def initialize_images(self, mode=None, current_image_path=None):
self._all_images: List = file_walker.walk(mode)
self._current_index = 0
self._shuffle_start_index = 0
if current_image_path:
self._current_index = self.get_index_from_image_path(
current_image_path)
self._shuffle_start_index = self._current_index
# self.image_shuffle()
self._set_image(self._current_index)
def initialize_images(self,
mode=None,
current_image_path=None,
to_sort: bool = False,
reverse_sort: bool = False):
self._all_images: List = file_walker.walk(mode,
is_sorted=to_sort,
reverse_sort=reverse_sort)
self._current_index = 0
self._shuffle_start_index = 0
if current_image_path:
self._current_index = self.get_index_from_image_path(
current_image_path)
self._shuffle_start_index = self._current_index
# self.image_shuffle()
self._set_image(self._current_index)
def backup_image(old_image_path, backup_path):
command = "mv '{old}' {backup_path}".format(old=old_image_path,
backup_path=backup_path)
print(command)
subprocess.check_output(shlex.split(command))
# def compress_first_10_images():
if __name__ == '__main__':
hard_limit = 5491520
# 5491520 bytes = 5240 KB = 5MB
image_paths: List = walker.walk(increasing_size_sort=True)
new_suffix = "_comp_la"
new_ext = '.jpg'
for image_path in image_paths:
if new_suffix in image_path:
continue
if os.path.getsize(image_path) < hard_limit:
print(image_path)
print("image size > hard limit; stopping")
break
old_image_path: str = image_path
new_image_path = old_image_path.replace(
os.path.splitext(old_image_path)[1], new_suffix + new_ext)
compress_image(old_image_path, new_image_path)
backup_image(old_image_path)
# break
def __init__(self,
data_list=["AFEW"],
dir_gt=None,
onlyFace=True,
image_size=224,
transform=None,
useIT=False,
augment=False,
step=1,
split=False,
nSplit=5,
listSplit=[0, 1, 2, 3, 4],
wHeatmap=False,
isVideo=False,
seqLength=None):
self.seq_length = seqLength
self.isVideo = isVideo
self.transform = transform
self.onlyFace = onlyFace
self.augment = augment
self.wHeatmap = wHeatmap
self.imageSize = image_size
self.imageHeight = image_size
self.imageWidth = image_size
self.useIT = useIT
self.curDir = "/homedtic/gulloa/ValenceArousal/images/"
list_gt = []
list_labels_t = []
list_labels_tE = []
counter_image = 0
annotL_name = 'annot'
annotE_name = 'annot2'
if dir_gt is not None:
annot_name = dir_gt
for data in data_list:
print(("Opening " + data))
for f in file_walker.walk(self.curDir + data + "/"):
if f.isDirectory: # Check if object is directory
#print((f.name, f.full_path)) # Name is without extension
for sub_f in f.walk():
if sub_f.isDirectory: # Check if object is directory
#AFEWVA
'''for sub_2_f in sub_f.walk():
if sub_2_f.isDirectory:
list_dta = []
#print(sub_f.name)
if(sub_2_f.name == annotL_name) : #If that's annot, add to labels_t
for sub_sub_f in sub_2_f.walk(): #this is the data
if(".npy" not in sub_sub_f.full_path):
list_dta.append(sub_sub_f.full_path)
list_labels_t.append(sorted(list_dta))
elif(sub_2_f.name == annotE_name) : #If that's annot, add to labels_t
for sub_sub_f in sub_2_f.walk(): #this is the data
if(".npy" not in sub_sub_f.full_path):
list_dta.append(sub_sub_f.full_path)
list_labels_tE.append(sorted(list_dta))
elif(sub_2_f.name == 'img'): #Else it is the image
for sub_sub_f in sub_2_f.walk(): #this is the data
if(".npy" not in sub_sub_f.full_path):
list_dta.append(sub_sub_f.full_path)
list_gt.append(sorted(list_dta))
counter_image+=len(list_dta)'''
#SEMAINE
list_dta = []
#print(sub_f.name)
if (sub_f.name == annotL_name
): #If that's annot, add to labels_t
for sub_sub_f in sub_f.walk(
): #this is the data
if (".npy" not in sub_sub_f.full_path):
list_dta.append(sub_sub_f.full_path)
list_labels_t.append(sorted(list_dta))
elif (sub_f.name == annotE_name
): #If that's annot, add to labels_t
for sub_sub_f in sub_f.walk(
): #this is the data
if (".npy" not in sub_sub_f.full_path):
list_dta.append(sub_sub_f.full_path)
list_labels_tE.append(sorted(list_dta))
elif (sub_f.name == 'img'): #Else it is the image
for sub_sub_f in sub_f.walk(
): #this is the data
if (".npy" not in sub_sub_f.full_path):
list_dta.append(sub_sub_f.full_path)
list_gt.append(sorted(list_dta))
counter_image += len(list_dta)
self.length = counter_image
print("Now opening keylabels")
list_labelsN = []
list_labelsEN = []
list_labels = []
list_labelsE = []
for ix in range(len(
list_labels_t)): #lbl,lble in (list_labels_t,list_labels_tE) :
lbl_68 = [] #Per folder
lbl_2 = [] #Per folder
lbl_n68 = [] #Per folder
lbl_n2 = [] #Per folder
for jx in range(len(list_labels_t[ix])): #lbl_sub in lbl :
lbl_sub = list_labels_t[ix][jx]
if ('pts' in lbl_sub):
x = []
#print(lbl_sub)
with open(lbl_sub) as file:
data2 = [re.split(r'\t+', l.strip()) for l in file]
for i in range(len(data2)):
if (i not in [0, 1, 2, len(data2) - 1]):
x.append([float(j) for j in data2[i][0].split()])
lbl_68.append(np.array(x).flatten('F')) #1 record
lbl_n68.append(lbl_sub)
lbl_subE = list_labels_tE[ix][jx]
if ('aro' in lbl_subE):
x = []
#print(lbl_sub)
with open(lbl_subE) as file:
data2 = [re.split(r'\t+', l.strip()) for l in file]
for i in range(len(data2)):
x.append([float(j) for j in data2[i][0].split()])
lbl_2.append(np.array(x).flatten('F'))
lbl_n2.append(lbl_sub)
list_labelsN.append(lbl_n68)
list_labelsEN.append(lbl_n2)
list_labels.append(lbl_68)
list_labelsE.append(lbl_2)
t_l_imgs = []
t_l_gt = []
t_l_gtE = []
t_list_gt_names = []
t_list_gtE_names = []
for i in range(0, len(list_gt)): #For each dataset
list_images = []
list_gt_names = []
list_gtE_names = []
indexer = 0
list_ground_truth = np.zeros([len(list_gt[i]), 136])
list_ground_truthE = np.zeros([len(list_gt[i]), 2])
for j in range(
0, len(list_gt[i]), step
): #for number of data #n_skip is usefull for video data
list_images.append(list_gt[i][j])
list_gt_names.append(list_labelsN[i][j])
list_gtE_names.append(list_labelsEN[i][j])
list_ground_truth[indexer] = np.array(
list_labels[i][j]).flatten('F')
list_ground_truthE[indexer] = np.array(
list_labelsE[i][j]).flatten('F')
indexer += 1
t_l_imgs.append(list_images)
t_l_gt.append(list_ground_truth)
t_l_gtE.append(list_ground_truthE)
t_list_gt_names.append(list_gt_names)
t_list_gtE_names.append(list_gtE_names)
self.l_imgs = []
self.l_gt = []
self.l_gtE = []
self.list_gt_names = []
self.list_gtE_names = []
if split:
indexer = 0
self.l_gt = []
self.l_gtE = []
totalData = len(t_l_imgs)
perSplit = int(truediv(totalData, nSplit))
for x in listSplit:
print('split : ', x)
begin = x * perSplit
if x == nSplit - 1:
end = begin + (totalData - begin)
else:
end = begin + perSplit
print(begin, end, totalData)
for i in range(begin, end):
for j in range(len(t_l_imgs[i])):
#print('append ',t_l_imgs[i][j])
self.l_imgs.append(t_l_imgs[i][j])
self.l_gt.append(t_l_gt[i][j])
self.l_gtE.append(t_l_gtE[i][j])
self.list_gt_names.append(t_list_gt_names[i][j])
self.list_gtE_names.append(t_list_gtE_names[i][j])
indexer += 1
print(len(self.l_imgs))
self.l_gt = np.asarray(self.l_gt)
self.l_gtE = np.asarray(self.l_gtE)
else:
self.l_gt = np.zeros([counter_image, 136])
self.l_gtE = np.zeros([counter_image, 2])
indexer = 0
for i in range(len(t_l_imgs)):
for j in range(len(t_l_imgs[i])):
self.l_imgs.append(t_l_imgs[i][j])
print(i, j, '-', len(t_l_imgs[i]))
self.l_gt[indexer] = t_l_gt[i][j]
self.l_gtE[indexer] = t_l_gtE[i][j]
self.list_gt_names.append(t_list_gt_names[i][j])
self.list_gtE_names.append(t_list_gtE_names[i][j])
indexer += 1
print('limgs : ', len(self.l_imgs))
def __init__(self,
data_list=["AFEW"],
dir_gt=None,
onlyFace=True,
image_size=224,
transform=None,
useIT=False,
augment=False,
step=1,
split=False,
nSplit=5,
listSplit=[0, 1, 2, 3, 4],
wHeatmap=False,
isVideo=False,
seqLength=None):
self.seq_length = seqLength
self.isVideo = isVideo
self.transform = transform
self.onlyFace = onlyFace
self.augment = augment
self.wHeatmap = wHeatmap
self.imageSize = image_size
self.imageHeight = image_size
self.imageWidth = image_size
self.useIT = useIT
self.curDir = "/homedtic/gulloa/ValenceArousal/images/"
list_gt = []
list_labels_t = []
list_labels_tE = []
counter_image = 0
annotL_name = 'annot'
annotE_name = 'annot2'
if dir_gt is not None:
annot_name = dir_gt
for data in data_list:
print(("Opening " + data))
for f in file_walker.walk(self.curDir + data + "/"):
if f.isDirectory: # Check if object is directory
#print((f.name, f.full_path)) # Name is without extension
for sub_f in f.walk():
if sub_f.isDirectory: # Check if object is directory
#AFEWVA
for sub_2_f in sub_f.walk():
if sub_2_f.isDirectory:
list_dta = []
#print(sub_f.name)
if (sub_2_f.name == annotL_name
): #If that's annot, add to labels_t
for sub_sub_f in sub_2_f.walk(
): #this is the data
if (".npy" not in
sub_sub_f.full_path):
list_dta.append(
sub_sub_f.full_path)
list_labels_t.append(sorted(list_dta))
elif (sub_2_f.name == annotE_name
): #If that's annot, add to labels_t
for sub_sub_f in sub_2_f.walk(
): #this is the data
if (".npy" not in
sub_sub_f.full_path):
list_dta.append(
sub_sub_f.full_path)
list_labels_tE.append(sorted(list_dta))
elif (sub_2_f.name == 'img'
): #Else it is the image
for sub_sub_f in sub_2_f.walk(
): #this is the data
if (".npy" not in
sub_sub_f.full_path):
list_dta.append(
sub_sub_f.full_path)
list_gt.append(sorted(list_dta))
counter_image += len(list_dta)
#SEMAINE
'''list_dta = []
#print(sub_f.name)
if(sub_f.name == annotL_name) : #If that's annot, add to labels_t
for sub_sub_f in sub_f.walk(): #this is the data
if(".npy" not in sub_sub_f.full_path):
list_dta.append(sub_sub_f.full_path)
list_labels_t.append(sorted(list_dta))
elif(sub_f.name == annotE_name) : #If that's annot, add to labels_t
for sub_sub_f in sub_f.walk(): #this is the data
if(".npy" not in sub_sub_f.full_path):
list_dta.append(sub_sub_f.full_path)
list_labels_tE.append(sorted(list_dta))
elif(sub_f.name == 'img'): #Else it is the image
for sub_sub_f in sub_f.walk(): #this is the data
if(".npy" not in sub_sub_f.full_path):
list_dta.append(sub_sub_f.full_path)
list_gt.append(sorted(list_dta))
counter_image+=len(list_dta)'''
self.length = counter_image
print("Now opening keylabels")
list_labelsN = []
list_labelsEN = []
list_labels = []
list_labelsE = []
for ix in range(len(
list_labels_t)): #lbl,lble in (list_labels_t,list_labels_tE) :
lbl_68 = [] #Per folder
lbl_2 = [] #Per folder
lbl_n68 = [] #Per folder
lbl_n2 = [] #Per folder
for jx in range(len(list_labels_t[ix])): #lbl_sub in lbl :
#print(os.path.basename(list_gt[ix][jx]))
#print(os.path.basename(list_labels_t[ix][jx]))
#print(os.path.basename(list_labels_tE[ix][jx]))
lbl_sub = list_labels_t[ix][jx]
if ('pts' in lbl_sub):
x = []
#print(lbl_sub)
with open(lbl_sub) as file:
data2 = [re.split(r'\t+', l.strip()) for l in file]
for i in range(len(data2)):
if (i not in [0, 1, 2, len(data2) - 1]):
x.append([float(j) for j in data2[i][0].split()])
lbl_68.append(np.array(x).flatten('F')) #1 record
lbl_n68.append(lbl_sub)
lbl_subE = list_labels_tE[ix][jx]
if ('aro' in lbl_subE):
x = []
#print(lbl_sub)
with open(lbl_subE) as file:
data2 = [re.split(r'\t+', l.strip()) for l in file]
for i in range(len(data2)):
x.append([float(j) for j in data2[i][0].split()])
lbl_2.append(np.array(x).flatten('F'))
lbl_n2.append(lbl_sub)
list_labelsN.append(lbl_n68)
list_labelsEN.append(lbl_n2)
list_labels.append(lbl_68)
list_labelsE.append(lbl_2)
'''for lbl in list_labels_t :
lbl_68 = [] #Per folder
for lbl_sub in lbl :
#print(lbl_sub)
if ('pts' in lbl_sub) :
x = []
#print(lbl_sub)
with open(lbl_sub) as file:
data2 = [re.split(r'\t+',l.strip()) for l in file]
for i in range(len(data2)) :
if(i not in [0,1,2,len(data2)-1]):
x.append([ float(j) for j in data2[i][0].split()] )
lbl_68.append(np.array(x).flatten('F')) #1 record
list_labels.append(lbl_68)
list_labelsE = []
for lbl in list_labels_tE :
lbl_2 = [] #Per folder
for lbl_sub in lbl :
#print(lbl_sub)
if ('aro' in lbl_sub) :
x = []
#print(lbl_sub)
with open(lbl_sub) as file:
data2 = [re.split(r'\t+',l.strip()) for l in file]
for i in range(len(data2)) :
x.append([ float(j) for j in data2[i][0].split()] )
lbl_2.append(np.array(x).flatten('F')) #1 record
list_labelsE.append(lbl_2)'''
'''print(len(list_labelsN[0]))
print(len(list_labelsEN[0]))
print(len(list_labels[0]))
print(len(list_labelsE[0]))'''
t_l_imgs = []
t_l_gt = []
t_l_gtE = []
t_list_gt_names = []
t_list_gtE_names = []
if not self.isVideo:
#Flatten it to one list
for i in range(0, len(list_gt)): #For each dataset
list_images = []
list_gt_names = []
list_gtE_names = []
indexer = 0
list_ground_truth = np.zeros([len(list_gt[i]), 136])
list_ground_truthE = np.zeros([len(list_gt[i]), 2])
for j in range(
0, len(list_gt[i]), step
): #for number of data #n_skip is usefull for video data
list_images.append(list_gt[i][j])
list_gt_names.append(list_labelsN[i][j])
list_gtE_names.append(list_labelsEN[i][j])
list_ground_truth[indexer] = np.array(
list_labels[i][j]).flatten('F')
list_ground_truthE[indexer] = np.array(
list_labelsE[i][j]).flatten('F')
indexer += 1
t_l_imgs.append(list_images)
t_l_gt.append(list_ground_truth)
t_l_gtE.append(list_ground_truthE)
t_list_gt_names.append(list_gt_names)
t_list_gtE_names.append(list_gtE_names)
else:
if self.seq_length is None:
list_ground_truth = np.zeros([
int(counter_image / (self.seq_length * step)),
self.seq_length, 136
])
indexer = 0
for i in range(0, len(list_gt)): #For each dataset
counter = 0
for j in range(
0, int(len(list_gt[i]) /
(self.seq_length *
step))): #for number of data/batchsize
temp = []
temp2 = np.zeros([self.seq_length, 136])
i_temp = 0
for z in range(counter,
counter + (self.seq_length * step),
step): #1 to seq_size
temp.append(list_gt[i][z])
temp2[i_temp] = list_labels[i][z]
i_temp += 1
list_images.append(temp)
list_ground_truth[indexer] = temp2
indexer += 1
counter += self.seq_length * step
#print counter
self.l_imgs = list_images
self.l_gt = list_ground_truth
else:
counter_seq = 0
for i in range(0, len(list_gt)): #For each dataset
indexer = 0
list_gt_names = []
list_gtE_names = []
list_ground_truth = np.zeros([
int(len(list_gt[i]) / (self.seq_length * step)),
self.seq_length, 136
]) #np.zeros([counter_image,136])
list_ground_truthE = np.zeros([
int(len(list_gt[i]) / (self.seq_length * step)),
self.seq_length, 2
]) #np.zeros([counter_image,2])
counter = 0
list_images = []
for j in range(
0, int(len(list_gt[i]) /
(self.seq_length *
step))): #for number of data/batchsize
tmpn68 = []
tmpn2 = []
temp = []
temp2 = np.zeros([self.seq_length, 136])
temp3 = np.zeros([self.seq_length, 2])
i_temp = 0
for z in range(counter,
counter + (self.seq_length * step),
step): #1 to seq_size
temp.append(list_gt[i][z])
temp2[i_temp] = list_labels[i][z].flatten('F')
temp3[i_temp] = list_labelsE[i][z].flatten('F')
tmpn68.append(list_labelsN[i][z])
tmpn2.append(list_labelsEN[i][z])
i_temp += 1
counter_seq += 1
list_images.append(temp)
list_ground_truth[indexer] = temp2
list_ground_truthE[indexer] = temp3
list_gt_names.append(tmpn68)
list_gtE_names.append(tmpn2)
indexer += 1
counter += self.seq_length * step
#print counter
t_l_imgs.append(list_images)
t_l_gt.append(list_ground_truth)
t_l_gtE.append(list_ground_truthE)
t_list_gt_names.append(list_gt_names)
t_list_gtE_names.append(list_gtE_names)
'''print('length : ',len(t_l_imgs)) #Folder
print('lengt2 : ',len(t_l_imgs[0])) #all/seq
print('lengt3 : ',len(t_l_imgs[0][0])) #seq
print('length4 : ',len(t_l_imgs[0][0][0]))'''
#[folder, all/seq,seq]
self.l_imgs = []
self.l_gt = []
self.l_gtE = []
self.list_gt_names = []
self.list_gtE_names = []
#print('cimage : ',counter_image)
if split:
'''print('splitting')
self.l_imgs = []
self.l_gt = []
self.l_gtE = []
totalData = len(list_images)
perSplit = int(truediv(totalData, nSplit))
for x in listSplit :
print('split : ',x)
begin = x*perSplit
if x == nSplit-1 :
end = begin + (totalData - begin)
else :
end = begin+perSplit
print(begin,end,totalData)
for x2 in range(begin,end) :
#print(x2,totalData)
self.l_imgs.append(list_images[x2])
self.l_gt.append(list_ground_truth[x2])
self.l_gtE.append(list_ground_truthE[x2])'''
indexer = 0
self.l_gt = []
self.l_gtE = []
'''else :
self.l_gt = np.zeros([counter_seq,self.seq_length,136])
self.l_gtE = np.zeros([counter_seq,self.seq_length,2])'''
totalData = len(t_l_imgs)
perSplit = int(truediv(totalData, nSplit))
for x in listSplit:
print('split : ', x)
begin = x * perSplit
if x == nSplit - 1:
end = begin + (totalData - begin)
else:
end = begin + perSplit
print(begin, end, totalData)
if not self.isVideo:
for i in range(begin, end):
for j in range(len(t_l_imgs[i])):
#print('append ',t_l_imgs[i][j])
self.l_imgs.append(t_l_imgs[i][j])
self.l_gt.append(t_l_gt[i][j])
self.l_gtE.append(t_l_gtE[i][j])
self.list_gt_names.append(t_list_gt_names[i][j])
self.list_gtE_names.append(t_list_gtE_names[i][j])
indexer += 1
else:
for i in range(begin, end):
for j in range(len(t_l_imgs[i])): #seq counter
t_img = []
t_gt = []
t_gtE = []
t_gt_N = []
t_gt_EN = []
tmp = 0
for k in range(len(t_l_imgs[i][j])): #seq size
t_img.append(t_l_imgs[i][j][k])
t_gt.append(t_l_gt[i][j][k])
t_gtE.append(t_l_gtE[i][j][k])
t_gt_N.append(t_list_gt_names[i][j][k])
t_gt_EN.append(t_list_gtE_names[i][j][k])
tmp += 1
#print('append ',t_img)
self.l_imgs.append(t_img)
self.l_gt.append(t_gt)
self.l_gtE.append(t_gtE)
self.list_gt_names.append(t_gt_N)
self.list_gtE_names.append(t_gt_EN)
indexer += 1
print(len(self.l_imgs))
self.l_gt = np.asarray(self.l_gt)
self.l_gtE = np.asarray(self.l_gtE)
else:
if not self.isVideo:
self.l_gt = np.zeros([counter_image, 136])
self.l_gtE = np.zeros([counter_image, 2])
indexer = 0
for i in range(len(t_l_imgs)):
for j in range(len(t_l_imgs[i])):
self.l_imgs.append(t_l_imgs[i][j])
print(i, j, '-', len(t_l_imgs[i]))
self.l_gt[indexer] = t_l_gt[i][j]
self.l_gtE[indexer] = t_l_gtE[i][j]
self.list_gt_names.append(t_list_gt_names[i][j])
self.list_gtE_names.append(t_list_gtE_names[i][j])
indexer += 1
else:
self.l_gt = np.zeros([counter_seq, self.seq_length, 136])
self.l_gtE = np.zeros([counter_seq, self.seq_length, 2])
indexer = 0
for i in range(len(t_l_imgs)): #dataset
for j in range(len(t_l_imgs[i])): #seq counter
t_img = []
t_gt = np.zeros([self.seq_length, 136])
t_gte = np.zeros([self.seq_length, 2])
t_gt_n = []
t_gt_en = []
i_t = 0
for k in range(len(t_l_imgs[i][j])): #seq size
t_img.append(t_l_imgs[i][j][k])
t_gt[i_t] = t_l_gt[i][j][k]
t_gte[i_t] = t_l_gtE[i][j][k]
t_gt_n.append(t_list_gt_names[i][j][k])
t_gt_en.append(t_list_gtE_names[i][j][k])
i_t += 1
self.l_imgs.append(t_img)
self.l_gt[indexer] = t_gt
self.l_gtE[indexer] = t_gte
self.list_gt_names.append(t_gt_n)
self.list_gtE_names.append(t_gt_en)
indexer += 1
print('limgs : ', len(self.l_imgs))