FAITH/wavelet/wavelet.py

import torch.nn as nn
import torch
import pdb
import os, torchvision
from PIL import Image
from torchvision import transforms as trans

def test3():
    from pytorch_wavelets import DWTForward, DWTInverse # (or import DWT, IDWT)
    #J为分解的层次数,wave表示使用的变换方法
    xfm = DWTForward(J=1, mode='zero', wave='haar')  # Accepts all wave types available to PyWavelets
    # ifm = DWTInverse(mode='zero', wave='haar')

    # img = Image.open('../../../ultraedit/UltraEdit/origin_dataset/CelebAMask-HQ/CelebA-HQ-img/37.jpg')
    # img = Image.open('../../../ultraedit/UltraEdit/sequential_deepfake_dataset/37/50_10_21_62.jpg')
    img = Image.open('../../../ultraedit/UltraEdit/sequential_deepfake_dataset/541/50_11_62_22.jpg')
    transform = trans.Compose([
        trans.ToTensor()
    ])
    img = transform(img).unsqueeze(0)
    print(img.shape)
    Yl, Yh = xfm(img)
    print(Yl.shape)
    print(len(Yh))
    # print(Yh[0].shape)
    i = 0
    # for i in range(len(Yh)):
    if 1:
        print(Yh[i].shape)
        if i == len(Yh)-1:
            h = torch.zeros([4,3,Yh[i].size(3),Yh[i].size(3)]).float()
            h[0,:,:,:] = Yl
        else:
            h = torch.zeros([3,3,Yh[i].size(3),Yh[i].size(3)]).float()
        
        for j in range(3):
            if i == len(Yh)-1:
                h[j+1,:,:,:] = Yh[i][:,:,j,:,:]
            else:
                h[j,:,:,:] = Yh[i][:,:,j,:,:]
        # pdb.set_trace()
        
        if i == len(Yh)-1:
            img_grid = torchvision.utils.make_grid(h, 2) #一行2张图片
        else:
            img_grid = torchvision.utils.make_grid(h, 3)
        
        s_img = Yh[0][:,:,2,:,:]
        print(f"s_img.shape: {s_img.shape}")
        # print(torch.max(s_img) + torch.min(s_img))
        
        # s_img = s_img * 20
        # print(torch.max(s_img) + torch.min(s_img))
        s_img[s_img < torch.max(s_img) / 20] = 0

        s_img = s_img[0][0] * 12
        # print(f"s_img.shape: {s_img.shape}" )
        torchvision.utils.save_image(s_img, "test_HH.jpg")

        # img_grid = img_grid * 100
        # img_grid[img_grid < 1] = 0
        # torchvision.utils.save_image(img_grid, 'test.jpg')
        # pdb.set_trace()

if __name__ == '__main__':
    test3()