FAITH/models/DCT.py

import cv2
import numpy as np
from scipy.fftpack import dctn, idctn
from torch_dct import dct_2d, idct_2d


import pdb
import torch
from torch import nn
from PIL import Image
from torchvision import transforms


class DCT(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv = nn.Conv2d(1, 1, kernel_size=16, stride=16, padding=0, bias=False)
        # self.pool = nn.AdaptiveAvgPool2d((32, 32))

    def forward(self, images):
        """ 输入维度: [B, C, H, W] """
        # 灰度化（保持GPU计算）
        images_gray = torch.mean(images, dim=1)  # [B, H, W]

        # 二维DCT变换
        dct_coeff = dct_2d(images_gray, norm='ortho')  # [B, H, W]

        # 创建高通滤波器掩膜（保留右下四分之一）
        B, H, W = dct_coeff.shape
        mask = torch.zeros((H, W), device=images.device)
        mask[H//2:, W//2:] = 1  # 右下区域
        mask = mask.unsqueeze(0)  # [1, H, W] 用于广播

        # 应用高通滤波
        dct_highpass = dct_coeff * mask  # 广播乘法 [B, H, W]

        # 逆DCT变换
        img_highpass = idct_2d(dct_highpass, norm='ortho')  # [B, H, W]
        img_highpass = torch.abs(img_highpass)  # 确保数值稳定性

        # # masked_image = Image.fromarray(img_highpass)       # .size: [512, 512]
        # masked_image = transforms.ToPILImage()(img_highpass[0])       # .size: [512, 512]
        # masked_image.save("./dct_test.jpg")

        # 通过卷积层（保持通道维度）
        coeffs = self.conv(img_highpass.unsqueeze(1)).flatten(1)  # [B, 1024]

        return coeffs


    def forward_single_image(self, image):
        image = np.array(image)
        # 执行二维DCT变换
        dct_coeff = dctn(image.astype(float), norm='ortho')

        # 创建高频掩码（保留右下四分之一）
        rows, cols = image.shape
        mask = np.zeros((rows, cols))
        mask[rows//2:, cols//2:] = 1

        # 提取高频分量
        high_freq_dct = dct_coeff * mask

        # 逆DCT变换恢复高频图像
        masked_image = idctn(high_freq_dct, norm='ortho')
        masked_image = np.clip(masked_image, 0, 255).astype(np.uint8) * 10

        masked_image = Image.fromarray(masked_image)       # .size: [512, 512]
        masked_image.save("./dct_test.jpg")

        return masked_image



if __name__ == "__main__":
    img_path = "../../../ultraedit/UltraEdit/sequential_deepfake_dataset/0/50.jpg"
    image = Image.open(img_path).convert('RGB')

    trans = transforms.Compose([
        transforms.ColorJitter(brightness=[0.5, 1.3], contrast=[
                                0.8, 1.5], saturation=[0.2, 1.5]),  # 修改图像的亮度、对比度和饱和度
        transforms.RandomHorizontalFlip(),      # 按照概率p水平翻转图像
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
    ])

    input_image = trans(image).unsqueeze(0)
    print(input_image.shape)

    # pdb.set_trace()
    mask_generator = DCT()
    output = mask_generator.forward(input_image)