A data science exercise to programmatically find buildings that *used* to be Pizza Hut restaurants...
My main inspiration for this project: https://www.reddit.com/r/FormerPizzaHuts/
Pizza Hut has an API that returns store information by Store Number. Fortunately, this API has no real form of rate limitation, so I iterated through all possible store combinations.
To return store information you simply send a POST request with form data that contains a storeNum parameter to the following API endpoint:
https://www.pizzahut.com/api.php/site/api_ajax/stores/getStoreAjax
You will get a response that looks similar to this:
{
"success": 1,
"response": {
"storeNum": "027543",
"address1": "8024 Augusta Rd",
"address2": "Piedmont, SC 29673",
"zip": "29673",
"phone": "(864) 277-1222",
"landmark": "",
"hrs_available": 1,
"hrs_concepts": [{
"service": "carryout"
}, {
"service": "delivery"
}],
"hrs_times": [{
"day": "Sun",
"col0": "11:00am - 11:00pm",
"col1": "11:00am - 11:00pm"
}, {
"day": "Mon",
"col0": "11:00am - 11:00pm",
"col1": "11:00am - 11:00pm"
}, {
"day": "Tues",
"col0": "11:00am - 11:00pm",
"col1": "11:00am - 11:00pm"
}, {
"day": "Weds",
"col0": "11:00am - 11:00pm",
"col1": "11:00am - 11:00pm"
}, {
"day": "Thurs",
"col0": "11:00am - 11:00pm",
"col1": "11:00am - 11:00pm"
}, {
"day": "Fri",
"col0": "11:00am - 12:00am",
"col1": "11:00am - 12:00am"
}, {
"day": "Sat",
"col0": "11:00am - 12:00am",
"col1": "11:00am - 12:00am"
}],
"dinein_available": 0,
"services": "Carryout, Delivery",
"wing_street": 1,
"latitude": 34.696159,
"longitude": -82.398854,
"closure_reason": "",
"carryout_future_order": true,
"delivery_future_order": true,
"city": "Piedmont",
"offline": false,
"status": "open",
"occasion_status": "DC",
"store_status": "DC",
"delivery_available": true,
"offline_status_msg": "",
"first_delivery": "11:00 AM",
"first_carryout": "11:00 AM",
"open_later_today_delivery": "",
"open_later_today_carryout": "",
"loyalty": {
"address": "8024 Augusta Rd",
"city": "Piedmont",
"state": "SC",
"state_name": "South Carolina",
"zip": 29673,
"phone": "(864) 277-1222",
"status": "DC",
"carryout_open": "11:00AM",
"carryout_close": "11:00PM",
"carryout_future_order": true,
"delivery_open": "11:00AM",
"delivery_close": "11:00PM",
"delivery_future_order": true,
"open_for_delivery": true,
"closure_reason": "",
"onlineStatus": "online",
"firstTimeDiscount": false,
"currentlyOpen": true,
"deliveryAvailable": true,
"carryoutAvailable": true,
"acceptFutureOrders": true,
"landmark": null,
"futuredelivery": true,
"futurecarryout": true,
"pizzaEvoTestStore": true,
"offlineStatusMsg": null,
"loyalty": "Y",
"tmp_offline_msg": "",
"store_lastUpdate": null,
"showDeliveryTime": "Y",
"promiseTimeDel": 50,
"promiseTimeLastUpdateDel": {
"date": "2018-06-24 16:27:00.000000",
"timezone_type": 3,
"timezone": "US\/Central"
},
"showCarryoutTime": "Y",
"promiseTimeCo": 15,
"promiseTimeLastUpdateCo": {
"date": "2018-06-24 16:25:00.000000",
"timezone_type": 3,
"timezone": "US\/Central"
},
"promiseTime": 50
}
}
}Here is the Python function used to determine if the store was Open, Closed, or Non-Existant (Note that this simply prints out the results, however the actual code used in main.py will save store information to a local file.)
def checkStore(storeNums):
data = '{"storeNum":"NUM"}'.replace("NUM", str(storeNum))
response = requests.post('https://www.pizzahut.com/api.php/site/api_ajax/stores/getStoreAjax', data=data)
if response.json()['response']['phone'] == None:
print("Not a Real Store")
elif response.json()['response']['longitude'] == 0:
print("Closed Store")
else:
print("Open Store")After finding restaraunt addresses that have recently closed, the program uses Google Maps to extract photos from the location. It does this by making a Google search for the address to extract the Panoid value from the first Google Maps URL in the search results. The Panoid is then used to directly pull images using the following API endpoint:
After pulling the panoid it will extract 17 images from each address using camera orientation values ranging from 0 to 340. Additionally, camera orientation values are "randomized" by adding a random float between 0 and 1 to the orientaiton. This does not significantly affect the camera orientation, but it prevents bot detection when accessing this API endpoint frequently.
def grabImagesFromAddress(address):
imageList = []
# Contains a list of image URLs
addressURL = address.replace(" ", "+")
# Converts address to a valid URL address
url = "https://www.google.com/maps/place/" + addressURL
# Gens URL for this address
res = grabSite(url)
# Pulls the url that was previously defined
page = bs4.BeautifulSoup(res.text, 'lxml')
# Converts it into a BS4 Object
panoidVal = grabPanoidFromHTML(page)
# This grabs the Panoid val from that address
if panoidVal != None:
# This means the address did exist
for cameraOrientationVal in genListOfCameraOrientations():
# Iterates through all possible camera orientations
imageList.append(GOOGLE_STREETVIEW_API.format(panoidVal, cameraOrientationVal))
# Appends them to the list of images
return imageListThe most time intensive part of this project was identifying the best camera orientation for each set of location images. The program pulls 17 camera orientations for each address, and the pizza hut is usually in only 1 or 2 of these images.
The most cost effective way of finding the ideal camera orientation was using an unrelated service called DeathByCapthca. DBC uses real people to solve Captcha images, but the service is usually used by spammers and bot creators. I theorized that if I turned my Pizza Hut images into Captchas, DBC would send back the image coordinates of the Pizza Hut locations.
Here is an example of the generated Captcha image:
Captcha images were then sent to DeathByCaptcha, and the quantity of solved coordinates were used to find the best image of each restaraunt.
Here is an example using psuedocode:
# This is pseudocode, but the captcha.py file contains the actual python code
listOfImages = GrabImages("123 Test Street, Palo Alto CA")
# Grabs the Pizza Hut images at 123 Test Street, Palo Alto CA...
listOfImageInfo = []
for image in listOfImages:
# Iterates through all 17 camera orientations
fakeCaptcha = generateCaptcha(image)
# Generates a "Fake" captcha image
coordinates = SolveCaptcha(fakeCaptcha)
# DBC returns coordinates of the squares that contain restaraunts
listOfImageInfo({"image_file": image, "number_of_restaraunts": len(coordinates)})
# Creates a dictionary with image file and the number of squares containing restaraunts
mostRestaraunts = 0
for imageInfo in listOfImageInfo:
if imageInfo['number_of_restaraunts'] > mostRestaraunts:
bestImage = imageInfo['image_file']
return bestImageThe captcha method worked for most images, but there was still a handful of photos that either didn't contain a Pizza Hut or that contained photos of other buildings in the image. To resolve this issue, I created a python script that iterates through all remaining addresses, and opens up a window that allows you to move through images quickly to pick the image that best displays the former Pizza Hut location. This also allows you to eliminate Pizza Hut locations that had been torn down and replaced with a building that didn't keep the iconic Pizza Hut building style...
cameraTilt = list(range(0, 340, 20))
# This contains all camera tilt orientations in the Google Car
currentOrientation = cameraTilt[0]
while True:
fileName = genFileName(address, currentOrientation)
# Filename for this specific camera orientation
keyChoice = viewImage(fileName)
# Keychoice is the key a user presses in the image window
if keyChoice == ord('n'):
# The user inputted the letter N
newTilt = cameraTilt.index(currentOrientation) + 1
if newTilt == len(cameraTilt):
# This is going to refresh the orientation from 320+ to 0
# ^ This allows for a smoothless transition between images
newTilt = 0
currentOrientation = cameraTilt[newTilt]
# Sets new orientation
if keyChoice == ord("p"):
# The user inputted the letter P
currentOrientation = cameraTilt[cameraTilt.index(currentOrientation) - 1]
# Sets new orientation
if keyChoice == ord("q"):
# The user inputted the letter Q
# This indicates that this image did not have a Pizza hut
break
# Quits the while loop
if keyChoice == ord("s"):
# The user inputted the letter S
# This indicates the user wants to save on the current image
saveImage(fileName)
# Saves the image
break
# Quits the while loop
print("{} IN LOOP {}".format(currentOrientation, fileName))
# Prints out information about the image
I wanted to get as much project feedback as possible from Reddit, so I used the Reddit Post Bigquery Dataset to find the most ideal time to post the project on /r/FormerPizzaHuts.
I used the following SQL query to pull upvote count by UTC-Timestamp:
#standardSQL
SELECT
created_utc, score
FROM `fh-bigquery.reddit_posts.201*`
WHERE subreddit = "FormerPizzaHuts"After analyzing the dataset I found that posting this project on Reddit on a Monday at 9:00PM PST would result in the highest upvote count.
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8800 Hampton Mall Dr N, Capitol Heights,MD
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8514 Holcomb Bridge Rd, Alpharetta,GA
4705 River Oaks Blvd, Fort Worth,TX
3245 Sierra Rd, San Jose,CA
1823 W Hill Ave, Valdosta,GA
127 Twin City Mall, Crystal City,MO
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