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| 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("FINAL-EFFICIENTNETV2-B0") | |
| 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 work of \"Achieving Face Swapped Deepfakes Detection Using EfficientNetV2\" by 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: [email protected], the researcher will immediately take down the examples used.\ | |
| " | |
| examples = [ | |
| ['Video1-fake-1-ff.mp4'], | |
| ['Video6-real-1-ff.mp4'], | |
| ['Video3-fake-3-ff.mp4'], | |
| ['Video8-real-3-ff.mp4'], | |
| ['real-1.mp4'], | |
| ['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() | |