Install Detection Studio application
The application can be directly downloaded from the repository releases.
To run the app, first give executable permissions running:
chmod a+x DetectionStudioxxxxx.AppImage
and run it using:
./DetectionStudioxxxxx -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
To install Pytorch:
pip install torch
Compile and Install from source
To use the latest version of Detection Studio you need to compile and install it from source. To get started you can either read along or follow these video tutorials.
Also, just add qt in your PATH by running:
OpenCV 4.2 (with CUDA GPU support)
If you don’t need GPU support (only applicable for Darknet YOLO with OpenCV), just ignore cmake options related with CUDA and GPU.
|You must change CUDA_ARCH_BIN version to yours GPU architecture version.
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
Detection Studio can currently read ROS and ICE Camera Streams. So, to enable Streaming support, install any one of them.
Detection Studio currently supports many Inferencing Frameworks namely Darknet, TensorFlow, Keras, PyTorch and Caffe. Each one of them has some dependencies, and are mentioned below.
Choose your favourite one and go ahead.
Darknet (jderobot fork)
Included in OpenCV libraries.
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.
To use Caffe as an inferencing framework, it is necessary to install OpenCV.
Note: Be Sure to checkout functionality for tutorials on how to use the above mentioned functionalities and frameworks.
How to compile Detection Studio:
Once you have all the required dependencies installed just run:
git clone https://github.com/JdeRobot/DetectionStudio cd DetectionStudio/DetectionStudio mkdir build && cd build
Note: GPU support is enabled by default
Once it is built, you will find various executables in different folders ready to be executed .
Starting with Detection Studio
The best way to start is with our beginner’s tutorial for Detection Studio.