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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 = faces.astype(np.uint8)
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: tp054565@mail.apu.edu.my, 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()