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preprocessing.py
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preprocessing.py
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from utils.preprocessing import GenericVideoPreprocessing
from utils.utils import expand_index_by_multiplier, load_pickle, save_to_pickle, get_filename_from_a_folder_given_extension, ensure_dir
import os
import scipy.io as sio
import pandas as pd
import numpy as np
class Preprocessing(GenericVideoPreprocessing):
def __init__(self, config):
super().__init__(config)
def generate_iterator(self):
path = os.path.join(self.config['root_directory'], self.config['raw_data_folder'])
iterator = [os.path.join(path, file) for file in sorted(os.listdir(path), key=float)]
return iterator
def generate_per_trial_info_dict(self):
per_trial_info_path = os.path.join(self.config['output_root_directory'], "processing_records.pkl")
if os.path.isfile(per_trial_info_path):
per_trial_info = load_pickle(per_trial_info_path)
else:
per_trial_info = {}
pointer = 0
sub_trial_having_continuous_label = self.get_sub_trial_info_for_continuously_labeled()
all_continuous_labels = self.read_all_continuous_label()
iterator = self.generate_iterator()
for idx, file in enumerate(iterator):
kwargs = {}
this_trial = {}
print(file)
session = int(file.split(os.sep)[-1])
subject_no, trial_no = session // 130 + 1, session % 130
this_trial['has_continuous_label'] = 0
if subject_no == sub_trial_having_continuous_label[pointer][0] and trial_no == sub_trial_having_continuous_label[pointer][1]:
this_trial['has_continuous_label'] = 1
if not this_trial['has_continuous_label']:
continue
time_stamp_file = get_filename_from_a_folder_given_extension(file, "tsv", "All-Data")[0]
video_trim_range = self.read_start_end_from_mahnob_tsv(time_stamp_file)
if video_trim_range is not None:
this_trial['video_trim_range'] = video_trim_range
else:
this_trial['discard'] = 1
continue
this_trial['continuous_label'] = None
this_trial['annotated_index'] = None
annotated_index = np.arange(this_trial['video_trim_range'][0][1])
if this_trial['has_continuous_label']:
raw_continuous_label = all_continuous_labels[pointer]
this_trial['continuous_label'] = raw_continuous_label
annotated_index = self.process_continuous_label(raw_continuous_label)
this_trial['annotated_index'] = annotated_index
pointer += 1
this_trial['video_path'] = get_filename_from_a_folder_given_extension(file, "avi")[0].split(os.sep)
this_trial['extension'] = "mp4"
# Some trials has no EEG recordings
this_trial['has_eeg'] = 1
eeg_path = get_filename_from_a_folder_given_extension(file, "bdf")
if len(eeg_path) == 1:
this_trial['eeg_path'] = eeg_path[0].split(os.sep)
else:
this_trial['eeg_path'] = None
this_trial['has_eeg'] = 0
this_trial['audio_path'] = ""
this_trial['subject_no'] = subject_no
this_trial['trial_no'] = trial_no
this_trial['trial'] = "P{}-T{}".format(str(subject_no), str(trial_no))
this_trial['target_fps'] = 64
kwargs['feature'] = "video"
kwargs['has_continuous_label'] = this_trial['has_continuous_label']
this_trial['video_annotated_index'] = self.get_annotated_index(annotated_index, **kwargs)
per_trial_info[idx] = this_trial
save_to_pickle(per_trial_info_path, per_trial_info)
self.per_trial_info = per_trial_info
def generate_dataset_info(self):
for idx, record in self.per_trial_info.items():
self.dataset_info['trial'].append(record['processing_record']['trial'])
self.dataset_info['trial_no'].append(record['trial_no'])
self.dataset_info['subject_no'].append(record['subject_no'])
self.dataset_info['has_continuous_label'].append(record['has_continuous_label'])
self.dataset_info['has_eeg'].append(record['has_eeg'])
if record['has_continuous_label']:
self.dataset_info['length'].append(len(record['continuous_label']))
else:
self.dataset_info['length'].append(len(record['video_annotated_index']) // 16)
self.dataset_info['multiplier'] = self.config['multiplier']
self.dataset_info['data_folder'] = self.config['npy_folder']
path = os.path.join(self.config['output_root_directory'], 'dataset_info.pkl')
save_to_pickle(path, self.dataset_info)
def extract_continuous_label_fn(self, idx, npy_folder):
if self.per_trial_info[idx]["has_continuous_label"]:
raw_continuous_label = self.per_trial_info[idx]['continuous_label']
if self.config['save_npy']:
filename = os.path.join(npy_folder, "continuous_label.npy")
if not os.path.isfile(filename):
ensure_dir(filename)
np.save(filename, raw_continuous_label)
def load_continuous_label(self, path, **kwargs):
cols = [emotion.lower() for emotion in self.config['emotion_list']]
if os.path.isfile(path):
continuous_label = pd.read_csv(path, sep=";",
skipinitialspace=True, usecols=cols,
index_col=False).values.squeeze()
else:
continuous_label = 0
return continuous_label
def get_annotated_index(self, annotated_index, **kwargs):
feature = kwargs['feature']
multiplier = self.config['multiplier'][feature]
if kwargs['has_continuous_label']:
annotated_index = expand_index_by_multiplier(annotated_index, multiplier)
# If the trial is not continuously labeled, then the whole facial video is used.
else:
pass
return annotated_index
def get_sub_trial_info_for_continuously_labeled(self):
label_file = os.path.join(self.config['root_directory'], "lable_continous_Mahnob.mat")
mat_content = sio.loadmat(label_file)
sub_trial_having_continuous_label = mat_content['trials_included']
return sub_trial_having_continuous_label
@staticmethod
def read_start_end_from_mahnob_tsv(tsv_file):
if os.path.isfile(tsv_file):
data = pd.read_csv(tsv_file, sep='\t', skiprows=23)
end = data[data['Event'] == 'MovieEnd'].index[0]
start_end = [(0, end)]
else:
start_end = None
return start_end
def read_all_continuous_label(self):
r"""
:return: the continuous labels for each trial (dict).
"""
label_file = os.path.join(self.config['root_directory'], "lable_continous_Mahnob.mat")
mat_content = sio.loadmat(label_file)
annotation_cell = np.squeeze(mat_content['labels'])
label_list = []
for index in range(len(annotation_cell)):
label_list.append(annotation_cell[index].T)
return label_list
@staticmethod
def init_dataset_info():
dataset_info = {
"trial": [],
"subject_no": [],
"trial_no": [],
"length": [],
"has_continuous_label": [],
"has_eeg": [],
}
return dataset_info
if __name__ == "__main__":
from configs import config
pre = Preprocessing(config)
pre.generate_per_trial_info_dict()
pre.prepare_data()