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| # We will use this file to create a dataloader for the real and fake dataset | |
| import os | |
| import json | |
| import torch | |
| from torchvision import transforms | |
| from torch.utils.data import DataLoader, Dataset | |
| from PIL import Image | |
| import numpy as np | |
| import pandas as pd | |
| import cv2 | |
| import cv2 | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import pywt | |
| class Extracted_Frames_Dataset(Dataset): | |
| def __init__(self, root_dir, split = "train", transform = None, extend = 'None', multi_modal = "dct"): | |
| """ | |
| Args: | |
| returns: | |
| """ | |
| AssertionError(split in ["train", "val", "test"]), "Split must be one of (train, val, test)" | |
| self.multi_modal = multi_modal | |
| self.root_dir = root_dir | |
| self.split = split | |
| self.transform = transform | |
| if extend == 'faceswap': | |
| self.dataset = pd.read_csv(os.path.join(root_dir, f"faceswap_extended_{self.split}.csv")) | |
| elif extend == 'fsgan': | |
| self.dataset = pd.read_csv(os.path.join(root_dir, f"fsgan_extended_{self.split}.csv")) | |
| else: | |
| self.dataset = pd.read_csv(os.path.join(root_dir, f"{self.split}.csv")) | |
| def __len__(self): | |
| return len(self.dataset) | |
| def __getitem__(self, idx): | |
| sample_input = self.get_sample_input(idx) | |
| return sample_input | |
| def get_sample_input(self, idx): | |
| rgb_image = self.get_rgb_image(idx) | |
| label = self.get_label(idx) | |
| if self.multi_modal == "dct": | |
| dct_image = self.get_dct_image(idx) | |
| sample_input = {"rgb_image": rgb_image, "dct_image": dct_image, "label": label} | |
| # dct_image = self.get_dct_image(idx) | |
| elif self.multi_modal == "fft": | |
| fft_image = self.get_fft_image(idx) | |
| sample_input = {"rgb_image": rgb_image, "dct_image": fft_image, "label": label} | |
| elif self.multi_modal == "hh": | |
| hh_image = self.get_hh_image(idx) | |
| sample_input = {"rgb_image": rgb_image, "dct_image": hh_image, "label": label} | |
| else: | |
| AssertionError("multi_modal must be one of (dct:discrete cosine transform, fft: fast forier transform, hh)") | |
| return sample_input | |
| def get_fft_image(self, idx): | |
| gray_image_path = self.dataset.iloc[idx, 0] | |
| gray_image = cv2.imread(gray_image_path, cv2.IMREAD_GRAYSCALE) | |
| fft_image = self.compute_fft(gray_image) | |
| if self.transform: | |
| fft_image = self.transform(fft_image) | |
| return fft_image | |
| def compute_fft(self, image): | |
| f = np.fft.fft2(image) | |
| fshift = np.fft.fftshift(f) | |
| magnitude_spectrum = 20 * np.log(np.abs(fshift) + 1) # Add 1 to avoid log(0) | |
| return magnitude_spectrum | |
| def get_hh_image(self, idx): | |
| gray_image_path = self.dataset.iloc[idx, 0] | |
| gray_image = cv2.imread(gray_image_path, cv2.IMREAD_GRAYSCALE) | |
| hh_image = self.compute_hh(gray_image) | |
| if self.transform: | |
| hh_image = self.transform(hh_image) | |
| return hh_image | |
| def compute_hh(self, image): | |
| coeffs2 = pywt.dwt2(image, 'haar') | |
| LL, (LH, HL, HH) = coeffs2 | |
| return HH | |
| def get_rgb_image(self, idx): | |
| rgb_image_path = self.dataset.iloc[idx, 0] | |
| rgb_image = Image.open(rgb_image_path) | |
| if self.transform: | |
| rgb_image = self.transform(rgb_image) | |
| return rgb_image | |
| def get_dct_image(self, idx): | |
| rgb_image_path = self.dataset.iloc[idx, 0] | |
| rgb_image = cv2.imread(rgb_image_path) | |
| dct_image = self.compute_dct_color(rgb_image) | |
| if self.transform: | |
| dct_image = self.transform(dct_image) | |
| return dct_image | |
| def get_label(self, idx): | |
| return self.dataset.iloc[idx, 1] | |
| def compute_dct_color(self, image): | |
| image_float = np.float32(image) | |
| dct_image = np.zeros_like(image_float) | |
| for i in range(3): | |
| dct_image[:, :, i] = cv2.dct(image_float[:, :, i]) | |
| return dct_image | |