Playing with Exit Signs / Destination Signs.
High-level idea:
Extract location for destination=* and exit_to=* tags from OSM ways and nodes and query Mapillary to retrieve dashcam images.
Use these labeled images to train a model that is able to predict signs from unlabeled images.
Automatically extracted dashcam images
Segmentation and Region proposals
Mapillary images licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).
Build the sign location extractor:
./deps.sh
make
Grab some OSM extracts:
./osm.sh
Get a clientId from Mapillary and export it for image fetching:
export EXIT_SIGNS_CLIENT_ID='YOUR-CLIENT-ID'
Run the Sign location extractor on the extract and save out a list of locations:
./build/Release/extract-locations osm/*.osm.pbf > signLocations.csv
Fetch images for locations:
mkdir -p signImages
./fetch-images.py signLocations.csv signImages
Clean up images and label them with rectangles to generate a training set:
for image in signImages; do ./label-image.py signImages/$image signImages/labels.csv; done
Train the R-CNN model on labeled images:
./region-cnn.py --train signImages/labels.csv
Predict signs on a new image:
./region-cnn.py --predict image.jpg
Copyright © 2016 Daniel J. Hofmann
Distributed under the MIT License (MIT).