Converting Labelme annotations to COCO dataset annotations

This is a short blog about how I converted Labelme annotations to COCO dataset annotations.

mlearning

I created the repo mlearning for storing Machine Learning utilities, helper code, etc…

The first main addition to this repo is the converter that I wrote. I takes labelme annotated images, and converts them to the COCO dataset 2014 annotations format.

Why?

I’m training a model on the COCO dataset, so I need a way to generate my own labeled data that can be used by this model. I tried out a few data labeling softwares, like RectLabel and LabelBox, but they were freemium’s and didn’t give me the output, or weren’t that great to use. labelme is open source. I plan to contribute back my COCO dataset converter once I use it more, and it becomes more stable.

Let’s see the converter in action.

Example Output

Here’s example output from the mlearning Github repo.

%matplotlib inline

import os

from matplotlib import pyplot as plt
import matplotlib.pylab as pylab

from mlearning import util
from mlearning.coco import Annotation
from mlearning.plotting import plot_bboxes_and_masks

pylab.rcParams['figure.figsize'] = 12, 12

# must set file paths for one's own data!!!
ann = Annotation(path=os.path.join(os.path.expanduser('~'), 'Desktop/license_plate_detection'))

# this is the function that displays a random example
plot_bboxes_and_masks(ann)

Future plans

I plan to label more images and then train them on the model. I am still doing a proof of concept that I can train on my own hardware, and that the labelme-to-COCO dataset converter that I wrote works.