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https://github.com/CDOTAD/AlphaGAN-Matting
This project is an unofficial implementation of AlphaGAN: Generative adversarial networks for natural image matting published at the BMVC 2018
https://github.com/CDOTAD/AlphaGAN-Matting
gan matting pytorch
Last synced: 29 days ago
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This project is an unofficial implementation of AlphaGAN: Generative adversarial networks for natural image matting published at the BMVC 2018
- Host: GitHub
- URL: https://github.com/CDOTAD/AlphaGAN-Matting
- Owner: CDOTAD
- Created: 2018-11-21T15:11:13.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-07-19T14:41:14.000Z (over 4 years ago)
- Last Synced: 2024-08-03T03:01:32.572Z (4 months ago)
- Topics: gan, matting, pytorch
- Language: Python
- Homepage:
- Size: 33.4 MB
- Stars: 152
- Watchers: 7
- Forks: 35
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-neural-art - AlphaGAN - Unofficial implementation of [AlphaGAN: Generative adversarial networks for natural image matting](https://arxiv.org/pdf/1807.10088.pdf). Uses a GAN as the name suggests. (Background subtraction)
README
# AlphaGAN
    
This project is an unofficial implementation of [AlphaGAN: Generative adversarial networks for natural image matting](https://arxiv.org/pdf/1807.10088.pdf) published at the BMVC 2018. As for now, the result of my experiment is not as good as the paper's.
# Dataset
## Adobe Deep Image Matting Dataset
Follow the [instruction](https://sites.google.com/view/deepimagematting) to contact the author for the dataset
You might need to follow the method mentioned in the **Deep Image Matting** to generate the trimap using the alpha mat.
The trimap are generated while the data are loaded.
```python
import numpy as np
import cv2 as cv
def generate_trimap(alpha):
k_size = random.choice(range(2, 5))
iterations = np.random.randint(5, 15)
kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (k_size, k_size))
dilated = cv.dilate(alpha, kernel, iterations=iterations)
eroded = cv.erode(alpha, kernel, iterations=iterations)
trimap = np.zeros(alpha.shape, dtype=np.uint8)
trimap.fill(128)
trimap[eroded >= 255] = 255
trimap[dilated <= 0] = 0
return trimap
```
See `scripts/MattingTrain.ipynb` and `scripts/MattingTest.ipynb` to compose the training/testing set.
The Dataset structure in my project
```Bash
Train
├── alpha # the alpha ground-truth
├── fg # the foreground image
├── merged_cv # the real image composed by the fg & bg
MSCOCO
├── train2014 # the background image
```
# Running the Codes
```shell
python train.py --dataroot ${YOUR_DIM_DATASET_ROOT} \
--training_file ${THE TRAINING FILE OF THE DIM DATASET}
```
# Differences from the original paper
- SyncBatchNorm instead of pytorch original BatchNorm when use multi GPU.
- Training batch_size = 1 [[1]](#ref1) [[2]](#ref2)
- Using GroupNorm [[2]](#ref2)
- Using Warmup [[3]](#ref3) [[4]](#ref4)
# Records
4 GPUS 32 batch size, and SyncBatchNorm
- Achieved **SAD=78.22** after 21 epoches.
1 GPU 1 batch size, and GroupNorm
- Achieved [**SAD=68.61 MSE=0.03189**](https://drive.google.com/open?id=1yFRSjTNlAycmio8B-aibR7ZfYB9oZ-H3) after 33 epoches.
- Achieved [**SAD=61.9 MSE=0.02878**](https://drive.google.com/open?id=1mICVWsQYGz3FrwiVZCnhsp56OAh-9coS) after xx epoches.
# Results
| image | trimap | alpha(predicted) |
|:---: | :--: | :---: |
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# Acknowledgments
My code is inspired by:
- [1] [pytorch-deep-image-matting](https://github.com/huochaitiantang/pytorch-deep-image-matting)
- [2] [FBA-Matting](https://github.com/MarcoForte/FBA-Matting)
- [3] [GCA-Matting](https://github.com/MarcoForte/FBA-Matting)
- [4] [reid-strong-baseline](https://github.com/michuanhaohao/reid-strong-baseline)
- [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)
- [pytorch-book](https://github.com/chenyuntc/pytorch-book) chapter7 generate anime head portrait with GAN
- [pytorch-deeplab-xception](https://github.com/jfzhang95/pytorch-deeplab-xception)
- [indexnet_matting](https://github.com/poppinace/indexnet_matting)