This repo contains the tools for training, running, and evaluating detectors and classifiers for images collected from motion-triggered camera traps. The core functionality provided is:
- Data parsing from frequently-used camera trap metadata formats into a common format
- Training and evaluation of detectors, particularly our "MegaDetector", which does a pretty good job finding animals, people, and vechicles (and therefore is pretty good at finding empty images) in a variety of terrestrial ecosystems
- A batch processing API that runs MegaDetector on large image collections, to accelerate population surveys
- A real-time API that runs MegaDetector (and some species classifiers) synchronously, primarily to support anti-poaching scenarios (e.g. see this blog post describing how this API supports Wildlife Protection Solutions)
- A Web-based demo that calls our real-time API
- Training and evaluation of species-level classifiers for specific data sets
- Miscellaneous useful tools for manipulating camera trap data
- Research experiments we're doing around camera trap data (i.e., some directories are highly experimental and you should take them with a grain of salt)
Classifiers and detectors are trained using TensorFlow.
This repo is maintained by folks in the Microsoft AI for Earth program who like looking at pictures of animals. I mean, we want to use machine learning to support conservation too, but we also really like looking at pictures of animals.
We work with ecologists all over the world to help them spend less time annotating images and more time thinking about conservation. You can read a little more about how this works on our AI for Earth camera trap collaborations page.
You can also read about what we do to support camera trap researchers in our recent blog post.
Here are a few of the organizations that have used AI for Earth camera trap tools... we're only listing organizations who (a) we know about and (b) have generously allowed us to refer to them here, so if you're using MegaDetector or other tools from this repo and would like to be added to this list, email us!
- Idaho Department of Fish and Game
- San Diego Zoo Global
- University of Washington Quantitative Ecology Lab
- University of Idaho
- Borderlands Research Institute at Sul Ross State University
- Borneo Nature Foundation
- Parks Canada
- Australian Wildlife Conservancy
- Lab of Dr. Bilal Habib at the Wildlife Institute of India
- Royal Society for the Protection of Birds (RSPB)
- Wildlife Protection Solutions
- Island Conservation
- Synthetaic
- School of Natural Sciences, University of Tasmania
- Arizona Department of Environmental Quality
- Wildlife Research, Oregon Department of Fish and Wildlife
- National Wildlife Refuge System, Southwest Region, US Fish and Wildlife
- Mammal Spatial Ecology and Conservation Lab at Washington State University
- Point No Point Treaty Council
- SPEA (Portuguese Society for the Study of Birds)
- Ghost Cat Analytics
- EcoLogic Consultants Ltd.
- Smithsonian Northern Great Plains Program
- Federal University of Amapá, Ecology and Conservation of Amazonian Vertebrates Research Group
- Hamaarag, The Steinhardt Museum of Natural History, Tel Aviv University
- Czech University of Life Sciences Prague
- Ramat Hanadiv Nature Park, Israel
- TU Berlin, Department of Ecology
- DC Cat Count, led by the Humane Rescue Alliance
- Center for Biodiversity and Conservation at the American Museum of Natural History
- Camelot
- Graeme Shannon's Research Group at Bangor University
- Snapshot USA
- University of British Columbia Wildlife Coexistence Lab
- Michigan Department of Natural Resources, Wildlife Division
- Serra dos Órgãos National Park / ICMBio
- McLoughlin Lab in Population Ecology, University of Saskatchewan
- Upper Yellowstone Watershed Group
- Blumstein lab, UCLA
- National Park Service Santa Monica Mountains Recreation Area
- Conservation X Labs
- The Nature Conservancy in Wyoming
This repo does not directly host camera trap data, but we work with our collaborators to make data and annotations available whenever possible on lila.science.
The main model that we train and run using tools in this repo is MegaDetector, an object detection model that identifies animals, people, and vehicles in camera trap images. This model is trained on several hundred thousand bounding boxes from a variety of ecosystems. Lots more information – including download links – is available on the MegaDetector page.
Here's a "teaser" image of what detector output looks like:
Image credit University of Washington.
For questions about this repo, contact cameratraps@lila.science.
This repo is organized into the following folders...
Code for hosting our models as an API, either for synchronous operation (e.g. for real-time inference or for our Web-based demo) or as a batch process (for large biodiversity surveys).
Experimental code for training species classifiers on new data sets, generally trained on MegaDetector crops. Currently the main pipeline described in this folder relies on a large database of labeled images that is not publicly available; therefore, this folder is not yet set up to facilitate training of your own classifiers. However, it is useful for users of the classifiers that we train, and contains some useful starting points if you are going to take a "DIY" approach to training classifiers on cropped images.
The folder also contains a tutorial on training your own classifier using MegaDetector, which has no dependencies on private data, although this tutorial uses somehwat obsolete frameworks and may not be the best starting point for new projects.
All that said, here's another "teaser image" of what you get at the end of training and running a classifier:
Code for:
- Converting frequently-used metadata formats to COCO Camera Traps format
- Creating, visualizing, and editing COCO Camera Traps .json databases
- Generating tfrecords
Source for the Web-based demo of our MegaDetector model (we'll release the demo soon!).
Code for training and evaluating detectors.
Ongoing research projects that use this repository in one way or another; as of the time I'm editing this README, there are projects in this folder around active learning and the use of simulated environments for training data augmentation.
Random things that don't fit in any other directory. Currently contains a single file, a not-super-useful but super-duper-satisfying and mostly-successful attempt to use OCR to pull metadata out of image pixels in a fairly generic way, to handle those pesky cases when image metadata is lost.
We use conda to manage our Python package dependencies. Conda is a package and environment management system. You can install a lightweight distribution of conda (Miniconda) for your OS via installers at https://docs.conda.io/en/latest/miniconda.html.
The required Python packages for running utility and visualization scripts in this repo are listed in environment.yml. To set up your environment for these scripts, in your shell, navigate to the root directory of this repo and issue the following command to create a virtual environment via conda called cameratraps (specified in the environment file) and install the required packages:
conda env create --file environment.yml
For unix users, you need to have gcc installed in order to compile the pip packages. If you do not already have gcc installed, run the following command before creating the conda environment:
sudo apt update
sudo apt install build-essentialScripts that execute machine learning code – specifically, scripts in the folders api, detection, and classification – require additional depdendencies. In particular, the detection/run_tf_detector*.py scripts should use environment-detector.yml to set up the environment, as follows:
conda env create --file environment-detector.yml
This environment file allows any TensorFlow version from 1.9 to 1.15 to be installed, but you may need to adjust that version for your environment. Specifically, if you are running on an Azure Data Science Virtual Machine (which has CUDA 10.1 as of the time I'm writing this), you may receive a CUDA error, in which case you should change the line:
- tensorflow-gpu>=1.9.0, <1.15.0
...to:
- tensorflow-gpu=1.13.1
...before creating your environment.
If you run into an error while creating either of the above environments, try updating conda to version 4.5.11 or above. Check the version of conda using conda --version.
To enter the conda virtual environment at your current shell, run:
conda activate cameratraps
...or, if you used the environment-detector.yml file above:
conda activate cameratraps-detector
You should see (cameratraps) prepended to the command line prompt. Invoking python or jupyter notebook will now be using the interpreter and packages available in this virtual env.
To exit the virtual env, issue conda deactivate.
If you need to use additional packages, add them to the environment file and run
conda env update --name cameratraps --file environment.yml --pruneor
conda env update --name cameratraps-detector --file environment-detector.yml --pruneIn some scripts, we also assume that you have the AI for Earth utilities repo (ai4eutils) cloned and its path appended to PYTHONPATH. You can append a path to PYTHONPATH for the current shell session by executing the following on Windows:
set PYTHONPATH="%PYTHONPATH%;c:\wherever_you_put_the_ai4eutils_repo"
You can do this with the following on Linux:
export PYTHONPATH="$PYTHONPATH:/absolute/path/to/repo/ai4eutils"
Adding this line to your ~/.bashrc (on Linux) modifies PYTHONPATH permanently.
We also do our best to follow Google's Python Style Guide, and we have adopted their pylintrc file, with the following differences:
- indent code blocks with 4 spaces (instead of 2)
To lint a file, run pylint with the CameraTraps repo folder as the current working directory. This allows pylint to recognize the pylintrc file. For example,
pylint classification/train_classifier.py
Image credit USDA, from the NACTI data set.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
This repository is licensed with the MIT license.