This works by first detecting objects with Detectron2 , then cutting out the desired masks with openCV and cropping out the object to its bounding boxes.

Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. There is no GPU required if you use pretrained models

List of currently supported objects

The extracted objects are not cut out pixel-perfect, but it can be a tremendous workload reduction to be able to process thousands of images at once. For example as a data set for other ML applications.

-1. Installing Detectron2 on Linux systems is pretty straightforward.

For Windows 10, a relatively easy solution is to install the Windows subsystem for Linux, instructions here: https://docs.microsoft.com/en-us/windows/wsl/install-win10. You will have to install the following packages as well (in the shell): python, python-dev and opencv-python.

For OS X, you can try to follow the Anaconda instructions posted here: https://medium.com/deepvisionguru/how-to-embed-detectron2-in-your-computer-vision-project-817f29149461. Please note that this is not yet compatible with our demo.py script, we hope to fix that soon.

0. Clone this repository and enter folder (or download, extract and enter folder):

git clone https://github.com/DDemmer1/ai-background-remove.git

cd ai-background-remove

1. Create virtual environment to make sure we don't mess with your system python install and install all needed packages:

If you have a Conda python install, try this:

conda create --nsame detectron2

conda activate detectron2

For vanilla python, try this:

python -m venv detectron2

source detectron2/bin/activate

pip install -r detectron2/requirements.txt

2. Install precompiled Detectron2 with CPU support only:

pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/index.html

OR: Install precompiled Detectron2 for CUDA 10.1:

pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/index.html

3. Create a new Jupyter kernel which uses the virtual environment you have just created:

ipython kernel install --user --name=detectron2

1. Copy your images with the objects to extract to ai-background-remove/input

2. Run the following network: (If you use a Python version other than 3.7. Make sure to change the version in the path )

python detectron2/demo.py --config-file detectron2/lib/python3.7/site-packages/detectron2/model_zoo/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml --input input/* --output output  --opts MODEL.DEVICE cpu MODEL.WEIGHTS detectron2://COCO-PanopticSegmentation/panoptic_fpn_R_101_3x/139514519/model_final_cafdb1.pkl

3. Detected objects are marked and a visualization is saved in your /output folder.

Run Jupyter notebook

jupyter notebook

For background removal open bg-remove.ipynb and make sure the 'detectron2' kernel is used and run the cells

If you want to instead analyse your input corpus open deep_watching.ipynb and run the cells (make sure the 'detectron2' kernel is used)