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import torch.nn.functional as F
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from torch.optim.lr_scheduler import CosineAnnealingLR
from tqdm import tqdm
import warnings
warnings.filterwarnings("ignore")
import cv2
import numpy as np
import matplotlib.pyplot as plt
import pywt
from utils.config import cfg
from dataset.real_n_fake_dataloader import Extracted_Frames_Dataset
from utils.data_transforms import get_transforms_train, get_transforms_val
from net.Multimodalmodel import Image_n_DCT
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 argparse
class Test_Dataset(Dataset):
def __init__(self, test_data_path = None, transform = None, image_path = None, multi_modal = "dct"):
"""
Args:
returns:
"""
self.multi_modal = multi_modal
if test_data_path is None and image_path is not None:
self.dataset = [[image_path, 2]]
self.transform = transform
else:
self.transform = transform
self.real_data = os.listdir(test_data_path + "/real")
self.fake_data = os.listdir(test_data_path + "/fake")
self.dataset = []
for image in self.real_data:
self.dataset.append([test_data_path + "/real/" + image, 1])
for image in self.fake_data:
self.dataset.append([test_data_path + "/fake/" + image, 0])
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[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[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[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[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[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
class Test:
def __init__(self, model_path, multi_modal = "dct"):
self.model_path = model_path
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(self.device)
# Load the model
self.model = Image_n_DCT()
self.model.load_state_dict(torch.load(self.model_path, map_location = self.device))
self.model.to(self.device)
self.model.eval()
self.multi_modal = multi_modal
def testimage(self, image_path):
test_dataset = Test_Dataset(transform = get_transforms_val(), image_path = image_path, multi_modal = self.multi_modal)
inputs = test_dataset[0]
rgb_image, dct_image = inputs['rgb_image'].to(self.device), inputs['dct_image'].to(self.device)
output = self.model(rgb_image.unsqueeze(0), dct_image.unsqueeze(0))
# print(output.shape)
_, predicted = torch.max(output.data, 1)
return 'real' if predicted==1 else 'fake' |