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from __future__ import print_function, division
import os
import torch
import pandas as pd
import cv2
import numpy as np
import random
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import pdb
import math
import os
import copy
import imgaug.augmenters as iaa
# face_scale = 1.3 #default for test, for training , can be set from [1.2 to 1.5]
# data augment from 'imgaug' --> Add (value=(-40,40), per_channel=True), GammaContrast (gamma=(0.5,1.5))
seq = iaa.Sequential([
iaa.Add(value=(-40, 40), per_channel=True), # Add color
iaa.GammaContrast(gamma=(0.5, 1.5)) # GammaContrast with a gamma of 0.5 to 1.5
])
# array
class RandomErasing(object):
'''
Class that performs Random Erasing in Random Erasing Data Augmentation by Zhong et al.
-------------------------------------------------------------------------------------
probability: The probability that the operation will be performed.
sl: min erasing area
sh: max erasing area
r1: min aspect ratio
mean: erasing value
-------------------------------------------------------------------------------------
'''
def __init__(self, probability=0.5, sl=0.01, sh=0.05, r1=0.5, mean=[0.4914, 0.4822, 0.4465]):
self.probability = probability
self.mean = mean
self.sl = sl
self.sh = sh
self.r1 = r1
def __call__(self, sample):
img, map_x, spoofing_label = sample['image_x'], sample['map_x'], sample['spoofing_label']
if random.uniform(0, 1) < self.probability:
attempts = np.random.randint(1, 3)
for attempt in range(attempts):
area = img.shape[0] * img.shape[1]
target_area = random.uniform(self.sl, self.sh) * area
aspect_ratio = random.uniform(self.r1, 1 / self.r1)
h = int(round(math.sqrt(target_area * aspect_ratio)))
w = int(round(math.sqrt(target_area / aspect_ratio)))
if w < img.shape[1] and h < img.shape[0]:
x1 = random.randint(0, img.shape[0] - h)
y1 = random.randint(0, img.shape[1] - w)
img[x1:x1 + h, y1:y1 + w, 0] = self.mean[0]
img[x1:x1 + h, y1:y1 + w, 1] = self.mean[1]
img[x1:x1 + h, y1:y1 + w, 2] = self.mean[2]
return {'image_x': img, 'map_x': map_x, 'spoofing_label': spoofing_label}
# Tensor
class Cutout(object):
def __init__(self, length=50):
self.length = length
def __call__(self, sample):
img, map_x, spoofing_label = sample['image_x'], sample['map_x'], sample['spoofing_label']
h, w = img.shape[1], img.shape[2] # Tensor [1][2], nparray [0][1]
mask = np.ones((h, w), np.float32)
y = np.random.randint(h)
x = np.random.randint(w)
length_new = np.random.randint(1, self.length)
y1 = np.clip(y - length_new // 2, 0, h)
y2 = np.clip(y + length_new // 2, 0, h)
x1 = np.clip(x - length_new // 2, 0, w)
x2 = np.clip(x + length_new // 2, 0, w)
mask[y1: y2, x1: x2] = 0.
mask = torch.from_numpy(mask)
mask = mask.expand_as(img)
img *= mask
return {'image_x': img, 'map_x': map_x, 'spoofing_label': spoofing_label}
class Normaliztion(object):
"""
same as mxnet, normalize into [-1, 1]
image = (image - 127.5)/128
"""
def __call__(self, sample):
image_x, map_x, spoofing_label = sample['image_x'], sample['map_x'], sample['spoofing_label']
new_image_x = (image_x - 127.5) / 128 # [-1,1]
new_map_x = map_x / 255.0 # [0,1]
return {'image_x': new_image_x, 'map_x': new_map_x, 'spoofing_label': spoofing_label}
class RandomHorizontalFlip(object):
"""Horizontally flip the given Image randomly with a probability of 0.5."""
def __call__(self, sample):
image_x, map_x, spoofing_label = sample['image_x'], sample['map_x'], sample['spoofing_label']
new_image_x = np.zeros((256, 256, 3))
new_map_x = np.zeros((32, 32))
p = random.random()
if p < 0.5:
# print('Flip')
new_image_x = cv2.flip(image_x, 1)
new_map_x = cv2.flip(map_x, 1)
return {'image_x': new_image_x, 'map_x': new_map_x, 'spoofing_label': spoofing_label}
else:
# print('no Flip')
return {'image_x': image_x, 'map_x': map_x, 'spoofing_label': spoofing_label}
class ToTensor(object):
"""
Convert ndarrays in sample to Tensors.
process only one batch every time
"""
def __call__(self, sample):
image_x, map_x, spoofing_label = sample['image_x'], sample['map_x'], sample['spoofing_label']
# swap color axis because
# numpy image: (batch_size) x H x W x C
# torch image: (batch_size) x C X H X W
image_x = image_x[:, :, ::-1].transpose((2, 0, 1))
image_x = np.array(image_x)
map_x = np.array(map_x)
spoofing_label_np = np.array([0], dtype=np.long)
spoofing_label_np[0] = spoofing_label
return {'image_x': torch.from_numpy(image_x.astype(np.float)).float(),
'map_x': torch.from_numpy(map_x.astype(np.float)).float(),
'spoofing_label': torch.from_numpy(spoofing_label_np.astype(np.long)).long()}
class Spoofing_train_g(Dataset):
def __init__(self, info_list, root_dir, map_dir, transform=None):
# +1,1_1_21_1
self.landmarks_frame = pd.read_csv(info_list, delimiter=',', header=None)
self.root_dir = root_dir
self.map_dir = map_dir
self.transform = transform
def __len__(self):
return len(self.landmarks_frame)
def __getitem__(self, idx):
# 1_1_30_1
videoname = str(self.landmarks_frame.iloc[idx, 1])
image_path = os.path.join(self.root_dir, videoname)
map_path = os.path.join(self.map_dir, videoname)
image_x, map_x = self.get_single_image_x(image_path, map_path, videoname)
spoofing_label = self.landmarks_frame.iloc[idx, 0]
if spoofing_label == 1:
spoofing_label = 1 # real
else:
spoofing_label = 0
map_x = np.zeros((32, 32)) # fake
sample = {'image_x': image_x, 'map_x': map_x, 'spoofing_label': spoofing_label}
if self.transform:
sample = self.transform(sample)
return sample
def get_idx(self):
real_data_idx = []
fake_data_idx = []
i, j = 0, 0
for idx_all in range(self.__len__()):
videoname = str(self.landmarks_frame.iloc[idx_all, 1])
if videoname[:1] == 'p':
fake_data_idx.append(i)
i += 1
else:
real_data_idx.append(j)
j += 1
return real_data_idx, fake_data_idx
def get_single_image_x(self, images_path, maps_path, videoname):
frame_total = len([name for name in os.listdir(images_path) if os.path.isfile(os.path.join(images_path, name))])
# random choose 1 frame
image_id = np.random.randint(1, frame_total)
if videoname[:1] == 'p':
image_id = np.random.randint(1, 100)
s = "%d_scene" % image_id
image_name = s + '.jpg'
# /home/shejiahui5/notespace/data/oulu_img/train_bbox_files/p2_0_1_30/21_scence.jpg
s = "%d_depth1D" % image_id
map_name = s + '.jpg'
else:
image_id = np.random.randint(1, frame_total)
s = "_%d_scene" % image_id
image_name = videoname + s + '.jpg'
s = "_%d_depth1D" % image_id
map_name = videoname + s + '.jpg'
image_path = os.path.join(images_path, image_name)
map_path = os.path.join(maps_path, map_name)
map_x = np.zeros((32, 32))
# RGB
image_x = cv2.imread(image_path)
image_x = cv2.resize(image_x, (256, 256))
# data augment from 'imgaug' --> Add (value=(-40,40), per_channel=True), GammaContrast (gamma=(0.5,1.5))
image_x_aug = seq.augment_image(image_x)
# gray-map
if os.path.exists(map_path):
map_x = cv2.imread(map_path, 0)
map_x = cv2.resize(map_x, (32, 32))
return image_x_aug, map_x
class SeparateBatchSampler(object):
def __init__(self, real_data_idx, fake_data_idx, batch_size, ratio, put_back=False):
self.batch_size = batch_size
self.ratio = ratio
self.real_data_num = len(real_data_idx)
self.fake_data_num = len(fake_data_idx)
self.max_num_image = max(self.real_data_num, self.fake_data_num)
self.real_data_idx = real_data_idx
self.fake_data_idx = fake_data_idx
self.processed_idx = copy.deepcopy(self.real_data_idx)
def __len__(self):
return self.max_num_image // (int(self.batch_size * self.ratio))
def __iter__(self):
batch_size_real_data = int(math.floor(self.ratio * self.batch_size))
batch_size_fake_data = self.batch_size - batch_size_real_data
self.processed_idx = copy.deepcopy(self.real_data_idx)
rand_real_data_idx = np.random.permutation(len(self.real_data_idx) // 2)
for i in range(self.__len__()):
batch = []
idx_fake_data = random.sample(self.fake_data_idx, batch_size_fake_data)
for j in range(batch_size_real_data // 2):
idx = rand_real_data_idx[(i * batch_size_real_data + j) % (self.real_data_num // 2)]
batch.append(self.processed_idx[2 * idx])
batch.append(self.processed_idx[2 * idx + 1])
for idx in idx_fake_data:
batch.append(idx + self.real_data_num)
# batch.append(2 * idx + 1 + self.real_data_num)
yield batch
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