def extract_all_labels(sbj_id, day, src_folder, vid_folder, dst_folder):
labels=pd.read_csv(src_folder + sbj_id + "_" + str(day) + ".txt", sep=':')
tracks = ["Laughing", "Movement.Head", "Movement.Other",
"Movement.arm", "Movement.arm", "Speaking", "Multiple_people",
"Sleeping","Eating", "Listening.Watching_Media",
"Listening.Listening_to_family_member",
"Listening.Listening_to_staff", "Rest"]
pretty_tracks = ["Laugh", "Head_mv", "Other_mv",
"Arm_mv_right", "Arm_mv_left" "Speak", "Mult_ppl",
"Sleep","Eat", "Watch",
"Listen_fam",
"Listen_staff", "Rest"]
for i in xrange(800):
file_num = str(i).zfill(4)
sbj_indexes = np.hstack([np.where(np.array(labels.filename)== file_num)[0],
np.where(np.array(labels.filename)== file_num + '-rs')[0]])
if sbj_indexes.shape[0]>0:
vid_length = get_len(vid_folder + "\\" + sbj_id
+ "_" + str(day) + "\\" + sbj_id
+ "_" + str(day) + "_" + file_num + ".avi")
result = convert_labels_to_array(labels,sbj_indexes, vid_length, tracks, pretty_tracks)
pickle.dump(result, open(dst_folder + "\\" + sbj_id +"_" + day + "_" + file_num + ".p", "wb"))
def extract_detailed_mvmt_labels(sbj_id, day, labels_file, vid_folder, dst_folder):
labels=pd.read_csv(labels_file, sep=' ', dtype=str)
tracks = ["Head", "Left.shoulder", "Left.elbow", "Left.wrist","Right.shoulder", "Right.elbow", "Right.wrist"]
pretty_tracks = tracks
for i in xrange(800):
file_num = "%s_%s_%s" %(sbj_id, day, str(i).zfill(4))
sbj_indexes = np.where(np.array(labels.filename)== file_num)[0]
#pdb.set_trace()
if sbj_indexes.shape[0] > 0:
vid_length = get_len("%s\\%s_%s_%04i.avi" % (vid_folder, sbj_id, day, i))
result = convert_detailed_mvmt_labels_to_array(labels,sbj_indexes, vid_length, tracks, pretty_tracks)
pickle.dump(result, open(dst_folder + "\\" + file_num + ".p", "wb"))
def extract_labeller_reduced_labels(sbj_id, day, src_folder, vid_folder, dst_folder, labeller):
labels=pd.read_csv(src_folder + sbj_id + "_" + str(day) + ".txt", sep=':', dtype=str)
tracks = ["Laughing", "Movement.Head", "Movement.Other",
"Movement.arm", "Speaking", "Multiple_people",
"Sleeping","Eating", "Listening.Watching_Media",
"Listening.Listening_to_family_member",
"Listening.Listening_to_staff", "Rest"]
tracks_reduced = ["Mvmt", "Sound", "Rest", "Other"]
for i in xrange(800):
file_num = str(i).zfill(4)
sbj_indexes = np.where(np.array(labels.filename)== file_num + '-' + labeller)[0]
if sbj_indexes.shape[0]>0:
vid_length = get_len(vid_folder + "\\" + sbj_id
+ "_" + str(day) + "\\" + sbj_id
+ "_" + str(day) + "_" + file_num + ".avi")
result = convert_reduced_labels_to_array(labels,sbj_indexes, vid_length, tracks, tracks_reduced)
pickle.dump(result, open(dst_folder + "\\" + sbj_id + "_" + str(day) +
"_" + file_num + "_" + labeller + ".p", "wb"))
