import gradio as gr import torch import torch.nn.functional as F from facenet_pytorch import MTCNN, InceptionResnetV1 import os import numpy as np from PIL import Image import zipfile import cv2 from pytorch_grad_cam import GradCAM from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget from pytorch_grad_cam.utils.image import show_cam_on_image gr.themes.Glass() with zipfile.ZipFile("examples.zip","r") as zip_ref: zip_ref.extractall(".") DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' '''cuda:0''' mtcnn = MTCNN( select_largest=False, post_process=False, device=DEVICE ).to(DEVICE).eval() model = InceptionResnetV1( pretrained="vggface2", classify=True, num_classes=1, device=DEVICE ) checkpoint = torch.load("resnetinceptionv1_epoch_32.pth", map_location=torch.device('cpu')) model.load_state_dict(checkpoint['model_state_dict']) model.to(DEVICE) model.eval() EXAMPLES_FOLDER = 'examples' examples_names = os.listdir(EXAMPLES_FOLDER) examples = [] for example_name in examples_names: example_path = os.path.join(EXAMPLES_FOLDER, example_name) label = example_name.split('_')[0] example = { 'path': example_path, 'label': label } examples.append(example) np.random.shuffle(examples) # shuffle def predict(input_image:Image.Image, true_label:str): """Predict the label of the input_image""" face = mtcnn(input_image) if face is None: raise Exception('No face detected') face = face.unsqueeze(0) # add the batch dimension face = F.interpolate(face, size=(256, 256), mode='bilinear', align_corners=False) # convert the face into a numpy array to be able to plot it prev_face = face.squeeze(0).permute(1, 2, 0).cpu().detach().int().numpy() prev_face = prev_face.astype('uint8') face = face.to(DEVICE) face = face.to(torch.float32) face = face / 255.0 face_image_to_plot = face.squeeze(0).permute(1, 2, 0).cpu().detach().int().numpy() target_layers=[model.block8.branch1[-1]] use_cuda = True if torch.cuda.is_available() else False #print ("Cuda :: ", use_cuda) cam = GradCAM(model=model, target_layers=target_layers) #, use_cuda=use_cuda) targets = [ClassifierOutputTarget(0)] grayscale_cam = cam(input_tensor=face, targets=targets, eigen_smooth=True) grayscale_cam = grayscale_cam[0, :] visualization = show_cam_on_image(face_image_to_plot, grayscale_cam, use_rgb=True) face_with_mask = cv2.addWeighted(prev_face, 1, visualization, 0.5, 0) with torch.no_grad(): output = torch.sigmoid(model(face).squeeze(0)) prediction = "real" if output.item() < 0.5 else "fake" real_prediction = 1 - output.item() fake_prediction = output.item() confidences = { 'real': real_prediction, 'fake': fake_prediction } return confidences, true_label, face_with_mask title = "Deepfake Image Detection" description = "~ AI - ML implementation for fake and real image detection technics." article = "

" interface = gr.Interface( fn=predict, inputs=[ gr.inputs.Image(label="Input Image", type="pil") #, #"text" ], outputs=[ gr.outputs.Label(label="Prediction on % of Fake/Real detection :") #, #"text", gr.outputs.Image(label="Face with Explainability") ], title = title, description = description, article = article #examples=[[examples[i]["path"], examples[i]["label"]] for i in range(10)] ).launch() #share=True)