/reflection-removal

Primary LanguagePython

perceptual-reflection-removal

Setup

  • Clone/Download this repo
  • $ cd perceptual-reflection-removal
  • $ mkdir VGG_Model
  • Download VGG-19. Search imagenet-vgg-verydeep-19 in this page and download imagenet-vgg-verydeep-19.mat. We need the pre-trained VGG-19 model for our hypercolumn input and feature loss
  • move the downloaded vgg model to folder VGG_Model

Conda environment

A minimal conda environment to test the pretrained model is provided.

conda env create -f env.yml

Testing

  • Download pre-trained model here
  • $ tar -xvzf pre-trained.tar.gz
  • this should extract the models into a newly created folder called pre-trained
  • Change test_path (line 419) to your test image folder. If you want to test on the provided test images (e.g. in ./test_images/real/), keep it as it is.
  • test results can be found in ./test_results/

Then, run

$ python3 main.py --task pre-trained --is_training 0

Acknowledgement

Original model belongs to CeciliaVision (https://github.com/ceciliavision)