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/

2. 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

3. 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.