIONS_DIRECTORY]"
-unseen_protocols=['','-LOO_fakehead','-LOO_flexiblemask','-LOO_glasses','-LOO_papermask','-LOO_prints','-LOO_replay','-LOO_rigidmask']
+unseen_protocols = ['', '-LOO_fakehead', '-LOO_flexiblemask', '-LOO_glasses', '-LOO_papermask', '-LOO_prints', '-LOO_replay', '-LOO_rigidmask']
PROTOCOL = 'grandtest-color-50'+unseen_protocols[0]
@@ -22,10 +22,10 @@ database = BatlPadDatabase(
protocol=PROTOCOL,
original_directory=ORIGINAL_DIRECTORY,
original_extension=ORIGINAL_EXTENSION,
- landmark_detect_method="mtcnn",
+ landmark_detect_method="mtcnn",
exclude_attacks_list=['makeup'],
- exclude_pai_all_sets=True,
- append_color_face_roi_annot=False)
+ exclude_pai_all_sets=True,
+ append_color_face_roi_annot=False)
"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
database settings.
diff --git a/bob/paper/mccnn/tifs2018/database/batl_db_depth.py b/bob/paper/mccnn/tifs2018/database/batl_db_depth.py
index 5e982a6725850d237544a5ca700c90d435c66796..0803ad383730e987ee85267a0ce04307a70311df 100644
--- a/bob/paper/mccnn/tifs2018/database/batl_db_depth.py
+++ b/bob/paper/mccnn/tifs2018/database/batl_db_depth.py
@@ -14,7 +14,7 @@ ORIGINAL_EXTENSION = ".h5" # extension of the data files
ANNOTATIONS_TEMP_DIR = "[YOUR_WMCA_ANNOTATIONS_DIRECTORY]"
-unseen_protocols=['','-LOO_fakehead','-LOO_flexiblemask','-LOO_glasses','-LOO_papermask','-LOO_prints','-LOO_replay','-LOO_rigidmask']
+unseen_protocols = ['', '-LOO_fakehead', '-LOO_flexiblemask', '-LOO_glasses', '-LOO_papermask', '-LOO_prints', '-LOO_replay', '-LOO_rigidmask']
PROTOCOL = 'grandtest-depth-50'+unseen_protocols[0]
@@ -22,10 +22,10 @@ database = BatlPadDatabase(
protocol=PROTOCOL,
original_directory=ORIGINAL_DIRECTORY,
original_extension=ORIGINAL_EXTENSION,
- landmark_detect_method="mtcnn",
+ landmark_detect_method="mtcnn",
exclude_attacks_list=['makeup'],
- exclude_pai_all_sets=True,
- append_color_face_roi_annot=False)
+ exclude_pai_all_sets=True,
+ append_color_face_roi_annot=False)
"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
database settings.
diff --git a/bob/paper/mccnn/tifs2018/database/batl_db_infrared.py b/bob/paper/mccnn/tifs2018/database/batl_db_infrared.py
index e69e8c966f170db221f981fb19ffefe85d7351e6..5e14b962fbec6fc747f166f042274239356fa577 100644
--- a/bob/paper/mccnn/tifs2018/database/batl_db_infrared.py
+++ b/bob/paper/mccnn/tifs2018/database/batl_db_infrared.py
@@ -14,7 +14,7 @@ ORIGINAL_EXTENSION = ".h5" # extension of the data files
ANNOTATIONS_TEMP_DIR = "[YOUR_WMCA_ANNOTATIONS_DIRECTORY]"
-unseen_protocols=['','-LOO_fakehead','-LOO_flexiblemask','-LOO_glasses','-LOO_papermask','-LOO_prints','-LOO_replay','-LOO_rigidmask']
+unseen_protocols = ['', '-LOO_fakehead', '-LOO_flexiblemask', '-LOO_glasses', '-LOO_papermask', '-LOO_prints', '-LOO_replay', '-LOO_rigidmask']
PROTOCOL = 'grandtest-infrared-50'+unseen_protocols[0]
@@ -22,10 +22,10 @@ database = BatlPadDatabase(
protocol=PROTOCOL,
original_directory=ORIGINAL_DIRECTORY,
original_extension=ORIGINAL_EXTENSION,
- landmark_detect_method="mtcnn",
+ landmark_detect_method="mtcnn",
exclude_attacks_list=['makeup'],
- exclude_pai_all_sets=True,
- append_color_face_roi_annot=False)
+ exclude_pai_all_sets=True,
+ append_color_face_roi_annot=False)
"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
database settings.
diff --git a/bob/paper/mccnn/tifs2018/database/batl_db_rgb_ir_d_t_grandtest.py b/bob/paper/mccnn/tifs2018/database/batl_db_rgb_ir_d_t_grandtest.py
index aa05a0ec6dec25507e33e930affec234fa5791e7..6ed39f9ace3f09321f2238fbb69fe7ff0a54580c 100644
--- a/bob/paper/mccnn/tifs2018/database/batl_db_rgb_ir_d_t_grandtest.py
+++ b/bob/paper/mccnn/tifs2018/database/batl_db_rgb_ir_d_t_grandtest.py
@@ -14,7 +14,7 @@ ORIGINAL_EXTENSION = ".h5" # extension of the data files
ANNOTATIONS_TEMP_DIR = "[YOUR_WMCA_ANNOTATIONS_DIRECTORY]"
-unseen_protocols=['','-LOO_fakehead','-LOO_flexiblemask','-LOO_glasses','-LOO_papermask','-LOO_prints','-LOO_replay','-LOO_rigidmask']
+unseen_protocols = ['', '-LOO_fakehead', '-LOO_flexiblemask', '-LOO_glasses', '-LOO_papermask', '-LOO_prints', '-LOO_replay', '-LOO_rigidmask']
PROTOCOL = 'grandtest-color*infrared*depth*thermal-50'+unseen_protocols[0]
@@ -22,10 +22,10 @@ database = BatlPadDatabase(
protocol=PROTOCOL,
original_directory=ORIGINAL_DIRECTORY,
original_extension=ORIGINAL_EXTENSION,
- landmark_detect_method="mtcnn",
+ landmark_detect_method="mtcnn",
exclude_attacks_list=['makeup'],
- exclude_pai_all_sets=True,
- append_color_face_roi_annot=False)
+ exclude_pai_all_sets=True,
+ append_color_face_roi_annot=False)
"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
database settings.
diff --git a/bob/paper/mccnn/tifs2018/database/batl_db_thermal.py b/bob/paper/mccnn/tifs2018/database/batl_db_thermal.py
index c04b715d228fb0ffe639f3657efbe26f8d2f6df6..e1518f47b23397f58f38b92d5ddae699f23f964c 100644
--- a/bob/paper/mccnn/tifs2018/database/batl_db_thermal.py
+++ b/bob/paper/mccnn/tifs2018/database/batl_db_thermal.py
@@ -14,7 +14,7 @@ ORIGINAL_EXTENSION = ".h5" # extension of the data files
ANNOTATIONS_TEMP_DIR = "[YOUR_WMCA_ANNOTATIONS_DIRECTORY]"
-unseen_protocols=['','-LOO_fakehead','-LOO_flexiblemask','-LOO_glasses','-LOO_papermask','-LOO_prints','-LOO_replay','-LOO_rigidmask']
+unseen_protocols = ['', '-LOO_fakehead', '-LOO_flexiblemask', '-LOO_glasses', '-LOO_papermask', '-LOO_prints', '-LOO_replay', '-LOO_rigidmask']
PROTOCOL = 'grandtest-thermal-50'+unseen_protocols[0]
@@ -22,10 +22,10 @@ database = BatlPadDatabase(
protocol=PROTOCOL,
original_directory=ORIGINAL_DIRECTORY,
original_extension=ORIGINAL_EXTENSION,
- landmark_detect_method="mtcnn",
+ landmark_detect_method="mtcnn",
exclude_attacks_list=['makeup'],
- exclude_pai_all_sets=True,
- append_color_face_roi_annot=False)
+ exclude_pai_all_sets=True,
+ append_color_face_roi_annot=False)
"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
database settings.
diff --git a/bob/paper/mccnn/tifs2018/extractor/HaralickRDWT.py b/bob/paper/mccnn/tifs2018/extractor/HaralickRDWT.py
index 13bea64c5eb15aa8a6d9e0a0cd8929376bb46031..aede4aafb79279987e3888d431e47c1c3b2d3053 100644
--- a/bob/paper/mccnn/tifs2018/extractor/HaralickRDWT.py
+++ b/bob/paper/mccnn/tifs2018/extractor/HaralickRDWT.py
@@ -4,7 +4,7 @@ import bob.bio.video
import bob.ip.base
import numpy as np
-#Extra packages
+# Extra packages
import pywt
import importlib
@@ -42,14 +42,12 @@ class HaralickRDWT(Extractor):
n_hor=n_hor,
n_vert=n_vert)
-
self.dtype = dtype
- self.wavelet=wavelet
+ self.wavelet = wavelet
self.n_hor = n_hor
self.n_vert = n_vert
-
- def min_max_norm(self,img,do_norm):
+ def min_max_norm(self, img, do_norm):
"""
Normalizes the image to 0-255 range based on min max range, and cast it to 'int8'
@@ -62,12 +60,10 @@ class HaralickRDWT(Extractor):
data_n = ((img-t_min)/(t_max-t_min))*255.0
else:
- data_n=img.copy()
-
+ data_n = img.copy()
return data_n.astype('uint8')
-
def comp_block_rwdt_haralick(self, data):
"""
Extracts RDWT decompositiond and therir haralick descriptors from a gray-scale image/block.
@@ -88,35 +84,31 @@ class HaralickRDWT(Extractor):
assert isinstance(data, np.ndarray)
# 1 level SWT/ UDWT decomposition
-
- coeff=pywt.swt2(data, self.wavelet,1)
+
+ coeff = pywt.swt2(data, self.wavelet, 1)
LL, (LH, HL, HH) = coeff[0]
- decompositions=[LL,LH,HL,HH,data] # all four decompositions and the original data
+ decompositions = [LL, LH, HL, HH, data] # all four decompositions and the original data
- features=[]
+ features = []
try:
- mahotas=importlib.import_module('mahotas')
+ mahotas = importlib.import_module('mahotas')
except:
- pass # TODO: test
-
+ pass # TODO: test
for decomposition in decompositions:
- ## the type should be decided; haralick needs it to be uint8
- feat=mahotas.features.haralick(f=self.min_max_norm(decomposition,True),return_mean=True, return_mean_ptp=False,use_x_minus_y_variance=False) # this gives one mean
+ # the type should be decided; haralick needs it to be uint8
+ feat = mahotas.features.haralick(f=self.min_max_norm(decomposition, True), return_mean=True, return_mean_ptp=False, use_x_minus_y_variance=False) # this gives one mean
features.append(feat)
# feature vector for the patch
- comb_patch=np.array(features).reshape(1,-1)
+ comb_patch = np.array(features).reshape(1, -1)
return comb_patch
-
-
-
def __call__(self, mcdata):
"""
Extracts RDWT+ Haralick features from multi-channel images, blockwise.
@@ -135,19 +127,19 @@ class HaralickRDWT(Extractor):
assert isinstance(mcdata, np.ndarray)
- if len(mcdata.shape)>2:
- channels=mcdata.shape[0]
+ if len(mcdata.shape) > 2:
+ channels = mcdata.shape[0]
else:
- channels=1
- mcdata=np.expand_dims(mcdata,0)
+ channels = 1
+ mcdata = np.expand_dims(mcdata, 0)
- haralick_feat=[]
+ haralick_feat = []
for channel in range(channels):
- data=mcdata[channel,:] # 2D image
+ data = mcdata[channel, :] # 2D image
- #print("data.shape",data.shape)
+ # print("data.shape",data.shape)
# Make sure the data can be split into equal blocks:
row_max = int(data.shape[0] / self.n_vert) * self.n_vert
@@ -160,8 +152,6 @@ class HaralickRDWT(Extractor):
haralick_feat.append(np.array(patch_haralick_feat).flatten())
- feat=np.array(haralick_feat).flatten() # flatten the features
+ feat = np.array(haralick_feat).flatten() # flatten the features
return feat
-
-
\ No newline at end of file
diff --git a/bob/paper/mccnn/tifs2018/script/automate_v2.py b/bob/paper/mccnn/tifs2018/script/automate_v2.py
index 7866d3096af8a05b478604fec6188f1c93352eec..8456dc258e3708910e3fc3fd35edd508feb12269 100644
--- a/bob/paper/mccnn/tifs2018/script/automate_v2.py
+++ b/bob/paper/mccnn/tifs2018/script/automate_v2.py
@@ -1,7 +1,7 @@
import os
-import numpy as np
+import numpy as np
import argparse
@@ -10,45 +10,42 @@ import datetime
import subprocess
+# Modififiable parameters
-## Modififiable parameters
+PROTOCOL_INDEX = 0
-PROTOCOL_INDEX=0
-
-SELECTED_CHANNELS = [0,1,2,3]
+SELECTED_CHANNELS = [0, 1, 2, 3]
ADAPTED_LAYERS = 'conv1-block1-group1-ffc'
ADAPT_REF_CHANNEL = False
-################# Template paths
+# Template paths
-_template_trainer_config_path= '<PATH_TEMPLATE_TRAIN_CONFIG>'
-_template_trainer_script_path= '<PATH_TO_TEMPLATE_TRAINER_SCRIPT>'
-_template_pipeline_config_path= '<PATH_TO_PIPELINE>'
-_template_pipeline_script_path= '<PATH_TO_PIPELINE_SCRIPT_TEMPLATE>'
+_template_trainer_config_path = '<PATH_TEMPLATE_TRAIN_CONFIG>'
+_template_trainer_script_path = '<PATH_TO_TEMPLATE_TRAINER_SCRIPT>'
+_template_pipeline_config_path = '<PATH_TO_PIPELINE>'
+_template_pipeline_script_path = '<PATH_TO_PIPELINE_SCRIPT_TEMPLATE>'
-################################################
+################################################
-unseen_protocols=['','-LOO_fakehead','-LOO_flexiblemask','-LOO_glasses','-LOO_papermask','-LOO_prints','-LOO_replay','-LOO_rigidmask']
+unseen_protocols = ['', '-LOO_fakehead', '-LOO_flexiblemask', '-LOO_glasses', '-LOO_papermask', '-LOO_prints', '-LOO_replay', '-LOO_rigidmask']
-protocols="grandtest-color-50"+unseen_protocols[PROTOCOL_INDEX] # makeup is excluded anyway here
+protocols = "grandtest-color-50"+unseen_protocols[PROTOCOL_INDEX] # makeup is excluded anyway here
UID = "_".join([str(i) for i in SELECTED_CHANNELS])+"_"+str(ADAPT_REF_CHANNEL)+"_"+ADAPTED_LAYERS+"_"+str(len(SELECTED_CHANNELS))+"_"+protocols
-MODEL_FILE='<MCCNN_OUTPUT_DIR>'+'/{}/model_25_0.pth'.format(UID)
-
-_trainer_saveloc='<MCCNN_OUTPUT_DIR>'+'/{}/'.format(UID)
-_score_loc= '<MCCNN_OUTPUT_DIR>'+'{}/{}/'.format(UID,protocols)
+MODEL_FILE = '<MCCNN_OUTPUT_DIR>'+'/{}/model_25_0.pth'.format(UID)
-_training_config_save_loc= '<MCCNN_OUTPUT_DIR>'+'/automate/'
-_training_config_save_name=_training_config_save_loc+UID+'.py'
-
-_extractor_config_saveloc='<MCCNN_OUTPUT_DIR>'+'extractor_v2_configs/automate/'
-_extractor_config_savename=_extractor_config_saveloc+UID+'.py'
+_trainer_saveloc = '<MCCNN_OUTPUT_DIR>'+'/{}/'.format(UID)
+_score_loc = '<MCCNN_OUTPUT_DIR>'+'{}/{}/'.format(UID, protocols)
+_training_config_save_loc = '<MCCNN_OUTPUT_DIR>'+'/automate/'
+_training_config_save_name = _training_config_save_loc+UID+'.py'
+_extractor_config_saveloc = '<MCCNN_OUTPUT_DIR>'+'extractor_v2_configs/automate/'
+_extractor_config_savename = _extractor_config_saveloc+UID+'.py'
"""
@@ -57,15 +54,15 @@ Generates a job script and launches it with qsub.
"""
-source_fname= parse_arguments( cmd_params=cmd_params)
+source_fname = parse_arguments(cmd_params=cmd_params)
-save_path,name=os.path.split(source_fname)
+save_path, name = os.path.split(source_fname)
# get the index from the filename
-stripped_index=int(name.split('.py')[0].split('v')[1])
+stripped_index = int(name.split('.py')[0].split('v')[1])
# sample showing contents of base
@@ -75,25 +72,18 @@ with open(source_fname, 'r') as file:
# read a list of lines into data
data = file.readlines()
-dt_line=data[31]
-
-print("DT_LINE",dt_line)
-
-print("Protocol",data[34])
-
-
+dt_line = data[31]
-all_log=[]
+print("DT_LINE", dt_line)
+print("Protocol", data[34])
-
-for nidx, prot in zip(range(stripped_index,stripped_index+14),protocols_base):
- print(nidx,prot)
-
-
+all_log = []
+for nidx, prot in zip(range(stripped_index, stripped_index+14), protocols_base):
+ print(nidx, prot)
with open(source_fname, 'r') as file:
# read a list of lines into data
@@ -101,57 +91,44 @@ for nidx, prot in zip(range(stripped_index,stripped_index+14),protocols_base):
#print (data)
+ protocol_line = "base_protocol='Protocol_{}' # 0 for other experiments\n".format(prot)
- protocol_line="base_protocol='Protocol_{}' # 0 for other experiments\n".format(prot)
-
+ dt_line = data[31].replace(str(stripped_index), str(nidx))
- dt_line=data[31].replace(str(stripped_index),str(nidx))
+ # print(protocol_line)
-
- #print(protocol_line)
-
- #print(dt_line)
+ # print(dt_line)
## replace in data
- data[31]=dt_line
-
- data[34]=protocol_line
+ data[31] = dt_line
+ data[34] = protocol_line
- save_name=save_path+'/cnn_rgb_automate_v{}.py'.format(str(nidx))
-
- #write file out
+ save_name = save_path+'/cnn_rgb_automate_v{}.py'.format(str(nidx))
+ # write file out
with open(save_name, 'w') as file:
- file.writelines( data )
-
+ file.writelines(data)
print("isub {}".format(save_name)+"\n")
-
- command="isub {}".format(save_name) + " && "
+ command = "isub {}".format(save_name) + " && "
all_log.append(command)
-
-
#subprocess.call(command, shell=True)
-
-
-
-
-info="generated scripte from {} to {}".format(stripped_index,stripped_index+13)
+info = "generated scripte from {} to {}".format(stripped_index, stripped_index+13)
print(info)
print("".join(all_log))
-log_file='<MCCNN_OUTPUT_DIR>'+'/logs.txt'
+log_file = '<MCCNN_OUTPUT_DIR>'+'/logs.txt'
with open(log_file, "a") as myfile:
myfile.write("------------------------------\n")
@@ -161,7 +138,3 @@ with open(log_file, "a") as myfile:
myfile.write(dt_line+"\n")
myfile.write("Generated at:"+str(datetime.datetime.now())+"\n")
myfile.write("------------------------------\n")
-
-
-
-
diff --git a/bob/paper/mccnn/tifs2018/script/mean_fusion.py b/bob/paper/mccnn/tifs2018/script/mean_fusion.py
index 730e672fb27f58b55aeba55ebb76bc0a9ba5e788..4de14d9b521c169fbe66204a5eb5536e0a889873 100644
--- a/bob/paper/mccnn/tifs2018/script/mean_fusion.py
+++ b/bob/paper/mccnn/tifs2018/script/mean_fusion.py
@@ -12,39 +12,38 @@ import argparse
import os
-## Preprocessors
+# Preprocessors
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
-modalities=['color','thermal','infrared','depth']
-
-groups=['dev','eval']
+modalities = ['color', 'thermal', 'infrared', 'depth']
+groups = ['dev', 'eval']
class Mean():
- def __init__(self,weights=None):
+ def __init__(self, weights=None):
if weights is not None:
- self.weights=weights/sum(weights)
+ self.weights = weights/sum(weights)
else:
- self.weights=None
-
- def fit(self,X,y):
+ self.weights = None
+
+ def fit(self, X, y):
pass
- def predict_proba(self,X):
- #2nd column is used
-
+
+ def predict_proba(self, X):
+ # 2nd column is used
+
if self.weights is not None:
- X=X*self.weights
-
- prob=np.mean(X,axis=1)
- res=np.vstack((1.0-prob,prob)).T
+ X = X*self.weights
+
+ prob = np.mean(X, axis=1)
+ res = np.vstack((1.0-prob, prob)).T
return res
-
-def parse_arguments(cmd_params=None):
+def parse_arguments(cmd_params=None):
"""
Parse the command line arguments.
@@ -52,18 +51,17 @@ def parse_arguments(cmd_params=None):
``color_path``: py:class:`string`
An absolute base path for extracted features from color channel
-
+
``out_path``: py:class:`string`
An absolute base path to save the concatenated features
"""
parser = argparse.ArgumentParser(description=__doc__)
-
parser.add_argument("-c", "--color-dev", type=str, help="An absolute path to the scores-dev file from color channel=.",
- default = "")
+ default="")
parser.add_argument("-s", "--save-path", type=str, help="An absolute base path to the folder to save the fused results =.",
- default = ".")
+ default=".")
if cmd_params is not None:
args = parser.parse_args(cmd_params)
@@ -76,80 +74,71 @@ def parse_arguments(cmd_params=None):
return color_dev, save_path
-
def main(cmd_params=None):
-
"""
./bin/python -m batl.utils.score_batl --h for help.
Sample usage: ./bin/python -m -c -o
"""
-
-
- color_dev, save_path = parse_arguments( cmd_params=cmd_params)
- print("color_dev",color_dev)
+ color_dev, save_path = parse_arguments(cmd_params=cmd_params)
- scaler=StandardScaler()
+ print("color_dev", color_dev)
- train_on_dev=True
+ scaler = StandardScaler()
- clf=Mean()
+ train_on_dev = True
+ clf = Mean()
- protocol=color_dev.split('/')[-3]
+ protocol = color_dev.split('/')[-3]
if save_path == ".":
- save_path=protocol+'/fusion/'
-
+ save_path = protocol+'/fusion/'
for group in groups:
- save_name=save_path+'scores_mean_fused_'+group
+ save_name = save_path+'scores_mean_fused_'+group
- eval_file_names=[]
+ eval_file_names = []
for modality in modalities:
- temp=color_dev.replace("color",modality)
- temp=temp.replace("dev",group)
+ temp = color_dev.replace("color", modality)
+ temp = temp.replace("dev", group)
eval_file_names.append(temp)
-
- eval_df= {}
- scores=[]
-
+ eval_df = {}
+ scores = []
## Order is important
for eval_file in eval_file_names:
- df=pd.read_csv(eval_file,sep=" ",names=['A','B','path','score'],dtype='str')
- eval_df[eval_file]=df
+ df = pd.read_csv(eval_file, sep=" ", names=['A', 'B', 'path', 'score'], dtype='str')
+ eval_df[eval_file] = df
scores.append(df['score'].values)
-
-
- X_test=np.stack(scores).T
- X_test=X_test.astype('float64')
+ X_test = np.stack(scores).T
+
+ X_test = X_test.astype('float64')
# Handling NaN
- X_test=np.nan_to_num(X_test)
+ X_test = np.nan_to_num(X_test)
# Fitting and transforming scaler
- #X_test=scaler.transform(X_test)
+ # X_test=scaler.transform(X_test)
# Fitting classifier
- score_predict=clf.predict_proba(X_test)[:,1]
+ score_predict = clf.predict_proba(X_test)[:, 1]
#
- df['score']=score_predict
+ df['score'] = score_predict
- os.makedirs(save_path,exist_ok=True)
+ os.makedirs(save_path, exist_ok=True)
#
df.to_csv(save_name, sep=" ", na_rep='', float_format=None, columns=None, header=False, index=False)
if __name__ == "__main__":
main(cmd_params=None)
-
diff --git a/bob/paper/mccnn/tifs2018/script/scoring.py b/bob/paper/mccnn/tifs2018/script/scoring.py
index a61b03032ca4be3543f0262bb8e6ae3e9b5bfee3..80c97d65bfa032fe229115bdad86c9e4b9b9012d 100644
--- a/bob/paper/mccnn/tifs2018/script/scoring.py
+++ b/bob/paper/mccnn/tifs2018/script/scoring.py
@@ -29,101 +29,90 @@ The measure type of the development set can be changed to compute "HTER" or
"""
-import sys, os, glob
+import sys
+import os
+import glob
import argparse
import numpy
-import bob.bio.base.score
+import bob.bio.base.score
import bob.measure
import pandas as pd
from bob.measure import (
- far_threshold, eer_threshold, min_hter_threshold,frr_threshold)
+ far_threshold, eer_threshold, min_hter_threshold, frr_threshold)
class custom_df():
"""
Custom class mimicking PrettyTable type functionality with pandas Dataframe
"""
- def __init__(self,columns):
- self.columns=columns
- self.frames=[]
-
- def add_row(self,ccolumn):
-
- t_df=pd.DataFrame(ccolumn, self.columns).T
- self.frames.append(t_df)
-
+
+ def __init__(self, columns):
+ self.columns = columns
+ self.frames = []
+
+ def add_row(self, ccolumn):
+
+ t_df = pd.DataFrame(ccolumn, self.columns).T
+ self.frames.append(t_df)
+
def get_df(self):
return pd.concat(self.frames)
+def get_metrics(dev_fname, eval_fname, legend):
+
+ dev_neg, dev_pos = bob.bio.base.score.split(dev_fname, ncolumns=None, sort=True)
+
+ eval_neg, eval_pos = bob.bio.base.score.split(eval_fname, ncolumns=None, sort=True)
+
+ # for BPCER20
+
+ # compute all thresholds
+
+ bpcer_1_percent_threshold = frr_threshold(dev_neg, dev_pos, 0.01, True) # threshold for BPCER 1%
+ eer_thresh = eer_threshold(dev_neg, dev_pos, True)
+ # Old standard
-def get_metrics(dev_fname,eval_fname,legend):
+ dev_far, dev_frr = bob.measure.farfrr(dev_neg, dev_pos, eer_thresh)
+ dev_hter = (dev_far + dev_frr)/2.0
- dev_neg, dev_pos=bob.bio.base.score.split(dev_fname, ncolumns=None, sort=True)
+ EER = float("{0:.2f}".format(dev_hter*100))
- eval_neg, eval_pos=bob.bio.base.score.split(eval_fname, ncolumns=None, sort=True)
+ eval_far, eval_frr = bob.measure.farfrr(eval_neg, eval_pos, eer_thresh)
+ eval_hter = (eval_far + eval_frr)/2.0
+ APCER_dev, BPCER_dev = bob.measure.farfrr(dev_neg, dev_pos, bpcer_1_percent_threshold)
+ ACER_dev = (APCER_dev+BPCER_dev)/2.0
- # for BPCER20
+ # BPCER_dev should be 1%
- ## compute all thresholds
-
- bpcer_1_percent_threshold=frr_threshold(dev_neg, dev_pos, 0.01, True)# threshold for BPCER 1%
-
+ APCER_eval, BPCER_eval = bob.measure.farfrr(eval_neg, eval_pos, bpcer_1_percent_threshold)
- eer_thresh = eer_threshold(dev_neg,dev_pos, True)
-
+ ACER_eval = (APCER_eval+BPCER_eval)/2.0
- # Old standard
+ APCER_dev = float("{0:.2f}".format(APCER_dev*100))
- dev_far, dev_frr = bob.measure.farfrr(dev_neg,dev_pos, eer_thresh)
- dev_hter = (dev_far + dev_frr)/2.0
+ BPCER_dev = float("{0:.2f}".format(BPCER_dev*100))
- EER=float("{0:.2f}".format(dev_hter*100))
-
+ ACER_dev = float("{0:.2f}".format(ACER_dev*100))
- eval_far, eval_frr = bob.measure.farfrr(eval_neg, eval_pos, eer_thresh)
- eval_hter = (eval_far + eval_frr)/2.0
-
-
- APCER_dev, BPCER_dev = bob.measure.farfrr(dev_neg,dev_pos, bpcer_1_percent_threshold)
-
- ACER_dev= (APCER_dev+BPCER_dev)/2.0
-
- # BPCER_dev should be 1%
-
-
- APCER_eval, BPCER_eval = bob.measure.farfrr(eval_neg,eval_pos, bpcer_1_percent_threshold)
-
- ACER_eval= (APCER_eval+BPCER_eval)/2.0
-
+ APCER_eval = float("{0:.2f}".format(APCER_eval*100))
- APCER_dev=float("{0:.2f}".format( APCER_dev*100))
-
- BPCER_dev=float("{0:.2f}".format( BPCER_dev*100))
-
- ACER_dev=float("{0:.2f}".format( ACER_dev*100))
-
- APCER_eval=float("{0:.2f}".format( APCER_eval*100))
-
- BPCER_eval=float("{0:.2f}".format( BPCER_eval*100))
-
- ACER_eval=float("{0:.2f}".format( ACER_eval*100))
-
- print("bpcer_1_percent_threshold : ",bpcer_1_percent_threshold,"BPCER_dev : ",BPCER_dev)
-
+ BPCER_eval = float("{0:.2f}".format(BPCER_eval*100))
+ ACER_eval = float("{0:.2f}".format(ACER_eval*100))
- metrics=[legend,APCER_dev,ACER_dev,APCER_eval,BPCER_eval,ACER_eval]
+ print("bpcer_1_percent_threshold : ", bpcer_1_percent_threshold, "BPCER_dev : ", BPCER_dev)
- return metrics
+ metrics = [legend, APCER_dev, ACER_dev, APCER_eval, BPCER_eval, ACER_eval]
+ return metrics
def parse_arguments(cmd_params=None):
@@ -136,10 +125,10 @@ def parse_arguments(cmd_params=None):
An absolute path of the score file.
``eval_name``: py:class:`string`
An absolute path of the score file.
-
+
``legends``: py:class:`string`
Legends of the score files.
-
+
``save_path``: py:class:`string`
An absolute base path for saving the performance table and ROCs.
@@ -147,7 +136,7 @@ def parse_arguments(cmd_params=None):
"""
parser = argparse.ArgumentParser(description=__doc__)
-
+
parser.add_argument("-df", "--dev-score-files", nargs="+", help="A list of dev score files in Bob 4-column format",
default=[""])
parser.add_argument("-ef", "--eval-score-files", nargs="+", help="A list of eval score files in Bob 4-column format",
@@ -156,7 +145,7 @@ def parse_arguments(cmd_params=None):
parser.add_argument("-l", "--legends", nargs="+", help="Legends", default=[])
parser.add_argument("-s", "--save-path", type=str, help="An absolute base path for saving the performance table and ROCs, default=.",
- default = "dump")
+ default="dump")
if cmd_params is not None:
args = parser.parse_args(cmd_params)
@@ -168,54 +157,46 @@ def parse_arguments(cmd_params=None):
legends = args.legends
save_path = args.save_path
- return dev_file_names,eval_file_names, legends, save_path
-
+ return dev_file_names, eval_file_names, legends, save_path
def main(cmd_params=None):
-
"""
"""
-
-
- dev_file_names,eval_file_names, legends, save_path = parse_arguments( cmd_params=cmd_params)
- assert(len(dev_file_names)==len(eval_file_names))
+ dev_file_names, eval_file_names, legends, save_path = parse_arguments(cmd_params=cmd_params)
- if len(legends)!=len(eval_file_names):
- legends=[]
- for i in range(len(eval_file_names)):
- tname='System_'+str(i)
+ assert(len(dev_file_names) == len(eval_file_names))
- legends.append(tname)
+ if len(legends) != len(eval_file_names):
+ legends = []
+ for i in range(len(eval_file_names)):
+ tname = 'System_'+str(i)
+ legends.append(tname)
+ column_names = ["Method", "APCER", "ACER", "APCER", "BPCER", "ACER"] # BPCER to de removed later
- column_names=["Method","APCER","ACER","APCER","BPCER","ACER"] # BPCER to de removed later
-
- df=custom_df(column_names)
-
- #df.add_row([EER,APCER,BPCER,ACER,BPCER10,BPCER20,BPCER100])
+ df = custom_df(column_names)
+ # df.add_row([EER,APCER,BPCER,ACER,BPCER10,BPCER20,BPCER100])
for i in range(len(dev_file_names)):
-
- tmetrics=get_metrics(dev_file_names[i],eval_file_names[i],legends[i])
- df.add_row(tmetrics)
+ tmetrics = get_metrics(dev_file_names[i], eval_file_names[i], legends[i])
- performance_table=df.get_df()
+ df.add_row(tmetrics)
+ performance_table = df.get_df()
print(performance_table)
- os.makedirs(save_path,exist_ok=True)
+ os.makedirs(save_path, exist_ok=True)
- savename_csv=save_path+"/performance_table.csv"
+ savename_csv = save_path+"/performance_table.csv"
- performance_table.to_csv(savename_csv,index=False)
+ performance_table.to_csv(savename_csv, index=False)
if __name__ == "__main__":
main(cmd_params=None)
-
diff --git a/bob/paper/mccnn/tifs2018/script/string_replacer.py b/bob/paper/mccnn/tifs2018/script/string_replacer.py
index 71daee037a7e6357eca9a48112fd32141e660b87..83c7ac012638f9dcf397ff90308da0d1db2d4f41 100644
--- a/bob/paper/mccnn/tifs2018/script/string_replacer.py
+++ b/bob/paper/mccnn/tifs2018/script/string_replacer.py
@@ -1,41 +1,38 @@
import numpy as np
-string='python bin/scoring.py -df \
+string = 'python bin/scoring.py -df \
<PATH_TO_RESULTS>/grandtest-color-50-LOO_fakehead/scores_mean_fused_dev -ef \
<PATH_TO_RESULTS>/grandtest-color-50-LOO_fakehead/scores_mean_fused_eval'
-channels=['color','depth','infrared','thermal']
+channels = ['color', 'depth', 'infrared', 'thermal']
-unseen_protocols=['-LOO_fakehead','-LOO_flexiblemask','-LOO_glasses','-LOO_papermask','-LOO_prints','-LOO_replay','-LOO_rigidmask']
+unseen_protocols = ['-LOO_fakehead', '-LOO_flexiblemask', '-LOO_glasses', '-LOO_papermask', '-LOO_prints', '-LOO_replay', '-LOO_rigidmask']
-commands=[]
+commands = []
-for idx,protocol in enumerate(unseen_protocols):
+for idx, protocol in enumerate(unseen_protocols):
+ nstring = string.replace('-LOO_fakehead', protocol) + " "
- nstring=string.replace('-LOO_fakehead',protocol)+ " "
+ print(nstring)
- print(nstring)
-
- commands.append(nstring)
+ commands.append(nstring)
print("ALL")
print("&&".join(commands))
-
-commands=[]
+commands = []
for channel in channels:
- nstring=string.replace('color',channel)+ " "
+ nstring = string.replace('color', channel) + " "
- print(nstring)
+ print(nstring)
- commands.append(nstring)
+ commands.append(nstring)
print("ALL")
print("&&".join(commands))
-
diff --git a/bob/paper/mccnn/tifs2018/script/version.py b/bob/paper/mccnn/tifs2018/script/version.py
index 6d1f941d192c658823fcd9bb6f9c6c164d0795f1..e675c943d089c48d93945765c55cb7acc5b8e25d 100644
--- a/bob/paper/mccnn/tifs2018/script/version.py
+++ b/bob/paper/mccnn/tifs2018/script/version.py
@@ -5,9 +5,9 @@
Print the text
"""
-def main():
- """Print the text"""
- print ("Print test text")
- return 0
+def main():
+ """Print the text"""
+ print("Print test text")
+ return 0
diff --git a/bob/paper/mccnn/tifs2018/trainer_configs/wmca_fasnet.py b/bob/paper/mccnn/tifs2018/trainer_configs/wmca_fasnet.py
index e16c8191c53ad62c8cead2a89cc8b0b66da61bc1..76a9662ee557a83fada7c458b4f690a9ab502ade 100644
--- a/bob/paper/mccnn/tifs2018/trainer_configs/wmca_fasnet.py
+++ b/bob/paper/mccnn/tifs2018/trainer_configs/wmca_fasnet.py
@@ -32,39 +32,39 @@ from bob.learn.pytorch.datasets import ChannelSelect, RandomHorizontalFlipImage
"""
#==============================================================================
-# Initialize the bob database instance
+# Initialize the bob database instance
-data_folder_train='<FASNET_PREPROCESSED_FOLDER>'
+data_folder_train = '<FASNET_PREPROCESSED_FOLDER>'
-output_base_path='<FASNET_CNN_OUTPUT_PATH>'
+output_base_path = '<FASNET_CNN_OUTPUT_PATH>'
-unseen_protocols=['','-LOO_fakehead','-LOO_flexiblemask','-LOO_glasses','-LOO_papermask','-LOO_prints','-LOO_replay','-LOO_rigidmask']
+unseen_protocols = ['', '-LOO_fakehead', '-LOO_flexiblemask', '-LOO_glasses', '-LOO_papermask', '-LOO_prints', '-LOO_replay', '-LOO_rigidmask']
-PROTOCOL_INDEX=0
+PROTOCOL_INDEX = 0
####################################################################
-frames=50
+frames = 50
-extension='.h5'
+extension = '.h5'
-train_groups=['train'] # only 'train' group is used for training the network
+train_groups = ['train'] # only 'train' group is used for training the network
-val_groups=['dev']
+val_groups = ['dev']
-do_crossvalidation=True
+do_crossvalidation = True
####################################################################
if do_crossvalidation:
- phases=['train','val']
+ phases = ['train', 'val']
else:
- phases=['train']
+ phases = ['train']
-groups={"train":['train'],"val":['dev']}
+groups = {"train": ['train'], "val": ['dev']}
-protocols="grandtest-color-50"+unseen_protocols[PROTOCOL_INDEX] # makeup is excluded anyway here
+protocols = "grandtest-color-50"+unseen_protocols[PROTOCOL_INDEX] # makeup is excluded anyway here
-exlude_attacks_list=["makeup"]
+exlude_attacks_list = ["makeup"]
bob_hldi_instance = BatlPadDatabase(
protocol=protocols,
@@ -73,36 +73,35 @@ bob_hldi_instance = BatlPadDatabase(
landmark_detect_method="mtcnn", # detect annotations using mtcnn
exclude_attacks_list=exlude_attacks_list,
exclude_pai_all_sets=True, # exclude makeup from all the sets, which is the default behavior for grandtest protocol
- append_color_face_roi_annot=False)
+ append_color_face_roi_annot=False)
#==============================================================================
# Initialize the torch dataset, subselect channels from the pretrained files if needed.
-SELECTED_CHANNELS = [0,1,2]
+SELECTED_CHANNELS = [0, 1, 2]
####################################################################
-img_transform={}
+img_transform = {}
-img_transform['train'] = transforms.Compose([transforms.ToPILImage(),transforms.RandomHorizontalFlip(),transforms.Resize(224, interpolation=2),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],
- std=[0.229, 0.224, 0.225])])
+img_transform['train'] = transforms.Compose([transforms.ToPILImage(), transforms.RandomHorizontalFlip(), transforms.Resize(224, interpolation=2), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225])])
-img_transform['val'] = transforms.Compose([transforms.ToPILImage(),transforms.Resize(224, interpolation=2),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],
- std=[0.229, 0.224, 0.225])])
+img_transform['val'] = transforms.Compose([transforms.ToPILImage(), transforms.Resize(224, interpolation=2), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225])])
-dataset={}
+dataset = {}
for phase in phases:
- dataset[phase] = DataFolder(data_folder=data_folder_train,
- transform=img_transform[phase],
- extension='.hdf5',
- bob_hldi_instance=bob_hldi_instance,
- groups=groups[phase],
- protocol=protocols,
- purposes=['real', 'attack'],
- allow_missing_files=True)
-
+ dataset[phase] = DataFolder(data_folder=data_folder_train,
+ transform=img_transform[phase],
+ extension='.hdf5',
+ bob_hldi_instance=bob_hldi_instance,
+ groups=groups[phase],
+ protocol=protocols,
+ purposes=['real', 'attack'],
+ allow_missing_files=True)
#==============================================================================
@@ -116,18 +115,17 @@ ADAPT_REF_CHANNEL = False
####################################################################
-
batch_size = 32
num_workers = 0
-epochs=25
-learning_rate=0.0001
+epochs = 25
+learning_rate = 0.0001
seed = 3
use_gpu = False
adapted_layers = ADAPTED_LAYERS
adapt_reference_channel = ADAPT_REF_CHANNEL
verbose = 2
UID = "_".join([str(i) for i in SELECTED_CHANNELS])+"_"+str(ADAPT_REF_CHANNEL)+"_"+ADAPTED_LAYERS+"_"+str(NUM_CHANNELS)+"_"+protocols
-training_logs= output_base_path+UID+'/train_log_dir/'
+training_logs = output_base_path+UID+'/train_log_dir/'
output_dir = output_base_path+UID
@@ -135,6 +133,6 @@ output_dir = output_base_path+UID
# Load the architecture
-assert(len(SELECTED_CHANNELS)==NUM_CHANNELS)
+assert(len(SELECTED_CHANNELS) == NUM_CHANNELS)
-network=FASNet(pretrained=True)
+network = FASNet(pretrained=True)
diff --git a/bob/paper/mccnn/tifs2018/trainer_configs/wmca_mccnn.py b/bob/paper/mccnn/tifs2018/trainer_configs/wmca_mccnn.py
index 62c473b6b226e8ce556f8963b1407cebf4a0d04e..1ac899f3b06c2639066bf151d6a1ed9e606bf597 100644
--- a/bob/paper/mccnn/tifs2018/trainer_configs/wmca_mccnn.py
+++ b/bob/paper/mccnn/tifs2018/trainer_configs/wmca_mccnn.py
@@ -32,26 +32,26 @@ from bob.learn.pytorch.datasets import ChannelSelect, RandomHorizontalFlipImage
"""
#==============================================================================
-# Initialize the bob database instance
+# Initialize the bob database instance
-data_folder_train='<MCCNN_PREPROCESSED_PATH>'
+data_folder_train = '<MCCNN_PREPROCESSED_PATH>'
-output_base_path='<MCCNN_CNN_OUTPUT_PATH>'
+output_base_path = '<MCCNN_CNN_OUTPUT_PATH>'
-unseen_protocols=['','-LOO_fakehead','-LOO_flexiblemask','-LOO_glasses','-LOO_papermask','-LOO_prints','-LOO_replay','-LOO_rigidmask']
+unseen_protocols = ['', '-LOO_fakehead', '-LOO_flexiblemask', '-LOO_glasses', '-LOO_papermask', '-LOO_prints', '-LOO_replay', '-LOO_rigidmask']
-PROTOCOL_INDEX=0
+PROTOCOL_INDEX = 0
####################################################################
-frames=50
+frames = 50
-extension='.h5'
+extension = '.h5'
-train_groups=['train'] # only 'train' group is used for training the network
+train_groups = ['train'] # only 'train' group is used for training the network
-protocols="grandtest-color-50"+unseen_protocols[PROTOCOL_INDEX] # makeup is excluded anyway here
+protocols = "grandtest-color-50"+unseen_protocols[PROTOCOL_INDEX] # makeup is excluded anyway here
-exlude_attacks_list=["makeup"]
+exlude_attacks_list = ["makeup"]
bob_hldi_instance_train = BatlPadDatabase(
protocol=protocols,
@@ -60,25 +60,24 @@ bob_hldi_instance_train = BatlPadDatabase(
landmark_detect_method="mtcnn", # detect annotations using mtcnn
exclude_attacks_list=exlude_attacks_list,
exclude_pai_all_sets=True, # exclude makeup from all the sets, which is the default behavior for grandtest protocol
- append_color_face_roi_annot=False)
+ append_color_face_roi_annot=False)
#==============================================================================
# Initialize the torch dataset, subselect channels from the pretrained files if needed.
-SELECTED_CHANNELS = [0,1,2,3]
+SELECTED_CHANNELS = [0, 1, 2, 3]
####################################################################
-img_transform_train = transforms.Compose([ChannelSelect(selected_channels = SELECTED_CHANNELS),RandomHorizontalFlipImage(p=0.5),transforms.ToTensor()])
+img_transform_train = transforms.Compose([ChannelSelect(selected_channels=SELECTED_CHANNELS), RandomHorizontalFlipImage(p=0.5), transforms.ToTensor()])
dataset = DataFolder(data_folder=data_folder_train,
- transform=img_transform_train,
- extension='.hdf5',
- bob_hldi_instance=bob_hldi_instance_train,
- groups=train_groups,
- protocol=protocols,
- purposes=['real', 'attack'],
- allow_missing_files=True)
-
+ transform=img_transform_train,
+ extension='.hdf5',
+ bob_hldi_instance=bob_hldi_instance_train,
+ groups=train_groups,
+ protocol=protocols,
+ purposes=['real', 'attack'],
+ allow_missing_files=True)
#==============================================================================
@@ -92,18 +91,17 @@ ADAPT_REF_CHANNEL = False
####################################################################
-
batch_size = 32
num_workers = 0
-epochs=25
-learning_rate=0.0001
+epochs = 25
+learning_rate = 0.0001
seed = 3
use_gpu = False
adapted_layers = ADAPTED_LAYERS
adapt_reference_channel = ADAPT_REF_CHANNEL
verbose = 2
UID = "_".join([str(i) for i in SELECTED_CHANNELS])+"_"+str(ADAPT_REF_CHANNEL)+"_"+ADAPTED_LAYERS+"_"+str(NUM_CHANNELS)+"_"+protocols
-training_logs= output_base_path+UID+'/train_log_dir/'
+training_logs = output_base_path+UID+'/train_log_dir/'
output_dir = output_base_path+UID
@@ -111,6 +109,6 @@ output_dir = output_base_path+UID
# Load the architecture
-assert(len(SELECTED_CHANNELS)==NUM_CHANNELS)
+assert(len(SELECTED_CHANNELS) == NUM_CHANNELS)
-network=MCCNN(num_channels = NUM_CHANNELS)
+network = MCCNN(num_channels=NUM_CHANNELS)
diff --git a/conda/meta.yaml b/conda/meta.yaml
index 69218db851cbea4dbb89b8a88c19fba41064dd94..d2f3d75ff32a3ac1df5899586ee50db365723d5a 100644
--- a/conda/meta.yaml
+++ b/conda/meta.yaml
@@ -25,15 +25,13 @@ requirements:
- six {{ six }}
- numpy
- bob.extension
- - bob.bio.base
- - bob.bio.video
- bob.pad.face
- - bob.learn.pytorch
+ - bob.learn.pytorch # [linux]
run:
- python
- setuptools
- six {{ six }}
- - numpy
+ - {{ pin_compatible('numpy') }}
test:
imports:
@@ -51,7 +49,6 @@ test:
- coverage
- sphinx
- sphinx_rtd_theme
- - gridtk
# extend this list with further test-time-only dependencies
about:
@@ -59,3 +56,5 @@ about:
license: GNU General Public License v3 (GPLv3)
summary: Implements tools for reproducing Multi Channel CNN based face PAD experiments.
license_family: GPL
+
+
diff --git a/doc/index.rst b/doc/index.rst
index d2752acd1789535ea78a383d4bca7cdd354c6b57..6569c150b560ce84b6c10cd9b4a04ef5cb3441ad 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -25,9 +25,9 @@ User guide
.. toctree::
:maxdepth: 2
- running_baslines
+ running_baselines
running_fasnet
- running_mccn
+ running_mccnn
references
diff --git a/doc/running_baslines.md b/doc/running_baselines.rst
similarity index 95%
rename from doc/running_baslines.md
rename to doc/running_baselines.rst
index a1070229e00e70396a4fee858911bf5ce1e0115d..fea3a3ab97a911ebe793be4d82773b2a83f612b9 100644
--- a/doc/running_baslines.md
+++ b/doc/running_baselines.rst
@@ -14,8 +14,8 @@ can be installed as
The steps to reproduce the results for grandtest protocol are listed below.
-Color channel
--------------
+A. Color channel
+----------------
1.A.1. IQM - LR
@@ -140,7 +140,7 @@ D. Thermal channel
<PATH_TO_BASELINE_RESULTS>/thermal/haralicksvm/grandtest-thermal-50/scores/scores-eval
-E. Score fusion (haralick-svm)
+E. Score fusion (Haralick-SVM)
------------------------------
.. code-block:: sh
@@ -169,12 +169,12 @@ F. Score fusion (IQM-LBP-LR)
<PATH_TO_BASELINE_RESULTS>/mean_fusion/grandtest/scores_mean_fused_eval
-BASELINES in LOO protocols (for scorefusion)
-============================================
+BASELINES in LOO protocols (for score fusion)
+=============================================
For the experiments first do individual experiments for all channels, after that perform fusion. This needs to be repeated for all the protocols
-A. color
+A. Color
--------
unseen_protocols=['','-LOO_fakehead','-LOO_flexiblemask','-LOO_glasses','-LOO_papermask','-LOO_prints','-LOO_replay','-LOO_rigidmask']
@@ -184,8 +184,6 @@ The protocols can be easily indexed as
PROTOCOL = 'grandtest-color-50'+unseen_protocols[0]
-1.
-
.. code-block:: sh
./bin/spoof.py \
@@ -199,11 +197,9 @@ PROTOCOL = 'grandtest-color-50'+unseen_protocols[0]
-B. depth
+B. Depth
--------
-1.
-
.. code-block:: sh
./bin/spoof.py \
@@ -220,8 +216,6 @@ B. depth
C. Infrared
-----------
-1.
-
.. code-block:: sh
./bin/spoof.py \
@@ -237,8 +231,6 @@ C. Infrared
D. Thermal
----------
-1.
-
.. code-block:: sh
./bin/spoof.py \
@@ -255,10 +247,8 @@ D. Thermal
E. Score fusion
---------------
-The protocola are, '-LOO_fakehead','-LOO_flexiblemask','-LOO_glasses','-LOO_papermask','-LOO_prints','-LOO_replay','-LOO_rigidmask'
-
+The protocols are, '-LOO_fakehead','-LOO_flexiblemask','-LOO_glasses','-LOO_papermask','-LOO_prints','-LOO_replay','-LOO_rigidmask'
-1.
.. code-block:: sh
./bin/python bin/mean_fusion.py -c \
@@ -274,11 +264,11 @@ The protocola are, '-LOO_fakehead','-LOO_flexiblemask','-LOO_glasses','-LOO_pape
Similarly, repeat the experiment for all protocols.
-BASELINES in LOO (haralick svm)
-===============================
+BASELINES in LOO protocols (Haralick-SVM)
+=========================================
-color
+Color
-----
.. code-block:: sh
@@ -286,8 +276,8 @@ color
Repeat the same procedure for all four channels.
-Scorefusion Haralick-SVM
-------------------------
+Score fusion Haralick-SVM
+-------------------------
Once scores from all channels are available. Run the following command.
@@ -295,7 +285,7 @@ Once scores from all channels are available. Run the following command.
./bin/python bin/mean_fusion.py -c <PATH_TO_BASELINE_RESULTS>/LOO/color/haralicksvm/grandtest-color-50-LOO_fakehead/grandtest-color-50-LOO_fakehead/scores/scores-dev -s <PATH_TO_BASELINE_RESULTS>/mean_fusion/haralick/grandtest-color-50-LOO_fakehead/
-scoring for Haralick Mean fusion
+Scoring for Haralick Mean fusion
--------------------------------
.. code-block:: sh
diff --git a/doc/running_fasnet.md b/doc/running_fasnet.rst
similarity index 78%
rename from doc/running_fasnet.md
rename to doc/running_fasnet.rst
index b080ce729b708360b44a642e1d5f4251798af8d6..8a57b02edb7683cf128dfb8cee98a392e98c5894 100644
--- a/doc/running_fasnet.md
+++ b/doc/running_fasnet.rst
@@ -1,6 +1,6 @@
Training FASNet for face PAD
-===========================
+============================
This section describes running our implementation of FASNet on WMCA dataset. It is **strongly recommended** to check the publication for better understanding
of the described work-flow.
@@ -11,9 +11,10 @@ FASNet accepts RGB images only, hence the preprocesing is done first. This can b
.. code-block:: sh
+
./bin/spoof.py \
- <PATH_TO_CONFIG>/wmca_grandtest_dbconfig.py \
- <PATH_TO_CONFIG>/wmca_config_pytorch_extractor.py \
+ wmca-color \
+ fasnet \
--execute-only preprocessing \
--sub-directory <FASNET_PIPELINE_FOLDER>
--grid idiap
@@ -23,8 +24,8 @@ which is notated from here onwards as ``<FASNET_PREPROCESSED_FOLDER>``.
-Training FASTNET
---------------
+Training FASNET
+---------------
Once the preprocessing is done, the next step is to train the FASNET architecture. A config file is defined which
can define the transforms, image resolution, training parameters such as number of epochs, learning rate and so on.
@@ -34,7 +35,7 @@ Once the config file is defined, training the network can be done with the follo
.. code-block:: sh
./bin/train_fasnet.py \ # script used for FASNET training
- <PATH_TO_TRAINER_CONFIG>/wmca_FASNet.py \ # configuration file defining the FASNET network, database, and training parameters
+ <PATH_TO_TRAINER_CONFIG>/wmca_faset.py \ # configuration file defining the FASNET network, database, and training parameters
-vv # set verbosity level
People in Idiap can benefit from GPU cluster, running the training as follows:
@@ -46,7 +47,7 @@ People in Idiap can benefit from GPU cluster, running the training as follows:
--log-dir <FOLDER_TO_SAVE_THE_RESULTS>/logs/ \ # substitute the path to save the logs to (Idiap only)
--environment="PYTHONUNBUFFERED=1" -- \ #
./bin/train_fasnet.py \ # script used for FASNET training
- <PATH_TO_TRAINER_CONFIG>/wmca_FASNet.py \ # configuration file defining the FASNET network, database, and training parameters
+ <PATH_TO_TRAINER_CONFIG>/wmca_fasnet.py \ # configuration file defining the FASNET network, database, and training parameters
--use-gpu \ # enable the GPU mode
-vv # set verbosity level
@@ -57,7 +58,7 @@ For a more detailed documentation of functionality available in the training scr
./bin/train_fasnet.py --help # note: remove ./bin/ if buildout is not used
-Please inspect the corresponding configuration file, ``wmca_FASNet.py`` for example, for more details on how to define the database, network architecture and training parameters.
+Please inspect the corresponding configuration file, ``wmca_faset.py`` for example, for more details on how to define the database, network architecture and training parameters.
The protocols, and channels used in the experiments can be easily configured in the configuration file.
@@ -72,10 +73,10 @@ For **grandtest** protocol this can be done as follows.
.. code-block:: sh
./bin/spoof.py \
- <PATH_TO_DATABASE_CONFIG>/wmca_grandtest_dbconfig.py \
- <PATH_TO_EXTRACTORS>/wmca_config_pytorch_extractor_v1_FASNet.py \
+ wmca-color \
+ fasnet \
--protocol grandtest \
- --sub-directory <FOLDER_TO_SAVE_MCCNN_RESULTS> -vv
+ --sub-directory <FOLDER_TO_SAVE_FASNET_RESULTS> -vv
@@ -87,8 +88,8 @@ To evaluate the models run the following command.
.. code-block:: python
./bin/scoring.py -df \
- <PATH_TO_SCORES>/scores-dev -ef \
- <PATH_TO_SCORES>/scores-eval
+ <FOLDER_TO_SAVE_FASNET_RESULTS>/grandtest/scores/scores-dev -ef \
+ <FOLDER_TO_SAVE_FASNET_RESULTS>/grandtest/scores/scores-eval
diff --git a/doc/running_mccn.md b/doc/running_mccnn.rst
similarity index 95%
rename from doc/running_mccn.md
rename to doc/running_mccnn.rst
index 2d2a67b1d24d25ea3ed73fb7a7def1173dffbc12..47ee3473de3c24bc789c5a5420e4ac3ce66410d4 100644
--- a/doc/running_mccn.md
+++ b/doc/running_mccnn.rst
@@ -7,7 +7,7 @@ of the described work-flow.
.. note::
- For the experiments discussed in this section, the WMCA dataset needs to be downloaded and installed in your system. Please refer to :ref:`bob.pad.face.baselines` section in the documentation of ``bob.pad.face`` package for more details on how to run the face PAD experiments and setup the databases. Fror downloading the WMCA database, please check the following link in PyTorch `Download Instructions <https://www.idiap.ch/dataset/wmca>`_.
+ For the experiments discussed in this section, the WMCA dataset needs to be downloaded and installed in your system. Please refer to :ref:`bob.pad.face.baselines` section in the documentation of ``bob.pad.face`` package for more details on how to run the face PAD experiments and setup the databases.
For reproducing the experiments with MCCNN. There are mainly four stages. Each of them are described here.
@@ -100,8 +100,8 @@ To evaluate the models run the following command.
.. code-block:: python
./bin/scoring.py -df \
- <PATH_TO_SCORES>/scores-dev -ef \
- <PATH_TO_SCORES>/scores-eval
+ <FOLDER_TO_SAVE_MCCNN_RESULTS>/grandtest/scores/scores-dev -ef \
+ <FOLDER_TO_SAVE_MCCNN_RESULTS>/grandtest/scores/scores-eval
Using pretrained models
=======================
diff --git a/dump/performance_table.csv b/dump/performance_table.csv
new file mode 100644
index 0000000000000000000000000000000000000000..82424a03b0d86b3549faa3ab8bc8ad231afcb391
--- /dev/null
+++ b/dump/performance_table.csv
@@ -0,0 +1,2 @@
+Method,APCER,ACER,APCER,BPCER,ACER
+System_0,0.68,0.84,0.6,0.0,0.3
diff --git a/setup.py b/setup.py
index 0f0f33ed519eb94c7fe37a51be653b718d422b89..9a595fba0a3e6377c54c4e4f5b2f3df91c832c90 100644
--- a/setup.py
+++ b/setup.py
@@ -109,7 +109,7 @@ setup(
'lbp-lr-infrared = bob.paper.mccnn.tifs2018.config.lbp_lr_infrared',
'lbp-lr-thermal= bob.paper.mccnn.tifs2018.config.lbp_lr_thermal',
'lbp-lr-depth= bob.paper.mccnn.tifs2018.config.lbp_lr_depth',
- 'fastnet = bob.paper.mccnn.tifs2018.config.FASNet_config',
+ 'fasnet = bob.paper.mccnn.tifs2018.config.FASNet_config',
'mccnn = bob.paper.mccnn.tifs2018.config.MCCNN_config',
'iqm-lr = bob.paper.mccnn.tifs2018.config.iqm_lr',
'haralick-svm = bob.paper.mccnn.tifs2018.config.haralick_svm',],
diff --git a/submitted.sql3 b/submitted.sql3
new file mode 100644
index 0000000000000000000000000000000000000000..46418b0e76730cd3f0a48125ab2b30f96b46f7ad
Binary files /dev/null and b/submitted.sql3 differ