Deepfake_1_(Frame_Extraction).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Deepfake - 1 (Frame Extraction)",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Atharva-Peshkar/DeepFake-Detector/blob/main/Deepfake_1_(Frame_Extraction).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dO9nGx9yDbLG"
      },
      "source": [
        "###Installing Dependecies, configuring notebook and downloading dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EGauv2qQ5xht",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c97dfe20-dabe-4c22-f67d-28308b9825c3"
      },
      "source": [
        "'''!pip uninstall -y kaggle\n",
        "!pip install --upgrade pip\n",
        "!pip install kaggle==1.5.6\n",
        "!kaggle -v\n",
        "!pip install kaggle --upgrade'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'!pip uninstall -y kaggle\\n!pip install --upgrade pip\\n!pip install kaggle==1.5.6\\n!kaggle -v\\n!pip install kaggle --upgrade'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2BVFrLyz6n_x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dac5e2f1-100a-4b70-f516-6a4fee6a93c7"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "%cd /gdrive/My Drive/Colab Notebooks/DeepFake/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /gdrive\n",
            "/gdrive/My Drive/Colab Notebooks/DeepFake\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJZdIuOX4O2I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "3ddef592-06de-40c9-f1ea-a8d6a63e9247"
      },
      "source": [
        "'''import os\n",
        "os.environ['KAGGLE_USERNAME'] = \"peshkaratharva\" \n",
        "os.environ['KAGGLE_KEY'] = \"993b3b76891f8707cec9d09b2463d78d\"\n",
        "!ls\n",
        "!kaggle competitions download -c deepfake-detection-challenge'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'import os\\nos.environ[\\'KAGGLE_USERNAME\\'] = \"peshkaratharva\" \\nos.environ[\\'KAGGLE_KEY\\'] = \"993b3b76891f8707cec9d09b2463d78d\"\\n!ls\\n!kaggle competitions download -c deepfake-detection-challenge'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUckkCgC7y5N"
      },
      "source": [
        "#!ls"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QD2Qvr3t4lJC"
      },
      "source": [
        "#!unzip '/gdrive/My Drive/Colab Notebooks/DeepFake/deepfake-detection-challenge.zip'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EE6M2SIDNkRi"
      },
      "source": [
        "#!pip install face_recognition"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TuieoYHPDh9k"
      },
      "source": [
        "###Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-QnaPQq7fqp"
      },
      "source": [
        "import os\n",
        "import shutil\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HemSqEjyHWYG"
      },
      "source": [
        "#this is the base working directory\n",
        "\n",
        "main_dir = '/gdrive/My Drive/Colab Notebooks/DeepFake/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAcwkoAOL9eU"
      },
      "source": [
        "metadata = pd.read_json(os.path.join(main_dir,'train_sample_videos/metadata.json')).T\n",
        "print(pd.Series(metadata['original']).value_counts())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TLmF0nsPvWD_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "1240fa85-99e8-42cb-9903-8402aea054d4"
      },
      "source": [
        "print(metadata.label.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "aagfhgtpmv.mp4    FAKE\n",
            "aapnvogymq.mp4    FAKE\n",
            "abarnvbtwb.mp4    REAL\n",
            "abofeumbvv.mp4    FAKE\n",
            "abqwwspghj.mp4    FAKE\n",
            "Name: label, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tEWFZYwsRkq2"
      },
      "source": [
        "train_list = os.listdir(os.path.join(main_dir,'train_sample_videos/'))\n",
        "meta = np.array(list(metadata.index))\n",
        "storage = np.array([file for file in train_list if  file.endswith('mp4')])\n",
        "print(f\"Metadata: {meta.shape[0]}, Folder: {storage.shape[0]}\")\n",
        "print(f\"Files in metadata and not in folder: {np.setdiff1d(meta,storage,assume_unique=False).shape[0]}\")\n",
        "print(f\"Files in folder and not in metadata: {np.setdiff1d(storage,meta,assume_unique=False).shape[0]}\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cixI2mvHUA-Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "6d2e6521-ee6a-4d09-e96d-973a843ece97"
      },
      "source": [
        "#Data Distribution Analysis\n",
        "import matplotlib.pyplot as plt\n",
        "print(metadata.label.unique())\n",
        "print(f\"Real: {(metadata.label=='REAL').sum()}\")\n",
        "print(f\"Fake: {(metadata.label=='FAKE').sum()}\")\n",
        "plt.bar(['Real','Fake'],[(metadata.label=='REAL').sum(),(metadata.label=='FAKE').sum()],color=['blue','red'])\n",
        "plt.title('Data Distribution')\n",
        "plt.xlabel('Data Label')\n",
        "plt.ylabel(\"Number of videos\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['FAKE' 'REAL']\n",
            "Real: 77\n",
            "Fake: 323\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of videos')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DXvNvyFuBuOO"
      },
      "source": [
        "###Moving files from training folder to real and fake folder respectively, after creating them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dLPJt110BnsA"
      },
      "source": [
        "#Function to create new directories if they do not exist. Syntax: dir_creator(parent_dir_path , new_dir_name)\n",
        "\n",
        "def dir_creator(path_val,dir_name):\n",
        "  if(os.path.isdir(os.path.join(path_val,dir_name))):\n",
        "    print(f\"Folder named: {dir_name}, already exists, good to go!\\n\")\n",
        "  else:\n",
        "    print(f\"Folder named: {dir_name}, created for you, go ahead!\\n\")\n",
        "    os.mkdir(os.path.join(path_val,dir_name))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WhvywfBs5M-h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e159b619-e78b-4680-f236-ab7a7d5406f7"
      },
      "source": [
        "'''creating the directories and sub-directories for segregating videos\n",
        "Directory structure:\n",
        "\n",
        "+ DeepFake                      |       +DeepFake\n",
        "  + Real                        |         +Fake\n",
        "    +videos                     |           +videos\n",
        "      =>data-points             |             => data-points '''\n",
        "\n",
        "\n",
        "source = os.path.join(main_dir,'train_sample_videos/')\n",
        "dir_creator(main_dir,'Real')\n",
        "dir_creator(os.path.join(main_dir,'Real'),'videos')\n",
        "dir_creator(main_dir,'Fake')\n",
        "dir_creator(os.path.join(main_dir,'Fake'),'videos')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Folder named: Real, already exists, good to go!\n",
            "\n",
            "Folder named: videos, already exists, good to go!\n",
            "\n",
            "Folder named: Fake, already exists, good to go!\n",
            "\n",
            "Folder named: videos, already exists, good to go!\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjXloZ2I5RvL"
      },
      "source": [
        "#Segregating all the REAL and FAKE label videos into different folders, from the main train_sample_videos directory\n",
        "for fn in metadata.index:\n",
        "  if(metadata.loc[fn]['label']=='REAL'):\n",
        "    shutil.move(os.path.join(source,fn),os.path.join(real_dest,fn),copy_function = shutil.copytree)\n",
        "  elif(metadata.loc[fn]['label']=='FAKE'):\n",
        "    shutil.move(os.path.join(source,fn),os.path.join(fake_dest,fn),copy_function = shutil.copytree)\n",
        "  else:\n",
        "    print(\"File not found!\")\n",
        "    continue"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxLdX_GG2BED",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "84dfe66f-2977-44e6-e420-7fe9f4f4ce59"
      },
      "source": [
        "#diagnostic step, checking if all the files are there.\n",
        "print(f'Files in train directory : {len(os.listdir(source))}')\n",
        "print(f'Files in REAL directory : {len(os.listdir(real_dest))}')\n",
        "print(f'Files in FAKE directory : {len(os.listdir(fake_dest))}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files in train directory : 4\n",
            "Files in REAL directory : 1\n",
            "Files in FAKE directory : 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5eit2B45DWPm"
      },
      "source": [
        "###Splitting the videos into frames"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IdrlP7KLGR2c"
      },
      "source": [
        "##Function for extracting frames from a given video. Syntax: frame_extractor(base_dir,dest_dir)\n",
        "\n",
        "def frame_extractor(base_dir,dest_dir):\n",
        "  import cv2 \n",
        "  import os \n",
        "  \n",
        "  frame_list = [f for f in os.listdir(base_dir) if f.endswith('.mp4')]\n",
        "\n",
        "  for fn in frame_list:\n",
        "    cam = cv2.VideoCapture(os.path.join(base_dir,fn)) \n",
        "    cam.set(cv2.CAP_PROP_FPS, 24)\n",
        "    while(True): \n",
        "      \n",
        "      # reading from frame \n",
        "      ret,frame = cam.read() \n",
        "\n",
        "      if ret: \n",
        "        # if video is still left continue creating images \n",
        "        name = dest_dir+'frame' + str(cam.get(cv2.CAP_PROP_POS_FRAMES)) + '.jpg'\n",
        "        print ('Creating...' + name) \n",
        "\n",
        "        # writing the extracted images \n",
        "        cv2.imwrite(name, frame) \n",
        "\n",
        "        # increasing counter so that it will \n",
        "        # show how many frames are created \n",
        "        #currentframe += 1\n",
        "      else: \n",
        "        break\n",
        "\n",
        "    # Release all space and windows once done \n",
        "    cam.release() \n",
        "    cv2.destroyAllWindows() \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XH0RJETXDcpd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3f4611d-4b62-4a6f-b0c0-5b52677780b4"
      },
      "source": [
        "#create these folders to store all the extracted frames from videos, in the next cell.\n",
        "dir_creator(os.path.join(main_dir,'Fake/'),'FakeFrames')  \n",
        "dir_creator(os.path.join(main_dir,'Real/'),'RealFrames')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Folder named: FakeFrames, already exists, good to go!\n",
            "\n",
            "Folder named: RealFrames, already exists, good to go!\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0MYetv1vMLSJ"
      },
      "source": [
        "FakeBase = os.path.join(main_dir,'Fake/videos/')                   #the base directory where the FAKE videos are stored\n",
        "FakeDest = os.path.join(main_dir,'Fake/FakeFrames/')               #the directory where you want to store the FAKE extracted frames\n",
        "RealBase = os.path.join(main_dir,'Real/videos/')                   #the base directory where the REAL videos are stored\n",
        "RealDest = os.path.join(main_dir,'Real/RealFrames/')               #the directory where you want to store the REAL extracted frames"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xU6prxtos_3W"
      },
      "source": [
        "###These processes below can take a lot of time. Upto 4 Hours"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQbzmkF7VljT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0ce47c9-b471-4dd5-917d-4f71825578e3"
      },
      "source": [
        "frame_extractor(RealBase,RealDest)    #Extracting REAL FRAMES\n",
        "print(\"\\n *** Frames extracted successfully! *** \\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating.../gdrive/My Drive/Colab Notebooks/DeepFake/Real/RealFrames/frame1.0.jpg\n",
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            "Creating.../gdrive/My Drive/Colab Notebooks/DeepFake/Real/RealFrames/frame295.0.jpg\n",
            "Creating.../gdrive/My Drive/Colab Notebooks/DeepFake/Real/RealFrames/frame296.0.jpg\n",
            "Creating.../gdrive/My Drive/Colab Notebooks/DeepFake/Real/RealFrames/frame297.0.jpg\n",
            "Creating.../gdrive/My Drive/Colab Notebooks/DeepFake/Real/RealFrames/frame298.0.jpg\n",
            "Creating.../gdrive/My Drive/Colab Notebooks/DeepFake/Real/RealFrames/frame299.0.jpg\n",
            "Creating.../gdrive/My Drive/Colab Notebooks/DeepFake/Real/RealFrames/frame300.0.jpg\n",
            "\n",
            " *** Frames extracted successfully! *** \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12BY88jEWkK6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44fa74bb-0cef-49a1-c9c7-4c1a409bfe40"
      },
      "source": [
        "frame_extractor(FakeBase,FakeDest)    #Extracting FAKE FRAMES\n",
        "print(\"\\n *** Frames extracted successfully! *** \\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating.../gdrive/My Drive/Colab Notebooks/DeepFake/Fake/FakeFrames/frame1.0.jpg\n",
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            "\n",
            " *** Frames extracted successfully! *** \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VryA4_vaGpOl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}