From bbe0e08c959f352c6ca7efad4026f70e34895025 Mon Sep 17 00:00:00 2001
From: Guillaume HEUSCH <guillaume.heusch@idiap.ch>
Date: Mon, 2 Jul 2018 16:26:07 +0200
Subject: [PATCH] [preprocessor, extractor] fixed docstrings (again)
---
bob/pad/face/extractor/LTSS.py | 46 +++++++++++---------------
bob/pad/face/extractor/LiFeatures.py | 39 +++++++++-------------
bob/pad/face/extractor/PPGSecure.py | 32 ++++++------------
bob/pad/face/preprocessor/Chrom.py | 40 +++++++++++-----------
bob/pad/face/preprocessor/Li.py | 29 ++++++++--------
bob/pad/face/preprocessor/PPGSecure.py | 31 ++++++-----------
bob/pad/face/preprocessor/SSR.py | 4 +--
7 files changed, 91 insertions(+), 130 deletions(-)
diff --git a/bob/pad/face/extractor/LTSS.py b/bob/pad/face/extractor/LTSS.py
index 3b3555f1..c1077a88 100644
--- a/bob/pad/face/extractor/LTSS.py
+++ b/bob/pad/face/extractor/LTSS.py
@@ -15,29 +15,23 @@ class LTSS(Extractor, object):
The features are described in the following article:
- @Article {
- Author = {Muckenhirn, Hannah and Korshunov, Pavel and
- Magimai-Doss, Mathew and Marcel, Sebastien }
- Title = {Long-Term Spectral Statistics for Voice Presentation
- Attack Detection},
- Journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
- Volume = {25},
- Number = {11},
- Pages = {2098--2111},
- year = 2017
- }
+ H. Muckenhirn, P. Korshunov, M. Magimai-Doss, and S. Marcel
+ Long-Term Spectral Statistics for Voice Presentation Attack Detection,
+ IEEE/ACM Trans. Audio, Speech and Language Processing. vol 25, n. 11, 2017
Attributes
----------
- framerate: int
+ window_size : :obj:`int`
+ The size of the window where FFT is computed
+ framerate : :obj:`int`
The sampling frequency of the signal (i.e the framerate ...)
- nfft: int
+ nfft : :obj:`int`
Number of points to compute the FFT
- debug: bool
+ debug : :obj:`bool`
Plot stuff
- concat: bool
+ concat : :obj:`bool`
Flag if you would like to concatenate features from the 3 color channels
- time: int
+ time : :obj:`int`
The length of the signal to consider (in seconds)
"""
@@ -46,19 +40,19 @@ class LTSS(Extractor, object):
Parameters
----------
- window_size: int
+ window_size : :obj:`int`
The size of the window where FFT is computed
- framerate: int
+ framerate : :obj:`int`
The sampling frequency of the signal (i.e the framerate ...)
- nfft: int
+ nfft : :obj:`int`
Number of points to compute the FFT
- concat: bool
- Flag if you would like to concatenate features from the 3 color channels
- debug: bool
+ debug : :obj:`bool`
Plot stuff
- time: int
+ concat : :obj:`bool`
+ Flag if you would like to concatenate features from the 3 color channels
+ time : :obj:`int`
The length of the signal to consider (in seconds)
-
+
"""
super(LTSS, self).__init__()
self.framerate = framerate
@@ -73,12 +67,12 @@ class LTSS(Extractor, object):
Parameters
----------
- signal: numpy.ndarray
+ signal : :py:class:`numpy.ndarray`
The signal
Returns
-------
- ltss: numpy.ndarray
+ :py:class:`numpy.ndarray`
The spectral statistics of the signal.
"""
diff --git a/bob/pad/face/extractor/LiFeatures.py b/bob/pad/face/extractor/LiFeatures.py
index 9fbd4c48..0e25a0b0 100644
--- a/bob/pad/face/extractor/LiFeatures.py
+++ b/bob/pad/face/extractor/LiFeatures.py
@@ -13,43 +13,34 @@ class LiFeatures(Extractor, object):
"""Compute features from pulse signals in the three color channels.
The features are described in the following article:
-
- @InProceedings{li-icpr-2016,
- Author = {Li, X. and Komulainen, J. and Zhao, G. and Yuen, P-C.
- and Pietik\"ainen, M.},
- Title = {Generalized {F}ace {A}nti-spoofing by {D}etecting
- {P}ulse {F}rom {F}ace {V}ideos},
- BookTitle = {Intl {C}onf. on {P}attern {R}ecognition ({ICPR})},
- Pages = {4244-4249},
- year = 2016
- }
-
+
+ X. Li, J. Komulainen, G. Zhao, P-C Yuen and M. Pietikainen,
+ Generalized Face Anti-spoofing by Detecting Pulse From Face Videos
+ Intl Conf. on Pattern Recognition (ICPR), 2016.
Attributes
----------
- framerate: int
+ framerate : :obj:`int`
The sampling frequency of the signal (i.e the framerate ...)
- nfft: int
+ nfft : :obj:`int`
Number of points to compute the FFT
- debug: bool
+ debug : :obj:`bool`
Plot stuff
"""
-
def __init__(self, framerate=25, nfft=512, debug=False, **kwargs):
"""Init function
Parameters
----------
- framerate: int
+ framerate : :obj:`int`
The sampling frequency of the signal (i.e the framerate ...)
- nfft: int
+ nfft : :obj:`int`
Number of points to compute the FFT
- debug: bool
+ debug : :obj:`bool`
Plot stuff
-
+
"""
-
super(LiFeatures, self).__init__()
self.framerate = framerate
self.nfft = nfft
@@ -61,13 +52,13 @@ class LiFeatures(Extractor, object):
Parameters
----------
- signal: numpy.ndarray
+ signal : :py:class:`numpy.ndarray`
The signal
Returns
- -------
- feature: numpy.ndarray
- the computed features
+ -------
+ :py:class:`numpy.ndarray`
+ the computed features
"""
# sanity check
diff --git a/bob/pad/face/extractor/PPGSecure.py b/bob/pad/face/extractor/PPGSecure.py
index bf4a8ddd..1865ec69 100644
--- a/bob/pad/face/extractor/PPGSecure.py
+++ b/bob/pad/face/extractor/PPGSecure.py
@@ -15,27 +15,17 @@ class PPGSecure(Extractor, object):
The feature are extracted according to what is described in
the following article:
- @InProceedings{nowara-afgr-2017,
- Author = {E. M. Nowara and A. Sabharwal and A. Veeraraghavan},
- Title = {P{PGS}ecure: {B}iometric {P}resentation {A}ttack
- {D}etection {U}sing {P}hotopletysmograms},
- BookTitle = {I{EEE} {I}ntl {C}onf on {A}utomatic {F}ace and
- {G}esture {R}ecognition ({AFGR})},
- Volume = {},
- Number = {},
- Pages = {56-62},
- issn = {},
- seq-number = {69},
- year = 2017
- }
+ E.M Nowara, A. Sabharwal and A. Veeraraghavan,
+ "PPGSecure: Biometric Presentation Attack Detection using Photoplethysmograms",
+ IEEE Intl Conf. on Automatic Face and Gesture Recognition, 2017.
Attributes
----------
- framerate: int
+ framerate : :obj:`int`
The sampling frequency of the signal (i.e the framerate ...)
- nfft: int
+ nfft : :obj:`int`
Number of points to compute the FFT
- debug: bool
+ debug : :obj:`bool`
Plot stuff
"""
@@ -44,11 +34,11 @@ class PPGSecure(Extractor, object):
Parameters
----------
- framerate: int
+ framerate : :obj:`int`
The sampling frequency of the signal (i.e the framerate ...)
- nfft: int
+ nfft : :obj:`int`
Number of points to compute the FFT
- debug: bool
+ debug : :obj:`bool`
Plot stuff
"""
@@ -63,12 +53,12 @@ class PPGSecure(Extractor, object):
Parameters
----------
- signal: numpy.ndarray
+ signal : :py:class:`numpy.ndarray`
The signal
Returns
-------
- fft: numpy.ndarray
+ :py:class:`numpy.ndarray`
the computed FFT features
"""
diff --git a/bob/pad/face/preprocessor/Chrom.py b/bob/pad/face/preprocessor/Chrom.py
index ec210f96..987492ea 100644
--- a/bob/pad/face/preprocessor/Chrom.py
+++ b/bob/pad/face/preprocessor/Chrom.py
@@ -25,26 +25,26 @@ class Chrom(Preprocessor, object):
The pulse is extracted according to the CHROM algorithm.
- See the documentation of :py:mod:`bob.rppg.base`
+ See the documentation of `bob.rppg.base`
Attributes
----------
- skin_threshold: float
+ skin_threshold : :obj:`float`
The threshold for skin color probability
- skin_init: bool
+ skin_init : :obj:`bool`
If you want to re-initailize the skin color distribution at each frame
- framerate: int
+ framerate : :obj:`int`
The framerate of the video sequence.
- bp_order: int
+ bp_order : :obj:`int`
The order of the bandpass filter
- window_size: int
+ window_size : :obj:`int`
The size of the window in the overlap-add procedure.
- motion: float
+ motion : :obj:`float`
The percentage of frames you want to select where the
signal is "stable". 0 mean all the sequence.
- debug: boolean
+ debug : :obj:`bool`
Plot some stuff
- skin_filter: :py:class:`bob.ip.skincolorfilter.SkinColorFilter`
+ skin_filter : :py:class:`bob.ip.skincolorfilter.SkinColorFilter`
The skin color filter
"""
@@ -54,24 +54,23 @@ class Chrom(Preprocessor, object):
Parameters
----------
- skin_threshold: float
+ skin_threshold : :obj:`float`
The threshold for skin color probability
- skin_init: bool
+ skin_init : :obj:`bool`
If you want to re-initailize the skin color distribution at each frame
- framerate: int
+ framerate : :obj:`int`
The framerate of the video sequence.
- bp_order: int
+ bp_order : :obj:`int`
The order of the bandpass filter
- window_size: int
+ window_size : :obj:`int`
The size of the window in the overlap-add procedure.
- motion: float
+ motion : :obj:`float`
The percentage of frames you want to select where the
signal is "stable". 0 mean all the sequence.
- debug: boolean
+ debug : :obj:`bool`
Plot some stuff
"""
-
super(Chrom, self).__init__()
self.skin_threshold = skin_threshold
self.skin_init = skin_init
@@ -87,14 +86,14 @@ class Chrom(Preprocessor, object):
Parameters
----------
- frames: :py:class:`bob.bio.video.utils.FrameContainer`
+ frames : :py:class:`bob.bio.video.utils.FrameContainer`
video data
- annotations: :py:class:`dict`
+ annotations : :py:class:`dict`
the face bounding box, as follows: ``{'topleft': (row, col), 'bottomright': (row, col)}``
Returns
-------
- pulse: numpy.ndarray
+ :obj:`numpy.ndarray`
The pulse signal
"""
@@ -239,4 +238,3 @@ class Chrom(Preprocessor, object):
pyplot.show()
return pulse
-
diff --git a/bob/pad/face/preprocessor/Li.py b/bob/pad/face/preprocessor/Li.py
index 143497cc..5f54a1bb 100644
--- a/bob/pad/face/preprocessor/Li.py
+++ b/bob/pad/face/preprocessor/Li.py
@@ -22,7 +22,7 @@ class Li(Preprocessor):
The pulse is extracted according to Li's CVPR 14 algorithm.
- See the documentation of :py:mod:`bob.rppg.base`
+ See the documentation of `bob.rppg.base`
Note that this is a simplified version of the original
pulse extraction algorithms (mask detection in each
@@ -31,17 +31,17 @@ class Li(Preprocessor):
Attributes
----------
- indent: int
+ indent : :obj:`int`
Indent (in percent of the face width) to apply to keypoints to get the mask.
- lamda_: int
+ lamda_ : :obj:`int`
the lamba value of the detrend filter
- window: int
+ window : :obj:`int`
The size of the window of the average filter
- framerate: int
+ framerate : :obj:`int`
The framerate of the video sequence.
- bp_order: int
+ bp_order : :obj:`int`
The order of the bandpass filter
- debug: bool
+ debug : :obj:`bool`
Plot some stuff
"""
@@ -51,19 +51,18 @@ class Li(Preprocessor):
Parameters
----------
- indent: int
+ indent : :obj:`int`
Indent (in percent of the face width) to apply to keypoints to get the mask.
- lamda_: int
+ lamda_ : :obj:`int`
the lamba value of the detrend filter
- window: int
+ window : :obj:`int`
The size of the window of the average filter
- framerate: int
+ framerate : :obj:`int`
The framerate of the video sequence.
- bp_order: int
+ bp_order : :obj:`int`
The order of the bandpass filter
- debug: bool
+ debug : :obj:`bool`
Plot some stuff
-
"""
super(Li, self).__init__(**kwargs)
@@ -86,7 +85,7 @@ class Li(Preprocessor):
Returns
-------
- pulse: numpy.ndarray
+ :obj:`numpy.ndarray`
The pulse signal, in each color channel (RGB)
"""
diff --git a/bob/pad/face/preprocessor/PPGSecure.py b/bob/pad/face/preprocessor/PPGSecure.py
index 2a09fa76..36b201d1 100644
--- a/bob/pad/face/preprocessor/PPGSecure.py
+++ b/bob/pad/face/preprocessor/PPGSecure.py
@@ -22,41 +22,30 @@ class PPGSecure(Preprocessor):
The pulse is extracted according to what is described in
the following article:
- @InProceedings{nowara-afgr-2017,
- Author = {E. M. Nowara and A. Sabharwal and A. Veeraraghavan},
- Title = {P{PGS}ecure: {B}iometric {P}resentation {A}ttack
- {D}etection {U}sing {P}hotopletysmograms},
- BookTitle = {I{EEE} {I}ntl {C}onf on {A}utomatic {F}ace and
- {G}esture {R}ecognition ({AFGR})},
- Volume = {},
- Number = {},
- Pages = {56-62},
- issn = {},
- seq-number = {69},
- year = 2017
- }
+ E.M Nowara, A. Sabharwal and A. Veeraraghavan,
+ "PPGSecure: Biometric Presentation Attack Detection using Photoplethysmograms",
+ IEEE Intl Conf. on Automatic Face and Gesture Recognition, 2017.
Attributes
----------
- framerate: int
+ framerate : :obj:`int`
The framerate of the video sequence.
- bp_order: int
+ bp_order : :obj:`int`
The order of the bandpass filter
- debug: bool
+ debug : :obj:`bool`
Plot some stuff
"""
-
def __init__(self, framerate=25, bp_order=32, debug=False, **kwargs):
"""Init function
Parameters
----------
- framerate: int
+ framerate : :obj:`int`
The framerate of the video sequence.
- bp_order: int
+ bp_order : :obj:`int`
The order of the bandpass filter
- debug: bool
+ debug : :obj:`bool`
Plot some stuff
"""
@@ -83,7 +72,7 @@ class PPGSecure(Preprocessor):
Returns
-------
- pulse: numpy.ndarray
+ :obj:`numpy.ndarray`
The pulse signal, in each color channel (RGB)
"""
diff --git a/bob/pad/face/preprocessor/SSR.py b/bob/pad/face/preprocessor/SSR.py
index edb3ed91..4c5f702a 100644
--- a/bob/pad/face/preprocessor/SSR.py
+++ b/bob/pad/face/preprocessor/SSR.py
@@ -23,7 +23,7 @@ class SSR(Preprocessor, object):
The pulse is extracted according to the SSR algorithm.
- See the documentation of :py:mod:`bob.rppg.base`
+ See the documentation of :py:module::`bob.rppg.base`
Attributes
----------
@@ -35,7 +35,7 @@ class SSR(Preprocessor, object):
The temporal stride.
debug: boolean
Plot some stuff
- skin_filter: :py:class:`bob.ip.skincolorfilter.SkinColorFilter`
+ skin_filter: :py:class::`bob.ip.skincolorfilter.SkinColorFilter`
The skin color filter
"""
--
GitLab