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import gradio as gr
import cv2
import numpy as np
import tensorflow as tf
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
class DetectionPipeline:
def __init__(self, detector, n_frames=None, batch_size=60, resize=None):
self.detector = detector
self.n_frames = n_frames
self.batch_size = batch_size
self.resize = resize
def __call__(self, filename):
v_cap = cv2.VideoCapture(filename)
v_len = int(v_cap.get(cv2.CAP_PROP_FRAME_COUNT))
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)
faces = []
frames = []
dummy_data = np.zeros((224, 224, 3), dtype=np.uint8)
face2 = dummy_data
for j in range(v_len):
success = v_cap.grab()
if j in sample:
success, frame = v_cap.retrieve()
if not success:
continue
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if self.resize is not None:
frame = cv2.resize(frame, (int(frame.shape[1] * self.resize), int(frame.shape[0] * self.resize)))
frames.append(frame)
if len(frames) % self.batch_size == 0 or j == sample[-1]:
boxes, _ = self.detector.detect(frames)
for i in range(len(frames)):
if boxes[i] is None:
faces.append(face2)
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)
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.saved_model.load("p1")
def deepfakespredict(input_video):
faces = detection_pipeline(input_video)
total = 0
real = 0
fake = 0
for face in faces:
face2 = (face / 255).astype(np.float32)
pred = model(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()
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