Inspiration
As a little boy, Huy Ha, a Vietnamese professional photographer and filmmaker, always loved that his mom took her time to document all the precious moments of their lives through video recordings. She showed him the value of holding onto memories because they can fade away just as quickly as they can be made. But as he grew older, the films became much more than just documentation, they were a way for him to revisit memories and the different places he has been. Films and photographs allowed him to reflect on himself and ponder about how he and his surrounding have changed over time.
However, when Huy arrived in Manhattan, he wanted to know where the best photo shoot locations were, but Google could only point him to tourist attractions. There were no other extensive and reliable location scouting tool for photographers. A great photograph is so powerful, not only to the photographer, but also the viewer. For a photographer to be able to find the most ideal locations to take photos gives a photographer the power to convey the message and emotion without the struggle of searching aimlessly to find the best location. All in all, the background of a photo is just as important as the object of the photo when it comes to conveying an idea or message.
What it does
Pholosco is a web app that allows not only professional photographers, but also novice enthusiasts to effortlessly locate the most ideal spots to take photos based on the input of photos and ratings from a community of photographers. Drawing data from Flickr, Pholosco loads the information onto Google Maps. Because we use the heat map within the Google Maps API, the user can easily identify the locations with the most photographs taken. The user can also create an account allowing users to connect.
How we built it
Flickr API, Google Maps API, Flask, SQLITE3 database, Python, JS, HTML, CSS, GCP to host remotely, VENV
Challenges we ran into
We had trouble setting up a remote database using MongoDB; therefore, we ended up using SQLite 3 to store usernames and passwords. Also getting the heat map to work was extremely difficult because data from flickr’s api would not cooperate with the google map api.
Accomplishments that we're proud of
We have the framework required for a website. We were able to successfully incorporate a user log-in system.
We were able to use the Google API in order to implement a system to draw heat maps.
We were able to have a python script use the Flickr API to pull a set amount of pictures from Flickr and store them in custom made objects until their data is manipulated by the user. Able to set up a method to remotely host the website on a Google Compute Cloud instance.
Use Git efficiently and effectively as a team.
We have a vision for where we want the web app to head.
What we learned
Huy: first time coding in Python and a lso his first Hackathon, and he was able to learn how to use Flickr’s API, draw user data, sort the data
William: Learned about Flask development and building a website. Worked on a docker style deployment and learned how to implement a DB and a login system on a Flask website. Also learned how to use Bootstrap and Blocking content to build websites.
Ciaran: I learned how to generate API keys for Google projects and then use Google’s Maps API for JavaScript. I added heatmap data points to our maps. I also researched and used various BootStrap elements to add a modern look for our web app.
Angela: Learned how to use github to pull and push changes. She has never worked with APIs before and learned how to implement Flickr API.
What's next for pholosco
In the future we hope to allow other photographers to create their own profile and store their information so people who enjoy their content could follow them, contact them for shooting opportunities, and for fellow photographers to share tips amongst themselves.
In the future we hope to enable a host of more computationally intensive functionality: real time data streams to display live content being taken, photo maps that show the locations recent photos being taken all around the world. Machine learning for photo identification and automatic tagging. Multiple location specific filters so that photographers can find locations that meet their specific criterion for that perfect shot.
Log in or sign up for Devpost to join the conversation.