andrewhou1/blindshadowremoval

Official Github repository for "Blind Removal of Facial Foreign Shadows", accepted to BMVC 2022.

Blind Removal of Facial Foreign Shadows

Official Github repository for "Blind Removal of Facial Foreign Shadows", accepted to BMVC 2022.

Yaojie Liu*, Andrew Hou*, Xinyu Huang, Liu Ren, Xiaoming Liu (* denotes equal contribution).

Dependencies

Our method has the following dependencies:

1. Tensorflow 2.3.0
2. Python 3.7.10
3. OpenCV 4.7.0
4. NumPy 1.18.5
5. Tensorflow keras 2.4.0
6. sklearn 0.24.2
7. scipy 1.4.1
8. matplotlib 3.4.2
9. PIL 8.2.0
10. skimage 0.18.1
11. tensorflow_addons 0.13.0
12. os, glob, time, sys, random, math, natsort, face-alignment

Preprocessing New Images

Before feeding an image to our method, use bmvc2022_dataprocess.py to compute landmarks for all images in the sample_uncropped_images/ folder. You can empty this folder initially and just place all of your images in (you only need .png, it will generate the .npy files that contain the landmarks).

Next, run dataprocess.py. This will generate a folder for each image in the sample_uncropped_images_cropped/ folder, where each folder contains the landmarks and a cropped image.

Note that these steps only need to be performed for new in-the-wild images. You do not need to preprocess any of the UCB test set or SFW images: this has already been done.

Testing Procedures

To test on in-the-wild images, place the resulting folder with your cropped image and landmarks in the sample_imgs/ folder and run:

python train_test_GSC.py

To run the SFW evaluation on shadow segmentation performance (AUC) and shadow removal performance, run:

python train_with_TSM.py

To test on the UCB test set, also run:

python train_test_GSC.py

You will need to change two lines in this file first. Change

DATA_DIR_TEST = ['sample_imgs/*'] to DATA_DIR_TEST = ['UCB/train/input/*']

and

fsr.testFFHQ(dataset_test) to fsr.test(dataset_test)

You will also need to change one line in dataset.py

Change dataset = dataset.map(map_func=self.parse_fn_test_FFHQ, num_parallel_calls=autotune) to dataset = dataset.map(map_func=self.parse_fn_test, num_parallel_calls=autotune)

SFW Dataset

Link to the original SFW video dataset: https://drive.google.com/file/d/1H4WFtDCGp4Bk1EeTjX1F_LXDmxxlxUHm/view?usp=share_link

Citation

If you utilize our code in your work, please cite our BMVC 2022 paper.

@inproceedings{ blind-removal-of-facial-foreign-shadows,
  author = { Yaojie Liu* and Andrew Hou* and Xinyu Huang and Liu Ren and Xiaoming Liu },
  title = { Blind Removal of Facial Foreign Shadows },
  booktitle = { In Proceedings of British Machine Vision Conference (BMVC) },
  address = { London, UK },
  month = { November },
  year = { 2022 },
}

Contact

If there are any questions, please feel free to post here or contact the first author at [email protected]