OSMDeepOD - OpenStreetMap (OSM) and Machine Learning (Deep Learning) based Object Detection from Aerial Imagery (Formerly also known as "OSM-Crosswalk-Detection").
OSMDeepOD is a project about object detection from aerial imagery using open data from OpenStreetMap (OSM). The project uses the open source software library TensorFlow, with a retrained Inception V3 neuronal network.
This work started as part of a semester thesis autumn 2015 at Geometa Lab, University of Applied Sciences Rapperswil (HSR). See Twitter hashtag #OSMDeepOD for news.
The simplest way to use the detection process is to clone the repository and build/start the docker container.
git clone https://github.com/geometalab/OSMDeepOD.git
cd OSMDeepOD/docker/
sudo docker build . -t osmdeepod
sudo docker run -it --name osmdeepod -v ./:/objects osmdeepod bash
After the previous shell commands you have started a standalone instance of OSMDeepOD and you are connected to it. If you have a nvida GPU and nvidia-docker installed, you could use the "nvidia-docker" command to run the container for automatically usage of the GPU1.
To start the detection process use the src/role/main.py2 script.
python3 main.py --config ./config.ini --standalone manager 9.345101 47.090794 9.355947 47.097288
After the detection process has finished a "detected_nodes.json" file will appear with the results. If you like to use OSMDeepOD in a more parallel and distributed way have a look at the https://github.com/geometalab/OSMDeepOD-Visualize repository. There you have got the ability to use redis as a message queue and you can run many OSMDeepOD instances as workers.
The configuration works with an INI file. The file looks like the following:
[DETECTION]
Network = /path/to/the/trained/convnet
Labels = /path/to/the/label/file/of/the/convnet
Barrier = 0.99
Word = crosswalk
Key = highway
Value = crossing
ZoomLevel = 19
Compare = yes
Orthofoto = other
FollowStreets = yes
StepWidth = 0.66
[REDIS]
Server = 127.0.0.1
Port = 40001
Password = crosswalks
[JOB]
BboxSize = 2000
Timeout = 5400
Some hints to the config file:
To use your own Orthofotos you have to do the following steps:
src/data/orthofoto
<your_new_directory>_api.py
<Your_new_directory>Api
(First letter needs to be uppercase)def get_image(self, bbox):
and returns a pillow image of the bbox--orthofots <your_new_directory>
If you have problems with the implementation have a look at the wms or other example.
During this work, we have collected our own dataset with swiss crosswalks and non-crosswalks. The pictures have a size of 50x50 pixels and are available by request.
Picture 3: Crosswalk Examples
Picture 4: No Crosswalk Examples
Python
At the moment, we support python 3.5
Docker
In order to use volumes, I recommend using docker >= 1.9.x
Bounding Box of area to analyze
To start the extraction of crosswalks within a given area, the bounding box of this area is required as arguments for the manager. To get the bounding box the desired area, you can use https://www.openstreetmap.org/export to select the area and copy paste the corresponding coordinates. Use the values in the following order when used as positional arguments to manager: left bottom right top
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