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https://github.com/bcmi/libcom

Image composition toolbox: everything you want to know about image composition or object insertion
https://github.com/bcmi/libcom

cross-domain-image-composition foreground-object-search generative-image-composition image-blending image-composition image-composition-library image-composition-toolbox image-harmonization inharmonious-region-localization object-insertion object-placement painterly-image-harmonization shadow-generation

Last synced: about 1 month ago
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Image composition toolbox: everything you want to know about image composition or object insertion

Host: GitHub
URL: https://github.com/bcmi/libcom
Owner: bcmi
License: apache-2.0
Created: 2023-10-19T05:08:12.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2024-11-10T13:55:33.000Z (about 1 month ago)
Last Synced: 2024-11-10T14:35:52.729Z (about 1 month ago)
Topics: cross-domain-image-composition, foreground-object-search, generative-image-composition, image-blending, image-composition, image-composition-library, image-composition-toolbox, image-harmonization, inharmonious-region-localization, object-insertion, object-placement, painterly-image-harmonization, shadow-generation
Language: Python
Homepage:
Size: 66.8 MB
Stars: 532
Watchers: 13
Forks: 32
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-production-machine-learning - libcom - libcom is an image composition toolbox. (Industry Strength CV)

README

        






libcom: everything about image composition


[![PyPI](https://img.shields.io/pypi/v/libcom)](https://pypi.org/project/libcom)

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[![Static Badge](https://img.shields.io/badge/Image%20Composition%20Demo-Green)](https://bcmi.sjtu.edu.cn/home/niuli/demo_image_composition/)

[![Static Badge](https://img.shields.io/badge/Resources-Awesome-green?style=flat&label=Resources)](https://github.com/bcmi/Awesome-Image-Composition)

[![Static Badge](https://img.shields.io/badge/survey-arxiv%3A2106.14490-red)](https://arxiv.org/pdf/2106.14490.pdf)

**We co-founded a startup company [miguo.ai](https://miguocomics.com/), dedicated to accelerating the production of comics and animations using AIGC technology. If you are seeking internship or full-time positions, please feel free to send your resume to [email protected].**

---

## Introduction

**_libcom_ (the library of image composition) is an image composition toolbox.** The goal of image composition (object insertion) is inserting one foreground into a background image to get a realistic composite image, by addressing the inconsistencies (appearance, geometry, and semantic inconsistency) between foreground and background. Generally speaking, image composition could be used to combine the visual elements from different images.







**_libcom_ covers a diversity of related tasks in the field of image composition**, including image blending, standard/painterly image harmonization, shadow generation, object placement, generative composition, quality evaluation, *etc*. For each task, we integrate one or two selected methods considering both efficiency and effectiveness. The selected methods will be continuously updated upon the emergence of better methods. 

**The ultimate goal of this library is solving all the problems related to image composition with simple `import libcom`.**

### Main Functions

- **get_composite_image** generates composite images using naive copy-and-paste followed by image blending.

- **OPAScoreModel** [[OPA]](https://github.com/bcmi/Object-Placement-Assessment-Dataset-OPA) evaluates the rationality of foreground object placement in a composite image.

- **FOPAHeatMapModel** [[FOPA]](https://github.com/bcmi/FOPA-Fast-Object-Placement-Assessment) can predict the rationality scores for all locations/scales given a background-foreground pair, and output the composite image with optimal location/scale.  

- **color_transfer** adjusts the foreground color to match the background using traditional color transfer method.

- **ImageHarmonizationModel** [[CDTNet]](https://github.com/bcmi/CDTNet-High-Resolution-Image-Harmonization)  [[PCTNet]](https://github.com/rakutentech/PCT-Net-Image-Harmonization) adjusts the foreground illumination to be compatible the background given photorealistic background and photorealistic foreground.

- **PainterlyHarmonizationModel** [[PHDNet]](https://github.com/bcmi/PHDNet-Painterly-Image-Harmonization)  [[PHDiffusion]](https://github.com/bcmi/PHDiffusion-Painterly-Image-Harmonization) adjusts the foreground style to be compatible with the background given artistic background and photorealistic foreground.

- **HarmonyScoreModel** [[BargainNet]](https://github.com/bcmi/BargainNet-Image-Harmonization) evaluates the harmony level between foreground and background in a composite image.

- **InharmoniousLocalizationModel** [[MadisNet]](https://github.com/bcmi/MadisNet-Inharmonious-Region-Localization) localizes the inharmonious region in a synthetic image.

- **FOSScoreModel** [[DiscoFOS]](https://github.com/bcmi/Foreground-Object-Search-Dataset-FOSD) evaluates the compatibility between foreground and background in a composite image in terms of geometry and semantics.

- **ShadowGenerationModel** [[ControlNet]](https://github.com/lllyasviel/ControlNet) generates plausible shadow for the inserted object in a composite image. 

- **ControlComModel** [[ControlCom]](https://github.com/bcmi/ControlCom-Image-Composition) is a generative image composition model which unifies image blending and image harmonization. The generated foreground has high fidelity. 

- **ObjectStitchModel** [[ObjectStitch]](https://github.com/bcmi/ObjectStitch-Image-Composition) is another generative image composition model which can adjust the pose and view of foreground, but the fidelity of generated foreground is worse than ControlComModel. 

**For the detailed method descriptions, code examples, visualization results, and performance comments, please refer to our [[documents]](https://libcom.readthedocs.io/en/latest/).** If the model performance is not satisfactory, you can finetune the pretrained model on your own dataset using the source repository and replace the original model. 

## Requirements

The main branch is built on the Linux system with **Python 3.8** and **PyTorch>=1.10.1**. For other dependencies, please refer to [[conda_env]](https://github.com/bcmi/libcom/blob/main/requirements/libcom.yaml) and [[runtime_dependencies]](https://github.com/bcmi/libcom/blob/main/requirements/runtime.txt).

## Get Started

Please refer to [[Installation]](https://github.com/bcmi/libcom/blob/main/docs/get_started.md) for installation instructions and [[documents]](https://libcom.readthedocs.io/en/latest/) for user guidance.

## Contributors

- Institution: [Brain-like Computing and Machine Intelligence (BCMI) Lab](https://bcmi.sjtu.edu.cn/).

- Project Initiator & Team Manager: [Li Niu](https://www.ustcnewly.com/index.html). 

- Architect & Lead Developer: [Bo Zhang](https://bo-zhang-cs.github.io/).   

- Documentation Manager: [Jiacheng Sui](https://github.com/charlessjc).

- Module Developers: [Jiacheng Sui](https://github.com/charlessjc), [Binjie Gao](https://github.com/WhynotHAHA), [Lingxiao Lu](https://github.com/pokaaa), [Xinhao Tao](https://github.com/taoxinhao13), [Junyan Cao](https://github.com/cjy-4), [Junqi You](https://github.com/dhmbb2).

## License

This project is released under the [Apache 2.0 license](https://github.com/bcmi/libcom/blob/main/LICENSE).

## Bibtex

If you use our toolbox, please cite our survey paper using the following BibTeX  [[arxiv](https://arxiv.org/pdf/2106.14490.pdf)]:

```

@article{niu2021making,

  title={Making images real again: A comprehensive survey on deep image composition},

  author={Niu, Li and Cong, Wenyan and Liu, Liu and Hong, Yan and Zhang, Bo and Liang, Jing and Zhang, Liqing},

  journal={arXiv preprint arXiv:2106.14490},

  year={2021}

}

```