This Github repo is for a proactive camera controller. We control two main camera attributes: exposure time (shutter speed) and gain simultaneously. The overall procedure consists of three modules,

• image synthesis module
• metric evaluation module
• control module module

We provide Matlab and C/C++ code. Since this code is to control a camera, the code should be compiled together with the camera driver. In this sample code, we implement our controller code with Bluefox camera driver.

In the experiment, we have used a sensor rig consists of three bluefox cameras ().

Cite our work via

Joowan Kim, Younggun Cho and Ayoung Kim, Proactive Camera Attribute Control using Bayesian Optimization for Illumination-Resilient Visual Navigation. IEEE Transactions on Robotics, 2020.

@ARTICLE{jwkim-2020-tro,
         author={J. Kim and Y. Cho and A. Kim},
         journal={IEEE Transactions on Robotics},
         title={Proactive Camera Attribute Control Using Bayesian Optimization for Illumination-Resilient Visual Navigation},
         year={2020},
         note={Accepted. In print.}}

The author version of the paper can be downloaded in this link.

Matlab folder contains the following subfolders.

• controller
  Run exp_gain_controller to run with sample images.
• synthetic
  Run run_synth_gen to synthesize using a sample image.
• data
  This folder contains sample images.
• fcns_synth and fcns_gp
  These folders contain libraries to run camera control and synthesis.

Matlab scripts illustrate how the algorithm works using sample images. In the Matlab examples, we do not control the camera.

To effectively use this camera controller, the algorithm should run begin coupled with the camera driver. In this sample code, we show how to use with Bluefox camera driver. Thus some code has a high dependency with the Bluefox2Driver, but you are welcome to adjust the code to couple with your own camera driver.

The src folder contains C/C++ code to run camera controller.

• crf_fitting
  Given four images, we fit a CRF curve.
• gpgo
  Using Gaussian Process Global Optimzer, compute optimal exposure time and gain.
• data_collector
  Image collector using Bluefox2Driver.
• exp_ctrl
  This contains the main executable camera exposure controller. Similar functionality as in tester code except it captures an image from the camera directly using Bluefox2Driver.
• irp_imgeval++ and irp_gpgo++
  Image evaluation and camera attribute control library.

We have tested the code using OpenCV 3.4.0. You can skip this steps if you have OpenCV 3.4.0 or higher installed.

mkdir opencv
cd opencv
wget -O opencv.zip https://github.com/opencv/opencv/archive/3.4.0.zip
wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/3.4.0.zip
unzip opencv.zip
unzip opencv_contrib.zip
cd opencv-3.4.0

Compile OpenCV

mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D WITH_TBB=OFF \
-D WITH_IPP=OFF \
-D WITH_1394=OFF \
-D BUILD_WITH_DEBUG_INFO=OFF \
-D BUILD_DOCS=OFF \
-D INSTALL_C_EXAMPLES=ON \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D BUILD_EXAMPLES=OFF \
-D BUILD_TESTS=OFF \
-D BUILD_PERF_TESTS=OFF \
-D WITH_QT=OFF \
-D WITH_GTK=ON \
-D WITH_OPENGL=ON \
-D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib-3.4.0/modules \
-D WITH_V4L=ON \
-D WITH_FFMPEG=ON \
-D WITH_XINE=ON \
-D BUILD_NEW_PYTHON_SUPPORT=ON \
-D PYTHON2_INCLUDE_DIR=/usr/include/python2.7 \
-D PYTHON2_NUMPY_INCLUDE_DIRS=/usr/lib/python2.7/dist-packages/numpy/core/include/ \
-D PYTHON2_PACKAGES_PATH=/usr/lib/python2.7/dist-packages \
-D PYTHON2_LIBRARY=/usr/lib/x86_64-linux-gnu/libpython2.7.so \
-D PYTHON3_INCLUDE_DIR=/usr/include/python3.6m \
-D PYTHON3_NUMPY_INCLUDE_DIRS=/usr/lib/python3/dist-packages/numpy/core/include/ \
-D PYTHON3_PACKAGES_PATH=/usr/lib/python3/dist-packages \
-D PYTHON3_LIBRARY=/usr/lib/x86_64-linux-gnu/libpython3.6m.so \
../
make

Install OpenCV

sudo make install
sudo sh -c echo '/usr/local/lib/' > sudo /etc/ld.so.conf.d/opencv.conf
sudo ldconfig

In this README, we assume that you are using a Bluefox camera (mvBlueFOX-IGC200wG). The source code is not compilable without the Bluefox camera driver. However, the controller scheme is totally general for all cameras as long as they allow attribute control. If you are using a different camera, please modify the code accordingly and make it installable without Bluefox driver.

To install Bluefox driver, go to third-party and install the driver.

cd third-party
./install_mvBlueFOX.sh

To check if the driver is properly installed, you can run the viewer provided with the driver.

wxPropView

Also you need to install lcm since our driver relies on lcm.

unzip lcm-1.4.0.zip
cd lcm-1.4.0/
mkdir build
cd build
cmake ..
make
sudo make install

You can use the following commands to download and compile the package.

mkdir build
cd build
cmake ..
make

The main executable file is exp_ctrl while others are the testers.

The main exposure (exposure time and gain) controller. It may crash because this binary assumes a bluefox camera is on the system. Again in this README, we are assuming the camera is Bluefox. First, prepare your config file.

cd config
ln -s bluefox2-2122.json master.json

In the configuration setup,expose_us determines the initial seed image for the camera controller. We found that 5000 for outdoor and 1000 for indoor works okay. Depending on your target environment, you might want to adjust this initial exposure time. The default is 5000.

"expose_us": 5000,

Then run the binary file.

./exp_ctrl

We have included our master.json file for the bluefox camera.

Using captured images, control exposure for a single image.

./exp_ctrl_1step

Example of generating camera response function (CRF) and saved in csv format.

./crf_fitting

Test GP based controller for camera gain.

./gp_gain_tester

Test GP based controller for camera exposure time.

./gp_exp_tester

Example of creating synthetic images for exposure time and gain. By running, it shows image synthesizing process.

./img_synthesizer <image filename>

For example, you can run with the sample data attached.

./img_synthesizer ../../data/3.png

It shows evaluation metric computed from image gradient and entropy.

./img_evaluator <image filename>