项目介绍

wasidennis/DeepHarmonization 是一个托管在 GitHub 上的开源项目,主要使用 Python 编写,目前获得 150 个 Star、33 次 Fork。

仓库全名
wasidennis/DeepHarmonization
Star 数
150
Fork 数
33
未关闭 Issue
2

README 摘要

Deep Image Harmonization Project webpage: https://sites.google.com/site/yihsuantsai/research/cvpr17-harmonization Contact: Yi-Hsuan Tsai (wasidennis at gmail dot com) Paper Deep Image Harmonization Yi-Hsuan Tsai, Xiaohui Shen, Zhe Lin, Kalyan Sunkavalli, Xin Lu and Ming-Hsuan Yang IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. This is the authors' demo code described in the above paper…

完整内容见 GitHub 原始 README 或本页 。

常见问题

DeepHarmonization 用什么语言开发?

主要使用 Python 编写,目前有 150 个 Star。

如何获取 DeepHarmonization 的源码?

可以直接克隆仓库:git clone https://github.com/wasidennis/DeepHarmonization.git,默认分支为 master;安装与运行步骤见仓库 README。

DeepHarmonization 最近还在维护吗?

仓库最近一次更新为 2020年1月29日,当前有 2 个未关闭 Issue;Star 数为 150,可作为社区活跃度的参考。

关于

Demo code of the paper: "Deep Image Harmonization", Y.-H. Tsai, X. Shen, Z. Lin, K. Sunkavalli, X. Lu and M.-H. Yang, CVPR 2017

描述取自 GitHub 官方 API,中文内容由 GitRead 中文社区 编辑团队整理。

简要分析

热度分
220
价值分
100
活跃状态
活跃

README

Deep Image Harmonization

Alt Text

Project webpage: https://sites.google.com/site/yihsuantsai/research/cvpr17-harmonization
Contact: Yi-Hsuan Tsai (wasidennis at gmail dot com)

Paper

Deep Image Harmonization
Yi-Hsuan Tsai, Xiaohui Shen, Zhe Lin, Kalyan Sunkavalli, Xin Lu and Ming-Hsuan Yang
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

This is the authors’ demo code described in the above paper. Please cite our paper if you find it useful for your research.

Installation and Usage

  • Download and unzip the code.

  • Install Caffe: http://caffe.berkeleyvision.org/.

  • Download the pre-trained caffe model and move it under the model folder.

  • Run demo.py on real composite images (including our test set collected in the paper).

Evaluation Set

  • Download our complete set of real composite images, including our harmonization results here.

Note

The model, code and dataset are available for non-commercial research purposes only.

Log

  • 03/2017: demo code released
  • 05/2017: complete evaluation set released

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