import gradio as gr import cv2 import numpy as np import tensorflow as tf import tensorflow_addons from facenet_pytorch import MTCNN from PIL import Image import moviepy.editor as mp 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() # Load face detector mtcnn = MTCNN(margin=14, keep_all=True, factor=0.7, device='cpu') #Face Detection function, Reference: (Timesler, 2020); Source link: https://www.kaggle.com/timesler/facial-recognition-model-in-pytorch class DetectionPipeline: """Pipeline class for detecting faces in the frames of a video file.""" def __init__(self, detector, n_frames=None, batch_size=60, resize=None): """Constructor for DetectionPipeline class. Keyword Arguments: n_frames {int} -- Total number of frames to load. These will be evenly spaced throughout the video. If not specified (i.e., None), all frames will be loaded. (default: {None}) batch_size {int} -- Batch size to use with MTCNN face detector. (default: {32}) resize {float} -- Fraction by which to resize frames from original prior to face detection. A value less than 1 results in downsampling and a value greater than 1 result in upsampling. (default: {None}) """ self.detector = detector self.n_frames = n_frames self.batch_size = batch_size self.resize = resize def __call__(self, filename): """Load frames from an MP4 video and detect faces. Arguments: filename {str} -- Path to video. """ # Create video reader and find length v_cap = cv2.VideoCapture(filename) v_len = int(v_cap.get(cv2.CAP_PROP_FRAME_COUNT)) # Pick 'n_frames' evenly spaced frames to sample if self.n_frames is None: sample = np.arange(0, v_len) else: sample = np.linspace(0, v_len - 1, self.n_frames).astype(int) # Loop through frames faces = [] frames = [] for j in range(v_len): success = v_cap.grab() if j in sample: # Load frame success, frame = v_cap.retrieve() if not success: continue frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # frame = Image.fromarray(frame) # Resize frame to desired size if self.resize is not None: frame = frame.resize([int(d * self.resize) for d in frame.size]) frames.append(frame) # When batch is full, detect faces and reset frame list if len(frames) % self.batch_size == 0 or j == sample[-1]: boxes, probs = self.detector.detect(frames) for i in range(len(frames)): if boxes[i] is None: faces.append(face2) #append previous face frame if no face is detected continue box = boxes[i][0].astype(int) frame = frames[i] face = frame[box[1]:box[3], box[0]:box[2]] if not face.any(): faces.append(face2) #append previous face frame if no face is detected continue face2 = cv2.resize(face, (224, 224)) faces.append(face2) frames = [] v_cap.release() return faces detection_pipeline = DetectionPipeline(detector=mtcnn,n_frames=20, batch_size=60) model = tf.keras.models.load_model("./EfficientNetV2_Deepfakes_Video_Detector/p1") def deepfakespredict(input_video): faces = detection_pipeline(input_video) total = 0 real = 0 fake = 0 for face in faces: face2 = face/255 pred = model.predict(np.expand_dims(face2, axis=0))[0] total+=1 pred2 = pred[1] if pred2 > 0.5: fake+=1 else: real+=1 fake_ratio = fake/total text ="" text2 = "Deepfakes Confidence: " + str(fake_ratio*100) + "%" if fake_ratio >= 0.5: text = "The video is FAKE." else: text = "The video is REAL." face_frames = [] for face in faces: face_frame = Image.fromarray(face.astype('uint8'), 'RGB') face_frames.append(face_frame) face_frames[0].save('results.gif', save_all=True, append_images=face_frames[1:], duration = 250, loop = 100 ) clip = mp.VideoFileClip("results.gif") clip.write_videofile("video.mp4") return text, text2, "video.mp4" title="EfficientNetV2 Deepfakes Video Detector" description="This is a demo implementation of EfficientNetV2 Deepfakes Image Detector by using frame-by-frame detection. \ To use it, simply upload your video, or click one of the examples to load them.\ This demo and model represent the Final Year Project titled \"Achieving Face Swapped Deepfakes Detection Using EfficientNetV2\" by a CS undergraduate Lee Sheng Yeh. \ The examples were extracted from Celeb-DF(V2)(Li et al, 2020) and FaceForensics++(Rossler et al., 2019). Full reference details is available in \"references.txt.\" \ 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.\ " examples = [ ['./EfficientNetV2_Deepfakes_Video_Detector/Video1-fake-1-ff.mp4'], ['./EfficientNetV2_Deepfakes_Video_Detector/Video6-real-1-ff.mp4'], ['./EfficientNetV2_Deepfakes_Video_Detector/Video3-fake-3-ff.mp4'], ['./EfficientNetV2_Deepfakes_Video_Detector/Video8-real-3-ff.mp4'], ['./EfficientNetV2_Deepfakes_Video_Detector/real-1.mp4'], ['./EfficientNetV2_Deepfakes_Video_Detector/fake-1.mp4'], ] gr.Interface(deepfakespredict, inputs = ["video"], outputs=["text","text", gr.outputs.Video(label="Detected face sequence")], title=title, description=description, examples=examples ).launch()