The ultimate goal of this repo is to (as the title suggests) classify memes.
How it works
Currently, the the algorithm works by comparing a provided image to one of many memes
provided in he
comparisons/ directory. The atual comparsion is essentially just comparing the
individual rgb values for each pixel after resizing the comparison image to the dimensions of
the meme I would like to classify. This could loosely be defined as a euclidean distance
classifier since I'm only comparing against 1 sample per class.
Hopefully this will go into further development the further my class advances into the Pattern Recognition class I'm in.
Both numpy and opencv are dependencies for this project. Since I have trouble installing numpy in particular to a virtualenv, I would suggest installing it into your global pip:
$ sudo apt-get install python-opencv $ sudo pip instal numpy
If you plan to run the flask server, you will also need to install whatever is listed in
$ sudo pip install -r requirements.txt
There is currently a web interface for classifying memes at
but a script is also provided that can run on a bash termial.
To classify an image on your local machine:
$ python classify.py /path/to/image
To classify an image at a url, just add the
$ python classify.py http://image.ur/path/to/image --url
To start the flask server like the one that's running on the website:
$ python __init__.py
- I would like to develop this enough that I could use it as my term project for my pattern recognition class.
- Instead of comparing against 1 image, collect data (from, lets say, reddit) to compare against training data via a bayesian or k-nearest-neighbors classifier.
- See if it's possible to eveolve this to a point that memes could be generated from this.
- If a meme is classified as a Pepe, also display it's rarity in how frequently it appears.