Create app.py

app.py

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+import gradio as gr
+import cv2
+import librosa
+import numpy as np
+from keras.models import load_model
+import os
+import cv2
+import json
+import pickle
+import librosa
+import shutil
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from scipy.io import wavfile
+from moviepy.editor import VideoFileClip
+from keras.utils import np_utils
+from sklearn.preprocessing import LabelEncoder
+
+# 데이터 전처리
+def preprocess_video(video_path):
+    face_cascade = cv2.CascadeClassifier('/content/drive/Shareddrives/23 인공지능 모델링_돌핀/haarcascade_frontalface_default.xml')
+    cnn_data = []
+    rnn_data = []
+
+    cap = cv2.VideoCapture(video_path)
+    count = 0
+    while len(cnn_data) < 2:
+        ret, frame = cap.read()
+        if ret:
+            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
+            for (x, y, w, h) in faces:
+                face_img = gray[y:y+h, x:x+w]
+                resized_img = cv2.resize(face_img, (224, 224))
+                cnn_data.append(resized_img)
+                count += 1
+                if count >= 15:
+                    break
+        else:
+            break
+
+    if len(cnn_data) < 281:
+        video_clip = VideoFileClip(video_path)
+        audio_clip = video_clip.audio
+        audio_clip.write_audiofile("audio.wav")
+        y, sr = librosa.load("audio.wav", sr=44100)
+        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
+        mfcc = mfcc[:, :400]
+        rnn_data.append(mfcc)
+        os.remove("audio.wav")
+
+    cnn_data = np.array(cnn_data)
+    rnn_data = np.array(rnn_data)
+    return cnn_data, rnn_data
+
+# 딥페이크 영상 유무 판별
+def detect_deepfake(video_path):
+    cnn_data, rnn_data = preprocess_video(video_path)
+
+    cnn_data_np = np.array(cnn_data)
+    rnn_data_np= np.array(rnn_data)
+
+    def augment_data(data, target_size):
+      # 증강된 데이터 배열 초기화
+      augmented_data = np.empty((target_size,) + data.shape[1:])
+
+      # RNN 데이터를 반전하여 복사
+      for i in range(target_size):
+          augmented_data[i] = np.flip(data[i % data.shape[0]], axis=0)
+
+      return augmented_data
+
+    # RNN 데이터 증강
+    augmented_rnn_data = augment_data(rnn_data_np, cnn_data_np.shape[0])
+
+    y_pred = multimodal_model.predict([cnn_data, augmented_rnn_data])
+
+    #print(y_pred)
+    max_prob = np.max(y_pred)
+    print(max_prob)
+
+    if max_prob < 0.5:
+        result =  "Deepfake"
+    else:
+        result =  "Real"
+    return result
+iface = gr.Interface(
+    fn=detect_deepfake,
+    inputs="video",
+    outputs="text",
+    title="Video Deepfake Detection",
+    description="Upload a video to check if it contains deepfake content.",
+    allow_flagging=False,
+    analytics_enabled=False
+)
+
+iface.launch()