face_recognition_dataset.ipynb

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  "metadata": {
    "colab": {
      "name": "face_recognition_dataset.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO5QN6MOuL6s9oQdnecnehC",
      "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/Forkyknight/Face_recognition/blob/master/face_recognition_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3HcpQ4gCcJ6e",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "3f6131db-1c45-4e75-b2e8-9ded9b005a27"
      },
      "source": [
        "from google.colab import files\n",
        "my_file=files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-6d0c1b3d-0ab8-4ac1-9aaf-fb662f4d3237\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-6d0c1b3d-0ab8-4ac1-9aaf-fb662f4d3237\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKtC4ih2djJG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "cred = pd.read_json(\"kaggle.json\",lines=True)\n",
        "os.environ['KAGGLE_USERNAME'] = cred.iloc[0][0]\n",
        "os.environ['KAGGLE_KEY'] = cred.iloc[0][1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O8Usw2ReeG8E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "1212b819-47ab-40fd-f07e-f36d5a09d7a2"
      },
      "source": [
        "!kaggle datasets download -d chiragsoni/ferdata"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading ferdata.zip to /content\n",
            " 68% 41.0M/60.3M [00:01<00:00, 22.2MB/s]\n",
            "100% 60.3M/60.3M [00:01<00:00, 52.0MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWNiqpIys5mX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c8da1f34-6c51-4277-cc62-7b8400e3c579"
      },
      "source": [
        "!unzip \"ferdata.zip\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EcmXWJIsF1MS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir face_rec_data\n",
        "!mkdir face_rec_data/unknown\n",
        "!mkdir face_rec_data/forkyknight\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J8P-7tIBZXzx",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bcKmziQsDwOE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mv /content/train/neutral/*\".jpg\" /content/face_rec_data/unknown\n",
        "!mv /content/train/happy/*\".jpg\" /content/face_rec_data/unknown\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mpmblm0fZC2V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir frames"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WiE40sPlEozr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "def frame(path):\n",
        "  cap= cv2.VideoCapture(path)\n",
        "  i=0\n",
        "  while(cap.isOpened()):\n",
        "      ret, frame = cap.read()\n",
        "      if ret == False:\n",
        "          break\n",
        "      cv2.imwrite(\"/content/frames/\"+str(i)+\".jpg\",frame)\n",
        "      \n",
        "      i+=1\n",
        "  \n",
        "  cap.release()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNdP3Gh0Yght",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "frame(\"1.mp4\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZuulVvBaag2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lst=os.listdir(\"/content/frames/\")\n",
        "for imag in lst:\n",
        "  img = cv2.imread(\"/content/frames/\"+imag)\n",
        "  imgr = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)\n",
        "  cv2.imwrite( \"/content/frames/\"+imag,imgr)\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "apoPGohDcEhO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#extracting\n",
        "from PIL import Image as IM\n",
        "\n",
        "def cord_face(imagePath,j):\n",
        "    image = cv2.imread(imagePath)\n",
        "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "    faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + \"haarcascade_frontalface_default.xml\")\n",
        "    faces = faceCascade.detectMultiScale(\n",
        "        gray,\n",
        "        scaleFactor=1.3,\n",
        "        minNeighbors=3,\n",
        "        minSize=(30, 30)\n",
        "    )\n",
        "    extract_face(faces,imagePath,j)\n",
        "  \n",
        "def extract_face(faces,imagePath,j):\n",
        "  \n",
        "  image = cv2.imread(imagePath)\n",
        "  for (x, y, w, h) in faces:\n",
        "    roi_color = image[y:y + h, x:x + w] \n",
        "    \n",
        "    gray = cv2.cvtColor(roi_color, cv2.COLOR_BGR2GRAY)\n",
        "    I=IM.fromarray(gray)\n",
        "    I.save(\"/content/face_rec_data/forkyknight/\"+str(j)+\".jpg\")\n",
        "    j=j+1\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_rBRhD9YnwL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lst=os.listdir(\"/content/frames/\")\n",
        "j=0\n",
        "for img in lst:\n",
        "  cord_face(\"/content/frames/\"+img,j)\n",
        "  j=j+1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ih_YdVvxdWXm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!rm /content/frames/*\".jpg\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LBwrjlsdgSSn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jFYeSTKpmXcT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fb56973f-f930-4c16-d8b7-3a7575866eaf"
      },
      "source": [
        "!kaggle datasets init -p /content/face_rec_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data package template written to: /content/face_rec_data/dataset-metadata.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nI78cLX2ssPF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "a65672e6-856d-4790-acdb-de5f3e1fa609"
      },
      "source": [
        "!kaggle datasets create -p /content/face_rec_data -u -r zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Starting upload for file forkyknight.zip\n",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.4)\n",
            "100% 1.99M/1.99M [00:00<00:00, 9.74MB/s]\n",
            "Upload successful: forkyknight.zip (2MB)\n",
            "Starting upload for file unknown.zip\n",
            "100% 19.6M/19.6M [00:00<00:00, 44.3MB/s]\n",
            "Upload successful: unknown.zip (20MB)\n",
            "Your public Dataset is being created. Please check progress at https://www.kaggle.com/chiragsoni/faceRecognition\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Dy-u_5mnNOb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bf8d8033-8bbf-4ba6-ec7c-2d9db0179121"
      },
      "source": [
        "!help zip\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/bin/bash: line 0: help: no help topics match `zip'.  Try `help help' or `man -k zip' or `info zip'.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0K70NL4gmanE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "a53992b4-fb25-4254-98b5-4c44f3c317d3"
      },
      "source": [
        "from kora.drive import upload_public\n",
        "url = upload_public('/content/face_rec_data')\n",
        "HTML(f\"\"\"<video src={url} width=500 controls/>\"\"\")\n",
        "url"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-9b08954237cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mkora\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrive\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mupload_public\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mupload_public\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/face_rec_data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mHTML\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"\"\"<video src={url} width=500 controls/>\"\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'kora'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qw8MKHkuoCJg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}