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load_data.py
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load_data.py
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# ------------------------------------------------------------------------------
# Author: Xiao Guo (guoxia11@msu.edu)
# CVPR2023: Hierarchical Fine-Grained Image Forgery Detection and Localization
# ------------------------------------------------------------------------------
from os.path import isfile, join
from PIL import Image
from torchvision import transforms
import numpy as np
import abc
import cv2
import torch.utils.data as data
import torch.nn.functional as F
import random
random.seed(1234567890)
from random import randrange
import torch.nn as nn
import torch
import imageio
import time
import math
import torch
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
self.file_path = '/user/guoxia11/cvlshare/cvl-guoxia11/IMDL/REAL'
self.file_path_fake = '/user/guoxia11/cvlshare/cvl-guoxia11/IMDL/FAKE'
# Real and Fake images.
self.image_names = []
self.image_class = self._img_list_retrieve()
for idx, _ in enumerate(self.image_class):
self.image_names += _
def __getitem__(self, index):
res = self.get_item(index)
return res
def __len__(self):
return len(self.image_names)
@abc.abstractmethod
def _img_list_retrieve():
pass
def _resize_func(self, input_img):
'''resize the input image into the crop size.'''
input_img = Image.fromarray(input_img)
input_img = input_img.resize(self.crop_size, resample=Image.BICUBIC)
input_img = np.asarray(input_img)
return input_img
def get_image(self, image_name, aug_index=None):
'''transform the image.'''
image = imageio.imread(image_name)
if image.shape[-1] == 4:
image = self.rgba2rgb(image)
image = self._resize_func(image)
image = image.astype(np.float32) / 255.
image = torch.from_numpy(image)
return image.permute(2, 0, 1)
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')
def generate_4masks(self, mask):
'''generate 4 masks at different scale.'''
crop_height, crop_width = self.crop_size
ma_height, ma_width = mask.shape[:2]
mask_pil = Image.fromarray(mask)
if ma_height != crop_height or ma_width != crop_width:
mask_pil = mask_pil.resize(self.crop_size, resample=Image.BICUBIC)
(width2, height2) = (mask_pil.width // 2, mask_pil.height // 2)
(width3, height3) = (mask_pil.width // 4, mask_pil.height // 4)
(width4, height4) = (mask_pil.width // 8, mask_pil.height // 8)
mask2 = mask_pil.resize((width2, height2))
mask3 = mask_pil.resize((width3, height3))
mask4 = mask_pil.resize((width4, height4))
mask = np.asarray(mask_pil)
mask = mask.astype(np.float32) / 255
mask[mask > 0.5] = 1
mask[mask <= 0.5] = 0
mask2 = np.asarray(mask2).astype(np.float32) / 255
mask2[mask2 > 0.5] = 1
mask2[mask2 <= 0.5] = 0
mask3 = np.asarray(mask3).astype(np.float32) / 255
mask3[mask3 > 0.5] = 1
mask3[mask3 <= 0.5] = 0
mask4 = np.asarray(mask4).astype(np.float32) / 255
mask4[mask4 > 0.5] = 1
mask4[mask4 <= 0.5] = 0
mask = torch.from_numpy(mask)
mask2 = torch.from_numpy(mask2)
mask3 = torch.from_numpy(mask3)
mask4 = torch.from_numpy(mask4)
# print(mask.size(), mask2.size(), mask3.size(), mask4.size())
return mask, mask2, mask3, mask4
def get_mask(self, image_name, cls, aug_index=None):
'''given the cls, we return the mask.'''
# authentic
if cls in [0,1,2,3,4]:
mask = self.load_mask('', real=True, aug_index=aug_index)
return_res = [0,0,0,0]
# splice
elif cls == 5:
if '.jpg' in image_name:
mask_name = image_name.replace('fake', 'mask').replace('.jpg', '.png')
else:
mask_name = image_name.replace('fake', 'mask').replace('.tif', '.png')
mask = self.load_mask(mask_name, aug_index=aug_index)
return_res = [1,1,1,cls - 4]
# inpainting
elif cls == 6:
mask_name = image_name.replace('/fake/', '/mask/').replace('.jpg', '.png')
mask = self.load_mask(mask_name, aug_index=aug_index)
return_res = [1,1,1,cls - 4]
# copy-move
elif cls == 7:
mask_name = image_name.replace('.png', '_mask.png')
mask_name = mask_name.replace('CopyMove', 'CopyMove_mask')
mask = self.load_mask(mask_name, aug_index=aug_index)
return_res = [1,1,1,cls - 4]
# faceshifter
elif cls == 8:
image_id = image_name.split('/')[-1].split('.')[0]
mask_name = image_name.replace(image_id, f'mask/{image_id}_mask')
mask = self.load_mask(mask_name, aug_index=aug_index)
return_res = [1,2,2,cls - 4]
# STGAN
elif cls == 9:
image_id = image_name.split('/')[-1].split('.')[0]
mask_name = image_name.replace('fake', 'mask').replace(image_id, f'{image_id}_label')
mask = self.load_mask(mask_name, aug_index=aug_index)
return_res = [1,2,2,cls - 4]
## they are star2, hisd, stylegan2, stylegan3, ddpm, ddim, latent, guided
elif cls in [10,11,12,13,14,15,16,17]:
mask = self.load_mask('', real=False, full_syn=True, aug_index=aug_index)
if cls in [10,11]:
return_res = [2,3,3,cls-4]
elif cls in [12,13]:
return_res = [2,3,4,cls-4]
elif cls in [14,15]:
return_res = [2,4,5,cls-4]
elif cls in [16,17]:
return_res = [2,4,6,cls-4]
else:
print(cls, index)
raise Exception('class is not defined!')
return mask, return_res
def load_mask(self, mask_name, real=False, full_syn=False, gray=True, aug_index=None):
'''binarize the mask, given the mask_name.'''
if real:
mask = np.zeros(self.crop_size)
else:
if not full_syn:
mask = imageio.imread(mask_name) if not gray else np.asarray(Image.open(mask_name).convert('RGB').convert('L'))
mask = mask.astype(np.float32)
else:
mask = np.ones(self.crop_size)
mask = self.generate_4masks(mask)
return mask
def get_cls(self, image_name):
'''return the forgery/authentic cls given the image_name.'''
if '/authentic/' in image_name:
return_cls = 0
elif '/REAL/LSUN/' in image_name:
return_cls = 0
elif '/afhq_v2/' in image_name:
return_cls = 1
elif '/CelebAHQ/' in image_name:
return_cls = 2
elif '/FFHQ/' in image_name:
return_cls = 3
elif '/Youtube' in image_name:
return_cls = 4
elif '/splice' in image_name:
return_cls = 5
elif '/Inpainting' in image_name:
return_cls = 6
elif '/CopyMove' in image_name:
return_cls = 7
elif '/FaShifter' in image_name:
return_cls = 8
elif '/STGAN' in image_name:
return_cls = 9
elif '/Star2' in image_name:
return_cls = 10
elif '/HiSD' in image_name:
return_cls = 11
elif '/STYL2' in image_name:
return_cls = 12
elif '/STYL3' in image_name:
return_cls = 13
elif '/DDPM_' in image_name:
return_cls = 14
elif '/DDIM_' in image_name:
return_cls = 15
elif '/D_latent' in image_name:
return_cls = 16
elif '/GLIDE/' in image_name:
return_cls = 17
else:
print(image_name)
raise ValueError
return return_cls
class TrainData(BaseData):
'''
The dataset used for the IFDL dataset.
'''
def __init__(self, args):
self.is_train = True
self.val_num = 90000
super(TrainData, self).__init__(args)
def img_retrieve(self, file_text, file_folder, real=True):
'''
Parameters:
file_text: str, text file for images.
file_folder: str, images folder.
Returns:
the image list.
'''
result_list = []
val_num = self.val_num * 3 if file_text in ["Youtube", "FaShifter"] else self.val_num
data_path = self.file_path if real else self.file_path_fake
data_text = join(data_path, file_text)
data_path = join(data_path, file_folder)
file_handler = open(data_text)
contents = file_handler.readlines()
if self.is_train:
contents_lst = contents[:val_num]
else:
contents_lst = contents[val_num:]
for content in contents_lst:
if '.npy' not in content and 'mask' not in content:
img_name = content.strip()
img_name = join(data_path, img_name)
result_list.append(img_name)
file_handler.close()
## only truncate the val_num images.
if len(result_list) < val_num:
mul_factor = (val_num//len(result_list)) + 2
result_list = result_list * mul_factor
result_list = result_list[-val_num:]
return result_list
def get_item(self, index):
'''
given the index, this function returns the image with the forgery mask
this function calls get_image, get_mask for the image and mask torch tensor.
'''
image_name = self.image_names[index]
cls = self.get_cls(image_name)
# image and mask
aug_index = randrange(0, 8)
image = self.get_image(image_name, aug_index)
mask, return_res = self.get_mask(image_name, cls, aug_index)
return image, mask, return_res[0], return_res[1], return_res[2], return_res[3]
def _img_list_retrieve(self):
'''Returns image list for different authentic and forgery image.'''
authentic_names = self.img_retrieve('authentic.txt', 'authentic')
splice_names = self.img_retrieve('splice_randmask.txt', 'splice_randmask/fake',False)
inpainting_names = self.img_retrieve('Inpainting.txt', 'Inpainting/fake', False)
copymove_names = self.img_retrieve('copy_move.txt', 'CopyMove', False)
STGAN_names = self.img_retrieve('STGAN.txt', 'STGAN/fake', False)
FaShifter_names = self.img_retrieve('FaShifter.txt', 'FaShifter', False)
return [authentic_names, splice_names, inpainting_names, copymove_names, STGAN_names, FaShifter_names]
class ValData(BaseData):
'''
The dataset used for the IFDL dataset.
'''
def __init__(self, args):
self.is_train = False
self.val_num = 900
super(ValData, self).__init__(args)
def img_retrieve(self, file_text, file_folder, real=True):
'''
Parameters:
file_text: str, text file for images.
file_folder: str, images folder.
Returns:
the image list.
'''
result_list = []
val_num = self.val_num * 3 if file_text in ["Youtube", "FaShifter"] else self.val_num
data_path = self.file_path if real else self.file_path_fake
data_text = join(data_path, file_text)
data_path = join(data_path, file_folder)
file_handler = open(data_text)
contents = file_handler.readlines()
for content in contents[-val_num:]:
if '.npy' not in content and 'mask' not in content:
img_name = content.strip()
img_name = join(data_path, img_name)
result_list.append(img_name)
file_handler.close()
## only truncate the val_num images.
if len(result_list) < val_num:
mul_factor = (val_num//len(result_list)) + 2
result_list = result_list * mul_factor
result_list = result_list[-val_num:]
return result_list
def get_item(self, index):
'''
given the index, this function returns the image with the forgery mask
this function calls get_image, get_mask for the image and mask torch tensor.
'''
image_name = self.image_names[index]
cls = self.get_cls(image_name)
# image
image = self.get_image(image_name)
mask, return_res = self.get_mask(image_name, cls)
return image, mask, return_res[0], return_res[1], return_res[2], return_res[3], image_name
def _img_list_retrieve(self):
'''Returns image list for different authentic and forgery image.'''
STGAN_names = self.img_retrieve('STGAN.txt', 'STGAN/fake', False)
FaShifter_names = self.img_retrieve('FaShifter.txt', 'FaShifter', False)
return [STGAN_names, FaShifter_names]