detector_codes/FreqNet-DeepfakeDetection-main/data/datasets.py

import cv2
import numpy as np
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
from random import random, choice
from io import BytesIO
from PIL import Image
from PIL import ImageFile
from scipy.ndimage.filters import gaussian_filter
from torchvision.transforms import InterpolationMode
import torch
import random

ImageFile.LOAD_TRUNCATED_IMAGES = True

def dataset_folder(opt, root):
    if opt.mode == 'binary':
        return binary_dataset(opt, root)
    if opt.mode == 'filename':
        return FileNameDataset(opt, root)
    raise ValueError('opt.mode needs to be binary or filename.')


class RandomGaussianBlur():
    def __init__(self, kernel_size, sigma=(0.1, 2.0), p=1.0):
        self.blur = transforms.GaussianBlur(kernel_size=kernel_size, sigma=sigma)
        self.p = p

    def __call__(self, img):
        if random.random() < self.p:
            return self.blur(img)
        return img


class RandomMask(object):
    def __init__(self, ratio=0.5, patch_size=16, p=0.5):
        """
        Args:
            ratio (float or tuple of float): If float, the ratio of the image to be masked.
                                             If tuple of float, random sample ratio between the two values.
            patch_size (int): the size of the mask (d*d).
        """
        if isinstance(ratio, float):
            self.fixed_ratio = True
            self.ratio = (ratio, ratio)
        elif isinstance(ratio, tuple) and len(ratio) == 2 and all(isinstance(r, float) for r in ratio):
            self.fixed_ratio = False
            self.ratio = ratio
        else:
            raise ValueError("Ratio must be a float or a tuple of two floats.")

        self.patch_size = patch_size
        self.p = p

    def __call__(self, tensor):

        if random.random() > self.p: return tensor

        _, h, w = tensor.shape
        mask = torch.ones((h, w), dtype=torch.float32)

        if self.fixed_ratio:
            ratio = self.ratio[0]
        else:
            ratio = random.uniform(self.ratio[0], self.ratio[1])

        # Calculate the number of masks needed
        num_masks = int((h * w * ratio) / (self.patch_size ** 2))

        # Generate non-overlapping random positions
        selected_positions = set()
        while len(selected_positions) < num_masks:
            top = random.randint(0, (h // self.patch_size) - 1) * self.patch_size
            left = random.randint(0, (w // self.patch_size) - 1) * self.patch_size
            selected_positions.add((top, left))

        for (top, left) in selected_positions:
            mask[top:top+self.patch_size, left:left+self.patch_size] = 0

        return tensor * mask.expand_as(tensor)

def binary_dataset(opt, root):
    if opt.isTrain:
        crop_func = transforms.RandomCrop(opt.cropSize)
        rotation_func = transforms.RandomRotation(180)
        jitter_func = transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5)
        mask_func = RandomMask(ratio=(0.00, 0.75), patch_size=16, p=0.5)
    elif opt.no_crop:
        crop_func = transforms.Lambda(lambda img: img)
        rotation_func = transforms.Lambda(lambda img: img)
        jitter_func = transforms.Lambda(lambda img: img)
        mask_func = transforms.Lambda(lambda img: img)
    else:
        crop_func = transforms.CenterCrop(opt.cropSize)
        rotation_func = transforms.Lambda(lambda img: img)
        jitter_func = transforms.Lambda(lambda img: img)
        mask_func = transforms.Lambda(lambda img: img)

    if opt.isTrain and not opt.no_flip:
        flip_func = transforms.RandomHorizontalFlip()
    else:
        flip_func = transforms.Lambda(lambda img: img)
    if not opt.isTrain and opt.no_resize:
        rz_func = transforms.Lambda(lambda img: img)
    else:
        # rz_func = transforms.Lambda(lambda img: custom_resize(img, opt))
        rz_func = transforms.Resize((opt.loadSize, opt.loadSize))
        # rz_func = transforms.CenterCrop(opt.cropSize)

    dset = datasets.ImageFolder(
            root,
            transforms.Compose([
                rz_func,
                # transforms.Lambda(lambda img: data_augment(img, opt)),
                crop_func,
                flip_func,
                # rotation_func,
                # jitter_func,
                transforms.ToTensor(),
                transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
                # mask_func
            ]))
    return dset


class FileNameDataset(datasets.ImageFolder):
    def name(self):
        return 'FileNameDataset'

    def __init__(self, opt, root):
        self.opt = opt
        super().__init__(root)

    def __getitem__(self, index):
        # Loading sample
        path, target = self.samples[index]
        return path


def data_augment(img, opt):
    img = np.array(img)

    if random() < opt.blur_prob:
        sig = sample_continuous(opt.blur_sig)
        gaussian_blur(img, sig)

    if random() < opt.jpg_prob:
        method = sample_discrete(opt.jpg_method)
        qual = sample_discrete(opt.jpg_qual)
        img = jpeg_from_key(img, qual, method)

    return Image.fromarray(img)


def sample_continuous(s):
    if len(s) == 1:
        return s[0]
    if len(s) == 2:
        rg = s[1] - s[0]
        return random() * rg + s[0]
    raise ValueError("Length of iterable s should be 1 or 2.")


def sample_discrete(s):
    if len(s) == 1:
        return s[0]
    return choice(s)


def gaussian_blur(img, sigma):
    gaussian_filter(img[:,:,0], output=img[:,:,0], sigma=sigma)
    gaussian_filter(img[:,:,1], output=img[:,:,1], sigma=sigma)
    gaussian_filter(img[:,:,2], output=img[:,:,2], sigma=sigma)


def cv2_jpg(img, compress_val):
    img_cv2 = img[:,:,::-1]
    encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), compress_val]
    result, encimg = cv2.imencode('.jpg', img_cv2, encode_param)
    decimg = cv2.imdecode(encimg, 1)
    return decimg[:,:,::-1]


def pil_jpg(img, compress_val):
    out = BytesIO()
    img = Image.fromarray(img)
    img.save(out, format='jpeg', quality=compress_val)
    img = Image.open(out)
    # load from memory before ByteIO closes
    img = np.array(img)
    out.close()
    return img


jpeg_dict = {'cv2': cv2_jpg, 'pil': pil_jpg}
def jpeg_from_key(img, compress_val, key):
    method = jpeg_dict[key]
    return method(img, compress_val)


# rz_dict = {'bilinear': Image.BILINEAR,
           # 'bicubic': Image.BICUBIC,
           # 'lanczos': Image.LANCZOS,
           # 'nearest': Image.NEAREST}
rz_dict = {'bilinear': InterpolationMode.BILINEAR,
           'bicubic': InterpolationMode.BICUBIC,
           'lanczos': InterpolationMode.LANCZOS,
           'nearest': InterpolationMode.NEAREST}
def custom_resize(img, opt):
    interp = sample_discrete(opt.rz_interp)
    return TF.resize(img, (opt.loadSize,opt.loadSize), interpolation=rz_dict[interp])