# ProgressiveColorTransfer_Pytorch

**Repository Path**: fengfeng043/ProgressiveColorTransfer_Pytorch

## Basic Information

- **Project Name**: ProgressiveColorTransfer_Pytorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-01-08
- **Last Updated**: 2025-01-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ProgressiveColorTransfer_Pytorch
CSCI5210 Project | Pytorch Re-Implementation: Progressive Color Transfer with Dense Semantic Correspondences (SIGGRAPH 2019)

It is just a non-official course project implementation, offcial C++ implementation is [here](https://github.com/hmmlillian/Neural-Color-Transfer).

```bash
export PYTHONPATH=/path/to/the/project/ProgressiveColorTransfer_Pytorch:$PYTHONPATH
cd ProgressiveColorTransfer_Pytorch
bash exps/00-baseline/inference.sh
```
## Results
![](demo/vis.png)
## Notes

The patchmatch is superslow since it was a GPU version. You can get intermediate results by reduce the number of feature extraction layers in the configuration file. Some details may different with the original paper, so I just find out a workable setting through experiments.

I did not implement the non-local loss term.

Thanks for the code of [Deep Image Analogy PyTorch](https://github.com/harveyslash/Deep-Image-Analogy-PyTorch) and [Neural Color Transfer PyTorch Implementation](https://github.com/rassilon712/Neural_Color_Transfer).