By Soumyadip Sengupta, Vivek Jayaram, Brian Curless, Steve Seitz, and Ira Kemelmacher-Shlizerman
This paper will be presented in IEEE CVPR 2020.
Go to Project page for additional details and results.
- Soumyadip Sengupta, University of Washington
- Vivek Jayaram, University of Washington
- Brian Curless, University of Washington
- Steve Seitz, University of Washington
- Ira Kemelmacher-Shlizerman, University of Washington
This work is licensed under the Creative Commons Attribution NonCommercial ShareAlike 4.0 License.
- Updates
- Getting Started
- Inference Code on images
- Inference Code on videos
- Notes on capturing images
- Training code (coming soon ...)
- Captured Data (coming soon ...)
- Citations
April 9, 2020
- Issues:
- Updated alignment function in pre-processing code. Python version uses AKAZE features (SIFT and SURF is not available with opencv3), MATLAB version also provided uses SURF features.
- New features:
April 8, 2020
- Issues:
- Turning off adjustExposure() for bias-gain correction in test_pre_processing.py. (Bug found, need to be fixed)
- Incorporating 'uncropping' operation in test_background-matting_image.py. (Output will be of same resolution and aspect-ratio as input)
Clone repository:
git clone https://github.com/senguptaumd/Background-Matting.git
Please use Python 3. Create an Anaconda environment and install the dependencies. Our code is tested with Pytorch=1.1.0, Tensorflow=1.14 with cuda10.0
conda create --name back-matting python=3.6
conda activate back-matting
Make sure CUDA 10.0 is your default cuda. If your CUDA 10.0 is installed in /usr/local/cuda-10.0, apply
export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64
export PATH=$PATH:/usr/local/cuda-10.0/bin
Install PyTorch, Tensorflow (needed for segmentation) and dependencies
conda install pytorch=1.1.0 torchvision cudatoolkit=10.0 -c pytorch
pip install tensorflow-gpu==1.14.0
pip install -r requirements.txt
Note: The code is likely to work on other PyTorch and Tensorflow versions compatible with your system CUDA. If you already have a working environment with PyTorch and Tensorflow, only install dependencies with pip install -r requirements.txt. If our code fails due to different versions, then you need to install specific CUDA, PyTorch and Tensorflow versions.
To perform Background Matting based green-screening, you need to capture:
- (a) Image with the subject (use
_img.pngextension) - (b) Image of the background without the subject (use
_back.pngextension) - (c) Target background to insert the subject (place in
data/background)
Use sample_data/ folder for testing and prepare your own data based on that. This data was collected with a hand-held camera.
Please download the pre-trained models from Google Drive and place Models/ folder inside Background-Matting/.
Note: syn-comp-adobe-trainset model was trained on the training set of the Adobe dataset. This was the model used for numerical evaluation on Adobe dataset.
- Segmentation
Background Matting needs a segmentation mask for the subject. We use tensorflow version of Deeplabv3+.
cd Background-Matting/
git clone https://github.com/tensorflow/models.git
cd models/research/
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
cd ../..
python test_segmentation_deeplab.py -i sample_data/input
You can replace Deeplabv3+ with any segmentation network of your choice. Save the segmentation results with extension _masksDL.png.
- Alignment
Skip this step, if your data is captured with fixed-camera.
- For hand-held camera, we need to align the background with the input image as a part of pre-processing. We apply simple hoomography based alignment.
- We ask users to disable the auto-focus and auto-exposure of the camera while capturing the pair of images. This can be easily done in iPhone cameras (tap and hold for a while).
Run python test_pre_process.py -i sample_data/input for pre-processing. It aligns the background image _back.png and changes its bias-gain to match the input image _img.png
We used AKAZE features python code (since SURF and SIFT unavilable in opencv3) for alignment. We also provide an alternate MATLAB code (test_pre_process.m), which uses SURF features. MATLAB code also provides a way to visualize feature matching and alignment. Bad alignment will produce bad matting output.
Bias-gain adjustment is turned off in the Python code due to a bug, but it is present in MATLAB code. If there are significant exposure changes between the captured image and the captured background, use bias-gain adjustment to account for that.
Feel free to write your own alignment code, choose your favorite feature detector, feature matching and alignment.
python test_background-matting_image.py -m real-hand-held -i sample_data/input/ -o sample_data/output/ -tb sample_data/background/0001.pngFor images taken with fixed camera (with a tripod), choose -m real-fixed-cam for best results. -m syn-comp-adobe lets you use the model trained on synthetic-composite Adobe dataset, without real data (worse performance).
This is almost exactly similar as that of the image with few small changes.
To perform Background Matting based green-screening, you need to capture:
- (a) Video with the subject (
teaser.mov) - (b) Image of the background without the subject (use
teaser_back.pngextension) - (c) Target background to insert the subject (place in
target_back.mov)
We provide sample_video/ captured with hand-held camera and sample_video_fixed/ captured with fixed camera for testing. Please download the data and place both folders under Background-Matting. Prepare your own data based on that.
- Frame extraction:
cd Background-Matting/sample_video
mkdir input background
ffmpeg -i teaser.mov input/%04d_img.png -hide_banner
ffmpeg -i target_back.mov background/%04d.png -hide_banner
Repeat the same for sample_video_fixed
- Segmentation
cd Background-Matting/models/research/
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
cd ../..
python test_segmentation_deeplab.py -i sample_video/input
Repeat the same for sample_video_fixed
- Alignment
No need to run alignment for sample_video_fixed or videos captured with fixed-camera.
Run python test_pre_process_video.py -i sample_video/input -v_name sample_video/teaser_back.png for pre-processing. Alternately you can also use test_pre_process_video.m in MATLAB.
For hand-held videos, like sample_video:
python test_background-matting_image.py -m real-hand-held -i sample_video/input/ -o sample_video/output/ -tb sample_video/background/For fixed-camera videos, like sample_video_fixed:
python test_background-matting_image.py -m real-fixed-cam -i sample_video_fixed/input/ -o sample_video_fixed/output/ -tb sample_video_fixed/background/ -b sample_video_fixed/teaser_back.pngTo obtain the video from the output frames, run:
cd Background-Matting/sample_video
ffmpeg -r 60 -f image2 -i output/%04d_matte.png -vcodec libx264 -crf 15 -s 1280x720 -pix_fmt yuv420p teaser_matte.mp4
ffmpeg -r 60 -f image2 -i output/%04d_compose.png -vcodec libx264 -crf 15 -s 1280x720 -pix_fmt yuv420p teaser_compose.mp4
Repeat same for sample_video_fixed
For best results capture images following these guidelines:
- Choose a background that is mostly static, can be both indoor and outdoor.
- Avoid casting any shadows of the subject on the background.
- place the subject atleast few feets away from the background.
- if possible adjust the lighting to avoid strong shadows on the background.
- Avoid large color coincidences between subject and background. (e.g. Do not wear a white shirt in front of a white wall background.)
- Lock AE/AF (Auto-exposure and Auto-focus) of the camera.
- For hand-held capture, you need to:
- allow only small camera motion by continuing to holding the camera as the subject exists the scene.
- avoid backgrounds that has two perpendicular planes (homography based alignment will fail) or use a background very far away.
- The above restirctions do not apply for images captured with fixed camera (on a tripod)
We will also release the training code, which will allow users to train on labelled data and also on unlabelled real data.
Coming soon ...
We collected 50 videos with both fixed and hand-held camera in indoor and outdoor settings. We plan to release this data to encourage future research on improving background matting.
Coming soon ...
We are eager to hear how our algorithm works on your images/videos. If the algorithm fails on your data, please feel free to share it with us at soumya91@cs.washington.edu. This will help us in improving our algorithm for future research. Also, feel free to share any cool results.
If you use this code for your research, please consider citing:
@InProceedings{BMSengupta20,
title={Background Matting: The World is Your Green Screen},
author = {Soumyadip Sengupta and Vivek Jayaram and Brian Curless and Steve Seitz and Ira Kemelmacher-Shlizerman},
booktitle={Computer Vision and Pattern Regognition (CVPR)},
year={2020}
}