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https://github.com/PetervanLunteren/EcoAssist

Simplify camera trap image analysis with ML species recognition models based around the MegaDetector model
https://github.com/PetervanLunteren/EcoAssist

animals annotation-tool cameratraps classifier-model conservation deploy ecology linux machine-learning macos megadetector object-detection python windows yolov5

Last synced: about 1 month ago
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Simplify camera trap image analysis with ML species recognition models based around the MegaDetector model

Host: GitHub
URL: https://github.com/PetervanLunteren/EcoAssist
Owner: PetervanLunteren
License: mit
Created: 2022-01-29T13:43:22.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2024-04-13T23:08:59.000Z (5 months ago)
Last Synced: 2024-04-14T07:12:21.913Z (5 months ago)
Topics: animals, annotation-tool, cameratraps, classifier-model, conservation, deploy, ecology, linux, machine-learning, macos, megadetector, object-detection, python, windows, yolov5
Language: Python
Homepage:
Size: 62.8 MB
Stars: 91
Watchers: 7
Forks: 12
Open Issues: 7
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: citation.cff

Awesome Lists containing this project

awesome-yolo-object-detection - PetervanLunteren/EcoAssist - code platform to train and deploy YOLOv5 object detection models. (Applications)
open-sustainable-technology - EcoAssist - An open-source application designed to streamline the work of ecologists dealing with camera trap images. (Biosphere / Terrestrial Wildlife)

README

        


  





[![status](https://joss.theoj.org/papers/dabe3753aae2692d9908166a7ce80e6e/status.svg)](https://joss.theoj.org/papers/dabe3753aae2692d9908166a7ce80e6e)

[![Project Status: Active The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)

![GitHub](https://img.shields.io/github/license/PetervanLunteren/EcoAssist)

![GitHub last commit](https://img.shields.io/github/last-commit/PetervanLunteren/EcoAssist)



---



  

[![](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/PetervanLunteren)



  

Official website: https://addaxdatascience.com/ecoassist/





EcoAssist is an application designed to streamline the work of ecologists dealing with camera trap images. It’s an AI platform that allows you to analyse images with machine learning models for automatic detection, offering ecologists a way to save time and focus on conservation efforts.



  

  

   

   



## Cite

Please use the following citations if you used EcoAssist in your research.

EcoAssist




[Link to paper](https://joss.theoj.org/papers/10.21105/joss.05581)

```BibTeX

@article{van_Lunteren_EcoAssist_2023,

  author = {van Lunteren, Peter},

  doi = {10.21105/joss.05581},

  journal = {Journal of Open Source Software},

  month = aug,

  number = {88},

  pages = {5581},

  title = {{EcoAssist: A no-code platform to train and deploy custom YOLOv5 object detection models}},

  url = {https://joss.theoj.org/papers/10.21105/joss.05581},

  volume = {8},

  year = {2023}

}

```

MegaDetector




[Link to paper](https://arxiv.org/abs/1907.06772)

```BibTeX

@article{Beery_Efficient_2019,

  title     = {Efficient Pipeline for Camera Trap Image Review},

  author    = {Beery, Sara and Morris, Dan and Yang, Siyu},

  journal   = {arXiv preprint arXiv:1907.06772},

  year      = {2019}

}

```

## Contribute

Interested in contributing to this project? There are always things to do. Do you feel comfortable handling one of the tasks below? [Let me know](mailto:[email protected])! And I will guide you in the right direction. Thanks!

- [ ] **Improve installation** - At the moment users still have to install dependeceies and use CLI. Perferably, we would like to package EcoAssist as a standalone folder with all dependencies.

- [ ] **Camtrap DP format** - Add a postprocess option to export results to camtrap DP format: https://camtrap-dp.tdwg.org/.

- [ ] **Improve human-in-loop** - At the moment the human-in-the-loop feature works with two separate windows. Perferably, we would like to merge these two.

- [ ] **Sequence smoothing** - There is no option to smoothen results based on sequences, yet. Dan Morris has already created [the code to do so](https://github.com/agentmorris/MegaDetector/blob/main/api/batch_processing/data_preparation/manage_local_batch.py#L1302). This needs to be implemented into the existing workflow.

- [ ] **Sequence ID** - Add column for unique sequence_id in CSV export.

## Uninstall

All files are located in one folder, called `EcoAssist_files`. You can uninstall EcoAssist by simply deleting this folder. Please be aware that it's hidden, so you'll probably have to adjust your settings before you can see it (find out how to: [macOS](https://www.sonarworks.com/support/sonarworks/360003040160-Troubleshooting/360003204140-FAQ/5005750481554-How-to-show-hidden-files-Mac-and-Windows-), [Windows](https://support.microsoft.com/en-us/windows/view-hidden-files-and-folders-in-windows-97fbc472-c603-9d90-91d0-1166d1d9f4b5#WindowsVersion=Windows_11), [Linux](https://askubuntu.com/questions/232649/how-to-show-or-hide-a-hidden-file)). If you're planning on updating EcoAssist, there is no need to uninstall it first. It will do that automatically. 

Location on Windows




  

```r

# All users

─── 📁Program Files

    └── 📁EcoAssist_files

# Single user

─── 📁Users

    └── 📁

        └── 📁EcoAssist_files

```

Location on macOS




  

```r

─── 📁Applications

    └── 📁.EcoAssist_files

```

Location on Linux




  

```r

─── 📁home

    └── 📁

        └── 📁.EcoAssist_files

```