def read_affectiva(affectivafile, orig_cols=False):
"""
This function reads in affectiva file processed through
the https://github.com/cosanlab/affectiva-api-app.
Args:
affectivafile: file to read
orig_cols: If True, convert original colnames to FACS names
"""
d = pd.read_json(affectivafile, lines=True)
rep_dict = {
"anger": "Anger",
"attention": "Attention",
"contempt": "Contempt",
"disgust": "Disgust",
"engagement": "Engagement",
"fear": "Fear",
"joy": "Joy",
"sadness": "Sadness",
"smirk": "Smirk",
"surprise": "Surprise",
"valence": "Valence",
"browFurrow": "AU04",
"smile": "AU12",
"browRaise": "AU02",
"cheekRaise": "AU06",
"chinRaise": "AU17",
"dimpler": "AU14",
"eyeClosure": "AU43",
"eyeWiden": "AU05",
"innerBrowRaise": "AU01",
"jawDrop": "AU26",
"lidTighten": "AU07",
"lipCornerDepressor": "AU15",
"lipPress": "AU24",
"lipPucker": "AU18",
"lipStretch": "AU20",
"lipSuck": "AU28",
"mouthOpen": "AU25",
"noseWrinkle": "AU09",
"upperLipRaise": "AU10",
}
affectiva_au_columns = [col for col in rep_dict.values() if "AU" in col]
affectiva_emotion_columns = list(
set(rep_dict.values()) - set(affectiva_au_columns))
if not orig_cols:
new_cols = []
for col in d.columns:
try:
new_cols.append(rep_dict[col])
except:
new_cols.append(col)
d.columns = new_cols
return feat.Fex(
d,
filename=affectivafile,
au_columns=affectiva_au_columns,
emotion_columns=affectiva_emotion_columns,
detector="Affectiva",
)
def read_affectiva(affectivafile, orig_cols=False):
'''
This function reads in affectiva file processed through
the https://github.com/cosanlab/affectiva-api-app.
Args:
affectivafile: file to read
orig_cols: If True, convert original colnames to FACS names
'''
d = pd.read_json(affectivafile, lines=True)
rep_dict = {
'anger': 'Anger',
'attention': 'Attention',
'contempt': 'Contempt',
'disgust': 'Disgust',
'engagement': 'Engagement',
'fear': 'Fear',
'joy': 'Joy',
'sadness': 'Sadness',
'smirk': 'Smirk',
'surprise': 'Surprise',
'valence': 'Valence',
'browFurrow': "AU04",
"smile": 'AU12',
'browRaise': 'AU02',
'cheekRaise': 'AU06',
'chinRaise': 'AU17',
'dimpler': 'AU14',
'eyeClosure': 'AU43',
'eyeWiden': 'AU05',
'innerBrowRaise': 'AU01',
'jawDrop': 'AU26',
'lidTighten': 'AU07',
'lipCornerDepressor': 'AU15',
'lipPress': 'AU24',
'lipPucker': 'AU18',
'lipStretch': 'AU20',
'lipSuck': 'AU28',
'mouthOpen': 'AU25',
'noseWrinkle': 'AU09',
'upperLipRaise': 'AU10'
}
affectiva_au_columns = [col for col in rep_dict.values() if 'AU' in col]
affectiva_emotion_columns = list(
set(rep_dict.values()) - set(affectiva_au_columns))
if not orig_cols:
new_cols = []
for col in d.columns:
try:
new_cols.append(rep_dict[col])
except:
new_cols.append(col)
d.columns = new_cols
return feat.Fex(d,
filename=affectivafile,
au_columns=affectiva_au_columns,
emotion_columns=affectiva_emotion_columns,
detector="Affectiva")
def read_feat(fexfile):
"""This function reads files extracted using the Detector from the Feat package.
Args:
fexfile: [description]
"""
d = pd.read_csv(fexfile)
return feat.Fex(d,
filename=fexfile,
au_columns=None,
emotion_columns=FEAT_EMOTION_COLUMNS,
facebox_columns=FEAT_FACEBOX_COLUMNS,
time_columns=FEAT_TIME_COLUMNS,
detector='Feat')
def read_feat(fexfile):
"""This function reads files extracted using the Detector from the Feat package.
Args:
fexfile: [description]
"""
d = pd.read_csv(fexfile)
return feat.Fex(
d,
filename=fexfile,
au_columns=jaanet_AU_presence,
emotion_columns=FEAT_EMOTION_COLUMNS,
landmark_columns=openface_2d_landmark_columns,
facebox_columns=FEAT_FACEBOX_COLUMNS,
time_columns=FEAT_TIME_COLUMNS,
detector="Feat",
)
def read_feat(fexfile):
"""This function reads files extracted using the Detector from the Feat package.
Args:
fexfile: Path to facial expression file.
Returns:
Fex of processed facial expressions
"""
d = pd.read_csv(fexfile)
au_columns = [col for col in d.columns if "AU" in col]
return feat.Fex(
d,
filename=fexfile,
au_columns=au_columns,
emotion_columns=FEAT_EMOTION_COLUMNS,
landmark_columns=openface_2d_landmark_columns,
facebox_columns=FEAT_FACEBOX_COLUMNS,
time_columns=FEAT_TIME_COLUMNS,
detector="Feat",
)
def read_openface(openfacefile, features=None):
"""
This function reads in an OpenFace exported facial expression file.
Args:
features: If a list of column names are passed, those are returned. Otherwise, default returns the following features:
['frame', 'timestamp', 'confidence', 'success', 'gaze_0_x',
'gaze_0_y', 'gaze_0_z', 'gaze_1_x', 'gaze_1_y', 'gaze_1_z',
'pose_Tx', 'pose_Ty', 'pose_Tz', 'pose_Rx', 'pose_Ry', 'pose_Rz',
'x_0', 'x_1', 'x_2', 'x_3', 'x_4', 'x_5', 'x_6', 'x_7', 'x_8',
'x_9', 'x_10', 'x_11', 'x_12', 'x_13', 'x_14', 'x_15', 'x_16',
'x_17', 'x_18', 'x_19', 'x_20', 'x_21', 'x_22', 'x_23', 'x_24',
'x_25', 'x_26', 'x_27', 'x_28', 'x_29', 'x_30', 'x_31', 'x_32',
'x_33', 'x_34', 'x_35', 'x_36', 'x_37', 'x_38', 'x_39', 'x_40',
'x_41', 'x_42', 'x_43', 'x_44', 'x_45', 'x_46', 'x_47', 'x_48',
'x_49', 'x_50', 'x_51', 'x_52', 'x_53', 'x_54', 'x_55', 'x_56',
'x_57', 'x_58', 'x_59', 'x_60', 'x_61', 'x_62', 'x_63', 'x_64',
'x_65', 'x_66', 'x_67', 'y_0', 'y_1', 'y_2', 'y_3', 'y_4', 'y_5',
'y_6', 'y_7', 'y_8', 'y_9', 'y_10', 'y_11', 'y_12', 'y_13', 'y_14',
'y_15', 'y_16', 'y_17', 'y_18', 'y_19', 'y_20', 'y_21', 'y_22',
'y_23', 'y_24', 'y_25', 'y_26', 'y_27', 'y_28', 'y_29', 'y_30',
'y_31', 'y_32', 'y_33', 'y_34', 'y_35', 'y_36', 'y_37', 'y_38',
'y_39', 'y_40', 'y_41', 'y_42', 'y_43', 'y_44', 'y_45', 'y_46',
'y_47', 'y_48', 'y_49', 'y_50', 'y_51', 'y_52', 'y_53', 'y_54',
'y_55', 'y_56', 'y_57', 'y_58', 'y_59', 'y_60', 'y_61', 'y_62',
'y_63', 'y_64', 'y_65', 'y_66', 'y_67', 'X_0', 'X_1', 'X_2', 'X_3',
'X_4', 'X_5', 'X_6', 'X_7', 'X_8', 'X_9', 'X_10', 'X_11', 'X_12',
'X_13', 'X_14', 'X_15', 'X_16', 'X_17', 'X_18', 'X_19', 'X_20',
'X_21', 'X_22', 'X_23', 'X_24', 'X_25', 'X_26', 'X_27', 'X_28',
'X_29', 'X_30', 'X_31', 'X_32', 'X_33', 'X_34', 'X_35', 'X_36',
'X_37', 'X_38', 'X_39', 'X_40', 'X_41', 'X_42', 'X_43', 'X_44',
'X_45', 'X_46', 'X_47', 'X_48', 'X_49', 'X_50', 'X_51', 'X_52',
'X_53', 'X_54', 'X_55', 'X_56', 'X_57', 'X_58', 'X_59', 'X_60',
'X_61', 'X_62', 'X_63', 'X_64', 'X_65', 'X_66', 'X_67', 'Y_0',
'Y_1', 'Y_2', 'Y_3', 'Y_4', 'Y_5', 'Y_6', 'Y_7', 'Y_8', 'Y_9',
'Y_10', 'Y_11', 'Y_12', 'Y_13', 'Y_14', 'Y_15', 'Y_16', 'Y_17',
'Y_18', 'Y_19', 'Y_20', 'Y_21', 'Y_22', 'Y_23', 'Y_24', 'Y_25',
'Y_26', 'Y_27', 'Y_28', 'Y_29', 'Y_30', 'Y_31', 'Y_32', 'Y_33',
'Y_34', 'Y_35', 'Y_36', 'Y_37', 'Y_38', 'Y_39', 'Y_40', 'Y_41',
'Y_42', 'Y_43', 'Y_44', 'Y_45', 'Y_46', 'Y_47', 'Y_48', 'Y_49',
'Y_50', 'Y_51', 'Y_52', 'Y_53', 'Y_54', 'Y_55', 'Y_56', 'Y_57',
'Y_58', 'Y_59', 'Y_60', 'Y_61', 'Y_62', 'Y_63', 'Y_64', 'Y_65',
'Y_66', 'Y_67', 'Z_0', 'Z_1', 'Z_2', 'Z_3', 'Z_4', 'Z_5', 'Z_6',
'Z_7', 'Z_8', 'Z_9', 'Z_10', 'Z_11', 'Z_12', 'Z_13', 'Z_14', 'Z_15',
'Z_16', 'Z_17', 'Z_18', 'Z_19', 'Z_20', 'Z_21', 'Z_22', 'Z_23',
'Z_24', 'Z_25', 'Z_26', 'Z_27', 'Z_28', 'Z_29', 'Z_30', 'Z_31',
'Z_32', 'Z_33', 'Z_34', 'Z_35', 'Z_36', 'Z_37', 'Z_38', 'Z_39',
'Z_40', 'Z_41', 'Z_42', 'Z_43', 'Z_44', 'Z_45', 'Z_46', 'Z_47',
'Z_48', 'Z_49', 'Z_50', 'Z_51', 'Z_52', 'Z_53', 'Z_54', 'Z_55',
'Z_56', 'Z_57', 'Z_58', 'Z_59', 'Z_60', 'Z_61', 'Z_62', 'Z_63',
'Z_64', 'Z_65', 'Z_66', 'Z_67', 'p_scale', 'p_rx', 'p_ry', 'p_rz',
'p_tx', 'p_ty', 'p_0', 'p_1', 'p_2', 'p_3', 'p_4', 'p_5', 'p_6',
'p_7', 'p_8', 'p_9', 'p_10', 'p_11', 'p_12', 'p_13', 'p_14', 'p_15',
'p_16', 'p_17', 'p_18', 'p_19', 'p_20', 'p_21', 'p_22', 'p_23',
'p_24', 'p_25', 'p_26', 'p_27', 'p_28', 'p_29', 'p_30', 'p_31',
'p_32', 'p_33', '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',
'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', 'AU28_c', 'AU45_c']
Returns:
dataframe of processed facial expressions
"""
d = pd.read_csv(openfacefile, sep=",")
d.columns = d.columns.str.strip(" ")
# Check if features argument is passed and return only those features, else return basic emotion/AU features
if isinstance(features, list):
try:
d = d[features]
except:
raise KeyError([features, "not in openfacefile"])
elif isinstance(features, type(None)):
features = OPENFACE_ORIG_COLUMNS
try:
d = d[features]
except:
pass
return feat.Fex(
d,
filename=openfacefile,
au_columns=openface_AU_columns,
emotion_columns=None,
facebox_columns=None,
landmark_columns=openface_2d_landmark_columns,
facepose_columns=openface_facepose_columns,
gaze_columns=openface_gaze_columns,
time_columns=openface_time_columns,
detector="OpenFace",
)
def read_facet(facetfile, features=None, raw=False, sampling_freq=None):
"""This function reads in an iMotions-FACET exported facial expression file.
Args:
facetfile: iMotions-FACET file. Files from iMotions 5, 6, and 7 have been tested and supported
features: If a list of iMotion-FACET column names are passed, those are returned. Otherwise, default columns are returned in the following format:['Timestamp','FaceRectX','FaceRectY','FaceRectWidth','FaceRectHeight', 'Joy','Anger','Surprise','Fear','Contempt', 'Disgust','Sadness','Confusion','Frustration', 'Neutral','Positive','Negative','AU1','AU2', 'AU4','AU5','AU6','AU7','AU9','AU10', 'AU12','AU14','AU15','AU17','AU18','AU20', 'AU23','AU24','AU25','AU26','AU28','AU43', 'Yaw', 'Pitch', 'Roll']. Note that these column names are different from the original files which has ' Evidence', ' Degrees' appended to each column.
raw (default=False): Set to True to return all columns without processing.
sampling_freq: sampling frequency to pass to Fex
Returns:
dataframe of processed facial expressions
"""
# Check iMotions Version
versionstr = ""
try:
with open(facetfile, "r") as f:
studyname = f.readline().replace("\t", "").replace("\n", "")
studydate = f.readline().replace("\t", "").replace("\n", "")
versionstr = f.readline().replace("\t", "").replace("\n", "")
versionnum = int(versionstr.split(" ")[-1].split(".")[0])
except:
raise TypeError(
"Cannot infer version of iMotions-FACET file. Check to make sure this is the raw iMotions-FACET file."
)
d = pd.read_csv(facetfile, skiprows=5, sep="\t")
# Check if features argument is passed and return only those features, else return all columns
if isinstance(features, list):
try:
d = d[features]
if raw:
return feat.Fex(d, filename=facetfile)
except:
raise KeyError([features, "not in facetfile"])
elif isinstance(features, type(None)):
if raw:
return feat.Fex(d, filename=facetfile)
else:
fex_columns = [
col.replace(" Evidence", "").replace(" Degrees", "")
for col in d.columns if "Evidence" in col or "Degrees" in col
]
# Remove Intensity as this has been deprecated
cols2drop = [col for col in d.columns if "Intensity" in col]
d = d.drop(columns=cols2drop)
d.columns = [col.replace(" Evidence", "") for col in d.columns]
d.columns = [col.replace(" Degrees", "") for col in d.columns]
d.columns = [col.replace(" ", "") for col in d.columns]
# d._metadata = fex_columns
au_columns = [col for col in d.columns if "AU" in col]
return feat.Fex(
d,
filename=facetfile,
au_columns=au_columns,
emotion_columns=FACET_EMOTION_COLUMNS,
facebox_columns=FACET_FACEBOX_COLUMNS,
facepose_columns=FACET_FACEPOSE_COLUMNS,
time_columns=FACET_TIME_COLUMNS,
design_columns=FACET_DESIGN_COLUMNS,
detector="FACET",
sampling_freq=sampling_freq,
)