def build_audioFAUsFeatures_downsampledDataset_test(path, downsamplingFactor):
"""
FUNCTION NAME: build_audioFAUsFeatures_downsampledDataset
This function creates audio+FAUs features .csv files so
they can be used in the machine learning stage of our
working pipeline. This function is aimed to be used when the
dataset available is reduced by a certain factor. This function
is aimed to be used when generating features data from the
testing set.
INPUT:
------
-> path: path to data
-> downsamplingFactor: factor by which the original dataset
is downsampled
OUTPUT:
-------
"""
features = []
labels = []
header = True
headerStringFeatures = ['subjectID', 'storyID', 'frameID', \
'f_1', 'f_2', 'f_3','f_4', 'f_5', 'f_6', 'f_7', 'f_8', 'f_9', 'f_10', \
'f_11', 'f_12', 'f_13', 'f_14', 'f_15', 'f_16', 'f_17', 'f_18', 'f_19', 'f_20', \
'f_21', 'f_22', 'f_23', 'f_24', 'f_25', 'f_26', 'f_27', 'f_28', 'f_29', 'f_30', \
'f_31', 'f_32', 'f_33', 'f_34', 'f_35', 'f_36', 'f_37', 'f_38', 'f_39', 'f_40', \
'f_41', 'f_42', 'f_43', 'f_44', 'f_45', 'f_46', 'f_47', 'f_48', 'f_49', 'f_50', \
'f_51', 'f_52', 'f_53', 'f_54', 'f_55', 'f_56', 'f_57', 'f_58', 'f_59', 'f_60', \
'f_61', 'f_62', 'f_63', 'f_64', 'f_65', 'f_66', 'f_67', 'f_68', 'f_69', 'f_70', \
'f_71', 'f_72', 'f_73', 'f_74', 'f_75', 'f_76', 'f_77', 'f_78', 'f_79', 'f_80', \
'f_81', 'f_82', 'f_83', 'f_84', 'f_85', 'f_86', 'f_87', 'f_88', \
'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']
outputFeaturesFile = path + '/DataMatrices/openSMILE_FAUs_downsamplingFactor' + str(
downsamplingFactor) + '_features.csv'
folders = sorted(os.listdir(path + '/AudioFeatures'))
for folder in folders:
if (os.path.isdir(path + '/AudioFeatures' + '/' + folder)) and (len(folder.split('_')) == 5) and \
(folder.split('_')[4] == 'downsampledDataBy' + str(downsamplingFactor)):
print 'Building data for: ' + folder + ' ...'
files = sorted(os.listdir(path + '/AudioFeatures' + '/' + folder))
# FAU features path
FAUpath = path + '/VideoFeatures/' + folder.split(
'_downsampledDataBy')[0] + '.csv'
subjectID = int(folder.split('_')[1])
storyID = int(folder.split('_')[3])
print ' Reading openSMILE features information ...'
openSMILEfeatures = []
for file in files:
if file.endswith('.csv'):
openSMILEpath = path + '/AudioFeatures/' + folder + '/' + file
# Read features information
currentFeat = DE.read_openSMILEfeatures(openSMILEpath)
# Reshape features information
currentFeat = DE.reshape_openSMILEfeaturesVector(
currentFeat)
# Concatenate openSMILE features information
if len(openSMILEfeatures) == 0:
openSMILEfeatures = currentFeat
else:
openSMILEfeatures = DE.incrementalMatrix(
openSMILEfeatures, currentFeat)
print ' Reading FAUs features information ...'
# Read FAUs information
currentFAUs = DE.read_FAUs(FAUpath)
# Select the proper instances according to the downsampling
dwIDs = DE.get_downsamplingIDs(currentFAUs, downsamplingFactor)
subjectID, storyID, framesID = DE.get_infoFromFAUs(
currentFAUs, subjectID, storyID)
# Concatenate openSMILE + FAUs features information
features = DE.concatenate_info_openSMILE_FAUs(
subjectID[dwIDs, :], storyID[dwIDs, :], framesID[dwIDs, :],
openSMILEfeatures, currentFAUs[dwIDs, :])
# Write data in .csv file
if header == True:
f = pd.DataFrame(features)
f.to_csv(outputFeaturesFile,
header=headerStringFeatures,
float_format='%e',
index=False,
mode='w')
else:
f = pd.DataFrame(features)
f.to_csv(outputFeaturesFile,
header=header,
float_format='%e',
index=False,
mode='a')
header = False
def build_audioFeatures(path):
"""
FUNCTION NAME: build_audioFeatures
This function creates audio features and label csv files, so
they can be used in the machine learning stage of our
working pipeline.
INPUT:
------
-> path: path to data
OUTPUT:
-------
"""
features = []
labels = []
header = True
headerStringFeatures = ['subjectID', 'storyID', 'frameID', \
'f_1', 'f_2', 'f_3','f_4', 'f_5', 'f_6', 'f_7', 'f_8', 'f_9', 'f_10', \
'f_11', 'f_12', 'f_13', 'f_14', 'f_15', 'f_16', 'f_17', 'f_18', 'f_19', 'f_20', \
'f_21', 'f_22', 'f_23', 'f_24', 'f_25', 'f_26', 'f_27', 'f_28', 'f_29', 'f_30', \
'f_31', 'f_32', 'f_33', 'f_34', 'f_35', 'f_36', 'f_37', 'f_38', 'f_39', 'f_40', \
'f_41', 'f_42', 'f_43', 'f_44', 'f_45', 'f_46', 'f_47', 'f_48', 'f_49', 'f_50', \
'f_51', 'f_52', 'f_53', 'f_54', 'f_55', 'f_56', 'f_57', 'f_58', 'f_59', 'f_60', \
'f_61', 'f_62', 'f_63', 'f_64', 'f_65', 'f_66', 'f_67', 'f_68', 'f_69', 'f_70', \
'f_71', 'f_72', 'f_73', 'f_74', 'f_75', 'f_76', 'f_77', 'f_78', 'f_79', 'f_80', \
'f_81', 'f_82', 'f_83', 'f_84', 'f_85', 'f_86', 'f_87', 'f_88']
headerStringLabels = ['subjectID', 'storyID', 'frameID', 'valence']
outputFeaturesFile = path + '/DataMatrices/openSMILE_features.csv'
outputLabelsFile = path + '/DataMatrices/openSMILE_labels.csv'
folders = sorted(os.listdir(path + '/AudioFeatures'))
for folder in folders:
if os.path.isdir(path + '/AudioFeatures' + '/' + folder):
print 'Building data for: ' + folder + ' ...'
files = sorted(os.listdir(path + '/AudioFeatures' + '/' + folder))
for file in files:
if file.endswith('.csv'):
featuresFile = path + '/AudioFeatures/' + folder + '/' + file
labelsFile = path + '/Annotations/' + folder + '.csv'
frameNumber = int(file.split('.csv')[0][-6:])
subjectID = int(folder.split('_')[1])
storyID = int(folder.split('_')[3])
# Read features information
currentFeat = DE.read_openSMILEfeatures(featuresFile)
# Reshape features information
currentFeat = DE.reshape_openSMILEfeaturesVector(
currentFeat)
# Concatenate features information
features = DE.concatenate_info(subjectID, storyID,
frameNumber, currentFeat)
# Read labels information
annotation = DE.get_annotationFromFrameNumber(
labelsFile, frameNumber)
# Reshape labels information
annotation = DE.reshape_annotation(annotation)
# Concatenate labels information
labels = DE.concatenate_info(subjectID, storyID,
frameNumber, annotation)
# Write data in .csv file
if header == True:
f = pd.DataFrame(features)
f.to_csv(outputFeaturesFile,
header=headerStringFeatures,
float_format='%e',
index=False,
mode='w')
l = pd.DataFrame(labels)
l.to_csv(outputLabelsFile,
header=headerStringLabels,
index=False,
mode='w')
else:
f = pd.DataFrame(features)
f.to_csv(outputFeaturesFile,
header=header,
float_format='%e',
index=False,
mode='a')
l = pd.DataFrame(labels)
l.to_csv(outputLabelsFile,
header=header,
index=False,
mode='a')
header = False
def build_audioFAUsFeatures(path):
"""
FUNCTION NAME: build_audioFAUsFeatures
This function creates openSMILE and FAUs features and
label .csv files so they can be used in the machine
learning stage of our working pipeline.
INPUT:
------
-> path: path to data
OUTPUT:
-------
"""
features = []
labels = []
header = True
headerStringFeatures = ['subjectID', 'storyID', 'frameID', \
'f_1', 'f_2', 'f_3','f_4', 'f_5', 'f_6', 'f_7', 'f_8', 'f_9', 'f_10', \
'f_11', 'f_12', 'f_13', 'f_14', 'f_15', 'f_16', 'f_17', 'f_18', 'f_19', 'f_20', \
'f_21', 'f_22', 'f_23', 'f_24', 'f_25', 'f_26', 'f_27', 'f_28', 'f_29', 'f_30', \
'f_31', 'f_32', 'f_33', 'f_34', 'f_35', 'f_36', 'f_37', 'f_38', 'f_39', 'f_40', \
'f_41', 'f_42', 'f_43', 'f_44', 'f_45', 'f_46', 'f_47', 'f_48', 'f_49', 'f_50', \
'f_51', 'f_52', 'f_53', 'f_54', 'f_55', 'f_56', 'f_57', 'f_58', 'f_59', 'f_60', \
'f_61', 'f_62', 'f_63', 'f_64', 'f_65', 'f_66', 'f_67', 'f_68', 'f_69', 'f_70', \
'f_71', 'f_72', 'f_73', 'f_74', 'f_75', 'f_76', 'f_77', 'f_78', 'f_79', 'f_80', \
'f_81', 'f_82', 'f_83', 'f_84', 'f_85', 'f_86', 'f_87', 'f_88', \
'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']
headerStringLabels = ['subjectID', 'storyID', 'frameID', 'valence']
outputFeaturesFile = path + '/DataMatrices/openSMILE_FAUs_features.csv'
outputLabelsFile = path + '/DataMatrices/openSMILE_FAUs_labels.csv'
folders = sorted(os.listdir(path + '/AudioFeatures'))
for folder in folders:
if os.path.isdir(path + '/AudioFeatures' + '/' + folder):
print 'Building data for: ' + folder + ' ...'
files = sorted(os.listdir(path + '/AudioFeatures' + '/' + folder))
# FAU features path
FAUpath = path + '/VideoFeatures/' + folder + '.csv'
# Annotations path
valencePath = path + '/Annotations/' + folder + '.csv'
subjectID = int(folder.split('_')[1])
storyID = int(folder.split('_')[3])
print ' Reading openSMILE features information ...'
openSMILEfeatures = []
for file in files:
if file.endswith('.csv'):
openSMILEpath = path + '/AudioFeatures/' + folder + '/' + file
# Read features information
currentFeat = DE.read_openSMILEfeatures(openSMILEpath)
# Reshape features information
currentFeat = DE.reshape_openSMILEfeaturesVector(
currentFeat)
# Concatenate openSMILE features information
if len(openSMILEfeatures) == 0:
openSMILEfeatures = currentFeat
else:
openSMILEfeatures = DE.incrementalMatrix(
openSMILEfeatures, currentFeat)
print ' Reading FAUs features information ...'
# Read FAUs information
currentFAUs = DE.read_FAUs(FAUpath)
subjectID, storyID, framesID = DE.get_infoFromFAUs(
currentFAUs, subjectID, storyID)
print ' Reading valence labels information ...'
# Read valence labels information
annotations = DE.get_annotationsFromFile(valencePath)
# Concatenate labels information
labels = DE.concatenate_info_FAUs(subjectID, storyID, framesID,
annotations)
# Concatenate openSMILE + FAUs features information
features = DE.concatenate_info_openSMILE_FAUs(
subjectID, storyID, framesID, openSMILEfeatures, currentFAUs)
# Write data in .csv file
if header == True:
f = pd.DataFrame(features)
f.to_csv(outputFeaturesFile,
header=headerStringFeatures,
float_format='%e',
index=False,
mode='w')
l = pd.DataFrame(labels)
l.to_csv(outputLabelsFile,
header=headerStringLabels,
index=False,
mode='w')
else:
f = pd.DataFrame(features)
f.to_csv(outputFeaturesFile,
header=header,
float_format='%e',
index=False,
mode='a')
l = pd.DataFrame(labels)
l.to_csv(outputLabelsFile,
header=header,
index=False,
mode='a')
header = False
def build_audioFeatures_downsampledDataset_test(path, downsamplingFactor):
"""
FUNCTION NAME: build_audioFeatures_downsampledDataset_test
This function creates audio features as csv files, so
they can be used in the machine learning stage of our
working pipeline. This function is aimed to be used when the
dataset available is reduced by a certain factor and when
preparing the features to be used during test.
INPUT:
------
-> path: path to data
-> downsamplingFactor: factor by which the original dataset
is downsampled
OUTPUT:
-------
"""
features = []
labels = []
header = True
headerStringFeatures = ['subjectID', 'storyID', 'frameID', \
'f_1', 'f_2', 'f_3','f_4', 'f_5', 'f_6', 'f_7', 'f_8', 'f_9', 'f_10', \
'f_11', 'f_12', 'f_13', 'f_14', 'f_15', 'f_16', 'f_17', 'f_18', 'f_19', 'f_20', \
'f_21', 'f_22', 'f_23', 'f_24', 'f_25', 'f_26', 'f_27', 'f_28', 'f_29', 'f_30', \
'f_31', 'f_32', 'f_33', 'f_34', 'f_35', 'f_36', 'f_37', 'f_38', 'f_39', 'f_40', \
'f_41', 'f_42', 'f_43', 'f_44', 'f_45', 'f_46', 'f_47', 'f_48', 'f_49', 'f_50', \
'f_51', 'f_52', 'f_53', 'f_54', 'f_55', 'f_56', 'f_57', 'f_58', 'f_59', 'f_60', \
'f_61', 'f_62', 'f_63', 'f_64', 'f_65', 'f_66', 'f_67', 'f_68', 'f_69', 'f_70', \
'f_71', 'f_72', 'f_73', 'f_74', 'f_75', 'f_76', 'f_77', 'f_78', 'f_79', 'f_80', \
'f_81', 'f_82', 'f_83', 'f_84', 'f_85', 'f_86', 'f_87', 'f_88']
headerStringLabels = ['subjectID', 'storyID', 'frameID', 'valence']
outputFeaturesFile = path + '/DataMatrices/openSMILE_downsamplingFactor' + str(
downsamplingFactor) + '_features.csv'
folders = sorted(os.listdir(path + '/AudioFeatures'))
for folder in folders:
if (os.path.isdir(path + '/AudioFeatures' + '/' + folder)) and (len(folder.split('_')) == 5) and \
(folder.split('_')[4] == 'downsampledDataBy' + str(downsamplingFactor)):
print 'Building data for: ' + folder + ' ...'
files = sorted(os.listdir(path + '/AudioFeatures' + '/' + folder))
for file in files:
if file.endswith('.csv'):
featuresFile = path + '/AudioFeatures/' + folder + '/' + file
frameNumber = int(file.split('.csv')[0][-6:])
subjectID = int(folder.split('_')[1])
storyID = int(folder.split('_')[3])
# Read features information
currentFeat = DE.read_openSMILEfeatures(featuresFile)
# Reshape features information
currentFeat = DE.reshape_openSMILEfeaturesVector(
currentFeat)
# Concatenate features information
features = DE.concatenate_info(
subjectID, storyID,
int(downsamplingFactor * frameNumber), currentFeat)
# Write data in .csv file
if header == True:
f = pd.DataFrame(features)
f.to_csv(outputFeaturesFile,
header=headerStringFeatures,
float_format='%e',
index=False,
mode='w')
else:
f = pd.DataFrame(features)
f.to_csv(outputFeaturesFile,
header=header,
float_format='%e',
index=False,
mode='a')
header = False