StreetviewTest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division,absolute_import,print_function\n",
    "import requests\n",
    "import shutil\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.interpolate import interp1d \n",
    "import os\n",
    "save_dir = 'F:\\streetview'\n",
    "road = 'TajmahalRoad'\n",
    "district = 'Dhaka'\n",
    "start = [23.764260, 90.365447]\n",
    "end = [23.7625406,90.3592206]\n",
    "interpolation = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use [HERE api](https://developer.here.com/documentation/routing/topics/what-is.html) to get coordinates along path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "app_id = \"DemoAppId01082013GAL\"\n",
    "app_code = \"AJKnXv84fjrb0KIHawS0Tg\"\n",
    "request = \"https://route.cit.api.here.com/routing/7.2/calculateroute.json?waypoint0=\"+str(start[0])+\"%2C\"+str(start[1])+\"&waypoint1=\"+str(end[0])+\"%2C\"+str(end[1])+\"&mode=fastest%3Bcar%3Btraffic%3Adisabled&routeattributes=shape&app_id=\"+app_id+\"&app_code=\"+app_code+\"&departure=now\"\n",
    "response = requests.get(request)\n",
    "if response.status_code == 200:\n",
    "    links = response.json()['response']['route'][0]['shape']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot path GPS coordinates of response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "links_ = [np.asarray(x.split(',')).astype(float) for x in links]\n",
    "links_ = np.reshape(np.hstack(links_),(-1,2))\n",
    "plt.scatter(links_[:,0],links_[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpolate along the coordinates and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f2 = interp1d(links_[:,0],links_[:,1], kind='linear')\n",
    "xnew = np.linspace(links_[0,0],links_[-1,0], num=interpolation)\n",
    "plt.scatter(xnew,f2(xnew))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Turn coordinates to String"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "links_ = zip(xnew,f2(xnew))\n",
    "links_ = [str(x[0])+\",\"+str(x[1]) for x in links_]\n",
    "print(links_[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get unique PanoID and fit coordinates for the predicted path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "panoID = []\n",
    "latitude = []\n",
    "longitude = []\n",
    "for coordinates in links_:\n",
    "    request = \"https://cbks0.google.com/cbk?cb_client=apiv3&authuser=0&hl=en&output=polygon&it=1%3A1&rank=closest&ll=\"+coordinates+\"&radius=10\"\n",
    "    response = requests.get(request)\n",
    "    if response.status_code == 200:\n",
    "        try:\n",
    "            panoID.append(response.json()['result'][0]['id'])\n",
    "            latitude.append(response.json()['result'][0]['latitude'])\n",
    "            longitude.append(response.json()['result'][0]['longitude'])\n",
    "        except KeyError:\n",
    "            print(response.content) \n",
    "df = pd.DataFrame(panoID,columns={\"panoID\"})\n",
    "df['latitude'] = latitude\n",
    "df['longitude'] = longitude\n",
    "df = df.drop_duplicates(subset=\"panoID\")\n",
    "df['road'] = [road]*df['panoID'].count()\n",
    "df['district'] = [district]*df['panoID'].count() \n",
    "df.head()\n",
    "panoID = list(set(panoID)) # uniques for google api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_file = os.path.join(save_dir,\"metadata_{},{}.csv\".format(road,district))\n",
    "df.to_csv(csv_file,index=False)\n",
    "df['panoID'].to_csv(os.path.join(save_dir,'downloadlist.txt'),index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use Google Street View Api to download segments of panos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_key = \"AIzaSyDA1bRkrS0i8d1C4QaDz3WGK7B8sMuIHsA\"\n",
    "fov = 90\n",
    "size = '640x640'\n",
    "pitch = '10'\n",
    "headings = list(range(0,360,45))\n",
    "headings.remove(90)\n",
    "headings.remove(270)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for pano in panoID:\n",
    "    for heading in headings:\n",
    "        request = \"https://maps.googleapis.com/maps/api/streetview?size=\"+size+\"&pano=\"+pano+\"&fov=\"+str(fov)+\"&heading=\"+str(heading)+\"&pitch=\"+pitch+\"&key=\"+api_key\n",
    "        response = requests.get(request,stream=True)\n",
    "        if response.status_code == 200:\n",
    "            try:\n",
    "                if response.headers['Content-Type'] == 'image/jpeg':\n",
    "                    image_name = \"img_pano{}_heading{}_pitch{}_fov{}.jpeg\".format(pano,heading,pitch,fov)\n",
    "                    with open(os.path.join(save_dir,image_name), 'wb') as out_file:\n",
    "                        shutil.copyfileobj(response.raw, out_file)\n",
    "            except KeyError:\n",
    "                print(response.content)\n",
    "            del response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "panoID[27:32]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}