Object and lane detection for self-driving cars

Object and lane detection for videos and/or images using py-faster-rcnn and OpenCV.

Object detection is based on the research conducted by Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun (Microsoft Research) described in this paper.

Installation

Install pre-requisites

• Install the following packages if not already installed

  sudo apt-get update
  sudo updatedb
  sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev python-pip cmake cython python-opencv
  sudo apt-get install python-setuptools libgfortran3 build-essential gfortran python-all-dev libatlas-base-dev
  sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
  sudo pip install numpy

Install CUDA

You can refer this to install CUDA for your system. Or, you can follow steps given below:

• Note: Depending upon platform and CUDA version, below names and deb package will defer.

  sudo apt-get install nvidia-cuda-toolkit
  sudo apt-get install --no-install-recommends libboost-all-dev
  wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers  
  sudo dpkg -i cuda-repo-ubuntu1404-8-0-local-ga2_8.0.61-1_amd64-deb
  sudo apt-get install cuda 

• Add following lines in ~/.bashrc

  export CUDA_HOME=/usr/local/cuda-8.0 
  export LD_LIBRARY_PATH=${CUDA_HOME}/lib64 
  PATH=${CUDA_HOME}/bin:${PATH} 
  export PATH

• Source the bashrc file

source ~/.bashrc

Build modules

1. Clone this repository

   git clone https://github.com/onkarganjewar/cmpe295-masters-project
   

2. Build cython modules

   cd faster-rcnn-resnet/lib/
   make
   

3. Update Makefile.config (Sample Makefile.config can be found here)

   cd ../caffe-fast-rcnn/
   cp Makefile.config.example Makefile.config
   # In your Makefile.config, uncomment following lines
   WITH_PYTHON_LAYER := 1
   USE_CUDNN := 1
   

4. Build Caffe and Pycaffe

  cd faster-rcnn-resnet/caffe-fast-rcnn
  mkdir build
  cd build/
  cmake ..
  make all
  make install
  make pycaffe  

Demo

1. Store the input files at

  cd faster-rcnn-resnet/data/input/

1. Run the demo

  cd faster-rcnn-resnet/tools/

  # When input is ONLY image files, run this command
  python demo.py

  # When input is ONLY video files, run this command
  python demo.py --vdo

1. Retrieve the output files stored at

  cd faster-rcnn-resnet/data/output/

Output video

Click on the thumbnail below to play the sample output video on YouTube.

Built With

• c
• c++
• cmake
• css
• cuda
• html
• javascript
• jupyter-notebook
• m
• makefile
• matlab
• protocol-buffer
• python
• shell