https://github.com/agentmorris/megadetector
MegaDetector is an AI model that helps conservation folks spend less time doing boring things with camera trap images.
https://github.com/agentmorris/megadetector
camera-traps cameratraps computer-vision conservation ecology machine-learning megadetector wildlife
Last synced: 25 days ago
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MegaDetector is an AI model that helps conservation folks spend less time doing boring things with camera trap images.
- Host: GitHub
- URL: https://github.com/agentmorris/megadetector
- Owner: agentmorris
- License: mit
- Created: 2023-05-20T01:46:37.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-29T20:10:15.000Z (6 months ago)
- Last Synced: 2024-10-29T22:43:37.691Z (6 months ago)
- Topics: camera-traps, cameratraps, computer-vision, conservation, ecology, machine-learning, megadetector, wildlife
- Language: Jupyter Notebook
- Homepage:
- Size: 185 MB
- Stars: 109
- Watchers: 9
- Forks: 24
- Open Issues: 1
-
Metadata Files:
- Readme: README-package.md
- License: LICENSE
- Citation: citation.cff
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README
# MegaDetector
This package is a pip-installable version of the support/inference code for [MegaDetector](https://github.com/agentmorris/MegaDetector/?tab=readme-ov-file#megadetector), an object detection model that helps conservation biologists spend less time doing boring things with camera trap images. Complete documentation for this Python package is available at [megadetector.readthedocs.io](https://megadetector.readthedocs.io).
If you aren't looking for the Python package specifically, and you just want to learn more about what MegaDetector is all about, head over to the [MegaDetector repo](https://github.com/agentmorris/MegaDetector/?tab=readme-ov-file#megadetector).
If you don't want to run MegaDetector, and you just want to use the utilities in this package - postprocessing, manipulating large volumes of camera trap images, etc. - you may want to check out the [megadetector-utils](https://pypi.org/project/megadetector-utils/) package, which is identical to this one, but excludes all of the PyTorch/YOLO dependencies, and is thus approximately one zillion times smaller.
## Installation
Install with:
`pip install megadetector`
MegaDetector model weights aren't downloaded at the time you install the package, but they will be (optionally) automatically downloaded the first time you run the model.
## Package reference
See [megadetector.readthedocs.io](https://megadetector.readthedocs.io).
## Examples of things you can do with this package
### Run MegaDetector on one image and count the number of detections
```
from megadetector.utils import url_utils
from megadetector.visualization import visualization_utils as vis_utils
from megadetector.detection import run_detector
# This is the image at the bottom of this page, it has one animal in it
image_url = 'https://github.com/agentmorris/MegaDetector/raw/main/images/orinoquia-thumb-web.jpg'
temporary_filename = url_utils.download_url(image_url)
image = vis_utils.load_image(temporary_filename)
# This will automatically download MDv5a; you can also specify a filename.
model = run_detector.load_detector('MDV5A')
result = model.generate_detections_one_image(image)
detections_above_threshold = [d for d in result['detections'] if d['conf'] > 0.2]
print('Found {} detections above threshold'.format(len(detections_above_threshold)))
```
### Run MegaDetector on a folder of images
```
from megadetector.detection.run_detector_batch import \
load_and_run_detector_batch, write_results_to_file
from megadetector.utils import path_utils
import os
# Pick a folder to run MD on recursively, and an output file
image_folder = os.path.expanduser('~/megadetector_test_images')
output_file = os.path.expanduser('~/megadetector_output_test.json')
# Recursively find images
image_file_names = path_utils.find_images(image_folder,recursive=True)
# This will automatically download MDv5a; you can also specify a filename.
results = load_and_run_detector_batch('MDV5A', image_file_names)
# Write results to a format that Timelapse and other downstream tools like.
write_results_to_file(results,
output_file,
relative_path_base=image_folder,
detector_file=detector_filename)
```
## Contact
Contact [email protected] with questions.
## Gratuitous animal picture
Image credit University of Minnesota, from the [Orinoquía Camera Traps](http://lila.science/datasets/orinoquia-camera-traps/) data set.