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from flask import Flask, render_template, request, redirect, url_for |
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import gradio as gr |
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import cv2 |
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import numpy as np |
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import tensorflow as tf |
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from facenet_pytorch import MTCNN |
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import moviepy.editor as mp |
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from PIL import Image |
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import os |
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import zipfile |
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import json |
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import base64 |
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from tensorflow_addons.optimizers import RectifiedAdam |
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from keras.utils import get_custom_objects |
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get_custom_objects().update({"RectifiedAdam": RectifiedAdam}) |
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app = Flask(__name__) |
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mtcnn = MTCNN(margin=14, keep_all=True, factor=0.7, device='cpu') |
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class DetectionPipeline: |
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def __init__(self, detector, n_frames=None, batch_size=60, resize=None): |
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self.detector = detector |
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self.n_frames = n_frames |
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self.batch_size = batch_size |
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self.resize = resize |
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def __call__(self, filename): |
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v_cap = cv2.VideoCapture(filename) |
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v_len = int(v_cap.get(cv2.CAP_PROP_FRAME_COUNT)) |
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if self.n_frames is None: |
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sample = np.arange(0, v_len) |
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else: |
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sample = np.linspace(0, v_len - 1, self.n_frames).astype(int) |
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faces = [] |
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frames = [] |
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for j in range(v_len): |
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success = v_cap.grab() |
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if j in sample: |
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success, frame = v_cap.retrieve() |
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if not success: |
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continue |
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) |
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if self.resize is not None: |
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frame = frame.resize([int(d * self.resize) for d in frame.size]) |
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frames.append(frame) |
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if len(frames) % self.batch_size == 0 or j == sample[-1]: |
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boxes, probs = self.detector.detect(frames) |
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for i in range(len(frames)): |
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if boxes[i] is None: |
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faces.append(face2) |
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continue |
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box = boxes[i][0].astype(int) |
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frame = frames[i] |
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face = frame[box[1]:box[3], box[0]:box[2]] |
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if not face.any(): |
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faces.append(face2) |
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continue |
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face2 = cv2.resize(face, (224, 224)) |
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faces.append(face2) |
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frames = [] |
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v_cap.release() |
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return faces |
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detection_pipeline = DetectionPipeline(detector=mtcnn, n_frames=20, batch_size=60) |
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model = tf.keras.models.load_model("./Detecto-DeepFake_Video_Detector/p1") |
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def deepfakespredict(input_video): |
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faces = detection_pipeline(input_video) |
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total = 0 |
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real = 0 |
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fake = 0 |
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for face in faces: |
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face2 = face / 255 |
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pred = model.predict(np.expand_dims(face2, axis=0))[0] |
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total += 1 |
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pred2 = pred[1] |
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if pred2 > 0.5: |
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fake += 1 |
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else: |
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real += 1 |
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fake_ratio = fake / total |
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text = "" |
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text2 = f"Deepfakes Confidence: {fake_ratio * 100:.2f}%" |
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if fake_ratio >= 0.5: |
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text = "The video is FAKE." |
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else: |
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text = "The video is REAL." |
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face_frames = [] |
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for face in faces: |
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face_frame = Image.fromarray(face.astype('uint8'), 'RGB') |
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face_frames.append(face_frame) |
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face_frames[0].save('results.gif', save_all=True, append_images=face_frames[1:], duration=250, loop=100) |
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clip = mp.VideoFileClip("results.gif") |
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clip.write_videofile("video.mp4") |
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return text, text2, "video.mp4" |
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iface = gr.Interface( |
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fn=deepfakespredict, |
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inputs=gr.inputs.Video(type="mp4"), |
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outputs=[ |
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gr.outputs.Text(label="Detection Result"), |
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gr.outputs.Text(label="Confidence"), |
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gr.outputs.File(label="Result Video") |
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], |
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live=True, |
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title="EfficientNetV2 Deepfakes Video Detector", |
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description="This is a demo implementation of EfficientNetV2 Deepfakes Image Detector ", |
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examples=[ |
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[open('./Detecto-DeepFake_Video_Detector/Video1-fake-1-ff.mp4', 'rb')], |
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[open('./Detecto-DeepFake_Video_Detector/Video6-real-1-ff.mp4', 'rb')], |
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[open('./Detecto-DeepFake_Video_Detector/Video3-fake-3-ff.mp4', 'rb')], |
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[open('./Detecto-DeepFake_Video_Detector/Video8-real-3-ff.mp4', 'rb')], |
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[open('./Detecto-DeepFake_Video_Detector/real-1.mp4', 'rb')], |
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[open('./Detecto-DeepFake_Video_Detector/fake-1.mp4', 'rb')] |
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] |
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) |
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@app.route('/') |
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def index(): |
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iface.launch(share=True) |
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return iface.ui() |
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if __name__ == '__main__': |
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app.run(debug=True) |
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