realndfakefacedetector.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "realndfakefacedetector.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "2259xAlMbOaO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "07e763a3-48eb-42aa-8ddc-9a2d40b39b51"
      },
      "source": [
        "!pip uninstall -y tensorflow"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uninstalling tensorflow-2.2.0:\n",
            "  Successfully uninstalled tensorflow-2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "swU9jGkAbS-V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "outputId": "d371ec20-01ba-4906-c724-2603230bf51b"
      },
      "source": [
        "!pip install tensorflow"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3d/be/679ce5254a8c8d07470efb4a4c00345fae91f766e64f1c2aece8796d7218/tensorflow-2.2.0-cp36-cp36m-manylinux2010_x86_64.whl (516.2MB)\n",
            "\u001b[K     |████████████████████████████████| 516.2MB 31kB/s \n",
            "\u001b[?25hRequirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.30.0)\n",
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            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow) (49.1.0)\n",
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            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow) (2020.6.20)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow) (2.10)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow) (1.7.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow) (4.6)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow) (4.1.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow) (3.1.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow) (3.1.0)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3\"->google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow) (0.4.8)\n",
            "Installing collected packages: tensorflow\n",
            "Successfully installed tensorflow-2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Qq-5xyHZfqH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "45fd89ae-da82-4ad2-ffdb-241546ca91d1"
      },
      "source": [
        "!pip install tensorflow-gpu==2.0.0-alpha\n",
        "!pip install split-folders"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow-gpu==2.0.0-alpha in /usr/local/lib/python3.6/dist-packages (2.0.0a0)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha) (1.0.8)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha) (0.3.3)\n",
            "Requirement already satisfied: tb-nightly<1.14.0a20190302,>=1.14.0a20190301 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha) (1.14.0a20190301)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha) (1.1.0)\n",
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            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha) (1.12.0)\n",
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            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha) (0.8.1)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha) (0.9.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha) (1.1.2)\n",
            "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha) (1.18.5)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha) (3.10.0)\n",
            "Requirement already satisfied: tf-estimator-nightly<1.14.0.dev2019030116,>=1.14.0.dev2019030115 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha) (1.14.0.dev2019030115)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==2.0.0-alpha) (2.10.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha) (1.0.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha) (3.2.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu==2.0.0-alpha) (49.1.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha) (1.7.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha) (3.1.0)\n",
            "Requirement already satisfied: split-folders in /usr/local/lib/python3.6/dist-packages (0.3.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LalBsb7Ja94l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "b8a5f935-8b6a-4f6b-e6fb-555403c81e33"
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from tensorflow.keras.applications import MobileNetV2\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras import Sequential\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWzL-jjhZxIA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import tensorflow as tf\n",
        "import split_folders\n",
        "import numpy as np\n",
        "import cv2\n",
        "import os\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPM74HxLaTJ8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5bb1d83c-e0ab-4b61-8c62-c8323736be4d"
      },
      "source": [
        "import os\n",
        "print(os.listdir(\"/content/\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['.config', 'sample_data']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wC0RsOXOpurJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip '/content/real-and-fake-face-detection.zip'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJCB0AakbwWR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "real = \"/content/real_and_fake_face/training_real\"\n",
        "fake = \"/content/real_and_fake_face/training_fake\"\n",
        "\n",
        "real_path = os.listdir(real)\n",
        "fake_path = os.listdir(fake)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ItWAfT5e6C-6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_img(path):\n",
        "    image = cv2.imread(path)\n",
        "    image = cv2.resize(image, (224, 224))\n",
        "    return image[...,::-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79NDtkBS6DWK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fig = plt.figure(figsize=(4, 4))\n",
        "\n",
        "for i in range(16):\n",
        "    plt.subplot(4, 4, i+1)\n",
        "    plt.imshow(load_img(real + real_path[i]), cmap='gray')\n",
        "    plt.title(\"real face\")\n",
        "    plt.axis('off')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3N9IQoHJ6GGU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fig = plt.figure(figsize=(4, 4))\n",
        "\n",
        "for i in range(16):\n",
        "    plt.subplot(4, 4, i+1)\n",
        "    plt.imshow(load_img(fake + fake_path[i]), cmap='gray')\n",
        "    plt.title(\"fake face\")\n",
        "    plt.axis('off')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwZcpFKi6Iux",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_datagen = ImageDataGenerator(horizontal_flip=True,\n",
        "                                   vertical_flip=False,\n",
        "                                   rescale=1./255,\n",
        "                                   )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s9A_Bj5v6LTu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset_path = \"/content/real_and_fake_face\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWKSGJ2u6ROm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = train_datagen.flow_from_directory(dataset_path,\n",
        "                                          class_mode=\"binary\",\n",
        "                                          target_size=(96, 96),\n",
        "                                          batch_size=32)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUayDLeR6VqK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mobilenetV2 = MobileNetV2(input_shape=(96, 96, 3),\n",
        "                          include_top=False,\n",
        "                          weights='imagenet'\n",
        "                          )\n",
        "\n",
        "average_layer = GlobalAveragePooling2D()\n",
        "\n",
        "model = Sequential([\n",
        "    mobilenetV2,\n",
        "    average_layer,\n",
        "    Dense(256, activation=tf.nn.relu),\n",
        "    BatchNormalization(),\n",
        "    Dropout(0.2),\n",
        "    Dense(2, activation=tf.nn.softmax)\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTHjECI46WF-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer=Adam(lr=0.001),\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YI1FoZXu6a4y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def scheduler(epoch):\n",
        "    if epoch <= 2:\n",
        "        return 0.001\n",
        "    elif epoch > 2 and epoch <= 15:\n",
        "        return 0.0001 \n",
        "    else:\n",
        "        return 0.00001\n",
        "\n",
        "lr_callbacks = tf.keras.callbacks.LearningRateScheduler(scheduler)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZuwwkwD6c8x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.fit_generator(train,\n",
        "                    epochs=50,\n",
        "                    callbacks=[lr_callbacks])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8V58mHv_6fQy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.evaluate_generator(train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5esXmeSv6ik9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save(\"model.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gyr_Sl2C7YTm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## due to slow net pblm i ve not run the code"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}