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added lower and upper half composite exp

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This commit is contained in:
Vidya Koesmahargyo
2020-12-03 10:16:36 -05:00
parent c190d6f86b
commit 847e01fc7c
6 changed files with 101 additions and 82 deletions
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46
dbm_lib/dbm_features/raw_features/util/video_util.py
View File

@@ -11,32 +11,32 @@ from dbm_lib.dbm_features.raw_features.util import util as ut
def smooth(x,window_len=11,window='hanning'):
"""smooth the data using a window with requested size.
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
in the begining and end part of the output signal.
input:
x: the input signal
x: the input signal
window_len: the dimension of the smoothing window; should be an odd integer
window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'
flat window will produce a moving average smoothing.
output:
the smoothed signal
example:
t=linspace(-2,2,0.1)
x=sin(t)+randn(len(t))*0.1
y=smooth(x)
see also:
see also:
numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve
scipy.signal.lfilter
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.
"""
@@ -62,7 +62,7 @@ def filter_by_confidence_and_thresh(x, fea, thresh):
return x[fea]
else:
return np.NaN
def add_au_emotion(x, emotion,emotion_type,exp_type):
"""
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
sum_r += x[au_r_label]
cnt += 6
if exp_type=='full' and x[au_c_label]==0: #Logic to compute emotion expressivity when all AU's are present
cnt = 0
if exp_type=='full' and x[au_c_label]==0: #Logic to compute emotion expressivity when all AU's are present
cnt = 0
break
if cnt > 0:
if cnt > 0:
sum_r /= cnt
else:
sum_r = 0
v_emo = x[emotion_type] + sum_r
v_emo = x[emotion_type] + sum_r
else:
v_emo = np.NaN
error_reason = 'confidence less than 80%'
return v_emo, error_reason
def add_au_occ(x, emotion,emotion_type):
"""
computing individula emotion presence
@@ -107,14 +107,14 @@ def add_au_occ(x, emotion,emotion_type):
au_c_label = " AU{:02d}_c".format(au)
if x[au_c_label]==1: #there are data with face in, but au_c=0
au_pres.append(1)
if len(au_pres) == len(emotion):
em_pres = 1
else:
em_pres = np.NaN
error_reason = 'confidence less than 80%'
return em_pres, error_reason
def emotion_exp(em_au,of,em_col,err_col):
"""
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:
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')
def emotion_pres(em_au,of,em_col,err_col):
"""
Computing individual emotion expressivity and adding it to dataframe
"""
for emotion in em_au:
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):
"""
Creating dataframe for emotion expressivity
@@ -142,7 +142,7 @@ def calc_of_for_video(of,face_cfg,fe_cfg):
fe_cfg.com_exp_full]
of[new_cols] = pd.DataFrame([[0] * len(new_cols)], index=of.index)
of[fe_cfg.err_reason] = 'Pass'
#Composite happiness expressivity
emotion_exp(face_cfg.happiness,of,[fe_cfg.hap_exp,fe_cfg.hap_exp_full],fe_cfg.err_reason)
#Composite sadness expressivity
@@ -167,6 +167,10 @@ 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)
#Composite Expressivity
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)
#AU happiness presence
emotion_pres(face_cfg.happiness,of,fe_cfg.happ_occ,fe_cfg.err_reason)
#AU Sad presence

12
dbm_lib/dbm_features/raw_features/video/face_config/face_config_reader.py
View File

@@ -18,18 +18,20 @@ class ConfigFaceReader(object):
Args:
service_config_yml (None, optional): yml file defined service configuration
"""
if service_config_yml is None:
service_config = DBMLIB_FACE_CONFIG
else:
service_config = service_config_yml
with open(service_config, 'r') as ymlfile:
config = yaml.load(ymlfile)
self.ACTION_UNITS = config['cdx_face_config']['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.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']['LOWER_ACTION_UNITS']
self.happiness = config['cdx_face_config']['happiness']
self.sadness = config['cdx_face_config']['sadness']
self.surprise = config['cdx_face_config']['surprise']
@@ -44,7 +46,7 @@ class ConfigFaceReader(object):
self.AU_fl = config['cdx_face_config']['AU_filters']
self.au_int = config['cdx_face_config']['au_intensity']
self.au_prs = config['cdx_face_config']['au_presence']
def get_action_unit(self):
"""Summary
Returns:
@@ -134,4 +136,4 @@ class ConfigFaceReader(object):
Returns:
TYPE: end point
"""
return self.cai
return self.cai