This project is based on the thesis《Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs》。

The author's project address is：nothinglo/Deep-Photo-Enhancer

My code is based on https://github.com/mtics/deep-photo-enhancer

hyerania\Deep-Learning-Project

中文文档说明请看这里

https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

The latest code is on the dev branch

• Python 3.6
• CUDA 10.0
• To install requirements： pip install -r requirements.txt

Expert-C on MIT-Adobe FiveK dataset

1. All hyperparameters are in libs\constant.py

All this extra foders are created by calling

python directory_strcture.py

2. There are some folders need to be created, to do that just call python directory_structure.py:
   1. images_LR：Used to store datasets
      1. Expert-C
      2. input
      3. In each of the above two folders, the following three new folders need to be created:
         1. Testing
         2. Training1
         3. Training2
   2. models：Used to store all the training generated files：
      1. gt_images
      2. input_images
      3. pretrain_checkpoint
      4. pretrain_images
      5. test_images
      6. train_checkpoint
      7. train_images
      8. train_test_images
      9. In each of the above folders, the following two new folders need to be created:
         1. 1Way
         2. 2Way
   3. model: Used to store log_PreTraining.txt

1. 1. images_LR/input/Training1 should contain folders containing images you want to correct.
   2. images_LR/Expert-C/Training2 should contain folders containing the images of the type you want to obtain.
   3. images_LR/input/Testing images_LR/Expert-C/Testing should contain folders containing the images to get the test scores. In the former you should put the 'bad' sample and on the latter the 'good' sample of the same image

2. Run the following line to get all the images to max_size 512 ...change the commented directoies as needed python reize.py

3. Run for training

python 2WayGAN_Train_v3.py

For now, the evaluation and training are simultaneous. So there is no need to run anything.

To evaluate my model, I use PSNR in “XWayGAN_Train.py”

This is inference for general ized images, it may be improved by using a loader.

python unbounded_inference.py --generator_model 'gan2_train_28_40.pth' --input_image 'input_image' --output_image 'output_image'

1. There may be a problem in computing the value of PSNR or not. It needs to be proved.
2. The compute functions in libs\compute.py are wrong, which cause the discriminator loss cannot converge and the output is awful.

This repo is released under the MIT License

For now, This repo is maintained by Felipe Bivort Haiek.

Welcome to join me to maintain it together.