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Invisibility Cloak OpenCV with Deep Learning

Invisibility Cloak Project

Computer Vision with OpenCV

Graduation Project in 2021 Fall Semester @UNIST

For this project, FGVC(Flow-edge Guided Video Completion) deep learning model was used for object removal.

Result

✔ Youtube | Presentation (Kor) | Report (Kor)

Environment

Package Version
anaconda (x64) 4.10.3
cuda 10.2.89
matplotlib 3.4.3
numpy 1.21.4
opencv 4.5.4
os Windows 10
pip 21.0.1
python 3.8.12
pytorch 1.6.0
scipy 1.6.2

Usage

Install

$ conda create -n (name)
$ conda activate (name)
$ conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch
$ pip install matplotlib scipy opencv imageio imageio-ffmpeg scikit-image imutils

Run Project

  • Download and unzip weight.zip into the modules.
  • Prepare video sequences dataset of color and mask for project. (Data Samples : tennis | f250)
  • Run project
# Remove __pycache__ if you want.
$ find . | grep -E "(__pycache__|\.pyc|\.pyo$)" | xargs rm -rf

# Run video inpainting.
$ python run_inpainting.py \
> --path      './data/tennis_color' \
> --path_mask './data/tennis_mask' \
> --outroot   './data/tennis_result' \
> --merge \
> --run

Update History

v1.3

  • Add to remove object with FGVC
  • Set the main cloak color with RED
  • Update cloak mask and noise.
  • Rename ftn -> image_stack.

v1.2

  • Add to save output video.
  • Add color selection mode : RED || GREEN
  • Get object removal result by FGVC.

v1.1

  • Add specific noise filtering conditions.
  • Modify detected color : RED -> GREEN

v1.0

  • Project first commit.
  • Test module ftn for showing image stack view.
  • Test HSV Detector using track bar.
  • Test Invisibility Cloak Demo.

Updated : 2021-11-28 00:19