coo_extractor.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "coo_extractor",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP80u5R6XSKfvSVsNJwzz3x",
      "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/wanderloop/WanderlustAI/blob/master/coo_extractor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgyGYGlkohWL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pickle import load\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import patches\n",
        "plt.style.use('fivethirtyeight')\n",
        "from os import system\n",
        "\n",
        "# Run this program in Linux command line since it won't return anything on Google Colab\n",
        "# This program will read line by line of names and extract XY coordinates from screen \n",
        "\n",
        "system(\"/content/1.jpg\")\n",
        "def predict(x_mid,\n",
        "            y_mid,):\n",
        " pickle_in=open(\"dict.pickle\",\n",
        "                \"rb\",)\n",
        " regr=load(pickle_in)\n",
        " x=regr.predict([[x_mid,\n",
        "                  y_mid,]])\n",
        " pickle_in.close()\n",
        " pickle_in=open(\"dict1.pickle\",\n",
        "                \"rb\",)\n",
        " regr=load(pickle_in)\n",
        " y=regr.predict([[x_mid,\n",
        "                  y_mid,]])\n",
        " pickle_in.close()\n",
        " return(x,\n",
        "        y,)\n",
        " \n",
        "def search_keyword_person(line):\n",
        "    v=line.split(':')\n",
        "    if v[0]=='person':\n",
        "        return(1)\n",
        "    else:\n",
        "        return(0)\n",
        "    \n",
        "def parser(string):\n",
        "    q=string.split(':')\n",
        "    q=q[1].split(',')\n",
        "    ak_lis=[]\n",
        "    for i in q:\n",
        "        j=i.split('=')\n",
        "        ak_lis.append(int(j[1]))\n",
        "    return(ak_lis)   \n",
        "    \n",
        "fh = open('coo_extract',\n",
        "          'r',)\n",
        "\n",
        "k=0\n",
        "count=0\n",
        "imp_lis=[]\n",
        "while True:\n",
        "    line = fh.readline()\n",
        "    j=search_keyword_person(line)\n",
        "    if j==1:\n",
        "        imp_lis.append(k+2)\n",
        "        count=count+1\n",
        "    if not line:\n",
        "        #print(count)\n",
        "        break\n",
        "    k=k+1\n",
        "\n",
        "fh.close()\n",
        "with open(\"coo_extract\") as f:\n",
        "    lines = f.readlines()\n",
        "coo_lis=[]\n",
        "for i  in imp_lis:\n",
        "    v=parser(lines[int(i)-1])\n",
        "    coo_lis.append(v)\n",
        "f.close()\n",
        "\n",
        "# print(coo_lis,\n",
        "#    ' count : '\n",
        "#     + str(count),)\n",
        "# print(coo_lis)\n",
        "\n",
        "for i in coo_lis:\n",
        "    a=(i[0]+i[2])/2\n",
        "    b=(i[1]+i[3])/2\n",
        "    x,y=predict(int(a),\n",
        "                int(b),)\n",
        "    print(x,\n",
        "          y,)\n",
        "    \n",
        "f = open(\"coo_extract\",\n",
        "         \"w\",)\n",
        "f.close()"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}