def k_fold(folder_k):
folder_k_fold = SAVE_SETS + "/K-fold"
if not os.path.exists(folder_k_fold): os.makedirs(folder_k_fold)
def folder_k_lines(dataset):
random.shuffle(dataset)
n = int(len(dataset) / folder_k)
r = len(dataset) % folder_k
lines = []
a = 1
start = 0
for i in range(folder_k - 1):
if r == 0: a = 0
else: r = r - 1
lines = lines + ['folder{}:\n'.format(i + 1)
] + dataset[start:start + n + a]
start = start + n + a
lines = lines + ['folder{}:\n'.format(i + 2)
] + dataset[start:len(dataset)]
return lines
dtset_hp.write_lines(folder_k_fold + "/SAMM_k_fold.txt",
folder_k_lines(SAMM_all))
dtset_hp.write_lines(folder_k_fold + "/micro_k_fold.txt",
folder_k_lines(micro))
def LOVO():
folder_LOVO = SAVE_SETS + "/LOVO"
if not os.path.exists(folder_LOVO): os.makedirs(folder_LOVO)
random.shuffle(SAMM_all)
random.shuffle(micro)
dtset_hp.write_lines(folder_LOVO + "/SAMM_LOVO.txt", SAMM_all)
dtset_hp.write_lines(folder_LOVO + "/micro_LOVO.txt", micro)
def hold_out(ratio):
folder_hold_out = SAVE_SETS + "/Hold-Out"
if not os.path.exists(folder_hold_out):
os.makedirs(folder_hold_out)
micro_train, micro_test = train_test_split(micro,
test_size=1 - ratio,
random_state=0)
SAMM_train, SAMM_test = train_test_split(SAMM_all,
test_size=1 - ratio,
random_state=0)
micro_hold_out = ['trainval:\n'] + micro_train + ['test:\n'
] + micro_test
SAMM_hold_out = ['trainval:\n'] + SAMM_train + ['test:\n'
] + SAMM_test
dtset_hp.write_lines(folder_hold_out + "/SAMM_hold_out.txt",
SAMM_hold_out)
dtset_hp.write_lines(folder_hold_out + "/micro_hold_out.txt",
micro_hold_out)
def hold_out(ratio):
folder_hold_out = SAVE_SETS + "/Hold-Out"
if not os.path.exists(folder_hold_out):
os.makedirs(folder_hold_out)
micro_train, micro_test = train_test_split(micro,
test_size=1 - ratio,
random_state=0)
macro_train, macro_test = train_test_split(macro,
test_size=1 - ratio,
random_state=0)
micro_hold_out = ['trainval:\n'] + micro_train + ['test:\n'
] + micro_test
macro_hold_out = ['trainval:\n'] + macro_train + ['test:\n'
] + macro_test
ME2_train = micro_train + macro_train
ME2_test = micro_test + macro_test
random.shuffle(ME2_train)
random.shuffle(ME2_test)
ME2_hold_out = ['trainval:\n'] + ME2_train + ['test:\n'] + ME2_test
dtset_hp.write_lines(folder_hold_out + "/ME2_hold_out.txt",
ME2_hold_out)
dtset_hp.write_lines(folder_hold_out + "/micro_hold_out.txt",
micro_hold_out)
dtset_hp.write_lines(folder_hold_out + "/macro_hold_out.txt",
macro_hold_out)
def LOSO():
folder_LOSO = SAVE_SETS + "/LOSO"
if not os.path.exists(folder_LOSO): os.makedirs(folder_LOSO)
def loso_lines(dataset):
random.shuffle(dataset)
map_subject = {}
for line in dataset:
subject = line.split()[0]
if subject in map_subject:
map_subject[subject].append(line)
else:
map_subject[subject] = [line]
lines = []
for i, value in enumerate(map_subject.values()):
lines = lines + ['subject{}:\n'.format(i + 1)] + value
return lines
dtset_hp.write_lines(folder_LOSO + "/SAMM_LOSO.txt",
loso_lines(SAMM_all))
dtset_hp.write_lines(folder_LOSO + "/micro_LOSO.txt",
loso_lines(micro))
def generate_dataset(self):
wb = xlrd.open_workbook(SOURCE_DATA + "/SAMM_Micro_FACS_Codes.xlsx")
sheet_micro = wb.sheet_by_name('MICRO_ONLY')
nrows = sheet_micro.nrows
allInfo = []
micro_all = []
SAMM_all = []
for row_id in range(nrows):
if row_id <= 10:
continue
folder = sheet_micro.cell_value(row_id, 0)
video = str(sheet_micro.cell_value(row_id, 1)).split('_')[1]
cls = "micro-expression"
onset_frame = sheet_micro.cell_value(row_id, 3)
apex_frame = sheet_micro.cell_value(row_id, 4)
offset_frame = sheet_micro.cell_value(row_id, 5)
info_line = "{} {} {} {} {} {}\n".format(folder, video, cls,
onset_frame, apex_frame,
offset_frame)
allInfo.append(info_line)
video_line = "{} {}\n".format(folder, video)
micro_all.append(video_line)
root, folders, _ = dtset_hp.return_oswalk(SOURCE_DATA)
for folder in folders:
videos_path = os.path.join(root, folder)
_, videos, _ = dtset_hp.return_oswalk(videos_path)
for video in videos:
video_line = "{} {}\n".format(folder, video)
SAMM_all.append(video_line)
micro_all = list(set(micro_all))
dtset_hp.write_lines(SAVE_SETS + "/all_Info.txt", allInfo)
dtset_hp.write_lines(SAVE_SETS + "/SAMM_all.txt", SAMM_all)
dtset_hp.write_lines(SAVE_SETS + "/micro_all.txt", micro_all)
def generate_dataset(self):
wb = xlrd.open_workbook(SOURCE_DATA + "/CAS(ME)^2code_final.xlsx")
sheet_CASFEcode = wb.sheet_by_name('CASFEcode_final')
sheet_namingRule1 = wb.sheet_by_name('naming rule1')
sheet_namingRule2 = wb.sheet_by_name('naming rule2')
video_map = {} # { anger1:0401, ...} name to code
for i in range(sheet_namingRule2.nrows):
code = sheet_namingRule2.cell_value(i, 0)
name = sheet_namingRule2.cell_value(i, 1)
video_map[name] = code
nrows = sheet_CASFEcode.nrows
allInfo = []
ME2_all = []
micro_all = []
macro_all = []
for row_id in range(nrows):
folder = sheet_namingRule1.cell_value(row_id, 1)
video_name = sheet_CASFEcode.cell_value(row_id, 1).split('_')[0]
video = video_map[video_name]
cls = sheet_CASFEcode.cell_value(row_id, 7)
onset_frame = sheet_CASFEcode.cell_value(row_id, 2)
apex_frame = sheet_CASFEcode.cell_value(row_id, 3)
offset_frame = sheet_CASFEcode.cell_value(row_id, 4)
if offset_frame == 0:
offset_frame = apex_frame
if apex_frame == 0:
apex_frame = (onset_frame + offset_frame) / 2
info_line = "{} {} {} {} {} {}\n".format(folder, video, cls,
onset_frame, apex_frame,
offset_frame)
allInfo.append(info_line)
video_line = "{} {}\n".format(folder, video)
if cls == "micro-expression":
micro_all.append(video_line)
if cls == "macro-expression":
macro_all.append(video_line)
root, folders, _ = dtset_hp.return_oswalk(SOURCE_DATA + '/rawpic')
for folder in folders:
videos_path = os.path.join(root, folder)
_, videos, _ = dtset_hp.return_oswalk(videos_path)
for video in videos:
video = video.split('_')[1][0:4]
video_line = "{} {}\n".format(folder, video)
ME2_all.append(video_line)
ME2_all = list(set(ME2_all))
micro_all = list(set(micro_all))
macro_all = list(set(macro_all))
macro_all = [x for x in macro_all if x not in micro_all]
dtset_hp.write_lines(SAVE_SETS + "/all_Info.txt", allInfo)
dtset_hp.write_lines(SAVE_SETS + "/ME2_all.txt", ME2_all)
dtset_hp.write_lines(SAVE_SETS + "/micro_all.txt", micro_all)
dtset_hp.write_lines(SAVE_SETS + "/macro_all.txt", macro_all)
