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Merge pull request #14 from AiCure/revert-13-exp_vars

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Revert "Exp vars"
This commit is contained in:
vjbytes102
2020-12-14 18:09:57 -05:00
committed by GitHub
parent 3a7757ef84 17e1b0e39b
commit 9196ff9522
6 changed files with 35 additions and 70 deletions
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10
dbm_lib/config/config_raw_feature.py
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@@ -194,9 +194,6 @@ class ConfigRawReader(object):
self.neu_exp = config['raw_feature']['neu_exp'] self.neu_exp = config['raw_feature']['neu_exp']
self.cai_exp = config['raw_feature']['cai_exp'] self.cai_exp = config['raw_feature']['cai_exp']
self.com_exp = config['raw_feature']['com_exp'] self.com_exp = config['raw_feature']['com_exp']
self.com_lower_exp = config['raw_feature']['com_lower_exp']
self.com_upper_exp = config['raw_feature']['com_upper_exp']
self.pai_exp = config['raw_feature']['pai_exp']
self.hap_exp_full = config['raw_feature']['hap_exp_full'] self.hap_exp_full = config['raw_feature']['hap_exp_full']
self.sad_exp_full = config['raw_feature']['sad_exp_full'] self.sad_exp_full = config['raw_feature']['sad_exp_full']
self.sur_exp_full = config['raw_feature']['sur_exp_full'] self.sur_exp_full = config['raw_feature']['sur_exp_full']
@@ -209,10 +206,6 @@ class ConfigRawReader(object):
self.neu_exp_full = config['raw_feature']['neu_exp_full'] self.neu_exp_full = config['raw_feature']['neu_exp_full']
self.cai_exp_full = config['raw_feature']['cai_exp_full'] self.cai_exp_full = config['raw_feature']['cai_exp_full']
self.com_exp_full = config['raw_feature']['com_exp_full'] self.com_exp_full = config['raw_feature']['com_exp_full']
self.com_lower_exp_full = config['raw_feature']['com_lower_exp_full']
self.com_upper_exp_full = config['raw_feature']['com_upper_exp_full']
self.pai_exp_full = config['raw_feature']['pai_exp_full']
self.fac_AsymMaskMouth = config['raw_feature']['fac_AsymMaskMouth'] self.fac_AsymMaskMouth = config['raw_feature']['fac_AsymMaskMouth']
self.fac_AsymMaskEye = config['raw_feature']['fac_AsymMaskEye'] self.fac_AsymMaskEye = config['raw_feature']['fac_AsymMaskEye']
self.fac_AsymMaskEyebrow = config['raw_feature']['fac_AsymMaskEyebrow'] self.fac_AsymMaskEyebrow = config['raw_feature']['fac_AsymMaskEyebrow']
@@ -229,7 +222,7 @@ class ConfigRawReader(object):
self.mov_Hpose_Yaw = config['raw_feature']['mov_Hpose_Yaw'] self.mov_Hpose_Yaw = config['raw_feature']['mov_Hpose_Yaw']
self.mov_Hpose_Roll = config['raw_feature']['mov_Hpose_Roll'] self.mov_Hpose_Roll = config['raw_feature']['mov_Hpose_Roll']
self.mov_Hpose_Dist = config['raw_feature']['mov_Hpose_Dist'] self.mov_Hpose_Dist = config['raw_feature']['mov_Hpose_Dist']
self.mov_freq_trem_freq = config['raw_feature']['mov_freq_trem_freq'] self.mov_freq_trem_freq = config['raw_feature']['mov_freq_trem_freq']
self.mov_freq_trem_index = config['raw_feature']['mov_freq_trem_index'] self.mov_freq_trem_index = config['raw_feature']['mov_freq_trem_index']
self.mov_freq_trem_pindex = config['raw_feature']['mov_freq_trem_pindex'] self.mov_freq_trem_pindex = config['raw_feature']['mov_freq_trem_pindex']
@@ -276,4 +269,3 @@ class ConfigRawReader(object):
self.nlp_wordsPerMin = config['raw_feature']['nlp_wordsPerMin'] self.nlp_wordsPerMin = config['raw_feature']['nlp_wordsPerMin']
self.nlp_totalTime = config['raw_feature']['nlp_totalTime'] self.nlp_totalTime = config['raw_feature']['nlp_totalTime']

48
dbm_lib/dbm_features/raw_features/util/video_util.py
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@@ -11,32 +11,32 @@ from dbm_lib.dbm_features.raw_features.util import util as ut
def smooth(x,window_len=11,window='hanning'): def smooth(x,window_len=11,window='hanning'):
"""smooth the data using a window with requested size. """smooth the data using a window with requested size.
This method is based on the convolution of a scaled window with the signal. This method is based on the convolution of a scaled window with the signal.
The signal is prepared by introducing reflected copies of the signal The signal is prepared by introducing reflected copies of the signal
(with the window size) in both ends so that transient parts are minimized (with the window size) in both ends so that transient parts are minimized
in the begining and end part of the output signal. in the begining and end part of the output signal.
input: input:
x: the input signal x: the input signal
window_len: the dimension of the smoothing window; should be an odd integer window_len: the dimension of the smoothing window; should be an odd integer
window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman' window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'
flat window will produce a moving average smoothing. flat window will produce a moving average smoothing.
output: output:
the smoothed signal the smoothed signal
example: example:
t=linspace(-2,2,0.1) t=linspace(-2,2,0.1)
x=sin(t)+randn(len(t))*0.1 x=sin(t)+randn(len(t))*0.1
y=smooth(x) y=smooth(x)
see also: see also:
numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve
scipy.signal.lfilter scipy.signal.lfilter
TODO: the window parameter could be the window itself if an array instead of a string TODO: the window parameter could be the window itself if an array instead of a string
NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y. NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y.
""" """
@@ -62,7 +62,7 @@ def filter_by_confidence_and_thresh(x, fea, thresh):
return x[fea] return x[fea]
else: else:
return np.NaN return np.NaN
def add_au_emotion(x, emotion,emotion_type,exp_type): def add_au_emotion(x, emotion,emotion_type,exp_type):
""" """
computing individula emotion expressivity matrix computing individula emotion expressivity matrix
@@ -79,20 +79,20 @@ def add_au_emotion(x, emotion,emotion_type,exp_type):
if x[au_c_label]==1 and (not np.isnan(x[au_r_label])): #there are data with face in, but au_c=0 if x[au_c_label]==1 and (not np.isnan(x[au_r_label])): #there are data with face in, but au_c=0
sum_r += x[au_r_label] sum_r += x[au_r_label]
cnt += 6 cnt += 6
if exp_type=='full' and x[au_c_label]==0: #Logic to compute emotion expressivity when all AU's are present if exp_type=='full' and x[au_c_label]==0: #Logic to compute emotion expressivity when all AU's are present
cnt = 0 cnt = 0
break break
if cnt > 0: if cnt > 0:
sum_r /= cnt sum_r /= cnt
else: else:
sum_r = 0 sum_r = 0
v_emo = x[emotion_type] + sum_r v_emo = x[emotion_type] + sum_r
else: else:
v_emo = np.NaN v_emo = np.NaN
error_reason = 'confidence less than 80%' error_reason = 'confidence less than 80%'
return v_emo, error_reason return v_emo, error_reason
def add_au_occ(x, emotion,emotion_type): def add_au_occ(x, emotion,emotion_type):
""" """
computing individula emotion presence computing individula emotion presence
@@ -107,14 +107,14 @@ def add_au_occ(x, emotion,emotion_type):
au_c_label = " AU{:02d}_c".format(au) au_c_label = " AU{:02d}_c".format(au)
if x[au_c_label]==1: #there are data with face in, but au_c=0 if x[au_c_label]==1: #there are data with face in, but au_c=0
au_pres.append(1) au_pres.append(1)
if len(au_pres) == len(emotion): if len(au_pres) == len(emotion):
em_pres = 1 em_pres = 1
else: else:
em_pres = np.NaN em_pres = np.NaN
error_reason = 'confidence less than 80%' error_reason = 'confidence less than 80%'
return em_pres, error_reason return em_pres, error_reason
def emotion_exp(em_au,of,em_col,err_col): def emotion_exp(em_au,of,em_col,err_col):
""" """
Computing individual emotion expressivity and adding it to dataframe Computing individual emotion expressivity and adding it to dataframe
@@ -122,14 +122,14 @@ def emotion_exp(em_au,of,em_col,err_col):
for emotion in em_au: for emotion in em_au:
of[[em_col[0],err_col]]=of.apply(add_au_emotion, args=(emotion,em_col[0],'partial',), axis=1, result_type='expand') of[[em_col[0],err_col]]=of.apply(add_au_emotion, args=(emotion,em_col[0],'partial',), axis=1, result_type='expand')
of[[em_col[1],err_col]]=of.apply(add_au_emotion, args=(emotion,em_col[1],'full',), axis=1, result_type='expand') of[[em_col[1],err_col]]=of.apply(add_au_emotion, args=(emotion,em_col[1],'full',), axis=1, result_type='expand')
def emotion_pres(em_au,of,em_col,err_col): def emotion_pres(em_au,of,em_col,err_col):
""" """
Computing individual emotion expressivity and adding it to dataframe Computing individual emotion expressivity and adding it to dataframe
""" """
for emotion in em_au: for emotion in em_au:
of[[em_col,err_col]]=of.apply(add_au_occ, args=(emotion,em_col,), axis=1, result_type='expand') of[[em_col,err_col]]=of.apply(add_au_occ, args=(emotion,em_col,), axis=1, result_type='expand')
def calc_of_for_video(of,face_cfg,fe_cfg): def calc_of_for_video(of,face_cfg,fe_cfg):
""" """
Creating dataframe for emotion expressivity Creating dataframe for emotion expressivity
@@ -142,7 +142,7 @@ def calc_of_for_video(of,face_cfg,fe_cfg):
fe_cfg.com_exp_full] fe_cfg.com_exp_full]
of[new_cols] = pd.DataFrame([[0] * len(new_cols)], index=of.index) of[new_cols] = pd.DataFrame([[0] * len(new_cols)], index=of.index)
of[fe_cfg.err_reason] = 'Pass' of[fe_cfg.err_reason] = 'Pass'
#Composite happiness expressivity #Composite happiness expressivity
emotion_exp(face_cfg.happiness,of,[fe_cfg.hap_exp,fe_cfg.hap_exp_full],fe_cfg.err_reason) emotion_exp(face_cfg.happiness,of,[fe_cfg.hap_exp,fe_cfg.hap_exp_full],fe_cfg.err_reason)
#Composite sadness expressivity #Composite sadness expressivity
@@ -167,12 +167,6 @@ def calc_of_for_video(of,face_cfg,fe_cfg):
emotion_exp(face_cfg.cai,of,[fe_cfg.cai_exp,fe_cfg.cai_exp_full],fe_cfg.err_reason) emotion_exp(face_cfg.cai,of,[fe_cfg.cai_exp,fe_cfg.cai_exp_full],fe_cfg.err_reason)
#Composite Expressivity #Composite Expressivity
emotion_exp(face_cfg.ACTION_UNITS,of,[fe_cfg.com_exp,fe_cfg.com_exp_full],fe_cfg.err_reason) emotion_exp(face_cfg.ACTION_UNITS,of,[fe_cfg.com_exp,fe_cfg.com_exp_full],fe_cfg.err_reason)
#Composite lower face expressivity
emotion_exp(face_cfg.LOWER_ACTION_UNITS,of,[fe_cfg.com_lower_exp,fe_cfg.com_lower_exp_full],fe_cfg.err_reason)
#Composite upper face Expressivity
emotion_exp(face_cfg.UPPER_ACTION_UNITS,of,[fe_cfg.com_upper_exp,fe_cfg.com_upper_exp_full],fe_cfg.err_reason)
#Composite pain expressivity
emotion_exp(face_cfg.pain,of,[fe_cfg.pai_exp,fe_cfg.pai_exp_full],fe_cfg.err_reason)
#AU happiness presence #AU happiness presence
emotion_pres(face_cfg.happiness,of,fe_cfg.happ_occ,fe_cfg.err_reason) emotion_pres(face_cfg.happiness,of,fe_cfg.happ_occ,fe_cfg.err_reason)
#AU Sad presence #AU Sad presence

13
dbm_lib/dbm_features/raw_features/video/face_config/face_config_reader.py
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@@ -18,20 +18,18 @@ class ConfigFaceReader(object):
Args: Args:
service_config_yml (None, optional): yml file defined service configuration service_config_yml (None, optional): yml file defined service configuration
""" """
if service_config_yml is None: if service_config_yml is None:
service_config = DBMLIB_FACE_CONFIG service_config = DBMLIB_FACE_CONFIG
else: else:
service_config = service_config_yml service_config = service_config_yml
with open(service_config, 'r') as ymlfile: with open(service_config, 'r') as ymlfile:
config = yaml.load(ymlfile) config = yaml.load(ymlfile)
self.ACTION_UNITS = config['cdx_face_config']['ACTION_UNITS'] self.ACTION_UNITS = config['cdx_face_config']['ACTION_UNITS']
self.NEG_ACTION_UNITS = config['cdx_face_config']['NEG_ACTION_UNITS'] self.NEG_ACTION_UNITS = config['cdx_face_config']['NEG_ACTION_UNITS']
self.POS_ACTION_UNITS = config['cdx_face_config']['POS_ACTION_UNITS'] self.POS_ACTION_UNITS = config['cdx_face_config']['POS_ACTION_UNITS']
self.NET_ACTION_UNITS = config['cdx_face_config']['NET_ACTION_UNITS'] self.NET_ACTION_UNITS = config['cdx_face_config']['NET_ACTION_UNITS']
self.LOWER_ACTION_UNITS = config['cdx_face_config']['LOWER_ACTION_UNITS']
self.UPPER_ACTION_UNITS = config['cdx_face_config']['UPPER_ACTION_UNITS']
self.happiness = config['cdx_face_config']['happiness'] self.happiness = config['cdx_face_config']['happiness']
self.sadness = config['cdx_face_config']['sadness'] self.sadness = config['cdx_face_config']['sadness']
self.surprise = config['cdx_face_config']['surprise'] self.surprise = config['cdx_face_config']['surprise']
@@ -39,7 +37,6 @@ class ConfigFaceReader(object):
self.anger = config['cdx_face_config']['anger'] self.anger = config['cdx_face_config']['anger']
self.disgust = config['cdx_face_config']['disgust'] self.disgust = config['cdx_face_config']['disgust']
self.contempt = config['cdx_face_config']['contempt'] self.contempt = config['cdx_face_config']['contempt']
self.pain = config['cdx_face_config']['pain']
self.cai = config['cdx_face_config']['CAI'] self.cai = config['cdx_face_config']['CAI']
self.SELECTED_FEATURES = config['cdx_face_config']['SELECTED_FEATURES'].split(',') self.SELECTED_FEATURES = config['cdx_face_config']['SELECTED_FEATURES'].split(',')
self.face_expr_dir = config['cdx_face_config']['face_expr_dir'] self.face_expr_dir = config['cdx_face_config']['face_expr_dir']
@@ -47,7 +44,7 @@ class ConfigFaceReader(object):
self.AU_fl = config['cdx_face_config']['AU_filters'] self.AU_fl = config['cdx_face_config']['AU_filters']
self.au_int = config['cdx_face_config']['au_intensity'] self.au_int = config['cdx_face_config']['au_intensity']
self.au_prs = config['cdx_face_config']['au_presence'] self.au_prs = config['cdx_face_config']['au_presence']
def get_action_unit(self): def get_action_unit(self):
"""Summary """Summary
Returns: Returns:
@@ -137,4 +134,4 @@ class ConfigFaceReader(object):
Returns: Returns:
TYPE: end point TYPE: end point
""" """
return self.cai return self.cai

12
resources/features/derived_feature.yml
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@@ -36,11 +36,9 @@ derive_feature:
'fac_AU06pres', 'fac_AU07pres', 'fac_AU09pres', 'fac_AU10pres', 'fac_AU12pres', 'fac_AU14pres', 'fac_AU15pres', 'fac_AU06pres', 'fac_AU07pres', 'fac_AU09pres', 'fac_AU10pres', 'fac_AU12pres', 'fac_AU14pres', 'fac_AU15pres',
'fac_AU17pres', 'fac_AU20pres', 'fac_AU23pres', 'fac_AU25pres', 'fac_AU26pres', 'fac_AU28pres', 'fac_AU45pres'] 'fac_AU17pres', 'fac_AU20pres', 'fac_AU23pres', 'fac_AU25pres', 'fac_AU26pres', 'fac_AU28pres', 'fac_AU45pres']
FAC_EXP: ['hap_exp', 'sad_exp', 'sur_exp', 'fea_exp', 'ang_exp', 'dis_exp', 'con_exp', 'happ_occ', 'sad_occ', FAC_EXP: ['hap_exp', 'sad_exp', 'sur_exp', 'fea_exp', 'ang_exp', 'dis_exp', 'con_exp', 'happ_occ', 'sad_occ',
'sur_occ', 'fea_occ', 'ang_occ', 'dis_occ', 'con_occ', 'pos_exp', 'neg_exp', 'com_exp','com_lower_exp','com_upper_exp', 'hap_exp_full', 'sur_occ', 'fea_occ', 'ang_occ', 'dis_occ', 'con_occ', 'pos_exp', 'neg_exp', 'com_exp', 'hap_exp_full',
'sad_exp_full', 'sur_exp_full','fea_exp_full', 'ang_exp_full', 'dis_exp_full', 'con_exp_full', 'pos_exp_full', 'sad_exp_full', 'sur_exp_full','fea_exp_full', 'ang_exp_full', 'dis_exp_full', 'con_exp_full', 'pos_exp_full',
'neg_exp_full', 'com_exp_full','com_lower_exp_full','com_upper_exp_full', 'pai_exp', 'pai_exp_full'] 'neg_exp_full', 'com_exp_full']
FAC_LMK: ['fac_LMK00disp', 'fac_LMK01disp', 'fac_LMK02disp', 'fac_LMK03disp', 'fac_LMK04disp', FAC_LMK: ['fac_LMK00disp', 'fac_LMK01disp', 'fac_LMK02disp', 'fac_LMK03disp', 'fac_LMK04disp',
'fac_LMK05disp', 'fac_LMK06disp', 'fac_LMK07disp', 'fac_LMK08disp', 'fac_LMK09disp', 'fac_LMK10disp', 'fac_LMK05disp', 'fac_LMK06disp', 'fac_LMK07disp', 'fac_LMK08disp', 'fac_LMK09disp', 'fac_LMK10disp',
'fac_LMK11disp', 'fac_LMK12disp', 'fac_LMK13disp', 'fac_LMK14disp', 'fac_LMK15disp', 'fac_LMK16disp', 'fac_LMK11disp', 'fac_LMK12disp', 'fac_LMK13disp', 'fac_LMK14disp', 'fac_LMK15disp', 'fac_LMK16disp',
@@ -147,9 +145,6 @@ derive_feature:
neg_exp: ['mean', 'std', 'pct'] neg_exp: ['mean', 'std', 'pct']
neu_exp: ['mean', 'std', 'pct'] neu_exp: ['mean', 'std', 'pct']
com_exp: ['mean', 'std', 'pct'] com_exp: ['mean', 'std', 'pct']
com_lower_exp: ['mean','std','pct']
com_upper_exp: ['mean','std','pct']
pai_exp: ['mean','std','pct']
hap_exp_full: ['mean', 'std'] hap_exp_full: ['mean', 'std']
sad_exp_full: ['mean', 'std'] sad_exp_full: ['mean', 'std']
sur_exp_full: ['mean', 'std'] sur_exp_full: ['mean', 'std']
@@ -161,9 +156,6 @@ derive_feature:
neg_exp_full: ['mean', 'std'] neg_exp_full: ['mean', 'std']
neu_exp_full: ['mean', 'std'] neu_exp_full: ['mean', 'std']
com_exp_full: ['mean', 'std'] com_exp_full: ['mean', 'std']
com_lower_exp_full: ['mean','std']
com_upper_exp_full: ['mean', 'std']
pai_exp_full: ['mean','std']
#Facial Landmarks #Facial Landmarks
fac_LMK00disp: ['mean', 'std'] fac_LMK00disp: ['mean', 'std']

7
resources/features/raw_feature.yml
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@@ -26,10 +26,6 @@ raw_feature:
neu_exp: neu_exp neu_exp: neu_exp
cai_exp: cai_exp cai_exp: cai_exp
com_exp: fac_comintsoft com_exp: fac_comintsoft
com_lower_exp: fac_comlowintsoft
com_upper_exp: fac_comuppintsoft
pai_exp: fac_paiintsoft
hap_exp_full: fac_hapinthard hap_exp_full: fac_hapinthard
sad_exp_full: fac_sadinthard sad_exp_full: fac_sadinthard
sur_exp_full: fac_surinthard sur_exp_full: fac_surinthard
@@ -42,9 +38,6 @@ raw_feature:
neu_exp_full: neu_exp_full neu_exp_full: neu_exp_full
cai_exp_full: cai_exp_full cai_exp_full: cai_exp_full
com_exp_full: fac_cominthard com_exp_full: fac_cominthard
com_lower_exp_full: fac_comlowinthard
com_upper_exp_full: fac_comuppinthard
pai_exp_full: fac_paiinthard
#Facial asymmetry #Facial asymmetry
fac_AsymMaskMouth: fac_asymmaskmouth fac_AsymMaskMouth: fac_asymmaskmouth

15
resources/services/face_util.yml
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@@ -1,7 +1,5 @@
cdx_face_config: cdx_face_config:
ACTION_UNITS: [[6, 12],[1, 4, 15],[1, 2, 5, 26],[1, 2, 4, 5, 7, 20, 26],[4, 5, 7, 23],[9, 15],[12, 14]] ACTION_UNITS: [[6, 12],[1, 4, 15],[1, 2, 5, 26],[1, 2, 4, 5, 7, 20, 26],[4, 5, 7, 23],[9, 15],[12, 14]]
LOWER_ACTION_UNITS: [[12], [15], [26], [20, 26], [23], [15], [12, 14]]
UPPER_ACTION_UNITS: [[6], [1, 4], [1, 2, 5], [1, 2, 4, 5, 7], [4, 5, 7], [9]]
NEG_ACTION_UNITS: [[1, 4, 15], [1, 2, 4, 5, 7, 20, 26], [4, 5, 7, 23], [9, 15], [12, 14]] NEG_ACTION_UNITS: [[1, 4, 15], [1, 2, 4, 5, 7, 20, 26], [4, 5, 7, 23], [9, 15], [12, 14]]
POS_ACTION_UNITS: [[6, 12]] POS_ACTION_UNITS: [[6, 12]]
NET_ACTION_UNITS: [[1, 2, 5, 26]] NET_ACTION_UNITS: [[1, 2, 5, 26]]
@@ -12,17 +10,16 @@ cdx_face_config:
anger: [[4, 5, 7, 23]] anger: [[4, 5, 7, 23]]
disgust: [[9, 15]] disgust: [[9, 15]]
contempt: [[12, 14]] contempt: [[12, 14]]
pain: [[4, 6, 7, 9, 10, 12, 20, 26]]
CAI: [[6, 12],[1, 4, 15],[2, 5, 26],[7, 20, 26],[23],[9],[12, 14]] CAI: [[6, 12],[1, 4, 15],[2, 5, 26],[7, 20, 26],[23],[9],[12, 14]]
SELECTED_FEATURES: AU,POSE SELECTED_FEATURES: AU,POSE
face_expr_dir: /video/face_expressivity face_expr_dir: /video/face_expressivity
face_asym_dir: /video/face_asymmetry face_asym_dir: /video/face_asymmetry
AU_filters: ['frame', ' face_id', ' timestamp', ' confidence', ' success', ' AU01_r',' AU02_r',' AU04_r',' AU05_r', AU_filters: ['frame', ' face_id', ' timestamp', ' confidence', ' success', ' AU01_r',' AU02_r',' AU04_r',' AU05_r',
' AU06_r', ' AU07_r', ' AU09_r', ' AU10_r', ' AU12_r', ' AU14_r', ' AU15_r', ' AU17_r', ' AU20_r', ' AU06_r', ' AU07_r', ' AU09_r', ' AU10_r', ' AU12_r', ' AU14_r', ' AU15_r', ' AU17_r', ' AU20_r',
' AU25_r', ' AU26_r', ' AU45_r', ' AU01_c', ' AU02_c', ' AU04_c', ' AU05_c', ' AU06_c', ' AU07_c', ' AU25_r', ' AU26_r', ' AU45_r', ' AU01_c', ' AU02_c', ' AU04_c', ' AU05_c', ' AU06_c', ' AU07_c',
' AU10_c', ' AU12_c', ' AU14_c', ' AU15_c', ' AU17_c', ' AU20_c', ' AU23_c', ' AU25_c', ' AU26_c', ' AU10_c', ' AU12_c', ' AU14_c', ' AU15_c', ' AU17_c', ' AU20_c', ' AU23_c', ' AU25_c', ' AU26_c',
' AU28_c', ' AU45_c',' AU09_c',' AU23_r' ] ' AU28_c', ' AU45_c',' AU09_c',' AU23_r' ]
au_intensity: [' AU01_r',' AU02_r',' AU04_r',' AU05_r', ' AU06_r', ' AU07_r', ' AU09_r', ' AU10_r', ' AU12_r', au_intensity: [' AU01_r',' AU02_r',' AU04_r',' AU05_r', ' AU06_r', ' AU07_r', ' AU09_r', ' AU10_r', ' AU12_r',
' AU14_r', ' AU15_r', ' AU17_r', ' AU20_r',' AU23_r', ' AU25_r', ' AU26_r', ' AU45_r'] ' AU14_r', ' AU15_r', ' AU17_r', ' AU20_r',' AU23_r', ' AU25_r', ' AU26_r', ' AU45_r']
au_presence: [' AU01_c', ' AU02_c', ' AU04_c', ' AU05_c', ' AU06_c', ' AU07_c', ' AU09_c', ' AU10_c', ' AU12_c', au_presence: [' AU01_c', ' AU02_c', ' AU04_c', ' AU05_c', ' AU06_c', ' AU07_c', ' AU09_c', ' AU10_c', ' AU12_c',
' AU14_c', ' AU15_c', ' AU17_c', ' AU20_c', ' AU23_c', ' AU25_c', ' AU26_c', ' AU45_c'] ' AU14_c', ' AU15_c', ' AU17_c', ' AU20_c', ' AU23_c', ' AU25_c', ' AU26_c', ' AU45_c']