Saher El-Neklawy A blog

Layar POI service using Google App Engine

Layar is a cool handheld augmented reality application that allows to overlay "Layers" on the image seen by a handheld device's camera. One can think of these layers as content that is seen based on your current location. This allows to overlay digital data over actual live imagery.

The Layar API depends that the source of data (Points Of Interest) is a RESTful web service, that sends an HTTP GET request, and expects back  a JSON object. The details of the GET parameters, and the required JSON object could be found here in the layar API documentation.

To create a layer, one needs to provide such a service that provides the Points Of Interest (POI). Such a service could easily be written and provided by google app engine. This article will discuss how to do so using the google app engine python SDK, and is highly depends on the getting started guide for google app engine through python. Sections 1-4,6 are sufficient for understanding of the coming content.

Handling Requests

Since requests from layar to the POI web service come in the form of GET requests, the parameters can simply be accessed in the request handling method through:
[sourcecode language="python"]
self.request.get(parameter_name)
[/sourcecode]

POI Response

The POI response that layar expects is a json object. The API place many restrictions on the response. The first comes in the content type of the HTTP response. Thus, it has to be set as follows:
[sourcecode language="python"]
self.response.headers['Content-type'] = 'text/javascript; charset=utf-8'
[/sourcecode]

Another restriction is in JSON object returned as a response, where almost all the fields are required by layar. To make things simpler, the object is to be represented as a python object. This could be later converted to a JSON object. The JSON object responded with could be represented as follows (as a minumum):
[sourcecode lanaguage="javascript"]
{'layer':'layer name', 'hotspots':list_of_POI, 'errorCode':0, 'errorString':'ok'}
[/sourcecode]
Where hotspots is a list of POI object. These object could be represented as:
[sourcecode language="python"]
class POI:
def __init__(self,poi_id,title,lat,lang):
self.actions = []
self.id = poi_id
self.imageURL = None
self.lat = lat
self.lon = lang
self.distance = None
self.title = title
self.line2 = None
self.line3 = None
self.line4 = None
self.attribution = ""
self.type = 0
self.dimenion = 1
self.transform = {'rel':True, 'angle':0, 'scale':1.0}
self.object = {'baseURL': ""}
[/sourcecode]

From python to JSON

As python follows a batteries all included strategy, there are libraries that convert python dictionaries to JSON objects. Even though google app engine uses python, the "json" module is not available. Thankfully, an equivalent one is found through "from django.utils import simplejson". It could be used to convert a python dictionary (that looks frightfully like a JSON object) to a JSON object string as follows:
[sourcecode language="python"]
simplejson.dumps({'layer':'guc', 'hotspots':poi_list, 'errorCode':0, 'errorString':'ok'})
[/sourcecode]
The problem with simplejson is that it only takes dictionaries or lists. Thus, this poses a problem when using the POI class mentioned earlier. Again, python comes with a rescue, where a dictionary representing the object could be obtained as follows:
[sourcecode language="python"]
poi = POI('C1','C1',29986707,31438864)
poiDictionary = poi1.__dict__
[/sourcecode]

Putting the code together

Now that we have the POI object, and the conversion mechanism, we are ready to have a request handler for layar requests. An example of static POIs is as follows:
[sourcecode language="python"]
from django.utils import simplejson
from poi import POI
from google.appengine.ext import webapp
from google.appengine.ext.webapp.util import run_wsgi_app

class POIHandler(webapp.RequestHandler):
def get(self):
self.response.headers['Content-type'] = 'text/javascript; charset=utf-8'
#latitude and longitude is an integer will be divided by 10^6
# so take care of accuracy after the division
poi1 = POI('C1','C1',29986707,31438864).__dict__
poi2 = POI('C2','C2',29986744,31439272).__dict__
poi3 = POI('C3','C3',29986995,31438923).__dict__
poi4 = POI('C4','C4',29987153,31439245).__dict__
poi5 = POI('C5','C5',29986326,31438810).__dict__
poi6 = POI('C6','C6',29986688,31438569).__dict__
poi7 = POI('C7','C7',29986442,31438370).__dict__
pois = [poi1,poi2,poi3,poi4,poi5,poi6,poi7]
# final getPOI response dictionary
d = {'layer':'guc', 'hotspots':pois, 'errorCode':0, 'errorString':'ok'}
self.response.out.write(simplejson.dumps(d))

application = webapp.WSGIApplication(
[('/',POIHandler),
('/getPOI',POIHandler)],
debug=True)

def main():
run_wsgi_app(application)

if __name__=='__main__':
main()
[/sourcecode]

Updates

With the new layar 4 api coming soon, and the new features being in beta2, un update of how to add one of my favorite new features is needed. This is having different actions on the entire layar. It can be easily done by adding an action attribute to the final getPOI response dictionary (see code above). An example of the addition is as follows:
[sourcecode language="python"]
d['actions'] = [{'uri':'http://google.com','label':'action on the entire layar'}]
[/sourcecode]

SVN post-commit hook cronjob

Few SVN hosting sites provide ssh access to their svn servers. Such is needed to create hooks, like a post-commit hook that sends an email with every commit. Such hooks are often required by the development team. This article discusses the creation of a cron job that polls the SVN repository to simulate a post-commit hook that sends an email with every commit.

Setting up sendEmail

sendEmail is a nice tool to send emails via command line. This is needed to script in the cronjob sending an email. On ubuntu, install it from apt-get by:

sudo apt-get install sendemail libio-socket-ssl-perl libcrypt-ssleay-perl

The latter 2 packages are needed for connecting to an SMTP server that uses a secure connection (like Gmail, which is used in the example).

The post-commit hook script

The following is a python script, that could be added as an entry in the cron table
[sourcecode language="python"]
#! /usr/bin/env python

import os,re

log_file = LOG_DIR
svn_log_seperator = '------------------------------------------------------------------------'
svn_dir = SVN_DIR
mail_filter = '[special mail identifier in subject]'
message_file = '/tmp/commit_mail'

mail_from = MAIL_FROM
mail_to = MAIL_TO

smtp_server = "smtp.gmail.com:587"
smtp_user = SMTP_USER
smtp_pass = SMTP_PASS

def send_mail(subject,mail):
mail_file = open(message_file,'w')
mail_file.write(mail)
mail_file.close()
mail_command = 'sendEmail -f %s -t %s -u "%s" -s %s -xu "%s" -xp "%s" -o message-file="%s"' % (mail_from, mail_to, subject, smtp_server, smtp_user, smtp_pass,message_file)
print mail_command
os.system(mail_command)

print "analyzing old log file"

old_log_f = open(log_file)
old_log = old_log_f.read().split(svn_log_seperator)
old_log_f.close()

print "updating svn"
os.system("/usr/local/bin/svn update %s" % svn_dir)
os.system("/usr/local/bin/svn log %s > %s" % (svn_dir,log_file))
print "got new log"

new_log_f = open(log_file)
new_log = new_log_f.read().split(svn_log_seperator)
new_log_f.close()

delta = set(new_log) - set(old_log)

for commit in delta:
l = [x for x in commit.split('\n') if x]
details = l[0].split('|')
subject = "%s %s %s" %(mail_filter,details[0],details[1])
f_diff=os.popen('/usr/local/bin/svn diff -c %s %s' % (details[0], svn_dir))
diff = f_diff.read()
f_diff.close()
mail = "%s\n%s" % (commit, diff)
send_mail(subject,mail)
[/sourcecode]

Perquisites before running the script

      Having a working copy of the svn reposity
      Running the following command once before the script:

      svn log > $PATH_TO_LOG_FILE

Developer vs. User <=> Driver vs. Mechanic

Hypothesis: The Developer User relation is equivalent to the car driver mechanic relation.

Proof:
Through my few numbered years of driving in the lovely streets of Cairo, i faced car troubles every now and then. In the case of such circumstances, I head to my trusty mechanic. From here, starts the problem, when I am driving my car, and i notice a problem, it is very difficult to reproduce by the mechanic. Such a reproduction often happens when it's too late.

Here, comes the fitting analogy, mechanics are car using experts. They manage to use cars in an expert manner, one that hides any possible problems in the car.

The same maps to the computer software users, where they see problems that developers are unable to reproduce, simply because of their computer usage habits that are so much different from that of end users.

Quod Erat Demonstrandum (QED).

Thus, the lesson to learn here is, every time you want to feel like a user, think of yourself driving your car, and weird things starting to happen.