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load_edata.py
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load_edata.py
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from PIL import Image
from torchvision import transforms
from os.path import join
import abc
import numpy as np
import torch
import torch.utils.data as data
import imageio
import os
class BaseData(data.Dataset):
'''
The dataset used for the IFDL dataset.
'''
def __init__(self, args):
super(BaseData, self).__init__()
self.crop_size = args.crop_size
## demo dataset:
self.mani_data_dir = './data_dir'
## the full dataset:
# self.mani_data_dir = './data'
self.image_names = []
self.image_class = []
self.mask_names = []
def __getitem__(self, index):
res = self.get_item(index)
return res
def __len__(self):
return len(self.image_names)
def generate_mask(self, mask):
'''
generate the corresponding binary mask.
'''
mask = mask.astype(np.float32) / 255
mask[mask > 0.5] = 1
mask[mask <= 0.5] = 0
mask = np.expand_dims(mask, axis=0)
mask = torch.from_numpy(mask)
return mask
def rgba2rgb(self, rgba, background=(255, 255, 255)):
'''
turn rgba to rgb.
'''
row, col, ch = rgba.shape
rgb = np.zeros((row, col, 3), dtype='float32')
r, g, b, a = rgba[:, :, 0], rgba[:, :, 1], rgba[:, :, 2], rgba[:, :, 3]
a = np.asarray(a, dtype='float32') / 255.0
R, G, B = background
rgb[:, :, 0] = r * a + (1.0 - a) * R
rgb[:, :, 1] = g * a + (1.0 - a) * G
rgb[:, :, 2] = b * a + (1.0 - a) * B
return np.asarray(rgb, dtype='uint8') # the output value is uint8 that belongs to [0,255]
def get_image(self, image_name):
'''
return the image with the tensor.
'''
image = imageio.imread(image_name)
if len(image.shape) == 2:
image = imageio.imread(image_name, as_gray=False, pilmode="RGB")
if image.shape[-1] == 4:
image = self.rgba2rgb(image)
image = torch.from_numpy(image.astype(np.float32) / 255)
return image.permute(2, 0, 1)
def get_mask(self, mask_name):
'''
return the binary mask.
'''
mask = Image.open(mask_name).convert('L')
mask = mask.resize(self.crop_size, resample=Image.BICUBIC)
mask = np.asarray(mask)
mask = self.generate_mask(mask)
return mask
@abc.abstractmethod
def get_item(self, index):
'''
blur
image = Image.fromarray(image)
image = image.filter(ImageFilter.GaussianBlur(radius=7))
image = np.asarray(image)
resize
image = Image.fromarray(image)
image = image.resize((int(image.width*0.25), int(image.height*0.25)), resample=Image.BILINEAR)
image = np.asarray(image)
noise
import skimage
image = skimage.util.random_noise(image/255., mode='gaussian', mean=0, var=15/255) * 255
jpeg compression
im = Image.open(image_name)
temp_name = './temp/' + image_name.split('/')[-1][:-3] + 'jpg'
im.save(temp_name, 'JPEG', quality=50)
image = Image.open(temp_name)
image = np.asarray(image)
'''
pass
class ValColumbia(BaseData):
def __init__(self, args):
super(ValColumbia, self).__init__(args)
ddir = os.path.join(self.mani_data_dir, 'columbia')
with open(join(ddir, 'vallist.txt')) as f:
contents = f.readlines()
for content in contents:
_ = os.path.join(ddir, '4cam_splc', content.strip())
self.image_names.append(_)
self.image_class = [1] * len(self.image_names)
def get_item(self, index):
image_name = self.image_names[index]
cls = self.image_class[index]
# image
image = self.get_image(image_name)
# mask
if '4cam_splc' in image_name:
mask_name = image_name.replace('4cam_splc', 'mask').replace('.tif', '.jpg')
mask = self.get_mask(mask_name)
else:
mask = np.zeros((1, 256, 256), dtype='float32')
return image, mask, cls, image_name
class ValCoverage(BaseData):
def __init__(self, args):
super(ValCoverage, self).__init__(args)
ddir = os.path.join(self.mani_data_dir, 'Coverage')
with open(join(ddir, 'fake.txt')) as f:
contents = f.readlines()
for content in contents:
_ = os.path.join(ddir, 'image', content.strip())
self.image_names.append(_)
self.image_class = [2] * len(self.image_names)
def get_item(self, index):
image_name = self.image_names[index]
cls = self.image_class[index]
## read image.
image = self.get_image(image_name)
# mask
mask_name = image_name.replace('image', 'mask').replace('t.tif', 'forged.tif')
mask = self.get_mask(mask_name)
return image, mask, cls, image_name
class ValCasia(BaseData):
def __init__(self, args):
super(ValCasia, self).__init__(args)
ddir = os.path.join(self.mani_data_dir, 'CASIA/CASIA1')
with open(join(ddir, 'fake.txt')) as f:
contents = f.readlines()
for content in contents:
tag = content.split('/')[-1].split('_')[1]
if tag == 'D':
self.image_class.append(1)
elif tag == 'S':
self.image_class.append(2)
else:
raise Exception('unknown class: {}'.format(content))
self.image_names.append(os.path.join(ddir, 'fake', content.strip()))
ddir = os.path.join(self.mani_data_dir, 'CASIA/CASIA2')
with open(join(ddir, 'fake.txt')) as f:
contents = f.readlines()
for content in contents:
tag = content.split('/')[-1].split('_')[1]
if tag == 'D':
self.image_class.append(1)
elif tag == 'S':
self.image_class.append(2)
else:
raise Exception('unknown class: {}'.format(content))
self.image_names.append(os.path.join(ddir, 'fake', content.strip()))
def get_item(self, index):
image_name = self.image_names[index]
cls = self.image_class[index]
# image
image = self.get_image(image_name)
# mask
if '.jpg' in image_name:
mask_name = image_name.replace('fake', 'mask').replace('.jpg', '_gt.png')
else:
mask_name = image_name.replace('fake', 'mask').replace('.tif', '_gt.png')
mask = self.get_mask(mask_name)
return image, mask, cls, image_name
class ValNIST16(BaseData):
def __init__(self, args):
super(ValNIST16, self).__init__(args)
ddir = os.path.join(self.mani_data_dir, 'NIST16')
file_name = 'alllist.txt'
with open(join(ddir, file_name)) as f:
contents = f.readlines()
for content in contents:
image_name, mask_name = content.split(' ')
self.image_names.append(join(ddir, image_name))
self.mask_names.append(join(ddir, mask_name.strip()))
def get_item(self, index):
image_name = self.image_names[index]
mask_name = self.mask_names[index]
if 'splice' in mask_name:
cls = 1
elif 'manipulation' in mask_name:
cls = 2
elif 'remove' in mask_name:
cls = 3
else:
cls = 0
# image
image = self.get_image(image_name)
if image.size()[2]*image.size()[1] >= 1000*1000:
image = imageio.imread(image_name)
if image.shape[-1] == 4:
image = self.rgba2rgb(image)
image = Image.fromarray(image)
image = image.resize((1000, 1000), resample=Image.BICUBIC)
image = np.asarray(image)
image = torch.from_numpy(image.astype(np.float32) / 255)
image = image.permute(2, 0, 1)
# mask
mask = self.get_mask(mask_name)
mask = torch.abs(mask - 1)
return image, mask, cls, image_name
class ValIMD2020(BaseData):
def __init__(self, args):
super(ValIMD2020, self).__init__(args)
ddir = os.path.join(self.mani_data_dir, 'IMD2020')
file_name = 'fake.txt'
with open(join(ddir, file_name)) as f:
contents = f.readlines()
for content in contents:
image_name = content.strip()
if '.jpg' in image_name:
mask_name = image_name.replace('.jpg', '_mask.png')
else:
mask_name = image_name.replace('.png', '_mask.png')
self.image_names.append(join(ddir, 'fake_img', image_name))
self.mask_names.append(join(ddir, 'mask', mask_name))
self.image_class = [2] * len(self.image_names)
def get_item(self, index):
image_name = self.image_names[index]
mask_name = self.mask_names[index]
cls = self.image_class[index]
try:
image = self.get_image(image_name)
except:
print(f"Fail at {image_name}.")
mask = self.get_mask(mask_name)
return image, mask, cls, image_name