def portionBucket():
cryptorand = SystemRandom()
print("Portioning images from bucket..")
if len(os.listdir("PLACE_IMAGES_HERE/normal/")) > 0:
print("Portioning normal images..")
transferlist = list(Path('.').glob("PLACE_IMAGES_HERE/normal/*.jpg"))
cryptorand.shuffle(transferlist)
portionlimit = transferlist.__len__() / 3
index = 0
for s in transferlist:
index += 1
if index < portionlimit:
os.rename(s, "internal/test_set/normal/" + str(s)[25:])
else:
os.rename(s, "internal/training_set/normal/" + str(s)[25:])
else:
print("No normal images found.")
if len(os.listdir("PLACE_IMAGES_HERE/abnormal/")) > 0:
print("Portioning abnormal images..")
transferlist = list(Path('.').glob("PLACE_IMAGES_HERE/abnormal/*.jpg"))
cryptorand.shuffle(transferlist)
portionlimit = transferlist.__len__() / 3
index = 0
for s in transferlist:
index += 1
if index < portionlimit:
os.rename(s, "internal/test_set/abnormal/" + str(s)[27:])
else:
os.rename(s, "internal/training_set/abnormal/" + str(s)[27:])
else:
print("No abnormal images found.")
print("Images portioned.")
def generate_groups(self):
"""
Generates the groups based on a randomization algorithm.
:return: the generated groups: a list of lists (names)
"""
if not self.names:
raise AttributeError("Could not find names list. Try re-initializing Engine with valid filepath.")
groups = []
cryptogen = SystemRandom()
cryptogen.shuffle(self.names)
current_group = []
for name in self.names:
current_group.append(name)
is_last_name = self.names.index(name) == len(self.names) - 1
is_group_full = len(current_group) == self.group_size
if is_group_full or is_last_name:
groups.append(current_group)
current_group = []
logging.info("Created {} groups with max size {}.".format(len(groups), self.group_size))
return groups
def generate_groups(self):
"""
Generates the groups based on a randomization algorithm.
:return: the generated groups: a list of lists (names)
"""
if not self.names:
raise AttributeError(
"Could not find names list. Try re-initializing Engine with valid filepath."
)
groups = []
cryptogen = SystemRandom()
cryptogen.shuffle(self.names)
current_group = []
for name in self.names:
current_group.append(name)
is_last_name = self.names.index(name) == len(self.names) - 1
is_group_full = len(current_group) == self.group_size
if is_group_full or is_last_name:
groups.append(current_group)
current_group = []
logging.info("Created {} groups with max size {}.".format(
len(groups), self.group_size))
return groups
def main(args):
r = SystemRandom()
try:
try:
_min = int(args[1])
_max = int(args[2])
except:
_min = 0
_max = int(args[1])
except:
_min = 0
_max = 10
try:
if args[0] == 'simple':
l = list(string.letters + string.digits)
r.shuffle(l)
return ''.join(l[0:_max])
if args[0] == 'strong':
l = list(string.letters + string.digits + string.punctuation)
r.shuffle(l)
return ''.join(l[0:_max])
if args[0] == 'integer':
return r.randint(_min, _max)
return r.uniform(_min, _max)
except:
return r.random()
def tcp_scan(ips, ports, randomize=True):
loop = asyncio.get_event_loop()
if randomize:
rdev = SystemRandom()
ips = rdev.shuffle(ips)
ports = rdev.shuffle(ports)
tcp_Scanner_run(tcp_scanner(ip, port) for port in ports for ip in ips)
def scan(ips, ports, randomize=False):
"""Scan the ports"""
loop = asyncio.get_event_loop()
if randomize:
rdev = SystemRandom()
ips = rdev.shuffle(ips)
ports = rdev.shuffle(ports)
# let's pass list of task, not only one
run([scanner(ip, port) for port in ports for ip in ips])
def __init__(self, mode='test', limiter=0, shuffle_en=False):
name = 'waymo'
self.type = 'lidar'
db.__init__(self, name, mode)
self._train_scenes = []
self._val_scenes = []
self._test_scenes = []
if (mode == 'test'):
self._tod_filter_list = cfg.TEST.TOD_FILTER_LIST
else:
self._tod_filter_list = cfg.TRAIN.TOD_FILTER_LIST
self._uncertainty_sort_type = cfg.UC.SORT_TYPE
self._draw_width = int((cfg.LIDAR.X_RANGE[1] - cfg.LIDAR.X_RANGE[0]) *
(1 / cfg.LIDAR.VOXEL_LEN))
self._draw_height = int((cfg.LIDAR.Y_RANGE[1] - cfg.LIDAR.Y_RANGE[0]) *
(1 / cfg.LIDAR.VOXEL_LEN))
self._num_slices = cfg.LIDAR.NUM_SLICES
self._bev_slice_locations = [1, 2, 3, 4, 5, 7]
self._filetype = 'npy'
self._imtype = 'PNG'
self._scene_sel = True
#For now one large cache file is OK, but ideally just take subset of actually needed data and cache that. No need to load nusc every time.
self._classes = (
'dontcare', # always index 0
'vehicle.car')
# 'human.pedestrian',
#'vehicle.bicycle')
self.config = {'cleanup': True, 'matlab_eval': False, 'rpn_file': None}
self._class_to_ind = dict(
list(zip(self.classes, list(range(self.num_classes)))))
self._train_index = os.listdir(
os.path.join(self._devkit_path, 'train', 'point_clouds'))
self._val_index = os.listdir(
os.path.join(self._devkit_path, 'val', 'point_clouds'))
self._val_index.sort(key=natural_keys)
rand = SystemRandom()
if (shuffle_en):
print('shuffling pc indices')
rand.shuffle(self._train_index)
rand.shuffle(self._val_index)
if (limiter != 0):
if (limiter < len(self._val_index)):
self._val_index = self._val_index[:limiter]
if (limiter < len(self._train_index)):
self._train_index = self._train_index[:limiter]
if (limiter < len(self._test_index)):
self._test_index = self._test_index[:limiter]
#if(18000 < len(self._val_index)):
# self._val_index = self._val_index[:18000]
assert os.path.exists(
self._devkit_path), 'waymo dataset path does not exist: {}'.format(
self._devkit_path)
def bogo_solve(constraints):
names = get_names(constraints)
rng = SystemRandom()
while verify(names, constraints) != []:
rng.shuffle(names)
s = ""
for n in names:
s += n + " "
print "Ordering: {}".format(s)
def fillBucket():
cryptorand = SystemRandom()
print("Putting images into bucket..")
if len(os.listdir("internal/training_set/normal")) > 0:
print("Taking normal images..")
transferlist = list(
Path('.').glob("internal/training_set/normal/*.jpg"))
cryptorand.shuffle(transferlist)
for s in transferlist:
os.rename(s, "PLACE_IMAGES_HERE/normal/" + str(s)[29:])
transferlist = list(Path('.').glob("internal/test_set/normal/*.jpg"))
cryptorand.shuffle(transferlist)
for s in transferlist:
os.rename(s, "PLACE_IMAGES_HERE/normal/" + str(s)[25:])
else:
print("No normal images found.")
if len(os.listdir("internal/training_set/abnormal")) > 0:
print("Taking abnormal images..")
transferlist = list(
Path('.').glob("internal/training_set/abnormal/*.jpg"))
cryptorand.shuffle(transferlist)
for s in transferlist:
os.rename(s, "PLACE_IMAGES_HERE/abnormal/" + str(s)[31:])
transferlist = list(Path('.').glob("internal/test_set/abnormal/*.jpg"))
cryptorand.shuffle(transferlist)
for s in transferlist:
os.rename(s, "PLACE_IMAGES_HERE/abnormal/" + str(s)[27:])
else:
print("No abnormal images found.")
print("Images moved to bucket.")
def init_captcha():
dice = SystemRandom()
normalset, oddballset = dice.sample(current_app.captcha_data, 2)
normals = dice.sample(normalset, NORMALS)
oddballs = dice.sample(oddballset - normalset, ODDBALLS)
united = normals + oddballs
dice.shuffle(united)
challenge = ' '.join(united)
expiry = datetime.today() + AUTHENTICATION_TIME
session['captcha-answer'] = map(lambda s: s.lower(), oddballs)
session['captcha-expires'] = expiry
if 'captcha-quarantine' in session:
del session['captcha-quarantine']
session.modified = True
return {'captcha_expires': str(expiry), 'captcha_challenge': challenge}
def generate_wordlist(num_words, num_letters, num_digits, num_symbols):
rng = SystemRandom()
wordlist_set = set()
for _ in range(num_words):
letters = ''.join(
rng.choice(string.ascii_letters) for _ in range(num_letters))
digits = ''.join(rng.choice(string.digits) for _ in range(num_digits))
symbols = ''.join(
rng.choice(string.punctuation) for _ in range(num_symbols))
word = letters + digits + symbols
word_l = list(word)
rng.shuffle(word_l)
wordlist_set.add(''.join(word_l))
return wordlist_set
def generate_sequence():
cryptorand = SystemRandom()
sequence = []
for i in range(22):
flip = random.randint(1, 2)
if flip == 1:
sequence.append("0")
else:
sequence.append("O")
random.seed()
cryptorand.shuffle(sequence)
return "O" + "".join(sequence)
def start(format):
random = SystemRandom()
randomCharacterList = formatDict.get(format)[
1] # Get encoding alphabet based on user input
random.shuffle(
randomCharacterList) # Randomize the alphabet order for extra random
keyListLength = len(randomCharacterList)
keyList = []
for _ in range(
formatDict.get(format)
[0]): # Add random characters from the encoding alphabet to a list
i = random.randint(1, keyListLength)
n = randomCharacterList[i - 1]
keyList.append(n)
privateKey = "".join(keyList) # Join the character list into a key string
return privateKey
def __init__(self, mode='test', limiter=0):
name = 'nuscenes'
db.__init__(self, name)
self._train_scenes = []
self._val_scenes = []
self._test_scenes = []
self._train_index = []
self._val_index = []
self._test_index = []
self._devkit_path = self._get_default_path()
self._mode = mode
self._nusc = None
self._scene_sel = True
#For now one large cache file is OK, but ideally just take subset of actually needed data and cache that. No need to load nusc every time.
self._classes = (
'dontcare', # always index 0
'vehicle.car',
'human.pedestrian',
'vehicle.bicycle')
self.config = {'cleanup': True, 'matlab_eval': False, 'rpn_file': None}
self._class_to_ind = dict(
list(zip(self.classes, list(range(self.num_classes)))))
self._val_scenes = create_splits_scenes()['val']
self._train_scenes = create_splits_scenes()['train']
self._test_scenes = create_splits_scenes()['test']
#TODO: create custom scene list
#print(self._train_scenes)
for rec in self.nusc.sample_data:
if (rec['channel'] == 'CAM_FRONT' and rec['is_key_frame'] is True):
rec_tmp = deepcopy(rec)
#Reverse lookup, getting the overall sample from the picture sample token, to get the scene information.
scene_name = self.nusc.get(
'scene',
self.nusc.get('sample',
rec['sample_token'])['scene_token'])['name']
desc = self.nusc.get(
'scene',
self.nusc.get('sample', rec['sample_token'])
['scene_token'])['description'].lower()
if (self._scene_sel and 'night' not in desc
and 'rain' not in desc and 'cones' not in desc):
sample = self.nusc.get('sample', rec['sample_token'])
rec_tmp['anns'] = sample['anns']
rec_tmp['lidar_token'] = sample['data']['LIDAR_TOP']
if (scene_name in self._train_scenes):
self._train_index.append(rec_tmp)
elif (scene_name in self._val_scenes):
self._val_index.append(rec_tmp)
elif (scene_name in self._train_scenes):
self._test_index.append(rec_tmp)
rand = SystemRandom()
#Get global image info
if (mode == 'train'):
img_index = self._train_index
rand.shuffle(self._val_index)
elif (mode == 'val'):
img_index = self._val_index
elif (mode == 'test'):
img_index = self._test_index
self._imwidth = img_index[0]['width']
self._imheight = img_index[0]['height']
self._imtype = img_index[0]['fileformat']
rand = SystemRandom()
rand.shuffle(img_index)
if (limiter != 0):
img_index = img_index[:limiter]
if (mode == 'train'):
self._train_index = img_index
elif (mode == 'val'):
self._val_index = img_index
elif (mode == 'test'):
self._test_index = img_index
assert os.path.exists(
self._devkit_path
), 'nuscenes dataset path does not exist: {}'.format(self._devkit_path)
pseudo_id = namedtuple('pseudo_id', ['pseudonym', 'code', 'cryptonym'])
voter_list = voters_in_text.strip().splitlines()
word_list = unique_words.strip().splitlines()
random = SystemRandom()
random_words = random.sample(word_list, len(voter_list))
random_keys = random.sample(code_range, len(voter_list))
print("There are %d voters in upcoming election:\n" % len(voter_list))
for voter in voter_list:
print(voter)
random.shuffle(voter_list)
print()
print("Distributing pseudonyms...\n")
pseudo = []
for i in range(len(voter_list)):
current = pseudo_id(random_words[i], str(random_keys[i]),
random_words[i] + str(random_keys[i]))
pseudo.append(current)
i = -1
try:
def shuffle(elements):
cryptorand = SystemRandom()
cryptorand.shuffle(elements)
def __init__(self, nboxes, nguess):
self.nboxes = nboxes
self.nguess = nguess
rnd = SystemRandom()
self.boxes = list(range(1, nboxes + 1))
rnd.shuffle(self.boxes)
def __init__(self, mode='test',limiter=0, shuffle_en=True):
name = 'kitti'
db.__init__(self, name, mode)
self._devkit_path = self._get_default_path()
self._data_path = self._devkit_path
self._mode = mode
self._uncertainty_sort_type = cfg.UC.SORT_TYPE
self._draw_width = int((cfg.LIDAR.X_RANGE[1] - cfg.LIDAR.X_RANGE[0])*(1/cfg.LIDAR.VOXEL_LEN))
self._draw_height = int((cfg.LIDAR.Y_RANGE[1] - cfg.LIDAR.Y_RANGE[0])*(1/cfg.LIDAR.VOXEL_LEN))
self._num_slices = cfg.LIDAR.NUM_SLICES
self._frame_sub_dir = 'velodyne'
self._train_dir = os.path.join(self._data_path, 'training', self._frame_sub_dir)
self._val_dir = os.path.join(self._data_path, 'training', self._frame_sub_dir)
self._test_dir = os.path.join(self._data_path, 'testing', self._frame_sub_dir)
self._split_dir = os.path.join(self._data_path, 'splits')
self._test_index = open(self._split_dir+'/test.txt').read().splitlines()
self._train_index = open(self._split_dir+'/train.txt').read().splitlines()
self._val_index = open(self._split_dir+'/val.txt').read().splitlines()
self._filetype = 'bin'
self._imtype = 'PNG'
self.type = 'lidar'
self._bev_slice_locations = [1,2,3,4,5,7]
self._mode = mode
#Backwards compatibility
self._train_sub_folder = 'training'
self._val_sub_folder = 'training'
self._test_sub_folder = 'testing'
self._classes = (
'dontcare', # always index 0
#'Pedestrian',
#'Cyclist',
'Car')
self.config = {
'cleanup': True,
'matlab_eval': False,
'rpn_file': None
}
self._class_to_ind = dict(
list(zip(self.classes, list(range(self.num_classes)))))
#self._train_index = sorted([d for d in os.listdir(self._train_dir) if d.endswith('.bin')])
#self._val_index = sorted([d for d in os.listdir(self._val_dir) if d.endswith('.bin')])
#self._test_index = sorted([d for d in os.listdir(self._test_dir) if d.endswith('.bin')])
#Limiter
if(limiter != 0):
if(limiter < len(self._val_index)):
self._val_index = self._val_index[:limiter]
if(limiter < len(self._train_index)):
self._train_index = self._train_index[:limiter]
if(limiter < len(self._test_index)):
self._test_index = self._test_index[:limiter]
rand = SystemRandom()
if(shuffle_en):
print('shuffling frame indices')
rand.shuffle(self._val_index)
rand.shuffle(self._train_index)
rand.shuffle(self._test_index)
assert os.path.exists(self._devkit_path), \
'Kitti dataset path does not exist: {}'.format(self._devkit_path)
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def __init__(self, mode='test', limiter=0, shuffle_en=True):
name = 'cadc'
db.__init__(self, name, mode)
self._devkit_path = self._get_default_path()
self._data_path = self._devkit_path
self._mode = mode
self._uncertainty_sort_type = cfg.UC.SORT_TYPE
self._frame_sub_dir = 'image_00'
if (mode == 'test'):
self._tod_filter_list = cfg.TEST.CADC_FILTER_LIST
else:
self._tod_filter_list = cfg.TRAIN.CADC_FILTER_LIST
scene_desc_filename = os.path.join(self._data_path,
'cadc_scene_description.csv')
self._load_scene_meta(scene_desc_filename)
self._train_dir = os.path.join(self._data_path, 'train',
self._frame_sub_dir)
self._val_dir = os.path.join(self._data_path, 'val',
self._frame_sub_dir)
#self._test_dir = os.path.join(self._data_path, 'testing', self._frame_sub_dir)
crop_top = 150
crop_bottom = 250
self._imwidth = 1280
self._imheight = 1024 - crop_top - crop_bottom
self._imtype = 'png'
self._filetype = 'png'
self.type = 'image'
self._mode = mode
#Backwards compatibility
#self._train_sub_folder = 'training'
#self._val_sub_folder = 'evaluation'
#self._test_sub_folder = 'testing'
self._classes = (
'dontcare', # always index 0
#'Pedestrian',
#'Cyclist',
'Car')
self.config = {'cleanup': True, 'matlab_eval': False, 'rpn_file': None}
self._class_to_ind = dict(
list(zip(self.classes, list(range(self.num_classes)))))
self._train_index = sorted(
[d for d in os.listdir(self._train_dir) if d.endswith('.png')])
self._val_index = sorted(
[d for d in os.listdir(self._val_dir) if d.endswith('.png')])
#self._test_index = sorted([d for d in os.listdir(self._test_dir) if d.endswith('.png')])
#Limiter
if (limiter != 0):
if (limiter < len(self._val_index)):
self._val_index = self._val_index[:limiter]
if (limiter < len(self._train_index)):
self._train_index = self._train_index[:limiter]
#if(limiter < len(self._test_index)):
# self._test_index = self._test_index[:limiter]
rand = SystemRandom()
if (shuffle_en):
print('shuffling image indices')
rand.shuffle(self._val_index)
rand.shuffle(self._train_index)
#rand.shuffle(self._test_index)
assert os.path.exists(self._devkit_path), \
'cadc dataset path does not exist: {}'.format(self._devkit_path)
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
#!/usr/bin/env python3
from string import ascii_lowercase
from itertools import product
from random import SystemRandom
from math import ceil, log
from secretstuff import FLAG
random = SystemRandom()
ALPHABET = ascii_lowercase + "_"
assert all(char in ALPHABET for char in FLAG)
bigrams = [''.join(bigram) for bigram in product(ALPHABET, repeat=2)]
random.shuffle(bigrams)
S_box = {}
for i in range(len(ALPHABET)):
for j in range(len(ALPHABET)):
S_box[ALPHABET[i]+ALPHABET[j]] = bigrams[i*len(ALPHABET) + j]
assert len(set(S_box.keys())) == 27*27
def encrypt(message):
if len(message) % 2:
message += "_"
message = list(message)
rounds = int(2 * ceil(log(len(message), 2))) # The most secure amount of rounds
for round in range(rounds):
# Encrypt
from random import SystemRandom
random = SystemRandom()
lista = []
i,n = 0,0
qtd = int(input('Informe a quantidade de alunos na sala : '))
while i < qtd:
a = str(input('Digite o nome do {:2}° aluno : '.format(i+1)))
lista.append(a)
i+=1
random.shuffle(lista)
print('\nA lista sorteada foi :')
while n < qtd:
print('{:2}° aluno escolhido : {}'.format(n+1,lista[n]))
n+=1
def __init__(self, mode='test', limiter=0, shuffle_en=True):
name = 'cadc'
db.__init__(self, name, mode)
self._devkit_path = self._get_default_path()
self._data_path = self._devkit_path
self._mode = mode
if (mode == 'test'):
self._tod_filter_list = cfg.TEST.CADC_FILTER_LIST
else:
self._tod_filter_list = cfg.TRAIN.CADC_FILTER_LIST
scene_desc_filename = os.path.join(self._data_path,
'cadc_scene_description.csv')
self._load_scene_meta(scene_desc_filename)
self._uncertainty_sort_type = cfg.UC.SORT_TYPE
self._draw_width = int((cfg.LIDAR.X_RANGE[1] - cfg.LIDAR.X_RANGE[0]) *
(1 / cfg.LIDAR.VOXEL_LEN))
self._draw_height = int((cfg.LIDAR.Y_RANGE[1] - cfg.LIDAR.Y_RANGE[0]) *
(1 / cfg.LIDAR.VOXEL_LEN))
self._num_slices = cfg.LIDAR.NUM_SLICES
self._frame_sub_dir = 'point_clouds'
self._annotation_sub_dir = 'annotation_00'
self._calib_sub_dir = 'calib'
self._train_dir = os.path.join(self._data_path, 'train',
self._frame_sub_dir)
self._val_dir = os.path.join(self._data_path, 'val',
self._frame_sub_dir)
#self._test_dir = os.path.join(self._data_path, 'testing', self._frame_sub_dir)
self._filetype = 'bin'
self._imtype = 'PNG'
self.type = 'lidar'
self._bev_slice_locations = [1, 2, 3, 4, 5, 7]
self._mode = mode
#Backwards compatibility
#self._train_sub_folder = 'training'
#self._val_sub_folder = 'evaluation'
#self._test_sub_folder = 'testing'
self._classes = (
'dontcare', # always index 0
#'Pedestrian',
#'Cyclist',
'Car')
self.config = {'cleanup': True, 'matlab_eval': False, 'rpn_file': None}
self._class_to_ind = dict(
list(zip(self.classes, list(range(self.num_classes)))))
self._train_index = sorted(
[d for d in os.listdir(self._train_dir) if d.endswith('.bin')])
self._val_index = sorted(
[d for d in os.listdir(self._val_dir) if d.endswith('.bin')])
#self._test_index = sorted([d for d in os.listdir(self._test_dir) if d.endswith('.bin')])
#Limiter
if (limiter != 0):
if (limiter < len(self._val_index)):
self._val_index = self._val_index[:limiter]
if (limiter < len(self._train_index)):
self._train_index = self._train_index[:limiter]
#if(limiter < len(self._test_index)):
# self._test_index = self._test_index[:limiter]
rand = SystemRandom()
if (shuffle_en):
print('shuffling frame indices')
rand.shuffle(self._val_index)
rand.shuffle(self._train_index)
#rand.shuffle(self._test_index)
assert os.path.exists(self._devkit_path), \
'cadc dataset path does not exist: {}'.format(self._devkit_path)
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def __init__(self, mode='test', limiter=0, shuffle_en=True):
name = 'kitti'
db.__init__(self, name, mode)
self._devkit_path = self._get_default_path()
self._data_path = self._devkit_path
self._mode = mode
self._uncertainty_sort_type = cfg.UC.SORT_TYPE
self._frame_sub_dir = 'image_2'
self._train_dir = os.path.join(self._data_path, 'training',
self._frame_sub_dir)
self._val_dir = os.path.join(self._data_path, 'training',
self._frame_sub_dir)
self._test_dir = os.path.join(self._data_path, 'testing',
self._frame_sub_dir)
self._split_dir = os.path.join(self._data_path, 'splits')
self._imwidth = 1242
self._imheight = 375
self._imtype = 'png'
self._filetype = 'png'
self.type = 'image'
self._mode = mode
#Backwards compatibility
self._train_sub_folder = 'training'
self._val_sub_folder = 'training'
self._test_sub_folder = 'testing'
self._classes = (
'dontcare', # always index 0
#'Pedestrian',
#'Cyclist',
'Car')
self.config = {'cleanup': True, 'matlab_eval': False, 'rpn_file': None}
self._class_to_ind = dict(
list(zip(self.classes, list(range(self.num_classes)))))
self._test_index = open(self._split_dir +
'/test.txt').read().splitlines()
self._train_index = open(self._split_dir +
'/train.txt').read().splitlines()
self._val_index = open(self._split_dir +
'/val.txt').read().splitlines()
#self._train_index = self._train_index + self._val_index[250:]
#self._val_index = self._val_index[:250]
#self._train_index = sorted([d for d in os.listdir(self._train_dir) if d.endswith('.png')])
#self._val_index = sorted([d for d in os.listdir(self._val_dir) if d.endswith('.png')])
#self._test_index = sorted([d for d in os.listdir(self._test_dir) if d.endswith('.png')])
#Limiter
if (limiter != 0):
if (limiter < len(self._val_index)):
self._val_index = self._val_index[:limiter]
if (limiter < len(self._train_index)):
self._train_index = self._train_index[:limiter]
if (limiter < len(self._test_index)):
self._test_index = self._test_index[:limiter]
rand = SystemRandom()
if (shuffle_en):
print('shuffling image indices')
rand.shuffle(self._val_index)
rand.shuffle(self._train_index)
rand.shuffle(self._test_index)
assert os.path.exists(self._devkit_path), \
'Kitti dataset path does not exist: {}'.format(self._devkit_path)
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
class Encryptor(object):
def __init__(self, characters=None, backlog=-1):
self.random = SystemRandom()
if characters is None:
unicode_chars = [chr(i) for i in range(1, 1028)]
self.characters = [c for c in unicode_chars]
self.characters.remove('\r')
self.characters.remove('\f')
self.random.shuffle(self.characters)
self.rev_ord = {}
self.ord = {}
self.range = len(self.characters)
self.make_key(self.characters)
self.mix = backlog
def load_key_file(self, filepath='key'):
with open(filepath, 'r') as f:
key_data = f.read()
self.characters = [c for c in key_data]
self.make_key(self.characters)
return self
def save_key_file(self, filepath='key'):
with open(filepath, 'w') as f:
f.write(''.join(self.characters))
return self
def make_key(self, characters):
if characters is not self.characters:
self.characters = characters
self.rev_ord = {}
self.ord = {}
for i, char in enumerate(self.characters):
self.rev_ord[i + 1] = char
for key, value in self.rev_ord.items():
self.ord[value] = key
self.range = len(self.characters)
return self
def increment(self, start, amount):
rng = (1, self.range)
remainder = amount % rng[1]
amount = remainder
# Number is equally divisible and we can use the starting number
if amount >= rng[1] and remainder == 0:
return start
# Amount is more than the range and there is a remainder.
if amount >= rng[1] and remainder > 0:
return start + remainder
increase = start + amount
if increase <= rng[1]:
return increase
if increase > rng[1]:
return increase % rng[1]
def decrement(self, start, amount):
rng = (1, self.range)
remainder = amount % rng[1]
amount = remainder
if amount >= rng[1] and remainder == 0:
return start
if amount >= rng[1] and remainder > 0:
return rng[1] - ((amount - start) % rng[1])
if amount < rng[1] and start - amount < rng[0]:
return rng[1] - (amount - start)
if amount < rng[1] and start - amount >= rng[0]:
return start - amount
def encrypt(self, string):
if not string:
# raise ValueError("String cannot be empty.")
return string
first_letter_ord = self.ord[string[0]]
output = ""
mix = 0
for i, c in enumerate(string):
increase = sum([self.ord[x] for x in string[:i][self.mix:]])
c_ord = self.increment(self.ord[c], increase + mix)
mix += increase
if i == 0:
out = c
else:
_ = self.increment(first_letter_ord, c_ord)
out = self.rev_ord[_]
output += out
first_letter_ord = self.ord[c]
first_letter_number = self.increment(
self.ord[string[0]], sum([self.ord[x] for x in output[self.mix:]]))
output = self.rev_ord[first_letter_number] + output[1:]
return output
def decrypt(self, string):
if not string:
# raise ValueError("String cannot be empty.")
return string
first_letter_ord = self.ord[string[0]]
if not first_letter_ord:
raise ValueError("Your message is invalid.")
output = ""
# Set string's first letter to the correct un-encoded letter.
first_letter_number = self.decrement(
first_letter_ord, sum([self.ord[x] for x in string[self.mix:]]))
first_letter = self.rev_ord[first_letter_number]
string = first_letter + string[1:]
mix = 0
for i, c in enumerate(string):
increase = sum([self.ord[x] for x in output[:i][self.mix:]])
c_ord = self.decrement(self.ord[c], increase + mix)
mix += increase
if i == 0:
out = c
else:
last_c_ord = self.ord[output[-1]]
out_ord = self.decrement(c_ord, last_c_ord)
out = self.rev_ord[out_ord]
output += out
first_letter_ord = self.ord[out]
return output