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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 | |
| # 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. | |
| """ | |
| 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("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="Group 2- EfficientNetV2 based Deepfake Video Detector" | |
| description='''Please upload videos responsibly and await the results in a gif. The approach in place includes breaking down the video into several frames followed by collecting | |
| the frames that contain a face. Once these frames are collected the trained model attempts to predict if the face is fake or real and contribute to a deepfake confidence. This confidence level eventually | |
| determines if the video can be considered a fake or not.''' | |
| gr.Interface(deepfakespredict, | |
| inputs = ["video"], | |
| outputs=["text","text", gr.Video(label="Detected face sequence")], | |
| title=title, | |
| description=description | |
| ).launch() |