:smiley: machine_feeling :smiley:
An Emoji-ful introduction to computer vision and machine learning.
Introduction :smirk:
This small project is intended as a beginning to some of the fundamental ideas of machine learning and computer vision. Specifically, this is a look at the problem of categorizing images (along with some potential solutions).
Our goal in this project is to walk through how something like this may work.
Goal :relaxed:
The problem herein is that we want to be able to identify whether the emoji we see on the Internet is positive :blush:, neutral :neutral_face:, or negative :angry:. In this (somewhat contrived) scenario, we, the enterprising young computer scientists, have decided to build software to perform several tasks in accomplishing this goal:
- We need a way to "take a picture" or photograph something within our computer (i.e. something we see on the Internet). We could do this by using our phone or a screenshot software. But we are grade-A hipsters, so we'll be using OpenCV, a computer vision (buzzword!) platform, to build this ourselves.
- This picture should then be plopped into some machine-learning algorithm (yay! more buzzwords!) that will tell us (hopefully), which if our emoji is looking positive, negative or somewhere in between.
Some Notes :grin:
- Yes, this is an arguably absurdly contrived example on which to learn some basic machine learning and computer vision.
- Yes, there are likely better ways to do this than using machine learning, especially since we can usually tell what emotion an emoji is already
- Given the above two things, we designed the project this way because it seemed fun and entertaining
- Please do not take this project in any way, shape, or form as a representation of all that either machine learning or computer vision are capable - this barely scratches the surface of either field
To Get Started
- First, I would recommend using some form of dedicated environment, as with virtualenv, but if you don't wish to, that's ok too
- The rest is as easy as installing the requirements with
pip install -r requirements.txt
- Then, navigate to the directory containing run (assuming you're in the machine_feeling/ directory,
cd src; cd sample and run "run.py" ./run.py -h
- If you receive a message about your ability to run that file, go ahead and change the permissions
chmod +x run.py and you should be able to run it
- PLEASE NOTE, DUE TO A BUG WITH KERAS, YOU WILL LIKELY GET AN ERROR ABOUT SUBTRACTION AND NEGATIVE SIZES:
- the way I recommend is modifying the file at
~/.keras/keras.json where it reads "image_dim_ordering": "something here", and change that row to read "image_dim_ordering": "tf"
- YOU MAY ALSO GET AN ERROR ABOUT INPUT DIMENSIONS, IN WHICH CASE YOU SHOULD CHANGE THE MODEL SUCH THAT THE LAYERS PASS ON THE CORRECT DIMENSIONALITY TO THE LAYERS AFTER THEM
- All bugs should be fixed for distro code, please do let me know if anything doesn't work!
Conclusion :heart:
We sincerely hope that you enjoy working through this project and go on to pursue your interest in both machine learning and computer vision!
If you find a bug, a typo, or simply wish to contribute, please do make a pull request with your edit!
Sincerely,
potc
