This is a quick note on how to map images in a directory purely from their exif files. An exif file is one which contains the meta data of the image. If the image was taken by a phone of GPS enabled camera, then a location will also be contained within that file. To read that directory on the webserver we need to use a CGI script. For our purposes we will use python.

This is really a very simple concept:

    1. have a folder space on a webserver
    2. set that server up to to understand .py files as CGI scripts
    3. have a directory full of images
    4. have a web page which pulls up a Google Map
    5. have that map requst the geography of each image (via ajax and the CGI script)
    6. display the locations of the images on the map

This idea is simple, but requires some fiddling. Firstly you need to make your apache webserver execute python scripts. Thankfully there are numerous resources to help in this. With that in mind I will assume you now have a space on a webserver in which a python script can be executed. next make a directory called ‘images’ and put your images in it.

Next we will need a very simple web page. Ours will just have a single div called “map”

<html>
    <head>
        <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js">
        </script>
        <script src="https://maps.googleapis.com/maps/api/js?v=3.exp&sensor=false">
        </script>
        <style>
            html, body, #map {
                height: 100%;
                margin: 0px;
                padding: 0px;
            }
        </style>
    </head>
    <body>
        <div id="map"></div>
    </body>
</html>

And, we’ll need a smidge of JavaScript to display a Google Map, ask the python script for the coordinates from the images’ exif files and then provide listeners for the info boxes.

<script>
 
    $.ajax({
        url: "get_images.py",
        type: "GET",
        dataType: 'json',
        success: function(data){
            initialize_map(data)
        }
     });
     
     function initialize_map(data){
         var myLatlng = new google.maps.LatLng(-25.363882,131.044922);
         var mapOptions = {
             zoom: 4,
             center: myLatlng
         };
 
         var bounds = new google.maps.LatLngBounds();
         var map = new google.maps.Map(document.getElementById('map'), mapOptions);
 
         for (var image in data){
             lat = data[image].loc.split(',')[0]
             lon = data[image].loc.split(',')[1]
             var pos = new google.maps.LatLng(lat,lon);
             marker = new google.maps.Marker({
                 position: pos,
                 map: map 
             });
             bounds.extend(pos)
 
             attachInfo(marker, data[image].image)
 
             function attachInfo(marker, image) { 
                 var infowindow = new google.maps.InfoWindow({
                     content: '<img src="images/' + image + '" width=200/>'});
                     google.maps.event.addListener(marker, 'click', function() {
                         infowindow.open(map,marker);
                     }); 
                 })
             }
         map.fitBounds(bounds);
     }
 </script>

This provides a simple ajax using jquery request to the python script. It doesn’t have to do anything clever, it just says “run your get_images.py” script. In return its expecting a json object with image names and locations. The image names returned help build the info windows (by pulling in images to them) and the returned locations add the markers to the map.

The final piece of the puzzle is the python script.

#!/usr/bin/env/python
 # -*- coding: UTF-8 -*-
 import cgitb
import cgi
import os, sys
import mimetypes
import exifread
import json
from fractions import Fraction

cgitb.enable()

print "Content-Type: application/json"
print

source = "/path/to/your/images"
image_locate = {}
directory = os.listdir( source )

def _get_if_exist(data, key):
    if key in data:
        return data[key]
return None 

def _convert_to_degress(value):
    """Helper function to convert the GPS coordinates stored in the EXIF to degrees in float format"""
    d = float(Fraction(str(value.values[0])))
    m = float(Fraction(str(value.values[1]))) 
    s = float(Fraction(str(value.values[2])))
return d + (m / 60.0) + (s / 3600.0) 

def get_lat_lon(exif_data):
    """Returns the latitude and longitude, if available, from the provided exif_data (obtained through get_exif_data above)"""
    lat = None
    lon = None 
    gps_latitude = _get_if_exist(tags, "GPS GPSLatitude")
    gps_latitude_ref = _get_if_exist(tags, 'GPS GPSLatitudeRef').values
    gps_longitude = _get_if_exist(tags, 'GPS GPSLongitude')
    gps_longitude_ref = _get_if_exist(tags, 'GPS GPSLongitudeRef').values
    
    if gps_latitude and gps_latitude_ref and gps_longitude and gps_longitude_ref:
        lat = _convert_to_degress(gps_latitude)
    
    if gps_latitude_ref != "N":
        lat = 0 - lat
        lon = _convert_to_degress(gps_longitude)
    
    if gps_longitude_ref != "E":
        lon = 0 - lon
    
    return "%s, %s" % (lat, lon)

    for file in directory:
        mt = mimetypes.guess_type(file)[0]
        if mt:
            p = os.path.join(source, file)
            f = open(p, 'rb')
            tags = exifread.process_file(f) 
            image_locate[file] = get_lat_lon(tags)

print json.dumps([{'image': k, 'loc': v} for k,v in image_locate.items()], indent=4)

This file depends on the exifread library which very conveniently does all the heavy lifting in the exif reading. That read all we have to do is drop into our images folder then read through the files, check their mimetypes, if they are images then look for geography. The geography is the converted back from rational number representations of Degrees, Minutes, Seconds to the more versitile Decimal Degrees and finally passed back in a json objet to the javascript and the map.

There is little validation here and this is obviously just proof of concept code, but a useful example I think.

The code above assembles to look like this:

Sharing options

Observations • Will Cadell

Our Mapbox Services Partnership

For us, geospatial has always been about the consumer, or commercial applications. For this reason, we use Mapbox tools and services. Their technology is thoughtfully designed and enormously…

teal and red abstract painting

Observations • Will Cadell

/quick-map for Slack Update

Sometimes a chat needs a map, sometimes a map needs a chat. Either way, we created /quick-map last year to provide that functionality in Slack, the leading corporate…

Observations • Julien Jacques

Coral Reef Dataset Pipeline

Facing climate change and severe coral reef bleaching, which causes coral reefs to die off, coral reef scientists have been embracing new technologies to help diagnose the overall…

Need a geospatial partner?

Our team complements organizations like yours—by providing on-tap access to geospatial, analytics, and mapping expertise.

Let’s talk

Join our team?

We’re always looking for skilled technologists to help us build the future of geospatial. Got a minute to find out more about us?

Working Here

Sharing options