Skip to content

Machine learning and computer vision to count beer consumption

License

Notifications You must be signed in to change notification settings

gonzula/Brewereader

Repository files navigation

Brewereader

Machine learning and Computer Vision to read beer

Where I live, me and my friends mark the beer we drink on a paper like this, hanging on the wall.

Paper hanging on the wall

Once a month we type this paper on a spreadsheet and run a script on it to tell how much each person has to pay. This process takes 30 minutes approximately.

The spreadsheet

The solution

In an attempt to solve this wasted time I developed a script that reads the image of the paper and builds the spreadsheet by itself.

The only action required by the user is to take a picture of the paper.

The script, using OpenCV, separates each cell using a Hough transformation

Hough transformation

After that, runs a MLP, written in Python with numpy, on each cell Separated cells

Getting the dataset

To build the dataset required to train the mlp, I needed a large amount of cells filled with known data. So I asked my friends to fill up some sheets.

3 examples

And together, we managed to draw 33,000 sticks

A larger sample

The results

The algorithm gets 90% accuracy. Since this software deals with money, 90% accuracy is not enough, but the mistakes are very likely to be noticed before any payment is made. Once the spreadsheet knows how much beer has been bought, it knows how much has to be marked on the paper, and if the numbers don't match, probably we have an error.

Releases

No releases published

Packages

No packages published

Languages