datapreparation (1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EohuYsTrlVN4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install keras\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "JkMrJ6Afagdd",
        "outputId": "0dee8c4d-8aa5-4700-b954-6f9ebc7bea8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (3.4.1)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from keras) (1.4.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from keras) (1.26.4)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras) (13.8.0)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras) (0.0.8)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.10/dist-packages (from keras) (3.11.0)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras) (0.12.1)\n",
            "Requirement already satisfied: ml-dtypes in /usr/local/lib/python3.10/dist-packages (from keras) (0.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from keras) (24.1)\n",
            "Requirement already satisfied: typing-extensions>=4.5.0 in /usr/local/lib/python3.10/dist-packages (from optree->keras) (4.12.2)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras) (2.16.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras) (0.1.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow\n"
      ],
      "metadata": {
        "collapsed": true,
        "id": "Sq8FBd0TagTO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade pip\n"
      ],
      "metadata": {
        "collapsed": true,
        "id": "oYz5O1nKjWLZ",
        "outputId": "f0fc395e-6fb7-49b7-cf67-621ec30a37dc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (24.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-learn"
      ],
      "metadata": {
        "collapsed": true,
        "id": "F2yWVT7Tjf5X",
        "outputId": "3abb8d6e-2d47-4c83-b861-c49fd9e053a6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.3.2)\n",
            "Requirement already satisfied: numpy<2.0,>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "import cv2\n",
        "import os\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "eCGlzLVYagM5"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MiAR3VI0afBj",
        "outputId": "84968f2a-c1e1-4ac3-a246-c42145efa37a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image_folder = '/content/drive/MyDrive/complaint_media/images'\n",
        "video_folder = '/content/drive/MyDrive/complaint_media/videos'\n",
        "frame_output_folder = '/content/drive/MyDrive/complaint_media/video_frames'"
      ],
      "metadata": {
        "id": "pNd-gtsPkWpH"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_frames(video_path, output_folder, frame_rate=30):\n",
        "  vidcap = cv2.VideoCapture(video_path)\n",
        "  success,image = vidcap.read()\n",
        "  count = 0\n",
        "  while success:\n",
        "    if count % frame_rate == 0:\n",
        "      cv2.imwrite(f\"{output_folder}/frame_{count}.jpg\", image)\n",
        "    success, image = vidcap.read()\n",
        "    count +=1"
      ],
      "metadata": {
        "id": "4a-1vJT-ae6D"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_video_path = os.path.join(video_folder, 'sample_video.mp4')\n",
        "extract_frames(sample_video_path, frame_output_folder)"
      ],
      "metadata": {
        "id": "TU_ckuQBksQl"
      },
      "execution_count": 10,
      "outputs": []
    }
  ]
}