object-detection-with-street-view/1_Data_Acquisition_GSV.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "street_view.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "KQSP7h-nrure"
      },
      "source": [
        "#This code uses the google streetview.api and the haversine formula to download Google Street View."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLUJdoRpr5vL"
      },
      "source": [
        "!pip install google_streetview"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XPEWOX8mr-Z5"
      },
      "source": [
        "# Import google_streetview for the api module\n",
        "import google_streetview.api\n",
        "from google.colab import files as FILE\n",
        "import requests\n",
        "import os\n",
        "import re"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ISp63NDsGhM"
      },
      "source": [
        "'''\n",
        "Calculate distance using the Haversine Formula\n",
        "'''\n",
        "#The haversine formula determines the great-circle distance between two points on a sphere given their longitudes and latitudes\n",
        "\n",
        "def haversine(coord1: object, coord2: object):\n",
        "    import math\n",
        "\n",
        "    # Coordinates in decimal degrees (e.g. 2.89078, 12.79797)\n",
        "    lon1, lat1 = coord1\n",
        "    lon2, lat2 = coord2\n",
        "\n",
        "    R = 6371000  # radius of Earth in meters\n",
        "    phi_1 = math.radians(lat1)\n",
        "    phi_2 = math.radians(lat2)\n",
        "\n",
        "    delta_phi = math.radians(lat2 - lat1)\n",
        "    delta_lambda = math.radians(lon2 - lon1)\n",
        "\n",
        "    a = math.sin(delta_phi / 2.0) ** 2 + math.cos(phi_1) * math.cos(phi_2) * math.sin(delta_lambda / 2.0) ** 2\n",
        "    \n",
        "    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\n",
        "    head= math.atan2(math.sin(delta_lambda)*math.cos(phi_2), math.cos(phi_1)*math.sin(phi_2)-math.sin(phi_1)*math.cos(phi_2)*math.cos(delta_lambda))\n",
        "    meters = R * c  # output distance in meters\n",
        "    km = meters / 1000.0  # output distance in kilometers\n",
        "\n",
        "    meters = round(meters, 3)\n",
        "    headd=head*57.295779513 #convert radian to degree\n",
        "    km = round(km, 3)\n",
        "    kms=km/100\n",
        "    stepsze=kms/111.2    #the number of grid points in each direction. \n",
        "    print(f\"Distance: {meters} m\")\n",
        "    print(f\"Distance: {km} km\")\n",
        "    print(f\"stepsize: {kms} km\")\n",
        "    print(f\"stepsizedegreee: {stepsze} degree\")\n",
        "    print(f\"head: {headd} degree\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yc_enRjSsH5I"
      },
      "source": [
        "# da and db are the lengths of the sides of the rectangle in the latitude and longitude direction\n",
        "db=haversine([ 40.982199, 28.756978 ], [40.987382, 28.809335 ])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zpgm8-IHsMiG"
      },
      "source": [
        "da=haversine([ 40.987382, 28.809335 ], [40.982199, 28.756978  ])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkp1uS50sS2Q"
      },
      "source": [
        "image_links = []\n",
        "api_results=[]\n",
        "api_list=[]\n",
        "locs= []\n",
        "\n",
        "#Depending on the values ​​above, the values ​​they take should change.\n",
        "db=   0.0005255395683453238\n",
        "da=  0.0005255395683453238\n",
        "#longitudes and latitudes of two point \n",
        "rem=  41.016097\n",
        "rbm=  29.205892 \n",
        "ree=  41.127392\n",
        "rbe=29.296529\n",
        "while ree > rem and rbe > rbm:\n",
        "    rem=rem+db;\n",
        "    alst = list(str(rem)+ \",\")\n",
        "    rbm=rbm+db;\n",
        "    blst = list(str(rbm))\n",
        "    a = ''.join(alst); b = ''.join(blst) \n",
        "#At this stage, the latitude and longitude are combined into a format that we can use.         \n",
        "    my_list = [a,b]\n",
        "    my_string = ''.join(my_list)\n",
        "    my_string\n",
        "    locs.append(my_string)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IbdfaTs-sZqA"
      },
      "source": [
        "# Define parameters for street view api\n",
        "for i in range(150):\n",
        "  params = {\n",
        "\t'size': '300x300', # max 640x640 pixels\n",
        "\t'location': locs[i],\n",
        "\t'heading': '170;90;-90' #indicates the compass heading of the camera. Accepted values are from 0 to 360\n",
        "  'pitch': '-0.76', #(default is 0) specifies the up or down angle of the camera relative to the Street View vehicle.\n",
        "\t'key': '..... '\n",
        "}\n",
        "\n",
        "  api = google_streetview.helpers.api_list(params)\n",
        "  api_list.append(api)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPpRxcYgsdVP"
      },
      "source": [
        "# Create a results object\n",
        "for i in range(350):\n",
        "  api_result = google_streetview.api.results(api_list[i])\n",
        "  api_results.append(api_result)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGLdezbNsdoX"
      },
      "source": [
        "#Download images as a link\n",
        "for i in range(240):\n",
        "  link = api_results[i].links\n",
        "  image_links.append(link)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3_7gxlSslsX"
      },
      "source": [
        "#Example  link\n",
        "image_links[2][2]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i8hajtjqsdzl"
      },
      "source": [
        "# We get the data on our own google drive.\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dWx4-PL7srYE"
      },
      "source": [
        "#Download images\n",
        "for i in range(240):\n",
        "  img_data = requests.get(image_links[i][0]).content\n",
        "  with open('/content/gdrive/My Drive/buyukcekmece/lbktz' + str(i)+ '.jpg', 'wb') as handler:\n",
        "   handler.write(img_data)\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}