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"""
Video Face Manipulation Detection Through Ensemble of CNNs
Image and Sound Processing Lab - Politecnico di Milano
Nicolò Bonettini
Edoardo Daniele Cannas
Sara Mandelli
Luca Bondi
Paolo Bestagini
"""
import os
from pathlib import Path
from typing import List
import albumentations as A
import numpy as np
import pandas as pd
import torch
from PIL import Image
from albumentations.pytorch import ToTensorV2
from torch.utils.data import Dataset, IterableDataset
from .utils import extract_bb
def load_face(record: pd.Series, root: str, size: int, scale: str, transformer: A.BasicTransform) -> torch.Tensor:
path = os.path.join(str(root), str(record.name))
autocache = size < 256 or scale == 'tight'
if scale in ['crop', 'scale', ]:
cached_path = str(Path(root).joinpath('autocache', scale, str(size), str(record.name)).with_suffix('.jpg'))
else:
# when self.scale == 'tight' the extracted face is not dependent on size
cached_path = str(Path(root).joinpath('autocache', scale, str(record.name)).with_suffix('.jpg'))
face = np.zeros((size, size, 3), dtype=np.uint8)
if os.path.exists(cached_path):
try:
face = Image.open(cached_path)
face = np.array(face)
if len(face.shape) != 3:
raise RuntimeError('Incorrect format: {}'.format(path))
except KeyboardInterrupt as e:
# We want keybord interrupts to be propagated
raise e
except (OSError, IOError) as e:
print('Deleting corrupted cache file: {}'.format(cached_path))
print(e)
os.unlink(cached_path)
face = np.zeros((size, size, 3), dtype=np.uint8)
if not os.path.exists(cached_path):
try:
frame = Image.open(path)
bb = record['left'], record['top'], record['right'], record['bottom']
face = extract_bb(frame, bb=bb, size=size, scale=scale)
if autocache:
os.makedirs(os.path.dirname(cached_path), exist_ok=True)
face.save(cached_path, quality=95, subsampling='4:4:4')
face = np.array(face)
if len(face.shape) != 3:
raise RuntimeError('Incorrect format: {}'.format(path))
except KeyboardInterrupt as e:
# We want keybord interrupts to be propagated
raise e
except (OSError, IOError) as e:
print('Error while reading: {}'.format(path))
print(e)
face = np.zeros((size, size, 3), dtype=np.uint8)
face = transformer(image=face)['image']
return face
class FrameFaceIterableDataset(IterableDataset):
def __init__(self,
roots: List[str],
dfs: List[pd.DataFrame],
size: int, scale: str,
num_samples: int = -1,
transformer: A.BasicTransform = ToTensorV2(),
output_index: bool = False,
labels_map: dict = None,
seed: int = None):
"""
:param roots: List of root folders for frames cache
:param dfs: List of DataFrames of cached frames with 'bb' column as array of 4 elements (left,top,right,bottom)
and 'label' column
:param size: face size
:param num_samples:
:param scale: Rescale the face to the given size, preserving the aspect ratio.
If false crop around center to the given size
:param transformer:
:param output_index: enable output of df_frames index
:param labels_map: map from 'REAL' and 'FAKE' to actual labels
"""
self.dfs = dfs
self.size = int(size)
self.seed0 = int(seed) if seed is not None else np.random.choice(2 ** 32)
# adapt indices
dfs_adapted = [df.copy() for df in self.dfs]
for df_idx, df in enumerate(dfs_adapted):
mi = pd.MultiIndex.from_tuples([(df_idx, key) for key in df.index], names=['df_idx', 'df_key'])
df.index = mi
# Concat
self.df = pd.concat(dfs_adapted, axis=0, join='inner')
self.df_real = self.df[self.df['label'] == 0]
self.df_fake = self.df[self.df['label'] == 1]
self.longer_set = 'real' if len(self.df_real) > len(self.df_fake) else 'fake'
self.num_samples = max(len(self.df_real), len(self.df_fake)) * 2
self.num_samples = min(self.num_samples, num_samples) if num_samples > 0 else self.num_samples
self.output_idx = bool(output_index)
self.scale = str(scale)
self.roots = [str(r) for r in roots]
self.transformer = transformer
self.labels_map = labels_map
if self.labels_map is None:
self.labels_map = {False: np.array([0., ]), True: np.array([1., ])}
else:
self.labels_map = dict(self.labels_map)
def _get_face(self, item: pd.Index) -> (torch.Tensor, torch.Tensor) or (torch.Tensor, torch.Tensor, str):
record = self.dfs[item[0]].loc[item[1]]
face = load_face(record=record,
root=self.roots[item[0]],
size=self.size,
scale=self.scale,
transformer=self.transformer)
label = self.labels_map[record.label]
if self.output_idx:
return face, label, record.name
else:
return face, label
def __len__(self):
return self.num_samples
def __iter__(self):
random_fake_idxs, random_real_idxs = get_iterative_real_fake_idxs(
df_real=self.df_real,
df_fake=self.df_fake,
num_samples=self.num_samples,
seed0=self.seed0
)
while len(random_fake_idxs) >= 1 and len(random_real_idxs) >= 1:
yield self._get_face(random_fake_idxs.pop())
yield self._get_face(random_real_idxs.pop())
def get_iterative_real_fake_idxs(df_real: pd.DataFrame, df_fake: pd.DataFrame,
num_samples: int, seed0: int):
longer_set = 'real' if len(df_real) > len(df_fake) else 'fake'
worker_info = torch.utils.data.get_worker_info()
if worker_info is None:
seed = seed0
np.random.seed(seed)
worker_num_couple_samples = num_samples // 2
fake_idxs_portion = np.random.choice(df_fake.index, worker_num_couple_samples,
replace=longer_set == 'real')
real_idxs_portion = np.random.choice(df_real.index, worker_num_couple_samples,
replace=longer_set == 'fake')
else:
worker_id = worker_info.id
seed = seed0 + worker_id
np.random.seed(seed)
worker_num_couple_samples = (num_samples // 2) // worker_info.num_workers
if longer_set == 'fake':
fake_idxs_portion = df_fake.index[
worker_id * worker_num_couple_samples:(worker_id + 1) * worker_num_couple_samples]
real_idxs_portion = np.random.choice(df_real.index, worker_num_couple_samples, replace=True)
else:
real_idxs_portion = df_real.index[
worker_id * worker_num_couple_samples:(worker_id + 1) * worker_num_couple_samples]
fake_idxs_portion = np.random.choice(df_fake.index, worker_num_couple_samples,
replace=True)
random_fake_idxs = list(np.random.permutation(fake_idxs_portion))
random_real_idxs = list(np.random.permutation(real_idxs_portion))
assert (len(random_fake_idxs) == len(random_real_idxs))
return random_fake_idxs, random_real_idxs
class FrameFaceDatasetTest(Dataset):
def __init__(self, root: str, df: pd.DataFrame,
size: int, scale: str,
transformer: A.BasicTransform = ToTensorV2(),
labels_map: dict = None,
aug_transformers: List[A.BasicTransform] = None):
"""
:param root: root folder for frames cache
:param df: DataFrame of cached frames with 'bb' column as array of 4 elements (left,top,right,bottom)
and 'label' column
:param size: face size
:param num_samples:
:param scale: Rescale the face to the given size, preserving the aspect ratio.
If false crop around center to the given size
:param transformer:
:param labels_map: dcit to map df labels
:param aug_transformers: if not None, creates multiple copies of the same sample according to the provided augmentations
"""
self.df = df
self.size = int(size)
self.scale = str(scale)
self.root = str(root)
self.transformer = transformer
self.aug_transformers = aug_transformers
self.labels_map = labels_map
if self.labels_map is None:
self.labels_map = {False: np.array([0., ]), True: np.array([1., ])}
else:
self.labels_map = dict(self.labels_map)
def _get_face(self, item: pd.Index) -> (torch.Tensor, torch.Tensor) or (torch.Tensor, torch.Tensor, str):
record = self.df.loc[item]
label = self.labels_map[record.label]
if self.aug_transformers is None:
face = load_face(record=record,
root=self.root,
size=self.size,
scale=self.scale,
transformer=self.transformer)
return face, label
else:
faces = []
for aug_transf in self.aug_transformers:
faces.append(
load_face(record=record,
root=self.root,
size=self.size,
scale=self.scale,
transformer=A.Compose([aug_transf, self.transformer])
))
faces = torch.stack(faces)
return faces, label
def __len__(self):
return len(self.df)
def __getitem__(self, item):
return self._get_face(self.df.index[item])
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