deepfake.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\">Deepfake detection</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are trying to detect deepfake videos. Here we are using a dataset from Kaggle where the annotations are stored in a json file in the train \n",
    "sample videos folder.\n",
    "\n",
    "Steps:\n",
    "* Reading the videos and taking images from it\n",
    "* Reading the label in the json file and store the image in a folder according to it's label\n",
    "* Converting the image to an array and splitting the data into train and test\n",
    "* Customizing the InceptionResNetV2 and training the data on it\n",
    "* Testing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dlib\n",
    "import cv2\n",
    "import os\n",
    "import re\n",
    "import json\n",
    "from pylab import *\n",
    "from PIL import Image, ImageChops, ImageEnhance\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix,f1_score,classification_report\n",
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the dlib face detecter to convert video to face img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_frame_folder = 'train_sample_videos'\n",
    "\n",
    "#loading the data from json file\n",
    "with open(os.path.join(train_frame_folder, 'metadata.json'), 'r') as file:\n",
    "    data = json.load(file)\n",
    "    \n",
    "# Saving the names of videos in a list\n",
    "\n",
    "list_of_train_data = []\n",
    "\n",
    "for f in os.listdir(train_frame_folder):\n",
    "    if f.endswith('.mp4'):\n",
    "        list_of_train_data.append(f)\n",
    "\n",
    "detector = dlib.get_frontal_face_detector()\n",
    "for vid in list_of_train_data:\n",
    "    count = 0\n",
    "    cap = cv2.VideoCapture(os.path.join(train_frame_folder, vid))\n",
    "    frameRate = cap.get(5)\n",
    "    while cap.isOpened():\n",
    "        frameId = cap.get(1)\n",
    "        ret, frame = cap.read()\n",
    "        if ret != True:\n",
    "            break\n",
    "        if frameId % ((int(frameRate)+1)*1) == 0:\n",
    "            face_rects, scores, idx = detector.run(frame, 0)\n",
    "            for i, d in enumerate(face_rects):\n",
    "                x1 = d.left()\n",
    "                y1 = d.top()\n",
    "                x2 = d.right()\n",
    "                y2 = d.bottom()\n",
    "                crop_img = frame[y1:y2, x1:x2]\n",
    "                # Resizing the cropped image and saving it to dataset folder in png format\n",
    "                if data[vid]['label'] == 'REAL':\n",
    "                    cv2.imwrite('dataset/real/'+vid.split('.')[0]+'_'+str(count)+'.png', cv2.resize(crop_img, (224, 224)))\n",
    "                elif data[vid]['label'] == 'FAKE':\n",
    "                    cv2.imwrite('dataset/fake/'+vid.split('.')[0]+'_'+str(count)+'.png', cv2.resize(crop_img, (224, 224)))\n",
    "                count+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting image data to numpy array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_shape = (224, 224, 3)\n",
    "data_dir = 'dataset'\n",
    "\n",
    "\n",
    "#Storing the real and fake image names in lists\n",
    "real_data = []\n",
    "fake_data = []\n",
    "\n",
    "for f in os.listdir(data_dir+'/real'):\n",
    "    if f.endswith('.png'):\n",
    "        real_data.append(f)\n",
    "    \n",
    "for f in os.listdir(data_dir+'/fake'):\n",
    "    if f.endswith('.png'):\n",
    "        fake_data.append(f)    \n",
    "\n",
    "#Creating an empty array to store the real and fake image data\n",
    "X = []\n",
    "\n",
    "#Creating an empty array store the labels\n",
    "Y = []\n",
    "\n",
    "\n",
    "#Appending Numpy array info of image to X and label info to Y (1 for real and 0 for fake)\n",
    "for img in real_data:\n",
    "    X.append(img_to_array(load_img(data_dir+'/real/'+img)).flatten() / 255.0)\n",
    "    Y.append(1)\n",
    "for img in fake_data:\n",
    "    X.append(img_to_array(load_img(data_dir+'/fake/'+img)).flatten() / 255.0)\n",
    "    Y.append(0)\n",
    "\n",
    "Y_val_org = Y\n",
    "\n",
    "#Normalization\n",
    "X = np.array(X)\n",
    "Y = to_categorical(Y, 2)\n",
    "\n",
    "#Reshape\n",
    "X = X.reshape(-1, 224, 224, 3)\n",
    "\n",
    "#Train-Test split\n",
    "X_train, X_val, Y_train, Y_val = train_test_split(X, Y, test_size = 0.25, random_state=5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### We are using the Inception ResNetV2 model and customizing it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inception_resnet_v2 (Functio (None, 5, 5, 1536)        54336736  \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 1536)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 2)                 3074      \n",
      "=================================================================\n",
      "Total params: 54,339,810\n",
      "Trainable params: 54,279,266\n",
      "Non-trainable params: 60,544\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.applications import InceptionResNetV2\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, InputLayer, GlobalAveragePooling2D\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping\n",
    "\n",
    "googleNet_model = InceptionResNetV2(include_top=False, weights='imagenet', input_shape=input_shape)\n",
    "googleNet_model.trainable = True\n",
    "model = Sequential()\n",
    "model.add(googleNet_model)\n",
    "model.add(GlobalAveragePooling2D())\n",
    "#model.add(Dense(units=64, activation='relu'))\n",
    "model.add(Dense(units=2, activation='softmax'))\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.Adam(lr=1e-5, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False),\n",
    "              metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the Model on Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "110/110 [==============================] - 336s 3s/step - loss: 0.5704 - accuracy: 0.8082 - val_loss: 0.4815 - val_accuracy: 0.7930\n",
      "Epoch 2/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.3771 - accuracy: 0.8642 - val_loss: 0.4422 - val_accuracy: 0.8149\n",
      "Epoch 3/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.2240 - accuracy: 0.9376 - val_loss: 0.3248 - val_accuracy: 0.8719\n",
      "Epoch 4/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.1082 - accuracy: 0.9808 - val_loss: 0.3399 - val_accuracy: 0.8905\n",
      "Epoch 5/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0560 - accuracy: 0.9916 - val_loss: 0.3415 - val_accuracy: 0.8970\n",
      "Epoch 6/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0392 - accuracy: 0.9919 - val_loss: 0.3247 - val_accuracy: 0.9047\n",
      "Epoch 7/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0209 - accuracy: 0.9976 - val_loss: 0.2948 - val_accuracy: 0.9124\n",
      "Epoch 8/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0198 - accuracy: 0.9964 - val_loss: 0.2405 - val_accuracy: 0.9222\n",
      "Epoch 9/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0147 - accuracy: 0.9978 - val_loss: 0.2534 - val_accuracy: 0.9146\n",
      "Epoch 10/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0147 - accuracy: 0.9958 - val_loss: 0.2714 - val_accuracy: 0.9091\n",
      "Epoch 11/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0122 - accuracy: 0.9981 - val_loss: 0.2505 - val_accuracy: 0.9233\n",
      "Epoch 12/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0118 - accuracy: 0.9983 - val_loss: 0.2449 - val_accuracy: 0.9266\n",
      "Epoch 13/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0072 - accuracy: 0.9997 - val_loss: 0.2382 - val_accuracy: 0.9277\n",
      "Epoch 14/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0152 - accuracy: 0.9949 - val_loss: 0.2290 - val_accuracy: 0.9266\n",
      "Epoch 15/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0125 - accuracy: 0.9969 - val_loss: 0.2668 - val_accuracy: 0.9277\n",
      "Epoch 16/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0065 - accuracy: 0.9988 - val_loss: 0.2685 - val_accuracy: 0.9277\n",
      "Epoch 17/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0091 - accuracy: 0.9976 - val_loss: 0.2863 - val_accuracy: 0.9233\n",
      "Epoch 18/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0068 - accuracy: 0.9983 - val_loss: 0.2978 - val_accuracy: 0.9244\n",
      "Epoch 19/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.2909 - val_accuracy: 0.9266\n",
      "Epoch 20/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0104 - accuracy: 0.9991 - val_loss: 0.2916 - val_accuracy: 0.9299\n",
      "Epoch 21/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0049 - accuracy: 0.9996 - val_loss: 0.2926 - val_accuracy: 0.9266\n",
      "Epoch 22/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0049 - accuracy: 0.9989 - val_loss: 0.2704 - val_accuracy: 0.9299\n",
      "Epoch 23/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.2827 - val_accuracy: 0.9277\n",
      "Epoch 24/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0034 - accuracy: 0.9999 - val_loss: 0.2813 - val_accuracy: 0.9288\n",
      "Epoch 25/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0032 - accuracy: 0.9991 - val_loss: 0.2801 - val_accuracy: 0.9332\n",
      "Epoch 26/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0058 - accuracy: 0.9986 - val_loss: 0.2734 - val_accuracy: 0.9343\n",
      "Epoch 27/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0038 - accuracy: 0.9993 - val_loss: 0.2831 - val_accuracy: 0.9299\n",
      "Epoch 28/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0095 - accuracy: 0.9982 - val_loss: 0.2648 - val_accuracy: 0.9343\n",
      "Epoch 29/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0028 - accuracy: 0.9998 - val_loss: 0.2593 - val_accuracy: 0.9365\n",
      "Epoch 30/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0039 - accuracy: 0.9993 - val_loss: 0.2834 - val_accuracy: 0.9266\n",
      "Epoch 31/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 0.2508 - val_accuracy: 0.9398\n",
      "Epoch 32/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0025 - accuracy: 0.9998 - val_loss: 0.2667 - val_accuracy: 0.9299\n",
      "Epoch 33/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9321\n",
      "Epoch 34/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0077 - accuracy: 0.9980 - val_loss: 0.3030 - val_accuracy: 0.9288\n",
      "Epoch 35/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0061 - accuracy: 0.9977 - val_loss: 0.2836 - val_accuracy: 0.9299\n",
      "Epoch 36/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 0.2707 - val_accuracy: 0.9310\n",
      "Epoch 37/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0051 - accuracy: 0.9980 - val_loss: 0.2732 - val_accuracy: 0.9343\n",
      "Epoch 38/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0027 - accuracy: 0.9994 - val_loss: 0.3170 - val_accuracy: 0.9222\n",
      "Epoch 39/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0107 - accuracy: 0.9983 - val_loss: 0.2865 - val_accuracy: 0.9277\n",
      "Epoch 40/40\n",
      "110/110 [==============================] - 325s 3s/step - loss: 0.0046 - accuracy: 0.9986 - val_loss: 0.2604 - val_accuracy: 0.9288\n"
     ]
    }
   ],
   "source": [
    "early_stopping = EarlyStopping(monitor='val_loss',\n",
    "                               min_delta=0,\n",
    "                               patience=2,\n",
    "                               verbose=0, mode='auto')\n",
    "EPOCHS = 40\n",
    "BATCH_SIZE = 25\n",
    "history = model.fit(X_train, Y_train, batch_size = BATCH_SIZE, epochs = EPOCHS, validation_data = (X_val, Y_val), verbose = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting the Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(25, 6))\n",
    "t = f.suptitle('Pre-trained InceptionResNetV2 Transfer Learn with Fine-Tuning & Image Augmentation Performance ', fontsize=12)\n",
    "f.subplots_adjust(top=0.85, wspace=0.3)\n",
    "\n",
    "epoch_list = list(range(1,EPOCHS+1))\n",
    "ax1.plot(epoch_list, history.history['accuracy'], label='Train Accuracy')\n",
    "ax1.plot(epoch_list, history.history['val_accuracy'], label='Validation Accuracy')\n",
    "ax1.set_xticks(np.arange(0, EPOCHS+1, 1))\n",
    "ax1.set_ylabel('Accuracy Value')\n",
    "ax1.set_xlabel('Epoch #')\n",
    "ax1.set_title('Accuracy')\n",
    "l1 = ax1.legend(loc=\"best\")\n",
    "\n",
    "ax2.plot(epoch_list, history.history['loss'], label='Train Loss')\n",
    "ax2.plot(epoch_list, history.history['val_loss'], label='Validation Loss')\n",
    "ax2.set_xticks(np.arange(0, EPOCHS+1, 1))\n",
    "ax2.set_ylabel('Loss Value')\n",
    "ax2.set_xlabel('Epoch #')\n",
    "ax2.set_title('Loss')\n",
    "l2 = ax2.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function to print Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True positive =  2880\n",
      "False positive =  42\n",
      "False negative =  39\n",
      "True negative =  690\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Output confusion matrix\n",
    "def print_confusion_matrix(y_true, y_pred):\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    print('True positive = ', cm[0][0])\n",
    "    print('False positive = ', cm[0][1])\n",
    "    print('False negative = ', cm[1][0])\n",
    "    print('True negative = ', cm[1][1])\n",
    "    print('\\n')\n",
    "    df_cm = pd.DataFrame(cm, range(2), range(2))\n",
    "    sn.set(font_scale=1.4) # for label size\n",
    "    sn.heatmap(df_cm, annot=True, annot_kws={\"size\": 16}) # font size\n",
    "    plt.ylabel('Actual label', size = 20)\n",
    "    plt.xlabel('Predicted label', size = 20)\n",
    "    plt.xticks(np.arange(2), ['Fake', 'Real'], size = 16)\n",
    "    plt.yticks(np.arange(2), ['Fake', 'Real'], size = 16)\n",
    "    plt.ylim([2, 0])\n",
    "    plt.show()\n",
    "    \n",
    "print_confusion_matrix(Y_val_org, np.argmax(model.predict(X),axis=-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score : 0.944558521560575 \n",
      "\n",
      "Classification report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      2922\n",
      "           1       0.94      0.95      0.94       729\n",
      "\n",
      "    accuracy                           0.98      3651\n",
      "   macro avg       0.96      0.97      0.97      3651\n",
      "weighted avg       0.98      0.98      0.98      3651\n",
      "\n"
     ]
    }
   ],
   "source": [
    "f1 = f1_score(Y_val_org, np.argmax(model.predict(X),axis=-1))\n",
    "cr = classification_report(Y_val_org, np.argmax(model.predict(X),axis = -1))\n",
    "print(f'f1 score : {f1} \\n')\n",
    "print(f'Classification report : \\n{cr}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving the Model for future use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('deepfake-detection-model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Saved Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('deepfake-detection-model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing on the sample videos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Video_name\tPredicted Actual\n",
      "-------------------------------\n",
      "ahqqqilsxt.mp4 :   REAL   REAL \n",
      "ajwpjhrbcv.mp4 :   FAKE   FAKE \n",
      "aapnvogymq.mp4 :   FAKE   FAKE \n",
      "ajqslcypsw.mp4 :   REAL   REAL \n",
      "aipfdnwpoo.mp4 :   FAKE   FAKE \n",
      "abqwwspghj.mp4 :   FAKE   FAKE \n",
      "abofeumbvv.mp4 :   FAKE   FAKE \n",
      "aklqzsddfl.mp4 :   FAKE   FAKE \n",
      "aagfhgtpmv.mp4 :   FAKE   FAKE \n",
      "abarnvbtwb.mp4 :   REAL   REAL \n"
     ]
    }
   ],
   "source": [
    "test_frame_folder = 'test_videos'\n",
    "list_of_test_data = [f for f in os.listdir(test_frame_folder) if f.endswith('.mp4')]\n",
    "detector = dlib.get_frontal_face_detector()\n",
    "with open(os.path.join(test_frame_folder, 'metadata.json'), 'r') as file:\n",
    "    data_test = json.load(file)\n",
    "    \n",
    "actual_value = []\n",
    "predicted_value = []\n",
    "    \n",
    "print('Video_name\\tPredicted Actual')\n",
    "print('-------------------------------')\n",
    "    \n",
    "for vid in list_of_test_data:\n",
    "    count = 0\n",
    "    cap = cv2.VideoCapture(os.path.join(test_frame_folder, vid))\n",
    "    frameRate = cap.get(5)\n",
    "    while cap.isOpened():\n",
    "        frameId = cap.get(1)\n",
    "        ret, frame = cap.read()\n",
    "        if ret != True:\n",
    "            break\n",
    "        if frameId % ((int(frameRate)+1)*1) == 0:\n",
    "            face_rects, scores, idx = detector.run(frame, 0)\n",
    "            for i, d in enumerate(face_rects):\n",
    "                x1 = d.left()\n",
    "                y1 = d.top()\n",
    "                x2 = d.right()\n",
    "                y2 = d.bottom()\n",
    "                crop_img = frame[y1:y2, x1:x2]\n",
    "                data = img_to_array(cv2.resize(crop_img, (224, 224))).flatten() / 255.0\n",
    "                data = data.reshape(-1, 224, 224, 3)\n",
    "    actual = data_test[vid]['label']\n",
    "    predicted = 'FAKE' if np.argmax(model.predict(data),axis=-1) ==[0] else 'REAL'\n",
    "    print(\"{vid} :   {predicted}   {actual} \".format(vid = vid,predicted = predicted,actual=actual))\n",
    "    \n",
    "   # Storing the predictions in an array\n",
    "    if np.argmax(model.predict(data),axis=-1) ==[0]:\n",
    "        predicted_value.append(0)\n",
    "    elif np.argmax(model.predict(data),axis=-1) ==[1]:\n",
    "        predicted_value.append(1)\n",
    "        \n",
    "    # The label in the json file is a string so in order to get the metrics wee need to convert them to arrays\n",
    "    if actual == 'FAKE':\n",
    "        actual_value.append(0)\n",
    "    elif actual == 'REAL':\n",
    "        actual_value.append(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_value = np.array(predicted_value)  \n",
    "actual_value = np.array(actual_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score : 1.0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "f1 = f1_score(actual_value,predicted_value)\n",
    "print(f'f1 score : {f1} \\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True positive =  7\n",
      "False positive =  0\n",
      "False negative =  0\n",
      "True negative =  3\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_confusion_matrix(actual_value,predicted_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         7\n",
      "           1       1.00      1.00      1.00         3\n",
      "\n",
      "    accuracy                           1.00        10\n",
      "   macro avg       1.00      1.00      1.00        10\n",
      "weighted avg       1.00      1.00      1.00        10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cr = classification_report(actual_value,predicted_value)\n",
    "print(f'Classification report : \\n{cr}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}