Install DetectionSuite application
The application can be directly downloaded from the repository releases.
To run the app, first give executable permissions running:
chmod a+x DetectionSuitexxxxx.AppImage
and run it using:
./DetectionSuitexxxxx -c configFile
To use Tensorflow and/or Keras, you would need to install them.
To install Tensorflow:
pip install tensorflow
pip install tensorflow-gpu
To install Keras:
pip install keras
First you need to have docker installed in your computer. If no, assuming you are in Ubuntu, install it using the following command:
sudo apt install docker.io
Now you need to start the docker deamon using
sudo service docker start
After starting the deamon , get DetectionSuite docker file using
sudo docker pull jderobot/dl-detectionsuite
Now run the docker image using
sudo docker run -it -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix jderobot/dl-detectionsuite
-it flag instructs Docker to allocate a pseudo-TTY connected to the container’s stdin; creating an interactive
bash shell in the container ,
-e tag is used to set the environment variable, in this case
DISPLAY, this is required because you will be using GUI for interaction through out rest of the tutorials.
Voila!! You can now use DetectionSuite. Proceed to beginner’s tutorial to get started.
Compile and Install from source
To use the latest version of DetectionSuite you need to compile and install it from source. To get started you can either read along or follow these video tutorials.
sudo apt install build-essential git cmake rapidjson-dev libssl-dev sudo apt install libboost-dev libboost-filesystem-dev libboost-system-dev libboost-program-options-dev
sudo easy_install numpy brew install cmake boost rapidjson
sudo apt install libgoogle-glog-dev libyaml-cpp-dev qt5-default libqt5svg5-dev
brew install glog yaml-cpp qt
Also, just add qt in your PATH by running:
echo 'export PATH="/usr/local/opt/qt/bin:$PATH"' >> ~/.bash_profile
Install OpenCV 3.4
git clone https://github.com/opencv/opencv.git cd opencv git checkout 3.4 mkdir build && cd build cmake -D WITH_QT=ON -D WITH_GTK=OFF .. make -j4 sudo make install
brew install opencv
CUDA (For GPU support)
NVIDIA_GPGKEY_SUM=d1be581509378368edeec8c1eb2958702feedf3bc3d17011adbf24efacce4ab5 && \ NVIDIA_GPGKEY_FPR=ae09fe4bbd223a84b2ccfce3f60f4b3d7fa2af80 && \ sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub && \ sudo apt-key adv --export --no-emit-version -a $NVIDIA_GPGKEY_FPR | tail -n +5 > cudasign.pub && \ echo "$NVIDIA_GPGKEY_SUM cudasign.pub" | sha256sum -c --strict - && rm cudasign.pub && \ sudo sh -c 'echo "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64 /" > /etc/apt/sources.list.d/cuda.list' && \ sudo sh -c 'echo "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64 /" > /etc/apt/sources.list.d/nvidia-ml.list'
Update and install
sudo apt-get update sudo apt-get install -y cuda
Below is a list of more optional dependencies you may require depending on your usage.
Camera Streaming Support
DetectionSuite can currently read ROS and ICE Camera Streams. So, to enable Streaming support, install any one of them.
DetectionSuite currently supports many Inferencing Frameworks namely Darknet, TensorFlow, Keras and Caffe. Each one of them has some dependencies, and are mentioned below.
Choose your favourite one and go ahead.
Darknet (jderobot fork)
Darknet supports both GPU and CPU builds, and GPU build is enabled by default. If your computer doesn’t have a NVIDIA Graphics card, then it is necessary to turn of GPU build in cmake by passing
-DUSE_GPU=OFFas an option in cmake.
git clone https://github.com/JdeRobot/darknet cd darknet mkdir build && cd build
For GPU users:
cmake -DCMAKE_INSTALL_PREFIX=<DARKNET_DIR> ..
For Non-GPU users (CPU build):
cmake -DCMAKE_INSTALL_PREFIX=<DARKNET_DIR> -DUSE_GPU=OFF ..
<DARKNET_DIR>to your custom installation path.
make -j4 sudo make -j4 install
The only dependency for using TensorFlow as an inferencing framework is TensorFlow. So, just install TensorFlow. It should be 1.4.1 or greater.
Similarly, the only dependency for using Keras as an inferencing framework is Keras.
For using Caffe as an inferencing framework, it is necessary to install OpenCV 3.4 or greater.
Note: Be Sure to checkout functionality for tutorials on how to use the above mentioned functionalities and frameworks.
How to compile DetectionSuite:
Once you have all the required dependencies installed just run:
git clone https://github.com/JdeRobot/DetectionSuite cd DetectionSuite/DeepLearningSuite mkdir build && cd build
To enable Darknet support with GPU:
cmake -DARKNET_PATH=<DARKNET_INSTALLETION_DIR> -DUSE_GPU_DARKNET=ON ..
Note: GPU support is enabled by default for other Frameworks
Note: To enable Darknet support just pass an optimal parameter in cmake
-D DARKNET_PATH equal to Darknet installation directory, and is same as
<DARKNET_DIR> passed above in darknet installation.
Once it is built, you will find various executables in different folders ready to be executed .
Starting with DetectionSuite
The best way to start is with our beginner’s tutorial for DetectionSuite.