Ron0420/EfficientNetV2_Deepfakes_Image_Detector

1import gradio as gr2 3import cv24from mtcnn.mtcnn import MTCNN5import tensorflow as tf6import tensorflow_addons7import numpy as np8 9import os10import zipfile11 12local_zip = "FINAL-EFFICIENTNETV2-B0.zip"13zip_ref = zipfile.ZipFile(local_zip, 'r')14zip_ref.extractall('FINAL-EFFICIENTNETV2-B0')15zip_ref.close()16 17model = tf.keras.models.load_model("FINAL-EFFICIENTNETV2-B0")18 19detector = MTCNN()20 21def deepfakespredict(input_img ):22 23    labels = ['real', 'fake']24    pred = [0, 0]25    text =""26    text2 =""27    28    face = detector.detect_faces(input_img)29 30    if len(face) > 0:31        x, y, width, height = face[0]['box']32        x2, y2 = x + width, y + height33 34        cv2.rectangle(input_img, (x, y), (x2, y2), (0, 255, 0), 2)35 36        face_image = input_img[y:y2, x:x2]37        face_image2 = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)38        face_image3 = cv2.resize(face_image2, (224, 224))39        face_image4 = face_image3/25540 41        pred = model.predict(np.expand_dims(face_image4, axis=0))[0]42 43        if pred[1] >= 0.6:44            text = "The image is FAKE."45        elif pred[0] >= 0.6:46            text = "The image is REAL."47        else:48            text = "The image may be REAL or FAKE."49            50    else:51        text = "Face is not detected in the image."52    53    text2 = "REAL: " + str(np.round(pred[0]*100, 2)) + "%, FAKE: " + str(np.round(pred[1]*100, 2)) + "%"54 55    return input_img, text, text2, {labels[i]: float(pred[i]) for i in range(2)}56 57 58title="EfficientNetV2 Deepfakes Image Detector"59description="This is a demo implementation of EfficientNetV2 Deepfakes Image Detector. \60            To use it, simply upload your image, or click one of the examples to load them.  \61            This demo and model represent the Final Year Project titled \"Achieving Face Swapped Deepfakes Detection Using EfficientNetV2\" by a CS undergraduate Lee Sheng Yeh.  \62            The examples were extracted from Celeb-DF(V2)(Li et al, 2020) and FaceForensics++(Rossler et al., 2019). Full reference detail is available in \"references.txt.\" \63            The examples are used under fair use to demo the working of the model only. If any copyright is infringed, please contact the researcher via this email: tp054565@mail.apu.edu.my.\64            "65            66examples = [67                ['Fake-1.png'],68                ['Fake-2.png'],69                ['Fake-3.png'],70                ['Fake-4.png'],71                ['Fake-5.png'],72  73                ['Real-1.png'],74                ['Real-2.png'],75                ['Real-3.png'],76                ['Real-4.png'],77                ['Real-5.png']78                79           ]80           81             82gr.Interface(deepfakespredict,83                     inputs = ["image"],84                     outputs=[gr.outputs.Image(type="pil", label="Detected face"), 85                         "text", 86                         "text", 87                         gr.outputs.Label(num_top_classes=None, type="auto", label="Confidence")],                     88                     title=title,89                     description=description,90                     examples = examples, 91                     examples_per_page = 592                     ).launch()