Semantically identifiy hazard and create a 3D color coded cloud
This package is tested with TensorFlow 2.0.0, CUDA 10.1. It's higly recommended to run keras-segmentation using GPU to have higher execution speed.
Some required packges/modules before installing image-segmentation-keras
pip install opencv-python==4.2.0.32
pip install Keras==2.3.1
pip install launchpadlib==1.10.6
pip install setuptools==41.0.0
pip install tensorflow==2.1.0
pip install cntk
pip install 'scikit-image<0.15'
pip install Theano
cd ~
git clone https://github.com/Theano/libgpuarray.git
cd libgpuarray
git checkout tags/v0.6.5 -b v0.6.9
mkdir Build
cd Build
cmake .. -DCMAKE_BUILD_TYPE=Release # or Debug if you are investigating a crash
make
sudo make install
cd ..
# for pygpu
# This must be done after libgpuarray is installed as per instructions above.
python setup.py build
python setup.py install
sudo ldconfig
sudo apt-get install -y python-mako
apt-get install -y libsm6 libxext6 libxrender-devcd ~
git clone https://github.com/divamgupta/image-segmentation-kerasIn predict.py, change the line with return pr to return seg_img
cd image-segmentation-keras
python setup.py installmkdir -p ~/catkin_ws/src
cd ~/catkin_ws
catkin init
catkin config --cmake-args -DCMAKE_BUILD_TYPE=Release
catkin config --merge-devel
cd ~/catkin_ws/src
# You need access to clone this package
git clone https://github.com/kucars/semantic_hazard_cloud.git
cd ..
catkin buildYou need custom trained network which is available in folder models_trained. You also need a rosbag to test the package.
# In one terminal
roslaunch semantic_hazard_cloud semantic_mapping.launch
# In another terminal
rosbag play 2020-06-09-10-18-31.bagAn application of this package for environmental exploration with semantic mapping using a PX4-powered drone is available in the semantic_based_exploration