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| import gradio as gr | |
| import cv2 | |
| from mtcnn.mtcnn import MTCNN | |
| import tensorflow as tf | |
| import tensorflow_addons | |
| import numpy as np | |
| import os | |
| import zipfile | |
| local_zip = "FINAL-EFFICIENTNETV2-B0.zip" | |
| zip_ref = zipfile.ZipFile(local_zip, 'r') | |
| zip_ref.extractall('FINAL-EFFICIENTNETV2-B0') | |
| zip_ref.close() | |
| local_zip = "FINAL-EFFICIENTNETV2-S.zip" | |
| zip_ref = zipfile.ZipFile(local_zip, 'r') | |
| zip_ref.extractall('FINAL-EFFICIENTNETV2-S') | |
| zip_ref.close() | |
| local_zip = "deepfakes-test-images.zip" | |
| zip_ref = zipfile.ZipFile(local_zip, 'r') | |
| zip_ref.extractall('deepfakes-test-images') | |
| zip_ref.close() | |
| model_b0 = tf.keras.models.load_model("FINAL-EFFICIENTNETV2-B0") | |
| model_s = tf.keras.models.load_model("FINAL-EFFICIENTNETV2-S") | |
| detector = MTCNN() | |
| def deepfakespredict(select_model, input_img ): | |
| tf.keras.backend.clear_session() | |
| if select_model == "EfficientNetV2-B0": | |
| model = model_b0 | |
| elif select_model == "EfficientNetV2-B0": | |
| model = model_s | |
| text ="" | |
| face = detector.detect_faces(input_img) | |
| if len(face) > 0: | |
| x, y, width, height = face[0]['box'] | |
| x2, y2 = x + width, y + height | |
| cv2.rectangle(input_img, (x, y), (x2, y2), (0, 255, 0), 2) | |
| face_image = input_img[y:y2, x:x2] | |
| face_image2 = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB) | |
| face_image3 = cv2.resize(face_image2, (224, 224)) | |
| face_image4 = face_image3/255 | |
| pred = model.predict(np.expand_dims(face_image4, axis=0))[0] | |
| if pred[1] >= 0.6: | |
| text = "The image is fake." | |
| elif pred[0] >= 0.6: | |
| text = "The image is real." | |
| else: | |
| text = "The image might be real or fake." | |
| else: | |
| text = "Face is not detected in the image." | |
| return text, input_img, {labels[i]: float(pred[i]) for i in range(2)} | |
| title="EfficientNetV2 Deepfakes Image Detector" | |
| description="This is a demo implementation of EfficientNetV2 Deepfakes Image Detector. To use it, simply upload your image, or click one of the examples to load them." | |
| examples = [ | |
| [ | |
| ['deepfakes-test-images/Fake-1.jpg'], | |
| ['deepfakes-test-images/Fake-2.jpg'], | |
| ['deepfakes-test-images/Fake-3.jpg'], | |
| ['deepfakes-test-images/Fake-4.jpg'], | |
| ['deepfakes-test-images/Fake-5.jpg'] | |
| ], | |
| [ | |
| ['deepfakes-test-images/Real-1.jpg'], | |
| ['deepfakes-test-images/Real-2.jpg'], | |
| ['deepfakes-test-images/Real-3.jpg'], | |
| ['deepfakes-test-images/Real-4.jpg'], | |
| ['deepfakes-test-images/Real-5.jpg'], | |
| ] | |
| ] | |
| gr.Interface(deepfakespredict, | |
| inputs = [gr.inputs.Radio(["EfficientNetV2-B0", "EfficientNetV2-S"], label = "Select model:"), "image"], | |
| outputs=["text", gr.outputs.Image(type="pil", label="Detected face"), gr.outputs.Label(num_top_classes=None, type="auto", label="Confidence")], | |
| title=title, | |
| description=description, | |
| ).launch() |