addition of test file

.gitignore

@@ -42,5 +42,4 @@ Thumbs.db
 
 
 ___pycache__/
-test_image.py
 *.pyc

test_image.py

@@ -0,0 +1,162 @@
+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'