{"id":13738254,"url":"https://github.com/codeslake/Color_Transfer_Histogram_Analogy","last_synced_at":"2025-05-08T16:33:13.482Z","repository":{"id":194668963,"uuid":"288594367","full_name":"codeslake/Color_Transfer_Histogram_Analogy","owner":"codeslake","description":"[CGI 2020] Official PyTorch Implementation for \"Deep Color Transfer using Histogram Analogy\"","archived":false,"fork":false,"pushed_at":"2024-04-02T21:46:22.000Z","size":14221,"stargazers_count":176,"open_issues_count":1,"forks_count":35,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-05-03T09:51:34.111Z","etag":null,"topics":["color-transfer","deep-learning","histogram-analogy","pytorch"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"agpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/codeslake.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-08-19T00:33:03.000Z","updated_at":"2025-04-23T09:24:39.000Z","dependencies_parsed_at":null,"dependency_job_id":"d7236b58-dcfb-4b68-96cd-efa119a40a99","html_url":"https://github.com/codeslake/Color_Transfer_Histogram_Analogy","commit_stats":null,"previous_names":["codeslake/color_transfer_histogram_analogy"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeslake%2FColor_Transfer_Histogram_Analogy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeslake%2FColor_Transfer_Histogram_Analogy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeslake%2FColor_Transfer_Histogram_Analogy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeslake%2FColor_Transfer_Histogram_Analogy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/codeslake","download_url":"https://codeload.github.com/codeslake/Color_Transfer_Histogram_Analogy/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253105423,"owners_count":21855023,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["color-transfer","deep-learning","histogram-analogy","pytorch"],"created_at":"2024-08-03T03:02:16.068Z","updated_at":"2025-05-08T16:33:12.434Z","avatar_url":"https://github.com/codeslake.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"## Deep Color Transfer using Histogram Analogy\u003cbr\u003e\u003csub\u003eOfficial PyTorch Implementation of the CGI 2020 Paper\u003c/sub\u003e\u003cbr\u003e\u003csub\u003e[Project](https://junyonglee.me/projects/CTHA) | [Paper](https://link.springer.com/epdf/10.1007/s00371-020-01921-6?sharing_token=m2UzXwVlSCP8CbRYNrEcnve4RwlQNchNByi7wbcMAY5_mQV2iPdNT8_ORizvbX3p8mina4UHEjoKsvegf0S_FwC3Yo3cBRV6mlx1mdbvv3CiiREpz3ZqyJuRGmHbygkNL_7X-3hd2CMGSxgPtF22LPsyjpEfhG1R_bNHSSVNvbc%3D) | [Supp](https://www.dropbox.com/s/jxvg6ize41g43vj/Additional_Result.pdf?raw=1) | [Slide](https://www.dropbox.com/s/jbnp7omqre2pu9b/2020_junyonglee.pdf?raw=1)\u003c/sub\u003e\n\nThis repo contains the evaluation code for the following paper:\n\n\u003e [**Deep Color Transfer using Histogram Analogy**](https://junyonglee.me/projects/CTHA)\u003cbr\u003e\n\u003e [Junyong Lee](https://junyonglee.me)\u003csup\u003e1\u003c/sup\u003e, [Hyeongseok Son](https://sites.google.com/site/sonhspostech/)\u003csup\u003e1\u003c/sup\u003e, Gunhee Lee\u003csup\u003e2\u003c/sup\u003e, Jonghyeop Lee\u003csup\u003e1\u003c/sup\u003e, [Sunghyun Cho](https://www.scho.pe.kr/)\u003csup\u003e1\u003c/sup\u003e, and [Seungyong Lee](http://cg.postech.ac.kr/leesy/)\u003csup\u003e1\u003c/sup\u003e\u003cbr\u003e\n\u003e \u003csup\u003e1\u003c/sup\u003ePOSTECH, \u003csup\u003e2\u003c/sup\u003eNCSOFT\u003cbr\u003e\n\u003e *The Visual Computer (special issue on CGI 2020) 2020*\u003cbr\u003e\n\u003e \n\n\u003cp align=\"left\"\u003e\n  \u003ca href=\"https://junyonglee.me/projects/CTHA\"\u003e\n    \u003cimg width=85% src=\"./assets/teaser.gif\"/\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"left\"\u003e\n  \u003cimg width=85% src=\"./assets/figure.jpg\"/\u003e\n\u003c/p\u003e\n\n**Figure:** *Color transfer results on various source and reference image pairs. For visualization, the reference image is cropped to make a same size with other images.*\n\n\n## Getting Started\n### Prerequisites\n*Tested environment*\n\n![Ubuntu](https://img.shields.io/badge/Ubuntu-18.0.4-blue.svg?style=plastic)\n![Python](https://img.shields.io/badge/Python-3.8.8-green.svg?style=plastic)\n![PyTorch](https://img.shields.io/badge/PyTorch-1.8.0-green.svg?style=plastic)\n![CUDA](https://img.shields.io/badge/CUDA-10.2-green.svg?style=plastic)\n\n1. **Install requirements**\n    * `pip install -r requirements.txt`\n    * \n2. **Pre-trained models**\n    * Download and unzip pretrained weights ([OneDrive](https://onedrive.live.com/download?resid=94530B7E5F49D254%21484\u0026authkey=!AIw6wh6Vjo-IFWs) | [Dropbox](https://www.dropbox.com/s/lkwo9xg168e650i/checkpoints.zip?dl=1)) under `[CHECKPOINT_ROOT]`:\n\n        ```\n        ├── [CHECKPOINT_ROOT]\n        │   ├── *.pth\n        ```\n\n        \u003e **NOTE:**\n        \u003e \n        \u003e `[CHECKPOINT_ROOT]` can be specified with the option `--checkpoints_dir`.\n\n\n## Testing the network\n* To test the network:\n\n  ```bash\n  python test.py --dataroot [test folder path] --checkpoints_dir [CHECKPOINT_ROOT]\n  # e.g., python test.py --dataroot test --checkpoints_dir checkpoints\n  ```\n\n  \u003e **Note:**\n  \u003e\n  \u003e * Input images and their segment maps should be placed under `./test/input` and `./test/seg_in`, respectively.\n  \u003e * Target images and their segment maps should be placed under `./test/target` and `./test/seg_tar`, respectively. \n  \u003e * The test results will be saved under `./results/`.\n\n* To turn on *semantic replacement*, add `--is_SR`:\n\n  ```bash\n  python test.py --dataroot [test folder path] --checkpoints_dir [ckpt path] --is_SR\n  ```\n\n## Contact\nOpen an issue for any inquiries.\nYou may also have contact with [junyonglee@postech.ac.kr](mailto:junyonglee@postech.ac.kr)\n\n## Resources\n\nAll material related to our paper is available via the following links:\n\n## License\n![License CC BY-NC](https://img.shields.io/badge/license-GNU_AGPv3-green.svg?style=plastic)\u003cbr\u003e\nThis software is being made available under the terms in the [LICENSE](LICENSE) file.\nAny exemptions to these terms require a license from the Pohang University of Science and Technology.\n\n## Citation\nIf you find this code useful, please consider citing:\n```\n@Article{Lee2020CTHA,\n    author  = {Junyong Lee and Hyeongseok Son and Gunhee Lee and Jonghyeop Lee and Sunghyun Cho and Seungyong Lee},\n    title   = {Deep Color Transfer using Histogram Analogy},\n    journal = {The Visual Computer},\n    volume  = {36},\n    number  = {10},\n    pages   = {2129--2143},\n    year    = {2020},\n}\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodeslake%2FColor_Transfer_Histogram_Analogy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcodeslake%2FColor_Transfer_Histogram_Analogy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodeslake%2FColor_Transfer_Histogram_Analogy/lists"}