get_eyes.ipynb

{
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  "metadata": {
    "colab": {
      "name": "get_eyes.ipynb",
      "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/anmorgul/ml20192/blob/master/get_eyes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFT2mfHb2x6v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import urllib.request\n",
        "import csv\n",
        "import cv2\n",
        "import dlib\n",
        "import fnmatch\n",
        "from google.colab import drive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lx2JBETf9jv8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "outputId": "ac5db594-63b1-419a-d236-e42351123ae5"
      },
      "source": [
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38Bqhk9e2Ho_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "outputId": "c77860ef-22b1-443c-dc8a-14c4e9912893"
      },
      "source": [
        "!git clone https://github.com/SilvesterHsu/ORLFaceRecognition-PCA.git\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'ORLFaceRecognition-PCA'...\n",
            "remote: Enumerating objects: 604, done.\u001b[K\n",
            "remote: Total 604 (delta 0), reused 0 (delta 0), pack-reused 604\u001b[K\n",
            "Receiving objects: 100% (604/604), 3.59 MiB | 27.05 MiB/s, done.\n",
            "Resolving deltas: 100% (93/93), done.\n",
            "ORLFaceRecognition-PCA\tsample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q5ijFtCE4re3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "detector = dlib.get_frontal_face_detector()\n",
        "urllib.request.urlretrieve('https://github.com/AKSHAYUBHAT/TensorFace/raw/master/openface/models/dlib/shape_predictor_68_face_landmarks.dat', 'shape_predictor_68_face_landmarks.dat')\n",
        "predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2nVvnkFp9510",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def geteyes(landmarks, l, r):\n",
        "  return landmarks.part(l).x, landmarks.part(l).y, landmarks.part(r).x, landmarks.part(r).y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YYrEaT8N2UTH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open('/content/drive/My Drive/eyes.csv', mode='w') as csv_file:\n",
        "  csv_writer = csv.writer(csv_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
        "  csv_writer.writerow(['Image', \"left_eye_x\", \"left_eye_y\", \"right_eye_x\", \"right_eye_y\"])\n",
        "  for i in os.walk(os.path.join('ORLFaceRecognition-PCA','att_faces')):\n",
        "    address, dirs, files = i[0], i[1], i[2]\n",
        "    person = address.split('/')[-1]\n",
        "    for filename in fnmatch.filter(files, '*.pgm'):\n",
        "      img = cv2.imread(os.path.join(address,filename))\n",
        "      gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "      faces, scores, idx = detector.run(gray, 1, -0.6)\n",
        "      for face in faces:\n",
        "        csv_writer.writerow([os.path.join(person,filename), *geteyes(predictor(gray, face), 36, 45)])\n",
        "\n",
        "      "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "icEj-Nqh9VQK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}