This project implements the 2016 paper Deep Image Homography Estimation by DeTone, Malisiewicz, and Rabinovich. We create an image homography training set by randomly warping the dataset presented in the 2015 paper Microsoft COCO: Common Objects in Context by Lin et al. We then architect and train a deep convolutional neural network to learn how to compute a 3x3 homography matrix given an image pair.
This procedure was tested on Ubuntu 16.04 and Mac OS X 10.11.6 (El Capitan). GPU-accelerated training is supported on Ubuntu only.
Prerequisites: Install Python package dependencies using my instructions. Then, activate the environment:
source activate deep-learning
Optional, but recommended on Ubuntu: Install support for NVIDIA GPU acceleration with CUDA v8.0 and cuDNN v5.1:
wget https://www.dropbox.com/s/08ufs95pw94gu37/cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda
wget https://www.dropbox.com/s/9uah11bwtsx5fwl/cudnn-8.0-linux-x64-v5.1.tgz
tar -xvzf cudnn-8.0-linux-x64-v5.1.tgz
cd cuda/lib64
export LD_LIBRARY_PATH=`pwd`:$LD_LIBRARY_PATH
cd ..
export CUDA_HOME=`pwd`
sudo apt-get install libcupti-dev
pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.1.0-cp35-cp35m-linux_x86_64.whl
Clone the deep_homography_estimation repo:
git clone https://github.com/alexhagiopol/deep_homography_estimation
cd deep_homography_estimation
Download the MSCOCO Dataset:
mkdir MSCOCO
cd MSCOCO
wget http://msvocds.blob.core.windows.net/coco2014/train2014.zip
wget http://msvocds.blob.core.windows.net/coco2014/val2014.zip
wget http://msvocds.blob.core.windows.net/coco2014/test2014.zip
wget http://msvocds.blob.core.windows.net/coco2015/test2015.zip
unzip *.zip
tar -xvzf traffic-signs-data.tar.gz
rm -rf traffic-signs-data.tar.gz
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